Uptake of postnatal care and its determinants in Ethiopia: a positive deviance approach

BMC Pregnancy and Childbirth, Volume 22, No. 1, Year 2022

Interpretation

Background: Postnatal care (PNC) services are an essential intervention for improving maternal and child health. In Ethiopia, PNC service has been poorly implemented, despite the governments and partners’ attempt to improve maternal and child health service utilization. Moreover, many literatures identified that women with no education are significantly underutilized the PNC services. Thus, this study aimed to assess the PNC service uptake among women at high risk for underutilization of PNC services and to identify the individual and community level determinants of PNC services uptake in Ethiopia using the positive deviance approach. Methods: Data from the Ethiopia Demographic and Health Survey 2016 were used. A total of 2417 deviant women (women with no education) were identified through a two-stage stratified sampling technique and included in this analysis. A multilevel mixed-effect binary logistic regression analysis was computed to identify the individual and community-level determinants of PNC services uptake among deviant women. In the final model, a p-value of less than 0.05 and adjusted odds ratio (AOR) with 95% confidence interval (CI) were used to declare statistically significant determinants of PNC services uptake. Results: In this analysis, the uptake of PNC service among deviant women was 5.8% [95% CI: 4.9–6.8]. Working in the agriculture (AOR = 2.15, 95% CI: 1.13–3.52), being Orthodox religion follower (AOR = 2.56, 95% CI: 1.42–4.57), living in the highest wealth quantile (AOR = 2.22, 95% CI: 1.25–3.91) were the individual level determinants, whereas residing in the city administration (AOR: 3.17, 95% CI: 1.15–8.71), and living closer to health facility (AOR: 1.57, 95% CI: 1.03–2.39) were the community level determinants. Conclusion: The study highlighted a better PNC service uptake among deviant women who are working in the agriculture, follows orthodox religion, lives in highest household wealth status, resides in city administration, and living closer to the health facility. The positive deviance approach provides evidences for health policy makers and program implementers to improve health behavior in specific target population, and ultimately to bring better maternal and child health outcomes, despite acknowledged adverse risk profile. Such strategy and knowledge could facilitate targeted efforts aimed at achieving national goals of maternal and newborn mortality reduction in the country. The study used the EDHS 2016 data, a nationally representative household survey that has been implemented by the Central Statistical Agency (CSA) of Ethiopia every 5 years [28]. Ethiopia is a home country of an estimated 114 million population (CSA 2015). Administratively, the country is divided into nine regions (Tigray, Afar, Amhara, Oromia, Benishangul, Gambela, South Nation Nationalities and Peoples’ Region (SNNPR), Harari and Somali) and two City Administrations (Addis Ababa and Dire-Dawa). Those nine regions can be divided in to developed regions (Tigray, Amhara, Oromia, South Nation Nationalities and Peoples’ Region (SNNPR), and Harari), and emerging regions (Afar, Benishangul, Gambela, and Somali). The 2016 EDHS used the 2007 Ethiopian population and housing census as a sampling frame, which was conducted by the CSA of Ethiopia and a complete list of 84,915 enumeration areas (EAs) were used in the census. The 2016 EDHS sample was stratified in two stages. A sample of EAs were selected from each stratum, independently. Then a total of 645 EAs were selected with probability proportional to the EA size, and each sampling stratum was selected from the given samples. The total residential households in the EA were the EA size, and a household listing operation was implemented. Then, the resulting lists of households were used as the sampling frame for selecting households in the second stage. Accordingly, all women aged 15–49 years who are regular members of the selected households were eligible for the survey. Finally, from a total of 4081 women identified from the EDHS 2016, 2417 deviant women were included in this analysis, and data were extracted from the datasets using STATA version 14 software. Variables at the individual and community-level