Migration and child immunization in Nigeria: Individual- and community-level contexts

BMC Public Health, Volume 10, Year 2010

Interpretation

Background: Vaccine-preventable diseases are responsible for severe rates of morbidity and mortality in Africa. Despite the availability of appropriate vaccines for routine use on infants, vaccine-preventable diseases are highly endemic throughout sub-Saharan Africa. Widespread disparities in the coverage of immunization programmes persist between and within rural and urban areas, regions and communities in Nigeria. This study assessed the individual- and community-level explanatory factors associated with child immunization differentials between migrant and non-migrant groups. Methods. The proportion of children that received each of the eight vaccines in the routine immunization schedule in Nigeria was estimated. Multilevel multivariable regression analysis was performed using a nationally representative sample of 6029 children from 2735 mothers aged 15-49 years and nested within 365 communities. Odds ratios with 95% confidence intervals were used to express measures of association between the characteristics. Variance partition coefficients and Wald statistic i.e. the ratio of the estimate to its standard error were used to express measures of variation. Results. Individual- and community contexts are strongly associated with the likelihood of receiving full immunization among migrant groups. The likelihood of full immunization was higher for children of rural non-migrant mothers compared to children of rural-urban migrant mothers. Findings provide support for the traditional migration perspectives, and show that individual-level characteristics, such as, migrant disruption (migration itself), selectivity (demographic and socio-economic characteristics), and adaptation (health care utilization), as well as community-level characteristics (region of residence, and proportion of mothers who had hospital delivery) are important in explaining the differentials in full immunization among the children. Conclusion. Migration is an important determinant of child immunization uptake. This study stresses the need for community-level efforts at increasing female education, measures aimed at alleviating poverty for residents in urban and remote rural areas, and improving the equitable distribution of maternal and child health services. © 2010 Antai. Data from the 2003 Nigeria Demographic and Health Survey (DHS) was used in this study. This is a nationally-representative probability sample, collected using a stratified two-stage cluster sampling procedure. A full report and detailed description of the data collection procedures are presented elsewhere [22]. Birth history data, such as, sex, month and year of birth, survivorship status and current age or, if the child had died, ages at death were also collected for each of these births. 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 theirchild had received BCG, polio, DPT (including the number of doses for each) and measles vaccinations. The outcome variable is the risk of a child 12 months of age and older receiving full immunization (i.e. all of the eight required vaccinations in the EPI programme). Routine immunization schedule in Nigeria stipulates that infants should be vaccinated with the following vaccines: a dose of Bacillus Calmette-Guerin (BCG) vaccine at birth (or as soon as possible); three doses of diphtheria, pertussis and tetanus (DPT) vaccine at 6, 10 and 14 weeks of age; at least three doses of oral polio vaccine (OPV) – at birth, and at 6, 10 and 14 weeks of age; and one dose of measles vaccine at 9 months of age [23,24]. A child was considered to have received full immunization status when they have received the full complement of eight vaccinations according to the EPI programme mentioned above. Migrant status was categorized as: urban non-migrant, rural non-migrant and rural-urban migrant. A migrant was defined as a person who moved between any combination of rural and urban areas in the 10 years prior to the survey. Migration histories are not routinely collected in the Demographic and Health Surveys; however, basic information relating to number of years spent in the respondents current place of residence are collected, as well as place of residence (previous and current). These were used to establish migration status and to identify four migration streams: urban-to-urban, rural-to-rural, rural-to-urban and urban-to-rural. A variable that categorized the migration streams into rural-to-urban migrants, rural non-migrants, and urban non-migrants was created. Migrants in the rural-to-rural and urban-to-urban streams made up the rural- and urban non-migrants, while urban-to-rural migrants were excluded from the analysis. Migration status of a person was defined by a person changing their place of residence across an administrative boundary. Visitors were excluded from the analysis. For instance, a woman who reported previous residence as rural and current residence as urban was classified as a rural-urban migrant. The non-migrant groups are classified as rural- or urban non-migrant depending upon their reported duration at the place of residence as “always”. A number of child- and mother-level characteristics may potentially confound the relationship between migration status and likelihood of full immunization among children younger than 5 years of age. Demographic characteristics assessed included: as: a) birth order/birth interval, created by merging “birth order” and the “preceding birth interval” into one variable. The variable ‘preceding birth interval’ is the interval before the birth of the child in question. As such, the effect of the preceding birth interval is considered in relation to the younger of the two children. Ideally, first births are left out of the analysis of preceding birth interval and survival of the preceding child because they are not preceded by another birth. In order to enable the inclusion of first births in the analysis, first births in this study were merged with those with a preceding birth interval of 24 months or longer. This merged variable was classified into seven categories 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); b) sex of the child, categorized as: male and female; c) mother's age, grouped as: 15-18, 19-23, 24-28, 29-33, and 34 years and older; d) mother's age at birth of first child, categorized as: 18 years or less and 19 years or older; and e) marital status, categorized as: single, married and formerly married. Socio-economic characteristics were assessed as: a) mothers' education, categorized as: no