Determinants of maternal high-risk fertility behaviors and its correlation with child stunting and anemia in the East Africa region: A pooled analysis of nine East African countries

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Study Justification:
– High-risk fertility behavior is a prevalent public health concern in low-income nations.
– Unmet family planning needs, child marriage, and a weak health system contribute to high-risk fertility behavior.
– Understanding the factors that influence high-risk fertility behavior and its impact on child stunting and anemia is crucial for developing effective interventions.
Study Highlights:
– The study analyzed data from nine East African countries to determine the determinants of high-risk fertility behavior and its correlation with child stunting and anemia.
– Approximately 57.6% of women in the region engaged in high-risk fertility behavior.
– Disparities in high-risk fertility behavior were found across countries and women’s residences.
– Factors associated with high-risk fertility behavior included living in rural areas, healthcare access challenges, history of abortion, better socio-economic conditions, and antenatal care follow-up.
– High-risk fertility behaviors such as young maternal age at first birth, narrow birth intervals, and high birth orders were associated with increased occurrences of child stunting and anemia.
Study Recommendations:
– Interventions focused on health education and behavioral change of women should be implemented to avert risky fertility behaviors.
– Encouraging contraceptive utilization and creating awareness about birth spacing among reproductive-age women would be helpful.
– Frequent nutritional screening and early intervention for children born from women with high-risk fertility characteristics are necessary to reduce chronic malnutrition.
Key Role Players:
– Policy makers and government officials responsible for public health and reproductive health programs.
– Healthcare providers and professionals involved in maternal and child health services.
– Community leaders and organizations working on women’s health and empowerment.
– Non-governmental organizations (NGOs) specializing in reproductive health and family planning.
Cost Items for Planning Recommendations:
– Health education and awareness campaigns.
– Training programs for healthcare providers on reproductive health and family planning.
– Access to contraceptives and family planning services.
– Nutritional screening and intervention programs for children.
– Infrastructure improvements to enhance healthcare access in rural areas.
– Monitoring and evaluation systems to assess the effectiveness of interventions.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study utilized secondary data from recent demography and health surveys of nine East African countries, which provides a large sample size and a diverse population. The study also used a mixed-effect logistic regression model to analyze the data, which is appropriate for the hierarchical structure of the data. However, the abstract does not provide information on the specific variables included in the analysis or the statistical significance of the findings. To improve the evidence, the abstract should include a summary of the main variables and their associations with high-risk fertility behavior, child stunting, and anemia. Additionally, providing the p-values and confidence intervals for the adjusted odds ratios would strengthen the evidence.

Background In low-income nations, high-risk fertility behavior is a prevalent public health concern that can be ascribed to unmet family planning needs, child marriage, and a weak health system. As a result, this study aimed to determine the factors that influence high-risk fertility behavior and its impact on child stunting and anemia. Method This study relied on secondary data sources from recent demography and health surveys of nine east African countries. Relevant data were extracted from Kids Record (KR) files and appended for the final analysis; 31,873 mother-child pairs were included in the final analysis. The mixed-effect logistic regression model (fixed and random effects) was used to describe the determinants of high-risk fertility behavior (HRFB) and its correlation with child stunting and anemia. Result According to the pooled study about 57.6% (95% CI: 57.7 to 58.2) of women had at least one high-risk fertility behavior, with major disparities found across countries and women’s residences. Women who lived in rural areas, had healthcare access challenges, had a history of abortion, lived in better socio-economic conditions, and had antenatal care follow-up were more likely to engage in high-risk fertility practices. Consequently, Young maternal age at first birth (<18), narrow birth intervals, and high birth orders were HRFBs associated with an increased occurrences of child stunting and anemia. Conclusion This study revealed that the magnitude of high-risk fertility behavior was higher in east Africa region. The finding of this study underscores that interventions focused on health education and behavioral change of women, and improvement of maternal healthcare access would be helpful to avert risky fertility behaviors. In brief, encouraging contraceptive utilization and creating awareness about birth spacing among reproductive-age women would be more helpful. Meanwhile, frequent nutritional screening and early intervention of children born from women who had high-risk fertility characteristics are mandatory to reduce the burden of chronic malnutrition.

