Mapping and systematic appraisal of umbrella reviews in epidemiological research: a protocol for a meta-epidemiological study.

INTRODUCTION: Umbrella review is one of the terms used to describe an overview of systematic reviews. During the last years, a rapid increase in the number of umbrella reviews on epidemiological studies has been observed, but there is no systematic assessment of their methodological and reporting characteristics. Our study aims to fill this gap by performing a systematic mapping of umbrella reviews in epidemiological research. METHODS: We will perform a meta-epidemiological study including a systematic review in MEDLINE and EMBASE to identify all the umbrella reviews that focused on systematic reviews of epidemiological studies and were published from inception until December 31, 2022. We will consider eligible any research article which was designed as an umbrella review and summarized systematic reviews and meta-analyses of epidemiological studies. From each eligible article, we will extract information about the research topic, the methodological characteristics, and the reporting characteristics. We will examine whether the umbrella reviews assessed the strength of the available evidence and the rigor of the included systematic reviews. We will also examine whether these characteristics change across time. DISCUSSION: Our study will systematically appraise the methodological and reporting characteristics of published umbrella reviews in epidemiological literature. The findings of our study can be used to improve the design and conduct of future umbrella reviews, to derive a standardized set of reporting and methodological guidelines for umbrella reviews, and to allow further meta-epidemiological work. SYSTEMATIC REVIEW REGISTRATION: osf.io/sxzc6.

Reproducibility of prediction models in health services research.

The field of health services research studies the health care system by examining outcomes relevant to patients and clinicians but also health economists and policy makers. Such outcomes often include health care spending, and utilization of care services. Building accurate prediction models using reproducible research practices for health services research is important for evidence-based decision making. Several systematic reviews have summarized prediction models for outcomes relevant to health services research, but these systematic reviews do not present a thorough assessment of reproducibility and research quality of the prediction modelling studies. In the present commentary, we discuss how recent advances in prediction modelling in other medical fields can be applied to health services research. We also describe the current status of prediction modelling in health services research, and we summarize available methodological guidance for the development, update, external validation and systematic appraisal of prediction models.

Multivariable prediction models for health care spending using machine learning: a protocol of a systematic review.

BACKGROUND: With rising cost pressures on health care systems, machine-learning (ML)-based algorithms are increasingly used to predict health care costs. Despite their potential advantages, the successful implementation of these methods could be undermined by biases introduced in the design, conduct, or analysis of studies seeking to develop and/or validate ML models. The utility of such models may also be negatively affected by poor reporting of these studies. In this systematic review, we aim to evaluate the reporting quality, methodological characteristics, and risk of bias of ML-based prediction models for individual-level health care spending. METHODS: We will systematically search PubMed and Embase to identify studies developing, updating, or validating ML-based models to predict an individual's health care spending for any medical condition, over any time period, and in any setting. We will exclude prediction models of aggregate-level health care spending, models used to infer causality, models using radiomics or speech parameters, models of non-clinically validated predictors (e.g., genomics), and cost-effectiveness analyses without predicting individual-level health care spending. We will extract data based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS), previously published research, and relevant recommendations. We will assess the adherence of ML-based studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and examine the inclusion of transparency and reproducibility indicators (e.g. statements on data sharing). To assess the risk of bias, we will apply the Prediction model Risk Of Bias Assessment Tool (PROBAST). Findings will be stratified by study design, ML methods used, population characteristics, and medical field. DISCUSSION: Our systematic review will appraise the quality, reporting, and risk of bias of ML-based models for individualized health care cost prediction. This review will provide an overview of the available models and give insights into the strengths and limitations of using ML methods for the prediction of health spending.

Conducting umbrella reviews.

In this article, Lazaros Belbasis and colleagues explain the rationale for umbrella reviews and the key steps involved in conducting an umbrella review, using a working example.

Prognostic factors for adverse outcomes in patients with COVID-19: a field-wide systematic review and meta-analysis.

