Finding a Predictive Model for Post-Hospitalization Adverse EventsHenry Carretta1, PhD, MPH; Katrina McAfee1,2, MS; Dennis Tsilimingras1,3, MD, MPH

1 Department of Behavioral Sciences and Social Medicine, Florida State University College of Medicine, Tallahassee, FL2 Florida State University Department of Statistics, Tallahassee, FL3 Department of Family Medicine and Public Health Services, Wayne State University School of Medicine, Detroit, MI

AbstractAs 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

Area Year Source Metric CriteriaZip code population density

Zip codes 2010 US Census Bureau 100 persons per square mile Investigator defined

2010 census tract population density Census tracts 2010 US Census Bureau 100 persons per

square mile Investigator defined

Census tract RUCA categories CT RUCA 2010 US Department of

AgricultureRural Urban Commuting Areas RUCA Category A

Zip code RUCA categories Zip codes 2004 University of

WashingtonRural Urban Commuting Areas RUCA Category A

Urban clusters Census tract clusters 2010 US Census Bureau Census urban

cluster definitionNot a census UC, then rural

CMS Primary Care Shortage Designation

Zip codes 2012Centers for Medicare & Medicaid Services

Primary care physicians per 1,000 population

Program categories for rural & super rural

County population density

Counties & zip codes 2008 Florida Legislature 100 persons per

square mileOriginal 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 2008. 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

Frequency Percent

Urban Rural Urban Rural

2010 Zip Code Pop/Sq Mi 389 295 56.87 43.13

2010 Census CT Pop/Sq Mi 425 259 62.13 37.87

2010 Census CT RUCA Score 564 120 82.46 17.54

2004 Zip Code RUCA Score 565 119 82.60 17.40

2010 Urbanized Clusters & Areas 354 330 51.75 48.25

2012 CMS Medicare/Medicaid Primary Care Shortage Designation 490 194 71.64 28.36

2008 FL Legislative Designation of Rural Counties 340 344 49.71 50.29

2010 HRSA Office of Rural Health Policy 575 109 84.06 15.94

Cases Where All Rural Definitions Agree 325 47.25

Results & Conclusion

Odds Ratio Estimates Models using Distance from

Hospital (Meters)Models using Driving time from Hospital (Seconds)

EffectPoint

Estimate95% Wald

Confidence LimitsPoint

Estimate95% Wald

Confidence Limits

2010 Zip Code Pop/Sq Mi 0 vs 1 0.790 0.466 1.339 0.944 0.565 1.578

2010 Census CT Pop/Sq Mi 0 vs 1 1.455 0.949 2.232 1.616 1.039 2.513

2010 Census CT RUCA Score 0 vs 1 1.598 0.700 3.649 1.728 0.857 3.486

2004 Zip Code RUCA Score 0 vs 1 1.276 0.564 2.891 1.467 0.733 2.935

2010 Urbanized Clusters & Areas 0 vs 1 1.002 0.688 1.460 1.056 0.720 1.550

2012 CMS Medicare/Medicaid Primary Care Shortage Designation 0 vs 1

1.360 0.750 2.465 1.542 0.901 2.640

2008 FL Legislative Designation of Rural Counties 0 vs 1 0.779 0.478 1.269 0.896 0.551 1.459

2010 HRSA Office of Rural Health Policy 0 vs 1 1.626 0.715 3.699 1.796 0.867 3.722

Distance from Hospital (Miles) 1.000 1.000 1.000 x x x

Driving Time from Hospital (Seconds)* x x x 1.000 1.000 1.000

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 1.616 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