Evidence of Concurrent Stunting and Obesity among Children under 2 Years from Socio-Economically Disadvantaged Backgrounds in the Era of the Integrated Nutrition Programme in South Africa

International Journal of Environmental Research and Public Health, Volume 19, No. 19, Year 2022

Interpretation

In view of persistent stunting and increasing rates of obesity coexisting among children in the era of the Integrated Nutrition Programme, a cross-sectional study was conducted to determined concurrent stunting and obesity (CSO) and related factors using a random sample of child–mother pairs (n = 400) in Mbombela, South Africa. Sociodemographic data was collected using a validated questionnaire, and stunting (≥2SD) and obesity (>3SD) were assessed through respective length-for-age (LAZ) and body mass index (BAZ) z-scores. Using SPSS 26.0, the mean age of children was 8 (4; 11) months, and poor sociodemographic status was observed, in terms of maternal singlehood (73%), no education or attaining primary education only (21%), being unemployed (79%), living in households with a monthly income below R10,000 (≈$617), and poor sanitation (84%). The z-test for a single proportion showed a significant difference between the prevalence of CSO (41%) and non-CSO (69%). Testing for the two hypotheses using the Chi-square test showed no significant difference of CSO between boys (40%) and girls (41%), while CSO was significantly different and high among children aged 6–11 months (55%), compared to those aged 0–5 months (35%) and ≥12 months (30%). Further analysis using hierarchical logistic regression showed significant associations of CSO with employment (AOR = 0.34; 95%CI: 0.14–0.78), maternal education status (AOR = 0.39; 95%CI: 0.14–1.09) and water access (AOR = 2.47; 95%CI: 1.32; 4.63). Evidence-based and multilevel intervention programs aiming to prevent CSO and addressing stunting, while improving weight status in children with social disadvantages, are necessary. A cross-sectional study was conducted among children aged under two years attending CHCs with their mothers in at Mbombela, in Mpumalanga Province, South Africa. This paper is part of a larger study, which determined the maternal feeding practices and nutritional knowledge, and the nutritional status of infants attending CHCs of Mbombela. The larger study was conceptualized using combinations of the UNICEF conceptual framework for malnutrition of children (immediate and underlying causes of child malnutrition) [51], WHO conceptual framework on Childhood Stunting (context on the community and societal factors, household and family factors-related causes, and consequences with an emphasis on complementary feeding) [52], and the Bronfenbrenner’s social ecological model for child growth and development (different contexts in which human development takes place, especially the influence of the primary caregiver in the early stages of a child’s life) [53,54]. The current paper reports on the prevalence of CSO and related factors using child–mother pairs. The study was conducted from May 2021 to November 2021. Mbombela is one of the four local municipalities in the Ehlanzeni District, situated in Mpumalanga Province of South Africa. The local municipality is situated in the north-eastern part of South Africa within the low veld sub region of the Mpumalanga Province and is the capital city of the province, which includes urban and rural population [55]. Mbombela municipality has two district hospitals and 31 primary healthcare facilities, of which six of them are where the study was conducted. The six primary health care facilities selected were those found in the deep rural areas of Mbombela, as reported by Drigo et al. [56]. The study population was children under two years attending childcare services with their mothers at the selected CHCs. The study excluded mothers who were not mentally fit to be interviewed, and those who were below 18 years and could not obtain consent from their parents/guardians to participate in the study. We also excluded children who were above 2 years, and whose biological mothers were not available to participate. The Raosoft sample size calculator [57] was used to calculate a sample size, considering an estimated population of approximately 3000 children attending CHCs for childcare services [58]. Within the selected six CHCs, a systematic random sampling was used to select children and their mothers. Every 3rd mother was recruited while on the queue waiting to consult, given a slip, and asked to come to the data collection site in the facility, after having been attended to, for further activities. Initially, 426 mothers with their children were recruited, and data were collected from 405 child–mother pairs. However, a final sample size of 400 was obtained, following exclusion of five questionnaires, which had over 10% of missing data, mostly including the dependent variable (i.e., LAZ and/or BAZ). The response rate of the study was 95%. Maternal sociodemographic data (i.e., age, marital status, education, employment, as well as household information) obstetric history (i.e., parity and obstetric complications), and child (i.e., birth) information (i.e., gender, birth weight, birth order, and delivery details) were collected using a researcher-administered, and structured questionnaire adapted from the literature [8,9,13,59] and validated through construct, content, and face validity, as well as translation and a pilot study. The questionnaire was first prepared in English and translated into the local language; SiSwati [60]. Independent translators who speak SiSwati as their mother tongue and are conversant with English did forward and backward translations of the questionnaire. An expert committee approved the final version of the translated questionnaire. The research assistants who speak SiSwati were trained on conducting the interviews in a local language before a pilot study commenced. During the pilot study, a questionnaire was pre-tested, and the research assistants were assessed while administering a questionnaire to participants in SiSwati and measuring anthropometry. Internal consistency (reliability) of a questionnaire was measured using Cronbach’s Alpha and yielded a reliability coefficient of 0.82. The feasibility of the study was tested among 30 child–mother pairs where their results did not form part of the main study. After pretesting the questionnaire, we considered minimal clarity of wording, and further simplified the layout and style of the questionnaire. Trained research assistants measured the anthropometry of children according to WHO procedures [61]. Weight was measured using a Seca 354 baby electronic scale distributed by medicare hospital equipment in South Africa, manufactured in Germany. Recumbent length (L) was measured using a Seca 210 measuring mat distributed by medicare hospital equipment in South Africa, manufactured in Germany. Results were recorded in the Section A of the questionnaire, and anthropometric data was captured on the WHO Anthro software v3.22 and analyzed according to WHO Z-scores classification for length-for-age (LAZ) and BMI-for-age (BAZ) [62]. The WHO defines stunting by LAZ/HAZ < −2 SD, overweight by BAZ > 2 SD, and obesity by BAZ > 3 SD [61]. SPSS version 26.0 (IBM SPSS Statistics, Armonk, NY, USA) was used to compute descriptive and inferential statistics. A complete case analysis was used to identify participants with missing data. Questionnaires with more than 10% of missing/incomplete data, including missing information for the dependent variables, were excluded from the study (i.e., five questionnaires). Descriptive statistics (frequency, percentages, and cross tabulation) for the children’s age, and anthropometric measurements and indices were computed [i.e., medians (Interquartile range (IQR)], after data distribution was checked with a Shapiro–Wilk test. Mann–Whitney U test was used to compare medians (IQR) between two groups by sex categories, while Kruskal–Wallis test was used to compare the medians (IQR) for LAZ and BAZ by age categories. The z-test for a single proportion was applied to determine the significant difference between the prevalence of CSO and non-CSO within a population. Chi square test was used to test for the two hypotheses by comparing the percentages of children with stunting, overweight/obesity, and CSO by sex and age categories. The Pearson correlation coefficient (r) was used to determine a linear correlation between stunting, and overweight, obesity, and combined overweight/obesity. Hierarchical logistic regression analysis was used to determine the association between the CSO and the covariates. Variables that had a p-value ≤ 0.2 were used in multivariate logistic regression. A stepwise backward elimination procedure was employed controlling for confounding. Adjusted odds ratios (AOR) with a 95% confidence interval (CI) were generated and used to determine the independent strength of the associations, and significance was considered at p < 0.05.

