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.

Trends in treatment costs of U.S. emergency department visits.

BACKGROUND: Spending on emergency department (ED) services in recent years has increased faster than spending in any other area of healthcare. Analyzing growth rates of ED treatment costs by patient and hospital attributes may illuminate ways to reduce overall hospital cost growth. Prior studies have examined changes in ED visit charges and expenditures over time, but little research has focused on changes in ED treatment costs. METHODS: We analyzed trends in ED treatment costs by applying the Healthcare Cost and Utilization Project (HCUP) Cost-to-Charge Ratios for ED Files to the 2012-2019 HCUP Nationwide Emergency Department Sample. Specifically, we estimated treatment cost per ED visit, mean and total costs by patient and hospital characteristics, and compound annual growth rate in costs and patient volumes. RESULTS: During 2012-2019, ED treatment costs increased from $54 billion to $88 billion, a 5.4% annual growth rate-with 4.4 percentage points attributable to higher treatment cost per visit. Growth rates varied by patient and hospital attribute. CONCLUSIONS: By highlighting overall ED cost trends, as well as specific segments of the delivery system with the most rapidly increasing costs, this study provides important information for policymakers and hospital decisionmakers.

Methods for estimating the cost of treat-and-release emergency department visits.

OBJECTIVE: To evaluate and compare approaches to estimating the service delivery cost of emergency department (ED) visits from total charge data only. DATA SOURCES: The 2013-2017 Healthcare Cost and Utilization Project's (HCUP) State Emergency Department Databases (SEDD) and the Centers for Medicare and Medicaid Services Healthcare Cost Report Information System (HCRIS) public use files. STUDY DESIGN: Compare a baseline approach (requiring cost-center-level charge detail) and four alternative methods (relying on total charges only) for estimating ED visit costs. Estimation errors are calculated after applying each method to a sample of ED visits, treating estimates from the baseline approach as the "true" cost. Performance metrics are calculated at the visit and hospital levels. DATA COLLECTION/EXTRACTION METHODS: The charges, revenue center codes, and patient/hospital characteristics were extracted from the SEDD. Detailed costs and charges were extracted from HCRIS public use files. PRINCIPAL FINDINGS: Baseline ("true") ED visit costs increased from $383 to $420 per visit between 2013 and 2017. Three methods performed comparatively well estimating mean cost per visit. The method using an overall cost-to-charge ratio (CCR) for all ancillary cost centers without regression adjustment (ANC-CCR) performed the worst, overestimating "true" costs by $63-$113 per visit. The other three methods, which used CCRs computed from selected cost centers, exhibited much smaller bias, with two of the methods yielding estimates within $2 of the "true" cost in 2017. Compared with ANC-CCR, the other three methods had more compact estimation error distributions. The estimated mean visit costs from all four methods have relatively small statistical variance, with 95% confidence intervals for mean cost in a hospital with 25,000 ED visits ranging between $4 and $7. CONCLUSIONS: When cost-center-level charge detail for ED visits is unavailable, alternative methods relying on total ED charges can estimate ED service costs for patient and hospital segments.

Effects of Hurricanes on Emergency Department Utilization: An Analysis Across 7 US Storms.

OBJECTIVE: Emergency departments (EDs) are critical sources of care after natural disasters such as hurricanes. Understanding the impact on ED utilization by subpopulation and proximity to the hurricane's path can inform emergency preparedness planning. This study examines changes in ED utilization for residents of 344 counties after the occurrence of 7 US hurricanes between 2005 and 2016. METHODS: This retrospective observational study used ED data from the Healthcare Cost and Utilization Project State Inpatient Databases and State Emergency Department Databases. ED utilization rates for weeks during and after hurricanes were compared with pre-hurricane rates, stratified by the proximity of the patient county to the hurricane path, age, and disease category. RESULTS: The overall population rate of weekly ED visits changed little post-hurricane, but rates by disease categories and age demonstrated varying results. Utilization rates for respiratory disorders exhibited the largest post-hurricane increase, particularly 2-3 weeks following the hurricane. The change in population rates by disease categories and age tended to be larger for people residing in counties closer to the hurricane path. CONCLUSIONS: Changes in ED utilization following hurricanes depend on disease categories, age, and proximity to the hurricane path. Emergency managers could incorporate these factors into their planning processes.

Changes in hospital service demand, cost, and patient illness severity following health reform.

