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.

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.

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.

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 Pedatric 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.

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.

Changes in US Veterans' Access to Specialty Care During the COVID-19 Pandemic

This cross-sectional study identifies changes in referral volume and wait times for veterans seeking care from the specialists in the Veterans Health Administration or the community.

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.

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.

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.

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].

Medicaid Subscription-Based Payment Models and Implications for Access to Hepatitis C Medications

Question

Did the use of direct-acting antiviral hepatitis C virus (HCV) medications change after implementation of subscription-based payment models for these drugs in Washington and Louisiana?

Findings

In this cross-sectional study, Louisiana experienced a 534.5% increase in HCV prescription fills after implementation of a subscription-based payment model, but no significant change in prescription fills was observed in Washington.

Meaning

In this study, subscription-based payment models in Louisiana and Washington were differentially associated with use of Medicaid-covered HCV medications, which may reflect state-level differences in implementation, historical restrictions on access to these medications, and responses to the COVID-19 pandemic.

Importance

Hepatitis C virus (HCV) can be cured with direct-acting antiviral medications, but state Medicaid programs often restrict access to these lifesaving medications owing to their high costs. Subscription-based payment models (SBPMs), wherein states contract with a single manufacturer to supply prescriptions at a reduced price, may offer a solution that increases access. Whether SBPMs are associated with changes in HCV medication use is unknown.

Objective

To estimate changes in Medicaid-covered HCV prescription fills after Louisiana and Washington implemented SBPMs on July 1, 2019.

Design, Setting, and Participants

This cross-sectional study examined trends in prescription fills of Medicaid-covered direct-acting antiviral HCV medications in Louisiana and Washington after implementation of SBPMs. A synthetic control approach was used to compare changes in HCV prescription fills between states that did and did not implement SBPMs. The unit of analysis was state-quarter. Outpatient direct-acting antiviral HCV prescription fills from the Medicaid State Drug Utilization Data files were obtained from all 50 US states and the District of Columbia from January 1, 2017, to June 30, 2020.

Exposures

Implementation of SBPMs for Medicaid-covered direct-acting antiviral HCV medications.

Main Outcomes and Measures

Direct-acting antiviral HCV prescriptions filled per 100 000 Medicaid enrollees.

Results

In the year preceding SBPM implementation, the mean (SD) rate of quarterly HCV prescription fills per 100 000 Medicaid enrollees was 43.1 (8.6) prescriptions in Louisiana and 50.1 (4.1) in Washington. After SBPM implementation, the mean (SD) rate of quarterly HCV prescription fills per 100 000 enrollees was 206.0 (51.2) prescriptions in Louisiana and 53.9 (11.0) in Washington. In synthetic control models, SBPM implementation in Louisiana was associated with an increase of 173.5 (95% CI, 74.3-265.3) quarterly prescription fills per 100 000 Medicaid enrollees during the following year, a relative increase of 534.5% (95% CI, 228.7%-1125.0%). Washington did not experience a significant change in prescription fills following SBPM implementation.

Conclusions and Relevance

In this cross-sectional study, Louisiana experienced substantial increases in HCV medication use among its Medicaid-enrolled population following SBPM implementation, whereas Washington did not. These differences may partially be explained by state-level variation in SBPM implementation, historical restrictions on access to HCV medications, and responses to the COVID-19 pandemic.

Association of Medicaid Managed Care Drug Carve Outs With Hepatitis C Virus Prescription Use

Importance

Medicaid enrolls a disproportionate share of US adults with hepatitis C virus (HCV), and most receive Medicaid benefits through managed care organizations (MCOs). Medicaid MCOs often impose stricter requirements to access HCV medications than traditional fee-for-service Medicaid, which may inhibit use. Though Medicaid MCOs generally cover prescription drugs, several states have carved out direct-acting antiviral HCV medications from MCO coverage and opted to cover them under fee-for-service. Whether these carve outs were associated with changes in medication use is unknown.

Objective

To examine the association between Medicaid-covered HCV medication fills and carve outs of these medications from MCO coverage.

Design Setting and Participants

This cross-sectional study examined changes in fills of Medicaid-covered direct-acting antiviral HCV medications in 4 states (Indiana, Michigan, New Hampshire, and West Virginia) that carved out these drugs from Medicaid MCOs between 2015 and 2017. A synthetic control approach was used to compare changes in HCV prescription fills between states that did and did not carve out these medications from MCO prescription drug coverage. Data of direct-acting antiviral HCV prescription fills were obtained from the Medicaid State Drug Utilization Data files, January 2015 to June 2020. Data analysis was conducted from November 2020 to June 2021.

