Estimating state-specific population-based hospitalization rates from in-state hospital discharge data.

OBJECTIVE: To 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 SETTING: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project (HCUP), State Inpatient Databases (SID), 2018-2019, 47 states+DC. STUDY DESIGN: We 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 METHODS: SID are based on administrative billing records collected by hospitals, shared with statewide data organizations, and provided to HCUP. PRINCIPAL FINDINGS: Of 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). CONCLUSIONS: The 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.

Effects of the COVID-19 early pandemic on delivery outcomes among women with and without COVID-19 at birth.

BACKGROUND: The COVID-19 pandemic may influence delivery outcomes through direct effects of infection or indirect effects of disruptions in prenatal care. We examined early pandemic-related changes in birth outcomes for pregnant women with and without a COVID-19 diagnosis at delivery. METHODS: We compared four delivery outcomes-preterm delivery (PTD), severe maternal morbidity (SMM), stillbirth, and cesarean birth-between 2017 and 2019 (prepandemic) and between April and December 2020 (early-pandemic) using interrupted time series models on 11.8 million deliveries, stratified by COVID-19 infection status at birth with entropy weighting for historical controls, from the Healthcare Cost and Utilization Project across 43 states and the District of Columbia. RESULTS: Relative to 2017-2019, women without COVID-19 at delivery in 2020 had lower odds of PTD (OR = 0.93; 95% CI = 0.92-0.94) and SMM (OR = 0.88; 95% CI = 0.85-0.91) but increased odds of stillbirth (OR = 1.04; 95% CI = 1.01-1.08). Absolute effects were small across race/ethnicity groups. Deliveries with COVID-19 had an excess of each outcome, by factors of 1.07-1.46 for outcomes except SMM at 4.21. The effect for SMM was more pronounced for Asian/Pacific Islander non-Hispanic (API; OR = 10.51; 95% CI = 5.49-20.14) and Hispanic (OR = 5.09; 95% CI = 4.29-6.03) pregnant women than for White non-Hispanic (OR = 3.28; 95% CI = 2.65-4.06) women. DISCUSSION: Decreasing rates of PTD and SMM and increasing rates of stillbirth among deliveries without COVID-19 were small but suggest indirect effects of the pandemic on maternal outcomes. Among pregnant women with COVID-19 at delivery, adverse effects, particularly SMM for API and Hispanic women, underscore the importance of addressing health disparities.