COVID-19 Admission Rates and Changes in Care Quality in US Hospitals.

Importance: Unprecedented increases in hospital occupancy rates during COVID-19 surges in 2020 caused concern over hospital care quality for patients without COVID-19. Objective: To examine changes in hospital nonsurgical care quality for patients without COVID-19 during periods of high and low COVID-19 admissions. Design, Setting, and Participants: This cross-sectional study used data from the 2019 and 2020 Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project State Inpatient Databases. Data were obtained for all nonfederal, acute care hospitals in 36 states with admissions in 2019 and 2020, and patients without a diagnosis of COVID-19 or pneumonia who were at risk for selected quality indicators were included. The data analysis was performed between January 1, 2023, and March 15, 2024. Exposure: Each hospital and week in 2020 was categorized based on the number of COVID-19 admissions per 100 beds: less than 1.0, 1.0 to 4.9, 5.0 to 9.9, 10.0 to 14.9, and 15.0 or greater. Main Outcomes and Measures: The main outcomes were rates of adverse outcomes for selected quality indicators, including pressure ulcers and in-hospital mortality for acute myocardial infarction, heart failure, acute stroke, gastrointestinal hemorrhage, hip fracture, and percutaneous coronary intervention. Changes in 2020 compared with 2019 were calculated for each level of the weekly COVID-19 admission rate, adjusting for case-mix and hospital-month fixed effects. Changes during weeks with high COVID-19 admissions (≥15 per 100 beds) were compared with changes during weeks with low COVID-19 admissions (<1 per 100 beds). Results: The analysis included 19 111 629 discharges (50.3% female; mean [SD] age, 63.0 [18.0] years) from 3283 hospitals in 36 states. In weeks 18 to 48 of 2020, 35 851 hospital-weeks (36.7%) had low COVID-19 admission rates, and 8094 (8.3%) had high rates. Quality indicators for patients without COVID-19 significantly worsened in 2020 during weeks with high vs low COVID-19 admissions. Pressure ulcer rates increased by 0.09 per 1000 admissions (95% CI, 0.01-0.17 per 1000 admissions; relative change, 24.3%), heart failure mortality increased by 0.40 per 100 admissions (95% CI, 0.18-0.63 per 100 admissions; relative change, 21.1%), hip fracture mortality increased by 0.40 per 100 admissions (95% CI, 0.04-0.77 per 100 admissions; relative change, 29.4%), and a weighted mean of mortality for the selected indicators increased by 0.30 per 100 admissions (95% CI, 0.14-0.45 per 100 admissions; relative change, 10.6%). Conclusions and Relevance: In this cross-sectional study, COVID-19 surges were associated with declines in hospital quality, highlighting the importance of identifying and implementing strategies to maintain care quality during periods of high hospital use.

Duration Of Uninsured Spells For Nonelderly Adults Declined After 2014.

Using longitudinal data from the Medical Expenditure Panel Survey-Household Component (MEPS-HC), we found that nonelderly respondents in 2014-15, following implementation of ACA coverage provisions, experienced shorter periods of being uninsured than did respondents in 2012-13 and 2013-14. This was particularly true for people with preexisting (or "high-risk-pool") health conditions.

Do People with Health Insurance Coverage Who Live in Areas with High Uninsurance Rates Pay More for Emergency Department Visits?

OBJECTIVE: To investigate the relationship between the percent uninsured in a county and expenditures associated with the typical emergency department visit. DATA SOURCES: The Medical Expenditure Panel Survey linked to county-level data from the American Community Survey, the Healthcare Cost and Utilization Project, and the Area Health Resources Files. STUDY DESIGN: We use a nationally representative sample of emergency department visits that took place between 2009 and 2013 to estimate the association between the percent uninsured in counties and the amount paid for a typical visit. Final estimates come from a diagnosis-level fixed-effects model, with additional controls for a wide variety of visit, individual, and county characteristics. PRINCIPAL FINDINGS: Among those with private insurance, we find that an increase of 1 percentage point in the county uninsurance rate is associated with a $20 increase in the mean emergency department payment. No such association is observed among visits covered by other insurance types. CONCLUSIONS: Results provide tentative evidence that the costs associated with high uninsurance rates spill over to those with insurance, but future research needs to replicate these findings with longitudinal data and methods before drawing causal conclusions. Recent data on changes in area uninsurance rates following the ACA's insurance expansions and subsequent changes in emergency department expenditures afford a valuable opportunity to do this.

