Physicians and Specialties in the Veterans Health Administration's Community Care Network.

This cross-sectional study of data from the US Veterans Health Administration examines the availability of services provided through community care networks by specialty and clinical characteristics.

Injuries From Legal Interventions Involving Conducted Energy Devices.

This cross-sectional study evaluates emergency department visits for physical injuries from use of conducted energy devices by police departments.

Trends in U.S. self-reported health and self-care behaviors during the COVID-19 pandemic.

IMPORTANCE: The COVID-19 pandemic represents a unique stressor in Americans' daily lives and access to health services. However, it remains unclear how the pandemic impacted perceived health status and engagement in health-related behaviors. OBJECTIVE: To assess changes in self-reported health outcomes during the COVID-19 pandemic, and to explore trends in health-related behaviors that may underlie the observed health changes. DESIGN: Interrupted time series stratified by age, gender, race/ethnicity, educational attainment, household income, and employment status. SETTING: United States. PARTICIPANTS: All adult respondents to the 2016-2020 Behavioral Risk Factor Surveillance System (N = 2,146,384). EXPOSURE: Survey completion following the U.S. public health emergency declaration (March-December 2020). January 2019 to February 2020 served as our reference period. MAIN OUTCOMES AND MEASURES: Self-reported health outcomes included the number of days per month that respondents spent in poor mental health, physical health, or when poor health prevented their usual activities of daily living. Self-reported health behaviors included the number of hours slept per day, number of days in the past month where alcohol was consumed, participation in any exercise, and current smoking status. RESULTS: The national rate of days spent in poor physical health decreased overall (-1.00 days, 95% CI: -1.10 to -0.90) and for all analyzed subgroups. The rate of poor mental health days or days when poor health prevented usual activities did not change overall but exhibited substantial heterogeneity by subgroup. We also observed overall increases in mean sleep hours per day (+0.09, 95% CI 0.05 to 0.13), the percentage of adults who report any exercise activity (+3.28%, 95% CI 2.48 to 4.09), increased alcohol consumption days (0.27, 95% CI 0.18 to 0.37), and decreased smoking prevalence (-1.11%, 95% CI -1.39 to -0.83). CONCLUSIONS AND RELEVANCE: The COVID-19 pandemic had deleterious but heterogeneous effects on mental health, days when poor health prevented usual activities, and alcohol consumption. In contrast, the pandemic's onset was associated with improvements in physical health, mean hours of sleep per day, exercise participation, and smoking status. These findings highlight the need for targeted outreach and interventions to improve mental health in individuals who may be disproportionately affected by the pandemic.

Impact of notification policy on patient-before-clinician review of immediately released test results.

The 21st Century Cures Act mandates immediate availability of test results upon request. The Cures Act does not require that patients be informed of results, but many organizations send notifications when results become available. Our medical center implemented 2 sequential policies: immediate notifications for all results, and notifications only to patients who opt in. We used over 2 years of data from Vanderbilt University Medical Center to measure the effect of these policies on rates of patient-before-clinician result review and patient-initiated messaging using interrupted time series analysis. When releasing test results with immediate notification, the proportion of patient-before-clinician review increased 4-fold and the proportion of patients who sent messages rose 3%. After transition to opt-in notifications, patient-before-clinician review decreased 2.4% and patient-initiated messaging decreased 0.4%. Replacing automated notifications with an opt-in policy provides patients flexibility to indicate their preferences but may not substantially alleviate clinicians' messaging workload.

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.

Validation of the Clinicians' Cultural Sensitivity Survey for Use in Pediatric Primary Care Settings.

Incorporating cultural sensitivity into healthcare settings is important to deliver high-quality and equitable care, particularly for marginalized communities who are non-White, non-English speaking, or immigrants. The Clinicians' Cultural Sensitivity Survey (CCSS) was developed as a patient-reported survey assessing clinicians' recognition of cultural factors affecting care quality for older Latino patients; however, this instrument has not been adapted for use in pediatric primary care. Our objective was to examine the validity and reliability of a modified CCSS that was adapted for use with parents of pediatric patients. A convenience sampling approach was used to identify eligible parents during well-child visits at an urban pediatric primary care clinic. Parents were administered the CCSS via electronic tablet in a private location. We first conducted exploratory factor analyses (EFAs) to explore the dimensionality of survey responses in the adapted CCSS, and then conducted a series of confirmatory factor analyses (CFAs) using maximum likelihood estimation based on the results of the EFAs. Exploratory and confirmatory factor analyses (N = 212 parent surveys) supported a three-factor structure assessing racial discrimination ([Formula: see text]=0.96), culturally-affirming practices ([Formula: see text]=0.86), and causal attribution of health problems ([Formula: see text]=0.85). In CFAs, the three-factor model also outperformed other potential factor structures in terms of fit statistics including scaled root mean square error approximation (0.098), Tucker-Lewis Index (0.936), Comparative Fit Index (0.950), and demonstrated adequate fit according to the standardized root mean square residual (0.061). Our findings support the internal consistency, reliability, and construct validity of the adapted CCSS for use in a pediatric population.

