Multilevel analysis of individual and contextual factors associated with polio non-vaccination in africa: Further analyses to enhance policy and opportunity to save more lives

Vaccines, Volume 9, No. 7, Year 2021

Interpretation

Background: Africa was certified polio-free in 2020 and to maintain the polio-free status, African countries need to attain and maintain optimal routine polio vaccination coverage. One indicator for optimal polio vaccination coverage is the prevalence of children who have received no polio vaccination through routine services. The objective of the study was to examine the individual-, neighbourhood-, and country-level factors associated with non-vaccination against polio in Africa. Methods: We applied multivariable multilevel logistic regression analyses on recent demographic and health survey data collected from 2010 onwards in Africa. We identified 64,867 children aged 12–23 months (Level 1) nested within 16,283 neighbourhoods (Level 2) from 32 countries (Level 3). Results: The prevalence of non-vaccination for polio ranged from 2.19% in Egypt to 32.74% in Guinea. We found the following factors to be independent predictors of the increased odds of non-vaccination for polio: being a male child, born to mother with no formal education, living in poorer households; being from a polygamous family, living in neighbourhoods with high maternal illiteracy, high unemployment rate, and low access to media. Conclusions: We found that both individual and contextual factors are associated with non-vaccination for Polio. Data for this cross-sectional study were obtained from Demographic and Health Surveys (DHS), which are nationally representative household surveys conducted in Africa. This study used data from 32 recent DHS surveys conducted from 2010 in African countries available as of March 2021. Demographic and Health Surveys (DHS) are nationally representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The DHS employs a stratified, multistage sampling approach, with homes serving as the sampling unit [6]. All women and men who match the eligibility criteria are interviewed in each sample household. Weights are calculated to account for differential selection probabilities as well as non-response because the surveys are not self-weighting. The results of the survey, when weighted, represent the entire target population. A household questionnaire, a women’s questionnaire, and, in most countries, a men’s questionnaire are all included in the DHS surveys. DHS surveys collect primary data using four different types of model questionnaires. A household questionnaire is used to gather information about the characteristics of the household’s dwelling unit as well as the characteristics of regular residents and visitors. It is also used to identify household members who are eligible for an individual interview. individual woman’s or man’s questionnaire is then used to interview eligible respondents. The biomarker questionnaire is used to gather biomarker information from children, women, and men. The woman’s questionnaire collects data on the following topics: background characteristics, reproductive behaviour and intentions, contraception, antenatal, delivery, and postnatal care, breastfeeding and nutrition, children’s health, status of women, HIV and other sexually transmitted infections, husband’s background, and other topics: questions examine behaviour related to environmental health, the use of tobacco, and health insurance. To achieve comparable data across countries, all DHS questionnaires were implemented with similar interviewer training, supervision, and implementation protocols. Procedures for collecting data have been published elsewhere [6]. In a nutshell, data were gathered by visiting households and conducting face-to-face interviews to obtain information on maternal and child health indicators, among other things. This study is based on an analysis of existing survey data with all identifier information removed. The surveys were approved by the Ethics Committee of the ICF at Rockville, MD, in the USA and by the corresponding National Ethics Committee in the Ministries of Health from each country. All study participants gave informed consent before participation, and all information was collected confidentially. Children who have received no vaccines through routine vaccination services are referred to as zero-dose children, which we refer to in this study as polio non-vaccination. We chose the term polio non-vaccination to avoid confusion with the birth dose of polio, which is often referred to in African countries as “polio zero dose”. Non-vaccinated child for polio was described as a binary variable that takes the value 1 if a child aged 12–23 months has not received any of the four routine doses of oral polio vaccine (polio 0 at birth, polio 1 at 6 weeks, polio 2 at 10 weeks, and polio 3 at 14 weeks) and 0 otherwise. We included the following individual level factors: child’s age (in months), child sex (male or female), high birth order (less than 24 months), number of under-five children, polygamous family, mother’s age (completed years) wealth index (poorer, middle, or richer), mother’s and father’s education (no education, primary, secondary, or higher), employment status (working