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

Explaining Racial and Ethnic Explaining Racial and Ethnic Differences in Children’s Use of Differences in Children’s Use of

Stimulant MedicationsStimulant Medications

J.L. HudsonJ.L. HudsonG.E. Miller G.E. Miller J.B. KirbyJ.B. Kirby

September 8, 2008September 8, 2008

Published ResearchPublished Research

This presentation is based on the results This presentation is based on the results from the following published paper:from the following published paper:

– Hudson, J., Miller, G.E. and Kirby, J.B. Hudson, J., Miller, G.E. and Kirby, J.B. (2007). “Explaining Racial and Ethnic (2007). “Explaining Racial and Ethnic Differences in Children’s Use of Stimulant Differences in Children’s Use of Stimulant Medications.” Medications.” Medical CareMedical Care 45(11). 45(11).

MotivationMotivation

Sharp increase in stimulant use by Sharp increase in stimulant use by children in early 1990’schildren in early 1990’s

Highlighted concerns with:Highlighted concerns with:– ToleranceTolerance– DependenceDependence– Side EffectsSide Effects

Case studies report over/under Case studies report over/under prescribing of stimulantsprescribing of stimulants

Large differences across race/ethnicityLarge differences across race/ethnicity

Our ResearchOur Research

Important to understand factors that Important to understand factors that contribute to racial/ethnic differences in contribute to racial/ethnic differences in stimulant use among childrenstimulant use among children

Use of MEPS with secondary data Use of MEPS with secondary data sources tosources to– identify differences in characteristics identify differences in characteristics

across racial/ethnic groups across racial/ethnic groups

– quantify the role these characteristics play quantify the role these characteristics play in differential use of stimulantsin differential use of stimulants

LiteratureLiterature

Blacks and Hispanic differ from Whites on the Blacks and Hispanic differ from Whites on the following dimensions: following dimensions: – Usual Source of CareUsual Source of Care

– RX ExpendituresRX Expenditures

Differential Use of Stimulants found in:Differential Use of Stimulants found in:– Case StudiesCase Studies

– Medicaid Claims DataMedicaid Claims Data

– Nationally Representative SurveysNationally Representative Surveys

DataData

All Children ages 5-17All Children ages 5-17

Medical Expenditure Panel Survey 2000-2002Medical Expenditure Panel Survey 2000-2002– Stimulant UseStimulant Use– Family characteristicsFamily characteristics– Insurance StatusInsurance Status– Health StatusHealth Status– Race (Hispanic, Non-Hispanic White, Non-Hispanic Black)Race (Hispanic, Non-Hispanic White, Non-Hispanic Black)

Medications identified using National Drug Code to Medications identified using National Drug Code to link to Multum Lexicon databaselink to Multum Lexicon database

Local Area Characteristics at Block Level from 2000 Local Area Characteristics at Block Level from 2000 Decennial Census Decennial Census

Top Selling Stimulants among US Top Selling Stimulants among US Children 5-17, MEPS 2000-2002Children 5-17, MEPS 2000-2002

DrugDrug Common Common Brand Brand

Name(s)Name(s)

Annual Annual PurchasesPurchases

Total Total (millions)(millions)

Pct Pct

All StimulantsAll Stimulants 13.913.9 100100

MethylphenidateMethylphenidate Concerta Concerta RitilinRitilin

8.38.3 59.659.6

Amphetamine-Amphetamine-DextroamphetamineDextroamphetamine

Adderall Adderall Adderall XRAdderall XR

4.84.8 34.834.8

DextroamphetamineDextroamphetamine Dexedrine Dexedrine DexostatDexostat

0.80.8 5.55.5

Stimulant Use & Treatment for ADHD Stimulant Use & Treatment for ADHD Children 5-17 - MEPS 2000-2002Children 5-17 - MEPS 2000-2002

AllAll WhiteWhite BlackBlack HispanicHispanic

Any Any Stimulant Stimulant UseUse

4.2 4.2 (0.2)(0.2)

5.1 5.1 (0.3)(0.3)

2.8* 2.8* (0.4)(0.4)

2.1* 2.1* (0.3)(0.3)

Any Any Treatment Treatment for ADHDfor ADHD

4.7 4.7 (0.3)(0.3)

5.8 5.8 (0.4)(0.4)

2.8* 2.8* (0.4)(0.4)

2.4* 2.4* (0.3)(0.3)

