The association of depression and all-cause and cause-specific mortality: an umbrella review of systematic reviews and meta-analyses.

BACKGROUND: Depression is a prevalent and disabling mental disorder that frequently co-occurs with a wide range of chronic conditions. Evidence has suggested that depression could be associated with excess all-cause mortality across different settings and populations, although the causality of these associations remains unclear. METHODS: We conducted an umbrella review of systematic reviews and meta-analyses of observational studies. PubMed, PsycINFO, and Embase electronic databases were searched through January 20, 2018. Systematic reviews and meta-analyses that investigated associations of depression and all-cause and cause-specific mortality were selected for the review. The evidence was graded as convincing, highly suggestive, suggestive, or weak based on quantitative criteria that included an assessment of heterogeneity, 95% prediction intervals, small-study effects, and excess significance bias. RESULTS: A total of 26 references providing 2 systematic reviews and data for 17 meta-analytic estimates met inclusion criteria (19 of them on all-cause mortality); data from 246 unique studies (N = 3,825,380) were synthesized. All 17 associations had P < 0.05 per random effects summary effects, but none of them met criteria for convincing evidence. Associations of depression and all-cause mortality in patients after acute myocardial infarction, in individuals with heart failure, in cancer patients as well as in samples from mixed settings met criteria for highly suggestive evidence. However, none of the associations remained supported by highly suggestive evidence in sensitivity analyses that considered studies employing structured diagnostic interviews. In addition, associations of depression and all-cause mortality in cancer and post-acute myocardial infarction samples were supported only by suggestive evidence when studies that tried to adjust for potential confounders were considered. CONCLUSIONS: Even though associations between depression and mortality have nominally significant results in all assessed settings and populations, the evidence becomes weaker when focusing on studies that used structured interviews and those that tried to adjust for potential confounders. A causal effect of depression on all-cause and cause-specific mortality remains unproven, and thus interventions targeting depression are not expected to result in lower mortality rates at least based on current evidence from observational studies.

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.

Physical activity and cancer: an umbrella review of the literature including 22 major anatomical sites and 770 000 cancer cases.

OBJECTIVE: To provide an overview of the breadth and validity of claimed associations between physical activity and risk of developing or dying from cancer. DESIGN: Umbrella review. DATA SOURCES: We searched Medline, Embase, Cochrane Database and Web of Science. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Systematic reviews about physical activity and cancer incidence and cancer mortality in different body sites among general population. RESULTS: We included 19 reviews covering 22 cancer sites, 26 exposure-outcome pairs meta-analyses and 541 original studies. Physical activity was associated with lower risk of seven cancer sites (colon, breast, endometrial, lung, oesophageal, pancreas and meningioma). Only colon (a protective association with recreational physical activity) and breast cancer (a protective association with overall physical activity) were supported by strong evidence and highly suggestive evidence, respectively. Evidence from endometrial, lung, oesophageal, pancreas and meningioma presented hints of uncertainty and bias in the literature (eg, not reaching P values<10-6) showing large between-study heterogeneity and/or not demonstrating a definite direction for the effect when 95% prediction intervals were considered. Four of the 26 meta-analyses showed small study effects and 4 showed excess significance. CONCLUSION: Physical activity is associated with a lower risk of several cancers, but only colon and breast cancer associations were supported by strong or highly suggestive evidence, respectively. Evidence from other cancer sites was less consistent, presenting hints of uncertainty and/or bias.

Short-term economic impact of the Zika virus outbreak.

Zika virus (ZIKV) is mainly transmitted by mosquitoes bites. However, transmission by sexual contacts has been reported in 11 non endemic countries. The rapid spread of ZIKV in Latin American and Caribbean Countries (LCR), person-to-person transmission and perceived risk on people's well being can affect the emerging economies of LCR which historically dependent on truism. Here we present an analysis on economic outputs for assessing the current impact of ZIKV on markets. Our analysis show an unexpected resilience of LCR markets to international alerts. This positive response represents an opportunity to scale-up interventions for preventing the further spreading of the ZIKV epidemic.

Association between obesity and postoperative atrial fibrillation in patients undergoing cardiac operations: a systematic review and meta-analysis.

In a systematic review and random-effects meta-analysis, we evaluated whether obesity is associated with postoperative atrial fibrillation (POAF) in patients undergoing cardiac operations. We selected 18 observational studies until December 2011 that excluded patients with preoperative AF (n=36,147). Obese patients had a modest higher risk of POAF compared with nonobese (odds ratio, 1.12; 95% confidence interval, 1.04 to 1.21; p=0.002). The association between obesity and POAF did not vary substantially by type of cardiac operation, study design, or year of publication. POAF was significantly associated with a higher risk of stroke, respiratory failure, and operative death.

Empirical evaluation of very large treatment effects of medical interventions.

