Military Medicine

Sitler, Collin; Lustik, Michael; Levy, Gary; Pier, Bruce

doi: 10.1093/milmed/usaa119pmid: 32633326

ABSTRACT Introduction Because of increased morbidity seen in multiple gestations, the American Society of Reproductive Medicine recommends transfer of blastocysts one at a time for most patients. While cost-effectiveness models have compared single embryo transfer (SET) versus double embryo transfer (DET), few incorporate maternal and neonatal morbidity, and none have been performed in U.S. Military facilities. The purpose of this study was to determine the cost effectiveness of sequential SET versus DET in a U.S. Military treatment facility. Materials and Methods A cost-effectiveness model was created based on 250 patients between the ages of 20–44 who previously underwent in vitro fertilization (IVF) at our facility. The model consisted of patients pursuing either SET or DET with two total embryos. Cycle outcomes were determined using the published SARTCORS success calculator. Neonatal and obstetrical outcomes were simulated based on singleton and twin IVF pregnancies. Neonatal and obstetrical cost estimates were based on internal data as well. Results If 250 model patients pursue SET, 140 live births would occur, with total cost of $5.7 million, and cost per delivery of $40,500. If the model patients pursued DET, 117 live births would occur, with total cost of $9.2 million and a cost per delivery of $77.700. DET would lead to more total infants (207 vs. 143 in SET cohort). Personal costs are higher in SET versus DET cohorts ($23,036 vs. $20,535). Conclusions SET in a system with no infertility coverage saves approximately $3.5 million per 250 patients. Higher personal costs as seen with SET may incentivize patients to seek DET. The total savings should encourage alteration to practice patterns with the U.S Military Healthcare System. INTRODUCTION The aim of assisted reproductive technology (ART) is to achieve pregnancy while minimizing the risk of multiple gestations. When compared with elective single embryo transfer (SET), double embryo transfer (DET) demonstrates a superior pregnancy and live birth rate with a significantly higher risk of multiple gestations.1,2 Despite achieving a higher pregnancy rate, DET in ART leads to an increase in multiple gestation pregnancies, which are associated with increased maternal and neonatal morbidity and mortality.3 Twin pregnancies have a higher incidence of preterm deliveries, cesarean sections, admissions to the neonatal intensive care unit (NICU), and pre-eclampsia, when compared with singleton pregnancies.4,5 The American Society of Reproductive Medicine provides guidance on the number of embryos to be transferred, with one blastocyst recommended for transfer in patients under 38.6 Despite this recommendation, greater than 60% of patients under 35 years old do not receive SET.7 Most patients prefer to have two embryos transferred, with financial considerations being the chief reason. Studies demonstrate that patients are more likely to choose SET when educated regarding the risks of multiple gestation pregnancy, but select two embryos for transfer if they perceive a lower pregnancy rate when compared with transferring one embryo.8 Cost-effectiveness research comparing single versus DET after in vitro fertilization (IVF) has previously been performed, with most studies taking place in areas with mandated IVF insurance coverage, and few incorporating hospital costs linked to obstetrical and neonatal outcomes.9–13 Crawford et al. looked at the cost of sequential SET versus DET in U.S. population and found sequential SET to provide an approximate of 34% in savings and an increase in pregnancies than DET. This study estimated cost per type of pregnancy (singleton, twin, triplet), however did not assess the patient and insurance company cost (if in a nonmandated ART coverage state), and therefore may not fully delineate cost-savings for both patient and insurer.14 The U.S. Military population provides a unique opportunity to study cost effectiveness of SET and DET in a model similar to U.S. states that do not have IVF insurance mandates. IVF is a partially covered benefit of TRICARE, the insurance provider of the U.S. Military population. The cost of an IVF cycle is shared by the patient and TRICARE (patients pay for the IVF cycle, while TRICARE covers the cost of medications and appointments leading up to oocyte retrieval and embryo transfer), while obstetrical and neonatal care is fully covered by TRICARE.15 Inherently, this may incentivize the patient to request transfer of more than one embryo, as they expect DET gives them the best chance for a pregnancy. With this mind, we designed a cost-effectiveness analysis to test the hypothesis that stepwise SET is more cost-effective for patients and TRICARE military health insurance, with cost per delivery (CPD) as our primary outcome. MATERIALS AND METHODS This study was approved by the Tripler Army Medical Center (TAMC) Institutional Review Board and complied with applicable regulations regarding human subject’s research. We developed a decision-analysis model to compare sequential SET and DET based on maternal characteristics for 250 patients who underwent IVF at our facility and maternal and neonatal cost and complication data for 2,304 deliveries at TAMC (Fig. 1). SET is defined as transferring one embryo and freezing the remaining embryo in this model, and if not pregnant after the first embryo transfer, the thawed embryo is transferred in a following frozen embryo transfer cycle. The following data were obtained from a de-identified data set for 250 patients who had an autologous IVF cycle in 2016 and 2017 at our facility: age, body mass index (BMI), and infertility diagnosis. These patient data were used to obtain expected live birth rates, singleton pregnancy rates, and twin pregnancy rates, for both stepwise SET and DET, by the use of a nationally verified and peer reviewed success calculator available through the Society of Assisted Reproductive Technology (SART) CORS.16,17 We then used this data to populate our cost-effectiveness model. In the SET model, patients would have two embryos available for transfer after IVF, one to transfer and one to freeze. If no pregnancy was achieved with the first transfer, the model patients underwent a second transfer. In the DET model, patients would have two embryos available for transfer after IVF, and both would be transferred with no embryos frozen or available to transfer if pregnancy was not successful. Possible outcomes included singleton pregnancy, multiple gestation pregnancy and failure. FIGURE 1 Open in new tabDownload slide Decision tree diagram detailing cost-effectiveness study design. IVF Cost IVF cost data were based on a standard 10 day gonadotropin releasing hormone antagonist protocol, which included 150 units recombinant follicle stimulating hormone (Gonal-F) and 150 units human menopausal gonadotropin (Menopur) daily. Medication costs were computed including intramuscular progesterone for luteal support for 10 weeks total, and 5 day courses of doxycycline and methylprednisolone (to reduce infection risk and inflammatory response).18 Costs were also added for facility fees regarding four transvaginal ultrasound visits and lab draws for estradiol and progesterone. Fixed costs were used for IVF cycles, which included intracytoplasmic sperm injection and the cost of the first embryo transfer. For frozen embryo transfers, cost for estradiol (Estrace) and intramuscular progesterone were used. Also, cost of two ultrasound and laboratory visits was added (Supplemental Table I). Maternal and Neonatal Cost and Risk Calculations The U.S. Army Women’s Product Line (Fort Sam Houston, Texas) was queried to provide cost data for neonatal and obstetrical outcomes and cost for all deliveries at our institution in 2016 (n = 2304). Our analysis was based on 2,261 women who gave birth to singletons and 43 women who gave birth to twins. Maternal and neonatal morbidity was established by the presence of ICD-10 diagnosis codes for each condition(s). Rates of cesarean section, preterm delivery, admission to NICU, preterm delivery, small for gestational age, jaundice, sepsis, and respiratory disease were compared with previously published peer reviewed literature regarding obstetrical and neonatal risk in singleton and twin pregnancies resultant from IVF in the United States, and found to mirror in previously reported results.5,19–21 Model Simulation/Statistical Analysis Cost-effectiveness ratios were defined as total CPD. The incremental cost-effectiveness ratio was defined as the cost per each additional pregnancy. A bootstrap approach was performed, which linked together maternal and neonatal cost and complication risk, summing the costs and counting the number of each complication for twins (eg, number of twins with jaundice could be either 0, 1 or 2). If the model generated result from the livebirth probabilities was a singleton or twin, corresponding records were randomly selected from the corresponding singleton or twin file to get the associated costs. If the randomly generated result from the livebirth probabilities was no birth, IVF costs only were factored. Results are based on 10,000 iterations of simulating livebirths and costs for cohorts of 250 women. Analyses were performed using SAS version 9.4 (SAS Institute, Cary, North Carolina). RESULTS Input Data Demographics of the 250 patients enrolled in IVF at TAMC are listed in Table I. Ages ranged from 20 to 44 years with a mean of 33.7 (SD = 4.6). Nearly 80% had BMI levels of normal or overweight, with only 22% obese. The primary infertility diagnoses were male factor and tubal disease (tubal ligation and other tubal). Maternal and neonatal characteristics of deliveries are presented in Table II and Supplemental Table II. About one in four deliveries were by cesarean section for singletons compared with 79% of twins. Fifty-eight percent of twins were delivered before 37 weeks versus 6% of singletons, and complication rates were much higher for twin deliveries than for singletons. Because of the higher rates of cesarean sections, early delivery, and complications, combined obstetric and neonatal costs were much higher on average for twins than for singletons (mean = $70.9 K for twins vs. $31.5 K for singletons). TABLE I Baseline Demographics of 250 Cost-Effectiveness Model Patients. Data Presented as Percentage of 250 Patients, Unless Otherwise Stated Patients . (N) . (%) . Infertility diagnosis . (N) . (%) . 250 . . . 100.0 . Maternal age (years) 100.0 Male infertility 70 28.0  <29 47 18.8 Endometriosis 11 4.4  30–34 92 36.8 Polycystic ovary syndrome 33 13.2  35–39 79 31.6 Diminished ovarian reserve 30 12.0  40–44 32 12.8 Tubal ligation 17 6.8  45+ 0 0.0 Tubal hydrosalpinx/other 42 16.8 BMI 100.0 Uterine factors 1 0.4  <25 119 47.6 Unexplained factors 25 10.0  25 ≤ 30 77 30.8 Other nonfertile 13 5.2  30 ≤ 35 39 15.6 Other PGD 2 0.8  ≥35 15 6.0 Patients . (N) . (%) . Infertility diagnosis . (N) . (%) . 250 . . . 100.0 . Maternal age (years) 100.0 Male infertility 70 28.0  <29 47 18.8 Endometriosis 11 4.4  30–34 92 36.8 Polycystic ovary syndrome 33 13.2  35–39 79 31.6 Diminished ovarian reserve 30 12.0  40–44 32 12.8 Tubal ligation 17 6.8  45+ 0 0.0 Tubal hydrosalpinx/other 42 16.8 BMI 100.0 Uterine factors 1 0.4  <25 119 47.6 Unexplained factors 25 10.0  25 ≤ 30 77 30.8 Other nonfertile 13 5.2  30 ≤ 35 39 15.6 Other PGD 2 0.8  ≥35 15 6.0 Note. BMI, body mass index; PGD, pregenetic diagnosis. Open in new tab TABLE I Baseline Demographics of 250 Cost-Effectiveness Model Patients. Data Presented as Percentage of 250 Patients, Unless Otherwise Stated Patients . (N) . (%) . Infertility diagnosis . (N) . (%) . 250 . . . 100.0 . Maternal age (years) 100.0 Male infertility 70 28.0  <29 47 18.8 Endometriosis 11 4.4  30–34 92 36.8 Polycystic ovary syndrome 33 13.2  35–39 79 31.6 Diminished ovarian reserve 30 12.0  40–44 32 12.8 Tubal ligation 17 6.8  45+ 0 0.0 Tubal hydrosalpinx/other 42 16.8 BMI 100.0 Uterine factors 1 0.4  <25 119 47.6 Unexplained factors 25 10.0  25 ≤ 30 77 30.8 Other nonfertile 13 5.2  30 ≤ 35 39 15.6 Other PGD 2 0.8  ≥35 15 6.0 Patients . (N) . (%) . Infertility diagnosis . (N) . (%) . 250 . . . 100.0 . Maternal age (years) 100.0 Male infertility 70 28.0  <29 47 18.8 Endometriosis 11 4.4  30–34 92 36.8 Polycystic ovary syndrome 33 13.2  35–39 79 31.6 Diminished ovarian reserve 30 12.0  40–44 32 12.8 Tubal ligation 17 6.8  45+ 0 0.0 Tubal hydrosalpinx/other 42 16.8 BMI 100.0 Uterine factors 1 0.4  <25 119 47.6 Unexplained factors 25 10.0  25 ≤ 30 77 30.8 Other nonfertile 13 5.2  30 ≤ 35 39 15.6 Other PGD 2 0.8  ≥35 15 6.0 Note. BMI, body mass index; PGD, pregenetic diagnosis. Open in new tab TABLE II Maternal/Neonatal Morbidity Statistics Used in Cost-Effectiveness Model. Data Presented as Percentage of 250 Patients, Unless Otherwise Stated . SET COHORT . DET cohort . Mean . 95% CI . Mean . 95% CI . Maternal  Cesarean Birth 28.0% (20.9–35.3%) 65.8% (57.1–73.9%)  Delivery complications 27.6% (20.4–35.1%) 69.1% (60.7–77.2%)  Hypertensive disorders 16.0% (10.1–22.3%) 33.4% (25.0–42.1%)  Diabetic disorders 6.1% (2.6–10.3%) 5.0% (1.6–9.3%) Neonate  GA < 32 weeks 1.6% (0.0–3.9%) 7.3% (2.9–12.4%)  GA 32–37 weeks 7.3% (3.4–11.9%) 38.2% (29.4–47.0%)  Small for GA 2.9% (0.8–5.4%) 10.3% (6.7–13.6%)  Jaundice 15.7% (10.5–20.5%) 31.9% (27.0–36.0%)  NICU admit 12.5% (7.5–16.9%) 39.3% (33.7–44.1%)  Respiratory disease 5.9% (2.3–9.6%) 19.6% (14.6–23.7%)  Sepsis 0.5% (0.0–1.8%) 1.1% (0.0–2.5%) . SET COHORT . DET cohort . Mean . 95% CI . Mean . 95% CI . Maternal  Cesarean Birth 28.0% (20.9–35.3%) 65.8% (57.1–73.9%)  Delivery complications 27.6% (20.4–35.1%) 69.1% (60.7–77.2%)  Hypertensive disorders 16.0% (10.1–22.3%) 33.4% (25.0–42.1%)  Diabetic disorders 6.1% (2.6–10.3%) 5.0% (1.6–9.3%) Neonate  GA < 32 weeks 1.6% (0.0–3.9%) 7.3% (2.9–12.4%)  GA 32–37 weeks 7.3% (3.4–11.9%) 38.2% (29.4–47.0%)  Small for GA 2.9% (0.8–5.4%) 10.3% (6.7–13.6%)  Jaundice 15.7% (10.5–20.5%) 31.9% (27.0–36.0%)  NICU admit 12.5% (7.5–16.9%) 39.3% (33.7–44.1%)  Respiratory disease 5.9% (2.3–9.6%) 19.6% (14.6–23.7%)  Sepsis 0.5% (0.0–1.8%) 1.1% (0.0–2.5%) Note. Delivery complications including hypertensive disorders, gestational/pre-gestational diabetes, obesity, and other maternal co-morbidities (see Supplemental Table I). SET, sequential single embryo transfer; DET, double embryo transfer; GA, gestational age; NICU, neonatal intensive care unit. Open in new tab TABLE II Maternal/Neonatal Morbidity Statistics Used in Cost-Effectiveness Model. Data Presented as Percentage of 250 Patients, Unless Otherwise Stated . SET COHORT . DET cohort . Mean . 95% CI . Mean . 95% CI . Maternal  Cesarean Birth 28.0% (20.9–35.3%) 65.8% (57.1–73.9%)  Delivery complications 27.6% (20.4–35.1%) 69.1% (60.7–77.2%)  Hypertensive disorders 16.0% (10.1–22.3%) 33.4% (25.0–42.1%)  Diabetic disorders 6.1% (2.6–10.3%) 5.0% (1.6–9.3%) Neonate  GA < 32 weeks 1.6% (0.0–3.9%) 7.3% (2.9–12.4%)  GA 32–37 weeks 7.3% (3.4–11.9%) 38.2% (29.4–47.0%)  Small for GA 2.9% (0.8–5.4%) 10.3% (6.7–13.6%)  Jaundice 15.7% (10.5–20.5%) 31.9% (27.0–36.0%)  NICU admit 12.5% (7.5–16.9%) 39.3% (33.7–44.1%)  Respiratory disease 5.9% (2.3–9.6%) 19.6% (14.6–23.7%)  Sepsis 0.5% (0.0–1.8%) 1.1% (0.0–2.5%) . SET COHORT . DET cohort . Mean . 95% CI . Mean . 95% CI . Maternal  Cesarean Birth 28.0% (20.9–35.3%) 65.8% (57.1–73.9%)  Delivery complications 27.6% (20.4–35.1%) 69.1% (60.7–77.2%)  Hypertensive disorders 16.0% (10.1–22.3%) 33.4% (25.0–42.1%)  Diabetic disorders 6.1% (2.6–10.3%) 5.0% (1.6–9.3%) Neonate  GA < 32 weeks 1.6% (0.0–3.9%) 7.3% (2.9–12.4%)  GA 32–37 weeks 7.3% (3.4–11.9%) 38.2% (29.4–47.0%)  Small for GA 2.9% (0.8–5.4%) 10.3% (6.7–13.6%)  Jaundice 15.7% (10.5–20.5%) 31.9% (27.0–36.0%)  NICU admit 12.5% (7.5–16.9%) 39.3% (33.7–44.1%)  Respiratory disease 5.9% (2.3–9.6%) 19.6% (14.6–23.7%)  Sepsis 0.5% (0.0–1.8%) 1.1% (0.0–2.5%) Note. Delivery complications including hypertensive disorders, gestational/pre-gestational diabetes, obesity, and other maternal co-morbidities (see Supplemental Table I). SET, sequential single embryo transfer; DET, double embryo transfer; GA, gestational age; NICU, neonatal intensive care unit. Open in new tab Output Data The cost-effectiveness model found that on average, 57% of women gave birth under SET compared with 47% under DET (Table III). Ninety-five percent of SET deliveries were for singletons, while 75% of DET deliveries were for twins. Because there were more twin births under DET, the DET cohort was more likely to have delivery complications, which require a cesarean delivery and have infants affected by preterm delivery, admission to the NICU, have jaundice, respiratory disease, and be small for gestational age. TABLE III Total and Average Cost Statistics for IVF, Maternal, and Neonatal Morbidity Statistics . SET cohort . DET cohort . Mean . 95% CI . Mean . 95% CI . Percentage of women who give birth 57% (51–63%) 47% (41–53%) Number of women who delivered 143 (127–158) 118 (103–133) Number of neonates delivered 150 (133–166) 207 (178–236) Percentage of number of infants born  0 42.9% (36.8–49.2%) 52.8% (46.8–58.8%)  1 54.4% (48–60.4%) 11.6% (8.0–15.6%)  2 2.7% (0.8–4.8%) 35.5% (29.6–41.6%) Total government cost for all 250 women 3,651,389 (2.91–4.80 × 10−6) 7,710,479 (5.43–10.74 × 10−6) Total IVF cost for all 250 women 3,204,801 (3.16–3.25 × 10−6) 2,408,868 (2.41–2.41 × 10−6) Total maternal hospital costs 1,374,462 (1.17–1.62 × 10−6) 1,672,378 (1.41–1.95 × 10−6) Total neonatal hospital costs 1,190,603 (0.56–2.34 × 10−6) 5,069,023 (2.90–8.02 × 10−6) Total hospital costs for mother and infants 2,565,065 (1.82–3.79 × 10−6) 6,741,402 (4.46–9.77 × 10−6) Total personal cost for all women 2,118,477 (2.09–2.15 × 10−6) 1,439,790 (1.44–1.44 × 10−6) Total costs for all women 5,769,866 (5.03–6.99 × 10−6) 9,150,269 (6.87–12.18 × 10−6) Average IVF cost per woman 12,819 (12,652–12,982) 9,635 (9,635–9,635) Average maternal hospital cost per delivery 9,627 (8,656–10,979) 14,184 (12,951–15,540) Average neonatal hospital cost per disposition 8,337 (4,011–16,389) 42,988 (24,915–66,844) Average hospital cost for mother and infant(s) 17,964 (13,138–26,396) 57,172 (39,030–81,162) Average personal cost per patient (n = 250) 8,474 (8,355–8,589) 5,759 (5,759–5,759) Average government cost per patient (n = 250) 14,606 (11,623–19,487) 30,842 (21,705–42,950) Average total cost per patient (n = 250) 23,079 (20,128–27,967) 36,601 (27,464–48,709) . SET cohort . DET cohort . Mean . 95% CI . Mean . 95% CI . Percentage of women who give birth 57% (51–63%) 47% (41–53%) Number of women who delivered 143 (127–158) 118 (103–133) Number of neonates delivered 150 (133–166) 207 (178–236) Percentage of number of infants born  0 42.9% (36.8–49.2%) 52.8% (46.8–58.8%)  1 54.4% (48–60.4%) 11.6% (8.0–15.6%)  2 2.7% (0.8–4.8%) 35.5% (29.6–41.6%) Total government cost for all 250 women 3,651,389 (2.91–4.80 × 10−6) 7,710,479 (5.43–10.74 × 10−6) Total IVF cost for all 250 women 3,204,801 (3.16–3.25 × 10−6) 2,408,868 (2.41–2.41 × 10−6) Total maternal hospital costs 1,374,462 (1.17–1.62 × 10−6) 1,672,378 (1.41–1.95 × 10−6) Total neonatal hospital costs 1,190,603 (0.56–2.34 × 10−6) 5,069,023 (2.90–8.02 × 10−6) Total hospital costs for mother and infants 2,565,065 (1.82–3.79 × 10−6) 6,741,402 (4.46–9.77 × 10−6) Total personal cost for all women 2,118,477 (2.09–2.15 × 10−6) 1,439,790 (1.44–1.44 × 10−6) Total costs for all women 5,769,866 (5.03–6.99 × 10−6) 9,150,269 (6.87–12.18 × 10−6) Average IVF cost per woman 12,819 (12,652–12,982) 9,635 (9,635–9,635) Average maternal hospital cost per delivery 9,627 (8,656–10,979) 14,184 (12,951–15,540) Average neonatal hospital cost per disposition 8,337 (4,011–16,389) 42,988 (24,915–66,844) Average hospital cost for mother and infant(s) 17,964 (13,138–26,396) 57,172 (39,030–81,162) Average personal cost per patient (n = 250) 8,474 (8,355–8,589) 5,759 (5,759–5,759) Average government cost per patient (n = 250) 14,606 (11,623–19,487) 30,842 (21,705–42,950) Average total cost per patient (n = 250) 23,079 (20,128–27,967) 36,601 (27,464–48,709) Note. Data presented as dollars, unless otherwise stated. SET, sequential single embryo transfer; DET, double embryo transfer; IVF, in vitro fertilization. Open in new tab TABLE III Total and Average Cost Statistics for IVF, Maternal, and Neonatal Morbidity Statistics . SET cohort . DET cohort . Mean . 95% CI . Mean . 95% CI . Percentage of women who give birth 57% (51–63%) 47% (41–53%) Number of women who delivered 143 (127–158) 118 (103–133) Number of neonates delivered 150 (133–166) 207 (178–236) Percentage of number of infants born  0 42.9% (36.8–49.2%) 52.8% (46.8–58.8%)  1 54.4% (48–60.4%) 11.6% (8.0–15.6%)  2 2.7% (0.8–4.8%) 35.5% (29.6–41.6%) Total government cost for all 250 women 3,651,389 (2.91–4.80 × 10−6) 7,710,479 (5.43–10.74 × 10−6) Total IVF cost for all 250 women 3,204,801 (3.16–3.25 × 10−6) 2,408,868 (2.41–2.41 × 10−6) Total maternal hospital costs 1,374,462 (1.17–1.62 × 10−6) 1,672,378 (1.41–1.95 × 10−6) Total neonatal hospital costs 1,190,603 (0.56–2.34 × 10−6) 5,069,023 (2.90–8.02 × 10−6) Total hospital costs for mother and infants 2,565,065 (1.82–3.79 × 10−6) 6,741,402 (4.46–9.77 × 10−6) Total personal cost for all women 2,118,477 (2.09–2.15 × 10−6) 1,439,790 (1.44–1.44 × 10−6) Total costs for all women 5,769,866 (5.03–6.99 × 10−6) 9,150,269 (6.87–12.18 × 10−6) Average IVF cost per woman 12,819 (12,652–12,982) 9,635 (9,635–9,635) Average maternal hospital cost per delivery 9,627 (8,656–10,979) 14,184 (12,951–15,540) Average neonatal hospital cost per disposition 8,337 (4,011–16,389) 42,988 (24,915–66,844) Average hospital cost for mother and infant(s) 17,964 (13,138–26,396) 57,172 (39,030–81,162) Average personal cost per patient (n = 250) 8,474 (8,355–8,589) 5,759 (5,759–5,759) Average government cost per patient (n = 250) 14,606 (11,623–19,487) 30,842 (21,705–42,950) Average total cost per patient (n = 250) 23,079 (20,128–27,967) 36,601 (27,464–48,709) . SET cohort . DET cohort . Mean . 95% CI . Mean . 95% CI . Percentage of women who give birth 57% (51–63%) 47% (41–53%) Number of women who delivered 143 (127–158) 118 (103–133) Number of neonates delivered 150 (133–166) 207 (178–236) Percentage of number of infants born  0 42.9% (36.8–49.2%) 52.8% (46.8–58.8%)  1 54.4% (48–60.4%) 11.6% (8.0–15.6%)  2 2.7% (0.8–4.8%) 35.5% (29.6–41.6%) Total government cost for all 250 women 3,651,389 (2.91–4.80 × 10−6) 7,710,479 (5.43–10.74 × 10−6) Total IVF cost for all 250 women 3,204,801 (3.16–3.25 × 10−6) 2,408,868 (2.41–2.41 × 10−6) Total maternal hospital costs 1,374,462 (1.17–1.62 × 10−6) 1,672,378 (1.41–1.95 × 10−6) Total neonatal hospital costs 1,190,603 (0.56–2.34 × 10−6) 5,069,023 (2.90–8.02 × 10−6) Total hospital costs for mother and infants 2,565,065 (1.82–3.79 × 10−6) 6,741,402 (4.46–9.77 × 10−6) Total personal cost for all women 2,118,477 (2.09–2.15 × 10−6) 1,439,790 (1.44–1.44 × 10−6) Total costs for all women 5,769,866 (5.03–6.99 × 10−6) 9,150,269 (6.87–12.18 × 10−6) Average IVF cost per woman 12,819 (12,652–12,982) 9,635 (9,635–9,635) Average maternal hospital cost per delivery 9,627 (8,656–10,979) 14,184 (12,951–15,540) Average neonatal hospital cost per disposition 8,337 (4,011–16,389) 42,988 (24,915–66,844) Average hospital cost for mother and infant(s) 17,964 (13,138–26,396) 57,172 (39,030–81,162) Average personal cost per patient (n = 250) 8,474 (8,355–8,589) 5,759 (5,759–5,759) Average government cost per patient (n = 250) 14,606 (11,623–19,487) 30,842 (21,705–42,950) Average total cost per patient (n = 250) 23,079 (20,128–27,967) 36,601 (27,464–48,709) Note. Data presented as dollars, unless otherwise stated. SET, sequential single embryo transfer; DET, double embryo transfer; IVF, in vitro fertilization. Open in new tab The main outcome of this model is the CPD in SET versus DET for a cohort of 250 women undergoing IVF. The expected number of deliveries for women undergoing SET was 143, resulting in 150 infants, compared with 118 deliveries for women undergoing DET, resulting in 207 infants. The expected total cost for all women under SET was $5.7 million compared with $9.2 million under DET, leading to an average CPD of $40.5 K for SET compared with $77.7 K for DET. In this construct, DET was less effective and more expensive than SET. SET cost $37.2 K less on average per delivery than DET. Out of pocket costs for the patient were about $2,700 less for DET ($5,759 per patient in the DET cohort versus $8,474 in the SET cohort), but government costs in the SET model were about 16,000 dollars less ($14,606 per patient for SET compared with $30,842 per patient for DET). Overall, for a cohort of 250 women, total government cost for the DET cohort was $7.71 million, approximately $2 million more than the total cost of the SET cohort (personal and government cost). DISCUSSION In our model, stepwise SET would provide an overall savings of $37,210 per delivery. The savings of approximately $37,000 per delivery reflects the increase in obstetrical and neonatal costs seen in DET given the accompanying complications of multiple gestation pregnancies. In our simulation, seven deliveries after SET resulted in twin deliveries. However, 89 of the deliveries following DETs resulted in twin delivery. The significant increase in morbidity and associated cost of multiple gestations should lead reproductive specialists to transfer one embryo unless extenuating circumstances exist.22 This finding is in line with recently updated American Society of Reproductive Medicine guidance on the limits to the number of embryos to transfer, stating a single embryo be transferred unless evidence of unfavorable prognosis exists. Factors that predict a favorable prognosis include: euploid embryos at any age, one or more embryos available for cryopreservation in fresh cycles, previous live birth after an IVF cycle, first frozen cycle, and availability of vitrified day 5 or 6 blastocyst.6 Clinical Implications Our model demonstrates that in 250 model patients, SET is cost effective when compared to DET by more than $3.3 million. In our model, however, the total patient cost is increased by more than $675,000 in the model of 250 patients when compared with DET. This financial discrepancy may represent the barrier that reproductive specialists face when counseling patients on elective SET. This situation is not unique to the TRICARE beneficiaries. Research demonstrates that when patients have insurance coverage, they are more likely to consider SET.23 When insurance coverage is not available, greater than 50% of patients strongly desire DET.24 A strategy to consider in light of this study’s data is to offer IVF treatments as a covered TRICARE benefit. In this strategy, TRICARE would pay approximately an additional $2,000 per delivery, however, the morbidity from ART associated twin deliveries may be nearly eliminated, making this a desirable strategy to consider. Research Implications SET is more cost effective in our study, but more costly to patients. Previous research has demonstrated that by reducing patient financial burden through insurance coverage, patients are more likely to accept elective SET.8 In 2012, Wu et al. estimated that if TRICARE were to cover IVF treatments, the seven current military treatment facilities offering IVF would increase IVF volume by 700%.15 While it may be early to call for complete TRICARE coverage for IVF based on the findings presented within, a financial incentive may increase elective embryo transfer beyond the current rate. Strengths and Limitations Our study strengths include a novel approach to determining the number of singleton and twin deliveries in our cohorts using the SART patient prediction tool.24 Also, consideration of antepartum and neonatal outcomes, while not unique to this study, has not been completed in a study in a military facility before our knowledge.9–13 Furthermore, this is one of the first cost-effectiveness analyses to our knowledge that looks at the shared cost model of IVF care available to the U.S. Department of Defense medical beneficiaries. Limitations are also present in our study. Risks were inferred for maternal and neonatal risks from a de-identified dataset from all deliveries at our institution in 2016, the overwhelming majority from non-IVF pregnancies. While the risk of maternal and neonatal morbidity is elevated in IVF patients who undergo fresh embryo transfer, many of the risks noted in our SET and DET cohorts mirrored those previously published in IVF pregnancies, supporting our methodology.5,19–21 In addition, our study did not include preimplantation genetic testing for aneuploidy. A recent cost-effectiveness analysis demonstrated PGT-A decreases healthcare cost, but the cost for PGT-A in that study was lower than the cost our patients pay locally, and as such, most of our patients do not choose PGT-A, therefore we did not include this modality in our model.25 Also, maternal co-morbidities in our study were lumped into a large subheading (pregnancy with complication). While this included hypertensive disorders and gestational and pregestational diabetes, it also included several other diagnoses. It could be that using a complete pregnancy record for cost (instead of cost per morbid condition) may introduce extra cost for other co-morbidities (eg, obesity and hypertension). We feel that by using the complete pregnancy record, our study is more applicable to other military centers practicing IVF. Also, our study design compares SET and DET, however, DET is not the typical practice in the U.S. Military. Our review of military clinics reporting to SART (n = 4) found the average embryo number transferred to be 1.5.26 Our study, therefore, may not demonstrate the true financial benefit of mandating SETs in our population. Finally, our patient population differs somewhat from what is reported nationally in terms of infertility diagnosis.27 Our patient population had a higher rate of tubal factor infertility. This is likely specific, however, to military populations, as tubal factor may be related to the presence of a higher incidence of sexually transmitted infections in some military populations.28 We feel, therefore, this study represents an accurate look at cost associated with IVF and subsequent pregnancy and neonatal care in our patient population. CONCLUSIONS In our study, we found SET to be more cost effective than DET, providing a total savings of approximately $4 million while resulting in higher personal costs. These data suggest that providing financial incentives to patients undergoing IVF in the military to choose SET may result in decreased overall cost to the U.S. Government. Presented in part as an oral presentation at the 67th Annual Pacific Coast Reproductive Society Meeting, Indian Wells, California, April 3–7, 2019. The views expressed in this abstract/manuscript are those of the authors and do not reflect the official policy or position of the Department of the Army, Department of Defense, or the U.S. Government. REFERENCES 1. Thurin AM , Hausken J, Hillensjö T, et al. : Elective single-embryo transfer versus double-embryo transfer in in vitro fertilization . N Engl J Med 2004 ; 351 : 2392 – 402 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Pandian Z , Marjoribanks J, Ozturk O, et al. : Number of embryos for transfer following in vitro fertilisation or intra-cytoplasmic sperm injection . Cochrane Database Syst Rev 2013 ; 7 : 1 – 54 . Google Scholar OpenURL Placeholder Text WorldCat 3. Ombelet W , de Sutter P, van der Elst J, et al. : Multiple gestation and infertility treatment: registration, reflection and reaction—the Belgian project . Hum Reprod Update 2005 ; 11 : 3 – 14 . Google Scholar Crossref Search ADS PubMed WorldCat 4. Obmelet W , Martens G, De Sutter P, et al. : Perinatal outcome of 12,021 singleton and 3108 twin births after non-IVF-assisted reproduction: a cohort study . Hum Reprod 2006 ; 21 : 1025 – 32 . Google Scholar Crossref Search ADS PubMed WorldCat 5. Sazonova A , Källen K, Thurin-Kjellberg A, et al. : Neonatal and maternal outcomes comparing women undergoing two in vitro fertilization (IVF) singleton pregnancies and women undergoing one IVF twin pregnancy . Fertil Steril 2013 ; 99 : 731 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat 6. American Society for Reproductive Medicine : Guidance on the limits to the number of embryos to transfer: a committee opinion . Fertil Steril 2017 ; 107 : 901 – 3 . Crossref Search ADS PubMed WorldCat 7. American Society for Reproductive Medicine : Elective single-embryo transfer . Fertil Steril 2012 ; 97 : 835 – 42 . Crossref Search ADS PubMed WorldCat 8. Sunderam S , Boulet SL, Jamieson DJ, et al. : Effects of patient education on desire for twins and use of elective single embryo transfer procedures during ART treatment: a systematic review . Reprod Biomed Soc Online 2018 ; 6 : 102 – 19 . Google Scholar Crossref Search ADS PubMed WorldCat 9. De Sutter P , Gerris J, Dhont M: A health-economic decision-analytic model comparing double with single embryo transfer in IVF/ICSI . Hum Reprod 2002 ; 17 : 2891 – 6 . Google Scholar Crossref Search ADS PubMed WorldCat 10. Kjellberg A , Carlsson P, Bergh C: Randomized single versus double embryo transfer: obstetric and paediatric outcome and cost-effectiveness analysis . Hum Reprod 2006 ; 21 : 210 – 6 . Google Scholar Crossref Search ADS PubMed WorldCat 11. Fiddelers AA , van Montfoort AP, Dirksen CD, et al. : Single versus double embryo transfer: cost-effectiveness analysis alongside a randomized clinical trial . Hum Reprod 2006 ; 21 : 2090 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat 12. Dixon S , Faghih Nasiri F, Ledger WL, et al. : Cost-effectiveness analysis of different embryo transfer strategies in England . BJOG 2008 ; 115 : 758 – 66 . Google Scholar Crossref Search ADS PubMed WorldCat 13. Van Heesch MMJ , Van Asselt ADI, Evers JLH, et al. : Cost-effectiveness of embryo transfer strategies: a decision analytic model using long-term costs and consequences of singletons and multiples born as a consequence of IVF . Hum Reprod 2016 ; 31 : 2527 – 40 . Google Scholar Crossref Search ADS PubMed WorldCat 14. Crawford S , Boulet SL, Mneimneh AS, et al. : Costs of achieving live birth from assisted reproductive technology: a comparison of sequential single and double embryo transfer approaches . Fertil Steril 2016 ; 105 : 444 – 50 . Google Scholar Crossref Search ADS PubMed WorldCat 15. Wu M , Henne M, Propst A: Tax credits, insurance, and in vitro fertilization in the US military health care system . Mil Med 2012 ; 177 : 745 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat 16. Luke B , Brown M, Wantman E, et al. : A prediction model for live birth and multiple births within the first three cycles of assisted reproductive technology . Fertil Steril 2014 ; 102 : 744 – 52 . Google Scholar Crossref Search ADS PubMed WorldCat 17. Luke B , Brown MB, Wantman E, et al. : Application of a validated prediction model for in vitro fertilization: comparison of live birth rates and multiple birth rates with one embryo transferred over two cycles versus two embryos in one cycle . Am J Obstet Gynecol 2015 ; 212 : 676.e1 – 7 . Google Scholar Crossref Search ADS WorldCat 18. Reichman D , Rosenwaks Z. GnRH antagonist-based protocols for in vitro fertilization. In: Human Fertility, 2014. Methods in Molecular Biology (Methods and Protocols) , Vol. 1154 . Edited by Rosenwaks Z, Wassarman P. New York, NY , Humana Press , 2014 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 19. Van Heesch MMJ , Evers JLH, Dumoulin JCM, et al. : A comparison of perinatal outcomes in singletons and multiples born after in vitro fertilization or intracytoplasmic sperm injection stratified for neonatal risk criteria . Acta Obstet Gynecol Scand 2014 ; 93 : 277 – 86 . Google Scholar Crossref Search ADS PubMed WorldCat 20. Declercq E , Luke B, Belanoff C, et al. : Perinatal outcomes associated with assisted reproductive technology: the Massachusetts outcomes study of assisted reproductive technologies (MOSART) . Fertil Steril 2015 ; 103 : 888 – 95 . Google Scholar Crossref Search ADS PubMed WorldCat 21. Luke B , Brown MB, Wantman E, et al. : Risk of severe maternal morbidity by maternal fertility status: a US study in 8 states . Am J Obstet Gynecol 2019 ; 220 : 195.e1 – 195 . Google Scholar Crossref Search ADS WorldCat 22. Cutting R : Single embryo transfer for all . Best Pract Res Clin Obstet Gynaecol 2018 ; 53 : 30 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat 23. Griffin D , Brown L, Feinn R, et al. : Impact of an educational intervention and insurance coverage on patients' preferences to transfer multiple embryos . Reprod Biomed Online 2012 ; 25 : 204 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat 24. SART : What Are My Chances with ART? Available at https://www.sartcorsonline.com/Predictor/Patient; accessed June 27, 2019 . 25. Neal SA , Morin SJ, Franasiak JM, et al. : Preimplantation genetic testing for aneuploidy is cost-effective, shortens treatment time, and reduces the risk of failed embryo transfer and clinical miscarriage . Fertil Steril 2018 ; 110 : 896 – 904 . Google Scholar Crossref Search ADS PubMed WorldCat 26. SART : Final Clinical Summary Report , 2016 . Available at https://www.sartcorsonline.com/rptCSR_PublicMultYear.aspx?ClinicPKID=2253; accessed June 27, 2019 . 27. Baker V , Luke B, Brown M, et al. : Multivariate analysis of factors affecting probability of pregnancy and live birth with in vitro fertilization: an analysis of the Society for Assisted Reproductive Technology Clinic Outcomes Reporting System . Fertil Steril 2010 ; 94 : 1410 – 6 . Google Scholar Crossref Search ADS PubMed WorldCat 28. Jordan N , Lee SE, Nowak G, et al. : Chlamydia trachomatis reported among U.S. active duty service members, 2000-2008 . Mil Med 2011 ; 176 : 312 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2020. This work is written by US Government employees and is in the public domain in the US. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2020.

