Inequitable childhood immunization uptake in Nigeria: A multilevel analysis of individual and contextual determinants

BMC Infectious Diseases, Volume 9, Year 2009

Interpretation

Background: Immunization coverage in many parts of Nigeria is far from optimal, and far from equitable. Methods: Nigeria accounts for half of the deaths from Measles in Africa, the highest prevalence of circulating wild poliovirus in the world, and the country is among the ten countries in the world with vaccine coverage below 50 percent. Studies focusing on community-level determinants therefore have serious policy implications. Results: Multilevel multivariable regression analysis was used on a nationally-representative sample of women aged 15-49 years from the 2003 Nigeria Demographic and Health Survey. Multilevel regression analysis was performed with children (level 1) nested within mothers (level 2), who were in turn nested within communities (level 3). Conclusion: Results show that the pattern of full immunization clusters within families and communities, and that socio-economic characteristics are important in explaining the differentials in full immunization among the children in the study. At the individual level, ethnicity, mothers’ occupation, and mothers’ household wealth were characteristics of the mothers associated with full immunization of the children. At the community level, the proportion of mothers that had hospital delivery was a determinant of full immunization status. Significant community-level variation remaining after having controlled for child- and mother-level characteristics is indicative of a need for further research on community-levels factors, which would enable extensive tailoring of community-level interventions aimed at improving full immunization and other child health outcomes. © 2009 Antai; licensee BioMed Central Ltd. Data on the health and mortality of children in Nigeria were collected as part of the Nigeria Demographic and Health Survey (DHS). This study uses data from the 2003 edition of this survey, which is a nationally-representative probability sample, collected using a stratified two-stage cluster sampling procedure. Sampling of women was performed according to the list of enumeration areas developed from the 1991 Population Census sampling frame. The initial sampling stage involved selecting 365 clusters, also known as primary sampling units (PSUs) with a probability proportional to the size. The size, in this case, is the number of households in the cluster. Subsequent sampling involved systematically selecting households from the already selected clusters. This resulted in a probability sample of 7864 households, from which data was collected by face-to-face interviews from 3725 women aged 15 to 49 years. These women contributed a total of 6029 live born children born to the survey. Information collected included birth histories, in-depth demographic and socio-economic information on illnesses, medical care, immunizations, and anthropometric details of children [20]. Immunization status of a child was determined from vaccination cards shown to the DHS interviewer. In the absence of vaccination cards, mothers were asked to recall whether the child had received BCG, Polio, DPT (including the number of doses for each) and Measles vaccinations. The outcome variable is the likelihood of a child 12 months of age and older having received all of the eight required vaccinations (full immunization). Eight additional child- and mother-level variables of interest were examined: i) sex of the child, assessed as: male and female; ii) birth order and interval between births, created by merging “birth order” and “preceding birth interval” classified as: first births, birth order 2-4 with short birth interval (<24 months), birth order 2-4 with medium birth interval (24-47 months), birth order 2-4 with long birth interval (48+ months), birth order 5+ with short birth interval (<24 months), birth order 5+ with medium birth interval (24-47 months), and birth order 5+ with long birth interval (48 months); iii) mothers' age, grouped as: 15-18, 19-23, 24-28, 29-33, and 34 years and older; iv) marital status, grouped as: single, married, and divorced; iv) ethnicity, categorized as: a) Hausa/Fulani/Kanuri (grouped on the basis that these ethnic groups either speak a common language or dialect, share a common sense of identity, cohesion and history; or have a single set of customs and behavioural rules as in marriage, clothing, diet, taboos); b) Igbo; c) Yoruba; and d) Others (a merger of various other minority ethnic groups from the more than 374 identifiable ethnic groups in Nigeria); v) vi) mothers' education, categorized as: no education, primary, and secondary or higher education; vii) mothers' occupation, categorized as: professional/technical/managerial, clerical/sales/services/skilled manual, agricultural self-employed/agricultural employee/household & domestic/unskilled manual occupations, and not working; and viii) mothers' household wealth index, categorized into five quintiles as: poorest, poorer, middle, richer and richest. Primary sampling units or clusters are administratively-defined areas used as proxies for "neighbourhoods" or "communities" [21,22], and are relevant when the hypothesis involves policies. Primary sampling units are small and designed to be fairly homogenous