Explaining changes in wealth inequalities in child health: The case of stunting and wasting in Nigeria

PLoS ONE, Volume 15, No. 9 September, Year 2020

Interpretação

Background Malnutrition is a major cause of child death, and many children suffer from acute and chronic malnutrition. Nigeria has the second-highest burden of stunting globally and a higher-than-average child wasting prevalence. Moreover, there is substantial spatial variation in the prevalence of stunting and wasting in Nigeria. This paper assessed the socioeconomic inequalities and determinants of the change in socioeconomic inequalities in child stunting and wasting in Nigeria between 2013 and 2018. Methods Data came from the 2013 and 2018 Nigeria Demographic and Health Survey. Socioeconomic inequalities in stunting and wasting were measured using the concentration curve and Erreygers’ corrected concentration index. A pro-poor concentration index is negative, meaning that the poor bear a disproportionately higher burden of stunting or wasting than the wealthy. A positive or pro-rich index is the opposite. Standard methodologies were applied to decompose the concentration index (C) while the Oaxaca-Blinder approach was used to decompose changes in the concentration indices (ΔC). Findings The socioeconomic inequalities in child stunting and wasting were pro-poor in 2013 and 2018. The concentration indices for stunting reduced from -0.298 (2013) to -0.330 (2018) (ΔC = -0.032). However, the concentration indices for wasting increased from -0.066 to -0.048 (ΔC = 0.018). The changes in the socioeconomic inequalities in stunting and wasting varied by geopolitical zones. Significant determinants of these changes for both stunting and wasting were changes in inequalities in wealth, maternal education and religion. Under-five dependency, access to improved toilet facilities and geopolitical zone significantly explained changes in only stunting inequality, while access to improved water facilities only significantly determined the change in inequality in wasting. Conclusion Addressing the socio-economic, spatial and demographic determinants of the changes in the socioeconomic inequalities in child stunting and wasting, especially wealth, maternal education and access to sanitation is critical for improving child stunting and wasting in Nigeria. Data come from the 2013 and 2018 rounds of the nationally representative Nigeria Demographic and Health Survey (NDHS), the two most recent rounds of the NDHS. A stratified three-stage cluster design was used for the 2013 NDHS, comprising 904 primary sampling units (PSUs) which served as the clusters: 372 urban and 532 rural. The 2013 sample selected 40,680 households, where a minimum of 943 interviews were completed in each state. (Nigeria has 36 states and an autonomous Federal Capital Territory). A more detailed account of the dataset is also available elsewhere [10]. The 2018 NDHS was conducted between August and December 2018 via a two-stage stratified cluster sampling design, with each cluster or PSU defined based on enumeration areas from the 2006 population census frame. The survey, which was billed to be conducted in 1 400 clusters comprising 580 urban and 820 rural clusters, actually took place in 1 389 clusters due to feasibility issues. In total, 41 821 (13 311) eligible women (men) were interviewed as part of the 2018 NDHS. A more detailed description of the 2018 NDHS sampling design is available elsewhere [11]. Both datasets allow for the consistent measurement of indicators at the national, zonal (i.e. the six geopolitical zones) and state levels, while survey weights were appropriately calculated to ensure national representativity. This paper used the Birth Recode data file, which contains information on every child ever born to each interviewed woman (up to a maximum of 20 children). This data file also links children to their mothers, and it contains other relevant household information. For each NDHS round, the analysis was restricted to the sample of children aged 0–59 months. After data cleaning, the final sample sizes were 23,992 and 11 150 children in the 2013 and 2018 NDHS, respectively. The outcome variables are indicators of child stunting and wasting. Both follow their respective standard definitions. A child with a height-for-age z-score that is less than the negative of twice the standard deviation of the WHO Child Growth Standards median is considered stunted. A child is wasted if the weight-for-height z-score is less than the negative of twice the standard deviation of the WHO Child Growth Standards median [12]. Given that the NDHS does not contain household expenditure information, this paper used the wealth index created in the DHS dataset as an indicator of a child’s socioeconomic status [10]. The wealth index in the NDHS was obtained by applying principal components analysis on household assets [13], accounting for rural-urban differences. Variables used to decompose the change in stunting and wasting were the child’s characteristics (age, sex and religion), mother’s characteristics (education, marital and employment status), and household characteristics (wealth, location, sanitary condition, dependency ratio, and the characteristics of the household head). These variables are correlated with child nutritional status in prior empirical studies. For instance, a child’s age is associated with a child’s nutritional health outcome [14, 15]. Similarly, a child’s sex is associated with nutritional health outcomes, particularly stunting [16–18], while religion and culture are important correlates of a child’s nutritional outcomes [19, 20]. Furthermore, a child with an educated mother is less likely to be stunted or wasted [21–23]. Other mothers’ characteristics like marital status and employment are also associated with children’s nutritional status [24, 25]. Children’s nutritional health outcomes are also affected by household characteristics like wealth and sanitary conditions [15, 21, 23]. In this paper, sanitary conditions were proxied by a household’s water and toilet conditions. Improved water and toilet conditions were created based on the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene classification [26]. A child’s location is also associated with the child’s health status [27–29]. Moreover, the age and sex of the household head are correlated with children’s nutritional outcomes [30, 31], while high dependency