Prevalence and associated factors of common childhood illnesses in sub-Saharan Africa from 2010 to 2020: A cross-sectional study

BMJ Open, Volume 12, No. 11, Year 2022

Interpretation

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.

AI Digest

Study Justification:

– The study aimed to assess the prevalence and determinants of common childhood illnesses in sub-Saharan Africa.
– This is important because childhood illnesses have a significant impact on child health and well-being, and understanding the factors associated with these illnesses can inform interventions and policies to reduce their prevalence.

Study Highlights:

– The prevalence of common childhood illnesses among under-5 children in sub-Saharan Africa was found to be 50.71%.
– There was a large variation in prevalence between countries, ranging from 23.26% in Sierra Leone to 87.24% in Chad.
– Factors positively associated with common childhood illnesses included living in rural areas, mothers who are currently breastfeeding, low maternal education, substandard floor material, high community women education, and high community poverty.
– Factors negatively associated with common childhood illnesses included children from older age mothers, children from the richest households, children from large family sizes, and having media access, electricity, a refrigerator, and improved toilets.

Recommendations for Lay Reader:

– The prevalence of common childhood illnesses is relatively high in sub-Saharan Africa.
– Improving housing conditions, interventions to improve toilets, and strengthening the economic status of families and communities are recommended to reduce common childhood diseases.

Recommendations for Policy Maker:

– Policies and interventions should focus on improving housing conditions, particularly addressing substandard floor materials and improving access to improved toilets.
– Efforts should be made to increase maternal education and reduce community poverty, as these factors were found to be associated with common childhood illnesses.
– Programs should also target older age mothers, children from the richest households, and children from large family sizes to reduce the prevalence of common childhood illnesses.
– Access to media, electricity, and refrigerators should be promoted as they were found to be negatively associated with common childhood illnesses.

Key Role Players:

– Health ministries and departments in sub-Saharan African countries
– Non-governmental organizations (NGOs) working in child health and development
– Community health workers and volunteers
– Education ministries and departments
– Poverty alleviation programs and organizations

Cost Items for Planning Recommendations:

– Housing improvement programs (e.g., flooring materials, toilet facilities)
– Education programs and initiatives
– Poverty alleviation programs and interventions
– Health promotion and awareness campaigns
– Training and capacity building for health workers and volunteers
– Monitoring and evaluation systems for tracking progress and impact

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is based on a cross-sectional study design using secondary data analysis from recent Demographic and Health Survey datasets. The study included a large sample size of 208,415 under-5 children from 33 sub-Saharan African countries. The prevalence of common childhood illnesses was assessed, and associated factors were identified using multilevel binary logistic regression. The study provides adjusted odds ratios with 95% confidence intervals and p-values to declare significantly associated factors. The study design and analysis methods are appropriate for the research question. However, to improve the evidence, it would be beneficial to include information on the representativeness of the sample and any potential limitations of the study, such as data quality or potential confounding factors. Additionally, providing more details on the data collection process and survey tools used in the Demographic and Health Survey would enhance the transparency and replicability of the study.

Abstract

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.

Materials

Innovations

Based on the information provided, it seems that the study focused on assessing the prevalence and determinants of common childhood illnesses in sub-Saharan Africa. The study used secondary data analysis from recent Demographic and Health Survey datasets from 33 sub-Saharan African countries. The study identified several factors associated with common childhood illnesses, including maternal and child characteristics, household characteristics, and community-level factors.

To improve access to maternal health, here are some potential recommendations based on the findings of the study:

1. Strengthen maternal education: The study found that educated mothers were associated with a lower prevalence of common childhood illnesses. Promoting and providing access to education for women can empower them to make informed decisions about their health and the health of their children.

2. Improve housing conditions: The study found that substandard floor material was positively associated with common childhood illnesses. Improving housing conditions, such as ensuring clean and safe living environments, can help reduce the risk of illness for both mothers and children.

3. Enhance community-level factors: The study identified community poverty and low community women’s education as factors positively associated with common childhood illnesses. Implementing interventions to address poverty and improve women’s education at the community level can have a positive impact on maternal health and the health of children.

