Moderate and severe household food insecurity predicts stunting and severe stunting among Rwanda children aged 6–59 months residing in Gicumbi district

Maternal and Child Nutrition, Volume 15, No. 3, Year 2019

Ukuhunyushwa

Household food insecurity (HFI) plays an important role in child malnutrition in many low-income countries. We determined the association between HFI and stunting and severe stunting among Rwandan children from the Gicumbi district, aged 6–59 months using a cross-sectional study of 2,222 children. HFI factor was calculated by summing all seven HFI (access) frequency questions and was categorised into food security, mildly food insecurity, moderately food insecurity, and severe food insecurity. The association between stunting, severe stunting, and HFI was determined using the multiple logistic regression analyses that adjust for clustering and sampling weights. The odds of moderate and severe HFI were significantly higher among stunted children aged 6–59 months than those who were not stunted (adjusted odds ratio [AOR] = 1.43; 95% confidence interval [CI] [1.11, 1.84] and AOR = 1.35; 95% CI [1.08, 1.69], respectively). Children from households with moderate food insecurity were 2.47 times more likely to be severely stunted (AOR = 2.47; 95% CI [1.77, 3.46]), and those from households with severe food insecurity were more likely to be severely stunted (AOR = 1.82; 95% CI [1.34, 2.48]), compared with children aged 6–59 months from households with food security. Other factors included male children and children who did not attend monthly growth monitoring sessions. This study showed that moderate and severe HFI correlated with stunting and severe stunting. Interventions to improve stunting in Gicumbi children should also focus on male children, children who did not attend monthly growth monitoring sessions, and households with moderate and severe food insecurity. Gicumbi district is located in the northern province of Rwanda closer to the border with Uganda. Gicumbi district comprises 21 sectors, 109 cells, and 630 villages (Imidugudu). The population is more of rural than urban. The topography of Gicumbi is more of steep slopes and mountainous but surrounded by steep ravines with small valleys segmented by multiple swamps. A cross‐sectional study was conducted during harvest period, from January 21 to 31, 2016, in Gicumbi district covering 32 villages as part of World Vision Rwanda’s funding service agreement to generate evidence to influence maternal and child health programmes. The study population shared similar characteristics (homogeneous, i.e., all household from a low socio‐economic group). The respondents were enrolled in a Maternal Newborn Child Health intervention at the household level with the specific criteria for household inclusion being a presence of a pregnant woman or breastfeeding mother. The sampling frame produced by the 2010 Rwanda Population and Housing Census projection was used in the sampling process of the survey (RDHS, 2010; Rurangirwa, Mogren, Nyirazinyoye, Ntaganira, & Krantz, 2017). The survey sample was selected in two stages. In the first stage, a total of 20 villages (clusters) were selected from the cells. In the second stage, 32 households were randomly selected in each selected villages (clusters). All selected villages were visited, and none was replaced, regardless of reason(s) encountered or given. The total sample of the survey consists of 20 clusters. All 660 (including nonresponse rate) households completed the mother’s/caregiver interviews, yielding a response rate of 100%. The high response rate for this survey was because before conducting the interview, World Vision Rwanda mobilised the local leaders, community health workers, and team leaders of community health workers for the survey. For reporting district‐level results, sample weights will be used, and sampling weight was calculated by the product of the reciprocal of the sampling fractions employed in the selection of cells and villages. For the analysis to be achieved, it is important to calculate the required sample size that will be enough to detect any statistical difference. We estimate that this sample has 90% power and alpha level of 5%, to detect an odds ratio (OR) of at least 1.6, assuming an alpha level of 5%, prevalence of <4 times antenatal care (ANC) of 55% (Rurangirwa et al., 2017), a design effect of 3.2 (based on the average of 32 children per cluster and expected relative difference of about 10%) and a total sample of about 664 households is required for the study, and we consider this sufficient statistical power to examine differences in <4 times ANC that would be of public health significance. The questionnaires that were used in the survey included household information, which was used to collect information on household members (usual residents), and women's questionnaire administered to mothers or caretakers for all children under 5 years. The women or caretaker's questionnaire included the women or caretaker's demographic characteristics: antenatal, delivery, and post‐natal care, breastfeeding, and child nutrition. The questionnaires were installed on tablets using the Open Data Kit. Open Data Kit is a suite tool that allows data collection using mobile devices and data submission to an online server. World Vision office in Kigali provided the