Plan Selection, Enrollee Risk, and Health Spending on the Patient Protection and Affordable Care Act Individual Marketplaces, 2019.

Importance: The Patient Protection and Affordable Care Act (ACA) individual marketplaces are a source of insurance for millions of residents in the US. However, the association between enrollee risk, health spending, and metal tier selection remains unclear. Objectives: To describe individual marketplace enrollees' metal tier selections by risk score and assess enrollees' health spending by metal tier, risk score, and spending type. Design, Setting, and Participants: This retrospective, cross-sectional study analyzed claims data from the Wakely Consulting Group ACA database, a deidentified claims database built on data voluntarily submitted by insurers. Enrollees with continuous, full-year enrollment in on-exchange or off-exchange ACA-qualified health plans during the 2019 contract year were included. Data analysis was conducted from March 2021 to January 2023. Main Outcomes and Measures: Enrollment totals, total spending, and out-of-pocket cost were calculated, stratified by metal tier and the Department of Health and Human Services (HHS) Hierarchical Condition Category (HCC) risk score for 2019. Results: Enrollment and claims data were obtained for 1 317 707 enrollees (53.5% female; mean [SD] age, 46.35 [13.43] years) across all census areas, age groups, and sexes. Of these, 34.6% were on plans with cost-sharing reductions (CSRs), 75.5% did not have an assigned HCC, and 84.0% submitted at least 1 claim. Compared with enrollees in bronze plans (17.2%), enrollees were more likely to be classified in the top HHS-HCC risk quartile if they selected platinum (42.0%), gold (34.4%), or silver (29.7%) plans. The highest share of enrollees with $0 total spending was noted with the catastrophic (26.4%) and bronze (22.7%) plans, while gold plans had the lowest share (8.1%). Median total spending was lower among bronze plan enrollees ($593; IQR, $28-$2100) vs platinum ($4111; IQR, $992-$15 821) or gold ($2675; IQR, $728-$9070). Within the top risk score decile, CSR enrollees had less average total spending than any other metal tier by more than 10%. Conclusions and Relevance: In this cross-sectional study of the ACA individual marketplace, enrollees who selected plans with higher actuarial value also had greater mean HHS-HCC risk scores and health spending. The findings suggest these differences may be associated with variation in benefit generosity by metal tier, enrollee's perceptions of future health needs, or other barriers to care access.

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.

Geographic Variation in Appointment Wait Times for US Military Veterans.

Importance: Timely access to medical care is an important determinant of health and well-being. The US Congress passed the Veterans Access, Choice, and Accountability Act in 2014 and the VA MISSION (Maintaining Systems and Strengthening Integrated Outside Networks) Act in 2018, both of which allow veterans to access care from community-based clinicians, but geographic variation in appointment wait times after the passage of these acts have not been studied. Objective: To describe geographic variation in wait times experienced by veterans for primary care, mental health, and other specialties. Design, Setting, and Participants: This is a cross-sectional study using data from the Veterans Health Administration (VHA) Corporate Data Warehouse. Participants include veterans who sought medical care from January 1, 2018, to June 30, 2021. Data analysis was performed from February to June 2022. Exposures: Referral to either VHA or community-based clinicians. Main Outcomes and Measures: Total appointment wait times (in days) for 3 care categories: primary care, mental health, and all other specialties. VHA medical centers are organized into regions called Veterans Integrated Services Networks (VISNs); wait times were aggregated to the VISN level. Results: The final sample included 22 632 918 million appointments for 4 846 892 unique veterans (77.3% male; mean [SD] age, 61.6 [15.5] years). Among non-VHA appointments, mean (SD) VISN-level appointment wait times were 38.9 (8.2) days for primary care, 43.9 (9.0) days for mental health, and 41.9 (5.9) days for all other specialties. Among VHA appointments, mean (SD) VISN-level appointment wait times were 29.0 (5.5) days for primary care, 33.6 (4.6) days for mental health, and 35.4 (2.7) days for all other specialties. There was substantial geographic variation in appointment wait times. Among non-VHA appointments, VISN-level appointment wait times ranged from 25.4 to 52.4 days for primary care, from 29.3 to 65.7 days for mental health, and from 34.7 to 54.8 days for all other specialties. Among VHA appointments, wait times ranged from 22.4 to 43.4 days for primary care, from 24.7 to 42.0 days for mental health, and from 30.3 to 41.9 days for all other specialties. There was a correlation between wait times across care categories and setting (VHA vs community care). Conclusions and Relevance: This cross-sectional study found substantial variation in wait times across care type and geography, and VHA wait times in a majority of VISNs were lower than those for community-based clinicians, even after controlling for differences in specialty mix. These findings suggest that liberalized access to community care under the Veterans Access, Choice, and Accountability Act and the VA MISSION Act may not result in lower wait times within these regions.

Local Supply Of Postdischarge Care Options Tied To Hospital Readmission Rates.

