Comparing approaches to identify live births using the Transformed Medicaid Statistical Information System.

OBJECTIVE: To evaluate the performance of different approaches for identifying live births using Transformed Medicaid Statistical Information System Analytic Files (TAF). DATA SOURCES: The primary data source for this study were TAF inpatient (IP), other services (OT), and demographic and eligibility files. These data contain administrative claims for Medicaid enrollees in all 50 states and the District of Columbia from January 1, 2018 to December 31, 2018. STUDY DESIGN: We compared five approaches for identifying live birth counts obtained from the TAF IP and OT data with the Centers for Disease Control and Prevention (CDC) Natality data-the gold standard for birth counts at the state level. DATA COLLECTION/EXTRACTION METHODS: The five approaches used varying combinations of diagnosis and procedure, revenue, and place of service codes to identify live births. Approaches 1 and 2 follow guidance developed by the Centers for Medicare and Medicaid Services (CMS). Approaches 3 and 4 build on the approaches developed by CMS by including all inpatient hospital claims in the OT file and excluding codes related to delivery services for infants, respectively. Approach 5 applied Approach 4 to only the IP file. PRINCIPAL FINDINGS: Approach 4, which included all inpatient hospital claims in the OT file and excluded codes related to infants to identify deliveries, achieved the best match of birth counts relative to CDC birth record data, identifying 1,656,794 live births-a national overcount of 3.6%. Approaches 1 and 3 resulted in larger overcounts of births (20.5% and 4.5%), while Approaches 2 and 5 resulted in undercounts of births (-3.4% and -6.8%). CONCLUSIONS: Including claims from both the IP and OT files, and excluding codes unrelated to the delivery episode and those specific to services rendered to infants improves accuracy of live birth identification in the TAF data.

Medicaid policy data for evaluating eligibility and programmatic changes.

OBJECTIVES: Medicaid and the Children's Health Insurance Program (CHIP) provide health insurance coverage to more than 90 million Americans as of early 2023. There is substantial variation in eligibility criteria, application procedures, premiums, and other programmatic characteristics across states and over time. Analyzing changes in Medicaid policies is important for state and federal agencies and other stakeholders, but such analysis requires data on historical programmatic characteristics that are often not available in a form ready for quantitative analysis. Our objective is to fill this gap by synthesizing existing qualitative policy data to create a new data resource that facilitates Medicaid policy research. DATA DESCRIPTION: Our source data were the 50-state surveys of Medicaid and CHIP eligibility, enrollment, and cost-sharing policies, and budgets conducted near annually by KFF since 2000, which we coded through 2020. These reports are a rich source of point-in-time information but not operationalized for quantitative analysis. Through a review of the measures captured in the KFF surveys, we developed five Medicaid policy domains with 122 measures in total, each coded by state-quarter-1) eligibility (28 measures), 2) enrollment and renewal processes (39 measures), 3) premiums (16 measures), 4) cost-sharing (26 measures), and 5) managed care (13 measures).

Using Modernized Medicaid Data to Advance Evidence-Based Improvements in Maternal Health.

Medicaid is the primary payor for nearly half of all births in the United States and plays a disproportionate role in covering maternity care for low-income people, rural people, and minoritized racial groups. Newly available, modernized Medicaid claims data-the Transformed Medicaid Statistical Information System Analytic Files (TAF)-offer a significant opportunity to conduct novel research that can drive the development of evidence-based programs and policies for Medicaid beneficiaries before, during, and after pregnancy. Yet, the public health research community has so far underused the TAF for maternal health research. We provide an overview of the TAF and how they compare to other major data sets available to study maternal health. We highlight some major limitations of the TAF and offer strategies to maximize the potential of these novel data to accelerate timely, rigorous research to improve maternal health and health equity. (Am J Public Health. 2023;113(7):805-810. https://doi.org/10.2105/AJPH.2023.307287).

Excess Mortality at Veterans Health Administration Facilities During the COVID-19 Pandemic.