education, primary, and secondary or higher education; b) mother's occupation, grouped as: professional/technical/managerial; clerical/sales/services/skilled manual; agricultural self employed/agricultural employee/household & domestic/unskilled manual occupations; and not working; and c) wealth index, which is used in the absence reliable data on incomes and expenditures in the demographic and health survey. This is a composite index and indicator of the socio-economic status of households that assigns weights or factor scores generated by principal component analysis to information on household assets collected from censuses and surveys. Household socio-economic indicators included those relating to household ownership of durable assets and household environmental conditions; these were used to compute the index. Principal components analysis allows each asset owned to be given a score and the factor loading scores used to create linear composites of each household socio-economic status variable. The socio-economic index generated is subsequently divided into quintiles of socio-economic status, categorized as: poorest, poorer, middle, richer and richest. Health care utilization was assessed as: a) mother received tetanus toxoid injections in pregnancy, categorized as: yes and no; b) place of delivery of child, categorized as: home, and hospital facility; and c) prenatal care by doctor, categorized as: yes and no. These included: a) mothers' 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; and b) three contextual variables, which were at the level of the primary sampling unit (PSU) (n = 365) were used.; i) community prenatal care by doctor, defined as the percentage of mothers who received prenatal care by a doctor during pregnancy within the PSU, and categorized as: low, and high; ii) community hospital delivery, defined as the percentage of mothers who delivered their child in a hospital facility within the PSU, and categorized as: low, middle, and high. Prenatal care directly increases the chances that the mother would subsequently access health care services for her child, such as institutional delivery and immunization [25,26]. Thus, 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 [27,28]; and iii) community mother's education, defined as the percentage of mothers with secondary or higher education within the PSU, and categorized as: low and high. Higher levels of maternal education are associated with better child health outcomes, such as child immunization rates [29,30]. PSUs or clusters are administratively-defined areas used as proxies for "neighbourhoods" or "communities" [31]. They are small and designed to be fairly homogenous units with respect to population socio-demographic characteristics, economic status and living conditions, and are made up of one or more enumeration areas (EAs), which are the smallest geographic units for which census data are available in Nigeria. Each cluster consisted of a minimum of 50 households, with a contiguous EA being added when a cluster had less than 50 households [22]. The simultaneous inclusion of both individual- and neighbourhood-level predictors in regression equations with individuals as the units of analysis, permits: i) the examination of neighbourhood or area effects after individual-level confounders have been controlled; ii) the examination of individual-level characteristics as modifiers of the area effect (and vice versa); and iii) the simultaneous examination of within- and between neighbourhood variability in outcomes, and of the extent to which between-neighbourhood variation is "explained" by individual- and neighbourhood-level characteristics [31,32]. The distribution of the children and mothers in the sample was assessed by migration status and socio-economic characteristics. Normalized sample weights provided in the DHS data were used for all analyses in order to adjust for non-response and enable generalization of findings to the general population. These analyses were done using Stata 10 [33]. A three-level multilevel logistic regression model to account for the hierarchical structure of the DHS data [34] was used. Children (level 1), were nested within mothers (level 2), who were in turn nested within communities (level 3). Five models were fitted containing variables of interest, grouped into categories. Model 1 contained only mother's migration status as the only exposure variable. Model 2 included migration status and demographic characteristics of children and mothers (sex of the child, birth order/birth interval, mother's age and mother's age at birth of first child). Model 3 contained migration status and socio-economic variables (mother's education, mother's occupation and wealth index), and Model 4 contained migration status and health care utilization (mother received tetanus toxoid injections in pregnancy, place of delivery of child and prenatal care by doctor). Finally, Model 5 contained community-level variables (mother's region of residence, community prenatal care by doctor, community hospital delivery, and community mother's education). In each of the five models, migration status was fitted with a different category of exposure variables against the risk of full immunization. This modelling strategy is intended to enable a comparison of the influence of each of the different exposure variables on the association between migration and the likelihood of full immunization. The association between the likelihood of full immunization and migration status were expressed as odds ratio (OR) and 95% confidence intervals (95% CIs). The random effects (measures of variation) were expressed as Variance Partition Coefficient (VPC) and proportional change in variance (PCV). The variance partition coefficient (VPC) measures the extent that siblings resemble each other more than they resemble children from other families in relation to the likelihood of full immunization. A large VPC value (close to 1) indicates maximally segregated clusters, and a low VPC value (close to zero) suggests homogeneous risk of under-five mortality among clusters. Statistical testing of the population variance was performed using the Wald statistic i.e. the ratio of the estimate to its standard error [35]. The multilevel analyses were performed using MLwiN software package 2.0.2 [36], with Binomial, Penalized Quasi-Likelihood (PQL) procedures [37]. Random slope variance indicates whether contextual phenomenon differs in magnitude for different groups of people and whether the community level modifies associations between individual-level exposures. 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:

