Does it really matter where you live? A multilevel analysis of factors associated with missed opportunities for vaccination in sub-Saharan Africa

  • Human Vaccines and Immunotherapeutics, Volume 14, No. 10, Year 2018

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Study Justification:
– There is an urgent need to examine the magnitude and factors responsible for missed opportunities for vaccination in sub-Saharan Africa.
– Rapid achievement of national immunization targets requires understanding the factors influencing missed opportunities for vaccination.
Study Highlights:
– The study examined the influence of individual, neighborhood, and country-level socioeconomic position on missed opportunities for vaccination in sub-Saharan Africa.
– Data from 43,637 children aged 12 to 23 months, nested within 15,122 neighborhoods from 35 countries, were analyzed using multilevel logistic regression analysis.
– Risk factors for increased odds of missed opportunities for vaccination included high birth order, high number of under-five children in the house, poorest household, lack of maternal education, lack of media access, and living in a poorer neighborhood.
– Country and neighborhood-level factors accounted for 18.4% and 37.4% of the variance in odds of missed opportunities for vaccination, respectively.
– Moving to a country or neighborhood with a higher probability of missed opportunities for vaccination increased the odds by 2.47 and 2.56 fold, respectively.
– The study revealed that missed opportunities for vaccination in sub-Saharan Africa are influenced by individual factors as well as compositional factors such as family’s financial capacity and place of birth and upbringing.
Recommendations for Lay Reader and Policy Maker:
– Increase access to vaccination services in sub-Saharan Africa, particularly in poorer neighborhoods.
– Target interventions towards households with high birth order and a high number of under-five children.
– Improve maternal education and media access to increase awareness and knowledge about vaccination.
– Address the socioeconomic factors that contribute to missed opportunities for vaccination, such as poverty and lack of resources.
– Consider the impact of neighborhood and country-level factors when designing vaccination programs and policies.
Key Role Players:
– National and local governments
– Ministries of Health
– Non-governmental organizations (NGOs)
– Community health workers
– Healthcare providers
– Educators and schools
– Media organizations
Cost Items for Planning Recommendations:
– Vaccination program implementation and expansion
– Training and capacity building for healthcare providers and community health workers
– Outreach and awareness campaigns
– Development and distribution of educational materials
– Infrastructure improvements for healthcare facilities
– Monitoring and evaluation systems
– Research and data collection
– Collaboration and coordination between stakeholders

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There is an urgent need to examine the magnitude and factors responsible for missed opportunities for vaccination, to rapidly achieve national immunization targets. The objective of the study was to examine the influence of individual, neighbourhood and country level socioeconomic position on missed opportunities for vaccination (MOV) in Sub-Saharan Africa. We used multilevel logistic regression analysis on Demographic and Health Survey data collected between 2007 and 2016 in sub-Saharan Africa. We analysed data on 43,637 children aged 12 to 23 months (Level 1) nested within 15,122 neighbourhoods (Level 2) from 35 countries (Level 3). After adjustment for individual-, neighbourhood- and country-level factors, the following appeared as significant risk factors for increased odds of MOV: high birth order, high number of under-five children in the house, poorest household, lack of maternal education, lack of media access, and living in poorer neighbourhood. According to the intra-country and intra-neighbourhood correlation coefficient, 18.4% and 37.4% of the variance in odds of MOV could be attributed to the country and neighbourhood level factors, respectively; and if a child moved to another country or neighbourhood with a higher probability of MOV, the median increase in their odds of MOV would be 2.47 and 2.56 fold respectively. This study has revealed that the risk of missed opportunities for vaccination in sub-Saharan Africa is influenced by not only individual factors but also by compositional factors such as family’s financial capacity, place of birth and upbringing.

