Dataset Information

The Impact of Disability and Social Determinants of Health on Condition-Specific Readmissions beyond Medicare Risk Adjustments: A Cohort Study.

ABSTRACT:

Background

Readmission rates after pneumonia, heart failure, and acute myocardial infarction hospitalizations are risk-adjusted for age, gender, and medical comorbidities and used to penalize hospitals.

Objective

To assess the impact of disability and social determinants of health on condition-specific readmissions beyond current risk adjustment.

Design, setting, and participants

Retrospective cohort study of Medicare patients using 1) linked Health and Retirement Study-Medicare claims data (HRS-CMS) and 2) Healthcare Cost and Utilization Project State Inpatient Databases (Florida, Washington) linked with ZIP Code-level measures from the Census American Community Survey (ACS-HCUP). Multilevel logistic regression models assessed the impact of disability and selected social determinants of health on readmission beyond current risk adjustment.

Main measures

Outcomes measured were readmissions ?30 days after hospitalizations for pneumonia, heart failure, or acute myocardial infarction. HRS-CMS models included disability measures (activities of daily living [ADL] limitations, cognitive impairment, nursing home residence, home healthcare use) and social determinants of health (spouse, children, wealth, Medicaid, race). ACS-HCUP model measures were ZIP Code-percentage of residents ?65 years of age with ADL difficulty, spouse, income, Medicaid, and patient-level and hospital-level race.

Key results

For pneumonia, ?3 ADL difficulties (OR 1.61, CI 1.079-2.391) and prior home healthcare needs (OR 1.68, CI 1.204-2.355) increased readmission in HRS-CMS models (N?=?1631); ADL difficulties (OR 1.20, CI 1.063-1.352) and 'other' race (OR 1.14, CI 1.001-1.301) increased readmission in ACS-HCUP models (N?=?27,297). For heart failure, children (OR 0.66, CI 0.437-0.984) and wealth (OR 0.53, CI 0.349-0.787) lowered readmission in HRS-CMS models (N?=?2068), while black (OR 1.17, CI 1.056-1.292) and 'other' race (OR 1.14, CI 1.036-1.260) increased readmission in ACS-HCUP models (N?=?37,612). For acute myocardial infarction, nursing home status (OR 4.04, CI 1.212-13.440) increased readmission in HRS-CMS models (N?=?833); 'other' patient-level race (OR 1.18, CI 1.012-1.385) and hospital-level race (OR 1.06, CI 1.001-1.125) increased readmission in ACS-HCUP models (N?=?17,496).

Conclusions

Disability and social determinants of health influence readmission risk when added to the current Medicare risk adjustment models, but the effect varies by condition.

SUBMITTER: Meddings J

PROVIDER: S-EPMC5215164 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Impact of Disability and Social Determinants of Health on Condition-Specific Readmissions beyond Medicare Risk Adjustments: A Cohort Study.

Meddings Jennifer J Reichert Heidi H Smith Shawna N SN Iwashyna Theodore J TJ Langa Kenneth M KM Hofer Timothy P TP McMahon Laurence F LF

Journal of general internal medicine 20161115 1

<h4>Background</h4>Readmission rates after pneumonia, heart failure, and acute myocardial infarction hospitalizations are risk-adjusted for age, gender, and medical comorbidities and used to penalize hospitals.<h4>Objective</h4>To assess the impact of disability and social determinants of health on condition-specific readmissions beyond current risk adjustment.<h4>Design, setting, and participants</h4>Retrospective cohort study of Medicare patients using 1) linked Health and Retirement Study-Med ...[more]

PMID: 27848189

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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.

Project description:ImportanceDespite substantial geographic variation in Medicare per beneficiary spending in the US, little is known about the extent to which social determinants of health (SDoH) are associated with this variation.ObjectiveTo determine the associations between SDoH and county-level price-adjusted Medicare per beneficiary spending.Design, setting, and participantsThis cross-sectional study used county-level data on 2017 Medicare fee-for-service (FFS) spending, patient demographic characteristics (eg, age and gender) and clinical risk score, supply of health care resources (eg, number of hospital beds), and SDoH measures (eg, median income and unemployment rate) from multiple sources. Multivariable regressions were used to estimate the association of the variation in spending across quintiles with SDoH.Main outcomes and measures2017 county-level price-adjusted Medicare Parts A and B spending per beneficiary. SDoH measures included socioeconomic position, race/ethnicity, social relationships, and residential and community context.ResultsAmong 3038 counties with 33 495 776 Medicare FFS beneficiaries (18 352 336 [54.8%] women; mean [SD] age, 72 [1.5] years), mean Medicare price-adjusted per beneficiary spending for counties in the highest spending quintile was $3785 (95% CI, $3706-$3862) higher, or 49% higher, than spending for bottom-quintile counties (mean [SD] spending per beneficiary, $11 464 [735] vs $7679 [522]; P < .001). The total contribution (including through both direct and indirect pathways) of SDoH was 37.7% ($1428 of $3785) of this variation, compared with 59.8% ($2265 of $3785) by patient clinical risk, 14.5% ($549 of $3785) by supply of health care resources, and 19.8% ($751 of $3785) by patient demographic characteristics. When all factors were included within the same model, the direct contribution of SDoH was associated with 5.8% of the variation, compared with 4.6% by supply, 4.7% by patient demographic characteristics, and 62.0% by patient clinical risk.Conclusions and relevanceThese findings suggest social determinants of health are associated with considerable proportions of geographic variation in Medicare spending. Policies addressing SDoH for disadvantaged patients in certain regions have the potential to contain health care spending and improve the value of health care; patient SDoH may need to be accounted for in publicly reported physician performance, and in value-based purchasing incentive programs for health care professionals.