units with respect to population socio-demographic characteristics, economic status and living conditions, and consist of one or more enumeration areas (EAs), which are the smallest geographic units for which census data are available in Nigeria. Each cluster was made up of a minimum of 50 households; in the case of less than 50 households, a contiguous enumeration area was added [20]. Four community-level variables were assessed. Community prenatal care by doctor was assessed because prenatal care directly increases the chances that mothers would access subsequent health care services for their child, such as institutional delivery and immunization [23,24]. Community hospital delivery was included because the proportion of mothers that delivered in a hospital setting is a predictor of child immunization uptake. Hospital delivery is one of the most important preventive measures against maternal and child health outcomes, and an important determinant of full immunization [25,26]. Community mother's education was assessed because higher levels of maternal education are associated with better child health outcomes, such as child immunization rates [23,24]. These community-level variables were: i) community mother's education, defined as the percentage of mothers with secondary or higher education in the primary sampling unit, and categorized as: low, middle, and high (cut-off at median value in all primary sampling units combined; "middle" referring to the proportion at the median value, "low" referring to the proportion below the median value, and "high" referring to the proportion above the median value); ii) community hospital delivery, defined as the percentage of mothers who delivered their child in the hospital, and categorized as: low, middle, and high (cut-off at median value in all primary sampling units combined); iii) Community prenatal care by doctor, defined as the percentage of mothers who received prenatal care by a doctor and categorized as: low, and high (cut-off at 13% in all primary sampling units combined); and iv) mother's region of residence, categorized according to the six geo-political zones in Nigeria, as: North Central, North East, North West, South East, South South, and South West. Community-level variables were estimated at the level of the primary sampling unit (n = 365). The distribution of the children and mothers in the sample by full immunization status was assessed. Normalized sample weights provided in the DHS data were used for all analyses using Stata 10 software package [27], so as to adjust for non-response and enable generalization of findings to the general population. A three-level multilevel logistic regression model was applied in order to account for the hierarchical structure of the DHS data [28]. Children (level 1), were nested within mothers (level 2), who were in turn nested within communities (level 3). Four models containing variables of interest were fitted. Model 0 (empty model) contained no exposure variable and only focused on decomposing the total variance into its mother and community components. Model 1 contained child-level variables (sex of the child, birth order/birth interval of the children) and Model 2 included mother-level variables (mothers' age, marital status, ethnicity, mothers' education, mothers' occupation, and mothers' household wealth index). Model 3 contained community-level variables (community mother's education, community hospital delivery, community prenatal care by doctor, and mothers' region of residence). The three-level multilevel model is written as follows: where πijk is the probability of dying for the ith child of the jth mother in the kth community, eijk is a child-level error term distributed as Bernoulli constant, Xijk is a vector of covariates corresponding to the ith child of the jth mother in the kth community including mother's ethnicity, and educational background, β0 is a vector of unknown parameters, u0jk is the random effect at the mother level, and v0k is the random effect at the community level. The intercept or average probability of being fully immunized is assumed to vary randomly across mothers and communities. The fixed effects (measures of association) are expressed as odds ratio (OR) and 95% confidence intervals (95% CI). The random effects (measures of variation) are expressed as Variance Partition Coefficient (VPC) and proportional change in variance (PCV). We appraised the precision by the standard error (SE) of the explanatory variables, and tested parameters using the Wald statistic i.e. the ratio of the estimated variance to its standard error [29], and we calculated p-values. MLwiN software package 2.0.2 [30] was used for the multilevel analyses, with Binomial, Penalized Quasi-Likelihood (PQL) procedures [31]. Missing data were excluded from the analysis. This study is based on analysis of secondary data with all participant identifiers removed. The survey was approved by the National Ethics Committee in the Federal Ministry of Health, Nigeria and the Ethics Committee of the Opinion Research Corporation Macro International, Incorporated (ORC Macro Inc.), Calverton, USA. Informed consent was obtained from the participants prior to participation in the survey, and data collection was done confidentially. Permission to use the DHS data in this study was obtained from ORC Macro Inc.