Knutson, Kristine M; Gotts, Stephen J; Wassermann, Eric M; Lewis, Jeffrey D

doi: 10.1093/milmed/usaa142pmid: 32776114

Abstract Introduction The purpose of this study was to explore the effect of low testosterone level on whole-brain resting state (RS) connectivity in male veterans with symptoms such as sleep disturbance, fatiguability, pain, anxiety, irritability, or aggressiveness persisting after mild traumatic brain injury (mTBI). Follow-up analyses were performed to determine if sleep scores affected the results. Materials and Methods In our cross-sectional design study, RS magnetic resonance imaging scans on 28 veterans were performed, and testosterone, sleep quality, mood, and post-traumatic stress symptoms were measured. For each participant, we computed the average correlation of each voxel’s time-series with the rest of the voxels in the brain, then used AFNI’s 3dttest++ on the group data to determine whether the effects of testosterone level on whole-brain connectivity were significant. We then performed follow-up region of interest-based RS analyses of testosterone, with and without sleep quality as a covariate. The study protocol was approved by the National Institute of Health’s Combined Neuroscience Institutional Review Board. Results Sixteen participants reported repeated blast exposure in theater, leading to symptoms; the rest reported exposure to a single blast or a nonblast TBI. Thirty-three percent had testosterone levels <300 ng/dL. Testosterone level was lower in participants who screened positive for post-traumatic stress disorder compared to those who screened negative, but it did not reach statistical significance. Whole-brain connectivity and testosterone level were positively correlated in the left parahippocampal gyrus (LPhG), especially in its connectivity with frontal areas, the lingual gyrus, cingulate, insula, caudate, and right parahippocampal gyrus. Further analysis revealed that the effect of testosterone on LPhG connectivity is only partially mediated by sleep quality. Sleep quality by itself had an effect on connectivity of the thalamus, cerebellum, precuneus, and posterior cingulate. Conclusion Lower testosterone levels were correlated with lower connectivity of the LPhG. Weaknesses of this study include a retrospective design based on self-report of mTBI and the lack of a control group without TBI. Without a control group or pre-injury testosterone measures, we were not able to attribute the rate of low testosterone in our participants to TBI per se. Also testosterone levels were checked only once. The high rate of low testosterone level that we found suggests there may be an association between low testosterone level and greater post-traumatic stress disorder symptoms following deployment, but the causality of the relationships between TBI and deployment stress, testosterone level, behavioral symptomatology, and LPhG connectivity remains to be determined. Our study on men with persistent symptoms postdeployment and post-mTBI may help us understand the role of low testosterone and sleep quality in persistent symptoms and may be important in developing therapeutic interventions. Our results highlight the role of the LPhG, as we found that whole-brain connectivity in that region was positively associated with testosterone level, with only a limited portion of that effect attributable to sleep quality. INTRODUCTION Following deployment, military service members often report symptoms such as sleep difficulties, fatigue, and low mood,1 each of which has been associated with testosterone.2–6 Symptoms have frequently been attributed to a mild traumatic brain injury (mTBI) or post-traumatic stress disorder (PTSD).1,7,8 The stress of the military environment, particularly during deployment, has mixed effects on serum hormone levels, including testosterone.9 For example, serum testosterone levels were significantly reduced after weeks-long military training, but recovered within 3 days.10–12 However, stress can also increase testosterone levels. For example, testosterone levels were significantly increased 1 and 6 months after return from deployment compared to pre-deployment levels.9 Another study of combat veterans13 reported no difference in plasma testosterone levels between those with combat-related PTSD and healthy controls, possibly reflecting endocrine levels in chronic, rather than acute, stress; however, men with depression were excluded from that study. Our study was part of a broader study of postdeployment symptoms in U.S. service members and veterans experiencing at least one deployment-related concussion occurring during deployment in Iraq and Afghanistan in support of Operation Enduring Freedom (2001–2014), Operation Iraqi Freedom (2003–2010), and Operation New Dawn (2010–2011). In light of the known effects of TBI on testosterone, and testosterone’s beneficial effects on mood, functional improvement post-TBI, behavior (especially aggression and competition), and cognition (including spatial cognition),14–18 we performed an exploratory, data-driven analysis of the effects of testosterone level on whole-brain resting state (RS) connectivity. RS functional connectivity is a measure of the correlation between the fluctuations in blood oxygen level dependent signals from two brain areas while the subject is at rest, and it is considered to be a measure of the strength of the neural connection between them.19 Correlational analyses can be performed between connectivity and behavioral or clinical measures to explore for regions whose connectivity is related to those measures. Following an exploratory analysis, a detected region can be used as a seed to determine more specifically which regions drove the connectivity results.20,21 As sleep quality is related to depression and likely contributes to the onset of PTSD, we wanted to explore its effects on our RS connectivity results.8 Our RS connectivity analyses looked primarily at the effects of testosterone alone, and secondarily with sleep quality as an additional covariate, and with sleep quality alone. We also tested for correlations among testosterone, sleep quality, and depression to better understand the effects of testosterone on symptoms persisting after mTBI. We also split participants into a positive PTSD screen group and a negative one to compare mean testosterone levels, and we performed voxel-based morphometry (VBM) to find associations between gray matter volume (GMV) and testosterone level. MATERIALS AND METHODS Participants We enrolled 28 male active duty U.S. service members and veterans with a history of deployment-related mTBI including blast exposure meeting the definition in the Department of Veterans Affairs/Department of Defense Clinical Practice Guidelines for the Management of Concussion, 2009, occurring at least 6 months prior. Recruitment occurred between 2012 and 2015. Initial contact with prospective TBI participants was made by members of the Defense and Veterans Brain Injury Center network of sites at military treatment facilities and the Department of Veterans Affairs medical centers, and their associated staff. The study protocol was approved by the National Institute of Health’s Combined Neuroscience Institutional Review Board. Written informed consent was obtained from all participants after the procedures had been fully explained. Inclusion criteria included self-report of any of the following somatic or behavioral symptoms, not present before, but developing within 3 months after mTBI, and may or may not have been present at enrollment: sleep disturbance, easy fatiguability, headache or other chronic widespread pain unrelated to physical injury; emotional lability, apathy or lack of spontaneity; lack of motivation; feelings of anxiety; personality change noticed by self or others; and irritability or aggressiveness. Exclusion criteria were inability to tolerate magnetic resonance imaging (MRI); daily use of psychomotor stimulants, narcotics, hypnotics, or anxiolytics; diagnosis of sleep apnea, thyroid disorder, or rheumatoid arthritis; history of brain injury associated with loss of consciousness lasting longer than 24 hours; daily use of more than five cups of coffee; or more than one headache per month before deployment. Mean age was 34.4 ± 7.5 (range 26–49). Educational level ranged from 13 to 18 years (mean = 14.6). All participants were right-handed. Sixteen reported repeated blast exposure in theater leading to symptoms; the rest reported exposure to a single blast or a non-blast TBI. See Supplementary Table S1 for details on participants’ head and body injuries. Clinical MRI scans were interpreted by a board-certified neuroradiologist. One participant did not complete the RS scanning session because of claustrophobia and another’s RS dataset was unusable because of excessive motion/scanner artifact, resulting in a total of 26 participants for RS analysis; however, serum testosterone levels were not obtained for two other participants, leaving a total of 24 participants for RS analysis using testosterone measures (see Fig. 1). Anatomical images were available for all 28, so those with testosterone measures (n = 26) were included in the VBM analysis. FIGURE 1 Open in new tabDownload slide Enrollment Numbers at Each Step. Procedure Participants were evaluated over 2 days at the Clinical Research Center of the National Institutes of Health in Bethesda, Maryland, USA. On day 1, a clinical history, a neurological examination, and experimental neuropsychological testing were performed. MRI scans were performed in the afternoon. On day 2, serum testosterone levels were measured at 8 a.m. Samples were analyzed by the National Institutes of Health Clinical Research Center clinical pathology laboratory. Experimental neuropsychological testing continued on day 2. MRI Acquisition Each participant completed an MRI session on a Siemens 3T Biograph scanner provided by the Center for Neuroscience and Regenerative Medicine. We acquired a T1-weighted MPRAGE (TR = 2530, TE = 3.03, matrix size = 256 × 256, flip angle = 7, slices = 176, slice thickness = 1), along with a 7.5 minutes RS scan of 150 T2*-weighted echoplanar volumes (TR 3000 = ms, TE = 25 ms, flip angle = 90°, slice thickness = 3 mm, slices = 43, voxel size = 1.7188 × 1.7188 × 3 mm, FOV = 1540 × 1540 mm, matrix = 128 × 128), during which participants were asked to keep their eyes closed, but not to sleep. We did not verify wakefulness. Neuropsychological Tests As part of a larger neuropsychological test battery, participants completed the Pittsburgh Sleep Quality Index (PSQI), which indicates the likelihood of a sleep disorder22; The Center for Epidemiological Studies Depression scale (CES-D), a self-report measure of depressive symptoms23; and the PTSD Checklist (PCL), a brief screening checklist for PTSD.24 Statistical Analysis MRI Analysis Preprocessing of the RS MRI data included despiking, interpolation to correct for slice time acquisition differences, and spatial registration to the first volume to minimize effects of head motion using AFNI.25 The RS images were blurred using a FWHM of 6 mm and rescaled to percent signal change. Anatomical images were segmented into maps of gray matter (GM), white matter, and cerebral spinal fluid using FreeSurfer, Version 5.3.0.26 Next, we followed ANATICOR procedures to remove artifacts, including six motion parameters, average signal (within a radius of 15 mm) within eroded white matter and cerebral spinal fluid, along with respiration and cardiac signals (nine Retroicor regressors and five Respiration Volume per Time, regressors).27–29 Cardiac and respiration signals were not available for three participants; these participants were included in the analyses. The residual time-series data were then transformed into Talairach space and standardized to z scores using the Fisher transformation. Global GM was not regressed out to avoid potential distortion of the correlation values.30 Following preprocessing, we created whole-brain “connectedness” maps for each subject’s RS data. Whole-brain connectedness is the average correlation of each voxel’s time-series with the rest of the voxels in the brain. AFNI’s 3dttest++, which runs t-tests on groups of 3D datasets, was performed to determine the effect of the continuous covariate of each subject’s testosterone level while removing the effect of head motion on their whole-brain connectedness data at each voxel. This procedure included only voxels where at least 90% of participants had data. In follow-up analyses, we used the significant result from the first analysis as a seed region, computed the average RS time-series in that seed region and correlated it with that of all other voxels in the brain for each participant. This resulted in a voxelwise functional connectivity map from the seed region for each participant. Testosterone level was then correlated with the seed-based functional connectivity maps across subjects in each voxel. These voxelwise correlations with testosterone level were then thresholded, correcting for multiple comparisons using cluster size. The first follow-up analysis showed which brain regions contributed to the connectedness results for testosterone. We then added sleep quality with and without testosterone as a covariate in a second and third follow-up analysis. To control for different motion levels across participants, each participant’s measure of transient head motion across consecutive time points (AFNI’s @1dDiffMag), which is comparable to the average framewise displacement,31 was calculated and entered as a mean-centered covariate into each group-level analysis. Our analyses were two-tailed and used a voxelwise threshold of P < 0.005, corrected to an alpha of 0.05 using AFNI’s empirically modeled spatial auto-correlation function. This resulted in a cluster size threshold of 166 voxels to be considered significant in our RS analyses. For VBM, T1 images were segmented, normalized, modulated, and smoothed using an 8 × 8 × 8 full width at half maximum Gaussian kernel using SPM12 and the CAT12 toolbox,32 running on MATLAB R2016a using the defaults unless otherwise specified. A positive multiple regression was performed on the segmented GMVs and level of testosterone (where significant results would mean higher levels of testosterone were associated with increased tissue volume). A negative test was also computed as a quality control check. An overall significance threshold of P = 0.001 with family-wise error correction for multiple comparisons was used. Age and total intracranial volume were included as covariates of no interest. In addition to the whole-brain VBM analysis, a VBM analysis was performed in the LPhG region of interest (ROI) that resulted from the RS analysis with testosterone to determine whether there were any structural changes within this ROI that correlated with testosterone levels. Neuropsychological Tests Analysis We used SPSS (Version 20) to calculate the means and standard deviations for PSQI, CES-D, and PCL scores. We also used SPSS to compute the Spearman rank order correlation coefficients between PSQI and CES-D scores with testosterone level at P < 0.05. We used Spearman’s Rho as the variables were not normally distributed. We split participants into two groups based on PCL scores, those with scores above the cut-off of 50 (positive PTSD screen) and those below (negative PTSD screen), then used an independent samples t-test to compare the mean testosterone level between groups. RESULTS Testosterone Level Results The mean serum testosterone level for those participants with usable RS data (n = 24) was 376 ± 107 ng/dL. Thirty-three percent of those participants had testosterone levels below 300 ng/dL, a level the American Urological Society considers a reasonable cut-off for diagnosis of low testosterone.33 For comparison, the mean serum testosterone level for all 26 participants with testosterone measures was similar: 383 ± 107 ng/dL, with 30.77% below the cut-off. As expected for the age range of our participants, testosterone level was not significantly associated with age (Spearman’s one-tailed, P = 0.09). Testosterone and Neuropsychological Test Results The mean PSQI score was 11.3 ± 3.9, with 24 of 26 participants scoring higher than five, indicating the likelihood of a sleep disorder. The mean CES-D score was 15.9 ± 10.3, with 12 of 28 participants scoring above 16 and therefore considered at risk for depression. The mean PCL score for those participants who also had testosterone measures was 39.2 ± 15.4, with six out of 26 participants scoring higher than the cut-off of 50 for positive screening for PTSD. These six participants had lower testosterone levels than the group with a negative PTSD screen, but the difference did not quite reach statistical significance (312.0 vs. 404.6, t24 = 1.96, P = 0.06; see Fig. 2). FIGURE 2 Open in new tabDownload slide Boxplots Comparing Serum Testosterone Levels for the Group of Subjects Who Screened Negative for PTSD (n = 20) vs. Those Who Screened Positive (n = 6). There was a significant negative correlation between testosterone and PSQI score (R = −0.36, P = 0.04), but not between testosterone and CES-D score (R = −0.18, P = 0.19). PSQI, CES-D, and PCL scores were all highly correlated (Rs > 0.58, Ps ≤ 0.001). Imaging Results As shown in Supplementary Table S1, some participants had microhemorrhages or small white matter lesions, but otherwise normal MRIs. As these regions were small and most affected white matter, we did not expect them to affect the results of RS analyses and did not remove any lesion areas from our analyses. Whole Brain RS Results Testosterone level had a significant positive association with whole-brain RS connectivity in the left parahippocampal gyrus (LPhG). No other area met the cluster size threshold (see Fig. 3a). FIGURE 3 Open in new tabDownload slide (a) Coronal View of Testosterone’s Effects on Whole-Brain RS Connectivity Overlaid on the AFNI TT_N27 Atlas. Warm Colors Indicate Significantly Increased Whole-Brain RS Connectivity with Increasing Testosterone Level in the LPhG (Peak at −25, −7, −22, Size = 196 Voxels), Indicating that Subjects With High Testosterone Levels Tended to Have More Whole Brain Connectivity to/from the LPhG. P < 0.005, Corrected to P < 0.05, Two-Tailed, Minimum Cluster Size = 166 Contiguous Voxels. (b) Scatterplot of Testosterone Level and LPhG Connectivity. ROI RS Results In a follow-up seed-to-whole-brain RS analysis, we used the LPhG results from our first RS analysis as the seed ROI and testosterone level as a covariate. We found that testosterone level was significantly correlated with LPhG RS connectivity to many brain areas (see Supplementary Table S2; Fig. 4. FIGURE 4 Open in new tabDownload slide Regions Meeting the Cluster Size Threshold Where Whole-Brain Connectivity to/from the LPhG ROI was Significantly Correlated with Testosterone Level Across Subjects. The LPhG ROI Seed Used in This Analysis Resulted From the Whole-Brain Testosterone Analysis. P < 0.005, Corrected to P < 0.05, Two-Tailed, Minimum Cluster Size = 166 Contiguous Voxels. When we performed this ROI analysis again with testosterone level as a covariate, but removed the effects of PSQI score, some areas of connectivity were lost or attenuated, including the precuneus, cerebellum, caudate and left inferior and medial frontal gyri, but others remained, including the right anterior cingulate, right precuneus, left middle frontal gyrus, right lingual gyrus, bilateral superior parietal cortex, and the cerebellum (see Supplementary Table S3 and Supplementary Fig. S1). When examining the effects of PSQI alone (without considering testosterone), significant negative correlations were observed in a bilateral cluster that included parts of the thalamus and cerebellum (size = 1590 voxels; peak 3, −7, −2) and the precuneus and cingulate gyrus (BA 31; size = 379 voxels, peak −1, −63, 28; see Supplementary Fig. S2). Disordered sleep, as indicated by the PSQI, was associated with weaker connectivity with the LPhG in these regions. Notably, the effects of PSQI alone did not overlap with the effects of testosterone that were lost when PSQI was added as a covariate. Taken together, these results suggest that a limited portion of the effect of testosterone on functional connectivity of the LPhG is attributable to sleep quality. Testosterone and VBM Results A significant positive association between testosterone level and GMV, which met the family-wise error correction for size, was found only in the posterior lobe of the right cerebellar hemisphere (39, −68, −26; 55 voxels). No significant negative correlations were found. The VBM analysis within the LPhG ROI did not produce any significant results. DISCUSSION Testosterone Levels One-third of our participants had levels below the cut-off for low testosterone, which has been associated with PTSD.9 Our PTSD positive screen group had lower testosterone levels than the negative group, but the difference did not reach statistical significance (P = 0.06). We found that testosterone level was significantly and positively correlated with whole-brain RS functional connectivity of the LPhG, and we found a significant negative correlation between testosterone level and sleep problems. The effect of testosterone on the parahippocampal gyrus (PhG) and sleep quality is discussed below. PHG and Testosterone The PhG is considered a primary hub of the default mode network,34 and its connectivity and function are affected by testosterone. For example, the long-range functional connectivity of the PhG was found to be higher in men than women, possibly because of the effects of testosterone on this region.35 Also, supraphysiological testosterone doses in eugonadal elderly men were associated with decreased activity in the right entorhinal cortex/amygdala, and increased activity in left entorhinal cortex, RPhG, right posterior hippocampus, and bilateral prefrontal cortex.36 Both exogenous and endogenous testosterone affected bilateral PhG activation in men in response to affective stimuli.37 Studies of GMV in patients with medical conditions affecting testosterone level have revealed volume differences in the PhG compared to normal controls. For instance, an increase in GMV in familial male precocious puberty with its continuous unregulated testosterone secretion38 and a positive correlation between testosterone and GMV in bilateral parahippocampus in Klinefelter syndrome where testosterone is deficient39 were found. We found, however, that testosterone level did not affect LPhG GMV, but was associated with GMV only in the right cerebellum, which is consistent with the right cerebellum being larger in men than women.39 Our imaging results show that testosterone level has an effect on RS connectivity of the PhG, but not its volume. Sleep Quality We found a significant negative association between testosterone level and PSQI score, meaning that lower testosterone level was associated with disrupted sleep. Others have found similar relationships.5 Sleep disturbances may have contributed to our participants’ low testosterone levels, as well as their fatigue and mood complaints. Sleep complaints are common following deployment,40 as well as mTBI,41 and this was true for our participants. Even though we excluded those with sleep apnea, 24 of 26 participants scored greater than five on the PSQI and were therefore at risk for sleep problems. When we removed the effect of PSQI score in our LPhG ROI-to-whole-brain connectivity analysis with testosterone level, we found fewer areas of connectivity than when only testosterone level was included. The results show that in our participants, sleep quality had an effect on LPhG functional connectivity which was different from that of testosterone level. Others have also found that the PhG is associated with sleep quality. For example, reduced GM volume in the LPhG was found in people with obstructive sleep disorder and GM volume increased after treatment.42 Also, significantly decreased fractional anisotropy in PhG was found bilaterally in patients with mTBI and sleep disturbance, compared to those with histories of mTBI but without sleep disturbance.43 LIMITATIONS Limitations of this study include a retrospective design based on self-report of mTBI—many could not quantify their number of blast exposures—and the lack of a control group without TBI. Importantly, without a control group or pre-injury testosterone measures, we were not able to attribute the rate of low testosterone in our participants to TBI per se, although several studies have found low testosterone to be common after TBI, even in the chronic stage.14,15,44 Another limitation is that testosterone levels were checked only once during the study. Also, since sleep problems, depression and PTSD symptoms were highly correlated, we cannot separate their effects. There is a need to determine whether changes in LPhG connectivity associated with testosterone level cause changes in learning and sensitivity to reward45 or emotional memory,46,47 or increased fatigue,16 and to help untangle the complex relationships between testosterone, LPhG connectivity, and persistent symptoms after mTBI, but we were not able to do so because of correction for multiple comparisons considerations. The causality of the relationships between TBI and deployment stress, testosterone level, behavioral symptomatology, and LPhG connectivity remains to be determined. In addition to TBI, all of our participants were exposed to the stress of prolonged deployment in a conflict zone. Deployment stress affects the prevalence and severity of postconcussive symptoms and may have other effects which are difficult to distinguish from those of mTBI.48 To dissociate the effects of physical trauma and stress in this population would require study of previously deployed service members with equal stress exposure but no histories of TBI, and individuals with comparable histories of TBI, but no deployment stress, groups which are difficult to recruit. However, the aim of this study was to explore the physiological basis of a commonly recognized and socially important syndrome48 and develop hypotheses relevant to treatment, rather than to determine its ultimate causation. CONCLUSION In this exploratory study, we found that testosterone level wassignificantly and positively correlated with whole-brain RS functional connectivity of the LPhG in male service members/veterans with history of deployment-related mTBI and persistent complaints, and that only a limited portion of that effect was attributable to sleep quality. We also found an association between low testosterone level and sleep problems. These results might suggest that testosterone level be considered when making treatment recommendations for patients with persistent complaints after TBI. However, as there have been only a few studies of testosterone therapy after TBI,6,49,50 further research is needed to determine whether low testosterone and the associated lowered LPhG connectivity contribute to persistent symptoms following TBI. Our connectivity results are intriguing given the role of the PhG in the default mode network, and further investigation should be performed to determine whether PhG connectivity could be used to monitor treatment efficacy. A poster summarizing this work was presented at the Organization of Human Brain Mapping Conference, Vancouver, Canada, June 28, 2017. The work was performed at the Behavioral Neurology Unit, National Institute of Neurological Disorders and Stroke Clinical Neuroscience Program, National Institutes of Health, Bethesda, Maryland, USA. Funding came from the Clinical Neuroscience Program of the National Institute of Neurological Disorders and Stroke, National Institutes of Health (1ZIANS002977). Dr Gotts is supported by the National Institute of Mental Health, Division of Intramural Research (1ZIAMH002588). The view(s) expressed herein are those of the authors and do not reflect the official policy or position of the Department of the Air Force, Department of Defense, or the US Government. The authors declare they have no financial relationships with commercial interests. ACKNOWLEDGMENTS We appreciate the technical support of the Center for Neuroscience and Regenerative Medicine, and we gratefully acknowledge the generous time and effort the service members made in supporting this study. REFERENCES 1. Lei K , Metzger-Smith V, Golshan S, et al. : The prevalence of headaches, pain, and other associated symptoms in different Persian gulf deployment periods and deployment durations . SAGE Open Med 2019 ; 7 : 1 – 12 . Google Scholar Crossref Search ADS WorldCat 2. Bercea RM , Mihaescu T, Cojocaru C, et al. : Fatigue and serum testosterone in obstructive sleep apnea patients . Clin Respir J 2015 ; 9 : 342 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat 3. 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This work is written by US Government employees and is in the public domain in the US. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2020. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Pak, Kevin; Junga, Zachary; Mertz, Andrew; Singla, Manish

