Individual- And Community-Level Determinants for Complete Vaccination among Children Aged 12-23 Months in Ethiopia: A Multilevel Analysis

BioMed Research International, Volume 2020, Year 2020

Interpretação

Background. Childhood vaccination continues to increase dramatically. In spite of the success of immunization programs to date, millions of children continued to die each year, and sub-Saharan Africa (SSA) accounted for the world’s highest neonatal deaths. Childhood vaccination was designed as one of the most effective ways to reduce child mortalities from fatal vaccine-preventable diseases. Therefore, this study is aimed at investigating the individual- and community-level determinants of childhood complete vaccination in Ethiopia. Methods. A secondary data analysis was done based on the 2016 Ethiopian Demographic and Health Survey (EDHS). A total weighted sample of 1,984 children aged 12-23 months was included for analysis. Considering the hierarchical nature of EDHS data, a two-level multilevel analysis for assessing individual- and community-level determinants of childhood complete vaccination was done. The intraclass correlation coefficient (ICC), Median Odds Ratio (MOR), Proportional Change in Variance (PCV), and deviance (-2LL) were used for model comparison and for checking model fitness. Variables with p value < 0.2 in the bivariable multilevel analysis were considered for the multivariable multilevel analysis. In the multivariable multilevel logistic regression analysis, the Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) was reported to declare significant determinants of complete childhood vaccination. Results. Overall complete vaccination status among children aged 12-23 months was 39% (95% CI: 36.8, 41.2). In the multilevel analysis, secondary or above educated mothers (AOR=2.48; 95% CI: 1.41, 4.36), richest wealth status (AOR=2.24; 95% CI: 1.16, 4.32), ≥four ANC visits (AOR=2.77; 95% CI: 1.90-4.02), employed mothers (AOR=1.66; 95% CI: 1.26, 2.18), urban residence (AOR=1.84; 95% CI: 1.00, 3.51), and children in city administration (AOR=2.66; 9% CI: 1.53, 4.62) were positively associated with vaccination status. On the other hand, children with a female household head (AOR=0.68; 95% CI: 0.48, 0.96) were negatively associated. Conclusion. Overall, childhood full vaccination status was low compared with the WHO targets. Maternal education, wealth status, ANC visit, maternal occupation, residence, region, and sex of household head were significant predictors of childhood complete vaccination. As a result, it is better to design a compensation mechanism to the costs associated with childhood vaccination for the poor households and strengthen awareness creation for rural residents to improve the access, utilization, and continuum of vaccination service. A secondary data analysis was done based on the 2016 Ethiopian Demographic and Health Survey (EDHS). The 2016 EDHS is the fourth DHS survey in Ethiopia collected in the nine administrative regions, namely, Tigray, Afar, Amhara, Benishangul-Gumuz, Gambela, Harari, Oromia, Somali, and Southern Nations, Nationalities, and People's Region (SNNP), and two city administrative regions (Addis Ababa and Dire Dawa) (Figure 1). Most of the population of Ethiopia is an agrarian society, and about 43 percent of the Gross Domestic Product (GDP) of the country has been accounted for by agriculture, and 84% of the population lives in rural areas. More than 80 percent of the country's total population lives in the regional states of Amhara, Oromia, and SNNP [24]. In addition, Ethiopia is the thirteenth in the world and the second in Africa's most populous countries with a 4.46 fertility rate. Ethiopia has followed 3 tiers of preventive healthcare system approaches. These are primary-level healthcare comprising of a primary hospital, health center, and health post; secondary-level healthcare (general hospital); and tertiary-level healthcare (specialized hospital). The number of hospitals varies from region to region in relation to the size of the population. The Oromia region has the highest number of hospitals (30), and only one hospital is found in the Gambela region [25]. Map of the nine regions and two city administrations of Ethiopia, 2016. All children aged 12-23 months in Ethiopia were the source population, while children aged 12-23 months in the selected enumeration areas before five years of the survey were the study population. In EDHS, a stratified two-stage cluster sampling technique was employed to select the study participants using the 2007 Population and Housing Census (PHC) as a sampling frame. Stratification was achieved by separating each region into urban and rural areas. A total of 21 sampling strata have been created because the Addis Ababa region is entirely urban. In the first stage, 645 enumeration areas (EAs) were selected proportionally to the size of EAs with independent selection from the sampling stratum. Consecutively, a complete list of the households was carried out in all selected EAs before the actual data collection period, and 28 households were selected using a systematic sampling technique. Of these, 18,008 households and 16,583 eligible women were included, and the detailed sampling procedure was