OBJECTIVE: To estimate the effects of the health insurance exchange and Medicaid coverage expansions on hospital inpatient and emergency department (ED) utilization rates, cost, and patient illness severity, and also to test the association between changes in outcomes and the size of the uninsured population eligible for coverage in states. DATA SOURCES: Healthcare Cost and Utilization Project State Inpatient and Emergency Department Databases, 2011-2015, Nielsen Demographic Data, and the American Community Survey. STUDY DESIGN: Retrospective study using fixed-effects regression to estimate the effects in expansion and nonexpansion states by age/sex demographic groups. FINDINGS: In Medicaid expansion states, rates of uninsured inpatient discharges and ED visits fell sharply in many demographic groups. For example, uninsured inpatient discharge rates across groups, except young females, decreased by ≥39 percent per capita on average in expansion states. In nonexpansion states, uninsured utilization rates remained unchanged or increased slightly (0-9.2 percent). Changes in all-payer and private insurance rates were more muted. Changes in inpatient costs per discharge were negative, and all-payer inpatient costs per discharge declined <6 percent in most age/sex groups. The size of the uninsured population eligible for coverage was strongly associated with changes in outcomes. For example, among males aged 35-54 years in expansion states, there was a 0.793 percent decrease in the uninsured discharge rate per unit increase in the coverage expansion ratio (the ratio of the size of the population eligible for coverage to the size of the previously covered population within an age/sex/payer/geographic group). CONCLUSIONS: Significant shifts in cost per discharge and patient severity were consistent with selective take-up of insurance. The "treatment intensity" of expansions may be useful for anticipating future effects.

Early Impact of the Affordable Care Act Coverage Expansion on Safety-Net Hospital Inpatient Payer Mix and Market Shares.

OBJECTIVE: To examine the impact of the Affordable Care Act's coverage expansion on safety-net hospitals (SNHs). STUDY SETTING: Nine Medicaid expansion states. STUDY DESIGN: Differences-in-differences (DID) models compare payer-specific pre-post changes in inpatient stays of adults aged 19-64 years at SNHs and non-SNHs. DATA COLLECTION METHODS: 2013-2014 Healthcare Cost and Utilization Project State Inpatient Databases. PRINCIPAL FINDINGS: On average per quarter postexpansion, SNHs and non-SNHs experienced similar relative decreases in uninsured stays (DID = -2.2 percent, p = .916). Non-SNHs experienced a greater percentage increase in Medicaid stays than did SNHs (DID = 13.8 percent, p = .041). For SNHs, the average decrease in uninsured stays (-146) was similar to the increase in Medicaid stays (153); privately insured stays were stable. For non-SNHs, the decrease in uninsured (-63) plus privately insured (-33) stays was similar to the increase in Medicaid stays (105). SNHs and non-SNHs experienced a similar absolute increase in Medicaid, uninsured, and privately insured stays combined (DID = -16, p = .162). CONCLUSIONS: Postexpansion, non-SNHs experienced a greater percentage increase in Medicaid stays than did SNHs, which may reflect patients choosing non-SNHs over SNHs or a crowd-out of private insurance. More research is needed to understand these trends.

Changes in Hospital Inpatient Utilization Following Health Care Reform.

OBJECTIVE: To estimate the effects of 2014 Medicaid expansions on inpatient outcomes. DATA SOURCES: Health Care Cost and Utilization Project State Inpatient Databases, 2011-2014; population and unemployment estimates. STUDY DESIGN: Retrospective study estimating effects of Medicaid expansions using difference-in-differences regression. Outcomes included total admissions, referral-sensitive surgical and preventable admissions, length of stay, cost, and patient illness severity. FINDINGS: In 2014 quarter four, compared with nonexpansion states, Medicaid admissions increased (28.5 percent, p = .006), and uninsured and private admissions decreased (-55.1 percent, p = .001, and -6.6 percent, p = .052), whereas all-payer admissions showed little change. Uninsured expansion effects were negative for preventable admissions (-24.4 percent, p = .068), length of stay (-9.3 percent, p = .039), total cost (-9.2 percent, p = .128), and illness severity (-4.5 percent, p = .397). Significant positive expansion effects were found for Medicaid referral-sensitive surgeries (11.8 percent, p = .021) and patient illness severity (2.3 percent, p = .015). Private and all-payer expansion effects for outcomes other than admission volume were small and mainly nonsignificant (p > .05). CONCLUSION: Medicaid expansions did not change all-payer admission volumes, but they were associated with increased Medicaid and decreased uninsured volumes. Results suggest those previously uninsured with greater needs for inpatient services were most likely to gain coverage. Compositional changes in uninsured and Medicaid admissions may be due to selection.