doi: 10.1093/milmed/usaa141pmid: 32633752

Abstract Introduction Helicobacter pylori (H. pylori) infection affects about half of the world’s population and can lead to multiple complications if left untreated. Testing for H. pylori infection in appropriate patients with prompt treatment followed by the testing of eradication is the standard of care in the United States. Active Duty Service members (ADSMs) in the U.S. military are a unique patient population that may be at higher risk for acquiring H. pylori infection given frequent deployments to developing countries. Noninvasive diagnostic strategies include the urea breath test, the stool antigen test, and serologic testing, which include H. pylori immunoglobulin M (IgM), immunoglobulin A (IgA), and immunoglobulin G (IgG) antibodies. Among noninvasive methods, the least sensitive is serology, and although there is clinical utility in testing for H. pylori IgG antibodies, H. pylori IgA or IgM antibodies have limited clinical utility. Despite this, H. pylori IgA and IgM antibodies are still widely ordered across the Military Health System. Materials and Methods In order to determine how frequently this testing is being ordered and the associated cost, we conducted a retrospective cross-sectional study of H. pylori serologic testing utilization in the MHS from October 2015 to September 2018 in adult patients using the MHS Data Repository. All H. pylori IgM, IgA, and IgG antibodies, H. pylori stool antigen tests, and H. pylori urea breath tests were queried during this time period across all ADSMs, retirees, and ADSM dependents for all adults. Cost information was obtained from LabCorp, and the institutional price used for cost analysis was the same throughout all military treatment facilities in the Department of Defense (DOD). Results We discovered that over a 3-yr period, 1,916 H. pylori IgA and 2,492 IgM antibodies were ordered. In total, the DOD spent close to $400,000 on antibody-based testing for H. pylori not accounting for indirect associated costs like personnel, supplies, repeat testing, as well as the costs of delayed diagnosis and associated complications. Conclusion H. pylori IgM and IgA have limited clinical utility, are inaccurate, and are costly to maintain, especially when more accurate alternative tests are available. Based on our analysis, we strongly recommend the removal of the H. pylori IgA and IgM serologic tests throughout the DOD in order to improve the efficiency and quality of care for patients suspected of having an H. pylori infection. Further research is needed to determine how these tests are ordered, how providers are responding to the results of the serologic tests, and if noninvasive testing is being ordered appropriately. INTRODUCTION Helicobacter pylori (H. pylori) infection is one of the most common infections worldwide; estimates show almost half of the global population has been colonized.1,2 Patients chronically infected or colonized, with H. pylori have a 10 to 20% lifetime risk of developing peptic ulcer disease (PUD) and a 1 to 2% risk of developing gastric carcinoma.3,4,H. pylori infection is also associated with mucosa-associated lymphoid tissue lymphoma.3,5,6 Once diagnosed, H. pylori can be treated with a combination of antimicrobial agents and proton pump inhibitors.5 Active Duty Service members (ADSMs) in the U.S. military are at higher risk for infection compared with adults in developed countries because of frequent deployments to developing countries throughout the world.7 Without efficient diagnosis and treatment, unaddressed H. pylori infection can have serious long-term health effects.3 ADSMs use a considerable amount of nonsteroidal anti-inflammatory drugs (NSAIDs),8 and H. pylori infection increases the risk of NSAID-related gastrointestinal complications.1,9 This increased risk of complications directly impacts medical readiness. Testing is recommended in patients with active PUD, past history of PUD without documentation of prior cure of H. pylori infection, low-grade mucosa-associated lymphoid tissue lymphoma, history of endoscopic resection of early gastric cancer, uninvestigated dyspepsia with age less than 60 yr, long-term low-dose aspirin use, unexplained iron deficiency anemia, and idiopathic thrombocytopenic purpura.1 There are many diagnostic modalities available for testing patients suspected of having H. pylori infection. Among them is serologic testing of H. pylori antibodies, which include immunoglobulin M (IgM), immunoglobulin A (IgA), and immunoglobulin G (IgG) antibodies. Among the serologic tests, the most sensitive is the H. pylori IgG10, whereas the H. pylori IgM and IgA arguably have no substantial clinical utility.10–12H. pylori serologic testing for IgA and IgM is being used despite more accurate tests being available. We conducted a retrospective analysis of H. pylori serologic testing utilization across the Military Health System (MHS) from fiscal year October 2015 to September 2018 in adult patients in order to better quantify use and spending on low yield tests, which may complicate the diagnosis and treatment of H. pylori infections in the Department of Defense (DOD). METHODS We conducted a retrospective cross-sectional study using the MHS Data Repository from October 2015 to September 2018. The MHS Data Repository includes records of all TRICARE beneficiaries to include ADSMs, their dependents, and retirees across all military treatment facilities (MTF) in both inpatient and outpatient settings across the DOD. This data set did not include blood work conducted outside an MTF. All H. pylori IgM, IgA, and IgG antibodies, H. pylori stool antigen tests (SATs), and H. pylori urea breath tests (UBTs) were queried during this time period across all ADSMs, retirees, and ADSM dependents for all adults (≥18-yr-old). The test type and results were extracted for the above dates. Test totals were determined accounting for varying methods of ordering serologies to include various combination panels of IgM, IgA, and IgG. Cost information was obtained from LabCorp. The institutional price that was used for the cost analysis was the same price across all Military Treatment Facilities (MTFs) in the DOD. This study was approved by the institutional review board at the Walter Reed National Military Medical Center. RESULTS From October 2015 to September 2018, 6,018 nonactive duty patients and 1,483 ADSMs had H. pylori testing performed in the MHS. There were 20,548 individual, unique tests; 362 were UBTs, 7,215 were SATs, and 12,971 were serologic H. pylori tests (Table I). Among the serologic tests, 1,546 H. pylori panels consisting of all 3 H. pylori immunoglobulins were ordered. There were 2,492 H. pylori IgM antibodies, and 1,916 H. pylori IgA antibodies ordered. Thus, 4,408 H. pylori IgM and IgA tests were ordered over a 3-yr period—approximately 21.5% of all H. pylori testing, not including gastric biopsies. In addition, 2750 patients of the 7501 (37%) who underwent testing had an IgA and/or an IgM ordered. The price of each H. pylori antibody test in the DOD is $30.01. In comparison, the price of each SAT is $50.52, and the price of UBT was $63.35. In total, the DOD spent approximately $389,919.93 on antibody-based testing for H. pylori IgA and IgM during the study period. This estimation only accounts for the direct charge for testing and does not include ancillary costs incurred with serological testing or costs associated with misdiagnosis or delays in care. Table I Number of H. pylori Tests Across the DOD (October 2015 to September 2018) IgG . 8,563 . IgA 1,916 IgM 2,492 Total Ig 12,971 Stool Ag 7,215 UBT 362 Total tests 20,548 IgG . 8,563 . IgA 1,916 IgM 2,492 Total Ig 12,971 Stool Ag 7,215 UBT 362 Total tests 20,548 DOD, Department of Defense; IgG, immunoglobulin G; IgA, immunoglobulin A; IgM, immunoglobulin M; Ig, immunoglobulin; Ag, antigen; UBT, urea breath test. Open in new tab Table I Number of H. pylori Tests Across the DOD (October 2015 to September 2018) IgG . 8,563 . IgA 1,916 IgM 2,492 Total Ig 12,971 Stool Ag 7,215 UBT 362 Total tests 20,548 IgG . 8,563 . IgA 1,916 IgM 2,492 Total Ig 12,971 Stool Ag 7,215 UBT 362 Total tests 20,548 DOD, Department of Defense; IgG, immunoglobulin G; IgA, immunoglobulin A; IgM, immunoglobulin M; Ig, immunoglobulin; Ag, antigen; UBT, urea breath test. Open in new tab DISCUSSION A common paradigm for the management of H. pylori is the “test and treat” model: testing for H. pylori infection and, if positive, starting treatment without resorting to a biopsy.13 The “gold-standard” for diagnosing H. pylori infection has been biopsy with histologic examination.3,6 UBT has a sensitivity of 95% and a specificity of 96%, and those of SATs are 95% and 94%, respectively.2 Serologic testing has a sensitivity from 85 to 92% and a specificity from 79 to 83%2; however, serologic testing cannot distinguish between active infection and prior exposure, so it cannot be used to confirm eradication.2,3,6,14 The H. pylori IgG is the only appropriate antibody test to use in patients with documented PUD and/or active bleeding given that H. pylori infection is a chronic infection.1,13 The IgM and IgA are rarely useful.10,12 The sensitivity and specificity of H. pylori IgG is 80 to 100% and 69 to 95%, respectively.15 The sensitivity of H. pylori IgM for adults, using SAT as the gold standard, is 4.4% and the specificity is 93.4%.10 IgM antibodies are only elevated in acute infections and can miss chronic H. pylori infections.16 Regarding H. pylori IgA, one comparison found that the sensitivity and specificity of IgG was 96 and 75%, respectively, and those of IgA were 81 and 79%, respectively.12 Another comparison reported the sensitivity and specificity of IgG as 92 and 84%, respectively, and those of IgA as 80 and 89%, respectively.17 Although the H. pylori IgA antibody detects chronic infections like IgG, it is not as accurate as the IgG given IgA antibodies are localized to the mucosa, which may allow for IgA antibody levels to under-detect the infection.12,16,H. pylori IgA antibody may be useful when ordered with the IgG antibody, as one study demonstrated that using both tests resulted in a positive predictive value of 95.7% and a negative predictive value of 97.7%, which are greater than each test by itself11. However, keeping the IgA antibody for this sole purpose retains the potential for misuse, because it assumes that providers across the MHS are aware of this synergistic effect. Clinical guidelines do not mention the utility of either IgM or IgA antibodies. Also, in the clinical setting, any discrepancy between the IgG and IgA tests would likely prompt the clinician to order more accurate tests like the SAT or UBT, which are readily available. Although each antibody individually is less expensive than the UBT or SAT, often providers order all of the H. pylori antibodies, thus increasing the cost of testing. Our reported total cost is likely an underestimation. Further indirect costs include the time of the phlebotomists, basic supplies required for sampling, and costs and risks associated with delays in treatment for H. pylori or phlebitis associated with venipuncture for blood samples. When compared with other noninvasive tests, serologic tests can be misleading. As antibodies to H. pylori can remain detectable in the serum a long time after eradication, a positive H. pylori serology can be a false-positive resulting in unnecessary treatment in patients who no longer have active infection or who already completed eradication therapy.2,3,6,14 Because of the false-positive rates associated with the antibody-based tests, providers will usually check either a UBT or SAT in patients with a positive H. pylori antibody: there may be a double cost associated with providers ordering H. pylori serologies. As the numbers show, over 20% of H. pylori testing consists of serology testing for IgA and IgM, and over a third of patients received IgA and/or IgM testing that was suboptimal and often misleading. If H. pylori IgM and IgA were removed as testing options, we posit that we would have more efficient testing with better diagnosis and timely treatment. By extension, we recommend that patients with suspected H. pylori infection should be ideally assessed with UBT or SAT and that serologic testing for H. pylori infection should be limited only to the IgG antibody. In conclusion, H. pylori IgM and IgA have limited clinical utility, are inaccurate, and are costly to maintain, especially when more accurate alternatives are available. Based on the data collected and our analysis of the performance of the serologic tests, we strongly recommend the removal of the H. pylori IgM and IgA serologic tests throughout the MHS in order to improve the efficiency and quality of care for patients suspected of having an H. pylori infection as well as to reduce unnecessary spending in the DOD. Of note, the analysis presented does not factor behavioral responses of the providers ordering the tests; it only quantifies the number of the tests and cost associated. Research regarding whether or not the providers are ordering the aforementioned tests appropriately, how these providers are ordering serologic tests, and how they are responding to serologic test results require future investigations. In addition, it may be useful to determine the degree to which ADSMs are using NSAIDs chronically. Given the increased risk of ulcer complications in patients using long-term NSAIDs, current guidelines recommend that patients initiating long-term NSAID therapies should be tested for H. pylori infection1. NSAID use among ADSMs is widespread and knowing whether or not more individuals need to be tested for H. pylori based on the pattern of NSAID use further supports the need for efficient and streamlined diagnosis of H. pylori infection in the military8. The views expressed in this manuscript are those of the author and do not reflect the official policy of the Department of Army, Navy, Air Force, Department of Defense, or U.S. Government. REFERENCES 1. 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J Clin Microbiol 1993 ; 31 : 1450 – 3 . Google Scholar Crossref Search ADS PubMed WorldCat Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2020. This work is written by US Government employees and is in the public domain in the US. This work is written by US Government employees and is in the public domain in the US. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2020. This work is written by US Government employees and is in the public domain in the US.

