Exploring the determinants of exclusive breastfeeding among infants under-six months in Ethiopia using multilevel analysis

listen audio

Study Justification:
– Exclusive breastfeeding is the safest and healthiest option for infants in the first 6 months.
– Previous studies have focused on individual-level determinants, but this study aims to identify determinants at both the individual and community levels.
– Understanding these determinants will help design appropriate strategies to improve exclusive breastfeeding practices.
Study Highlights:
– The study used data from the 2016 Ethiopian Demographic and Health Survey.
– A total of 1185 infants under 6 months of age were included in the analysis.
– Multilevel logistic regression was used to investigate factors associated with exclusive breastfeeding.
– Individual-level determinants included age of the infant, sex of the infant, infant comorbidities, household wealth index, and antenatal care.
– Community-level determinants included contextual region, community-level postnatal visit, and community-level maternal employment.
– The study found that 46.8% of the variation in exclusive breastfeeding was explained by factors at the individual and community levels.
Recommendations:
– Promote and enhance antenatal and postnatal care services utilization to improve exclusive breastfeeding practice.
– Give more emphasis to infants with comorbid conditions and those living in pastoralist regions.
Key Role Players:
– Health policymakers and government officials responsible for maternal and child health programs.
– Healthcare providers, including doctors, nurses, and midwives.
– Community health workers and volunteers.
– Non-governmental organizations (NGOs) working in maternal and child health.
Cost Items for Planning Recommendations:
– Training and capacity building for healthcare providers and community health workers.
– Development and dissemination of educational materials for mothers and families.
– Strengthening antenatal and postnatal care services, including infrastructure and staffing.
– Community outreach programs and awareness campaigns.
– Monitoring and evaluation of exclusive breastfeeding programs.
– Research and data collection to inform evidence-based interventions.

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 using a large sample size from the 2016 Ethiopian Demographic and Health Survey. The study employed a multilevel logistic regression model to investigate the determinants of exclusive breastfeeding at both individual and community levels. The adjusted odds ratios and 95% confidence intervals were used to measure the association of variables, and measures of random effects (intracluster correlation, median odds ratio, and proportional change in variance) were used to assess the variation in exclusive breastfeeding. The study provides valuable insights into the factors influencing exclusive breastfeeding in Ethiopia and suggests actionable steps to improve the practice, such as promoting antenatal and postnatal care services utilization and focusing on infants with comorbid conditions and those living in pastoralist regions. However, to further strengthen the evidence, it would be beneficial to include more details about the sampling procedure, data collection methods, and statistical analysis techniques in the abstract.