INTRODUCTION: The individual prognostic factors for coronavirus disease 2019 (COVID-19) are unclear. For this reason, we aimed to present a state-of-the-art systematic review and meta-analysis on the prognostic factors for adverse outcomes in COVID-19 patients. METHODS: We systematically reviewed PubMed from 1 January 2020 to 26 July 2020 to identify non-overlapping studies examining the association of any prognostic factor with any adverse outcome in patients with COVID-19. Random-effects meta-analysis was performed, and between-study heterogeneity was quantified using I2 statistic. Presence of small-study effects was assessed by applying the Egger's regression test. RESULTS: We identified 428 eligible articles, which were used in a total of 263 meta-analyses examining the association of 91 unique prognostic factors with 11 outcomes. Angiotensin-converting enzyme inhibitors, obstructive sleep apnoea, pharyngalgia, history of venous thromboembolism, sex, coronary heart disease, cancer, chronic liver disease, COPD, dementia, any immunosuppressive medication, peripheral arterial disease, rheumatological disease and smoking were associated with at least one outcome and had >1000 events, p<0.005, I2<50%, 95% prediction interval excluding the null value, and absence of small-study effects in the respective meta-analysis. The risk of bias assessment using the Quality in Prognosis Studies tool indicated high risk of bias in 302 out of 428 articles for study participation, 389 articles for adjustment for other prognostic factors and 396 articles for statistical analysis and reporting. CONCLUSIONS: Our findings could be used for prognostic model building and guide patient selection for randomised clinical trials.

Tobacco Smoking and Risk for Pulmonary Fibrosis: A Prospective Cohort Study From the UK Biobank.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease of unknown origin. A limited number of small studies show an effect of tobacco smoking on risk of IPF, but second-hand smoking has not been examined. RESEARCH QUESTION: Are smoking-related exposures associated with risk of IPF and does interaction between them exist? STUDY DESIGN AND METHODS: We designed a prospective cohort study using UK Biobank data, including 437,453 nonrelated men and women of White ethnic background (40-69 years of age at baseline). We assessed the effect of tobacco smoking-related exposures on risk for IPF using Cox regression adjusted for age, sex, Townsend deprivation index, and home area population density. We also examined potential additive and multiplicative interaction between these exposures. Multiple imputation with chained equations was used to address missing data. RESULTS: We identified 802 incident IPF cases. We showed an association between smoking status (hazard ratio [HR], 2.12; 95% CI, 1.81-2.47), and maternal smoking (HR, 1.38; 95% CI, 1.18-1.62) with risk of IPF. In ever smokers, a dose-response relationship was observed between pack-years of smoking and risk of IPF (HR per 1-pack-year increase, 1.013; 95% CI, 1.009-1.016). Furthermore, an additive and multiplicative interaction was observed between maternal smoking and smoking status, with a relative excess risk due to interaction of 1.00 (95% CI, 0.45-1.54) and a ratio of HRs of 1.50 (95% CI, 1.05-2.14). INTERPRETATION: Active and maternal tobacco smoking have an independent detrimental effect on risk of IPF and work synergistically. Also, intensity of smoking presents a dose-response association with IPF, strengthening the hypothesis for a potentially causal association.

Early-Life Factors and Risk of Multiple Sclerosis: An MR-EWAS.

BACKGROUND: Although several risk factors are associated with multiple sclerosis (MS) in adulthood, evidence for risk factors acting from birth to adolescence is scarce. METHODS: We conceived a 2-step study design, where signals from an Environment-Wide Association Study are prioritized for follow-up in a Mendelian Randomization study (MR-EWAS), to examine the association of early-life factors with risk of MS. The EWAS was conducted in UK Biobank, where we agnostically selected all the available risk factors acting from the perinatal period until the adolescence, including perinatal factors, anthropometric characteristics during childhood, male and female sexual factors, and skin phenotypic characteristics. We prioritized statistically significant risk factors to perform a 2-sample MR study using publicly available summary-level genetic data. We also calculated the power of the 2-step MR-EWAS approach under several scenarios and compared it against a 1-step hypothesis-free MR approach to detect risk factors of MS. RESULTS: In the EWAS, an increase per 1 year in age at menarche was associated with a lower risk of MS (OR = 0.93; 95% CI: 0.90-0.96) and a plumper than average body size at the age of 10 was associated with a higher risk of MS (OR = 1.42; 95% CI: 1.24-1.61). Individuals getting very tanned or moderately tanned were at higher risk of MS compared with individuals that never tan or get mildly to occasionally tanned (OR = 0.86; 95% CI: 0.79-0.94). The MR analysis supported the association of age at menarche and childhood body mass index (BMI) without presence of pleiotropic effects. In the multivariable MR analysis, the association of age at menarche was not statistically significant after adjusting for childhood BMI. The MR analysis for ease of tanning did not reveal a statistically significant association. In multiple scenarios, the power of MR-EWAS approach was larger than the power of a hypothesis-free MR approach. CONCLUSIONS: We introduced the MR-EWAS, a 2-step approach that is more powerful compared with the hypothesis-free MR approach under certain scenarios, to test potential causal signals. Our comprehensive assessment of early-life risk factors of MS highlighted a potential causal role of early menarche and elevated childhood BMI for risk of MS.

