Incomplete childhood immunization in Nigeria: A multilevel analysis of individual and contextual factors

BMC Public Health, Volume 17, No. 1, Year 2017

Interpretation

Background: Under-five mortality remains high in sub-Saharan Africa despite global decline. One quarter of these deaths are preventable through interventions such as immunization. The aim of this study was to examine the independent effects of individual-, community- and state-level factors on incomplete childhood immunization in Nigeria, which is one of the 10 countries where most of the incompletely immunised children in the world live. Methods: The study was based on secondary analyses of cross-sectional data from the 2013 Nigeria Demographic and Health Survey (DHS). Multilevel multivariable logistic regression models were applied to the data on 5,754 children aged 12-23 months who were fully immunized or not (level 1), nested within 896 communities (level 2) from 37 states (level 3). Results: More than three-quarter of the children (76.3%) were not completely immunized. About 83% of children of young mothers (15-24 years) and 94% of those whose mothers are illiterate did not receive full immunization. In the fully adjusted model, the chances of not being fully immunized reduced for children whose mothers attended antenatal clinic (adjusted odds ratio [aOR] = 0.49; 95% credible interval [CrI] = 0.39-0.60), delivered in health facility (aOR = 0.62; 95% CrI = 0.51-0.74) and lived in urban area (aOR = 0.66; 95% CrI = 0.50-0.82). Children whose mothers had difficulty getting to health facility (aOR = 1.28; 95% CrI = 1.02-1.57) and lived in socioeconomically disadvantaged communities (aOR = 2.93; 95% CrI = 1.60-4.71) and states (aOR = 2.69; 955 CrI =1.37-4.73) were more likely to be incompletely immunized. Conclusions: This study has revealed that the risk of children being incompletely immunized in Nigeria was influenced by not only individual factors but also community- and state-level factors. Interventions to improve child immunization uptake should take into consideration these contextual characteristics. This study used data from the Nigeria Demographic and Health Survey (NDHS) 2013 which is a population-based cross-sectional survey. The selection of sample was based on clusters and households and this involved a three-stage sampling technique. Nigeria was divided into strata which consist of all the 36 states and the Federal Capital Territory (FCT). Enumeration areas (EAs) were created in every state for easy access to the respondents. In the first stage, 896 clusters were randomly selected. The second stage involved a random selection of one EA from most of the clusters and this resulted in the selection of 372 EAs from the urban areas and 532 from the rural areas. A total of 45 households were selected from each rural and urban area. Altogether, 40,680 households were sampled for the survey; 23,940 in the rural areas and 16,740 in the urban areas. Complete details of the methods used in the NDHS have been published elsewhere [14]. Data were collected through the use of questionnaires that were administered by conducting face-to-face interviews. Information obtained through this process covered socioeconomic characteristics, reproductive history, prenatal and postnatal care, nutrition, immunization and HIV/AIDS. Information on immunization was collected through vaccination cards and mothers’ verbal reports. Interviewers asked mothers to present the vaccination cards in order to obtain vaccination dates. In the absence of vaccination cards, such mothers were asked to recall the vaccination administered on to their children. Details of the data collection procedure have been published elsewhere [14]. We limited our study to children aged 12–23 months as children at this age are expected to have received all the basic doses of immunization. The outcome variable was calculated using 9 doses of 4 vaccines; Bacillus Calmette–Guérin (BCG) (1 dose), Polio (4 doses), DTP (3 doses) and Measles (1 dose) (see Table 1). The DTP-containing vaccine currently used in Nigeria is a pentavalent vaccine that also includes Haemophilus influenzae type b and hepatitis B virus antigens. The NDHS applied the WHO recommendations that stipulate that a child is considered fully immunized if he or she received BCG against tuberculosis; 3 doses of vaccine against DTP; at least 3 doses of vaccine against polio and 1 dose of vaccine against measles [14]. The outcome variable was derived from nine variables which represent the doses. Children who received all the nine doses were categorized as fully immunized and those who received less than nine doses were defined as not completely immunized. Routine immunization schedule for children in Nigeria Source: Federal Ministry of Health, Nigeria [4] The following variables were considered in the study: mother’s age, education, wealth index, marital status, occupation, sex of child, and birth order. Others include size of child at birth, exposure to mass media, antenatal care and place of delivery. Mother’s age was grouped into 15–24, 25–34 and 35+. Education was defined as no education, primary and secondary or higher. Wealth index was originally presented in 5 quintiles by DHS which were derived