Socio-demographic and environmental determinants of under-5 stunting in Rwanda: Evidence from a multisectoral study

Frontiers in Public Health, Volume 11, Year 2023

Interpretation

Child stunting is an important household, socio-economic, environmental and nutritional stress indicator. Nationally, 33% of children under 5 in Rwanda are stunted necessitating the need to identify factors perpetuating stunting for targeted interventions. Our study assessed the individual and community-level determinants of under-5 stunting essential for designing appropriate policy and program responses for addressing stunting in Rwanda. A cross-sectional study was conducted between September 6 and October 9, 2022, in five districts of Rwanda including, Kicukiro, Ngoma, Burera, Nyabihu and Nyanza. 2788 children and their caregivers were enrolled in the study and data on the individual level (child, caregiver/household characteristics), and community-level variables were collected. A multilevel logistic regression model was used to determine the influence of individual and community-level factors on stunting. The prevalence of stunting was 31.4% (95% CI: 29.5–33.1). Of this, 12.2% were severely stunted while 19.2% were moderately stunted. In addition, male gender, age above 11 months, child disability, more than six people in the household, having two children below the age of five, a child having diarrhea 1–2 weeks before the study, eating from own plate when feeding, toilet sharing, and open defecation increased the odds of childhood stunting. The full model accounted for 20% of the total variation in the odds of stunting. Socio-demographic and environmental factors are significant determinants of childhood stunting in Rwanda. Interventions to address under-five stunting should be tailored toward addressing individual factors at household levels to improve the nutritional status and early development of children. This was a cross-sectional study conducted between September 6 and October 9 2022, in five districts of Rwanda including, Kicukiro, Ngoma, Burera, Nyabihu, and Nyanza. Gikuriro Kuri Bose is a multisectoral and transdisciplinary project being implemented in five districts of Rwanda, each being drawn from one province. Rwanda has four geopolitical provinces and the City of Kigali. The provinces and the City of Kigali are further subdivided into 30 districts and districts subdivided into sectors (416 sectors in total) and sectors subdivided into cells (2,148 cells) and cells subdivided into villages (14,837 villages). Villages comprise about 100 households while cells constitute between five-seven villages. The study districts included Nyabihu from the western province, Burera from the northern province, Kicukiro from the city of Kigali, Nyanza in the south and Ngoma in the eastern province (Figure 1). Map of Rwanda showing the administrative districts and project implementation areas. This study was based on 2,788 children and their mothers/legal guardians. To determine the sample size, the current prevalence of stunting (33%) (7) was considered as an indicator of the nutritional status. Using a 95% confidence interval and the equation proposed by Lwanga et al. (11) as n = Z1-a22 (1-p)/ ε2p, where p = prevalence, ε = relative precision, and n = sample size with a relative precision for the study to be between 5 and 10% of the true prevalence (0.05 < ε < 0.10), a sample size of 713–2,854 pairs of mothers/guardians and children as adequate. From each household, children under five and their legal guardians were selected for inclusion in the study. In this study, the sampling unit was a cell. To obtain a representative sample, the study used a two-stage probabilistic sampling method. The first stage involved the random selection of cells from the sector and the second stage involved a systematic sampling of households from the selected cells. Approval to conduct the study was granted by the University of Global Health Equity Institutional Review Board (UGHE-IRB: Ref: UGHE-IRB/2022/034). Furthermore, legal guardians of children were asked for consent, and this was provided in writing. The dependent variable in this study was stunting, and this was a categorical binary variable (yes = 1 or no = 0). Stunting was defined as height for age z-score <-2 standard deviations using the WHO growth standards (12). Furthermore, using WHO classifications, children with height for age z-score of ≤-2 standard deviations and ≥-3 standard deviation were classified as moderately stunted while those with height for age z-score <-3 standard deviations were classified as severely stunted (13). There were three levels of the independent variables. These were categorized as individual (child and maternal/household) characteristics, community and environmental factors which included topography of the area, water, hygiene, and sanitation variables. To collect this information, a structured pre-tested questionnaire was administered to mothers/legal guardians of the children who had been included in the study. The questionnaire collected information on the child's age, sex, maternal/guardian's age, level of education, socio-economic class also called