Defining the emergency general surgery patient population in the era of ICD-10 : Evaluating an established crosswalk from ICD-9 to ICD-10 diagnosis codes.

INTRODUCTION: In 2015, the United States moved from the International Classification of Diseases, Ninth Revision ( ICD-9 ), to the International Classification of Diseases, Tenth Revision ( ICD-10 ), coding system. The American Association for the Surgery of Trauma Committee on Severity Assessment and Patient Outcomes previously established a list of ICD-9 diagnoses to define the field of emergency general surgery (EGS). This study evaluates the general equivalence mapping (GEM) crosswalk to generate an equivalent list of ICD-10 -coded EGS diagnoses. METHODS: The GEM was used to generate a list of ICD-10 codes corresponding to the American Association for the Surgery of Trauma ICD-9 EGS diagnosis codes. These individual ICD-9 and ICD-10 codes were aggregated by surgical area and diagnosis groups. The volume of patients admitted with these diagnoses in the National Inpatient Sample in the ICD-9 era (2013-2014) was compared with the ICD-10 volumes to generate observed to expected ratios. The crosswalk was manually reviewed to identify the causes of discrepancies between the ICD-9 and ICD-10 lists. RESULTS: There were 485 ICD-9 codes, across 89 diagnosis categories and 11 surgical areas, which mapped to 1,206 unique ICD-10 codes. A total of 196 (40%) ICD-9 codes have an exact one-to-one match with an ICD-10 code. The median observed to expected ratio among the diagnosis groups for a primary diagnosis was 0.98 (interquartile range, 0.82-1.12). There were five key issues identified with the ability of the GEM to crosswalk ICD-9 EGS diagnoses to ICD-10 : (1) changes in admission volumes, (2) loss of necessary modifiers, (3) lack of specific ICD-10 code, (4) mapping to a different condition, and (5) change in coding nomenclature. CONCLUSION: The GEM provides a reasonable crosswalk for researchers and others to use when attempting to identify EGS patients in with ICD-10 diagnosis codes. However, we identify key issues and deficiencies, which must be accounted for to create an accurate patient cohort. This is essential for ensuring the validity of policy, quality improvement, and clinical research work anchored in ICD-10 coded data. LEVEL OF EVIDENCE: Diagnostic Test/Criteria; Level III.

Reassessing the July Effect: 30 Years of Evidence Show No Difference in Outcomes.

OBJECTIVE: The aim of this study was to critically evaluate whether admission at the beginning versus end of the academic year is associated with increased risk of major adverse outcomes. SUMMARY BACKGROUND DATA: The hypothesis that the arrival of new residents and fellows is associated with increases in adverse patient outcomes has been the subject of numerous research studies since 1989. Methods: We conducted a systematic review and random-effects meta-analysis of July Effect studies published before December 20, 2019, looking for differences in mortality, major morbidity, and readmission. Given a paucity of studies reporting readmission, we further analyzed 7 years of data from the Nationwide Readmissions Database to assess for differences in 30-day readmission for US patients admitted to urban teaching versus nonteach-ing hospitals with 3 common medical (acute myocardial infarction, acute ischemic stroke, and pneumonia) and 4 surgical (elective coronary artery bypass graft surgery, elective colectomy, craniotomy, and hip fracture) conditions using risk-adjusted logistic difference-in-difference regression. RESULTS: A total of 113 studies met inclusion criteria; 92 (81.4%) reported no evidence of a July Effect. Among the remaining studies, results were mixed and commonly pointed toward system-level discrepancies in efficiency. Metaanalyses of mortality [odds ratio (95% confidence interval): 1.01 (0.98-1.05)] and major morbidity [1.01 (0.99-1.04)] demonstrated no evidence of a July Effect, no differences between specialties or countries, and no change in the effect over time. A total of 5.98 million patient encounters were assessed for readmission. No evidence of a July Effect on readmission was found for any of the 7 conditions. CONCLUSION: The preponderance of negative results over the past 30 years suggests that it might be time to reconsider the need for similarly-themed studies and instead focus on system-level factors to improve hospital efficiency and optimize patient outcomes.

Long-term Mental Health Trajectories of Injured Military Servicemembers: Comparing Combat to Noncombat Related Injuries.