AI Digest

Study Justification:

The study aimed to investigate the prevalence of concurrent stunting and obesity (CSO) among children under 2 years from socio-economically disadvantaged backgrounds in Mbombela, South Africa. This is important because CSO is a significant public health concern, and understanding its prevalence and related factors can inform interventions to address both stunting and obesity in this vulnerable population.

Highlights:

– The study found a significant prevalence of CSO among children under 2 years from socio-economically disadvantaged backgrounds in Mbombela, South Africa.
– Factors associated with CSO included maternal employment, education status, and water access.
– The study highlights the need for evidence-based and multilevel intervention programs to prevent CSO and improve weight status in children with social disadvantages.

Recommendations:

– Implement intervention programs that address both stunting and obesity in children under 2 years from socio-economically disadvantaged backgrounds.
– Focus on improving maternal employment opportunities, education, and access to clean water as potential strategies to reduce CSO.
– Collaborate with relevant stakeholders, including healthcare providers, policymakers, and community organizations, to develop and implement comprehensive interventions.

Key Role Players:

– Healthcare providers: They play a crucial role in implementing intervention programs and providing healthcare services to children and their mothers.
– Policymakers: They can create policies and allocate resources to support interventions targeting CSO and improve socio-economic conditions.
– Community organizations: They can contribute to community engagement, awareness, and support for interventions.
– Researchers: They can continue to conduct studies to monitor the prevalence of CSO and evaluate the effectiveness of interventions.

