Spatial variation of overweight/obesity and associated factor among reproductive age group women in Ethiopia, evidence from EDHS 2016

  • PLoS ONE, Volume 17, No. 11 November, Year 2022

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Study Justification:
– Overweight and obesity are significant global health issues, leading to millions of deaths worldwide.
– In Ethiopia, the prevalence of overweight and obesity among women has been increasing.
– However, there is a lack of research on the spatial variation of overweight/obesity and associated factors in Ethiopia using geospatial techniques.
– This study aims to fill this research gap by exploring the spatial variation of overweight/obesity and associated factors among reproductive age group women in Ethiopia.
Highlights:
– The study used data from the Ethiopian Demographic and Health Surveys (EDHS) conducted in 2016.
– A total of 10,928 reproductive age women were included in the study.
– Geospatial techniques, including ArcGIS and SaTScan, were used to analyze the spatial distribution and clustering of overweight/obesity.
– The study identified statistically significant high hotspots of overweight/obesity in Addis Ababa, Harari, and Dire Dawa.
– Geographically weighted regression analysis revealed that factors such as wealth index, women’s age, watching TV, internet use, and employment status were associated with the spatial variation of overweight/obesity.
Recommendations for Lay Reader:
– The study findings highlight the spatial variation of overweight/obesity among reproductive age group women in Ethiopia.
– The identified high hotspots of overweight/obesity can guide targeted interventions and policies to address this issue.
– Initiatives such as funding for physical education and recreational centers in communities most in need can be considered.
– Public and private mass media can play a role in promoting awareness of healthy lifestyles and increased physical activity.
Recommendations for Policy Maker:
– The study provides evidence of the spatial distribution and clustering of overweight/obesity in Ethiopia.
– The identified high hotspots can inform the development of targeted interventions and policies to address overweight/obesity.
– Consideration can be given to providing funding for physical education and recreational centers in areas with high prevalence.
– Health education campaigns through mass media can be promoted to raise awareness of healthy lifestyles and encourage physical activity.
Key Role Players:
– Ministry of Health
– Ethiopian Public Health Institute
– Local health authorities
– Community leaders and organizations
– Non-governmental organizations (NGOs) working in health and nutrition
Cost Items for Planning Recommendations:
– Funding for physical education programs and recreational centers
– Health education campaigns through mass media
– Training and capacity building for healthcare professionals and community health workers
– Research and data collection on overweight/obesity prevalence and associated factors
– Monitoring and evaluation of interventions and policies
– Collaboration and coordination between relevant stakeholders and organizations

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a detailed description of the study methodology, including the sample size, statistical analysis methods, and results. However, it lacks information on the specific limitations of the study and potential biases. To improve the evidence, the abstract could include a discussion of the study’s limitations, such as potential selection bias or confounding factors. Additionally, it would be helpful to provide information on the generalizability of the findings and any recommendations for future research.

Background Globally, at least 4.7 million people die from being overweight or obese. In Ethiopia, the level of overweight and obesity among women grew from 3% to 8%. However, as far as my literature searching, studies concerning the spatial variation of overweight/obesity and factors associated are not researched in Ethiopia using geospatial techniques. Therefore, this study aimed to explore the spatial variation of overweight/obesity and factor associated among reproductive age group women in Ethiopia using geospatial techniques. Mothed A total weighted sample of 10,928 reproductive age women were included in the study. ArcGIS version10.7 was used to explore the spatial variation of overweight/obesity. Bernoulli based model was used to analyze the purely spatial cluster detection of overweight/obesity through SaTScan version 9.6.1 software. Ordinary Least Square analysis and geographically weighted regression analysis was employed to assess the association between an outcome variable and explanatory variables by using ArcGIS 10.7 software. P value of less than 0.05 was used to declare statically significant. Result The spatial distribution of overweight/obesity in Ethiopia was clustered. Statistically, a significant-high hot spot overweight/obesity was identified at Addis Ababa, harrari, Dire Dawa. SaTScan identified 66 primary spatial clusters (RR = 4.17, P < 0.001) located at Addis Ababa, southeast amhara, central part of oromia region and northern part of SNNP region. In geographically weighted regression, rich wealth index, women’s age (35–39 and 40–44 years), watching TV, internet use and not working were statistically significant that affecting spatial variation of overweight/obesity. Conclusion In Ethiopia, overweight/obesity varies across the region. Statistically, significant-high hot spots of overweight/obesity were detected in Addis Ababa, Harari, Dire Dawa, some parts of Amhara and afar region, most of the Oromia and Somalia region, and the South Nation Nationality and People region of Ethiopia. Therefore, the ministry of health and the Ethiopian public health institute, try to initiate policies and practices that could include providing funding for physical education as well as recreational centers in communities most in need. In addition, public and private mass media create awareness of healthy lifestyles is promoted by health education regarding increased physical activity and reduced sedentary behavior through various media platforms.