or not working), has health insurance, media access (access to radio, television, or newspaper), and maternal health-seeking behaviours (prenatal visits, tetanus injection during pregnancy, medical assistance at delivery, knowledge of oral rehydration solution (ORS), and possession of a health card for the child). The DHS did not collect any direct data on household’s income and spending. As a proxy for socioeconomic status, we used the DHS wealth index. The methods used to calculate the DHS wealth index have previously been defined [7,8]. In brief, an index of economic status was created for each household utilizing principal components analysis based on the following household variables: number of rooms per home, ownership of a vehicle, motorcycle, bicycle, fridge, television, and telephone, as well as any type of heating system. The tertiles of the DHS wealth index (poor, middle, and rich) were estimated and used in the subsequent modelling. We used the word “neighbourhood” to describe a grouping of people who live in the same geographical area [9]. Within the DHS data, neighbourhoods were defined based on the presence of a common primary sample unit [10]. The models contained the following neighbourhood-level factors: where: Scores can range from 0 to approximately 1. For clarity of interpretation, each diversity index is multiplied by 100; the larger the index, the greater diversity there is in the area. If an area’s entire population belongs to one ethnic group, then an area has zero diversity. An area’s diversity index increases to 100 when the population is evenly divided into ethnic groups. Data at the country level were gathered from reports released by the United Nations Development Program [15]. We included the intensity of deprivation at the national level, which is the average percentage of deprivation faced by people living in multidimensional poverty, and this was categorized into two (low and high). We categorised community- and country-level variables into two categories (low and high) to allow for non-linear effects and provide more readily interpretable results in the policy arena. Median values served as the reference group for comparison. The year the DHS was conducted was included as a partial control for a period trend to control for effects of unknown factors that may have been introduced due to different timing of surveys across countries. In the descriptive statistics, respondents’ distribution by key variables was expressed as percentages. We used multivariable logistic multilevel regression models to analyse the association between individual compositional and contextual factors associated with polio non-vaccination. We specified a 3-level model for binary response reporting children that did not receive any polio vaccine (at level 1), in a neighbourhood (at level 2) living in a country (at level 3). Five models were built. The first model, a null model with no explanatory variables, was used to decompose the amount of variance between country and neighbourhood levels. The second model only included individual-level factors, the third model only included neighbourhood-level factors, and the fourth model only included country-level factors. Finally, the fifth model accounted for person-, neighbourhood-, and country-level factors all at the same time (full model). Fixed-effect findings (measures of association) were recorded as odds ratios (ORs) with 95 percent credible intervals (CrIs). The possible contextual effects were measured by the intra-class correlation (ICC) and median odds ratio (MOR). We measured similarity between respondents in the same neighbourhood and within the same country using ICC. The ICC represents the percentage of the total variance in the probability of non-vaccination for polio that is related to the neighbourhood- and country-level, i.e., measure of clustering of odds of non-vaccination for polio in the same neighbourhood and country. The ICC was calculated by the linear threshold (latent variable method) [16]. Following the ideas of Larsen et al. on neighbourhood effects [17], we reported the random effects in terms of odds. The MOR measures the second or third level (neighbourhood or country) variance as odds ratio and estimates the probability of non-vaccination for polio attributed to neighbourhood and country context. MOR equal to one indicates no neighbourhood or country variance. Conversely, the higher the MOR, the more important are the contextual effects for understanding the probability of non-vaccination for polio. We examined the multicollinearity among explanatory variables. The multilevel models were fitted using the MLwinN programme, version 2.31 [18,19]. We used the Bayesian Deviance Information Criterion to measure how well different models fitted the data [20].

AI Digest

Study Justification:

The study aims to examine the individual, neighborhood, and country-level factors associated with non-vaccination against polio in Africa. This is important because Africa was certified polio-free in 2020, and maintaining this status requires optimal routine polio vaccination coverage. Understanding the factors that contribute to non-vaccination can help inform policies and interventions to improve vaccination rates and prevent the resurgence of polio.

