Effects of early feeding on growth velocity and overweight/obesity in a cohort of HIV unexposed South African infants and children

International Breastfeeding Journal, Volume 10, No. 1, Year 2015

Interpretation

Background: South Africa has the highest prevalence of overweight/obesity in Sub-Saharan Africa. Assessing the effect of modifiable factors such as early infant feeding on growth velocity and overweight/obesity is therefore important. This paper aimed to assess the effect of infant feeding in the transitional period (12 weeks) on 12-24 week growth velocity amongst HIV unexposed children using WHO growth velocity standards and on the age and sex adjusted body mass index (BMI) Z-score distribution at 2 years. Methods: Data were from 3 sites in South Africa participating in the PROMISE-EBF trial. We calculated growth velocity Z-scores using the WHO growth standards and assessed feeding practices using 24-hour and 7-day recall data. We used quantile regression to study the associations between 12 week infant feeding and 12-24 week weight velocity (WVZ) with BMI-for-age Z-score at 2 years. We included the internal sample quantiles (70th and 90th centiles) that approximated the reference cut-offs of +2 (corresponding to overweight) and +3 (corresponding to obesity) of the 2 year BMI-for-age Z-scores. Results: At the 2-year visit, 641 children were analysed (median age 22 months, IQR: 17-26 months). Thirty percent were overweight while 8.7% were obese. Children not breastfed at 12 weeks had higher 12-24 week mean WVZ and were more overweight and obese at 2 years. In the quantile regression, children not breastfed at 12 weeks had a 0.37 (95% CI 0.07, 0.66) increment in BMI-for-age Z-score at the 50th sample quantile compared to breast-fed children. This difference in BMI-for-age Z-score increased to 0.46 (95% CI 0.18, 0.74) at the 70th quantile and 0.68 (95% CI 0.41, 0.94) at the 90th quantile. The 12-24 week WVZ had a uniform independent effect across the same quantiles. Conclusions: This study demonstrates that the first 6 months of life is a critical period in the development of childhood overweight and obesity. Interventions targeted at modifiable factors such as early infant feeding practices may reduce the risks of rapid weight gain and subsequent childhood overweight/obesity. The present paper includes data from the three South African sites of the PROMISE-EBF behavioural-intervention trial that sought to improve EBF rates through peer counselling, conducted between 2006 and January 2008: Paarl (mixed peri-urban/rural area), Rietvlei (rural area) and Umlazi (peri-urban formal township). Trial methods have been described in detail elsewhere [28,29]. Briefly, pregnant women in their last trimester of pregnancy were screened for inclusion into the study. A total of 964 HIV negative and 184 HIV positive women and their singleton children were enrolled at the 3 week postnatal visit and followed up at 6, 12 and 24 weeks . Six hundred and fifty four HIV unexposed children (67.8% of original cohort) followed up again between March and September 2008 at a median age of 22 months (IQR: 9–34 months), which we refer to as the 2 year visit, were considered for this analysis due to the described negative effect of HIV infection on growth [27]. We compared baseline characteristics of participants that were followed-up at 2 years with those that were not (see Additional file 1) and observed no systematic differences, besides the proportion of male children, between the groups. This suggests that the sample that was followed-up is generally representative of the children in the whole cohort. A further 13 children were excluded because of extreme and implausible anthropometric values leading to a final sample of 641 children. Standardised questionnaires were used to collect interview data during pregnancy and postnatally at 3, 6, 12 and 24 weeks, and at the primary endpoint of 2 years of age. Maternal variables included: age, parity and education which were captured during recruitment; delivery mode and reported HIV status collected at the 3 week visit. The questionnaires also addressed infant feeding practices through 24-hour and 7-day recall of a list of 23 foods commonly consumed in the study sites. No food diaries were used. Data on child birth weight were extracted from perinatal records. Field staff measured child weight and recumbent length/height during the 3, 6, 12, and 24 weeks visits and at 2 years. Children were weighed to the nearest 0.1 kg on Masskot (SOS Series) electronic pan scales, which were calibrated weekly using a 2 kg weight, wearing minimum clothing and no shoes. Depending on the study site, recumbent length measurements were obtained to the nearest 0.1 cm using TALC roller meters (Oxford, UK) or Shorr Height-Length Measuring Board (Maryland, USA) while height was measured using a validated ustom-made stadiometer. All field workers were trained on anthropometric techniques. In order to improve validity and reduce inter and intra-observer bias, the anthropometry data collection was validated periodically. Child age was calculated using the date of birth from the Road to Health card and the date of the interview. Data were double-entered into a Microsoft Access database and analysed using Stata SE 12 [30] and IBM SPSS Statistics 21 [31]. The primary outcome measure was BMI-for-age Z-scores at 2 years; secondary outcomes were weight velocity Z-scores (WVZ) and length velocity Z-scores (LVZ). We calculated BMI-for-age Z-scores at the 12 week and 2 year visits, standardised for sex and actual age at the respective visit, using the WHO growth standards [32]. We considered children as “overweight” and “obese” if their BMI-for-age