Ubudehe, breastfeeding and complementary feeding practices, hygiene and handwashing practices, household water availability and access, availability, and types of sanitary facilities, and socio-economic characteristics of the household. Additionally, information about the guardian and child's illnesses and disabilities (yes = 1 or no = 0) was collected. The classification of Ubudehe in Rwanda has been explained further in Supplementary material 1. The weight of the children was measured using the SECA electronic scales to a precision of 0.1 kg while the height was taken to the nearest 0.1 cm using a UNICEF height/length board. To measure the height, children between the age of 24–59 months were made stand-upright without shoes and their height was taken using a stadiometer in a Frankfurt vertical position and to the nearest 0.1 cm. For children aged 0–23 months, their height/length was taken using a vertical measuring board while in a horizontal position. Before the measurements, it was ensured that the head, shoulders, and buttocks touched the board. To ascertain the validity of the anthropometry measurements, duplicate measurements were done for 10–15% of the sample and the variations for the duplicate measurements were below 5%. The age of the children was obtained from the Ifishi Y'Ubuzima Bw'umwana (vaccination card). The study included children aged between 0 and 59 months who were attending routine hospital outpatient visitations. Furthermore, the study included those without medical complications and those whose legal guardians consented to participate and signed the consent forms. All children in this age category but not fulfilling the inclusion criteria were excluded from the study. To enhance the precision of the measurements, the SECA weighing scales were calibrated daily before the commencement of data collection. All data collectors were trained in the taking of child anthropometric measurements and administration of the face-to-face questionnaire interviews before data collection. Community health workers who were part of the data collection teams assisted with the taking of anthropometric measurements on all children. For children who could not be weighed on the SECA scale, the weight of the mother/legal guardian was initially taken. Thereafter, the weight of the mother/legal guardian while holding the child was taken. The difference between the two weights was taken as the weight of the child. Descriptive analysis was used to summarize continuous and categorical variables, showing their distribution with the outcome variable. The Z-score value for height-for-age was calculated using the ANTHRO PLUS software (14). In the bivariate and multivariate analysis, the response variable, stunting, was turned into a binary variable thus allowing us to logistic models. To determine the relationship between the various individual, community and environmental factors, a bivariate analysis was used. A multivariate multilevel logistic regression was used to examine the individual, community and environmental factors associated with under-five stunting. The multilevel models were deemed suitable for the analysis because of the hierarchical structure of the data and its ability to allow for the determination of the residual components associated with each level of the hierarchy. Furthermore, the multilevel models also allow for the estimation of group-level variables while estimating the group effects. Three models were fit in the overall analysis. The first model was a null model, and this included the response variable only without any predictor variable and this was done to estimate its variance. In the second model which was a fixed effects model, we controlled for individual-level variables, and this included the children's demographic characteristics, history of diarrhea, breastfeeding and complementary feeding practices and child morbidity. In this model, district and sector were added as random intercept terms. Maternal (legal guardian) variables included education level and feeding structure, age and morbidity and water, hygiene, and environmental variables such as sanitation practices were also included. District and place of residence were added as random effects. The final model included both individual and contextual level factors which were the place of residence and district. The results demonstrating measures of association have been presented as adjusted odds ratios (aOR) together with their corresponding 95% confidence intervals (CIs) and p-values. The intraclass correlation coefficient (ICC), median odds ratio (MOR) and proportional change in variance (PCV) were used as a measure of the random effect. The ICC, which shows the proportion of total variance in the outcome attributable to districts, sectors and cells was calculated as shown by Merlo et al. (15). MOR is the measure of heterogeneity, and the PVC is the measure of the total variation of stunting in the final model (models with individual and environmental variables) comparative to the null model and was determined as described elsewhere (16, 17). Data analysis was carried out using StataSE STATA version 17 (StataCorp, College Station, TX, USA).

