Childhood undernutrition and its predictors in a rural health and demographic surveillance system site in South Africa

International Journal of Environmental Research and Public Health, Volume 16, No. 17, Year 2019

Interpretation

Background: Overweight and obesity are increasing at an alarming rate in South Africa, while childhood undernutrition remains persistently high. This study determined the magnitude and predictors of stunting and underweight among schoolchildren in the Dikgale and Health Demographic Surveillance System Site, a rural site in South Africa. Methods: A cross sectional study using multistage sampling was conducted among 508 schoolchildren and their mothers. Anthropometric measurements were taken from children and their mothers, while sociodemographic information was obtained from mothers using a questionnaire. The World Health Organization Anthro Plus was used to generate height-for-age and weight-for-age z-scores to indicate stunting and underweight, respectively, among the children. Maternal overweight and obesity were assessed using body mass index. Bivariate and multivariate logistic regression analyses were used to evaluate the predictors of stunting and underweight among schoolchildren. Results: Twenty-two percent (22%) of children were stunted and 27% were underweight, while 27.4% of the mothers were overweight and 42.3% were obese. The odds of being stunted were lower in younger children, whereas having a mother who was overweight/obese and had a short stature increased the odds of stunting. Access to water, having a refrigerator, and having a young mother were protective against being underweight. Having a mother who was overweight/obese increased the odds of being underweight. Conclusions: The study showed a high prevalence of stunting and underweight among children, and overweight and obesity among mothers, indicating a household double burden of malnutrition. The age of the child and maternal overweight/obesity and short stature were predictors of stunting and underweight, while having a younger mother and access to water and a refrigerator were protective against being underweight. The need for an evidence-based and feasible nutrition program for schoolchildren, especially those in rural schools, cannot be over-emphasized. This paper is an extraction from a doctoral dissertation written by the first author, which determined the growth patterns of primary school children and the maternal factors influencing these growth patterns. The doctoral dissertation also explored the influence of the cultural beliefs and practices of mothers on the growth of children in the Dikgale Health and Demographic Surveillance System Site. The study used a convergent mixed method design with parallel phases of quantitative and qualitative enquiry. A cross-sectional quantitative survey was used to determine the growth patterns of schoolchildren using nutritional indicators for stunting, underweight, thinness, and overweight/obesity. In addition, data on the anthropometry, socio-demographics, obstetric history, knowledge of nutrition and child growth, the influence of societal cultural beliefs and practices on child nutrition and food security were collected from the mothers. In the qualitative phase of the enquiry, focus group discussions were conducted to explore the influence of socio-cultural beliefs and practices of the mothers on their children’s growth and nutrition. The study was conducted from August 2017 to December 2017. This paper reports on the prevalence and predictors of stunting and underweight among schoolchildren in the research population in a rural context. The study was conducted in the Dikgale Health and Demographic Surveillance System Site (DHDSSS). The DHDSSS is a well-researched rural site that was founded in 1995 and forms part of the International Network for the Demographic Evaluation of Populations and their Health (INDEPTH). INDEPTH is an umbrella organization for a group of independent health research centers operating 43 Health and Demographic Surveillance Sites in 20 LMICs [35]. The DHDSSS is situated approximately 40 km northeast of Polokwane, the capital city of the Limpopo Province, in South Africa. The area comprises of communities clustered in 16 villages with a population of approximately 36,000 with poor infrastructure. Electricity and mobile phone networks are found everywhere, while the supply of piped water is more problematic [36]. A poor socio-economic status, characterized by high unemployment and poverty, has been reported in this area [37,38]. There are 19 public primary schools in the villages forming part of the DHDSSS, with an estimated total enrolment number (EN) of learners of 7772 in 2016, ranging from an enrollment number of 112 children in the smallest school to 776 in the largest school [39]. The primary schools in this area belong to quintile three (Q3), which in South Africa are declared as no-fee schools, and therefore do not charge school fees. These schools receive the majority of their funding from the government [40]. In addition, primary schools belonging to this site have a feeding program to provide learners with meals during school hours. Although the DHDSSS is a well-researched site, there is a paucity of data on the nutritional status of children in this area. This was a child–mother paired