Trends and Spatio-temporal variation of female genital mutilation among reproductive-age women in Ethiopia: A Spatio-temporal and multivariate decomposition analysis of Ethiopian demographic and health surveys

BMC Public Health, Volume 20, No. 1, Year 2020

Interpretação

Background: Female genital mutilation (FGM) is a serious health problem globally with various health, social and psychological consequences for women. In Ethiopia, the prevalence of female genital mutilation varied across different regions of the country. Therefore, this study aimed to investigate the trend and determinants of female genital mutilation among reproductive-age women over time. Methods: A secondary data analysis was done using 2000, 2005, and 2016 Demographic Health Surveys (DHSs) of Ethiopia. A total weighted sample of 36,685 reproductive-age women was included for analysis from these three EDHS Surveys. Logit based multivariate decomposition analysis was employed for identifying factors contributing to the decrease in FGM over time. The Bernoulli model was fitted using spatial scan statistics version 9.6 to identify hotspot areas of FGM, and ArcGIS version 10.6 was applied to explore the spatial distribution FGM across the country. Results: The trends of FGM practice has been decreased from 79.9% in 2000 to 70.4% in 2016 with an annual reduction rate of 0.8%. The multivariate decomposition analysis revealed that about 95% of the overall decrease in FGM practice from 2000 to 2016 was due to the difference in the effects of women’s characteristics between the surveys. The difference in the effects of residence, religion, occupation, education, and media exposure were significant predictors that contributed to the decrease in FGM over time. The spatial distribution of FGM showed variation across the country. The SaTScan analysis identified significant hotspot areas of FGM in Somali, Harari, and Afar regions consistently over the three surveys. Conclusion: Female genital mutilation practice has shown a remarkable decrease over time in Ethiopia. Public health programs targeting rural, non-educated, unemployed, and those women with no access to media would be helpful to maintain the decreasing trend of FGM practice. The significant Spatio-temporal clustering of FGM was observed across regions in Ethiopia. Public health interventions must target the identified clusters as well. We used 2000, 2005, and 2016 Ethiopian Demographic and Health surveys (EDHSs). These EDHSs are nationally representative cross-sectional surveys performed in 9 regions and 2 country city administrations every five years (Fig. 1). In each of the surveys, stratified two-stage sampling of clusters was carried out. Stratification was achieved by separating each region into urban and rural areas. Accordingly, a total of 21 sampling strata have been created. In the first stage, a total of 539 Enumeration Areas (EAs) for EDHS 2000, 540 EAs for EDHS 2005, and 645 EAs for EDHS 2016 were randomly selected proportional to the EA size. At the second stage, on average 27 to 32 households per EA were selected. A total weighted sample of 36,685 (15,367 in EDHS 2000, 14,070 in EDHS 2005 and 7248 in EDHS 2016) reproductive-age women used for this study. The comprehensive procedure for sampling was described in the complete EDHS report [10, 11, 23]. Map of the study area (Source; Shape file from CSA, 2013, done using ArcGIS version 10.6 and SaTScan version 9.6) The outcome variable for this study was experienced FGM and coded as “Yes = 1” or “No = 0”. The EDHS asked women to answer the question “have you ever been circumcised?”. So, the response variable of the ith mother Yi was measured as a dichotomous variable with possible values Yi = Yes if ith mother had experienced circumcision and Yi = No if mother did not experience circumcision. The independent variables included in this study were: residence, religion, geographic region, responded age, maternal education, women occupation, media exposure, and wealth index. The data were extracted from the Individual Record (IR) data sets. Before any statistical analysis, the data were weighted using sampling weight, primary sampling unit, and strata, to restore the representativeness of the survey and get reliable statistical estimates. Trend analysis of FGM and decomposition of the decrease in the prevalence of FGM over time was done. The trend analysis has been done in three phases, phase 1 (2000–2005), phase 2 (2005–2016) and the overall trend (2000–2016), the trend and determinants was examined separately. For the trend analysis multivariate decomposition analysis for non-linear response outcome was employed to identify the factors contributed to the decrease in FGM practice across the surveys. For our study, Logit based decomposition analysis was employed. The Logit based multivariate decomposition analysis utilizes the output from the logistic regression model to parcel out the observed decrease in FGM over time into components. The main aim of multivariate decomposition is to identify the factors contributing to the decrease in FGM practice for the last 16 years. The decrease in FGM practice can be explained by the compositional difference between surveys (i.e. differences in characteristics) and/or the difference in effects of explanatory variables (i.e. differences in the coefficients) between the surveys. Hence, the observed decrease in FGM over time is additively decomposed into a characteristics (or endowments) component and a coefficient (or effects of characteristics) component. For logistic regression, the Logit or log-odd of FGM is taken as: E C The E component refers to the part of the differential owing to differences in endowments or characteristics. The C component refers to that part of the differential attributable to differences in coefficients or effects. The recently developed multivariate decomposition for the non-linear model was used for the decomposition analysis of female genital mutilation using the mvdcmp STATA command [24]. ArcGIS version 10.6 and SaTScan version 9.6 software were used for spatial analysis. The spatial autocorrelation (Global Moran’s I) statistic was used to assess whether there was significant clustering of FGM [25]. Moran’s I has a value ranging from-1 to 1. Positive Moran’s I value shows that FGM is clustered while negative Moran’s I indicates that FGM is dispersed [26]. The value of Moran’s I near zero has revealed that FGM is randomly distributed. Both Z-score and P-value are generated to assess the significance of the Moran index. In spatial scan statistical analysis, Bernoulli based model was employed to identify significant spatial high FGM clusters using Kuldorff’s SaTScan version 9.6 software. The SaTScan uses a circular scanning window that moves across the study area. Women who were circumcised were taken as cases whereas those who were not circumcised were taken as controls to fit the Bernoulli model. The default maximum spatial cluster size of < 50% of the population was used as an upper limit, which allowed both small and large clusters to be detected and ignored clusters that contained more than the maximum limit. For each potential cluster, a likelihood ratio test statistic and the p-value were used to determine significant clusters. The scanning window with maximum likelihood was the most likely performing cluster. The primary and secondary clusters were identified and ranked based on their likelihood ratio test, based on 999 Monte Carlo replications [27]. The Ordinary Kriging spatial interpolation method was used to predict the un-sampled/unmeasured values from the sampled measurements. Since the study was a secondary data analysis of publically available survey data from MEASURE DHS program, ethical approval and participant consent were not necessary for this particular study. We requested DHS Program and permission was granted to download and use the data for this study from http://www.dhsprogram.com. There are no names of individuals or household addresses in the data files. The geographic identifiers only go down to the regional level (where regions are typically very large geographical areas encompassing several states/provinces. In surveys that collect GIS coordinates in the field, the coordinates are only for the enumeration area (EA) as a whole, and not for individual households, and the measured coordinates are randomly displaced within a large geographic area so that specific enumeration areas cannot be identified.