Said, Sayf, A;Ordeñana, Carlos, X;Rezaei,, Majid;Figueroa, Brian, A;Dasarathy,, Srinivasan;Brunengraber,, Henri;Rampazzo,, Antonio;Gharb, Bahar, Bassiri

doi: 10.1093/milmed/usaa081pmid: 32627828

The article, “Ex-Vivo Normothermic Limb Perfusion With a Hemoglobin-Based Oxygen Carrier (HBOC) Perfusate,” that was published on February 19, 2020 contained an error. The fifth author Dr S. Dasarathy would like to declare his funding sources. The funding sources are National Institutes of Health (R21 AA022742, RO1 DK 113196, RO1 GM119174, R56HL141744, P50 AA024333, UO1 AA021890, UO1 AA026975, and UO1 DK061732) and the Mikati Foundation Grant support. The authors regret this error. Reference 1. Said SA, Ordeñana CX, Rezaei M et al: Ex-vivo normothermic limb perfusion with a hemoglobin-based oxygen carrier perfusate. Mil Med 2020; 185(Supplement_1): 110–20. Presented as a poster at the 2018 Military Health System Research Symposium, August 2018, Kissimmee, FL; abstract # MHSRS-18-1264 There are no conflict of interests to disclose. HBOC-201 was donated by Hemoglobin Oxygen Therapeutics LLC., Home Souderton, PA, USA. © The Association of Military Surgeons of the United States. All rights reserved. For permissions, please e-mail: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Khorram-Manesh, Amir; Robinson, Yohan; Boffard, Ken; Örtenwall, Per

doi: 10.1093/milmed/usaa071pmid: 32313926

Abstract Introduction The interaction between military and civilian healthcare systems has contributed to the development of medical care. Swedish innovations such as the Seldinger technique for angiography, Leksell Gamma Knife for cranial surgery, and the introduction of pacemakers and ultrasound have contributed to the global development of medicine. Several authors have described the Swedish civilian healthcare system and its development. However, the development and history of its military healthcare system and its influence on the civilian healthcare system remain untold. This review aims to describe the historical development of the Swedish military healthcare system and its path toward civilian-military collaboration and a total defense healthcare system. Material and Methods A search for all published scientific papers in Swedish and English, along with available legal documents and directives, was made. We used CINAHL, PubMed, Scopus, and Gothenburg University’s databases and search engines. The following keywords, Swedish, military, civilian, healthcare, collaboration, and development, were searched for, alone or in combination, using a PRISMA flow chart. Duplicates, abstracts, and nonscientific publications were excluded. Results Each of the four distinct periods of historical development in the Swedish military healthcare system can be characterized by the changes necessary for transforming Sweden from an aggressive to a defensive and collaborative nation, with national and international engagement. Collaboration not only encompasses readiness and willingness to share resources and information, and to adjust routines and guidelines, but also needs a culture of consensus and respect for each other’s limitations and capabilities. The definition of military medicine and the military physician’s role in Sweden is imperative for further civilian-military collaboration. Conclusions Recent global sociopolitical changes necessitate civilian-military healthcare collaboration. Although civilian-military healthcare partnerships in various medical fields have been reported earlier, the Swedish concept of total defense’s healthcare system integration and collaboration may be a more fruitful approach. The collaboration within the total defense healthcare system will result in technical achievements, innovations, and medical advancements for the benefit of the whole nation. Introduction The interaction between military and civilian healthcare systems has contributed to the development of medical care. It was during the Napoleonic Wars in the early nineteenth century that the organized form of military healthcare appeared. The use of ambulances during combat (ambulance volante), the introduction of triage, and the use of field hospitals a few miles back from the battlefield were all important factors in the development of military medicine. The need to support troops over long distances contributed to the development of medical logistics. The introduction of new evacuation systems and principles of resuscitation and trauma management from the point of injury is another important step that has gradually increased the survival rates in the military population. All these contributions have since then been disseminated successfully within the civilian healthcare system by physicians and others, many of whom were serving reservist physicians.1–4 Swedish innovations have contributed to the global development of medicine. Olof Rudbeck discovered the lymph vessels in 1652. Nils Rosén von Rosenstein published the first textbook in pediatric medicine and initiated the use of medical records in daily hospital care. Carl von Linné, a botanist, zoologist, and physician, became the father of modern taxonomy.5,6 Seldinger introduced the modern technique for angiography.7 The Gamma Knife for cranial surgery8 and pacemakers and ultrasound were introduced in medicine in the 1950s and 1970s, respectively.9,10 Omeprazole was introduced in the twentieth century.11 Several authors have described the advancement of the Swedish civilian healthcare system,12 but there are few, if any, descriptions regarding the development of the Swedish military healthcare system. Such a report can be of interest since, as a neutral country, Sweden has not been involved in a declared conflict for the last 200 years (since 1814), yet is actively engaged in multinational peacekeeping missions. Figure 1 Open in new tabDownload slide Flow diagram showing database outputs and selection of included studies for both objectives based on the PRISMA flow diagram.11 Figure 1 Open in new tabDownload slide Flow diagram showing database outputs and selection of included studies for both objectives based on the PRISMA flow diagram.11 The objective of this review is to describe the Swedish military healthcare system’s path toward civilian-military collaboration from a historical point of view and the current policy aiming at civilian-military collaboration and total defense healthcare. Method and Material A search for all published scientific papers in Swedish and English, along with available legal documents and directives, was made. We used CINAHL, PubMed, Scopus, and Gothenburg University’s databases and search engines. The following keywords, Swedish, military, civilian, healthcare, collaboration, and development, were searched for, alone or in combination, using the PRISMA flow chart.13Figure 1 shows the process of search and the number of hits in each step. Duplicates, abstracts, and nonscientific publications were excluded. Results Historically, the development of Swedish civilian and military healthcare has been closely associated and can be divided into four distinct periods: (1) war period (before 1814–1911), (2) peace and development period (1813–1911), (3) period of neutrality and international engagement (1912–1945), and (4) period of civilian-military collaboration (1946 to current). Swedish Military Healthcare Before 1814: The War Period During this period, Sweden was constantly involved in wars with all neighboring countries. The treatment of war injuries and of infectious disease was practiced by both monks and barbers. The monks’ influence on surgery declined gradually, allowing the barbers to practice surgery by performing different tasks such as cutting beard and hair, phlebotomy, tooth extractions, and drainage of abscesses in the civilian population.14,15 During the wars in the sixteenth to nineteenth centuries, there were increasing demands for amputation, abdominal procedures, and other surgical interventions. The military forces hired barbers for the care of the casualties.16 However, since the barbers found it risky to follow on military campaigns and often refused the task, they were often themselves forced to enlist. Uppsala University recognized medicine as an academic specialty in the 1600s, and as a result the number of trained physicians gradually increased. In 1663, the newly founded Swedish Collegium Medicum received the task of opposing quacks and charlatanism within the medical profession. A few years later, they were given the responsibility of licensing medical and pharmacy graduates and, in 1685, supervision of surgical education.15 However, they had no authority to supervise the barbers’ skills and knowledge. As a result of the plague epidemic (1710–1713) and several outbreaks of measles, the Serafimer hospital opened in Stockholm in 1752 to serve the whole country. The war with Russia in the late 1780s led to a new era in Swedish military healthcare, since infectious diseases and surgical conditions dominated the causes of death among soldiers.14,15 The barbers’ limited knowledge and lack of insight regarding field conditions resulted in a suggestion to set up temporary educational institutions for the training of field surgeons (1789–1808). As a result, the Karolinska Institute was formed in 1810, with the task of setting up a 2-year training program, after which the participants could work as interns in the field. They were also allowed to continue their medical studies after the war. The same year the Collegium Medicum was divided into civilian and military divisions,16 a garrison hospital was built, and two new professors were installed: one in medicine and the other in the surgery.14–16 Swedish Military Healthcare Between 1814 and 1911: Peace and Development Period At the beginning of the nineteenth century, the Swedish economy deteriorated, due to internal social issues, though there were no concerns about external threats. After several years of wars, the new foreign policy known as the Policy of 1812 resulted in a long period of peace and development after 1814. Collegium Medicum was modernized in 1813, and a new medical organization, “Sundhetskollegium,” consisting of four divisions, civilian healthcare, military healthcare, obstetric and vaccination care, and pharmaceutical division, was formed. In 1878, Medicinalstyrelsen replaced Sundhetskollegium and was gradually given a broader task for the supervision of other areas such as public health, veterinary care, dental care, forensic medicine, etc.14,16,17 With the birth of the Red Cross and the Geneva Convention, voluntary activities started in different countries, including Sweden. The official position of the Swedish Government was not to get involved in armed conflict. However, Swedish physicians and nurses actively participated through various help organizations in other armed conflicts such as the Danish-German War (1864), the Franco-German War (1870–1871), and the Boer War (1899–1902). The knowledge, as well as the experience they gained, was later used at home.18 According to the reigning Swedish policy, military healthcare and hospitals aimed to provide medical care for the Swedish army. Nevertheless, the introduction of more efficient weapons (eg, machine guns) which caused more severe injuries and greater numbers of casualties forced the armies in several countries, including Sweden, to build military hospitals and hire specialist doctors to manage trauma.16 Garrison hospitals in Linköping, Karlsborg, Sollefteå, Stockholm, Skövde, and Boden and the regiment hospital in Södermansland were built during this period. The size and the characteristics of these hospitals depended on the prevailing sociopolitical conditions. They were all eventually closed after WW2.14–16,18–20 Swedish Military Healthcare Between 1912 and 1945: Period of Neutrality and International Engagement Its position as a neutral country during WWI and WWII gave Sweden an opportunity to develop social welfare and healthcare systems. The introduction of air raids into warfare, causing both military and civilian casualties, initiated the development of civil defense, that is, a more formalized approach to civilian-military cooperation. The first report recommending civilian-military collaboration in war was published in 1936, and a plan was delivered 1 year later. However, the partnership did not start until the end of WWII. In 1940, the first preparedness department was established within the Medicinalstyrelsen, and a link between civilian and military healthcare was established by creating a position as defense assistant. The task of the department was further developed to become the healthcare preparedness committee (SBN = Sjukvårdens beredskapsnämnd).17 Swedish volunteers continued their involvement, and considerable medical and nonmedical support was offered to victims of the war.21 Numerous voluntary physicians and nurses participated in the Balkan War (1912–1913) by joining the Swedish Red Cross. The most significant activity during the WW1 was the establishment of a Swedish hospital in Vienna (1917–1918) with around 400 beds. The Swedish contribution in the Finnish Civil War (1918) was the four-staffed ambulances from the Swedish Red Cross. In this war, another voluntary organization, “Swedish Red Star,” offered veterinary help. In 1937, 12 Swedish physicians, medical students, and nurses took part in the Second Italo-Ethiopian War (1935–1937). The Spanish Civil War gathered many participants through the Swedish Red Cross and other voluntary organizations such as the Swedish-Spanish Help Organization. A hospital was gradually built with 700 beds to offer medical care to both military and civilian casualties. During WWII, as many as 102 Swedish physicians worked within different units abroad. The establishment of war hospitals in Visby (1939–1945), Lärbo (1942–1945), Hemse (1939–1945), and Klinte (1939–1945) offered care for victims of the war, including many refugees. One of the most reported activities of a Swedish voluntary organization during WWII was the so-called white buses, which saved the lives of many refugees held in camps, by transferring them to Sweden.18,19,22 Although Sweden never got directly involved in any of these wars, the knowledge and experience gained by voluntary healthcare professionals could be used in civilian healthcare after their return. It also helped to organize better medical assistance in other conflicts such as in the Korean War, where through the Swedish Red Cross, 170 Swedish healthcare staff joined the American and South Korean medical teams to take care of the military personnel and casualties. The Swedish mission continued by offering healthcare services to the Korean civilian population. The Korean mission inspired many of the participants who also took part in later United Nations missions, such as in Congo.18 Swedish Military Healthcare Since 1945: Toward Civilian-Military Collaboration Because of the growing risk of nuclear conflict during the Cold War, the Swedish defense policy was based on both national defense and involvement in international peacekeeping missions. However, the main aim was to defend the Swedish territory from invasion. National Preparedness Several years of discussions and investigations after WWII resulted in the creation of a medical disaster committee in 1965. Its main task was to increase Swedish preparedness for unexpected incidents and disasters by issuing recommendations and guidelines. Three years later, the National Board of Health and Welfare was established to cover all areas of health and social care in peace and war, which implied collaboration with the armed forces. This collaboration developed gradually from 1969 to reach its peak in 1980 with regular meetings, planning, and mutual projects.17 The national preparedness implied that most of the national resources would be immediately ready for use after a rapid mobilization. Several preparedness hospitals (civilian buildings that could be converted into hospitals) were created, where medical equipment, supplies, etc. were stored. Additionally, a civil defense system with the responsibility of protecting the civilian population during the war was created. Many shelters were built, extensive plans for the evacuation of the people from urban areas were made, and stockpiles of food, fuel, drugs, etc. were created. The program was estimated to involve 2.8 million people after a full mobilization in the early 1980s.14–17,23,24 The provision of medical care in this plan was the subject of several investigations. As an example, a government’s report proposed to give military medicine a position as a medical specialty of its own and to require such competence for holding certain positions within the military healthcare system. Also, the report proposed the establishment of research programs in the military to deal specifically with military medical issues. Finally, a small number of permanent employees were recruited as senior executives in military medicine to create career development opportunities, enhance recruitment of medical staff, and stimulate military healthcare.23,24 International Missions From the late 1960s, the Cold War became less cold, and the risk for Swedish involvement in armed conflicts was considered less likely. Thus, Swedish preparedness was reduced significantly. Some of the 50+ field hospitals and naval combat hospitals that the Swedish Armed Forces (SAF) had in their possession were used in various international assignments. The remaining facilities were dismantled around the turn of the millennium due to the collapse of the Warsaw Pact and the Soviet Union. The reduced risk of large-scale armed conflicts on European soil led to a new era in Swedish military healthcare strategy and organization.14,23,24 The new defense policy aimed to build Sweden’s security abroad in coalition with forces from other nations. The result was Sweden’s participation in major peacekeeping operations such as the Bosnian War as part of the United Nations Protection Force (Former Yugoslavia) UNPROFOR, the war in Afghanistan as part of the International Security Assistance Force (ISAF), the Libyan Civil War, and the ongoing conflict in Mali.15,24,25 Simultaneously SAF was significantly reduced in size and went from conscript service to employ only professional staff. International missions need risk assessment and preventive measures; thus, medical intelligence and preventive medicine had to be developed. New medical treatment facilities were built based on modular units to be used nationally or internationally during armed conflicts as well as in humanitarian missions. Most of the medical staff were hired part-time, combining both civilian and military careers. In addition to their civilian skills, that is, skills equivalent to advanced trauma life support (ATLS), they also needed basic military training, as well as military trauma-focused specialized training, for example, Battlefield Advanced Trauma Life Support (BATLS) and Definitive Surgical Trauma Care (DSTC).20,24,25 All soldiers were trained to use tourniquets as well as some medications such as fentanyl in the prehospital setting. In each group of eight soldiers, a “combat lifesaver” (CLS) was included, given 7 weeks of additional medical training and who carried more medical equipment. Registered nurses staffed military ambulances. Furthermore, physicians staffed ambulance helicopters, as well as light maneuver units (NATO Role 2LM), forward surgical teams (FST), and field hospitals (NATO Role 2). For hospital care following repatriation, the SAF signed an agreement with one of the university hospitals in the Stockholm region (NATO Role 4 hospital). Strategic evacuation (Stratevac) of injured Swedish soldiers was included in the contract, to be carried out by a civil aviation operator with the support of a medical team from the hospital. Admitted patients were to be transferred to their county hospitals, as soon as their medical condition permitted. The latter were responsible for further care, including rehabilitation, prosthetic care, psychological support, etc.24,25 A New Era and New Scenarios: Toward Civilian-Military Healthcare Collaboration The political tension in Europe has increased during the last decade.25,26 Russia has upgraded and expanded its military forces. Conflicts in Georgia, Ukraine, and Syria have proved that nations are capable and willing to use military force to gain political goals. In Sweden, the reduction of in size of the SAF, including the military healthcare system, has impaired the capability to handle military casualties resulting from a possible armed attack on Sweden itself. This responsibility thus became the challenge for the civilian healthcare system. However, the civilian healthcare system has during the same period reduced the number of hospital beds by >50% due to strained finances and associated technological developments and has changed from stockpiling to same-day delivery of supplies. Thus, there is an obvious need for collaboration between the civilian and military healthcare systems.26,27 Consequently, the SAF and the National Board of Health and Welfare initiated the project “total defense’s healthcare system” in 2015.27 The project aimed to identify risks, vulnerabilities, and measures needed to provide the necessary medical care in the event of major incidents, disasters, and wars. Within the framework of the collaboration, the authorities have developed a concept for civilian-military coordination in planning, education, skills provision, and resources to increase the national capacity. Furthermore, there is the intention to develop national plans for trauma and emergency medical education, support and focus on research through the Knowledge Centers for Disaster Medicine, and the development of national guidelines for multi-professional exercises and training. The project report provides a basis for continued dialogue with relevant stakeholders such as the government, authorities, and healthcare professionals and initiates a new era in the development of Swedish military healthcare system.27 Discussion and Conclusion Each of the four distinct periods of historical development in the Swedish military healthcare system was characterized by the changes necessary for transferring Sweden from an offensive to a defensive and collaborative nation, with national and international engagement, to finally achieve the conditions required for successful civilian-military collaboration and the concept of total defense healthcare.14–18,28 Although the partnership encompasses readiness and willingness to share resources and to adjust routines and guidelines, it also needs a culture of consensus and respect for each other’s limitations and capabilities.29 Although associated with challenges, interorganizational collaboration is an essential factor in effective crisis and disaster management. One of the main characteristics of the Swedish civilian disaster and crisis response system is its firm emphasis on shared and collaborative actions,30 a concept that has developed during a long period of peace, sociopolitical stability, and civil defense improvement. The first plan for civilian-military collaboration was published in the late 1930s, yet it took many years of partnership, discussion, and meetings until the beginning of the twenty-first century to harmonize such concert.17 A fruitful and fair healthcare collaboration encompasses all major nonmedical and medical elements of crisis management such as command, control, communication, information sharing, standard treatment guidelines, routines, etc.27,31,32 There are medical and nonmedical contact points and challenges for future Swedish civilian-military collaboration.24,26,27,31,32 These discouraging factors must be reviewed, and proper actions should be implemented to enhance civilian-military collaboration. Interactive training and education might be one way to stimulate a fruitful collaboration between civilian and military healthcare systems. The SAF has participated in joint simulation exercises with civilian healthcare. Such a partnership allows both systems to identify strengths and weaknesses in their networks and to use their resources favorably. It also increases their ability to understand each other’s limitations as well as capabilities and respect the areas of responsibilities. Besides organizational rapprochements, professional development can be achieved by mutual medical courses, simulation exercises, and linking military medicine and military healthcare staff to academia.24,27,32 The latter also provides a new opportunity for military medicine-related research activities.31,33 Defense funding of research in physiology, trauma, emergency medicine, public health, preventive medicine, and other areas of mutual interest not only increases the readiness of the military forces but also benefits the Swedish civilian healthcare system.31 From a Swedish perspective, military operational activity cannot be maintained without an integration between military and civilian healthcare philosophy.31,34 Having that in mind, the current Swedish approach to integration and collaborative work is logical for the proper use of resources, sharing knowledge, and determination of responsibilities and limitations. Such integration also has a profound impact on guidelines and procedures as well as the choice of equipment to support the most critically injured in the military context. Investing in all aspects of civilian healthcare development, including research and recognition of military medicine, as part of integration policy, is an essential step that needs plans, time, and policy discussion and constitutes the foundation for a total defense healthcare system.31,34 In defining military medicine as “a specialty consisting of all forms of medicine, such as military emergency care, military traumatology, military occupational and environmental medicine, veteran care, medical intelligence, as well as medical leadership and administration,” the primary task of military medicine remains to maintain the combat readiness of the troops. Consequently, besides surgical procedures as an essential part of military medicine, combat-related injuries also require prehospital care as well as transport to a qualified medical facility, medical intelligence, preventive medicine, emergency and daily medical care, as well as rehabilitation.35–37 These areas constitute potential areas of collaboration between the civilian and military healthcare systems.24,27,31 Furthermore, medical intelligence follows the process of risk identification, assessment, and management, which is an essential part of disaster and major incident management.38 Despite earlier recommendations,22,23 military medicine has not yet been established as a specialty in Sweden. There are some common denominators between the tasks of the military and civilian healthcare systems. These denominators are mutually interdependent, but the successful accomplishment of any one of them depends upon the efficient performance of the others.27,31,33,34 The definition of military medicine and the military physician’s role in Sweden is imperative for further civilian-military collaboration. The collaboration within the total defense healthcare system will result in technical achievements, innovations, and medical advancements for the benefit of the whole nation. In conclusion, recent global sociopolitical changes necessitate civilian-military healthcare collaboration. Although subject-related civilian-military healthcare partnerships have been reported earlier,39,40 the Swedish concept of healthcare systems’ integration and collaboration may be a more fruitful approach, which demands a long period of stability, peace, and a culture of collaboration and consensus.41 AK conceived the idea for the manuscript, undertook the literature review, and wrote the draft. 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Google Scholar OpenURL Placeholder Text WorldCat 30. Strandh V : Sweden’s capacity to prepare and respond to a terrorist attack on rail-bound traffic-promising practices and obstacles to inter-organizational collaboration . JRACR 2015 ; 5 ( 4 ): 215 – 25 . Google Scholar OpenURL Placeholder Text WorldCat 31. Khorram-Manesh A : Facilitators and constrainers of civilian-military collaboration: the Swedish perspectives . Eur J Trauma Emerg Surg 2018 . doi: 10.1007/s00068-018-1058-9 . Google Scholar OpenURL Placeholder Text WorldCat Crossref 32. Khorram-Manesh A , Angthong C, Pangma A et al. : Hospital evacuation; learning from the past? Flooding of Bangkok 2011 . BJMMR 2014 ; 4 ( 1 ): 395 – 415 . Google Scholar OpenURL Placeholder Text WorldCat 33. Rasmussen TE : Why military medical research? Mil Med 2014 ; 179 ( 1 ): 1 – 2 . Google Scholar OpenURL Placeholder Text WorldCat 34. Hodgetts TJ : The future character of military medicine . JR Army Corps 2019 ; 158 ( 3 ): 271 – 8 . 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For permissions, please e-mail: [email protected] This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] © Association of Military Surgeons of the United States 2020. All rights reserved. For permissions, please e-mail: [email protected]

