Using Integrated City Data and Machine Learning to Identify and Intervene Early on Housing-Related Public Health Problems.

ABSTRACT:

Context

Housing is more than a physical structure-it has a profound impact on health. Enforcing housing codes is a primary strategy for breaking the link between poor housing and poor health.

Objective

The objective of this study was to determine whether machine learning algorithms can identify properties with housing code violations at a higher rate than inspector-informed prioritization. We also show how city data can be used to describe the prevalence and location of housing-related health risks, which can inform public health policy and programs.

Setting

This study took place in Chelsea, Massachusetts, a demographically diverse, densely populated, low-income city near Boston.

Design

Using data from 1611 proactively inspected properties, representative of th

SUBMITTER: Robb K 

PROVIDER: S-EPMC8781224 | biostudies-literature | 2022 Mar-Apr 01

REPOSITORIES: biostudies-literature