Z-scores were above +2 and +3 respectively as recommended by the World Health Organisation [33]. A macro based on the WHO-2009 growth velocity standards was used to compute the WVZ and LVZ. Velocities were calculated for a first period, namely from 3 or 6 to 12 weeks post-delivery, and for a second period, namely 12 to 24 weeks post-delivery. In cases where the 3 or 6 week weight was missing we used the birth weight for the calculation of velocity in the first period. The age intervals and child ages observed in the study did not always correspond exactly with those of the velocity standards. Thus the velocity Z-scores were calculated, as recommended by WHO, by identifying the best-fitting age interval for each child period observed and linearly extrapolating the observed increment in the child period to the duration of the best-fitting target interval [26]. Anthropometric measurement values and Z-scores were flagged for verification if any of the following criteria were met: a) decrease in length of more than 2 cm between two consecutive visits; b) WAZ <-6 or >5, WLZ <-5 or >5, LAZ <-6 or >6, WLZ >3 and LAZ <-3; c) extreme changes in LAZ between visits defined as LAZ at 3 weeks < 2 and LAZ at 24 weeks > 2.5, or LAZ at 24 weeks < 2 and LAZ at 36 weeks > 2.5; d) changes > 4 or <-4 Z-scores between 24 and 36 weeks and BMI-for-age Z-score ≥6. All the flagged anthropometric observations were assessed and values treated as missing if no plausible explanation was determined. We used a combination of 24-hour and 7-day infant feeding recall data at each follow-up visit to generate time specific food consumption indicator variables (for breast milk, water, sugar water, formula, cereals, fruits/vegetables, traditional herbs, prescribed and non-prescribed medicines) with 3 categories: yes, no and missing. For example if the caregiver said that she gave the child breast milk in the previous 24-hours or 7-days then we coded that child as having received breast milk. If the caregiver said “no” to both questions on breast milk, the child was then considered as one that did not receive breast milk. The response was coded as “missing” for breast milk if data were missing for both questions. Cross-tabulation of the 12 week breast milk and formula indicators revealed that all children had consumed at least one of the two foods. Based on exploratory analysis we combined two of the three combinations of these feeding indicators and this resulted in a binary ‘ever breastfed” variable with the following categories: yes (received breast milk with other solids and liquids which may include formula) and no (received formula and other liquids and solids except breast milk). The 12 week breastfeeding cessation variable was defined as no breastfeeding at the 12 week interview (based on 24-hour and 7-day recall) and no breastfeeding reported for the subsequent final 24-week interview. Only children who initiated breastfeeding by the 3 week visit were considered in this definition. Unlike the ordinary least squares (OLS) regression which only considers the conditional mean function, we used quantile regression which is a statistical technique that provides a more detailed analysis of the relationship between the dependent variable and its independent variables because it provides conditional regression coefficients for each quantile, [34,35]. We used univariate and multivariate simultaneous quantile regression to test whether 12 week infant feeding and 12–24 week growth velocity (adjusting for other variables) had increased effects over the upper tails of the conditional distribution of BMI-for-age Z-scores at 2 years. For this analysis we included the internal sample quantiles (70th and 90th centiles) that approximate the reference cut-offs of +2 and +3 Z-scores for BMI-for-age around 2 years. We also performed OLS regression modelling. The following variables were adjusted for in the multivariate models because of their epidemiologic or clinical importance: birth weight, maternal age, parity, maternal education, study arm and site. Although the child’s age and sex were taken into account in the BMI-for-age Z-score estimations, based on previous literature [36] we included an interaction term between the infant feeding and sex variables in initial regression models to test whether sex modifies the relationship between feeding and BMI-for-age Z-score. This interaction term was excluded from the final models as no effect measure modification was detected. Maternal age and parity were excluded from the final multivariate model because they were not significantly associated with 2-year BMI in the univariate analysis. The Breusch-Pagan / Cook-Weisberg test was used to check for heteroskedasticity and trends across the quantile regression percentiles were also tested. Continuous data are presented as mean ± SD or median (IQR) while categorical variables are presented as frequencies. We used the Student t-test to compare means and the Pearson chi-square test to examine associations in the cross-tabulations. Statistical tests were two-sided and performed at the 5% significance level. Kernel density functions were used to estimate the 2 year BMI-for-age Z-score distribution stratified by the 12 week overweight and breastfeeding while the two-sample Kolmogorov-Smirnov test was used to test for equality of the distribution functions. The PROMISE-EBF trial was approved by the Regional Committees for Medical and Health Research Ethics (REK VEST) in Norway (issue number 05/8197), University of the Western Cape (research registration number 0607/8) and the South African Medical Research Council (protocol ID: ECO7-001). Informed consent was obtained from all participants.

