ObjectivesWe aimed to develop a systematic synthesis of systematic reviews of health impacts of climate change, by synthesising studies’ characteristics, climate impacts, health outcomes and key findings.DesignWe conducted an overview of systematic reviews of health impacts of climate change. We registered our review in PROSPERO (CRD42019145972). No ethical approval was required since we used secondary data. Additional data are not available.Data sourcesOn 22 June 2019, we searched Medline, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Cochrane and Web of Science.Eligibility criteriaWe included systematic reviews that explored at least one health impact of climate change.Data extraction and synthesisWe organised systematic reviews according to their key characteristics, including geographical regions, year of publication and authors’ affiliations. We mapped the climate effects and health outcomes being studied and synthesised major findings. We used a modified version of A MeaSurement Tool to Assess systematic Reviews-2 (AMSTAR-2) to assess the quality of studies.ResultsWe included 94 systematic reviews. Most were published after 2015 and approximately one-fifth contained meta-analyses. Reviews synthesised evidence about five categories of climate impacts; the two most common were meteorological and extreme weather events. Reviews covered 10 health outcome categories; the 3 most common were (1) infectious diseases, (2) mortality and (3) respiratory, cardiovascular or neurological outcomes. Most reviews suggested a deleterious impact of climate change on multiple adverse health outcomes, although the majority also called for more research.ConclusionsMost systematic reviews suggest that climate change is associated with worse human health. This study provides a comprehensive higher order summary of research on health impacts of climate change. Study limitations include possible missed relevant reviews, no meta-meta-analyses, and no assessment of overlap. Future research could explore the potential explanations between these associations to propose adaptation and mitigation strategies and could include broader sociopsychological health impacts of climate change.

The models most commonly used, to study the effects of psychosocial work factors on workers' health, are the Demand-Control-Support (DCS) model and Effort-Reward Imbalance (ERI) model. An emerging body of research has identified Organisational Justice as another model that can help to explain deleterious health effects. This review aimed: (1) to identify prospective studies of the associations between organisational justice and mental health in industrialised countries from 1990 to 2010; (2) to evaluate the extent to which organisational justice has an effect on mental health independently of the DCS and ERI models; and (3) to discuss theoretical and empirical overlap and differences with previous models. The studies had to present associations between organisational justice and a mental health outcome, be prospective, and be entirely available in English or in French. Duplicated papers were excluded. Eleven prospective studies were selected for this review. They provide evidence that procedural justice and relational justice are associated with mental health. These associations remained significant even after controlling for the DCS and ERI models. There is a lack of prospective studies on distributive and informational justice. In conclusion, procedural and relational justice can be considered a different and complementary model to the DCS and ERI models. Future studies should evaluate the effect of change in exposure to organisational justice on employees' mental health over time.

IMPORTANCE Mental health problems are associated with considerable occupational, medical, social, and economic burdens. Psychosocial stressors at work have been associated with a higher risk of mental disorders, but the risk of sickness absence due to a diagnosed mental disorder, indicating a more severe condition, has never been investigated in a systematic review and meta-analysis. OBJECTIVETo synthesize the evidence of the association of psychosocial stressors at work with sickness absence due to a diagnosed mental disorder among adult workers.DATA SOURCES Seven electronic databases (MEDLINE, Embase, PsycInfo, Web of Science, CINAHL, Sociological Abstracts, and International Bibliography of the Social Sciences), 3 gray literature databases (Grey Literature Report, WHO-IRIS and Open Grey), and the reference lists of all eligible studies and reviews were searched in January 2017 and updated in February 2019.STUDY SELECTION Only original prospective studies evaluating the association of at least 1 psychosocial stressor at work from the 3 most recognized theoretical models were eligible: the job demand-control-support model, including exposure to job strain (high psychological demands with low job control); effort-reward imbalance model; and organizational justice model. Study selection was performed in duplicate by blinded independent reviewers. Among the 28 467 citations screened, 23 studies were eligible for systematic review.DATA EXTRACTION AND SYNTHESIS This meta-analysis followed the PRISMA and MOOSE guidelines. Data extraction and risk of bias evaluation, using the Risk of Bias in Nonrandomized Studies-Interventions tool, were performed in duplicate by blinded independent reviewers. Data were pooled using random-effect models. MAIN OUTCOMES AND MEASURES Sickness absence due to a mental disorder with a diagnosis obtained objectively.RESULTS A total of 13 studies representing 130 056 participants were included in the 6 meta-analyses. Workers exposed to low reward were associated with a higher risk of sickness absence due to a diagnosed mental disorder compared with nonexposed workers (pooled risk ratio [RR], 1.76 [95% CI, 1.49-2.08]), as were those exposed to effort-reward imbalance (pooled RR, 1.66 [95% CI, 1.37-2.00]), job strain (pooled RR, 1.47 [95% CI, 1.24-1.74]), low job control (pooled RR, 1.25 [95% CI, 1.02-1.53]), and high psychological demands (pooled RR, 1.23 [95% CI, 1.04-1.45]).CONCLUSIONS AND RELEVANCE This meta-analysis found that workers exposed to psychosocial stressors at work were associated with a higher risk of sickness absence due to a mental disorder. A better understanding of the importance of these stressors could help physicians when evaluating their patients' mental health and work capacity.

