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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.

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2011 ahrq annual conference

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

uses of data for disparities elimination
Uses of Data for Disparities Elimination
  • Identify, Locate and Quantify Disparities
  • Understand Causes of Disparities and Plan Interventions
  • Track Progress Towards Elimination
causal chain for health outcomes
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)

data sources for health outcomes
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.

health outcomes utilization
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.

what maryland has done
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.
what maryland has done 2
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
mortality data by race and county l4
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

cause specific mortality data by race and county l4
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)

rate ratio vs rate difference
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

ratio vs difference implications for trends and evaluation
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.

us renal data system data for esrd incidence l3
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.

brfss data for risk factor prevalence l2
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.

utilization analysis for cost of disparities
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.

discharge data analysis of cost of disparities
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.
medicare data analysis of cost of disparities for maryland
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.

discharge data analysis of cost of disparities1
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%

contact information
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