Determinants and causes of neonatal mortality in jimma Zone, Southwest Ethiopia: A multilevel analysis of prospective follow up study

PLoS ONE, Volume 9, No. 9, Year 2014

Interpretation

Background: Ethiopia is among the countries with the highest neonatal mortality with the rate of 37 deaths per 1000 live births. In spite of many efforts by the government and other partners, non-significant decline has been achieved in the last 15 years. Thus, identifying the determinants and causes are very crucial for policy and program improvement. However, studies are scarce in the country in general and in Jimma zone in particular. This study was a community-based prospective follow up conducted in Jimma Zone from September 2012-December 2013. Jimma Zone is one of the 17 Zones of the Oromia Regional State of Ethiopia having a total of 17 rural districts and two town administrations. According to the 2007 national population and housing census, the Zone has a total population of 2.6 million, of which 88.7% are rural residents [6], [7]. The minimum required sample size for this study was determined by using Epi-Info V.3.5.1 by considering two sample comparisons of proportions based on the following assumptions. The outcome variable was neonatal mortality. Among all the determinants of neonatal mortality considered, educational status of mothers was found to give the largest sample size. Based on this, the prevalence of neonatal mortality among mothers having educational status of secondary or above was estimated to be 4.0% (P1 = 0.040) and among those who didn’t attend secondary education was to be 8.1% (P2 = 0.081) [8]; 95% level of confidence and 80% power were considered. A ratio of 1∶3 was used (r = 3). As multistage-clustered sampling method was used, a design effect of 2 was considered. Finally, 10% was added for non-responses and miss-to-follow up and the final sample size became 3604. Multistage-clustered sampling technique was used to identify a cohort of pregnant women to be enrolled in the follow up for the study. At first stage, the Zone was stratified as rural districts (17 in number) and town administrations (2 in number, Jimma and Agaro). Then, by considering time and logistics, 5 districts (30%) were selected by simple random sampling from the 17 districts. At second stage, all the selected 5 districts were clustered by ‘Kebeles’ (A ‘kebele’ is the smallest administrative unit having 5000 population in average) and stratified in to urban and rural ‘Kebeles’. Then, by simple random sampling method, 9 rural ‘Kebeles’ and 2 urban ‘Kebeles’ were selected from each selected district. This number of clusters (‘kebeles’) was determined based on expected number of pregnant women per ‘Kebele’. Jimma town administration and Agaro town administration have 13 and 5 ‘Kebeles’, respectively and all were included purposefully. With this, a total of 73 Clusters (‘Kebeles’) were included in the study. Then, for all selected ‘kebeles’, pregnant women were enumerated by using house-to-house visit and all obtained were enrolled in the study (Figure S1). The dependent variable for this study was neonatal mortality and the independent variables were divided into two levels. Level 2 (higher-level variables) included community or cluster level variables such as place of residence, access to health centers and access to hospitals. Level 1 (lower-level variables) included individual and household characteristics such as: socio-demography, wealth quintiles, maternal obstetric factors, maternal health care use, conditions of labor, characteristics of the neonates and neonatal care practices. The detail descriptions and measurements are given below (Table 1). The data were collected by using pre-tested interviewer administered structured questionnaires which were adapted from different literatures. The indicators for the wealth index were adapted from Ethiopian Demographic and Health Survey (EDHS) [5]. Indicators to measure birth preparedness and complication readiness (BP & CR) were adapted from the safe motherhood questionnaires developed by maternal and neonatal health program of Johns Hopkins Program for International Education in Gynecology and Obstetrics (JHPIEGO) [9]. Indicators for neonatal care practices were adapted from the World Health Organization (WHO) minimum neonatal care packages [10]. Data on causes of neonatal death were collected by using structured verbal autopsy questionnaire adapted from the standard VA questionnaire developed and validated by WHO, Johns Hopkins University (JHU) and London School of Hygiene and Tropical Medicine [11]. All the questionnaires were prepared in English, then translated to local languages ‘Afan Oromoo’ and Amharic and used to collect the data after back translating to English by different experts to check its consistency. As this was prospective follow up study, data were collected in three phases. First, home-to-home visit was made to enumerate pregnant women from the selected 73 clusters. Then, all the identified pregnant women were enrolled in the study as a cohort. At a baseline, data on basic socio-demography, economy and birth