OBJECTIVE: We sought to quantify the impact of injury characteristics and setting on the development of mental health conditions, comparing combat to noncombat injury mechanisms. BACKGROUND: Due to advances in combat casualty care, military service-members are surviving traumatic injuries at substantial rates. The nature and setting of traumatic injury may influence the development of subsequent mental health disorders more than clinical injury characteristics. METHODS: TRICARE claims data was used to identify servicemembers injured in combat between 2007 and 2011. Controls were servicemembers injured in a noncombat setting matched by age, sex, and injury severity. The rate of development, and time to diagnosis [in days (d)], of 3 common mental health conditions (post-traumatic stress disorder, depression, and anxiety) among combat-injured servicemembers were compared to controls. Risk factors for developing a new mental health condition after traumatic injury were evaluated using multivariable logistic regression that controlled for confounders. RESULTS: There were 3979 combat-injured servicemember and 3979 matched controls. The majority of combat injured servicemembers (n = 2524, 63%) were diagnosed with a new mental health condition during the course of follow-up, compared to 36% (n = 1415) of controls ( P < 0.001). In the adjusted model, those with combat-related injury were significantly more likely to be diagnosed with a new mental health condition [odds ratio (OR): 3.18, [95% confidence interval (CI): 2.88-3.50]]. Junior (OR: 3.33, 95%CI: 2.66-4.17) and senior enlisted (OR: 2.56, 95%CI: 2.07-3.17) servicemem-bers were also at significantly greater risk. CONCLUSIONS: We found significantly higher rates of new mental health conditions among servicemembers injured in combat compared to service-members sustaining injuries in noncombat settings. This indicates that injury mechanism and environment are important drivers of mental health sequelae after trauma.

The Hidden Costs of War: Healthcare Utilization Among Individuals Sustaining Combat-related Trauma (2007-2018).

OBJECTIVE: We sought to evaluate long-term healthcare requirements of American military servicemembers with combat-related injuries. SUMMARY OF BACKGROUND DATA: US military conflicts since 2001 have produced the most combat casualties since Vietnam. Long-term consequences on healthcare utilization and associated costs remain unknown. METHODS: We identified servicemembers who were treated for combat-related injuries between 2007 and 2011. Controls consisted of active-duty servicemembers injured in the civilian sector, without any history of combat-related trauma, matched (1:1) on year of injury, biologic sex injury severity, and age at time of injury. Surveillance was performed through 2018. Total annual healthcare expenditures were evaluated overall and then as expenditures in the first year after injury and for subsequent years. Negative binomial regression was used to identify the adjusted influence of combat injury on healthcare costs. RESULTS: The combat-injured cohort consisted of 3981 individuals and we identified 3979 controls. Total healthcare utilization during the follow-up period resulted in median costs of $142,214 (IQR $61,428, $323,060) per combat-injured servicemember as compared to $50,741 (IQR $26,669, $104,134) among controls. Median expenditures, adjusted for duration of follow-up, for the combat-injured were $45,211 (IQR $18,698, $105,437). In adjusted analysis, overall costs were 30% higher (1.30; 95% confidence interval: 1.23, 1.37) for combat-injured personnel. CONCLUSION: This investigation represents the longest continuous observation of healthcare utilization among individuals after combat injury and the first to assess costs. Expenditures were 30% higher for individuals injured as a result of combat-related trauma when compared to those injured in the civilian sector.

In-hospital Outcomes for COVID-19 Patients in the Military Health System: Comparison of Military and Civilian Facility Treatment.

INTRODUCTION: Beneficiaries of TRICARE, an insurance program of the military health system, can choose to receive care within the private sector (fee-for-service) or direct (budget-based facilities with salaried providers) care setting. Previous studies in several specialties have shown that there are disparities in both resource utilization and outcomes between the two settings. In this study, we sought to determine differences in outcomes between coronavirus disease 2019 (COVID-19) patients treated in the private sector versus direct care. MATERIALS AND METHODS: Using TRICARE claims data, we identified patients admitted to the hospital for COVID-19 between March and September 2020. Cases were classified, according to the facility where they were admitted for treatment, as private sector or direct care. We abstracted patient sociodemographic characteristics, comorbid conditions, and outcomes including in-hospital mortality, intensive care unit (ICU) admission, ventilator use, in-hospital complications, and 30-day readmission. We used multivariable regression models, adjusted for covariates, to determine the association between health care settings and outcomes. RESULTS: A total of 3,177 patients were included. Of these, 2,147 (68%) and 1,030 (32%) received care in the private sector and direct care settings, respectively. The average age of the study cohort was 52 years (SD = 21), and 84% had at least one medical comorbidity. In adjusted analyses, we found significant differences in the rates of ICU admission, with patients treated in private sector care having lower odds of being admitted to the ICU (odds ratio, 0.64; 95% CI, 0.53-0.76). There were no significant differences in the rates of in-hospital mortality, ventilator use, in-hospital complications, and 30-day readmissions. CONCLUSION: With the exception of ICU admission rates, which are higher in the direct care setting, we encountered comparable hospital-based outcomes for patients treated for COVID-19 within the military health system, whether care was received under private sector or direct care.