ratios are detrimental to children’s health outcomes [32]. Basic descriptive statistics were computed and compared between 2013 and 2018. Percentages were computed for categorical variables while mean values were reported for continuous/count variables. Even though there may be skewness, the mean of continuous/count variables were reported to ease comparison with results in the literature. A concentration curve, suited to assess socioeconomic inequalities [33], depicts the cumulative shares of stunted or wasted children against the cumulative population shares, ranked by socioeconomic status. A line of equality is depicted by a 45-degree line, with a proportional concentration curve coinciding with this line. A pro-rich concentration curve—which depicts a disproportionate concentration of stunting or wasting on the rich—lies below the line of equality. Conversely, a pro-poor curve indicating the disproportionate concentration of stunting or wasting on the poor lies above the line of equality [34]. The concentration index, obtained from the concentration curve, is twice the covariance between the health outcome and the fractional rank in the wealth distribution, divided by the mean of the health outcome indicator [34–37]: where CH refers to the concentration index of the health outcome (H); μH refers to the mean of the health outcome (i.e. stunting or wasting), and r is the fractional rank of the individual/household in the wealth distribution. For continuous outcomes, the index lies in the [-1, +1] interval. A negative (positive) index typically indicates a pro-poor (pro-rich) distribution of stunted or wasted children, while a zero index indicates perfect equality. A zero concentration index may also result from a complex relationship where the concentration curve crosses the line of equality [34]. For categorical variables like indicators of stunting and wasting, the concentration index should be normalised as it may not lie between -1 and +1 [38]. The Erreygers’ normalisation approach was used in this paper [39, 40] to obtain the Erreygers’ normalised concentration index (EC) as follows [41]: where a and b refer to the lower and upper limits of the ordinal health indicator, respectively; and μH and CH remain as earlier defined. Changes in the concentration indices or curves between 2013 and 2018 were assessed using the general characterisation of a pro-poor or a pro-rich change or shift laid out by Ataguba [42]. Let Ct−1 and Ct represent the value of the concentration indices at time t − 1 and t, respectively. A pro-poor change in the concentration indices occurs when Ct−1 > Ct while a pro-rich change corresponds to Ct−1 < Ct. Only two indices are suitable for assessing relative socioeconomic inequalities in health—the slope index of inequality and the concentration index [33]. They are consistent with ranking individuals across socio-economic groupings, sensitive to changes in population distribution across socio-economic groups and consistent in the distribution of stunting and wasting across the distribution of socioeconomic status [33, 43]. The concentration index was used in this paper because it is decomposable to ascertain the factors that significantly explain socioeconomic inequalities in stunting and wasting, which is relevant to policymakers for evidence-based policy interventions. Socioeconomic inequalities in stunting and wasting were decomposed using the Wagstaff et al.’s [44] approach. The relationship between a child’s health outcomes (i.e. stunting or wasting) (H) and associated determinants (z) can be denoted as: where φ and β are parameters, and ε is the error term. Eq (3) was estimated using linear probability models because binary indicators of stunting and wasting were used, with each model appropriately weighted to the population while correcting for heteroscedasticity. For each year, the concentration index in Eq (1) can be decomposed as (the time subscripts are suppressed here to avoid notational clutter): where (βkz-kμH=ηk) denotes the elasticity of stunting or wasting to marginal changes in the k-th explanatory variable, while Ck refers to the concentration index of the k-th explanatory variable. GCε refers to the generalised concentration index of the error term, and (GCεμH) captures the unexplained/residual component. While Eq (4) enables us to decompose observed socioeconomic inequalities in stunting and wasting at each point in time, it does not explain temporal changes in such inequalities. Sequel to decomposing the health concentration index for the relevant health outcome in each year as shown in Eq (4), the change in the concentration index can be decomposed between both periods using the Oaxaca-Blinder decomposition as follows [34]: where t indicates the time period (with the convention that t − 1 and t denote the first (i.e. 2013) and second (i.e. 2018) periods respectively, while other terms in Eq (5) are as earlier defined). Eq (5) decomposes the temporal change in the socioeconomic inequality in stunting or wasting into a part that captures the temporal changes in socioeconomic inequality in the determinants of stunting or wasting: ∑k ηk,t(Ck,t − Ck,t−1), and another component that captures the changes in the elasticities of stunting or wasting with respect to these determinants: ∑k Ck,t−1(ηk,t − ηk,t−1). The final term in Eq (5), Δ(GCε,tμt), measures the temporal difference in the error/residual component [34]. Note that in Eq (5), the temporal changes in the concentration indices of the predictors/determinants are weighted by the elasticities of the second period, while the first-period concentration indices are used to weight the change in the elasticities. The Oaxaca-Blinder decomposition is not unique [34], as an alternative decomposition would be to weight the difference in the concentration indices by the first-period elasticities and the difference in the elasticities by the second-period concentration indices. Given that there are no analytical standard errors for the estimates derived from Eqs (4) and (5), bootstrap routines [45, 46] with 1,000 replications were used, accounting for the full sampling structure of the NDHS. All analyses were implemented using Stata® [47]. The conindex routine was employed in obtaining the Erreygers’ normalised or corrected concentration indices, while accounting for the survey design (clustering, stratification and the resulting survey weights) in each NDHS round [48].

