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Background: E-values are a recently introduced approach to evaluate confounding in observational studies. We aimed to empirically assess the current use of E-values in published literature.Methods: We conducted a systematic literature search for all publications, published up till the end of 2018, which cited at least one of two inceptive E-value papers and presented E-values for original data. For these case publications we identified control publications, matched by journal and issue, where the authors had not calculated E-values.Results: In total, 87 papers presented 516 E-values. Of the 87 papers, 14 concluded that residual confounding likely threatens at least some of the main conclusions. Seven of these 14 named potential uncontrolled confounders. 19 of 87 papers related E-value magnitudes to expected strengths of field-specific confounders. The median E-value was 1.88, 1.82, and 2.02 for the 43, 348, and 125 E-values where confounding was felt likely to affect the results, unlikely to affect the results, or not commented upon, respectively. The 69 case-control publication pairs dealt with effect sizes of similar magnitude. Of 69 control publications, 52 did not comment on unmeasured confounding and 44/69 case publications concluded that confounding was unlikely to affect study conclusions.Conclusions: Few papers using E-values conclude that confounding threatens their results, and their E-values overlap in magnitude with those of papers acknowledging susceptibility to confounding. Facile automation in calculating E-values may compound the already poor handling of confounding. E-values should not be a substitute for careful consideration of potential sources of unmeasured confounding. If used, they should be interpreted in the context of expected confounding in specific fields. [ABSTRACT FROM AUTHOR]

In the 1980s debate intensified over whether there was a protective effect of high-density lipoprotein cholesterol (HDL-C) or an adverse effect of triglycerides on coronary heart disease (CHD) risk. In a 1991 paper reprinted in the IJE we suggested that the high degree of correlation between the two, together with plausible levels of measurement error, made it unlikely that conventional epidemiological approaches could contribute to causal understanding. The consensus that HDL-C was protective, popularly reified in the notion of 'good cholesterol', strengthened over subsequent years. Reviewing the biostatistical and epidemiological literature from before and after 1991 we suggest that within the observational epidemiology pantheon only Mendelian randomization studies-that began to appear at the same time as the initial negative randomized controlled trials-made a meaningful contribution. It is sobering to realize that many issues that appear suitable targets for epidemiological investigation are simply refractory to conventional approaches. The discipline should surely revisit this and other high-profile cases of consequential epidemiological failure-such as that with respect to vitamin E supplementation and CHD risk-rather than pass them over in silence. [ABSTRACT FROM AUTHOR]

Background: Rigorously designed longitudinal studies can inform how best to reduce the widening health gap between Indigenous and non-Indigenous children.Methods: A systematic review was performed to identify and present the breadth and depth of longitudinal studies reporting the health and well-being of Indigenous children (aged 0-18 years) globally. Databases were searched up to 23 June 2020. Study characteristics were mapped according to domains of the life course model of health. Risk of bias was assessed using the National Institutes of Health (NIH) Study Quality Assessment Tools. Reported level of Indigenous involvement was also appraised; PROSPERO registration CRD42018089950.Results: From 5545 citations, 380 eligible papers were included for analysis, representing 210 individual studies. Of these, 41% were located in Australia (n = 88), 22.8% in the USA (n = 42), 11.9% in Canada (n = 25) and 10.9% in New Zealand (n = 23). Research tended to focus on either health outcomes (50.9%) or health-risk exposures (43.8%); 55% of studies were graded as 'good' quality; and 89% of studies made at least one reference to the involvement of Indigenous peoples over the course of their research.Conclusions: We identified gaps in the longitudinal assessment of cultural factors influencing Indigenous child health at the macrosocial level, including connection to culture and country, intergenerational trauma, and racism or discrimination. Future longitudinal research needs to be conducted with strong Indigenous leadership and participation including holistic concepts of health. This is critical if we are to better understand the systematic factors driving health inequities experienced by Indigenous children globally. [ABSTRACT FROM AUTHOR]

