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CX 3 Tier 1/Mapping Training

CX 3 Tier 1/Mapping Training. An investment in neighborhoods October 2011. Valerie Quinn, M.Ed, Network for a Healthy California. Research: Clear connection between neighborhood factors & health outcomes. Neighborhoods Matter. Neighborhoods Matter. Neighborhoods Matter. Store Marketing.

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CX 3 Tier 1/Mapping Training

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  1. CX3 Tier 1/Mapping Training An investment in neighborhoods October 2011 Valerie Quinn, M.Ed, Network for a Healthy California

  2. Research: Clear connection between neighborhood factors & health outcomes

  3. Neighborhoods Matter

  4. Neighborhoods Matter

  5. Neighborhoods Matter

  6. Store Marketing • Include Walgreen’s picture?

  7. CX3: Project Background Objectives: • Collect real-world data that accurately conveys neighborhood conditions 2. Activate consumers to improve food availability in low-income neighborhoods* Focused initiative with local health depts. • Began 2006 (6 pilot sites) • Collect neighborhood-level data using CX3 standardized tools & methods • Over 100 neighborhoods * >50% 185% FPL

  8. CX3 Sites CX3 LOCAL HEALTH DEPARTMENTS Alameda (2006)▲ San Bernardino (2006)▲ Berkeley (2006)▲ San Diego (2007) Contra Costa (2007) San Joaquin (2009) Humboldt (2011) San Mateo (2009) Kern (2006)▲ Santa Barbara (2008) Long Beach (2009) Santa Clara (2006)▲ Marin (2007) Shasta (2008) Monterey (2008) Solano (2007 Orange (2008) Sonoma (2008) Pasadena (2008) Stanislaus (2008) Riverside (2006/2011)▲ Tulare (2009) Sacramento (2007) Ventura (2007) Yolo (2011) ▲ Pilot sites

  9. Data Uses • Design/tailor nutrition education interventions • Expose disparities • Resource allocation • Engage residents in advancing change • Real-world data to create healthier neighborhoods • City/Co Planners, Redevelopment agencies, inform General Plan, health impact, etc. • Program evaluation

  10. Neighborhood Focus

  11. Top Picks

  12. CX3 – How does it work? Standardized Indicators Tools & Methods Real world data 4 STEPS 1. Compile localized data 2. Set priorities based on data 3. Implement strategic, community-focused action plan 4. Evaluate progress over time

  13. Neighborhood Marketplace How are the consumers influenced by neighborhood in their food choices? Collecting neighborhood nutrition data… • Are healthy food choices available and accessible to residents of low-income neighborhoods? • What is the density of FF outlets in neighborhood and around schools? • How much and what are the types of healthy/unhealthy marketing messages on billboards around schools? • Is it safe to walk to store? Is it accessible? • What kinds of food are available from emergency food outlets? Nutrition education? • And more!

  14. Tier 1 Tier 2 Community Assets

  15. Building aNeighborhood Profile… …to inspire community action!

  16. Key to success: Community involvement

  17. CX3 Benefits • Advances local change – Proven! • Puts local groups at forefront for obesity prevention • Standardized indicators, measures, tools • Appealing to funders

  18. CX3 Team • Valerie Quinn, M.Ed. Network 916.552.9908, valerie.quinn@cdph.ca.gov • Alyssa Ghirardelli, RD, MPH, Network916.449.5342, alyssa.ghirardelli@cdph.ca.gov • Lynn Moreno - Fuhrman, - Network 916.552.9862, lynn.moreno@cdph.ca.gov • Chris Bilbrey – Network 916.319.9809, chris.bilbrey@cdph.ca.gov • Network Program Managers • CX3 Local Health Departments YOU!!!

  19. Real Freshness!

  20. Neighborhood Selection

  21. CX3Neighborhood Selection • Don’t take it lightly! • Investment in Community, Future So… ponder, connect, discuss!

  22. CX3 Neighborhood Selection– cont. 4 Requirements • SNAP-Ed eligible census tracts (>50% at <185% FPL; aka dark blue zones)

  23. CX3 Neighborhood Selection– cont. • BUT now required to use American Community Survey data. • Race/ethnicity data layers by 185% FPL due soon (early 2012)

  24. CX3 Neighborhood Selection – Cont. 2a. Pick 3 – 7 neighborhoods: Urban, rural, suburban, remote, dense... What mix do you want?

  25. CX3 Neighborhood Selection – Cont. 2b. Reassessing- Which ones did you implement interventions in? Must do those for program evaluation purposes.

  26. CX3 Neighborhood Selection – Cont. 3. “Recognized” Neighborhood

  27. CX3 Neighborhood Selection – Cont. 4. Middle or High School (preferred)

  28. CX3 Neighborhood Selection – Cont. If not using SNAP-Ed funding, #1 and 2 don’t apply. Do a middle/upper income neighborhood?

  29. CX3 Neighborhood Selection – Cont. Population Considerations • Level of Poverty • Racial/Ethnic Makeup • Geographic Diversity • Obesity rates • Comparison neighborhoods– upper income (if other funding sources)

  30. CX3 Neighborhood Selection – Cont. Program Considerations • Research/New Focus Area • Map More, Survey Fewer • Community Activism • Additional Program Resources, Partnerships or Similar Initiatives • Political Will • Other?

  31. CX3 Neighborhood Selection – Cont. Strategic, leveraging resources • Building Healthy Communities grants- TCE foundation, 10 year commitment  • Community Transformation Grants- Several CA counties! • Strategic Growth Council’s Sustainable Communities Planning grants http://sgc.ca.gov/selected_apps_2010.html

  32. CX3 Neighborhood Selection – Cont. Tip: Set up a special CX3 planning team or workgroup to participate in making decisions about neighborhoods.

  33. Thank you! This material was produced by the California Department of Public Health’s Network for a Healthy California with funding from USDA SNAP, known in California as CalFresh (formerly Food Stamps). These institutions are equal opportunity providers and employers. CalFresh provides assistance to low-income households and can help buy nutritious foods for better health. For CalFresh information, call 877-847-3663. For important nutrition information, visit www.cachampionsforchange.net.

  34. Reassessing • Of CX3 neighborhoods, which ones did you implement interventions? • As program evaluation: • Required to reassess any neighborhood where conducted intervention work. • Comparison CX3 neighborhood if possible (and no intervention work) • Data complexities, expecting more precise federal data in early 2012 (Race/ethnicity data layers by 185% FPL)

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