Emergency Department Visits And Hospital Capacity In The US: Trends In The Medicare Population During The COVID-19 Pandemic.

Although emergency department (ED) and hospital overcrowding were reported during the later parts of the COVID-19 pandemic, the true extent and potential causes of this overcrowding remain unclear. Using data on the traditional fee-for-service Medicare population, we examined patterns in ED and hospital use during the period 2019-22. We evaluated trends in ED visits, rates of admission from the ED, and thirty-day mortality, as well as measures suggestive of hospital capacity, including hospital Medicare census, length-of-stay, and discharge destination. We found that ED visits remained below baseline throughout the study period, with the standardized number of visits at the end of the study period being approximately 25 percent lower than baseline. Longer length-of-stay persisted through 2022, whereas hospital census was considerably above baseline until stabilizing just above baseline in 2022. Rates of discharge to postacute facilities initially declined and then leveled off at 2 percent below baseline in 2022. These results suggest that widespread reports of overcrowding were not driven by a resurgence in ED visits. Nonetheless, length-of-stay remains higher, presumably related to increased acuity and reduced available bed capacity in the postacute care system.

Weak correlations in health services research: Weak relationships or common error?

OBJECTIVE: To examine whether the correlation between a provider's effect on one population of patients and the same provider's effect on another population is underestimated if the effects for each population are estimated separately as opposed to being jointly modeled as random effects, and to characterize how the impact of the estimation procedure varies with sample size. DATA SOURCES: Medicare claims and enrollment data on emergency department (ED) visits, including patient characteristics, the patient's hospitalization status, and identification of the doctor responsible for the decision to hospitalize the patient. STUDY DESIGN: We used a three-pronged investigation consisting of analytical derivation, simulation experiments, and analysis of administrative data to demonstrate the fallibility of stratified estimation. Under each investigation method, results are compared between the joint modeling approach to those based on stratified analyses. DATA COLLECTION/EXTRACTION METHODS: We used data on ED visits from administrative claims from traditional (fee-for-service) Medicare from January 2012 through September 2015. PRINCIPAL FINDINGS: The simulation analysis demonstrates that the joint modeling approach is generally close to unbiased, whereas the stratified approach can be severely biased in small samples, a consequence of joint modeling benefitting from bivariate shrinkage and the stratified approach being compromised by measurement error. In the administrative data analyses, the estimated correlation of doctor admission tendencies between female and male patients was estimated to be 0.98 under the joint model but only 0.38 using stratified estimation. The analogous correlations for White and non-White patients are 0.99 and 0.28 and for Medicaid dual-eligible and non-dual-eligible patients are 0.99 and 0.31, respectively. These results are consistent with the analytical derivations. CONCLUSIONS: Joint modeling targets the parameter of primary interest. In the case of population correlations, it yields estimates that are substantially less biased and higher in magnitude than naive estimators that post-process the estimates obtained from stratified models.

Analysis of Consistency in Emergency Department Physician Variation in Propensity for Admission Across Patient Sociodemographic Groups.