Gaining Coverage In 2014: New Estimates Of Marketplace And Medicaid Transitions.

We used data from the Medical Expenditure Panel Survey-Household Component to examine coverage transitions for nonelderly US adults. We found that 71.5 percent of Marketplace enrollees in 2014 had some period of uninsurance before enrollment. In Medicaid expansion states, 17.4 percent of adults who were uninsured throughout 2013 gained Medicaid coverage in 2014, compared with only 5.6 percent in those states between 2012 and 2013.

Fee-For-Service, While Much Maligned, Remains The Dominant Payment Method For Physician Visits.

Recent concerted efforts have sought to shift provider payment away from fee-for-service and toward risk-based alternatives. Despite these efforts, fee-for-service not only remains the dominant payment method but has continued to grow, with nearly 95 percent of all physician office visits in 2013 reimbursed in this fashion.

The capacity of the Medical Expenditure Panel Survey to inform the Affordable Care Act.

The Affordable Care Act (ACA) was enacted with major provisions to expand health insurance coverage, control health care costs, and improve the health care delivery system. Essential data resources will be required for effective program planning, administration, and management, in addition to facilitating evaluations of program performance. The Medical Expenditure Panel Survey (MEPS) is one of the core data resources that has been used to inform several provisions of the ACA. This paper provides a summary of the capacity of the MEPS to inform program planning, implementation, and evaluations of program performance for several components of the ACA.

State- and payer-specific estimates of annual medical expenditures attributable to obesity.

The goal of this study is to expand prior analyses by presenting current state-level estimates of the costs of obesity in total and separately for Medicare and Medicaid. Quantifying current Medicare and Medicaid expenditures attributable to obesity is important because high public sector costs of obesity have been a primary motivation for publicly funded obesity prevention efforts at the state level. We also present estimates of the obesity-attributable fraction (OAF) of total, Medicare, and Medicaid expenditures and the percentage of total obesity costs within each state that is funded by the public sector. We used the 2006 Medical Expenditure Panel Survey, nationally representative data that include information on obesity and medical expenditures, to generate an equation that predicts annual medical expenditures as a function of obesity status. We used the 2006 Behavioral Risk Factor Surveillance System, state representative data, and the equation generated from the national model to predict state (and payer within state) expenditures and the fraction of expenditures attributable to obesity for each state. Across states, annual medical expenditures would be between 6.7 and 10.7% lower in the absence of obesity. Between 22% (Virginia) and 55% (Rhode Island) of the state-level costs of obesity are financed by the public sector via Medicare and Medicaid. The high costs of obesity at the state level emphasize the need to prevent and control obesity as a way to manage state medical costs.

Paying physicians by capitation: is the past now prologue?

In the 1980s and 1990s, physician capitation-in which participating physicians received a fixed sum for each insured patient regardless of how much care the patient received-was widely touted as a way to restrain costs and encourage more-efficient care. Capitation remained prevalent in markets with a substantial health maintenance organization (HMO) presence but virtually disappeared elsewhere as HMO enrollment declined. By 2007, only 7 percent of all physician office visits were covered under capitation arrangements. Given this history, markets that now lack infrastructure to handle physician risk sharing will probably be challenged by current proposals for payment reform, many of which incorporate components of capitation.

Using information on clinical conditions to predict high-cost patients.

OBJECTIVE: To compare the ability of different models to predict prospectively whether someone will incur high medical expenditures. DATA SOURCE: Using nationally representative data from the Medical Expenditure Panel Survey (MEPS), prediction models were developed using cohorts initiated in 1996-1999 (N=52,918), and validated using cohorts initiated in 2000-2003 (N=61,155). STUDY DESIGN: We estimated logistic regression models to predict being in the upper expenditure decile in Year 2 of a cohort, based on data from Year 1. We compared a summary risk score based on diagnostic cost group (DCG) prospective risk scores to a count of chronic conditions and indicators for 10 specific high-prevalence chronic conditions. We examined whether self-rated health and functional limitations enhanced prediction, controlling for clinical conditions. Models were evaluated using the Bayesian information criterion and the c-statistic. PRINCIPAL FINDINGS: Medical condition information substantially improved prediction of high expenditures beyond gender and age, with the DCG risk score providing the greatest improvement in prediction. The count of chronic conditions, self-reported health status, and functional limitations were significantly associated with future high expenditures, controlling for DCG score. A model including these variables had good discrimination (c=0.836). CONCLUSIONS: The number of chronic conditions merits consideration in future efforts to develop expenditure prediction models. While significant, self-rated health and indicators of functioning improved prediction only slightly.

