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Objective: To quantify the degree to which health care service area (HCSA) definitions captured hospitalizations and heterogeneity in social determinants of health (SDOH). Data Sources and Study Setting: Geospatial data from the Centers for Medicare and Medicaid Services, the Census Bureau, and the Dartmouth Institute. Drive-time isochrones from MapBox. Area Deprivation Index (ADI) data. 2017 inpatient discharge data from Arizona, Florida, Iowa, Maryland, Nebraska, New Jersey, New York, and Wisconsin, State Emergency Department Databases and State Inpatient Databases, Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality; and Fee-For-Service Medicare data in 48 states. Study Design: Cross-sectional, descriptive analysis. Data Collection/Extraction Methods: The capture rate was the percentage of inpatient discharges occurring in the same HCSA as the hospital. We compared capture rates for each HCSA definition for different populations and by hospital type. We measured SDOH heterogeneity using the coefficient of variation of the ADI among ZIP codes within each HCSA. Principal Findings: HCSA definitions captured a wide range of inpatient discharges, ranging from 20% to 50% for Public Use Microdata Areas (PUMAs) to 93%-97% for Metropolitan Statistical Areas (MSAs). Three-quarters of inpatient discharges were from facilities within the same county as the patient's residential ZIP code, while nearly two-thirds were within the same Hospital Service Area. From the hospital perspective, 74.7% of inpatient discharges originated from within a 30-min drive and 90.1% within a 60-min drive. Capture rates were the lowest for teaching hospitals. PUMAs and drive-time-based HCSAs encompassed more homogenous populations while MSAs, Commuting Zones, and Hospital Referral Regions captured the most variation. Conclusions: The proportion of hospital discharges captured by each HCSA varied, with MSAs capturing the highest proportion of discharges and PUMAs capturing the lowest. Additionally, researchers face a trade-off between capture rate and population homogeneity when deciding which HCSA to use. KEYWORDS access/demand/utilization of services, geographic/spatial factors/small-area variations, hospitals, social determinants of health, 1 | INTRODUCTION The geography of health care utilization is important to the substance, methods, and impact of health policy and health services research. For example, geographic service area boundaries [...]

Objective: To quantify shared patient relationships between primary care physicians (PCPs) and cardiologists and oncologists and the degree to which those relationships were captured within insurance networks. Data Sources: Secondary analysis of Vericred data on physician networks, CareSet data on physicians' shared Medicare patients, and insurance plan attributes from Health Insurance Compare. Data validation exercises used data from Physician Compare and IQVIA. Study Design: Cross-sectional study of the PCP-to-specialist in-network shared patient percentage (primary outcome). We also categorized networks by insurance market segment (Medicare Advantage [MA], Medicaid managed care, small-group or individually purchased), insurance plan type, and network breadth. Data Extraction: We analyzed data on 219,982 PCPs, 29,400 cardiologists, and 22,745 oncologists who, in 2021, accepted MA (n = 941 networks), Medicaid managed care (n = 293), and individually-purchased (n = 332) and small-group (n = 501) plans. Principal Findings: Networks captured, on average, 64.6% of PCP-cardiology shared patient ties, and 61.8% of PCP-oncologist ties. Less than half of in-network ties (44.5% and 38.9%, respectively) were among physicians with a common organizational affiliation. After adjustment for network breadth, we found no evidence of differences in the shared patient percentage across insurance market segments or networks of different types (p-value >0.05 for all comparisons). An exception was among national versus local and regional networks, where we found that national plans captured fewer shared patient ties, particularly among the narrowest networks (58.4% for national networksvs. 64.7% for local and regional networks for PCP-cardiology). Conclusions: Given recent trends toward narrower networks, our findings underscore the importance of incorporating additional and nuanced measures of network composition to aid plan selection (for patients) and to guide regulatory oversight. KEY WORDS health policy, insurance, insurance benefits, managed care programs, provider networks What is known on this topic * Enrollment in private insurance plans is expected to grow, and private plans are increasingly limiting the number of in-network providers and/or limiting coverage of out-of-network care. * Interspecialty communication among physicians is an important determinant of care quality, spending, and the experience of care for both physicians and patients. * Health services research on provider networks focuses almost exclusively on the breadth of networks, but not on their composition or quality. What this study adds * We develop a novel measure of the degree to which insurance plan networks capture interspecialty relationships among primary care physicians and specialists in oncology and cardiology. * We investigate variation in this measure across insurance market segments, plan types, and network breadth sizes. * In 2021, private insurance networks captured approximately two-thirds of patient-sharing relationships. We find substantial variation in the degree to which narrow network plans capture these relationships., 1 | INTRODUCTION The majority of insured people in the US are covered by private health plans. With few exceptions, these plans require receipt of care from in-network clinicians to [...]

Objective: To discuss and develop difference-in-difference estimators for categorical outcomes and apply them to estimate the effect of the Affordable Care Act's Medicaid expansion on insurance coverage. Data Sources: Secondary analysis of Survey on Income and Program Participation (SIPP) data on health insurance coverage types before (January 2013) and after (December 2015) Medicaid expansion in 39 US states (19 expansion and 20 non-expansion). Study Design: We develop difference-in-difference methods for repeated measures (panel data) of categorical outcomes. We discuss scale-dependence of DID assumptions for marginal and transition effect estimates and specify a new target estimand: the difference between outcome category transitions under treatment versus no treatment. We establish causal assumptions about transitions that are sufficient to identify this and a marginal target estimand. We contrast the marginal estimands identified by the transition approach versus an additive assumption only about marginal evolution. We apply both the marginal and transition approaches to estimate the effects of Medicaid expansion on health insurance coverage types (employer-sponsored; other private, non-group; public; and uninsured). Data Extraction: We analyzed 16,027 individual survey responses from people aged 18-62 years in the 2014 SIPP panel. Principal Findings: We show that the two identifying assumptions are equivalent (on the scale of the marginals) if either the baseline marginal distributions are identical or the marginals are constant in both groups. Applying our transitions approach to the SIPP data, we estimate a differential increase in transitions from uninsured to public coverage and differential decreases in transitions from uninsured to private, non-group coverage and in remaining uninsured. Conclusions: By comparing the assumption that marginals are evolving in parallel to an assumption about transitions across outcome values, we illustrate the scaledependence of difference-in-differences. Our application shows that studying transitions can illuminate nuances obscured by changes in the marginals. KEYWORDS control groups, data science, health care reform, health policy, insurance, methods, models, econometric, models, statistical, regression analysis What is known on this topic * Many researchers use difference-in-differences to estimate causal impacts in nonrandomized settings. * Causal impacts on transitions among categorical outcome values (like self-reported health status or insurance coverage type) may be of policy interest. What this study adds * We propose a novel estimand that captures causal effects on transitions among categorical outcome values and show that assumptions about transitions can identify both our transition estimand and a marginal estimand. * We compare this transition approach to one based only on marginal distributions to illustrate the importance of scale in difference-in-differences studies. * In our example, the transition approach illuminates nuances obscured by a marginal-only approach., 1 | INTRODUCTION Difference-in-differences (DID) is a workhorse for causal inference in non-randomized settings. In a DID study, the pre- to post-intervention outcome changes among those exposed to the intervention [...]