ArticleHealth services research2023
Estimating state-specific population-based hospitalization rates from in-state hospital discharge data.
Article in Health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Estimating state-specific population-based hospitalization rates from in-state hospital discharge data.Health services research · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop weights to estimate state population-based hospitalization rates for all residents of a state using only data from in-state hospitals which exclude residents treated in other states. DATA SOURCES AND STUDY
settingAgency for Healthcare Research and Quality, Healthcare Cost and Utilization Project (HCUP), State Inpatient Databases (SID), 2018-2019, 47 states+DC. STUDY
designWe identified characteristics for patients hospitalized in each state differentiating movers (discharges for patients hospitalized outside state of residence) from stayers (discharges for patients hospitalized in state of residence) and created weights based on 2018 data informed by these characteristics. We calculated standard errors using a sampling framework and compared weight-based estimates against complete observed values for 2019. DATA COLLECTION/EXTRACTION
methodsSID are based on administrative billing records collected by hospitals, shared with statewide data organizations, and provided to HCUP. PRINCIPAL
findingsOf 34,186,766 discharged patients in 2018, 4.2% were movers. A higher share of movers (vs. stayers) lived in state border and rural counties; a lower share had discharges billed to Medicaid or were hospitalized for maternal/neonatal services. The difference between 2019 observed and estimated total discharges for all included states and DC was 9402 (mean absolute percentage error = 0.2%). We overestimated discharges with an expected payer of Medicaid, from the lowest income communities, and for maternal/neonatal care. We underestimated discharges with an expected payer of private insurance, from the highest income communities, and with injury diagnoses and surgical services. Estimates for most subsets were not within a 95% confidence interval, likely due to factors impossible to account for (e.g., hospital closures/openings, shifting consumer preferences).
conclusionsThe weights offer a practical solution for researchers with access to only a single state's data to account for movers when calculating population-based hospitalization rates.
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