tablets that were used in the data collection exercise. Data were posted daily after fieldwork, and this enabled daily review of work done to check for inconsistencies and errors. In the child nutrition questionnaire, measurements of height were obtained for children under the age of five in all of the selected households. Each enumerator carried a scale and measuring board. Measurements were made using lightweight SECA scales (with digital screens) designed and manufactured under the authority of the UNICEF. The measuring boards employed were specially made by Shorr Productions for use in survey settings. Children under the age of 2 were measured lying down on the board (recumbent length), and standing height was measured for all other children. The primary outcome variables were stunting and severe stunting. The outcome variables were expressed as a dichotomous variable, that is, Category 0 (not stunted [greater than −2 standard deviations {SDs} of the WHO Child Growth Standards median] or not severely stunted [greater than −3 SD]) and Category 1 (stunted [less than −2 SD] or severely stunted [less than −3 SD]). The household food security tool consists of seven questions, which are aimed at extracting information required for defining the household's food security status. The responses are “rarely,” “something,” or “often” or “rarely” in the past 12 months. The HFI factor was calculated by summing all the seven HFI (access) frequency questions with scores ranging from 0 to 21. The households were also categorised into four groups, such as food secure (0), mildly food insecure (1–2), moderately food insecure (3–10), and severely food insecure (more than 10; Swindale & Bilinsky, 2007). Our choice of potential confounding factors was based on similar studies that examined the relationship between stunting and severe stunting by food security status in developing countries (Ali et al., 2013; Ali Naser et al., 2014; Singh, Singh, & Ram, 2014). These potential confounders were classified into four distinct groups: socio‐economic and demographic (sectors, primary caregiver, education level, marital status, and household wealth index); child (sex of baby and child's age in months); maternal and child health (ANC, duration of breastfeeding, and attended child monthly growth monitoring sessions); and health services and environmental factors (quality of care from health services, place of delivery, water available all year, sources of drinking water, and type of toilet facility). The household wealth index variable measures basic household needs for all children 5–18 years. The household wealth index was constructed by assigning weights to three basic household needs for children 5–18 years (i.e., difficulty providing at least two sets of clothes for all children aged 5–18 years living in the household, difficulty providing a pair of shoes for all children aged 5–18 years living in the household, and difficulty paying school fees or school contribution for all children aged 5–18 years living in the household) using the principle components analysis. The household wealth index was divided into three categories (poorest, middle and least poor; Filmer & Pritchett, 2001), and improved and unimproved sources of drinking water and type of toilet facility were categorised based on the WHO and UNICEF Joint Monitoring Programme guidelines (WHO/UNICEF, 2014). Data analysis was performed using the survey (SVY) commands of Stata version 14.1 (Stata Corp, College Station, TX, USA), which adjust for sampling weights and cluster sampling design and the calculation of standard errors. Preliminary analyses involved percentage and frequency count of all selected characteristics; this was followed by estimation of prevalence of stunting and severe stunting by HFI among children aged 6–59 months. The Taylor series linearization method was used in the surveys when estimating 95% confidence intervals (CIs) around prevalence estimates. Survey logistic regression that adjusted for cluster and survey weights was used to determine the association between HFI and stunting and severe stunting among Rwandan children aged 6–59 months. First, univariate binary logistic regression analysis was performed to examine the unadjusted OR. A staged modelling technique was employed for the multiple logistic regression analyses. In the first stage, the socio‐economic and demographic factors were entered into the baseline multiple logistic regression model to examine their association with the study outcome. After that, a manual elimination process was performed, and variables that were associated with the study outcomes were retained in the model. Second, child factors were added into significant model retained in the first stage. In the third and fourth stages, maternal and child's health factors and health services and environmental factors were added to the significant variable retained in the second stage. As before, those factors with p values <0.05 were retained. In the final stage of the analysis, the main study factor (HFI) was added to the significant variables obtained from the third and fourth stages, and variables with a p value <0.05 were retained in the final. The ORs and their 95% CIs obtained from the adjusted multiple logistics model were used to determine the association of HFI fuels on stunting and severe stunting.