Exposures

Carve outs of direct-acting antiviral HCV medications from Medicaid MCO prescription drug coverage.

Main Outcomes and Measures

Direct-acting antiviral HCV prescriptions filled per 100 000 Medicaid enrollees.

Results

In this cross-sectional study, carve outs were associated with a mean quarterly increase of 22.1 (95% CI, 12.7-34.1) HCV prescriptions per 100 000 Medicaid enrollees, a relative increase of 86.3% compared with synthetic control states. Compared with each state's respective synthetic control, HCV prescription fills were associated with an increase of 11.5 (95% CI, 5.1-19.0) HCV prescription fills per 100 000 Medicaid enrollees per quarter in Indiana, 36.6 (95% CI, 23.5-53.9) in Michigan, 20.7 (95% CI, 11.1-32.8) in West Virginia, and 43.6 (95% CI, 25.9-68.4) in New Hampshire.

Conclusions and Relevance

In this cross-sectional study of data from 39 states and the District of Columbia, carve outs of direct-acting antiviral HCV medications from Medicaid MCO prescription drug coverage were associated with significant increases in HCV medication use. Given their clinical benefits, greater uptake of HCV medication may help improve the health of Medicaid enrollees with HCV and reduce the economic burden of untreated HCV on the US health care system.

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.

Implications of county-level variation in U.S. opioid distribution

BACKGROUND

Prescription opioids accounted for the majority of opioid-related deaths in the United States prior to 2010, and continue to contribute to opioid misuse and mortality. We used a novel dataset to investigate the distributional patterns of prescription opioids, whether opioid pill volume was associated with opioid-related mortality, and whether early state Medicaid expansions were associated with either pill volume or opioid-related mortality.

METHODS

Data on opioid shipments to retail pharmacies for 2006-2013 were obtained from the U.S. Drug Enforcement Administration, and opioid-related deaths (ORDs) were obtained from the Centers for Disease Control and Prevention. We first compared characteristics of counties in the highest and lowest quartiles for per capita pill volume (PCPV). We used adjusted difference-in-differences regression models to identify factors associated with PCPV or ORDs, and whether early state Medicaid expansions were associated with either outcome. All models were estimated as linear regressions with standard errors clustered by county, and weighted by county population.

RESULTS

We found large geographic variations in opioid distribution, and this variation appears to be driven by differences in demographics, healthcare access, and healthcare supply. In adjusted models, a one-pill increase in PCPV was associated with a 0.20 increase in ORDs per 100,000 population (95 % CI 0.11-0.30). Early Medicaid expansions were associated with lower PCPV (-2.20, 95 % CI -2.97 to -1.43).

CONCLUSIONS

Our findings validate the relationship between PCPV and ORDs, identify important environmental drivers of the opioid epidemic, and suggest early state Medicaid expansions were beneficial in reducing opioid pill volume.

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.

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.

Effectiveness of COVID-19 shelter-in-place orders varied by state

State "shelter-in-place" (SIP) orders limited the spread of COVID-19 in the U.S. However, impacts may have varied by state, creating opportunities to learn from states where SIPs have been effective. Using a novel dataset of state-level SIP order enactment and county-level mobility data form Google, we use a stratified regression discontinuity study design to examine the effect of SIPs in all states that implemented them. We find that SIP orders reduced mobility nationally by 12 percentage points (95% CI: -13.1 to -10.9), however the effects varied substantially across states, from -35 percentage points to +11 percentage points. Larger reductions were observed in states with higher incomes, higher population density, lower Black resident share, and lower 2016 vote shares for Donald J. Trump. This suggests that optimal public policies during a pandemic will vary by state and there is unlikely to be a "one-size fits all" approach that works best.