AI Digest

Study Justification:

– South Africa has the highest prevalence of overweight/obesity in Sub-Saharan Africa.
– Assessing the effect of modifiable factors such as early infant feeding on growth velocity and overweight/obesity is important.
– This study aimed to assess the effect of infant feeding in the transitional period on growth velocity and BMI Z-score distribution in South African infants and children.

Study Highlights:

– Data from the PROMISE-EBF trial in South Africa were analyzed.
– 641 children were included in the analysis at the 2-year visit.
– Children not breastfed at 12 weeks had higher 12-24 week mean weight velocity and were more overweight and obese at 2 years.
– Quantile regression analysis showed that children not breastfed at 12 weeks had higher BMI-for-age Z-scores at the 50th, 70th, and 90th quantiles.
– The study demonstrates that the first 6 months of life is a critical period in the development of childhood overweight and obesity.
– Interventions targeting early infant feeding practices may reduce the risks of rapid weight gain and subsequent childhood overweight/obesity.

Recommendations for Lay Reader:

– Breastfeeding in the first 6 months of life is important for preventing childhood overweight and obesity.
– Parents should be encouraged to breastfeed their infants for at least 12 weeks.
– Early infant feeding practices can have a long-term impact on a child’s weight and health.

Recommendations for Policy Maker:

– Implement interventions to promote and support breastfeeding in the first 6 months of life.
– Provide education and resources for parents on the benefits of breastfeeding and proper infant feeding practices.
– Develop policies and programs to address the high prevalence of overweight/obesity in South African children.

Key Role Players:

– Health professionals (doctors, nurses, lactation consultants) to provide education and support for breastfeeding.
– Community health workers to promote breastfeeding and provide resources to parents.
– Government agencies and policymakers to develop and implement breastfeeding promotion programs.

Cost Items for Planning Recommendations:

– Education and training materials for health professionals and community health workers.
– Public awareness campaigns to promote breastfeeding.
– Support services for breastfeeding mothers (such as lactation consultants).
– Monitoring and evaluation of breastfeeding promotion programs.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on data from a large cohort study and uses quantile regression to analyze the associations between infant feeding and growth velocity. However, to improve the evidence, the abstract could provide more details on the study design, such as the sample size and characteristics of the participants, as well as the statistical methods used. Additionally, including information on potential confounding factors and limitations of the study would further strengthen the evidence.