study. The study population comprised of primary school learners and their mothers. The sample size was calculated using Rao software [41]. The software takes into consideration the population size, a 5% margin of error, a 95% confidence level, and a 30% non-response rate. A total of 508 child–mother pairs were taken as the sample size. A multistage sampling technique was used. First, the schools were stratified by the size of enrollment and five of the largest schools were selected. Second, in each selected school, one class per grade was randomly selected. Third, all learners in the selected class were included. The study excluded children who were younger than five years, had physical disabilities that compromised their stature, or whose biological mothers were not available to participate. A structured interviewer-administered questionnaire, translated from English to a local language (Sepedi), was used to collect data. The questionnaire took into consideration the determinants of nutritional status [42] and covered a range of topics on socio-demographics and the household situation of mothers, in accordance with the variables used in other studies conducted in the study area [37,43]. The questionnaire was pre-tested in a pilot study and four trained research assistants were employed to collect the data. The anthropometry (weights and heights) of the children and their mothers was recorded using a well-calibrated, smart D-quip electronic scale and a height measuring board, respectively. Height was measured to the nearest 0.1 cm and weight to the nearest 0.1 kg. All measurements were taken three times, and the average recorded. A non-stretchable plastic tape was used to measure the waist and hip circumferences of the mothers, which were recorded to the nearest 0.1 cm. All measurements were done according to WHO recommendations [44,45]. For the children, anthropometric measurements were converted to height-for-age z scores (HAZ) and weight-for-age z scores (WAZ) and compared to reference data for 5–19 year olds. The children were classified as stunted if the HAZ was less than or equal to −2SD or underweight if the WAZ was less than or equal to −2SD. The Anthro-plus software was unable to generate weight-for-age (WAZ) values for 189 children because the indicator excludes children aged 11 years and above. Thus, a sample of 319 was analyzed for WAZ for children 10 years old and younger. According to the software, WAZ reference data are not available beyond 10 years of age because this indicator does not distinguish between height and body mass in the age period where many children experience pubertal growth spurts and may appear to have excess weight (by weight-for-age) when in fact they are just tall [45]. For the mothers, body mass index (BMI) was calculated as the weight in kilograms divided by the height in meters squared (BMI (kg/m2) = weight (kg)/height (m2)). Normal BMI is within 19 to 24 kg/m2. Underweight is defined as BMI < 18.5 kg/m2, overweight as BMI of 25 to 29.9 kg/m2, and obesity as BMI ≥ 30 kg/m2. The cut-off point for central obesity in females is a waist circumference ≥88 cm [46]. The waist–hip ratio (WHR) was computed as the waist circumference divided by the hip circumference. The WHR cut-off point (i.e., abdominal obesity) for females is ≥0.85 [46]. The data were analyzed using STATA version 14. Descriptive statistics for the age, body weight (W), height (H), and HAZ and WAZ of the children were computed for the mean, the standard deviation (SD), the median, and the interquartile range (IQR). Comparison of the means was done using a Mann–Whitney test, while the percentages of children with variables below, on, or above the cut-off points were compared using a chi-square test. Bivariate and multivariate logistic regression analysis was used to determine the association between the nutritional status indicators of children, their stunting and underweight, and independent variables. Bivariate analyses were used to identify the association between the dependent variables and each of the independent variables. Independent variables that had a p-value of 0.1 were used in the multivariate logistic regression with a stepwise backward elimination procedure controlling for confounding. During multivariate logistic regression analysis, child gender, learning grade, maternal age, WHR, WC, marital status, employment, education, household income, and household size were controlled to determine the association of stunting with covariates. For underweight, child age and gender, learning grade, maternal WHR, WC, height, marital status, employment, education, household income, and household size were controlled. Adjusted odds ratios (AOR) with a 95% confidence interval (CI) were generated and used to determine the independent strength of the associations. Significance was considered at p < 0.05. This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by Sefako Makgatho Health Sciences University Research and Ethics Committee (SMUREC) (SMUREC/H/161/2016: PG). Furthermore, this study received permission from the Department of Education (DoE) in the Limpopo Province, South Africa. The nature of the study was explained to the mothers of the children prior to their participation. Informed consent was obtained from the mothers and verbal assent was obtained from the children.

AI Digest

Study Justification:

– Childhood undernutrition remains persistently high in South Africa, despite the increasing rates of overweight and obesity.
– The study aimed to determine the magnitude and predictors of stunting and underweight among schoolchildren in a rural site in South Africa.
– The study provides valuable insights into the prevalence and predictors of childhood undernutrition, highlighting the need for evidence-based and feasible nutrition programs for schoolchildren, especially in rural areas.

Study Highlights:

– The study found that 22% of children were stunted and 27% were underweight, while 27.4% of mothers were overweight and 42.3% were obese.
– Younger children had lower odds of being stunted, while having an overweight/obese mother with a short stature increased the odds of stunting.
– Access to water, having a refrigerator, and having a young mother were protective against being underweight, while having an overweight/obese mother increased the odds of being underweight.
– The study revealed a household double burden of malnutrition, with high prevalence of stunting and underweight among children, and overweight and obesity among mothers.

Study Recommendations:

– The study recommends the implementation of evidence-based and feasible nutrition programs for schoolchildren, particularly those in rural schools.
– The recommendations emphasize the importance of addressing the predictors of stunting and underweight, such as maternal overweight/obesity and short stature, through targeted interventions.
– The study highlights the protective factors against underweight, such as access to water, having a refrigerator, and having a young mother, which should be considered in nutrition programs.

Key Role Players:

– Researchers and academics in the field of nutrition and public health
– Government agencies responsible for health and education policies
– Non-governmental organizations (NGOs) working on nutrition and child health
– Community leaders and local authorities
– School administrators and teachers

Cost Items for Planning Recommendations:

– Development and implementation of nutrition programs
– Training and capacity building for healthcare professionals and educators
– Monitoring and evaluation of program effectiveness
– Awareness campaigns and community engagement activities
– Research and data collection on nutrition indicators
– Infrastructure improvement, such as access to water and refrigeration facilities in schools
– Collaboration and coordination among stakeholders

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a cross-sectional study using multistage sampling, which provides a moderate level of evidence. The study includes a sample size calculation, data collection methods, and statistical analyses. However, the study design is limited in its ability to establish causality. To improve the strength of the evidence, a longitudinal study design could be considered to assess the temporal relationship between predictors and outcomes. Additionally, including a control group and randomization could help reduce bias. Finally, conducting a systematic review or meta-analysis of similar studies could provide a higher level of evidence.