Cost Items:

– Program development and implementation: This includes costs associated with designing and implementing intervention programs, such as staff salaries, training, materials, and monitoring and evaluation.
– Healthcare services: Costs related to providing healthcare services to children and their mothers, including consultations, screenings, and treatments.
– Education and training: Costs associated with providing education and training to healthcare providers, policymakers, and community organizations involved in the interventions.
– Infrastructure and resources: Costs for improving infrastructure and resources, such as healthcare facilities, water supply systems, and educational materials.
– Research and evaluation: Costs for conducting research and evaluation studies to assess the impact and effectiveness of interventions.
Please note that the provided cost items are general categories and may vary depending on the specific context and requirements of the interventions.

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 with a sample size of 400 child-mother pairs. The study collected sociodemographic data using a validated questionnaire and assessed stunting and obesity through length-for-age and body mass index z-scores. The prevalence of concurrent stunting and obesity (CSO) was found to be 41%. The study also identified significant associations of CSO with employment, maternal education status, and water access. The evidence is relatively strong as it provides specific findings and statistical analysis. However, to improve the strength of the evidence, future studies could consider a larger sample size and longitudinal design to establish causality and generalizability.

Abstract

A cross-sectional study was conducted among children aged under two years attending CHCs with their mothers in at Mbombela, in Mpumalanga Province, South Africa. This paper is part of a larger study, which determined the maternal feeding practices and nutritional knowledge, and the nutritional status of infants attending CHCs of Mbombela. The larger study was conceptualized using combinations of the UNICEF conceptual framework for malnutrition of children (immediate and underlying causes of child malnutrition) [51], WHO conceptual framework on Childhood Stunting (context on the community and societal factors, household and family factors-related causes, and consequences with an emphasis on complementary feeding) [52], and the Bronfenbrenner’s social ecological model for child growth and development (different contexts in which human development takes place, especially the influence of the primary caregiver in the early stages of a child’s life) [53,54]. The current paper reports on the prevalence of CSO and related factors using child–mother pairs. The study was conducted from May 2021 to November 2021. Mbombela is one of the four local municipalities in the Ehlanzeni District, situated in Mpumalanga Province of South Africa. The local municipality is situated in the north-eastern part of South Africa within the low veld sub region of the Mpumalanga Province and is the capital city of the province, which includes urban and rural population [55]. Mbombela municipality has two district hospitals and 31 primary healthcare facilities, of which six of them are where the study was conducted. The six primary health care facilities selected were those found in the deep rural areas of Mbombela, as reported by Drigo et al. [56]. The study population was children under two years attending childcare services with their mothers at the selected CHCs. The study excluded mothers who were not mentally fit to be interviewed, and those who were below 18 years and could not obtain consent from their parents/guardians to participate in the study. We also excluded children who were above 2 years, and whose biological mothers were not available to participate. The Raosoft sample size calculator [57] was used to calculate a sample size, considering an estimated population of approximately 3000 children attending CHCs for childcare services [58]. Within the selected six CHCs, a systematic random sampling was used to select children and their mothers. Every 3rd mother was recruited while on the queue waiting to consult, given a slip, and asked to come to the data collection site in the facility, after having been attended to, for further activities. Initially, 426 mothers with their children were recruited, and data were collected from 405 child–mother pairs. However, a final sample size of 400 was obtained, following exclusion of five questionnaires, which had over 10% of missing data, mostly including the dependent variable (i.e., LAZ and/or BAZ). The response rate of the study was 95%. Maternal sociodemographic data (i.e., age, marital status, education, employment, as well as household information) obstetric history (i.e., parity and obstetric complications), and child (i.e., birth) information (i.e., gender, birth weight, birth order, and delivery details) were collected using a researcher-administered, and structured questionnaire adapted from the literature [8,9,13,59] and validated through construct, content, and face validity, as well as translation and a pilot study. The questionnaire was first prepared in English and translated into the local language; SiSwati [60]. Independent translators who speak SiSwati as their mother tongue and are conversant with English did forward and backward translations of the questionnaire. An expert committee approved the final version of the translated questionnaire. The research assistants who speak SiSwati were trained on conducting the interviews in a local language before a pilot study commenced. During the pilot study, a questionnaire was pre-tested, and the research assistants were assessed while administering a questionnaire to participants in SiSwati and measuring anthropometry. Internal consistency (reliability) of a questionnaire was measured using Cronbach’s Alpha and yielded a reliability coefficient of 0.82. The feasibility of the study was tested among 30 child–mother pairs where their results did not form part of the main study. After pretesting the questionnaire, we considered minimal clarity of wording, and further simplified the layout and style of the questionnaire. Trained research assistants measured the anthropometry of children according to WHO procedures [61]. Weight was measured using a Seca 354 baby electronic scale distributed by medicare hospital equipment in South Africa, manufactured in Germany. Recumbent length (L) was measured using a Seca 210 measuring mat distributed by medicare hospital equipment in South Africa, manufactured in Germany. Results were recorded in the Section A of the questionnaire, and anthropometric data was captured on the WHO Anthro software v3.22 and analyzed according to WHO Z-scores classification for length-for-age (LAZ) and BMI-for-age (BAZ) [62]. The WHO defines stunting by LAZ/HAZ 2 SD, and obesity by BAZ > 3 SD [61]. SPSS version 26.0 (IBM SPSS Statistics, Armonk, NY, USA) was used to compute descriptive and inferential statistics. A complete case analysis was used to identify participants with missing data. Questionnaires with more than 10% of missing/incomplete data, including missing information for the dependent variables, were excluded from the study (i.e., five questionnaires). Descriptive statistics (frequency, percentages, and cross tabulation) for the children’s age, and anthropometric measurements and indices were computed [i.e., medians (Interquartile range (IQR)], after data distribution was checked with a Shapiro–Wilk test. Mann–Whitney U test was used to compare medians (IQR) between two groups by sex categories, while Kruskal–Wallis test was used to compare the medians (IQR) for LAZ and BAZ by age categories. The z-test for a single proportion was applied to determine the significant difference between the prevalence of CSO and non-CSO within a population. Chi square test was used to test for the two hypotheses by comparing the percentages of children with stunting, overweight/obesity, and CSO by sex and age categories. The Pearson correlation coefficient (r) was used to determine a linear correlation between stunting, and overweight, obesity, and combined overweight/obesity. Hierarchical logistic regression analysis was used to determine the association between the CSO and the covariates. Variables that had a p-value ≤ 0.2 were used in multivariate logistic regression. A stepwise backward elimination procedure was employed controlling for confounding. Adjusted odds ratios (AOR) with a 95% confidence interval (CI) were generated and used to determine the independent strength of the associations, and significance was considered at p < 0.05.

