Evidence map›Paper›PMID 33112401›Full record

ArticleJAMA network open2020

Assessment of Changes in Rural and Urban Primary Care Workforce in the United States From 2009 to 2017.

Donglan Zhang, Heejung Son, Ye Shen, Zhuo Chen, Janani Rajbhandari-Thapa, Yan Li, Heesun Eom, Daniel Bu, Lan Mu, Gang Li and 1 more

Abstract read
In one paragraph

Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed, 1 pooled it
–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

56 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Rural Health Workforce Development Through the AHEC Scholars Model.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2026
    Article
  7. Article
  8. Valley Fever in Central California: Diagnosis is Not the Finish Line, the Real Gap is Longitudinal Care.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
    Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Rural-urban disparities in primary care and geographic continuity of care for children with medical complexity.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2026
    Article
  14. Article
  15. Article
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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

11 authors.

Donglan ZhangDepartment of Health Policy and Management, College of Public Health, University of Georgia, Athens.
Heejung SonDepartment of Health Policy and Management, College of Public Health, University of Georgia, Athens.
Ye ShenDepartment of Epidemiology & Biostatistics, College of Public Health, University of Georgia, Athens.
Zhuo ChenDepartment of Health Policy and Management, College of Public Health, University of Georgia, Athens.
Janani Rajbhandari-ThapaDepartment of Health Policy and Management, College of Public Health, University of Georgia, Athens.
Yan LiDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York.
Heesun EomDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York.
Daniel BuDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York.
Lan MuDepartment of Geography, University of Georgia, Athens.
Gang LiDepartment of Health Policy and Management, College of Public Health, University of Georgia, Athens.
José A PagánDepartment of Public Health Policy and Management, School of Global Public Health, New York University, New York.

Funding

Using machine learning and causal inference approaches to investigate disparities in Alzheimer’s disease and related dementias outcomesR01MD013886 · NIMHD · UNIVERSITY OF GEORGIA · PI ZHANG, DONGLAN · 2019 to 2022
$2.4M
Conduits: MSHS Translational Science HubTL1TR001434 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI GABRILOVE, JANICE L · 2015 to 2019
$2.1M
NCATS NIH HHS TL1 TR001434NIMHD NIH HHS R01 MD013886
6 · The paper itself

Abstract

Importance: Access to primary care clinicians, including primary care physicians and nonphysician clinicians (nurse practitioners and physician assistants) is necessary to improving population health. However, rural-urban trends in primary care access in the US are not well studied. Objective: To assess the rural-urban trends in the primary care workforce from 2009 to 2017 across all counties in the US. Design, Setting, and Participants: In this cross-sectional study of US counties, county rural-urban status was defined according to the national rural-urban classification scheme for counties used by the National Center for Health Statistics at the Centers for Disease Control and Prevention. Trends in the county-level distribution of primary care clinicians from 2009 to 2017 were examined. Data were analyzed from November 12, 2019, to February 10, 2020. Main Outcomes and Measures: Density of primary care clinicians measured as the number of primary care physicians, nurse practitioners, and physician assistants per 3500 population in each county. The average annual percentage change (APC) of the means of the density of primary care clinicians over time was calculated, and generalized estimating equations were used to adjust for county-level sociodemographic variables obtained from the American Community Survey. Results: The study included data from 3143 US counties (1167 [37%] urban and 1976 [63%] rural). The number of primary care clinicians per 3500 people increased significantly in rural counties (2009 median density: 2.04; interquartile range [IQR], 1.43-2.76; and 2017 median density: 2.29; IQR, 1.57-3.23; P < .001) and urban counties (2009 median density: 2.26; IQR. 1.52-3.23; and 2017 median density: 2.66; IQR, 1.72-4.02; P < .001). The APC of the mean density of primary care physicians in rural counties was 1.70% (95% CI, 0.84%-2.57%), nurse practitioners was 8.37% (95% CI, 7.11%-9.63%), and physician assistants was 5.14% (95% CI, 3.91%-6.37%); the APC of the mean density of primary care physicians in urban counties was 2.40% (95% CI, 1.19%-3.61%), nurse practitioners was 8.64% (95% CI, 7.72%-9.55%), and physician assistants was 6.42% (95% CI, 5.34%-7.50%). Results from the generalized estimating equations model showed that the density of primary care clinicians in urban counties increased faster than in rural counties (β = 0.04; 95% CI, 0.03 to 0.05; P < .001). Conclusions and Relevance: Although the density of primary care clinicians increased in both rural and urban counties during the 2009-2017 period, the increase was more pronounced in urban than in rural counties. Closing rural-urban gaps in access to primary care clinicians may require increasingly intensive efforts targeting rural areas.

Indexed as

Cross-Sectional StudiesHumansPersonnel Staffing and SchedulingPrimary Health CareRural PopulationUnited StatesUrban PopulationWorkforce

Identifiers

PMID33112401
PMCPMC7593812

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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.