Pahissa, Robert, A;Lin-Hurtubise, Kevin, M

doi: 10.1093/milmed/usaa039pmid: 32196111

Abstract Acute appendicitis is one of the most prevalent causes of an acute abdomen. Although the cause of appendicitis is not completely understood, the theory of luminal obstruction is a popular belief, with appendicoliths being a common etiology. While appendicoliths are quite common, giant appendicoliths >2 cm are rare. Although previous reports cite only two or three other occurrences of giant appendicoliths, we found at least 11 reported cases in the literature. We present a young male diagnosed preoperatively on computed tomography to have a large appendiceal mass of 2.2 cm. This case is presented for the rarity of giant appendicoliths along with a review of the literature. Introduction Appendicitis is one of the most common causes for emergency surgery and accounts for ~1% of all surgical operations. While the diagnosis is largely based on clinical exam, supplemental imaging and laboratory studies are often used. Appendicoliths are present in 3% of the general population and in 10% of appendicitis cases.1 Although most patients with appendicoliths are asymptomatic, they may be associated with an increased risk of perforation or abscess formation. Appendicoliths are called giant when they are >2 cm in size and are rare. Case Report A 19-year-old male presented to the emergency room with a 3-day history of abdominal pain focused in the right lower quadrant associated with nausea and vomiting. On physical examination, he was focally tender to palpation with involuntary guarding over McBurney’s point but without rebound tenderness. White blood cell count was 22,300 cell/mm3 and he has given piperacillin and tazobactam. Abdominal/pelvic computed tomography (CT) with oral and IV contrast was remarkable for a 2.2 × 1.3 × 2.1 cm mass associated with adjacent enhanced bowel wall thickening and periappendiceal fat stranding. Because of the size and unclear nature of the mass, we were not able to rule out a neoplastic process in association with acute appendicitis versus a large fecalith. There was no free fluid or air in the abdominal cavity, as well as no signs of abscess formation (Fig. 1). Given that the patient had no history of recent unexplained weight loss, melena, hematochezia, or family history of gastrointestinal cancer, concern for neoplasm was decreased and the decision was made to undergo diagnostic laparoscopy and laparoscopic appendectomy (LA). A thickened omentum was found to be adhered to both the cecum and appendix upon entry into the peritoneal cavity. The adherent omentum was bluntly dissected, exposing a contained perforation of a large diameter appendix with surrounding inflammation and phlegmon. As the appendix appeared consistent with perforated appendicitis with appendicolith, a right hemicolectomy and frozen intraoperative slides were deemed unnecessary. Laparoscopic appendectomy was performed successfully, notable for a very large fecalith (greatest dimension of 2.4 cm) at the site of perforation, which fell out of the appendix into the pelvis during the dissection and was promptly retrieved and removed (Fig. 2). The patient’s postoperative course was uneventful. Histopathological examination revealed a phlegmonous appendicitis (Fig. 3) and confirmed the giant appendicolith. The patient was discharged home in a much-improved clinical condition on the second postoperative day with 7 days of oral amoxicillin and clavulanic acid. Follow-up at 2 weeks postop was unremarkable. Discussion The etiology of appendicitis remains uncertain, although luminal obstruction is thought to be a possible cause.2 Appendicoliths (a mass formed by concretion of calcified deposits in the appendix made of packed stool and occasionally mineral deposits), lymphoid hyperplasia (more common in children, often after a viral infection), and malignancy are among the possible causes of luminal obstruction. The obstruction of the proximal appendiceal lumen results in a closed-loop obstruction which leads to an increase of luminal pressure. Vascular congestion develops and can lead to ischemia of the appendiceal wall ultimately culminating in gangrene and perforation if unresolved. While appendicoliths have been believed to be the most common cause of nonperforated appendicitis, some studies refute this.3 Appendicoliths are more common in the male, pediatric, and young adult (<35) population, similar to our patient, and the most common age group in military units. Atypical presentation can include colicky pain, which may cause providers to consider urolithiasis.4 The diagnosis of appendicitis is largely based on clinical judgment, but laboratory values and radiologic imaging studies are often used to increase diagnostic accuracy. In a 2011 study of 2,871 patients, multidetector CT had a sensitivity of 98.5% and a specificity of 98% for appendicitis.5 Appendicolith detected on CT had a sensitivity of 65%, specificity of 86%, and positive predictive value of 74% for the diagnosis of acute appendicitis.6 In a study looking to see if CT features accurately differentiate complicated versus uncomplicated appendicitis, 9 out of 10 features had high specificity (range: 74–100%) but low sensitivity (range: 14–59%) while the 10th feature had high sensitivity (94%) but low specificity (40%).7 Hence, overall, complicated appendicitis is not identified before surgery about half of the time. FIGURE 1 Open in new tabDownload slide Abdominal computed topography showing appendiceal mass. FIGURE 1 Open in new tabDownload slide Abdominal computed topography showing appendiceal mass. FIGURE 2 Open in new tabDownload slide Removed giant appendicolith measuring 2.4 cm. FIGURE 2 Open in new tabDownload slide Removed giant appendicolith measuring 2.4 cm. FIGURE 3 Open in new tabDownload slide Phlegmonous appendicitis. Invading neutrophils (black arrow) and site of perforation (red arrow). FIGURE 3 Open in new tabDownload slide Phlegmonous appendicitis. Invading neutrophils (black arrow) and site of perforation (red arrow). The treatment approach for appendicitis is a topic of debate between surgical excision via open appendectomy (OA) or LA versus nonoperative management (NOM) with antibiotics. While the greater arguments for and against each management approach is beyond the scope of this report, it is interesting to note that a study of 50 children treated with NOM for appendicitis found a higher complication rate for patients with an appendicolith. The study noted that 68% of patients avoided appendectomy and that those with an appendicolith had a higher initial failure rate (37%) compared to patients without one (10%; P < .05). They stated that “the presence of an appendicolith was associated with a higher failure rate but is not an absolute contraindication for NOM.”8 Since the presence of appendicoliths in tandem with acute appendicitis portend an increased risk of associated complication (prevalence of 39.4–50% perforation and abscess formation), appendectomy should be performed in this setting.9 Similarly, we were able to safely manage our patient with laparoscopic appendectomy with no short-term morbidity. However, there are several reports of appendicolith being managed conservatively without surgery, although these were discovered incidentally and not in association with appendiceal inflammation.10 Although most case reports described two or three cases of giant appendicoliths, we found a total of 11 cases in the literature.4,9,10–18 Surgical approaches varied between LA and OA. Of significance, the largest stone was 3 × 2.5 cm,17 the youngest occurrence was in a 3-year-old girl,18 and several had atypical presentation concerning for urolithiasis.4,9,13 One case had chronic abdominal pain and an initial diagnosis of cecal cancer before removal.15 Endoscopic removal of a giant appendicolith was performed in a patient with stump appendicitis 35 years after appendectomy.14 Concerning the preferred method for surgical excision for appendicitis, a 2018 Cochrane Database review found that “except for a higher rate of intra-abdominal abscesses after LA in adults, LA showed advantages over OA in pain intensity on day one, wound infections, length of hospital stay and time until return to normal activity in adults.”19 Of relevance to military medicine, the review states that the time until return to normal activity was 5 days earlier after LA than OA, although the quality of evidence was low. A 2019 study found a decrease in hospital length of stay after implementing an outpatient LA protocol at Walter Reed National Military Medical Center with minimal complications in select patients.20 As such, service members may be able to return to duty in less time with LA. Conclusion Appendicitis is a common cause of an acute abdomen and while it is typically a clinical diagnosis, supplemental radiologic and laboratory studies are often utilized. Appendicoliths are present in 10% of appendicitis cases and have a high association with complicated appendicitis, thus appendectomy is recommended in an acute setting. Giant appendicolith with acute appendicitis is a rare presentation and can safely be managed with laparoscopic appendectomy, reducing the overall morbidity from an open approach. The views expressed are solely those of the authors and do not reflect the official policy or position of the Uniformed Services University U.S. Army, U.S. Navy, U.S. Air Force, the Department of Defense, or the US Government. Presented previously as a stand-alone poster presentation at the Uniformed Services University of the Health Sciences Founder’s Day Event on September 20, 2019. References 1. Jones BA , Demetriades D, Segal I, Burkitt DP: The prevalence of appendiceal fecaliths in patients with and without appendicitis. A comparative study from Canada and South Africa . Ann Surg 1985 ; 202 : 80 – 2 . doi: 10.1097/00000658-198507000-00013 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 2. Baird DLH , Simillis C, Kontovounisios C, Rasheed S, Tekkis P: Acute appendicitis . BMJ 2017 ; 357 : j1703 . doi: 10.1136/bmj.j1703 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 3. Singh JP , Mariadason JG: Role of the faecolith in modern-day appendicitis . Ann R Coll Surg Engl 2013 ; 95 ( 1 ): 48 – 51 . doi: 10.1308/003588413x13511609954851 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 4. Teke Z , Kabay B, Erbiş H, Tuncay OL: Appendicolithiasis causing diagnostic dilemma: a rare cause of acute appendicitis (report of a case) . Ulus Travma Acil Cerrahi Derg 2008 ; 14 : 323 – 5 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 5. Pickhardt PJ , Lawrence EM, Pooler BD, Bruce RJ: Diagnostic performance of multidetector computed tomography for suspected acute appendicitis . Ann Intern Med 2011 ; 154 ( 12 ): 789 . doi: 10.7326/0003-4819-154-12-201106210-00006 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 6. Lowe LH , Penney MW, Scheker LE, et al. : Appendicolith revealed on CT in children with suspected appendicitis . AJR Am J Roentgenol 2000 ; 175 ( 4 ): 981 – 4 . doi: 10.2214/ajr.175.4.1750981 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 7. Kim HY , Park JH, Lee YJ, Lee SS, Jeon JJ, Lee KH: Systematic review and meta-analysis of CT features for differentiating complicated and uncomplicated appendicitis . Radiology 2018 ; 287 ( 1 ): 104 – 15 . doi: 10.1148/radiol.2017171260 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 8. Scott A , Lee SL, DeUgarte DA, Shew SB, Dunn JCY, Shekherdimian S: Nonoperative management of appendicitis . Clin Pediatr (Phila) 2018 ; 57 ( 2 ): 200 – 4 . doi: 10.1177/0009922817696465 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 9. Kumar K , Lewis D: Diagnostic confusion caused by a giant appendicolith: a case report . J Med Cases 2015 ; 6 ( 2 ): 71 – 3 . doi: 10.14740/jmc2032w . Google Scholar Crossref Search ADS WorldCat Crossref 10. Scroggie DL , Al-Whouhayb M: Asymptomatic giant appendicolith managed conservatively . J Surg Case Rep 2015 ; 2015 ( 11 ): rjv149 . doi: 10.1093/jscr/rjv149 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 11. Garg PK , Jain BK, Rathi V, Mohanty D, Vaibhaw K: Giant appendicolith . Indian J Gastroenterol 2011 ; 30 ( 5 ): 243 – 3 . doi: 10.1007/s12664-011-0115-75 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 12. Keating J , Memon S: Giant appendicolith . Gastrointest Endosc 2005 ; 61 ( 2 ): 292 – 3 . doi: 10.1016/s0016-5107(04)02547-7 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 13. Kaya B , Eris C: Different clinical presentation of appendicolithiasis. The report of three cases and review of the literature . Clin Med Insights Pathol 2011 ; 4 : 1 – 4 . doi: 10.4137/CPath.S6757 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 14. Kim du J , Park SW, Choi SH, et al. : A case of endoscopic removal of a giant appendicolith combined with stump appendicitis . Clin Endosc 2014 ; 47 ( 1 ): 112 – 4 . doi: 10.5946/ce.2014.47.1.112 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 15. Abebe E , Abebe K: Giant appendicolithiasis presenting with chronic abdominal pain and mass: a case report . Ethiop J Health Sci 2019 ; 29 ( 3 ): 417 – 9 . doi: 10.4314/ejhs.v29i3.16 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Crossref 16. Adorisio O , De Peppo F, Silveri M, et al. : Giant appendicolith causing severe lameness in a child . Pediatr Int 2017 ; 59 ( 3 ): 381 – 2 . doi: 10.1111/ped.13220 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 17. Singhal S , Singhal A, Mahajan H, et al. : Giant appendicolith: rare finding in a common ailment . J Minim Access Surg 2016 ; 12 : 170 – 2 . doi: 10.4103/0972-9941.178514 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 18. Athawale HR , Vageriya N, Dhende NP, Patil S, Kotawala H: Giant appendicolith mimicking as foreign body: a case report . J Pediatr Surg Case Rep 2016 ; 4 : 54 – 7 . doi: 10.1016/j.epsc.2015.11.005 . Google Scholar Crossref Search ADS WorldCat Crossref 19. Jaschinski T , Mosch CG, Eikermann M, Neugebauer EA, Sauerland S: Laparoscopic versus open surgery for suspected appendicitis . Cochrane Database Syst Rev 2018 ; 11 : CD001546 . doi: 10.1002/14651858.CD001546.pub4 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Crossref 20. Bradley M , Kindvall A, Logan J, Bailey J, Elster E, Rodriguez C: Successful implementation of an appendectomy process improvement protocol . Trauma Surg Acute Care Open 2019 ; 4 : e000303 . doi: 10.1136/tsaco-2019-000303 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref © Association of Military Surgeons of the United States 2020. All rights reserved. For permissions, please e-mail: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Nursey,, Jane;Sbisa,, Alyssa;Knight,, Holly;Ralph,, Naomi;Cowlishaw,, Sean;Forbes,, David;O’Donnell,, Meaghan;Hinton,, Mark;Cooper,, John;Hopwood,, Malcolm;McFarlane,, Alexander;Herring,, Sally;Fitzgerald,, Paul