IMPORTANCE: The COVID-19 pandemic resulted in excess mortality among the general US population and at Veterans Health Administration (VHA) facilities. It is critical to understand the characteristics of facilities that experienced the highest and lowest pandemic-related mortality to inform future mitigation efforts. OBJECTIVE: To identify facility-level excess mortality during the pandemic and to correlate these estimates with facility characteristics and community-wide rates of COVID-19 burden. DESIGN: We used pre-pandemic data to estimate mortality risk prediction models using 5-fold cross-validation and Poisson quasi-likelihood regression. We then estimated excess mortality and observed versus expected (O/E) mortality ratios by the VHA facility from March to December 2020. We examined facility-level characteristics by excess mortality quartile. PARTICIPANTS: Overall, there were 11.4 million VHA enrollees during 2016 and 2020. MAIN MEASURES: Facility-level O/E mortality ratios and excess all-cause mortality. RESULT: VHA-enrolled veterans experienced 52,038 excess deaths from March to December 2020, equating to 16.8% excess mortality. Facility-specific rates ranged from -5.5% to +63.7%. Facilities in the lowest quartile for excess mortality experienced fewer COVID-19 deaths (0.7-1.51, P <0.001) and cases (52.0-63.0, P =0.002) per 1,000 population compared with the highest quartile. The highest quartile facilities had more hospital beds (276.7-187.6, P =0.024) and a higher percent change in the share of visits conducted via telehealth from 2019 to 2020 (183%-133%, P <0.008). CONCLUSIONS: There was a large variation in mortality across VHA facilities during the pandemic, which was only partially explained by the local COVID-19 burden. Our work provides a framework for large health care systems to identify changes in facility-level mortality during a public health emergency.

State Variation in Severe Maternal Morbidity Among Individuals With Medicaid Insurance.

OBJECTIVE: To measure variation in delivery-related severe maternal morbidity (SMM) among individuals with Medicaid insurance by state and by race and ethnicity across and within states. METHODS: We conducted a pooled, cross-sectional analysis of the 2016-2018 TAF (Transformed Medicaid Statistical Information System Analytic Files). We measured overall and state-level rates of SMM without blood transfusion for all individuals with Medicaid insurance with live births in 49 states and Washington, DC. We also examined SMM rates among non-Hispanic Black and non-Hispanic White individuals with Medicaid insurance in a subgroup of 27 states (and Washington, DC). We generated unadjusted rates of composite SMM and the individual indicators of SMM that comprised the composite. Rate differences and rate ratios were calculated to compare SMM rates for non-Hispanic Black and non-Hispanic White individuals with Medicaid insurance. RESULTS: The overall rate of SMM without blood transfusion was 146.2 (95% CI 145.1-147.3) per 10,000 deliveries (N=4,807,143). Rates of SMM ranged nearly threefold, from 80.3 (95% CI 71.4-89.2) per 10,000 deliveries in Utah to 210.4 (95% CI 184.6-236.1) per 10,000 deliveries in Washington, DC. Non-Hispanic Black individuals with Medicaid insurance (n=629,774) experienced a higher overall rate of SMM (212.3, 95% CI 208.7-215.9) compared with non-Hispanic White individuals with Medicaid insurance (n=1,051,459); (125.3, 95% CI 123.2-127.4) per 10,000 deliveries (rate difference 87.0 [95% CI 82.8-91.2]/10,000 deliveries; rate ratio 1.7 [95% CI 1.7-1.7]). The leading individual indicator of SMM among all individuals with Medicaid insurance was eclampsia, although the leading indicators varied across states and by race and ethnicity. Many states were concordant in leading indicators among the overall, non-Hispanic Black, and non-Hispanic White populations (ie, in Oklahoma sepsis was the leading indicator for all three). Most states, however, were discordant in leading indicators across the three groups (ie, in Texas eclampsia was the leading indicator overall, pulmonary edema or acute heart failure was the leading indicator among the non-Hispanic Black population, and sepsis was the leading indicator among the non-Hispanic White population). CONCLUSION: Interventions aimed at reducing SMM and, ultimately, mortality among individuals with Medicaid insurance may benefit from the data generated from this study, which highlights states that have the greatest burden of SMM, the differences in rates among non-Hispanic Black populations compared with non-Hispanic White populations, and the leading indicators of SMM overall, by state, and by race and ethnicity.