AI Digest

Study Justification:

– Female genital mutilation (FGM) is a serious health problem globally with various health, social, and psychological consequences for women.
– The prevalence of FGM in Ethiopia varies across different regions of the country.
– This study aimed to investigate the trend and determinants of FGM among reproductive-age women over time in Ethiopia.

Study Highlights:

– The prevalence of FGM practice in Ethiopia has decreased from 79.9% in 2000 to 70.4% in 2016, with an annual reduction rate of 0.8%.
– Factors such as residence, religion, occupation, education, and media exposure have contributed significantly to the decrease in FGM over time.
– Significant hotspot areas of FGM were consistently identified in Somali, Harari, and Afar regions over the three surveys.
– Public health programs targeting rural, non-educated, unemployed, and women with no access to media can help maintain the decreasing trend of FGM practice.
– Public health interventions should focus on the identified clusters of FGM in different regions of Ethiopia.

Recommendations for Lay Reader:

– Support public health programs that aim to reduce FGM practice in Ethiopia.
– Promote education and awareness about the negative consequences of FGM.
– Advocate for policies that protect women and girls from undergoing FGM.
– Encourage community engagement and dialogue to change cultural norms and attitudes towards FGM.

Recommendations for Policy Maker:

– Allocate resources to implement public health programs targeting FGM reduction.
– Develop and enforce policies that criminalize FGM and provide support for survivors.
– Invest in education and awareness campaigns to change societal attitudes towards FGM.
– Collaborate with local communities, NGOs, and international organizations to address FGM effectively.