* Statistically different from whites at 5% level

Oxaca-Blinder Oxaca-Blinder DecompositionDecomposition

Regression based decompositionRegression based decomposition

Any differences in stimulant use across Any differences in stimulant use across two groups must result from the two groups must result from the following:following:– Difference in characteristics of the groups Difference in characteristics of the groups

(means)(means)– Difference in how characteristics affect Difference in how characteristics affect

stimulant use across the two groups stimulant use across the two groups (coefficients)(coefficients)

Oxaca-Blinder WagesOxaca-Blinder Wages

First used to study wage discrimination First used to study wage discrimination between men and women in 1970’sbetween men and women in 1970’s

Consider educationConsider education– Women were less likely to have college degree Women were less likely to have college degree

than men (mean)than men (mean)

– In wage regressions by gender - having a college In wage regressions by gender - having a college degree provided a larger boost to wage for men degree provided a larger boost to wage for men (coefficient)(coefficient)

– Potential sign of discrimination because men and Potential sign of discrimination because men and women with same level of education were not paid women with same level of education were not paid the same the same

Oxaca-BlinderOxaca-Blinder

Using means and coefficients – can calculate Using means and coefficients – can calculate what percent of gap in stimulant use is due to what percent of gap in stimulant use is due to differences in a particular characteristic differences in a particular characteristic

For a characteristic to explain difference in For a characteristic to explain difference in outcome – it MUST have a significant impact outcome – it MUST have a significant impact on the outcome in questionon the outcome in question– Consider eye color and Male-Female wagesConsider eye color and Male-Female wages– If women were more likely to have brown eyes If women were more likely to have brown eyes

than menthan men– But having brown eyes makes no difference in a But having brown eyes makes no difference in a

wage regressionwage regression– Differences in eye color cannot explain differences Differences in eye color cannot explain differences

in wagesin wages

Linear Probability ModelLinear Probability Model

Sample – all children 5-17Sample – all children 5-17 Regressions run separately by race/ethnicityRegressions run separately by race/ethnicity Dependent Variable – Any Stimulant Use in the yearDependent Variable – Any Stimulant Use in the year Independent VariablesIndependent Variables

– AgeAge– Family Income as percent of poverty lineFamily Income as percent of poverty line– Parental Education Parental Education – Insurance Status (private, public, uninsured)Insurance Status (private, public, uninsured)– Family StructureFamily Structure– Census RegionCensus Region– Health relatedHealth related

Usual Source of CareUsual Source of Care Fair/Poor HealthFair/Poor Health Fair/Poor Mental HealthFair/Poor Mental Health Child LimitationChild Limitation Columbia Impairment Scale – behavioral health measureColumbia Impairment Scale – behavioral health measure

Mean Characteristics by Mean Characteristics by Race/Ethnicity - FamilyRace/Ethnicity - Family

WhiteWhite BlackBlack HispanicHispanic

No HS No HS degree degree (parent)(parent) 4.84.8 15.8*15.8* 37.6*37.6*

Below Below 100% 100% Poverty Poverty 10.010.0 32.5*32.5* 27.0*27.0*

Two Two parentsparents

78.778.7 39.8*39.8* 68.1*68.1*

* Significantly different from whites at 5% level

Mean Characteristics by Mean Characteristics by Race/Ethnicity - InsuranceRace/Ethnicity - Insurance


PublicPublic 13.413.4 40.4*40.4* 36.1*36.1*

Private Private 80.280.2 52.0*52.0* 44.1*44.1*

UninsuredUninsured 6.446.44 7.67.6 19.9*19.9*


Mean Characteristics by Mean Characteristics by Race/Ethnicity – Health StatusRace/Ethnicity – Health Status


Fair/poor Fair/poor HealthHealth

2.42.4 3.5*3.5* 3.8*3.8*

Fair/Poor Fair/Poor Mental HealthMental Health

2.82.8 3.23.2 3.13.1

Child Child LimitationLimitation

6.86.8 6.06.0 4.7*4.7*

CIS CIS BehavioralBehavioral

12.012.0 11.411.4 19.2*19.2*


Coefficients by Coefficients by Race/Ethnicity - FamilyRace/Ethnicity - Family


No HS No HS degree degree (parent)(parent)

Not statistically significantNot statistically significantBelow Below 100% 100% Poverty Poverty Two Two parentsparents

Coefficients by Race/Ethnicity - Coefficients by Race/Ethnicity - InsuranceInsurance


