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Oral Health Assessment by Lay Personnel for Older Adults

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Published on: February 2, 2020

Housing-Sensitive Health Conditions Can Predict Poor-Quality Housing.

Ougni Chakraborty¹, Kacie L Dragan², Ingrid Gould Ellen³

¹Ougni Chakraborty, New York University, New York, New York.

Health Affairs (Project Hope)

|February 5, 2024

Summary

This study created a housing health index using machine learning to identify buildings likely affecting resident health. This tool helps target housing inspections to improve public health outcomes.

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Area of Science:

Environmental Health
Public Health
Data Science

Background:

Housing quality significantly impacts resident health.
Identifying buildings in poor repair for targeted interventions is challenging.
Existing methods lack precision in linking housing quality to specific health outcomes.

Purpose of the Study:

To develop a novel method for health-related building inspection targeting.
To identify specific housing-sensitive health conditions correlated with building quality.
To create a Housing Health Index (HHI) for prioritizing building inspections.

Main Methods:

Linked New York City Medicaid claims data with Landlord Watchlist data.
Applied machine learning to identify housing-sensitive health conditions.
Generated a building-level HHI based on health claims data.

Main Results:

Identified 23 housing-sensitive health conditions across five categories.
Developed an HHI that effectively ranks buildings by potential health impacts.
Validated the HHI by showing buildings with higher scores had worse housing quality indicators.

Conclusions:

The HHI provides a data-driven approach to target building inspections.
This method can help local governments prioritize interventions to improve housing quality and resident health.
The findings support the use of health data and machine learning for public health initiatives.