In EDHS 2016, a community-based cross-sectional study was conducted by the Central Statistical Agency (CSA) from January 18 to June 27, 2016, in Ethiopia [15]. It has nine Regional states (Afar, Amhara, Benishangul-Gumuz, Gambella, Harari, Oromia, Somali, Southern Nations, Nationalities, and People’s Region (SNNP) and Tigray) and two city Administrative (Addis Ababa and Dire-Dawa) (Fig 1). The source of population was all reproductive-age women within five years before the survey in Ethiopia. A weighted total of 10,928 reproductive age group women who had a complete answer to all variables of interest were included from the total 15,683 women aged 15–49 years were interviewed. All women those who had underweight and women for whom there was missing information on height and/or weight and/or women those who had pregnant and observation from enumeration areas with zero coordinate were excluded. The data was acquired from the Demographic and Health Surveys (DHS) Program by requesting for this work and accessed www.dhsprogram.com website. In the 2016 EDHS, a standardized and validated questionnaire were adapted from the DHS Program’s standard questionnaires in a way to reveal the population and health issues relevant to Ethiopia. A two stage stratified sampling technique was employed to select representative samples for the country as whole. The regions of the country were stratified into urban and rural areas. Then, samples of enumeration areas (EAs) were selected in each stratum in two stages. In the first stage, 645 EAs were selected with probability proportional to the EA size. The EA size is the number of residential households in the EA as determined in the 2007 Ethiopian Population and Housing Census. In the second stage, a fixed number of 28 households per cluster were selected randomly from the household listing [15]. For the purpose of this study, the women’s data (IR) from the 2016 EDHS was utilized. The detailed sampling procedure is available in the Ethiopian Demographic and Health Survey reports from Measure DHS website (www.dhsprogram.com). The outcome variable of this study was Body Mass Index (BMI). BMI is a measure for quantifying nutritional status in adults which is calculated by using the formula weight in kg divided by height in meter square. This study utilized the WHO-specific cut-offs value to categorize BMI of the reproductive age group women. Normal weight: 18.5–24.9 kg/m2, Overweight: 25.0–29.99 kg/m2 and Obese: ≥30.0 kg/m2 [1]. For the purpose of this study, a BMI ≥25.0 kg/m2 was categorized as overweight/obesity and coded as 1, while a BMI 18.5–24.99 kg/m2 was categorized as normal and coded as 0. Socio-demographic factor marital status (labelled as single, married, divorced/separated and widowed), Women’s education status (labelled as no education, primary, secondary, and higher education, maternal occupation (labelled as working or not working), parity(labelled as 0 children, 1–3 children and 4 and above children), wealth index (labelled as poor, middle and rich) and maternal age. Behavioral factors such as alcohol use (labeled as yes and No), cigarette smoking (labeled as yes and No) and contraceptive use (labeled as yes and No). Exposure to mass media (whether the woman read any newspapers/magazines, listened to the radio, or watched television and internet use) were labeled as (less than once a week and at least once a week) and not at all). The data was clean by STATA version 14.1 software and Microsoft Excel, for data analysis we used Arc GIS 10.7 and SaTScan 9.6. Spatial autocorrelation (Global Moran’s I) statistic measure is used to assess whether overweight/obesity is dispersed, clustered, or randomly distributed in Ethiopia. Moran’s I value close to − 1 shows dispersed overweight/obesity, close to + 1 shows clustered, and if Moran’s I value zero shows randomly distributed and a statistically significant Moran’s I (p < 0.05) leads to rejection of the null hypothesis [16]. Hot Spot Analysis (the Getis-Ord Gi * statistic) of the z-scores and significant p-values tells the features with either hot spot or cold spot values for the clusters spatially [17]. For measuring spatial autocorrelation for a set of distances, a line graph of those distances and their related Z-scores was generated. The level of spatial clustering and statistical significance are represented by Z-scores. Peak Z -scores show distances where spatial processes promoting clustering are most pronounced. These peak distances are often proper values to use for tools with a Distance Band or Distance Radius parameter. This tool can help to select an appropriate distance threshold or radius for tools that have these parameters, such as hot spot analysis [18]. The spatial interpolation technique is used to predict overweight/obesity for unsampled areas based on sampled clusters [19]. We used deterministic and geostatistical interpolation methods. To compare the above interpolation method we employed geostatistical analysis based on result with lowest mean predicted error (MPE) and root mean square predicted error (RMSPE) was the best fitted interpolation technique for overweight/obesity. Those small values indicate that predicted values are close to the observed values and vice versa [20]. For this study ordinary kriging interpolation method was selected, since lowest mean predicted error (MPE) and root mean square predicted error (RMSPE). Bernoulli based model spatial Kuldorff’s Scan statistics was used to determine the geographical locations of statistically significant spatial window for overweight/obesity using SaTScan version 9.6.1 software [21]. The outcome variable has a Bernoulli distribution, so Bernoulli model was used by applying the Kuldorff’s method for purely spatial analysis. The scanning window that moves across the study area in which women give overweight/obesity was taken as case and those women who give normal body weight was taken as control to fit the Bernoulli model. The default maximum spatial cluster size of < 50% of the population was used as an upper limit and most likely clusters was identified by using p-values and likelihood ratio tests based on 999 Monte Carlo replications. To generate secondary clusters, we employed non-overlapping options by SaTScan version 9.6.1 and ArcGIS software version 10.7 was used to map the cluster and attribute of overweight produced by SaTScan™. Exploratory Regression was used to find a model that meets the OLS method’s assumptions, all while identifying models with a high Adjusted R2 value. Ordinary Least Square regression (OLS) model is a global model that estimates only one single coefficient per explanatory variable over the entire study region. We used to check assumptions of spatial regression using explanatory regression with the particular tests. The Jarque-Bera test was used to assess the normality assumption for residuals. As residuals are not spatially auto-correlated, the statistically significant Koenker (BP) statistic shows that the relationships modeled are not consistent (either due to non-stationarity or heteroscedasticity). Multicollinearity (Variance Inflation Factor) was used to check redundancy among predictor variables, coefficients have the expected sign and statically significant, and strong adjusted R2 values. A geographically weighted regression model is an extension of the OLS regression model. It gives local parameter estimates to reflect variations over space in the association between an outcome and predictor variables [22]. For geographically weighted regression analysis, the aggregated proportion of overweight/obesity among reproductive-age women and all the predictor variables are considered for each cluster. To determine the predictor variables for overweight/obesity among reproductive-age women, we used a geographically weighted regression model. The model structure of geographically weighted regression written as: Where Yi is the outcome variable, (ui, vi) represents the coordinates of the ith point in space, β0 is the intercept at the (ui, vi) coordinate, βk is the coefficient of the covariate X at the (ui, vi) coordinate, and i is the random error term. Geographical heterogeneity for each coefficient can be measured by comparing the AICc between the GWR model and the global OLS regression model. The corrected Akaike Information Criteria (AIC) and Adjusted R-squared was used for model comparison of OLS (global model) and GWR (local) model. A model with the lowest AICc value and a higher adjusted R-squared value was used to determine the best fit model for local parameter estimates. Ethical clearance was obtained from the ethical review board of the University Of Gondar Institute of Public Health, CMHS. The guidelines expressed in Ethiopia’s Declaration of Central Statistical Agency (DCSA) guided the countrywide survey. The survey was also approved by CSA’s Ethical Review Board (ERB), and everyone who decided to take part in it completed a consent form. Permission for data access was acquired from the measure demographic and health survey through an online request by written letter of objective and significance of the study from http://www.dhsprogram.com. Moreover, for Geographic information system coordinates, the coordinates are only for the enumeration area (EA) as a whole and the measured coordinates were randomly displaced within a large geographic area so that no particular enumeration areas can be identified.

