Underweight, overweight and obesity among reproductive Bangladeshi women: A nationwide survey

Nutrients, Volume 13, No. 12, Year 2021

Interpretation

The double burden of malnutrition is becoming more prevalent among Bangladeshi women. Underweight, overweight, and obesity were examined among women aged 15–49 years using the 2017–2018 Bangladesh Demographic and Health Survey (BDHS). A dataset of 20,127 women aged 15–49 years with complete Body Mass Index (BMI) measurements were extracted and categorized as underweight, normal weight, overweight, and obesity. A multiple logistic regression that adjusts for clustering and sampling weights was used to examine underweight, overweight, and obesity among reproductive age Bangladeshi women. Our analyses revealed that the odds of being overweight and obese were higher among women who completed primary and secondary or more levels of education, rich households, breastfeeding women, and women exposed to media (newspapers and television (TV). Women from the poorest households were significantly more likely to be underweight (AOR = 3.86, 95%CI: 2.94–5.07) than women from richer households. The likelihood of being underweight was higher among women with no schooling, adolescent women, and women not using contraceptives. Conclusions: Overweight and obesity was higher among educated and affluent women while underweight was higher among women from low socioeconomic status, indicating that tailored messages to combat overweight and obesity should target educated and affluent Bangladeshi women while improving nutrition among women from low socioeconomic status. The 2017–2018 BDHS represents a national survey because it encompasses the whole population living in Bangladeshi non-institutional housing units. The BBS 2011 sampling framework used in the survey included enumeration areas (EAs) of the 2011 Population and Housing Census, which are provided by the Bangladesh Bureau of Statistics (BBS). The Primary Sampling Unit (PSU) (i.e., clusters) for the survey includes an EA of about 120 households on average. Each cluster was considered as a community based on previous studies [2,31]. Bangladesh is made up of eight divisions, including Barishal, Chattogram, Dhaka, Mymensingh, Khulna, Rajshahi, Rangpur, and Sylhet. There are zilas (districts) for each division, and each zila is subdivided further in Upazilas (sub-district). Each urban area of Upazila is split into wards, further divided into Mohallas, whereas each rural area in Upazila is separated into parishes of union (UPs). There are Mouzas in UPs, and all these divisions enable the division into rural and urban areas. Figure 1 presents the sampling procedure. The survey included a stratified two-stage sample of households: 675 EAs with probability proportions to the size of EA were selected in the first phase. In urban areas, there were 250 EAs, and in rural areas, 425 EAs. The sample was drawn by BBS in the first stage, in accordance with the specifications of the DHS team. The selection resulted in 20,250 residential households in accordance with this design. Approximately 20,100 married women aged 15–49 years are expected to complete interviews. The survey report contains details of the sample structure, including the sample framework and the sample implementations [5]. Sampling frame, BDHS2017–2018. Weight measurement using the lightweight scale SECA787 (with digital screens). Heights were measured using an adjustable wood measuring board, designed specifically to provide an accurate reading of 0.1 cm to take the developed countries into account. Through weight and height measurements, their BMI was calculated. The survey also included information on fertility, contraceptive use, maternal and child health, mother’s nutritional status, women’s empowerment, and sociodemographic characteristics. Data for 20,127 women 15 to 49 years of age who were not pregnant have been used following exclusion. The dependent variables were ordered as normal, underweight, overweight, and obesity which was based on the WHO classes for BMI: underweight (<18.50 kg/m2), normal (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obesity (≥30.0 kg/m2) (48). In order to ensure the quality, all weight/height were recorded, BMI has been continuously extended in conservative form. The independent variables were the individual-, household- and community-level factors identified in the conceptual framework. The individual-level variables included all relevant attributes of the respondents, including maternal work status, parents’ level of education, mother’s marital status, mother’s age, mother’s literacy status, access to health care services (autonomy to health care), access to the media (newspaper, radio and Television (TV)), and power over family income. Household-level variables consisted of the source of drinking water (improved or unimproved) and household wealth index into five categories [poorest, poorer, middle, richer, and richest]. In creating a wealth index [32], principal component analysis was used to estimate the index weights based on acquired information on various household assets, including ownership of different means of transport and other sustainable domestic goods. This index was divided into five categories, and one of five categories was allocated to each household. Variables at the community level included residence (urban/rural) and geographical area (Barishal, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, and Sylhet). All analyses were conducted using Stata version 14.1 (Stata Corp 2015, College Station, TX, USA). The command ‘Svy’ has been used for the adaptation of the cluster sampling design, weights, and the Taylor series linearized procedure was used to calculate the standard errors. The dependent variable was always expressed as binary, with number ‘1’ assigned as underweight, overweight, and obesity, while 0 was normal weight. Frequencies or proportions were used to show the prevalence of overweight and obesity and their 95 percent confidence intervals, using descriptive statistics and surveying tabulation. Logistics regression was adjusted using the cluster and survey weights. Multivariable logistic regression analysis was performed to obtain the association of each independent variable with the dependent variable (i.e., underweight, overweight, and obesity), utilizing a normal BMI range as the reference value. Crude and adjusted regression models were built and variables with a pre-specified significance value of <0.2 in the unadjusted model were eligible for inclusion in the final adjusted multivariable models [27]. Association results of multivariable regression analysis were presented by odds ratio (OR) at 95% confidence intervals (CIs). Statistical significance was considered with a p-value < 0.05. Our final model was tested for any co-linearity. The adjusted regression models’ odds ratios and the 95% confidence intervals (CI) were determined.

