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Record W4391648129 · doi:10.2196/46591

Association Between Long‑Term Exposure to Air Pollution and the Rate of Mortality After Hip Fracture Surgery in Patients Older Than 60 Years: Nationwide Cohort Study in Taiwan

2024· article· en· W4391648129 on OpenAlexvenueno aff
Shu‐Han Chuang, Yi‐Jie Kuo, Shu-Wei Huang, Hanwei Zhang, Hsiao-Ching Peng, Yu‐Pin Chen

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHip fractureCohort studyEpidemiologyCohortEnvironmental healthMortality rateTerm (time)Air pollutionSurgeryOsteoporosisInternal medicine

Abstract

fetched live from OpenAlex

Background To enhance postoperative patient survival, particularly in older adults, understanding the predictors of mortality following hip fracture becomes paramount. Air pollution, a prominent global environmental issue, has been linked to heightened morbidity and mortality across a spectrum of diseases. Nevertheless, the precise impact of air pollution on hip fracture outcomes remains elusive. Objective This retrospective study aims to comprehensively investigate the profound influence of a decade-long exposure to 12 diverse air pollutants on the risk of post–hip fracture mortality among older Taiwanese patients (older than 60 years). We hypothesized that enduring long-term exposure to air pollution would significantly elevate the 1-year mortality rate following hip fracture surgery. Methods From Taiwan’s National Health Insurance Research Database, we obtained the data of patients who underwent hip fracture surgery between July 1, 2003, and December 31, 2013. Using patients’ insurance registration data, we estimated their cumulative exposure levels to sulfur dioxide (SO2), carbon dioxide (CO2), carbon monoxide (CO), ozone (O3), particulate matter having a size of <10 μm (PM10), particulate matter having a size of <2.5 μm (PM2.5), nitrogen oxides (NOX), nitrogen monoxide (NO), nitrogen dioxide (NO2), total hydrocarbons (THC), nonmethane hydrocarbons (NMHC), and methane (CH4). We quantified the dose-response relationship between these air pollutants and the risk of mortality by calculating hazard ratios associated with a 1 SD increase in exposure levels over a decade. Results Long-term exposure to SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 demonstrated significant associations with heightened all-cause mortality risk within 1 year post hip fracture surgery among older adults. For older adults, each 1 SD increment in the average exposure levels of SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 corresponded to a substantial escalation in mortality risk, with increments of 14%, 49%, 18%, 12%, 41%, 33%, 38%, 20%, 9%, and 26%, respectively. We further noted a 35% reduction in the hazard ratio for O3 exposure suggesting a potential protective effect, along with a trend of potentially protective effects of CO2. Conclusions This comprehensive nationwide retrospective study, grounded in a population-based approach, demonstrated that long-term exposure to specific air pollutants significantly increased the risk of all-cause mortality within 1 year after hip fracture surgery in older Taiwanese adults. A reduction in the levels of SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 may reduce the risk of mortality after hip fracture surgery. This study provides robust evidence and highlights the substantial impact of air pollution on the outcomes of hip fractures.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.024
GPT teacher head0.312
Teacher spread0.287 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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