Good data curation is integral to cohort studies, but it is not always done to a level necessary to ensure the longevity of the data a study holds. In this opinion paper, we introduce the concept of data curation debt-the data curation equivalent to the software engineering principle of technical debt. Using the context of UK cohort studies, we define data curation debt-describing examples and their potential impact. We highlight that accruing this debt can make it more difficult to use the data in the future. Additionally, the long-running nature of cohort studies means that interest is accrued on this debt and compounded over time-increasing the impact a debt could have on a study and its stakeholders. Primary causes of data curation debt are discussed across three categories: longevity of hardware, software and data formats; funding; and skills shortages. Based on cross-domain best practice, strategies to reduce the debt and preventive measures are proposed-with importance given to the recognition and transparent reporting of data curation debt. Describing the debt in this way, we encapsulate a multi-faceted issue in simple terms understandable by all cohort study stakeholders. Data curation debt is not only confined to the UK, but is an issue the international community must be aware of and address. This paper aims to stimulate a discussion between cohort studies and their stakeholders on how to address the issue of data curation debt. If data curation debt is left unchecked it could become impossible to use highly valued cohort study data, and ultimately represents an existential risk to studies themselves. [ABSTRACT FROM AUTHOR]

Background: Directed acyclic graphs (DAGs) are of great help when researchers try to understand the nature of causal relationships and the consequences of conditioning on different variables. One fundamental feature of causal relations that has not been incorporated into the standard DAG framework is interaction, i.e. when the effect of one variable (on a chosen scale) depends on the value that another variable is set to. In this paper, we propose a new type of DAG-the interaction DAG (IDAG), which can be used to understand this phenomenon.Methods: The IDAG works like any DAG but instead of including a node for the outcome, it includes a node for a causal effect. We introduce concepts such as confounded interaction and total, direct and indirect interaction, showing that these can be depicted in ways analogous to how similar concepts are depicted in standard DAGs. This also allows for conclusions on which treatment interactions to account for empirically. Moreover, since generalizability can be compromised in the presence of underlying interactions, the framework can be used to illustrate threats to generalizability and to identify variables to account for in order to make results valid for the target population.Conclusions: The IDAG allows for a both intuitive and stringent way of illustrating interactions. It helps to distinguish between causal and non-causal mechanisms behind effect variation. Conclusions about how to empirically estimate interactions can be drawn-as well as conclusions about how to achieve generalizability in contexts where interest lies in estimating an overall effect. [ABSTRACT FROM AUTHOR]

Background: Systematic reviews of prenatal alcohol exposure effects generally only include conventional observational studies. However, estimates from such studies are prone to confounding and other biases.Objectives: To systematically review the evidence on the effects of prenatal alcohol exposure from randomized controlled trials (RCTs) and observational designs using alternative analytical approaches to improve causal inference.Search Strategy: Medline, Embase, Web of Science, PsychINFO from inception to 21 June 2018. Manual searches of reference lists of retrieved papers.Selection Criteria: RCTs of interventions to stop/reduce drinking in pregnancy and observational studies using alternative analytical methods (quasi-experimental studies e.g. Mendelian randomization and natural experiments, negative control comparisons) to determine the causal effects of prenatal alcohol exposure on pregnancy and longer-term offspring outcomes in human studies.Data Collection and Analysis: One reviewer extracted data and another checked extracted data. Risk of bias was assessed using customized risk of bias tools. A narrative synthesis of findings was carried out and a meta-analysis for one outcome.Main Results: Twenty-three studies were included, representing five types of study design, including 1 RCT, 9 Mendelian randomization and 7 natural experiment studies, and reporting on over 30 outcomes. One study design-outcome combination included enough independent results to meta-analyse. Based on evidence from several studies, we found a likely causal detrimental role of prenatal alcohol exposure on cognitive outcomes, and weaker evidence for a role in low birthweight.Conclusion: None of the included studies was judged to be at low risk of bias in all domains, results should therefore be interpreted with caution.Systematic Review Registration: This study is registered with PROSPERO, registration number CRD42015015941. [ABSTRACT FROM AUTHOR]

