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Record W2920850455 · doi:10.1016/s0140-6736(19)30220-x

Trends in cause-specific mortality among children aged 5–14 years from 2005 to 2016 in India, China, Brazil, and Mexico: an analysis of nationally representative mortality studies

2019· article· en· W2920850455 on OpenAlexaffabout
Shaza A. Fadel, Cynthia Boschi-Pinto, Shicheng Yu, Luz Myriam Reynales-Shigematsu, Geetha R. Menon, Leslie Newcombe, Kathleen Strong, Qiqi Wang, Prabhat Jha

Bibliographic record

VenueThe Lancet · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMaternal and Neonatal Healthcare
Canadian institutionsUniversity of TorontoCentre for Global Health ResearchSt. Michael's Hospital
FundersWorld Health Organization
KeywordsDemographyChinaMedicineCause of deathMortality rateCommunicable diseaseChild mortalityDiseaseEnvironmental healthGeographyPublic healthPopulationSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: With global survival increasing for children younger than 5 years of age, attention is required to reduce the approximately 1 million deaths of children aged 5-14 years occurring every year. Causes of death at these ages remain poorly documented. We aimed to explore trends in mortality by causes of death in India, China, Brazil, and Mexico, which are home to about 40% of the world's children aged 5-14 years and experience more than 200 000 deaths annually at these ages. METHODS: We examined data on 244 401 deaths in children aged 5-14 years from four nationally representative data sources that obtained direct distributions of causes of death: the Indian Million Death Study, the Chinese Disease Surveillance Points, mortality data from the Mexican Instituto Nacional de Estadística y Geografía, and mortality data from the Brazilian Institute of Geography and Statistics. We present data on 12 main disease groups in all countries, with breakdown by communicable and nutritional diseases, non-communicable diseases, injuries, and ill-defined causes. To calculate age-specific and sex-specific death rates for each cause, we applied the national cause of death distribution to the UN mortality envelopes for 2005-16 for each country. FINDINGS: Unlike Brazil, China, and Mexico, communicable diseases still account for nearly half of deaths in India in children aged 5-14 years (73 920 [46·1%] of 160 330 estimated deaths in 2016). In 2016, India had the highest death rates in nearly every category, including from communicable diseases. Fast declines among girls in communicable disease mortality narrowed the gap by 2016 with boys in India (32·6 deaths per 100 000 girls vs 26·2 per 100 000 boys) and China (1·7 vs 1·5). In China, injuries accounted for the greatest proportions of deaths (20 970 [53·2%] of 39 430 estimated deaths, in which drowning was a leading cause). The homicide death rate at ages 10-14 years was higher for boys than for girls in Brazil, increasing annually by an average of 0·7% (0·3-1·1). In India and China, the suicide death rates were higher for girls than for boys at ages 10-14 years. By contrast, in Mexico it was higher for boys than for girls, increasing annually by an average of 2·8% (2·0-3·6). Deaths from transport injuries, drowning, and cancer are common in all four countries, with transport accidents among the top three causes of death for both sexes in all countries, except for Indian girls, and cancer in the top three causes for both sexes in Mexico, Brazil, and China. INTERPRETATION: Most of the deaths that occurred between 2005 and 2016 in children aged 5-14 years in India, China, Brazil, and Mexico arose from preventable or treatable conditions. This age group is important for extending some of the global disease-specific targets developed for children younger than 5 years of age. Interventions to control non-communicable diseases and injuries and to strengthen cause of death reporting systems are also required. FUNDING: WHO and the University of Toronto Connaught Global Challenge.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
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 head0.094
GPT teacher head0.459
Teacher spread0.365 · 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

Citations95
Published2019
Admission routes2
Has abstractyes

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