We used cross-sectional data from Demographic and Health Surveys (DHS), which are nationally representative household surveys conducted in sub-Saharan Africa. This study used data from 35 recent DHS surveys conducted between 2007 and 2016 available as of May 2018. The DHS uses a multi-stage, stratified sampling design with households as the sampling unit.24 Eligible women and men living in households were interviewed. The survey data are comparable across countries as all surveys instruments and procedures were implemented similarly. We used the World Health Organisation (WHO) definition of missed opportunity for vaccination (MOV) as the outcome variable, defined as a binary variable that takes the value of 1 if the child 12–23 months had any contact with health services but remained unavaccinated to any vaccine doses for which the child is eligible. Contact with health services were defined using the following six variables: skilled birth attendance, baby postnatal check within 2 months, received vitamin A dose in first 2 months after delivery, has health card and medical treatment of diarrhea/fever/cough. The following individual-level factors were included in the models: child’s age, sex of the child (male and female), high birth order (> 4 birth order), number of under five children in the household, maternal age (15 to 24, 25 to 34, 35 or older), employment status (working or not working), maternal education (no education, primary or secondary or higher), media access (radio, television or newspaper), and wealh index (poorest, poorer, middle, richer and richest).20,25 We considered neighbourhood socioeconomic disadvantage for the community-level variable in this study. Neighbourhood socioeconomic disadvantage was operationalized with a principal component comprised of the proportion of respondents with: no formal education, unemployed, rural resident, and living below the poverty level (asset index below 20% poorest quintile). A standardized score with mean 0 and standard deviation 1 was generated from this index; with higher scores indicative of lower socieo-economic position (SEP). We divided the resultants scores into quintiles. Country level data were collected from the reports published by the United Nations Development Program.26 At country-level, we included human development index, a measure of country’s intensity of deprivation, which is the average percentage of deprivation experienced by people in multidimensional poverty. Like wealth index, intensity of deprivation was computed using principal components based on data on household deprivations in education, health and living standards, however, at the country-level26. The country-level variables were categorized into three tertiles (low, middle and high levels). We used multivariable multilevel logistic regression models to analyse the association between individual, compositional and contextual factors associated with missed opportunity for vaccination. We specified a 3-level model for binary response reporting missed opportunity for vaccination or not, for a child (at level 1), in a neighbourhood (at level 2) living in a country (at level 3) (see Figure 3). Five different models were developed. First, was the unconditional or empty model without any determinant variables. The aim of this model was to decompose the amount of variance in odds of missed opportunity vaccination between countries and neighbourhoods. Model 2 included only individual-level factor, model 3 included only neighbourhood-level factors, and model 4 included only the country-level factors. The fifth model, included all individual-, neighbourhood- and country-level factors simulteneously. We reported the measures of association as odds ratios (ORs) with their 95% credible intervals (CrIs). Measures of variations were explored using the intraclass correlation (ICC) and median odds ratio (MOR) 27,28. The ICC represents the percentage of the total variance in the odds of missed opportunities for vaccination that is related to the neighbourhood and country level, i.e. measure of clustering of odds of missed opportunities for vaccination in the same neighbourhood and country. MOR estimates the probability of missed opportunities for vaccination that can be attributed to neighbourhood and country context. Multilevel analysis was performed using the MLwinN software, version 2.3129,30 using the Bayesian Markov Chain Monte Carlo procedure.29 We generated scatter plots of performance, as a percentage, against the number of missed opportunities for vaccination children (the denominator for the percentage). The mean country performance and exact binomial 3 sigma limits were calculated for all possible values for the number of cases and used to create a funnel plot using the method described by Spiegelhalter.31,32 If a state lies with the 99% CI, it has crude missed opportunities for vaccination rate that is statistically consistent with the average rate (common-cause variation). If a country lies outside the 99% CI, then it has crude missed opportunities for vaccination rate that is statistically different from the average rate (special-cause variation).

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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 mobile applications or text messaging services that provide pregnant women with information about prenatal care, vaccination schedules, and reminders for appointments. This can help overcome barriers to accessing healthcare by providing information directly to women’s smartphones.

2. Telemedicine: Implement telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare providers remotely. This can help address the lack of healthcare facilities in certain regions and improve access to prenatal care.

3. Community Health Workers: Train and deploy community health workers who can provide basic prenatal care, education, and support to pregnant women in their communities. These workers can help bridge the gap between healthcare facilities and remote areas, ensuring that women receive the necessary care during pregnancy.

4. Transportation Solutions: Develop transportation systems or programs that provide affordable and accessible transportation for pregnant women to reach healthcare facilities. This can help overcome geographical barriers and ensure that women can access prenatal care and vaccination services.

5. Financial Support: Implement financial support programs that provide subsidies or incentives for pregnant women to seek prenatal care and vaccinations. This can help alleviate the financial burden associated with accessing healthcare and encourage women to prioritize their maternal health.

6. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of prenatal care and vaccinations. These campaigns can be conducted through various channels, such as radio, television, and community outreach programs, to reach women in different settings.

