Evidence map›Paper›PMID 41757338›Full record

ArticleAJPM focus2026

Aggregated 50-State, Regional, and State-Level Trends in State and Local Government Health Employees in the U.S. From 2000 Through 2023.

Lijing Wei, Melody S Goodman, Jemar R Bather

Abstract read
In one paragraph

Article in AJPM focus, 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
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lijing WeiDepartment of Biostatistics, NYU School of Global Public Health, New York, New York.
Melody S GoodmanDepartment of Biostatistics, NYU School of Global Public Health, New York, New York.
Jemar R BatherDepartment of Biostatistics, NYU School of Global Public Health, New York, New York.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The authors investigated trends in U.S. state and local government health employees per million persons at the aggregated 50-state, regional, and state levels. Methods: The authors used repeated cross-sectional data from the Annual Survey of Public Employment & Payroll. Joinpoint regression was used to estimate average annual percentage changes and annual percentage changes from 2000 through 2023. State and local full-time and part-time government health employees included public health and several other categories of health workers. Results: State and local government health employees per million persons remained stable in the U.S. from 2000 through 2023. Heterogeneous trends in state and local government health employees were observed by region: Northeast (average annual percentage change=0.5% increase, 95% CI=0.3%, 0.7%), Midwest (average annual percentage change=0.9% increase, 95% CI=0.6%, 1.1%), South (average annual percentage change= -0.7% decrease, 95% CI= -0.9%, -0.5%), and West (average annual percentage change=0.1% increase, 95% CI= -0.1%, 0.2%). The authors observed further variation in state-stratified analyses. Conclusions: Most U.S. states experienced decreasing trends during the Great Recession (2007-2009) and increasing trends during the COVID-19 pandemic (2020-2023). Stable and long-term funding streams are essential to support consistent recruitment, training, and retention of state and local government health employees. Health policies should account for regional variations in health needs and employment trends when planning the state and local government health hiring.

Indexed as

Change-point analysishealth departmentsegmented regressiontemporal trendtrend analysisworkforce development

Identifiers

PMID41757338
PMCPMC12934315

What OpenQuestion holds

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LicenceCC BY
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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.