CONTEXT: Most medical interventions have modest effects, but occasionally some clinical trials may find very large effects for benefits or harms. OBJECTIVE: To evaluate the frequency and features of very large effects in medicine. DATA SOURCES: Cochrane Database of Systematic Reviews (CDSR, 2010, issue 7). STUDY SELECTION: We separated all binary-outcome CDSR forest plots with comparisons of interventions according to whether the first published trial, a subsequent trial (not the first), or no trial had a nominally statistically significant (P < .05) very large effect (odds ratio [OR], ≥5). We also sampled randomly 250 topics from each group for further in-depth evaluation. DATA EXTRACTION: We assessed the types of treatments and outcomes in trials with very large effects, examined how often large-effect trials were followed up by other trials on the same topic, and how these effects compared against the effects of the respective meta-analyses. RESULTS: Among 85,002 forest plots (from 3082 reviews), 8239 (9.7%) had a significant very large effect in the first published trial, 5158 (6.1%) only after the first published trial, and 71,605 (84.2%) had no trials with significant very large effects. Nominally significant very large effects typically appeared in small trials with median number of events: 18 in first trials and 15 in subsequent trials. Topics with very large effects were less likely than other topics to address mortality (3.6% in first trials, 3.2% in subsequent trials, and 11.6% in no trials with significant very large effects) and were more likely to address laboratory-defined efficacy (10% in first trials,10.8% in subsequent, and 3.2% in no trials with significant very large effects). First trials with very large effects were as likely as trials with no very large effects to have subsequent published trials. Ninety percent and 98% of the very large effects observed in first and subsequently published trials, respectively, became smaller in meta-analyses that included other trials; the median odds ratio decreased from 11.88 to 4.20 for first trials, and from 10.02 to 2.60 for subsequent trials. For 46 of the 500 selected topics (9.2%; first and subsequent trials) with a very large-effect trial, the meta-analysis maintained very large effects with P < .001 when additional trials were included, but none pertained to mortality-related outcomes. Across the whole CDSR, there was only 1 intervention with large beneficial effects on mortality, P < .001, and no major concerns about the quality of the evidence (for a trial on extracorporeal oxygenation for severe respiratory failure in newborns). CONCLUSIONS: Most large treatment effects emerge from small studies, and when additional trials are performed, the effect sizes become typically much smaller. Well-validated large effects are uncommon and pertain to nonfatal outcomes.

Comparative effectiveness of medical interventions in adults versus children.

OBJECTIVE: To estimate the comparative effectiveness of medical interventions in adults versus children. STUDY DESIGN: We identified from the Cochrane Database of Systematic Reviews (Issue 1, 2007) meta-analyses with data on at least 1 adult and 1 pediatric randomized trial with binary primary efficacy outcome. For each meta-analysis, we calculated the summary odds ratio of the adult trials and the pediatric trials, respectively; the relative odds ratio (ROR) of the adult versus pediatric odds ratios per meta-analysis; and the summary ROR across all meta-analyses. ROR <1 means that the experimental intervention is more unfavorable in children than adults. RESULTS: Across 128 eligible meta-analyses (1051 adult and 343 pediatric trials), the summary ROR did not show a statistically significant difference between adults and children (0.96; 95% confidence intervals, 0.86 to 1.08). However, in all meta-analyses except for 1, the individual ROR's 95% confidence intervals could not exclude a relative difference in efficacy over 20%. In two-thirds, the relative difference in observed point estimates exceeded 50%. Nine statistically significant discrepancies were identified; 4 of them were also clinically important. CONCLUSIONS: Treatment effects are on average similar in adults and children, but available evidence leaves large uncertainty about their relative efficacy. Clinically important discrepancies may occur.

Critical interpretation of Cochran's Q test depends on power and prior assumptions about heterogeneity.

We describe how an appropriate interpretation of the Q-test depends on its power to detect a given typical amount of between-study variance (τ(2)) as well as prior beliefs on heterogeneity. We illustrate these concepts in an evaluation of 1011 meta-analyses of clinical trials with ⩾4 studies and binary outcomes. These concepts can be seen as an application of the Bayes theorem. Across the 1011 meta-analyses, power to detect typical heterogeneity was low in most situations. Thus, usually a non-significant Q test did not change perceptibly prior convictions on heterogeneity. Conversely, significant results for the Q test typically augmented considerably the probability of heterogeneity. The posterior probability of heterogeneity depends on what τ(2) we want to detect. With the same approach, one may also estimate the posterior probability for the presence of heterogeneity that is large enough to annul statistically significant summary effects; that is half the average within-study variance of the combined studies; and that is able to change the summary effect estimate of the meta-analysis by 20%. The discussed analyses are exploratory, and may depend heavily on prior assumptions when power for the Q-test is low. Statistical heterogeneity in meta-analyses should be cautiously interpreted considering the power to detect a specific τ(2) and prior assumptions about the presence of heterogeneity. Copyright © 2010 John Wiley & Sons, Ltd.

Discovery properties of genome-wide association signals from cumulatively combined data sets.

Genetic effects for common variants affecting complex disease risk are subtle. Single genome-wide association (GWA) studies are typically underpowered to detect these effects, and combination of several GWA data sets is needed to enhance discovery. The authors investigated the properties of the discovery process in simulated cumulative meta-analyses of GWA study-derived signals allowing for potential genetic model misspecification and between-study heterogeneity. Variants with null effects on average (but also between-data set heterogeneity) could yield false-positive associations with seemingly homogeneous effects. Random effects had higher than appropriate false-positive rates when there were few data sets. The log-additive model had the lowest false-positive rate. Under heterogeneity, random-effects meta-analyses of 2-10 data sets averaging 1,000 cases/1,000 controls each did not increase power, or the meta-analysis was even less powerful than a single study (power desert). Upward bias in effect estimates and underestimation of between-study heterogeneity were common. Fixed-effects calculations avoided power deserts and maximized discovery of association signals at the expense of much higher false-positive rates. Therefore, random- and fixed-effects models are preferable for different purposes (fixed effects for initial screenings, random effects for generalizability applications). These results may have broader implications for the design and interpretation of large-scale multiteam collaborative studies discovering common gene variants.