2011 AHRQ Annual Conference

1. 2011 AHRQ Annual Conference Maryland’s Approach to Racial and Ethnic Minority Health Data Analysis and Reporting Dr. David A. Mann September 21, 2011 Office of Minority Health and Health Disparities Maryland Department of Health and Mental Hygiene

2. Uses of Data for Disparities Elimination • Identify, Locate and Quantify Disparities • Understand Causes of Disparities and Plan Interventions • Track Progress Towards Elimination

3. Causal Chain for Health Outcomes (4 levels of illness) Social Determinants of Health Level 1 Genetics: At each step, individual or group genetic patterns can influence the susceptibility to moving from one level to the next. Public Health Risk Factor Prevalence Level 2 Health Care Access and Quality Disease Frequency Level 3 Case-Specific Event Rates Morbidity and Mortality Level 4 Example: Food desert + no safe place for exercise (level 1) >> Obesity (level 2) >> Diabetes (level 3) >> Diabetes-related: blindness, ESRD, amputations, death (level 4)

4. Data Sources for Health Outcomes (4 levels of illness) Social Determinants of Health Non health data sources: Poverty rate, unemployment rate HS graduation rate, crime rate, etc. Level 1 Risk Factor Prevalence BRFSS data, other local surveys, registries, “claims-coded prevalence”* Level 2 Disease Frequency BRFSS data, other local surveys, registries, “claims-coded prevalence”* Level 3 Morbidity and Mortality Vital Statistics data, CDC Wonder, BRFSS, registries, “claims-coded prevalence”* Level 4 *“Claims-coded prevalence”: prevalence estimate using the count with relevant codes from administrative data as numerator; and one of three denominators: Utilizers, enrollees, or an entire population.

5. Health Outcomes >> Utilization (4 levels of illness) Social Determinants of Health Case-Specific Event Rates Level 1 Risk Factor Prevalence Health Care Utilization Data*: Disparities in Utilization Rates More may be better: Joint replacement, cardiac revascularization, etc. More is worse: diabetic amputations Disparities in Costs: Frequency Disparity in Cost Severity Disparity in Cost Level 2 Disease Frequency Level 3 Morbidity and Mortality Level 4 *Utilization data may be provider-based (hospital discharge or ER data), or may be payer-based (insurance data). In the future it may be medical record based (EMR + HIE). Data accuracy and unique ID may vary by source.

6. What Maryland Has Done • (L4) Mortality:Vital Statistics Reports and CDC Wonder • (L3) Disease Frequency • Incidence: Cancer Registry, HIV/AIDS registry, US Renal Data System (ESRD incidence) • Prevalence: BRFSS (prevalence of doctor diagnosis only) • (L2) Risk Factor Prevalence • Behavioral factors from BRFSS: smoking, obesity, physical activity. Smoking also from state tobacco survey. • Screening factors from BRFSS: mammography, colonoscopy • (L1) Social Determinants of Health • County level social risk profiles.

7. What Maryland Has Done (2) • Cost of disparities analysis in discharge data • Hospital discharge data analysis of Black-White hospitalization disparities • Cost of disparities analysis in Medicare data • Analysis of ACSC admissions in Medicare recipients age 65+ • Removes problem of out of state admissions • Examples of this work, which illustrate various themes and lessons, follow. • Issues of age-adjustment are central to most analyses • Pros and cons of rate ratios vs. rate differences are important

8. Mortality Data byRace and County (L4) Age-Adjusted All-Cause Mortality (rate per 100,000) by Black or White Race and by Jurisdiction, Maryland 2004-2006 Pooled Somerset has a smaller disparity than Montgomery … But Somerset has much worse Black mortality than Montgomery, and the 2nd worst White mortality Lesson: The disparity metric displayed alone can be misleading !!! Age-adjusted death rates for Blacks could not be calculated for Garrett County Source: CDC Wonder Mortality Data 2004-2006