AI Digest

Study Justification:

This study aims to investigate the socioeconomic inequalities and determinants of changes in child stunting and wasting in Nigeria between 2013 and 2018. Malnutrition is a major cause of child death, and Nigeria has a high burden of stunting and wasting. Understanding the factors contributing to these inequalities is crucial for developing effective interventions to improve child health in Nigeria.

Highlights:

1. The study found that socioeconomic inequalities in child stunting and wasting were pro-poor in both 2013 and 2018.
2. The concentration indices for stunting decreased slightly from -0.298 in 2013 to -0.330 in 2018, indicating a reduction in inequality. However, the concentration indices for wasting increased from -0.066 to -0.048, indicating an increase in inequality.
3. The changes in socioeconomic inequalities varied by geopolitical zones, highlighting the importance of considering regional differences in addressing child malnutrition.
4. Significant determinants of changes in stunting and wasting inequalities were changes in wealth, maternal education, religion, under-five dependency, access to improved toilet facilities, access to improved water facilities, and geopolitical zone.

Recommendations:

1. Addressing the socioeconomic, spatial, and demographic determinants of changes in child stunting and wasting is crucial for improving child health in Nigeria.
2. Policies and interventions should focus on reducing wealth inequalities, improving maternal education, and increasing access to sanitation facilities.
3. Regional differences should be taken into account when designing interventions, as the determinants of inequalities vary across geopolitical zones.