I-AI Digest

Study Justification:

This study aimed to investigate the association between household food insecurity (HFI) and stunting and severe stunting among children aged 6-59 months in the Gicumbi district of Rwanda. The justification for this study is that HFI is known to play a significant role in child malnutrition in low-income countries. Understanding the relationship between HFI and stunting can help inform interventions and policies to improve child nutrition in this region.

Highlights:

– The study found that moderate and severe HFI were significantly associated with stunting and severe stunting among children aged 6-59 months in Gicumbi district.
– Children from households with moderate food insecurity were 2.47 times more likely to be severely stunted, and those from households with severe food insecurity were also more likely to be severely stunted.
– Other factors associated with stunting and severe stunting included being male, not attending monthly growth monitoring sessions, and not receiving adequate antenatal care.

Recommendations for Lay Reader:

– Interventions to improve child nutrition in Gicumbi district should focus on addressing household food insecurity, particularly moderate and severe food insecurity.
– Efforts should also be made to target male children, promote attendance at monthly growth monitoring sessions, and ensure access to adequate antenatal care.
– These recommendations aim to reduce the prevalence of stunting and severe stunting among children in the district and improve their overall health and well-being.

Recommendations for Policy Maker:

– Policies and programs should be developed and implemented to address household food insecurity in Gicumbi district, with a focus on households experiencing moderate and severe food insecurity.
– Strategies should be put in place to improve access to antenatal care and promote attendance at monthly growth monitoring sessions.
– Targeted interventions should be designed to address the specific needs of male children, who are at higher risk of stunting and severe stunting.
– Collaboration between government agencies, non-governmental organizations, and community stakeholders is crucial for the successful implementation of these interventions.

Key Role Players:

– Government agencies responsible for health and nutrition policies and programs
– Non-governmental organizations working in the field of child nutrition and food security
– Community leaders and local health workers
– Researchers and academics specializing in child nutrition and public health

Cost Items for Planning Recommendations:

– Funding for interventions targeting household food insecurity, such as providing food assistance or implementing income-generating programs
– Resources for improving access to antenatal care and monthly growth monitoring sessions, including training for healthcare providers and transportation support for families
– Awareness campaigns and educational materials to promote healthy nutrition practices and raise awareness about the importance of antenatal care and growth monitoring
– Monitoring and evaluation activities to assess the effectiveness of interventions and make necessary adjustments
– Coordination and collaboration costs for stakeholders involved in implementing the recommendations.

Amandla Obufakazi

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is moderately strong. The study design is a cross-sectional study, which limits the ability to establish causality. However, the study includes a large sample size of 2,222 children and adjusts for clustering and sampling weights in the analysis. The study also uses multiple logistic regression analyses to determine the association between household food insecurity (HFI) and stunting and severe stunting. The odds ratios (AORs) are reported with 95% confidence intervals (CIs), which adds to the strength of the evidence. To improve the strength of the evidence, future studies could consider using a longitudinal design to establish causality and include a control group for comparison. Additionally, collecting data on potential confounding factors such as maternal education and income level could further strengthen the evidence.

Abstract

Gicumbi district is located in the northern province of Rwanda closer to the border with Uganda. Gicumbi district comprises 21 sectors, 109 cells, and 630 villages (Imidugudu). The population is more of rural than urban. The topography of Gicumbi is more of steep slopes and mountainous but surrounded by steep ravines with small valleys segmented by multiple swamps. A cross‐sectional study was conducted during harvest period, from January 21 to 31, 2016, in Gicumbi district covering 32 villages as part of World Vision Rwanda’s funding service agreement to generate evidence to influence maternal and child health programmes. The study population shared similar characteristics (homogeneous, i.e., all household from a low socio‐economic group). The respondents were enrolled in a Maternal Newborn Child Health intervention at the household level with the specific criteria for household inclusion being a presence of a pregnant woman or breastfeeding mother. The sampling frame produced by the 2010 Rwanda Population and Housing Census projection was used in the sampling process of the survey (RDHS, 2010; Rurangirwa, Mogren, Nyirazinyoye, Ntaganira, & Krantz, 2017). The survey sample was selected in two stages. In the first stage, a total of 20 villages (clusters) were selected from the cells. In the second stage, 32 households were randomly selected in each selected villages (clusters). All selected villages were visited, and none was replaced, regardless of reason(s) encountered or given. The total sample of the survey consists of 20 clusters. All 660 (including nonresponse rate) households completed the mother’s/caregiver interviews, yielding a response rate of 100%. The high response rate for this survey was because before conducting the interview, World Vision Rwanda mobilised the local leaders, community health workers, and team leaders of community health workers for the survey. For reporting district‐level results, sample weights will be used, and sampling weight was calculated by the product of the reciprocal of the sampling fractions employed in the selection of cells and villages. For the analysis to be achieved, it is important to calculate the required sample size that will be enough to detect any statistical difference. We estimate that this sample has 90% power and alpha level of 5%, to detect an odds ratio (OR) of at least 1.6, assuming an alpha level of 5%, prevalence of <4 times antenatal care (ANC) of 55% (Rurangirwa et al., 2017), a design effect of 3.2 (based on the average of 32 children per cluster and expected relative difference of about 10%) and a total sample of about 664 households is required for the study, and we consider this sufficient statistical power to examine differences in <4 times ANC that would be of public health significance. The questionnaires that were used in the survey included household information, which was used to collect information on household members (usual residents), and women's questionnaire administered to mothers or caretakers for all children under 5 years. The women or caretaker's questionnaire included the women or caretaker's demographic characteristics: antenatal, delivery, and post‐natal care, breastfeeding, and child nutrition. The questionnaires were installed on tablets using the Open Data Kit. Open Data Kit is a suite tool that allows data collection using mobile devices and data submission to an online server. World Vision office in Kigali provided the tablets that were used in the data collection exercise. Data were posted daily after fieldwork, and this enabled daily review of work done to check for inconsistencies and errors. In the child nutrition questionnaire, measurements of height were obtained for children under the age of five in all of the selected households. Each enumerator carried a scale and measuring board. Measurements were made using lightweight SECA scales (with digital screens) designed and manufactured under the authority of the UNICEF. The measuring boards employed were specially made by Shorr Productions for use in survey settings. Children under the age of 2 were measured lying down on the board (recumbent length), and standing height was measured for all other children. The primary outcome variables were stunting and severe stunting. The outcome variables were expressed as a dichotomous variable, that is, Category 0 (not stunted [greater than −2 standard deviations {SDs} of the WHO Child Growth Standards median] or not severely stunted [greater than −3 SD]) and Category 1 (stunted [less than −2 SD] or severely stunted [less than −3 SD]). The household food security tool consists of seven questions, which are aimed at extracting information required for defining the household's food security status. The responses are “rarely,” “something,” or “often” or “rarely” in the past 12 months. The HFI factor was calculated by summing all the seven HFI (access) frequency questions with scores ranging from 0 to 21. The households were also categorised into four groups, such as food secure (0), mildly food insecure (1–2), moderately food insecure (3–10), and severely food insecure (more than 10; Swindale & Bilinsky, 2007). Our choice of potential confounding factors was based on similar studies that examined the relationship between stunting and severe stunting by food security status in developing countries (Ali et al., 2013; Ali Naser et al., 2014; Singh, Singh, & Ram, 2014). These potential confounders were classified into four distinct groups: socio‐economic and demographic (sectors, primary caregiver, education level, marital status, and household wealth index); child (sex of baby and child's age in months); maternal and child health (ANC, duration of breastfeeding, and attended child monthly growth monitoring sessions); and health services and environmental factors (quality of care from health services, place of delivery, water available all year, sources of drinking water, and type of toilet facility). The household wealth index variable measures basic household needs for all children 5–18 years. The household wealth index was constructed by assigning weights to three basic household needs for children 5–18 years (i.e., difficulty providing at least two sets of clothes for all children aged 5–18 years living in the household, difficulty providing a pair of shoes for all children aged 5–18 years living in the household, and difficulty paying school fees or school contribution for all children aged 5–18 years living in the household) using the principle components analysis. The household wealth index was divided into three categories (poorest, middle and least poor; Filmer & Pritchett, 2001), and improved and unimproved sources of drinking water and type of toilet facility were categorised based on the WHO and UNICEF Joint Monitoring Programme guidelines (WHO/UNICEF, 2014). Data analysis was performed using the survey (SVY) commands of Stata version 14.1 (Stata Corp, College Station, TX, USA), which adjust for sampling weights and cluster sampling design and the calculation of standard errors. Preliminary analyses involved percentage and frequency count of all selected characteristics; this was followed by estimation of prevalence of stunting and severe stunting by HFI among children aged 6–59 months. The Taylor series linearization method was used in the surveys when estimating 95% confidence intervals (CIs) around prevalence estimates. Survey logistic regression that adjusted for cluster and survey weights was used to determine the association between HFI and stunting and severe stunting among Rwandan children aged 6–59 months. First, univariate binary logistic regression analysis was performed to examine the unadjusted OR. A staged modelling technique was employed for the multiple logistic regression analyses. In the first stage, the socio‐economic and demographic factors were entered into the baseline multiple logistic regression model to examine their association with the study outcome. After that, a manual elimination process was performed, and variables that were associated with the study outcomes were retained in the model. Second, child factors were added into significant model retained in the first stage. In the third and fourth stages, maternal and child's health factors and health services and environmental factors were added to the significant variable retained in the second stage. As before, those factors with p values <0.05 were retained. In the final stage of the analysis, the main study factor (HFI) was added to the significant variables obtained from the third and fourth stages, and variables with a p value <0.05 were retained in the final. The ORs and their 95% CIs obtained from the adjusted multiple logistics model were used to determine the association of HFI fuels on stunting and severe stunting.