from the measurements of ownership of household items such as car, radio, television, and dwelling features like toilet facilities, water source and type of roofing/floor. This mode of measurement has been used by the World Bank to categorize households into poverty levels based on principal components analysis [15, 16]. For easy interpretation, we reclassified the weighted scores into three tertiles (poor, middle and rich). Marital status was grouped into never married and ever married. We categorized maternal occupation as not working and working. Sex of child was defined as male and female. Birth order of children was categorized into 1st -3rd order, 4th -6th order and 7th + order. Size of child at birth was categorized into three; large, average and small. Exposure to mass media refers to the frequency of access to newspaper, radio and television. Those who had access to any of the three outlets (for any number of times in a week) were defined as exposed and others were considered never exposed. Antenatal care was dichotomized as attended for women who paid at least one visit to the clinic during pregnancy and never attended for others. Place of delivery was categorized into health facility for women who delivered at either public or private hospital and home for those who delivered elsewhere. The factors that were considered at the community level included place of residence, difficulty experienced in getting to health facilities, ethnicity diversity index and community socioeconomic status. Place of residence was grouped into urban and rural. Difficulty experienced in getting to health facilities was defined in terms of distance and lack of transportation. This was categorized into having a problem getting to health facilities and not having a problem. Ethnicity diversity index was defined through a formula given as follows Where: x i= population of ethnic group i of the area, y = total population of the area, n = number of ethnic groups in the area. The index measures the number of ethnic groups in a locality [17]. The formula calculates scores from 0 to approximately 1 and each index is multiplied by 100 to show the diversity. A higher index score implies better ethnic diversity. While a diversity of 0 indicates the community is dominated by one ethnic group, a 100 diversity index implies such a community is populated by various ethnic groups that are equally represented. Socioeconomic status of the community was computed from the socioeconomic characteristics such as education, occupation and wealth of individuals living in the same community. Through a principal components method, the proportion of individuals who are; uneducated, unemployed and poor was calculated. A standardized score of 0 mean and 1 standard deviation was derived from the proportion. The scores were then grouped into three tertiles (least disadvantaged, tertile 2 and most disadvantaged) with the highest score representing a lower socioeconomic status. State socioeconomic status was arrived at through socioeconomic characteristics of individuals living within the same state. Such characteristics include education, occupation and wealth. Using a principal components method, proportion of individuals in the same state who are; uneducated, unemployed and poor was calculated. A standardized score of 0 mean and 1 standard deviation was derived from the proportion. The scores were then categorized into three tertiles (least disadvantaged, tertile 2 and most disadvantaged) with the highest score representing a lower socioeconomic status. The descriptive analysis was presented by showing the distributions of independent variables by the outcome variable. The distributions were expressed as numbers and percentages. We calibrated a three-level binomial logistic regression that had a structure of children with incomplete vaccination or not at level 1 nested within communities at level 2 from a State at level 3. We have applied binomial logistic regression because of the dichotomous nature of the outcome variable. Four models were constructed. The first model is a null model with no independent variables. This model was included to decompose the amount of variance that existed between the community and state levels. Second model contained individual-level variables. The third and fourth were expanded to include community- and state-level variables respectively. Fixed effects were presented as odds ratios (OR) with their 95% credible intervals (CrI). The results of random effects comprised an intra-cluster correlation (ICC), a variance partition coefficient (VPC) and median odds ratio (MOR). MOR is the unexplained cluster heterogeneity. Details of the methods applied for computing MOR have been published elsewhere [18, 19]. We used the Bayesian Deviance Information Criterion (DIC) to assess the goodness of fit of the model. Variance Inflation Factor (VIF) was applied to test for multicollinearity. All the multilevel modelling operations were executed using MLwiN 2.35 [20] calling Stata Statistical Software for windows version 14 [21] using (runmlwin). Markov Chain Monte Carlo (MCMC) estimation was used for the multilevel logistic regression models [22]. P value of < 0.05 was used to define statistical significance.

