Community Health Data
Scan for Connecticut
Educational Briefing
Wed., March 28, 2007
Why Community Health Data Matters?
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Discover areas of need
Set funding priorities
Target interventions
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For specific area/groups of people
Evaluate long-term effectiveness of
interventions
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Community Health Data Scan for Connecticut
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Broad range of indicators
Quantitative
Focus on race/ethnic disparities in
context of kind of community
Innovative use of “Health Reference
Groups” of cities and towns
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Health Reference Groups Map
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Use the Data Scan to:
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Generate hypotheses about health
disparities
Estimate rates for individual communities,
or groups of communities
Link to other sources of research about
particular conditions
Develop public policy recommendations
Evaluate change over time
Use maps, charts and tables on
www.cthealth.org (available April 2007)
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Why Health Data Matters - Two Cases
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Binge Drinking
Metabolic Syndrome Diseases
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Binge Drinking by HRG & Race/Ethnicity
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Binge Drinking by Age & Race/Ethnicity
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Metabolic Syndrome Disease
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Definition
Diabetes
 Heart disease
 Stroke
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Rates
Different types of communities
 Different race/ethnicity groups
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Diabetes Varies by Race/Ethnicity &
Health Reference Group
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Diabetes Mortality Varies by
Race/Ethnicity
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White – 16.6
Black, Not Hispanic – 46.4
Asian, Not Hispanic – 11.8
Hispanic – 26.9
Rates are per 100,000 population,
age-adjusted, 2000-2004 (see Data
Scan Table 113, p 180).
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Obesity Varies by Race/Ethnicity &
Health Reference Group
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Signs of a Coming Epidemic
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Metabolic Syndrome Starts Early
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Significant Disparities in Youth
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Overweight
Lack of exercise
TV watching time
Connecticut lacks good youth data
on out-of-school activity to help
target programs
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Notes on Data Scan Methods
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Community rate estimation
Problems in community health data
Solutions for community health data
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Problem of Community Rate Estimation
“There is no such thing as a ‘true’
value. There are only estimates,
with a probable range, and with a
risk of being wrong.”
Paraphrase of W. Edwards Deming
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Key Problems
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Town problem
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Unreliable rates due to small numbers
Data suppression for privacy/confidentiality
Inconsistency in race and ethnicity coding
Since 2000, no race-ethnicity-gender-age
population estimates for towns
Median ages are very different
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White, Not Hispanic – 40.2
Black – 29.9
Asian – 30.7
Hispanic – 25.4
Source: U.S. Census 2000
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Key Solutions
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Town Problem
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Race and ethnicity coding
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Combine several years of data
Combine cities and towns into six
demographically meaningful clusters called
“Health Reference Groups”
Use confidence intervals to help interpret all
differences
Use standard U.S. Census classification
Population estimates
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Use Census 2000
Age-adjustment of all rates to the U.S. 2000
standard population
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Health Reference Groups (HRGs)
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Urban Centers (3)
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Bridgeport
Hartford
New Haven
Manufacturing Centers (10)
Diverse Suburbs (15)
Wealthy Suburbs (27)
Mill Towns (39)
Rural Towns (75)
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Health Reference Groups Map
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Why Use Both Health Reference
Groups & Race/Ethnicity?
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Avoid the “broad brush” approach
Avoid stereotyping Asians, blacks,
Hispanics & whites as being the
same regardless of community
context
Allow a more realistic view of
community differences that may
have public policy and program
implications
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Avoid “Broad Brush” Error
Not accounting for differences within
broad race/ethnicity groups
 74.4 percent of Hispanic residents
in the Urban Centers are of Puerto
Rican origin
 23.4 percent of Hispanic residents
in the Wealthy Suburbs are of
Puerto Rican origin
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Take Account of both Race/Ethnicity
and Context.