Environmental factors and risk of multiple sclerosis: Findings from meta-analyses and Mendelian randomization studies.

Multiple sclerosis (MS) is a chronic demyelinating disease that is associated with permanent disability and low quality of life. Development of MS is attributed to a combination of genetic and environmental factors. Genome-wide association studies revealed more than 200 variants that are associated with risk of MS. An umbrella review showed that smoking, history of infectious mononucleosis, and anti-Epstein-Barr virus nuclear antigen (anti-EBNA) immunoglobulin G (IgG) seropositivity are credible risk factors of MS. In the present narrative review, we updated our published umbrella review, showing that body mass index in childhood and adolescence and anti-viral capsid antigen (anti-VCA) IgG seropositivity are additional credible risk factors of MS. In addition, we discuss the findings from Mendelian randomization studies, which present evidence for a potential causal role of serum vitamin D and adulthood body mass index on risk of MS. Finally, we discuss the potential limitations of meta-analyses, umbrella reviews, and Mendelian randomization studies in the search for risk factors of MS.

Environmental factors, serum biomarkers and risk of atrial fibrillation: an exposure-wide umbrella review of meta-analyses.

Atrial fibrillation (AF) is the most common cardiac arrhythmia. We designed an umbrella review to systematically assess the epidemiological credibility of the associations of non-genetic factors with risk of AF. We searched PubMed and EMBASE from inception to December 31, 2018 to identify systematic reviews and meta-analyses of observational studies for the association of non-genetic factors with risk of AF. For each meta-analysis, we used the random-effects model, and we estimated the 95% confidence and prediction intervals. We also assessed between-study heterogeneity, small-study effects and excess significance bias. We identified 34 eligible papers that examined 51 associations of 42 unique non-genetic factors with risk of AF. Eighteen associations remained statistically significant at P value < 1 × 10-6. Thirty-one associations presented large or very large between-study heterogeneity. Eight associations presented evidence for small-study effects and 13 associations had evidence for excess significance bias. Ten associations, i.e. corrected QT interval, alcohol consumption (highest vs. lowest category, per 1 drink/day increase), body mass index (> 30 units vs. < 30 units, per 5 units increase), waist circumference, body weight, type 2 diabetes mellitus, and smoking (ever vs. never, per 10 cigarettes/day increase) were supported by convincing or highly suggestive evidence in meta-analyses of prospective cohort studies. Type 2 diabetes mellitus, markers of adiposity, alcohol consumption, smoking, and corrected QT interval constitute credible risk factors of AF. Our proposed grading may guide the design of future studies, including Mendelian randomization studies, to assess whether these associations are causal.

Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal.

