Performance of health care service area definitions for capturing variation in inpatient care and social determinants of health

Abstract

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.

FIGURE 1

Median percentage of population capture rate by health care service area definition and patient population. Vertical black bars on the graph represent the Interquartile Range (IQR); FFS, fee‐for‐service; HCUP, hospital cost and utilization project; MC, medicare. Source: Authors' analyses of Medicare, Hospital Cost and Utilization Project, and American Hospital Association Data.

FIGURE 2

Median percent of hospital utilization captured by health care service area definition and hospital type for medicare fee‐for‐service (FFS) patients. HRR, hospital referral region; HSA, hospital service area; MSA, metropolitan statistical area; PUMA, public use microdata area. Source: Authors' analyses of Medicare, Hospital Cost and Utilization Project, and American Hospital Association Data.

FIGURE 3

Inpatient capture rate and socioeconomic heterogeneity index value, by health care service area definition type, hospital cost and utilization project sample, ages 19–64 sample. Source: Authors' analyses of Medicare, Hospital Cost and Utilization Project, and American Hospital Association Data.