The extent to which patients' risk for readmission after a hospitalization is influenced by local availability of postdischarge care options is not currently known. We used national, hospital-level data to assess whether the supply of postdischarge care options in hospitals' catchment areas was associated with readmission rates for Medicare patients after hospitalizations for acute myocardial infarction, heart failure, or pneumonia. Overall, readmission rates were negatively associated with per capita supply of primary care physicians (-0.16 percentage points per standard deviation) and licensed nursing home beds (-0.09 percentage points per standard deviation). In contrast, readmission rates were positively associated with per capita supply of nurse practitioners (0.09 percentage points per standard deviation). Our results suggest potential modifications to the Hospital Readmissions Reduction Program to account for local health system characteristics when assigning penalties to hospitals.

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.

A novel dataset of predictors of mortality for older Veterans living with type II diabetes.

The dataset summarized in this article includes a nationwide prevalence sample of U.S. military Veterans who were aged 65 years or older, dually enrolled in the Veterans Health Administration and traditional Medicare and had a previous diagnosis of diabetes (diabetes mellitus) as of December 2005 (N = 275,190) [1]. Our data were originally used to develop and validate prognostic indices of 5- and 10-year mortality among older Veterans with diabetes. We include various potential predictors including demographics (e.g., sex, age, marital status, and VA priority group), healthcare utilization (e.g., # of outpatient visits, # days of inpatient stays), medication history, and major comorbidities. This novel dataset provides researchers with an opportunity to study the associations between a large variety of individual-level risk factors and longevity for patients living with diabetes.

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.

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].

Cost Analysis of an Annual School-Based Pediatric Hearing Screening Program in Semi-Rural Kenya.

INTRODUCTION: Approximately 8.9 million children in Sub-Saharan Africa have disabling hearing loss, accounting for 11% of the global child healthcare hearing costs. For children living in Low- and Middle-Income Countries (LMICs), 75% of hearing loss is preventable. METHODS: We evaluate the overall intervention and expansion costs of a humanitarian, pediatric hearing health and screening program in Malindi, Kilifi County, Kenya. A cost analysis is conducted from the provider perspective, identifying the mean cost incurred for each case of newly identified hearing loss. Estimates were made for 3 different cost scenarios. A one-way sensitivity analysis and probabilistic sensitivity analysis using Monte Carlo simulation determined the impact of variations in individual cost parameters. These results were used to project scale-up costs to achieve sub-county expansion of the program. RESULTS: 155 children ages 5 to 16 years old were screened, of which 5.8% were diagnosed with hearing impairment. The total cost for implementation in four schools was $6,783 USD, thus a mean cost of $212 per diagnosis of hearing loss. The highest proportion of costs were recurrent costs of resident travel (27.9%), capital costs for providing audiometric testing (25.3%), and equipment maintenance (18.7%). Expansion of an exclusively CHW-run program across all 77 primary public schools in Malindi is projected to be $130,573 (range $119,352 to $142,240). CONCLUSION: We provide relevant cost-estimation for an expansion of an intervention which identified higher than average rates of hearing loss. Humanitarian aid plays a key role in the sustainability and feasibility of expanding this program.

Increasing Insurance Choices In The Affordable Care Act Marketplaces, 2018-21.

The Affordable Care Act (ACA) relies on insurers to offer health plans in the individual health insurance Marketplaces. Since the ACA's implementation, levels of Marketplace competition have varied, reaching a nadir in 2018. We examined the characteristics of counties that experienced changes in insurers' participation in the ACA Marketplaces from 2016 to 2021. Using data from the Kaiser Family Foundation and other sources, we found that 1,968 counties (accounting for 66 percent of the US population younger than age sixty-five) have more insurers in 2021 than in 2018, whereas only twelve counties (comprising 0.4 percent of the US nonelderly population) have fewer insurers. The number of counties with monopolist Marketplace insurers declined from 1,616 in 2018 to 294 in 2021. Recent Marketplace insurer gains were more likely in counties that lost insurers from 2016 to 2018 or had a monopolist insurer in 2018. Increased competition may lead to lower gross premiums in the ACA Marketplaces. Given the Biden administration's support for the ACA Marketplaces, it appears likely that the ACA individual health insurance market will be stable and profitable for the next several years.

Appointment wait time data for primary & specialty care in veterans health administration facilities vs. community medical centers.

The datasets summarized in this article include more than 38 million appointment wait times that U.S. military veterans experienced when seeking medical care since January 2014. Our data include both within Veterans Health Administration (VHA) facilities and community medical centers, and wait times are stratified by primary/specialty care type. Deidentified wait time data are reported at the referral-level, at the VHA facility-level, and at the patient's 3-digit ZIP code-level. As of this writing, no other U.S. health care system has made their wait times publicly available. Our data thus represent the largest, national, and most representative measures of timely access to care for patients of both VHA and community providers. Researchers may use these datasets to identify variations in appointment wait times both longitudinally and cross-sectionally, conduct research on policies and interventions to improve access to care, and to incorporate fine-grained measures of wait times into their analyses.

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.