Annual medical spending attributable to obesity: payer-and service-specific estimates.

In 1998 the medical costs of obesity were estimated to be as high as $78.5 billion, with roughly half financed by Medicare and Medicaid. This analysis presents updated estimates of the costs of obesity for the United States across payers (Medicare, Medicaid, and private insurers), in separate categories for inpatient, non-inpatient, and prescription drug spending. We found that the increased prevalence of obesity is responsible for almost $40 billion of increased medical spending through 2006, including $7 billion in Medicare prescription drug costs. We estimate that the medical costs of obesity could have risen to $147 billion per year by 2008.

The medical expenditure panel survey: a national information resource to support healthcare cost research and inform policy and practice.

BACKGROUND: The Medical Expenditure Panel Survey (MEPS) collects detailed information regarding the use and payment for health care services from a nationally representative sample of Americans. The survey is designed to provide analysts with the data they need to support policy-relevant research on health care expenses, utilization, insurance coverage, and access in the United States and to provide policymakers with the results and data they need to make informed decisions. OBJECTIVES: This article summarizes the capacity of this broad-based and publicly available information resource to support research efforts directed towards achieving a better understanding of the dynamics of American healthcare and to better characterize its current state. METHODS: The MEPS comprises a nationally representative sample of the civilian noninstitutionalized population in the United States, and collects comprehensive data on individuals and their health care experiences over a span of 2 years. Household survey data are collected by means of computer-assisted personal interviews, and those data are supplemented by information collected directly from the medical providers used by survey participants. Insurance data are collected both from households and through a separate state and nationally representative survey of business establishments, which collects information on health insurance provided by United States employers. RESULTS: The MEPS has been used extensively in scientific publications and published reports, as well as by the Federal and state governments to examine the delivery and financing of healthcare in the United States. CONCLUSIONS: The analytical findings generated by the MEPS are key inputs to facilitate the development, implementation, and evaluation of policies and practices addressing health care in the United States and its related costs. Recent efforts to reconcile MEPS and the National Health Expenditure Accounts have the potential to provide an even more accurate and powerful data tool for research and policy analysis.

Prescription drugs and the changing concentration of health care expenditures.

Health care expenditures are highly concentrated in the United States, with a small fraction of the population accounting for a large share of total health spending. This concentration has proved remarkably stable over time; however, the degree of concentration has declined over the past decade. Using data from the 1996-2003 Medical Expenditure Panel Survey (MEPS), we explore why. We find that rapid growth in prescription drug spending, which is diffused over a large fraction of the population, versus slower growth in spending for inpatient care largely accounts for the recent change in concentration. We discuss the potential implications for current cost containment and reform efforts.

Using the SF-12 health status measure to improve predictions of medical expenditures.

BACKGROUND: Relatively few studies have used self-reported health status in models to predict medical expenditures, and many of these have used the SF-36. OBJECTIVES: We sought to examine the ability of the briefer SF-12 measure of health status to predict medical expenditures in a nationally representative sample. METHODS: We used data from the 2000-2001 panel of the Medical Expenditure Panel Study. Respondents (n = 5542) completed the SF-12 in a questionnaire. Interviews obtained data on demographics and selected chronic conditions. Data on expenditures incurred subsequent to the interview were obtained in part from provider records. We examined different regression model specifications and compared different statistical estimation techniques. RESULTS: Adding the SF-12 to a regression model improved the prediction of subsequent medical expenditures. In a model with only age and gender, adding the SF-12 increased R from 0.06 to 0.13. The coefficients for the Physical Component Summary (PCS) and the Mental Component Summary (MCS) of the SF-12 for this model were -0.045 (P < 0.01) and -0.012 (P < 0.01), respectively. In a model including demographic characteristics, chronic conditions, and previous expenditures, adding the SF-12 increased the R from 0.26 to 0.29. The coefficients for the PCS and the MCS for this model were -0.025 (P < 0.001) and -0.005 (P = 0.15), respectively. A single general health status question performed almost as well as the full SF-12. Models estimated using ordinary least squares had undesirable properties. In terms of R, a generalized linear model (GLM) with a Poisson variance function was consistently superior to a GLM with a gamma variance function. CONCLUSIONS: Information on self-reported health status is useful in predicting medical expenditures. The extent to which the SF-12 adds predictive power over a comprehensive array of diagnostic data remains to be examined.