AI Digest

Study Justification:

– Child stunting is an important indicator of household, socio-economic, environmental, and nutritional stress.
– In Rwanda, 33% of children under 5 are stunted, highlighting the need for targeted interventions.
– This study aims to identify the factors perpetuating stunting to inform policy and program responses.

Study Highlights:

– Cross-sectional study conducted in five districts of Rwanda.
– 2,788 children and their caregivers were enrolled.
– Individual and community-level determinants of under-5 stunting were assessed.
– Prevalence of stunting was 31.4%, with 12.2% severely stunted and 19.2% moderately stunted.
– Factors such as male gender, age above 11 months, child disability, household size, having multiple young children, recent diarrhea, feeding practices, toilet sharing, and open defecation increased the odds of stunting.
– Socio-demographic and environmental factors play a significant role in childhood stunting in Rwanda.

Recommendations:

– Interventions to address under-five stunting should focus on individual factors at the household level.
– Tailored approaches are needed to improve the nutritional status and early development of children.

Key Role Players:

– Ministry of Health, Rwanda
– Ministry of Education, Rwanda
– Ministry of Gender and Family Promotion, Rwanda
– Ministry of Infrastructure, Rwanda
– Ministry of Local Government, Rwanda
– District Health Offices
– Community Health Workers
– Non-governmental organizations working in child health and nutrition

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers and community health workers
– Development and dissemination of educational materials on proper nutrition and feeding practices
– Implementation of hygiene and sanitation programs
– Improvement of water supply and access to clean water
– Monitoring and evaluation of interventions
– Advocacy and awareness campaigns
– Coordination and collaboration between relevant ministries and stakeholders

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study design is cross-sectional, which limits the ability to establish causality. To improve the evidence, a longitudinal study design could be considered to assess the long-term effects of the determinants on under-5 stunting. Additionally, the sample size of 2,788 children is adequate, but increasing the sample size could enhance the generalizability of the findings. Finally, conducting further statistical analyses, such as subgroup analyses or sensitivity analyses, could provide more robust evidence.

