End of Life Expenditure Patterns for Medicaid Eligible Infants and Children

1. End of Life Expenditure Patterns for Medicaid Eligible Infants and Children Caprice Knapp, PhD Lindsay Thompson, MD MS Bruce Vogel, PhD Elizabeth Shenkman, PhD

2. Background • Annually, 50,000 children die from injuries, congenital anomalies, cancer, and other diseases • An additional 500,000 children are coping with life-limiting illnesses • The goal of palliative care is to provide comprehensive and effective care for children and families (pain and symptom control) while addressing health, spirituality, and emotional well being • Limited information available

3. Goals of our Study • Describe the end of life usage patterns over a diverse set of service categories (inpatient, outpatient, emergency dept., pharmacy, skilled nursing, and hospice care) • Investigate the association between demographic and health status characteristics on spending

4. Census Characteristics • N=1,282 infants and N=1,934 children who died from 2003-2006. All in Florida’s Medicaid program • Costs during the last 12 months of life • About 60% males (both infants and children) • More Black infants (37%) and more White children (40%) than all other race categories (Hispanic and Other) • Less than 10% live in a rural area • Infants age=3 months, children=12 • Average months enrolled for children=9

6. Health Status Characteristics • Clinical Risk Groups (CRGs) were used to classify children into one of six categories: • Non Acute Non Chronic, • Significant Acute, • Chronic Minor, • Chronic Moderate, • Chronic Major, and • Unassigned. • More infants were classified as unassigned (41%) and more children were classified with a major chronic condition (35%)

7. Analytical Strategy • Two part models were used: • Estimate the odds of having any expenditures, • For spenders only, estimate an ordinary least squares (OLS) model, • Combine the results to determine the overall effects. • Outcome variables: expenditures for the service categories • Expenditures are highly skewed so we log transformed the dependent variables, and then transformed them back for ease of interpretation. • Predictor variables: CRGs, months enrolled, and demographics

8. Results- Logistic Model • CRGS had the greatest effect on the odds of incurring any expenditures for both infants and children • Longer months enrolled increased the odds for infants-- 15% inpatient to 58% total costs • Black infants had decreased odds versus Whites-- 35% less ED, 40% less Outpatient, and 35% less Pharmacy • Black children had increased odds versus Whites-- 60% more ED costs • Hispanic infants had three times the odds of incurring hospice costs than Whites. • Black children 50% less likely than Whites to incur hospice expenses.

9. Combined Results for the Overall Effect on Cost • More severe conditions increased total expenditures for children and infants. • $5,673 for non-acute non-chronic children, • $18,200 for significant acute children, and • $38,300 for major chronic condition children. • Black and Hispanic infants and children had higher inpatient expenditures– • $30,783 for White non-Hispanic infants, • $50,484 for Black non-Hispanic infants, and • $32,630 for Hispanic infants • Rural children had lower pharmacy expenditures– • $4,024 for urban, and • $3,098 for rural. • Hispanic infants had lower pharmacy expenditures– • $253 for White non-Hispanic, • $255 for Black non-Hispanic, and • $83 for Hispanic

10. Discussion • For both infants and children: • Health status had the greatest effect on spending • Months enrolled had positive effect • Age had a slightly negative effect for children • Race/ethnicity variables showed some expected and unexpected results: • Expected • Black and Hispanic infants spend less than Whites for pharmacy and outpatient, and • Black and Hispanic children spend more on inpatient and emergency department care than Whites. • Unexpected • Hispanic infants are about 3 times more than White infants to use hospice services, and • Children in rural areas spend more inpatient but less on pharmacy than children in urban areas.

11. Future Research • What drives the differences in service usage across racial groups? • Would the patterns of use differ if a longer time span was used?