Home care before and after the Balanced Budget Act of 1997: shifts in financing and services.

PURPOSE: This article describes the pattern of change in home-care use and expenditures, the distribution of payments by source, and the mix of skilled versus nonskilled services before and after 1996. DESIGN AND METHODS: The analysis is based on tabulations of the 1987 National Medical Expenditure Survey and the 1996, 1998, and 1999 Medical Expenditure Panel Surveys. Estimates are weighted to represent the U.S. civilian noninstitutionalized population. RESULTS: After increasing dramatically between 1987 and 1996, formal home-care use and expenditures fell between 1996 and 1999. The decline was largely due to a decrease in funding under Medicare, which coincided with changes initiated in the Balanced Budget Act of 1997 (BBA). Declines in total spending were attenuated by increases in expenditures under state and local programs. After the BBA, fewer skilled services were provided to the elderly population and more unskilled services were provided to the nonelderly population. IMPLICATIONS: These findings highlight the increasing role of state governments in funding home care after the BBA. However, more recent pressure on state budgets and the institution of prospective payment under Medicare for home care may alter these trends.

Has the increase in HMO enrollment within the Medicaid population changed the pattern of health service use and expenditures?

OBJECTIVE: To describe changes in health services use and expenditures within the Medicaid population between 1987 and 1997 and to estimate the extent to which the increase in Health Maintenance Organization (HMO) enrollment has influenced these changes. SUBJECTS: Individuals under the age of 65 years in the 1987 National Medical Expenditure Survey and the 1997 Medical Expenditure Panel Survey enrolled in Medicaid the entire year. RESEARCH DESIGN: Using bivariate and multivariate techniques, we compared several measures of health services use and expenditures across three groups: (1) individuals enrolled in Medicaid for all of 1987; (2) individuals enrolled in Medicaid for all of 1997 but never enrolled in an HMO; and (3) individuals enrolled in Medicaid for all of 1997 and enrolled in an HMO for at least part of the year. RESULTS: Medicaid enrollees in 1997 differ little from Medicaid recipients in 1987 with respect to use and expenditures. Modest but statistically significant differences emerge, however, when a distinction is made between HMO enrollees and non-HMO enrollees in 1997. Specifically, 1997 Medicaid HMO enrollees have significantly fewer hospital visits than 1987 Medicaid enrollees and spend significantly less on health services than 1997 non-HMO enrollees. CONCLUSIONS: Our findings suggest that the increase in HMO enrollment may have held down use and expenditures to rates modestly lower than what would have been expected had HMO enrollment not increased.

Spending and service use among people with the fifteen most costly medical conditions, 1997.

This study addresses the Institute of Medicine's recommendation that AHRQ use MEPS data to identify a set of priority conditions to inform efforts at improving quality of care. Using MEPS data we identify the fifteen most expensive conditions in the U.S. in 1997: chronic diseases such as heart disease, cancer, and diabetes, and acute conditions such as trauma, pneumonia, and infectious disease. Comorbidities were also associated with increased expenses. Type-of-service and source-of-payment distributions varied considerably across this set of conditions. Our findings highlight some of the challenges likely to be encountered in efforts to reform the current system.

A guide to comparing health care expenditures in the 1996 MEPS to the 1987 NMES.

Substantial changes in the organization, delivery, and financing of health care over the last decade, combined with data collection and methodological improvements in the 1996 Medical Expenditure Panel Survey (MEPS), pose special challenges in comparing expenditure estimates in MEPS with those in the 1987 National Medical Expenditure Survey (NMES). The 1987 NMES used charges as its fundamental expenditure concept, whereas the 1996 MEPS used actual payments as its expenditure measure. In spite of these differences, researchers and policymakers will want to be able to analyze trends in health care expenditures using these two surveys. We discuss these issues in detail and present a simple, straightforward adjustment method that can be applied to the 1987 NMES public use expenditure data to improve comparability to the MEPS. We base this adjustment method on an analysis of provider-reported payment data collected in NMES. We present several examples of the application of this method that illustrate the importance of the adjustments for analyses of trends in health care spending.