Stunting in infancy is associated with decreased risk of high body mass index for age at 8 and 12 years of age

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Study Justification:
This study aimed to investigate the effects of early-life stunting on the development of adiposity (body fat) in later childhood. The researchers wanted to understand the relationship between stunting in infancy and the risk of high body mass index (BMI) for age at 8 and 12 years old. This study is important because the effects of early-life stunting on later adiposity development are not well understood, particularly in relation to the onset of overweight and obesity.
Highlights:
– The study analyzed data from 1942 Peruvian children in the Young Lives cohort study at ages 1, 5, 8, and 12 years.
– Stunting at age 1 year was associated with a lower prevalence of high BMI-for-age at age 8 and 12 years.
– Stunting was not associated with an increased risk of developing high BMI-for-age.
– Stunting at age 5 years was positively associated with the risk of reversion from high BMI-for-age.
– The association of stunting with high BMI-for-age was stronger for urban children at ages 5 and 8 years, and for nonindigenous children at age 8 years.
Recommendations:
Based on the findings of this study, the following recommendations can be made:
1. Further research should be conducted to understand the underlying mechanisms through which stunting in infancy affects adiposity development later in life.
2. Interventions should be implemented to address stunting in infancy, with a focus on improving nutrition and overall child health.
3. Public health programs should prioritize efforts to prevent and reduce childhood obesity, taking into account the potential protective effect of stunting in infancy.
Key Role Players:
To address the recommendations, the following key role players are needed:
1. Researchers and scientists to conduct further studies and investigate the mechanisms behind the association between stunting and adiposity development.
2. Health professionals and policymakers to develop and implement interventions targeting stunting in infancy and childhood obesity prevention.
3. Community leaders and organizations to raise awareness about the importance of nutrition and child health, and to support initiatives aimed at improving child growth and development.
Cost Items:
While the actual cost of implementing the recommendations cannot be estimated without a detailed budget analysis, the following cost items should be considered in planning:
1. Research funding for further studies and investigations.
2. Resources for implementing interventions, such as nutritional supplements, healthcare services, and educational materials.
3. Training and capacity building for health professionals and community leaders involved in the implementation of interventions.
4. Monitoring and evaluation costs to assess the effectiveness of interventions and track progress towards reducing stunting and childhood obesity.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides results from a cohort study with a large sample size and includes statistical analysis. However, the rating is not higher because the abstract does not provide information on the study design, potential limitations, or implications of the findings. To improve the evidence, the abstract could include a brief description of the study design (e.g., prospective cohort study), mention any limitations or potential biases, and provide a sentence on the implications of the findings for future research or public health interventions.

Background: Effects of early-life stunting on adiposity development later in childhood are not well understood, specifically with respect to age in the onset of overweight and obesity. Objectives: We analyzed associations of infant stunting with prevalence of, incidence of, and reversion from high body mass index-for-age z score (BMIZ) later in life. We then estimated whether associations of infant stunting with BMIZ varied by sex, indigenous status, and rural or urban residence. Methods: Data were collected from 1942 Peruvian children in the Young Lives cohort study at ages 1, 5, 8, and 12 y. Multivariable generalized linear models estimated associations of stunting (height-for-age z score 1 and BMIZ > 2 prevalence, incidence (moving above a BMIZ threshold between ages), and reversion (moving below a BMIZ threshold between ages) at later ages. Results: After adjustment for covariates, stunting at age 1 y was associated with a lower prevalence of BMIZ > 1 at age 8 y (RR: 0.81; 95% CI: 0.66, 1.00; P = 0.049) and 12 y (RR: 0.75; 95% CI: 0.61, 0.91;P=0.004), aswell as a lower prevalence of BMIZ > 2 at age 8 y. Stunting was not associated with incident risk of BMIZ > 1 or BMIZ > 2. Stunting was positively associated at age 5 y with risk of reversion from BMIZ > 1 (RR: 1.22; 95% CI: 1.05, 1.42; P = 0.008) and BMIZ > 2. We found evidence that the association of stunting with prevalent and incident BMIZ > 1 was stronger for urban children at ages 5 and 8 y, and for nonindigenous children at age 8 y. Conclusions: Stunting predicted a lower risk of prevalent BMIZ > 1 and BMIZ > 2, even after controlling for potential confounders. This finding may be driven in part by a higher risk of reversion from BMIZ > 1 by age 5 y. Our results contribute to an understanding of how nutritional stunting in infancy is associated with BMIZ later in life.

