Spatial distribution and determinants of the change in pre-lacteal feeding practice over time in Ethiopia: A spatial and multivariate decomposition analysis

PLoS ONE, Volume 16, No. 1 January, Year 2021

Ukuhunyushwa

Background Pre-lacteal feeding persists in low and middle-income countries as deep-rooted nutritional malpractice. It imposes significant negative consequences on neonatal health, including increased risk of illness and mortality. Different studies revealed that pre-lacteal feeding practice is decreased over time. Even though different studies are done on the prevalence and determinants of pre-lacteal feeding practice, up to our knowledge, the spatial distribution and the determinants of the change in pre-lacteal feeding practice over time are not researched. Objective To assess the spatial distribution and determinants of the change in pre-lacteal feeding practice over time in Ethiopia. Methods We used the Ethiopian demographic and health surveys (EDHSs) data. For this study, a total weighted sample of 14672 (5789 from EDHS 2005, 4510 from EDHS 2011, and 4373 from EDHS 2016) reproductive-age women who gave birth within two years preceding the respective surveys and whoever breastfeed were used. The logit-based multivariate decomposition analysis was used to identify factors that contributed to the decrease in pre-lacteal feeding practice over the last 10 years (from 2005 to 2016). Using the 2016 EDHS data, we also conducted a spatial analysis by using ArcGIS version 10.3 and SaTScan version 9.6 software to explore the spatial distribution and hotspot clusters of pre-lacteal feeding practice. Result Pre-lacteal feeding practice was decreased from 29% [95% Confidence interval (CI): 27.63–29.96%] in 2005 to 8% [95% CI: 7.72–8.83%] in 2016 with annual rate of reduction of 7.2%. The overall decomposition analysis showed that about 20.31% of the overall decrease in pre-lacteal feeding practice over the last 10 years was attributable to the difference in composition of women (endowment) across the surveys, while, the remaining 79.39% of the overall decrease was explained by the difference in the effect of characteristics (coefficient) across the surveys. In the endowment component, the difference in composition of residence, perception of distance from the health facility, maternal educational level, wealth status, occupation, ANC visit, place of delivery, the timing of breastfeeding initiation, and wanted last-child/pregnancy were found to be significant contributing factors for the decrease in pre-lacteal feeding practice. After controlling for the role of compositional changes, the difference in the effect of distance from the health facility, wealth status, occupation, antenatal care (ANC) visit, and wanted last-child/pregnancy across the surveys were significantly contributed to the observed decrease in pre-lacteal feeding practice. Regarding the spatial distribution, pre-lacteal feeding practice was non-random in Ethiopia in which the primary and secondary clusters’ of pre-lacteal feeding identified in Somalia and the Afar region respectively. Conclusion Pre-lacteal feeding practice has shown a significant decline over the 10-year period. Program interventions considering women with poor maternal health service utilization such as ANC visits, women with poor socioeconomic status, women with an unintended pregnancy, and women from remote areas especially at border areas such as Somali and Afar could decrease pre-lacteal feeding practice in Ethiopia. We used the three Ethiopian demographic and health surveys (EDHSs) (2005, 2011, and 2016) data, which are the nationally representative surveys performed in Ethiopia. In each of the surveys, a two-stage cluster sampling was employed. In the first stage, 540 Enumeration Areas (EAs) for EDHS 2005, 624 EAs for EDHS 2011, and 645 EAs for EDHS 2016 were randomly selected proportional to the EA size and, on average, 27 to 32 households per EAs were selected in the second stage. A total weighted sample of 14672 (5789 from EDHS 2005, 4510 from EDHS 2011, and 4373 from EDHS 2016) reproductive-age women who gave birth within two years preceding the respective surveys and whoever breastfeed were used for this study. There is detailed and comprehensive information relating to the sampling process and other information in each survey report [27–29]. The outcome variable was feeding of the child other than breast milk within three days, which was a binary outcome variable coded as “1” if the mother gave anything other than breast milk and “0” if a mother gave nothing for her newborn child within three