Impact of Socioeconomic Status on Unplanned Hospital Admissions and Readmissions in Medicare Yan Tang, PhD Christopher Beadles, MD, PhD Amy Mills, BS Musetta Leung, PhD www.rti.org RTI International is a registered trademark and a trade name of Research Triangle Institute.
Acknowledgements and Disclaimer Acknowledgements RTI International – Olivia Berzin – Anna Prigozhin – Pavel Semukhin Disclaimer – This project was funded by the Centers for Medicare & Medicaid Services under contract no. HHSM-500-2011-00152G. – The statements contained in this presentation are solely those of the authors and do not necessarily reflect the views or policies of the Centers for Medicare & Medicaid Services. RTI assumes responsibility for the accuracy and completeness of the information contained in this report.
Background Unplanned hospital admissions and readmissions are prevalent and expensive Empirical evidence suggests health care utilization may differ by socioeconomic status (SES) (DHHS, 2016; Lewis et al., 2017;Singh and Siahpush, 2006) Despite efforts to improve health outcomes for low SES individuals, research suggests gaps likely remain (Kochanek et al., 2015; Singh and Siahpush, 2014) Understanding the relationship between individual SES and health care utilization is important to designing innovative interventions to deliver high-quality health care
Research Objectives To describe socioeconomic status using a composite SES measure based on neighborhood SES status from the US Census data To examine the association between the neighborhood socioeconomic status on unplanned hospital admissions and readmissions in a Medicare population
Methods Data – 2015 Medicare fee-for-service claims data for beneficiaries assigned to the Shared Savings Program Accountable Care Organizations (ACOs) – 2010-2014 American Community Survey 5-year estimates data Study sample – Four cohorts (≥65 years old): Diabetes Heart failure Multiple chronic conditions (MCC) ≥2 of eight chronic disease groups: acute myocardial infarction, Alzheimer’s disease, atrial fibrillation, chronic kidney disease, COPD and asthma, depression, heart failure, stroke and transient ischemic attack Primarily defined by CMS’s Chronic Condition Data Warehouse (CCW) Hospitalization
Methods (cont.) Outcomes (patient-level) – Three unplanned admission measures: Dummy variable indicating any unplanned hospital admission for individuals with diabetes, heart failure, or MCC, respectively – One unplanned readmission measure: Dummy variable indicating any unplanned readmission within 30 days of discharge from the index hospitalization Identification of unplanned admissions – Admissions for some types of care are always considered planned Transplant surgery Maintenance chemotherapy, radiotherapy, immunotherapy Rehabilitation – Otherwise, a non-acute admission for a scheduled procedure is considered planned – Admissions for acute illness or complications of care are unplanned
Methods (cont.) Key independent variable – ZIP code-level SES index score according to the Agency for Healthcare Research and Quality (AHRQ) SES algorithm (AHRQ, 2008) Normalized score based on high occupancy housing, property values, poverty, median household income, education, and unemployment rate Ranges from 0-100, with higher score indicating better SES status Control Variables – Individual demographic information (e.g., age, sex) – Comorbidity status (e.g., CMS Condition Category groups) Statistical analysis – Generalized linear mixed models with a binomial distribution and a logit link, with ACO-level random effects
Results Characteristics of the Study Sample Characteristic Diabetes Heart Failure MCC Hospitalization Sample size 750,620 584,127 1,195,909 1,041,289 Female (%) 53.8 54.8 58.4 57.4 Hospital admissions/readmissions 29.1 38.9 34.1 15.7 per 100 individuals SES index score distribution
Results (cont.) Regression Results: Estimated Effects of SES on the Outcomes Only SES Index SES Index + Covariates Measure Odds Ratios (95% CI) Odds Ratios (95% CI) Admissions for individuals with diabetes 0.985 (0.984 - 0.986) 0.987 (0.986 - 0.988) Admissions for individuals with heart failure 0.988 (0.987 - 0.990) 0.988 (0.987 - 0.990) Admissions for individuals with MCC 0.986 (0.985 - 0.987) 0.989 (0.988 - 0.990) Hospital readmissions 0.983 (0.982 - 0.984) 0.994 (0.993 - 0.995) All odds ratios were significant at p<0.001 level
Results (cont.) Adjusted Admissions for Individuals with Diabetes, by SES 28.7 30 27.2 Unplanned Admissions per 100 26.3 25.2 25 20 Individuals 15 10 5 0 Quartile 1 Quartile 2 Quartile 3 Quartile 4 Quartile of SES Index Measure was adjusted for individual demographic information and comorbidity status
Results (cont.) Adjusted Admissions for Individuals with Heart Failure, by SES 39.7 38.2 40 Unplanned Admissions per 100 36.9 36.2 35 30 Individuals 25 20 15 10 5 0 Quartile 1 Quartile 2 Quartile 3 Quartile 4 Quartile of SES Index Measure was adjusted for individual demographic information and comorbidity status
Results (cont.) Adjusted Admissions for Individuals with MCC, by SES 34.5 35 33.0 Unplanned Admissions per 100 32.0 31.5 30 25 Individuals 20 15 10 5 0 Quartile 1 Quartile 2 Quartile 3 Quartile 4 Quartile of SES Index Measure was adjusted for individual demographic information and comorbidity status
Results (cont.) Adjusted Hospital Readmissions, by SES Unplanned Readmissions per 13.4 14.0 13.0 12.6 12.6 12.0 100 Individuals 10.0 8.0 6.0 4.0 2.0 0.0 Quartile 1 Quartile 2 Quartile 3 Quartile 4 Quartile of SES Index Measure was adjusted for individual demographic information and comorbidity status
Summary of Key Findings Higher SES index score was associated with lower unplanned hospital admissions/readmissions The magnitude of the associations between SES index score and the four measures was small Risk adjustment for individual comorbidities generally mitigated this association Limitations: – ZIP code- level SES index score serves as a proxy for individual’s socioeconomic status – Unmeasured potential confounding (e.g., provider characteristics)
Implications The observed associations between SES status and hospital admission and readmission measures were explained in part by differences in patient medical complexity Quality improvement programs intended to enhanced care delivery for individuals with low SES status may promote the delivery of high- quality health care These findings are among Medicare beneficiaries assigned to SSP ACOs; it is unknown whether other accountable care organization models would have similar findings
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