Causal inference requires theory and prior knowledge to structure analyses, and is not usually thought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential outcomes approaches, often include processing steps before the final estimation step. The purposes of this paper are: (i) to overview the recent emergence of prediction underpinning steps in contemporary causal inference methods as a useful perspective on contemporary causal inference methods, and (ii) explore the role of machine learning (as one approach to 'best prediction') in causal inference. Causal inference methods covered include propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum likelihood estimation (TMLE). Machine learning has been used more for propensity scores and TMLE, and there is potential for increased use in G computation and estimation of IPTWs. [ABSTRACT FROM AUTHOR]

Interrupted time series designs are a valuable quasi-experimental approach for evaluating public health interventions. Interrupted time series extends a single group pre-post comparison by using multiple time points to control for underlying trends. But history bias-confounding by unexpected events occurring at the same time of the intervention-threatens the validity of this design and limits causal inference. Synthetic control methodology, a popular data-driven technique for deriving a control series from a pool of unexposed populations, is increasingly recommended. In this paper, we evaluate if and when synthetic controls can strengthen an interrupted time series design. First, we summarize the main observational study designs used in evaluative research, highlighting their respective uses, strengths, biases and design extensions for addressing these biases. Second, we outline when the use of synthetic controls can strengthen interrupted time series studies and when their combined use may be problematic. Third, we provide recommendations for using synthetic controls in interrupted time series and, using a real-world example, we illustrate the potential pitfalls of using a data-driven approach to identify a suitable control series. Finally, we emphasize the importance of theoretical approaches for informing study design and argue that synthetic control methods are not always well suited for generating a counterfactual that minimizes critical threats to interrupted time series studies. Advances in synthetic control methods bring new opportunities to conduct rigorous research in evaluating public health interventions. However, incorporating synthetic controls in interrupted time series studies may not always nullify important threats to validity nor improve causal inference. [ABSTRACT FROM AUTHOR]

It has long been recognized that sample size calculations for cluster randomized trials require consideration of the correlation between multiple observations within the same cluster. When measurements are taken at anything other than a single point in time, these correlations depend not only on the cluster but also on the time separation between measurements and additionally, on whether different participants (cross-sectional designs) or the same participants (cohort designs) are repeatedly measured. This is particularly relevant in trials with multiple periods of measurement, such as the cluster cross-over and stepped-wedge designs, but also to some degree in parallel designs. Several papers describing sample size methodology for these designs have been published, but this methodology might not be accessible to all researchers. In this article we provide a tutorial on sample size calculation for cluster randomized designs with particular emphasis on designs with multiple periods of measurement and provide a web-based tool, the Shiny CRT Calculator, to allow researchers to easily conduct these sample size calculations. We consider both cross-sectional and cohort designs and allow for a variety of assumed within-cluster correlation structures. We consider cluster heterogeneity in treatment effects (for designs where treatment is crossed with cluster), as well as individually randomized group-treatment trials with differential clustering between arms, for example designs where clustering arises from interventions being delivered in groups. The calculator will compute power or precision, as a function of cluster size or number of clusters, for a wide variety of designs and correlation structures. We illustrate the methodology and the flexibility of the Shiny CRT Calculator using a range of examples. [ABSTRACT FROM AUTHOR]

The stepped-wedge cluster randomized trial (SW-CRT) involves the sequential transition of clusters (such as hospitals, public health units or communities) from control to intervention conditions in a randomized order. The use of the SW-CRT is growing rapidly. Yet the SW-CRT is at greater risks of bias compared with the conventional parallel cluster randomized trial (parallel-CRT). For this reason, the CONSORT extension for SW-CRTs requires that investigators provide a clear justification for the choice of study design. In this paper, we argue that all other things being equal, the SW-CRT is at greater risk of bias due to misspecification of the secular trends at the analysis stage. This is particularly problematic for studies randomizing a small number of heterogeneous clusters. We outline the potential conditions under which an SW-CRT might be an appropriate choice. Potentially appropriate and often overlapping justifications for conducting an SW-CRT include: (i) the SW-CRT provides a means to conduct a randomized evaluation which otherwise would not be possible; (ii) the SW-CRT facilitates cluster recruitment as it enhances the acceptability of a randomized evaluation either to cluster gatekeepers or other stakeholders; (iii) the SW-CRT is the only feasible design due to pragmatic and logistical constraints (for example the roll-out of a scare resource); and (iv) the SW-CRT has increased statistical power over other study designs (which will include situations with a limited number of clusters). As the number of arguments in favour of an SW-CRT increases, the likelihood that the benefits of using the SW-CRT, as opposed to a parallel-CRT, outweigh its risks also increases. We argue that the mere popularity and novelty of the SW-CRT should not be a factor in its adoption. In situations when a conventional parallel-CRT is feasible, it is likely to be the preferred design. [ABSTRACT FROM AUTHOR]

Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study's inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given. [ABSTRACT FROM AUTHOR]

Randomized controlled trials (RCTs)-the gold standard for evaluating the effects of medical interventions-are notoriously challenging in terms of logistics, planning and costs. The cohort multiple randomized controlled trial approach is designed to facilitate randomized trials for pragmatic evaluation of (new) interventions and is a promising variation from conventional pragmatic RCTs. In this paper, we evaluate methodological challenges of conducting an RCT within a cohort. We argue that equally valid results can be obtained from trials conducted within cohorts as from pragmatic RCTs. However, whether this design is more efficient compared with conducting a pragmatic RCT depends on the amount and nature of non-compliance in the intervention arm. [ABSTRACT FROM AUTHOR]

Risk and rate advancement periods (RAP) measure the impact of an exposure on the relation of age to disease. Specifically, they quantify the time by which the risk or rate of a disease is advanced among exposed subjects conditional on disease-free survival to a certain baseline age. The fact that these measures incorporate timing of disease occurrence makes them appealing for risk communication, and their use has increased over recent years. Unfortunately, their misinterpretations have also increased. In particular, RAP is often misinterpreted as the difference in mean survival time, when in fact it is a distinct concept and a profoundly different quantity. Other misinterpretations include interpreting RAP as the time by which a survival curve is shifted between exposed and unexposed, and equating RAP to a simple function of relative risks. In this paper we review RAP and show why common misinterpretations are flawed. We also show how RAP is profoundly sensitive to the specification of disease dependence on age. We conclude that whereas RAP remains a potentially useful measure, it requires multiple cautions beyond those needed for traditional measures of association, especially when computed for rates. [ABSTRACT FROM AUTHOR]

The generalization of flexible labour markets, the declining influence of unions and the degradation of social protection has led to the emergence of new forms of employment at the expense of the Standard Employment Relationship, as well as a considerable amount of research across social and scientific disciplines. Years ago we suggested the urgent need to disentangle the consequences of new types of employment for the health and well-being of workers, contending that the study of precarious employment and health is in its infancy. Today, research challenges include clearer, more precise definitions of the original concepts, a more detailed understanding of the pathways and mechanisms through which precarious employment harms worker health, stronger information systems for monitoring the problem and a complex systems approach to employment conditions and health research. All of these must be guided by the theoretical and policy debates linking precarious employment and health, and be geared towards developing better tools for the design, implementation and evaluation of policies intended to minimize precariousness in the labour market and its effects on public health and health inequalities. Our aim in this paper is to outline an agenda for the next decade of research on precarious employment and health, establishing a compelling programme that expands our understanding of complex causes and links. [ABSTRACT FROM AUTHOR]

Background: Understanding the relationship between different aspects of disadvantage over time and domains of child development will facilitate the formulation of more precise policy responses. We examined the association between exposure to aspects of disadvantage over the childhood period (from 0-9 years) and child development at 10-11 years.Methods: We used data from the nationally representative birth cohort of the Longitudinal Study of Australian Children (n = 4979). Generalized linear models with log-Poisson link were used to estimate the association between previously derived disadvantage trajectories (in each of four lenses of sociodemographic, geographic environments, health conditions and risk factors, and a composite of these) and risk of poor child developmental outcomes. Population-attributable fractions were calculated to quantify the potential benefit of providing all children with optimal conditions for each developmental outcome.Results: Trajectories of disadvantage were associated with developmental outcomes: children in the most disadvantaged composite trajectory had seven times higher risk of poor outcomes on two or more developmental domains, compared with those most advantaged. Trajectories of disadvantage in different lenses were varyingly associated with the child development domains of socio-emotional adjustment, physical functioning and learning competencies. Exposure to the most advantaged trajectory across all lenses could reduce poor developmental outcomes by as much as 70%.Conclusions: Exposure to disadvantage over time is associated with adverse child development outcomes. Developmental outcomes varied with the aspects of disadvantage experienced, highlighting potential targets for more precise policy responses. The findings provide evidence to stimulate advocacy and action to reduce child inequities. [ABSTRACT FROM AUTHOR]

