Evidence map›Paper›PMID 42597240›Full record

ArticleHumanities & social sciences communications2026

Beyond "care deserts": a new metric for mapping formal care employment equity in the United States.

Yoonjung Ahn, Joseph Bommarito

Abstract read
In one paragraph

Article in Humanities & social sciences communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Yoonjung AhnUniversity of Kansas, Lawrence, KS USA.
Joseph BommaritoUniversity of Kansas, Lawrence, KS USA.

Funding

Women's Alzheimer's Risk Reduction in MidlifeP20GM152280 · NIGMS · UNIVERSITY OF KANSAS LAWRENCE · PI HEATHER R DESAIRE · 2024 to 2026
$10.0M
NIGMS NIH HHS P20 GM152280
6 · The paper itself

Abstract

The formal care economy, including paid labor in health care, education, and daily living support, is essential to individual well-being and national productivity. Yet access to these services remains geographically unequal across the United States. Existing approaches commonly measure either within-area inequality (e.g., Gini coefficient) or regional specialization/adequacy (e.g., location quotient (LQ)), but rarely integrate both dimensions in a way that supports consistent classification and comparison across places, sectors, and time. Here, we develop the Care Resource Equity Score (CaRES), which integrates the Gini coefficient and the Location Quotient to quantify the joint distribution of care employment across internal regions and its regional concentration. Using U.S. census tract data aggregated to all counties from 2009-2021, we classify counties into four care-employment typologies using common thresholds: low inequality (Gini < 0.5) vs. high inequality (Gini > 0.5), and below-average concentration (LQ < 1.0) vs. above-average concentration (LQ > 1.0, relative to the national average). The resulting categories are Equitable Coverage (low Gini, high LQ), Concentrated Access (high Gini, high LQ), Even Desert (low Gini, low LQ), and Unequal Scarcity (high Gini, low LQ). Applying CaRES across health care, education, and daily living sectors, we found that most counties fall into either Unequal Scarcity or Concentrated Access, indicating that care employment is typically either under-supplied and unevenly distributed or abundant but spatially concentrated. Nonmetropolitan counties, especially small-town and rural areas, disproportionately exhibit Even Deserts, whereas metropolitan areas more often exhibit Concentrated Access, reflecting localized inaccessibility despite overall resource abundance. Over time, average within-county inequality declines, but this trend is often accompanied by a decline in relative care-employment concentration, suggesting that more even spatial distribution can coincide with growing scarcity. The resulting typology provides an interpretable, scalable tool for identifying distinct types of care-access challenges and for targeting policy responses that differ between places facing scarcity and those facing concentration.

Indexed as

Environmental social sciencesGeographySocial sciences

Identifiers

PMID42597240
PMCPMC13468136

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.