preparedness and complication readiness were collected. Then, just at the end of neonatal period, maternal service use (antenatal care (ANC), delivery place and attendant and postnatal care), conditions of labor, neonatal characteristics and neonatal care practices were collected. For died neonates, VAs were conducted within 15–30 days of death. Females, who had completed 10th grade or above were recruited, trained and collected the data. The VAs were conducted by two experienced females. The data collection process was supervised strictly by trained supervisors and principal investigators. To control the quality of data, in addition to training, pretest, supervision and use of local languages, the inter-item consistency of the indicators to measure the composite score of wealth index, BP & CR and neonatal care practices were checked by using Chronbach-alpha at 0.7 cut-off points. The collected data were coded and entered into Epidata V.3.1 to minimize logical errors and design skipping patterns. Then, the data were exported to SPSS for windows version 20.0 for cleaning, editing and analysis. Descriptive analysis was done by computing proportions and summary statistics. Socioeconomic quintiles were determined by using Principal Component Analysis (PCA). Birth preparedness and complication readiness was computed by composite indicator of five items. Similarly, neonatal care practice was determined by composite variable of 12 items by using PCA. As Jimma and Agaro town administrations were both purposefully included, the status of neonatal mortality was estimated by calculating weighted percentage based on the complex sample survey procedure to avoid underestimation. Bivariate analysis was done by using cross-tabulation to see associations between the dependent and independent variables. Then, all variables having P-value <0.25 were considered as candidates for the final model. As multistage-clustered sampling technique was used because of the different levels of factors, mixed-effects multilevel logistic regression model was used by using STATA 13. This model was preferred in order to avoid the clustering effects as well as ecological fallacy. ‘Kebeles’ were considered as clusters and ‘kebele’ level variables were taken as higher-level (level 2). Neonates were nested within their family and households. As a result, neonatal individual variables, delivery conditions and household characteristics were taken as lower level (level 1) variables (Table 1). Goodness of fit of the multilevel model was tested by the log likelihood ratio (LR) test. To evaluate the extent of the cluster variation in influencing neonatal mortality, intercept-only model was fitted as Logit (pij) = γ00+u0j and Interclass Correlation Coefficient (ICC = ρ) was determined by dividing variance between groups (δ2µ0) by variance between groups (δ2µ0) plus variance within groups (δ2e). However, within group variance (δ2e) can't be directly obtained for dichotomous outcome variables; instead, it was estimated by dividing δ2µ0 by δ2µ0 plus ∏2/3. Then, to identify the determinant factors, the full model was fitted as: Logit(pij) = γ00 + γ01 Zj + γ10 Xij + u0j + u1j Xij. Where: Logit(pij) = dependent variable at unit i in cluster j, Xij = individual explanatory variable in cluster j, Zj = group level explanatory variable, γ00 = fixed intercept, γ01 and γ10 = fixed slopes and u0j and u1j = random effects at level 2. Multicollinearity between the independent variables was assessed by using variance inflation factors (VIF>10 considered as existence of multicollinearity) before interpreting the final output. However, only skill of delivery attendant (VIF = 10.9) had multicollinearity with place of delivery (VIF = 9.1, reduced to 1.8 when delivery attendant was dropped). As a result, they were included in the model alternatively by dropping the other. For the rest of the variables, the VIF was <3. In addition, cross-level two-way interactions were checked, particularly between place of residence, access to health facilities and maternal health care use (ANC, place of delivery and delivery attendants). Individual-level two-way interactions between prematurity (gestation age at birth), twin births and size of neonate at birth were checked. However, no significant interaction was detected (P>0.05 for each). The VAs were interpreted by two independent pediatricians and third pediatrician interpreted in case of disagreements. Ethical approval was obtained from the Institutional Review Board (IRB) of College of Health Sciences of Addis Ababa University as well as IRB of Oromia Regional State Health Bureau. Following this, formal letters and permissions were secured from all respective local administrators. Written informed consent was obtained from each respondent before actual data collection. Issues of confidentiality were maintained by removing any identifiers from the questionnaire. To protect vulnerable group, data collectors were trained to maintain confidentiality and provide necessary health information based on the need of the participants and arrange referral to health facilities for sick neonates.