Accessibility of Level III trauma centers for underserved populations: A cross-sectional study.

BACKGROUND: By providing definitive care for many, and rapid assessment, resuscitation, stabilization, and transfer to Level I/II centers when needed, Level III trauma centers can augment capacity in high resource regions and extend the geographic reach to lower resource regions. We sought to (1) characterize populations served principally by Level III trauma centers, (2) estimate differences in time to care by trauma center level, and (3) update national estimates of trauma center access. METHODS: In a cross-sectional study (United States, 2019), we estimated travel time from census block groups to the nearest Level I/II trauma center and nearest Level III trauma center. Block groups were categorized based on the level of care accessible within 60 minutes, then distributions of population characteristics and differences in time to care were estimated. RESULTS: An estimated 22.8% of the US population (N = 76,119,228) lacked access to any level of trauma center care within 60 minutes, and 8.8% (N = 29,422,523) were principally served by Level III centers. Black and American Indian/Alaska Native (AIAN) populations were disproportionately represented among those principally served by Level III centers (39.1% and 12.2%, respectively). White and AIAN populations were disproportionately represented among those without access to any trauma center care (26.2% and 40.8%, respectively). Time to Level III care was shorter than Level I/II for 27.9% of the population, with a mean reduction in time to care of 28.9 minutes (SD = 31.4). CONCLUSION: Level III trauma centers are a potential source of trauma care for underserved populations. While Black and AIAN disproportionately rely on Level III centers for care, most with access to Level III centers also have access to Level I/II centers. The proportion of the US population with timely access to trauma care has not improved since 2010. LEVEL OF EVIDENCE: Prognostic/Epidemiological; Level IV.

Opioid administration in the prehospital setting for patients sustaining traumatic injuries: An evaluation of national emergency medical services data.

INTRODUCTION: Despite concerns about long-term dependence, opioids remain the mainstay of treatment for acute pain from traumatic injuries. Additionally, early pain management has been associated with improved long-term outcomes in injured patients. We sought to identify the patterns of prehospital pain management across the United States. METHODS: We used 2019 national emergency medical services (EMS) data to identify the use of pain management for acutely injured patients. Opioid specific dosing was calculated in morphine milligram equivalents (MME). The effects of opioids as well as adverse events were identified through objective patient data and structured provider documentation. RESULTS: We identified a total of 3,831,768 injured patients, 85% of whom were treated by an advanced life support (ALS) unit. There were 269,281 (7.0%) patients treated with opioids, including a small number of patients intubated by EMS (n = 1537; 0.6%). The median opioid dose was 10 MME [IQR 5-10] and fentanyl was the most commonly used opioid (88.2%). Patients treated with opioids had higher initial pain scores documented by EMS than those not receiving opioids (median: 9 vs 4, p<0.001), and had a median reduction in pain score of 3 points (IQR 1-5) based on the final prehospital pain score. Adverse events associated with opioid administration, including episodes of altered mental status (n = 453; 0.2%) and respiratory compromise (n = 252; 0.1%), were rare. For patients with severe pain (≥8/10), 27.3% of patients with major injuries (ISS ≥15) were treated with opioids, compared with 24.8% of those with moderate injuries (ISS 9-14), and 21.4% of those with minor (ISS 1-8) injuries (p<0.001). CONCLUSION: The use of opioids in the prehospital setting significantly reduced pain among injured patients with few adverse events. Despite its efficacy and safety, the majority of patients with major injuries and severe pain do not receive opioid analgesia in the prehospital setting.

Defining Referral Regions for Inpatient Trauma Care: The Utility of a Novel Geographic Definition.

INTRODUCTION: Geographic variation is an inherent feature of the US health system. Despite efforts to account for geographic variation in trauma system strengthening, it remains unclear how trauma "regions" should be defined. The objective of this study is to evaluate the utility of a novel definition of Trauma Referral Regions (TRR) for assessing geographic variation in inpatient trauma across the age span of hospitalized trauma patients. METHODS: Using 2016-2017 State Inpatient Databases, we assessed the extent of geographic variability in three common metrics of hospital use (localization index, market share index, net patient flow) among TRRs and, as a comparison, trauma regions alternatively defined based on Hospital Referral Regions, Hospital Service Areas, and counties. RESULTS: A total of 860,593 admissions from 102 TRRs, 127 Hospital Referral Regions, 884 Hospital Service Areas, and 583 counties were included. Consistent with expectations for distinct trauma regions, TRR presented with high average localization indices (mean [standard deviation]: 83.4 [11.7%]), low market share indices (mean [standard deviation]: 11.9 [7.0%]), and net patient flows close to 1.00. Similar results were found among stratified pediatric, adult, and older adult patients. Associations between TRRs and variations in important demographic features (e.g., travel time by road to the nearest Level I or II Trauma Center) suggest that while indicative of standalone trauma regions, TRRs are also able to simultaneously capture critical variations in regional trauma care. CONCLUSIONS: TRRs offer a standalone set of geographic regions with minimal variation in common metrics of hospital use, minimal geographic clustering, and preserved associations with important demographic factors. They provide a needed, valid means of assessing geographic variation among trauma systems.