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Based on the information provided, here are some potential innovations that could 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, exercise, and access to healthcare services. These apps can be easily accessible to women in remote areas, providing them with essential information and connecting them to healthcare providers.

2. Telemedicine: Implement telemedicine services that allow pregnant women to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide timely medical advice and support to women in areas with limited access to healthcare facilities.

3. Community Health Workers: Train and deploy community health workers who can provide education, support, and basic healthcare services to pregnant women in their communities. These workers can help bridge the gap between healthcare facilities and remote areas, ensuring that women receive the necessary care during pregnancy.

4. Maternal Health Vouchers: Introduce voucher programs that provide financial assistance to pregnant women, enabling them to access essential maternal health services such as prenatal care, delivery, and postnatal care. These vouchers can be distributed through community health workers or local healthcare facilities.

5. Public-Private Partnerships: Foster collaborations between the government, private sector, and non-profit organizations to improve access to maternal health services. This can involve initiatives such as building and upgrading healthcare facilities, training healthcare professionals, and implementing outreach programs in underserved areas.

6. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of maternal health and promote healthy behaviors during pregnancy. These campaigns can utilize various media platforms, including radio, television, and the internet, to reach a wide audience.

7. Transportation Support: Develop transportation support systems that help pregnant women in remote areas reach healthcare facilities for prenatal visits, delivery, and postnatal care. This can involve providing subsidized transportation services or establishing community-based transportation networks.

8. Maternal Health Monitoring Systems: Implement digital monitoring systems that track the health status of pregnant women and provide real-time data to healthcare providers. This can help identify high-risk pregnancies and ensure timely interventions and referrals.