Racial and ethnic disparities in excess mortality among U.S. veterans during the COVID-19 pandemic.

OBJECTIVE: The COVID-19 pandemic disproportionately affected racial and ethnic minorities among the general population in the United States; however, little is known regarding its impact on U.S. military Veterans. In this study, our objectives were to identify the extent to which Veterans experienced increased all-cause mortality during the COVID-19 pandemic, stratified by race and ethnicity. DATA SOURCES: Administrative data from the Veterans Health Administration's Corporate Data Warehouse. STUDY DESIGN: We use pre-pandemic data to estimate mortality risk models using five-fold cross-validation and quasi-Poisson regression. Models were stratified by a combined race-ethnicity variable and included controls for major comorbidities, demographic characteristics, and county fixed effects. DATA COLLECTION: We queried data for all Veterans residing in the 50 states plus Washington D.C. during 2016-2020. Veterans were excluded from analyses if they were missing county of residence or race-ethnicity data. Data were then aggregated to the county-year level and stratified by race-ethnicity. PRINCIPAL FINDINGS: Overall, Veterans' mortality rates were 16% above normal during March-December 2020 which equates to 42,348 excess deaths. However, there was substantial variation by racial and ethnic group. Non-Hispanic White Veterans experienced the smallest relative increase in mortality (17%, 95% CI 11%-24%), while Native American Veterans had the highest increase (40%, 95% CI 17%-73%). Black Veterans (32%, 95% CI 27%-39%) and Hispanic Veterans (26%, 95% CI 17%-36%) had somewhat lower excess mortality, although these changes were significantly higher compared to White Veterans. Disparities were smaller than in the general population. CONCLUSIONS: Minoritized Veterans experienced higher rates excess of mortality during the COVID-19 pandemic compared to White Veterans, though with smaller differences than the general population. This is likely due in part to the long-standing history of structural racism in the United States that has negatively affected the health of minoritized communities via several pathways including health care access, economic, and occupational inequities.

Effect of a Veterans Health Administration mandate to case review patients with opioid prescriptions on mortality among patients with opioid use disorder: a secondary analysis of the STORM randomized control trial.

AIMS: The Veterans Health Administration (VHA) implemented the Stratification Tool for Opioid Risk Mitigation (STORM) to reduce the risk of serious adverse events (SAE) among patients with opioid analgesic prescriptions. VHA facilities were mandated to case review patients identified as high risk by STORM. The aim of this study was to measure the effect of this mandate on all-cause mortality and SAEs among VHA patients newly diagnosed with opioid use disorder (OUD). DESIGN: Secondary analysis of a stepped-wedged cluster randomized controlled trial conducted at all 140 VHA facilities, with facility as the unit of randomization, from 2018 to 2020. SETTING AND PARTICIPANTS: United States VHA facilities were randomized to case review the top 1 or 5% of high-risk patients prescribed opioid analgesics identified by STORM. A total of 28 251 patients were diagnosed with OUD during the trial and were considered control or treatment depending on the status of the facility where they received their OUD diagnosis. Post-hoc analyses among patients who had at least one opioid analgesic prescription in the 90 days prior to diagnosis were conducted and were then stratified by receipt of a prescription in the 90 days following diagnosis to assess the sensitivity of results to opioid discontinuation. MEASUREMENTS: All-cause mortality and opioid-related, drug-related, suicide-related and other SAEs within 90 days of OUD diagnosis. FINDINGS: Mandated case review increased the odds of 90-day mortality [odds ratio (OR) = 1.74, 95% confidence interval (CI) = 1.06, 2.87], but did not significantly change the odds of SAEs. Among patients who received an opioid prescription prior to but not after OUD diagnosis, the odds of all-cause mortality within 90 days was 5.87 (95% CI = 1.85, 18.58) relative to control patients. CONCLUSIONS: Veterans Health Administration patients newly diagnosed with opioid use disorder experienced increased all-cause mortality following expansion of a case review mandate for high-risk patients prescribed opioids.

