Journal of Infection and Public Health (May 2024)
Impact of the COVID-19 pandemic on child malnutrition in Selangor, Malaysia: A pilot study
Abstract
Background: Child malnutrition risk factors are globally recognized, but the specific impact of the COVID-19 pandemic on the prevalence of child malnutrition, considering socioeconomic burdens and changes in family lifestyles, remains underexplored. This study aims to identify the significance of COVID-19-related factors in relation to the prevalence of child malnutrition in Selangor, Malaysia. Methods: Purposive sampling was employed in this pilot study to select the households with under-5 children and, a structured questionnaire was developed to gather data. Chi-squared tests, logistic regression modelling and World Health Organisation AnthroPlus software-based visualization were used for analyses. Results: The present study’s findings indicate that demographic and social factors, including 'Citizenship,' 'Type of House,' 'Number of Earning Members,' 'Father's Highest Educational Level,' and 'Number of Children in a Family,' have a statistically significant association with Wasting. Additionally, the mother's 'Highest Educational Level' is found to be linked to underweight prevalence. Within COVID-19 factors, ''COVID-19 Impact on Employment/Business'' demonstrated significance for both stunting and wasting. Multivariate analysis revealed disparities in childhood malnutrition by gender, age, and factors such as ''COVID-19 impact on children's physical activity'' and ''COVID-19 impact on children's decrease in health over the last two weeks.'' Conclusions: This study identified COVID-19 factors alongside sociodemographic variables with statistically significant relationships impacting childhood malnutrition in Selangor, Malaysia. The results underscored the substantial influence of the COVID-19 pandemic on child malnutrition prevalence. Decision-makers at family and community levels can benefit by considering these factors in their actions. However, the study's limitation lay in its dataset, urging larger-scale analyses to explore further sub-categories of the examined variables.