AI Digest

Study Justification:

– Neonatal mortality is a significant issue in Ethiopia, with a rate of 37 deaths per 1000 live births.
– Despite efforts by the government and partners, there has been little decline in neonatal mortality in the past 15 years.
– Identifying the determinants and causes of neonatal mortality is crucial for improving policies and programs.
– However, there is a scarcity of studies on this topic in Ethiopia, especially in the Jimma Zone.

Study Highlights:

– The study was a community-based prospective follow-up conducted in the Jimma Zone from September 2012 to December 2013.
– The study included a total of 3604 pregnant women from rural districts and town administrations in the Jimma Zone.
– Data was collected through structured questionnaires and verbal autopsy questionnaires.
– The study analyzed both individual and community-level factors that contribute to neonatal mortality.
– Multilevel logistic regression models were used to analyze the data and identify determinant factors.

Study Recommendations:

– The study recommends focusing on improving educational status among mothers, as it was found to be a significant determinant of neonatal mortality.
– Access to health centers and hospitals should be improved in rural areas to ensure timely and adequate care for pregnant women and neonates.
– Birth preparedness and complication readiness should be promoted to ensure that pregnant women are well-prepared for childbirth and potential complications.
– Neonatal care practices should be improved, following the guidelines provided by the World Health Organization.

Key Role Players:

– Ministry of Health: Responsible for implementing policies and programs to address neonatal mortality.
– Regional Health Bureau: Responsible for coordinating and overseeing health services in the Jimma Zone.
– Health Center Staff: Provide antenatal care, delivery services, and postnatal care to pregnant women and neonates.
– Community Health Workers: Play a crucial role in educating and supporting pregnant women and their families.

Cost Items for Planning Recommendations:

– Education Programs: Budget for programs aimed at improving educational status among mothers.
– Infrastructure Development: Budget for improving access to health centers and hospitals in rural areas.
– Training and Capacity Building: Budget for training healthcare providers on birth preparedness, complication readiness, and neonatal care practices.
– Community Outreach Programs: Budget for community health workers to conduct awareness campaigns and provide support to pregnant women and their families.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because the study provides detailed information on the methodology, sample size determination, data collection process, and statistical analysis. However, the abstract does not mention the specific results or findings of the study, which limits the ability to fully evaluate the strength of the evidence. To improve the evidence, the abstract should include a summary of the key findings and their implications for policy and program improvement.

