Rural-urban disparities in missed opportunities for vaccination in sub-Saharan Africa: a multi-country decomposition analyses

Human Vaccines and Immunotherapeutics, Volume 15, No. 5, Year 2019

Ukuhunyushwa

Background: In this study, we aimed to explore the rural-urban disparities in the magnitude and determinants of missed opportunities for vaccination (MOV) in sub-Saharan Africa. Methods: This was a cross-sectional study using nationally representative household surveys conducted between 2007 and 2017 in 35 countries across sub-Saharan Africa. The risk difference in MOV between rural or urban dwellers were calculated. Logistic regression method was used to investigate the urban-rural disparities in multivariable analyses. Then Blinder-Oaxaca method was used to decompose differences in MOV between rural and urban dwellers. Results: The median number of children aged 12 to 23 months was 2113 (Min: 370, Max: 5896). There was wide variation in the the magnitude of MOV among children in rural and urban areas across the 35 countries. The magnitude of MOV in rural areas varied from 18.0% (95% CI 14.7 to 21.4) in the Gambia to 85.2% (81.2 to 88.9) in Gabon. Out of the 35 countries included in this analysis, pro-rural inequality was observed in 16 countries (i.e. MOV is prevalent among children living in rural areas) and pro-urban inequality in five countries (i.e. MOV is prevalent among children living in urban areas). The contributions of the compositional ‘explained’ and structural ‘unexplained’ components varied across the countries. However, household wealth index was the most frequently identified factor. Conclusions: Variation exists in the level of missed opportunities for vaccination between rural and urban areas, with widespread pro-rural inequalities across Africa. Although several factors account for these rural-urban disparities in various countries, household wealth was the most common. Data for this cross-sectional study was 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.24 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-responses. With weights applied, survey findings represent the full target populations. The DHS surveys collects data using a household questionnaire. For eligible individuals within households, interviews are conducted using a woman’s or man’s questionnaire. DHS surveys are implemented across countries with standardized interviewer training, supervision, and implementation protocols. We used the World Health Organization (WHO) definition of missed opportunity for vaccination (MOV) as the outcome variable, defined as a binary variable that takes the value of 1 if a child who is eligible for vaccination, 12–23 months had any contact with health services, which does not result in the child receiving one or more of the vaccine doses for which he or she is eligible. Contact with health services was defined using the following five variables: skilled birth attendance, baby postnatal check within 2 months, received vitamin A dose in first 2 months after delivery, has a health card and received medical treatment for diarrhea/fever/cough. Place of residence which was categorised as rural or urban areas. The following factors were included in the models: child’s age, sex (male versus female), high birth order (>4 birth order), number of under five children in the household, maternal age in completed years (15 to 24, 25 to 34, 35 to 49), employment status (working or not working), maternal education (no education, primary or secondary or higher) and media access (radio, television or newspaper). Media access was assessed using the following indicators: access to information measured via frequency of watching television, listening to radio, and reading newspapers/magazine. To allow meaningful analysis, we dichotomized the response levels “less than one week”, “at least once a week”, and “almost every day” as one group and the response level “not at all” as the other group. We then created an additive media access variable (from 0 to 3) that counted the number of media type each respondent had access to. Wealth index was used as a proxy indicator for socioeconomic position. The methods used in calculating DHS wealth index have been described elsewhere.25,26 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) were calculated and used in the subsequent modelling. The analytical approach included descriptive statistics, univariable analysis and Blinder-Oaxaca decomposition techniques using logistic regressions. We used 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. In the descriptive statistics the distribution of respondents by key variables were expressed as percentages. All cases in the DHS data were given weights to adjust for differences in probability of selection and to adjust for non-response in order to produce the proper representation. Individual weights were used for descriptive statistics in this study. We calculate risk difference in missed opportunities between the two group, living in rural or urban areas. A risk difference greater than 0 suggests that missed opportunities is prevalent among children living in rural areas (pro-rural inequality). Conversely, a negative risk difference indicates that missed opportunities for vaccination is prevalent among children living in urban areas (pro-urban inequality). Finally, we adopted logistic regression method using the pooled cross-sectional data to investigate the urban-rural disparities in multivariable analyses adjusted for explanatory variables. The Blinder-Oaxaca decomposition was a counterfactual method with an assumption that “what the probability of missed opportunities for vaccination would be if children living in rural areas had the same characteristics as their urban counterparts?”.27,28 The Blinder-Oaxaca method allows for the decomposition of the difference in an outcome variable between 2 groups into 2 components.27,28 The first component is the “explained” portion of that gap that captures differences in the distributions of the measurable characteristics (referred as “compositional” or “endowments”) of these groups.27,28 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.27,28 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. The DHS stratification and the unequal sampling weights as well as household clustering effects were considered in the analysis to correct standard errors. All tests were two tailed and p < 0.05 was considered significant. Regression diagnostics were used to judge the goodness-of-fit of the model. They included the tolerance test for multicollinearity, its reciprocal variance inflation factors (VIF), presence of outliers and estimates of adjusted R square of the regression model. We checked for multi-collinearity among explanatory variables examining the variance inflation factor (VIF),29 all diagonal elements in the variance-covariance (τ) matrix for correlation between −1 and 1, and diagonal elements for any elements close to zero. The largest VIF greater than 10 or the mean VIF greater than 6 represent severe multicollinearity. None of the results of the tests provided reasons for concern. Thus, the models provide robust and valid results.