This study was based on the secondary data from nine East African Demography and Health the most recent Survey (Burundi, Ethiopia, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zimbabwe, and Madagascar) with the analysis period ranged from July 1–30, 2020. The appended datasets of countries were used to estimate the magnitude of high-risk fertility behavior and its effects among reproductive-age women. We included women in this study who had given birth in the five years before the survey and had a child under the age of five. We used Kids Record (KR) files, which contain information about women and children, for this specific research. In terms of data extraction, we took women who were married and had completed data for the main variables, as well as children’s anthropometric measurements. The data includes socioeconomic, reproductive health, and infant traits such as height for age and hemoglobin level. After data cleaning, the final sample size was 31,873 mothers-children pair who were included in the final analysis. To select study participants in each enumeration region, the DHS used a two-stage stratified sampling technique. We combined data from nine DHS surveys conducted in East African countries, yielding a weighted sample of 31,873 women and children. The strategy is described in detail in the DHS methodology section [16]. Maternal health outcome. For this study, maternal high-risk fertility behavior was the primary outcome variable which is defined based on several criteria’s as follow; Children health outcomes. another objective of this study was to see the association between maternal risky fertility behaviors and chronic malnutrition and anemia in children. Socio-demographic and maternal health services like age group, sex of household headed, women’s educational status, husband’s educational status, maternal occupation status, marital status, media exposure, wealth status, sex of the child, birth order, antenatal care visits, sources of family planning, postnatal care visit, place of delivery, birth attendants, and healthcare access problems were independent variables. After extracting the variables based on literature, data from the nine East African countries were combined. To restore the representativeness of the survey and take sampling design into account when calculating standard errors and reliable estimates, the data were weighted using sampling weight, main sampling unit, and strata before any statistical analysis. STATA version 14 was used to perform cross-tabulations and summary statistics. Using a forest plot, the overall magnitude of high-risk fertility behavior, stunting, and anemia was estimated with the 95 percent Confidence Interval (CI). The DHS data had a hierarchical structure for the determinant factors, which contradicts the classical logistic regression model’s independence of observations and equal variance assumptions. As a result, children are nested within a cluster, and we anticipate that children in the same cluster will be more similar than children across the country. This means that advanced models should be used to account for the variability between clusters. As a result, a mixed effect logistic regression model was fitted (with both fixed and random effects). Standard logistic regression and Generalized Linear Mixed Models (GLMM) were used because the outcome variable was binary (presence or absence of high-risk fertility behavior in women, stunting, and anemia in children). Since the models were nested, the Intra-class Correlation Coefficient (ICC), Likelihood Ratio (LR) test, Median Odds Ratio (MOR), and deviance (-2LLR) values were used to compare and assess model fitness. It was decided to use the model with the lowest deviance. As a result, the mixed-effect logistic regression model fits the data the best. In the multivariable mixed-effect logistic regression model, variables with a p-value of less than 0.2 in the bivariable analysis were considered. The multivariable model used Adjusted Odds Ratios (AOR) with a 95 percent Confidence Interval (CI) and p-value 0.05 to declare major factors high-risk fertility behavior. A multivariable Generalized Linear Mixed Models (GLMM) model was also fitted to see the relationship between HRFB and infant stunting and anemia. The HRFB had a major impact on stunting and anemia, as measured by the AOR with 95 percent confidence intervals and variables with a p-value less than 0.05. Measure DHS provided ethical clearance after filling out a request for data access form. The data used in this study is aggregated secondary data that is publicly accessible and does not contain any personal identifying information that can be related to study participants. The data was kept confidential in an anonymous manner.

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

1. Mobile Health (mHealth) Solutions: Develop and implement mobile applications or text messaging services to provide pregnant women with information and reminders about antenatal care visits, nutrition, and family planning. This can help overcome barriers such as lack of awareness and access to healthcare facilities.

2. Telemedicine: Establish telemedicine platforms that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls or phone consultations. This can help address the issue of healthcare access challenges faced by women in rural areas.

3. Community Health Workers (CHWs): Train and deploy CHWs in communities to provide education, counseling, and basic healthcare services to pregnant women. CHWs can play a crucial role in improving maternal health outcomes by promoting healthy behaviors, facilitating access to healthcare services, and identifying high-risk pregnancies.

4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with financial assistance to cover the costs of antenatal care, delivery, and postnatal care. This can help reduce the financial barriers that prevent women from seeking appropriate maternal healthcare.

5. Public-Private Partnerships: Foster collaborations between public and private sectors to improve maternal health services. This can involve leveraging private healthcare providers to expand access to quality maternal healthcare, especially in areas with limited public healthcare infrastructure.

6. Maternal Waiting Homes: Establish maternal waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to travel long distances to access healthcare services. These homes can provide a safe and comfortable environment for women to stay before and after delivery.

7. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of family planning, birth spacing, and nutrition during pregnancy. These campaigns can be conducted through various channels, including mass media, community meetings, and social media.

