Overweight and obesity among women: Analysis of demographic and health survey data from 32 Sub-Saharan African Countries

BMC Public Health, Volume 16, No. 1, Year 2016

Interpretation

Background: Overweight and obesity are risk factors for many chronic diseases globally. However, the extent of the problem in low-income countries like Sub-Saharan Africa is unclear. We assessed the magnitude and disparity of both phenomena by place of residence, level of education and wealth quintile using cross-sectional data from 32 countries. Methods: Demographic and Health Survey (DHS) data collected in 32 Sub-Saharan African countries between January 2005 and December 2013 were used. A total of 250651 women (aged 15-49 years) were analyzed. Trained personnel using a standardized procedure measured body weight and height. Body mass index (BMI) was calculated by dividing body weight by height squared. Prevalence of overweight (25.0-29.9 kg/m2) and obesity (≥30.0 kg/m2) were estimated for each country. Analysis of the relationships of overweight and obesity with place of residence, education and wealth index were carried out using logistic regression. Results: The pooled prevalence of overweight for the region was 15.9 % (95 % CI, 15.7-16.0) with the lowest in Madagascar 5.6 % (95 % CI, 5.1-6.1) and the highest in Swaziland 27.7 % (95 % CI, 26.4-29.0). Similarly, the prevalence of obesity was also lowest in Madagascar 1.1 % (95 % CI, 0.9-1.4) and highest in Swaziland 23.0 (95 % CI, 21.8-24.2). The women in urban residence and those who were classified as rich, with respect to the quintile of the wealth index, had higher likelihood of overweight and obesity. In the pooled results, high education was significantly associated with overweight and obesity. Conclusions: The prevalence of overweight and obesity varied highly between the countries and wealth index (rich vs. poor) was found to be the strongest predictor in most of the countries. Interventions that will address the socio-cultural barriers to maintaining healthy body size can contribute to curbing the overweight and obesity epidemic in Africa. Thirty-two nationally representative cross-sectional data from the most recent Demographic and Health survey (DHS) conducted between January 1, 2005, and December 31, 2013 in Sub-Saharan Africa were used. The DHS survey data were collected at about 5-year intervals across low and middle-income countries. DHS collect data on health and welfare by interviewing women of reproductive age (15–49 years), their children, and their households. In this analysis only women who had information on age and height were included. DHS surveys are available to investigators through the World Wide Web (http://www.dhsprogram.com). All 32 countries, DHS survey followed the same standard procedures. Detail descriptions of DHS sampling procedures, validation of questionnaire, and data collection methods are published elsewhere (http://www.dhsprogram.com). Briefly, the DHS used a stratified two-stage random sampling approach, consisting of a selection of census enumeration areas based on a probability, followed by a random selection of household from a complete listing of a household within the selected enumeration areas. In this study, all together 366885 women from 32 countries responded to the surveys with the response rates varying from 86.2 to 100.0 %. However, the present analysis is based on all women who had information on weight and height (N = 250651). Women granted written informed consent before interviewing them. Ethical approval was given by ICF International (Calverton, MD, USA) institutional review board and by individual review boards within every participating country. In all DHS survey, trained personnel measured the height and weight using a standardized procedure. Weight was measured using solar-powered scales with accuracy of 0.1 kg and height was measured using standardized measuring boards with accuracy to 0.1 cm. Body mass index (BMI) was calculated by dividing body weight (kg) by squared height (m2). Overweight and obesity were defined as recommended by World Health Organization [5]: overweight 25.0–29.9 kg/m2 and obesity ≥30.0 kg/m2. As the prevalence of obesity was low (<1 %) in some countries, the categories of overweight and obesity were combined together in the logistic regression analyses. Only the respondents who had information on BMI were included in the analyses. The participants’ place of residence was designated as rural and urban according to country specific definitions. The wealth index was calculated using easy-to-collect data on a household’s ownership of selected assets (e.g. televisions, bicycles, cars, materials used for housing construction and types of water access and sanitation facilities). The wealth index was then generated as a composite variable by demographic and health survey (DHS) staff using principal components analysis. Continuous scale of relative wealth was then categorized into five (poorest, poorer, middle, richer, and richest) according to the quintile of the sample. Wealth index was not available from the DHS data from Chad. Maternal education was assessed from self-report of the completed educational level (no education, primary, secondary, or higher). Sample weights were applied to the data to remove the bias due to unequal selection probabilities. Descriptive figures of the study participants are reported in percentages and the prevalence of overweight and obesity with their 95 % confidence intervals (CIs) are reported separately for each country. The pooled prevalence for the region was also estimated. Scatterplots were used to visualize the relationship between no education and overweight and obesity with the size of a marker relative to the number of outcome events to reflect their influence on the correlation. Analyses of the relationships between overweight and obesity and place of residence, education and wealth index were carried out. Odds ratios (ORs) and their 95 % confidence intervals (CIs) for overweight and obesity (<25.0 = 0, ≥ 25.0 = 1) were estimated in multivariate logistic regressions model including the place of residence (urban = 0, rural =1), maternal education (secondary or higher =0, no or primary education =1) and wealth index (richer or richest =0, poorest to middle =1). All the analyses were performed using the SPSS statistical software package version 21 and Stata version 13.