Abstract

The present paper includes data from the three South African sites of the PROMISE-EBF behavioural-intervention trial that sought to improve EBF rates through peer counselling, conducted between 2006 and January 2008: Paarl (mixed peri-urban/rural area), Rietvlei (rural area) and Umlazi (peri-urban formal township). Trial methods have been described in detail elsewhere [28,29]. Briefly, pregnant women in their last trimester of pregnancy were screened for inclusion into the study. A total of 964 HIV negative and 184 HIV positive women and their singleton children were enrolled at the 3 week postnatal visit and followed up at 6, 12 and 24 weeks . Six hundred and fifty four HIV unexposed children (67.8% of original cohort) followed up again between March and September 2008 at a median age of 22 months (IQR: 9–34 months), which we refer to as the 2 year visit, were considered for this analysis due to the described negative effect of HIV infection on growth [27]. We compared baseline characteristics of participants that were followed-up at 2 years with those that were not (see Additional file 1) and observed no systematic differences, besides the proportion of male children, between the groups. This suggests that the sample that was followed-up is generally representative of the children in the whole cohort. A further 13 children were excluded because of extreme and implausible anthropometric values leading to a final sample of 641 children. Standardised questionnaires were used to collect interview data during pregnancy and postnatally at 3, 6, 12 and 24 weeks, and at the primary endpoint of 2 years of age. Maternal variables included: age, parity and education which were captured during recruitment; delivery mode and reported HIV status collected at the 3 week visit. The questionnaires also addressed infant feeding practices through 24-hour and 7-day recall of a list of 23 foods commonly consumed in the study sites. No food diaries were used. Data on child birth weight were extracted from perinatal records. Field staff measured child weight and recumbent length/height during the 3, 6, 12, and 24 weeks visits and at 2 years. Children were weighed to the nearest 0.1 kg on Masskot (SOS Series) electronic pan scales, which were calibrated weekly using a 2 kg weight, wearing minimum clothing and no shoes. Depending on the study site, recumbent length measurements were obtained to the nearest 0.1 cm using TALC roller meters (Oxford, UK) or Shorr Height-Length Measuring Board (Maryland, USA) while height was measured using a validated ustom-made stadiometer. All field workers were trained on anthropometric techniques. In order to improve validity and reduce inter and intra-observer bias, the anthropometry data collection was validated periodically. Child age was calculated using the date of birth from the Road to Health card and the date of the interview. Data were double-entered into a Microsoft Access database and analysed using Stata SE 12 [30] and IBM SPSS Statistics 21 [31]. The primary outcome measure was BMI-for-age Z-scores at 2 years; secondary outcomes were weight velocity Z-scores (WVZ) and length velocity Z-scores (LVZ). We calculated BMI-for-age Z-scores at the 12 week and 2 year visits, standardised for sex and actual age at the respective visit, using the WHO growth standards [32]. We considered children as “overweight” and “obese” if their BMI-for-age Z-scores were above +2 and +3 respectively as recommended by the World Health Organisation [33]. A macro based on the WHO-2009 growth velocity standards was used to compute the WVZ and LVZ. Velocities were calculated for a first period, namely from 3 or 6 to 12 weeks post-delivery, and for a second period, namely 12 to 24 weeks post-delivery. In cases where the 3 or 6 week weight was missing we used the birth weight for the calculation of velocity in the first period. The age intervals and child ages observed in the study did not always correspond exactly with those of the velocity standards. Thus the velocity Z-scores were calculated, as recommended by WHO, by identifying the best-fitting age interval for each child period observed and linearly extrapolating the observed increment in the child period to the duration of the best-fitting target interval [26]. Anthropometric measurement values and Z-scores were flagged for verification if any of the following criteria were met: a) decrease in length of more than 2 cm between two consecutive visits; b) WAZ 5, WLZ 5, LAZ 6, WLZ >3 and LAZ <-3; c) extreme