Importance: Sociodemographic disparities in health care and variation in physician practice patterns have been well documented; however, the contribution of variation in individual physician care practices to health disparities is challenging to quantify. Emergency department (ED) physicians vary in their propensity to admit patients. The consistency of this variation across sociodemographic groups may help determine whether physician-specific factors are associated with care differences between patient groups. Objective: To estimate the consistency of ED physician admission propensities across categories of patient sex, race and ethnicity, and Medicaid enrollment. Design, Setting, and Participants: This cross-sectional study analyzed Medicare fee-for-service claims for ED visits from January 1, 2016, to December 31, 2019, in a 10% random sample of hospitals. The allocation of patients to ED physicians in the acute care setting was used to isolate physician-level variation in admission rates that reflects variation in physician decision-making. Multi-level models with physician random effects and hospital fixed effects were used to estimate the within-hospital physician variation in admission propensity for different patient sociodemographic subgroups and the covariation in these propensities between subgroups (consistency), adjusting for primary diagnosis and comorbidities. Main Outcomes and Measures: Admission from the ED. Results: The analysis included 4 567 760 ED visits involving 2 334 361 beneficiaries and 15 767 physicians in 396 EDs. The mean (SD) age of the beneficiaries was 78 (8.2) years, 2 700 661 visits (59.1%) were by women, and most patients (3 839 055 [84.1%]) were not eligible for Medicaid. Of 4 473 978 race and ethnicity reports on enrollment, 103 699 patients (2.3%) were Asian/Pacific Islander, 421 588 (9.4%) were Black, 257 422 (5.8%) were Hispanic, and 3 691 269 (82.5%) were non-Hispanic White. Within hospitals, adjusted rates of admission were higher for men (36.8%; 95% CI, 36.8%-36.9%) than for women (33.7%; 95% CI, 33.7%-33.8%); higher for non-Hispanic White (36.0%; 95% CI, 35.9%-36.0%) than for Asian/Pacific Islander (33.6%; 95% CI, 33.3%-33.9%), Black (30.2%; 95% CI, 30.0%-30.3%), or Hispanic (31.1%; 95% CI, 30.9%-31.2%) beneficiaries; and higher for beneficiaries dually enrolled in Medicaid (36.3%; 95% CI, 36.2%-36.5%) than for those who were not (34.7%; 95% CI, 34.7%-34.8%). Within hospitals, physicians varied in the percentage of patients admitted, ranging from 22.4% for physicians at the 10th percentile to 47.6% for physicians at the 90th percentile of the estimated distribution. Physician admission propensities were correlated between men and women (r = 0.99), Black and non-Hispanic White patients (r = 0.98), and patients who were dually enrolled and not dually enrolled in Medicaid (r = 0.98). Conclusions and Relevance: This cross-sectional study indicated that, although overall rates of admission differ systematically by patient sociodemographic factors, an individual physician's propensity to admit relative to other physicians appears to be applied consistently across sociodemographic groups of patients.

National Trends In ED Visits, Hospital Admissions, And Mortality For Medicare Patients During The COVID-19 Pandemic.

Concerns about avoidance or delays in seeking emergency care during the COVID-19 pandemic are widespread, but national data on emergency department (ED) visits and subsequent rates of hospitalization and outcomes are lacking. Using data on all traditional Medicare beneficiaries in the US from October 1, 2018, to September 30, 2020, we examined trends in ED visits and rates of hospitalization and thirty-day mortality conditional on an ED visit for non-COVID-19 conditions during several stages of the pandemic and for areas that were considered COVID-19 hot spots versus those that were not. We found reductions in ED visits that were largest by the first week of April 2020 (52 percent relative decrease), with volume recovering somewhat by mid-June (25 percent relative decrease). These reductions were of similar magnitude in counties that were and were not designated as COVID-19 hot spots. There was an early increase in hospitalizations and in the relative risk for thirty-day mortality, starting with the first surge of the pandemic, peaking at just over a 2-percentage-point increase. These results suggest that patients were presenting with more serious illness, perhaps related to delays in seeking care.

Variation in Rates of Hospital Admission from the Emergency Department Among Medicare Patients at the Regional, Hospital, and Physician Levels.

STUDY OBJECTIVE: Rates of admission from the emergency department (ED) vary widely across regions of the country, hospitals within regions, and physicians within hospitals. Our objective was to determine the extent to which variation in admission decisions was described by differences in admission rates at these 3 levels. This understanding will serve to better target interventions to modify rates of admission where appropriate. METHODS: In this cross-sectional observational cohort study, we analyzed Medicare fee-for-service claims for ED visits from 2012 to 2015 in a 20% random sample of beneficiaries. We first estimated the total regional-, hospital-, and physician-level variations in rates of admission and their proportions of the total variation after adjusting for patient and each level's covariates. We then estimated the extent to which each level's characteristics accounted for variation at that respective level. RESULTS: Our study sample included 5,778,218 visits with 45,491 physicians at 3,480 EDs across 306 hospital referral regions. The mean rate of admission was 38.9% and ranged from 21.4% to 53.0% for physicians at the 10th and 90th percentile of the distribution, respectively. The residual (unexplained) variations at the regional, hospital, and physician levels were 13.3% (95% confidence interval [CI], 11.2 to 15.5%), 60.1% (57.1 to 62.9%), and 26.7% (26.4 to 26.9%), respectively. Regional, hospital, and physician characteristics accounted for 9.1% (95% CI, -5.6 to 23.8%), 51.1% (48.8 to 53.5%), and 2.7% (1.3 to 4.1%), respectively, of the explained variation at their respective levels. CONCLUSION: Within-area variation, both across hospitals within a region and across physicians within a hospital, is a more substantial component of observed variation in admission rates from the ED than regional level variation. These findings suggest that variation in admission rates is at least in part related to institutional norms and cultures as well as heterogeneity of physician decisionmaking within hospitals, both of which could be targets of interventions to modify rates of admission.