AI Digest

Study Justification:

– Overweight and obesity are risk factors for chronic diseases globally.
– The extent of overweight and obesity in low-income countries like Sub-Saharan Africa is unclear.
– This study aims to assess the magnitude and disparity of overweight and obesity in Sub-Saharan Africa by place of residence, level of education, and wealth quintile.

Highlights:

– The study analyzed data from 32 Sub-Saharan African countries.
– Prevalence of overweight and obesity were estimated for each country.
– The study found that the prevalence of overweight and obesity varied greatly between countries.
– Urban residence and higher wealth index were associated with higher likelihood of overweight and obesity.
– High education was also significantly associated with overweight and obesity.

Recommendations:

– Interventions should be implemented to address socio-cultural barriers to maintaining a healthy body size.
– Policies should focus on reducing the prevalence of overweight and obesity, especially among urban residents and those with higher wealth index.
– Education programs should be developed to promote healthy lifestyles and raise awareness about the risks of overweight and obesity.

Key Role Players:

– Government health departments
– Non-governmental organizations (NGOs) working in public health
– Community leaders and influencers
– Healthcare professionals and organizations
– Education institutions

Cost Items for Planning Recommendations:

– Development and implementation of educational programs
– Training and capacity building for healthcare professionals
– Awareness campaigns and communication materials
– Research and data collection on overweight and obesity prevalence
– Monitoring and evaluation of interventions
– Infrastructure and equipment for healthcare facilities
– Collaboration and coordination between stakeholders

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study used nationally representative cross-sectional data from 32 Sub-Saharan African countries, which increases the generalizability of the findings. The study also followed standardized procedures for data collection and analysis. However, the abstract could be improved by providing more details about the sample size and the specific statistical methods used. Additionally, it would be helpful to include information about potential limitations of the study, such as any biases or confounding factors that may have influenced the results. Overall, the evidence is strong, but providing more information and addressing potential limitations would further enhance the quality of the abstract.