OBJECTIVE: To map and assess prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease (COPD). DESIGN: Systematic review. DATA SOURCES: PubMed until November 2018 and hand searched references from eligible articles. ELIGIBILITY CRITERIA FOR STUDY SELECTION: Studies developing, validating, or updating a prediction model in COPD patients and focusing on any potential clinical outcome. RESULTS: The systematic search yielded 228 eligible articles, describing the development of 408 prognostic models, the external validation of 38 models, and the validation of 20 prognostic models derived for diseases other than COPD. The 408 prognostic models were developed in three clinical settings: outpatients (n=239; 59%), patients admitted to hospital (n=155; 38%), and patients attending the emergency department (n=14; 3%). Among the 408 prognostic models, the most prevalent endpoints were mortality (n=209; 51%), risk for acute exacerbation of COPD (n=42; 10%), and risk for readmission after the index hospital admission (n=36; 9%). Overall, the most commonly used predictors were age (n=166; 41%), forced expiratory volume in one second (n=85; 21%), sex (n=74; 18%), body mass index (n=66; 16%), and smoking (n=65; 16%). Of the 408 prognostic models, 100 (25%) were internally validated and 91 (23%) examined the calibration of the developed model. For 286 (70%) models a model presentation was not available, and only 56 (14%) models were presented through the full equation. Model discrimination using the C statistic was available for 311 (76%) models. 38 models were externally validated, but in only 12 of these was the validation performed by a fully independent team. Only seven prognostic models with an overall low risk of bias according to PROBAST were identified. These models were ADO, B-AE-D, B-AE-D-C, extended ADO, updated ADO, updated BODE, and a model developed by Bertens et al. A meta-analysis of C statistics was performed for 12 prognostic models, and the summary estimates ranged from 0.611 to 0.769. CONCLUSIONS: This study constitutes a detailed mapping and assessment of the prognostic models for outcome prediction in COPD patients. The findings indicate several methodological pitfalls in their development and a low rate of external validation. Future research should focus on the improvement of existing models through update and external validation, as well as the assessment of the safety, clinical effectiveness, and cost effectiveness of the application of these prognostic models in clinical practice through impact studies. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017069247.

Elucidating the environmental risk factors for rheumatic diseases: An umbrella review of meta-analyses.

AIMS: Although rheumatic diseases constitute a leading cause of disability, the environmental risk factors for these diseases are not clarified. In the present study, we aim to systematically appraise the epidemiological credibility of the environmental risk factors for rheumatic diseases. METHODS: We systematically searched PubMed to capture meta-analyses of observational studies on environmental risk factors for the most prevalent rheumatic diseases. For each association, we estimated the summary effect size estimate, the 95% confidence and prediction intervals, and the I2 metric. We further examined the presence of small-study effects and excess significance bias. RESULTS: Overall, we identified 30 eligible papers describing 42 associations. Thirty-three associations were statistically significant at P < 0.05, whereas 13 of them were statistically significant at P < 1 × 10-6 . Thirty-two associations had large or very large between-study heterogeneity. In 12 associations, evidence of small-study effects and/or excess significance bias was found. Six risk factors (nine associations) presented convincing or highly suggestive evidence of association: smoking and pack-years of smoking for rheumatoid arthritis; BMI (per 5 kg/m2 increase) for gout and hip osteoarthritis; alcohol consumption for gout; BMI (overweight vs lean, obese vs lean), knee injury and participation in heavy work for knee osteoarthritis. CONCLUSION: Our umbrella review indicated that a narrow range of risk factors has been examined for rheumatic diseases. Current evidence strongly supports that smoking, obesity, alcohol consumption, knee injury, and work activities are associated with risk for at least one rheumatic disease.

Introduction to Epidemiological Studies.

The basic epidemiological study designs are cross-sectional, case-control, and cohort studies. Cross-sectional studies provide a snapshot of a population by determining both exposures and outcomes at one time point. Cohort studies identify the study groups based on the exposure and, then, the researchers follow up study participants to measure outcomes. Case-control studies identify the study groups based on the outcome, and the researchers retrospectively collect the exposure of interest. The present chapter discusses the basic concepts, the advantages, and disadvantages of epidemiological study designs and their systematic biases, including selection bias, information bias, and confounding.

Mapping risk factors for depression across the lifespan: An umbrella review of evidence from meta-analyses and Mendelian randomization studies.