AI Digest

Study Justification:

– Under-five mortality remains high in sub-Saharan Africa despite global decline.
– One quarter of these deaths are preventable through interventions such as immunization.
– Nigeria is one of the 10 countries where most of the incompletely immunized children in the world live.
– The aim of this study was to examine the independent effects of individual-, community-, and state-level factors on incomplete childhood immunization in Nigeria.

Highlights:

– More than three-quarters of the children (76.3%) in Nigeria were not completely immunized.
– Children of young mothers (15-24 years) and those whose mothers are illiterate were less likely to receive full immunization.
– Factors associated with increased likelihood of incomplete immunization include difficulty getting to health facility, living in socioeconomically disadvantaged communities and states.
– Factors associated with decreased likelihood of incomplete immunization include mothers attending antenatal clinic, delivering in a health facility, and living in urban areas.

Recommendations:

– Interventions to improve child immunization uptake should take into consideration individual, community, and state-level factors.
– Efforts should be made to improve access to health facilities, especially for mothers who have difficulty getting to them.
– Socioeconomic disparities should be addressed to ensure equal access to immunization services.
– Education and awareness programs should target young mothers and those who are illiterate.
– Collaboration between different stakeholders, including government agencies, healthcare providers, and community organizations, is crucial for implementing and sustaining immunization programs.

Key Role Players:

– Government agencies responsible for healthcare and immunization policies and programs.
– Healthcare providers, including doctors, nurses, and community health workers.
– Community organizations and leaders who can help raise awareness and facilitate access to immunization services.
– Non-governmental organizations (NGOs) involved in immunization advocacy and support.

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers and community workers.
– Development and dissemination of educational materials and campaigns.
– Infrastructure improvements to ensure access to health facilities.
– Outreach programs to reach remote and underserved communities.
– Monitoring and evaluation systems to assess the impact of interventions.
– Collaboration and coordination efforts among stakeholders.
– Research and data collection to inform evidence-based decision making.

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 large sample size (5,754 children) and utilizes multilevel multivariable logistic regression models. The study also provides specific percentages and odds ratios to support its findings. To improve the evidence, the abstract could include information on the response rate of the survey and any potential limitations of the study, such as selection bias or measurement error.