Variation In Emergency Department Admission Rates Among Medicare Patients: Does The Physician Matter?

Hospitalizations account for the largest share of health care spending. New payment models increasingly encourage health care providers to reduce hospital admissions. Although emergency department (ED) physicians play a major role in the decision to admit a patient, the extent to which admission rates vary among ED physicians even within the same hospital remains poorly understood. In this study we examined physician-level variation in ED admission rates for Medicare patients. We found meaningful variation in admission rates: The mean physician-level adjusted admission rate was 38.9 percent and ranged from 32.2 percent to 45.6 percent for physicians at the tenth and ninetieth percentiles, respectively, of the estimated distribution within the same hospital. In contrast, the predicted risk for admission based on patient characteristics varied little among these physicians, suggesting that the variation in admission rates was not due to differences in patients seen. Our results suggest that strategies targeting physician decision making could modify (by either increasing or decreasing when appropriate) rates of admissions.

The validity of race and ethnicity in enrollment data for Medicare beneficiaries.

OBJECTIVE: To assess the validity of race/ethnicity in Medicare databases for studies of racial/ethnic disparities. DATA SOURCES: The 2010 Medicare Consumer Assessments of Healthcare Providers and Systems (CAHPS(®)) survey was linked to Medicare enrollment data and local area characteristics from the 2000 Census. STUDY DESIGN: Race/ethnicity was cross-tabulated for CAHPS and Medicare data. Within each self-reported category, demographic, geographic, health, and health care variables were compared between those that were and were not similarly identified in Medicare data. DATA COLLECTION METHODS: The Medicare CAHPS survey included 343,658 responses from elderly participants (60 percent response rate). Data were weighted for sampling and nonresponse to be representative of the national population of elderly Medicare beneficiaries. PRINCIPAL FINDINGS: Self-reported Hispanics, Asians, Pacific Islanders, and American Indians were underidentified in Medicare enrollment data. Individuals in these groups who were identified in Medicare data tended to be more strongly identified with their group, poorer, and in worse health and to report worse health care experiences than those who were not so identified. CONCLUSIONS: Self-reported members of racial and ethnic groups other than Whites and Blacks who are identified in Medicare data differ substantially from those who are not so identified. These differences should be considered in assessments of disparities in health and health care among Medicare beneficiaries.

Profiling hospitals by survival of patients with colorectal cancer.

OBJECTIVE: To profile hospitals by survival rates of colorectal cancer patients in multiple periods after initial treatment. DATA SOURCES: California Cancer Registry data from 50,544 patients receiving primary surgery with curative intent for stage I-III colorectal cancer in 1994-1998, supplemented with hospital discharge abstracts. STUDY DESIGN: We estimated a single Bayesian hierarchical model to quantify associations of survival to 30 days, 30 days to 1 year, and 1-5 years by hospital, adjusted for patient age, sex, race, stage, tumor site, and comorbidities. We compared two profiling methods for 30-day survival and four longer-term profiling methods by the fractions of hospitals with demonstrably superior survival profiles and of hospital pairs whose relative standings could be established confidently. PRINCIPAL FINDINGS: Interperiod correlation coefficients of the random effects are (95 percent credible interval 0.27, 0.85), (0.20, 0.76), and (0.19, 0.82). The three-period model ranks 5.4 percent of pairwise comparisons by 30-day survival with at least 95 percent confidence, versus 3.3 percent of pairs using a single-period model, and 15-20 percent by weighted multiperiod methods. CONCLUSIONS: The quality of care for colorectal cancer provided by a hospital system is somewhat consistent across the immediate postoperative and long-term follow-up periods. Combining mortality profiles across longer periods may improve the statistical reliability of outcome comparisons.