days. The independent variables included (after searching of literatures) for our study were region, place of residence, perception of distance from the health facility, age, educational level, wealth index, occupation, mass media exposure, parity, ANC visit, place of delivery, delivery by cesarean section, size of the child at birth, and timing of initiation of breastfeeding. Mass media exposure: Created by combining whether a respondent reads a newspaper, listen to the radio, and watch television and coded as yes (if a woman had exposed to at least one of these media) and no (if women were not exposed to at least one of the media). Size of the child at birth: It is defined as the size of the child during delivery, which is based on the mere report of mothers and categorized in the surveys as very small, small, average, large, and very large and recoded as average, small (includes very small and small), and large (includes large and very large) for this analysis. The other independent variable definitions are self-explanatory and more information about these variables can get from the EDHS 2016 report [28]. The data were extracted and recoded using Stata version 14. Throughout the analysis, the data were weighted to make it representative and to provide better statistical estimates. The trend and multivariate decomposition analyses were done using Stata version 14. The trend of pre-lacteal feeding practice was examined separately for the periods 2005–2011, 2011–2016, and 2005–2016. The trend of pre-lacteal feeding in each of the selected sociodemographic characteristics of respondents was also analyzed using descriptive analysis. The multivariate decomposition analysis technique was used to analyze the difference in pre-lacteal feeding practice between two points in time (2005 and 2016). It is widely practiced in public health studies to identify components of a change over time and identify contributing factors for the change [30,31]. The analysis decomposes the differences in pre-lacteal feeding practice over time into two components (the endowment part and coefficient part). For our study, the 2016 EDHS data was appended to the 2005 EDHS data using the “append” Stata command, and the logit based multivariate decomposition analysis (using mvdcmp STATA command) was used to identify factors that contributed to the decrease in pre-lacteal feeding practice over the last 10 years. Therefore, the observed decrease in pre-lacteal feeding practice was additively decomposed into differences due to endowment/characteristic and differences due to coefficient/effects of the characteristic component. In doing the decomposition analysis, the Logit or log-odd of pre-lacteal feeding practice is taken as [31]: In which, the “E” component is the part of the differential due to differences in characteristics while the “C” component refers to the part of the differential attributable due to differences in coefficients or effects of characteristics. We conducted a spatial analysis using ArcGIS version 10.3 and SaTScan version 9.6 software. To assess whether the spatial distribution of pre-lacteal feeding practice was random or non-random (spatial autocorrelation), Global Moran’s I statistic was used. Kriging spatial interpolation technique was used to predict pre-lacteal feeding practice in the un-sampled areas based on the values from sampled measurements. Besides, Getis Ord Gi* statistical hotspot analysis was done to identify the significant hot spot areas (areas with high rates of pre-lacteal feeding practice) and cold spot areas (areas with lower rates of pre-lacteal feeding practice). Moreover, we used Bernoulli based spatial scan statistical analysis to detect statistically significant clusters. To fit the model women who gave anything within three days for the newborn were taken as cases and those who gave nothing were taken as controls. The primary and secondary clusters were identified and p values were assigned and ranked using their log-likelihood ratio (LLR) test based on the 999 Monte Carlo replications. Areas with high LLR and significant p-value were considered as clusters with higher rates of pre-lacteal feeding practice and the spatial window with the highest significant LLR test statistic was defined as the most likely (primary) cluster. Since this is a secondary analysis of the Demographic and Health Survey (DHS) data, ethical approval was not necessary. However, we registered and requested the datasets from DHS on-line archive and received permission to access and download the data files. 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.