Key Role Players:

1. Government agencies responsible for health and nutrition policies and programs.
2. Non-governmental organizations (NGOs) working on child health and nutrition.
3. Health professionals and researchers specializing in child health and nutrition.
4. Community leaders and local organizations involved in community development and health promotion.

Cost Items for Planning Recommendations:

1. Funding for research and data collection to monitor changes in child stunting and wasting.
2. Investment in education and awareness campaigns to promote maternal education and improve knowledge about child nutrition.
3. Financial support for programs aimed at reducing wealth inequalities and improving access to sanitation facilities.
4. Resources for training healthcare professionals and community workers to provide effective interventions and support for child health and nutrition.
Please note that the cost items provided are general suggestions and may vary depending on the specific context and implementation strategies.

Força da prova

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a detailed description of the methods used, the data sources, and the findings. However, it lacks information on the sample size and the representativeness of the sample. To improve the evidence, the abstract could include information on the sample size and how it was determined to ensure the findings are generalizable to the population. Additionally, it would be helpful to provide information on the limitations of the study and potential sources of bias.

Resumo

Data come from the 2013 and 2018 rounds of the nationally representative Nigeria Demographic and Health Survey (NDHS), the two most recent rounds of the NDHS. A stratified three-stage cluster design was used for the 2013 NDHS, comprising 904 primary sampling units (PSUs) which served as the clusters: 372 urban and 532 rural. The 2013 sample selected 40,680 households, where a minimum of 943 interviews were completed in each state. (Nigeria has 36 states and an autonomous Federal Capital Territory). A more detailed account of the dataset is also available elsewhere [10]. The 2018 NDHS was conducted between August and December 2018 via a two-stage stratified cluster sampling design, with each cluster or PSU defined based on enumeration areas from the 2006 population census frame. The survey, which was billed to be conducted in 1 400 clusters comprising 580 urban and 820 rural clusters, actually took place in 1 389 clusters due to feasibility issues. In total, 41 821 (13 311) eligible women (men) were interviewed as part of the 2018 NDHS. A more detailed description of the 2018 NDHS sampling design is available elsewhere [11]. Both datasets allow for the consistent measurement of indicators at the national, zonal (i.e. the six geopolitical zones) and state levels, while survey weights were appropriately calculated to ensure national representativity. This paper used the Birth Recode data file, which contains information on every child ever born to each interviewed woman (up to a maximum of 20 children). This data file also links children to their mothers, and it contains other relevant household information. For each NDHS round, the analysis was restricted to the sample of children aged 0–59 months. After data cleaning, the final sample sizes were 23,992 and 11 150 children in the 2013 and 2018 NDHS, respectively. The outcome variables are indicators of child stunting and wasting. Both follow their respective standard definitions. A child with a height-for-age z-score that is less than the negative of twice the standard deviation of the WHO Child Growth Standards median is considered stunted. A child is wasted if the weight-for-height z-score is less than the negative of twice the standard deviation of the WHO Child Growth Standards median [12]. Given that the NDHS does not contain household expenditure information, this paper used the wealth index created in the DHS dataset as an indicator of a child’s socioeconomic status [10]. The wealth index in the NDHS was obtained by applying principal components analysis on household assets [13], accounting for rural-urban differences. Variables used to decompose the change in stunting and wasting were the child’s characteristics (age, sex and religion), mother’s characteristics (education, marital and employment status), and household characteristics (wealth, location, sanitary condition, dependency ratio, and the characteristics of the household head). These variables are correlated with child nutritional status in prior empirical studies. For instance, a child’s age is associated with a child’s nutritional health outcome [14, 15]. Similarly, a child’s sex is associated with nutritional health outcomes, particularly stunting [16–18], while religion and culture are important correlates of a child’s nutritional outcomes [19, 20]. Furthermore, a child with an educated mother is less likely to be stunted or wasted [21–23]. Other mothers’ characteristics like marital status and employment are also associated with children’s nutritional status [24, 25]. Children’s nutritional health outcomes are also affected by household characteristics like wealth and sanitary conditions [15, 21, 23]. In this paper, sanitary conditions were proxied by a household’s water and toilet conditions. Improved water and toilet conditions were created based on the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene classification [26]. A child’s location is also associated with the child’s health status [27–29]. Moreover, the age and sex of the household head are correlated with children’s nutritional outcomes [30, 31], while high dependency ratios are detrimental to children’s health outcomes [32]. Basic descriptive statistics were computed and compared between 2013 and 2018. Percentages were computed for categorical variables while mean values were reported for continuous/count variables. Even though there may be skewness, the mean of continuous/count variables were reported to ease comparison with results in the