Abstract

This study used data from the Nigeria Demographic and Health Survey (NDHS) 2013 which is a population-based cross-sectional survey. The selection of sample was based on clusters and households and this involved a three-stage sampling technique. Nigeria was divided into strata which consist of all the 36 states and the Federal Capital Territory (FCT). Enumeration areas (EAs) were created in every state for easy access to the respondents. In the first stage, 896 clusters were randomly selected. The second stage involved a random selection of one EA from most of the clusters and this resulted in the selection of 372 EAs from the urban areas and 532 from the rural areas. A total of 45 households were selected from each rural and urban area. Altogether, 40,680 households were sampled for the survey; 23,940 in the rural areas and 16,740 in the urban areas. Complete details of the methods used in the NDHS have been published elsewhere [14]. Data were collected through the use of questionnaires that were administered by conducting face-to-face interviews. Information obtained through this process covered socioeconomic characteristics, reproductive history, prenatal and postnatal care, nutrition, immunization and HIV/AIDS. Information on immunization was collected through vaccination cards and mothers’ verbal reports. Interviewers asked mothers to present the vaccination cards in order to obtain vaccination dates. In the absence of vaccination cards, such mothers were asked to recall the vaccination administered on to their children. Details of the data collection procedure have been published elsewhere [14]. We limited our study to children aged 12–23 months as children at this age are expected to have received all the basic doses of immunization. The outcome variable was calculated using 9 doses of 4 vaccines; Bacillus Calmette–Guérin (BCG) (1 dose), Polio (4 doses), DTP (3 doses) and Measles (1 dose) (see Table 1). The DTP-containing vaccine currently used in Nigeria is a pentavalent vaccine that also includes Haemophilus influenzae type b and hepatitis B virus antigens. The NDHS applied the WHO recommendations that stipulate that a child is considered fully immunized if he or she received BCG against tuberculosis; 3 doses of vaccine against DTP; at least 3 doses of vaccine against polio and 1 dose of vaccine against measles [14]. The outcome variable was derived from nine variables which represent the doses. Children who received all the nine doses were categorized as fully immunized and those who received less than nine doses were defined as not completely immunized. Routine immunization schedule for children in Nigeria Source: Federal Ministry of Health, Nigeria [4] The following variables were considered in the study: mother’s age, education, wealth index, marital status, occupation, sex of child, and birth order. Others include size of child at birth, exposure to mass media, antenatal care and place of delivery. Mother’s age was grouped into 15–24, 25–34 and 35+. Education was defined as no education, primary and secondary or higher. Wealth index was originally presented in 5 quintiles by DHS which were derived from the measurements of ownership of household items such as car, radio, television, and dwelling features like toilet facilities, water source and type of roofing/floor. This mode of measurement has been used by the World Bank to categorize households into poverty levels based on principal components analysis [15, 16]. For easy interpretation, we reclassified the weighted scores into three tertiles (poor, middle and rich). Marital status was grouped into never married and ever married. We categorized maternal occupation as not working and working. Sex of child was defined as male and female. Birth order of children was categorized into 1st -3rd order, 4th -6th order and 7th + order. Size of child at birth was categorized into three; large, average and small. Exposure to mass media refers to the frequency of access to newspaper, radio and television. Those who had access to any of the three outlets (for any number of times in a week) were defined as exposed and others were considered never exposed. Antenatal care was dichotomized as attended for women who paid at least one visit to the clinic during pregnancy and never attended for others. Place of delivery was categorized into health facility for women who delivered at either public or private hospital and home for those who delivered elsewhere. The factors that were considered at the community level included place of residence, difficulty experienced in getting to health facilities, ethnicity diversity index and community socioeconomic status. Place of residence was grouped into urban and rural. Difficulty experienced in getting to health facilities was defined in terms of distance and lack of transportation. This was categorized into having a problem getting to health facilities and not having a problem. Ethnicity diversity index was defined through a formula given as follows Where: x i= population of ethnic group i of the area, y = total population of the area, n = number of ethnic groups in the area. The index measures the number of ethnic groups in a locality [17]. The formula calculates scores from 0 to approximately 1 and each index is multiplied by 100 to show the diversity. A higher index score implies better ethnic diversity. While a diversity of 0 indicates the community is dominated by one ethnic group, a 100 diversity index implies such a community is populated by various ethnic groups that are equally represented. Socioeconomic status of the community was computed from the socioeconomic characteristics such as education, occupation and wealth of individuals living in the same community. Through a principal components method, the proportion of individuals who are; uneducated, unemployed and poor was calculated. A standardized score of 0 mean and 1 standard deviation was derived from the proportion. The scores were then grouped into three tertiles (least disadvantaged, tertile 2 and most disadvantaged) with the highest score representing a lower socioeconomic status. State socioeconomic status was arrived at through socioeconomic characteristics of individuals living within the same state. Such characteristics include education, occupation and wealth. Using a principal components method, proportion of individuals in the same state who are; uneducated, unemployed and poor was calculated. A standardized score of 0 mean and 1 standard deviation was derived from the proportion. The scores were then categorized into three tertiles (least disadvantaged, tertile 2 and most disadvantaged) with the highest score representing a lower socioeconomic status. The descriptive analysis was presented by showing the distributions of independent variables by the outcome variable. The distributions were expressed as numbers and percentages. We calibrated a three-level binomial logistic regression that had a structure of children with incomplete vaccination or not at level 1 nested within communities at level 2 from a State at level 3. We have applied binomial logistic regression because of the dichotomous nature of the outcome variable. Four models were constructed. The first model is a null model with no independent variables. This model was included to decompose the amount of variance that existed between the community and state levels. Second model contained individual-level variables. The third and fourth were expanded to include community- and state-level variables respectively. Fixed effects were presented as odds ratios (OR) with their 95% credible intervals (CrI). The results of random effects comprised an intra-cluster correlation (ICC), a variance partition coefficient (VPC) and median odds ratio (MOR). MOR is the unexplained cluster heterogeneity. Details of the methods applied for computing MOR have been published elsewhere [18, 19]. We used the Bayesian Deviance Information Criterion (DIC) to assess the goodness of fit of the model. Variance Inflation Factor (VIF) was applied to test for multicollinearity. All the multilevel modelling operations were executed using MLwiN 2.35 [20] calling Stata Statistical Software for windows version 14 [21] using (runmlwin). Markov Chain Monte Carlo (MCMC) estimation was used for the multilevel logistic regression models [22]. P value of < 0.05 was used to define statistical significance.

Materials

N/A

Innovations

The study “Incomplete childhood immunization in Nigeria: A multilevel analysis of individual and contextual factors” examined the factors contributing to incomplete childhood immunization in Nigeria. The study used data from the 2013 Nigeria Demographic and Health Survey (NDHS) and applied multilevel multivariable logistic regression models to analyze the data.

The study found that more than three-quarters of the children in Nigeria were not completely immunized. Factors associated with incomplete immunization included young maternal age, maternal illiteracy, difficulty accessing health facilities, living in socioeconomically disadvantaged communities and states, and not attending antenatal care or delivering in a health facility. On the other hand, factors associated with higher immunization rates included attending antenatal care, delivering in a health facility, and living in urban areas.

The study highlights the importance of considering individual, community, and state-level factors in interventions to improve childhood immunization rates. It suggests that efforts should be made to improve access to healthcare facilities, particularly for disadvantaged communities, and to promote antenatal care and facility-based deliveries. These interventions can help increase immunization rates and reduce under-five mortality in Nigeria.

AI Innovations Description

The study titled “Incomplete childhood immunization in Nigeria: A multilevel analysis of individual and contextual factors” aimed to examine the factors contributing to incomplete childhood immunization in Nigeria. The study used data from the 2013 Nigeria Demographic and Health Survey (NDHS) and applied multilevel multivariable logistic regression models.

The study found that more than three-quarters of the children in Nigeria (76.3%) were not completely immunized. Factors associated with incomplete immunization included young maternal age (15-24 years), maternal illiteracy, difficulty in accessing health facilities, living in socioeconomically disadvantaged communities and states, and not attending antenatal care or delivering in a health facility. On the other hand, children whose mothers attended antenatal care, delivered in a health facility, and lived in urban areas had a reduced likelihood of being incompletely immunized.

The study highlights the importance of considering individual, community, and state-level factors in interventions aimed at improving child immunization uptake. Recommendations to improve access to maternal health and increase immunization rates could include:

1. Strengthening antenatal care services: Providing comprehensive antenatal care services that include immunization education and counseling can help increase awareness and knowledge among pregnant women about the importance of childhood immunization.

