DR. ROBERT VANDERSLICE DR. PETER SIMON NANCY SUTTON RHODE ISLAND DEPARTMENT OF HEALTH Health...

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Transcript of DR. ROBERT VANDERSLICE DR. PETER SIMON NANCY SUTTON RHODE ISLAND DEPARTMENT OF HEALTH Health...

DR. ROBERT VANDERSLICEDR. PETER SIMONNANCY SUTTONRHODE ISLAND DEPARTMENT OF HEALTH

Health Partnerships for Healthy Housing

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

Higher Lead Exposure = More Chronic Absence

Higher Lead Exposure = More Grade Repetition

Higher Lead Exposure = Lower Achievement

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

Operational Data: Healthy Housing Mapper

NANCY SUTTONRHODE ISLAND DEPARTMENT OF HEALTH

Asthma Insurance Claims Project

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

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

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

Claims Data

• Address, Name, DOB

• # of Asthma Cases

•# of Asthma ED Visits

•# of Asthma inpatient admissions

•One Data Request = 3 insurers, 5 different

datasets!

First Run: Basic maps

Address data allow much more accurate mapping than ED/Discharge data from hospitals

Name and DOB will allow HUB linkage

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

Imagine this analysis for Asthma

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

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