The development of depression may involve a complex interplay of environmental and genetic risk factors. PubMed and PsycInfo databases were searched from inception through August 3, 2017, to identify meta-analyses and Mendelian randomization (MR) studies of environmental risk factors associated with depression. For each eligible meta-analysis, we estimated the summary effect size and its 95% confidence interval (CI) by random-effects modeling, the 95% prediction interval, heterogeneity with I2, and evidence of small-study effects and excess significance bias. Seventy meta-analytic reviews met the eligibility criteria and provided 134 meta-analyses for associations from 1283 primary studies. While 109 associations were nominally significant (P < 0.05), only 8 met the criteria for convincing evidence and, when limited to prospective studies, convincing evidence was found in 6 (widowhood, physical abuse during childhood, obesity, having 4-5 metabolic risk factors, sexual dysfunction, job strain). In studies in which depression was assessed through a structured diagnostic interview, only associations with widowhood, job strain, and being a Gulf War veteran were supported by convincing evidence. Additionally, 8 MR studies were included and provided no consistent evidence for the causal effects of obesity, smoking, and alcohol consumption. The proportion of variance explained by genetic risk factors was extremely small (0.1-0.4%), which limited the evidence provided by the MR studies. Our findings suggest that despite the large number of putative risk factors investigated in the literature, few associations were supported by robust evidence. The current findings may have clinical and research implications for the early identification of individuals at risk for depression.

Gender-specific estimates of COPD prevalence: a systematic review and meta-analysis.

Rationale: COPD has been perceived as being a disease of older men. However, >7 million women are estimated to live with COPD in the USA alone. Despite a growing body of literature suggesting an increasing burden of COPD in women, the evidence is limited. Objectives: To assess and synthesize the available evidence among population-based epidemiologic studies and calculate the global prevalence of COPD in men and women. Materials and methods: A systematic review and meta-analysis reporting gender-specific prevalence of COPD was undertaken. Gender-specific prevalence estimates were abstracted from relevant studies. Associated patient characteristics as well as custom variables pertaining to the diagnostic method and other important epidemiologic covariates were also collected. A Bayesian random-effects meta-analysis was performed investigating gender-specific prevalence of COPD stratified by age, geography, calendar time, study setting, diagnostic method, and disease severity. Measurements and main results: Among 194 eligible studies, summary prevalence was 9.23% (95% credible interval [CrI]: 8.16%-10.36%) in men and 6.16% (95% CrI: 5.41%-6.95%) in women. Gender prevalences varied widely by the World Health Organization Global Burden of Disease subregions, with the highest female prevalence found in North America (8.07% vs 7.30%) and in participants in urban settings (13.03% vs 8.34%). Meta-regression indicated that age ≥40 and bronchodilator testing contributed most significantly to heterogeneity of prevalence estimates across studies. Conclusion: We conducted the largest ever systematic review and meta-analysis of global prevalence of COPD and the first large gender-specific review. These results will increase awareness of COPD as a critical woman's health issue.

Risk factors for type 2 diabetes mellitus: An exposure-wide umbrella review of meta-analyses.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a global epidemic associated with increased health expenditure, and low quality of life. Many non-genetic risk factors have been suggested, but their overall epidemiological credibility has not been assessed. METHODS: We searched PubMed to capture all meta-analyses and Mendelian randomization studies for risk factors of T2DM. For each association, we estimated the summary effect size, its 95% confidence and prediction interval, and the I2 metric. We examined the presence of small-study effects and excess significance bias. We assessed the epidemiological credibility through a set of predefined criteria. RESULTS: We captured 86 eligible papers (142 associations) covering a wide range of biomarkers, medical conditions, and dietary, lifestyle, environmental and psychosocial factors. Adiposity, low hip circumference, serum biomarkers (increased level of alanine aminotransferase, gamma-glutamyl transferase, uric acid and C-reactive protein, and decreased level of adiponectin and vitamin D), an unhealthy dietary pattern (increased consumption of processed meat and sugar-sweetened beverages, decreased intake of whole grains, coffee and heme iron, and low adherence to a healthy dietary pattern), low level of education and conscientiousness, decreased physical activity, high sedentary time and duration of television watching, low alcohol drinking, smoking, air pollution, and some medical conditions (high systolic blood pressure, late menarche age, gestational diabetes, metabolic syndrome, preterm birth) presented robust evidence for increased risk of T2DM. CONCLUSIONS: A healthy lifestyle pattern could lead to decreased risk for T2DM. Future randomized clinical trials should focus on identifying efficient strategies to modify harmful daily habits and predisposing dietary patterns.

