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DR. ROBERT VANDERSLICE DR. PETER SIMON NANCY SUTTON RHODE ISLAND DEPARTMENT OF HEALTH Health Partnerships for Healthy Housing.

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Presentation on theme: "DR. ROBERT VANDERSLICE DR. PETER SIMON NANCY SUTTON RHODE ISLAND DEPARTMENT OF HEALTH Health Partnerships for Healthy Housing."— Presentation transcript:

1 DR. ROBERT VANDERSLICE DR. PETER SIMON NANCY SUTTON RHODE ISLAND DEPARTMENT OF HEALTH Health Partnerships for Healthy Housing

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

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6 Higher Lead Exposure = More Chronic Absence

7 Higher Lead Exposure = More Grade Repetition

8 Higher Lead Exposure = Lower Achievement

9 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

10 Operational Data: Healthy Housing Mapper

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12 NANCY SUTTON RHODE ISLAND DEPARTMENT OF HEALTH Asthma Insurance Claims Project

13 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

14 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

15 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

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

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

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20 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

21 Imagine this analysis for Asthma

22 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

23 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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