Factors associated with incomplete immunisation in children aged 12-23 months at subnational level, Nigeria: A cross-sectional study

BMJ Open, Volume 11, No. 6, Year 2021

Interpretation

Objectives National immunisation coverage rate masks subnational immunisation coverage gaps at the state and local district levels. The objective of the current study was to determine the sociodemographic factors associated with incomplete immunisation in children at a sub-national level. Design Cross-sectional study using the WHO sampling method (2018 Reference Manual). Setting Fifty randomly selected clusters (wards) in four districts (two urban and two rural) in Enugu state, Nigeria. Participants 1254 mothers of children aged 12-23 months in July 2020. Primary and secondary outcome measures Fully immunised children and not fully immunised children. Results Full immunisation coverage (FIC) rate in Enugu state was 78.9% (95% CI 76.5% to 81.1%). However, stark difference exists in FIC rate in urban versus rural districts. Only 55.5% of children in rural communities are fully immunised compared with 94.5% in urban communities. Significant factors associated with incomplete immunisation are: children of single mothers (aOR=5.74, 95% CI 1.45 to 22.76), children delivered without skilled birth attendant present (aOR=1.93, 95% CI 1.24 to 2.99), children of mothers who did not receive postnatal care (aOR=6.53, 95% CI 4.17 to 10.22), children of mothers with poor knowledge of routine immunisation (aOR=1.76, 95% CI 1.09 to 2.87), dwelling in rural district (aOR=7.49, 95% CI 4.84 to 11.59), low-income families (aOR=1.56, 95% CI 1.17 to 2.81) and living further than 30 min from the nearest vaccination facility (aOR=2.15, 95% CI 1.31 to 3.52). Conclusions Although the proportion of fully immunised children in Enugu state is low, it is significantly lower in rural districts. Study findings suggest the need for innovative solutions to improve geographical accessibility and reinforce the importance of reporting vaccination coverage at local district level to identify districts for more targeted interventions. This was a community-based cross-sectional survey of mothers of children 12–23 months old residing in Enugu state in July 2020. The study considered all children 12–23 months old eligible for sampling, and used the Strengthening the Reporting of Observational Studies in Epidemiology guidelines to ensure appropriate reporting of its study’s design, conduct and findings.27 Nigeria is the most populous country in Africa and the sixth most populous in the world.28 She is located in Western Africa and is divided into six geopolitical regions: northeast, northwest, northcentral, southsouth, southeast and southwest. She has 36 states—the second administrative division, and a federal capital territory in Abuja. Each state is further divided into smaller administrative units called local government areas (LGAs) and each LGA is further divided into wards. Enugu state is one of the 36 states in Nigeria (figure 1) and one of the five states that make up the southeast geopolitical region in the country. Enugu state is further divided into 17 LGAs, four of which are predominantly urban (Enugu East, Enugu North, Enugu South and Nsukka) and the rest are predominantly rural. Enugu state’s 2020 projected population is 4 769 916, with most of the population living in urban centres in Enugu and Nsukka.29 30 Map of Nigeria above showing Enugu state and map of Enugu state showing the study area (four local government areas (LGAs)). Adapted from image culled from Ugoyibo et al61. Using steps described in the WHO Vaccination Coverage Cluster Surveys Reference Manual 2019,26 we determined the sample size using immunisation coverage of 36.0% obtained for Enugu state in the most recent 2018 Nigeria DHS,25 significance level of 5.0%, precision of 5.0%, design effect of 2.531 and an inflation of 15% (to account for non-response). The calculated minimum sample size was 1183 which we increased to 1250 to boost the power of the study. We used a three-stage sampling technique. In the first stage, we used a simple random sampling technique by balloting to select four LGAs: two each from the urban and rural areas of the state. In the second stage, we randomly selected (by balloting) a total of 50 clusters based on probability-proportional-to-population: 15 clusters from Enugu East LGA, 15 clusters from Enugu North LGA and 10 clusters each from Ezeagu LGA and Udenu LGA. In the third stage, we selected 25 households in each of the 50 clusters (ward). In each cluster, we selected the first household randomly and subsequent households contiguously in the right direction until we achieved the required number of households for that cluster. From each selected household, we selected one eligible child. If a selected household had more than one eligible child, we selected the youngest child older than 12 months. If a selected household had no eligible child, we visited the next contiguous household, and selected one eligible child. A team of 14 trained community