Health-Care Utilization and Expenditures Associated with Long-Term Treatment After Combat and Non-Combat-Related Orthopaedic Trauma.

BACKGROUND: The long-term consequences of musculoskeletal trauma can be profound and can extend beyond the post-injury period. The surveillance of long-term expenditures among individuals who sustain orthopaedic trauma has been limited in prior work. We sought to compare the health-care requirements of active-duty individuals who sustained orthopaedic injuries in combat and non-combat (United States) environments using TRICARE claims data. METHODS: We identified service members who sustained combat or non-combat musculoskeletal injuries between 2007 and 2011. Combat-injured personnel were matched to those in the non-combat-injured cohort on a 1:1 basis using biologic sex, year of the injury, Injury Severity Score (ISS), and age at the index hospitalization. Health-care utilization was surveyed through 2018. The total health-care expenditures over the post-injury period were the primary outcome. These were assessed as a total overall cost and then as costs adjusted per year of follow-up. We used negative binomial regression to identify the independent association between risk factors and health-care expenditures. RESULTS: We identified 2,119 individuals who sustained combat-related orthopaedic trauma and 2,119 individuals who sustained non-combat injuries. The most common mechanism of injury within the combat-injured cohort was blast-related trauma (59%), and 418 individuals (20%) sustained an amputation. The total costs were $156,886 for the combat-injured group compared with $55,873 for the non-combat-injured group (p < 0.001). Combat-related orthopaedic injuries were associated with a 43% increase in health-care expenditures (incidence rate ratio, 1.43 [95% confidence interval, 1.19 to 1.73]). Severe ISS at presentation, ≥2 comorbidities, and amputations were also significantly associated with health-care utilization, as was junior enlisted rank, our proxy for socioeconomic status. CONCLUSIONS: Health-care requirements and associated costs are substantial among service members sustaining combat and non-combat orthopaedic trauma. Given the sociodemographic characteristics of our cohort, we believe that these results are translatable to civilians who sustain similar types of musculoskeletal trauma.

Geospatial Analysis of Access to Emergency Cesarean Delivery for Military and Civilian Populations in the US.

Importance: Many women in the US, particularly those living in rural areas, have limited access to obstetric care. Military-civilian partnership could improve access to obstetric care and benefit military personnel, their civilian dependents, and the civilian population as a whole. Objective: To identify medical facilities within military and civilian geographic areas that present opportunities for military-civilian partnership in obstetric care and to assess whether civilian use of military medical treatment facilities (MTFs) could improve access to emergency cesarean delivery care in the US. Design, Setting, and Participants: This geospatial epidemiological population-based cross-sectional study was conducted from November 2020 to March 2021. ArcGIS Pro software, version 2.7 (Esri), was used to assess population coverage for TRICARE (military insurance) beneficiaries and civilian populations and to estimate 30-minute travel time to 2392 total military and civilian medical facilities that were capable of providing emergency cesarean delivery care in the continental US. Data on health insurance coverage for TRICARE beneficiaries and their civilian dependents per county were obtained from the American Community Survey tables available through ArcGIS Pro software. Demographic characteristics of the general population were obtained from the 2020 key demographic indicators published by Esri. Race and ethnicity were not examined because the data used for this study were aggregated and did not include further categorization by race or ethnicity. Main Outcomes and Measures: Population coverage rates (measured in percentages) within 30-minute catchment areas, defined as areas that were within a 30-minute travel time to a medical facility capable of providing emergency cesarean delivery care. Results: A total of 29 MTFs and 2363 civilian hospitals capable of providing emergency cesarean delivery were identified across the contiguous US. Overall, an estimated 167 759 762 women (3 640 000 TRICARE beneficiaries and 164 119 762 civilians) were included in these service areas. The analysis identified 17 of 29 MTFs (58.6%) capable of providing emergency cesarean delivery care that were located within 30-minute catchment areas. Of those, 3 MTFs were the only facilities capable of providing emergency cesarean delivery care within a 30-minute travel time in those regions, and 14 additional MTFs had catchment areas partially overlapping with civilian hospitals that also covered areas without alternative access to emergency cesarean delivery. Expanded use of these 14 MTFs could enhance access to emergency cesarean delivery care not otherwise covered by current civilian hospitals. Conclusions and Relevance: In this study, 58.6% of MTFs capable of providing emergency cesarean delivery care were located in areas with the potential to improve access to obstetric care within a 30-minute travel time. Maintenance of MTFs in these important access regions could be prioritized in the context of restructuring MTFs. This prioritization has the potential to improve access to emergency cesarean delivery care for underserved civilian populations in the US, particularly among those living in rural areas.