Abstract

This was a cross-sectional study conducted between September 6 and October 9 2022, in five districts of Rwanda including, Kicukiro, Ngoma, Burera, Nyabihu, and Nyanza. Gikuriro Kuri Bose is a multisectoral and transdisciplinary project being implemented in five districts of Rwanda, each being drawn from one province. Rwanda has four geopolitical provinces and the City of Kigali. The provinces and the City of Kigali are further subdivided into 30 districts and districts subdivided into sectors (416 sectors in total) and sectors subdivided into cells (2,148 cells) and cells subdivided into villages (14,837 villages). Villages comprise about 100 households while cells constitute between five-seven villages. The study districts included Nyabihu from the western province, Burera from the northern province, Kicukiro from the city of Kigali, Nyanza in the south and Ngoma in the eastern province (Figure 1). Map of Rwanda showing the administrative districts and project implementation areas. This study was based on 2,788 children and their mothers/legal guardians. To determine the sample size, the current prevalence of stunting (33%) (7) was considered as an indicator of the nutritional status. Using a 95% confidence interval and the equation proposed by Lwanga et al. (11) as n = Z1-a22 (1-p)/ ε2p, where p = prevalence, ε = relative precision, and n = sample size with a relative precision for the study to be between 5 and 10% of the true prevalence (0.05 < ε < 0.10), a sample size of 713–2,854 pairs of mothers/guardians and children as adequate. From each household, children under five and their legal guardians were selected for inclusion in the study. In this study, the sampling unit was a cell. To obtain a representative sample, the study used a two-stage probabilistic sampling method. The first stage involved the random selection of cells from the sector and the second stage involved a systematic sampling of households from the selected cells. Approval to conduct the study was granted by the University of Global Health Equity Institutional Review Board (UGHE-IRB: Ref: UGHE-IRB/2022/034). Furthermore, legal guardians of children were asked for consent, and this was provided in writing. The dependent variable in this study was stunting, and this was a categorical binary variable (yes = 1 or no = 0). Stunting was defined as height for age z-score <-2 standard deviations using the WHO growth standards (12). Furthermore, using WHO classifications, children with height for age z-score of ≤-2 standard deviations and ≥-3 standard deviation were classified as moderately stunted while those with height for age z-score <-3 standard deviations were classified as severely stunted (13). There were three levels of the independent variables. These were categorized as individual (child and maternal/household) characteristics, community and environmental factors which included topography of the area, water, hygiene, and sanitation variables. To collect this information, a structured pre-tested questionnaire was administered to mothers/legal guardians of the children who had been included in the study. The questionnaire collected information on the child's age, sex, maternal/guardian's age, level of education, socio-economic class also called Ubudehe, breastfeeding and complementary feeding practices, hygiene and handwashing practices, household water availability and access, availability, and types of sanitary facilities, and socio-economic characteristics of the household. Additionally, information about the guardian and child's illnesses and disabilities (yes = 1 or no = 0) was collected. The classification of Ubudehe in Rwanda has been explained further in Supplementary material 1. The weight of the children was measured using the SECA electronic scales to a precision of 0.1 kg while the height was taken to the nearest 0.1 cm using a UNICEF height/length board. To measure the height, children between the age of 24–59 months were made stand-upright without shoes and their height was taken using a stadiometer in a Frankfurt vertical position and to the nearest 0.1 cm. For children aged 0–23 months, their height/length was taken using a vertical measuring board while in a horizontal position. Before the measurements, it was ensured that the head, shoulders, and buttocks touched the board. To ascertain the validity of the anthropometry measurements, duplicate measurements were done for 10–15% of the sample and the variations for the duplicate measurements were below 5%. The age of the children was obtained from the Ifishi Y'Ubuzima Bw'umwana (vaccination card). The study included children aged between 0 and 59 months who were attending routine hospital outpatient visitations. Furthermore, the study included those without medical complications and those whose legal guardians consented to participate and signed the consent forms. All children in this age category but not fulfilling the inclusion criteria were excluded from the study. To enhance the precision of the measurements, the SECA weighing scales were calibrated daily before the commencement of data collection. All data collectors were trained in the taking of child anthropometric measurements and administration of the face-to-face questionnaire interviews before data collection. Community health workers who were part of the data collection teams assisted with the taking of anthropometric measurements on all children. For children who could not be weighed on the SECA scale, the weight of the mother/legal guardian was initially taken. Thereafter, the weight of the mother/legal guardian while holding the child was taken. The difference between the two weights was taken as the weight of the child. Descriptive analysis was used to summarize continuous and categorical variables, showing their distribution with the outcome variable. The Z-score value for height-for-age was calculated using the ANTHRO PLUS software (14). In the bivariate and multivariate analysis, the response variable, stunting, was turned into a binary variable thus allowing us to logistic models. To determine the relationship between the various individual, community and environmental factors, a bivariate analysis was used. A multivariate multilevel logistic regression was used to examine the individual, community and environmental factors associated with under-five stunting. The multilevel models were deemed suitable for the analysis because of the hierarchical structure of the data and its ability to allow for the determination of the residual components associated with each level of the hierarchy. Furthermore, the multilevel models also allow for the estimation of group-level variables while estimating the group effects. Three models were fit in the overall analysis. The first model was a null model, and this included the response variable only without any predictor variable and this was done to estimate its variance. In the second model which was a fixed effects model, we controlled for individual-level variables, and this included the children's demographic characteristics, history of diarrhea, breastfeeding and complementary feeding practices and child morbidity. In this model, district and sector were added as random intercept terms. Maternal (legal guardian) variables included education level and feeding structure, age and morbidity and water, hygiene, and environmental variables such as sanitation practices were also included. District and place of residence were added as random effects. The final model included both individual and contextual level factors which were the place of residence and district. The results demonstrating measures of association have been presented as adjusted odds ratios (aOR) together with their corresponding 95% confidence intervals (CIs) and p-values. The intraclass correlation coefficient (ICC), median odds ratio (MOR) and proportional change in variance (PCV) were used as a measure of the random effect. The ICC, which shows the proportion of total variance in the outcome attributable to districts, sectors and cells was calculated as shown by Merlo et al. (15). MOR is the measure of heterogeneity, and the PVC is the measure of the total variation of stunting in the final model (models with individual and environmental variables) comparative to the null model and was determined as described elsewhere (16, 17). Data analysis was carried out using StataSE STATA version 17 (StataCorp, College Station, TX, USA).