health workers collected the data using structured pretested interviewer-administered questionnaires. We constructed the questionnaire from a review of the available literature on immunisation surveys in similar contexts,32–34 and tested it for acceptability and logical structure in a sample of 20 mothers before the study. Prior to the survey, we trained the team on the study’s objectives, interpreting and extracting data from health cards/vaccination certificates, sampling techniques, walking distance estimation using Google Maps mobile app, ethical issues including the process of taking informed verbal consent and administration of the questionnaire. We administered the questionnaire in Igbo (the local language) except for a few non-Igbo speakers whom we administered the questionnaire in English. The research team directed the questions to the mothers and recorded only their responses. Data we collected include sociodemographic characteristics of mothers and children including maternal healthcare (MHC) utilisation (ante-natal care (ANC), skilled birth attendant (SBA) present at birth and post-natal care (PNC)), knowledge of mothers regarding RI, immunisation status of children and reasons for any non-vaccination. If the immunisation card was available, we recorded immunisation information of each inoculation the child received. If a child had never received an immunisation card or the mother was unable to present the immunisation card to the interviewer, the immunisation data/information for the child was based on the mother’s report. We used Google Map mobile app on smartphones to estimate the walking distance from each study participant’s house to the nearest vaccination centre in all but four clusters (in Ezeagu LGA). In these four clusters, we first identified the nearest routine childhood vaccination point in each cluster and then estimated the walking distance from this nearest vaccination facility to each household included in the study. To evaluate mothers’ knowledge of RI and vaccine-preventable diseases, the interviewers asked questions on the correct purpose of immunisation, different vaccine-preventable diseases, the correct age for receiving the vaccines and the total number of visits required to complete the recommended vaccination for the child. We evaluated the responses as per the National Primary Healthcare Development Agency RI schedule.35 We coded correct responses as 2 points, incorrect responses 1 point, ‘I do not know’ 0 (zero) point. We categorised children as fully immunised, partially immunised or unimmunised (zero-dose) based on the types and doses of antigens received. We defined a ‘fully immunised child’ as a child who had received one dose of BCG, three doses of polio vaccine (excluding Oral Polio Vaccine (OPV) given at birth), three doses of pentavalent vaccine and one dose of measles vaccine by 12 months of age. Likewise, we defined a partially immunised child as a child who missed at least any one of the above doses, and an ‘un-immunised’ or ‘zero-dose’ child as a child who had not received any vaccine by 12 months of age.36 Incomplete immunisation, in this study, includes partially immunised children and unimmunised (zero-dose) children. Immunisation status was based on mothers’ recall and immunisation card record (ie, where the mother presents an immunisation card, the child’s immunisation status is based on records in the card, but where an immunisation card is not available, the immunisation status is based on mothers’ recall) as recommended by the WHO.26 A number of other studies have used this method,32 37 which has proven to be a reliable assessment of immunisation coverage.38–40 We did not include vitamin A and yellow fever vaccines in determining complete immunisation status for this study. We entered the data into Microsoft Excel (Microsoft, Redmond, Washington, DC, USA), cleaned and transferred to IBM SPSS V.27.0 (IBM, Armonk, New York, USA) for statistical analyses. We used frequency and percentage to describe the data, and χ2 test to test for statistical significance. We used t-test to assess for statistical difference in the mean scores for knowledge of RI. We conducted multivariate logistics regression analyses to estimate adjusted ORs with 95% CI while adjusting for mothers age, marital status, mothers educational status, mothers occupation, religion, ethnic/tribal group, family monthly income, sex of the index child and source of information on immunisation. We dichotomised aggregate scores for questions on awareness of RI into satisfactory knowledge (10 points and above) and poor knowledge (less than 10 points) prior to inclusion in the regression model. We used p<0.05 to define statistical significance, and all tests were two-tailed. No patients nor the public were involved in developing the research question and study design or in the implementation of the study design, the interpretation of the results and writing of the manuscript. There are no plans to share the study with patients, will share with the public through open access publishing.