were also extracted and further analyzed. We used the Anderson’s behavioral model of health service use [29] and other related studies [30, 31] to identify the positive deviant for PNC services uptake. Accordingly, education is the major determinant of health services utilization. We selected women who had no education as a sub-group with a very low likelihood of PNC services utilization, as education was the strongest predictor of PNC after adjusting for the other risk factors associated with PNC in this population. Positive deviant women were those who had no education but had an adequate uptake of PNC services. Finally, in the analysis, we compared the characteristics of the PD women to those of their counterparts. Due to significant variations by clusters in the overall use of PNC and also the individual and household level data were nested under the community level data, the analysis was stratified by individual and community level. Uptake of PNC services among deviant women was the dependent variable. The uptake was assessed when a woman received PNC services within 2 months after delivery, irrespective of their place of delivery. The information on the uptake of PNC services for their recent birth was assessed based on the women’s verbal responses during the survey. Accordingly, it was categorized as “yes” if a woman received at least one PNC visit, otherwise “no”. Individual-level variables; socio-demographic and economic variables (age, occupational status, religion, marital status, age at first birth, desire for child, household wealth status) were included in this analysis. On the other hand, place of residence, region, living closer to health facility, and media exposure were the community-level variables. Household wealth status was assessed using the asset index based on data from the entire sample on separate scores prepared for rural and urban households, and combined to produce a single asset index for all households and ranked into three (lowest, middle, and highest). The difficulty of getting health services was assessed by the question “living closer to health facility” and the responses were categorized as “yes” or “no”. Media exposure was assessed based on whether people had access to read newsletters, listen to the radio, and watch TV. Accordingly, if they have access to all three media (newsletter, radio, and TV) at least once a week, we categorized them as “yes”, otherwise “no”. The data were extracted, cleaned, re-coded, and analyzed using STATA version 17. The data were weighted using sampling weight during the statistical analysis to adjust for unequal probability of selection due to the sampling design used in DHS data. Tables and narrations were used to present the descriptive statistics. Since the DHS data are hierarchical (individual were nested within communities), a two-level binary logistic regression model was fitted to estimate the effect of both individual and community-level variables on PNC services uptake [32]. In this multilevel analysis, we fitted four models; i) Model 0: an empty (null) model without any explanatory variables, ii) Model 1: a model with individual-level variables, iii) Model 2: a model with community-level variables, and iv) Model 3: a model with both individual and community -level variables. In the survey, the individual and household level data were nested under the community level data, for model comparison, Intra-class Correlation Coefficient and deviance (− 2* log likelihood ratio) were used. Accordingly, a model with lowest deviance was chosen. The variation between clusters was assessed by computing Intra-lass Correlation Coefficient (ICC) [33]. The ICC is the proportion of variance explained by the grouping structure in the population which: ICC=; Where, the standard logit distribution has a variance of,, indicates the cluster variance. The ICC greater than 5% is eligible for multilevel analysis and in our analysis, the ICC was 22.5%. A mixed effect multilevel binary logistic regression analysis was done. A low deviance value was used to estimate the model goodness of fit by comparing the full model with the preceding three models. Finally, a p-value of less than 0.05 and an adjusted odds ratio (AOR) with 95% confidence interval (CI) were used to declare statistically significant factors associated with PNC services uptake among deviant women.

