CREAT: Census Research Exploration and Analysis Tool

The Hidden Costs of Decline: Health Disparities in America's Diminishing Micropolitan Areas

September 2025

Written by: Todd Gardner

Working Paper Number:

CES-25-70

Abstract

This study examines the relationship between long-term population change and health outcomes in U.S. micropolitan areas, with a focus on life expectancy and mortality disparities. Using a county typology based on the historical population trajectories of micropolitan cores from 1940 to 2020, this analysis reveals that health outcomes are substantially worse in places that experienced sustained decline. These disparities persist even after controlling for demographic and socioeconomic characteristics, suggesting that population loss itself is a key driver of poor public health. Declining micropolitan areas are older, less educated, and report high rates of behavioral risk factors, including smoking, excessive drinking, and physical inactivity. By linking historical demographic trends to tract-level data, this analysis highlights the distinct challenges facing the urban cores of shrinking micropolitan areas. Population decline emerges not only as a demographic trend, but as a marker of structural disadvantage with measurable consequences for community health.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
metropolitan, country, rural, disadvantaged, population, urban, town, urbanization, city, socioeconomic, poverty, health, resident, disparity, prevalence, mortality, urbanized, suburbanization, suburban, death

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

The model is able to label words and phrases by part-of-speech, including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are identified to contain references to specific institutions, datasets, and other organizations.
:
Department of Agriculture, Decennial Census, Economic Research Service, American Community Survey, National Center for Health Statistics, Core Based Statistical Area, Centers for Disease Control and Prevention, Longitudinal Data Base, Robert Wood Johnson Foundation

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