Transcript DetroitKidsData.org

DetroitKidsData.org
An Informational Resource of
Children, Family and Community
Status for Metropolitan Detroit
A Children’s Bridge Program
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Funded by Wayne State
University’s
2004 Research Enhancement
Program awarded to:
Paul T. Giblin, Department of
Pediatrics
Lee Kallenbach, Department of
Community Medicine
Kurt Metzger, Center for Urban
Studies
DetroitKidsData.org
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What is DKD?
Why DKD
A “cooks tour” of DKD
DKD measures applied
What Is DKD ?
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DKD is a web based highly
interactive data repository of child
and family information for Wayne,
Macomb and Oakland Counties and
the City of Detroit
DKD’s information is presented in
geographic units as small as zip
codes and as large as cities and
counties and is compared to state
and national norms
What is DKD?
DKD’s data is presented in 4 domains
1. Predisposing/mediating measures
of child health and development
2. Child developmental status and
behavioral measures
3. Child health status and access to
service
4. Neighborhood demographic
characteristics
What is DKD?
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DKD presents data as tables, as graphs
and as maps
DKD presents data organized by specific
child or family measures expressed across
communities or focuses on a specific
community and reports its full spectrum
of child and family measures
DKD turns data into information by
highlighting disparities between
communities and illustrating
correspondence between measures
Why DKD?
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Data is cool
Web sites are cool
Those who do data and web sites
are really cool
Why DKD?
A child development/family
systems perspective
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An ecological model of child health
and development suggests the
relevance of a wide range of familial
and community measures
An aggregation of measures by
community suggest a unit of
interpretation and influence
Presenting data as information
supports action
Why DKD?:
A policy and program perspective
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“New Federalism”---the dissemination of
responsibility to the lowest unit of action
urges the “democratization” of
information
Web based technology and merged extant
data systems provide data repositories
allowing timely programmatic response
Epidemiologic surveillance allows the
measurement of community benefit
resulting from program action or
institutional mission
Why DKD?
A public health perspective
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The IOM’s Community Health
Improvement Process (CHIP) begins with
problem identification based on ongoing
monitoring of community measures
Problem identification also requires an
ongoing forum for community
participation in which data is presented in
a timely manner and transformed into
information
Health promotion begins with provision of
health information to improve life skills
and make healthy choices
Why DKD?
A university perspective
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A shared baseline for problem
identification, program justification
and evaluation of progress
A single source of measures
employed by varied social science,
human service and health science
disciplines
A common ground to foster
university and community
collaboration
A “Cook’s Tour” of DKD
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Home Page
Accessing Data
Measures by DKD district
Measure across regions
Groupings of Measures
Demographic information
Predisposing/mediating measures
Child developmental/behavioral
measures
Health status and access to services
Web Page Walk Through
DKD Measures Applied
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“Neighborhoods (are) a potent
source of unequal opportunity”
(Brooks-Gunn, et al, 1993 p.385)
“Poverty is social quicksand; it
swallows up community”
(Garbarino, 1998 p.114)
“I’ve been rich and I’ve been poor
and rich is better.” (Sophie Tucker)
Neighborhood
Neighborhood implies the
existence
of both a structural boundary and
a social context
Neighborhood
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Neighborhood factors which may
influence child outcomes include:
Demographic composition (income,
ethnicity, positive role models, racial
segregation)
Social organization or
disorganization and the presence or
absence of social controls
(economic decline, population
turnover, decreased institutional
resources)
Neighborhood
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Stressors in the neighborhood
(violence, incarceration, housing
density, environmental pollutants)
Parenting practices and social
networks (parenting styles, two
versus single parent households,
divorce, multiple siblings closely
spaced)
(Ingoldsby& Shaw, 2002)
Poverty
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Poverty is a measure of
subsistence---the “poverty line” for
the U.S. is set by the Department of
Agriculture’s estimate of food costs
for a basic nutritionally adequate
diet multiplied by three (assuming
that a subsistence level family
spends 1/3 of its income on food)
Poverty
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Poverty is more concentrated in
children because of the declining
incomes of young families and the
increase in female headed single
parent families
Poverty expresses itself through
maternal stress, and inadequate
emotional, informational and social
supports
Poverty
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Outcomes of poverty include
disparities in survival (neonatal and
infant mortality, mortality from
trauma) and morbidity (intellectual
performance, social/emotional
functioning, chronic medical
conditions, nutrition and growth,
lead poisoning, asthma) and
homelessness
DKD Measures Applied
How may neighborhood conditions
affect child health and
development?