Background: Estimates of the relative contribution of different pathogens to all-cause diarrhoea mortality are needed to inform global diarrhoea burden models and prioritize interventions. We aimed to investigate and estimate heterogeneity in the case fatality risk (CFR) of different diarrhoeal pathogens.Methods: We conducted a systematic review and meta-analysis of studies that reported cases and deaths for 15 enteric pathogens published between 1990 and 2019. The primary outcome was the pathogen-specific CFR stratified by age group, country-specific under-5 mortality rate, setting, study year and rotavirus vaccine introduction status. We developed fixed-effects and multilevel mixed-effects logistic regression models to estimate the pooled CFR overall and for each pathogen, controlling for potential predictors of heterogeneity.Results: A total of 416 studies met review criteria and were included in the analysis. The overall crude CFR for all pathogens was 0.65%, but there was considerable heterogeneity between and within studies. The overall CFR estimated from a random-effects model was 0.04% (95% CI: 0.026%-0.062%), whereas the pathogen-specific CFR estimates ranged from 0% to 2.7%. When pathogens were included as predictors of the CFR in the overall model, the highest and lowest odds ratios were found for enteropathogenic Escherichia coli (EPEC) [odds ratio (OR) = 3.0, 95% CI: 1.28-7.07] and rotavirus (OR = 0.23, 95% CI: 0.13-0.39), respectively.Conclusion: We provide comprehensive estimates of the CFR across different diarrhoeal pathogens and highlight pathogens for which more studies are needed. The results motivate the need for diarrhoeal interventions and could help prioritize pathogens for vaccine development. [ABSTRACT FROM AUTHOR]

Background: Children in low- and middle-income countries are particularly vulnerable in the months following an initial health event (IHE), with increased risk of mortality caused mostly by infectious diseases. Due to exposure to a wide range of environmental stressors, hospitalization in itself might increase child vulnerability at discharge. The goal of this study was to disentangle the role of hospitalization on the risk of subsequent infection.Methods: Data from a prospective, longitudinal, international, multicenter mother-and-child cohort were analysed. The main outcome assessed was the risk of subsequent infection within 3 months of initial care at hospital or primary healthcare facilities. First, risk factors for being hospitalized for the IHE (Step 1) and for having a subsequent infection (Step 2) were identified. Then, inpatients were matched with outpatients using propensity scores, considering the risk factors identified in Step 1. Finally, adjusted on the risk factors identified in Step 2, Cox regression models were performed on the matched data set to estimate the effect of hospitalization at the IHE on the risk of subsequent infection.Results: Among the 1312 children presenting an IHE, 210 (16%) had a subsequent infection, mainly lower-respiratory infections. Although hospitalization did not increase the risk of subsequent diarrhoea or unspecified sepsis, inpatients were 1.7 (95% Confidence Intervals [1.0-2.8]) times more likely to develop a subsequent lower-respiratory infection than comparable outpatients.Conclusion: For the first time, our findings suggest that hospitalization might increase the risk of subsequent lower-respiratory infection adjusted on severity and symptoms at IHE. This highlights the need for robust longitudinal follow-up of at-risk children and the importance of investigating underlying mechanisms driving vulnerability to infection. [ABSTRACT FROM AUTHOR]