Materials

https://efashare.b-cdn.net/materials/522ef31bb65c60ecb71839ce5d21737e36592f28019302c1aa297ef4611df352/Data_Sheet_1.docx

Innovations

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

1. Mobile Health (mHealth) Solutions: Develop and implement mobile applications or text messaging services to 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. Community Health Worker (CHW) Programs: Expand and strengthen community health worker programs to provide education, support, and referrals for maternal health services. CHWs can play a crucial role in reaching remote and underserved areas, conducting home visits, and promoting healthy behaviors during pregnancy and postpartum.

3. Telemedicine and Teleconsultations: Establish telemedicine platforms and teleconsultation services to enable pregnant women and new mothers to access healthcare professionals remotely. This can help overcome geographical barriers and improve access to specialized care, especially in rural areas.

4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with financial assistance to access essential maternal health services, including antenatal care, skilled birth attendance, and postnatal care. These vouchers can be distributed through community health centers or mobile platforms.

5. Transportation Support: Develop transportation initiatives to address the challenge of reaching healthcare facilities. This could involve providing subsidized transportation services or partnering with existing transportation networks to ensure pregnant women have reliable and affordable means of reaching healthcare facilities for prenatal and postnatal care.

6. Maternal Health Education Campaigns: Launch targeted education campaigns to raise awareness about the importance of maternal health and the available services. These campaigns can be conducted through various channels, such as radio, television, community meetings, and social media, to reach a wide audience.

7. Maternity Waiting Homes: Establish maternity waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to stay closer to the facility during the final weeks of pregnancy. These homes can provide a safe and supportive environment for women to wait for labor and delivery.

8. Strengthening Health Infrastructure: Invest in improving the infrastructure of healthcare facilities, particularly in rural areas, to ensure they have the necessary equipment, supplies, and skilled healthcare providers to deliver quality maternal health services.

9. Public-Private Partnerships: Foster collaborations between the public and private sectors to leverage resources and expertise in improving access to maternal health. This can involve partnerships with private healthcare providers, pharmaceutical companies, technology companies, and non-profit organizations.

10. Maternal Health Financing: Explore innovative financing mechanisms, such as health insurance schemes or microfinance programs, to make maternal health services more affordable and accessible to all women, regardless of their socioeconomic status.

It is important to note that the specific recommendations for improving access to maternal health should be tailored to the context and needs of the local population in Rwanda.

AI Innovations Description

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

1. Implement targeted interventions: Based on the findings of the study, it is important to design and implement targeted interventions to address the individual factors at the household level that contribute to childhood stunting. These interventions should focus on improving the nutritional status and early development of children.

2. Enhance maternal education and awareness: Promote maternal education and awareness about the importance of proper nutrition, breastfeeding, and complementary feeding practices. This can be done through community-based education programs, workshops, and counseling sessions.

3. Improve access to clean water and sanitation facilities: Enhance access to clean water and sanitation facilities in households and communities. This can help reduce the risk of diarrheal diseases and improve overall hygiene practices, which are important factors in preventing childhood stunting.

4. Strengthen community health systems: Strengthen community health systems by training and empowering community health workers to provide education, support, and monitoring for maternal and child health. This can include regular home visits, health screenings, and referrals to appropriate healthcare services.