Frankel,, Dianne;Banaag,, Amanda;Madsen,, Cathaleen;Koehlmoos,, Tracey

doi: 10.1093/milmed/usaa153pmid: 32633784

ABSTRACT Introduction Diabetes is one of the most common chronic conditions in the United States and has a cost burden over $120 billion per year. Readmissions following hospitalization for diabetes are common, particularly in minority patients, who experience greater rates of complications and lower quality healthcare compared to white patients. This study examines disparities in diabetes-related readmissions in the Military Health System, a universally insured, population of 9.5 million beneficiaries, who may receive care from military (direct care) or civilian (purchased care) facilities. Methods The study identified a population of 7,605 adult diabetic patients admitted to the hospital in 2014. Diagnostic codes were used to identify hospital readmissions, and logistic regression was used to analyze associations among race, beneficiary status, patient or sponsor’s rank, and readmissions at 30, 60, and 90 days. Results A total of 239 direct care patients and 545 purchased care patients were included in our analyses. After adjusting for age and sex, we found no significant difference in readmission rates for black versus white patients; however, we found a statistically significant increase in the likelihood for readmission of Native American/Alaskan Native patients compared to white patients, which persisted in direct care at 60 days (adjusted odds ratio [AOR] 11.51, 95% CI 1.11–119.41) and 90 days (AOR 18.42, 95% CI 1.78–190.73), and in purchased care at 90 days (AOR 4.54, 95% CI 1.31–15.74). Conclusion Our findings suggest that universal access to healthcare alleviates disparities for black patients, while Native America/Alaskan Native populations may still be at risk of disparities associated with readmissions among diabetic patients in both the closed direct care system and the civilian fee for service purchased care system. INTRODUCTION Diabetes is one of the most common chronic conditions in the United States and was associated with $124 billion in direct medical costs in 2012.1 Among patients with diabetes who were hospitalized, at least 30% were subsequently readmitted, accounting for over 50% of total hospitalizations and hospital costs.2 The 30-day readmission rate of diabetic patients in 2014 was 14.4–22.7% and constituted over $25 billion dollars in healthcare cost.3 In 2012, a 5% reduction in the 30-day readmission rate would result in an estimated annual cost savings of $1.2 billion.1 With the implementation of the Affordable Care Act, readmission rates have been determined to be an important indicator of hospital performance, efficiency, reduction of healthcare cost, and Medicare and Medicaid reimbursement.4 Known risk factors for readmission include lower socioeconomic status (SES), public insurance, emergent or urgent admission, and a history of recent prior hospitalization,3 as well as comorbidities including cardiovascular disease and history of dysglycemia.5,6 Minorities continue to have a significant increase in diabetes-related complications, overall mortality, and lower quality of care when compared to whites.1 Poor glycemic control seems to be a significant predictor for hospitalizations, with black and Hispanic patients less likely to monitor blood glucose levels.7 This is often linked to insurance status, with black and Hispanic patients having lower rates of insurance coverage and also greater rates of diabetic complications than their white counterparts.8 However, disparities in rates of complications and risk of readmission persist even in guaranteed-access systems such as Medicare.7 Despite these known factors, diabetes-related hospitalization and readmission are understudied in minority populations. The U.S. Military Health System (MHS) is another guaranteed-access system, providing medical care to a universally insured population of 9.5 million military and civilian beneficiaries. These may receive direct care at Military Treatment Facilities (MTFs) or purchased care through TRICARE at civilian facilities (known as purchased care).9 Because of this bifurcated structure, the MHS offers a unique ability to assess healthcare disparities among a universally insured population, comparing the closed MTF environment to the fee-for-service civilian private sector environment. Additionally, the MHS has recently begun to investigate Prevention Quality Indicators, developed by the Agency for Healthcare Research and Quality, associated with diabetes complication admission rates and global 30-day readmissions.9 This makes the study of disparities in diabetes readmissions both timely and relevant. Therefore, this study applied the methodology developed by Jiang et al.7 to assess racial disparities in diabetes readmissions in the MHS. Results are expected to inform discussion on the effects of universal insurance in mitigation of racial disparities, as well as discussion of quality and care improvement within the MHS. METHODS Data Source and Study Design This cross-sectional study utilized administrative claims data from the MHS Data Repository (MDR) for diabetes-related admissions during fiscal year 2014. The MDR captures all care provided to MHS beneficiaries in the direct and purchased care systems. It does not include care provided in combat zones, or through the Veterans Administration, which is a completely separate health system. MHS beneficiaries are sociodemographically representative of the U.S. population under age 65 and include ~20% active duty personnel and 80% dependent family members and retirees.10–12 Data from the MDR have been used in multiple previous studies investigating healthcare disparities.10,13–17 This work was found exempt by the Institutional Review Board of the Uniformed Services University of the Health Sciences. Study Sample We identified all patients admitted to a direct or purchased care facility for primary or secondary diabetes-related issues during fiscal year 2014. Patients were identified by, and diagnoses classified according to, the International Classification of Diseases-9th revision-Clinical Modification (ICD-9-CM) diabetes-related codes utilized by Jiang et al.7 (Supplementary Table I). We excluded patients <18 and >64 years of age. Patients >64 years of age were excluded because Medicare was the primary insurance. Additional demographics such as gender, race, rank, and beneficiary status were obtained for patients meeting inclusion criteria. Rank was used as a surrogate for SES as supported in previous studies.11–17 Both inclusion criteria for the index admission and readmission(s) included the primary diagnosis code of diabetes or secondary diagnosis of diabetes, with a principal diagnosis of a condition associated with diabetes at 30, 60, and 90 days postinitial hospital admission. Furthermore, patients with multiple readmissions within the defined time periods following an index admission were only counted as one readmission event in each time period. Statistical Analyses Descriptive statistics (frequencies and proportions) were performed on patient demographics. Patients with missing race and/or rank were matched to their sponsor’s race and/or rank. Fisher’s exact test was performed on patient demographics in readmitted patients compared to non-readmitted patients. The most common primary readmission diagnoses for diabetic patients in direct care were compared to purchased care. Multivariate logistic regressions were performed to determine unadjusted and adjusted odds ratios for readmissions at 30, 60, and 90 days in both direct and purchased care. Patient race, beneficiary status, and rank were used as predictor variables in the regression models, and age and gender were used in the adjusted regression models. A P < 0.05 determined statistical significance. All analyses were performed using SAS version 9.4 (Cary, North Carolina) and Stata/IC version 15. RESULTS During fiscal year 2014, a total of 2,915 patients were admitted to direct care with a diabetes-related diagnosis, 239 (8.2%) of which had a diabetes-related readmission within the 90-day observation period. A total of 4,690 patients were admitted to purchased care with a diabetes-related diagnosis, 545 (11.6%) of which had a diabetes-related readmission within the 90-day observation period. The majority of diabetes-related admissions and readmissions in both direct and purchased care were patients aged 45 to 64 years old, of white race, and retired from the armed forces or dependents of retirees. Over 80% of admitted and readmitted patients were associated with senior enlisted status; likely as dependents of senior enlisted sponsors, as the overall rate of active duty personnel hospitalized for diabetes was <5%. Full demographic information is available in Table I. TABLE I Demographics of Diabetes Readmission Status by Direct and Purchased Care . Direct care . Purchased care . . Non-readmitted (n = 2,676) . Readmitted (n = 239) . P-value . Non-readmitted (n = 4,145) . Readmitted (n = 545) . P-value . Gender 0.875 0.061  Male 1419 (53.0) 128 (53.6) 2215 (53.4) 268 (49.2)  Female 1257 (47.0) 111 (46.4) 1930 (46.6) 277 (50.8) Age group 0.446 0.025  18–24 119 (4.5) 12 (5.0) 189 (4.6) 35 (6.4)  25–34 104 (3.9) <11 150 (3.6) 16 (2.9)  35–44 256 (9.6) 14 (5.9) 260 (6.3) 27 (5.0)  45–54 662 (24.7) 61 (25.5) 933 (22.5) 99 (18.2)  55–64 1535 (57.4) 143 (59.8) 2613 (63.0) 368 (67.5) Race/Sponsor’s race 0.124 0.048  Black 706 (26.4) 54 (22.6) 763 (18.4) 99 (18.2)  White 1243 (46.5) 120 (50.2) 1760 (42.5) 208 (38.2)  Asian 154 (5.8) 17 (7.1) 104 (2.5) 11 (2.0)  Native American/Alaskan Native <11 <11 24 (0.6) <11  Other 500 (18.7) 42 (17.6) 89 (2.2) <11  Unknown 70 (2.6) <11 1405 (33.9) 209 (38.4) Beneficiary status 0.964 0.015  Active duty 148 (5.5) 11 (4.6) 85 (2.1) <11  Dependent of active duty 277 (10.4) 25 (10.5) 266 (6.4) 39 (7.2) Retiree 1244 (46.5) 109 (45.6) 2013 (48.6) 252 (46.2)  Dependent of retiree 885 (33.1) 82 (34.3) 1481 (35.7) 211 (38.7)  Other 122 (4.6) 12 (5.0) 282 (6.8) 36 (6.6)  Unknown — — 18 (0.4) <11 Rank 0.723 0.631  Junior enlisted 154 (5.8) 15 (6.3) 210 (5.1) 30 (5.5)  Senior enlisted 2223 (83.1) 199 (83.3) 3496 (84.3) 472 (86.6)  Junior officer 125 (4.7) <11 188 (4.5) 19 (3.5)  Senior officer 109 (4.1) 12 (5.0) 137 (3.3) 13 (2.4)  Warrant officer 64 (2.4) <11 99 (2.4) <11  Missing/Other <11 — 15 (0.4) <11 . Direct care . Purchased care . . Non-readmitted (n = 2,676) . Readmitted (n = 239) . P-value . Non-readmitted (n = 4,145) . Readmitted (n = 545) . P-value . Gender 0.875 0.061  Male 1419 (53.0) 128 (53.6) 2215 (53.4) 268 (49.2)  Female 1257 (47.0) 111 (46.4) 1930 (46.6) 277 (50.8) Age group 0.446 0.025  18–24 119 (4.5) 12 (5.0) 189 (4.6) 35 (6.4)  25–34 104 (3.9) <11 150 (3.6) 16 (2.9)  35–44 256 (9.6) 14 (5.9) 260 (6.3) 27 (5.0)  45–54 662 (24.7) 61 (25.5) 933 (22.5) 99 (18.2)  55–64 1535 (57.4) 143 (59.8) 2613 (63.0) 368 (67.5) Race/Sponsor’s race 0.124 0.048  Black 706 (26.4) 54 (22.6) 763 (18.4) 99 (18.2)  White 1243 (46.5) 120 (50.2) 1760 (42.5) 208 (38.2)  Asian 154 (5.8) 17 (7.1) 104 (2.5) 11 (2.0)  Native American/Alaskan Native <11 <11 24 (0.6) <11  Other 500 (18.7) 42 (17.6) 89 (2.2) <11  Unknown 70 (2.6) <11 1405 (33.9) 209 (38.4) Beneficiary status 0.964 0.015  Active duty 148 (5.5) 11 (4.6) 85 (2.1) <11  Dependent of active duty 277 (10.4) 25 (10.5) 266 (6.4) 39 (7.2) Retiree 1244 (46.5) 109 (45.6) 2013 (48.6) 252 (46.2)  Dependent of retiree 885 (33.1) 82 (34.3) 1481 (35.7) 211 (38.7)  Other 122 (4.6) 12 (5.0) 282 (6.8) 36 (6.6)  Unknown — — 18 (0.4) <11 Rank 0.723 0.631  Junior enlisted 154 (5.8) 15 (6.3) 210 (5.1) 30 (5.5)  Senior enlisted 2223 (83.1) 199 (83.3) 3496 (84.3) 472 (86.6)  Junior officer 125 (4.7) <11 188 (4.5) 19 (3.5)  Senior officer 109 (4.1) 12 (5.0) 137 (3.3) 13 (2.4)  Warrant officer 64 (2.4) <11 99 (2.4) <11  Missing/Other <11 — 15 (0.4) <11 Open in new tab TABLE I Demographics of Diabetes Readmission Status by Direct and Purchased Care . Direct care . Purchased care . . Non-readmitted (n = 2,676) . Readmitted (n = 239) . P-value . Non-readmitted (n = 4,145) . Readmitted (n = 545) . P-value . Gender 0.875 0.061  Male 1419 (53.0) 128 (53.6) 2215 (53.4) 268 (49.2)  Female 1257 (47.0) 111 (46.4) 1930 (46.6) 277 (50.8) Age group 0.446 0.025  18–24 119 (4.5) 12 (5.0) 189 (4.6) 35 (6.4)  25–34 104 (3.9) <11 150 (3.6) 16 (2.9)  35–44 256 (9.6) 14 (5.9) 260 (6.3) 27 (5.0)  45–54 662 (24.7) 61 (25.5) 933 (22.5) 99 (18.2)  55–64 1535 (57.4) 143 (59.8) 2613 (63.0) 368 (67.5) Race/Sponsor’s race 0.124 0.048  Black 706 (26.4) 54 (22.6) 763 (18.4) 99 (18.2)  White 1243 (46.5) 120 (50.2) 1760 (42.5) 208 (38.2)  Asian 154 (5.8) 17 (7.1) 104 (2.5) 11 (2.0)  Native American/Alaskan Native <11 <11 24 (0.6) <11  Other 500 (18.7) 42 (17.6) 89 (2.2) <11  Unknown 70 (2.6) <11 1405 (33.9) 209 (38.4) Beneficiary status 0.964 0.015  Active duty 148 (5.5) 11 (4.6) 85 (2.1) <11  Dependent of active duty 277 (10.4) 25 (10.5) 266 (6.4) 39 (7.2) Retiree 1244 (46.5) 109 (45.6) 2013 (48.6) 252 (46.2)  Dependent of retiree 885 (33.1) 82 (34.3) 1481 (35.7) 211 (38.7)  Other 122 (4.6) 12 (5.0) 282 (6.8) 36 (6.6)  Unknown — — 18 (0.4) <11 Rank 0.723 0.631  Junior enlisted 154 (5.8) 15 (6.3) 210 (5.1) 30 (5.5)  Senior enlisted 2223 (83.1) 199 (83.3) 3496 (84.3) 472 (86.6)  Junior officer 125 (4.7) <11 188 (4.5) 19 (3.5)  Senior officer 109 (4.1) 12 (5.0) 137 (3.3) 13 (2.4)  Warrant officer 64 (2.4) <11 99 (2.4) <11  Missing/Other <11 — 15 (0.4) <11 . Direct care . Purchased care . . Non-readmitted (n = 2,676) . Readmitted (n = 239) . P-value . Non-readmitted (n = 4,145) . Readmitted (n = 545) . P-value . Gender 0.875 0.061  Male 1419 (53.0) 128 (53.6) 2215 (53.4) 268 (49.2)  Female 1257 (47.0) 111 (46.4) 1930 (46.6) 277 (50.8) Age group 0.446 0.025  18–24 119 (4.5) 12 (5.0) 189 (4.6) 35 (6.4)  25–34 104 (3.9) <11 150 (3.6) 16 (2.9)  35–44 256 (9.6) 14 (5.9) 260 (6.3) 27 (5.0)  45–54 662 (24.7) 61 (25.5) 933 (22.5) 99 (18.2)  55–64 1535 (57.4) 143 (59.8) 2613 (63.0) 368 (67.5) Race/Sponsor’s race 0.124 0.048  Black 706 (26.4) 54 (22.6) 763 (18.4) 99 (18.2)  White 1243 (46.5) 120 (50.2) 1760 (42.5) 208 (38.2)  Asian 154 (5.8) 17 (7.1) 104 (2.5) 11 (2.0)  Native American/Alaskan Native <11 <11 24 (0.6) <11  Other 500 (18.7) 42 (17.6) 89 (2.2) <11  Unknown 70 (2.6) <11 1405 (33.9) 209 (38.4) Beneficiary status 0.964 0.015  Active duty 148 (5.5) 11 (4.6) 85 (2.1) <11  Dependent of active duty 277 (10.4) 25 (10.5) 266 (6.4) 39 (7.2) Retiree 1244 (46.5) 109 (45.6) 2013 (48.6) 252 (46.2)  Dependent of retiree 885 (33.1) 82 (34.3) 1481 (35.7) 211 (38.7)  Other 122 (4.6) 12 (5.0) 282 (6.8) 36 (6.6)  Unknown — — 18 (0.4) <11 Rank 0.723 0.631  Junior enlisted 154 (5.8) 15 (6.3) 210 (5.1) 30 (5.5)  Senior enlisted 2223 (83.1) 199 (83.3) 3496 (84.3) 472 (86.6)  Junior officer 125 (4.7) <11 188 (4.5) 19 (3.5)  Senior officer 109 (4.1) 12 (5.0) 137 (3.3) 13 (2.4)  Warrant officer 64 (2.4) <11 99 (2.4) <11  Missing/Other <11 — 15 (0.4) <11 Open in new tab Table II details the most common diagnoses in patients with diabetes readmissions in direct and purchased care. The most common diagnosis for both direct and purchased care was diabetes with cardiovascular disease. The categories of diabetes with hypertension or cerebrovascular disease and neurological complications showed the greatest differences between direct and purchased care; with 6.3% in direct care versus 3.1% in purchased care diagnosed with hypertension, and 8.0% in direct care versus 18.5% in purchased care diagnosed with cerebrovascular disease and neurological complications (Table II). TABLE II Most Common Clinical Readmissions for Patients in Direct Care and Purchased Care Clinical diagnosis . Direct care n (%) . Purchased care n (%) . Diabetes with cardiovascular disease 89 (37.2) 192 (35.2) Diabetes with ischemia 38 (15.9) 97 (17.8) Diabetes with peripheral circulation disease and lower extremity disease 25 (10.5) 60 (11.0) Diabetes with acute complications 21 (8.8) 66 (12.1) Diabetes with other complications 20 (8.4) 46 (8.4) Diabetes with renal disease 20 (8.4) 34 (6.2) Diabetes with cerebrovascular disease and neurological complications 19 (8.0) 101 (18.5) Diabetes with hypertension 15 (6.3) 17 (3.1) Diabetes without complications 6 (2.5) 11 (2.0) Clinical diagnosis . Direct care n (%) . Purchased care n (%) . Diabetes with cardiovascular disease 89 (37.2) 192 (35.2) Diabetes with ischemia 38 (15.9) 97 (17.8) Diabetes with peripheral circulation disease and lower extremity disease 25 (10.5) 60 (11.0) Diabetes with acute complications 21 (8.8) 66 (12.1) Diabetes with other complications 20 (8.4) 46 (8.4) Diabetes with renal disease 20 (8.4) 34 (6.2) Diabetes with cerebrovascular disease and neurological complications 19 (8.0) 101 (18.5) Diabetes with hypertension 15 (6.3) 17 (3.1) Diabetes without complications 6 (2.5) 11 (2.0) Open in new tab TABLE II Most Common Clinical Readmissions for Patients in Direct Care and Purchased Care Clinical diagnosis . Direct care n (%) . Purchased care n (%) . Diabetes with cardiovascular disease 89 (37.2) 192 (35.2) Diabetes with ischemia 38 (15.9) 97 (17.8) Diabetes with peripheral circulation disease and lower extremity disease 25 (10.5) 60 (11.0) Diabetes with acute complications 21 (8.8) 66 (12.1) Diabetes with other complications 20 (8.4) 46 (8.4) Diabetes with renal disease 20 (8.4) 34 (6.2) Diabetes with cerebrovascular disease and neurological complications 19 (8.0) 101 (18.5) Diabetes with hypertension 15 (6.3) 17 (3.1) Diabetes without complications 6 (2.5) 11 (2.0) Clinical diagnosis . Direct care n (%) . Purchased care n (%) . Diabetes with cardiovascular disease 89 (37.2) 192 (35.2) Diabetes with ischemia 38 (15.9) 97 (17.8) Diabetes with peripheral circulation disease and lower extremity disease 25 (10.5) 60 (11.0) Diabetes with acute complications 21 (8.8) 66 (12.1) Diabetes with other complications 20 (8.4) 46 (8.4) Diabetes with renal disease 20 (8.4) 34 (6.2) Diabetes with cerebrovascular disease and neurological complications 19 (8.0) 101 (18.5) Diabetes with hypertension 15 (6.3) 17 (3.1) Diabetes without complications 6 (2.5) 11 (2.0) Open in new tab Table III shows the unadjusted and adjusted logistic regression results for direct care. Compared to patients of white race, we found that Native Americans/Alaskan Natives had significantly higher odds of diabetes-related readmissions at both 60 days (unadjusted odds ratio [UOR] 11.41, 95% CI 1.12–115.97) and 90 days (UOR 19.02, 95% CI 1.84–196.49). Being a dependent of a retiree increased the odds of readmission at 60 days (UOR 1.76, 95% CI 1.04–2.98). Once adjusted for age and gender, Native Americans/Alaskan Natives still showed increased odds of readmission in direct care at both 60-day (adjusted odds ratio [AOR] 11.51, 95% CI 1.11–119.41) and 90-day (AOR 18.42, 95% CI 1.78–190.73) time periods (Table III). Table III Multivariate Logistic Regression Results for 30-, 60-, 90-day Readmission in Direct Care . Readmission OR (95% CI) . Unadjusted 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 0.86 (0.56–1.32) 0.69 (0.24–1.98) 1.27 (0.43–3.77)  Asian 1.12 (0.54–2.31) 0.64 (0.34–1.19) 0.98 (0.49–1.94)  Native American/Alaskan Native 4.18 (0.44–39.85) 11.41 (1.12–115.97)* 19.02 (1.84–196.49)*  Other 0.86 (0.52–1.42) 0.69 (0.35–1.36) 1.01 (0.49–2.09) Beneficiary status  Active duty 1.15 (0.54–2.45) 0.23 (0.03–1.89) 0.26 (0.03–2.42)  Dependent of active duty 0.54 (0.25–1.18) 0.86 (0.31–2.35) 1.66 (0.70–3.96)  Retiree (ref) 1 1 1  Dependent of retiree 0.82 (0.55–1.23) 1.76 (1.04–2.98)* 1.38 (0.75–2.55) Rank  Junior enlisted 0.87 (0.35–2.18) 1.91 (0.66–5.51) 2.19 (0.79–6.08)  Senior enlisted (ref) 1 1 1  Junior officer 0.85 (0.34–2.15) 0.31 (0.04–2.24) 0.88 (0.21–3.76)  Senior officer 1.50 (0.71–3.18) 1.29 (0.45–3.67) 2.06 (0.72–5.92)  Warrant officer 1.65 (0.64–4.24) 0.46 (0.06–3.51) 0.66 (0.09–5.08) Adjusteda 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 0.85 (0.55–1.31) 0.65 (0.35–1.22) 1.00 (0.50–2.00)  Asian 1.22 (0.60–2.46) 0.70 (0.23–2.09) 1.34 (0.45–3.98)  Native American/Alaskan Native 5.16 (0.53–50.71) 11.51 (1.11–119.41)* 18.42 (1.78–190.73)*  Other 0.90 (0.55–1.48) 0.69 (0.35–1.37) 0.99 (0.47–2.08) Beneficiary status  Active duty 2.04 (0.85–4.91) 0.30 (0.03–2.88) 0.16 (0.01–1.91)  Dependent of active duty 1.79 (0.71–4.51) 0.72 (0.17–3.07) 1.16 (0.30–4.53)  Retiree (ref) 1 1 1  Dependent of retiree 1.90 (0.95–3.81) 1.12 (0.41–3.04) 1.22 (0.44–3.38) Rank  Junior enlisted 1.12 (0.43–2.93) 1.92 (0.62–5.91) 1.50 (0.48–4.68)  Senior enlisted (ref) 1 1 1  Junior officer 0.80 (0.30–2.14) 0.31 (0.04–2.22) 0.89 (0.21–3.86)  Senior officer 1.24 (0.57–2.68) 1.28 (0.45–3.66) 2.03 (0.70–5.90)  Warrant officer 1.58 (0.61–4.1) 0.43 (0.06–3.27) 0.67 (0.09–5.23) . Readmission OR (95% CI) . Unadjusted 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 0.86 (0.56–1.32) 0.69 (0.24–1.98) 1.27 (0.43–3.77)  Asian 1.12 (0.54–2.31) 0.64 (0.34–1.19) 0.98 (0.49–1.94)  Native American/Alaskan Native 4.18 (0.44–39.85) 11.41 (1.12–115.97)* 19.02 (1.84–196.49)*  Other 0.86 (0.52–1.42) 0.69 (0.35–1.36) 1.01 (0.49–2.09) Beneficiary status  Active duty 1.15 (0.54–2.45) 0.23 (0.03–1.89) 0.26 (0.03–2.42)  Dependent of active duty 0.54 (0.25–1.18) 0.86 (0.31–2.35) 1.66 (0.70–3.96)  Retiree (ref) 1 1 1  Dependent of retiree 0.82 (0.55–1.23) 1.76 (1.04–2.98)* 1.38 (0.75–2.55) Rank  Junior enlisted 0.87 (0.35–2.18) 1.91 (0.66–5.51) 2.19 (0.79–6.08)  Senior enlisted (ref) 1 1 1  Junior officer 0.85 (0.34–2.15) 0.31 (0.04–2.24) 0.88 (0.21–3.76)  Senior officer 1.50 (0.71–3.18) 1.29 (0.45–3.67) 2.06 (0.72–5.92)  Warrant officer 1.65 (0.64–4.24) 0.46 (0.06–3.51) 0.66 (0.09–5.08) Adjusteda 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 0.85 (0.55–1.31) 0.65 (0.35–1.22) 1.00 (0.50–2.00)  Asian 1.22 (0.60–2.46) 0.70 (0.23–2.09) 1.34 (0.45–3.98)  Native American/Alaskan Native 5.16 (0.53–50.71) 11.51 (1.11–119.41)* 18.42 (1.78–190.73)*  Other 0.90 (0.55–1.48) 0.69 (0.35–1.37) 0.99 (0.47–2.08) Beneficiary status  Active duty 2.04 (0.85–4.91) 0.30 (0.03–2.88) 0.16 (0.01–1.91)  Dependent of active duty 1.79 (0.71–4.51) 0.72 (0.17–3.07) 1.16 (0.30–4.53)  Retiree (ref) 1 1 1  Dependent of retiree 1.90 (0.95–3.81) 1.12 (0.41–3.04) 1.22 (0.44–3.38) Rank  Junior enlisted 1.12 (0.43–2.93) 1.92 (0.62–5.91) 1.50 (0.48–4.68)  Senior enlisted (ref) 1 1 1  Junior officer 0.80 (0.30–2.14) 0.31 (0.04–2.22) 0.89 (0.21–3.86)  Senior officer 1.24 (0.57–2.68) 1.28 (0.45–3.66) 2.03 (0.70–5.90)  Warrant officer 1.58 (0.61–4.1) 0.43 (0.06–3.27) 0.67 (0.09–5.23) aRegression models adjusted for age and gender. *Statistically significant, P < 0.05. Open in new tab Table III Multivariate Logistic Regression Results for 30-, 60-, 90-day Readmission in Direct Care . Readmission OR (95% CI) . Unadjusted 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 0.86 (0.56–1.32) 0.69 (0.24–1.98) 1.27 (0.43–3.77)  Asian 1.12 (0.54–2.31) 0.64 (0.34–1.19) 0.98 (0.49–1.94)  Native American/Alaskan Native 4.18 (0.44–39.85) 11.41 (1.12–115.97)* 19.02 (1.84–196.49)*  Other 0.86 (0.52–1.42) 0.69 (0.35–1.36) 1.01 (0.49–2.09) Beneficiary status  Active duty 1.15 (0.54–2.45) 0.23 (0.03–1.89) 0.26 (0.03–2.42)  Dependent of active duty 0.54 (0.25–1.18) 0.86 (0.31–2.35) 1.66 (0.70–3.96)  Retiree (ref) 1 1 1  Dependent of retiree 0.82 (0.55–1.23) 1.76 (1.04–2.98)* 1.38 (0.75–2.55) Rank  Junior enlisted 0.87 (0.35–2.18) 1.91 (0.66–5.51) 2.19 (0.79–6.08)  Senior enlisted (ref) 1 1 1  Junior officer 0.85 (0.34–2.15) 0.31 (0.04–2.24) 0.88 (0.21–3.76)  Senior officer 1.50 (0.71–3.18) 1.29 (0.45–3.67) 2.06 (0.72–5.92)  Warrant officer 1.65 (0.64–4.24) 0.46 (0.06–3.51) 0.66 (0.09–5.08) Adjusteda 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 0.85 (0.55–1.31) 0.65 (0.35–1.22) 1.00 (0.50–2.00)  Asian 1.22 (0.60–2.46) 0.70 (0.23–2.09) 1.34 (0.45–3.98)  Native American/Alaskan Native 5.16 (0.53–50.71) 11.51 (1.11–119.41)* 18.42 (1.78–190.73)*  Other 0.90 (0.55–1.48) 0.69 (0.35–1.37) 0.99 (0.47–2.08) Beneficiary status  Active duty 2.04 (0.85–4.91) 0.30 (0.03–2.88) 0.16 (0.01–1.91)  Dependent of active duty 1.79 (0.71–4.51) 0.72 (0.17–3.07) 1.16 (0.30–4.53)  Retiree (ref) 1 1 1  Dependent of retiree 1.90 (0.95–3.81) 1.12 (0.41–3.04) 1.22 (0.44–3.38) Rank  Junior enlisted 1.12 (0.43–2.93) 1.92 (0.62–5.91) 1.50 (0.48–4.68)  Senior enlisted (ref) 1 1 1  Junior officer 0.80 (0.30–2.14) 0.31 (0.04–2.22) 0.89 (0.21–3.86)  Senior officer 1.24 (0.57–2.68) 1.28 (0.45–3.66) 2.03 (0.70–5.90)  Warrant officer 1.58 (0.61–4.1) 0.43 (0.06–3.27) 0.67 (0.09–5.23) . Readmission OR (95% CI) . Unadjusted 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 0.86 (0.56–1.32) 0.69 (0.24–1.98) 1.27 (0.43–3.77)  Asian 1.12 (0.54–2.31) 0.64 (0.34–1.19) 0.98 (0.49–1.94)  Native American/Alaskan Native 4.18 (0.44–39.85) 11.41 (1.12–115.97)* 19.02 (1.84–196.49)*  Other 0.86 (0.52–1.42) 0.69 (0.35–1.36) 1.01 (0.49–2.09) Beneficiary status  Active duty 1.15 (0.54–2.45) 0.23 (0.03–1.89) 0.26 (0.03–2.42)  Dependent of active duty 0.54 (0.25–1.18) 0.86 (0.31–2.35) 1.66 (0.70–3.96)  Retiree (ref) 1 1 1  Dependent of retiree 0.82 (0.55–1.23) 1.76 (1.04–2.98)* 1.38 (0.75–2.55) Rank  Junior enlisted 0.87 (0.35–2.18) 1.91 (0.66–5.51) 2.19 (0.79–6.08)  Senior enlisted (ref) 1 1 1  Junior officer 0.85 (0.34–2.15) 0.31 (0.04–2.24) 0.88 (0.21–3.76)  Senior officer 1.50 (0.71–3.18) 1.29 (0.45–3.67) 2.06 (0.72–5.92)  Warrant officer 1.65 (0.64–4.24) 0.46 (0.06–3.51) 0.66 (0.09–5.08) Adjusteda 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 0.85 (0.55–1.31) 0.65 (0.35–1.22) 1.00 (0.50–2.00)  Asian 1.22 (0.60–2.46) 0.70 (0.23–2.09) 1.34 (0.45–3.98)  Native American/Alaskan Native 5.16 (0.53–50.71) 11.51 (1.11–119.41)* 18.42 (1.78–190.73)*  Other 0.90 (0.55–1.48) 0.69 (0.35–1.37) 0.99 (0.47–2.08) Beneficiary status  Active duty 2.04 (0.85–4.91) 0.30 (0.03–2.88) 0.16 (0.01–1.91)  Dependent of active duty 1.79 (0.71–4.51) 0.72 (0.17–3.07) 1.16 (0.30–4.53)  Retiree (ref) 1 1 1  Dependent of retiree 1.90 (0.95–3.81) 1.12 (0.41–3.04) 1.22 (0.44–3.38) Rank  Junior enlisted 1.12 (0.43–2.93) 1.92 (0.62–5.91) 1.50 (0.48–4.68)  Senior enlisted (ref) 1 1 1  Junior officer 0.80 (0.30–2.14) 0.31 (0.04–2.22) 0.89 (0.21–3.86)  Senior officer 1.24 (0.57–2.68) 1.28 (0.45–3.66) 2.03 (0.70–5.90)  Warrant officer 1.58 (0.61–4.1) 0.43 (0.06–3.27) 0.67 (0.09–5.23) aRegression models adjusted for age and gender. *Statistically significant, P < 0.05. Open in new tab Table IV shows the unadjusted and adjusted logistic regression results for diabetes-related readmissions in purchased care. At 90 days, Native Americans/Alaskan Natives (UOR 4.61, 95% CI 1.34–15.89), dependents of active duty service members (UOR 2.04, 95% CI 1.06–3.95), dependents of retirees (UOR 1.68, 95% CI 1.08–2.62), and those associated with a junior enlisted rank (UOR 2.02, 95% 1.06–3.82) had statistically significant increased odds of being readmitted. After adjusting for age and gender, only Native American/Alaskan Native race (AOR 4.54, 95% CI 1.31–15.74) showed increased odds of readmission in purchased care at the 90-day time period (Table IV). TABLE IV Multivariate Logistic Regression Results for 30-, 60-, 90-day Readmission in Purchased Care . Readmission OR (95% CI) . Unadjusted 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 1.29 (0.95–1.75) 0.98 (0.62–1.57) 0.89 (0.51–1.55)  Asian 0.53 (0.19–1.46) 0.56 (0.14–2.32) 1.88 (0.73–4.86)  Native American/Alaskan Native 2.14 (0.74–6.21) 1.05 (0.14–7.83) 4.61 (1.34–15.89)*  Other 0.95 (0.41–2.21) 0.64 (0.15–2.66) 0.78 (0.19–3.27)  Unknown 1.35 (0.99–1.83) 3.33 (0.76–14.63) 1.14 (0.72–1.79) Beneficiary status  Active duty 0.17 (0.02–1.20) — 0.44 (0.06–3.30)  Dependent of active duty 1.06 (0.66–1.71) 0.78 (0.34–1.79) 2.04 (1.06–3.95)*  Retiree (ref) 1 1 1  Dependent of retiree 0.93 (0.80–1.24) 1.18 (0.78–1.79) 1.68 (1.08–2.62)*  Unknown 1.99 (0.58–6.82) 3.33 (0.76–14.63) 8.39 (2.34–30.11)* Rank  Junior enlisted 0.81 (0.46–1.42) 0.85 (0.36–2.03) 2.02 (1.06–3.82)*  Senior enlisted (ref) 1 1 1  Junior officer 0.78 (0.43–1.42) 0.74 (0.30–1.82) 0.65 90.23–1.78)  Senior officer 0.82 (0.41–1.63) 0.83 (0.30–2.30) —  Warrant officer 1.10 (0.55–2.21) 0.52 (0.13–2.16) — Adjusteda 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 1.33 (0.98–1.81) 1.03 (0.65–1.65) 0.87 (0.50–1.52)  Asian 0.53 (0.19–1.48) 0.55 (0.13–2.29) 1.87 (0.72–4.85)  Native American/Alaskan Native 2.03 (0.70–5.93) 1.00 (0.13–7.53) 4.54 (1.31–15.74)*  Other 0.90 (0.38–2.14) 0.53 (0.11–2.49) 0.79 (0.19–3.34)  Unknown 1.30 (0.96–1.77) 1.04 (0.66–1.62) 1.14 (0.72–1.80) Beneficiary status  Active duty 0.18 (0.02–1.34) — 0.32 (0.04–2.59)  Dependent of active duty 0.97 (0.48–1.98) 0.57 (0.17–1.86) 1.45 (0.52–4.05)  Retiree (ref) 1 1 1  Dependent of retiree 0.80 (0.49–1.31) 1.24 (0.60–2.57) 0.70 (0.72–3.18)  Unknown 1.59 (0.40–6.33) 2.48 (0.43–14.50) 4.75 (1.00–22.52)* Rank  Junior enlisted 0.78 (0.44–1.40) 0.75 (0.31–1.82) 1.79 (0.92–3.47)  Senior enlisted (ref) 1 1 1  Junior officer 0.79 (0.44–1.44) 0.72 (0.29–1.78) 0.65 (0.24–1.79)  Senior officer 0.80 (0.40–1.60) 0.84 (0.30–2.31) —  Warrant officer 1.07 (0.53–2.14) 0.52 (0.13–2.13) — . Readmission OR (95% CI) . Unadjusted 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 1.29 (0.95–1.75) 0.98 (0.62–1.57) 0.89 (0.51–1.55)  Asian 0.53 (0.19–1.46) 0.56 (0.14–2.32) 1.88 (0.73–4.86)  Native American/Alaskan Native 2.14 (0.74–6.21) 1.05 (0.14–7.83) 4.61 (1.34–15.89)*  Other 0.95 (0.41–2.21) 0.64 (0.15–2.66) 0.78 (0.19–3.27)  Unknown 1.35 (0.99–1.83) 3.33 (0.76–14.63) 1.14 (0.72–1.79) Beneficiary status  Active duty 0.17 (0.02–1.20) — 0.44 (0.06–3.30)  Dependent of active duty 1.06 (0.66–1.71) 0.78 (0.34–1.79) 2.04 (1.06–3.95)*  Retiree (ref) 1 1 1  Dependent of retiree 0.93 (0.80–1.24) 1.18 (0.78–1.79) 1.68 (1.08–2.62)*  Unknown 1.99 (0.58–6.82) 3.33 (0.76–14.63) 8.39 (2.34–30.11)* Rank  Junior enlisted 0.81 (0.46–1.42) 0.85 (0.36–2.03) 2.02 (1.06–3.82)*  Senior enlisted (ref) 1 1 1  Junior officer 0.78 (0.43–1.42) 0.74 (0.30–1.82) 0.65 90.23–1.78)  Senior officer 0.82 (0.41–1.63) 0.83 (0.30–2.30) —  Warrant officer 1.10 (0.55–2.21) 0.52 (0.13–2.16) — Adjusteda 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 1.33 (0.98–1.81) 1.03 (0.65–1.65) 0.87 (0.50–1.52)  Asian 0.53 (0.19–1.48) 0.55 (0.13–2.29) 1.87 (0.72–4.85)  Native American/Alaskan Native 2.03 (0.70–5.93) 1.00 (0.13–7.53) 4.54 (1.31–15.74)*  Other 0.90 (0.38–2.14) 0.53 (0.11–2.49) 0.79 (0.19–3.34)  Unknown 1.30 (0.96–1.77) 1.04 (0.66–1.62) 1.14 (0.72–1.80) Beneficiary status  Active duty 0.18 (0.02–1.34) — 0.32 (0.04–2.59)  Dependent of active duty 0.97 (0.48–1.98) 0.57 (0.17–1.86) 1.45 (0.52–4.05)  Retiree (ref) 1 1 1  Dependent of retiree 0.80 (0.49–1.31) 1.24 (0.60–2.57) 0.70 (0.72–3.18)  Unknown 1.59 (0.40–6.33) 2.48 (0.43–14.50) 4.75 (1.00–22.52)* Rank  Junior enlisted 0.78 (0.44–1.40) 0.75 (0.31–1.82) 1.79 (0.92–3.47)  Senior enlisted (ref) 1 1 1  Junior officer 0.79 (0.44–1.44) 0.72 (0.29–1.78) 0.65 (0.24–1.79)  Senior officer 0.80 (0.40–1.60) 0.84 (0.30–2.31) —  Warrant officer 1.07 (0.53–2.14) 0.52 (0.13–2.13) — aRegression models adjusted for age and gender. *Statistically significant, P < 0.05. Open in new tab TABLE IV Multivariate Logistic Regression Results for 30-, 60-, 90-day Readmission in Purchased Care . Readmission OR (95% CI) . Unadjusted 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 1.29 (0.95–1.75) 0.98 (0.62–1.57) 0.89 (0.51–1.55)  Asian 0.53 (0.19–1.46) 0.56 (0.14–2.32) 1.88 (0.73–4.86)  Native American/Alaskan Native 2.14 (0.74–6.21) 1.05 (0.14–7.83) 4.61 (1.34–15.89)*  Other 0.95 (0.41–2.21) 0.64 (0.15–2.66) 0.78 (0.19–3.27)  Unknown 1.35 (0.99–1.83) 3.33 (0.76–14.63) 1.14 (0.72–1.79) Beneficiary status  Active duty 0.17 (0.02–1.20) — 0.44 (0.06–3.30)  Dependent of active duty 1.06 (0.66–1.71) 0.78 (0.34–1.79) 2.04 (1.06–3.95)*  Retiree (ref) 1 1 1  Dependent of retiree 0.93 (0.80–1.24) 1.18 (0.78–1.79) 1.68 (1.08–2.62)*  Unknown 1.99 (0.58–6.82) 3.33 (0.76–14.63) 8.39 (2.34–30.11)* Rank  Junior enlisted 0.81 (0.46–1.42) 0.85 (0.36–2.03) 2.02 (1.06–3.82)*  Senior enlisted (ref) 1 1 1  Junior officer 0.78 (0.43–1.42) 0.74 (0.30–1.82) 0.65 90.23–1.78)  Senior officer 0.82 (0.41–1.63) 0.83 (0.30–2.30) —  Warrant officer 1.10 (0.55–2.21) 0.52 (0.13–2.16) — Adjusteda 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 1.33 (0.98–1.81) 1.03 (0.65–1.65) 0.87 (0.50–1.52)  Asian 0.53 (0.19–1.48) 0.55 (0.13–2.29) 1.87 (0.72–4.85)  Native American/Alaskan Native 2.03 (0.70–5.93) 1.00 (0.13–7.53) 4.54 (1.31–15.74)*  Other 0.90 (0.38–2.14) 0.53 (0.11–2.49) 0.79 (0.19–3.34)  Unknown 1.30 (0.96–1.77) 1.04 (0.66–1.62) 1.14 (0.72–1.80) Beneficiary status  Active duty 0.18 (0.02–1.34) — 0.32 (0.04–2.59)  Dependent of active duty 0.97 (0.48–1.98) 0.57 (0.17–1.86) 1.45 (0.52–4.05)  Retiree (ref) 1 1 1  Dependent of retiree 0.80 (0.49–1.31) 1.24 (0.60–2.57) 0.70 (0.72–3.18)  Unknown 1.59 (0.40–6.33) 2.48 (0.43–14.50) 4.75 (1.00–22.52)* Rank  Junior enlisted 0.78 (0.44–1.40) 0.75 (0.31–1.82) 1.79 (0.92–3.47)  Senior enlisted (ref) 1 1 1  Junior officer 0.79 (0.44–1.44) 0.72 (0.29–1.78) 0.65 (0.24–1.79)  Senior officer 0.80 (0.40–1.60) 0.84 (0.30–2.31) —  Warrant officer 1.07 (0.53–2.14) 0.52 (0.13–2.13) — . Readmission OR (95% CI) . Unadjusted 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 1.29 (0.95–1.75) 0.98 (0.62–1.57) 0.89 (0.51–1.55)  Asian 0.53 (0.19–1.46) 0.56 (0.14–2.32) 1.88 (0.73–4.86)  Native American/Alaskan Native 2.14 (0.74–6.21) 1.05 (0.14–7.83) 4.61 (1.34–15.89)*  Other 0.95 (0.41–2.21) 0.64 (0.15–2.66) 0.78 (0.19–3.27)  Unknown 1.35 (0.99–1.83) 3.33 (0.76–14.63) 1.14 (0.72–1.79) Beneficiary status  Active duty 0.17 (0.02–1.20) — 0.44 (0.06–3.30)  Dependent of active duty 1.06 (0.66–1.71) 0.78 (0.34–1.79) 2.04 (1.06–3.95)*  Retiree (ref) 1 1 1  Dependent of retiree 0.93 (0.80–1.24) 1.18 (0.78–1.79) 1.68 (1.08–2.62)*  Unknown 1.99 (0.58–6.82) 3.33 (0.76–14.63) 8.39 (2.34–30.11)* Rank  Junior enlisted 0.81 (0.46–1.42) 0.85 (0.36–2.03) 2.02 (1.06–3.82)*  Senior enlisted (ref) 1 1 1  Junior officer 0.78 (0.43–1.42) 0.74 (0.30–1.82) 0.65 90.23–1.78)  Senior officer 0.82 (0.41–1.63) 0.83 (0.30–2.30) —  Warrant officer 1.10 (0.55–2.21) 0.52 (0.13–2.16) — Adjusteda 30 day 60 day 90 day Race  White (ref) 1 1 1  Black 1.33 (0.98–1.81) 1.03 (0.65–1.65) 0.87 (0.50–1.52)  Asian 0.53 (0.19–1.48) 0.55 (0.13–2.29) 1.87 (0.72–4.85)  Native American/Alaskan Native 2.03 (0.70–5.93) 1.00 (0.13–7.53) 4.54 (1.31–15.74)*  Other 0.90 (0.38–2.14) 0.53 (0.11–2.49) 0.79 (0.19–3.34)  Unknown 1.30 (0.96–1.77) 1.04 (0.66–1.62) 1.14 (0.72–1.80) Beneficiary status  Active duty 0.18 (0.02–1.34) — 0.32 (0.04–2.59)  Dependent of active duty 0.97 (0.48–1.98) 0.57 (0.17–1.86) 1.45 (0.52–4.05)  Retiree (ref) 1 1 1  Dependent of retiree 0.80 (0.49–1.31) 1.24 (0.60–2.57) 0.70 (0.72–3.18)  Unknown 1.59 (0.40–6.33) 2.48 (0.43–14.50) 4.75 (1.00–22.52)* Rank  Junior enlisted 0.78 (0.44–1.40) 0.75 (0.31–1.82) 1.79 (0.92–3.47)  Senior enlisted (ref) 1 1 1  Junior officer 0.79 (0.44–1.44) 0.72 (0.29–1.78) 0.65 (0.24–1.79)  Senior officer 0.80 (0.40–1.60) 0.84 (0.30–2.31) —  Warrant officer 1.07 (0.53–2.14) 0.52 (0.13–2.13) — aRegression models adjusted for age and gender. *Statistically significant, P < 0.05. Open in new tab DISCUSSION As previously described, diabetes-related hospital readmissions are a significant cost driver in American healthcare, accounting for over 50% of total hospitalizations and costs.2 This disproportionately affects minorities, whose increased rates of poor glycemic control and subsequent complications are associated with insurance status.18 Racial disparities have been documented among Medicare and Medicaid recipients,19 suggesting that insurance is not the sole driver of such disparities. In particular, as these programs both serve vulnerable populations, socioeconomic factors such as age or poverty may serve as confounders to the study of disparities. Therefore, this study of disparities in diabetes readmissions among a universally insured, working-age, nationally representative population is both timely and relevant. To date, there have been no other studies published that evaluate readmission rates and health disparities for diabetic patients in the MHS. This study identified factors associated with readmissions among 7,605 patients hospitalized for diabetes in the MHS. Unadjusted risk of readmission was influenced by Native American/Alaskan Native race, dependent of active duty status, dependent of retirees status, unknown status, and junior enlisted rank. Adjusted risk was influenced by Native American/Alaskan Native race and unknown status. Native Americans/Alaskan Natives, who represented <1% of the total cohort, were more likely to be readmitted in both direct and purchased care. Native American and Alaska Natives, who represent <1% of the U.S. population,20 are 30% more likely to have obesity as adolescents and 50% more likely to have obesity as adults, compared to non-Hispanic whites.21 American Indian and Alaska Native individuals served by the Indian Health Service had a 2.3 times higher rate of a diagnosis of diabetes and nine times higher rate of being diagnosed at ages 10 to 19 when compared to whites.20 Risk factors for Native American/Alaskan Native patients in the MHS could be their overall health status or decreased access to healthcare before their acquisition of TRICARE. Conversely, the data appeared to show a lack of disparities in readmissions among black patients in both direct and purchased care. These similar findings seemed to correlate with recent MHS data addressing health disparities with patients’ length of stay following coronary bypass, and postoperative complications from major surgical interventions.11–17 However, published literature showed mixed results overall when considering race as a risk factor for readmission, with some studies showing no significant difference between black and white patients, and others showing black patients at increased risk of readmission.22 There was a statistically significant association with readmissions for dependents of retirees at 30 days in direct care. The 30-day findings are particularly significant in light of previous research which suggests that 30-day readmission rates better measure the quality of inpatient care, versus a later (180 days) period which better measures the quality of postdischarge outpatient care.7 The use of 30-day readmission data is used to assess the quality of healthcare services in the United States, Canada, Australia, UK, and New Zealand.23 A recent qualitative assessment identified some contributors for early (30-day) readmission risk to include poor health literacy, health system failures, and social determinants impeding care.3 An additional study showed that avoidable readmissions were related to failure to utilize services, breakdowns in communication, and lack of a multidisciplinary health system.24 However, our study revealed that differences were more likely to occur at 60 or 90 days not 30 days; this strongly suggests that outcomes are because of differences in outpatient instead of inpatient management. Interventions in outpatient care may reduce readmission risk by incorporating early follow-up care, case management, and nutritional services.6 LIMITATIONS A number of limitations for this study should be addressed. First, some findings were only marginally significant because of small sample size. In the future, this can be improved by increasing the time period beyond a single year, which would effectively increase the sample size. Furthermore, though age and sex were adjusted, this population likely could have had additional confounders such as increased comorbidities. The use of a Diabetes Complications Severity Index, Charlson Comorbidity Index, or clinical data could be used in the future to further assess the differences in case complexity, as well as to validate diagnosis codes and avoid misclassification of disease. Also, there is a great degree of coding “unknown” for race, rank, and status in the database, particularly in purchased care. Though race and rank were imputed using the sponsor’s race and/or rank, this can lead to potential errors in the data. Improved coding for these variables is needed to delineate any further health disparities associated with readmissions. Furthermore, we did not address the potential of crossover admissions between military and civilian hospitals. One must also consider that some readmissions are not preventable. Finally, the possibility of planned readmissions is a potential bias to this study, and this information cannot be gleaned from the MDR. Future public health recommendations include further research to evaluate health disparities and readmissions in the MHS by assessing length of stay, comorbidities, clinical diagnosis, and clinical data. Of note, ~14% of the diabetic patients for both direct care and purchased care were admitted more than once during the defined time period. Many areas of interventions are shown to decrease readmissions, including inpatient diabetes education, medication reconciliation, and follow-up appointments that specifically address diabetes.6 CONCLUSION This study determined that diabetic patients who were Native American/Alaska Native race, dependents of retirees, or unknown status were more likely to be readmitted in the MHS. In contrast to previously published literature, no significant disparities were observed between black and white patients’ odds of readmission. Further study is necessary to determine the exact cause of increased odds of readmission among at-risk groups, and to determine appropriate interventions for reducing preventable readmissions. The contents, views or opinions expressed in this presentation are those of the author(s) and do not necessarily reflect official policy or position of Uniformed Services University of the Health Sciences, the Department of Defense, Departments of the Army, Navy, or Air Force, or the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. The authors declare no conflict of interest. ACKNOWLEDGMENTS This research was supported in part by an appointment to the Postgraduate Research Participation Program at the U.S. Army Institute of Surgical Research administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and USAISR. Funding This study was funded through the Comparative Effectiveness and Provider-Induced Demand Collaboration (EPIC) / Low-Value Care in the National Capital Region Project, by the United States Defense Health Agency, Grant # HU0001-11-1-0023. REFERENCES 1. Rubin DJ : Correction to: hospital readmission of patients with diabetes . Curr Diab Rep 2018 ; 18 ( 4 ): 21 . doi: 10.1007/s11892-018-0989-1 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 2. Enomoto LM , Shrestha DP, Rosenthal MB, Hollenbeak CS, Gabbay RA: Risk factors associated with 30-day readmission and length of stay in patients with type 2 diabetes . J Diabetes Complications 2017 ; 31 ( 1 ): 122 – 7 . doi: 10.1016/j.jdiacomp.2016.10.021 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 3. Rubin DJ , Donnell-Jackson K, Jhingan R, Golden SH, Paranjape A: Early readmission among patients with diabetes: a qualitative assessment of contributing factors . 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J Minim Invasive Gynecol 2017 ; 24 ( 5 ): 790 – 6 . doi: 10.1016/j.jmig.2017.03.016 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 16. Chaudhary MA , Sharma M, Scully RE et al. : Universal insurance and an equal access healthcare system eliminate disparities for black patients after traumatic injury . Surgery 2018 ; 163 ( 4 ): 651 – 6 . Google Scholar Crossref Search ADS PubMed WorldCat 17. Chowdhury R , Davis WA, Chaudhary MA et al. : Race-based differences in duration of stay among universally insured coronary artery bypass graft patients in military versus civilian hospitals . Surgery 2017 ; 161 ( 4 ): 1090 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat 18. Nelson KM , Chapko MK, Reiber G, Boyko EJ: The association between health insurance coverage and diabetes care; data from the 2000 Behavioral risk factor surveillance system . Health Serv Res 2005 ; 40 ( 2 ): 361 – 72 . Google Scholar Crossref Search ADS PubMed WorldCat 19. Weech-Maldonado R , Elliott MN, Adams JL et al. : Health Serv Res 2015 ; 50 ( 6 ): 1829 – 49 . Crossref Search ADS PubMed 20. United States Department of Health and Human Services : Indian Health Service Division of Diabetes Treatment and Prevention. Diabetes in American Indians and Alaska Natives Facts at-a-Glance , 2012 . Available at https://www.ihs.gov/MedicalPrograms/Diabetes/HomeDocs/Resources/FactSheets/Fact_sheet_AIAN_508c.pdf 21. United States Department of Health and Human Services, Office of Minority Health : Minority Population Profiles: Diabetes and American Indians/Alaska Natives , 2016 . Available at https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlID=33 22. Karunakaran A , Zhao H, Rubin DJ: Pre- and post-discharge risk factors for hospital readmission among patients with diabetes . Med Care 2018 ; 56 ( 7 ): 634 – 42 . doi: 10.1097/MLR.0000000000000931 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 23. Liu X , Liu Y, Lv Y, Li C, Cui Z, Ma J: Prevalence and temporal pattern of hospital readmissions for patients with type I and type II diabetes . BMJ Open 2015 ; 5 ( 11 ): e007362 . doi: 10.1136/bmjopen-2014-007362 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref 24. Gillani SM , Aziz U, Blundell D, Singh BM: Non elective re-admissions to an acute hospital in people with diabetes: causes and the potential for avoidance. The WICKED project . Prim Care Diabetes 2015 ; 9 ( 5 ): 392 – 6 . doi: 10.1016/j.pcd.2015.01.005 . Google Scholar Crossref Search ADS PubMed WorldCat Crossref © Association of Military Surgeons of the United States 2020. All rights reserved. For permissions, please e-mail: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Deal, J Banks; Magee, Anthony J