AI Digest

Study Justification:

– The study focuses on childhood immunization uptake in Nigeria, which is far from optimal and equitable.
– Nigeria has high rates of measles deaths and low vaccine coverage, making this study important for policy implications.
– The study aims to identify individual and contextual determinants of immunization uptake to inform interventions and improve child health outcomes.

Highlights:

– The study used a nationally-representative sample of women aged 15-49 years from the 2003 Nigeria Demographic and Health Survey.
– Multilevel multivariable regression analysis was performed, with children nested within mothers, who were nested within communities.
– Results showed that full immunization clusters within families and communities, and socio-economic characteristics play a role in immunization differentials.
– Ethnicity, mothers’ occupation, and mothers’ household wealth were associated with full immunization at the individual level.
– Community-level factors, such as the proportion of mothers with hospital delivery, were also determinants of immunization status.
– Further research on community-level factors is needed to tailor interventions and improve immunization and child health outcomes.

Recommendations:

– Tailor community-level interventions to address the determinants of immunization uptake, such as socio-economic characteristics and hospital delivery rates.
– Conduct further research on community-level factors to better understand their impact on immunization and child health outcomes.
– Strengthen immunization programs and policies in Nigeria to improve coverage and equity.

Key Role Players:

– Ministry of Health: Responsible for implementing and coordinating immunization programs and policies.
– Healthcare Providers: Deliver immunization services and provide education to mothers and communities.
– Community Leaders: Engage and mobilize communities to promote immunization and address barriers.
– Non-Governmental Organizations (NGOs): Support immunization programs through advocacy, funding, and implementation.

Cost Items for Planning Recommendations:

– Training and Capacity Building: Budget for training healthcare providers on immunization practices and community engagement.
– Vaccine Supply and Logistics: Allocate funds for procuring vaccines, cold chain equipment, and transportation.
– Communication and Awareness Campaigns: Invest in public education campaigns to promote immunization and address misconceptions.
– Monitoring and Evaluation: Set aside resources for monitoring immunization coverage, conducting surveys, and evaluating program effectiveness.
– Research and Data Analysis: Allocate funds for further research on community-level factors and data analysis to inform interventions.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a nationally-representative sample and uses multilevel regression analysis. However, to improve the evidence, the study could include more recent data to ensure its relevance to the current situation in Nigeria.

