Dataset Information

Cutting medicare hospital prices leads to a spillover reduction in hospital discharges for the nonelderly.

ABSTRACT:

Objective

To measure spillover effects of Medicare inpatient hospital prices on the nonelderly (under age 65).

Primary data sources

Healthcare Cost and Utilization Project State Inpatient Databases (10 states, 1995-2009) and Medicare Hospital Cost Reports.

Study design

Outcomes include nonelderly discharges, length of stay and case mix, staffed hospital bed-days, and the share of discharges and days provided to the elderly. We use metropolitan statistical areas as our markets. We use descriptive analyses comparing 1995 and 2009 and panel data fixed-effects regressions. We instrument for Medicare prices using accumulated changes in the Medicare payment formula.

Principal findings

Medicare price reductions are strongly associated with reductions in nonelderly discharges and hospital capacity. A 10-percent reduction in the Medicare price is estimated to reduce discharges among the nonelderly by about 5 percent. Changes in the Medicare price are not associated with changes in the share of inpatient hospital care provided to the elderly versus nonelderly.

Conclusions

Medicare price reductions appear to broadly constrain hospital operations, with significant reductions in utilization among the nonelderly. The slow Medicare price growth under the Affordable Care Act may result in a spillover slowdown in hospital utilization and spending among the nonelderly.

SUBMITTER: White C

PROVIDER: S-EPMC4213050 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cutting medicare hospital prices leads to a spillover reduction in hospital discharges for the nonelderly.

White Chapin C

Health services research 20140521 5

<h4>Objective</h4>To measure spillover effects of Medicare inpatient hospital prices on the nonelderly (under age 65).<h4>Primary data sources</h4>Healthcare Cost and Utilization Project State Inpatient Databases (10 states, 1995-2009) and Medicare Hospital Cost Reports.<h4>Study design</h4>Outcomes include nonelderly discharges, length of stay and case mix, staffed hospital bed-days, and the share of discharges and days provided to the elderly. We use metropolitan statistical areas as our marke ...[more]

PMID: 24850524

Similar Datasets

Project description:ImportanceExcess 30-day readmissions have declined substantially in hospitals initially penalized for high readmission rates under the Medicare Hospital Readmissions Reduction Program (HRRP). Although a possible explanation is that the policy incentivized penalized hospitals to improve care processes, another is regression to the mean (RTM), a statistical phenomenon that predicts entities farther from the mean in one period are likely to fall closer to the mean in subsequent (or preceding) periods owing to random chance.ObjectiveTo quantify the contribution of RTM to declining readmission rates at hospitals initially penalized under the HRRP.Design, setting, and participantsThis study analyzed data from Medicare Provider and Analysis Review files to assess changes in readmissions going forward and backward in time at hospitals with high and low readmission rates during the measurement window for the first year of the HRRP (fiscal year [FY] 2013) and for a measurement window that predated the FY 2013 measurement window for the HRRP among hospitals participating in the HRRP. Hospital characteristics are based on the 2012 survey by the American Hospital Association. The analysis included fee-for-service Medicare beneficiaries 65 years or older with an index hospitalization for 1 of the 3 target conditions of heart failure, acute myocardial infarction, or pneumonia or chronic obstructive pulmonary disease and who were discharged alive from February 1, 2006, through June 30, 2014, with follow-up completed by July 30, 2014. Data were analyzed from January 23, 2018, through March 29, 2019.ExposuresHospital Readmission Reduction Program penalties.Main outcome and measuresThe excess readmission ratio (ERR), calculated as the ratio of a hospital's readmissions to the readmissions that would be expected based on an average hospital with similar patients. Hospitals with ERRs of greater than 1.0 were penalized.ResultsA total of 3258 hospitals were included in the study. For the 3 target conditions, hospitals with ERRs of greater than 1.0 during the FY 2013 measurement window exhibited decreases in ERRs in the subsequent 3 years, whereas hospitals with ERRs of no greater than 1.0 exhibited increases. For example, for patients with heart failure, mean ERRs declined from 1.086 to 1.038 (-0.048; 95% CI, -0.053 to -0.043; P < .001) at hospitals with ERRs of greater than 1.0 and increased from 0.917 to 0.957 (0.040; 95% CI, 0.036-0.044; P < .001) at hospitals with ERRs of no greater than 1.0. The same results, with ERR changes of similar magnitude, were found when the analyses were repeated using an alternate measurement window that predated the HRRP and followed up hospitals for 3 years (for patients with heart failure, mean ERRs declined from 1.089 to 1.044 [-0.045; 95% CI, -0.050 to -0.040; P < .001] at hospitals with below-mean performance and increased from 0.915 to 0.948 [0.033; 95% CI, 0.029 to 0.037; P < .001] at hospitals with above-mean performance). By comparing actual changes in ERRs with expected changes due to RTM, 74.3% to 86.5% of the improvement in ERRs for penalized hospitals was explained by RTM.Conclusions and relevanceMost of the decline in readmission rates in hospitals with high rates during the measurement window for the first year of the HRRP appeared to be due to RTM. These findings seem to call into question the notion of an HRRP policy effect on readmissions.

Project description:BACKGROUND:The Hospital Readmissions Reduction Program (HRRP) was established by the 2010 Patient Protection and Affordable Care Act (ACA) in an effort to reduce excess hospital readmissions, lower health care costs, and improve patient safety and outcomes. Although studies have examined the policy's overall impacts and differences by hospital types, research is limited on its effects for different types of vulnerable populations. The aim of this study was to analyze the impact of the HRRP on readmissions for three targeted conditions (acute myocardial infarction, heart failure, and pneumonia) among four types of vulnerable populations, including low-income patients, patients served by hospitals that serve a high percentage of low-income or Medicaid patients, and high-risk patients at the highest quartile of the Elixhauser comorbidity index score. METHODS:Data on patient and hospital information came from the Nationwide Readmission Database (NRD), which contained all discharges from community hospitals in 27 states during 2010-2014. Using difference-in-difference (DD) models, linear probability regressions were conducted for the entire sample and sub-samples of patients and hospitals in order to isolate the effect of the HRRP on vulnerable populations. Multiple combinations of treatment and control groups and triple difference (DDD) methods were used for testing the robustness of the results. All models controlled for the patient and hospital characteristics. RESULTS:There have been statistically significant reductions in readmission rates overall as well as for vulnerable populations, especially for acute myocardial infarction patients in hospitals serving the largest percentage of low-income patients and high-risk patients. There is also evidence of spillover effects for non-targeted conditions among Medicare patients compared to privately insured patients. CONCLUSIONS:The HRRP appears to have created the right incentives for reducing readmissions not only overall but also for vulnerable populations, accruing societal benefits in addition to previously found reductions in costs. As the reduction in the rate of readmissions is not consistent across patient and hospital groups, there could be benefits to adjusting the policy according to the socioeconomic status of a hospital's patients and neighborhood.

Dataset Information

Cutting medicare hospital prices leads to a spillover reduction in hospital discharges for the nonelderly.

Objective

Primary data sources

Study design

Principal findings

Conclusions

Publications

Cutting medicare hospital prices leads to a spillover reduction in hospital discharges for the nonelderly.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets