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.

Clinical Practice Guideline Recommendations in Pediatric Orthopaedic Injury: A Systematic Review.

BACKGROUND: Lack of adherence to recommendations on pediatric orthopaedic injury care may be driven by lack of knowledge of clinical practice guidelines (CPGs), heterogeneity in recommendations or concerns about their quality. We aimed to identify CPGs for pediatric orthopaedic injury care, appraise their quality, and synthesize the quality of evidence and the strength of associated recommendations. METHODS: We searched Medline, Embase, Cochrane CENTRAL, Web of Science and websites of clinical organizations. CPGs including at least one recommendation targeting pediatric orthopaedic injury populations on any diagnostic or therapeutic intervention developed in the last 15 years were eligible. Pairs of reviewers independently extracted data and evaluated CPG quality using the Appraisal of Guidelines Research and Evaluation (AGREE) II tool. We synthesized recommendations from high-quality CPGs using a recommendations matrix based on the GRADE Evidence-to-Decision framework. RESULTS: We included 13 eligible CPGs, of which 7 were rated high quality. Lack of stakeholder involvement and applicability (i.e., implementation strategies) were identified as weaknesses. We extracted 53 recommendations of which 19 were based on moderate or high-quality evidence. CONCLUSIONS: We provide a synthesis of recommendations from high-quality CPGs that can be used by clinicians to guide treatment decisions. Future CPGs should aim to use a partnership approach with all key stakeholders and provide strategies to facilitate implementation. This study also highlights the need for more rigorous research on pediatric orthopaedic trauma. LEVEL OF EVIDENCE: Level II-therapeutic study.

Pediatric Moderate and Severe Traumatic Brain Injury: A Systematic Review of Clinical Practice Guideline Recommendations.

Traumatic brain injury (TBI) is the leading cause of death and disability in children. Many clinical practice guidelines (CPGs) have addressed pediatric TBI in the last decade but significant variability in the use of these guidelines persists. Here, we systematically review CPGs recommendations for pediatric moderate-to-severe TBI, evaluate the quality of CPGs, synthesize the quality of evidence and strength of included recommendations, and identify knowledge gaps. A systematic search was conducted in MEDLINE®, Embase, Cochrane CENTRAL, Web of Science, and Web sites of organizations publishing recommendations on pediatric injury care. We included CPGs developed in high-income countries from January 2012 to May 2023, with at least one recommendation targeting pediatric (≤ 19 years old) moderate-to-severe TBI populations. The quality of included clinical practice guidelines was assessed using the AGREE II tool. We synthesized evidence on recommendations using a matrix based on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. We identified 15 CPGs of which 9 were rated moderate to high quality using AGREE II. We identified 90 recommendations, of which 40 (45%) were evidence based. Eleven of these were based on moderate to high quality evidence and were graded as moderate or strong by at least one guideline. These included transfer, imaging, intracranial pressure control, and discharge advice. We identified gaps in evidence-based recommendations for red blood cell transfusion, plasma and platelet transfusion, thromboprophylaxis, surgical antimicrobial prophylaxis, early diagnosis of hypopituitarism, and mental health mangement. Many up-to-date CPGs are available, but there is a paucity of evidence to support recommendations, highlighting the urgent need for robust clinical research in this vulnerable population. Our results may be used by clinicians to identify recommendations based on the highest level of evidence, by healthcare administrators to inform guideline implementation in clinical settings, by researchers to identify areas where robust evidence is needed, and by guideline writing groups to inform the updating of existing guidelines or the development of new ones.

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.