Season of Data Collection of Child Dietary Diversity Indicators May Affect Conclusions About Longer-Term Trends in Peru, Senegal, and Nepal

Background: The WHO-UNICEF minimum dietary diversity (MDD) indicator for children aged 6-23 mo is a global monitoring indicator used to track multi-year population-level changes in dietary quality, but the influence of seasonality on MDD estimates remains unclear. Objectives: To examine how seasonality of data collection may influence population-level MDD estimates and inferences about MDD changes over multiple survey years. Methods: We selected countries with 3 or more consecutive years of MDD data collection, including continuous national Demographic Health Surveys in Senegal (2012-2017; n = 12,183) and Peru (2005-2016; n = 35,272) and the Policy and Science for Health, Agriculture, and Nutrition sentinel site seasonal surveys (covering 3 seasons/y) in Nepal (2013-2016; n = 1309). The MDD prevalence (>= 5 of 8 food groups) and an 8-item continuous Food Group Score (FGS) and 95% CIs were estimated by month and compared for lean and non-lean seasons using ordinary least squares regression with dummy variables for year. Results: The national prevalence of MDD was higher in Peru (75.4%) than in Nepal (39.1%) or in Senegal (15.7%). Children in Peru were 1.8% (coefficient, -0.0179; 95% CI, -0.033 to -0.002) less likely to achieve MDD during the lean season. Similar seasonal magnitudes were observed in Senegal (coefficient, -0.0347; 95% CI, -0.058 to -0.011) and Nepal (coefficient, -0.0133; 95% CI, -0.107 to 0.081). The FGS was about 0.1 item lower during the lean season in all 3 countries. In comparison, MDD increased by an average rate of only 4.2 and 4.4 percentage points per 5 y in Peru and Senegal, respectively. Intakes of specific food groups were stable across months in all countries, with the provitamin A-rich food group exhibiting the most seasonality. Conclusions: The magnitude of seasonal variation in MDD prevalence was smaller than expected but large relative to longer-term changes. If large-scale surveys are not conducted in the same season, biased conclusions about trends are possible.

Automated summary

This study examines how seasonality of data collection may influence population-level MDD estimates, finding varying seasonal prevalence rates across different countries, with seasonality having a significant impact on MDD. Long-term trends are minimally affected by seasonal changes.