I-AI Digest

Study Justification:

The study aimed to investigate the rural-urban disparities in missed opportunities for vaccination (MOV) in sub-Saharan Africa. This is an important area of research because understanding the factors contributing to these disparities can help inform targeted interventions to improve vaccination coverage and reduce inequalities in healthcare access.

Highlights:

– The study used nationally representative household surveys conducted between 2007 and 2017 in 35 countries across sub-Saharan Africa.
– The magnitude of MOV varied widely among children in rural and urban areas across the 35 countries.
– Pro-rural inequality was observed in 16 countries, indicating that MOV is more prevalent among children living in rural areas.
– Pro-urban inequality was observed in five countries, indicating that MOV is more prevalent among children living in urban areas.
– Household wealth index was the most frequently identified factor contributing to rural-urban disparities in MOV.

Recommendations:

Based on the findings of the study, the following recommendations can be made:

1. Strengthen efforts to improve vaccination coverage in rural areas, where MOV is more prevalent.
2. Target interventions towards households with lower wealth index, as this was identified as a common factor contributing to rural-urban disparities in MOV.
3. Enhance access to healthcare services in rural areas, including skilled birth attendance, postnatal check-ups, and treatment for common childhood illnesses.
4. Conduct further research to explore other factors contributing to rural-urban disparities in MOV, such as cultural beliefs, healthcare infrastructure, and health literacy.

Key Role Players:

To address the recommendations, the following key role players are needed:

1. Ministries of Health: Responsible for implementing vaccination programs and coordinating efforts to improve coverage in rural areas.
2. Healthcare providers: Involved in delivering vaccination services and providing healthcare access in rural areas.
3. Community health workers: Play a crucial role in reaching remote communities and promoting vaccination.
4. Non-governmental organizations (NGOs): Can provide support in terms of funding, resources, and advocacy for improving vaccination coverage.
5. International organizations (e.g., World Health Organization, UNICEF): Provide technical guidance, resources, and support to countries in improving vaccination programs.

Cost Items:

While the actual cost of implementing the recommendations will vary depending on the context and specific interventions, some key cost items to consider in planning include:

1. Training and capacity building for healthcare providers and community health workers.
2. Infrastructure development, including the establishment or improvement of healthcare facilities in rural areas.
3. Outreach and awareness campaigns to promote vaccination and address barriers to access.
4. Procurement and distribution of vaccines, as well as cold chain management.
5. Monitoring and evaluation systems to assess the impact of interventions and ensure accountability.
Please note that the above cost items are general considerations and a detailed budget would need to be developed based on the specific context and interventions planned.

Amandla Obufakazi

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a cross-sectional study using nationally representative household surveys conducted in 35 countries across sub-Saharan Africa. The study used a multi-country decomposition analysis to explore rural-urban disparities in missed opportunities for vaccination. The evidence is supported by a large sample size and standardized data collection methods. However, the abstract does not provide information on the specific methodology used for data analysis, such as the statistical tests employed. To improve the evidence, the abstract could include more details on the statistical methods used and provide information on the reliability and validity of the data collection instruments.