2. Improving access to health facilities: Addressing the challenges faced by mothers in accessing health facilities, such as distance and lack of transportation, can be achieved through initiatives like mobile clinics, community health workers, and transportation support.

3. Enhancing community engagement: Engaging communities through community health workers, local leaders, and community-based organizations can help raise awareness about immunization, address misconceptions, and promote the importance of childhood immunization.

4. Targeting socioeconomically disadvantaged communities and states: Implementing targeted interventions in socioeconomically disadvantaged communities and states can help address the underlying social determinants of incomplete immunization, such as poverty, education, and employment.

5. Strengthening immunization monitoring and surveillance: Improving the monitoring and surveillance systems for immunization coverage can help identify areas with low coverage and enable targeted interventions to reach underserved populations.

It is important to note that these recommendations should be tailored to the specific context of Nigeria and should involve collaboration between government agencies, healthcare providers, community organizations, and other stakeholders to ensure effective implementation and sustainability.

AI Innovations Methodology

The study you provided focuses on incomplete childhood immunization in Nigeria and examines the factors that contribute to this issue at the individual, community, and state levels. The study used data from the 2013 Nigeria Demographic and Health Survey (NDHS), which is a population-based cross-sectional survey.

To improve access to maternal health, it is important to consider innovations that address the identified factors contributing to incomplete childhood immunization. Here are some potential recommendations:

1. Strengthening Antenatal Care: Promote and provide comprehensive antenatal care services to pregnant women, including education on the importance of immunization and ensuring access to immunization services during antenatal visits.

2. Improving Health Facility Delivery: Enhance access to health facility delivery by addressing barriers such as distance, lack of transportation, and financial constraints. This can be achieved through the establishment of more health facilities, mobile clinics, and transportation support for pregnant women.

3. Enhancing Community Awareness and Engagement: Conduct community-based awareness campaigns to educate community members, especially mothers, about the importance of childhood immunization. Engage community leaders and influencers to promote immunization and address any misconceptions or concerns.

4. Addressing Socioeconomic Disadvantages: Implement targeted interventions to address socioeconomic disadvantages that contribute to incomplete immunization. This can include providing financial support for families who cannot afford immunization costs and improving access to immunization services in disadvantaged communities.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed using a combination of quantitative and qualitative approaches. Here is a brief outline of a possible methodology:

1. Data Collection: Collect data on key indicators related to maternal health, including immunization coverage, antenatal care utilization, place of delivery, and socioeconomic factors. This can be done through surveys, interviews, and analysis of existing data sources.

2. Baseline Assessment: Conduct a baseline assessment to determine the current status of access to maternal health services, including immunization. This will provide a benchmark for evaluating the impact of the recommendations.

3. Intervention Implementation: Implement the recommended interventions in selected communities or regions. Monitor the implementation process and collect data on the reach and effectiveness of each intervention.

4. Impact Evaluation: Compare the pre- and post-intervention data to assess the impact of the recommendations on improving access to maternal health. This can be done through statistical analysis, such as regression modeling, to determine the association between the interventions and the outcomes.

5. Qualitative Analysis: Conduct qualitative interviews or focus group discussions with key stakeholders, including mothers, healthcare providers, and community leaders, to gather insights on the perceived impact of the interventions and identify any additional factors influencing access to maternal health.

6. Policy Recommendations: Based on the findings from the impact evaluation and qualitative analysis, develop policy recommendations to scale up successful interventions and address any remaining barriers to access.

It is important to note that this is a simplified outline, and the actual methodology would require more detailed planning and implementation. Additionally, the specific indicators, sample size, and data collection methods would need to be tailored to the context and resources available for the study.

Author

Dima Health

Statistics:

Citations: 77

Identifiers:

DOI: 10.1186/s12889-017-4137-7

Research Areas:

Community Interventions, Disparities, Environmental, Food Security, Genetics and Genomics, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Sexual and Reproductive Health, Social Determinants

Study Countries:

Nigeria

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male & Female