Izinto zokwakha

N/A

Emisha

Based on the information provided, here are some potential innovations that could improve access to maternal health in the context of Gicumbi district, Rwanda:

1. Mobile Health (mHealth) Solutions: Implementing mobile health technologies, such as text messaging or mobile apps, 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) Training and Support: Strengthening the capacity of community health workers through training programs and ongoing support to ensure they can effectively provide maternal health services, including antenatal care, postnatal care, and nutrition counseling.

3. Maternal Health Vouchers: Introducing a voucher system that provides pregnant women with access to essential maternal health services, including antenatal care visits, skilled birth attendance, and postnatal care, regardless of their ability to pay.

4. Transportation Support: Addressing transportation barriers by providing pregnant women with transportation vouchers or arranging for affordable transportation options to ensure they can access maternal health services, especially in remote areas.

5. Maternity Waiting Homes: Establishing maternity waiting homes near health facilities to accommodate pregnant women who live far away and need to stay closer to the facility in the weeks leading up to their expected delivery date. This can help ensure timely access to skilled birth attendance and emergency obstetric care.

6. Integration of Maternal Health Services: Integrating maternal health services with other existing healthcare programs, such as immunization services or family planning, to provide comprehensive care and improve overall health outcomes for women and their children.

7. Empowering Women and Promoting Gender Equality: Implementing programs that empower women, promote gender equality, and address social determinants of health, such as poverty and education, which can have a significant impact on maternal health outcomes.

These innovations can help improve access to maternal health services, reduce maternal and child mortality rates, and contribute to overall improvements in the health and well-being of women and children in Gicumbi district, Rwanda.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health and address the issue of stunting and severe stunting among children in Gicumbi district is as follows:

1. Implement interventions to improve household food security: Given the significant association between household food insecurity (HFI) and stunting/severe stunting, it is crucial to address this issue. Interventions should focus on improving access to nutritious food for households in Gicumbi district. This can be achieved through various approaches such as promoting sustainable agriculture, providing agricultural training and resources, and supporting income-generating activities for families.

