The Complexity of the Seasonality of Nutritional Status: Two Annul Peaks in Child Wasting in Eastern Chad.

BACKGROUND

Understanding seasonal patterns in nutritional status is critical for achieving and tracking global nutrition goals. However, the majority of nutrition seasonality research design draws on 2 or 3 within-year time points based on existing assumptions of seasonality, missing a more nuanced pattern.

OBJECTIVE

We aimed to identify the intra-year variability of childhood wasting, severe wasting, and weight-for-height z-scores (WHZ) in a dryland single wet-season context and illustrate an analytical approach for improving analysis of the seasonality of nutritional status.

METHODS

To quantify the intra-year variability in nutritional status, we use data from a 23-month panel study (May 2018 to March 2020) following 231 children (6-59 months of age) in eastern Chad. We apply a mixed-effects harmonic regression with child- and village-level fixed effects on the odds of being wasted, severely wasted, and on WHZ, testing for multiple and nonsymmetrical seasonal peaks, adjusted for child sex and age. We triangulate our findings using climate data on temperature, vegetation, and precipitation.

RESULTS

We identify 2 annual peaks of wasting and severe wasting. Wasting peaks at 14.7% (confidence interval [CI], 11.8-18.2) at the end of the dry season, while the smaller peak corresponds to the start of the harvest period at 13.4% (CI, 10.7-16.6). The odds of being wasted decline during the rainy season to 11.8% (CI, 9.4-14.7), with the lowest prevalence of 8.8% (CI, 6.9-11.1) occurring during the start of the dry season. In addition, a 1°C monthly increase in temperature is significantly associated with a 5% (CI, 1.4-8.7) and 12% (CI, 3.0-20.3) increase in the odds that a child is wasted and severely wasted, respectively.

CONCLUSIONS

Intra-year variability of child wasting is far more complex and nuanced than identified by the literature, with 2 peaks, as opposed to 1, likely corresponding to different seasonal drivers, such as food insecurity, disease, water contamination, and care practices at different times of year. Better seasonality analysis can go a long way in improving the timing and content of programming with the goal of reducing child wasting.