Evidence map›Paper›PMID 40687113›Full record

ArticleClinical parkinsonism & related disorders2025

Regional changes in the prevalence of Parkinson's disease over a ten-year period in the state of Texas.

Sean G Young, Kathryn Elwell, Christopher R Watts

Abstract read
In one paragraph

Article in Clinical parkinsonism & related disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Sean G YoungPeter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, United States.
Kathryn ElwellPeter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, United States.
Christopher R WattsHarris College of Nursing & Health Sciences, Texas Christian University, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Region specific epidemiological knowledge is needed to better help government and private entities plan and develop interventions to address accelerating diagnoses of Parkinson's disease (PD). We measured the within-state prevalence patterns of PD within the state of Texas, United States, over a ten-year period to include factors of sex and race/ethnicity, with a specific focus on county and regional patterns. Methods: Publicly available data from the State of Texas Outpatient Public Use Data File (PUDF) evaluated for the years 2012 and 2022. Individuals with a PD specific diagnosis code were included. Age group, race, ethnicity, sex, and county of residence were used to calculate state and county case counts for the years 2012 and 2022. Spatial cluster analysis was applied to assess concentrations of diagnoses across Texas. Results: Prevalence rates of PD increased substantially in Texas from 2012 to 2022, especially among those aged 65 and older and Hispanics. Males had a higher prevalence rate in both years, though both sexes doubled their prevalence over the ten-year period. Spatial cluster analysis showed changes in diagnosis concentration, from the central and northeastern regions to the southwestern border region. Conclusion: Outpatient diagnoses of PD increased substantially from 2012 to 2022. Spatial distribution of PD risk evolved over the decade; such that risk for PD was greatest in the southwestern region of Texas in 2022 compared to all other areas. Prevalence trends in Texas mirror global trends for PD and should inform prevention and intervention strategies in the future.

Indexed as

EpidemiologyParkinson’s diseasePrevalenceUnited States

Identifiers

PMID40687113
PMCPMC12275179

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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.