Systematic assessment of environmental risk factors for bipolar disorder: an umbrella review of systematic reviews and meta-analyses.

OBJECTIVES: The pathophysiology of bipolar disorder is likely to involve both genetic and environmental risk factors. In our study, we aimed to perform a systematic search of environmental risk factors for BD. In addition, we assessed possible hints of bias in this literature, and identified risk factors supported by high epidemiological credibility. METHODS: We searched the Pubmed/MEDLINE, EMBASE and PsycInfo databases up to 7 October 2016 to identify systematic reviews and meta-analyses of observational studies that assessed associations between putative environmental risk factors and BD. For each meta-analysis, we estimated its summary effect size by means of both random- and fixed-effects models, 95% confidence intervals (CIs), the 95% prediction interval, and heterogeneity. Evidence of small-study effects and excess of significance bias was also assessed. RESULTS: Sixteen publications met the inclusion criteria (seven meta-analyses and nine qualitative systematic reviews). Fifty-one unique environmental risk factors for BD were evaluated. Six meta-analyses investigated associations with a risk factor for BD. Only irritable bowel syndrome (IBS) emerged as a risk factor for BD supported by convincing evidence (k=6; odds ratio [OR]=2.48; 95% CI=2.35-2.61; P<.001), and childhood adversity was supported by highly suggestive evidence. Asthma and obesity were risk factors for BD supported by suggestive evidence, and seropositivity to Toxoplasma gondii and a history of head injury were supported by weak evidence. CONCLUSIONS: Notwithstanding that several environmental risk factors for BD were identified, few meta-analyses of observational studies were available. Therefore, further well-designed and adequately powered studies are necessary to map the environmental risk factors for BD.

Systematic evaluation of the associations between environmental risk factors and dementia: An umbrella review of systematic reviews and meta-analyses.

INTRODUCTION: Dementia is a heterogeneous neurodegenerative disease, whose etiology results from a complex interplay between environmental and genetic factors. METHODS: We searched PubMed to identify meta-analyses of observational studies that examined associations between nongenetic factors and dementia. We estimated the summary effect size using random-effects and fixed-effects model, the 95% CI, and the 95% prediction interval. We assessed the between-study heterogeneity (I-square), evidence of small-study effects, and excess significance. RESULTS: A total of 76 unique associations were examined. By applying standardized criteria, seven associations presented convincing evidence. These associations pertained to benzodiazepines use, depression at any age, late-life depression, and frequency of social contacts for all types of dementia; late-life depression for Alzheimer's disease; and type 2 diabetes mellitus for vascular dementia and Alzheimer's disease. DISCUSSION: Several risk factors present substantial evidence for association with dementia and should be assessed as potential targets for interventions, but these associations may not necessarily be causal.

Birth weight in relation to health and disease in later life: an umbrella review of systematic reviews and meta-analyses.

BACKGROUND: Birth weight, a marker of the intrauterine environment, has been extensively studied in epidemiological research in relation to subsequent health and disease. Although numerous meta-analyses have been published examining the association between birth weight and subsequent health-related outcomes, the epidemiological credibility of these associations has not been thoroughly assessed. The objective of this study is to map the diverse health outcomes associated with birth weight and evaluate the credibility and presence of biases in the reported associations. METHODS: An umbrella review was performed to identify systematic reviews and meta-analyses of observational studies investigating the association between birth weight and subsequent health outcomes and traits. For each association, we estimated the summary effect size by random-effects and fixed-effects models, the 95 % confidence interval, and the 95 % prediction interval. We also assessed the between-study heterogeneity, evidence for small-study effects and excess significance bias. We further applied standardized methodological criteria to evaluate the epidemiological credibility of the statistically significant associations. RESULTS: Thirty-nine articles including 78 associations between birth weight and diverse outcomes met the eligibility criteria. A wide range of health outcomes has been studied, ranging from anthropometry and metabolic diseases, cardiovascular diseases and cardiovascular risk factors, various cancers, respiratory diseases and allergies, musculoskeletal traits and perinatal outcomes. Forty-seven of 78 associations presented a nominally significant summary effect and 21 associations remained statistically significant at P < 1 × 10-6. Thirty associations presented large or very large between-study heterogeneity. Evidence for small-study effects and excess significance bias was present in 13 and 16 associations, respectively. One association with low birth weight (increased risk for all-cause mortality), two dose-response associations with birth weight (higher bone mineral concentration in hip and lower risk for mortality from cardiovascular diseases per 1 kg increase in birth weight) and one association with small-for-gestational age infants with normal birth weight (increased risk for childhood stunting) presented convincing evidence. Eleven additional associations had highly suggestive evidence. CONCLUSIONS: The range of outcomes convincingly associated with birth weight might be narrower than originally described under the "fetal origin hypothesis" of disease. There is weak evidence that birth weight constitutes an effective public health intervention marker.

