ObjectiveThe purpose of this study is to describe the volume, topics, and methodological nature of the existing research literature on research data management in academic institutions.Materials and methodsWe conducted a scoping review by searching forty literature databases encompassing a broad range of disciplines from inception to April 2016. We included all study types and data extracted on study design, discipline, data collection tools, and phase of the research data lifecycle.ResultsWe included 301 articles plus 10 companion reports after screening 13,002 titles and abstracts and 654 full-text articles. Most articles (85%) were published from 2010 onwards and conducted within the sciences (86%). More than three-quarters of the articles (78%) reported methods that included interviews, cross-sectional, or case studies. Most articles (68%) included the Giving Access to Data phase of the UK Data Archive Research Data Lifecycle that examines activities such as sharing data. When studies were grouped into five dominant groupings (Stakeholder, Data, Library, Tool/Device, and Publication), data quality emerged as an integral element.ConclusionMost studies relied on self-reports (interviews, surveys) or accounts from an observer (case studies) and we found few studies that collected empirical evidence on activities amongst data producers, particularly those examining the impact of research data management interventions. As well, fewer studies examined research data management at the early phases of research projects. The quality of all research outputs needs attention, from the application of best practices in research data management studies, to data producers depositing data in repositories for long-term use.

IntroductionPremature myocardial infarction (MI) generally refers to MI in men ≤55 years or women ≤65 years. Premature MI is a major contributor to cardiovascular disease (CVD), which claimed 17.6 million lives globally in 2016. Reducing premature MI and CVD is a key priority for all nations; however, there is sparse synthesis of information on risk factors associated with premature MI. To address this knowledge gap, we are conducting a systematic review to describe the association between risk factors (demographics, lifestyle factors and biomarkers) and premature MI.Methods and analysisThe following databases were searched from inception to June 2018: CENTRAL, CINAHL, Clinical Trials, EMBASE and MEDLINE. We will include original research articles (case–control, cohort and cross-sectional studies) that report a quantitative relationship between at least one risk factor and premature MI. Two investigators will use predetermined selection criteria and independently screen articles based on title and abstract (primary screening). Articles that meet selection criteria will undergo full-text screening based on criteria used for primary screening (secondary screening). Data will be extracted using predetermined data extraction forms. The Newcastle-Ottawa Scale for case–control and cohort studies will be used to evaluate the risk of bias and will be adapted for cross-sectional studies. Whenever feasible, data will be summarised into a random-effects meta-analysis.Ethics and disseminationTo our knowledge, this will be the first study to synthesise results on the relationship between risk factors and premature MI. These findings will inform healthcare providers on factors associated with risk of premature MI and potentially improve primary prevention efforts by guiding development of interventions. These findings will be summarised and presented at conferences and through publication in a peer-reviewed journal.PROSPERO registration numberCRD42018076862.

Understanding how child and adolescent health is influenced by fluctuations in socioeconomic status has important public health and policy implications, as children are often subjected to both micro and macro-level socioeconomic events. This study provides the first systematic review to date on the relationship between changes in household or parental socioeconomic status and subsequent child and adolescent health outcomes. Eighty articles were identified for inclusion in this review, examining 85 different socioeconomic exposures in five categories: Income (n = 64), Employment (n = 14), Socioeconomic Mobility (n = 3), Education (n = 2), and Food Insecurity (n = 2). The health outcomes analyzed by these eighty articles were separated into eight discrete categories, with many articles examining outcomes in more than one category: Anthropometric Measurements (n = 21), Cognition and Development (n = 15), Dental Health (n = 3), Health Behaviours (n = 9), Mental Health (n = 12), Overall Parent/Guardian Assessed health (n = 6); Physical Health Outcomes (n = 11), and Socio-Emotional Behaviour (n = 30). Several consistent patterns emerged in the literature, such as a link between increased income and improved, or decreased income and deteriorating, cognition, dental health, and physical health. The results of this review suggest a need to replicate current studies in diverse geographies to expand generalizability and clarify regional patterns. There should also be an effort to go beyond income, and employment, to assess the relationship between less frequently studied socioeconomic exposures and child health outcomes. Supplementary Information The online version contains supplementary material available at 10.1007/s40894-021-00151-8.

Objective: To evaluate the magnitude of the association between risk factors and premature myocardial infarction (MI) (men aged 18-55 years; women aged 18-65 years). Patients and Methods: We searched MEDLINE and other databases from inception through April 30, 2020, as well as bibliography of articles selected for data extraction. We selected observational studies reporting the magnitude of the association of at least 1 risk factor (demographic characteristics, lifestyle factors, clinical risk factors, or biomarkers) with premature MI and a control group. Pooled risk estimates (random effects) from all studies unadjusted and adjusted for risk factors were reported as summary odds ratios (ORs) with 95% CIs. Results: From 35,320 articles of 12.7 million participants, we extracted data on 19 risk factors from 77 studies across 58 countries. Men had a higher risk of premature MI (OR, 2.39; 95% CI, 1.71 to 3.35) than did women. Family history of cardiac disease was associated with a higher risk of premature MI (OR, 2.67; 95% CI, 2.29 to 3.27). Major modifiable risk factors associated with higher risk were current smoking (OR, 4.34; 95% CI, 3.68 to 5.12 vs no/former), diabetes mellitus (OR, 3.54; 95% CI, 2.69 to 4.65), dyslipidemia (OR, 2.94; 95% CI, 1.76 to 4.91), and hypertension (OR, 2.85; 95% CI, 2.48 to 3.27). Higher body mass index carried higher risk (OR, 1.46; 95% CI, 1.24 to 1.71 for !25 kg/m 2 vs <25 kg/ m 2 ). Biomarkers associated with 2-to 3-fold higher risk were total cholesterol levels greater than 200 mg/ dL, triglyceride levels higher than 150 mg/dL, and high-density lipoprotein cholesterol levels less than 60 mg/dL (to convert to mmol/L, multiply by 0.0259). Conclusion: Major risk factors for premature MI are mostly amenable to patient, population, and policy level interventions. Mild elevations in body mass index and triglyceride levels were associated with higher risk, which has implications for the growing worldwide epidemic of cardiometabolic diseases.