changes in LAZ between visits defined as LAZ at 3 weeks 2.5, or LAZ at 24 weeks 2.5; d) changes > 4 or <-4 Z-scores between 24 and 36 weeks and BMI-for-age Z-score ≥6. All the flagged anthropometric observations were assessed and values treated as missing if no plausible explanation was determined. We used a combination of 24-hour and 7-day infant feeding recall data at each follow-up visit to generate time specific food consumption indicator variables (for breast milk, water, sugar water, formula, cereals, fruits/vegetables, traditional herbs, prescribed and non-prescribed medicines) with 3 categories: yes, no and missing. For example if the caregiver said that she gave the child breast milk in the previous 24-hours or 7-days then we coded that child as having received breast milk. If the caregiver said “no” to both questions on breast milk, the child was then considered as one that did not receive breast milk. The response was coded as “missing” for breast milk if data were missing for both questions. Cross-tabulation of the 12 week breast milk and formula indicators revealed that all children had consumed at least one of the two foods. Based on exploratory analysis we combined two of the three combinations of these feeding indicators and this resulted in a binary ‘ever breastfed” variable with the following categories: yes (received breast milk with other solids and liquids which may include formula) and no (received formula and other liquids and solids except breast milk). The 12 week breastfeeding cessation variable was defined as no breastfeeding at the 12 week interview (based on 24-hour and 7-day recall) and no breastfeeding reported for the subsequent final 24-week interview. Only children who initiated breastfeeding by the 3 week visit were considered in this definition. Unlike the ordinary least squares (OLS) regression which only considers the conditional mean function, we used quantile regression which is a statistical technique that provides a more detailed analysis of the relationship between the dependent variable and its independent variables because it provides conditional regression coefficients for each quantile, [34,35]. We used univariate and multivariate simultaneous quantile regression to test whether 12 week infant feeding and 12–24 week growth velocity (adjusting for other variables) had increased effects over the upper tails of the conditional distribution of BMI-for-age Z-scores at 2 years. For this analysis we included the internal sample quantiles (70th and 90th centiles) that approximate the reference cut-offs of +2 and +3 Z-scores for BMI-for-age around 2 years. We also performed OLS regression modelling. The following variables were adjusted for in the multivariate models because of their epidemiologic or clinical importance: birth weight, maternal age, parity, maternal education, study arm and site. Although the child’s age and sex were taken into account in the BMI-for-age Z-score estimations, based on previous literature [36] we included an interaction term between the infant feeding and sex variables in initial regression models to test whether sex modifies the relationship between feeding and BMI-for-age Z-score. This interaction term was excluded from the final models as no effect measure modification was detected. Maternal age and parity were excluded from the final multivariate model because they were not significantly associated with 2-year BMI in the univariate analysis. The Breusch-Pagan / Cook-Weisberg test was used to check for heteroskedasticity and trends across the quantile regression percentiles were also tested. Continuous data are presented as mean ± SD or median (IQR) while categorical variables are presented as frequencies. We used the Student t-test to compare means and the Pearson chi-square test to examine associations in the cross-tabulations. Statistical tests were two-sided and performed at the 5% significance level. Kernel density functions were used to estimate the 2 year BMI-for-age Z-score distribution stratified by the 12 week overweight and breastfeeding while the two-sample Kolmogorov-Smirnov test was used to test for equality of the distribution functions. The PROMISE-EBF trial was approved by the Regional Committees for Medical and Health Research Ethics (REK VEST) in Norway (issue number 05/8197), University of the Western Cape (research registration number 0607/8) and the South African Medical Research Council (protocol ID: ECO7-001). Informed consent was obtained from all participants.