The Workforce wellness index: a method for valuing US workers' health.

OBJECTIVE: To devise a methodology to create a single health risk-cost score that can be applied to health risk assessment survey data and account for the medical costs associated with modifiable risks. METHODS: We linked person-level health risk assessment data with medical benefit eligibility and claims data for 341,650 workers for the period 2005 to 2010 and performed multivariate analyses to estimate costs associated with high risks. We used the estimated costs and risk prevalence rates to create a composite Workforce Wellness Index (WWI) score. RESULTS: Increasing obesity rates among employees was found to be the most important contributor to increased health care spending and the main reason the WWI score worsened over time. CONCLUSIONS: Employers that address employees' health risk factors may be able to reduce their medical spending and achieve an improvement in their WWI scores.

Association of a clinical knowledge support system with improved patient safety, reduced complications and shorter length of stay among Medicare beneficiaries in acute care hospitals in the United States.

BACKGROUND: Electronic clinical knowledge support systems have decreased barriers to answering clinical questions but there is little evidence as to whether they have an impact on health outcomes. METHODS: We compared hospitals with online access to UpToDate with other acute care hospitals included in the Thomson 100 Top Hospitals Database (Thomson database). Metrics used in the Thomson database differentiate hospitals on a variety of performance dimensions such as quality and efficiency. Prespecified outcomes were risk-adjusted mortality, complications, the Agency of Healthcare Research and Quality Patient Safety Indicators, and hospital length of stay among Medicare beneficiaries. Linear regression models were developed that included adjustment for hospital region, teaching status, and discharge volume. RESULTS: Hospitals with access to UpToDate (n=424) were associated with significantly better performance than other hospitals in the Thomson database (n=3091) on risk-adjusted measures of patient safety (P=0.0163) and complications (P=0.0012) and had significantly shorter length of stay (by on average 0.167 days per discharge, 95% confidence interval 0.081-0.252 days, P<0.0001). All of these associations correlated significantly with how much UpToDate was used at each hospital. Mortality was not significantly different between UpToDate and non-UpToDate hospitals. LIMITATIONS: The study was retrospective and observational and could not fully account for additional features at the included hospitals that may also have been associated with better health outcomes. CONCLUSIONS: An electronic clinical knowledge support system (UpToDate was associated with improved health outcomes and shorter length of stay among Medicare beneficiaries in acute care hospitals in the United States. Additional studies are needed to clarify whether use of UpToDate is a marker for the better performance, an independent cause of it, or a synergistic part of other quality improvement characteristics at better-performing hospitals.

Recent trends in hospital utilization for acute myocardial infarction and coronary revascularization in the United States.

Medical advances may be shifting patients with coronary artery disease away from the hospital setting despite an aging United States population. We explored this possibility using national inpatient data to estimate the number and population-based rates of hospitalization for acute myocardial infarction (AMI) and coronary revascularization from 2002 to 2005. Our primary data source was the Acute Care Tracker database, a proprietary administrative database that contains data on approximately 6 million discharges per year from 458 hospitals across the United States. Using the Acute Care Tracker database, we estimated the annual number and population-based rates of hospitalization for AMI (transmural, subendocardial) and coronary revascularization (percutaneous coronary intervention [PCI] and coronary artery bypass grafting [CABG]). Hospitalizations for AMI steadily decreased from 661,000 to 591,000 per year between 2002 and 2005, primarily due to decreases in transmural AMI. Hospitalizations for coronary revascularizations during this period varied between 794,000 and 815,000 per year, with the number of PCIs increasing and the number of CABGs decreasing. In addition, rates of hospitalization for AMI decreased from 309 to 266 per 100,000 persons between 2002 and 2005, with rates of transmural AMI decreasing substantially from 118 to 87 per 100,000 persons. Rates of hospitalization for coronary revascularization also decreased from 382 to 358 per 100,000 during this period, primarily due to decreases in CABG. In conclusion, the number and rates of hospitalization for AMI and coronary revascularization in the United States are decreasing.