Introduction Exclusive breastfeeding (EBF) is the safest and healthiest option of feeding among infants in the first 6 months throughout the world. Thus, the promotion of EBF is essential to prevent complex infant health problems even at the adulthood level. But the majority of previous studies focused on individual- level determinants of EBF by using basic regression models in localized areas. This study aimed to identify the determinants of EBF at the individual and community level which would be helpful to design appropriate strategies for improving the practice of EBF. Methods It is a secondary data analysis using the 2016 Ethiopian Demographic and Health Survey (EDHS) data. A total of 1185 infants under 6 months of age were included in the analysis. A Multilevel logistic regression model was employed to investigate factors significantly associated with EBF among under-six infants in Ethiopia. Adjusted odds ratio (AOR) with 95% confidence interval (CI) was used to measure the association of variables whereas Intracluster correlation (ICC), median odds ratio (MOR), and proportional change in variance (PCV) were used to measure random effects (variation). Result In multilevel logistic regression; 4–5 months age infant (AOR = 0.04, 95%CI:0.02–0.07), female infants (AOR = 2.51, 95%CI:1.61–3.91), infant comorbidities (AOR = 0.35, 95%CI: 0.21–0.57), richest household wealth index (AOR = 10.34, 95%CI: 3.14–34.03) and antenatal care (AOR = 2.25, 95%CI:1.32–3.82) were individual- level determinants significantly associated with exclusive breastfeeding. Whereas, contextual region (AOR = 0.30, 95%CI: 0.10–0.87), community- level of postnatal visit (AOR = 2.77, 95%CI: 1.26–6.58) and community -level of maternal employment (AOR = 2.8, 95%CI: 1.21–6.47) were community level determinants significantly associated with EBF. The full model showed that46.8% of the variation of exclusive breastfeeding was explained by the combined factors at the individual and community levels. Similarly, it showed that the variation in exclusive breastfeeding across communities remained statistically significant (ICC = 8.77% and variance = 0.32 with P75% of women are utilizing ANC) [25]. The Community level of PNC utilization was the proportion of women within specific cluster who visit PNC some number of times. It was categorized as low (when≤50% of women utilized PNC) and high (when>50% of women utilized PNC) [25]. Community- level of media exposure was an aggregate respondent level of exposure for different types of media categorized as “50% = high media utilized communities” [25]. Community level of poverty was an aggregate wealth index categorized as “50% = Low poverty communities” [25]. Contextual region Ethiopia is demarcated for administrative purpose into 11 regions, which are classified as an agrarian, pastoralist, and city based according to the living status of the population. The regions Tigray, Amhara, Oromia, SNNP, Gambella, and Benshangul Gumuz were categorized as agrarian. Somali and Afar regions were grouped to form pastoralist region and Harari region, Addis Ababa and Dire Dawa city administrations were grouped to form city- based populations [14,25]. Community- level of women education was the proportion of women in the community who have primary or higher education, which was categorized as low (when≤25% of women were educated), middle (when 25–75% of women were educated), and high (when >75% of women were educated) [25]. Community level employment status was the proportion of women who were employed (had to work) in the specific cluster. It was categorized as low (when≤50% of mothers were employed) and high (when>50% of mothers were employed) [24,25]. Sample weight was done to compensate for the unequal probability of selection between the strata that were geographically defined, as well as for non-responses. Weighing of individual interview produces the proper representation of exclusive breastfeeding and related factors. Coding, recoding, and exploratory analysis was performed. Categorization was done for continuous variables using information from different works of literatures and re-categorization was done for categorical variables accordingly. For data analysis, STATA version 14.1 was used and descriptive statistics were used to present frequencies, with percentages in tables and using texts. Four models were considered in the multilevel analysis to determine the model that best fits the data; Model one (Null model) without explanatory variable was developed to evaluate the null hypothesis that there is no cluster level difference in exclusive breastfeeding practice that specified only the random intercept and it presented the total variance in exclusive breastfeeding practice among clusters. Model two adjusted for an individual variable which assumes a cluster level difference of EBF practice is zero. Model three to evaluate community level factors by aggregate cluster difference of exclusive breastfeeding practice. Model four included both adjusted individual and community level factors. The log of the probability of Exclusive breastfeeding was modeled using a two-level multilevel model as follows: [26,27]. Where, i and j are the level 1 (individual) and level 2 (community) units, respectively; X and Z refer to individual and community-level variables, respectively; πij is the probability of exclusive breastfeeding for the ith infant in the jth community; the β’s is the fixed coefficients. Whereas, β0 is the intercept-the effect on the probability of exclusive breastfeeding use in the absence of influence of predictors and uj showed the random effect (effect of the community on exclusive breastfeeding) for the jth community, and eij showed random errors at the individual levels. By assuming each community had a different intercept (β0) and fixed coefficient (β), the clustered data nature and the within and between community, variations were taken into account. Multilevel logistic regression analysis was used to analyze the data since it is appropriate for DHS data as it had a hierarchical nature. Multilevel modeling was providing unexplained variation in exclusive breastfeeding due to unobserved cluster factors called the random effect. All models included a random intercept at the cluster level to capture the heterogeneity among clusters. The measures of association (fixed-effects) estimate the association between likelihood of infants to exclusively breastfeeding as the AOR with 95% CI of various explanatory variables were expressed. Crude association between independent variables and the dependent variable was done independently and variables having p ≤0.2 in Bi-variable analysis were used to fit multivariable analysis model. At multivariable analysis,variables with p≤0.05 with confidence interval not including the null value (OR = 1)were considered as statistically significant variables with exclusive breastfeeding practice. The measures of variation (random-effects) were reported using Intra-cluster correlation (ICC), Median Odds Ratio (MOR), and Proportional Change in Variance (PCV). ICC was used to explain cluster variation while MOR is a measure of unexplained cluster heterogeneity [27]. The ICC shows the variation in exclusive breastfeeding of infants undersix months of age due to community characteristics. The higher the ICC (ICC>5%), the more relevant was the community characteristics for understanding individual variation in exclusive breastfeeding of infants. The ICC can be calculated as follows: [ICC=δ2uδ2u+δ2e] where δ2u = between group variation, δ2e = with in group variation OR [ICC=δ2δ2+π23], where δ2 is the estimated variance of clusters [26]. The STATA software command can also compute the ICC value of each model. MOR is defined as the median value of the odds ratio between the area at highest likelihood and the area at the lowest likelihood of exclusive breastfeeding when randomly picking out two areas and it measures the unexplained cluster heterogeneity; the variation between clusters by comparing two persons from two randomly chosen different clusters. MOR can be calculated using the formula [26]. In this study, MOR shows the extent to which the individual probability of being exclusively breastfed is determined by residential area. The proportional change in variance [PCV = (VA − VB)/VA) * 100] where VA = Variance of initial model and VB = Variance of model with more terms measures the total variation attributed by individual level and community level factors in the multilevel model [26]. PCV was computed for each model concerning the empty model as a reference to show power of the factors in the model explains exclusive breastfeeding practice. Log- likelihood test, Deviance Information Criteria (DIC), and Akaike Information Criteria (AIC) were used to estimate the goodness of fit of the adjusted final model in comparison to the preceding models (individual and community level models). Thus, the model with the highest value of Log likelihood test and with lowest values of DIC and AIC was considered to be the best fit model. Ethical clearance was obtained from the Ethical Review Committee of the College of Medicine and Health Sciences, Wollo University with approval and supporting letter. Permission to access the data set was obtained from the Measure DHS International Program. The data was only used for purpose of this study and not shared with a third party. All data used in this study were anonymous publicly available and aggregated secondary data with not having any personal identity. The data was fully available on the full DHS website (www.measuredhs.com).