Non-genetic risk factors for cutaneous melanoma and keratinocyte skin cancers: An umbrella review of meta-analyses.

BACKGROUND: Skin cancers have a complex disease mechanism, involving both genetic and non-genetic risk factors. Numerous meta-analyses have been published claiming statistically significant associations between non-genetic risk factors and skin cancers without applying a thorough methodological assessment. OBJECTIVE: The present study maps the literature on the non-genetic risk factors of skin cancers, assesses the presence of statistical biases and identifies the associations with robust evidence. METHODS: We searched PubMed up to January 20, 2016 to identify systematic reviews and meta-analyses of observational studies that examined associations between non-genetic factors and skin cancers. For each meta-analysis, we estimated the summary effect size by random-effects and fixed-effects models, the 95% confidence interval and the 95% prediction interval. We also assessed the between-study heterogeneity (I2 metric), evidence for small-study effects and excess significance bias. RESULTS: Forty-four eligible papers were identified and included a total of 85 associations. Twenty-one associations were significant at P<10-6. Fifty-two associations had large or very large heterogeneity. Evidence for small-study effects and excess significance bias was found in fifteen and thirteen associations, respectively. Overall, thirteen associations (actinic keratosis, serum vitamin D, sunburns, and hair color for basal cell carcinoma and density of freckles, eye color, hair color, history of melanoma, skin type, sunburns, premalignant skin lesions, common and atypical nevi for melanoma) presented high level of credibility. CONCLUSION: The majority of meta-analyses on non-genetic risk factors for skin cancers suffered from large between-study heterogeneity and small-study effects or excess significance bias. The associations with convincing and highly suggestive evidence were mainly focused on skin photosensitivity and phenotypic characteristics.

Environmental Risk Factors and Amyotrophic Lateral Sclerosis: An Umbrella Review and Critical Assessment of Current Evidence from Systematic Reviews and Meta-Analyses of Observational Studies.

BACKGROUND: The pathogenesis of amyotrophic lateral sclerosis (ALS) involves both environmental and genetic factors. Our study aimed at summarising the environmental risk factors for ALS, assessing the evidence for diverse biases, and pinpointing risk factors with high epidemiological credibility. METHODS: We searched PubMed from inception to August 20, 2015, to identify systematic reviews and meta-analyses of observational studies examining associations between environmental factors and ALS. For each meta-analysis, we estimated the summary effect size by the use of random-effects and fixed-effects models, the 95% CI, the 95% prediction interval (PI), and the between-study heterogeneity. We assessed the evidence of small-study effects and excess significance bias. RESULTS: Sixteen unique meta-analyses of different risk factors and ALS were considered. Of them, 5 were statistically significant at p < 0.001 under the random-effects model. Only one factor presented robust evidence for a convincing association. This association pertained to chronic occupational exposure to lead (random-effects OR 1.81, 95% CI 1.39-2.35). CONCLUSIONS: A small number of published meta-analyses on environmental factors and risk of ALS was identified, a phenomenon that could be attributed to the challenges in studying a rare neurological disease. More observational studies with adequate sample size and study design are needed to clarify the environmental component of ALS pathogenesis.