Abstract

This study was a community-based prospective follow up conducted in Jimma Zone from September 2012-December 2013. Jimma Zone is one of the 17 Zones of the Oromia Regional State of Ethiopia having a total of 17 rural districts and two town administrations. According to the 2007 national population and housing census, the Zone has a total population of 2.6 million, of which 88.7% are rural residents [6], [7]. The minimum required sample size for this study was determined by using Epi-Info V.3.5.1 by considering two sample comparisons of proportions based on the following assumptions. The outcome variable was neonatal mortality. Among all the determinants of neonatal mortality considered, educational status of mothers was found to give the largest sample size. Based on this, the prevalence of neonatal mortality among mothers having educational status of secondary or above was estimated to be 4.0% (P1 = 0.040) and among those who didn’t attend secondary education was to be 8.1% (P2 = 0.081) [8]; 95% level of confidence and 80% power were considered. A ratio of 1∶3 was used (r = 3). As multistage-clustered sampling method was used, a design effect of 2 was considered. Finally, 10% was added for non-responses and miss-to-follow up and the final sample size became 3604. Multistage-clustered sampling technique was used to identify a cohort of pregnant women to be enrolled in the follow up for the study. At first stage, the Zone was stratified as rural districts (17 in number) and town administrations (2 in number, Jimma and Agaro). Then, by considering time and logistics, 5 districts (30%) were selected by simple random sampling from the 17 districts. At second stage, all the selected 5 districts were clustered by ‘Kebeles’ (A ‘kebele’ is the smallest administrative unit having 5000 population in average) and stratified in to urban and rural ‘Kebeles’. Then, by simple random sampling method, 9 rural ‘Kebeles’ and 2 urban ‘Kebeles’ were selected from each selected district. This number of clusters (‘kebeles’) was determined based on expected number of pregnant women per ‘Kebele’. Jimma town administration and Agaro town administration have 13 and 5 ‘Kebeles’, respectively and all were included purposefully. With this, a total of 73 Clusters (‘Kebeles’) were included in the study. Then, for all selected ‘kebeles’, pregnant women were enumerated by using house-to-house visit and all obtained were enrolled in the study (Figure S1). The dependent variable for this study was neonatal mortality and the independent variables were divided into two levels. Level 2 (higher-level variables) included community or cluster level variables such as place of residence, access to health centers and access to hospitals. Level 1 (lower-level variables) included individual and household characteristics such as: socio-demography, wealth quintiles, maternal obstetric factors, maternal health care use, conditions of labor, characteristics of the neonates and neonatal care practices. The detail descriptions and measurements are given below (Table 1). The data were collected by using pre-tested interviewer administered structured questionnaires which were adapted from different literatures. The indicators for the wealth index were adapted from Ethiopian Demographic and Health Survey (EDHS) [5]. Indicators to measure birth preparedness and complication readiness (BP & CR) were adapted from the safe motherhood questionnaires developed by maternal and neonatal health program of Johns Hopkins Program for International Education in Gynecology and Obstetrics (JHPIEGO) [9]. Indicators for neonatal care practices were adapted from the World Health Organization (WHO) minimum neonatal care packages [10]. Data on causes of neonatal death were collected by using structured verbal autopsy questionnaire adapted from the standard VA questionnaire developed and validated by WHO, Johns Hopkins University (JHU) and London School of Hygiene and Tropical Medicine [11]. All the questionnaires were prepared in English, then translated to local languages ‘Afan Oromoo’ and Amharic and used to collect the data after back translating to English by different experts to check its consistency. As this was prospective follow up study, data were collected in three phases. First, home-to-home visit was made to enumerate pregnant women from the selected 73 clusters. Then, all the identified pregnant women were enrolled in the study as a cohort. At a baseline, data on basic socio-demography, economy and birth preparedness and complication readiness were collected. Then, just at the end of neonatal period, maternal service use (antenatal care (ANC), delivery place and attendant and postnatal care), conditions of labor, neonatal characteristics and neonatal care practices were collected. For died neonates, VAs were conducted within 15–30 days of death. Females, who had completed 10th grade or above were recruited, trained and collected the data. The VAs were conducted by two experienced females. The data collection process was supervised strictly by trained supervisors and principal investigators. To control the quality of data, in addition to training, pretest, supervision and use of local languages, the inter-item consistency of the indicators to measure the composite score of wealth index, BP & CR and neonatal care practices were checked by using Chronbach-alpha at 0.7 cut-off points. The collected data were coded and entered into Epidata V.3.1 to minimize logical errors and design skipping patterns. Then, the data were exported to SPSS for windows version 20.0 for cleaning, editing and analysis. Descriptive analysis was done by computing proportions and summary statistics. Socioeconomic quintiles were determined by using Principal Component Analysis (PCA). Birth preparedness and complication readiness was computed by composite indicator of five items. Similarly, neonatal care practice was determined by composite variable of 12 items by using PCA. As Jimma and Agaro town administrations were both purposefully included, the status of neonatal mortality was estimated by calculating weighted percentage based on the complex sample survey procedure to avoid underestimation. Bivariate analysis was done by using cross-tabulation to see associations between the dependent and independent variables. Then, all variables having P-value 10 considered as existence of multicollinearity) before interpreting the final output. However, only skill of delivery attendant (VIF = 10.9) had multicollinearity with place of delivery (VIF = 9.1, reduced to 1.8 when delivery attendant was dropped). As a result, they were included in the model alternatively by dropping the other. For the rest of the variables, the VIF was 0.05 for each). The VAs were interpreted by two independent pediatricians and third pediatrician interpreted in case of disagreements. Ethical approval was obtained from the Institutional Review Board (IRB) of College of Health Sciences of Addis Ababa University as well as IRB of Oromia Regional State Health Bureau. Following this, formal letters and permissions were secured from all respective local administrators. Written informed consent was obtained from each respondent before actual data collection. Issues of confidentiality were maintained by removing any identifiers from the questionnaire. To protect vulnerable group, data collectors were trained to maintain confidentiality and provide necessary health information based on the need of the participants and arrange referral to health facilities for sick neonates.

Materials

Innovations

Based on the provided description, it is not clear what specific innovations are being used to improve access to maternal health. However, here are some potential recommendations for innovations that could be used:

1. Mobile health (mHealth) technology: Implementing mobile health applications or text messaging services to provide pregnant women with important health information, reminders for prenatal care appointments, and access to emergency services.

2. Telemedicine: Using telecommunication technology to connect pregnant women in remote or underserved areas with healthcare providers for virtual consultations, prenatal check-ups, and postnatal care.

3. Community health workers: Training and deploying community health workers to provide education, support, and basic healthcare services to pregnant women in their communities, especially in areas with limited access to healthcare facilities.

4. Transportation solutions: Developing innovative transportation solutions, such as mobile clinics or ambulances, to improve access to healthcare facilities for pregnant women in rural or remote areas.

5. Financial incentives: Implementing financial incentives, such as cash transfers or vouchers, to encourage pregnant women to seek and utilize maternal healthcare services.

6. Public-private partnerships: Collaborating with private sector organizations to improve access to maternal healthcare through initiatives such as mobile clinics, telemedicine services, or subsidized healthcare services.

7. Maternal health education programs: Developing and implementing comprehensive maternal health education programs that target both pregnant women and their families, providing them with knowledge and skills to make informed decisions about their healthcare.