We analyzed data from Peruvian children in the prospective Young Lives cohort study (23). In 2002, 2052 children aged ∼6–18 mo were recruited (round 1). Follow-up data were collected in 2006 when children were ∼5 y old (round 2), in 2009 when children were ∼8 y old (round 3), and in 2013 when children were ∼12 y old (round 4). To simplify reference to each of these rounds of data collection, we will refer to them as ages 1, 5, 8, and 12 y. Participants were selected through a multistage sampling process. Ten random draws of 20 sentinel sites were conducted from among the 1818 districts in Peru. Consistent with the study’s pro-poor focus, the wealthiest 5% of districts were excluded. From these random draws, one set of 20 sites was selected that best met the study aims of diverse coverage and logistical feasibility. Within selected districts, an initial community was randomly selected as the starting point for recruitment of age-eligible children. Full details of participant recruitment are available elsewhere (24). Weight and length at age 1 y were measured by 6 supervisors who used calibrated digital balances (Soehnle) with 100-g precision and locally made rigid stadiometers with 1-mm precision. At later ages, measurements were taken by all field staff with the use of similar digital platform balances (with 100-g precision), and standing height was measured with the use of locally made instruments accurate to 1 mm. The staff followed standard WHO procedures for measurement of weight, length, and height. To ensure inter- and intrarater reliability, standard measurement procedures were described in the training manual, and repeat measurements were conducted to ensure accuracy (25). HAZ and BMIZ were calculated according to age-appropriate WHO references (2, 4). Our predictor of interest was stunting at age 1 y, defined as HAZ 1 and BMIZ > 2. The WHO defines overweight and obesity differently for children <5 y of age and those 5–19 y. For children 2 and obesity is defined as BMIZ > 3 (2), whereas for children aged 5–19 y, overweight is defined as BMIZ > 1 and obesity is BMIZ > 2 (4). If we adhered to these definitions, children could be considered to develop overweight or obesity without any change in BMIZ. Therefore, for all ages, we consistently defined overweight as BMIZ > 1 and obesity as BMIZ > 2. To maintain clarity, we refer to the exact cutoffs used, rather than the terms overweight and obesity, when referring to the results from this analysis. If a child was above a given BMIZ threshold (i.e., BMIZ > 1 or BMIZ > 2) for the ith round, they were defined as a prevalent case for that threshold in the ith round. If a child was above the threshold at the ith round but below the threshold in the ith − 1 round, then we defined that child as an incident case for that threshold at the ith round. If a child was below the threshold at the ith round but was above the threshold in the ith − 1 round, then we defined that child as reverted from that threshold at the ith round. These transitions are illustrated graphically for the analyzed sample in Figure 1. Transitions across BMIZ > 1 threshold in Peruvian children in the Young Lives cohort at ages 1, 5, 8, and 12 y (n = 1755). Incidence refers to a transition from BMIZ ≤ 1 at a given age to a BMIZ > 1 at the next age. Reversion refers to a transition from BMIZ > 1 at a given age to a BMIZ ≤ 1 at the next age. BMIZ, body mass index–for-age z score. Covariates were selected for the model on the basis of the causal pathway structure supported by the literature, as well as the data available from the Young Lives study. The statistical significance of a covariate was not a criterion for inclusion in the model, although all covariates were significantly associated with stunting status at age 1 y (Table 1). We adjusted for covariates at the child, mother, and household level. At the child level, we adjusted for sex. Child age was not included because it was already adjusted through the BMIZ measure and the adjustment to HAZ in round 1. There was no association between age and BMIZ in any later round. We did not adjust for breastfeeding status because nearly all children (97.7%) had been breastfed for ≥6 mo. We also did not adjust for birth weight because we were interested in stunting at age 1 y as an indicator of chronic malnutrition. Characteristics of stunted and nonstunted Peruvian children at age 1 y in the Young Lives cohort study1 Maternal covariates included height and BMI in round 1. Maternal BMI was categorized into 3 mutually exclusive categories: normal [BMI (in kg/m2) <25], overweight (BMI ≥25 and <30) and obese (BMI ≥30). There were too few underweight women (BMI <18.5; 1.6%) to include in a separate category, so they were included in the normal BMI category. Mothers whose first language was not Spanish (defined by the language the grandmother spoke to the mother) were classified as indigenous. We also included a binary indicator of whether the mother had completed primary education (≥6 grades of schooling). Household characteristics included indicators of whether households had ≥6 members or were in rural areas, and geographic regions (coastal, jungle, or mountain). Household wealth was measured with the use of the Young Lives wealth index, which is the mean of 3 composite scores for housing quality, consumer durables, and service access. A detailed description of the wealth index is published elsewhere (24). Wealth was split into nominal quintile indicators for the statistical analysis. Of the 2052 children initially recruited, 23 were excluded because their ages at recruitment were outside the target range of 6–17 mo. Twenty children were excluded because of documented deaths after baseline, 45 children because of missing HAZ or BMIZ at age 1 y, 11 children because of improbable anthropometric z scores (HAZ 3 or BMIZ 5) (27) during any round, and 11 children because of missing covariate data at age 1 y. This resulted in a sample of 1942 children with complete data at baseline. An additional 187 children were missing BMIZ data at age 5, 8, or 12 y, resulting in 1755 cases with complete follow-up data for analysis. Details on baseline characteristics of subjects with and without missing follow-up BMIZ data are found in Supplemental Table 1. We observed that missingness was associated with some observed covariates, indicating that a complete case analysis might result in biased estimates. To account for potential selection bias (under the assumption of missing at random), we conducted multiple imputation with the use of chained equations to impute missing values of BMIZ (28). Thirty imputations for each missing value were performed (28). Linear regression was used in the multiple imputation procedure to impute predicted values for missing BMIZ at ages 5, 8, and 12 y. All covariates from the main analysis, baseline outcomes, and an indicator variable for the sampling cluster were included in the imputation models. We stratified the data on stunted status at age 1 y and calculated descriptive statistics. We tested differences in covariate values between stunted and nonstunted children at age 1 y, and between those lost to follow-up and those not lost to follow-up, with the use of Fisher’s exact test, Pearson’s chi-square test, and Student’s t test. We used generalized linear models with a Poisson distribution, log link, and robust variance (29) to estimate the association between stunting status at age 1 y and the risk of subsequent prevalence of, incidence of, and reversion from BMIZ > 1 or BMIZ > 2. Results from 3 models are reported: 1) bivariable regressions of outcomes on stunting at age 1 y; 2) multivariable regressions with controls for potentially confounding covariates with the use of observations with complete outcome data at all ages; and 3) multivariable regressions adjusted for the same covariates as in the second model, but with imputations for missing outcomes. In regressions of incidence and reversion on stunting status in model 3, the population at risk varied across imputed data sets. To permit analysis, we set the at-risk population across imputed data sets by using mean imputed values for BMIZ at age 5 y and age 8 y to determine whether children were at risk of incidence or reversion at ages 8 and 12 y, respectively. We examined, one interaction at a time, the significance of multiplicative interaction terms between stunting status at age 1 y and sex, indigenous status, and rural or urban status. Statistical significance was considered to be P < 0.05. Statistical analyses were conducted with the use of Stata version 13. Ethics committees at the University of Oxford and the Nutrition Research Institute in Lima approved the Peruvian Young Lives study. Parents provided written informed consent in round 1 and verbal reconsent in each subsequent round.