Project description:In appropriately selected patients with severe carotid stenosis, carotid revascularization reduces ischemic stroke. Prior clinical research has focused on the efficacy and safety of carotid revascularization, but few investigators have considered readmission as a clinically important outcome.The aims of this study were to examine frequency, timing, and diagnoses of 30-day readmission following carotid revascularization; to assess differences in 30-day readmission between patients undergoing carotid endarterectomy (CEA) and carotid artery stenting (CAS); to describe hospital variation in risk-standardized readmission rates (RSRR); and to examine whether hospital variation in the choice of procedure (CEA vs. CAS) is associated with differences in RSRRs.We used Medicare fee-for-service administrative claims data to identify acute care hospitalizations for CEA and CAS from 2009 to 2011. We calculated crude 30-day all-cause hospital readmissions following carotid revascularization. To assess differences in readmission after CAS compared with CEA, we used Kaplan-Meier survival curves and fitted mixed-effects logistic regression. We estimated hospital RSRRs using hierarchical generalized logistic regression. We stratified hospitals into 5 groups by their proportional CAS use and compared hospital group median RSRRs.Of 180,059 revascularizations from 2,287 hospitals, CEA and CAS were performed in 81.5% and 18.5% of cases, respectively. The unadjusted 30-day readmission rate following carotid revascularization was 9.6%. Readmission risk after CAS was greater than that after CEA. There was modest hospital-level variation in 30-day RSRRs (median: 9.5%; range 7.5% to 12.5%). Variation in proportional use of CAS was not associated with differences in hospital RSRR (range of median RSRR across hospital groups 9.49% to 9.55%; p = 0.771).Almost 10% of Medicare patients undergoing carotid revascularization were readmitted within 30 days of discharge. Compared with CEA, CAS was associated with a greater readmission risk. However, hospitals' RSRR did not differ by their proportional CAS use.

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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.

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Project description:BackgroundNonadherence among patients with chronic disease continues to be a significant concern, and the use of text message refill reminders has been effective in improving adherence. However, questions remain about how differences in patient characteristics and demographics might influence the likelihood of refill using this channel.ObjectiveThe aim of this study was to evaluate the efficacy of an SMS-based refill reminder solution using conversational artificial intelligence (AI; an automated system that mimics human conversations) with a large Medicare patient population and to explore the association and impact of patient demographics (age, gender, race/ethnicity, language) and social determinants of health on successful engagement with the solution to improve refill adherence.MethodsThe study targeted 99,217 patients with chronic disease, median age of 71 years, for medication refill using the mPulse Mobile interactive SMS text messaging solution from December 2016 to February 2019. All patients were partially adherent or nonadherent Medicare Part D members of Kaiser Permanente, Southern California, a large integrated health plan. Patients received SMS reminders in English or Spanish and used simple numeric or text responses to validate their identity, view their medication, and complete a refill request. The refill requests were processed by Kaiser Permanente pharmacists and support staff, and refills were picked up at the pharmacy or mailed to patients. Descriptive statistics and predictive analytics were used to examine the patient population and their refill behavior. Qualitative text analysis was used to evaluate quality of conversational AI.ResultsOver the course of the study, 273,356 refill reminders requests were sent to 99,217 patients, resulting in 47,552 refill requests (17.40%). This was consistent with earlier pilot study findings. Of those who requested a refill, 54.81% (26,062/47,552) did so within 2 hours of the reminder. There was a strong inverse relationship (r10=-0.93) between social determinants of health and refill requests. Spanish speakers (5149/48,156, 10.69%) had significantly lower refill request rates compared with English speakers (42,389/225,060, 18.83%; X21 [n=273,216]=1829.2; P<.001). There were also significantly different rates of refill requests by age band (X26 [n=268,793]=1460.3; P<.001), with younger patients requesting refills at a higher rate. Finally, the vast majority (284,598/307,484, 92.23%) of patient responses were handled using conversational AI.ConclusionsMultiple factors impacted refill request rates, including a strong association between social determinants of health and refill rates. The findings suggest that higher refill requests are linked to language, race/ethnicity, age, and social determinants of health, and that English speakers, whites, those younger than 75 years, and those with lower social determinants of health barriers are significantly more likely to request a refill via SMS. A neural network-based predictive model with an accuracy level of 78% was used to identify patients who might benefit from additional outreach to narrow identified gaps based on demographic and socioeconomic factors.

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Dataset Information

The Impact of Disability and Social Determinants of Health on Condition-Specific Readmissions beyond Medicare Risk Adjustments: A Cohort Study.

Background

Objective

Design, setting, and participants

Main measures

Key results

Conclusions

Publications

The Impact of Disability and Social Determinants of Health on Condition-Specific Readmissions beyond Medicare Risk Adjustments: A Cohort Study.

Similar Datasets

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