Materials

Innovations

Based on the provided information, 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 information and guidance on infant feeding practices, growth monitoring, and healthy lifestyle choices. These apps can also send reminders for important appointments and provide access to telemedicine consultations.

2. Community Health Workers: Train and deploy community health workers to provide education and support to pregnant women and new mothers in their communities. These workers can conduct home visits, provide counseling on infant feeding practices, and refer women to appropriate healthcare services.

3. Peer Support Programs: Establish peer support programs where experienced mothers can provide guidance and support to pregnant women and new mothers. These programs can be facilitated through community groups, online forums, or mobile applications.

4. Telemedicine Services: Implement telemedicine services to enable remote consultations between healthcare providers and pregnant women or new mothers. This can help overcome geographical barriers and improve access to specialized care.

5. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of early infant feeding and its impact on growth velocity and overweight/obesity. These campaigns can include public service announcements, community workshops, and educational materials.

6. Integration of Services: Improve coordination and integration of maternal health services with other healthcare services, such as HIV testing and treatment, to ensure comprehensive care for pregnant women and new mothers.

7. Quality Improvement Initiatives: Implement quality improvement initiatives in healthcare facilities to ensure that maternal health services are provided in a safe and effective manner. This can involve training healthcare providers, improving infrastructure and equipment, and implementing standardized protocols and guidelines.

8. Data Monitoring and Evaluation: Establish robust data monitoring and evaluation systems to track the impact of interventions and identify areas for improvement. This can help inform evidence-based decision making and ensure accountability in the delivery of maternal health services.

AI Innovations Description

The recommendation to improve access to maternal health based on the described study is to implement interventions targeted at improving early infant feeding practices. The study found that children who were not breastfed at 12 weeks had higher growth velocity and were more likely to be overweight or obese at 2 years of age. Therefore, promoting and supporting breastfeeding during the first 6 months of life may reduce the risks of rapid weight gain and subsequent childhood overweight/obesity. These interventions can include providing education and counseling to mothers on the benefits of breastfeeding, establishing breastfeeding support groups, and ensuring access to lactation consultants or breastfeeding experts. Additionally, healthcare providers can play a crucial role in promoting and supporting breastfeeding by providing accurate information, addressing concerns, and offering guidance on proper breastfeeding techniques.

AI Innovations Methodology

Based on the provided description, the study aims to assess the effect of infant feeding in the transitional period on growth velocity and overweight/obesity in HIV unexposed South African infants and children. The methodology used in the study includes data collection from three sites in South Africa participating in the PROMISE-EBF trial. The trial involved pregnant women in their last trimester of pregnancy who were screened for inclusion. The enrolled women and their singleton children were followed up at 6, 12, and 24 weeks. The primary outcome measure was BMI-for-age Z-scores at 2 years, and secondary outcomes were weight velocity Z-scores and length velocity Z-scores. The study used quantile regression to analyze the associations between infant feeding and growth velocity with BMI-for-age Z-scores at 2 years. The analysis included internal sample quantiles that approximated the reference cut-offs for overweight and obesity. The study also adjusted for other variables such as birth weight, maternal age, parity, maternal education, study arm, and site. Statistical tests were performed to examine associations and trends across the quantile regression percentiles. Informed consent was obtained from all participants, and the trial was approved by the relevant ethics committees.

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

1. Identify the recommendations: Based on the study findings and other relevant research, identify specific recommendations that can improve access to maternal health. For example, the study may recommend promoting and supporting early breastfeeding as a strategy to reduce the risks of rapid weight gain and subsequent childhood overweight/obesity.

2. Define the simulation model: Develop a simulation model that represents the current state of access to maternal health and the potential impact of the recommendations. The model should include relevant variables such as healthcare facilities, healthcare providers, transportation, financial resources, and community factors.

3. Collect data: Gather data on the current state of access to maternal health, including factors such as the number of healthcare facilities, availability of healthcare providers, transportation infrastructure, and financial resources allocated to maternal health.

4. Incorporate the recommendations: Modify the simulation model to incorporate the recommended interventions. For example, increase the availability of breastfeeding support programs, improve transportation infrastructure to enhance access to healthcare facilities, and allocate additional funding for maternal health services.

5. Run simulations: Run multiple simulations using the modified model to simulate the impact of the recommendations on improving access to maternal health. Vary the parameters and assumptions to explore different scenarios and assess the potential outcomes.

6. Analyze results: Analyze the simulation results to evaluate the impact of the recommendations on improving access to maternal health. Assess key indicators such as the number of women accessing maternal health services, the reduction in maternal mortality rates, and improvements in health outcomes for mothers and infants.

7. Refine and iterate: Based on the simulation results, refine the recommendations and the simulation model if necessary. Repeat the simulation process to further explore the potential impact of the refined recommendations.

By following this methodology, policymakers and healthcare professionals can gain insights into the potential impact of specific recommendations on improving access to maternal health. This information can inform decision-making and resource allocation to prioritize interventions that have the greatest potential for positive impact.

Author

Dima Health

Statistics:

Citations: 10

Identifiers:

DOI: 10.1186/s13006-015-0041-x

Research Areas:

Community Interventions, Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health

Study Countries:

South Africa

Study Design:

Cohort Study, Cross Sectional Study, Exploratory Study, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Male & Female