Abstract

Data for this cross-sectional study was 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.24 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-responses. With weights applied, survey findings represent the full target populations. The DHS surveys collects data using a household questionnaire. For eligible individuals within households, interviews are conducted using a woman’s or man’s questionnaire. DHS surveys are implemented across countries with standardized interviewer training, supervision, and implementation protocols. We used the World Health Organization (WHO) definition of missed opportunity for vaccination (MOV) as the outcome variable, defined as a binary variable that takes the value of 1 if a child who is eligible for vaccination, 12–23 months had any contact with health services, which does not result in the child receiving one or more of the vaccine doses for which he or she is eligible. Contact with health services was defined using the following five variables: skilled birth attendance, baby postnatal check within 2 months, received vitamin A dose in first 2 months after delivery, has a health card and received medical treatment for diarrhea/fever/cough. Place of residence which was categorised as rural or urban areas. The following factors were included in the models: child’s age, sex (male versus female), high birth order (>4 birth order), number of under five children in the household, maternal age in completed years (15 to 24, 25 to 34, 35 to 49), employment status (working or not working), maternal education (no education, primary or secondary or higher) and media access (radio, television or newspaper). Media access was assessed using the following indicators: access to information measured via frequency of watching television, listening to radio, and reading newspapers/magazine. To allow meaningful analysis, we dichotomized the response levels “less than one week”, “at least once a week”, and “almost every day” as one group and the response level “not at all” as the other group. We then created an additive media access variable (from 0 to 3) that counted the number of media type each respondent had access to. Wealth index was used as a proxy indicator for socioeconomic position. The methods used in calculating DHS wealth index have been described elsewhere.25,26 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) were calculated and used in the subsequent modelling. The analytical approach included descriptive statistics, univariable analysis and Blinder-Oaxaca decomposition techniques using logistic regressions. We used 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. In the descriptive statistics the distribution of respondents by key variables were expressed as percentages. All cases in the DHS data were given weights to adjust for differences in probability of selection and to adjust for non-response in order to produce the proper representation. Individual weights were used for descriptive statistics in this study. We calculate risk difference in missed opportunities between the two group, living in rural or urban areas. A risk difference greater than 0 suggests that missed opportunities is prevalent among children living in rural areas (pro-rural inequality). Conversely, a negative risk difference indicates that missed opportunities for vaccination is prevalent among children living in urban areas (pro-urban inequality). Finally, we adopted logistic regression method using the pooled cross-sectional data to investigate the urban-rural disparities in multivariable analyses adjusted for explanatory variables. The Blinder-Oaxaca decomposition was a counterfactual method with an assumption that “what the probability of missed opportunities for vaccination would be if children living in rural areas had the same characteristics as their urban counterparts?”.27,28 The Blinder-Oaxaca method allows for the decomposition of the difference in an outcome variable between 2 groups into 2 components.27,28 The first component is the “explained” portion of that gap that captures differences in the distributions of the measurable characteristics (referred as “compositional” or “endowments”) of these groups.27,28 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.27,28 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. The DHS stratification and the unequal sampling weights as well as household clustering effects were considered in the analysis to correct standard errors. All tests were two tailed and p < 0.05 was considered significant. Regression diagnostics were used to judge the goodness-of-fit of the model. They included the tolerance test for multicollinearity, its reciprocal variance inflation factors (VIF), presence of outliers and estimates of adjusted R square of the regression model. We checked for multi-collinearity among explanatory variables examining the variance inflation factor (VIF),29 all diagonal elements in the variance-covariance (τ) matrix for correlation between −1 and 1, and diagonal elements for any elements close to zero. The largest VIF greater than 10 or the mean VIF greater than 6 represent severe multicollinearity. None of the results of the tests provided reasons for concern. Thus, the models provide robust and valid results.

Izinto zokwakha

N/A

Emisha

Based on the information provided, it appears that the study focuses on exploring rural-urban disparities in missed opportunities for vaccination (MOV) in sub-Saharan Africa. The study uses data from nationally representative household surveys conducted between 2007 and 2017 in 35 countries across sub-Saharan Africa. The goal is to identify the magnitude and determinants of MOV and understand the factors contributing to rural-urban disparities in access to vaccination.

While the study does not explicitly mention innovations or recommendations to improve access to maternal health, based on the findings and the context of the study, here are some potential innovations that could be considered:

1. Mobile Health (mHealth) Interventions: Utilizing mobile phones and technology to deliver health information, reminders, and appointment notifications to pregnant women and new mothers in both rural and urban areas. This can help improve awareness and adherence to vaccination schedules.

2. Community Health Workers (CHWs): Training and deploying CHWs in rural areas to provide education, counseling, and vaccination services to pregnant women and new mothers. CHWs can bridge the gap between health facilities and remote communities, ensuring access to maternal health services.

3. Telemedicine: Implementing telemedicine services to connect pregnant women and new mothers in rural areas with healthcare providers in urban areas. This can enable remote consultations, advice, and guidance on vaccination and other maternal health issues.

4. Improving Health Infrastructure: Investing in the development and improvement of health facilities, particularly in rural areas, to ensure access to quality maternal health services, including vaccination.

5. Targeted Health Education Campaigns: Designing and implementing targeted health education campaigns to raise awareness about the importance of vaccination and address misconceptions or concerns among pregnant women and new mothers.