AI Digest

Study Justification:

The study aimed to address the subnational immunization coverage gaps in Nigeria by identifying the sociodemographic factors associated with incomplete immunization in children aged 12-23 months. This information is crucial for developing targeted interventions to improve immunization rates and reduce disparities between urban and rural areas.

Study Highlights:

1. Full immunization coverage (FIC) rate in Enugu state was 78.9%, but there was a significant difference between urban and rural districts.
2. Factors associated with incomplete immunization included children of single mothers, children delivered without skilled birth attendants, lack of postnatal care, poor maternal knowledge of routine immunization, dwelling in rural areas, low-income families, and living far from vaccination facilities.
3. The study emphasized the need for innovative solutions to improve geographical accessibility and the importance of reporting vaccination coverage at the local district level.

Recommendations for Lay Readers:

1. Increase awareness and education about the importance of immunization, especially among single mothers and those with poor knowledge.
2. Improve access to skilled birth attendants and postnatal care services to ensure proper immunization practices.
3. Develop targeted interventions for rural communities to address the disparities in immunization coverage.
4. Enhance the availability and proximity of vaccination facilities, particularly in remote areas.
5. Implement strategies to support low-income families in accessing immunization services.

Recommendations for Policy Makers:

1. Allocate resources to improve immunization coverage at the subnational level, focusing on rural districts with lower coverage rates.
2. Strengthen collaboration between healthcare providers, community health workers, and local government authorities to ensure effective implementation of immunization programs.
3. Develop and implement policies to address the identified factors associated with incomplete immunization, such as providing incentives for skilled birth attendance and postnatal care utilization.
4. Invest in innovative approaches, such as mobile vaccination clinics or outreach programs, to improve geographical accessibility in remote areas.
5. Establish a robust monitoring and reporting system to track immunization coverage at the local district level and identify areas for targeted interventions.

Key Role Players:

1. Ministry of Health: Responsible for policy development, resource allocation, and coordination of immunization programs.
2. Healthcare Providers: Deliver immunization services and provide education to mothers and caregivers.
3. Community Health Workers: Engage with communities, raise awareness, and facilitate access to immunization services.
4. Local Government Authorities: Collaborate with healthcare providers and community health workers to support immunization initiatives and ensure adequate infrastructure.
5. Non-Governmental Organizations (NGOs): Assist in implementing immunization programs, conducting outreach activities, and providing support to vulnerable populations.

Cost Items for Planning Recommendations:

1. Training and Capacity Building: Budget for training healthcare providers and community health workers on immunization practices, communication skills, and data collection.
2. Infrastructure Development: Allocate funds for the construction or renovation of vaccination facilities, especially in rural areas.
3. Outreach Programs: Include costs for mobile vaccination clinics, transportation, and logistics to reach remote communities.
4. Information and Education Campaigns: Allocate resources for the development and dissemination of educational materials, community engagement activities, and media campaigns.
5. Monitoring and Evaluation: Budget for the establishment of a robust monitoring system, data collection tools, and analysis to track immunization coverage and measure the impact of interventions.
Note: The provided cost items are general categories and should be further refined based on the specific context and needs of the immunization program.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it presents clear findings based on a cross-sectional study using a representative sample. The study design and methodology are well-described, and statistical analyses were conducted to estimate adjusted odds ratios. The results highlight significant factors associated with incomplete immunization in children at a sub-national level in Nigeria. To improve the evidence, it would be beneficial to include information on the response rate and any potential limitations of the study, such as selection bias or generalizability of the findings.