Long-Term Healthcare Expenditures Following Combat-Related Traumatic Brain Injury.

BACKGROUND: Traumatic brain injury (TBI) is one of the most common injuries resulting from U.S. Military engagements since 2001. Long-term consequences in terms of healthcare utilization are unknown. We sought to evaluate healthcare expenditures among U.S. military service members with TBI, as compared to a matched cohort of uninjured individuals. METHODS: We identified service members who were treated for an isolated combat-related TBI between 2007 and 2011. Controls consisted of hospitalized active duty service members, without any history of combat-related injury, matched by age, biologic sex, year of hospitalization, and duration of follow-up. Median total healthcare expenditures over the entire surveillance period represented our primary outcome. Expenditures in the first year (365 days) following injury (hospitalization for controls) and for subsequent years (366th day to last healthcare encounter) were considered secondarily. Negative binomial regression was used to identify the adjusted influence of TBI. RESULTS: The TBI cohort consisted of 634 individuals, and there were 1,268 controls. Healthcare expenditures among those with moderate/severe TBI (median $154,335; interquartile range [IQR] $88,088-$360,977) were significantly higher as compared to individuals with mild TBI (median $113,951; IQR $66,663-$210,014) and controls (median $43,077; IQR $24,403-$83,590; P < .001). Most expenditures were incurred during the first year following injury. CONCLUSION: This investigation represents the first continuous observation of healthcare utilization among individuals with combat-related TBI. Our findings speak to continued consumption of health care well beyond the immediate postinjury period, resulting in total expenditures approximately six to seven times higher than those of service members hospitalized for noncombat-related reasons.

A National Assessment of Trauma Systems Using the American College of Surgeons NBATS Tool: Geographic Distribution of Trauma Center Need.

OBJECTIVE: To compare the needs based assessment of trauma systems (NBATS) tool estimates of trauma center need to the existing trauma infrastructure using observed national trauma volume. SUMMARY OF BACKGROUND DATA: Robust trauma systems have improved outcomes for severely injured patients. The NBATS tool was created by the American College of Surgeons to align trauma resource allocation with regional needs. METHODS: Data from the Agency for Healthcare Research and Quality Healthcare Costs and Utilization Project State Inpatient Databases, the Trauma Information Exchange Program, and US Census was used to calculate an NBATS score for each trauma service area (TSA) as defined by the Pittsburgh Atlas. This score was used to estimate the number of trauma centers allocated to each TSA and compared to the number of existing trauma centers. RESULTS: NBATS predicts the need for 117 additional trauma centers across the United States to provide adequate access to trauma care nationwide. At least 1 additional trauma center is needed in 49% of TSAs. CONCLUSIONS: Application of the NBATS tool nationally shows the need for additional trauma infrastructure across a large segment of the United States. We identified some limitations of the NBATS tool, including preferential weighting based on current infrastructure. The NBATS tool provides a good framework to begin the national discussion around investing in the expansion of trauma systems nationally, however, in many instances lacks the granularity to drive change at the local level.

In defense of Direct Care: Limiting access to military hospitals could worsen quality and safety.