This study aims to investigate the factors contributing to disparities in child immunization rates between migrant and non-migrant groups in Nigeria. Vaccine-preventable diseases are a significant cause of morbidity and mortality in Africa, and despite the availability of vaccines, immunization coverage remains low in many areas. Understanding the individual and community-level factors that influence immunization rates can help inform targeted interventions to improve coverage and reduce disparities.

Highlights:

– The study found that both individual and community contexts are strongly associated with the likelihood of receiving full immunization among migrant groups.
– Children of rural non-migrant mothers had a higher likelihood of full immunization compared to children of rural-urban migrant mothers.
– Factors such as migrant disruption, selectivity, and adaptation, as well as community-level characteristics like region of residence and proportion of mothers with hospital delivery, were important in explaining immunization differentials.
– The study highlights the need for community-level efforts to increase female education, alleviate poverty in urban and remote rural areas, and improve the equitable distribution of maternal and child health services.

Recommendations:

– Implement community-level interventions to increase female education, which has been shown to improve child health outcomes, including immunization rates.
– Develop measures to alleviate poverty in urban and remote rural areas, as socioeconomic status is a significant factor in immunization differentials.
– Improve the equitable distribution of maternal and child health services to ensure that all communities have access to quality healthcare, including immunization services.

Key Role Players:

– Ministry of Health: Responsible for implementing and coordinating immunization programs and policies.
– Community Health Workers: Provide immunization services and education at the community level.
– Non-governmental Organizations (NGOs): Support immunization programs through advocacy, funding, and implementation of interventions.
– Education Department: Collaborate with the health sector to promote female education, which can have a positive impact on child health outcomes.

Cost Items for Planning Recommendations:

– Education programs: Budget for initiatives aimed at increasing female education, such as scholarships, school infrastructure improvements, and teacher training.
– Poverty alleviation programs: Allocate funds for poverty reduction measures, such as income-generating projects, social safety nets, and access to basic services in urban and remote rural areas.
– Health system strengthening: Include resources for improving the equitable distribution of maternal and child health services, such as expanding healthcare facilities and training healthcare workers.
– Immunization services: Ensure adequate funding for the procurement and distribution of vaccines, as well as training and capacity-building for healthcare providers involved in immunization programs.

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 of 6029 children from 2735 mothers aged 15-49 years and nested within 365 communities. The study uses multilevel multivariable regression analysis to assess the individual- and community-level factors associated with child immunization differentials between migrant and non-migrant groups. The study provides odds ratios with 95% confidence intervals to express measures of association between the characteristics. To improve the evidence, the study could include a larger sample size and consider other potential confounding factors such as access to healthcare facilities and cultural beliefs about immunization.

