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Record W4403272610 · doi:10.2196/60136

Disparities in the Prevalence of Urinary Diseases Among Prisoners in Taiwan: Population-Based Cross-Sectional Study

2024· article· en· W4403272610 on OpenAlexvenueno aff
Zhu Liduzi Jiesisibieke, Bing‐Long Wang, Ming‐Chon Hsiung, Tao‐Hsin Tung

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintCross-sectional studyEnvironmental healthMedicinePopulationEpidemiologyPathologyComputer science

Abstract

fetched live from OpenAlex

Background: Prisoner health is a major global concern, with prisoners often facing limited access to health care and enduring chronic diseases, infectious diseases, and poor mental health due to unsafe prison environments, unhygienic living conditions, and inadequate medical resources. In Taiwan, prison health is increasingly an issue, particularly concerning urinary diseases such as urinary tract infections. Limited access to health care and unsanitary conditions exacerbate these problems. Urinary disease epidemiology varies by sex and age, yet studies in Asia are scarce, and comprehensive data on urinary diseases in Taiwanese prisons remain limited. Objective: This study aimed to investigate the prevalence of urinary diseases among Taiwanese prisoners and explore the differences in disease prevalence between men and women, as well as across different age groups. Methods: This study used data on prisoners from the National Health Insurance Research Database covering the period from January 1 to December 31, 2013. Prisoners covered by National Health Insurance who were diagnosed with urinary diseases, identified by ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification) codes 580-599 based on their medical records, and had more than one medical visit to ambulatory care or inpatient services were included. Sex- and age-stratified analyses were conducted to determine the differences in the prevalence of urinary diseases. Results: We examined 83,048 prisoners, including 2998 with urinary diseases. The overall prevalence of urinary system diseases among prisoners was 3.61% (n=2998; n=574, 6.64% in men and n=2424, 3.26% in women). The prevalence rate in men was significantly lower than that in women (prevalence ratio: 0.46, P<.001). In age-stratified analysis, the prevalence rate among prisoners aged >40 years was 4.5% (n=1815), compared to 2.77% (n=1183) in prisoners aged ≤40 years. Prisoners aged >40 years had a higher prevalence (prevalence ratio: 1.69, P<.001). Other disorders of the urethra and urinary tract (ICD-9-CM: 599), including urinary tract infection, urinary obstruction, and hematuria, were the most prevalent diseases of the urethra and urinary tract across age and sex groups. Women and older prisoners had a higher prevalence of most urinary tract diseases. There were no significant sex-specific differences in adjusted prevalence ratios for acute glomerulonephritis, nephrotic syndrome, kidney infections, urethritis (nonsexually transmitted), or urethral syndrome. However, based on the age-specific adjusted prevalence ratio analysis, cystitis was more prevalent among younger prisoners (prevalence ratio: 0.69, P=.004). Conclusions: Urinary system infections and inflammation are common in prisons. Our findings advocate for policy reforms aimed at improving health care accessibility in prisons, with a particular focus on the needs of high-risk groups such as women and older prisoners. Further research linking claims data with prisoner information is crucial to providing more comprehensive medical services and achieving health equity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.373
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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