7. Strengthening Healthcare Systems: Invest in improving healthcare infrastructure, staffing, and resources in underserved areas. This can include building new healthcare facilities, training healthcare professionals, and ensuring the availability of essential medical supplies and equipment.

It is important to note that the specific implementation of these innovations would require further research, planning, and collaboration with relevant stakeholders to ensure their effectiveness and sustainability in improving access to maternal health.
AI Innovations Description
Based on the provided description, the recommendation to improve access to maternal health in sub-Saharan Africa is to implement targeted interventions that address the identified risk factors for missed opportunities for vaccination (MOV). These interventions should focus on the following areas:

1. High birth order: Implement strategies to ensure that children from higher birth orders receive timely and appropriate vaccinations. This could include targeted outreach and education campaigns to increase awareness among families with multiple children.

2. High number of under-five children in the household: Provide support and resources to families with multiple young children to ensure that all children receive their vaccinations. This could involve mobile vaccination clinics or home visits by healthcare providers.

3. Poorest households: Develop programs to address the financial barriers that prevent families from accessing vaccination services. This could include subsidies or financial assistance for vaccination costs, as well as improving the availability and affordability of transportation to healthcare facilities.

4. Lack of maternal education: Implement educational programs targeting mothers with low levels of education to increase their knowledge and understanding of the importance of vaccinations. This could involve community-based workshops, educational materials, and partnerships with local organizations.

5. Lack of media access: Utilize alternative communication channels, such as community radio stations or mobile phone messaging, to disseminate information about vaccinations and promote awareness among families with limited access to traditional media sources.

6. Living in poorer neighborhoods: Implement community-based interventions to address the socioeconomic disadvantages faced by families living in poorer neighborhoods. This could involve improving access to healthcare facilities, providing resources for community health workers, and addressing social determinants of health.

By targeting these specific risk factors and implementing tailored interventions, access to maternal health can be improved, leading to increased vaccination rates and better health outcomes for children in sub-Saharan Africa.
AI Innovations Methodology
Based on the provided description, here are some potential recommendations to improve access to maternal health:

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals in areas with limited access to maternal health services can help improve access and quality of care.

2. Mobile health (mHealth) interventions: Utilizing mobile technology to provide maternal health information, reminders for prenatal and postnatal care appointments, and access to telemedicine consultations can help overcome geographical barriers and improve access to care.

3. Community-based interventions: Implementing community health worker programs and training local volunteers to provide basic maternal health services, education, and support can help reach women in remote areas and increase access to essential care.

4. Financial incentives: Providing financial incentives, such as cash transfers or vouchers, to pregnant women or families who seek and complete maternal health services can help reduce financial barriers and increase utilization of care.

5. Transportation support: Establishing transportation systems or providing subsidies for transportation to healthcare facilities can address transportation challenges faced by pregnant women in accessing maternal health services.

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

1. Define the target population: Identify the specific population or geographic area for which the simulation will be conducted, such as a specific region or country in sub-Saharan Africa.

2. Collect baseline data: Gather relevant data on the current state of access to maternal health services in the target population, including indicators such as maternal mortality rates, utilization of antenatal care, and access to skilled birth attendance.

3. Define indicators and metrics: Determine the key indicators and metrics that will be used to measure the impact of the recommendations, such as changes in maternal mortality rates, increased utilization of antenatal care, or improved access to skilled birth attendance.

4. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the chosen indicators. This model should consider factors such as population size, healthcare infrastructure, financial resources, and existing healthcare systems.

5. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations on improving access to maternal health. This can involve adjusting variables related to the recommendations, such as the number of healthcare facilities or the coverage of mHealth interventions, and observing the resulting changes in the indicators.

6. Analyze and interpret results: Analyze the simulation results to determine the potential impact of the recommendations on improving access to maternal health. Assess the changes in the chosen indicators and evaluate the feasibility and effectiveness of the recommendations based on the simulation outcomes.

7. Refine and iterate: Use the simulation results to refine and iterate the recommendations, if necessary. Identify areas of improvement and adjust the simulation model accordingly to further optimize the impact on access to maternal health.

It is important to note that the methodology described above is a general framework and the specific details and techniques used may vary depending on the available data, resources, and expertise.

Statistics:
Citations: 27
Authors: 10
Identifiers:
DOI: 10.1080/21645515.2018.1504524
ISSN: 21645515
Research Areas:
Community Interventions, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants
Study Design:
Case-Control Study, Cross Sectional Study
Study Approach:
Quantitative
Participants Gender:
Male & Female
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