Abstract

Thirty-two nationally representative cross-sectional data from the most recent Demographic and Health survey (DHS) conducted between January 1, 2005, and December 31, 2013 in Sub-Saharan Africa were used. The DHS survey data were collected at about 5-year intervals across low and middle-income countries. DHS collect data on health and welfare by interviewing women of reproductive age (15–49 years), their children, and their households. In this analysis only women who had information on age and height were included. DHS surveys are available to investigators through the World Wide Web (http://www.dhsprogram.com). All 32 countries, DHS survey followed the same standard procedures. Detail descriptions of DHS sampling procedures, validation of questionnaire, and data collection methods are published elsewhere (http://www.dhsprogram.com). Briefly, the DHS used a stratified two-stage random sampling approach, consisting of a selection of census enumeration areas based on a probability, followed by a random selection of household from a complete listing of a household within the selected enumeration areas. In this study, all together 366885 women from 32 countries responded to the surveys with the response rates varying from 86.2 to 100.0 %. However, the present analysis is based on all women who had information on weight and height (N = 250651). Women granted written informed consent before interviewing them. Ethical approval was given by ICF International (Calverton, MD, USA) institutional review board and by individual review boards within every participating country. In all DHS survey, trained personnel measured the height and weight using a standardized procedure. Weight was measured using solar-powered scales with accuracy of 0.1 kg and height was measured using standardized measuring boards with accuracy to 0.1 cm. Body mass index (BMI) was calculated by dividing body weight (kg) by squared height (m2). Overweight and obesity were defined as recommended by World Health Organization [5]: overweight 25.0–29.9 kg/m2 and obesity ≥30.0 kg/m2. As the prevalence of obesity was low (<1 %) in some countries, the categories of overweight and obesity were combined together in the logistic regression analyses. Only the respondents who had information on BMI were included in the analyses. The participants’ place of residence was designated as rural and urban according to country specific definitions. The wealth index was calculated using easy-to-collect data on a household’s ownership of selected assets (e.g. televisions, bicycles, cars, materials used for housing construction and types of water access and sanitation facilities). The wealth index was then generated as a composite variable by demographic and health survey (DHS) staff using principal components analysis. Continuous scale of relative wealth was then categorized into five (poorest, poorer, middle, richer, and richest) according to the quintile of the sample. Wealth index was not available from the DHS data from Chad. Maternal education was assessed from self-report of the completed educational level (no education, primary, secondary, or higher). Sample weights were applied to the data to remove the bias due to unequal selection probabilities. Descriptive figures of the study participants are reported in percentages and the prevalence of overweight and obesity with their 95 % confidence intervals (CIs) are reported separately for each country. The pooled prevalence for the region was also estimated. Scatterplots were used to visualize the relationship between no education and overweight and obesity with the size of a marker relative to the number of outcome events to reflect their influence on the correlation. Analyses of the relationships between overweight and obesity and place of residence, education and wealth index were carried out. Odds ratios (ORs) and their 95 % confidence intervals (CIs) for overweight and obesity (<25.0 = 0, ≥ 25.0 = 1) were estimated in multivariate logistic regressions model including the place of residence (urban = 0, rural =1), maternal education (secondary or higher =0, no or primary education =1) and wealth index (richer or richest =0, poorest to middle =1). All the analyses were performed using the SPSS statistical software package version 21 and Stata version 13.

Materials

N/A

Innovations

Based on the provided information, here are some potential innovations that can be used to improve access to maternal health:

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide information and resources related to maternal health, including nutrition, prenatal care, and postpartum care. These apps can be easily accessed by women in Sub-Saharan Africa, even in remote areas, to improve their knowledge and access to healthcare services.

2. Telemedicine: Implement telemedicine programs that allow pregnant women to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide access to specialized care for high-risk pregnancies.

3. Community Health Workers: Train and deploy community health workers who can provide basic maternal health services, education, and support to women in their communities. These workers can play a crucial role in improving access to prenatal and postnatal care, as well as promoting healthy behaviors.

4. Maternal Health Vouchers: Introduce voucher programs that provide financial assistance to pregnant women, enabling them to access essential maternal health services, such as antenatal care, skilled birth attendance, and postpartum care.

5. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare providers to expand service delivery, improving infrastructure, and ensuring the availability of essential medical supplies and equipment.

6. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of maternal health and encourage women to seek timely and appropriate care. These campaigns can utilize various media channels, including radio, television, and community outreach programs.

7. Maternal Health Monitoring Systems: Develop and implement robust monitoring systems to track maternal health indicators and identify areas of improvement. This data can inform evidence-based decision-making and resource allocation to address gaps in access to maternal health services.

It is important to note that the specific implementation of these innovations should be tailored to the local context and needs of each country in Sub-Saharan Africa.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health based on the provided information is to implement interventions that address the socio-cultural barriers to maintaining a healthy body size in Sub-Saharan Africa. This can help in curbing the overweight and obesity epidemic in the region.