Materials

N/A

Innovations

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women and new mothers with access to important health information, appointment reminders, and personalized care plans. These apps can also facilitate communication with healthcare providers and offer telemedicine services for remote consultations.

2. Telemedicine Services: Establish telemedicine services that allow pregnant women and new mothers to consult with healthcare professionals remotely. This can help overcome geographical barriers and improve access to prenatal and postnatal care, especially for women in rural or underserved areas.

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

4. Maternal Health Vouchers: Implement voucher programs that provide financial assistance to pregnant women and new mothers, enabling them to access essential maternal health services. These vouchers can cover costs such as prenatal check-ups, delivery services, and postnatal care, making healthcare more affordable and accessible.

5. Maternal Health Clinics: Establish dedicated maternal health clinics that offer comprehensive services, including prenatal care, childbirth support, and postnatal care. These clinics can provide a one-stop solution for women’s healthcare needs, ensuring continuity of care throughout the pregnancy and postpartum period.

6. Public Awareness Campaigns: Launch public awareness campaigns to educate communities about the importance of maternal health and the available services. These campaigns can address cultural and social barriers, promote early and regular prenatal care, and encourage women to seek appropriate healthcare during pregnancy and after childbirth.

7. Maternal Health Hotlines: Set up toll-free hotlines staffed by trained healthcare professionals who can provide information, guidance, and support to pregnant women and new mothers. These hotlines can be available 24/7, offering a convenient and accessible resource for women seeking advice or assistance.

8. Maternal Health Education Programs: Develop and implement educational programs that focus on maternal health, covering topics such as nutrition, breastfeeding, safe childbirth practices, and postpartum care. These programs can be delivered through community workshops, online platforms, or mobile applications.

9. Partnerships with Non-Governmental Organizations (NGOs): Collaborate with NGOs that specialize in maternal health to leverage their expertise, resources, and networks. These partnerships can help expand access to maternal health services, improve the quality of care, and reach marginalized or vulnerable populations.