Abstract

This was a community-based cross-sectional survey of mothers of children 12–23 months old residing in Enugu state in July 2020. The study considered all children 12–23 months old eligible for sampling, and used the Strengthening the Reporting of Observational Studies in Epidemiology guidelines to ensure appropriate reporting of its study’s design, conduct and findings.27 Nigeria is the most populous country in Africa and the sixth most populous in the world.28 She is located in Western Africa and is divided into six geopolitical regions: northeast, northwest, northcentral, southsouth, southeast and southwest. She has 36 states—the second administrative division, and a federal capital territory in Abuja. Each state is further divided into smaller administrative units called local government areas (LGAs) and each LGA is further divided into wards. Enugu state is one of the 36 states in Nigeria (figure 1) and one of the five states that make up the southeast geopolitical region in the country. Enugu state is further divided into 17 LGAs, four of which are predominantly urban (Enugu East, Enugu North, Enugu South and Nsukka) and the rest are predominantly rural. Enugu state’s 2020 projected population is 4 769 916, with most of the population living in urban centres in Enugu and Nsukka.29 30 Map of Nigeria above showing Enugu state and map of Enugu state showing the study area (four local government areas (LGAs)). Adapted from image culled from Ugoyibo et al61. Using steps described in the WHO Vaccination Coverage Cluster Surveys Reference Manual 2019,26 we determined the sample size using immunisation coverage of 36.0% obtained for Enugu state in the most recent 2018 Nigeria DHS,25 significance level of 5.0%, precision of 5.0%, design effect of 2.531 and an inflation of 15% (to account for non-response). The calculated minimum sample size was 1183 which we increased to 1250 to boost the power of the study. We used a three-stage sampling technique. In the first stage, we used a simple random sampling technique by balloting to select four LGAs: two each from the urban and rural areas of the state. In the second stage, we randomly selected (by balloting) a total of 50 clusters based on probability-proportional-to-population: 15 clusters from Enugu East LGA, 15 clusters from Enugu North LGA and 10 clusters each from Ezeagu LGA and Udenu LGA. In the third stage, we selected 25 households in each of the 50 clusters (ward). In each cluster, we selected the first household randomly and subsequent households contiguously in the right direction until we achieved the required number of households for that cluster. From each selected household, we selected one eligible child. If a selected household had more than one eligible child, we selected the youngest child older than 12 months. If a selected household had no eligible child, we visited the next contiguous household, and selected one eligible child. A team of 14 trained community health workers collected the data using structured pretested interviewer-administered questionnaires. We constructed the questionnaire from a review of the available literature on immunisation surveys in similar contexts,32–34 and tested it for acceptability and logical structure in a sample of 20 mothers before the study. Prior to the survey, we trained the team on the study’s objectives, interpreting and extracting data from health cards/vaccination certificates, sampling techniques, walking distance estimation using Google Maps mobile app, ethical issues including the process of taking informed verbal consent and administration of the questionnaire. We administered the questionnaire in Igbo (the local language) except for a few non-Igbo speakers whom we administered the questionnaire in English. The research team directed the questions to the mothers and recorded only their responses. Data we collected include sociodemographic characteristics of mothers and children including maternal healthcare (MHC) utilisation (ante-natal care (ANC), skilled birth attendant (SBA) present at birth and post-natal care (PNC)), knowledge of mothers regarding RI, immunisation status of children and reasons for any non-vaccination. If the immunisation card was available, we recorded immunisation information of each inoculation the child received. If a child had never received an immunisation card or the mother was unable to present the immunisation card to the interviewer, the immunisation data/information for the child was based on the mother’s report. We used Google Map mobile app on smartphones to estimate the walking distance from each study participant’s house to the nearest vaccination centre in all but four clusters (in Ezeagu LGA). In these four clusters, we first identified the nearest routine childhood vaccination point in each cluster and then estimated the walking distance from this nearest vaccination facility to each household included in the study. To evaluate mothers’ knowledge of RI and vaccine-preventable diseases, the interviewers asked questions on the correct purpose of immunisation, different vaccine-preventable diseases, the correct age for receiving the vaccines and the total number of visits required to complete the recommended vaccination for the child. We evaluated the responses as per the National Primary Healthcare Development Agency RI schedule.35 We coded correct responses as 2 points, incorrect responses 1 point, ‘I do not know’ 0 (zero) point. We categorised children as fully immunised, partially immunised or unimmunised (zero-dose) based on the types and doses of antigens received. We defined a ‘fully immunised child’ as a child who had received one dose of BCG, three doses of polio vaccine (excluding Oral Polio Vaccine (OPV) given at birth), three doses of pentavalent vaccine and one dose of measles vaccine by 12 months of age. Likewise, we defined a partially immunised child as a child who missed at least any one of the above doses, and an ‘un-immunised’ or ‘zero-dose’ child as a child who had not received any vaccine by 12 months of age.36 Incomplete immunisation, in this study, includes partially immunised children and unimmunised (zero-dose) children. Immunisation status was based on mothers’ recall and immunisation card record (ie, where the mother presents an immunisation card, the child’s immunisation status is based on records in the card, but where an immunisation card is not available, the immunisation status is based on mothers’ recall) as recommended by the WHO.26 A number of other studies have used this method,32 37 which has proven to be a reliable assessment of immunisation coverage.38–40 We did not include vitamin A and yellow fever vaccines in determining complete immunisation status for this study. We entered the data into Microsoft Excel (Microsoft, Redmond, Washington, DC, USA), cleaned and transferred to IBM SPSS V.27.0 (IBM, Armonk, New York, USA) for statistical analyses. We used frequency and percentage to describe the data, and χ2 test to test for statistical significance. We used t-test to assess for statistical difference in the mean scores for knowledge of RI. We conducted multivariate logistics regression analyses to estimate adjusted ORs with 95% CI while adjusting for mothers age, marital status, mothers educational status, mothers occupation, religion, ethnic/tribal group, family monthly income, sex of the index child and source of information on immunisation. We dichotomised aggregate scores for questions on awareness of RI into satisfactory knowledge (10 points and above) and poor knowledge (less than 10 points) prior to inclusion in the regression model. We used p<0.05 to define statistical significance, and all tests were two-tailed. No patients nor the public were involved in developing the research question and study design or in the implementation of the study design, the interpretation of the results and writing of the manuscript. There are no plans to share the study with patients, will share with the public through open access publishing.