Cancer stage at presentation for incarcerated patients at a single urban tertiary care center

Patients who are incarcerated are a vulnerable patient population and may suffer from less access to routine cancer screenings compared to their non-incarcerated counterparts. Therefore, a thorough evaluation of potential differences in cancer diagnosis staging is needed. We sought to examine whether there are differences in cancer stage at initial diagnosis between non-incarcerated and incarcerated patients by pursuing a retrospective chart review from 2010–2017 for all patients who were newly diagnosed with cancer at an urban safety net hospital. Incarceration status was determined by insurance status. Our primary outcome was incarceration status at time of initial cancer diagnosis. Overall, patients who were incarcerated presented at a later cancer stage for all cancer types compared to the non-incarcerated (+.14 T stage, p = .033; +.23 N stage, p < .001). Incarcerated patients were diagnosed at later stages for colorectal (+0.93 T stage, p < .001; +.48 N stage, p < .001), oropharyngeal (+0.37 N stage, p = .003), lung (+0.60 N stage, p = .018), skin (+0.59 N stage, p = 0.014), and screenable cancers (colorectal, prostate, lung) as a whole (+0.23 T stage, p = 0.002; +0.17 N stage, p = 0.008). Incarcerated patients may benefit from more structured screening protocols in order to improve the stage at presentation for certain malignancies.

Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers

This cross-sectional study compares appointment wait times at Veterans Health Administration facilities and those at community medical centers accessed via the Veterans Choice Program.

Predicting 5- and 10-Year Mortality Risk in Older Adults With Diabetes

OBJECTIVE

Several diabetes clinical practice guidelines suggest that treatment goals may be modified in older adults on the basis of comorbidities, complications, and life expectancy. The long-term benefits of treatment intensification may not outweigh short-term risks for patients with limited life expectancy. Because of the uncertainty of determining life expectancy for individual patients, we sought to develop and validate prognostic indices for mortality in older adults with diabetes.

RESEARCH DESIGN AND METHODS

We used a prevalence sample of veterans with diabetes who were aged ≥65 years on 1 January 2006 (N = 275,190). Administrative data were queried for potential predictors that included patient demographics, comorbidities, procedure codes, laboratory values and anthropomorphic measurements, medication history, and previous health service utilization. Logistic least absolute shrinkage and selection operator regressions were used to identify variables independently associated with mortality. The resulting odds ratios were then weighted to create prognostic indices of mortality over 5 and 10 years.

RESULTS

Thirty-seven predictors of mortality were identified: 4 demographic variables, prescriptions for insulin or sulfonylureas or blood pressure medications, 6 biomarkers, previous outpatient and inpatient utilization, and 22 comorbidities/procedures. The prognostic indices showed good discrimination, with C-statistics of 0.74 and 0.76 for 5- and 10-year mortality, respectively. The indices also demonstrated excellent agreement between observed outcome and predictions, with calibration slopes of 1.01 for both 5- and 10-year mortality.

CONCLUSIONS

Prognostic indices obtained from administrative data can predict 5- and 10-year mortality in older adults with diabetes. Such a tool may enable clinicians and patients to develop individualized treatment goals that balance risks and benefits of treatment intensification.

Changes in Health Care Access, Behaviors, and Self-reported Health Among Low-income US Adults Through the Fourth Year of the Affordable Care Act

BACKGROUND

Health care access increased for low-income Americans under the Affordable Care Act (ACA). It is unknown whether these changes in access were associated with improved self-reported health.

OBJECTIVE

Determine changes in health care access, health behaviors, and self-reported health among low-income Americans over the first 4 years of the ACA, stratified by state Medicaid expansion status.

DESIGN

Interrupted time series and difference-in-differences analysis.

SUBJECTS

Noninstitutionalized US adults (18-64 y) in low income households (<138% federal poverty level) interviewed in the Behavioral Risk Factor Surveillance System 2011-2017 (N=505,824).

MEASURES

Self-reported insurance coverage, access to a primary care physician, avoided care due to cost; self-reported general health, days of poor physical health, days of poor mental health days, and days when poor health limited usual activities; self-reported health behaviors, use of preventive services, and diagnoses.

RESULTS

Despite increases in access, the ACA was not associated with improved physical or general health among low-income adults during the first 4 years of implementation. However, Medicaid expansion was associated with fewer days spent in poor mental health (-1.1 d/mo, 95% confidence interval: -2.1 to -0.5). There were significant changes in specific health behaviors, preventive service use, and diagnosis patterns during the same time period which may mediate the relationship between the ACA rollout and self-reported health.

CONCLUSION

In nationally-representative survey data, we observed improvements in mental but not physical self-reported health among low-income Americans after 4 years of full ACA implementation.