State-Level Variation in Supplemental Maternity Kick Payments in Medicaid Managed Care.

This cross-sectional study assesses the prevalence and magnitude of state-level delivery event­triggered kick payments to Medicaid managed care plans and their association with delivery costs.

Changes in Health Care Access by Race, Income, and Medicaid Expansion During the COVID-19 Pandemic.

BACKGROUND: The intersecting crises of the COVID-19 pandemic, job losses, and concomitant loss of employer-sponsored health insurance may have disproportionately affected health care access within minorized and lower-socioeconomic status communities. OBJECTIVE: To describe changes in access to care during the COVID-19 pandemic, stratified by race/ethnicity, household income, and state Medicaid expansion status. RESEARCH DESIGN: We used interrupted time series and difference-in-differences regression models, controlling for respondent characteristics and preexisting trends. SUBJECTS: Data were extracted for all adults aged 18-64 surveyed in the 2015-2020 Behavioral Risk Factor Surveillance System (N=1,731,699) from all 50 states and the District of Columbia. MEASURES: Our outcomes included indicators for whether respondents had any health insurance coverage or avoided seeking care because of cost within the prior year. The primary exposure was the onset of the COVID-19 pandemic in the United States in March 2020. RESULTS: The pandemic was associated with a 1.2 percentage point (pp) decline in uninsurance for Medicaid expansion states (95% CI, -1.8, -0.6); these reductions were concentrated among respondents who were Black, multiracial, or low income. The rates of uninsurance were generally stable in nonexpansion states. The rates of avoided care because of cost fell by 3.5 pp in Medicaid expansion states (95% CI, -3.9, -3.1), and by 3.6 pp (95% CI, 4.3-2.9) in nonexpansion states. These declines were concentrated among respondents who were Hispanic, Other Race, or low income. CONCLUSIONS: Our findings reinforce the value of Medicaid expansion as one tool to improve access to health insurance and care for marginalized and vulnerable populations.

Association Between Medicaid Managed Care Coverage of Substance Use Services and Treatment Utilization.

Importance: Medicaid insures a disproportionate share of adults with substance use disorder (SUD) and is thus uniquely positioned to facilitate access to care. Many enrollees receive coverage through Medicaid managed care (MMC) plans, which receive capitated payments in exchange for coverage of a defined set of benefits. Historically, coverage of substance use services has been carved out of MMC plans and financed fee-for-service (FFS) by state Medicaid programs, but in recent years, many states have opted to carve in this benefit. Little is known about whether MMC coverage of substance use services, relative to FFS coverage, is associated with changes in utilization. Objective: To examine the association between changes in MMC coverage of substance use services and admissions for substance use treatment. Design, Setting, and Participants: This cross-sectional study examined changes in admissions for substance use treatment in 2 states after coverage of substance use services was either carved into (Nebraska) or carved out of (Maryland) comprehensive MMC coverage. Synthetic control methods were used to compare changes in admissions between states that did and did not alter MMC coverage of substance use services. Data on substance use treatment admissions were obtained from the Treatment Episode Data Set-Admissions from 2010 to 2019. Exposures: Carve-outs or carve-ins of coverage for both inpatient and outpatient substance use services from comprehensive MMC coverage. Main Outcomes and Measures: Reported substance use treatment admissions per 100 000 residents and admissions by treatment type (ie, rehabilitation or residential, outpatient, and detoxification) per 100 000 residents. Results: Maryland's carve-out was associated with an additional mean 787.1 (95% CI, 624.6-1141.7) substance use admissions per 100 000 residents during 2015 and 2016, a relative increase of 104.4% (95% CI, 64.4%-154.1%) compared with its synthetic control. This increase was concentrated among changes in outpatient services utilization. In Nebraska, the carve-in was associated with a mean decrease of 97.2 (95% CI, -23.4 to 213.6) admissions per 100 000 residents, a relative decrease of 33.2% (95% CI, -54.1% to 29.6%) compared with its synthetic control and was concentrated primarily among admissions for detoxification services. Conclusions and Relevance: The results of this cross-sectional study suggest that carving out coverage of substance use services and financing them through FFS coverage may be associated with overall increases in treatment utilization but with heterogeneous associations across states and treatment types.