Key Role Players:

– Ministry of Health: Responsible for implementing and coordinating public health programs.
– Ministry of Women, Children, and Youth Affairs: Focused on protecting the rights of women and girls.
– Non-Governmental Organizations (NGOs): Involved in advocacy, awareness, and support services for FGM survivors.
– Community Leaders and Traditional Authorities: Play a crucial role in changing cultural norms and practices.
– International Organizations: Provide technical and financial support for FGM prevention and intervention programs.

Cost Items for Planning Recommendations:

– Funding for public health programs targeting FGM reduction.
– Resources for education and awareness campaigns.
– Support services for FGM survivors.
– Capacity building and training for healthcare professionals and community leaders.
– Research and monitoring activities to assess the effectiveness of interventions.

Força da prova

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a secondary data analysis of nationally representative surveys conducted over a period of 16 years. The study used a large weighted sample size of 36,685 reproductive-age women. The analysis employed multivariate decomposition and spatial analysis techniques to identify trends and determinants of female genital mutilation (FGM) in Ethiopia. The study found a significant decrease in FGM practice over time and identified factors such as residence, religion, occupation, education, and media exposure that contributed to the decrease. The spatial analysis identified hotspot areas of FGM in specific regions. To improve the evidence, the study could have provided more details on the methodology and statistical analysis techniques used, as well as the limitations of the study. Additionally, including information on the representativeness of the sample and any potential biases would have strengthened the evidence further.

Resumo

We used 2000, 2005, and 2016 Ethiopian Demographic and Health surveys (EDHSs). These EDHSs are nationally representative cross-sectional surveys performed in 9 regions and 2 country city administrations every five years (Fig. 1). In each of the surveys, stratified two-stage sampling of clusters was carried out. Stratification was achieved by separating each region into urban and rural areas. Accordingly, a total of 21 sampling strata have been created. In the first stage, a total of 539 Enumeration Areas (EAs) for EDHS 2000, 540 EAs for EDHS 2005, and 645 EAs for EDHS 2016 were randomly selected proportional to the EA size. At the second stage, on average 27 to 32 households per EA were selected. A total weighted sample of 36,685 (15,367 in EDHS 2000, 14,070 in EDHS 2005 and 7248 in EDHS 2016) reproductive-age women used for this study. The comprehensive procedure for sampling was described in the complete EDHS report [10, 11, 23]. Map of the study area (Source; Shape file from CSA, 2013, done using ArcGIS version 10.6 and SaTScan version 9.6) The outcome variable for this study was experienced FGM and coded as “Yes = 1” or “No = 0”. The EDHS asked women to answer the question “have you ever been circumcised?”. So, the response variable of the ith mother Yi was measured as a dichotomous variable with possible values Yi = Yes if ith mother had experienced circumcision and Yi = No if mother did not experience circumcision. The independent variables included in this study were: residence, religion, geographic region, responded age, maternal education, women occupation, media exposure, and wealth index. The data were extracted from the Individual Record (IR) data sets. Before any statistical analysis, the data were weighted using sampling weight, primary sampling unit, and strata, to restore the representativeness of the survey and get reliable statistical estimates. Trend analysis of FGM and decomposition of the decrease in the prevalence of FGM over time was done. The trend analysis has been done in three phases, phase 1 (2000–2005), phase 2 (2005–2016) and the overall trend (2000–2016), the trend and determinants was examined separately. For the trend analysis multivariate decomposition analysis for non-linear response outcome was employed to identify the factors contributed to the decrease in FGM practice across the surveys. For our study, Logit based decomposition analysis was employed. The Logit based multivariate decomposition analysis utilizes the output from the logistic regression model to parcel out the observed decrease in FGM over time into components. The main aim of multivariate decomposition is to identify the factors contributing to the decrease in FGM practice for the last 16 years. The decrease in FGM practice can be explained by the compositional difference between surveys (i.e. differences in characteristics) and/or the difference in effects of explanatory variables (i.e. differences in the coefficients) between the surveys. Hence, the observed decrease in FGM over time is additively decomposed into a characteristics (or endowments) component and a coefficient (or effects of characteristics) component. For logistic regression, the Logit or log-odd of FGM is taken as: E C The E component refers to the part of the differential owing to differences in endowments or characteristics. The C component refers to that part of the differential attributable to differences in coefficients or effects. The recently developed multivariate decomposition for the non-linear model was used for the decomposition analysis of female genital mutilation using the mvdcmp STATA command [24]. ArcGIS version 10.6 and SaTScan version 9.6 software were used for spatial analysis. The spatial autocorrelation (Global Moran’s I) statistic was used to assess whether there was significant clustering of FGM [25]. Moran’s I has a value ranging from-1 to 1. Positive Moran’s I value shows that FGM is clustered while negative Moran’s I indicates that FGM is dispersed [26]. The value of Moran’s I near zero has revealed that FGM is randomly distributed. Both Z-score and P-value are generated to assess the significance of the Moran index. In spatial scan statistical analysis, Bernoulli based model was employed to identify significant spatial high FGM clusters using Kuldorff’s SaTScan version 9.6 software. The SaTScan uses a circular scanning window that moves across the study area. Women who were circumcised were taken as cases whereas those who were not circumcised were taken as controls to fit the Bernoulli model. The default maximum spatial cluster size of < 50% of the population was used as an upper limit, which allowed both small and large clusters to be detected and ignored clusters that contained more than the maximum limit. For each potential cluster, a likelihood ratio test statistic and the p-value were used to determine significant clusters. The scanning window with maximum likelihood was the most likely performing cluster. The primary and secondary clusters were identified and ranked based on their likelihood ratio test, based on 999 Monte Carlo replications [27]. The Ordinary Kriging spatial interpolation method was used to predict the un-sampled/unmeasured values from the sampled measurements. Since the study was a secondary data analysis of publically available survey data from MEASURE DHS program, ethical approval and participant consent were not necessary for this particular study. We requested DHS Program and permission was granted to download and use the data for this study from http://www.dhsprogram.com. There are no names of individuals or household addresses in the data files. The geographic identifiers only go down to the regional level (where regions are typically very large geographical areas encompassing several states/provinces. In surveys that collect GIS coordinates in the field, the coordinates are only for the enumeration area (EA) as a whole, and not for individual households, and the measured coordinates are randomly displaced within a large geographic area so that specific enumeration areas cannot be identified.