literature. A concentration curve, suited to assess socioeconomic inequalities [33], depicts the cumulative shares of stunted or wasted children against the cumulative population shares, ranked by socioeconomic status. A line of equality is depicted by a 45-degree line, with a proportional concentration curve coinciding with this line. A pro-rich concentration curve—which depicts a disproportionate concentration of stunting or wasting on the rich—lies below the line of equality. Conversely, a pro-poor curve indicating the disproportionate concentration of stunting or wasting on the poor lies above the line of equality [34]. The concentration index, obtained from the concentration curve, is twice the covariance between the health outcome and the fractional rank in the wealth distribution, divided by the mean of the health outcome indicator [34–37]: where CH refers to the concentration index of the health outcome (H); μH refers to the mean of the health outcome (i.e. stunting or wasting), and r is the fractional rank of the individual/household in the wealth distribution. For continuous outcomes, the index lies in the [-1, +1] interval. A negative (positive) index typically indicates a pro-poor (pro-rich) distribution of stunted or wasted children, while a zero index indicates perfect equality. A zero concentration index may also result from a complex relationship where the concentration curve crosses the line of equality [34]. For categorical variables like indicators of stunting and wasting, the concentration index should be normalised as it may not lie between -1 and +1 [38]. The Erreygers’ normalisation approach was used in this paper [39, 40] to obtain the Erreygers’ normalised concentration index (EC) as follows [41]: where a and b refer to the lower and upper limits of the ordinal health indicator, respectively; and μH and CH remain as earlier defined. Changes in the concentration indices or curves between 2013 and 2018 were assessed using the general characterisation of a pro-poor or a pro-rich change or shift laid out by Ataguba [42]. Let Ct−1 and Ct represent the value of the concentration indices at time t − 1 and t, respectively. A pro-poor change in the concentration indices occurs when Ct−1 > Ct while a pro-rich change corresponds to Ct−1 < Ct. Only two indices are suitable for assessing relative socioeconomic inequalities in health—the slope index of inequality and the concentration index [33]. They are consistent with ranking individuals across socio-economic groupings, sensitive to changes in population distribution across socio-economic groups and consistent in the distribution of stunting and wasting across the distribution of socioeconomic status [33, 43]. The concentration index was used in this paper because it is decomposable to ascertain the factors that significantly explain socioeconomic inequalities in stunting and wasting, which is relevant to policymakers for evidence-based policy interventions. Socioeconomic inequalities in stunting and wasting were decomposed using the Wagstaff et al.’s [44] approach. The relationship between a child’s health outcomes (i.e. stunting or wasting) (H) and associated determinants (z) can be denoted as: where φ and β are parameters, and ε is the error term. Eq (3) was estimated using linear probability models because binary indicators of stunting and wasting were used, with each model appropriately weighted to the population while correcting for heteroscedasticity. For each year, the concentration index in Eq (1) can be decomposed as (the time subscripts are suppressed here to avoid notational clutter): where (βkz-kμH=ηk) denotes the elasticity of stunting or wasting to marginal changes in the k-th explanatory variable, while Ck refers to the concentration index of the k-th explanatory variable. GCε refers to the generalised concentration index of the error term, and (GCεμH) captures the unexplained/residual component. While Eq (4) enables us to decompose observed socioeconomic inequalities in stunting and wasting at each point in time, it does not explain temporal changes in such inequalities. Sequel to decomposing the health concentration index for the relevant health outcome in each year as shown in Eq (4), the change in the concentration index can be decomposed between both periods using the Oaxaca-Blinder decomposition as follows [34]: where t indicates the time period (with the convention that t − 1 and t denote the first (i.e. 2013) and second (i.e. 2018) periods respectively, while other terms in Eq (5) are as earlier defined). Eq (5) decomposes the temporal change in the socioeconomic inequality in stunting or wasting into a part that captures the temporal changes in socioeconomic inequality in the determinants of stunting or wasting: ∑k ηk,t(Ck,t − Ck,t−1), and another component that captures the changes in the elasticities of stunting or wasting with respect to these determinants: ∑k Ck,t−1(ηk,t − ηk,t−1). The final term in Eq (5), Δ(GCε,tμt), measures the temporal difference in the error/residual component [34]. Note that in Eq (5), the temporal changes in the concentration indices of the predictors/determinants are weighted by the elasticities of the second period, while the first-period concentration indices are used to weight the change in the elasticities. The Oaxaca-Blinder decomposition is not unique [34], as an alternative decomposition would be to weight the difference in the concentration indices by the first-period elasticities and the difference in the elasticities by the second-period concentration indices. Given that there are no analytical standard errors for the estimates derived from Eqs (4) and (5), bootstrap routines [45, 46] with 1,000 replications were used, accounting for the full sampling structure of the NDHS. All analyses were implemented using Stata® [47]. The conindex routine was employed in obtaining the Erreygers’ normalised or corrected concentration indices, while accounting for the survey design (clustering, stratification and the resulting survey weights) in each NDHS round [48].