I-AI Digest

Study Justification:

– Pre-lacteal feeding is a persistent issue in low and middle-income countries, with negative consequences for neonatal health.
– Previous studies have examined the prevalence and determinants of pre-lacteal feeding, but the spatial distribution and determinants of change over time have not been researched.
– Understanding the spatial distribution and determinants of change can inform targeted interventions to reduce pre-lacteal feeding practice.

Highlights:

– Pre-lacteal feeding practice in Ethiopia decreased from 29% in 2005 to 8% in 2016, with an annual rate of reduction of 7.2%.
– The decrease in pre-lacteal feeding practice over the last 10 years was attributed to both differences in the composition of women and the effects of characteristics.
– Factors such as residence, perception of distance from health facility, maternal education, wealth status, occupation, ANC visit, place of delivery, timing of breastfeeding initiation, and wanted last-child/pregnancy were significant contributors to the decrease in pre-lacteal feeding practice.
– Spatial analysis identified primary and secondary clusters of pre-lacteal feeding practice in Somalia and the Afar region, respectively.

Recommendations:

– Program interventions should target women with poor maternal health service utilization, including ANC visits, women with poor socioeconomic status, women with unintended pregnancies, and women from remote areas, especially at border areas such as Somali and Afar.
– Strategies to improve access to healthcare facilities, increase maternal education, and promote early initiation of breastfeeding should be implemented.

Key Role Players:

– Ministry of Health: Responsible for implementing and coordinating interventions to reduce pre-lacteal feeding practice.
– Non-governmental organizations (NGOs): Involved in implementing community-based interventions and providing support to women and families.
– Health professionals: Including doctors, nurses, and midwives, who play a crucial role in educating and counseling women on appropriate infant feeding practices.
– Community leaders and volunteers: Engaged in raising awareness, promoting behavior change, and supporting women in adopting optimal infant feeding practices.

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers and community volunteers.
– Development and dissemination of educational materials and resources.
– Outreach programs and community mobilization activities.
– Monitoring and evaluation of interventions.
– Research and data collection to assess the impact of interventions and track progress.
Please note that the cost items provided are general categories and may vary depending on the specific context and implementation strategies. A detailed budget would require a comprehensive analysis and consultation with relevant stakeholders.

Amandla Obufakazi

N/A

Abstract

We used the three Ethiopian demographic and health surveys (EDHSs) (2005, 2011, and 2016) data, which are the nationally representative surveys performed in Ethiopia. In each of the surveys, a two-stage cluster sampling was employed. In the first stage, 540 Enumeration Areas (EAs) for EDHS 2005, 624 EAs for EDHS 2011, and 645 EAs for EDHS 2016 were randomly selected proportional to the EA size and, on average, 27 to 32 households per EAs were selected in the second stage. A total weighted sample of 14672 (5789 from EDHS 2005, 4510 from EDHS 2011, and 4373 from EDHS 2016) reproductive-age women who gave birth within two years preceding the respective surveys and whoever breastfeed were used for this study. There is detailed and comprehensive information relating to the sampling process and other information in each survey report [27–29]. The outcome variable was feeding of the child other than breast milk within three days, which was a binary outcome variable coded as “1” if the mother gave anything other than breast milk and “0” if a mother gave nothing for her newborn child within three days. The independent variables included (after searching of literatures) for our study were region, place of residence, perception of distance from the health facility, age, educational level, wealth index, occupation, mass media exposure, parity, ANC visit, place of delivery, delivery by cesarean section, size of the child at birth, and timing of initiation of breastfeeding. Mass media exposure: Created by combining whether a respondent reads a newspaper, listen to the radio, and watch television and coded as yes (if a woman had exposed to at least one of these media) and no (if women were not exposed to at least one of the media). Size of the child at birth: It is defined as the size of the child during delivery, which is based on the mere report of mothers and categorized in the surveys as very small, small, average, large, and very large and recoded as average, small (includes very small and small), and large (includes large and very large) for this analysis. The other independent variable definitions are self-explanatory and more information about these variables can get from the EDHS 2016 report [28]. The data were extracted and recoded using Stata version 14. Throughout the analysis, the data were weighted to make it representative and to provide better statistical estimates. The trend and multivariate decomposition analyses were done using Stata version 14. The trend of pre-lacteal feeding practice was examined separately for the periods 2005–2011, 2011–2016, and 2005–2016. The trend of pre-lacteal feeding in each of the selected sociodemographic characteristics of respondents was also analyzed using descriptive analysis. The multivariate decomposition analysis technique was used to analyze the difference in pre-lacteal feeding practice between two points in time (2005 and 2016). It is widely practiced in public health studies to identify components of a change over time and identify contributing factors for the change [30,31]. The analysis decomposes the differences in pre-lacteal feeding practice over time into two components (the endowment part and coefficient part). For our study, the 2016 EDHS data was appended to the 2005 EDHS data using the “append” Stata command, and the logit based multivariate decomposition analysis (using mvdcmp STATA command) was used to identify factors that contributed to the decrease in pre-lacteal feeding practice over the last 10 years. Therefore, the observed decrease in pre-lacteal feeding practice was additively decomposed into differences due to endowment/characteristic and differences due to coefficient/effects of the characteristic component. In doing the decomposition analysis, the Logit or log-odd of pre-lacteal feeding practice is taken as [31]: In which, the “E” component is the part of the differential due to differences in characteristics while the “C” component refers to the part of the differential attributable due to differences in coefficients or effects of characteristics. We conducted a spatial analysis using ArcGIS version 10.3 and SaTScan version 9.6 software. To assess whether the spatial distribution of pre-lacteal feeding practice was random or non-random (spatial autocorrelation), Global Moran’s I statistic was used. Kriging spatial interpolation technique was used to predict pre-lacteal feeding practice in the un-sampled areas based on the values from sampled measurements. Besides, Getis Ord Gi* statistical hotspot analysis was done to identify the significant hot spot areas (areas with high rates of pre-lacteal feeding practice) and cold spot areas (areas with lower rates of pre-lacteal feeding practice). Moreover, we used Bernoulli based spatial scan statistical analysis to detect statistically significant clusters. To fit the model women who gave anything within three days for the newborn were taken as cases and those who gave nothing were taken as controls. The primary and secondary clusters were identified and p values were assigned and ranked using their log-likelihood ratio (LLR) test based on the 999 Monte Carlo replications. Areas with high LLR and significant p-value were considered as clusters with higher rates of pre-lacteal feeding practice and the spatial window with the highest significant LLR test statistic was defined as the most likely (primary) cluster. Since this is a secondary analysis of the Demographic and Health Survey (DHS) data, ethical approval was not necessary. However, we registered and requested the datasets from DHS on-line archive and received permission to access and download the data files. 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.

