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Enhancing Healthy Housing Partnerships for Lead and Asthma Prevention

Learn about the Rhode Island Department of Health's initiatives to address lead and asthma issues in older housing. Explore data-driven strategies, policy implications, and collaborative efforts involving schools and insurers.

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Enhancing Healthy Housing Partnerships for Lead and Asthma Prevention

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  1. Health Partnerships for Healthy Housing Dr. Robert VandersliceDr. Peter SimonNancy SuttonRhode Island Department of Health

  2. Healthy Housing • Two biggest issues: Lead and Asthma • Preventable +- • Older and poorly maintained housing • Concentrated in urban core, but not just an urban problem • Lead as proxy for other issues • Two kinds of data: Case-Making and Operational

  3. Higher Lead Exposure = More Chronic Absence

  4. Higher Lead Exposure = More Grade Repetition

  5. Higher Lead Exposure = Lower Achievement

  6. Policy Implications School performance improvement without a comprehensive, coordinated investment in social and environmental determinants of health will continue to produce unimpressive results.  This is work that Public Schools cannot do alone. • Changes in early intervention system: need more attention for 5-20 mcg/dl (more research!) • Not just Part C, more broad • Changes in prevention system: targeted, proactive enforcement

  7. Operational Data: Healthy Housing Mapper

  8. Asthma Insurance Claims Project Nancy SuttonRhode Island Department of Health

  9. Asthma • Traditionally tracked 15 datasets, sizable portion of Asthma Program budget • These are necessary but not sufficient • Much more precise data needed for case-making, operations • Enter… Insurance Data

  10. RI Insurance Claims Data Project • RI Health Plan Data • NHPRI • BCBSRI • UHC of New England • Purpose: • Map clustering of children w/asthma • Identify high risk homes, neighborhoods, communities • Document geographic clustering of asthma cases, hospitalizations, and ED visits

  11. RI Insurance Claims Data Project • Providence Plan - RI Data Hub • Explore relationships between asthma and: • academic performance • school absenteeism • age of housing • poverty • public v. private insurance

  12. Claims Data • Address, Name, DOB • # of Asthma Cases • # of Asthma ED Visits • # of Asthma inpatient admissions • One Data Request = 3 insurers, 5 different datasets!

  13. First Run: Basic maps Address data allow much more accurate mapping than ED/Discharge data from hospitals Name and DOB will allow HUB linkage

  14. Next Steps for Asthma • Combine with lead hotspots for HH Mapper • ID least healthy housing in city • DataHUB Link to students, schools • Confirm link to attendance, performance • ID disproportionate asthma in schools

  15. Imagine this analysis for Asthma

  16. Policy Implications • TARGETING LIMITED RESOURCES (e.g., Asthma Control Program) • Identify schools, health centers, communities with greatest need for intervention • Strengthens integration efforts • HEALTH CENTERS/PRIMARY CARE PROVIDERS • integrate asthma into QI/Patient-Centered Medical Home models • COMMUNITY PLANNING & DEVELOPMENT • provides evidence of association between poor housing/communities & health • sidewalks, bike routes/paths, public transit, traffic routes, open space

  17. Policy Implications • SCHOOLS & PUBLIC/SUBSIDIZED HOUSING • Proximity to highways, Diesel • IPM/pest management • Cleaning supplies/practices • mold/moisture • smoke-free • HOUSING • smoke free private housing rentals • code enforcement

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