doi: 10.1093/milmed/usaa077pmid: 32601681

Abstract Introduction The incidence of carpal tunnel syndrome (CTS) is increased in occupations exposed to repetitive motion, poor wrist posture, and vibration exposure. While all pilots are exposed to these factors, helicopter pilots are especially exposed to vibration. The study is to identify the incidence and risk factors for CTS in military aviators. Materials and Methods Clearance was obtained from Tripler Army Medical Center IRB. The Defense Medical Epidemiological Database was queried for all new cases of CTS from 2006 to 2015. Incidence rates (IRs) were determined for helicopter pilots, fixed-wing pilots, and nonpilot officers. Poisson regression analysis was used to calculate adjusted IR in order to control for demographic factors. Race was also taken into account, where pilots would classify themselves into a white or non-white race, defined by each individual. Race was assessed in the study to see if there were any differences in IR of CTS between white and non-white pilots. Results We identified 7,398 new cases of CTS among 2,319,352 person-years within the study period. Increasing age, female gender, and non-white race were significantly correlated with higher IR. Fixed-wing pilots demonstrated significantly lower adjusted IR than nonpilot officers in each age group. Helicopter pilots demonstrated higher IR than fixed-wing pilots in each age group. Helicopter pilots had lower incidence of CTS early in their career compared to nonpilot officers, but by age 40+, their IR ratio was significantly higher (1.21). Conclusion Analysis of the database indicates that fixed-wing pilot status is a protective factor against development of CTS among U.S. military officers. In contrast, helicopter pilots were found to be at an increased rate of CTS than their fixed-wing counterparts. Their incidence is comparable to their nonpilot officer peers early in their career, but is significantly increased at the senior level. Increasing age and female gender are risk factors in the military officer population as expected. Non-white race was found to increase risk in the military population, in contrast to reports of the civilian population. INTRODUCTION Carpal tunnel syndrome (CTS) is the most common nerve entrapment of the upper extremity. CTS is a neuritis most frequently caused by repetitive trauma from compression of the median nerve as it passes between the transverse carpal ligament and the carpus. Patients with CTS present with discomfort, sensory impairments along the thumb, index, long, and radial aspect of the ring finger, weakness, and weakness or atrophy of the thenar musculature. Its incidence is between 1.5 and 3.5 per 1,000 person-years in the general working population and as high as 3.95 per 1,000 person-years in the military population.1 Reported prevalence typically ranges from 2.7% to 4.9% depending on diagnostic criteria employed, but depends heavily on the population studied.2 Prevalence in working populations may be as high as 7.8%, with certain subgroups approaching 30%.3 The most commonly identified demographic risk factors are advancing age and female gender, while predisposing comorbidities include trauma, pregnancy, thyroid disorders, diabetes mellitus, Paget’s disease, and rheumatoid arthritis have been reported.4,5 Table I Demographic Distribution of Study Population . Demographics distribution . Cohort Fixed wing (%) Helicopter (%) Nonpilot officers (%) Age (years) 20–29 30.3 27.5 32.0 30–39 49.7 49.9 38.5 40+ 20.0 22.6 29.6 Gender Female 4.2 4.6 17.7 Male 95.8 95.4 82.3 Race White 90.7 86.9 75.9 Other 9.3 13.1 24.1 . Demographics distribution . Cohort Fixed wing (%) Helicopter (%) Nonpilot officers (%) Age (years) 20–29 30.3 27.5 32.0 30–39 49.7 49.9 38.5 40+ 20.0 22.6 29.6 Gender Female 4.2 4.6 17.7 Male 95.8 95.4 82.3 Race White 90.7 86.9 75.9 Other 9.3 13.1 24.1 Percentages given as total of person-years included in the analysis. Open in new tab Table I Demographic Distribution of Study Population . Demographics distribution . Cohort Fixed wing (%) Helicopter (%) Nonpilot officers (%) Age (years) 20–29 30.3 27.5 32.0 30–39 49.7 49.9 38.5 40+ 20.0 22.6 29.6 Gender Female 4.2 4.6 17.7 Male 95.8 95.4 82.3 Race White 90.7 86.9 75.9 Other 9.3 13.1 24.1 . Demographics distribution . Cohort Fixed wing (%) Helicopter (%) Nonpilot officers (%) Age (years) 20–29 30.3 27.5 32.0 30–39 49.7 49.9 38.5 40+ 20.0 22.6 29.6 Gender Female 4.2 4.6 17.7 Male 95.8 95.4 82.3 Race White 90.7 86.9 75.9 Other 9.3 13.1 24.1 Percentages given as total of person-years included in the analysis. Open in new tab Given the physical demands and occupational risk factors of military pilots, investigation of CTS in this population is of particular interest. The evidence of work-related exposures increasing risk for CTS is established and includes repetitive movements, forceful exertion, poor wrist posture, and whole body vibration exposure.6–8 These factors are variably present in fixed- and rotary-winged pilots, in whom these factors have been reported to drive increasing incidence and prevalence of spinal pathology.9–11 Given the vibration inherent in rotary-winged airframes and the constant, fine manipulation of avionic controls, it is suspected that CTS may be more prevalent among helicopter pilots than their fixed-wing colleagues. Previous work examining the incidence of CTS in the military population has not specifically addressed flight officers. The nearest report, from Garland, found increased incidence of CTS in U.S. Naval aviation-support technicians.5 CTS represents a substantial burden to the military because of disability, time lost during treatment, and potential for separation.1,12 MATERIALS AND METHODS After obtaining clearance from the Tripler Army Medical Center institutional review board, we searched the Defense Medical Epidemiological Database (DMED) for ambulatory diagnoses of carpal tunnel during the 10-year period from 2006 to 2015. The DMED is a de-identified database provided by the Armed Forces Health Surveillance Branch. It contains International Classification of Diseases (ICD) codes for all health care encounters serving U.S. active military personnel. Diagnoses are recorded as entered at the time of treatment by the provider. The ICD code 354.0 (CTS) was used, and only new diagnoses were included. Military and demographic characteristics are compiled along the clinical data before being de-identified by the system. Queries may be tailored by military and demographic factors including occupation, branch, self-reported race, gender, duty location, and ranges of rank, age, and case year. Results are shielded when case counts are low enough to allow for triangulation. Furthermore, the DMED provides population counts for the whole service, allowing denominators to be determined for incidence calculations. Data were tabulated for each permutation of occupation (helicopter pilot, fixed-wing pilot, and nonpilot officers), age group (20–29, 30–39, and 40+ years), gender, and race (white, black, and other). The fixed-wing pilot category was inclusive of the “fixed-wing fighter/bomber pilots” and “other fixed-wing pilots” categories available in DMED. The nonpilot category is comprised of all officers (including warrant officers) with subtraction of subjects who fell in the first two occupation categories. Because of limitations imposed by the anti-triangulation feature of the DMED, we combined the “black” and “other” race selection to form “non-white” group. Subdivision of results by branch resulted in degradation of data and was not pursued. Similarly, it was found that subdivision by marital status and grade did not contribute additional information that was not available by subdivision by age. We determined unadjusted incident rates (IR) and compared groups to determine unadjusted incidence rate ratios (IRRs). Then, a multivariate Poisson regression analysis was used to control for demographic factors after analysis showed no over-dispersion of the data. In this way, differences in incidence secondary to military occupation were determined, resulting in adjusted IR and IRR results. All IR values are given as cases per 1,000 person-years. Significance was established at P < 0.05. RESULTS We identified 7,398 new cases of CTS among 2,319,352 person-years in the study period. The IR per 1,000 person-years was found to be 2.52 in helicopter pilots, 1.19 in fixed-wing pilots, and 3.43 in nonpilot officers. The demographic distribution of the three groups varied significantly, as depicted in Table I. The two pilot groups were younger and more heavily composed of male, white officers than the nonpilot group. Compared to the 20 to 29 year age cohort, the 30 to 39 and 40+ cohorts demonstrated significantly higher rates of CTS, with IRRs of 2.80 and 6.16, respectively. Female gender demonstrated a significantly increased IRR of 2.60, and non-white race demonstrated a significantly increased IRR of 1.55 (Table I). Poisson regression analysis adjusting for effects because of age, gender and race demonstrated that fixed-wing officers were significantly less likely to develop CTS than helicopter pilots (IRR 0.51) or nonpilot officers (IRR 0.49) (Table II). When the age groups are analyzed separately, we see that fixed-wing pilots maintain a significantly lower IRR throughout their careers. As helicopter pilots progress from the 20 to 29 year age group to the 30 to 39 and 40+ groups, we find a stepwise increase in their IRR compared to nonpilot controls. This progresses from lower (0.70, P = 0.113 and 0.77, P = 0.006) to significantly higher (1.21, P = 0.010) (Table III). Table II Incidence Rate Comparison Before and After Poisson Regression Adjustment for Age, Gender, and Race, respectively Incident rate ratios . Unadjusted . Adjusted . Factor . IRR . 95% LB . 95% UB . P-value . IRR . 95% LB . 95% UB . P-value . Group  Fixed-wing vs. other officers 0.35 0.30 0.40 <0.0001 0.49 0.43 0.56 <0.0001  Rotary-wing vs. other officers 0.74 0.66 0.82 <0.0001 0.97 0.87 1.08 NS  Rotary wing vs. fixed wing 2.11 1.79 2.50 <0.001 1.97 1.67 2.33 <0.001 Age  30–39 vs. 20–29 2.80 2.58 3.04 <0.0001 2.95 2.71 3.20 <0.0001  >40 vs. 20–29 6.16 5.70 6.67 <0.0001 6.55 6.05 7.08 <0.0001 Gender  Female vs. male 2.60 2.48 2.73 <0.0001 2.65 2.52 2.79 <0.0001 Race  Other vs. white 1.55 1.47 1.63 <0.0001 1.32 1.25 1.39 <0.0001 Incident rate ratios . Unadjusted . Adjusted . Factor . IRR . 95% LB . 95% UB . P-value . IRR . 95% LB . 95% UB . P-value . Group  Fixed-wing vs. other officers 0.35 0.30 0.40 <0.0001 0.49 0.43 0.56 <0.0001  Rotary-wing vs. other officers 0.74 0.66 0.82 <0.0001 0.97 0.87 1.08 NS  Rotary wing vs. fixed wing 2.11 1.79 2.50 <0.001 1.97 1.67 2.33 <0.001 Age  30–39 vs. 20–29 2.80 2.58 3.04 <0.0001 2.95 2.71 3.20 <0.0001  >40 vs. 20–29 6.16 5.70 6.67 <0.0001 6.55 6.05 7.08 <0.0001 Gender  Female vs. male 2.60 2.48 2.73 <0.0001 2.65 2.52 2.79 <0.0001 Race  Other vs. white 1.55 1.47 1.63 <0.0001 1.32 1.25 1.39 <0.0001 LB and UB: Upper and lower bounds of the 95% confidence interval. Open in new tab Table II Incidence Rate Comparison Before and After Poisson Regression Adjustment for Age, Gender, and Race, respectively Incident rate ratios . Unadjusted . Adjusted . Factor . IRR . 95% LB . 95% UB . P-value . IRR . 95% LB . 95% UB . P-value . Group  Fixed-wing vs. other officers 0.35 0.30 0.40 <0.0001 0.49 0.43 0.56 <0.0001  Rotary-wing vs. other officers 0.74 0.66 0.82 <0.0001 0.97 0.87 1.08 NS  Rotary wing vs. fixed wing 2.11 1.79 2.50 <0.001 1.97 1.67 2.33 <0.001 Age  30–39 vs. 20–29 2.80 2.58 3.04 <0.0001 2.95 2.71 3.20 <0.0001  >40 vs. 20–29 6.16 5.70 6.67 <0.0001 6.55 6.05 7.08 <0.0001 Gender  Female vs. male 2.60 2.48 2.73 <0.0001 2.65 2.52 2.79 <0.0001 Race  Other vs. white 1.55 1.47 1.63 <0.0001 1.32 1.25 1.39 <0.0001 Incident rate ratios . Unadjusted . Adjusted . Factor . IRR . 95% LB . 95% UB . P-value . IRR . 95% LB . 95% UB . P-value . Group  Fixed-wing vs. other officers 0.35 0.30 0.40 <0.0001 0.49 0.43 0.56 <0.0001  Rotary-wing vs. other officers 0.74 0.66 0.82 <0.0001 0.97 0.87 1.08 NS  Rotary wing vs. fixed wing 2.11 1.79 2.50 <0.001 1.97 1.67 2.33 <0.001 Age  30–39 vs. 20–29 2.80 2.58 3.04 <0.0001 2.95 2.71 3.20 <0.0001  >40 vs. 20–29 6.16 5.70 6.67 <0.0001 6.55 6.05 7.08 <0.0001 Gender  Female vs. male 2.60 2.48 2.73 <0.0001 2.65 2.52 2.79 <0.0001 Race  Other vs. white 1.55 1.47 1.63 <0.0001 1.32 1.25 1.39 <0.0001 LB and UB: Upper and lower bounds of the 95% confidence interval. Open in new tab Table III Incident Rate Comparison Subdivided by Subject Age Groups, Before and After Poisson Regression Adjustment for Gender and Race Incident rate ratios by age . Unadjusted . Adjusted . Factor . IRR . 95% LB . 95% UB . P-value . IRR . 95% LB . 95% UB . P-value . Age 20–29  Fixed-wing vs. other officers 0.27 0.17 0.43 <0.0001 0.37 0.23 0.61 <0.0001  Rotary wing vs. other officers 0.51 0.33 0.78 0.002 0.70 0.45 1.09 NS Age 30–39  Fixed-wing vs. other officers 0.30 0.24 0.37 <0.0001 0.41 0.33 0.51 <0.0001  Rotary wing vs. other officers 0.58 0.49 0.70 <0.0001 0.77 0.64 0.93 0.006 Age 40+  Fixed-wing vs. other officers 0.50 0.42 0.60 <0.0001 0.61 0.51 0.73 <0.0001  Rotary wing vs. other officers 1.02 0.88 1.17 NS 1.21 1.05 1.39 0.010 Incident rate ratios by age . Unadjusted . Adjusted . Factor . IRR . 95% LB . 95% UB . P-value . IRR . 95% LB . 95% UB . P-value . Age 20–29  Fixed-wing vs. other officers 0.27 0.17 0.43 <0.0001 0.37 0.23 0.61 <0.0001  Rotary wing vs. other officers 0.51 0.33 0.78 0.002 0.70 0.45 1.09 NS Age 30–39  Fixed-wing vs. other officers 0.30 0.24 0.37 <0.0001 0.41 0.33 0.51 <0.0001  Rotary wing vs. other officers 0.58 0.49 0.70 <0.0001 0.77 0.64 0.93 0.006 Age 40+  Fixed-wing vs. other officers 0.50 0.42 0.60 <0.0001 0.61 0.51 0.73 <0.0001  Rotary wing vs. other officers 1.02 0.88 1.17 NS 1.21 1.05 1.39 0.010 LB and UB: Upper and lower bounds of the 95% confidence interval. Open in new tab Table III Incident Rate Comparison Subdivided by Subject Age Groups, Before and After Poisson Regression Adjustment for Gender and Race Incident rate ratios by age . Unadjusted . Adjusted . Factor . IRR . 95% LB . 95% UB . P-value . IRR . 95% LB . 95% UB . P-value . Age 20–29  Fixed-wing vs. other officers 0.27 0.17 0.43 <0.0001 0.37 0.23 0.61 <0.0001  Rotary wing vs. other officers 0.51 0.33 0.78 0.002 0.70 0.45 1.09 NS Age 30–39  Fixed-wing vs. other officers 0.30 0.24 0.37 <0.0001 0.41 0.33 0.51 <0.0001  Rotary wing vs. other officers 0.58 0.49 0.70 <0.0001 0.77 0.64 0.93 0.006 Age 40+  Fixed-wing vs. other officers 0.50 0.42 0.60 <0.0001 0.61 0.51 0.73 <0.0001  Rotary wing vs. other officers 1.02 0.88 1.17 NS 1.21 1.05 1.39 0.010 Incident rate ratios by age . Unadjusted . Adjusted . Factor . IRR . 95% LB . 95% UB . P-value . IRR . 95% LB . 95% UB . P-value . Age 20–29  Fixed-wing vs. other officers 0.27 0.17 0.43 <0.0001 0.37 0.23 0.61 <0.0001  Rotary wing vs. other officers 0.51 0.33 0.78 0.002 0.70 0.45 1.09 NS Age 30–39  Fixed-wing vs. other officers 0.30 0.24 0.37 <0.0001 0.41 0.33 0.51 <0.0001  Rotary wing vs. other officers 0.58 0.49 0.70 <0.0001 0.77 0.64 0.93 0.006 Age 40+  Fixed-wing vs. other officers 0.50 0.42 0.60 <0.0001 0.61 0.51 0.73 <0.0001  Rotary wing vs. other officers 1.02 0.88 1.17 NS 1.21 1.05 1.39 0.010 LB and UB: Upper and lower bounds of the 95% confidence interval. Open in new tab DISCUSSION The decreased incidence of CTS in fixed-wing aviators compared to nonpilot officers is an encouraging finding. Certainly, as the level of automation increases, civilian fixed-wing pilots have less hands-on exposure to control surfaces. This is evidenced by deterioration of manual flying skills, especially in long-haul pilots.13 Whether this effects military pilots remains to be shown. Though helicopter pilots demonstrate reduced incidence of CTS early in their career, the transition to higher IR than their nonpilot peers in the most senior age cohort is concerning. Civilian population studies show that the peak incidence of CTS occurs in the latter age cohort, and we expect to see the same thing in these pilots.7,14 Would the IR increase be even higher if a greater number of pilots in the fourth, fifth, and sixth decades could be included? The age distribution of the active duty pilot corps prevents that from being studied in the DMED, but it is possible that the uptick seen in the 40+ year cohort presages an even higher discrepancy in helicopter pilots after completion of military service. Ergonomic differences between fixed- and rotary-winged piloting may be responsible for the increased rate of CTS. While the fixed-wing pilot may be more hands off, this phenomenon is not applicable to the helicopter pilot who is engaged in frequent manipulation of the collective and cyclic controls. Furthermore, helicopter pilots may apply excessive manual force transmission to the controls during critical maneuvers. Pilots of the UH-1H Iroquois have been shown to exceed USAF recommendations for force generation by factors of 2 to 10. This discrepancy was greatest in upward force on the collective.15 Upper extremity and force generation was within thresholds during strength critical maneuvers in the C-130 Hercules.16 It should be noted that during routine maneuvers, force applied to the controls of the UH-1H Iroquois was small.17 In addition to peak force differences between fixed-wing and helicopter pilots, vibration constantly affects helicopter pilots. The effects of vibration on the spine of helicopter pilots are well researched. Vibration, in addition to ergonomic challenges, is thought to contribute to the exceedingly high rate of axial back pain and disk herniations in helicopters pilots.18 Pain in this group is more common than in nonpilot controls, occurs more frequently during trips, and interferes with flying in up to two-thirds of pilots.19 Less is known about the transmission of vibration to the upper extremity in pilots, but the literature shows that occupational upper extremity vibration exposure contributes to both CTS and hand-arm vibration syndrome.20 Currently designated vibration exposure limits, if adhered to, have been shown to be effective in reducing CTS.6 Specific interventions to reduce the incidence of CTS in pilots are not reported in the literature. Extrapolation from attempts to prevent CTS in other cohorts yields two broad categories for intervention: engineering and personal changes. Engineering interventions that improve the ergonomics of the flying environment—such as positioning and comfort of the control surfaces, dampening vibration transmission, reducing forces required to effect maneuvers, etc.—may prove to be beneficial.21 Additionally, educating pilots on the signs, symptoms, and occupation-related causes (namely, repetitive awkward motions that increase pressure in the carpal tunnel) may also demonstrate value. The present work is limited by factors inherent in all studies utilizing de-identified databases. Diagnoses of CTS are reported to the DMED as entered into the military health system by treating providers. The nature of the database employed does not allow for review of the diagnostic criteria employed by individual providers. Self-identified race, as recorded in the DMED, is troublesome to interpret as a purely biological variable and may be appropriately interpreted as a reflection of a social construct. The database does not provide aviation-relevant data such as flight hours, airframes used, and split between administrative and flying time. The subjects’ clinical symptoms, comorbidities, electro-diagnostic study results, treatment course, and outcomes are similarly unavailable. As this study specifically analyzes U.S. military pilots and the broader military officer corps, it is important to note that findings are not generalizable to other populations. Furthermore, because the DMED relies on diagnoses entered following voluntary patient presentations, it is subject to reporting biases. Aviators are often wary of presenting to their respective flight surgeons for any medical concerns, because of fear of losing their active flight status. Similarly, diagnoses appropriately made by the flight surgeons, in the tight-knit aviation community, may result in lack of formal reporting because of similar concerns. This may be especially true if the diagnosis does not necessarily hinder the pilot from safely performing their duties, as it is the flight surgeons responsibility not only to provide medical care to their respective units, but also to ensure that the unit has enough soldiers to be ready to deploy at any given time. Overcoming all risk of reporting bias would require mechanisms beyond the scope of this present study. This data has not been presented at any conference to date. The views expressed in this manuscript are those of the authors and do not reflect the official policy or position of the Department of the Army, Department of Defense, or the U.S. Government. References 1. Atroshi I , Gummesson C, Johnsson R, Ornstein E, Ranstam J, Rosen I: Prevalence of carpal tunnel syndrome in a general population . JAMA 1999 ; 282 ( 2 ): 153 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat 2. 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Dale AM , Harris-Adamson C, Rempel D, et al. : Prevalence and incidence of carpal tunnel syndrome in US working populations: pooled analysis of six prospective studies . Scand J Work Environ Health 2013 ; 39 ( 5 ): 495 – 505 . Google Scholar Crossref Search ADS PubMed WorldCat 7. Evanoff B , Dale AM, Deych E, Ryan D, Franzblau A: Risk factors for incident carpal tunnel syndrome: results of a prospective cohort study of newly-hired workers . Work 2012 ; 41 ( Suppl 1 ): 4450 – 2 . Google Scholar Crossref Search ADS PubMed WorldCat 8. Frank C , Garland CCG, Edward J, et al. : Carpal tunnel syndrome and occupation in U.S. navy enlisted personnel . Arch Environ Health 1996 ; 51 ( 5 ): 395 – 407 . Google Scholar Crossref Search ADS PubMed WorldCat 9. Gaydos SJ : Low back pain: considerations for rotary-wing aircrew . Aviat Space Environ Med 2012 ; 83 ( 9 ): 879 – 89 . Google Scholar Crossref Search ADS PubMed WorldCat 10. Gillibrand S , Ntani G, Coggon D: Do exposure limits for hand-transmitted vibration prevent carpal tunnel syndrome? Occup Med 2016 ; 66 ( 5 ): 399 – 402 . Google Scholar Crossref Search ADS WorldCat 11. Harrer KL , Yniguez D, Majar M, Ellenbecker D, Geiger M (editors). Whole Body Vibration Exposure for MH-60S Pilots . In: Forty Third Annual SAFE Association Symposium ., pp. 303–14, Salt Lake City , 2005 . 12. Haslbeck A , Hoermann HJ. Flying the needles: flight deck automation erodes fine-motor flying skills among airline pilots . Hum Factors 2016 ; 58 ( 4 ): 533 – 45 . Google Scholar Crossref Search ADS PubMed WorldCat 13. Hewson DJ , McNair PJ, Marshall RN: Aircraft control forces and EMG activity in a C-130 Hercules during strength-critical maneuvers . Aviat Space Environ Med 2001 ; 72 ( 3 ): 165 – 9 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 14. Hewson DJ , McNair PJ, Marshall RN: Aircraft control forces and EMG activity in a UH-1H Iroquois helicopter during emergency maneuvers . Aviat Space Environ Med 2000 ; 71 ( 8 ): 806 – 11 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 15. Hewson DJ , McNair PJ, Marshall RN: Aircraft control forces and EMG activity in a UH-1H Iroquois helicopter during routine maneuvers . Aviat Space Environ Med 2000 ; 71 ( 5 ): 470 – 5 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 16. Knox JB , Deal JB , Jr., Knox JA. Lumbar disc herniation in military helicopter pilots vs. matched controls . Aerosp Med Hum Perform 2018 ; 89 ( 5 ): 442 – 5 . Google Scholar Crossref Search ADS PubMed WorldCat 17. Kozak A , Schedlbauer G, Wirth T, Euler U, Westermann C, Nienhaus A: Association between work-related biomechanical risk factors and the occurrence of carpal tunnel syndrome: an overview of systematic reviews and a meta-analysis of current research . BMC Musculoskelet Disord 2015 ; 16 : 231 . Google Scholar Crossref Search ADS PubMed WorldCat 18. Palmer BN : Carpal tunnel syndrome, active component, U.S. armed forces, 2000-2010 . Msmr 2011 ; 18 ( 7 ): 12 – 5 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 19. Szabo RM : Carpal tunnel syndrome as a repetitive motion disorder . Clin Orthop Relat Res 1998 ; 351 : 78 – 89 . Google Scholar Crossref Search ADS WorldCat 20. Wolf JM , Mountcastle S, Owens BD: Incidence of carpal tunnel syndrome in the US military population . Hand (N Y) 2009 ; 4 ( 3 ): 289 – 93 . Google Scholar Crossref Search ADS PubMed WorldCat 21. Lincoln AE , Vernick JS, Ogaitis S, Smith GS, Mitchell CS, Agnew J: Interventions for the primary prevention of work-related carpal tunnel syndrome . Am J Prev Med 2000 ; 18 ( 4S ): 37–50. Google Scholar OpenURL Placeholder Text WorldCat Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2020. This work is written by US Government employees and is in the public domain in the US. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2020.