9. Maternal Health Financing: Explore innovative financing mechanisms, such as microinsurance or community-based health financing schemes, to make maternal health services more affordable and accessible to women in low-income communities.

10. Maternal Health Task Forces: Establish task forces or committees at the national and regional levels to coordinate efforts and develop strategies for improving maternal health. These task forces can bring together stakeholders from various sectors to address the challenges and gaps in maternal healthcare delivery.

It is important to note that these recommendations are general and may need to be tailored to the specific context and needs of Ethiopia.
AI Innovations Description
Based on the study “Spatial variation of overweight/obesity and associated factors among reproductive age group women in Ethiopia, evidence from EDHS 2016,” the following recommendation can be developed into an innovation to improve access to maternal health:

1. Initiate policies and practices: The Ministry of Health and the Ethiopian Public Health Institute should initiate policies and practices that include providing funding for physical education as well as recreational centers in communities most in need. This can help promote physical activity and reduce sedentary behavior, which are important for maintaining a healthy weight during pregnancy.

2. Create awareness through mass media: Public and private mass media should create awareness of healthy lifestyles and promote health education regarding increased physical activity and reduced sedentary behavior through various media platforms. This can help educate women about the importance of maintaining a healthy weight during pregnancy and provide them with information on how to achieve this.

3. Improve access to healthcare services: Efforts should be made to improve access to healthcare services, particularly in areas identified as high hot spots of overweight/obesity. This can include increasing the number of healthcare facilities, ensuring availability of trained healthcare professionals, and providing necessary resources for maternal health services.

4. Implement geospatial techniques: Geospatial techniques, such as spatial cluster detection and geographically weighted regression analysis, can be implemented to identify areas with high prevalence of overweight/obesity and understand the factors associated with it. This information can help target interventions and resources to specific areas and populations in need.

5. Collaborate with local communities: Collaboration with local communities is crucial for the success of any intervention. Engaging community leaders, healthcare providers, and women themselves can help in designing and implementing effective strategies to improve access to maternal health services and promote healthy behaviors.

By implementing these recommendations, it is possible to develop innovative approaches that can improve access to maternal health and address the issue of overweight/obesity among reproductive age group women in Ethiopia.
AI Innovations Methodology
Based on the information provided, here are some potential recommendations for improving access to maternal health:

1. Targeted interventions: Based on the spatial variation of overweight/obesity and associated factors, it is important to develop targeted interventions that address the specific needs of different regions. For example, in areas with high prevalence of overweight/obesity, programs can focus on promoting healthy eating habits, physical activity, and weight management.

2. Community-based education: Public and private mass media can play a crucial role in creating awareness about healthy lifestyles and the importance of maternal health. Health education campaigns can be conducted through various media platforms to reach a wide audience and promote increased physical activity and reduced sedentary behavior.

3. Recreational centers and physical education: Initiating policies and practices that provide funding for physical education and recreational centers in communities most in need can help improve access to maternal health. These centers can offer exercise classes, nutritional counseling, and support groups for pregnant women and new mothers.

4. Collaboration between the Ministry of Health and public health institutes: The Ministry of Health and the Ethiopian Public Health Institute can work together to develop and implement policies that prioritize maternal health. This collaboration can ensure that resources are allocated effectively and that evidence-based interventions are implemented.

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

1. Data collection: Gather data on the current status of maternal health, including access to healthcare facilities, maternal mortality rates, and prevalence of overweight/obesity among reproductive age group women. This data can be obtained from surveys, health records, and other relevant sources.

2. Spatial analysis: Use geospatial techniques, such as ArcGIS, to analyze the spatial variation of overweight/obesity and associated factors. This analysis can help identify areas with high prevalence and clusters of overweight/obesity, as well as factors that contribute to these patterns.

3. Modeling and simulation: Utilize statistical models, such as Bernoulli-based models and geographically weighted regression analysis, to simulate the impact of the recommended interventions on improving access to maternal health. These models can estimate the potential changes in overweight/obesity rates and associated factors based on the implementation of targeted interventions.

4. Evaluation and validation: Validate the simulation results by comparing them with real-world data and evaluating the effectiveness of the recommended interventions. This can involve monitoring changes in maternal health indicators, conducting surveys or interviews with affected populations, and assessing the overall impact of the interventions.

By following this methodology, policymakers and healthcare professionals can gain insights into the potential impact of specific recommendations on improving access to maternal health and make informed decisions regarding resource allocation and program implementation.

Statistics:
Authors: 3
Identifiers:
DOI: 10.1371/journal.pone.0277955
Research Areas:
Community Interventions, Genetics and Genomics, Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants, Substance Abuse
Study Countries:
Ethiopia, Multi-Countries, Somalia
Study Design:
Cross Sectional Study, Exploratory Study
Study Approach:
Quantitative
Participants Gender:
Female
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