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Record W3007112529 · doi:10.1371/journal.pmed.1003052

Secular trends in incidence of type 1 and type 2 diabetes in Hong Kong: A retrospective cohort study

2020· article· en· W3007112529 on OpenAlexaff
Andrea O. Y. Luk, Calvin Ke, Eric S. H. Lau, Hongjiang Wu, William B. Goggins, Ronald C.W., Elaine Chow, Alice P.S. Kong, Wing‐Yee So, Juliana C.N. Chan

Bibliographic record

VenuePLoS Medicine · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsUniversity of Toronto
FundersChinese University of Hong KongNovo NordiskSanofiHospital AuthorityAstraZenecaAmgenPfizerHong Kong GovernmentInnovation and Technology Commission
KeywordsMedicineIncidence (geometry)Type 2 diabetesDiabetes mellitusDemographyPopulationSecular variationType 1 diabetesCohort studyPediatricsInternal medicineEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: There is very limited data on the time trend of diabetes incidence in Asia. Using population-level data, we report the secular trend of the incidence of type 1 and type 2 diabetes in Hong Kong between 2002 and 2015. METHODS AND FINDINGS: The Hong Kong Diabetes Surveillance Database hosts clinical information on people with diabetes receiving care under the Hong Kong Hospital Authority, a statutory body that governs all public hospitals and clinics. Sex-specific incidence rates were standardised to the age structure of the World Health Organization population. Joinpoint regression analysis was used to describe incidence trends. A total of 562,022 cases of incident diabetes (type 1 diabetes [n = 2,426]: mean age at diagnosis is 32.5 years, 48.4% men; type 2 diabetes [n = 559,596]: mean age at diagnosis is 61.8 years, 51.9% men) were included. Among people aged <20 years, incidence of both type 1 and type 2 diabetes increased. For type 1 diabetes, the incidence increased from 3.5 (95% CI 2.2-4.9) to 5.3 (95% CI 3.4-7.1) per 100,000 person-years (average annual percentage change [AAPC] 3.6% [95% CI 0.2-7.1], p < 0.05) in boys and from 4.3 (95% CI 2.7-5.8) to 6.4 (95% CI 4.3-8.4) per 100,000 person-years (AAPC 4.7% [95% CI 1.7-7.7], p < 0.05] in girls; for type 2 diabetes, the incidence increased from 4.6 (95% CI 3.2-6.0) to 7.5 (95% CI 5.5-9.6) per 100,000 person-years (AAPC 5.9% [95% CI 3.4-8.5], p < 0.05) in boys and from 5.9 (95% CI 4.3-7.6) to 8.5 (95% CI 6.2-10.8) per 100,000 person-years (AAPC 4.8% [95% CI 2.7-7.0], p < 0.05) in girls. In people aged 20 to <40 years, incidence of type 1 diabetes remained stable, but incidence of type 2 diabetes increased over time from 75.4 (95% CI 70.1-80.7) to 110.8 (95% CI 104.1-117.5) per 100,000 person-years (AAPC 4.2% [95% CI 3.1-5.3], p < 0.05) in men and from 45.0 (95% CI 41.4-48.6) to 62.1 (95% CI 57.8-66.3) per 100,000 person-years (AAPC 3.3% [95% CI 2.3-4.2], p < 0.05) in women. In people aged 40 to <60 years, incidence of type 2 diabetes increased until 2011/2012 and then flattened. In people aged ≥60 years, incidence was stable in men and declined in women after 2011. No trend was identified in the incidence of type 1 diabetes in people aged ≥20 years. The present study is limited by its reliance on electronic medical records for identification of people with diabetes, which may result in incomplete capture of diabetes cases. The differentiation of type 1 and type 2 diabetes was based on an algorithm subject to potential misclassification. CONCLUSIONS: There was an increase in incidence of type 2 diabetes in people aged <40 years and stabilisation in people aged ≥40 years. Incidence of type 1 diabetes continued to climb in people aged <20 years but remained constant in other age groups.

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 score0.148
Threshold uncertainty score0.294

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.010
GPT teacher head0.245
Teacher spread0.235 · 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".

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Citations86
Published2020
Admission routes1
Has abstractyes

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