Izinto zokwakha

Emisha

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 pregnant women and new mothers with information and reminders about proper breastfeeding practices, including the importance of exclusive breastfeeding and the risks of pre-lacteal feeding. These apps can also provide access to virtual consultations with healthcare professionals and facilitate the scheduling of antenatal care visits.

2. Community Health Workers: Train and deploy community health workers to educate and support pregnant women and new mothers in their communities. These workers can provide counseling on breastfeeding practices, conduct home visits to monitor and support breastfeeding, and refer women to appropriate healthcare facilities when needed.

3. Telemedicine: Establish telemedicine services that allow pregnant women and new mothers in remote or underserved areas to access healthcare professionals for consultations and advice on breastfeeding practices. This can help overcome geographical barriers and improve access to timely and accurate information.

4. Maternal Health Hotline: Set up a toll-free hotline dedicated to maternal health, where women can call to seek advice and information on breastfeeding practices. Trained healthcare professionals can provide guidance and address concerns, ensuring that women receive accurate and timely support.

5. Peer Support Groups: Create peer support groups for pregnant women and new mothers, where they can share experiences, receive emotional support, and learn from each other about breastfeeding practices. These groups can be facilitated by trained healthcare professionals or community leaders and can help reduce social isolation and improve breastfeeding outcomes.

6. Targeted Education Campaigns: Develop targeted education campaigns to raise awareness about the importance of exclusive breastfeeding and the risks of pre-lacteal feeding. These campaigns can utilize various media channels, including radio, television, social media, and community gatherings, to reach a wide audience and promote behavior change.

7. Integration of Maternal Health Services: Ensure that maternal health services, including breastfeeding support, are integrated into existing healthcare systems. This includes training healthcare providers on evidence-based breastfeeding practices, establishing breastfeeding-friendly environments in healthcare facilities, and promoting the Baby-Friendly Hospital Initiative.

8. Financial Incentives: Explore the use of financial incentives, such as conditional cash transfers or vouchers, to encourage pregnant women and new mothers to seek and utilize maternal health services, including breastfeeding support. This can help address financial barriers and improve access to quality care.

9. Public-Private Partnerships: Foster collaborations between the public and private sectors to improve access to maternal health services. This can involve leveraging private sector resources and expertise to support innovative solutions, such as telemedicine platforms or mobile health applications.