AI Digest

Study Justification:

The study aimed to assess the uptake of postnatal care (PNC) services among women at high risk for underutilization in Ethiopia. This is important because PNC services are essential for improving maternal and child health, but they have been poorly implemented in the country. The study also aimed to identify the individual and community-level determinants of PNC service uptake using a positive deviance approach. This approach can provide evidence for health policymakers and program implementers to improve health behavior in specific target populations and ultimately achieve better maternal and child health outcomes.

Highlights:

– The study found that the uptake of PNC services among deviant women (women with no education) was 5.8%.
– Individual-level determinants of PNC service uptake among deviant women included working in agriculture, being an Orthodox religion follower, and living in the highest wealth quantile.
– Community-level determinants included residing in the city administration and living closer to a health facility.
– The positive deviance approach highlighted factors associated with better PNC service uptake, despite acknowledged adverse risk profiles.
– The study provides valuable insights for policymakers and program implementers to target efforts and improve health service utilization.

Recommendations:

– Policymakers should prioritize interventions to improve PNC service uptake among women with no education, particularly those working in agriculture and belonging to lower wealth quantiles.
– Efforts should be made to increase access to PNC services in rural areas and ensure proximity to health facilities.
– Health education and awareness campaigns should be tailored to specific communities and religious groups to address cultural and religious barriers to PNC service utilization.
– Strategies should be developed to improve the overall implementation of PNC services in Ethiopia, including strengthening health systems and increasing resources allocated to maternal and child health.

Key Role Players:

– Ministry of Health: Responsible for developing and implementing policies and programs related to maternal and child health, including PNC services.
– Non-governmental organizations (NGOs): Involved in implementing health programs and providing support for maternal and child health services.
– Health professionals: Including doctors, nurses, and midwives who provide PNC services and play a crucial role in educating and counseling women.
– Community leaders and religious leaders: Important in promoting awareness and acceptance of PNC services within their communities.

Cost Items for Planning Recommendations:

– Training and capacity building for health professionals on PNC service provision and counseling.
– Infrastructure development and improvement of health facilities to ensure adequate and accessible PNC services.
– Health education and awareness campaigns targeting specific communities and religious groups.
– Monitoring and evaluation activities to assess the implementation and impact of interventions.
– Research and data collection to inform evidence-based decision-making and policy development.
Please note that the cost items provided are general suggestions and may vary depending on the specific context and priorities of the Ethiopian healthcare system.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides specific details about the study design, data source, sample size, and statistical analysis. However, it does not mention the limitations of the study or potential biases. To improve the evidence, the abstract could include a discussion of the study’s limitations and potential biases, as well as suggestions for future research to address these limitations.