Highlights:

– The prevalence of non-vaccination for polio ranged from 2.19% in Egypt to 32.74% in Guinea.
– Factors associated with increased odds of non-vaccination for polio include being a male child, born to a mother with no formal education, living in poorer households, being from a polygamous family, living in neighborhoods with high maternal illiteracy, high unemployment rate, and low access to media.
– Individual and contextual factors are both important in understanding non-vaccination for polio.

Recommendations:

– Improve access to education for mothers, particularly those with no formal education, to increase awareness and understanding of the importance of polio vaccination.
– Implement targeted interventions in poorer households and neighborhoods with high maternal illiteracy, high unemployment rate, and low access to media to improve vaccination rates.
– Strengthen health systems to ensure routine polio vaccination services are accessible and available to all children, regardless of socioeconomic status or geographic location.
– Enhance media campaigns and community engagement to raise awareness about the benefits of polio vaccination and address misconceptions or concerns.

Key Role Players:

– Ministries of Health: Responsible for implementing and coordinating vaccination programs and policies.
– Community Health Workers: Engage with communities, provide education, and deliver vaccines.
– Non-Governmental Organizations (NGOs): Support vaccination campaigns, community outreach, and advocacy efforts.
– International Organizations (e.g., World Health Organization, UNICEF): Provide technical support, funding, and guidance for vaccination programs.
– Researchers and Academics: Conduct studies, analyze data, and provide evidence-based recommendations.

Cost Items for Planning Recommendations:

– Education and Training: Budget for training programs to educate mothers and community health workers on the importance of polio vaccination.
– Vaccine Supply and Distribution: Allocate funds for procuring and distributing vaccines to ensure availability in all areas.
– Media Campaigns: Set aside a budget for designing and implementing media campaigns to raise awareness about polio vaccination.
– Community Engagement: Allocate resources for community engagement activities, such as town hall meetings, community events, and door-to-door outreach.
– Monitoring and Evaluation: Include funding for monitoring and evaluating the impact of interventions and vaccination coverage rates.
Please note that the provided cost items are general categories and the actual cost will vary depending on the specific context and implementation strategies.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study used a large sample size and applied multivariable multilevel logistic regression analyses, which are robust statistical methods. The study also provided detailed information on the data source and methodology. However, to further strengthen the evidence, the abstract could include information on the statistical significance of the findings and the effect sizes of the predictors. Additionally, it would be helpful to provide information on the generalizability of the findings and any limitations of the study. Overall, the evidence is strong, but these suggested improvements would enhance its clarity and applicability.