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide information and support for exclusive breastfeeding, antenatal care, and postnatal care. These apps can provide personalized advice, reminders for appointments and medication, and educational resources for mothers.

2. Telemedicine Services: Implement telemedicine services to connect mothers in remote or underserved areas with healthcare professionals. This can help address the lack of access to healthcare facilities and provide timely advice and support for maternal health concerns.

3. Community Health Workers: Train and deploy community health workers to provide education and support for exclusive breastfeeding and maternal health. These workers can visit households, conduct group sessions, and provide personalized guidance to mothers in their communities.

4. Maternal Health Clinics: Establish dedicated maternal health clinics that provide comprehensive care for pregnant women and new mothers. These clinics can offer antenatal and postnatal care services, breastfeeding support, and counseling on nutrition and healthy lifestyles.

5. Public Awareness Campaigns: Launch public awareness campaigns to promote the importance of exclusive breastfeeding and maternal health. These campaigns can use various media channels, including radio, television, and social media, to reach a wide audience and educate them about the benefits and practices of exclusive breastfeeding.

6. Maternity Waiting Homes: Set up maternity waiting homes near healthcare facilities to accommodate pregnant women who live far away. These homes can provide a safe and comfortable environment for women to stay before and after giving birth, ensuring timely access to healthcare services.

7. Financial Incentives: Introduce financial incentives for mothers who exclusively breastfeed their infants. This can help motivate and encourage mothers to practice exclusive breastfeeding, especially in low-income communities where there may be competing demands on their time and resources.

8. Partnerships with Non-Governmental Organizations (NGOs): Collaborate with NGOs that specialize in maternal and child health to implement innovative programs and interventions. These partnerships can leverage the expertise and resources of NGOs to improve access to maternal health services and support.

9. Maternal Health Hotlines: Establish toll-free hotlines staffed by trained healthcare professionals to provide immediate assistance and advice to mothers regarding exclusive breastfeeding and other maternal health concerns. This can be particularly helpful for mothers who may have questions or need support outside of regular clinic hours.