Geographic area variations in the Medicare health plan era.

BACKGROUND: Prior research identified variations in care experiences across Medicare health plans (Medicare Advantage [MA]), but the relative amount of variation in MA and traditional fee-for-service (FFS) Medicare is unknown. OBJECTIVES: Compare variation and correlations of beneficiary reports of care experiences across geographic areas in MA and FFS. METHODS: Using the 2001 to 2004 Medicare CAHPS surveys, we analyzed 14 measures of care experiences and preventive services reported by 433,092 MA beneficiaries (82% response rate) and 244,731 in FFS (69% response rate). We estimated hierarchical regression models with random effects for state, hospital referral region, and plan, adjusting for respondent characteristics. We examined the relative variation in FFS and MA scores across areas and among individual MA plans, the correlation between FFS and MA scores across areas, and variability relative to average MA-FFS differences in scores. RESULTS: Although MA and FFS scores are highly correlated, variation is greater in MA than FFS across states and local areas for almost all measures. MA plan variation within areas accounts for 25% to 50% of explained MA variation. MA-FFS differences are smaller than the standard deviations of differences across areas for 10 of 14 measures. CONCLUSIONS: Relative performance between MA and FFS may differ across areas and locally between individual plans and FFS. Quality improvement initiatives should address local system factors that affect both MA and FFS, and identify organizational factors that make some MA plans more successful in improving quality.

Market and beneficiary characteristics associated with enrollment in Medicare managed care plans and fee-for-service.

BACKGROUND: Risk selection in the Medicare managed care program ("Medicare Advantage") is an important policy concern. Past research has shown that Medicare managed care plans tend to attract healthier beneficiaries and that market characteristics such as managed care penetration may also affect risk selection. OBJECTIVES: To assess whether patient enrollment in Medicare managed care (MMC) or traditional fee-for-service (FFS) Medicare is related to beneficiary and market characteristics and provide a baseline for understanding how changes in Medicare policy affect MMC enrollment over time. RESEARCH DESIGN: Data sources were the 2004 Medicare MMC and FFS CAHPS surveys, the Social Security Administration's Master Beneficiary Record, MMC Market Penetration Files, and 2000 Census data. We estimated logistic regression models to assess what beneficiary characteristics predict enrollment in MMC and the moderating effects of market characteristics. RESULTS: Enrollees in MMC plans tend to have better health than those in FFS. This effect is weaker in areas with more competition. Latinos and beneficiaries with less education and lower income, as indicated by earnings history or local-area median income, are more likely to enroll in MMC. CONCLUSIONS: Enrollment in MMC is related to beneficiary characteristics, including health status and socioeconomic status, and is modified by MMC presence in the local market. Because vulnerable subgroups are more likely to enroll in MMC plans, the Centers for Medicare & Medicaid Services should monitor how changes to Medicare Advantage policies and payment methods may affect beneficiaries in those groups.

Case-mix adjustment of the CAHPS Hospital Survey.

OBJECTIVES: To develop a model for case-mix adjustment of Consumer Assessment of Healthcare Providers and Systems (CAHPS) Hospital survey responses, and to assess the impact of adjustment on comparisons of hospital quality. DATA SOURCES: Survey of 19,720 patients discharged from 132 hospitals. METHODS: We analyzed CAHPS Hospital survey data to assess the extent to which patient characteristics predict patient ratings ("predictive power") and the heterogeneity of the characteristics across hospitals. We combined the measures to estimate the impact of each predictor ("impact factor") and selected high impact variables for adjusting ratings from the CAHPS Hospital survey. PRINCIPLE FINDINGS: The most important case-mix variables are: hospital service (surgery, obstetric, medical), age, race (non-Hispanic black), education, general health status (GHS), speaking Spanish at home, having a circulatory disorder, and interactions of each of these variables with service. Adjustment for GHS and education affected scores in each of the three services, while age and being non-Hispanic black had important impacts for those receiving surgery or medical services. Circulatory disorder, Spanish language, and Hispanic affected scores for those treated on surgery, obstetrics, and medical services, respectively. Of the 20 medical conditions we tested, only circulatory problems had an important impact within any of the services. Results were consistent for the overall ratings of nurse, doctor, and hospital. Although the overall impact of case-mix adjustment is modest, the rankings of some hospitals may be substantially affected. CONCLUSIONS: Case-mix adjustment has a small impact on hospital ratings, but can lead to important reductions in the bias in comparisons between hospitals.

