Finding a Predictive Model for Post-Hospitalization Adverse Events Henry Carretta 1, PhD, MPH; Katrina McAfee 1,2, MS; Dennis Tsilimingras 1,3, MD, MPH 1 Department of Behavioral Sciences and Social Medicine, Florida State University College of Medicine, Tallahassee, FL 2 Florida State University Department of Statistics, Tallahassee, FL 3 Department of Family Medicine and Public Health Services, Wayne State University School of Medicine, Detroit, MI Abstract As insurance companies increase demands on hospital treatment efficacy, there is urgency to determine the underlying causes that result in hospital readmissions. Hospitals with excessive readmissions, defined as an admission to a hospital within 30 days of a discharge from the same or another hospital, risk non-payment or reduced payments for treatment. This study explored possible patient demographic and health care system risk factors for adverse events post-hospitalization of 684 randomly sampled patients admitted to a large community hospital. Eligible patients were recruited at bedside into a 36-month prospective cohort study. Adverse events were assessed based on certainty by two physicians that the event was not due to the patient’s underlying medical conditions. Types of events include but are not limited to the following: fever, pain or discomfort, nausea or vomiting, cough, rash, or death. Using a logistic regression model, the following factors were predictive of adverse events post- hospitalization: whether or not the primary care provider knew of the patient’s initial hospitalization, patient alcohol use, patient prescription drug use, distance to the hospital from the patient’s residence, driving time to the hospital from the patient’s residence, patient education level, and indicators for patient urbanicity classification including population per square mile based on 2010 census tract, Rural Urban Commuting Area (RUCA) score classification based on 2000 census tract, RUCA score classification based on 2004 zip code information, and primary care shortage area classification by Medicare. The model has a 70.9% accuracy rate in the test dataset for predicting patient’s with post-hospitalization adverse events and may serve as a starting point in the discussion on how to reduce hospital readmission rates.

Background & Objective A random sample of n=684 patients admitted to one hospital is used for this study. Eligible patients met the following criteria: Admitted to this hospital in a 36-month study period Age 21 and older English speaking Could be contacted 42 days after hospital discharge Patients were stratified into groups based on residential classification. Patients were interviewed 6 weeks after discharge and consisted of: Patient understanding of their health care needs Patient’s use of health services since discharge Full review of organ systems Severity, timing, and resolution of reported symptoms Adverse events were assessed based on certainty the event was not due to patient’s underlying medical conditions. Adjudication was conducted by two physicians. Types of events include but are not limited to: Fever Pain or Discomfort Nausea or vomiting Cough Rash Death Gathered and recorded data of detailed adverse events, patient demographics, and socioeconomic factors resulting in 212 variables. For this analysis, 10 variables of interest were used involving distance and driving time to hospital and different classification metrics for defining rural populations. Objective: To determine if distance in miles or driving time to hospital is associated with post-discharge adverse events (AEs) in patients from a rural residence.

Defining Rural Name Underlying Geographic AreaYearSourceMetricCriteria Zip code population density Zip codes2010US Census Bureau 100 persons per square mile Investigator defined 2010 census tract population density Census tracts2010US Census Bureau 100 persons per square mile Investigator defined Census tract RUCA categories CT RUCA2010 US Department of Agriculture Rural Urban Commuting Areas RUCA Category A Zip code RUCA categories Zip codes2004 University of Washington Rural Urban Commuting Areas RUCA Category A Urban clusters Census tract clusters 2010US Census Bureau Census urban cluster definition Not a census UC, then rural CMS Primary Care Shortage Designation Zip codes2012 Centers for Medicare & Medicaid Services Primary care physicians per 1,000 population Program categories for rural & super rural County population density Counties & zip codes 2008Florida Legislature 100 persons per square mile Original study definition HRSA Office of Rural Health Policy Census tracts & counties 2010 Health Resources & Services Administration Counties not in metro area & CT RUCA codes 4-10 Program categories There are many different definitions of “rural” used by federal and state agencies and others. The original adverse event study definition used zip codes in Florida counties that were identified as rural by the Florida legislature in Other definitions defined by population density, Rural Urban Commuting Codes (RUCA’s), categorical program definitions, and census urban areas were also examined. Rural Classification Percentages Rural Definition FrequencyPercent UrbanRuralUrban Rural 2010 Zip Code Pop/Sq Mi Census CT Pop/Sq Mi Census CT RUCA Score Zip Code RUCA Score Urbanized Clusters & Areas CMS Medicare/Medicaid Primary Care Shortage Designation FL Legislative Designation of Rural Counties HRSA Office of Rural Health Policy Cases Where All Rural Definitions Agree

Results & Conclusion Odds Ratio Estimates Models using Distance from Hospital (Meters) Models using Driving time from Hospital (Seconds) Effect Point Estimate 95% Wald Confidence Limits Point Estimate 95% Wald Confidence Limits 2010 Zip Code Pop/Sq Mi 0 vs Census CT Pop/Sq Mi 0 vs Census CT RUCA Score 0 vs Zip Code RUCA Score 0 vs Urbanized Clusters & Areas 0 vs CMS Medicare/Medicaid Primary Care Shortage Designation 0 vs FL Legislative Designation of Rural Counties 0 vs HRSA Office of Rural Health Policy 0 vs Distance from Hospital (Miles)1.000 xxx Driving Time from Hospital (Seconds)* xxx1.000 Statistical Analysis 2010 Census CT Pop/Sq Mi is statistically significant in the model with driving time where the odds of an adverse event are times larger for an urban patient than the odds for a rural patient All other variables showed equal odds of an adverse event for urban and rural patients Final Conclusions Neither distance nor driving time from the hospital were significant predictors for modeling post- discharge adverse events The odds of having a post-discharge adverse event are increased for patients in urban residences, as opposed to their rural counterparts, using the 2010 Census Tract rural classification method A consensus should be reached on defining rural populations for further studies *Categorizing Driving Time from Hospital into 4 time intervals does not change interpretation