Materiais

N/A

Inovações

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide information and resources related to maternal health, including prenatal care, safe delivery practices, and postnatal care. These apps can be easily accessible to women in remote areas, providing them with vital information and guidance.

2. Telemedicine: Establish telemedicine services that allow pregnant women to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide access to medical expertise, especially in areas with limited healthcare facilities.

3. Community Health Workers: Train and deploy community health workers who can provide basic maternal healthcare services, education, and support to women in underserved areas. These workers can act as a bridge between the community and formal healthcare systems, ensuring that women receive appropriate care and referrals.

4. Maternal Health Vouchers: Implement 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 postnatal care. These vouchers can help reduce financial barriers and increase utilization of maternal health services.

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 coverage, implementing public-private financing mechanisms, and promoting innovative delivery models.

6. Health Information Systems: Strengthen health information systems to collect, analyze, and disseminate data on maternal health indicators. This can help identify gaps in service provision, monitor progress, and inform evidence-based decision-making for targeted interventions.

7. Maternal Health Education Programs: Develop comprehensive educational programs that focus on maternal health, targeting women, families, and communities. These programs can raise awareness about the importance of antenatal care, skilled birth attendance, and postnatal care, as well as address cultural practices that may pose risks to maternal health.

8. Transport and Referral Systems: Improve transportation infrastructure and establish efficient referral systems to ensure timely access to emergency obstetric care. This can involve providing ambulances or other means of transportation for pregnant women in need of emergency services.

9. Maternity Waiting Homes: Establish maternity waiting homes near healthcare facilities to accommodate pregnant women who live far away. These homes can provide a safe and supportive environment for women to stay during the final weeks of pregnancy, ensuring they are close to healthcare facilities when labor begins.

