Objective This study aimed to assess the prevalence and determinants of common childhood illnesses in sub-Saharan Africa. Design Cross-sectional study. Setting Sub-Saharan Africa. Participants Under-5 children. Primary outcome Common childhood illnesses. Methods Secondary data analysis was conducted using data from recent Demographic and Health Survey datasets from 33 sub-Saharan African countries. We used the Kids Record dataset file and we included only children under the age of 5 years. A total weighted sample size of 208 415 from the pooled (appended) data was analysed. STATA V.14.2 software was used to clean, recode and analyse the data. A multilevel binary logistic regression model was fitted, and adjusted OR with a 95% CI and p value of ≤0.05 were used to declare significantly associated factors. To check model fitness and model comparison, intracluster correlation coefficient, median OR, proportional change in variance and deviance (-2 log-likelihood ratio) were used. Result In this study, the prevalence of common childhood illnesses among under-5 children was 50.71% (95% CI: 44.18% to 57.24%) with a large variation between countries which ranged from Sierra Leone (23.26%) to Chad (87.24%). In the multilevel analysis, rural residents, mothers who are currently breast feeding, educated mothers, substandard floor material, high community women education and high community poverty were positively associated with common childhood illnesses in the sub-Saharan African countries. On the other hand, children from older age mothers, children from the richest household and children from large family sizes, and having media access, electricity, a refrigerator and improved toilets were negatively associated. Conclusions The prevalence of common illnesses among under-5 children was relatively high in sub-Saharan African countries. Individual-level and community-level factors were associated with the problem. Improving housing conditions, interventions to improve toilets and strengthening the economic status of the family and the communities are recommended to reduce common childhood diseases.
We used the Demographic and Health Survey (DHS) data which were collected using a cross-sectional study design. The DHS is a nationwide survey that collects data on maternal and child health, fertility, reproductive health, nutrition and adult self-reported health behaviours. In this study, we included 33 SSA countries which have recent DHS data. Therefore, the current study was based on DHS data which were conducted between 2010 and 2020 in SSA countries (figure 1). Study setting of common childhood illnesses. DHS, Demographic and Health Survey; ES; Estimate. The data for this study were drawn from recent nationally representative DHS data conducted in 33 countries in SSA. The DHS is routinely collected every 5 years across low/middle-income countries using structured, pretested and validated tools. These datasets were appended together to investigate the prevalence of common childhood illnesses and associated factors among under-5 children in SSA. The DHS employs a stratified two-stage sampling technique in each country. In the first stage, Enumeration Areas were randomly selected, while in the second stage households were selected by systematic sampling. We used the Kids Record (KR) dataset file and we included only children under age 5 years with at least one of the three diseases (ARI, diarrhoea, fever) at any time in the 2 weeks preceding each survey. Therefore, the total weighted sample size from the pooled (appended) data analysed in this study was 208 415. The dependent variable in this study was common childhood illnesses among under-5 children. The DHS has recorded common childhood diseases such as ARI, diarrhoea and fever in its survey. In this study, the child had an illness when he/she encountered at least one of the three childhood illnesses (ARI, diarrhoea, fever) and categorised as ‘yes’, while those who had none of them were categorised as ‘no’. For the ith children, the dependent variable was represented by a random variable Yi, with two possible values coded as 1 and 0. Therefore, Yi=1 if the child had at least one of the illnesses (ARI, diarrhoea, fever) while Yi=0 if the child had none of the three illnesses. Major explanatory variables were considered on two levels. Individual-level variables included maternal and child characteristics as well as household characteristics. Those factors were maternal age, maternal education, marital status, currently breast feeding, wealth index, family size, media access, household had electricity, household had a refrigerator, source of drinking water, type of toilet facility and floor material. On the other hand, place of residence (urban, rural), community poverty level (low, high), community literacy level (low, high) and community media exposure (low, high) were considered as the community-level factors. To generate community-level variables (community media exposure, community poverty and community women’s education), we did an aggregation of individual-level variables at the cluster level and categorised them as higher or lower based on a median value. Residence, which is a direct community-level variable, was used without any manipulation. Media exposure was generated from women’s responses to the questions related to the frequency of listening to the radio, watching television and reading newspapers in a week. It is categorised as ‘yes’ if women had exposure to at least one type of media: radio, newspaper or television, and ‘no’ otherwise. The wealth index was categorised into three: ‘poor’ (poorer and poorest), ‘middle’ and ‘rich’ (richer and richest). The type of toilet facility was categorised as improved and unimproved according to DHS.26 Community-level women’s education refers to the proportion of women in the community who have formal education (primary and above). It was categorised as low if communities in which <50% of respondents had formal education and high if ≥50% of respondents had attended formal education. Community-level poverty refers to the proportion of women in the community who had low wealth quintiles (poorest and poorer). It was categorised as low if the proportion of low wealth quintile households was <50% and high if the proportion was ≥50%. Community-level media usage is the proportion of women in the community who use radio, TV and newspaper, and it was categorised as low community-level media usage and high community-level media usage. ‘Low’ refers to communities in which <50% of respondents had media access while ‘high’ indicates communities in which ≥50% of respondents had media access. We extracted datasets from 33 SSA countries’ KR data files and appended them to generate pooled data. STATA V.14.2 was used to clean, recode and analyse the data. A multilevel binary logistic regression model was fitted to identify significantly associated factors with common childhood illnesses. Four models were constructed which comprised the null model (model 0) without any explanatory variables, model I with individual independent variables only, model II with community-level factors only, and model III with both individual-level and community-level variables. Since the models were nested, comparison was made using deviance (−2 log-likelihood) and model III was the best. Intracluster correlation coefficient (ICC), median OR (MOR) and proportional change in variance (PCV) were applied to measure the degree of heterogeneity and variation between clusters. All variables with a p value of ≤0.2 in the bivariable analysis were fitted in the multivariable model. Adjusted OR (AOR) with 95% CI and p<0.05 were presented to reveal significantly associated factors. The multicollinearity test was carried out using the variance inflation factor (VIF), and multicollinearity was not found because all variables have VIF <5. As our study used secondary analysis of DHS data, participants and the public were not involved in the study design or planning of the study. The study participants were not consulted to interpret the results and write or edit this document for readability or accuracy.