No abstract

Background Complications associated with preterm birth (PTB) are the largest contributor to under-five mortality globally. Success in reaching the Sustainable Development Goal target requires identifying potentially modifiable risk factors for PTB, estimating the relative importance of these risk factors, and identifying/implementing effective prevention strategies to address them. Methods We conducted a literature review to define risk relationships and estimate prevalence for established risk factors for spontaneous PTB (sPTB). We then estimated population attributable fractions (PAF) for the sPTB risk factors identified in the review as statistically significant for the 81 low- and middle-income (LMIC) countries included in the Countdown 2030 initiative. We summed country-level findings to produce PAFs for each risk factor and regional estimates for sub-Saharan Africa and South Asia. Results Forty-four potential sPTB risk factors were identified. and the final analysis included twenty-four risk factors with evidence of significant associations with sPTB. A second model with three additional risk factors with borderline insignificant associations was also run. Taken together, the twenty-four risk factors had a total PAF of 73% for all 81 countries and 77% and 72% of sPTB in sub-Saharan Africa and South Asia, respectively. For all countries, maternal undernutrition had the highest PAF (17.5%), followed by maternal infections (16.6%), environmental exposures (16%) and pregnancy history (8.7%). Conclusions While multiple risk factors contribute to sPTB, no single risk factor addresses a predominant fraction, and 27% of spontaneous preterm births are not associated with risk factors that we identified. Despite the significant role of preterm birth in child survival, there are major data gaps in LMIC settings. Furthermore, there is a paucity of evidence for effective interventions to prevent preterm birth. Preventing sPTB requires understanding underlying mechanisms leading to sPTB in different populations, and the identification/implementation of effective interventions.

Background Policymakers seeking to prioritize the use of restricted financial resources need to understand the relative costs and benefits of interventions for improving nutritional status. Improved linear growth can lead to increased education attainment and improved economic productivity in low- and middle-income countries (LMICs), though these non-health-related benefits are not reflected in current long-term modelling efforts, including the Lives Saved Tool (LiST). Our objective was to integrate the effects of improved linear growth on non-health related benefit into LiST by estimating subsequent gains in years of schooling and wage earnings. We then estimated the impacts of reaching the Sustainable Development Goals (SDGs) target for stunting in South Asian countries on lifetime productivity. Methods In the first step, we used LiST outputs to estimate the improved linear growth due to scaled-up nutrition interventions and used published estimates to quantify the education gain resulting from an increase in height for age z-score (HAZ). In the second step, we used published country-level estimates on economic returns to schooling to quantify the relative gains in wages that children born today will experience because of their additional education attainment in the future. In the last step, we used country-level data on wages to estimate the net present value of future earnings gained due to early childhood growth improvement per birth cohort. Results If South Asia countries reach the SDG target by 2025, an estimated 8.6 million years of schooling will be obtained by six birth cohorts of 2020 to 2025. These six birth cohorts will also gain an estimated US$64 893 million in the present value term, at a 5% discount rate, in lifetime earnings. India has the largest expected gain in years of schooling (7367 years) and lifetime earnings (US$59 390 million in present value terms, at a 5% discount rate). Conclusions Two non-health-related benefits of improved linear growth – additional years of schooling and lifetime earnings – are added in LiST. Together with LiST costing, users can now conduct both cost-effective and benefit-cost analyses. Using both analyses will provide more comprehensive insights into nutrition interventions' relative costs and benefits.