Abstract

This paper is an extraction from a doctoral dissertation written by the first author, which determined the growth patterns of primary school children and the maternal factors influencing these growth patterns. The doctoral dissertation also explored the influence of the cultural beliefs and practices of mothers on the growth of children in the Dikgale Health and Demographic Surveillance System Site. The study used a convergent mixed method design with parallel phases of quantitative and qualitative enquiry. A cross-sectional quantitative survey was used to determine the growth patterns of schoolchildren using nutritional indicators for stunting, underweight, thinness, and overweight/obesity. In addition, data on the anthropometry, socio-demographics, obstetric history, knowledge of nutrition and child growth, the influence of societal cultural beliefs and practices on child nutrition and food security were collected from the mothers. In the qualitative phase of the enquiry, focus group discussions were conducted to explore the influence of socio-cultural beliefs and practices of the mothers on their children’s growth and nutrition. The study was conducted from August 2017 to December 2017. This paper reports on the prevalence and predictors of stunting and underweight among schoolchildren in the research population in a rural context. The study was conducted in the Dikgale Health and Demographic Surveillance System Site (DHDSSS). The DHDSSS is a well-researched rural site that was founded in 1995 and forms part of the International Network for the Demographic Evaluation of Populations and their Health (INDEPTH). INDEPTH is an umbrella organization for a group of independent health research centers operating 43 Health and Demographic Surveillance Sites in 20 LMICs [35]. The DHDSSS is situated approximately 40 km northeast of Polokwane, the capital city of the Limpopo Province, in South Africa. The area comprises of communities clustered in 16 villages with a population of approximately 36,000 with poor infrastructure. Electricity and mobile phone networks are found everywhere, while the supply of piped water is more problematic [36]. A poor socio-economic status, characterized by high unemployment and poverty, has been reported in this area [37,38]. There are 19 public primary schools in the villages forming part of the DHDSSS, with an estimated total enrolment number (EN) of learners of 7772 in 2016, ranging from an enrollment number of 112 children in the smallest school to 776 in the largest school [39]. The primary schools in this area belong to quintile three (Q3), which in South Africa are declared as no-fee schools, and therefore do not charge school fees. These schools receive the majority of their funding from the government [40]. In addition, primary schools belonging to this site have a feeding program to provide learners with meals during school hours. Although the DHDSSS is a well-researched site, there is a paucity of data on the nutritional status of children in this area. This was a child–mother paired study. The study population comprised of primary school learners and their mothers. The sample size was calculated using Rao software [41]. The software takes into consideration the population size, a 5% margin of error, a 95% confidence level, and a 30% non-response rate. A total of 508 child–mother pairs were taken as the sample size. A multistage sampling technique was used. First, the schools were stratified by the size of enrollment and five of the largest schools were selected. Second, in each selected school, one class per grade was randomly selected. Third, all learners in the selected class were included. The study excluded children who were younger than five years, had physical disabilities that compromised their stature, or whose biological mothers were not available to participate. A structured interviewer-administered questionnaire, translated from English to a local language (Sepedi), was used to collect data. The questionnaire took into consideration the determinants of nutritional status [42] and covered a range of topics on socio-demographics and the household situation of mothers, in accordance with the variables used in other studies conducted in the study area [37,43]. The questionnaire was pre-tested in a pilot study and four trained research assistants were employed to collect the data. The anthropometry (weights and heights) of the children and their mothers was recorded using a well-calibrated, smart D-quip electronic scale and a height measuring board, respectively. Height was measured to the nearest 0.1 cm and weight to the nearest 0.1 kg. All measurements were taken three times, and the average recorded. A non-stretchable plastic tape was used to measure the waist and hip circumferences of the mothers, which were recorded to the nearest 0.1 cm. All measurements were done according to WHO recommendations [44,45]. For the children, anthropometric measurements were converted to height-for-age z scores (HAZ) and weight-for-age z scores (WAZ) and compared to reference data for 5–19 year olds. The children were classified as stunted if the HAZ was less than or equal to −2SD or underweight if the WAZ was less than or equal to −2SD. The Anthro-plus software was unable to generate weight-for-age (WAZ) values for 189 children because the indicator excludes children aged 11 years and above. Thus, a sample of 319 was analyzed for WAZ for children 10 years old and younger. According to the software, WAZ reference data are not available beyond 10 years of age because this indicator does not distinguish between height and body mass in the age period where many children experience pubertal growth spurts and may appear to have excess weight (by weight-for-age) when in fact they are just tall [45]. For the mothers, body mass index (BMI) was calculated as the weight in kilograms divided by the height in meters squared (BMI (kg/m2) = weight (kg)/height (m2)). Normal BMI is within 19 to 24 kg/m2. Underweight is defined as BMI < 18.5 kg/m2, overweight as BMI of 25 to 29.9 kg/m2, and obesity as BMI ≥ 30 kg/m2. The cut-off point for central obesity in females is a waist circumference ≥88 cm [46]. The waist–hip ratio (WHR) was computed as the waist circumference divided by the hip circumference. The WHR cut-off point (i.e., abdominal obesity) for females is ≥0.85 [46]. The data were analyzed using STATA version 14. Descriptive statistics for the age, body weight (W), height (H), and HAZ and WAZ of the children were computed for the mean, the standard deviation (SD), the median, and the interquartile range (IQR). Comparison of the means was done using a Mann–Whitney test, while the percentages of children with variables below, on, or above the cut-off points were compared using a chi-square test. Bivariate and multivariate logistic regression analysis was used to determine the association between the nutritional status indicators of children, their stunting and underweight, and independent variables. Bivariate analyses were used to identify the association between the dependent variables and each of the independent variables. Independent variables that had a p-value of 0.1 were used in the multivariate logistic regression with a stepwise backward elimination procedure controlling for confounding. During multivariate logistic regression analysis, child gender, learning grade, maternal age, WHR, WC, marital status, employment, education, household income, and household size were controlled to determine the association of stunting with covariates. For underweight, child age and gender, learning grade, maternal WHR, WC, height, marital status, employment, education, household income, and household size were controlled. Adjusted odds ratios (AOR) with a 95% confidence interval (CI) were generated and used to determine the independent strength of the associations. Significance was considered at p < 0.05. This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by Sefako Makgatho Health Sciences University Research and Ethics Committee (SMUREC) (SMUREC/H/161/2016: PG). Furthermore, this study received permission from the Department of Education (DoE) in the Limpopo Province, South Africa. The nature of the study was explained to the mothers of the children prior to their participation. Informed consent was obtained from the mothers and verbal assent was obtained from the children.