PublicPublic .029*.029* .034*.034* Not Not significantsignificant

Private Private .027*.027* .014*.014* Not Not significantsignificant

UninsuredUninsured Base categoryBase category* Significantly significant at 5% level

Coefficients by Race/Ethnicity – Coefficients by Race/Ethnicity – Health StatusHealth Status


Fair/poor Fair/poor HealthHealth

Not Not significantsignificant

Not Not significantsignificant

-.05*-.05*


.14*.14* Not Not significantsignificant

.15*.15*


.11*.11* .07*.07* .07*.07*


.07*.07* .04*.04* .06*.06*

* Significantly significant at 5% level

Oxaca Blinder CalculationsOxaca Blinder Calculations

Differences in mean characteristics explainDifferences in mean characteristics explain– None of the gap for whites – blacksNone of the gap for whites – blacks– 25% of gap for whites – Hispanics25% of gap for whites – Hispanics

BlacksBlacks– Many of the differences between the groups had Many of the differences between the groups had

no significant impact on stimulant use (family, no significant impact on stimulant use (family, health status)health status)

HispanicsHispanics– Differences can be explained by whites faring Differences can be explained by whites faring

better in terms of Insurance Status and Health better in terms of Insurance Status and Health Status Status

Comparative Means & Comparative Means & Coefficients for Health Status Coefficients for Health Status

MeansMeans CoefficientsCoefficients

WhiteWhite BlackBlack WhiteWhite BlackBlack


2.82.8 3.23.2 .14*.14* Not Not significantsignificant


6.86.8 6.06.0 .11*.11* .07*.07*


12.012.0 11.411.4 .07*.07* .04*.04*

* Significantly significant at 5% level

Race/Ethnicity Interacted Model Race/Ethnicity Interacted Model Health Status MeasuresHealth Status Measures

Most or all of racial/ethnic differences are due Most or all of racial/ethnic differences are due to differences in the way these groups to differences in the way these groups respond to the same characteristic in terms of respond to the same characteristic in terms of stimulant usestimulant use

Run a new Linear Probability Regression that Run a new Linear Probability Regression that includes all children in a single modelincludes all children in a single model

Model has interactions of race/ethnicity with Model has interactions of race/ethnicity with the following Mental Health measures:the following Mental Health measures:– Fair/Poor Mental HealthFair/Poor Mental Health– Child LimitationChild Limitation– CIS - BehavioralCIS - Behavioral

Interacted Model FindingsInteracted Model Findings

Significant difference in how whites and blacks Significant difference in how whites and blacks with the same mental health status use with the same mental health status use stimulantsstimulants

No significant difference between whites and No significant difference between whites and HispanicsHispanics

Black children reported to be in Fair/Poor Black children reported to be in Fair/Poor Mental Health are 10% points less likely to Mental Health are 10% points less likely to use stimulants than white children in Fair/poor use stimulants than white children in Fair/poor Mental HealthMental Health

Black children reported to have Behavioral Black children reported to have Behavioral Issues are 4% points less likely to use Issues are 4% points less likely to use stimulants than whites with Behavioral Issues stimulants than whites with Behavioral Issues

DiscussionDiscussion

Potential explanations for differences in Potential explanations for differences in “effect” of Mental Health characteristics “effect” of Mental Health characteristics on stimulant useon stimulant use– Cultural Differences across groupsCultural Differences across groups

Response to behavioral cuesResponse to behavioral cues Trust of medical systemTrust of medical system Beliefs – Willingness to “medicate”Beliefs – Willingness to “medicate”

– Environmental Differences across groupsEnvironmental Differences across groups School Policies in reporting ADHDSchool Policies in reporting ADHD Medical Treatment of ADHDMedical Treatment of ADHD

ConclusionConclusion

Our paper is the first to quantitatively explore Our paper is the first to quantitatively explore differences in stimulant use by race/ethnicitydifferences in stimulant use by race/ethnicity

Much of the difference is due to how Much of the difference is due to how racial/ethnic groups respond to characteristicsracial/ethnic groups respond to characteristics

Results are consistent with case studies Results are consistent with case studies suggesting cultural differences in treatment of suggesting cultural differences in treatment of mental health issues and corresponding use mental health issues and corresponding use of medicationsof medications

Results are consistent with research from 90’s Results are consistent with research from 90’s finding Black families are reluctant to use finding Black families are reluctant to use medications to treat psychiatric disorders medications to treat psychiatric disorders

Documents

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