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Record W4238792471 · doi:10.1371/journal.pmed.1002568

Distributional change of women’s adult height in low- and middle-income countries over the past half century: An observational study using cross-sectional survey data

2018· article· en· W4238792471 on OpenAlexaff
Jewel Gausman, Ivan Meija Guevara, S. V. Subramanian, Fahad Razak

Bibliographic record

VenuePLoS Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersNational Center for Advancing Translational SciencesNational Institutes of Health
KeywordsDemographyPercentilePopulationMedicineGeographyStatisticsMathematics

Abstract

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BACKGROUND: Adult height reflects childhood circumstances and is associated with health, longevity, and maternal-fetal outcomes. Mean height is an important population metric, and declines in height have occurred in several low- and middle-income countries, especially in Africa, over the last several decades. This study examines changes at the population level in the distribution of height over time across a broad range of low- and middle-income countries during the past half century. METHODS AND FINDINGS: The study population comprised 1,122,845 women aged 25-49 years from 59 countries with women's height measures available from four 10-year birth cohorts from 1950 to 1989 using data from the Demographic and Health Surveys (DHS) collected between 1993 and 2013. Multilevel regression models were used to examine the association between (1) mean height and standard deviation (SD) of height (a population-level measure of inequality) and (2) median height and the 5th and 95th percentiles of height. Mean-difference plots were used to conduct a graphical analysis of shifts in the distribution within countries over time. Overall, 26 countries experienced a significant increase, 26 experienced no significant change, and 7 experienced a significant decline in mean height between the first and last birth cohorts. Rwanda experienced the greatest loss in height (-1.4 cm, 95% CI: -1.84 cm, -0.96 cm) while Colombia experienced the greatest gain in height (2.6 cm, 95% CI: 2.36 cm, 2.84 cm). Between 1950 and 1989, 24 out of 59 countries experienced a significant change in the SD of women's height, with increased SD in 7 countries-all of which are located in sub-Saharan Africa. The distribution of women's height has not stayed constant across successive birth cohorts, and regression models suggest there is no evidence of a significant relationship between mean height and the SD of height (β = 0.015 cm, 95% CI: -0.032 cm, 0.061 cm), while there is evidence for a positive association between median height and the 5th percentile (β = 0.915 cm, 95% CI: 0.820 cm, 1.002 cm) and 95th percentile (β = 0.995 cm, 95% CI: 0.925 cm, 1.066 cm) of height. Benin experienced the largest relative expansion in the distribution of height. In Benin, the ratio of variance between the latest and earliest cohort is estimated as 1.5 (95% CI: 1.4, 1.6), while Lesotho and Uganda experienced the greatest relative contraction of the distribution, with the ratio of variance between the latest and earliest cohort estimated as 0.8 (95% CI: 0.7, 0.9) in both countries. Limitations of the study include the representativeness of DHS surveys over time, age-related height loss, and consistency in the measurement of height between surveys. CONCLUSIONS: The findings of this study indicate that the population-level distribution of women's height does not stay constant in relation to mean changes. Because using mean height as a summary population measure does not capture broader distributional changes, overreliance on the mean may lead investigators to underestimate disparities in the distribution of environmental and nutritional determinants of health.

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)0.006
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.282
GPT teacher head0.394
Teacher spread0.112 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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