Materiais

N/A

Inovações

Based on the provided information, here are some potential innovations that could be used to improve access to maternal health in Nigeria:

1. Mobile Health (mHealth) Solutions: Develop mobile applications or text messaging services that provide pregnant women and new mothers with important health information, reminders for prenatal and postnatal care appointments, and access to teleconsultations with healthcare providers.

2. Telemedicine: Implement telemedicine platforms that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls or phone consultations, reducing the need for travel and improving access to medical advice.

3. Community Health Workers: Train and deploy community health workers to provide education, support, and basic healthcare services to pregnant women and new mothers in rural areas. These workers can help bridge the gap between healthcare facilities and communities, ensuring that women receive the necessary care and guidance.

4. Maternal Health Vouchers: Introduce a voucher system that provides pregnant women with financial assistance to cover the costs of antenatal care, delivery, and postnatal care. This can help reduce financial barriers and increase access to quality maternal healthcare services.

5. Maternal Health Clinics: Establish dedicated maternal health clinics in underserved areas, equipped with skilled healthcare professionals and necessary facilities for antenatal care, delivery, and postnatal care. These clinics can provide comprehensive care specifically tailored to the needs of pregnant women and new mothers.

6. Transportation Support: Develop transportation initiatives that provide pregnant women with affordable and reliable transportation options to healthcare facilities. This can include partnerships with local transport providers or the use of ambulances for emergency cases.

7. Maternal Health Education Programs: Implement community-based education programs that focus on raising awareness about maternal health, including the importance of antenatal care, nutrition, hygiene, and family planning. These programs can empower women with knowledge to make informed decisions about their health and the health of their children.

8. Public-Private Partnerships: Foster collaborations between the government, private sector, and non-profit organizations to improve access to maternal health services. This can involve leveraging private sector resources and expertise to strengthen healthcare infrastructure, train healthcare professionals, and expand service delivery in underserved areas.

9. Maternal Health Financing: Explore innovative financing mechanisms, such as microinsurance or community-based health financing schemes, to ensure that pregnant women have access to affordable and comprehensive maternal healthcare services.

10. Data-driven Approaches: Utilize data analytics and digital health technologies to identify areas with high maternal health needs and allocate resources accordingly. This can help optimize the delivery of maternal health services and ensure that interventions are targeted where they are most needed.

It is important to note that the implementation of these innovations should be context-specific and tailored to the unique challenges and needs of the Nigerian healthcare system and population.