10. Research and Data Collection: Continue conducting research and data collection to better understand the determinants of pre-lacteal feeding practices and identify effective interventions. This can help inform the development and implementation of evidence-based strategies to improve access to maternal health and promote optimal breastfeeding practices.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health based on the study “Spatial distribution and determinants of the change in pre-lacteal feeding practice over time in Ethiopia: A spatial and multivariate decomposition analysis” is as follows:

1. Targeted Interventions: Implement targeted interventions that focus on women with poor maternal health service utilization, such as those who have low antenatal care (ANC) visit rates, poor socioeconomic status, unintended pregnancies, and women from remote areas, especially at border areas such as Somali and Afar. These interventions can include educational programs, community outreach, and improved access to healthcare facilities.

2. Awareness Campaigns: Conduct awareness campaigns to educate women and their families about the importance of exclusive breastfeeding and the negative consequences of pre-lacteal feeding. These campaigns can utilize various media channels, including radio, television, and newspapers, to reach a wide audience.

3. Strengthening Healthcare Infrastructure: Improve access to healthcare facilities, particularly in remote areas, by investing in the development and expansion of healthcare infrastructure. This can include building new healthcare facilities, improving transportation networks, and providing necessary resources and equipment to ensure quality maternal healthcare services.

4. Training and Capacity Building: Provide training and capacity building programs for healthcare providers, including doctors, nurses, and midwives, to enhance their knowledge and skills in promoting exclusive breastfeeding and providing appropriate maternal healthcare. This can include training on breastfeeding counseling, lactation support, and the management of common breastfeeding challenges.

5. Community Engagement: Engage local communities and community leaders in promoting exclusive breastfeeding and improving maternal health. This can involve establishing support groups, involving community health workers, and conducting community-based education programs to address cultural beliefs and practices related to infant feeding.

6. Monitoring and Evaluation: Establish a robust monitoring and evaluation system to track the progress and impact of interventions aimed at improving access to maternal health. This can include regular data collection, analysis, and reporting to identify gaps and inform evidence-based decision-making.

By implementing these recommendations, it is expected that access to maternal health will be improved, leading to a reduction in pre-lacteal feeding practices and better health outcomes for mothers and newborns in Ethiopia.

AI Innovations Methodology

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

1. Strengthening maternal health services: Increase the availability and accessibility of quality maternal health services, including antenatal care, skilled birth attendance, postnatal care, and family planning services. This can be achieved by improving infrastructure, training healthcare providers, and ensuring the availability of essential medicines and equipment.

2. Enhancing community engagement: Promote community involvement and participation in maternal health programs. This can be done through community education and awareness campaigns, mobilizing community health workers, and establishing community-based support groups for pregnant women and new mothers.

3. Addressing socio-economic barriers: Implement interventions to address socio-economic barriers that hinder access to maternal health services. This may include providing financial incentives or subsidies for maternal health services, improving transportation infrastructure, and addressing cultural and social norms that may discourage women from seeking care.

4. Utilizing technology and innovation: Explore the use of technology and innovation to improve access to maternal health services. This can include telemedicine, mobile health applications, and remote monitoring devices to provide remote consultations and support to pregnant women in remote or underserved areas.

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 reflect access to maternal health services, such as the percentage of pregnant women receiving antenatal care, the percentage of births attended by skilled birth attendants, and the percentage of women receiving postnatal care.

2. Collect baseline data: Gather baseline data on the selected indicators from relevant sources, such as national health surveys, health facility records, and community surveys. This will provide a starting point for comparison.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. This model should consider factors such as population demographics, healthcare infrastructure, socio-economic conditions, and technological feasibility.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations on the selected indicators. This can be done by adjusting the parameters related to each recommendation and observing the resulting changes in the indicators.

5. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on improving access to maternal health services. This can include assessing changes in the selected indicators, identifying areas or population groups that may benefit the most from the recommendations, and evaluating the cost-effectiveness of the interventions.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and validate it using additional data sources or expert input. This will ensure the accuracy and reliability of the simulation findings.

7. Communicate findings and inform decision-making: Present the simulation findings in a clear and concise manner to stakeholders, policymakers, and healthcare providers. Use the results to inform decision-making processes and prioritize interventions that have the greatest potential for improving access to maternal health services.

It is important to note that the methodology described above is a general framework and may need to be adapted based on the specific context and data availability.

Ababhali & Co-Ababhali

Statistics:

Authors: 3

Identifiers:

DOI: 10.1371/journal.pone.0244574

Research Areas:

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

Study Countries:

Ethiopia, Multi-Countries, Somalia

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

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