Evidence to prevent childhood obesity: The continuum of preconception, pregnancy, and postnatal interventions
Bibliographic record
Abstract
“You are what you eat”. If only it was that simple. Childhood obesity has increased tenfold in the last 40 years,1 and 124 million children and adolescents are obese today. This is the result not only of what children today are eating or the energy they are not expending being physically active but also due to exposures in the fetal environment and early in life. Childhood obesity is a global problem. The most recent global estimates from 2016 indicate that the rise in childhood obesity rates has recently accelerated, especially in Asia, where for decades, undernutrition has been the major consideration.1 Paradoxically, undernutrition, over generations, resulting in low birthweight and poor maternal nutrition, coupled with the rapidly changing food and physical activity environments today, are significant risk factors for childhood obesity across generations. The impacts are manifold, as childhood obesity affects children's physical and psychosocial health and educational attainment. Worryingly, childhood obesity shows a strong likelihood of continuing into adulthood. Obesity is associated with diabetes, cardiovascular diseases, and cancers in adulthood, which are on the rise in virtually every country, regardless of income status, and cause 75% of the 15 million premature deaths from noncommunicable diseases (NCDs) each year. An 85% of these premature deaths from NCDs occur in low- and middle-income countries.2 The World Health Organization (WHO) recognized the importance of starting obesity prevention early and established the Commission on Ending Childhood Obesity in 2014. The Commission's recommendations placed special emphasis on preconception and antenatal care and improving the health of adolescents, as the potential parents of tomorrow, in addition to addressing prevention and treatment for infants and children.3 However, no single intervention can hope to address a challenge as complex as obesity. Partnerships are key to build capacity and address the drivers of obesity. Action is needed across multiple sectors of government and the engagement of stakeholders to implement interventions to address, for example, the promotion and marketing of unhealthy foods, school and early childcare food and physical activity environments, urban design and transport barriers to being more physically active. Only through a life-course approach, coupled with strong public health policies to support healthy diets and physical activity, will any inroads be made to reverse this trend of increasing childhood obesity. The health of both parents and care that women receive preconception and during pregnancy has profound implications for the later health and development of their children. Early childhood is a critical window in which to establish the healthy diet and physical activity trajectories that will lead to optimal health, growth, and development. While there are still gaps in our knowledge, we must act now on the best evidence and to create more. The answers to some of these questions are identified in the systematic reviews in this supplement, providing clarity for options and approaches to prevent obesity and ill health in children, while also improving the health and well-being of women and parents. The Healthy Life Trajectories Initiative (HeLTI) is a collaboration between Canada, China, India, South Africa, and the WHO that has now established linked international intervention cohorts that will implement and test approaches to (a) prevent overweight and obesity in children and risk factors for NCDs and (b) improve early childhood development (ECD). In support of the initiative, WHO commissioned a number of systematic reviews regarding the efficacy of interventions in the preconception, pregnancy, and postpartum periods to reduce NCD risk factors. The goal of HeLTI is to generate evidence that will inform national policy and decision-making for the improvement of health and the prevention of NCDs throughout the life span and to improve ECD outcomes. Policy changes are needed to support maternal health as well as optimal nutrition and physical activity in infancy and beyond.4 These may face opposition from vested interests, but governments must recognize their obligation to protect children's rights to grow healthy and happy, by investing in evidence-based interventions from the very earliest stages of life to prevent childhood obesity and give children the best start in life. No conflict of interest statement. The views expressed by the authors do not necessarily represent the policies or position of the World Health Organization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".