Using Integrated City Data and Machine Learning to Identify and Intervene Early on Housing-Related Public Health Problems.
J Public Health Manag Pract
; 28(2): E497-E505, 2022.
Article
in En
| MEDLINE
| ID: mdl-33729188
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 the city's housing stock, we developed machine learning models to predict the probability that a given property would have (1) any housing code violation, (2) a set of high-risk health violations, and (3) a specific violation with a high risk to health and safety (overcrowding). We generated predicted probabilities of each outcome for all residential properties in the city (N = 5989).RESULTS:
Housing code violations were present in 54% of inspected properties, 85% of which were classified as high-risk health violations. We predict that if the city were to use integrated city data and machine learning to identify at-risk properties, it could achieve a 1.8-fold increase in the number of inspections that identify code violations as compared with current practices.CONCLUSION:
Given the strong connection between housing and health, reducing public health risk at more properties-without the need for additional inspection resources-represents an opportunity for significant public health gains. Integrated city data and machine learning can be used to describe the prevalence and location of housing-related health problems and make housing code enforcement more efficient, effective, and equitable in responding to public health threats.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Public Health
/
Housing
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Country/Region as subject:
America do norte
Language:
En
Journal:
J Public Health Manag Pract
Journal subject:
SAUDE PUBLICA
/
SERVICOS DE SAUDE
Year:
2022
Type:
Article