Abstract

Data for this cross-sectional study were obtained from Demographic and Health Surveys (DHS), which are nationally representative household surveys conducted in Africa. This study used data from 32 recent DHS surveys conducted from 2010 in African countries available as of March 2021. Demographic and Health Surveys (DHS) are nationally representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The DHS employs a stratified, multistage sampling approach, with homes serving as the sampling unit [6]. All women and men who match the eligibility criteria are interviewed in each sample household. Weights are calculated to account for differential selection probabilities as well as non-response because the surveys are not self-weighting. The results of the survey, when weighted, represent the entire target population. A household questionnaire, a women’s questionnaire, and, in most countries, a men’s questionnaire are all included in the DHS surveys. DHS surveys collect primary data using four different types of model questionnaires. A household questionnaire is used to gather information about the characteristics of the household’s dwelling unit as well as the characteristics of regular residents and visitors. It is also used to identify household members who are eligible for an individual interview. individual woman’s or man’s questionnaire is then used to interview eligible respondents. The biomarker questionnaire is used to gather biomarker information from children, women, and men. The woman’s questionnaire collects data on the following topics: background characteristics, reproductive behaviour and intentions, contraception, antenatal, delivery, and postnatal care, breastfeeding and nutrition, children’s health, status of women, HIV and other sexually transmitted infections, husband’s background, and other topics: questions examine behaviour related to environmental health, the use of tobacco, and health insurance. To achieve comparable data across countries, all DHS questionnaires were implemented with similar interviewer training, supervision, and implementation protocols. Procedures for collecting data have been published elsewhere [6]. In a nutshell, data were gathered by visiting households and conducting face-to-face interviews to obtain information on maternal and child health indicators, among other things. This study is based on an analysis of existing survey data with all identifier information removed. The surveys were approved by the Ethics Committee of the ICF at Rockville, MD, in the USA and by the corresponding National Ethics Committee in the Ministries of Health from each country. All study participants gave informed consent before participation, and all information was collected confidentially. Children who have received no vaccines through routine vaccination services are referred to as zero-dose children, which we refer to in this study as polio non-vaccination. We chose the term polio non-vaccination to avoid confusion with the birth dose of polio, which is often referred to in African countries as “polio zero dose”. Non-vaccinated child for polio was described as a binary variable that takes the value 1 if a child aged 12–23 months has not received any of the four routine doses of oral polio vaccine (polio 0 at birth, polio 1 at 6 weeks, polio 2 at 10 weeks, and polio 3 at 14 weeks) and 0 otherwise. We included the following individual level factors: child’s age (in months), child sex (male or female), high birth order (less than 24 months), number of under-five children, polygamous family, mother’s age (completed years) wealth index (poorer, middle, or richer), mother’s and father’s education (no education, primary, secondary, or higher), employment status (working or not working), has health insurance, media access (access to radio, television, or newspaper), and maternal health-seeking behaviours (prenatal visits, tetanus injection during pregnancy, medical assistance at delivery, knowledge of oral rehydration solution (ORS), and possession of a health card for the child). The DHS did not collect any direct data on household’s income and spending. As a proxy for socioeconomic status, we used the DHS wealth index. The methods used to calculate the DHS wealth index have previously been defined [7,8]. In brief, an index of economic status was created for each household utilizing principal components analysis based on the following household variables: number of rooms per home, ownership of a vehicle, motorcycle, bicycle, fridge, television, and telephone, as well as any type of heating system. The tertiles of the DHS wealth index (poor, middle, and rich) were estimated and used in the subsequent modelling. We used the word “neighbourhood” to describe a grouping of people who live in the same geographical area [9]. Within the DHS data, neighbourhoods were defined based on the presence of a common primary sample unit [10]. The models contained the following neighbourhood-level factors: where: Scores can range from 0 to approximately 1. For clarity of interpretation, each diversity index is multiplied by 100; the larger the index, the greater diversity there is in the area. If an area’s entire population belongs to one ethnic group, then an area has zero diversity. An area’s diversity index increases to 100 when the population is evenly divided into ethnic groups. Data at the country level were gathered from reports released by the United Nations Development Program [15]. We included the intensity of deprivation at the national level, which is the average percentage of deprivation faced by people living in multidimensional poverty, and this was categorized into two (low and high). We categorised community- and country-level variables into two categories (low and high) to allow for non-linear effects and provide more readily interpretable results in the policy arena. Median values served as the reference group for comparison. The year the DHS was conducted was included as a partial control for a period trend to control for effects of unknown factors that may have been introduced due to different timing of surveys across countries. In the descriptive statistics, respondents’ distribution by key variables was expressed as percentages. We used multivariable logistic multilevel regression models to analyse the association between individual compositional and contextual factors associated with polio non-vaccination. We specified a 3-level model for binary response reporting children that did not receive any polio vaccine (at level 1), in a neighbourhood (at level 2) living in a country (at level 3). Five models were built. The first model, a null model with no explanatory variables, was used to decompose the amount of variance between country and neighbourhood levels. The second model only included individual-level factors, the third model only included neighbourhood-level factors, and the fourth model only included country-level factors. Finally, the fifth model accounted for person-, neighbourhood-, and country-level factors all at the same time (full model). Fixed-effect findings (measures of association) were recorded as odds ratios (ORs) with 95 percent credible intervals (CrIs). The possible contextual effects were measured by the intra-class correlation (ICC) and median odds ratio (MOR). We measured similarity between respondents in the same neighbourhood and within the same country using ICC. The ICC represents the percentage of the total variance in the probability of non-vaccination for polio that is related to the neighbourhood- and country-level, i.e., measure of clustering of odds of non-vaccination for polio in the same neighbourhood and country. The ICC was calculated by the linear threshold (latent variable method) [16]. Following the ideas of Larsen et al. on neighbourhood effects [17], we reported the random effects in terms of odds. The MOR measures the second or third level (neighbourhood or country) variance as odds ratio and estimates the probability of non-vaccination for polio attributed to neighbourhood and country context. MOR equal to one indicates no neighbourhood or country variance. Conversely, the higher the MOR, the more important are the contextual effects for understanding the probability of non-vaccination for polio. We examined the multicollinearity among explanatory variables. The multilevel models were fitted using the MLwinN programme, version 2.31 [18,19]. We used the Bayesian Deviance Information Criterion to measure how well different models fitted the data [20].