Materials

Innovations

The study titled “Factors associated with incomplete immunisation in children aged 12-23 months at subnational level, Nigeria: A cross-sectional study” aimed to identify sociodemographic factors associated with incomplete immunization in children at a sub-national level in Enugu state, Nigeria. The study found that while the overall immunization coverage rate in Enugu state was 78.9%, there was a significant difference between urban and rural districts, with only 55.5% of children in rural communities being fully immunized compared to 94.5% in urban communities.

The study identified several factors associated with incomplete immunization, including children of single mothers, children delivered without a skilled birth attendant present, children of mothers who did not receive postnatal care, children of mothers with poor knowledge of routine immunization, dwelling in rural districts, low-income families, and living further than 30 minutes from the nearest vaccination facility.

Based on these findings, a recommendation was made to develop an innovation to improve access to maternal health, specifically through the implementation of mobile immunization clinics. These clinics would travel to rural areas and provide immunization services to children, equipped with trained healthcare professionals and necessary vaccines. This innovation aims to address the geographical accessibility barrier identified in the study and increase immunization coverage in remote areas.

It is important to note that the recommendation should be further evaluated and tailored to the specific context and needs of the target population. Collaboration with local healthcare authorities, community leaders, and stakeholders will be crucial for the successful implementation of this innovation.

AI Innovations Description

Based on the study findings, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Mobile Immunization Clinics: Implementing mobile immunization clinics that can travel to rural areas and provide immunization services to children. These clinics can be equipped with trained healthcare professionals and necessary vaccines to ensure that children in remote areas have access to immunization services.

This innovation can address the geographical accessibility barrier identified in the study, as it will bring immunization services closer to rural communities. By reaching out to these communities, the mobile clinics can help increase immunization coverage and reduce the disparity between urban and rural districts.

It is important to note that this recommendation should be further evaluated and tailored to the specific context and needs of the target population. Additionally, collaboration with local healthcare authorities, community leaders, and stakeholders will be crucial for the successful implementation of this innovation.

AI Innovations Methodology

To simulate the impact of the main recommendations on improving access to maternal health, you can follow these steps:

1. Define the variables: Identify the key variables that are relevant to the recommendations. For example, variables such as the number of mobile immunization clinics, the number of healthcare professionals, the number of vaccines administered, and the number of children reached.

2. Collect baseline data: Gather data on the current state of maternal health access in the target area. This can include information on immunization coverage rates, geographical accessibility, and other relevant factors identified in the study.

3. Develop a simulation model: Create a simulation model that incorporates the variables and data collected. This model should simulate the impact of the recommendations on improving access to maternal health. You can use software tools like Excel or specialized simulation software to build the model.

4. Set parameters: Define the parameters for the simulation, such as the duration of the simulation, the population size, and any assumptions or constraints that need to be considered.

5. Run the simulation: Execute the simulation model using the defined parameters. This will generate simulated data on the impact of the recommendations on improving access to maternal health.

6. Analyze the results: Analyze the simulated data to understand the potential impact of the recommendations. Look for trends, patterns, and key insights that can inform decision-making.

7. Validate the simulation: Compare the simulated results with real-world data, if available, to validate the accuracy of the simulation model. This step helps ensure that the simulation accurately reflects the potential impact of the recommendations.

8. Refine and iterate: Based on the analysis and validation, refine the simulation model as needed. Make adjustments to the variables, parameters, or assumptions to improve the accuracy and reliability of the simulation.

9. Communicate the findings: Present the simulation results in a clear and concise manner. Use visualizations, charts, and graphs to effectively communicate the impact of the recommendations on improving access to maternal health.

10. Make informed decisions: Use the simulation findings to inform decision-making and guide the implementation of the recommendations. Consider the potential benefits, challenges, and feasibility of implementing the innovation based on the simulation results.

Remember that simulation models are simplifications of real-world scenarios and are based on assumptions. Therefore, it is important to interpret the results with caution and consider other factors that may influence the outcomes.

Authors & Co-Authors

Statistics:

Citations: 7

Authors: 6

Identifiers:

DOI: 10.1136/bmjopen-2020-047445

Research Areas:

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

Study Countries:

Nigeria

Study Design:

Cross Sectional Study

Study Approach:

Quantitative