Materials

N/A

Innovations

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women and new mothers with access to important health information, reminders for prenatal and postnatal care appointments, and personalized advice for a healthy pregnancy and child development.

2. Telemedicine Services: Implement telemedicine services that allow pregnant women in rural areas to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide access to prenatal check-ups, consultations, and guidance without the need for travel.

3. Community Health Workers: Train and deploy community health workers in rural areas to provide education, support, and basic healthcare services to pregnant women and new mothers. These workers can conduct home visits, provide antenatal and postnatal care, and facilitate referrals to healthcare facilities when necessary.

4. Maternal Health Vouchers: Introduce a voucher system that provides pregnant women with financial assistance to access essential maternal health services, such as prenatal care, delivery, and postnatal care. This can help reduce financial barriers and increase utilization of healthcare services.

5. Maternal Health Clinics: Establish dedicated maternal health clinics in rural areas, equipped with skilled healthcare professionals and necessary facilities for prenatal and postnatal care. These clinics can provide comprehensive care, including regular check-ups, vaccinations, and counseling services.

6. Health Education Programs: Develop and implement health education programs that focus on maternal nutrition, breastfeeding, hygiene practices, and early childhood development. These programs can be delivered through community workshops, radio broadcasts, and mobile messaging platforms to reach a wider audience.

7. Transportation Support: Provide transportation support, such as subsidized or free transportation services, to pregnant women in rural areas to ensure they can access healthcare facilities for prenatal check-ups, delivery, and postnatal care.

8. Maternal Health Hotline: Establish a toll-free hotline staffed by trained healthcare professionals who can provide information, guidance, and support to pregnant women and new mothers. This can help address their concerns, provide advice, and connect them to appropriate healthcare services.

9. Maternal Health Awareness Campaigns: Conduct targeted awareness campaigns to educate communities about the importance of maternal health, dispel myths and misconceptions, and encourage early and regular utilization of healthcare services during pregnancy and after childbirth.

10. Partnerships with Non-Governmental Organizations (NGOs): Collaborate with NGOs working in maternal health to leverage their expertise, resources, and networks to improve access to maternal healthcare services in rural areas. This can include capacity building, training programs, and community outreach initiatives.

AI Innovations Description

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

1. Implement an evidence-based and feasible nutrition program for schoolchildren, especially those in rural schools, to address the high prevalence of stunting and underweight.

This program should focus on providing nutritious meals and educating children and their mothers about proper nutrition and healthy eating habits. It should also consider the cultural beliefs and practices of mothers that may influence their children’s growth and nutrition.