AI Digest

Study Justification:

The study aims to address the growing issue of the double burden of malnutrition among Bangladeshi women. It specifically focuses on examining the prevalence of underweight, overweight, and obesity among reproductive age women in Bangladesh. This is an important area of research as it provides insights into the nutritional status of women and highlights the need for targeted interventions to address these issues.

Highlights:

– The study utilized data from the 2017-2018 Bangladesh Demographic and Health Survey (BDHS), which represents a nationwide survey of the Bangladeshi population.
– A dataset of 20,127 women aged 15-49 years with complete Body Mass Index (BMI) measurements was analyzed.
– The study found that the odds of being overweight and obese were higher among women who completed primary and secondary or more levels of education, belonged to rich households, were breastfeeding, and were exposed to media (newspapers and television).
– Women from the poorest households were significantly more likely to be underweight compared to women from richer households.
– The likelihood of being underweight was higher among women with no schooling, adolescent women, and women not using contraceptives.
– The study concludes that tailored messages to combat overweight and obesity should target educated and affluent Bangladeshi women, while efforts should be made to improve nutrition among women from low socioeconomic status.

Recommendations:

– Develop targeted interventions to address overweight and obesity among educated and affluent Bangladeshi women, focusing on promoting healthy eating habits and physical activity.
– Implement nutrition programs specifically designed for women from low socioeconomic status to improve their nutritional status and reduce underweight prevalence.
– Incorporate nutrition education and awareness campaigns in schools and communities to promote healthy lifestyles and prevent the double burden of malnutrition.
– Strengthen access to healthcare services, including reproductive health services and contraceptive methods, to address the higher likelihood of underweight among women not using contraceptives.

Key Role Players:

– Ministry of Health: Responsible for developing and implementing national nutrition programs and policies.
– Department of Women’s Affairs: Involved in promoting women’s empowerment and addressing gender inequalities that contribute to nutritional disparities.
– Non-Governmental Organizations (NGOs): Collaborate with the government to implement nutrition interventions and provide support to vulnerable populations.
– Educational Institutions: Play a role in incorporating nutrition education in school curricula and promoting healthy lifestyles among students.

Cost Items for Planning Recommendations:

– Development and implementation of nutrition programs: Includes costs for program design, training of healthcare professionals, and distribution of educational materials.
– Awareness campaigns: Budget for media campaigns, printing of posters and brochures, and community outreach activities.
– Healthcare infrastructure: Investment in healthcare facilities and equipment to improve access to healthcare services.
– Research and monitoring: Funding for further research and monitoring of the effectiveness of interventions and the prevalence of underweight, overweight, and obesity among Bangladeshi women.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a nationwide survey, which provides a representative sample of Bangladeshi women. The survey methodology is described in detail, including the sampling procedure and data collection methods. The analysis includes multiple logistic regression to examine the factors associated with underweight, overweight, and obesity. However, the abstract does not provide information on the response rate or potential limitations of the study. To improve the evidence, it would be helpful to include the response rate and address any limitations of the study, such as potential biases or confounding factors.