Abstract

Data from the 2003 Nigeria Demographic and Health Survey (DHS) was used in this study. This is a nationally-representative probability sample, collected using a stratified two-stage cluster sampling procedure. A full report and detailed description of the data collection procedures are presented elsewhere [22]. Birth history data, such as, sex, month and year of birth, survivorship status and current age or, if the child had died, ages at death were also collected for each of these births. 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 theirchild had received BCG, polio, DPT (including the number of doses for each) and measles vaccinations. The outcome variable is the risk of a child 12 months of age and older receiving full immunization (i.e. all of the eight required vaccinations in the EPI programme). Routine immunization schedule in Nigeria stipulates that infants should be vaccinated with the following vaccines: a dose of Bacillus Calmette-Guerin (BCG) vaccine at birth (or as soon as possible); three doses of diphtheria, pertussis and tetanus (DPT) vaccine at 6, 10 and 14 weeks of age; at least three doses of oral polio vaccine (OPV) – at birth, and at 6, 10 and 14 weeks of age; and one dose of measles vaccine at 9 months of age [23,24]. A child was considered to have received full immunization status when they have received the full complement of eight vaccinations according to the EPI programme mentioned above. Migrant status was categorized as: urban non-migrant, rural non-migrant and rural-urban migrant. A migrant was defined as a person who moved between any combination of rural and urban areas in the 10 years prior to the survey. Migration histories are not routinely collected in the Demographic and Health Surveys; however, basic information relating to number of years spent in the respondents current place of residence are collected, as well as place of residence (previous and current). These were used to establish migration status and to identify four migration streams: urban-to-urban, rural-to-rural, rural-to-urban and urban-to-rural. A variable that categorized the migration streams into rural-to-urban migrants, rural non-migrants, and urban non-migrants was created. Migrants in the rural-to-rural and urban-to-urban streams made up the rural- and urban non-migrants, while urban-to-rural migrants were excluded from the analysis. Migration status of a person was defined by a person changing their place of residence across an administrative boundary. Visitors were excluded from the analysis. For instance, a woman who reported previous residence as rural and current residence as urban was classified as a rural-urban migrant. The non-migrant groups are classified as rural- or urban non-migrant depending upon their reported duration at the place of residence as “always”. A number of child- and mother-level characteristics may potentially confound the relationship between migration status and likelihood of full immunization among children younger than 5 years of age. Demographic characteristics assessed included: as: a) birth order/birth interval, created by merging “birth order” and the “preceding birth interval” into one variable. The variable ‘preceding birth interval’ is the interval before the birth of the child in question. As such, the effect of the preceding birth interval is considered in relation to the younger of the two children. Ideally, first births are left out of the analysis of preceding birth interval and survival of the preceding child because they are not preceded by another birth. In order to enable the inclusion of first births in the analysis, first births in this study were merged with those with a preceding birth interval of 24 months or longer. This merged variable was classified into seven categories 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); b) sex of the child, categorized as: male and female; c) mother's age, grouped as: 15-18, 19-23, 24-28, 29-33, and 34 years and older; d) mother's age at birth of first child, categorized as: 18 years or less and 19 years or older; and e) marital status, categorized as: single, married and formerly married. Socio-economic characteristics were assessed as: a) mothers' education, categorized as: no education, primary, and secondary or higher education; b) mother's occupation, grouped as: professional/technical/managerial; clerical/sales/services/skilled manual; agricultural self employed/agricultural employee/household & domestic/unskilled manual occupations; and not working; and c) wealth index, which is used in the absence reliable data on incomes and expenditures in the demographic and health survey. This is a composite index and indicator of the socio-economic status of households that assigns weights or factor scores generated by principal component analysis to information on household assets collected from censuses and surveys. Household socio-economic indicators included those relating to household ownership of durable assets and household environmental conditions; these were used to compute the index. Principal components analysis allows each asset owned to be given a score and the factor loading scores used to create linear composites of each household socio-economic status variable. The socio-economic index generated is subsequently divided into quintiles of socio-economic status, categorized as: poorest, poorer, middle, richer and richest. Health care utilization was assessed as: a) mother received