The objectives of this study were (1) to systematically review the literature on the association between birth weight in children born in the first and second generation; (2) to quantify this association by performing a meta-analysis. A systematic review was carried in six databases (Pubmed, Science Direct, Web of Science, Embase, Scopus, CINAHL and LILACS), in January 2021, for studies that recorded the birth weight of parents and children. A meta-analysis using random effects to obtain a pooled effect of the difference in birth weight and the association of low birth weight (LBW) between generations was performed. Furthermore, univariable meta-regression was conducted to assess heterogeneity. Egger’s tests were used to possible publication biases. Of the 9878 identified studies, 70 were read in full and 20 were included in the meta-analysis (10 prospective cohorts and 10 retrospective cohorts), 14 studies for difference in means and 11 studies for the association of LBW between generations (23 estimates). Across all studies, there was no statistically significant mean difference (MD) birth weight between first and second-generation (MD 19.26, 95% CI -28.85, 67.36; p= 0.43). Overall, children of LBW parents were 69% more likely to have low birth weight (pooled effect size (ES) 1.69, 95% CI 1.46, 1.95); I 2 : 85,8%). No source of heterogeneity was identified among the studies and no publication bias. The average birth weight of parents does not influence the average birth weight of children, however the proportion of LBW among the parents seems to affect the offspring’s birth weight.

RESUMO Objetivo: To describe the initial baseline results of a population-based study, as well as a protocol in order to evaluate the performance of different machine learning algorithms with the objective of predicting the demand for urgent and emergency services in a representative sample of adults from the urban area of Pelotas, Southern Brazil. Methods: The study is entitled “Emergency department use and Artificial Intelligence in PELOTAS (RS) (EAI PELOTAS)” (https://wp.ufpel.edu.br/eaipelotas/). Between September and December 2021, a baseline was carried out with participants. A follow-up was planned to be conducted after 12 months in order to assess the use of urgent and emergency services in the last year. Afterwards, machine learning algorithms will be tested to predict the use of urgent and emergency services over one year. Results: In total, 5,722 participants answered the survey, mostly females (66.8%), with an average age of 50.3 years. The mean number of household people was 2.6. Most of the sample has white skin color and incomplete elementary school or less. Around 30% of the sample has obesity, 14% diabetes, and 39% hypertension. Conclusion: The present paper presented a protocol describing the steps that were and will be taken to produce a model capable of predicting the demand for urgent and emergency services in one year among residents of Pelotas, in Rio Grande do Sul state.