10. Quality Improvement Initiatives: Implement quality improvement initiatives in healthcare facilities to enhance the provision of maternal health services. This can involve training healthcare providers, improving infrastructure and equipment, and strengthening infection prevention and control measures.

It is important to note that the specific context and needs of the target population should be considered when implementing these innovations. Additionally, continuous monitoring and evaluation should be conducted to assess their effectiveness and make necessary adjustments for optimal impact.

AI Innovations Description

Based on the description provided, the recommendation to improve access to maternal health based on the study findings would be to implement targeted public health programs. These programs should focus on rural areas, non-educated individuals, unemployed individuals, and women with limited access to media. By targeting these specific groups, the aim is to maintain the decreasing trend of female genital mutilation (FGM) practice in Ethiopia.

Additionally, the study identified significant hotspot areas of FGM in Somali, Harari, and Afar regions consistently over the three surveys. Therefore, public health interventions should also prioritize these identified clusters to effectively address the issue of FGM.

It is important to note that the study used data from the Ethiopian Demographic and Health Surveys (EDHSs) conducted in 2000, 2005, and 2016. The findings indicate a decrease in the prevalence of FGM over time, with an annual reduction rate of 0.8%. This suggests that efforts to combat FGM have been effective, but continued interventions are necessary to further reduce the practice and improve access to maternal health services.

It is worth mentioning that this study was a secondary data analysis of publicly available survey data from the MEASURE DHS program. Therefore, ethical approval and participant consent were not necessary for this particular study. The data used in the analysis did not contain any personal identifying information.

AI Innovations Methodology

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

1. Strengthening Public Health Programs: Implement targeted public health programs that focus on rural areas and communities with limited access to healthcare services. These programs should specifically address maternal health issues and provide education, resources, and support to pregnant women and new mothers.

2. Enhancing Education and Awareness: Develop comprehensive educational campaigns to raise awareness about maternal health, including the risks and consequences of female genital mutilation (FGM). These campaigns should target both women and men, as well as community leaders, to promote understanding and change social norms surrounding FGM.

3. Improving Healthcare Infrastructure: Invest in improving healthcare infrastructure, particularly in regions with high prevalence of FGM. This includes increasing the number of healthcare facilities, training healthcare professionals, and ensuring the availability of essential maternal health services.

4. Strengthening Collaboration and Partnerships: Foster collaboration between government agencies, non-governmental organizations (NGOs), and community-based organizations to address maternal health issues. This can involve sharing resources, expertise, and best practices to maximize impact and reach.

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 key indicators related to maternal health, such as maternal mortality rates, prevalence of FGM, healthcare infrastructure, education levels, and access to healthcare services. This data can be obtained from national surveys, health records, and other relevant sources.

2. Baseline Assessment: Establish a baseline assessment of the current state of maternal health access, including the prevalence of FGM and the availability of healthcare services. This will serve as a reference point for comparison.

3. Modeling and Simulation: Develop a mathematical or statistical model that incorporates the identified recommendations and their potential impact on improving access to maternal health. This model should consider factors such as population demographics, geographic distribution, and socio-economic characteristics.

4. Sensitivity Analysis: Conduct sensitivity analysis to assess the robustness of the model and evaluate the potential variations in outcomes based on different scenarios and assumptions. This will help identify the most influential factors and potential limitations of the recommendations.

5. Impact Assessment: Simulate the impact of the recommendations on improving access to maternal health by running the model with different input parameters and scenarios. This will provide estimates of the expected changes in key indicators, such as reduction in FGM prevalence, increase in healthcare utilization, and improvement in maternal health outcomes.

6. Evaluation and Refinement: Evaluate the results of the simulation and assess the effectiveness of the recommendations in improving access to maternal health. Identify any gaps or areas for improvement and refine the model accordingly.

7. Policy and Decision-Making: Communicate the findings of the simulation to policymakers, healthcare providers, and other stakeholders involved in maternal health. Use the results to inform policy decisions, resource allocation, and the implementation of targeted interventions.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of specific recommendations on improving access to maternal health and make informed decisions to address this critical issue.

Autor

Dima Health

Statistics:

Citations: 2

Identifiers:

DOI: 10.1186/s12889-020-08882-4

Research Areas:

Community Interventions, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Ethiopia

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

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