Specifically, the innovation could focus on:

1. Education and awareness: Develop educational programs and campaigns that promote healthy eating habits, physical activity, and the importance of maintaining a healthy body size during pregnancy. These programs should target women, families, and communities, and provide culturally appropriate information.

2. Nutritional support: Provide access to affordable and nutritious food options, especially in low-income areas. This can be done through initiatives such as community gardens, subsidized healthy food programs, and partnerships with local farmers and markets.

3. Healthcare infrastructure: Improve the availability and accessibility of healthcare facilities that provide maternal health services. This includes increasing the number of healthcare providers, improving transportation options, and ensuring that facilities are equipped to handle the specific needs of pregnant women.

4. Empowerment and support: Implement programs that empower women to make informed decisions about their health and well-being. This can include providing counseling services, support groups, and resources for women to access prenatal care and other maternal health services.

5. Policy and advocacy: Advocate for policies that prioritize maternal health and address the social determinants of health, such as poverty and education. This can include advocating for increased funding for maternal health programs, implementing regulations on food marketing and labeling, and promoting policies that support breastfeeding and healthy lifestyles.

By implementing these recommendations, it is possible to improve access to maternal health and reduce the prevalence of overweight and obesity among women in Sub-Saharan Africa.

AI Innovations Methodology

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

1. Increase awareness and education: Implement comprehensive health education programs that focus on the importance of maternal health, including the risks associated with overweight and obesity. This can be done through community outreach programs, workshops, and educational campaigns.

2. Improve healthcare infrastructure: Invest in improving healthcare facilities and infrastructure in low-income countries, particularly in rural areas where access to maternal health services may be limited. This includes ensuring the availability of skilled healthcare professionals, necessary medical equipment, and adequate transportation for pregnant women.

3. Strengthen antenatal care services: Enhance antenatal care services by providing regular check-ups, nutritional counseling, and weight management support for pregnant women. This can help identify and address overweight and obesity issues early on, leading to better maternal health outcomes.

4. Promote healthy lifestyle behaviors: Encourage pregnant women to adopt healthy lifestyle behaviors, such as regular physical activity and balanced diets, to prevent excessive weight gain during pregnancy. This can be achieved through targeted interventions, such as nutrition education programs and exercise classes specifically designed for pregnant women.

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

1. Define the target population: Identify the specific population group that will be the focus of the simulation, such as pregnant women in a particular region or country.

2. Collect baseline data: Gather relevant data on the current status of maternal health access, including information on overweight and obesity prevalence, healthcare infrastructure, and utilization of maternal health services.

3. Develop a simulation model: Create a mathematical or computational model that simulates the impact of the recommended interventions on improving access to maternal health. This model should take into account factors such as population size, demographic characteristics, healthcare resources, and the effectiveness of the interventions.

4. Input intervention parameters: Specify the parameters of the recommended interventions, such as the coverage and duration of health education programs, the level of investment in healthcare infrastructure, and the frequency and content of antenatal care services.

5. Run the simulation: Use the simulation model to generate projections of the potential impact of the interventions on improving access to maternal health. This can include estimates of changes in overweight and obesity prevalence, utilization of maternal health services, and maternal health outcomes.

6. Analyze the results: Evaluate the simulation results to assess the effectiveness and feasibility of the recommended interventions. This can involve comparing the projected outcomes with the baseline data and identifying any potential barriers or limitations to implementation.

7. Refine and iterate: Based on the analysis of the simulation results, refine the intervention parameters and run additional simulations to further optimize the recommendations for improving access to maternal health.

By following this methodology, policymakers and healthcare professionals can gain insights into the potential impact of different interventions and make informed decisions on how to allocate resources and implement strategies to improve access to maternal health.

Author

Dima Health

Statistics:

Citations: 89

Identifiers:

DOI: 10.1186/s12889-016-2698-5

Research Areas:

Community Interventions, Environmental, Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Sexual and Reproductive Health, Social Determinants

Study Countries:

Chad, Eswatini, Madagascar, Multi-Countries

Study Design:

Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female