AI Innovations Description

Based on the provided description, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Strengthening Maternal Education: Implement programs and initiatives that focus on improving maternal education levels. This can include providing access to quality education, vocational training, and awareness campaigns on the importance of education for maternal and child health.

2. Enhancing Wealth Distribution: Address wealth inequalities by implementing policies and programs that aim to reduce poverty and improve income distribution. This can include providing financial support, microfinance opportunities, and income-generating activities for women in low-income households.

3. Improving Sanitation Facilities: Increase access to improved toilet and water facilities, especially in rural areas. This can be achieved through infrastructure development projects, community-based initiatives, and partnerships with relevant stakeholders.

4. Geographic Targeting: Develop targeted interventions and strategies that focus on specific geographic areas with high prevalence rates of stunting and wasting. This can include allocating resources and implementing tailored programs to address the unique challenges faced by these regions.

5. Strengthening Health Systems: Invest in strengthening the overall health system, including maternal health services, by improving infrastructure, training healthcare providers, and ensuring the availability of essential medicines and equipment.

6. Community Engagement: Promote community engagement and participation in maternal health initiatives. This can involve creating community-based support groups, training community health workers, and raising awareness about the importance of maternal health within the community.

7. Data-driven Decision Making: Ensure the collection, analysis, and utilization of accurate and up-to-date data on maternal health indicators. This can help identify gaps, monitor progress, and inform evidence-based decision making for targeted interventions.

By implementing these recommendations, it is possible to develop innovative approaches that can improve access to maternal health and reduce socioeconomic inequalities in child stunting and wasting in Nigeria.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Invest in improving healthcare facilities, especially in rural areas, by increasing the number of well-equipped clinics and hospitals. This will ensure that pregnant women have access to quality maternal healthcare services.

2. Enhancing transportation services: Improve transportation infrastructure and services to ensure that pregnant women can easily access healthcare facilities. This could include providing ambulances or transportation vouchers for pregnant women in remote areas.

3. Increasing community awareness and education: Conduct awareness campaigns to educate communities about the importance of maternal health and the available healthcare services. This can help reduce cultural and social barriers that prevent women from seeking timely and appropriate care.

4. Training and capacity building: Provide training and capacity building programs for healthcare providers, especially in rural areas, to enhance their skills in providing maternal healthcare services. This can help improve the quality of care and ensure that healthcare providers are equipped to handle maternal health emergencies.

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

1. Define indicators: Identify key indicators that reflect access to maternal health, such as the number of pregnant women receiving prenatal care, the number of deliveries attended by skilled birth attendants, and the number of maternal deaths.

2. Collect baseline data: Gather data on the selected indicators from relevant sources, such as health facilities, surveys, and government reports. This will provide a baseline against which the impact of the recommendations can be measured.

3. Develop a simulation model: Create a simulation model that incorporates the baseline data and simulates the potential impact of the recommendations. The model should consider factors such as population demographics, healthcare infrastructure, transportation services, and community awareness.

4. Define scenarios: Define different scenarios that represent the implementation of the recommendations. For example, one scenario could assume the full implementation of all recommendations, while another scenario could assume partial implementation.

5. Simulate outcomes: Run the simulation model using the defined scenarios to estimate the potential outcomes. This could include projecting the number of pregnant women receiving prenatal care, the percentage of deliveries attended by skilled birth attendants, and the reduction in maternal deaths.

6. Analyze results: Analyze the simulated outcomes to assess the impact of the recommendations on improving access to maternal health. Compare the results of different scenarios to identify the most effective strategies.

7. Refine and iterate: Refine the simulation model based on the analysis results and feedback from stakeholders. Iterate the simulation process to further explore different scenarios and refine the recommendations.

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

Autor

Dima Health

Statistics:

Citations: 8

Identifiers:

DOI: 10.1371/journal.pone.0238191

Research Areas:

Community Interventions, Disparities, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants

Study Countries:

Nigeria

Study Design:

Cross Sectional Study, Phenomenological Study

Study Approach:

Quantitative

Participants Gender:

Male & Female

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