How Does Survey Timing Influence Apparent Wasting Trends? A Case Study from Senegal

Child wasting is known to exhibit seasonal patterns, but few studies have examined how the seasonality of wasting affects tracking of wasting trends over multiyear periods.

We explored the seasonality of wasting in Senegal relative to multiyear changes and examined implications for tracking. We tested whether month-fixed effects reduced bias in estimating longer-term wasting trends given variation in survey timing.

The average prevalence of child wasting (weight-for-height z-score < -2) and 95% confidence intervals were calculated by month and year from the continuous Demographic and Health Surveys (DHS) from 2012-2019. Peak and low wasting season estimates were defined as the 4 highest and the 4 lowest months of average wasting prevalence. Month-adjusted annual wasting estimates were generated using month-fixed effects linear regression, and the effectiveness of this method of bias adjustment was examined in simulated datasets.

Nationally, wasting fluctuated from 2013-2019, with the lowest annual prevalence of 6.0% (95% confidence interval [CI]: 5.1, 7.0%) recorded in 2014 and the highest in 2017 at 9.0% (95% CI: 8.3, 9.8%). Pooled across years, the peak wasting season prevalence was 8.8% (95% CI: 8.3, 9.3%), and low wasting season prevalence was 6.4% (95% CI: 5.7, 7.1%). Month-adjusted wasting estimates did not differ notably from raw wasting prevalence estimates. Simulations demonstrated that adjusting for months reduces bias in wasting when surveys are conducted 1 or 2 mo apart across waves but fails to reliably do so when surveys are conducted in different seasons across waves.

Seasonal fluctuations in the prevalence of wasting can be large enough to bias the interpretation of multiyear trends. Efforts should be made to conduct national surveys at the same time of year. Seasonality adjustment using month-fixed effects works more reliably when the differences in survey periods across waves are minimal.