Materials

N/A

Innovations

Based on the provided information, here are some potential innovations that could be used to improve access to maternal health:

1. Mobile Health (mHealth) Solutions: Develop and implement mobile applications or SMS-based systems that provide pregnant women with information about prenatal care, vaccination schedules, and reminders for appointments. These solutions can also enable women to communicate with healthcare providers and receive personalized advice.

2. Telemedicine: Establish telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls or phone consultations. This can help address the shortage of healthcare providers in certain regions and improve access to prenatal care.

3. Community Health Workers: Train and deploy community health workers (CHWs) who can provide basic prenatal care, education, and support to pregnant women in their communities. CHWs can conduct home visits, monitor the health of pregnant women, and refer them to healthcare facilities when necessary.

4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with financial assistance to access maternal health services, including prenatal care, delivery, and postnatal care. These vouchers can be distributed to women in low-income communities, enabling them to seek care from private healthcare providers.

5. Transportation Support: Develop transportation initiatives that provide pregnant women with affordable and reliable transportation to healthcare facilities. This can include partnerships with local transportation providers, subsidies for transportation costs, or the establishment of community-based transportation services.

6. Maternal Health Clinics: Establish dedicated maternal health clinics in underserved areas, equipped with trained healthcare providers and essential equipment for prenatal care, delivery, and postnatal care. These clinics can provide comprehensive care and reduce the need for pregnant women to travel long distances for healthcare services.

7. Health Education Campaigns: Conduct targeted health education campaigns to raise awareness about the importance of prenatal care, vaccination, and other maternal health practices. These campaigns can use various channels, such as radio, television, community meetings, and social media, to reach a wide audience.

8. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare facilities and resources to expand service coverage, implementing public-private insurance schemes, or establishing referral networks between public and private providers.

9. Maternal Health Information Systems: Develop and implement digital information systems that capture and track maternal health data, including vaccination records, prenatal care visits, and health outcomes. These systems can facilitate data-driven decision-making, improve coordination of care, and enable targeted interventions for at-risk populations.

10. Policy and Advocacy: Advocate for policy changes and increased investment in maternal health, both at the national and international levels. This can include advocating for increased funding for maternal health programs, improved healthcare infrastructure, and policies that prioritize maternal health outcomes.

It is important to note that the specific implementation of these innovations would require further research, planning, and collaboration with relevant stakeholders.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health would be to implement targeted interventions at multiple levels to address the factors associated with non-vaccination against polio in Africa. These interventions should focus on both individual and contextual factors identified in the study.