DKD Measures Applied
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Contagion model: The impact of
neighborhood peer influences on the
prevalence of child behavior problems in
socioeconomic and racially homogenous
communities
Collective socialization model: Child
outcomes as influenced by the prevalence
of neighborhood adults who can serve as
role models and monitors of the behavior
of neighborhood children
DKD Measures Applied
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Resource model: Child outcomes as
influenced by the level of resources
(community centers, parks, medical
care, daycare) available in a
neighborhood
Competition model: Neighborhood
effects on child outcomes as a
function of community residents
competing for scarce resources
(Jenks & Meyer, 1990)
DetroitKidsData.org
Predisposing/Mediating Neighborhood Factors
for Child Health and Development Measures:
Four Models of Influence
(Social Demographic)
DetroitKidsData.org
Predisposing/Mediating Neighborhood Factors
for Child Health and Development Measures:
Four Models of Influence
(Child Welfare)
DetroitKidsData.org
Predisposing/Mediating Neighborhood Factors
for Child Health and Development Measures:
Four Models of Influence
(Community Health)
DetroitKidsData.org
Predisposing/Mediating Neighborhood Factors
for Child Health and Development Measures:
Four Models of Influence
(Educational Resources)
Neighborhood and Child
Outcomes
Four Weddings and a Funeral
The Neighborhood Context of Adolescent Mental Health*
CAROL S. ANESHENSEL
CLEA A. SUCOFF
(Journal of Health and Social Behavior. 1996, 37 (December):293-310 )
Neighborhood Context
Structural
•median household incomes
•% population below poverty line,
•% labor force in professional,
executive managerial occupations
Racial Ethnic
•% Black % Hispanic
•Segregated vs integrated
neighborhoods
Family Background
•SES & racial ethnicity
•Family structure
•Parental mental health
Experiential
•Ambient hazards (11 Y/N
potential dangers)
•Social cohesion (Likert scale)
Adolescent Mental
Health
•Depression/Anxiety
•Oppositional defiant
disorders
•Conduct disorders
Do Neighborhoods Influence Child and Adolescent Development?'
Jeanne Brooks-Gunn,Greg J. Duncan,
Pamela Kato Klebanov, Naomi Sealand
(Amer J. Sociology; 1993; 99 (2): 353-95)
Neighborhood Effects
•% of families with
incomes<$10,000
•% families affluent
(>$30,000)
•Social Isolation: >40% of
neighborhood who were not
elderly were poor & no more
than 10% families earned
>$30,000
•% males working in
professional, managerial
occupations
•% Black
•% families with children
headed by women
•% families on assistance
•% adult males unemployed
Family Effects
•Maternal education
•Family income
•Female headship
status
Developmental
Outcomes
•Cognitive/ School
functioning
•Stanford-Binet at 36
months
•HS dropout rate
Some Ways in Which Neighborhoods, Nuclear Families, Friendship Groups, and Schools Jointly Affect Changes in Early
Adolescent Development
Thomas D. Cook, Melissa R.Hennan, Meredith Phillips, and Richard A. Settersten, Jr.
(Child Development. 2002; 73 (4): 1283-1309)
COMMUNITY CONTEXT
Family
 Structural features (size, income, parents)
 Process (parenting)
Schools
 Structure (size, location, class size)
 Process (valuing of academics)
Neighborhood
 Structure (employment)
 Process (social cohesion, social context)
Friendship Groups
Child Characteristics
 Gender
 Achievement test scores
 SES
 2-parent family
 Race/ ethnicity
BBehavioral Outcomes
 GPA
 Attendance
 Self Esteem
 Risk Behaviors
Contemporary Developmental Theory and Adolescence:
Developmental Systems and Applied Developmental Science
Richard M. Lerner, Ph.D. and Domini R. Castellino, Ph.D.
(J Adoles Health. 2002; 31: 122-135)
Figure 1. A developmental contextual model of adolescent-context relations (Lerner & Castellino, 2002).
DKD Measures Applied
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Caveats
Neighborhood boundaries as
experienced by a child may change
developmentally
The “social address” of a child may
extend beyond the neighborhood of
a child
Neighborhoods and children have a
transactional effect on each other
Neighborhoods are not static and
have temporal rhythms
DKD Measures Applied
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Program Recommendations for
Neighborhood Interventions:
Addressing only those families/children
with the highest scores on an index of risk
is unlikely to affect a community’s base
rate of risk factors
Adopt community wide primary
prevention programs which assure basic
preventive services---health care,
education and parent support
(Chamberlin, 1996)
Future Directions: Data
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More Measures (80 and counting)
More Time Points
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Baseline (Year 2000)
Current (2002-2003)
More Detail for Existing Measures
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Race / ethnicity
Age / gender
Other
Future Directions: Geography
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More Geographic Specificity
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In Detroit
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Subcommunities
Expand Coverage to Additional
Counties
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Statewide
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Requires additional resources
Future Directions: Functionality
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Geographic Areas
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Data Reporting
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Ability to Aggregate DKD Districts
Change Over Time
Custom / Interactive Maps
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User-specified
By Measure
 By Geography
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Considerations
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Small Numbers
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More Detail in Geography
More Detail in Population Subgroups
Change Over Time
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What is the denominator?
What Would You Like To See?
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Data
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Geography
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Functionality
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Other