8. Infrastructure development: Investing in the development and improvement of healthcare infrastructure, including the construction of healthcare facilities, upgrading existing facilities, and ensuring the availability of essential medical equipment and supplies.

It is important to note that these recommendations are general and may need to be tailored to the specific context and needs of the community in Jimma Zone, Southwest Ethiopia.

AI Innovations Description

The study mentioned above focuses on identifying the determinants and causes of neonatal mortality in Jimma Zone, Southwest Ethiopia. The goal of the study is to provide valuable information for policy and program improvement in order to reduce neonatal mortality rates.

Based on the findings of this study, here are some recommendations that can be developed into innovations to improve access to maternal health:

1. Strengthening education for mothers: The study found that the educational status of mothers was a significant determinant of neonatal mortality. Therefore, implementing programs that focus on improving access to education for women, especially in rural areas, can have a positive impact on maternal health outcomes.

2. Enhancing access to health centers and hospitals: The study identified access to health centers and hospitals as important factors influencing neonatal mortality. To improve access, innovative solutions such as mobile health clinics or telemedicine can be implemented to reach remote areas where health facilities are limited.

3. Promoting birth preparedness and complication readiness: The study used indicators to measure birth preparedness and complication readiness, which were found to be important in reducing neonatal mortality. Innovative approaches such as community-based education programs and mobile applications can be developed to provide information and support to pregnant women and their families, ensuring they are prepared for childbirth and potential complications.

4. Improving neonatal care practices: The study highlighted the importance of neonatal care practices in reducing neonatal mortality. Innovations such as training programs for healthcare providers and community health workers can be implemented to improve knowledge and skills in neonatal care. Additionally, community-based interventions can be developed to promote proper neonatal care practices among caregivers.

5. Strengthening antenatal and postnatal care: The study emphasized the significance of antenatal and postnatal care in reducing neonatal mortality. Innovative approaches such as mobile health applications or telemedicine can be utilized to provide remote access to antenatal and postnatal care services, especially in areas with limited healthcare facilities.

Overall, these recommendations can be developed into innovative interventions to improve access to maternal health and reduce neonatal mortality rates in Jimma Zone, Southwest Ethiopia.

AI Innovations Methodology

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

1. Strengthening education and awareness programs: Implementing educational programs to improve the knowledge and awareness of pregnant women and their families about maternal health, including the importance of antenatal care, skilled birth attendance, and postnatal care.

2. Increasing access to healthcare facilities: Expanding the availability and accessibility of healthcare facilities, especially in rural areas, by building more health centers and hospitals. This can include mobile clinics or telemedicine services to reach remote areas.

3. Improving transportation infrastructure: Enhancing transportation infrastructure, such as roads and transportation systems, to ensure pregnant women can easily access healthcare facilities during emergencies or for regular check-ups.

4. Training and capacity building: Providing training and capacity building programs for healthcare providers, including midwives and nurses, to improve their skills in providing quality maternal healthcare services.

5. Community engagement and involvement: Engaging and involving the community in maternal health initiatives, such as establishing community health workers or volunteers who can provide basic maternal healthcare services and education within their communities.

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

1. Define indicators: Identify specific indicators that can measure the impact of the recommendations, such as the number of women receiving antenatal care, the percentage of births attended by skilled birth attendants, or the distance to the nearest healthcare facility.

2. Data collection: Collect baseline data on the selected indicators before implementing the recommendations. This can be done through surveys, interviews, or existing data sources.

3. Implement recommendations: Implement the recommended interventions or strategies to improve access to maternal health, such as education programs, infrastructure improvements, or training programs.

4. Monitor and evaluate: Continuously monitor and evaluate the progress and impact of the implemented recommendations. This can involve collecting data on the selected indicators at regular intervals.

5. Analyze data: Analyze the collected data to assess the changes in the selected indicators after implementing the recommendations. This can be done using statistical analysis techniques to determine if there are significant improvements in access to maternal health.

6. Interpret results: Interpret the results of the analysis to understand the impact of the recommendations on improving access to maternal health. This can involve comparing the baseline data with the post-intervention data to determine the effectiveness of the interventions.

7. Adjust and refine: Based on the results and findings, make any necessary adjustments or refinements to the recommendations to further improve access to maternal health.

8. Repeat the process: Continuously repeat the process of monitoring, evaluating, and adjusting the recommendations to ensure sustained improvements in access to maternal health.

By following this methodology, policymakers and stakeholders can assess the effectiveness of the recommendations and make informed decisions on how to further improve access to maternal health.

Author

Dima Health

Statistics:

Citations: 110

Identifiers:

DOI: 10.1371/journal.pone.0107184

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female