Abstract

Data on the health and mortality of children in Nigeria were collected as part of the Nigeria Demographic and Health Survey (DHS). This study uses data from the 2003 edition of this survey, which is a nationally-representative probability sample, collected using a stratified two-stage cluster sampling procedure. Sampling of women was performed according to the list of enumeration areas developed from the 1991 Population Census sampling frame. The initial sampling stage involved selecting 365 clusters, also known as primary sampling units (PSUs) with a probability proportional to the size. The size, in this case, is the number of households in the cluster. Subsequent sampling involved systematically selecting households from the already selected clusters. This resulted in a probability sample of 7864 households, from which data was collected by face-to-face interviews from 3725 women aged 15 to 49 years. These women contributed a total of 6029 live born children born to the survey. Information collected included birth histories, in-depth demographic and socio-economic information on illnesses, medical care, immunizations, and anthropometric details of children [20]. Immunization status of a child was determined from vaccination cards shown to the DHS interviewer. In the absence of vaccination cards, mothers were asked to recall whether the child had received BCG, Polio, DPT (including the number of doses for each) and Measles vaccinations. The outcome variable is the likelihood of a child 12 months of age and older having received all of the eight required vaccinations (full immunization). Eight additional child- and mother-level variables of interest were examined: i) sex of the child, assessed as: male and female; ii) birth order and interval between births, created by merging “birth order” and “preceding birth interval” classified as: first births, birth order 2-4 with short birth interval (<24 months), birth order 2-4 with medium birth interval (24-47 months), birth order 2-4 with long birth interval (48+ months), birth order 5+ with short birth interval (<24 months), birth order 5+ with medium birth interval (24-47 months), and birth order 5+ with long birth interval (48 months); iii) mothers' age, grouped as: 15-18, 19-23, 24-28, 29-33, and 34 years and older; iv) marital status, grouped as: single, married, and divorced; iv) ethnicity, categorized as: a) Hausa/Fulani/Kanuri (grouped on the basis that these ethnic groups either speak a common language or dialect, share a common sense of identity, cohesion and history; or have a single set of customs and behavioural rules as in marriage, clothing, diet, taboos); b) Igbo; c) Yoruba; and d) Others (a merger of various other minority ethnic groups from the more than 374 identifiable ethnic groups in Nigeria); v) vi) mothers' education, categorized as: no education, primary, and secondary or higher education; vii) mothers' occupation, categorized as: professional/technical/managerial, clerical/sales/services/skilled manual, agricultural self-employed/agricultural employee/household & domestic/unskilled manual occupations, and not working; and viii) mothers' household wealth index, categorized into five quintiles as: poorest, poorer, middle, richer and richest. Primary sampling units or clusters are administratively-defined areas used as proxies for "neighbourhoods" or "communities" [21,22], and are relevant when the hypothesis involves policies. Primary sampling units are small and designed to be fairly homogenous units with respect to population socio-demographic characteristics, economic status and living conditions, and consist of one or more enumeration areas (EAs), which are the smallest geographic units for which census data are available in Nigeria. Each cluster was made up of a minimum of 50 households; in the case of less than 50 households, a contiguous enumeration area was added [20]. Four community-level variables were assessed. Community prenatal care by doctor was assessed because prenatal care directly increases the chances that mothers would access subsequent health care services for their child, such as institutional delivery and immunization [23,24]. Community hospital delivery was included because the proportion of mothers that delivered in a hospital setting is a predictor of child immunization uptake. Hospital delivery is one of the most important preventive measures against maternal and child health outcomes, and an important determinant of full immunization [25,26]. Community mother's education was assessed because higher levels of maternal education are associated with better child health outcomes, such as child immunization rates [23,24]. These community-level variables were: i) community mother's education, defined as the percentage of mothers with secondary or higher education in the primary sampling unit, and categorized as: low, middle, and high (cut-off at median value in all primary sampling units combined; "middle" referring to the proportion at the median value, "low" referring to the proportion below the median value, and "high" referring to the proportion above the median value); ii) community hospital delivery, defined as the percentage of mothers who delivered their child in the hospital, and categorized as: low, middle, and high (cut-off at median value in all primary sampling units combined); iii) Community prenatal care by doctor, defined as the percentage of mothers who received prenatal care by a doctor and categorized as: low, and high (cut-off at 13% in all primary sampling units combined); and iv) mother's region of residence, categorized according to the six geo-political zones in Nigeria, as: North Central, North East, North West, South East, South South, and South West. Community-level variables were estimated at the level of the primary sampling unit (n = 365). The distribution of the children and mothers in the sample by full immunization status was assessed. Normalized sample weights provided in the DHS data were used for all analyses using Stata 10 software package [27], so as to adjust for non-response and enable generalization of findings to the general population. A three-level multilevel logistic regression model was applied in order to account for the hierarchical structure of the DHS data [28]. Children (level 1), were nested within mothers (level 2), who were in turn nested within communities (level 3). Four models containing variables of interest were fitted. Model 0 (empty model) contained no exposure variable and only focused on decomposing the total variance into its mother and community components. Model 1 contained child-level variables (sex of the child, birth order/birth interval of the children) and Model 2 included mother-level variables (mothers' age, marital status, ethnicity, mothers' education, mothers' occupation, and mothers' household wealth index). Model 3 contained community-level variables (community mother's education, community hospital delivery, community prenatal care by doctor, and mothers' region of residence). The three-level multilevel model is written as follows: where πijk is the probability of dying for the ith child of the jth mother in the kth community, eijk is a child-level error term distributed as Bernoulli constant, Xijk is a vector of covariates corresponding to the ith child of the jth mother in the kth community including mother's ethnicity, and educational background, β0 is a vector of unknown parameters, u0jk is the random effect at the mother level, and v0k is the random effect at the community level. The intercept or average probability of being fully immunized is assumed to vary randomly across mothers and communities. The fixed effects (measures of association) are expressed as odds ratio (OR) and 95% confidence intervals (95% CI). The random effects (measures of variation) are expressed as Variance Partition Coefficient (VPC) and proportional change in variance (PCV). We appraised the precision by the standard error (SE) of the explanatory variables, and tested parameters using the Wald statistic i.e. the ratio of the estimated variance to its standard error [29], and we calculated p-values. MLwiN software package 2.0.2 [30] was used for the multilevel analyses, with Binomial, Penalized Quasi-Likelihood (PQL) procedures [31]. Missing data were excluded from the analysis. This study is based on analysis of secondary data with all participant identifiers removed. The survey was approved by the National Ethics Committee in the Federal Ministry of Health, Nigeria and the Ethics Committee of the Opinion Research Corporation Macro International, Incorporated (ORC Macro Inc.), Calverton, USA. Informed consent was obtained from the participants prior to participation in the survey, and data collection was done confidentially. Permission to use the DHS data in this study was obtained from ORC Macro Inc.