OBJECTIVE: Ongoing health care reforms within the US Military Health System (MHS) are expected to shift >1.9 million MHS beneficiaries from military treatment facilities (MTFs) into local civilian hospitals over the next 1-2 years. The objective of this study was to examine how such health care reforms are likely to affect the quality of MHS care. DATA SOURCES: Adult MHS beneficiaries, aged 18-64 years, treated in MTFs (under a program known as Direct Care) were compared against (1) MHS beneficiaries treated in locally available civilian hospitals (under a program known as Purchased Care) and (2) similarly-aged adult civilian patients across the United States. MHS beneficiaries in Direct and Purchased Care were identified from fiscal-year 2016-2018 MHS inpatient claims. National inpatients were identified in the 2017 Nationwide Readmissions Database. STUDY DESIGN: Retrospective cohort. DATA COLLECTION: Differences in quality were compared using two sets of quality metrics endorsed by the US Agency for Healthcare Research and Quality (AHRQ): Inpatient Quality Indicators, 19 quality metrics that look at differences in in-hospital mortality, and Patient Safety Indicators, 18 quality metrics that look at differences in in-hospital morbidity and adverse events. Among MHS beneficiaries (Direct and Purchased Care), we further simulated what changes in quality indicators might look like under various proposed scenarios of reduced access to Direct Care. PRINCIPAL FINDINGS: A total of 502,252 MHS admissions from 37 MTFs and surrounding civilian hospitals were included (326,076 Direct Care, 179,176 Purchased Care). Nationwide, 9.34 million adult admissions from 2453 hospitals were included. On average, MHS beneficiaries treated in MTFs experienced better inpatient quality and improved patient safety compared with MHS beneficiaries treated in locally available civilian hospitals (e.g., summary observed-to-expected ratio for medical mortality: 0.98 vs. 1.03, p < 0.001) and adult patients across the United States (0.98 vs. 1.02, p < 0.001). Simulations of proposed changes resulted in consistently worse outcomes for MHS patients, whether reducing MTF access by 10%, 20%, or 50% nationwide; limiting MTF access to active-duty beneficiaries; or closing MTFs with the worst performance on patient safety (p < 0.001 for overall quality indicators for each). CONCLUSIONS: Reducing access to MTFs could result in significant harm to MHS patients. The results underscore the importance of health-policy planning based on evidence-based evaluation and the need to consider the consequential downstream effects caused by changes in access to care.

Understanding the role of informal caregivers in postoperative care transitions for older patients.

BACKGROUND: Older adults may have new care needs and functional limitations after surgery. Many rely on informal caregivers (unpaid family or friends) after discharge but the extent of informal support is unknown. We sought to examine the role of informal postoperative caregiving on transitions of care for older adults undergoing routine surgical procedures. MATERIALS AND METHODS: We performed a retrospective cohort study using ACS NSQIP Geriatric Pilot Project data, 2014-2018. Patients were ≥65 years and underwent an inpatient surgical procedure. Patients who lived at home alone were compared with those who lived with support from informal caregivers (family and/or friends). Primary outcomes were discharge destination (home vs. post-acute care) and readmission within 30 days. Multivariable logistic regression was used to determine the association between support at home, discharge destination, and readmission. RESULTS: Of 18,494 patients, 25% lived alone before surgery. There was no difference in loss of independence (decline in functional status or new use of mobility aid) after surgery between patients who lived alone or with others (18.7% vs. 19.5%, p = 0.24). Nevertheless, twice as many patients who lived alone were discharged to a non-home location (10.2% vs. 5.1%; OR: 2.24, CI: 1.93-2.56). Patients who lived alone and were discharged home with new informal caregivers had increased odds of readmission (OR: 1.43, CI: 1.09-1.86). CONCLUSION: Living alone independently predicts discharge to post-acute care, and patients who received new informal caregiver support at home have higher odds of readmission. These findings highlight opportunities to improve discharge planning and care.

Analysis of Surgical Volume in Military Medical Treatment Facilities and Clinical Combat Readiness of US Military Surgeons.

Importance: Low surgical volume in the US Military Health System (MHS) has been identified as a challenge to military surgeon readiness. The Uniformed Services University of Health Sciences, in partnership with the American College of Surgeons, developed the Knowledge, Skills, and Abilities (KSA) Clinical Readiness Program that includes a tool for quantifying the clinical readiness value of surgeon workload, known as the KSA metric. Objective: To describe changes in US military general surgeon procedural volume and readiness using the KSA metric. Design, Setting, and Participants: This cohort study analyzed general surgery workload performed across the MHS, including military and civilian facilities, between fiscal year 2015 and 2019 and the calculated KSA metric value. The surgeon-level readiness among military general surgeons was calculated based on the KSA metric readiness threshold. Data were obtained from TRICARE, the US Department of Defense health insurance product. Main Outcomes and Measures: The main outcomes were general surgery procedural volumes and the KSA metric point value of those procedures across the MHS as well as the number of military general surgeons meeting the KSA metric readiness threshold. Aggregate facility and regional market-level claims data were used to calculate the procedural volumes and KSA metric readiness value of those procedures. Annual adjusted KSA metric points earned were used to determine the number of individual US military general surgeons meeting the readiness threshold. Results: The number of general surgery procedures generating KSAs in military hospitals decreased 25.6%, from 128 377 in 2015 to 95 461 in 2019, with a 19.1% decrease in the number of general surgeon KSA points (from 7 155 563 to 5 790 001). From 2015 to 2019, there was a 3.2% increase in both the number of procedures (from 419 980 to 433 495) and KSA points (from 21 071 033 to 21 748 984) in civilian care settings. The proportion of military general surgeons meeting the KSA metric readiness threshold decreased from 16.7% (n = 97) in 2015 to 10.1% (n = 68) in 2019. Conclusions and Relevance: This study noted that the number of KSA metric points and procedural volume in military hospitals has been decreasing since 2015, whereas both measures have increased in civilian facilities. The findings suggest that loss of surgical workload has resulted in further decreases in military surgeon readiness and may require substantial changes in patient care flow in the MHS to reverse the change.