9. Cause-Specific MortalityData by Race and County (L4) Age-Adjusted Mortality Rates (per 100,000), Selected Causes of Death for Blacks or African Americans and Whites, Somerset County, Maryland 2002-2006 Source: CDC Wonder online Database, Compressed Mortality Files 2002-2006 Lesson: For small counties (Iike Somerset)or small racial or ethnic groups, pooling multiple years of data can allow metric estimation even for less common outcomes (like diabetes compared to heart and cancer)

10. Rate Ratio vs. Rate Difference Black vs. White Mortality Disparity, 14 Leading Causes of Death, Maryland 2008 Largest Disparity By Rate Difference: Heart, Cancer Lesson: “Worst” Disparity Depends on Which Metric is Used Largest Disparity By Rate Ratio: HIV/AIDS, Homicide (Yellow highlight indicates Black or African American death rate higher than the White death rate) Source: Maryland Vital Statistics Annual Report 2008

11. Ratio vs. Difference: Implicationsfor Trends and Evaluation Lesson: Rate ratio disparity metrics, considered in isolation, can underestimate the success of minority health programs. This is crucial to understand if trends in such metrics are used for funding decisions.

12. US Renal Data System Datafor ESRD Incidence (L3) Lesson: Fine age stratification for age-adjustment, plus long multi-year pool can make the data robust for estimation in smaller groups.

13. BRFSS Data forRisk Factor Prevalence (L2) Percent of Adults Age 45-64 Classified as Obese, Maryland 2004-2008 18-44 and 65+ show a similar pattern to 45-64 * Source: Maryland BRFSS Data 2004 to 2008 Lesson: Coarse age stratification for age-adjustment, plus multi-year pooling can make the data robust for estimation in smaller groups.

14. Utilization Analysis for Cost of Disparities Black vs. White Disparity Ratios for Adults with Asthma, Maryland 2006 330% more ED visits and 140% more hospital admissions with only 30% more asthma indicates a disparity in disease management success. Source: This figure is Figure 8-5 from the DHMH report Asthma in Maryland 2007 Formula for attributable fraction in the exposed: (RR-1)/RR (2.4-1)/2.4 = 1.4/2.4 = 58.3% of Black Asthma hospitalizations are excess.

15. Discharge Data Analysis of Cost of Disparities • How might out of state • admissions be affecting • these estimates? • Check consistency with • Estimates in Baltimore City, • an “internal” jurisdiction • where admissions out of • state are less likely. • Check consistency with • estimates from Medicare • data, where the out of state • issue does not exist.

16. Medicare Data Analysis of Cost of Disparities for Maryland Analysis of Medicare data in persons age 65+ is consistent with the statewide discharge data analysis. Analysis of payer-based claims data (vs. provider- based data) where available avoids the missing out-of-state utilization issues. Frequency disparity vs. Severity disparity. Source: Differences in Hospitalizations for Ambulatory Care Sensitive Conditions Among Maryland Medicare Beneficiaries—2006. Maryland Health Care Commission.

17. Discharge Data Analysis of Cost of Disparities Why was analysis restricted to Black vs. White in 2004? Count of admissions missing race data: 30,087 Count of admissions missing Hispanic ethnicity data: 51,483 Count of admissions recorded as American Indian or Alaska Native: 1,537 Missing race as percent of known AIAN = 1957% Count of admissions recorded asAsian or Pacific Islander: 12,011 Missing race as percent of knownAPI = 250% Count of admissions recorded asHispanic: 19,449 Missing Hispanic ethnicity as percent of knownHispanic= 265% Count of admissions recorded as Black or African American: 207,495 Missing race as percent of known Black or African American = 15%

18. Contact Information Office of Minority Health and Health Disparities Maryland Department of Health and Mental Hygiene201 West Preston Street, Room 500 Baltimore, Maryland 21201Website: http://www.dhmh.maryland.gov/hd Chartbook:http://www.dhmh.state.md.us/hd/pdf/2010/Chartbook_2nd_Ed_Final_2010_04_28.pdf Phone: 410-767-7117Fax: 410-333-5100Email: healthdisparities@dhmh.state.md.us