Improving Access to High-Value, High-Cost Medicines: The Use of Subscription Models to Treat Hepatitis C Using Direct-Acting Antivirals in the United States.

State payers may face financial incentives to restrict use of high-cost medications. Yet, restrictions on access to high-value medications may have deleterious effects on population health. Direct-acting antivirals (DAAs), available since 2013, can cure chronic infection with hepatitis C virus (HCV). With prices upward of $90,000 for a treatment course, states have struggled to ensure access to DAAs for Medicaid beneficiaries and the incarcerated, populations with a disproportionate share of HCV. Advance purchase commitments (APCs), wherein a payer commits to purchase a certain quantity of medications at lower prices, offer payers incentives to increase access to high-value medications while also offering companies guaranteed revenue. This article discusses the use of subscription models, a type of APC, to support increased access to high-value DAAs for treating HCV. First, the authors provide background information about HCV, its treatment, and state financing of prescription medications. They then review the implementation of HCV subscription models in two states, Louisiana and Washington, and the early evidence of their impact. The article discusses challenges to evaluating state-sponsored subscription models, and it concludes by discussing implications of subscription models that target DAAs and other high-value, high-cost medicines.

Trends Among Female Pelvic Medicine and Reconstructive Surgery Fellowships and Graduates.

IMPORTANCE: Understanding differences in female pelvic medicine and reconstructive surgery (FPMRS) urology and gynecology-based fellowships is important because both are accredited by the American Board of Medical Subspecialties. OBJECTIVE: The aim of the study was to characterize urology-based and gynecology-based FPMRS fellowships. MATERIAL AND METHODS: An institutional review board-approved 21-item survey was emailed to Accreditation Council for Graduate Medical Education-accredited FPMRS fellowship program directors from January 8 through March 9, 2021. The survey aimed to characterize fellowships through a series of common and specialty-specific questions. Responses were collected with Qualtrics and analyzed using STATA/MP Version 16.1. RESULTS: The response rate was 75% (52/69). Many programs accept both gynecology- and urology-trained applicants (urology-based fellowships, 45.4%; gynecology-based fellowships, 68.3%) since the Accreditation Council for Graduate Medical Education accreditation in 2012. Within the gynecology-based cohort, there have been 10 urology-trained graduates among 7 programs (n = 1-2). Barriers to accepting urology applicants were limited gynecologic knowledge/experience (n = 14) and length of training (n = 11). Thirty-seven (94.8%) reported their graduates log more than 30 hysterectomies and 8.3% (n = 3) log 3 or more urinary diversions.Within the urology-based cohort, there have been 16 gynecology-trained graduates among 4 programs (n = 2-7). Lack of urologic clinical knowledge (n = 4) and training length (n = 2) were cited as barriers to accepting gynecology-trained applicants. Three (27%) reported that their graduates log more than 30 hysterectomies, while 8 (72.7%) reported that graduates log 3 or more urinary diversions. CONCLUSIONS: Despite many FPMRS programs stating that they accept gynecology or urology-trained applicants, few fellows graduate from outside specialty FPMRS training programs. Several barriers were identified that may prevent trainees acceptance outside of their residency specialty. Procedural training experience differs between urology- and gynecology-based fellowships.

Medicaid expansion and drug overdose mortality during the COVID-19 pandemic in the United States.