Abstract

The 2017–2018 BDHS represents a national survey because it encompasses the whole population living in Bangladeshi non-institutional housing units. The BBS 2011 sampling framework used in the survey included enumeration areas (EAs) of the 2011 Population and Housing Census, which are provided by the Bangladesh Bureau of Statistics (BBS). The Primary Sampling Unit (PSU) (i.e., clusters) for the survey includes an EA of about 120 households on average. Each cluster was considered as a community based on previous studies [2,31]. Bangladesh is made up of eight divisions, including Barishal, Chattogram, Dhaka, Mymensingh, Khulna, Rajshahi, Rangpur, and Sylhet. There are zilas (districts) for each division, and each zila is subdivided further in Upazilas (sub-district). Each urban area of Upazila is split into wards, further divided into Mohallas, whereas each rural area in Upazila is separated into parishes of union (UPs). There are Mouzas in UPs, and all these divisions enable the division into rural and urban areas. Figure 1 presents the sampling procedure. The survey included a stratified two-stage sample of households: 675 EAs with probability proportions to the size of EA were selected in the first phase. In urban areas, there were 250 EAs, and in rural areas, 425 EAs. The sample was drawn by BBS in the first stage, in accordance with the specifications of the DHS team. The selection resulted in 20,250 residential households in accordance with this design. Approximately 20,100 married women aged 15–49 years are expected to complete interviews. The survey report contains details of the sample structure, including the sample framework and the sample implementations [5]. Sampling frame, BDHS2017–2018. Weight measurement using the lightweight scale SECA787 (with digital screens). Heights were measured using an adjustable wood measuring board, designed specifically to provide an accurate reading of 0.1 cm to take the developed countries into account. Through weight and height measurements, their BMI was calculated. The survey also included information on fertility, contraceptive use, maternal and child health, mother’s nutritional status, women’s empowerment, and sociodemographic characteristics. Data for 20,127 women 15 to 49 years of age who were not pregnant have been used following exclusion. The dependent variables were ordered as normal, underweight, overweight, and obesity which was based on the WHO classes for BMI: underweight (<18.50 kg/m2), normal (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obesity (≥30.0 kg/m2) (48). In order to ensure the quality, all weight/height were recorded, BMI has been continuously extended in conservative form. The independent variables were the individual-, household- and community-level factors identified in the conceptual framework. The individual-level variables included all relevant attributes of the respondents, including maternal work status, parents’ level of education, mother’s marital status, mother’s age, mother’s literacy status, access to health care services (autonomy to health care), access to the media (newspaper, radio and Television (TV)), and power over family income. Household-level variables consisted of the source of drinking water (improved or unimproved) and household wealth index into five categories [poorest, poorer, middle, richer, and richest]. In creating a wealth index [32], principal component analysis was used to estimate the index weights based on acquired information on various household assets, including ownership of different means of transport and other sustainable domestic goods. This index was divided into five categories, and one of five categories was allocated to each household. Variables at the community level included residence (urban/rural) and geographical area (Barishal, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, and Sylhet). All analyses were conducted using Stata version 14.1 (Stata Corp 2015, College Station, TX, USA). The command ‘Svy’ has been used for the adaptation of the cluster sampling design, weights, and the Taylor series linearized procedure was used to calculate the standard errors. The dependent variable was always expressed as binary, with number ‘1’ assigned as underweight, overweight, and obesity, while 0 was normal weight. Frequencies or proportions were used to show the prevalence of overweight and obesity and their 95 percent confidence intervals, using descriptive statistics and surveying tabulation. Logistics regression was adjusted using the cluster and survey weights. Multivariable logistic regression analysis was performed to obtain the association of each independent variable with the dependent variable (i.e., underweight, overweight, and obesity), utilizing a normal BMI range as the reference value. Crude and adjusted regression models were built and variables with a pre-specified significance value of <0.2 in the unadjusted model were eligible for inclusion in the final adjusted multivariable models [27]. Association results of multivariable regression analysis were presented by odds ratio (OR) at 95% confidence intervals (CIs). Statistical significance was considered with a p-value < 0.05. Our final model was tested for any co-linearity. The adjusted regression models’ odds ratios and the 95% confidence intervals (CI) were determined.

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 breastfeeding. These apps can also send reminders for appointments and medication, and offer telemedicine consultations.

2. Community Health Workers: Train and deploy community health workers who can provide education, counseling, and support to pregnant women and new mothers in their communities. These workers can also conduct home visits to monitor maternal health and provide referrals when necessary.

3. Telemedicine: Establish telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare providers through video calls. This can help overcome geographical barriers and improve access to prenatal care and medical advice.

4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with financial assistance to access maternal health services, including antenatal care, delivery, and postnatal care. These vouchers can be distributed through healthcare facilities or community organizations.

5. Maternal Health Hotlines: Set up toll-free hotlines staffed by trained healthcare professionals who can provide information, counseling, and referrals related to maternal health. This can be particularly useful for women who have limited access to healthcare facilities or are unable to leave their homes.

6. Mobile Clinics: Deploy mobile clinics equipped with basic maternal health services to reach remote or underserved areas. These clinics can provide antenatal care, vaccinations, and basic screenings, and also serve as a referral point for more specialized care.

7. Health Education Campaigns: Conduct targeted health education campaigns that raise awareness about the importance of maternal health and provide information on healthy practices during pregnancy and childbirth. These campaigns can use various mediums such as radio, television, and community gatherings.