Knight, Ryan M; Moore, Charles H; Silverman, Montane B

doi: 10.1093/milmed/usaa059pmid: 32390038

BATTLEFIELD CARE THROUGHOUT HISTORY Battlefield medicine was pioneered at the turn of the 19th century by Napoleon’s surgeon-in-chief, Dominique-Jean Larrey. Larrey transformed a disorganized system that relied on untrained civilians and self-evacuation to clear the wounded from the battlefield to one with dedicated “flying ambulances.”1 This structured ambulance corps moved all casualties, friendly and enemy, 3 miles back from the front line to designated field hospitals.2 There, surgeons would triage the patients and provide the most up to date care before moving them again by designated ambulances to hospitals in France.1 Larrey moved the surgeon to the field, adopting the “24 hour principle” to wound care. Instead of completing amputations days later through gangrenous and infected tissue, popular at this time, Larrey’s surgeons operated promptly with increased survival using these techniques.1,2 At the start of the American Civil War, the disorganized Union Army took days to clear the First Battle of Bull Run because of reliance on quartermaster wagons and civilian drivers. Recognizing the need to bring order to this process, Major (Dr) Jonathan Letterman designed a similar system to efficiently evacuate and treat wounded casualties with oversight removed from the quartermaster and replaced by medical officers. Trained and dedicated ambulance teams would move onto the battlefield and evacuate the wounded to forward aid stations. Triage, lifesaving interventions, and stabilization of the wounded was conducted at these aid stations before casualties were moved further behind friendly lines to field hospitals that could provide more advanced surgical care.2 Letterman’s echelon of care system, which began at the point of injury (POI), incorporated aid stations and field hospitals, and ended at established medical centers created the foundation of American military medical doctrine. CURRENT ARMY DOCTRINE Current Army and Joint Service doctrine for evacuation and treatment of wounded personnel from the battlefield uses a similar tiered approach to these 18th and 19th century models. Treatment facilities are separated into Role 1, 2, 3, and 4, which define their capabilities.3 Role 1 care is the treatment provided prior to surgical intervention and includes self-aid, buddy-aid, combat lifesaver, tactical combat casualty care (TCCC), tactical evacuation, medical evacuation (MEDEVAC), and treatment at the battalion/brigade aid station (BAS). Role 1 aid stations are staffed by physicians, physician assistants (PA), and medics (combat medics—68W, special operations combat medics (SOCM), special forces medical sergeants that are assigned to the unit).4 The modified table of organization and equipment (MTOE) for a battalion or brigade surgeon is designated as an operational surgeon—immaterial (60A). This means these physicians are not always specifically trained in trauma medicine and instead are often residency trained in primary care or other nontrauma focused specialties.5,6 Role 1 doctrine is further defined by separate ambulance and treatment teams to help transport patients from a linear battlefield through the echelons of care prior to the Role 1. The ambulance teams, staffed by 68W medics, move forward from the aid station to receive patients treated by the integrated unit medics, and casualties are handed off at designated ambulance exchange points. The ambulance teams transport the patients back, behind the forward line of own troops, to the Role 1 aid station. This Role 1 aid station treatment section is the first place the wounded encounters a medical provider with an advanced scope of practice. The treatment section is divided into two teams with the physician leading one and the PA leading the other, in order to maximize the amount of lifesaving interventions. In this linear battlefield, the ambulance teams then take the stabilized patients to designated ambulance exchange points to hand off the casualties to the next ambulance team that will transport them to the next echelon of care further behind friendly lines.6,Table I, as adopted from Gerhardt et al7, summarizes the Role 1 and 2 echelons of the Army, Navy, and Air Force. TABLE I U.S. Army, Navy and Air Force Role I and II Level Care U.S. Army Role I/II . U.S. Navy/Marine Corps Role I/II . U.S. Air Force Role I/II . Focused on basic combat casualty care Echelons of care increase in capability Limited implementation of Role I/II Field care often affected by operational limitations Utilization of advanced life support in austere environments Far-forward deployability of advanced emergency medical care Advanced life support not conducted in far-forward settings MEDEVAC provides advanced life support level care MEDEVAC provides advanced life support level care Evacuation medical care may be conducted by lower level providers Far-forward, independent surgical capabilities Far-forward, independent surgical capabilities System focused on medic bringing patient to surgeon Lacking dedicated tactical MEDEVAC resources Deployability limited by existence of secured airstrip U.S. Army Role I/II . U.S. Navy/Marine Corps Role I/II . U.S. Air Force Role I/II . Focused on basic combat casualty care Echelons of care increase in capability Limited implementation of Role I/II Field care often affected by operational limitations Utilization of advanced life support in austere environments Far-forward deployability of advanced emergency medical care Advanced life support not conducted in far-forward settings MEDEVAC provides advanced life support level care MEDEVAC provides advanced life support level care Evacuation medical care may be conducted by lower level providers Far-forward, independent surgical capabilities Far-forward, independent surgical capabilities System focused on medic bringing patient to surgeon Lacking dedicated tactical MEDEVAC resources Deployability limited by existence of secured airstrip MEDEVAC, medical evacuation. Open in new tab TABLE I U.S. Army, Navy and Air Force Role I and II Level Care U.S. Army Role I/II . U.S. Navy/Marine Corps Role I/II . U.S. Air Force Role I/II . Focused on basic combat casualty care Echelons of care increase in capability Limited implementation of Role I/II Field care often affected by operational limitations Utilization of advanced life support in austere environments Far-forward deployability of advanced emergency medical care Advanced life support not conducted in far-forward settings MEDEVAC provides advanced life support level care MEDEVAC provides advanced life support level care Evacuation medical care may be conducted by lower level providers Far-forward, independent surgical capabilities Far-forward, independent surgical capabilities System focused on medic bringing patient to surgeon Lacking dedicated tactical MEDEVAC resources Deployability limited by existence of secured airstrip U.S. Army Role I/II . U.S. Navy/Marine Corps Role I/II . U.S. Air Force Role I/II . Focused on basic combat casualty care Echelons of care increase in capability Limited implementation of Role I/II Field care often affected by operational limitations Utilization of advanced life support in austere environments Far-forward deployability of advanced emergency medical care Advanced life support not conducted in far-forward settings MEDEVAC provides advanced life support level care MEDEVAC provides advanced life support level care Evacuation medical care may be conducted by lower level providers Far-forward, independent surgical capabilities Far-forward, independent surgical capabilities System focused on medic bringing patient to surgeon Lacking dedicated tactical MEDEVAC resources Deployability limited by existence of secured airstrip MEDEVAC, medical evacuation. Open in new tab Role 2, also known as forward resuscitative care, has the capability to manage more advanced trauma patients and continue more advanced resuscitative measures. Forward Surgical Teams and Forward Resuscitative Surgical Teams can be colocated at a Role 2 or operate independently. Role 3 is a theater hospital, which is able to provide treatment for all types of patients. Lastly, Role 4 are brick and mortar hospitals established both in the continental United States (previously referred to as Role 5) and outside the continental United States that provide definitive care.3,4 CHANGES TO THE CIVILIAN TRAUMA SYSTEM Army doctrine has minimally changed since the Napoleonic days and has used the same models, with increasing success, throughout the 19th and 20th centuries in conflict. However, over this same time, the civilian model for delivering trauma care has continuously evolved. Prior to the 1970s, hospitals staffed their emergency rooms with physicians of any specialty but largely with primary care physicians. This lack of specifically trained physicians to properly intervene and treat life-threatening conditions led to the birth of emergency medicine as a specialty and with it came better patient outcomes. When emergent patients are cared for and treated by emergency trained physicians when compared to general medical officers or primary care physicians, the result is reduced mortality.8 Emergency medical services (EMS) have also evolved and became more specialized. Medical directors were assigned who wrote treatment protocols, trained state and nationally licensed EMS personnel, professionally staffed the ambulances, and provided both direct and indirect medical direction to the prehospital providers.9 The civilian system also began designating trauma centers based on their capabilities and proved transporting patients to the facility with the proper capability led to a significant mortality benefit rather than merely transporting to the nearest hospital.10 The EMS system began implementing national guidelines, which included instructing EMS crews to bypass closer hospitals in order to take the most severe patients to the hospital with the proper capabilities for that patient.11 Though civilian EMS has continued to advance, the greatest evolution has arguably occurred in the helicopter emergency medical services (HEMS) community. HEMS units showed the benefit of staffing with advanced paramedics, flight nurses, and even physicians. European HEMS is often staffed with an emergency trained physician because of the clear and validated benefits in patient outcomes with this staffing model. These teams of advanced medical providers are able to provide a higher level of care to the patient and decrease the time to lifesaving interventions. In addition, they provide the experience and knowledge for early identification and management of life-threatening disease processes.12 THE CHANGING BATTLEFIELD The global war on terrorism, being fought on the nonlinear battlefields of Iraq and the great expanse and nonlinear battlefields of Afghanistan, Africa, and the Arabian Peninsula presented the military medical community with a great challenge. Current Army medical doctrine is designed for a linear battlefield with clear friendly and enemy lines as well as the movement of patients through an echeloned system. Role 1 aid stations were often located further away than Role 2 or even Role 3 facilities. Units quickly adapted and began taking their casualties directly to the facility with the highest capability rather than through the echeloned system. This has caused the Role 1 echelon to become rarely used on this modern battlefield. In 2009, Secretary of Defense Robert Gates issued the Gates Doctrine mandating that all approved missions must be able to transport casualties to a surgical facility within 60 min. By adopting the “Golden Hour” concept from civilian medicine, he improved mortality on the battlefield.13 However with this mandate, Role 1 care became even more irrelevant as commanders no longer considered getting their wounded to their unit medical personnel. Instead, mission planning was conducted against the “Golden Hour” and increasingly relied on MEDEVAC rings to enable these operations. Although the civilian HEMS sector had continued to advance, the Army MEDEVAC system did not keep pace. As the role of MEDEVAC increased in the operational environment the MEDEVAC community began to slowly evolve. In 2008, a National Guard MEDEVAC unit began showing improved survivability with their transports as compared to conventional, regular Army MEDEVAC.14 The MEDEVAC personnel in this National Guard unit also worked casualty evacuation in their civilian jobs. They replaced 68Ws with trained and experienced flight paramedics and flight nurses. In addition, the British Medical Evacuation Resuscitation Team (MERT) demonstrated improved survivability with even more complex patients and longer transports. The MERT is a MEDEVAC unit operating out of a CH47 Chinook helicopter composed of a flight nurse and flight paramedic team. The MERT-enhanced model incorporates a physician provider. This physician involvement, usually emergency medicine or anesthesiologist trained, was associated with increased survival.15 In this sector, online medical direction was utilized to triage and launched the MERT for the most critically wounded patients even if it meant a longer evacuation time. MERTs brought the resuscitation capability to the patient instead of simply transporting the patient back to a facility for resuscitation. Termed “scoop and play,” MERT is able to conduct resuscitation while transporting instead of having to decide between the conventional “stay and play” or “scoop and run” paradigms. By doing so, they significantly reduced the time to resuscitation even if this meant flying a longer distance than a conventional MEDEVAC. Upon analyzing these two scenarios, the U.S. military MEDEVAC community began to evolve and make changes to match these modern concepts of HEMS. For example, in 2012, the Army converted flight medics to flight paramedics, increasing the capability of the provider transporting the patient. ANSWERING THE NEED FOR CHANGE Through this revolution in medical evacuation, the Army has not changed its doctrine, training, or concept for Role 1 care. The model is largely the same as it was after Letterman made his changes and demonstrated their effectiveness at the Battle of Antietam in 1862. The doctrine has ignored this evolution in the civilian landscape regarding specialty trained emergency providers, bypassing facilities to deliver the patient to the most appropriate hospital rather than the closest facility, dedicated medical directors, standardized protocols for prehospital providers, and a quality improvement process. The 75th Ranger Regiment proposes to change this paradigm and follow the MEDEVAC community in acknowledging the need to evolve and learn from our civilian counterparts. The time has come to dissolve the traditional ambulance and treatment teams and form a new DCRT. The DCRT will utilize organic and MTOE’d Role 1 providers and personnel already present on the battlefield and maximize their lifesaving competencies and capabilities. This optimal and maximized Role 1 utilization will be paramount in a near-peer combat environment. The Damage Control Resuscitation Team (DCRT) is a modular concept that can be adapted to a linear or nonlinear battlefield. The core of the team will be comprised of a physician, PA, and senior SOCM medic. This core can work as one or split into one, two, or three teams augmented by the BAS medics to provide advanced resuscitation on the battlefield. The DCRT will be equipped to be mobile enough to move alongside their fellow Rangers but are agile enough to be employed in any CASEVAC platform or in a traditional Role 1 BAS. Their capability is to care for two critically injured Rangers for up to 6 h before logistical constraints become limiting. In a traditional linear battlefield, these teams can move forward to meet the combat medic and begin resuscitation en route back to the Role 1 BAS, decreasing the time to advanced team-based resuscitative care. They can also be employed on the non-linear battlefield, as they are organic to the unit and can accompany assaulters on a mission. Here, combat medics will provide the immediate TCCC and hand off to the DCRT at the casualty collection point, allowing the medic to return to the fighting formation. The DCRT can continue care, evacuate with the patient, or hand off to a MEDEVAC platform for transportation to a Role 2 or 3 facility. The key to this restructuring is in equipping and training. Physicians and PAs must be trained in emergency and critical care medicine. The emphasis of all medical training for these positions must remain focused on providing advanced care in an austere environment. Although courses and training in rehabilitation, infectious disease, tropical medicine, and sports medicine are important, they are not critical to emergently saving lives on the battlefield and should not be the focus of DCRT members. In addition to courses and training, DCRT members must receive patient care experience through different evacuation platforms in order to apply the lessons learned to real life patients. Finally, the team will be worked into the training cycle and support the battalion during all training events to develop tactics, techniques, and procedures for employing on a variety of terrains and missions. The DCRT will be specifically equipped to provide Damage Control Resuscitation (DCR) and Advanced Resuscitative Care (ARC). DCR and ARC can be defined as whole blood resuscitation, abdominopelvic hemorrhage control, advanced airway/monitoring, and resuscitation driven by labs (lactate). The traditional American College of Surgeons Advanced Trauma Life Support course, which currently serves as the foundation for Role 1 care in the Army, does not provide the sufficient methods for battlefield resuscitation and will not be the focus for the DCRT. The basic load out for the DCRT includes ventilators, invasive/noninvasive monitors, blood/fluid warmers, ultrasound, vasoactive medications, sedation medications, and blood products. These are all small, lightweight, and portable to allow for ease of carrying and mobility. Large equipment, such as full size monitors, oxygen tanks, and traditional ventilators, suitable for a traditional Role 1 BAS is not practical for a mobile team. SUMMARY Whereas civilian medicine has continuously evolved, Army doctrine for battlefield care has remained largely unchanged since the Civil War. The MEDEVAC community, when presented with statistics demonstrating the improved survivability in adapting the civilian model to the modern military, made changes to the training of medics to more appropriately match the civilian HEMS model. However, the Role 1 echelon has not changed even when faced with little utility on the modern battlefield. Instead, Role 1 care has evolved to provide preventative care, basic urgent care, and sports medicine for a largely healthy, homogenous population without improvement of battlefield survivability. With discussions regarding care in a near-peer conflict, attention has turned to training prolonged field care as the solution. The 75th Ranger Regiment proposes to change this model by resourcing, training, and employing DCRTs at each battalion by restructuring the training and equipment for the Role 1 BAS to provide emergency and critical care resuscitation at or near the POI, en route during CASEVAC, or in a traditional Role 1 BAS model. Guarantor: LTC Ryan Knight The views expressed are solely those of the authors and do not reflect the official policy or position of the U.S. Army, the Department of Defense, or the U.S. Government. References 1. Ortiz JM : The revolutionary flying ambulance of Napoleon’s surgeon . US Army Med Dep J 1998 ; 4 : 17 – 25 . Google Scholar OpenURL Placeholder Text WorldCat 2. Kharod CU , Shackelford BM, Mabry RL. Casualty transport and evacuation. In: Fundamentals of Military Medicine . Fort Detrick : Borden Institute , 2019 . pp 603 – 20 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 3. U.S. Department of Defense. Joint Health Services. 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This work is written by (a) US Government employee(s) and is in the public domain in the US. This work is written by US Government employees and is in the public domain in the US. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2020. This work is written by (a) US Government employee(s) and is in the public domain in the US.