Applying Health Outcome Data to Improve Health Equity Devon Williford, MPH, Health GIS Specialist Lorraine Dixon-Jones, Policy Analyst CDPHE Health Equity and Environmental Justice Collaborative Mile High Data Day February 19, 2016
Today Story of health inequities in Colorado Importance of geography in understanding this story CDPHE Health Equity and Environmental Justice Collaborative Complexity of health condition/outcome data systems 14 datasets representing key health conditions/outcomes and socio-demographic characteristics have been published (census tract geography) Connect via CDPHE Community Health Equity Maps Data Dissemination Plans Framework to address how programs can incorporate a Health Equity lens into their work Discussion/Feedback: How does this framework resonate within our community organizations? How can these data be disseminated more effectively for use? What are the next steps in engaging? Data to Action
Hot Topic Maps Marijuana in Denver: Map of pot-related businesses by neighborhood with income data, school locations: (Denver Post, January 3, 2016 ) http://www.denverpost.com/datacenter/ci_29337458/marijuana-denver-map-pot-related-businesses-by-neighborhood
Why we are here The risk of living in an unhealthy community is not uniformly distributed across all races, income brackets, or levels of educational attainment.
EHOH 6621 GIS for Public Health Research/Practice
Geographic Data and Connections to Decisions Jurisdictions Socio-Demographics Estimates Disability Rates Road Networks Fire Boundaries Flood Zones Emergency Responders Planning Policies Disease Tracking Health Agencies Health Care Facilities Physicians/Pharmacies Improve Resource and Information Communication Inform Emergency Planning Assess Community Inclusion Factors Inform Community Health Assessment Measure Health Burden Model Changes over Time Study Interaction with Environmental Exposures and Built Environment Inform Health and Planning Policies Precise Locational Data Improves our Ability to Make Decisions
Framework For Thinking About Data Healthy environments Equal access to care Reduction of negative health outcomes Requires Stakeholder engagement and Community partnerships Priorities based on Surveillance Identify Population Characteristics among Priorities Develop Geographic-Based Strategies Physical Environme nt Economic Factors Health Outcomes Factors Social Factors Health Behaviors Clinical Care Our data intrinsically have geographic characteristics Maps provide an environment to study data following ecological study model Model the data geographically (map) Compare different attributes of the data and perform Map Algebra Develop and employ a ranking system Visualize the cumulative product of different data Visualize trends of the data across Colorado (identify hotspots and over time) Precise Location Data Improves our Ability to Make Decisions
Social Determinants of Health No health burden or exposure to an environmental factor can be fully understood or effectively addressed without first understanding the socio-demographic characteristics (poverty, unemployment, educational attainment, race/ethnicity, linguistic isolation, etc.) that make up a community. The importance of understanding and visualizing the relationship between sociodemographic and economic factors among specific health outcomes or environmental exposures, and the inclusion of these correlations into population-level intervention strategies, are widely recognized by health professionals. Social Determinants of Health are conditions in the environments in which people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks. Conditions (e.g., social, economic, and physical) in these various environments and settings (e.g., school, church, workplace, and neighborhood) have been referred to as place (Healthy People 2020) Understanding the Social Determinants of Health
Importance to CDPHE and Colorado Data Utilization in Public Health (Data to Action)
Health Equity Advocacy and Spatial Data How can GIS Technologies use Spatial Data to assist with Advocacy? Analyze and identify policy issues: Detail inequities (location and size), Offer a platform for comparing other related data, Analyze associations Visually communicate the message to build public and political will Engage the community in policy research and development (Participatory GIS) Model policy solutions based on inequalities Examples: Analyzing neighborhood access to health-promoting land uses such as parks or zoning for grocery stores Targeting services, resources, and efforts to those communities most in need (Cervical Cancer Screening) Assessing exposure to environmental risk factors (complex) or Density of permitted emissions Illustrate change over time in Blood Lead Level testing after community engagement Treuhaft, S., Community Mapping for Health Equity Advocacy, PolicyLink June 2009, http://opportunityagenda.org/mapping
Health Equity Advocacy and Spatial Data
CDPHE Health Equity & Env. Justice Collaborative Vision: All Coloradans have equal opportunity to develop and achieve their full health potential and have equal access to health and environmental-related decision-making processes. Mission: To build an organizational culture that empowers and supports staff at every level in addressing the root causes of health inequity and environmental injustice in their work. CDPHE Health Equity and Environmental Justice Collaborative
Collaborative Data Workgroup Work Plan Goals: Utilize and promote the display and mapping of HE/EJ-related data as decision making tools in coordinated policy development, resource allocation, program development to examine health equity and environmental justice issues Objective 1: Utilize and promote sub-county health outcome, environmental facility, and sociodemographic data through use of internal web maps in CDPHE policy development, resource allocation, program development, and other department decisions concerning health equity and environmental justice Objective 2: Collaborate with internal/external stakeholders and build a focused public-facing website containing sub-county level maps of health & socio-demographic data Objective 3: Generate innovative strategies to collect data on populations not included in traditional surveillance systems Work Plan
Data Workgroup - Inventory and Indicators Inventory of Health Outcome, Socio- Demographic, and Environmental Facility Data to inform community-based activities Internal Map Viewer for CDPHE use Inform Permitting and Grant Processes Better understand what data we have, what data can we create (priority areas), and what data do local health and environmental health agencies need How do we best disseminate it to a wide range of audiences? Internal Application
Other Models Collaborative Efforts
Complexity of our Health Data Systems Birth Records Mortality Records Hospitalization Data Weighted Survey Data How is data assigned a geography? What is an age-adjusted rate? How can the data be assessed as high, low, or intermediate? How does aggregation into census tract geographies work? How often is data updated? Complexity of health outcome/condition data systems
Data
CDPHE Community Health Equity Map www.cohealthmaps.dphe.state.co.us/cdphe_community_health_equity_map
Data Dissemination
Data Dissemination to Community Partners
Data Driven Decisions/Models Community assessment Community intervention Program evaluation Data Field tested models Data Program strategy (e.g., Lean value stream mapping, social determinants of health) Partnerships (e.g., collective impact) Change management (e.g., ADKAR)
Proposed Framework for Incorporating Data in Programming Identify Problem Develop Strategy Conduct Evaluation Disproportionate asthma rates in low income populations Address the social determinants of health through collective impact Lead and lag measures Defined by Maps and Data Strategies Incorporate Model Field -tested model and mapping tool
Discussion Your take: How does this framework resonate within our community organizations? How can these data be disseminated more effectively for use? What are the next steps in engaging? Data Utilization