6. Public-Private Partnerships: Collaborating with private sector organizations, such as pharmaceutical companies or technology companies, to leverage their resources, expertise, and networks to improve access to maternal health services, including vaccination.

It is important to note that these recommendations are based on the general context of improving access to maternal health and may not directly address the specific findings of the study. Further research and analysis would be needed to tailor these recommendations to the specific rural-urban disparities in missed opportunities for vaccination identified in the study.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health is to address the rural-urban disparities in missed opportunities for vaccination (MOV) in sub-Saharan Africa. This can be achieved through the following steps:

1. Strengthen healthcare infrastructure in rural areas: Improve the availability and accessibility of healthcare facilities, including vaccination services, in rural areas. This may involve building new healthcare centers, upgrading existing facilities, and ensuring a sufficient supply of vaccines.

2. Increase awareness and education: Implement targeted awareness campaigns to educate rural communities about the importance of vaccination and maternal health. This can be done through community outreach programs, health education sessions, and the use of local media channels.

3. Improve transportation and logistics: Address transportation challenges by providing reliable and affordable transportation options for pregnant women and mothers to access healthcare facilities. This may involve establishing transportation networks, providing subsidies for transportation, or implementing mobile healthcare services.

4. Enhance healthcare workforce: Increase the number of healthcare professionals, particularly in rural areas, to ensure adequate coverage and availability of skilled personnel for vaccination services. This may involve recruiting and training more healthcare workers, incentivizing them to work in rural areas, and providing ongoing professional development opportunities.

5. Strengthen data collection and monitoring: Improve the collection and analysis of data on vaccination coverage and missed opportunities for vaccination. This will help identify specific areas with high rates of MOV and enable targeted interventions to address the underlying causes.

6. Address socioeconomic factors: Recognize that household wealth is a significant factor contributing to rural-urban disparities in MOV. Implement policies and programs that aim to reduce poverty and improve socioeconomic conditions in rural areas, such as providing financial support for healthcare expenses and promoting income-generating activities.

By implementing these recommendations, it is possible to reduce the rural-urban disparities in missed opportunities for vaccination and improve access to maternal health in sub-Saharan Africa.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, particularly in rural areas, can help improve access to maternal health services. This includes building and upgrading healthcare centers, ensuring availability of essential medical equipment and supplies, and training healthcare professionals.

2. Mobile health (mHealth) interventions: Utilizing mobile technology to provide maternal health information, reminders, and appointment scheduling can help overcome geographical barriers and improve access to healthcare services. This can be done through text messages, mobile applications, or interactive voice response systems.

3. Community-based interventions: Implementing community-based programs that involve trained community health workers can help reach women in remote areas who may have limited access to healthcare facilities. These workers can provide education, counseling, and basic healthcare services to pregnant women and new mothers.

4. Financial incentives: Providing financial incentives, such as cash transfers or vouchers, to pregnant women and new mothers can help reduce financial barriers and encourage them to seek maternal health services. This can be particularly beneficial for women in low-income households.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as the percentage of pregnant women receiving antenatal care, the percentage of births attended by skilled health personnel, or the percentage of women receiving postnatal care.

2. Collect baseline data: Gather data on the selected indicators from relevant sources, such as national surveys or health facility records. This will provide a baseline against which the impact of the recommendations can be measured.

3. Implement the recommendations: Introduce the recommended interventions, such as strengthening healthcare infrastructure, implementing mHealth interventions, or community-based programs. Ensure that these interventions are implemented in a controlled and standardized manner.

4. Monitor and collect data: Continuously monitor the implementation of the interventions and collect data on the selected indicators. This can be done through surveys, health facility records, or other data collection methods.

5. Analyze the data: Use statistical analysis techniques to compare the baseline data with the data collected after the implementation of the interventions. This will help determine the impact of the recommendations on improving access to maternal health.

6. Evaluate the results: Assess the findings of the data analysis to determine the effectiveness of the recommendations. This evaluation can include measuring changes in the selected indicators, identifying any disparities or challenges, and assessing the overall impact on access to maternal health.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different interventions on improving access to maternal health and make informed decisions on implementing the most effective strategies.

Umbhali

Dima Health

Statistics:

Citations: 7

Identifiers:

DOI: 10.1080/21645515.2019.1575163

ISSN: 21645515

Research Areas:

Disparities, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants

Study Countries:

Gabon, Gambia, Multi-Countries

Study Design:

Cross Sectional Study, Phenomenological Study

Study Approach:

Quantitative

Participants Gender:

Male & Female

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