8. Strengthening Health Systems: Invest in strengthening healthcare systems, including improving infrastructure, training healthcare professionals, and ensuring the availability of essential medicines and equipment for maternal healthcare.

It is important to note that the specific recommendations for improving access to maternal health should be tailored to the context and needs of the East Africa region, as identified through further research and consultation with relevant stakeholders.
AI Innovations Description
The study titled “Determinants of maternal high-risk fertility behaviors and its correlation with child stunting and anemia in the East Africa region: A pooled analysis of nine East African countries” provides valuable insights into the factors influencing high-risk fertility behavior and its impact on child stunting and anemia in the East Africa region. Based on the findings of the study, the following recommendations can be developed into an innovation to improve access to maternal health:

1. Health Education and Behavioral Change: Implement interventions focused on health education and behavioral change among women. This can include providing information and resources on family planning methods, birth spacing, and the importance of maternal healthcare.

2. Improved Maternal Healthcare Access: Enhance access to maternal healthcare services, particularly in rural areas where disparities were found to be higher. This can be achieved by increasing the number of healthcare facilities, improving transportation infrastructure, and ensuring availability of skilled healthcare providers.

3. Contraceptive Utilization: Encourage contraceptive utilization among reproductive-age women. This can be done through awareness campaigns, community outreach programs, and ensuring the availability and affordability of a wide range of contraceptive methods.

4. Birth Spacing: Promote awareness about the importance of birth spacing. Educate women about the risks associated with short birth intervals and encourage them to plan their pregnancies with adequate spacing between births.

5. Nutritional Screening and Early Intervention: Implement frequent nutritional screening and early intervention programs for children born to women who have engaged in high-risk fertility behaviors. This can help identify and address any nutritional deficiencies or health issues at an early stage, reducing the burden of chronic malnutrition.

By implementing these recommendations, it is possible to improve access to maternal health services, reduce high-risk fertility behaviors, and ultimately enhance the overall health and well-being of women and children in the East Africa region.
AI Innovations Methodology
Based on the provided description, here are some potential recommendations to improve access to maternal health:

1. Strengthening Health Education: Implement comprehensive health education programs that focus on reproductive health, family planning, and the importance of maternal healthcare. These programs should target women of reproductive age and provide them with accurate information and resources to make informed decisions about their reproductive health.

2. Enhancing Access to Contraceptives: Increase availability and accessibility of contraceptives, including a wide range of contraceptive methods, in both urban and rural areas. This can be achieved through the establishment of more family planning clinics, mobile clinics, and community-based distribution programs.

3. Improving Maternal Healthcare Facilities: Invest in improving the quality and accessibility of maternal healthcare facilities, particularly in rural areas. This includes ensuring the availability of skilled healthcare providers, necessary medical equipment, and essential drugs for maternal health services.

4. Promoting Birth Spacing: Raise awareness about the importance of birth spacing and provide counseling and support to women and couples on family planning methods that can help them achieve optimal birth intervals. This can be done through community outreach programs, antenatal care services, and postnatal care visits.

5. Strengthening Antenatal and Postnatal Care: Enhance the coverage and quality of antenatal and postnatal care services, including regular check-ups, screenings, and counseling on maternal and child health. This can help identify and address any potential risks or complications early on.

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

1. Define the indicators: Identify specific indicators that reflect access to maternal health, such as the percentage of women receiving antenatal care, the percentage of women using contraceptives, or the percentage of women delivering in healthcare facilities.

2. Collect baseline data: Gather baseline data on the selected indicators from the target population or relevant surveys. This could involve conducting surveys, reviewing existing data sources, or utilizing data from health facilities.

3. Implement interventions: Introduce the recommended interventions in the target population or specific areas. This could involve implementing health education programs, improving healthcare facilities, or increasing access to contraceptives.

4. Monitor and collect data: Continuously monitor the implementation of interventions and collect data on the selected indicators. This can be done through surveys, routine data collection systems, or monitoring and evaluation frameworks.

5. Analyze and compare data: Analyze the collected data to assess the impact of the interventions on the selected indicators. Compare the post-intervention data with the baseline data to determine any changes or improvements in access to maternal health.

6. Evaluate and adjust interventions: Evaluate the effectiveness of the interventions and identify areas for improvement. Based on the findings, make necessary adjustments to the interventions to further enhance access to maternal health.

7. Repeat the process: Continuously repeat the process of monitoring, analyzing, and evaluating to ensure ongoing improvement in access to maternal health.

By following this methodology, it is possible to simulate the impact of the recommended interventions on improving access to maternal health and make evidence-based decisions for further interventions or policy changes.

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