To improve access to maternal health, the program can include the following components:

– Mobile clinics: Set up mobile clinics that visit rural areas regularly to provide maternal health services, including prenatal care, postnatal care, and family planning. These clinics can be equipped with medical professionals, necessary equipment, and supplies to provide comprehensive care.

– Telemedicine: Utilize telemedicine technology to connect rural communities with healthcare providers. This can include virtual consultations, remote monitoring of maternal health indicators, and access to educational resources.

– Community health workers: Train and deploy community health workers in rural areas to provide basic maternal health services, health education, and referrals to higher-level healthcare facilities when needed. These community health workers can act as a bridge between the community and formal healthcare system.

– Health education and awareness: Conduct health education campaigns to raise awareness about the importance of maternal health and the available services. This can include workshops, community meetings, and distribution of educational materials in local languages.

– Infrastructure improvement: Invest in improving infrastructure in rural areas, such as roads, transportation, and healthcare facilities, to ensure better access to maternal health services.

– Collaboration and partnerships: Foster collaboration and partnerships between government agencies, non-governmental organizations, healthcare providers, and community leaders to collectively address the challenges and improve access to maternal health in rural areas.

By implementing these recommendations, it is possible to improve access to maternal health in rural areas and reduce the prevalence of stunting and underweight among schoolchildren.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and personnel in rural areas can improve access to maternal health services. This includes ensuring the availability of skilled healthcare providers, essential medicines, and necessary equipment for safe deliveries.

2. Mobile health clinics: Implementing mobile health clinics can bring maternal health services closer to remote and underserved communities. These clinics can provide prenatal care, antenatal check-ups, and postnatal care, making it easier for pregnant women to access essential healthcare services.

3. Telemedicine and telehealth services: Utilizing technology to provide remote consultations and monitoring can improve access to maternal health services, especially in areas with limited healthcare facilities. Telemedicine allows pregnant women to consult with healthcare providers, receive advice, and monitor their health remotely.

4. Community health workers: Training and deploying community health workers can help bridge the gap between healthcare facilities and communities. These workers can provide education, support, and basic healthcare services to pregnant women, ensuring they receive the necessary care throughout their pregnancy.

5. Health education and awareness programs: Implementing health education programs that focus on maternal health can empower women with knowledge about pregnancy, childbirth, and postnatal care. These programs can also raise awareness about the importance of seeking timely and appropriate healthcare services.

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 pregnant women receiving prenatal care, the percentage of deliveries attended by skilled birth attendants, or the distance to the nearest healthcare facility.

2. Collect baseline data: Gather data on the current status of the selected indicators in the target population. This can be done through surveys, interviews, or existing data sources.

3. Introduce the recommendations: Simulate the implementation of the recommended interventions by adjusting the relevant indicators based on the expected impact. For example, increase the number of pregnant women receiving prenatal care or the percentage of deliveries attended by skilled birth attendants.

4. Analyze the impact: Compare the baseline data with the simulated data to assess the impact of the recommendations on improving access to maternal health. Calculate the changes in the selected indicators and evaluate the extent to which the recommendations have achieved the desired outcomes.

5. Consider external factors: Take into account external factors that may influence the impact of the recommendations, such as socio-economic conditions, cultural beliefs, or policy changes. Adjust the simulation accordingly to reflect these factors.

6. Refine and iterate: Based on the analysis, refine the recommendations and simulation methodology as needed. Iterate the process to further improve access to maternal health and monitor the progress over time.

It is important to note that the specific methodology for simulating the impact may vary depending on the available data, resources, and context of the study.

Author


Dima Health

Statistics:

Citations: 18

Identifiers:

DOI: 10.3390/ijerph16173021

ISSN: 16617827

Research Areas:

Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Social Determinants

Study Countries:

South Africa

Study Design:

Cross Sectional Study

Study Approach:

Mixed-methods, Qualitative, Quantitative

Participants Gender:

Female