Materials

N/A

Innovations

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

1. Mobile Health (mHealth) Solutions: Develop mobile applications or text messaging services to provide pregnant women with information and reminders about prenatal care, immunizations, and other maternal health services. This can help increase awareness and adherence to recommended healthcare practices.

2. Community Health Workers: Train and deploy community health workers to provide education, counseling, and support to pregnant women and new mothers in their communities. These workers can help bridge the gap between healthcare facilities and remote or underserved areas, ensuring that women receive the necessary care and follow-up.

3. Telemedicine: Implement telemedicine services to enable remote consultations between healthcare providers and pregnant women. This can be particularly beneficial for women in rural or hard-to-reach areas who may have limited access to healthcare facilities.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with subsidized or free access to essential maternal health services, including prenatal care, delivery, and postnatal care. This can help reduce financial barriers and increase utilization of healthcare services.

5. Public-Private Partnerships: Foster collaborations between the government, private sector, and non-profit organizations to improve access to maternal health services. This can involve leveraging existing infrastructure, resources, and expertise to expand healthcare coverage and reach underserved populations.

6. Quality Improvement Initiatives: Implement quality improvement programs in healthcare facilities to enhance the delivery of maternal health services. This can involve training healthcare providers, improving infrastructure and equipment, and implementing standardized protocols and guidelines.

7. Maternal Health Information Systems: Develop and implement robust information systems to collect, analyze, and disseminate data on maternal health indicators. This can help identify gaps in service delivery, monitor progress, and inform evidence-based decision-making.

8. Transportation and Logistics Support: Improve transportation and logistics systems to ensure timely access to maternal health services. This can involve providing ambulances or transportation vouchers for pregnant women in need of emergency care or facilitating the delivery of medical supplies to healthcare facilities.

9. Maternity Waiting Homes: Establish maternity waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to travel for delivery. These homes can provide a safe and supportive environment for women during the final weeks of pregnancy, ensuring timely access to skilled birth attendants.

10. Health Education and Awareness Campaigns: Conduct targeted health education and awareness campaigns to promote maternal health practices and encourage women to seek timely care. This can involve community outreach programs, media campaigns, and partnerships with local influencers and community leaders.

AI Innovations Description

The recommendation to improve access to maternal health based on the study is to implement community-level interventions tailored to address the determinants of full immunization among children in Nigeria. The study found that socio-economic characteristics, such as ethnicity, mothers’ occupation, and mothers’ household wealth, were associated with full immunization of children. Additionally, the proportion of mothers that had hospital delivery was a determinant of full immunization status at the community level.

