Decomposing the gap in missed opportunities for vaccination between poor and non-poor in sub-Saharan Africa: A Multicountry Analyses

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Study Justification:
The study aims to understand the gaps in missed opportunities for vaccination (MOV) in sub-Saharan Africa in order to inform interventions for improving immunization coverage and achieving universal childhood immunization. By analyzing data from 35 Demographic and Health Surveys conducted between 2007 and 2016, the study provides insights into the factors contributing to the inequality in MOV between poor and non-poor households in sub-Saharan Africa.
Highlights:
– The study found that among the 35 countries analyzed, 19 showed pro-poor inequality, 5 showed pro-non-poor inequality, and 11 showed no statistically significant inequality in MOV.
– The risk difference in MOV between poor and non-poor households varied across countries, ranging from 4.2% in DR Congo to 20.1% in Kenya.
– Factors contributing to the inequality in MOV differed among countries. For example, in Madagascar, media access, number of under-five children, and maternal education were the largest contributors to inequality, while in Liberia, media access narrowed the inequality in MOV between poor and non-poor households.
– The findings suggest that socio-economic inequality in MOV is not solely determined by health system functions but is also influenced by factors beyond the scope of health authorities and care delivery systems.
– The study highlights the need to address social determinants of health in order to reduce the inequality in MOV and improve immunization coverage in sub-Saharan Africa.
Recommendations:
– Interventions should focus on addressing the social determinants of health that contribute to the inequality in MOV, such as improving media access, increasing maternal education, and reducing the number of under-five children in households.
– Health authorities and policymakers should collaborate with other sectors, such as education and media, to implement comprehensive strategies that address the underlying factors driving the inequality in MOV.
– Efforts should be made to strengthen health systems and improve the delivery of immunization services, particularly in poor households, to ensure that children have access to vaccines and do not miss opportunities for vaccination.
Key Role Players:
– Health authorities and policymakers
– Education sector
– Media organizations
– Non-governmental organizations (NGOs)
– Community leaders and influencers
Cost Items for Planning Recommendations:
– Media campaigns and outreach programs to improve media access and health education: production and dissemination of educational materials, advertisements, radio and television spots, etc.
– Training programs for healthcare providers to improve the delivery of immunization services: workshops, seminars, training materials, etc.
– Infrastructure improvements in healthcare facilities: renovation, equipment procurement, etc.
– Community engagement activities: community meetings, awareness campaigns, community health workers’ training and support, etc.
– Data collection and monitoring: surveys, data analysis, reporting, etc.
– Coordination and collaboration efforts: meetings, workshops, networking events, etc.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a detailed description of the study design, data source, and analytical methods used. It also presents findings on the gap in missed opportunities for vaccination between poor and non-poor households in sub-Saharan Africa. However, the abstract does not provide specific numerical results or statistical significance for the findings. To improve the evidence, the abstract could include more specific information on the magnitude of the gap and statistical significance, as well as any limitations of the study.

Understanding the gaps in missed opportunities for vaccination (MOV) in sub-Saharan Africa would inform interventions for improving immunisation coverage to achieving universal childhood immunisation. We aimed to conduct a multicountry analyses to decompose the gap in MOV between poor and non-poor in SSA. We used cross-sectional data from 35 Demographic and Health Surveys in SSA conducted between 2007 and 2016. Descriptive statistics used to understand the gap in MOV between the urban poor and non-poor, and across the selected covariates. Out of the 35 countries included in this analysis, 19 countries showed pro-poor inequality, 5 showed pro-non-poor inequality and remaining 11 countries showed no statistically significant inequality. Among the countries with statistically significant pro-illiterate inequality, the risk difference ranged from 4.2% in DR Congo to 20.1% in Kenya. Important factors responsible for the inequality varied across countries. In Madagascar, the largest contributors to inequality in MOV were media access, number of under-five children, and maternal education. However, in Liberia media access narrowed inequality in MOV between poor and non-poor households. The findings indicate that in most SSA countries, children belonging to poor households are most likely to have MOV and that socio-economic inequality in is determined not only by health system functions, but also by factors beyond the scope of health authorities and care delivery system. The findings suggest the need for addressing social determinants of health.