tetanus toxoid injections in pregnancy, categorized as: yes and no; b) place of delivery of child, categorized as: home, and hospital facility; and c) prenatal care by doctor, categorized as: yes and no. These included: a) mothers' 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; and b) three contextual variables, which were at the level of the primary sampling unit (PSU) (n = 365) were used.; i) community prenatal care by doctor, defined as the percentage of mothers who received prenatal care by a doctor during pregnancy within the PSU, and categorized as: low, and high; ii) community hospital delivery, defined as the percentage of mothers who delivered their child in a hospital facility within the PSU, and categorized as: low, middle, and high. Prenatal care directly increases the chances that the mother would subsequently access health care services for her child, such as institutional delivery and immunization [25,26]. Thus, 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 [27,28]; and iii) community mother's education, defined as the percentage of mothers with secondary or higher education within the PSU, and categorized as: low and high. Higher levels of maternal education are associated with better child health outcomes, such as child immunization rates [29,30]. PSUs or clusters are administratively-defined areas used as proxies for "neighbourhoods" or "communities" [31]. They are small and designed to be fairly homogenous units with respect to population socio-demographic characteristics, economic status and living conditions, and are made up of one or more enumeration areas (EAs), which are the smallest geographic units for which census data are available in Nigeria. Each cluster consisted of a minimum of 50 households, with a contiguous EA being added when a cluster had less than 50 households [22]. The simultaneous inclusion of both individual- and neighbourhood-level predictors in regression equations with individuals as the units of analysis, permits: i) the examination of neighbourhood or area effects after individual-level confounders have been controlled; ii) the examination of individual-level characteristics as modifiers of the area effect (and vice versa); and iii) the simultaneous examination of within- and between neighbourhood variability in outcomes, and of the extent to which between-neighbourhood variation is "explained" by individual- and neighbourhood-level characteristics [31,32]. The distribution of the children and mothers in the sample was assessed by migration status and socio-economic characteristics. Normalized sample weights provided in the DHS data were used for all analyses in order to adjust for non-response and enable generalization of findings to the general population. These analyses were done using Stata 10 [33]. A three-level multilevel logistic regression model to account for the hierarchical structure of the DHS data [34] was used. Children (level 1), were nested within mothers (level 2), who were in turn nested within communities (level 3). Five models were fitted containing variables of interest, grouped into categories. Model 1 contained only mother's migration status as the only exposure variable. Model 2 included migration status and demographic characteristics of children and mothers (sex of the child, birth order/birth interval, mother's age and mother's age at birth of first child). Model 3 contained migration status and socio-economic variables (mother's education, mother's occupation and wealth index), and Model 4 contained migration status and health care utilization (mother received tetanus toxoid injections in pregnancy, place of delivery of child and prenatal care by doctor). Finally, Model 5 contained community-level variables (mother's region of residence, community prenatal care by doctor, community hospital delivery, and community mother's education). In each of the five models, migration status was fitted with a different category of exposure variables against the risk of full immunization. This modelling strategy is intended to enable a comparison of the influence of each of the different exposure variables on the association between migration and the likelihood of full immunization. The association between the likelihood of full immunization and migration status were expressed as odds ratio (OR) and 95% confidence intervals (95% CIs). The random effects (measures of variation) were expressed as Variance Partition Coefficient (VPC) and proportional change in variance (PCV). The variance partition coefficient (VPC) measures the extent that siblings resemble each other more than they resemble children from other families in relation to the likelihood of full immunization. A large VPC value (close to 1) indicates maximally segregated clusters, and a low VPC value (close to zero) suggests homogeneous risk of under-five mortality among clusters. Statistical testing of the population variance was performed using the Wald statistic i.e. the ratio of the estimate to its standard error [35]. The multilevel analyses were performed using MLwiN software package 2.0.2 [36], with Binomial, Penalized Quasi-Likelihood (PQL) procedures [37]. Random slope variance indicates whether contextual phenomenon differs in magnitude for different groups of people and whether the community level modifies associations between individual-level exposures. 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 innovations that could improve access to maternal health:

