Explaining Racial and Ethnic Differences in Children’s Use of Stimulant Medications J.L. Hudson G.E. Miller J.B. Kirby September 8, 2008

Published Research This presentation is based on the results from the following published paper: This presentation is based on the results from the following published paper: – Hudson, J., Miller, G.E. and Kirby, J.B. (2007). “Explaining Racial and Ethnic Differences in Children’s Use of Stimulant Medications.” Medical Care 45(11).

Motivation Sharp increase in stimulant use by children in early 1990’s Sharp increase in stimulant use by children in early 1990’s Highlighted concerns with: Highlighted concerns with: – Tolerance – Dependence – Side Effects Case studies report over/under prescribing of stimulants Case studies report over/under prescribing of stimulants Large differences across race/ethnicity Large differences across race/ethnicity

Our Research Important to understand factors that contribute to racial/ethnic differences in stimulant use among children Important to understand factors that contribute to racial/ethnic differences in stimulant use among children Use of MEPS with secondary data sources to Use of MEPS with secondary data sources to – identify differences in characteristics across racial/ethnic groups – quantify the role these characteristics play in differential use of stimulants

Literature Blacks and Hispanic differ from Whites on the following dimensions: Blacks and Hispanic differ from Whites on the following dimensions: – Usual Source of Care – RX Expenditures Differential Use of Stimulants found in: Differential Use of Stimulants found in: – Case Studies – Medicaid Claims Data – Nationally Representative Surveys

Data All Children ages 5-17 All Children ages 5-17 Medical Expenditure Panel Survey Medical Expenditure Panel Survey – Stimulant Use – Family characteristics – Insurance Status – Health Status – Race (Hispanic, Non-Hispanic White, Non-Hispanic Black) Medications identified using National Drug Code to link to Multum Lexicon database Medications identified using National Drug Code to link to Multum Lexicon database Local Area Characteristics at Block Level from 2000 Decennial Census Local Area Characteristics at Block Level from 2000 Decennial Census

Top Selling Stimulants among US Children 5-17, MEPS Drug Common Brand Name(s) Annual Purchases Total (millions) Pct All Stimulants Methylphenidate Concerta Ritilin Amphetamine- Dextroamphetamine Adderall Adderall XR Dextroamphetamine Dexedrine Dexostat

Stimulant Use & Treatment for ADHD Children MEPS AllWhiteBlackHispanic Any Stimulant Use 4.2 (0.2) 5.1 (0.3) 2.8* (0.4) 2.1* (0.3) Any Treatment for ADHD 4.7 (0.3) 5.8 (0.4) 2.8* (0.4) 2.4* (0.3) * Statistically different from whites at 5% level

Oxaca-Blinder Decomposition Regression based decomposition Regression based decomposition Any differences in stimulant use across two groups must result from the following: Any differences in stimulant use across two groups must result from the following: – Difference in characteristics of the groups (means) – Difference in how characteristics affect stimulant use across the two groups (coefficients)

Oxaca-Blinder Wages First used to study wage discrimination between men and women in 1970’s First used to study wage discrimination between men and women in 1970’s Consider education Consider education – Women were less likely to have college degree than men (mean) – In wage regressions by gender - having a college degree provided a larger boost to wage for men (coefficient) – Potential sign of discrimination because men and women with same level of education were not paid the same

Oxaca-Blinder Using means and coefficients – can calculate what percent of gap in stimulant use is due to differences in a particular characteristic Using means and coefficients – can calculate what percent of gap in stimulant use is due to differences in a particular characteristic For a characteristic to explain difference in outcome – it MUST have a significant impact on the outcome in question For a characteristic to explain difference in outcome – it MUST have a significant impact on the outcome in question – Consider eye color and Male-Female wages – If women were more likely to have brown eyes than men – But having brown eyes makes no difference in a wage regression – Differences in eye color cannot explain differences in wages

Linear Probability Model Sample – all children 5-17 Sample – all children 5-17 Regressions run separately by race/ethnicity Regressions run separately by race/ethnicity Dependent Variable – Any Stimulant Use in the year Dependent Variable – Any Stimulant Use in the year Independent Variables Independent Variables – Age – Family Income as percent of poverty line – Parental Education – Insurance Status (private, public, uninsured) – Family Structure – Census Region – Health related Usual Source of Care Usual Source of Care Fair/Poor Health Fair/Poor Health Fair/Poor Mental Health Fair/Poor Mental Health Child Limitation Child Limitation Columbia Impairment Scale – behavioral health measure Columbia Impairment Scale – behavioral health measure