5. Collaborate with local stakeholders: Collaborate with local stakeholders, including government agencies, non-governmental organizations, and community leaders, to develop and implement comprehensive maternal health programs. This can ensure that interventions are culturally appropriate, sustainable, and effectively reach the target population.

6. Use technology for remote access: Utilize technology, such as telemedicine and mobile health applications, to provide remote access to maternal health services. This can help overcome geographical barriers and improve access to healthcare for women in remote or underserved areas.

7. Monitor and evaluate interventions: Establish a robust monitoring and evaluation system to assess the effectiveness of interventions and identify areas for improvement. This can help ensure that resources are allocated efficiently and interventions are continuously adapted to meet the evolving needs of the population.

By implementing these recommendations, it is possible to develop innovative approaches that improve access to maternal health and contribute to reducing childhood stunting in Rwanda.

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, including hospitals, clinics, and maternity centers, particularly in underserved areas. This can involve increasing the number of healthcare professionals, ensuring the availability of essential medical equipment and supplies, and improving the overall quality of care.

2. Enhancing community-based healthcare services: Implement community-based programs that provide maternal health services directly to women in their communities. This can include mobile clinics, outreach programs, and home visits by trained healthcare workers to provide prenatal care, postnatal care, and education on maternal health.

3. Improving transportation and logistics: Address transportation barriers by improving road infrastructure and transportation services to ensure that pregnant women can easily access healthcare facilities. This can involve providing transportation vouchers or subsidies for pregnant women, establishing emergency transportation systems, and improving the availability of ambulances.

4. Increasing awareness and education: Implement comprehensive maternal health education programs to raise awareness about the importance of prenatal care, safe delivery practices, and postnatal care. This can involve community workshops, educational campaigns, and the use of multimedia platforms to disseminate information.

5. Strengthening health information systems: Develop and implement robust health information systems to track maternal health indicators, monitor progress, and identify areas for improvement. This can involve the use of electronic health records, data collection tools, and analytics to inform decision-making and resource allocation.

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

1. Define the indicators: Identify specific indicators that measure access to maternal health, such as the number of prenatal visits, percentage of deliveries attended by skilled birth attendants, or maternal mortality rates.

2. Collect baseline data: Gather data on the current status of the selected indicators in the target population or region. This can involve conducting surveys, reviewing existing data sources, or collaborating with local health authorities.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. This can be done using statistical software or simulation tools that allow for scenario analysis and projection of outcomes.

4. Input data and parameters: Input the baseline data, as well as relevant parameters such as population size, healthcare infrastructure capacity, and resource availability. Define the assumptions and parameters for each recommendation, such as the expected increase in healthcare facilities or the percentage of the population reached by community-based programs.

5. Run simulations: Run the simulation model using different scenarios that reflect the implementation of the recommendations. This can involve adjusting the parameters and assumptions to simulate different levels of intervention coverage or resource allocation.

6. Analyze results: Analyze the simulation results to assess the potential impact of the recommendations on the selected indicators. Compare the outcomes of different scenarios to identify the most effective interventions and their expected outcomes.

7. Refine and validate the model: Refine the simulation model based on feedback and validation from experts in the field. Incorporate additional data or adjust parameters as needed to improve the accuracy and reliability of the simulations.

8. Communicate findings: Present the findings of the simulation study in a clear and concise manner, highlighting the potential impact of the recommendations on improving access to maternal health. Use visualizations and data summaries to effectively communicate the results to stakeholders and decision-makers.

By following these steps, a simulation study can provide valuable insights into the potential impact of different recommendations on improving access to maternal health, helping inform policy and programmatic decisions.

Author

Dima Health

Identifiers:

DOI: 10.3389/fpubh.2023.1107300

Research Areas:

Community Interventions, Disability, Environmental, Food Security, Genetics and Genomics, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants, Workforce

Study Countries:

Rwanda

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male