presented in the full 2016 EDHS report [6]. The full vaccination status of children in Ethiopia was the outcome variable for this study. The EDHS data about vaccination were collected from verbal reports of the mother and data extraction from the childhood immunization card. The detailed report is found in the 2016 EDHS report. A child that received one dose of BCG, 3 doses of pentavalent, 3 doses of polio, two doses of rota, three doses of PCV, and one dose of measles was considered fully vaccinated and categorized as “yes,” and the remaining was categorized as “no.” The response variable for the ith child is represented by a random variable Yi with two possible values coded as 1 and 0. As a result, the response variable of the ith child Yi was measured as a dichotomous variable with possible values of “Yi = 1” if the ith child was fully vaccinated and “Yi = 0” if the child was not fully vaccinated. Accordingly, consistent with the objective of the study and the hierarchical structural nature of the EDHS data, the women were nested within the cluster/community. As a result, we considered two levels of the independent variables. These are the individual-level (level one) factors including individual sociodemographic and economic factors, such as age, maternal education, paternal education, media exposure, wealth index, maternal occupation, health insurance, and sex of head of the household, and maternal obstetric-related factors, such as ANC visit, parity, preceding birth interval, and birth order. On the other hand, the community-level (level two) factors include the characteristics of the community such as the region, residence, community media exposure, community women education, region, and place of residence. This might help us to see whether the cluster-level variables had an effect on full childhood vaccination status. Community-level variables had two sources. These are direct community- and aggregated community-level variables used in the analysis of community-level variables. Some of the community-level variables such as community education and media exposure were an aggregate result of the individual data and categorized as low or high using the median value since the EDH data were not normally distributed. The data were weighted using sampling weight, primary sampling unit, and strata before any statistical analyses to restore the representativeness of the survey and to get reliable statistical estimates. Cross-tabulations and summary statistics were conducted to describe the study population using STATA version 14 software. In the EDHS data, children are nested within a cluster, and children within the same cluster might be more similar to each other than children in the rest of the country. This violates the assumption of a simple binary logistic regression model such as the independence of observations and equal variance across clusters. Therefore, a multilevel logistic regression model (both fixed and random effect) was fitted to take into account the clustering effect. Model comparison was done based on deviance since the models were nested. The likelihood ratio, intraclass correlation coefficient (ICC), Median Odds Ratio (MOR), and Proportional Change in Variance (PCV) were computed to measure the variation between clusters. The intraclass correlation coefficient (ICC) quantifies the degree of heterogeneity of childhood full vaccination between the clusters, or it is the proportion of the total observed individual variation in childhood full vaccination attributed to between-cluster variations. ICC = б2/(б2 + π2/3) [26], but MOR is quantifying the variation or heterogeneity in outcomes between clusters and defined as the median value of the odds ratio between the cluster at high likelihood of full vaccination and cluster at lower likelihood when randomly picking out two clusters or EAs. MOR=exp 2∗∂2∗0.6745~MOR=exp 0.95∗∂ [27]. ∂2 indicates the cluster variance, and PCV measures the total variation attributed to individual- and community-level factors in the multilevel model as compared to the null model: A two-level, at individual and community (cluster) levels, multilevel multivariable logistic regression was used to analyze factors associated with childhood full vaccination. Furthermore, four models were constructed for the multilevel logistic regression analysis. The first model was an empty model without any explanatory variable/s to determine the extent of cluster variation on full vaccination. The second model was adjusted with individual-level variables; the third model was adjusted for community-level variables, while the fourth was fitted with both individual- and community-level variables simultaneously. As a result, a model with the lowest deviance was chosen. Multicollinearity was also checked using the Variance Inflation Factor (VIF), and VIF < 10 and tolerance greater than 0.1 were used to declare the absence of multicollinearity. Variables with p values < 0.2 in the bivariable analysis for both individual- and community-level factors were fitted in the multivariable model. Adjusted Odds Ratio (AOR) with a 95% Confidence Interval (CI) and p values < 0.05 in the multivariable model were used to declare a significant association with childhood full vaccination.