1. Individual-level interventions:
– Promote education and awareness: Implement programs to increase maternal education and awareness about the importance of polio vaccination. This can include community-based education campaigns, workshops, and information dissemination through various media channels.
– Improve healthcare access: Enhance access to prenatal care, tetanus injections during pregnancy, and medical assistance at delivery. This can be achieved by strengthening healthcare infrastructure, increasing the number of healthcare facilities, and providing financial support for maternal healthcare services.
– Enhance media access: Improve access to media platforms such as radio, television, and newspapers to disseminate information about polio vaccination and its benefits. This can be done through partnerships with media organizations and community radio stations.

2. Contextual-level interventions:
– Address socioeconomic disparities: Implement policies and programs to reduce poverty and improve socioeconomic conditions in communities. This can include income generation initiatives, microfinance programs, and social welfare schemes targeted at vulnerable populations.
– Improve community resources: Enhance access to education and employment opportunities in communities. This can be achieved through vocational training programs, job creation initiatives, and support for entrepreneurship.
– Strengthen healthcare systems: Invest in healthcare infrastructure, equipment, and personnel to ensure adequate access to healthcare services in communities. This can include building and upgrading healthcare facilities, training healthcare workers, and improving supply chain management for vaccines.

By implementing these targeted interventions at both individual and contextual levels, access to maternal health can be improved, leading to increased polio vaccination coverage and ultimately saving more lives. It is important to collaborate with government agencies, non-governmental organizations, healthcare providers, and community leaders to ensure the successful implementation of these recommendations.

AI Innovations Methodology

To improve access to maternal health, here are some potential recommendations:

1. Mobile Health (mHealth) Solutions: Utilize mobile technology to provide maternal health information, reminders for prenatal care appointments, and access to telemedicine consultations.

2. Community Health Workers (CHWs): Train and deploy CHWs to provide maternal health education, prenatal care, and postnatal support in underserved areas.

3. Telemedicine: Implement telemedicine platforms to connect pregnant women in remote areas with healthcare providers for prenatal check-ups and consultations.

4. Transportation Support: Establish transportation services or subsidies to help pregnant women reach healthcare facilities for prenatal care and delivery.

5. Maternal Health Clinics: Set up specialized clinics that focus on providing comprehensive maternal health services, including prenatal care, delivery, and postnatal care.

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

1. Define the Outcome: Determine the specific outcome or indicator that reflects improved access to maternal health, such as increased utilization of prenatal care services or reduced maternal mortality rates.

2. Data Collection: Gather data on the current status of the outcome indicator and relevant factors that may influence access to maternal health, such as geographical location, socioeconomic status, and availability of healthcare facilities.

3. Model Development: Develop a simulation model that incorporates the identified factors and their relationships with the outcome indicator. This could be a mathematical model, such as a regression model or a system dynamics model, depending on the complexity of the problem.

4. Parameter Estimation: Estimate the parameters of the model using available data or expert opinions. This step involves quantifying the relationships between the factors and the outcome indicator.

5. Scenario Testing: Simulate different scenarios by manipulating the factors that can be influenced by the recommendations. For example, increase the number of CHWs or improve transportation services and observe the impact on the outcome indicator.

6. Evaluation and Analysis: Analyze the simulation results to assess the effectiveness of the recommendations in improving access to maternal health. Compare the outcomes of different scenarios and identify the most effective interventions.

7. Policy Recommendations: Based on the simulation results, provide evidence-based recommendations for policymakers and stakeholders to implement the most effective interventions and improve access to maternal health.

It is important to note that the accuracy and reliability of the simulation results depend on the quality of the data, the validity of the model assumptions, and the robustness of the parameter estimation methods. Regular monitoring and evaluation of the implemented interventions are also crucial to validate the simulation findings and make necessary adjustments.

Author

Dima Health

Statistics:

Citations: 2

Identifiers:

DOI: 10.3390/vaccines9070683

Research Areas:

Community Interventions, Disparities, Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Substance Abuse

Study Countries:

Egypt, Guinea, Multi-Countries

Study Design:

Case-Control Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female