Mean Characteristics by Race/Ethnicity - Family WhiteBlackHispanic No HS degree (parent) *37.6* Below 100% Poverty *27.0* Two parents *68.1* * Significantly different from whites at 5% level

Mean Characteristics by Race/Ethnicity - Insurance WhiteBlackHispanic Public *36.1* Private *44.1* Uninsured * * Significantly different from whites at 5% level

Mean Characteristics by Race/Ethnicity – Health Status WhiteBlackHispanic Fair/poor Health *3.8* Fair/Poor Mental Health Child Limitation * CIS Behavioral * * Significantly different from whites at 5% level

Coefficients by Race/Ethnicity - Family WhiteBlackHispanic No HS degree (parent) Not statistically significant Below 100% Poverty Two parents

Coefficients by Race/Ethnicity - Insurance WhiteBlackHispanic Public.029*.034* Not significant Private.027*.014* Uninsured Base category * Significantly significant at 5% level

Coefficients by Race/Ethnicity – Health Status WhiteBlackHispanic Fair/poor Health Not significant -.05* Fair/Poor Mental Health.14* Not significant.15* Child Limitation.11*.07*.07* CIS Behavioral.07*.04*.06* * Significantly significant at 5% level

Oxaca Blinder Calculations Differences in mean characteristics explain Differences in mean characteristics explain – None of the gap for whites – blacks – 25% of gap for whites – Hispanics Blacks Blacks – Many of the differences between the groups had no significant impact on stimulant use (family, health status) Hispanics Hispanics – Differences can be explained by whites faring better in terms of Insurance Status and Health Status

Comparative Means & Coefficients for Health Status MeansCoefficients WhiteBlackWhiteBlack Fair/Poor Mental Health * Not significant Child Limitation *.07* CIS Behavioral *.04* * Significantly significant at 5% level

Race/Ethnicity Interacted Model Health Status Measures Most or all of racial/ethnic differences are due to differences in the way these groups respond to the same characteristic in terms of stimulant use Most or all of racial/ethnic differences are due to differences in the way these groups respond to the same characteristic in terms of stimulant use Run a new Linear Probability Regression that includes all children in a single model Run a new Linear Probability Regression that includes all children in a single model Model has interactions of race/ethnicity with the following Mental Health measures: Model has interactions of race/ethnicity with the following Mental Health measures: – Fair/Poor Mental Health – Child Limitation – CIS - Behavioral

Interacted Model Findings Significant difference in how whites and blacks with the same mental health status use stimulants Significant difference in how whites and blacks with the same mental health status use stimulants No significant difference between whites and Hispanics No significant difference between whites and Hispanics Black children reported to be in Fair/Poor Mental Health are 10% points less likely to use stimulants than white children in Fair/poor Mental Health Black children reported to be in Fair/Poor Mental Health are 10% points less likely to use stimulants than white children in Fair/poor Mental Health Black children reported to have Behavioral Issues are 4% points less likely to use stimulants than whites with Behavioral Issues Black children reported to have Behavioral Issues are 4% points less likely to use stimulants than whites with Behavioral Issues

Discussion Potential explanations for differences in “effect” of Mental Health characteristics on stimulant use Potential explanations for differences in “effect” of Mental Health characteristics on stimulant use – Cultural Differences across groups Response to behavioral cues Response to behavioral cues Trust of medical system Trust of medical system Beliefs – Willingness to “medicate” Beliefs – Willingness to “medicate” – Environmental Differences across groups School Policies in reporting ADHD School Policies in reporting ADHD Medical Treatment of ADHD Medical Treatment of ADHD

Conclusion Our paper is the first to quantitatively explore differences in stimulant use by race/ethnicity Our paper is the first to quantitatively explore differences in stimulant use by race/ethnicity Much of the difference is due to how racial/ethnic groups respond to characteristics Much of the difference is due to how racial/ethnic groups respond to characteristics Results are consistent with case studies suggesting cultural differences in treatment of mental health issues and corresponding use of medications Results are consistent with case studies suggesting cultural differences in treatment of mental health issues and corresponding use of medications Results are consistent with research from 90’s finding Black families are reluctant to use medications to treat psychiatric disorders Results are consistent with research from 90’s finding Black families are reluctant to use medications to treat psychiatric disorders