AI Digest

Study Justification:

– Childhood vaccination is crucial for reducing child mortalities from vaccine-preventable diseases.
– Despite the success of immunization programs, child mortality rates remain high, particularly in sub-Saharan Africa.
– This study aims to investigate the determinants of childhood complete vaccination in Ethiopia to identify factors that can be targeted for intervention and improvement.

Highlights:

– The overall complete vaccination status among children aged 12-23 months in Ethiopia was 39%.
– Significant determinants of childhood complete vaccination include maternal education, wealth status, ANC visits, maternal occupation, residence, region, and sex of household head.
– Secondary or above educated mothers, richest wealth status, ≥four ANC visits, employed mothers, urban residence, and children in city administration were positively associated with vaccination status.
– Children with a female household head were negatively associated with vaccination status.

Recommendations for Lay Reader:

– Design a compensation mechanism to address the costs associated with childhood vaccination for poor households.
– Strengthen awareness creation for rural residents to improve access, utilization, and continuity of vaccination services.

Recommendations for Policy Maker:

– Implement policies to improve maternal education and employment opportunities.
– Develop strategies to address wealth disparities and provide financial support for vaccination.
– Enhance ANC services and promote the importance of ANC visits.
– Focus on improving vaccination coverage in urban areas and city administrations.
– Address gender disparities and promote female empowerment in households.

Key Role Players:

– Ministry of Health: Responsible for implementing vaccination policies and programs.
– Local Health Authorities: Involved in planning and implementing vaccination services at the community level.
– Non-Governmental Organizations (NGOs): Provide support and resources for vaccination campaigns and awareness programs.
– Health Workers: Responsible for administering vaccines and providing education to parents and caregivers.
– Community Leaders: Play a role in promoting vaccination and addressing community-specific challenges.

Cost Items for Planning Recommendations:

– Compensation Mechanism: Budget for providing financial support to poor households for vaccination costs.
– Awareness Creation: Allocate funds for developing and implementing awareness campaigns targeting rural residents.
– Maternal Education and Employment: Invest in educational programs and job creation initiatives for mothers.
– ANC Services: Allocate resources to strengthen ANC services and ensure adequate coverage.
– Urban Vaccination Services: Budget for improving vaccination services in urban areas and city administrations.
– Gender Empowerment: Invest in programs that promote gender equality and female empowerment.
Please note that the cost items provided are general categories and not actual cost estimates.

Força da prova

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study is based on a large sample size and uses a multilevel analysis to assess individual- and community-level determinants of childhood complete vaccination in Ethiopia. The study also reports adjusted odds ratios with 95% confidence intervals to declare significant determinants of complete childhood vaccination. However, the abstract does not provide information on the specific statistical methods used in the analysis, such as the type of multilevel model or the software used. Additionally, the abstract does not mention any limitations of the study or potential sources of bias. To improve the strength of the evidence, the abstract could include more details on the statistical methods used and address any limitations or potential sources of bias in the study.