Percent Told They Have diabetes, With Margins of Error
Source: Data Scan Web Page (available April 2007)
White, Not Hispanic
Black, Not Hispanic
%
Margin of
error
%
Margin of
error
Urban Centers
7.2
1.6
12.2
2.9
Manufacturing Centers
5.3
0.8
10.3
3.3
Diverse Suburbs
5.5
0.8
4.6
3.5
Wealthy Suburbs
2.7
0.6
Mill Towns
5.4
0.6
8.1
4.1
Rural Towns
4.2
0.6
Connecticut
4.7
0.4
9.8
1.8
Area
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CHF’s Data Scan Web Page
 Maps
 Charts
 Data
 Notes
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How to Access Data on CHF’s
Data Scan Web Page
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Visit www.cthealth.org
Click on “Community Health Data Scan”
link (available April 2007)
Select area of interest from banner
headlines
Select maps, charts or data tables from
topics list
Download notes that contain definitions
and caveats
Sign up to receive email notification of
data updates
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Data Scan Web Page
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How to Use the Data Scan & Web Page
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Legislative staff perspective
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Use legislative district data
Use HRG level data to understand the
context of disparities
Find tables that relate to a particular
area of concern
Frame additional detailed requests to
relevant agencies
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How to Use the Data Scan & Web Page
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Community-based organization
perspective
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Use HRG level data to understand
needs and disparities
Find tables that relate to a particular
area of concern
Explain that “rural” covers your town, if
you are developing a proposal that
focuses on one or more rural towns –
even if your town is not named
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Focus Area Criteria
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Disparities demonstrated by the
data
Within scope of study
Healthy People 2010
Significant problems encountered in
the study process
Science-based actions possible
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Focus Area 1
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Focus on the health reference
groups, and racial/ethnic groups in
greatest need.
The three Urban and ten Manufacturing
Centers in Connecticut need the greatest
health promoting investments. Within
these communities, black/African
American and Puerto Rican Hispanic
residents may be in greatest need.
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Focus Area 2
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Focus on diabetes and other
conditions in the metabolic
syndrome.
Risk factors for diabetes and
related conditions are prevalent in
Connecticut in all populations,
especially in the black/African
American population.
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Focus Area 3
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Focus on ensuring a medical home
for all Connecticut residents.
Overuse of emergency department (ED)
care and hospital admissions could be
avoided if every Connecticut resident
had, and used, a primary care “medical
home.” The medical home is a place to
discuss prevention (e.g., child and youth
risk issues, diet and physical activity).
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Focus Area 4
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Focus on the binge drinking and
smoking culture.
Smoking and binge drinking are major
contributors to many health problems
and premature mortality. Youth and
young adult white population is
especially at risk and these behaviors
may spread to immigrant populations as
they acculturate. Increase investment
in prevention by legal and educational
means.
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Focus Area 5
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Focus on youth risks and
opportunities.
Major youth health risks include
sexually transmitted diseases, teen
pregnancy, lack of use of seat
belts and bicycle helmets, and
child abuse. Black and Hispanic
children are most at risk.
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Focus Area 6
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Improve the health data system.
State data system should address:
access; customizing data for consumers;
data delays; community health
observations; more detail in
race/ethnicity categories and multiple
race options; health care quality and
disparities indices; mental health data;
out-of-school data; and documentation
about the data.
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Summary
Use the Data Scan to:
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Generate hypotheses about health
disparities
Estimate rates for individual communities,
or groups of communities
Link to other sources of research about
particular conditions
Develop public policy recommendations
Evaluate change over time
Use maps, charts and tables on
www.cthealth.org (available April 2007)
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Q&A
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The floor is yours!
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Acknowledgements
Thomas
Cooke - Geography
Denise Castronovo - Mapping
Karen Clements – BRFSS Analysis
Martha Laugen – Research Associate
Elisa Del Bonis – Web Page Design
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SigmaWorks Services
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Community and Regional Assessment
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Community Profiles
Risk Factors
Access to Care
Health Outcomes
Health Disparities
Training
Training
Training
Areas of
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and Consultation in Assessment
in Outcomes Measurement
in Continuous Quality Improvement
Expertise
Health
Education
Workforce Development
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Contact Information
Larry Finison, SigmaWorks &
Boston University:
lfinison@bu.edu, 781.483.3901
Monette Goodrich, Connecticut
Health Foundation,
monette@cthealth.org,
860.224.2208
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