BACKGROUND: The COVID-19 pandemic caused disruptions in the delivery of health services, which may have adversely affected access to substance use disorder (SUD) treatment services. Medicaid expansion has been previously associated with increased access to SUD services for low-income adults. Thus, the pandemic may have differentially impacted overdose mortality depending on expansion status. This study examined trends in overdose mortality nationally and by state Medicaid expansion status from 2013 to 2020. METHODS: State-level data on overdose mortality were obtained from the Centers for Disease Control and Prevention's WONDER database for 2013-2020 (N = 408 state-years). The primary outcomes were drug and opioid overdose deaths per 100,000 residents. The primary exposure was Medicaid expansion status as of January 1st, 2020. Difference-in-difference (DID) models were used to compare changes in outcomes between expansion and non-expansion states after the onset of the COVID-19 pandemic. RESULTS: The U.S. experienced 91,799 drug overdose deaths in 2020, a 29.9% relative increase from 2019. Expansion states experienced an adjusted increase of 7.0 drug overdose deaths per 100,000 residents (95% CI 3.3, 10.7) and non-expansion states experienced an increase of 4.3 deaths (95% CI 1.5, 8.2) from 2019 to 2020. Similar trends were observed in opioid overdose deaths. In DID models, Medicaid expansion was not associated with changes in drug (0.9 deaths, 95% CI -2.0, 3.7) or opioid overdose deaths (0.8 deaths, 95% CI -1.8, 3.5). CONCLUSIONS: The increase in drug or opioid overdose deaths experienced during the first year of the COVID-19 pandemic was similar in states with and without Medicaid expansion.

Compassion, stigma, and professionalism among emergency personnel responding to the opioid crisis: An exploratory study in New Hampshire, USA.

OBJECTIVE: Drug overdoses are the leading cause of death in the United States for those under 50 years of age, and New Hampshire has been disproportionately affected, resulting in increased encounters with the emergency response system. The ensuing impact on emergency personnel has received little attention. The present study aimed to explore the experiences and perspectives of emergency personnel responding to the opioid crisis in NH, with a focus on their views toward people who use opioids. METHODS: Thirty-six emergency personnel (emergency department clinicians, n = 18; emergency medical service providers, n = 6; firefighters, n = 6; and police officers, n = 6) in 6 New Hampshire counties were interviewed about their experiences responding to overdoses and their perspectives on individuals who use opioids. Directed content analysis was used to identify themes in the transcribed, semistructured interviews. The results were reviewed for consensus. RESULTS: Several categories of themes were identified among emergency personnel's accounts of their overdose response experiences and perspectives, including varied degrees of compassion and stigma toward people who use opioids; associations between compassion or stigma and policy- and practice-related themes, such as prehospital emergency care and the role of emergency departments (EDs); and primarily among personnel expressing compassion, a sense of professional responsibility that outweighed personal biases. CONCLUSIONS: Despite the magnitude of the ongoing opioid crisis, some emergency personnel in New Hampshire have sustained or increased their compassion for people who use opioids. Others' perspectives remain or have become increasingly stigmatizing. The associations of compassion and stigma with various policy- and practice-related themes warrant further investigation.

Mental health service use in a national sample of college students with co-occurring depression or anxiety and substance use.

Introduction: Campus health systems can provide timely and accessible resources for students with co-occurring substance use and mental illness, but little is known about the degree to which students use these systems. This study examined mental health service utilization among students with symptoms of anxiety or depression, stratified by substance use. Methods: This cross-sectional study used data came from the 2017-2020 Healthy Minds Study. Mental health service use was examined among students with clinically significant anxiety or depression (N = 65,969), stratified by substance use type (no use, alcohol or tobacco use, marijuana use, other drug use). We performed a series of weighted logistic regressions to assess the adjusted association of substance use type with past year use of campus, off-campus outpatient, emergency department, and hospital mental health services. Results: Among students, 39.3% reported exclusive use of alcohol or tobacco, 22.9% reported use of marijuana, and 5.9% reported use of other drugs. Use of alcohol or tobacco was not associated with mental health service utilization, while students who use marijuana faced increased odds of campus (OR 1.10, 95% CI 1.01, 1.20) and off-campus outpatient mental health service utilization (OR 1.27, 95% CI 1.17, 1.37). Other drug use was associated with increased odds of off-campus outpatient (OR 1.28, 95% CI 1.14, 1.48), emergency department (OR 2.13, 95% CI 1.50, 3.03) and hospital service utilization (OR 1.52, 95% CI 1.13, 2.04). Conclusions: Universities should consider screening for substance use and common mental illnesses to support the health of high-risk students.