Changes In Health Insurance Coverage, Access To Care, And Income-Based Disparities Among US Adults, 2011-17

The Affordable Care Act increased insurance coverage and access to care, according to numerous national studies. However, the administration of President Donald Trump implemented several policies that may have affected the act's effectiveness. It is unknown what effect these changes had on access to care. We used survey data for 2011-17 from the Behavioral Risk Factor Surveillance System to assess changes access to care among nonelderly adults from before to after the change in administration in 2017. We found that the proportion of adults who were uninsured or avoided care because of cost increased by 1.2 percentage points and 1.0 percentage points, respectively, during 2017. These changes were greater among respondents who had household incomes below 138 percent of the federal poverty level, resided in states that did not expand eligibility for Medicaid, or both. At the population level, our findings imply that approximately two million additional US adults experienced these outcomes at the end of 2017, compared to the end of 2016.

Changes in Insurance Coverage and Access to Care for Young Adults in 2017

PURPOSE

Previous work has demonstrated the Affordable Care Act (ACA) increased young adults' health care access during its first years. However, it is unclear if these trends continued through 2017; recent policies enacted by the Trump administration may have decreased the ACA's effectiveness. Our purpose was to determine changes in young adults' health care access during the transition from Obama to Trump administrations.

METHODS

Data on noninstitutionalized U.S. young adults (18-24 years) was obtained from the Behavioral Risk Factor Surveillance System 2011-2017 (N = 173,848). We used interrupted time series and difference-in-differences analysis to quantify changes in self-reported insurance coverage, access to a primary care physician, and unmet care because of cost from 2013 to 2017.

RESULTS

Young adults' health care access continued to improve through 2016; for instance, the percentage of respondents experiencing uninsurance declined by 8.7 points from 2013 to 2016 (95% confidence interval [CI] -9.4 to -8.0). However, these trends began to reverse and from 2016 to 2017, the percentage of young adults who experienced uninsurance increased by 1.4 points (95% CI .6-2.1), not having a personal doctor increased by 1.1 points (95% CI .2-2.0), and unmet care because of cost increased by 1.0 points (95% CI .3-1.7). The 2017 declines in access were concentrated in states which did not expand Medicaid and in households earning above 138% of federal poverty level.

CONCLUSIONS

Health care access declined for young adults in 2017, after several years of improvements. These changes correspond with recent policy actions, which may have weakened the ACA's reforms.

Call center performance affects patient perceptions of access and satisfaction

OBJECTIVES

There is little research on the relationship between call center performance and patient-centered outcomes. In this study, we quantified the relationships between 2 measures of telephone access, average speed of answer (ASA) and abandonment rate (AR), and patient satisfaction outcomes within the Veterans Health Administration (VHA).

STUDY DESIGN

We analyzed 2015 and 2016 data from the Survey of Healthcare Experiences of Patients and linked them with administrative data to gather features of the patient visit and monthly measures of telephone access for each medical center.

METHODS

We used mixed effects logistic regression models to estimate the effects of ASA and AR on a variety of access and satisfaction outcomes. Models were adjusted for patient-level demographics, time-varying facility-level characteristics, features of the patient visit, and facility-level random effects to control for care quality and case mix differences.

RESULTS

The VHA made substantial strides in both access measures between 2015 and 2016. We found that a center's ASA was inversely associated with patients' perceptions of their ability both to access urgent care appointments and to do so in a timely manner. In contrast, telephone AR was not associated with any of the patient satisfaction outcomes.

CONCLUSIONS

Our results associate decreased telephone waits with improved perceptions of urgent care access even without concomitant decreases in observed appointment waits. These findings may have important implications for regulators as well as for healthcare organizations that must decide resource levels for call centers, including hospitals, federal health insurance exchanges, and insurers.