Plan, geographical, and temporal variation of consumer assessments of ambulatory health care.

OBJECTIVE: To quantify contributions of health plans and geography to variation in consumer assessments of health plan quality. DATA SOURCES: Responses of beneficiaries of Medicare managed care plans to the Consumer Assessment of Health Plans Study (CAHPS(R)) survey. Our data included more than 700,000 survey responses assessing 381 Medicare managed care (MMC) contracts over a period of five years. STUDY DESIGN: The survey was administered to a nationally representative sample of beneficiaries of Medicare managed care plans. PRINCIPAL FINDINGS: Member assessments of their health plans, customer service functions, and prescription drug benefits varied most across health plans; these also varied the most over time. Assessments of direct interactions with doctors and their practices were more affected by geographical location, and these assessments were quite stable over time. A health plan's global rating often changed significantly between consecutive years, but only rarely were there such changes in ratings of care or doctor. Nationally, mean assessments tended to decrease over the study period. CONCLUSIONS: Our findings suggest that ratings of plans and reports about customer service and prescription access are affected by plan policies, benefits design, and administrative structures that can be changed relatively quickly. Conversely, assessments of other aspects of care are largely determined by characteristics of provider networks that are relatively stable. A consumer survey is unlikely to detect meaningful changes in quality of care from year to year unless quality improvement measures are developed that have substantially larger effects, possibly through area-wide initiatives, than historical temporal variations in quality.

Combining health plan performance indicators into simpler composite measures.

We investigated how the Consumer Assessment of Health Plan Study (CAHPS) survey and the Health Plan Employer Data Information System (HEDIS) measures from Medicare managed care (MMC) plans could be combined into fewer summary performance scores. Four scores summarize most of the variability in these measures, representing (1) care at the doctor's office, (2) customer service and access, (3) vaccinations, and (4) clinical quality measures. These summaries are substantively interpretable, internally consistent, and describe the majority of variation among units in the performance scores analyzed.

Factors affecting response rates to the Consumer Assessment of Health Plans Study survey.

OBJECTIVES: Assess the determinants of nonresponse to a consumer health care survey. METHODS: The first (1997; CAHPS 1.0) and third (1999; CAHPS 2.0) Medicare managed care (MMC) CAHPS surveys collected data on 215 and 365 health plan reporting units, respectively. Data indicated which beneficiaries responded by mail, responded by phone, could not be located, and did not respond. InterStudy data described plan characteristics. chi2 tests and logistic regression models, adjusted for clustering by plan, were used to test associations of individual and plan characteristics with availability of good contact information and response given good contact information. RESULTS: Response rates in the 1997 and 1999 surveys were 75% and 80%, respectively. Older and disabled beneficiaries, women, nonwhite beneficiaries, and persons living in areas with more residents who were nonwhite, on public assistance, and less educated had lower response rates. These associations were partly explained by the distribution of bad contact information, but even among beneficiaries who could be located plan response rates varied greatly. For-profit plans are significantly more likely to have high rates of bad contact information and lower response rates. Telephone follow-up improved the sociodemographic representativeness of the sample, for both high and low response rate plans. CONCLUSION: CAHPS-MMC survey procedures, in particular telephone follow-up, have resulted in high response rates, and current case-mix strategies compensate for some of the remaining effects of differing response rates on comparisons among plans. Further efforts to explore the determinants of response rates are warranted.

Adjusting Performance Measures to Ensure Equitable Plan Comparisons.

When comparing health plans on scores from the Medicare Managed Care Consumer Assessment of Health Plans (MMC-CAHPS®) survey, the results should be adjusted for patient characteristics, not under the control of health plans, that might affect survey results. We developed an adjustment model that uses self-reported measures of health status, age, education, and whether someone helped the respondent with the questionnaire. The associations of health and education with survey responses differed by HCFA administrative region. Consequently, we recommend that the case-mix model include regional interactions. Analyses of the impact of adjustment show that the adjustments were usually small but not negligible.