Resumo

A secondary data analysis was done based on the 2016 Ethiopian Demographic and Health Survey (EDHS). The 2016 EDHS is the fourth DHS survey in Ethiopia collected in the nine administrative regions, namely, Tigray, Afar, Amhara, Benishangul-Gumuz, Gambela, Harari, Oromia, Somali, and Southern Nations, Nationalities, and People's Region (SNNP), and two city administrative regions (Addis Ababa and Dire Dawa) (Figure 1). Most of the population of Ethiopia is an agrarian society, and about 43 percent of the Gross Domestic Product (GDP) of the country has been accounted for by agriculture, and 84% of the population lives in rural areas. More than 80 percent of the country's total population lives in the regional states of Amhara, Oromia, and SNNP [24]. In addition, Ethiopia is the thirteenth in the world and the second in Africa's most populous countries with a 4.46 fertility rate. Ethiopia has followed 3 tiers of preventive healthcare system approaches. These are primary-level healthcare comprising of a primary hospital, health center, and health post; secondary-level healthcare (general hospital); and tertiary-level healthcare (specialized hospital). The number of hospitals varies from region to region in relation to the size of the population. The Oromia region has the highest number of hospitals (30), and only one hospital is found in the Gambela region [25]. Map of the nine regions and two city administrations of Ethiopia, 2016. All children aged 12-23 months in Ethiopia were the source population, while children aged 12-23 months in the selected enumeration areas before five years of the survey were the study population. In EDHS, a stratified two-stage cluster sampling technique was employed to select the study participants using the 2007 Population and Housing Census (PHC) as a sampling frame. Stratification was achieved by separating each region into urban and rural areas. A total of 21 sampling strata have been created because the Addis Ababa region is entirely urban. In the first stage, 645 enumeration areas (EAs) were selected proportionally to the size of EAs with independent selection from the sampling stratum. Consecutively, a complete list of the households was carried out in all selected EAs before the actual data collection period, and 28 households were selected using a systematic sampling technique. Of these, 18,008 households and 16,583 eligible women were included, and the detailed sampling procedure was presented in the full 2016 EDHS report [6]. The full vaccination status of children in Ethiopia was the outcome variable for this study. The EDHS data about vaccination were collected from verbal reports of the mother and data extraction from the childhood immunization card. The detailed report is found in the 2016 EDHS report. A child that received one dose of BCG, 3 doses of pentavalent, 3 doses of polio, two doses of rota, three doses of PCV, and one dose of measles was considered fully vaccinated and categorized as “yes,” and the remaining was categorized as “no.” The response variable for the ith child is represented by a random variable Yi with two possible values coded as 1 and 0. As a result, the response variable of the ith child Yi was measured as a dichotomous variable with possible values of “Yi = 1” if the ith child was fully vaccinated and “Yi = 0” if the child was not fully vaccinated. Accordingly, consistent with the objective of the study and the hierarchical structural nature of the EDHS data, the women were nested within the cluster/community. As a result, we considered two levels of the independent variables. These are the individual-level (level one) factors including individual sociodemographic and economic factors, such as age, maternal education, paternal education, media exposure, wealth index, maternal occupation, health insurance, and sex of head of the household, and maternal obstetric-related factors, such as ANC visit, parity, preceding birth interval, and birth order. On the other hand, the community-level (level two) factors include the characteristics of the community such as the region, residence, community media exposure, community women education, region, and place of residence. This might help us to see whether the cluster-level variables had an effect on full childhood vaccination status. Community-level variables had two sources. These are direct community- and aggregated community-level variables used in the analysis of community-level variables. Some of the community-level variables such as community education and media exposure were an aggregate result of the individual data and categorized as low or high using the median value since the EDH data were not normally distributed. The data were weighted using sampling weight, primary sampling unit, and strata before any statistical analyses to restore the representativeness of the survey and to get reliable statistical estimates. Cross-tabulations and summary statistics were conducted to describe the study population using STATA version 14 software. In the EDHS data, children are nested within a cluster, and children within the same cluster might be more similar to each other than children in the rest of the country. This violates the assumption of a simple binary logistic regression model such as the independence of observations and equal variance across clusters. Therefore, a multilevel logistic regression model (both fixed and random effect) was fitted to take into account the clustering effect. Model comparison was done based on deviance since the models were nested. The likelihood ratio, intraclass correlation coefficient (ICC), Median Odds Ratio (MOR), and Proportional Change in Variance (PCV) were computed to measure the variation between clusters. The intraclass correlation coefficient (ICC) quantifies the degree of heterogeneity of childhood full vaccination between the clusters, or it is the proportion of the total observed individual variation in childhood full vaccination attributed to between-cluster variations. ICC = б2/(б2 + π2/3) [26], but MOR is quantifying the variation or heterogeneity in outcomes between clusters and defined as the median value of the odds ratio between the cluster at high likelihood of full vaccination and cluster at lower likelihood when randomly picking out two clusters or EAs. MOR=exp 2∗∂2∗0.6745~MOR=exp 0.95∗∂ [27]. ∂2 indicates the cluster variance, and PCV measures the total variation attributed to individual- and community-level factors in the multilevel model as compared to the null model: A two-level, at individual and community (cluster) levels, multilevel multivariable logistic regression was used to analyze factors associated with childhood full vaccination. Furthermore, four models were constructed for the multilevel logistic regression analysis. The first model was an empty model without any explanatory variable/s to determine the extent of cluster variation on full vaccination. The second model was adjusted with individual-level variables; the third model was adjusted for community-level variables, while the fourth was fitted with both individual- and community-level variables simultaneously. As a result, a model with the lowest deviance was chosen. Multicollinearity was also checked using the Variance Inflation Factor (VIF), and VIF < 10 and tolerance greater than 0.1 were used to declare the absence of multicollinearity. Variables with p values < 0.2 in the bivariable analysis for both individual- and community-level factors were fitted in the multivariable model. Adjusted Odds Ratio (AOR) with a 95% Confidence Interval (CI) and p values < 0.05 in the multivariable model were used to declare a significant association with childhood full vaccination.

Materiais

N/A

Inovações

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 information and reminders about maternal health, including vaccination schedules, antenatal care visits, and postnatal care. These apps can also provide access to telemedicine services for remote consultations with healthcare providers.

2. Community Health Workers: Train and deploy community health workers to provide education and support to pregnant women and new mothers in rural areas. These workers can conduct home visits, provide information on maternal health practices, and facilitate access to healthcare services.

3. Telemedicine: Establish telemedicine networks to connect remote areas with healthcare professionals. This would allow pregnant women and new mothers to receive medical advice, consultations, and follow-up care without having to travel long distances.

4. Maternal Health Vouchers: Implement a voucher system that provides financial assistance to pregnant women and new mothers, particularly those from low-income backgrounds, to cover the costs of maternal healthcare services, including vaccinations.

5. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private sector resources, such as transportation or mobile clinics, to reach underserved areas and provide essential maternal healthcare.

6. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of maternal health and vaccinations. These campaigns can utilize various communication channels, including radio, television, social media, and community gatherings, to reach a wide audience.

7. Strengthening Health Infrastructure: Invest in improving healthcare infrastructure, particularly in rural areas, by building and equipping health facilities that can provide comprehensive maternal healthcare services, including vaccination.

8. Maternal Health Financing: Develop innovative financing mechanisms, such as micro-insurance or community-based health financing schemes, to make maternal healthcare services more affordable and accessible to all women, regardless of their socioeconomic status.

9. Integration of Services: Integrate maternal health services with other healthcare programs, such as family planning, nutrition, and child health, to provide a comprehensive package of care. This can improve efficiency and ensure that women receive all the necessary services in one place.

10. Data-driven Decision Making: Utilize data analytics and digital health technologies to collect, analyze, and utilize data on maternal health outcomes and service utilization. This can help identify gaps in access and quality of care, and inform evidence-based decision making for targeted interventions.

These innovations, when implemented effectively, can help improve access to maternal health services, including vaccinations, and contribute to reducing maternal and neonatal mortality rates.

AI Innovations Description

Based on the study’s findings, here is a recommendation that can be developed into an innovation to improve access to maternal health:

Design a compensation mechanism to cover the costs associated with childhood vaccination for poor households and strengthen awareness creation for rural residents to improve the access, utilization, and continuum of vaccination services.

This recommendation addresses the identified determinants of childhood complete vaccination in Ethiopia, such as maternal education, wealth status, ANC visits, maternal occupation, residence, and sex of the household head. By providing financial support to poor households and increasing awareness among rural residents, barriers to accessing vaccination services can be reduced, leading to improved maternal and child health outcomes. This innovation can be implemented through partnerships with local communities, healthcare providers, and government agencies to ensure effective implementation and sustainability.

AI Innovations Methodology

Based on the provided information, the study aims to investigate the individual- and community-level determinants of childhood complete vaccination in Ethiopia. The methodology used in the study is a two-level multilevel analysis based on the 2016 Ethiopian Demographic and Health Survey (EDHS) data. Here is a brief description of the methodology:

1. Study Population: The study population includes all children aged 12-23 months in Ethiopia. The data for the analysis were obtained from the 2016 EDHS, which collected information from nine administrative regions and two city administrative regions in Ethiopia.

2. Sampling Technique: The EDHS used a stratified two-stage cluster sampling technique. Enumeration areas (EAs) were selected proportionally to the size of EAs, and households were selected using a systematic sampling technique. A total of 1,984 children aged 12-23 months were included in the analysis.

3. Data Collection: The data on childhood vaccination were collected through verbal reports from mothers and data extraction from the childhood immunization card. A child was considered fully vaccinated if they received one dose of BCG, 3 doses of pentavalent, 3 doses of polio, two doses of rota, three doses of PCV, and one dose of measles.

4. Hierarchical Structure: The data were analyzed using a multilevel logistic regression model to account for the hierarchical structure of the data. Children were nested within clusters/communities, and the analysis considered two levels of independent variables: individual-level factors (e.g., maternal education, wealth status, ANC visits) and community-level factors (e.g., region, residence, community education).

5. Model Comparison: Model comparison was done based on deviance, likelihood ratio, intraclass correlation coefficient (ICC), Median Odds Ratio (MOR), and Proportional Change in Variance (PCV) to measure the variation between clusters and assess model fitness.

6. Variable Selection: Variables with a p-value < 0.2 in the bivariable analysis for both individual- and community-level factors were included in the multivariable model. Multicollinearity was checked using the Variance Inflation Factor (VIF).

7. Analysis and Results: Four models were constructed for the multilevel logistic regression analysis: an empty model, a model adjusted for individual-level variables, a model adjusted for community-level variables, and a model fitted with both individual- and community-level variables simultaneously. The model with the lowest deviance was chosen. Adjusted Odds Ratio (AOR) with a 95% Confidence Interval (CI) and p-values < 0.05 were used to determine significant associations with childhood full vaccination.

In conclusion, the study used a multilevel analysis approach to identify individual- and community-level determinants of childhood complete vaccination in Ethiopia. The methodology involved data collection from the 2016 EDHS, hierarchical modeling, model comparison, and variable selection to determine significant factors associated with childhood full vaccination.

Autor

Dima Health

Statistics:

Citations: 3

Identifiers:

DOI: 10.1155/2020/6907395

ISSN: 23146133

Research Areas:

Community Interventions, Genetics and Genomics, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Mixed-methods, Quantitative, Systematic Review

Participants Gender:

Female

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