Background Patient decision aids should help people make evidence-informed decisions aligned with their values. There is limited guidance about how to achieve such alignment. Purpose To describe the range of values clarification methods available to patient decision aid developers, synthesize evidence regarding their relative merits, and foster collection of evidence by offering researchers a proposed set of outcomes to report when evaluating the effects of values clarification methods. Data Sources MEDLINE, EMBASE, PubMed, Web of Science, the Cochrane Library, and CINAHL. Study Selection We included articles that described randomized trials of 1 or more explicit values clarification methods. From 30,648 records screened, we identified 33 articles describing trials of 43 values clarification methods. Data Extraction Two independent reviewers extracted details about each values clarification method and its evaluation. Data Synthesis Compared to control conditions or to implicit values clarification methods, explicit values clarification methods decreased the frequency of values-incongruent choices (risk difference, –0.04; 95% confidence interval [CI], –0.06 to –0.02; P < 0.001) and decisional conflict (standardized mean difference, –0.20; 95% CI, –0.29 to –0.11; P < 0.001). Multicriteria decision analysis led to more values-congruent decisions than other values clarification methods (χ2 = 9.25, P = 0.01). There were no differences between different values clarification methods regarding decisional conflict (χ2 = 6.08, P = 0.05). Limitations Some meta-analyses had high heterogeneity. We grouped values clarification methods into broad categories. Conclusions Current evidence suggests patient decision aids should include an explicit values clarification method. Developers may wish to specifically consider multicriteria decision analysis. Future evaluations of values clarification methods should report their effects on decisional conflict, decisions made, values congruence, and decisional regret.

Background. High-quality health decisions are often defined as those that are both evidence informed and values congruent. A values-congruent decision aligns with what matters to those most affected by the decision. Values clarification methods are intended to support values-congruent decisions, but their effects on values congruence are rarely evaluated. Methods. We tested 11 strategies, including the 3 most commonly used values clarification methods, across 6 between-subjects online randomized experiments in demographically diverse US populations ( n1 = 1346, n2 = 456, n3 = 840, n4 = 1178, n5 = 841, n6 = 2033) in the same hypothetical decision. Our primary outcome was values congruence. Decisional conflict was a secondary outcome in studies 3 to 6. Results. Two commonly used values clarification methods (pros and cons, rating scales) reduced decisional conflict but did not encourage values-congruent decisions. Strategies using mathematical models to show participants which option aligned with what mattered to them encouraged values-congruent decisions and reduced decisional conflict when assessed. Limitations. A hypothetical decision was necessary for ethical reasons, as we believed some strategies may harm decision quality. Later studies used more outcomes and covariates. Results may not generalize outside US-based adults with online access. We assumed validity and stability of values during the brief experiments. Conclusions. Failing to explicitly support the process of aligning options with values leads to increased proportions of values-incongruent decisions. Methods representing more than half of values clarification methods commonly in use failed to encourage values-congruent decisions. Methods that use models to explicitly show people how options align with their values offer more promise for helping people make decisions aligned with what matters to them. Decisional conflict, while arguably an important outcome in and of itself, is not an appropriate proxy for values congruence.