Study protocol for UNICEF and WHO estimates of global, regional, and national low birthweight prevalence for 2000 to 2020

Julia Krasevec ORCID logo; Hannah Blencowe ORCID logo; Christopher Coffey; Yemisrach B Okwaraji ORCID logo; Diana Estevez ORCID logo; Gretchen A Stevens; Eric O Ohuma ORCID logo; Joel Conkle; Giovanna Gatica-Domínguez; Ellen Bradley ORCID logo; +5 more... Ben Kimathi Muthamia; Nita Dalmiya; Joy E Lawn ORCID logo; Elaine Borghi; Chika Hayashi ORCID logo; (2022) Study protocol for UNICEF and WHO estimates of global, regional, and national low birthweight prevalence for 2000 to 2020. Gates Open Research, 6. p. 80. ISSN 2572-4754 DOI: 10.12688/gatesopenres.13666.1
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<ns3:p><ns3:bold>Background</ns3:bold></ns3:p><ns3:p> Reducing low birthweight (LBW, weight at birth less than 2,500g) prevalence by at least 30% between 2012 and 2025 is a target endorsed by the World Health Assembly that can contribute to achieving Sustainable Development Goal 2 (Zero Hunger) by 2030. The 2019 LBW estimates indicated a global prevalence of 14.6% (20.5 million newborns) in 2015. We aim to develop updated LBW estimates at global, regional, and national levels for up to 202 countries for the period of 2000 to 2020.</ns3:p><ns3:p> <ns3:bold>Methods </ns3:bold></ns3:p><ns3:p> Two types of sources for LBW data will be sought: national administrative data and population-based surveys. Administrative data will be searched for countries with a facility birth rate ≥80% and included when birthweight data account for ≥80% of UN estimated live births for that country and year. Surveys with birthweight data published since release of the 2019 edition of the LBW estimates will be adjusted using the standard methodology applied for the previous estimates. Risk of bias assessments will be undertaken. Covariates will be selected based on a conceptual framework of plausible associations with LBW, covariate time-series data quality, collinearity between covariates and correlations with LBW. National LBW prevalence will be estimated using a Bayesian multilevel-mixed regression model, then aggregated to derive regional and global estimates through population-weighted averages.</ns3:p><ns3:p> <ns3:bold>Conclusion</ns3:bold></ns3:p><ns3:p> Whilst availability of LBW data has increased, especially with more facility births, gaps remain in the quantity and quality of data, particularly in low-and middle-income countries. Challenges include high percentages of missing data, lack of adherence to reporting standards, inaccurate measurement, and data heaping. Updated LBW estimates are important to highlight the global burden of LBW, track progress towards nutrition targets, and inform investments in programmes. Reliable, nationally representative data are key, alongside investments to improve the measurement and recording of an accurate birthweight for every baby.</ns3:p>

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