4. Increase access to healthcare services: Improving access to healthcare services, including prenatal care, skilled birth attendance, and postnatal care, can contribute to better maternal health outcomes. This can be achieved through the establishment of health facilities, training of healthcare providers, and implementing strategies to overcome barriers to accessing healthcare, such as geographical distance and financial constraints.

5. Promote health education and awareness: Increasing health education and awareness among mothers and communities can help prevent common childhood illnesses. This can include providing information on proper hygiene practices, nutrition, immunizations, and early recognition of illness symptoms.

It is important to note that these recommendations are based on the findings of the study and may need to be tailored to specific contexts and resources available in each sub-Saharan African country.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health based on the study findings would be to focus on the following interventions:

1. Improve housing conditions: The study found that substandard floor material was positively associated with common childhood illnesses. Therefore, efforts should be made to improve housing conditions, particularly the quality of floors, to reduce the risk of illness.

2. Enhance toilet facilities: The study found that improved toilets were negatively associated with common childhood illnesses. Therefore, it is recommended to invest in improving toilet facilities, ensuring proper sanitation and hygiene practices, which can contribute to reducing the prevalence of illnesses.

3. Strengthen economic status: The study found that high community poverty was positively associated with common childhood illnesses. To address this, interventions should focus on strengthening the economic status of families and communities, such as providing income-generating opportunities and social support programs.

4. Promote education: The study found that high community women’s education was positively associated with common childhood illnesses. Therefore, efforts should be made to improve access to education, particularly for women, as it can have a positive impact on maternal and child health outcomes.

By implementing these recommendations, it is expected that access to maternal health will be improved, leading to a reduction in common childhood illnesses in sub-Saharan Africa.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals in sub-Saharan Africa can help improve access to maternal health services. This includes establishing well-equipped maternity clinics and hospitals, ensuring availability of essential medicines and supplies, and training healthcare providers in maternal healthcare.

2. Increasing awareness and education: Implementing comprehensive maternal health education programs can help raise awareness about the importance of prenatal care, safe delivery practices, and postnatal care. This can be done through community outreach programs, workshops, and campaigns targeting both women and men.

3. Improving transportation and logistics: Enhancing transportation systems and logistics can help overcome geographical barriers and ensure timely access to maternal health services. This can involve providing ambulances or transportation vouchers for pregnant women in remote areas, improving road infrastructure, and establishing referral systems between healthcare facilities.

4. Empowering women and communities: Promoting women’s empowerment and involving communities in decision-making processes can contribute to better access to maternal health services. This can be achieved through initiatives that promote gender equality, women’s rights, and community engagement in healthcare planning and implementation.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the indicators: Identify specific indicators that reflect access to maternal health, such as the number of antenatal care visits, percentage of skilled birth attendance, or maternal mortality rate.

2. Collect baseline data: Gather existing data on the selected indicators before implementing the recommendations. This can be obtained from national health surveys, health facility records, or other relevant sources.

3. Implement the recommendations: Introduce the recommended interventions or innovations to improve access to maternal health. This could involve implementing the strategies mentioned earlier, such as strengthening healthcare infrastructure, increasing awareness and education, improving transportation, and empowering women and communities.

4. Monitor and collect data: Continuously monitor the implementation of the recommendations and collect data on the selected indicators. This can be done through routine data collection systems, surveys, or targeted studies.

5. Analyze the data: Analyze the collected data to assess the impact of the recommendations on the selected indicators. This can involve comparing the baseline data with the post-intervention data to determine any changes or improvements.

6. Evaluate the results: Evaluate the results of the analysis to determine the effectiveness of the recommendations in improving access to maternal health. This can include assessing the magnitude of change in the selected indicators and identifying any challenges or barriers that may have influenced the outcomes.

7. Refine and adjust: Based on the evaluation results, refine and adjust the recommendations as needed. This may involve scaling up successful interventions, addressing identified challenges, or exploring additional strategies to further improve access to maternal health.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of the recommendations on improving access to maternal health and make informed decisions for future interventions.

Authors & Co-Authors

Statistics:

Authors: 6

Identifiers:

DOI: 10.1136/bmjopen-2022-065257

Research Areas:

Community Interventions, Environmental, Food Security, Genetics and Genomics, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Sexual and Reproductive Health, Social Determinants

Study Countries:

Chad, Multi-Countries, Sierra Leone

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female