County-Level Impact of the COVID-19 Pandemic on Excess Mortality Among U.S. Veterans: A Population-Based Study.

BACKGROUND: As the novel coronavirus (COVID-19) continues to impact the world at large, Veterans of the US Armed Forces are experiencing increases in both COVID-19 and non-COVID-19 mortality. Veterans may be more susceptible to the pandemic than the general population due to their higher comorbidity burdens and older age, but no research has examined if trends in excess mortality differ between these groups. Additionally, individual-level data on demographics, comorbidities, and deaths are provided in near-real time for all enrolees of the Veterans Health Administration (VHA). These data provide a unique opportunity to identify excess mortality throughout 2020 at a subnational level, and to validate these estimates against local COVID-19 burden. METHODS: We queried VHA administrative data on demographics and comorbidities for 11.4 million enrolees during 2016-2020. Pre-pandemic data was used to develop and cross-validate eight mortality prediction models at the county-level including Poisson, Poisson quasi-likelihood, negative binomial, and generalized estimating equations. We then estimated county-level excess Veteran mortality during 2020 and correlated these estimates with local rates of COVID-19 confirmed cases and deaths. FINDINGS: All models demonstrated excellent agreement between observed and predicted mortality during 2016-2019; a Poisson quasi-likelihood with county fixed effects minimized median squared error with a calibration slope of 1.00. Veterans of the U.S. Armed Forces faced an excess mortality rate of 13% in 2020, which corresponds to 50,299 excess deaths. County-level estimates of excess mortality were correlated with both COVID-19 cases (R2=0.77) and deaths per 1,000 population (R2=0.59). INTERPRETATION: We developed sub-national estimates of excess mortality associated with the pandemic and shared our data as a resource for researchers and data journalists. Despite Veterans' greater likelihood of risk factors associated with severe COVID-19 illness, their excess mortality rate was slightly lower than the general population. Consistent access to health care and the rapid expansion of VHA telemedicine during the pandemic may explain this divergence. FUNDING: This work was supported by grants from the Department of Veterans Affairs Quality Enhancement Research Initiative [PEC 16-001]. Dr. Griffith's effort was supported in part by the Agency for Healthcare Research & Quality [K12 HS026395].

County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access.

The dataset summarized in this article is a combination of several of U.S. federal data resources for the years 2006-2013, containing county-level variables for opioid pill volumes, demographics (e.g. age, race, ethnicity, income), insurance coverage, healthcare demand (e.g. inpatient and outpatient service utilization), healthcare infrastructure (e.g. number of hospital beds or hospices), and the supply of various types of healthcare providers (e.g. medical doctors, specialists, dentists, or nurse practitioners). We also include indicators for states which permitted opioid prescribing by nurse practitioners. This dataset was originally created to assist researchers in identifying which factors predict per capita opioid pill volume (PCPV) in a county, whether early state Medicaid expansions increased PCPV, and PCPV's association with opioid-related mortality. Missing data were imputed using regression analysis and hot deck imputation. Non-imputed values are also reported. Taken together, our data provide a new level of precision that may be leveraged by scholars, policymakers, or data journalists who are interested in studying the opioid epidemic. Researchers may use this dataset to identify patterns in opioid distribution over time and characteristics of counties or states which were disproportionately impacted by the epidemic. These data may also be joined with other sources to facilitate studies on the relationships between opioid pill volume and a wide variety of health, economic, and social outcomes.