1. Mobile Health (mHealth) Solutions: Develop mobile applications or text messaging services to provide pregnant women with information about prenatal care, immunizations, and other maternal health services. These tools can also send reminders for appointments and provide educational resources.

2. Community Health Workers: Train and deploy community health workers to provide maternal health services, including immunizations, in rural and remote areas. These workers can educate women about the importance of immunizations and help them access healthcare facilities.

3. Telemedicine: Use telemedicine technologies to connect pregnant women in remote areas with healthcare providers. This can allow for virtual consultations, remote monitoring of maternal health, and timely access to medical advice.

4. Transportation Solutions: Improve transportation infrastructure and services in rural areas to ensure that pregnant women can easily access healthcare facilities for immunizations and other maternal health services. This could include providing transportation vouchers or implementing mobile clinics.

5. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of immunizations during pregnancy and early childhood. These campaigns can be conducted through various channels, such as radio, television, and community outreach programs.

6. Strengthening Health Systems: Invest in strengthening healthcare systems, particularly in rural and underserved areas, to ensure that maternal health services, including immunizations, are readily available and of high quality.

7. Public-Private Partnerships: Foster collaborations between the public and private sectors to improve access to maternal health services. This could involve partnering with private healthcare providers to expand service delivery or leveraging private sector resources for health education campaigns.

It is important to note that the specific recommendations for improving access to maternal health should be tailored to the local context and take into account the unique challenges and needs of the target population.

AI Innovations Description

The study titled “Migration and child immunization in Nigeria: Individual- and community-level contexts” explores the factors associated with child immunization differentials between migrant and non-migrant groups in Nigeria. The study highlights the importance of individual and community contexts in determining the likelihood of receiving full immunization among migrant groups.

The study used data from the 2003 Nigeria Demographic and Health Survey (DHS), which is a nationally-representative probability sample. The sample included 6029 children from 2735 mothers aged 15-49 years, nested within 365 communities.

The study found that individual-level characteristics such as migrant disruption, selectivity, and adaptation, as well as community-level characteristics such as region of residence and proportion of mothers who had hospital delivery, are important in explaining the differentials in full immunization among children.

Based on the findings, the study recommends several measures to improve access to maternal health and child immunization. These include:

1. Increasing female education: Community-level efforts should be made to increase female education, as higher levels of maternal education are associated with better child health outcomes, including immunization rates.

2. Alleviating poverty in urban and remote rural areas: Measures should be taken to alleviate poverty for residents in both urban and remote rural areas. Socio-economic factors play a significant role in determining access to healthcare services, including immunization.

3. Improving the equitable distribution of maternal and child health services: Efforts should be made to ensure that maternal and child health services are distributed equitably across different regions and communities. This includes increasing access to prenatal care, hospital delivery, and immunization services.

By implementing these recommendations, it is expected that access to maternal health and child immunization will be improved, leading to better health outcomes for mothers and children in Nigeria.

AI Innovations Methodology

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

1. Strengthen community-level efforts: Implement community-based interventions that focus on increasing female education, alleviating poverty in urban and remote rural areas, and improving the equitable distribution of maternal and child health services. This can be done through awareness campaigns, training of community health workers, and establishing partnerships with local organizations.

2. Improve access to prenatal care: Enhance access to prenatal care services, including tetanus toxoid injections during pregnancy, by ensuring that healthcare facilities are available and accessible to pregnant women. This can be achieved by increasing the number of healthcare facilities, especially in rural areas, and providing transportation services for pregnant women to reach these facilities.

3. Enhance immunization programs: Strengthen immunization programs by ensuring the availability and accessibility of vaccines for routine use on infants. This can be done by improving the supply chain management system, training healthcare workers on immunization practices, and implementing strategies to reach underserved populations.

4. Address migration-related factors: Develop targeted interventions to address migration-related factors that affect access to maternal health services. This can include providing support and resources to migrant women and their families, such as information on available healthcare services, assistance with transportation, and culturally sensitive healthcare practices.

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

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as the proportion of pregnant women receiving prenatal care, the proportion of women receiving tetanus toxoid injections during pregnancy, and the proportion of infants receiving full immunization according to the EPI program.

2. Collect baseline data: Gather baseline data on the selected indicators from relevant sources, such as health surveys, healthcare facilities, and community-level data.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. This model should consider factors such as population size, geographical distribution, healthcare infrastructure, and resource availability.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations on the selected indicators. This can be done by adjusting the parameters related to the recommendations, such as the coverage of community-level interventions, the availability of healthcare facilities, and the effectiveness of immunization programs.

5. Analyze results: Analyze the results of the simulations to determine the potential impact of the recommendations on improving access to maternal health. This can be done by comparing the simulated values of the selected indicators with the baseline data and assessing the magnitude of change.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and validate it using additional data sources or expert input. This will ensure the accuracy and reliability of the model in predicting the impact of the recommendations.

7. Communicate findings and make recommendations: Present the findings of the simulation analysis, including the potential impact of the recommendations on improving access to maternal health. Use these findings to inform policy and decision-making processes, and make recommendations for implementing the identified interventions.

It is important to note that the methodology described above is a general framework and can be adapted based on the specific context and data availability.

Author

Dima Health

Statistics:

Citations: 57

Identifiers:

DOI: 10.1186/1471-2458-10-116

Research Areas:

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

Study Countries:

Nigeria

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male & Female