Assessing the effect of drought severity on height-for-age z-score in Kenyan children: a secondary analysis
Bibliographic record
Abstract
Background Globally, droughts are occurring more regularly and are having negative effects on population health, particularly in countries such as Kenya, where agriculture is a primary driver of the economy and a source of subsistence for many communities. Children are particularly susceptible to weather-related shocks. Previous research has shown an association between drought and cross-sectional indicators of malnutrition, such as stunting. In this study, we explored various longitudinal and spatial analysis approaches to evaluating the effect of drought on height-for-age z-scores (HAZ) over time and space among young children in Kenya. Methods Using anthropometric data from three georeferenced Kenyan Demographic and Health Surveys (KDHS) and the self-calibrated Palmer Drought Severity Index, we developed multivariate linear regression and spatial lag and error models (with Moran's I calculations) to investigate the association between drought severity and HAZ in children aged 0–5 years. Initial covariates included age of the child, sex, maternal age, height and education, wealth index, urban or rural location, and size at birth. We then did multilevel and geographically weighted regression modelling using frequentist or Bayesian methods and with inclusion of household-level covariates, such as livelihood zones. To assess the effect of changes in drought severity on child HAZ over time, KDHS data from 2003, 2008–09, and 2014 were analysed with spatiotemporal modelling. Findings Preliminary results from the multivariate linear model showed a negative, non-significant association between drought severity and HAZ among Kenyan children in 2014 (β=0·033, p=0·101); however, there was a significant interaction between drought severity and age (β=–0·002, p<0·0007). The spatial lag model gave similar results. Other variables associated with HAZ included wealth index, age, sex, maternal education, and maternal height. Global Moran's I calculations indicated that there was a slight positive spatial autocorrelation across child HAZ (I=0·047, p<0·0001). Interpretation Increased drought severity was associated with a non-significant decrease in HAZ among Kenyan children. However, a significant interaction between age and drought severity indicates that the effect of drought on HAZ varies by age. Findings from this study will help to inform the development of methodological approaches for improving understanding of the effects of climate change on child health. Expanding these analyses to other east-African countries will also contribute to the development of national adaptation strategies and planning in anticipation of increased climate variability. Funding Canadian Institutes of Health Research (Institute of Public and Population Health).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".