2. Strengthen maternal and child health services: Enhancing access to maternal and child health services is essential to address the factors contributing to stunting and severe stunting. This includes promoting antenatal care (ANC) attendance, encouraging breastfeeding practices, and ensuring regular growth monitoring sessions for children. Efforts should be made to increase awareness and utilization of these services among pregnant women and caregivers.

3. Target vulnerable populations: The study highlights that male children and children who did not attend monthly growth monitoring sessions are at higher risk of severe stunting. To address this, targeted interventions should be implemented to reach these vulnerable populations. This may involve community outreach programs, health education campaigns, and incentives to encourage participation in growth monitoring sessions.

4. Collaborate with local leaders and community health workers: Engaging local leaders and community health workers is crucial for the success of maternal health programs. Their involvement can help mobilize communities, raise awareness, and ensure the effective implementation of interventions. Collaboration with these stakeholders should be prioritized to create a sustainable and community-driven approach to improving maternal health and reducing stunting.

5. Monitor and evaluate the impact of interventions: It is important to establish a monitoring and evaluation system to assess the effectiveness of the implemented interventions. Regular data collection and analysis will help track progress, identify areas for improvement, and ensure that the interventions are achieving the desired outcomes. This will enable evidence-based decision-making and facilitate continuous improvement of maternal health programs in Gicumbi district.

By implementing these recommendations, it is expected that access to maternal health will be improved, leading to a reduction in stunting and severe stunting among children in Gicumbi district.

AI Innovations Methodology

To improve access to maternal health in Gicumbi district, Rwanda, the following innovations could be considered:

1. Mobile Health Clinics: Implementing mobile health clinics that travel to remote areas of Gicumbi district can provide essential maternal health services, including prenatal care, vaccinations, and postnatal care. These clinics can reach women who may have limited access to healthcare facilities due to geographical barriers.

2. Telemedicine: Introducing telemedicine services can enable pregnant women in Gicumbi district to consult with healthcare professionals remotely. This innovation can provide access to medical advice, monitoring, and support, especially for women in remote areas who may have difficulty traveling to healthcare facilities.

3. Community Health Workers: Expanding the role of community health workers can improve access to maternal health services. These trained individuals can provide education, support, and basic healthcare services to pregnant women and new mothers in their communities, bridging the gap between healthcare facilities and remote areas.

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

1. Define Key Metrics: Identify key metrics that measure access to maternal health, such as the number of prenatal visits, percentage of women receiving postnatal care, and maternal mortality rate. These metrics will serve as indicators of improvement.

2. Baseline Data Collection: Collect baseline data on the identified metrics before implementing the innovations. This data will provide a starting point for comparison and evaluation.

3. Implement Innovations: Roll out the recommended innovations, such as mobile health clinics, telemedicine services, and expanded community health worker programs. Ensure proper training and resources are provided for successful implementation.

4. Data Collection during Implementation: Continuously collect data on the identified metrics during the implementation phase. This data will help track progress and identify any challenges or areas for improvement.

5. Analyze Data: Analyze the collected data to assess the impact of the innovations on improving access to maternal health. Compare the post-implementation data with the baseline data to determine the extent of improvement.

6. Evaluate Results: Evaluate the results of the analysis to determine the effectiveness of the innovations in improving access to maternal health. Identify any gaps or areas that require further attention.

7. Adjust and Refine: Based on the evaluation results, make any necessary adjustments or refinements to the implemented innovations. This iterative process will help optimize the impact on improving access to maternal health.

By following this methodology, stakeholders can gain insights into the effectiveness of the recommended innovations and make informed decisions on scaling up successful interventions to improve access to maternal health in Gicumbi district.

Umbhali

Dima Health

Statistics:

Citations: 13

Identifiers:

DOI: 10.1111/mcn.12767

ISSN: 17408695

Research Areas:

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

Study Countries:

Multi-Countries, Rwanda, Uganda

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Male & Female

Yabelana ngalokhu: