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Record W4400335803 · doi:10.2196/59237

Exploring the Birthday Week Effect on Hand, Foot, and Mouth Disease in Yunnan Province, China, From 2008 to 2022: Surveillance Data Analysis

2024· article· en· W4400335803 on OpenAlexvenueno aff
Pei Jiang, Xiangyu Yan, Tongjian Cai, Longxin Huang, Zhenzhong Liu, L. C. Hao, Tian Huang, Haijun Yang, Min Xu, Wenhui Shi, Tiejun Shui

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDemographyHand-foot-and-mouth diseasePoisson regressionFoot-and-mouth diseaseChinaEpidemiologyPediatricsPopulationInternal medicineGeographyEnvironmental healthImmunologyVirus

Abstract

fetched live from OpenAlex

Background: Hand, foot, and mouth disease (HFMD) is a notable infectious disease predominantly affecting infants and children worldwide. Previous studies on HFMD have primarily focused on natural patterns, such as seasonality, but research on the influence of important social time points is lacking. Several studies have indicated correlations between birthdays and certain disease outcomes. Objective: This study aimed to explore the association between birthdays and HFMD. Methods: Surveillance data on HFMD from 2008 to 2022 in Yunnan Province, China, were collected. We defined the period from 6 days before the birthday to the exact birthday as the "birthday week." The effect of the birthday week was measured by the proportion of cases occurring during this period, termed the "birthday week proportion." We conducted subgroup analyses to present the birthday week proportions across sexes, age groups, months of birth, and reporting years. Additionally, we used a modified Poisson regression model to identify conditional subgroups more likely to contract HFMD during the birthday week. Results: Among the 973,410 cases in total, 116,976 (12.02%) occurred during the birthday week, which is 6.27 times the average weekly proportion (7/365, 1.92%). While the birthday week proportions were similar between male and female individuals (68,849/564,725, 12.19% vs 48,127/408,685, 11.78%; χ21=153.25, P<.001), significant differences were observed among different age groups (χ23=47,145, P<.001) and months of birth (χ211=16,942, P<.001). Compared to other age groups, infants aged 0-1 year had the highest birthday week proportion (30,539/90,709, 33.67%), which is 17.57 times the average weekly proportion. Compared to other months, patients born from April to July and from October to December, the peak months of the HFMD epidemic, had higher birthday week proportions. Additionally, a decreasing trend in birthday week proportions from 2008 to 2022 was observed, dropping from 33.74% (3914/11,600) to 2.77% (2254/81,372; Cochran-Armitage trend test: Z=-102.53, P<.001). The results of the modified Poisson regression model further supported the subgroup analyses findings. Compared with children aged >7 years, infants aged 0-1 year were more likely to contract HFMD during the birthday week (relative risk 1.182, 95% CI 1.177-1.185; P<.001). Those born during peak epidemic months exhibited a higher propensity for contracting HFMD during their birthday week. Compared with January, the highest relative risk was observed in May (1.087, 95% CI 1.084-1.090; P<.001). Conclusions: This study identified a novel "birthday week effect" of HFMD, particularly notable for infants approaching their first birthday and those born during peak epidemic months. Improvements in surveillance quality may explain the declining trend of the birthday week effect over the years. Higher exposure risk during the birthday period and potential biological mechanisms might also account for this phenomenon. Raising public awareness of the heightened risk during the birthday week could benefit HFMD prevention and control.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.194
Threshold uncertainty score0.385

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.084
GPT teacher head0.352
Teacher spread0.268 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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