The impact of the Affordable Care Act's medicaid expansion on patients admitted for burns: An analysis of national data.

INTRODUCTION: The first states began implementing the Medicaid expansion provisions of the Patient Protection and Affordable Care Act (ACA) in 2014. Studies have yet to address its impact on burn patients. METHODS: Burn patients in geographic regions that expanded Medicaid coverage were compared to patients in regions that did not expand Medicaid before and after implementation of the ACA using bivariate statistics and a difference-in-differences model. A multivariable logistic regression was used to identify factors associated with having Medicaid insurance. The primary outcome of this study was the rate of Medicaid insurance. RESULTS: Of 25,331 discharges, we found greater increases in Medicaid coverage after the ACA in the Medicaid expander regions (23.4-40.2%) compared to the non-expander regions (18.5-20.1%). The difference-in-differences estimate between the expander and non-expander regions was 0.15 (95% CI: 0.11-0.18, p < 0.001). Patients admitted in expander regions were more likely to be insured by Medicaid (OR 1.57 [95%CI 1.21-2.05]), as were patients of Black race (OR 1.25 [95%CI 1.19-1.32), Hispanic ethnicity (OR 1.29 [95%CI 1.14-1.46]), and female sex (OR 1.59 [95%CI 1.11-2.27]). We also found a significant interaction between time period (pre-ACA/post-ACA) and expander region location (OR 2.10 [95%CI 1.67-2.62]). CONCLUSIONS: The Medicaid expansion provision of the ACA led to increased Medicaid coverage among burn patients which was significantly higher in areas with widespread implementation of the expansion.

The Utility of a Novel Definition of Health Care Regions in the United States in the Era of COVID-19: A Validation of the Pittsburgh Atlas Using Pneumonia Admissions.

STUDY OBJECTIVE: The COVID-19 pandemic in the United States has underscored the need to understand health care in a regional context. However, there are multiple definitions of health care regions available for conducting geospatial analyses. In this study, we compare the novel Pittsburgh Atlas, which defined regions for emergency care, with the existing definitions of regions, counties, and the Dartmouth Atlas, with respect to nonemergent acute medical conditions using pneumonia admissions. METHODS: We identified patients hospitalized with a primary diagnosis of pneumonia or a primary admitting diagnosis of sepsis with a secondary diagnosis of pneumonia in the Agency for Healthcare Research and Quality's State Inpatient Databases. We calculated the percentage of region concordant care, the localization index, and market share for 3 definitions of health care regions (the Pittsburgh Atlas, Dartmouth Atlas, and counties). We used logistic regression identified predictors of region concordant care. RESULTS: We identified 1,582,287 patients who met the inclusion criteria. We found that the Pittsburgh Atlas and Dartmouth Atlas definitions of regions performed similarly with respect to both localization index (92.0 [interquartile range 87.9 to 95.7] versus 90.3 [interquartile range 81.4 to 94.5]) and market share (8.5 [interquartile range 5.1 to 13.6] versus 9.4 [interquartile range 6.7 to 14.1]). Both atlases outperformed the localization index (67.5 [interquartile range 49.9 to 83.9]) and market share (20.0% [interquartile range 11.4 to 31.4]) of the counties. Within a given referral region, the demographic factors, including age, sex, race/ethnicity, insurance status, and the level of severity, affected concordance rates between residential and hospital regions. CONCLUSION: Because the Pittsburgh Atlas also has the benefit of respecting state and county boundaries, the use of this definition may have improved policy applicability without sacrificing accuracy in defining health care regions for acute medical conditions.

Long-term prescription opioid use among US military service members injured in combat.