10. Data-Driven Approaches: Utilize data from surveys and research studies, such as the Ethiopian Demographic and Health Survey, to identify specific areas and communities where access to maternal health services is limited. This can help target interventions and resources to areas with the greatest need, ensuring efficient and effective use of resources.
AI Innovations Description
The study titled “Exploring the determinants of exclusive breastfeeding among infants under-six months in Ethiopia using multilevel analysis” provides valuable insights into factors influencing exclusive breastfeeding practices in Ethiopia. Based on the findings of the study, the following recommendations can be made to improve access to maternal health and promote exclusive breastfeeding:

1. Promote and enhance antenatal care (ANC) services: The study found that ANC utilization was significantly associated with exclusive breastfeeding. Therefore, it is recommended to focus on improving access to and utilization of ANC services among pregnant women. This can be achieved through community-based education and awareness programs, ensuring availability of ANC services in remote areas, and training healthcare providers to provide comprehensive ANC services.

2. Enhance postnatal care (PNC) services: The study identified that the community-level of postnatal visit was significantly associated with exclusive breastfeeding. To improve access to maternal health, it is crucial to strengthen PNC services and encourage mothers to attend postnatal visits. This can be achieved by providing comprehensive PNC services, including breastfeeding support and counseling, and ensuring that PNC services are accessible and available to all mothers.

3. Focus on infants with comorbid conditions: The study found that infants with comorbidities were less likely to be exclusively breastfed. Therefore, it is recommended to provide targeted support and interventions for infants with comorbid conditions to promote exclusive breastfeeding. This can include training healthcare providers on managing comorbidities in infants, providing breastfeeding support to mothers of infants with comorbidities, and raising awareness among parents about the importance of exclusive breastfeeding for infants with health conditions.

4. Address regional disparities: The study highlighted that contextual region was a significant determinant of exclusive breastfeeding. To improve access to maternal health and promote exclusive breastfeeding, it is important to address regional disparities and ensure that interventions and resources are distributed equitably across different regions. This can be achieved through targeted interventions in regions with lower exclusive breastfeeding rates, improving healthcare infrastructure in underserved regions, and promoting community engagement and participation in maternal health programs.

Overall, the study emphasizes the importance of a multi-level approach to improve access to maternal health and promote exclusive breastfeeding. By addressing individual and community-level determinants, implementing targeted interventions, and ensuring equitable distribution of resources, it is possible to enhance maternal and child health outcomes in Ethiopia.
AI Innovations Methodology
The methodology used in the study titled “Exploring the determinants of exclusive breastfeeding among infants under-six months in Ethiopia using multilevel analysis” involved a secondary data analysis using the 2016 Ethiopian Demographic and Health Survey (EDHS) data. The study included a total of 1185 infants under 6 months of age.

A multilevel logistic regression model was employed to investigate the factors associated with exclusive breastfeeding among infants in Ethiopia. The model considered both individual-level and community-level factors. The outcome variable, exclusive breastfeeding, was measured using a 24-hour recall among mothers with infants under 6 months of age.

The study analyzed various individual-level factors such as infant age, sex, comorbidities, household wealth index, and antenatal care utilization. It also examined community-level factors including contextual region, community-level postnatal visit, and community-level maternal employment.

Four models were considered in the multilevel analysis: a null model without explanatory variables, a model adjusted for individual-level factors, a model adjusted for community-level factors, and a full model that included both individual and community-level factors. The measures of association, such as adjusted odds ratios (AOR) with 95% confidence intervals (CI), were used to assess the association between the variables and exclusive breastfeeding.

The study also calculated measures of variation, including the intracluster correlation (ICC), median odds ratio (MOR), and proportional change in variance (PCV). These measures were used to assess the random effects and the variation in exclusive breastfeeding across clusters.

The goodness of fit of the models was evaluated using log-likelihood tests, Deviance Information Criteria (DIC), and Akaike Information Criteria (AIC). The model with the highest log-likelihood test value and the lowest DIC and AIC values was considered the best fit model.

Ethical clearance was obtained from the Ethical Review Committee of the College of Medicine and Health Sciences, Wollo University, and permission to access the data set was obtained from the Measure DHS International Program. The study used anonymous and publicly available aggregated secondary data.

The findings of the study provided valuable insights into the determinants of exclusive breastfeeding in Ethiopia and can be used to inform strategies for improving access to maternal health and promoting exclusive breastfeeding.

Partilhar isto:
Facebook
Twitter
LinkedIn
WhatsApp
Email