Abstract

The study used the EDHS 2016 data, a nationally representative household survey that has been implemented by the Central Statistical Agency (CSA) of Ethiopia every 5 years [28]. Ethiopia is a home country of an estimated 114 million population (CSA 2015). Administratively, the country is divided into nine regions (Tigray, Afar, Amhara, Oromia, Benishangul, Gambela, South Nation Nationalities and Peoples’ Region (SNNPR), Harari and Somali) and two City Administrations (Addis Ababa and Dire-Dawa). Those nine regions can be divided in to developed regions (Tigray, Amhara, Oromia, South Nation Nationalities and Peoples’ Region (SNNPR), and Harari), and emerging regions (Afar, Benishangul, Gambela, and Somali). The 2016 EDHS used the 2007 Ethiopian population and housing census as a sampling frame, which was conducted by the CSA of Ethiopia and a complete list of 84,915 enumeration areas (EAs) were used in the census. The 2016 EDHS sample was stratified in two stages. A sample of EAs were selected from each stratum, independently. Then a total of 645 EAs were selected with probability proportional to the EA size, and each sampling stratum was selected from the given samples. The total residential households in the EA were the EA size, and a household listing operation was implemented. Then, the resulting lists of households were used as the sampling frame for selecting households in the second stage. Accordingly, all women aged 15–49 years who are regular members of the selected households were eligible for the survey. Finally, from a total of 4081 women identified from the EDHS 2016, 2417 deviant women were included in this analysis, and data were extracted from the datasets using STATA version 14 software. Variables at the individual and community-level were also extracted and further analyzed. We used the Anderson’s behavioral model of health service use [29] and other related studies [30, 31] to identify the positive deviant for PNC services uptake. Accordingly, education is the major determinant of health services utilization. We selected women who had no education as a sub-group with a very low likelihood of PNC services utilization, as education was the strongest predictor of PNC after adjusting for the other risk factors associated with PNC in this population. Positive deviant women were those who had no education but had an adequate uptake of PNC services. Finally, in the analysis, we compared the characteristics of the PD women to those of their counterparts. Due to significant variations by clusters in the overall use of PNC and also the individual and household level data were nested under the community level data, the analysis was stratified by individual and community level. Uptake of PNC services among deviant women was the dependent variable. The uptake was assessed when a woman received PNC services within 2 months after delivery, irrespective of their place of delivery. The information on the uptake of PNC services for their recent birth was assessed based on the women’s verbal responses during the survey. Accordingly, it was categorized as “yes” if a woman received at least one PNC visit, otherwise “no”. Individual-level variables; socio-demographic and economic variables (age, occupational status, religion, marital status, age at first birth, desire for child, household wealth status) were included in this analysis. On the other hand, place of residence, region, living closer to health facility, and media exposure were the community-level variables. Household wealth status was assessed using the asset index based on data from the entire sample on separate scores prepared for rural and urban households, and combined to produce a single asset index for all households and ranked into three (lowest, middle, and highest). The difficulty of getting health services was assessed by the question “living closer to health facility” and the responses were categorized as “yes” or “no”. Media exposure was assessed based on whether people had access to read newsletters, listen to the radio, and watch TV. Accordingly, if they have access to all three media (newsletter, radio, and TV) at least once a week, we categorized them as “yes”, otherwise “no”. The data were extracted, cleaned, re-coded, and analyzed using STATA version 17. The data were weighted using sampling weight during the statistical analysis to adjust for unequal probability of selection due to the sampling design used in DHS data. Tables and narrations were used to present the descriptive statistics. Since the DHS data are hierarchical (individual were nested within communities), a two-level binary logistic regression model was fitted to estimate the effect of both individual and community-level variables on PNC services uptake [32]. In this multilevel analysis, we fitted four models; i) Model 0: an empty (null) model without any explanatory variables, ii) Model 1: a model with individual-level variables, iii) Model 2: a model with community-level variables, and iv) Model 3: a model with both individual and community -level variables. In the survey, the individual and household level data were nested under the community level data, for model comparison, Intra-class Correlation Coefficient and deviance (− 2* log likelihood ratio) were used. Accordingly, a model with lowest deviance was chosen. The variation between clusters was assessed by computing Intra-lass Correlation Coefficient (ICC) [33]. The ICC is the proportion of variance explained by the grouping structure in the population which: ICC=; Where, the standard logit distribution has a variance of,, indicates the cluster variance. The ICC greater than 5% is eligible for multilevel analysis and in our analysis, the ICC was 22.5%. A mixed effect multilevel binary logistic regression analysis was done. A low deviance value was used to estimate the model goodness of fit by comparing the full model with the preceding three models. Finally, a p-value of less than 0.05 and an adjusted odds ratio (AOR) with 95% confidence interval (CI) were used to declare statistically significant factors associated with PNC services uptake among deviant women.

Materials

N/A

Innovations

The study “Uptake of postnatal care and its determinants in Ethiopia: a positive deviance approach” aimed to assess the uptake of postnatal care (PNC) services among women with no education in Ethiopia and identify the individual and community-level determinants of PNC service uptake. The study used data from the Ethiopia Demographic and Health Survey 2016.

The following innovations could be considered to improve access to maternal health based on the findings of the study:

1. Targeted education programs: Implement targeted education programs to improve awareness and knowledge about the importance of PNC services among women with no education. This could include community-based health education sessions, mobile health clinics, and the use of local influencers to disseminate information.

2. Strengthening agricultural programs: Since women working in agriculture showed higher PNC service uptake, integrating maternal health services into existing agricultural programs could be beneficial. This could involve providing PNC services at agricultural work sites or collaborating with agricultural extension workers to promote PNC utilization.

3. Addressing religious beliefs: Given that being an Orthodox religion follower was associated with higher PNC service uptake, engaging religious leaders and communities in promoting maternal health could be effective. This could involve incorporating maternal health messages into religious gatherings and partnering with religious institutions to provide PNC services.

4. Improving access to health facilities: Enhancing the proximity of health facilities to communities can increase PNC service uptake. This could be achieved by establishing additional health facilities or expanding existing ones in areas with low access. Additionally, improving transportation infrastructure and implementing mobile health clinics could help overcome geographical barriers.

5. Socio-economic empowerment: Since living in the highest wealth quantile was associated with higher PNC service uptake, interventions that focus on improving the socio-economic status of women could be beneficial. This could include income-generating programs, microfinance initiatives, and vocational training to enhance economic opportunities for women.