Data for this cross-sectional study were obtained from Demographic and Health Surveys (DHS), which are nationally representative household surveys conducted in low- and middle-income countries. This study used data from 35 recent DHS surveys conducted between 2007 and 2016 in sub-Saharan Africa available as of December 2017. The DHS uses a multi-stage, stratified sampling design with households as the sampling unit.18 Within each sample household, all women and men meeting the eligibility criteria are interviewed. Because the surveys are not self-weighting, weights are calculated to account for unequal selection probabilities as well as for non-response. With weights applied, survey findings represent the full target population. The DHS surveys include a household questionnaire, a women’s questionnaire, and in most countries, a men’s questionnaire. All three DHS questionnaires are implemented across countries with similar interviewer training, supervision, and implementation protocols. We used the World Health Organisation (WHO) definition of missed opportunity for vaccination (MOV) as the outcome variable, categorized as a binary variable that takes the value of ‘1’ if a child aged 12–23 months had any contact with health services who is eligible for vaccination (e.g. unvaccinated or partially vaccinated and free of contraindications to vaccination), which does not result in the child receiving one or more of the vaccine doses for which he or she is eligible, (and ‘0’ if otherwise). 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 We limited the analysis to one child per woman in order to minimise over-representation of women with more than one child in the age category. DHS did not collect direct information on household income and expenditure. We used DHS wealth index as a proxy indicator for socioeconomic position. The methods used in calculating DHS wealth index have been described elsewhere.19-20 Briefly, an index of economic status for each household was constructed using principal components analysis based on the following household variables: number of rooms per house, ownership of car, motorcycle, bicycle, fridge, television and telephone as well as any kind of heating device. From these criteria the DHS wealth index quintiles (poorest, poorer, middle, richer and richest) are derived. The bottom two quintiles (lower 40%) were considered as ‘poor’ and remaining three were classified as ‘non-poor’. The following factors were included in the models: child’s age, sex of the child (male versus female), high birth order (> 4 birth order), number of under five children in the household, maternal age completed years (15 to 24, 25 to 34, 35 or older), maternal education (no education, primary or secondary or higher), employment status (working or not working), and media access (radio, television or newspaper). The analytical approach included descriptive statistics, univariable analysis and Blinder-Oaxaca decomposition techniques using logistic regressions. We used the descriptive statistics to show the distribution of respondents by the key variables. Values were expressed as absolute numbers (percentages) and mean (standard deviation) for categorical and continuous variables respectively. We calculated the risk difference in missed opportunities between the two groups, from poor or non-poor households. A risk difference greater than 0 suggests that missed opportunities are prevalent among children from poor households (pro-poor inequality). Conversely, a negative risk difference indicates that missed opportunities for vaccination is prevalent among children from non-poor households (pro-non-poor inequality). Finally, we adopted logistic regression method using the pooled cross-sectional data to conduct the Blinder-Oaxaca decomposition analysis. The Blinder-Oaxaca decomposition21-22 is a counterfactual method with an assumption that “what the probability of missed opportunities for vaccination would be if children from poor households had the same characteristics as their non-poor counterparts?”. The Blinder-Oaxaca method allows for the decomposition of the difference in an outcome variable between 2 groups into 2 components. The first component is the “explained” portion of that gap that captures differences in the distributions of the measurable characteristics (referred to as “compositional” or “endowments”) of these groups. This illustrates the portion of the gap in missed opportunities for vaccination that is attributed to the differences in observable, measurable characteristics between the two groups. Using this method, we can quantify how much of the gap the “advantaged” and the “disadvantaged” groups is attributable to these differences in specific measurable characteristics. The second component is the “unexplained” part, meaning the portion of the gap due to the differences in the estimated regression coefficients and the unmeasured variables between the two groups. This is also referred to in the literature as the “structural” component or the “coefficient” portion of the decomposition. This reflects the remainder of the model not explained by the differences in measurable, objective characteristics. The “unexplained” portion arises from differentials in how the predictor variables are associated with the outcomes for the two groups. This portion would persist even if the disadvantaged group were to attain the same average levels of measured predictor variables as the advantaged group.

Based on the provided description, 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 text messaging services to provide pregnant women with information about prenatal care, vaccination schedules, and reminders for appointments.

2. Telemedicine: Establish telemedicine programs that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls or phone consultations, reducing the need for travel and improving access to medical advice.

3. Community Health Workers: Train and deploy community health workers to provide education, support, and basic healthcare services to pregnant women in their communities. These workers can help identify missed opportunities for vaccination and provide timely interventions.

4. Transportation Solutions: Improve transportation infrastructure and services in rural areas to ensure that pregnant women can easily access healthcare facilities for prenatal care, vaccinations, and emergency obstetric care.

5. Financial Incentives: Implement financial incentive programs to encourage pregnant women to seek prenatal care and vaccinations. This could include providing cash transfers or vouchers for healthcare services.