8. Maternity Waiting Homes: Establish maternity waiting homes near healthcare facilities for pregnant women who live far away and need to travel for delivery. These homes provide a safe and comfortable place for women to stay during the final weeks of pregnancy, ensuring timely access to skilled birth attendants.

9. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare providers and facilities to expand service coverage and reduce the burden on public healthcare systems.

10. Data-Driven Approaches: Utilize data from surveys and research studies, like the Bangladesh Demographic and Health Survey, to identify specific areas and populations with the greatest need for maternal health interventions. This can help target resources and interventions more effectively.

It is important to note that the specific implementation and effectiveness of these innovations may vary depending on the local context and resources available.

AI Innovations Description

Based on the information provided, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Tailored Messaging and Education: Develop targeted educational campaigns and messaging to combat overweight and obesity among educated and affluent Bangladeshi women. This can be done through various channels such as television, newspapers, and other media platforms to reach the target audience effectively.

2. Nutritional Support Programs: Implement nutrition programs specifically designed for women from low socioeconomic backgrounds to improve their nutritional status. These programs can include access to affordable and nutritious food, as well as education on healthy eating habits.

3. Healthcare Access: Improve access to healthcare services for all women, regardless of their socioeconomic status. This can be achieved by increasing the number of healthcare facilities in rural areas, providing transportation options for pregnant women to reach healthcare centers, and ensuring that healthcare services are affordable and accessible to all.

4. Empowerment and Family Income: Promote women’s empowerment and increase their decision-making power over family income. This can be done through initiatives that provide women with opportunities for income generation and financial independence, as well as programs that promote gender equality and women’s rights.

5. Contraceptive Education and Access: Increase awareness and access to contraceptives among women, particularly adolescent women. This can help prevent unintended pregnancies and reduce the risk of underweight pregnancies, as mentioned in the study.

By implementing these recommendations, it is possible to improve access to maternal health and address the double burden of malnutrition among Bangladeshi women.

AI Innovations Methodology

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

1. Targeted education and awareness campaigns: Tailored messages should be developed and disseminated to combat overweight and obesity among educated and affluent Bangladeshi women. These campaigns can focus on promoting healthy eating habits, regular physical activity, and the importance of maintaining a healthy weight during pregnancy.

2. Improved nutrition programs: Efforts should be made to improve nutrition among women from low socioeconomic status. This can include implementing nutrition programs that provide access to affordable and nutritious food, as well as education on balanced diets and healthy cooking practices.

3. Strengthening healthcare services: Access to quality healthcare services is crucial for maternal health. Investments should be made to improve the availability and affordability of healthcare facilities, especially in rural areas. This can include building more healthcare centers, training healthcare professionals, and ensuring the availability of essential maternal health services.

4. Empowering women: Women’s empowerment plays a significant role in improving maternal health outcomes. Efforts should be made to promote gender equality, increase women’s decision-making power regarding their own health, and provide opportunities for women to access education and economic resources.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define indicators: Identify key indicators that measure access to maternal health, such as the percentage of women receiving prenatal care, the percentage of women delivering in healthcare facilities, and the maternal mortality rate.

2. Baseline data collection: Collect baseline data on the selected indicators from existing sources, such as national surveys or health records. This will provide a starting point to compare the impact of the recommendations.

3. Develop a simulation model: Create a simulation model that incorporates the potential impact of the recommendations on the selected indicators. This model should consider factors such as population demographics, healthcare infrastructure, and socioeconomic factors.

4. Input data and parameters: Input relevant data and parameters into the simulation model, including information on the target population, the implementation timeline of the recommendations, and the expected coverage and effectiveness of each recommendation.

5. Run simulations: Run multiple simulations using different scenarios to assess the potential impact of the recommendations. This can include varying the coverage and effectiveness of each recommendation to understand their individual and combined effects.

6. Analyze results: Analyze the simulation results to determine the projected changes in the selected indicators. This can include comparing the baseline data with the simulated data to quantify the potential improvements in access to maternal health.

7. Validate and refine the model: Validate the simulation model by comparing the simulated results with real-world data, if available. Refine the model based on feedback and additional data to improve its accuracy and reliability.

8. Communicate findings: Present the findings of the simulation study to relevant stakeholders, such as policymakers, healthcare providers, and community organizations. Use the results to advocate for the implementation of the recommended interventions and to guide decision-making processes.

It is important to note that the methodology described above is a general framework and may require customization based on the specific context and available data.

Authors & Co-Authors

Statistics:

Citations: 2

Authors: 7

Identifiers:

DOI: 10.3390/nu13124408

Research Areas:

Community Interventions, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Design:

Case-Control Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female