By implementing these innovations, it is possible to improve access to maternal health services, particularly postnatal care, among women with no education in Ethiopia.

AI Innovations Description

The study titled “Uptake of postnatal care and its determinants in Ethiopia: a positive deviance approach” aimed to assess the uptake of postnatal care (PNC) services among women with no education in Ethiopia and identify the individual and community-level determinants of PNC service uptake. The study used data from the Ethiopia Demographic and Health Survey 2016.

The findings of the study revealed that the uptake of PNC services among deviant women (women with no education) was 5.8%. Several determinants were identified at both the individual and community levels. At the individual level, working in agriculture, being an Orthodox religion follower, and living in the highest wealth quantile were associated with higher PNC service uptake. At the community level, residing in the city administration and living closer to a health facility were associated with higher PNC service uptake.

The study highlights the importance of targeted efforts to improve PNC service uptake among women with no education. The positive deviance approach used in the study provides evidence for health policymakers and program implementers to improve health behavior in specific target populations, ultimately leading to better maternal and child health outcomes. The findings suggest that strategies focusing on improving education, access to health facilities, and addressing socio-economic factors can contribute to improving access to maternal health services in Ethiopia.

AI Innovations Methodology

Based on the provided description, the study “Uptake of postnatal care and its determinants in Ethiopia: a positive deviance approach” aimed to assess the uptake of postnatal care (PNC) services among women with no education in Ethiopia and identify the individual and community-level determinants of PNC service uptake. The study used data from the Ethiopia Demographic and Health Survey 2016.

To simulate the impact of recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Identify potential recommendations: Review existing literature, consult experts, and engage stakeholders to identify potential recommendations for improving access to maternal health. These recommendations could include interventions such as increasing education and awareness about maternal health, improving healthcare infrastructure and facilities, enhancing transportation systems, and implementing community-based programs.

2. Define indicators: Determine the key indicators that will be used to measure the impact of the recommendations on improving access to maternal health. These indicators could include the uptake of antenatal care, postnatal care, skilled birth attendance, and maternal mortality rates.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the defined indicators. The model should consider various factors such as population demographics, healthcare infrastructure, geographical accessibility, and socio-economic conditions.

4. Data collection: Gather relevant data to populate the simulation model. This data could include information on population demographics, healthcare facilities, transportation networks, and socio-economic factors. Data sources may include national surveys, health facility records, and other relevant databases.

5. Model calibration: Calibrate the simulation model using historical data or data from pilot interventions to ensure that it accurately represents the current situation and trends in maternal health access. Adjust the model parameters and assumptions as necessary.

6. Scenario analysis: Conduct scenario analyses using the simulation model to assess the potential impact of different combinations of recommendations on improving access to maternal health. Explore various scenarios, such as scaling up specific interventions, targeting specific populations, or implementing comprehensive packages of interventions.

7. Impact assessment: Evaluate the impact of the recommendations by comparing the simulated outcomes of the different scenarios. Assess the changes in the defined indicators, such as improvements in PNC service uptake, skilled birth attendance rates, and reductions in maternal mortality.

8. Sensitivity analysis: Perform sensitivity analyses to test the robustness of the simulation model and assess the uncertainty associated with the results. Vary key parameters and assumptions to understand their influence on the outcomes.

9. Policy recommendations: Based on the simulation results, provide evidence-based policy recommendations for improving access to maternal health. Consider the feasibility, cost-effectiveness, and sustainability of the recommended interventions.

10. Monitoring and evaluation: Continuously monitor and evaluate the implementation of the recommended interventions to assess their real-world impact on improving access to maternal health. Update the simulation model periodically with new data to refine the predictions and inform future decision-making.

By following this methodology, policymakers and program implementers can gain insights into the potential impact of different recommendations on improving access to maternal health and make informed decisions to address the identified gaps.

Authors & Co-Authors

Statistics:

Authors: 2

Identifiers:

DOI: 10.1186/s12884-022-04933-3

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female