6. Public Awareness Campaigns: Launch public awareness campaigns to educate communities about the importance of maternal health and vaccination. These campaigns can address cultural beliefs, myths, and misconceptions that may hinder access to care.

7. Integration of Services: Integrate maternal health services with existing healthcare programs, such as immunization programs or family planning services, to ensure comprehensive care and reduce missed opportunities for vaccination.

8. Quality Improvement Initiatives: Implement quality improvement initiatives in healthcare facilities to ensure that pregnant women receive timely and appropriate care, including vaccinations, during their visits.

9. Data Monitoring and Evaluation: Establish robust data monitoring and evaluation systems to track vaccination coverage and identify areas with high rates of missed opportunities. This information can guide targeted interventions and resource allocation.

10. Partnerships and Collaboration: Foster partnerships and collaboration between government agencies, non-governmental organizations, and private sector entities to leverage resources and expertise in improving access to maternal health services.

It’s important to note that these are general recommendations and may need to be adapted to the specific context and challenges of each country or region within sub-Saharan Africa.
AI Innovations Description
The recommendation that can be developed into an innovation to improve access to maternal health based on the provided description is to address the social determinants of health. The findings of the multicountry analyses indicate that children belonging to poor households in sub-Saharan Africa are more likely to have missed opportunities for vaccination (MOV). The socio-economic inequality in MOV is not solely determined by health system functions, but also by factors beyond the scope of health authorities and care delivery systems.

To improve access to maternal health, it is important to consider and address the social determinants of health that contribute to the inequality in MOV. This could involve implementing interventions that target the specific factors identified in each country. For example, in Madagascar, where media access, number of under-five children, and maternal education were found to be important contributors to inequality in MOV, interventions could focus on improving media access and promoting maternal education. In Liberia, where media access narrowed inequality in MOV between poor and non-poor households, efforts could be made to further enhance media access as a means of reducing inequality.

In addition to addressing specific factors, it is crucial to take a comprehensive approach that considers the broader social determinants of health. This may involve collaborating with other sectors such as education, employment, and housing to create supportive environments for maternal health. By addressing social determinants of health, it is possible to create sustainable and equitable improvements in access to maternal health services in sub-Saharan Africa.
AI Innovations Methodology
Based on the provided description, the study aims to analyze the gaps in missed opportunities for vaccination (MOV) between poor and non-poor households in sub-Saharan Africa. The methodology used in the study includes the following steps:

1. Data Collection: The study utilizes cross-sectional data from 35 Demographic and Health Surveys (DHS) conducted in sub-Saharan Africa between 2007 and 2016. The DHS surveys are nationally representative household surveys that collect information on various health indicators.

2. Sampling Design: The DHS surveys use a multi-stage, stratified sampling design with households as the sampling unit. Within each sample household, eligible women and men are interviewed. Weights are applied to account for unequal selection probabilities and non-response, ensuring that survey findings represent the full target population.

3. Outcome Variable: The outcome variable in this study is missed opportunities for vaccination (MOV). It is defined as a binary variable that takes the value of ‘1’ if a child aged 12-23 months had any contact with health services eligible for vaccination but did not receive the required vaccine doses, and ‘0’ otherwise. Contact with health services is determined using six variables related to skilled birth attendance, postnatal check, vitamin A dose, health card, and medical treatment.

4. Socioeconomic Position: The study uses the DHS wealth index as a proxy indicator for socioeconomic position. The wealth index is constructed using principal components analysis based on household variables such as number of rooms, ownership of assets, and heating devices. The bottom two quintiles are classified as ‘poor’ and the remaining three as ‘non-poor’.

5. Statistical Analysis: The study employs descriptive statistics, univariable analysis, and Blinder-Oaxaca decomposition techniques using logistic regressions. Descriptive statistics are used to understand the distribution of respondents by key variables. Univariable analysis examines the relationship between individual variables and missed opportunities for vaccination. The Blinder-Oaxaca decomposition method allows for the decomposition of the difference in MOV between poor and non-poor households into two components: the ‘explained’ portion attributed to differences in measurable characteristics and the ‘unexplained’ portion due to differences in estimated regression coefficients and unmeasured variables.

6. Interpretation of Results: The study quantifies the contribution of different factors to the gap in missed opportunities for vaccination between poor and non-poor households. It identifies the factors responsible for the observed inequality and highlights the need to address social determinants of health in order to improve immunization coverage and reduce disparities.

In summary, the methodology used in this study combines data analysis techniques, including descriptive statistics and Blinder-Oaxaca decomposition, to understand and quantify the factors contributing to the gap in missed opportunities for vaccination between poor and non-poor households in sub-Saharan Africa.

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