Altered lipid homeostasis in a PCB-resistant Atlantic killifish (Fundulus heteroclitus) population from New Bedford Harbor, MA, U.S.A

Sentinel species such as the Atlantic killifish (Fundulus heteroclitus) living in urban waterways can be used as toxicological models to understand impacts of environmental metabolism disrupting compound (MDC) exposure on both wildlife and humans. Exposure to MDCs is associated with increased risk of metabolic syndrome, including impaired lipid and glucose homeostasis, adipogenesis, appetite control, and basal metabolism. MDCs are ubiquitous in the environment, including in aquatic environments. New Bedford Harbor (NBH), Massachusetts is polluted with polychlorinated biphenyls (PCBs), and, as we show for the first time, tin (Sn). PCBs and organotins are ligands for two receptor systems known to regulate lipid homeostasis, the aryl hydrocarbon receptor (AHR) and the peroxisome proliferator-activated receptors (PPARs), respectively. In the current study, we compared lipid homeostasis in laboratory-reared killifish from NBH (F2) and a reference location (Scorton Creek, Massachusetts; F1 and F2) to evaluate how adaptation to local conditions may influence responses to MDCs. Adult killifish from each population were exposed to 3,3',4,4',5-pentachlorobiphenyl (PCB126, dioxin-like), 2,2',4,4',5,5'-hexachlorobiphenyl (PCB153, non-dioxin-like), or tributyltin (TBT, a PPARγ ligand) by a single intraperitoneal injection and analyzed after 3 days. AHR activation was assessed by measuring cyp1a mRNA expression. Lipid homeostasis was evaluated phenotypically by measuring liver triglycerides and organosomatic indices, and at the molecular level by measuring the mRNA expression of pparg and ppara and a target gene for each receptor. Acute MDC exposure did not affect phenotypic outcomes. However, overall NBH killifish had higher liver triglycerides and adiposomatic indices than SC killifish. Both season and population were significant predictors of the lipid phenotype. Acute MDC exposure altered hepatic gene expression only in male killifish from SC. PCB126 exposure induced cyp1a and pparg, whereas PCB153 exposure induced ppara. TBT exposure did not induce ppar-dependent pathways. Comparison of lipid homeostasis in two killifish populations extends our understanding of how MDCs act on fish and provides a basis to infer adaptive benefits of these differences in the wild.

The Person-Event Data Environment: leveraging big data for studies of psychological strengths in soldiers

The Department of Defense (DoD) strives to efficiently manage the large volumes of administrative data collected and repurpose this information for research and analyses with policy implications. This need is especially present in the United States Army, which maintains numerous electronic databases with information on more than one million Active-Duty, Reserve, and National Guard soldiers, their family members, and Army civilian employees. The accumulation of vast amounts of digitized health, military service, and demographic data thus approaches, and may even exceed, traditional benchmarks for Big Data. Given the challenges of disseminating sensitive personal and health information, the Person-Event Data Environment (PDE) was created to unify disparate Army and DoD databases in a secure cloud-based enclave. This electronic repository serves the ultimate goal of achieving cost efficiencies in psychological and healthcare studies and provides a platform for collaboration among diverse scientists. This paper provides an overview of the uses of the PDE to perform command surveillance and policy analysis for Army leadership. The paper highlights the confluence of both economic and behavioral science perspectives elucidating empirically-based studies examining relations between psychological assets, health, and healthcare utilization. Specific examples explore the role of psychological assets in major cost drivers such as medical expenditures both during deployment and stateside, drug use, attrition from basic training, and low reenlistment rates. Through creation of the PDE, the Army and scientific community can now capitalize on the vast amounts of personnel, financial, medical, training and education, deployment, and security systems that influence Army-wide policies and procedures.

Did we get our money's worth? Bridging economic and behavioral measures of program success in adolescent drug prevention

The recent U.S. Congressional mandate for creating drug-free learning environments in elementary and secondary schools stipulates that education reform rely on accountability, parental and community involvement, local decision making, and use of evidence-based drug prevention programs. By necessity, this charge has been paralleled by increased interest in demonstrating that drug prevention programs net tangible benefits to society. One pressing concern is precisely how to integrate traditional scientific methods of program evaluation with economic measures of “cost efficiency”. The languages and methods of each respective discipline don’t necessarily converge on how to establish the true benefits of drug prevention. This article serves as a primer for conducting economic analyses of school-based drug prevention programs. The article provides the reader with a foundation in the relevant principles, methodologies, and benefits related to conducting economic analysis. Discussion revolves around how economists value the potential costs and benefits, both financial and personal, from implementing school-based drug prevention programs targeting youth. Application of heterogeneous costing methods coupled with widely divergent program evaluation findings influences the feasibility of these techniques and may hinder utilization of these practices. Determination of cost-efficiency should undoubtedly become one of several markers of program success and contribute to the ongoing debate over health policy.