Social determinants of health and disparities in pediatric trauma care: protocol for a systematic review and meta-analysis.

BACKGROUND: Social determinants of health (SDH), including "the conditions in which individuals are born, grow, work, live and age" affect child health and well-being. Several studies have synthesized evidence about the influence of SDH on childhood injury risks and outcomes. However, there is no systematic evidence about the impact of SDH on accessing care and quality of care once a child has suffered an injury. We aim to evaluate the extent to which access to care and quality of care after injury are affected by children and adolescents' SDH. METHODS: Using Cochrane methodology, we will conduct a systematic review including observational and experimental studies evaluating the association between social/material elements contributing to health disparities, using the PROGRESS-Plus framework: place of residence, race/ethnicity/culture/language, occupation, gender/sex, religion, education, socioeconomic status, and social capital and care received by children and adolescents (≤ 19 years of age) after injury. We will consult published literature using PubMed, EMBASE, CINAHL, PsycINFO, Web of Science, and Academic Search Premier and grey literature using Google Scholar from their inception to a maximum of 6 months prior to submission for publication. Two reviewers will independently perform study selection, data extraction, and risk of bias assessment for included studies. The risk of bias will be assessed using the ROBINS-E and ROB-2 tools respectively for observational and experimental study designs. We will analyze data to perform narrative syntheses, and if enough studies are identified, we will conduct a meta-analysis using random effects models. DISCUSSION: This systematic review will provide a synthesis of evidence on the association between SDH and pediatric trauma care (access to care and quality of care) that clinicians and policymakers can use to better tailor care systems and promote equitable access and quality of care for all children. We will share our findings through clinical rounds, conferences, and publication in a peer-reviewed journal. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023408467.

On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modelling techniques.

Since the emergence of the novel 2019 coronavirus pandemic in December 2019 (COVID-19), numerous modellers have used diverse techniques to assess the dynamics of transmission of the disease, predict its future course and determine the impact of different control measures. In this study, we conducted a global systematic literature review to summarize trends in the modelling techniques used for Covid-19 from January 1st, 2020 to November 30th, 2020. We further examined the accuracy and precision of predictions by comparing predicted and observed values for cumulative cases and deaths as well as uncertainties of these predictions. From an initial 4311 peer-reviewed articles and preprints found with our defined keywords, 242 were fully analysed. Most studies were done on Asian (78.93%) and European (59.09%) countries. Most of them used compartmental models (namely SIR and SEIR) (46.1%) and statistical models (growth models and time series) (31.8%) while few used artificial intelligence (6.7%), Bayesian approach (4.7%), Network models (2.3%) and Agent-based models (1.3%). For the number of cumulative cases, the ratio of the predicted over the observed values and the ratio of the amplitude of confidence interval (CI) or credibility interval (CrI) of predictions and the central value were on average larger than 1 indicating cases of inaccurate and imprecise predictions, and large variation across predictions. There was no clear difference among models used for these two ratios. In 75% of predictions that provided CI or CrI, observed values fall within the 95% CI or CrI of the cumulative cases predicted. Only 3.7% of the studies predicted the cumulative number of deaths. For 70% of the predictions, the ratio of predicted over observed cumulative deaths was less or close to 1. Also, the Bayesian model made predictions closer to reality than classical statistical models, although these differences are only suggestive due to the small number of predictions within our dataset (9 in total). In addition, we found a significant negative correlation (rho = - 0.56, p = 0.021) between this ratio and the length (in days) of the period covered by the modelling, suggesting that the longer the period covered by the model the likely more accurate the estimates tend to be. Our findings suggest that while predictions made by the different models are useful to understand the pandemic course and guide policy-making, some were relatively accurate and precise while other not.