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Record W4386862265 · doi:10.2196/40149

Cancer and Atrial Fibrillation Comorbidities Among 25 Million Citizens in Shanghai, China: Medical Insurance Database Study

2023· article· en· W4386862265 on OpenAlexvenueno aff
Mu Chen, Cheng Li, Jiwei Zhang, Xin Cui, Wenqi Tian, Qunshan Wang, Jian Sun, Li Luo, Hong Wu, Yi‐Gang Li

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
FundersShanghai Hospital Development CenterScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of ChinaShanghai Municipal Health Commission
KeywordsMedicineCancerPopulationCancer registryAtrial fibrillationEpidemiologyBreast cancerInternal medicineColorectal cancerDemographyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: With population aging, the prevalence of both cancer and atrial fibrillation (AF) have increased. However, there is scarce epidemiological data concerning the comorbid state of cancer and AF in low- and middle-income countries, including China. OBJECTIVE: We aimed to evaluate the site-, sex-, and age-specific profiles of cancer and AF comorbidities in Chinese populations. METHODS: Data from the Shanghai Municipal Health Commission database between 2015 and 2020 were screened, covering all medical records of Shanghai residents with medical insurance. Site-specific cancer profiles were evaluated for the population with AF relative to the age- and sex-adjusted population of residents without AF. The sex distribution and peak age of cancer diagnosis were also assessed. RESULTS: A total of 25,964,447 adult patients were screened. Among them, 22,185 patients presented cancers comorbid with AF (median 77, IQR 67-82 years of age; men: n=13,631, 61.44%), while 839,864 presented cancers without AF (median 67, IQR 57-72 years of age; men: n=419,020, 49.89%), thus yielding a higher cancer prevalence among residents with AF (8.27%) than among those without AF (6.05%; P<.001). In the population with AF, the most prevalent cancer type was lung cancer, followed by colorectal, male genital organ, stomach, breast, liver, bladder, thyroid, leukemia, and esophageal cancers. AF was associated with an average of nearly 1.4-fold (prevalence ratio [PR] 1.37, 95% CI 1.35-1.38) increased prevalence of cancer after adjusting for age and sex. For site-specific analyses, an increased prevalence of cancer in the population with AF was observed in 20 of 21 cancer sites. This increased prevalence was most prominent for nonsolid tumors, including multiple myeloma (PR 2.56, 95% CI 2.28-2.87), leukemia (PR 1.73, 95% CI 1.57-1.90), and non-Hodgkin lymphoma (PR 1.59, 95% CI 1.43-1.77); intrathoracic malignancies, including mediastinum (PR 2.34, 95% CI 1.89-2.90), lung (PR 1.64, 95% CI 1.59-1.69), and esophageal cancers (PR 1.41, 95% CI 1.28-1.56); bone and soft tissue neoplasms (PR 1.56, 95% CI 1.37-1.77); and kidney cancer (PR 1.53, 95% CI 1.36-1.72). Cancer prevalence in the population with AF relative to that in the population without AF was higher in men than in women in 14 of 18 cancer sites, and female predominance was only observed for thyroid cancer. The peak age of index cancer diagnosis was lower in the population with AF (age group: 70-74 years) than in that without AF (age group: 75-79 years), especially for specific cancer types, including thyroid, central nervous system, mediastinum, esophageal, bladder, and biliary cancers. CONCLUSIONS: Patients with AF are associated with increased prevalence, heightened male predominance, and younger peak age of cancer. Further studies are needed to determine whether early screening of specific cancers is cost-effective and beneficial for patients with AF.

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)0.001
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 score0.073
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.074
GPT teacher head0.377
Teacher spread0.303 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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