To improve access to maternal health, the following actions can be taken:

1. Increase awareness and education: Implement community-based education programs to raise awareness about the importance of immunization and maternal health. This can include educating mothers about the benefits of full immunization and the risks of not vaccinating their children.

2. Improve access to healthcare facilities: Enhance access to healthcare facilities, particularly in rural areas, to ensure that mothers have the opportunity to deliver their babies in a hospital setting. This can be achieved by increasing the number of healthcare facilities, improving transportation infrastructure, and providing financial support for transportation costs.

3. Address socio-economic disparities: Implement policies and programs that address socio-economic disparities, such as poverty and education. This can include providing financial assistance to low-income families to cover the costs of immunization and maternal healthcare, as well as promoting educational opportunities for women.

4. Strengthen community-level healthcare services: Enhance community-level healthcare services, including prenatal care by doctors, to ensure that mothers receive adequate healthcare throughout their pregnancy. This can involve training and equipping healthcare workers at the community level, as well as improving the availability and accessibility of healthcare services.

5. Conduct further research: Further research is needed to understand the community-level factors that influence full immunization and other child health outcomes. This research can help tailor interventions to specific communities and identify additional strategies to improve access to maternal health.

By implementing these recommendations, it is possible to improve access to maternal health and increase full immunization rates among children in Nigeria.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals can improve access to maternal health services. This includes ensuring the availability of well-equipped clinics and hospitals, skilled birth attendants, and emergency obstetric care.

2. Increasing awareness and education: Implementing comprehensive education campaigns to raise awareness about the importance of maternal health and the available services can help increase utilization. This can include educating women and their families about the benefits of antenatal care, skilled birth attendance, and postnatal care.

3. Addressing socio-economic barriers: Poverty and lack of financial resources often hinder access to maternal health services. Implementing policies that provide financial support, such as subsidies or health insurance schemes, can help overcome these barriers and ensure that all women have access to quality maternal healthcare.

4. Improving transportation and infrastructure: In many remote areas, lack of transportation and poor infrastructure make it difficult for pregnant women to reach healthcare facilities. Investing in transportation systems, such as ambulances or community transport services, and improving road networks can help overcome these challenges.

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

1. Define indicators: Identify key indicators that measure access to maternal health, such as the percentage of women receiving antenatal care, skilled birth attendance, or postnatal care.

2. Collect baseline data: Gather data on the current status of these indicators in the target population. This can be done through surveys, interviews, or analysis of existing data sources such as the Nigeria Demographic and Health Survey.

3. Develop a simulation model: Create a mathematical or statistical model that simulates the impact of the recommendations on the selected indicators. This model should take into account factors such as population size, geographical distribution, and socio-economic characteristics.

4. Input intervention scenarios: Define different scenarios that represent the implementation of the recommendations. For example, one scenario could assume the full implementation of all recommendations, while another scenario could assume partial implementation or no implementation.

5. Run simulations: Use the simulation model to calculate the projected changes in the selected indicators under each intervention scenario. This can be done by applying the model to the baseline data and adjusting the relevant variables based on the assumptions of each scenario.

6. Analyze results: Compare the results of the different scenarios to assess the potential impact of the recommendations on improving access to maternal health. This can include analyzing changes in the selected indicators, identifying areas or population groups that would benefit the most from the interventions, and estimating the cost-effectiveness of each scenario.

7. Refine and iterate: Based on the analysis of the simulation results, refine the recommendations and the simulation model if necessary. Repeat the simulation process to further explore different intervention scenarios and optimize the strategies for improving access to maternal health.

It is important to note that the accuracy of the simulation results depends on the quality and representativeness of the data used, as well as the assumptions and limitations of the simulation model. Therefore, it is crucial to ensure the validity of the data sources and the robustness of the simulation methodology.

Author

Dima Health

Statistics:

Citations: 104

Identifiers:

DOI: 10.1186/1471-2334-9-181

Research Areas:

Community Interventions, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants, Technology and Innovations

Study Countries:

Nigeria

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Mixed-methods, Qualitative, Quantitative

Participants Gender:

Male & Female