INTRODUCTION: During the Global War on Terrorism, many US Military service members sustained injuries with potentially long-lasting functional limitations and chronic pain. We sought to understand the patterns of prescription opioid use among service members injured in combat. METHODS: We queried the Military Health System Data Repository to identify service members injured in combat between 2007 and 2011. Sociodemographics, injury characteristics, treatment information, and costs of care were abstracted for all eligible patients. We surveyed for prescription opioid utilization subsequent to hospital discharge and through 2018. Negative binomial regression was used to identify factors associated with cumulative prescription opioid use. RESULTS: We identified 3,981 service members with combat-related injuries presenting during the study period. The median age was 24 years (interquartile range [IQR], 22-29 years), 98.5% were male, and the median follow-up was 3.3 years. During the study period, 98% (n = 3,910) of patients were prescribed opioids at least once and were prescribed opioids for a median of 29 days (IQR, 9-85 days) per patient-year of follow-up. While nearly all patients (96%; n = 3,157) discontinued use within 6 months, 91% (n = 2,882) were prescribed opioids again after initially discontinuing opioids. Following regression analysis, patients with preinjury opioid exposure, more severe injuries, blast injuries, and enlisted rank had higher cumulative opioid use. Patients who discontinued opioids within 6 months had an unadjusted median total health care cost of US $97,800 (IQR, US $42,364-237,135) compared with US $230,524 (IQR, US $134,387-370,102) among those who did not discontinue opioids within 6 months (p < 0.001). CONCLUSION: Nearly all service members injured in combat were prescribed opioids during treatment, and the vast majority experienced multiple episodes of prescription opioid use. Only 4% of the population met the criteria for sustained prescription opioid use at 6 months following discharge. Early discontinuation may not translate to long-term opioid cessation in this population. LEVEL OF EVIDENCE: Epidemiology study, level III.

The impact of delayed management of fall-related hip fracture management on health outcomes for African American older adults.

BACKGROUND: Black hip fracture patients experience worse health outcomes than otherwise similar White patients, but causes of these disparities are not known. We sought to determine if delays in hip fracture surgery and/or hospital structures contribute to racial disparities in hip fracture outcomes. METHODS: Using 2006 to 2016 Trauma Quality Program Public Use Files, we identified hip fracture patients with primary mechanisms of fall from standing and determined surgical treatment category (no surgery, surgery within 24 hours after arrival, surgery 24-48 hours after arrival, surgery more than 48 hours after arrival) as well as hospital structure characteristics (trauma center designation, teaching status, profit status, bed size). We used generalized structural equation models to conduct path analyses and determine if hip fracture treatment and hospital characteristics mediated the relationship between race (non-Hispanic Black/non-Hispanic White) and outcomes (complications, length of stay, disposition). RESULTS: Non-Hispanic Black patients were more likely than non-Hispanic White patients to receive treatment at an academic medical center (49.1% vs. 28.0%), at a hospital with >600 inpatient beds (39.5% vs. 25.3%), and at a level I or II trauma center (86.8% vs. 77.7%); were more likely to go without hip fracture repair surgery (22.8% vs. 21.4%); and were more likely to have delayed surgery >48 hours after hospital arrival (15.5% vs. 10.6%). Path analysis suggests hip fracture treatment group and hospital characteristics mediate the relationship with complications, length of stay, and disposition. CONCLUSION: Non-Hispanic Black patients with fall-related hip fracture are more likely to experience delays in care, complications, and longer inpatient stays. Hospital characteristics contribute to increased risk of complications and longer length of stay, both as independent determinants of outcomes and as determinants of delays in hip fracture surgery. LEVEL OF EVIDENCE: Prognostic and epidemiologic, level III.

Strategies for spinal surgery reimbursement: bundling in the working-age population.

INTRODUCTION: Bundled payments for spine surgery, which is known for having high overall cost with wide variation, have been previously studied in older adults. However, there has been limited work examining bundled payments in working-age patients. We sought to identify the variation in the cost of spine surgery among working age adults in a large, national insurance claims database. METHODS: We queried the TRICARE claims database for all patients, aged 18-64, undergoing cervical and non-cervical spinal fusion surgery between 2012 and 2014. We calculated the case mix adjusted, price standardized payments for all aspects of care during the 60-, 90-, and 180-day periods post operation. Variation was assessed by stratifying Hospital Referral Regions into quintiles. RESULTS: After adjusting for case mix, there was significant variation in the cost of both cervical ($10,538.23, 60% of first quintile) and non-cervical ($20,155.59, 74%). Relative variation in total cost decreased from 60- to 180-days (63 to 55% and 76 to 69%). Index hospitalization was the primary driver of costs and variation for both cervical (1st-to-5th quintile range: $11,033-$19,960) and non-cervical ($18,565-$36,844) followed by readmissions for cervical ($0-$11,521) and non-cervical ($0-$13,932). Even at the highest quintile, post-acute care remained the lowest contribution to overall cost ($2070 & $2984). CONCLUSIONS: There is wide variation in the cost of spine surgery across the United States for working age adults, driven largely by index procedure and readmissions costs. Our findings suggest that implementing episodes longer than the current 90-day standard would do little to better control cost variation.