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Based on the provided information, it seems that the study is focused on analyzing the associations between early-life stunting and the risk of high body mass index (BMI) for age in later childhood. The study collected data from Peruvian children at ages 1, 5, 8, and 12 years. The associations between stunting at age 1 and the prevalence, incidence, and reversion of high BMIZ at later ages were examined.

To improve access to maternal health, some potential innovations or recommendations could include:

1. Strengthening prenatal and postnatal care: Ensuring that pregnant women and new mothers have access to comprehensive healthcare services, including regular check-ups, nutritional support, and counseling on breastfeeding and infant feeding practices.

2. Implementing community-based interventions: Establishing community health centers or mobile clinics to provide maternal health services closer to where women live, particularly in rural or remote areas where access to healthcare facilities may be limited.

3. Promoting maternal nutrition education: Educating women about the importance of a balanced diet during pregnancy and the postpartum period to support healthy fetal growth and development.

4. Enhancing healthcare infrastructure: Investing in the development and improvement of healthcare facilities, including maternity wards, delivery rooms, and neonatal intensive care units, to ensure safe and quality care for pregnant women and newborns.

5. Strengthening health information systems: Implementing electronic health records and data collection systems to improve the monitoring and tracking of maternal health indicators, enabling better identification of at-risk populations and targeted interventions.

6. Increasing awareness and reducing stigma: Conducting awareness campaigns to reduce stigma and promote positive attitudes towards maternal health, encouraging women to seek timely and appropriate care during pregnancy and childbirth.