10. Integration of Maternal Health Services: Integrate maternal health services with other healthcare programs, such as family planning, immunization, and HIV/AIDS prevention and treatment. This integration can ensure comprehensive care for women and their families, addressing multiple health needs simultaneously.

It’s important to note that the specific implementation of these innovations would require careful planning, stakeholder engagement, and consideration of local contexts and resources.

AI Innovations Description

The recommendation to improve access to maternal health based on the provided description is to implement evidence-based and multilevel intervention programs. These programs should aim to prevent concurrent stunting and obesity (CSO) among children under two years from socio-economically disadvantaged backgrounds in South Africa. The intervention programs should address stunting, improve weight status, and target children with social disadvantages.

Specifically, the programs should focus on the following areas:

1. Employment: Provide support and resources to improve employment opportunities for mothers. This can include job training, vocational programs, and access to job placement services. By improving employment opportunities, mothers will have better access to financial resources, which can positively impact their ability to provide for their children’s health and well-being.

2. Maternal education: Implement initiatives to improve maternal education levels. This can include adult education programs, literacy classes, and scholarships for further education. By increasing maternal education, mothers will have a better understanding of nutrition and health practices, leading to improved maternal and child health outcomes.

3. Water access: Improve access to clean and safe water for households. This can be achieved through infrastructure development, such as the installation of water supply systems and the promotion of proper water storage and hygiene practices. Access to clean water is essential for maintaining good hygiene and preventing waterborne diseases, which can negatively impact maternal and child health.

These intervention programs should be implemented at multiple levels, including individual, community, and societal levels. They should involve collaboration between healthcare providers, community organizations, government agencies, and other stakeholders to ensure comprehensive and sustainable improvements in access to maternal health.

It is important to note that these recommendations are based on the specific context and findings of the study conducted in Mbombela, South Africa. When implementing similar interventions in other settings, it is crucial to consider the local context, cultural factors, and available resources to ensure the effectiveness and relevance of the programs.

AI Innovations Methodology

To improve access to maternal health, here are some potential recommendations:

1. Mobile Clinics: Implementing mobile clinics that can reach remote areas and provide essential maternal health services such as prenatal care, vaccinations, and health education.

2. Telemedicine: Utilizing telemedicine technologies to connect pregnant women with healthcare professionals remotely, allowing them to receive consultations, advice, and monitoring without the need for physical visits.

3. Community Health Workers: Training and deploying community health workers who can provide basic maternal health services, education, and support in underserved areas.

4. Transportation Support: Establishing transportation support systems to ensure pregnant women have access to healthcare facilities, especially in rural areas where transportation infrastructure may be limited.

5. Health Education Programs: Developing and implementing health education programs that focus on maternal health, including prenatal care, nutrition, breastfeeding, and postnatal care, to empower women with knowledge and promote healthy practices.

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 women receiving skilled birth attendance, or maternal mortality rate.

2. Collect baseline data: Gather data on the current status of the indicators in the target population or area.

3. Define the intervention scenarios: Develop different scenarios based on the recommendations mentioned above, considering factors such as the number of mobile clinics, coverage of telemedicine services, number of community health workers, availability of transportation support, and reach of health education programs.

4. Simulate the impact: Use modeling techniques, such as mathematical models or simulation software, to estimate the potential impact of each scenario on the selected indicators. This could involve projecting changes in the indicators based on the assumed coverage and effectiveness of the interventions.

5. Analyze and compare results: Analyze the simulated results for each scenario and compare them to the baseline data. Assess the potential improvements in access to maternal health services and identify the most effective interventions.

6. Refine and optimize: Based on the analysis, refine the interventions and their parameters to optimize the impact on improving access to maternal health. This could involve adjusting the coverage, distribution, or implementation strategies of the interventions.

7. Monitor and evaluate: Implement the recommended interventions and continuously monitor and evaluate their impact on the selected indicators. This will help assess the effectiveness of the interventions in real-world settings and guide further improvements.

By following this methodology, policymakers and healthcare providers can make informed decisions on implementing innovations to improve access to maternal health and ensure better outcomes for mothers and their children.

Author

Dima Health

Statistics:

Citations: 1

Identifiers:

DOI: 10.3390/ijerph191912501

ISSN: 16617827

Research Areas:

Community Interventions, 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, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Male & Female