7. Empowering women and promoting gender equality: Addressing social and cultural barriers that hinder women’s access to maternal health services, such as gender-based discrimination and lack of decision-making power, through initiatives that promote women’s empowerment and gender equality.

It is important to note that these are general recommendations and may need to be tailored to the specific context and needs of the population being served. Additionally, further research and evaluation may be necessary to assess the effectiveness and feasibility of these innovations in improving access to maternal health.
AI Innovations Description
The recommendation that can be developed into an innovation to improve access to maternal health based on the provided information is to focus on addressing early-life stunting in infants. This can be achieved through the implementation of targeted interventions and programs that aim to improve nutrition and overall health during pregnancy and early childhood.

Some specific recommendations to address early-life stunting and improve access to maternal health include:

1. Promoting and supporting exclusive breastfeeding for the first six months of life, as breast milk provides essential nutrients and helps prevent stunting.

2. Providing nutritional education and counseling to pregnant women and new mothers, emphasizing the importance of a balanced diet and adequate intake of essential nutrients.

3. Implementing community-based interventions that focus on improving access to nutritious food, such as promoting home gardens and local food production.

4. Strengthening healthcare systems to ensure access to quality prenatal and postnatal care, including regular check-ups, screenings, and access to necessary supplements and medications.

5. Enhancing maternal and child health services in rural and remote areas, where access to healthcare facilities may be limited. This can be achieved through mobile clinics, telemedicine, and community health workers.

6. Collaborating with local communities, organizations, and stakeholders to raise awareness about the importance of maternal health and early childhood nutrition, and to develop culturally appropriate interventions.

7. Conducting research and monitoring programs to evaluate the effectiveness of interventions and identify areas for improvement.

By implementing these recommendations, it is possible to improve access to maternal health and reduce the prevalence of stunting in infants, ultimately leading to better health outcomes for both mothers and children.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Increase availability and accessibility of prenatal care: Ensure that pregnant women have access to regular check-ups, screenings, and necessary medical interventions throughout their pregnancy. This can be achieved by expanding the number of healthcare facilities, particularly in rural and underserved areas, and implementing mobile clinics or telemedicine options.

2. Improve education and awareness: Implement comprehensive maternal health education programs that provide information on nutrition, hygiene, and prenatal care to pregnant women and their families. This can be done through community health workers, educational campaigns, and partnerships with local organizations.

3. Strengthen referral systems: Establish effective referral systems between primary healthcare facilities and specialized maternal health centers. This will ensure that pregnant women with high-risk pregnancies or complications can receive timely and appropriate care from trained healthcare professionals.

4. Enhance transportation and infrastructure: Improve transportation infrastructure, especially in remote areas, to facilitate access to healthcare facilities. This can include building roads, providing transportation subsidies, or utilizing innovative solutions such as telemedicine or drone delivery of medical supplies.

5. Address socio-economic barriers: Implement policies and programs that address socio-economic barriers to maternal health, such as poverty, gender inequality, and lack of social support. This can involve providing financial assistance for healthcare expenses, promoting women’s empowerment, and engaging community leaders to advocate for maternal health.

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

1. Define indicators: Identify key indicators that reflect access to maternal health, such as the number of prenatal care visits, percentage of pregnant women receiving skilled birth attendance, or maternal mortality rates.

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

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. This model should consider factors such as population size, geographical distribution, healthcare infrastructure, and socio-economic conditions.

4. Input data and parameters: Input the baseline data and parameters into the simulation model. This includes information on the current availability of healthcare facilities, transportation infrastructure, education levels, and socio-economic factors.

5. Simulate scenarios: Run the simulation model with different scenarios that reflect the implementation of the recommendations. This can involve adjusting parameters such as the number of healthcare facilities, coverage of education programs, or improvements in transportation infrastructure.

6. Analyze results: Analyze the simulation results to assess the potential impact of the recommendations on the selected indicators. This can involve comparing the outcomes of different scenarios and identifying the most effective interventions.

7. Refine and validate the model: Continuously refine and validate the simulation model based on feedback from experts, stakeholders, and additional data sources. This will ensure that the model accurately reflects the real-world context and provides reliable insights.

By following this methodology, policymakers and healthcare professionals can gain valuable insights into the potential impact of different recommendations on improving access to maternal health. This can inform decision-making and resource allocation to effectively address the challenges in maternal healthcare.

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