Background: The association between body mass index (BMI) and oesophageal cancer (OC) has been consistently negative among Asians, whereas different associations based on histological OC subtypes have been observed in Europeans and North Americans. We examined the association between BMI and OC mortality in the Asia Cohort Consortium.Methods: We performed a pooled analysis to evaluate the association between BMI and OC mortality among 842 630 Asians from 18 cohort studies. Cox regression models were used to estimate hazard ratios (HRs) and 95% CIs.Results: A wide J-shaped association between BMI and overall OC mortality was observed. The OC mortality risk was increased for underweight (BMI <18.5 kg/m2: HR = 2.20, 95% CI 1.80-2.70) and extreme obesity (BMI ≥35 kg/m2: HR = 4.38, 95% CI 2.25-8.52) relative to the reference BMI (23-25 kg/m2). This association pattern was confirmed by several alternative analyses based on OC incidence and meta-analysis. A similar wide J-shaped association was observed in oesophageal squamous cell carcinoma (OSCC). Smoking and alcohol synergistically increased the OC mortality risk in underweight participants (HR = 6.96, 95% CI 4.54-10.67) relative to that in reference BMI participants not exposed to smoking and alcohol.Conclusion: Extreme obesity and being underweight were associated with an OC mortality risk among Asians. OC mortality and BMI formed a wide J-shaped association mirrored by OSCC mortality. Although the effect of BMI on OSCC and oesophageal adenocarcinoma mortality can be different in Asians, further research based on a large case-control study is recommended. [ABSTRACT FROM AUTHOR]

Background: Our hypothesis was that exposure to fine particulate matter (PM2.5) is related to abnormal cord insertion, which is categorized as a form of placental implantation abnormality. We investigated the association between exposure to total PM2.5 and its chemical components over the first trimester and abnormal cord insertion, which contributes to the occurrence of adverse birth outcomes.Methods: From the Japan Perinatal Registry Network database, we used data on 83 708 women who delivered singleton births at 39 cooperating hospitals in 23 Tokyo wards (2013-2015). We collected PM2.5 on a filter and measured daily concentrations of carbon and ion components. Then, we calculated the average concentrations over the first trimester (0-13 weeks of gestation) for each woman. A multilevel logistic-regression model with the hospital as a random effect was used to estimate the odds ratios (ORs) of abnormal cord insertion.Results: Among the 83 708 women (mean age at delivery = 33.7 years), the frequency of abnormal cord insertion was 4.5%, the median concentration [interquartile range (IQR)] of total PM2.5 was 16.1 (3.61) μg/m3 and the OR per IQR for total PM2.5 was 1.14 (95% confidence interval = 1.06-1.23). In the total PM2.5-adjusted models, total carbon, organic carbon, nitrate, ammonium and chloride were positively associated with abnormal insertion. Organic carbon was consistently, and nitrate tended to be, associated with specific types of abnormal insertion (marginal or velamentous cord insertion).Conclusions: Exposure to total PM2.5 and some of its components over the first trimester increased the likelihood of abnormal cord insertion. [ABSTRACT FROM AUTHOR]

Background: We studied the impact of fine particulate matter (PM2.5) exposure due to a remote wildfire event in the Pacific Northwest on daily outpatient respiratory and cardiovascular physician visits during wildfire (24-31 August, 2015) and post-wildfire period (1-30 September, 2015) relative to the pre-wildfire period (1-23 August, 2015) in the city of Calgary, Canada.Methods: A quasi-Poisson regression model was used for modelling daily counts of physician visits due to PM2.5 while adjusting for day of the week (weekday versus weekend or public holiday), wildfire exposure period (before, during, after), methane, relative humidity, and wind direction. A subgroup analysis of those with pre-existing diabetes or hypertension was performed.Results: An elevated risk of respiratory disease morbidity of 33% (relative risk: RR) [95% confidence interval (CI): 10%-59%] and 55% (95% CI: 42%-69%) was observed per 10µg/m3 increase in PM2.5 level during and after wildfire, respectively, relative to the pre-wildfire time period. Increased risk was observed for children aged 0-9 years during (RR = 1.57, 95% CI: 1.21-2.02) and after the wildfire (RR = 2.11, 95% CI: 1.86-2.40) especially for asthma, acute bronchitis and acute respiratory infection. The risk of physician visits among seniors increased by 11% (95% CI: 3%-21%), and 19% (95% CI: 7%-33%) post-wildfire for congestive heart failure and ischaemic heart disease, respectively. Individuals with pre-existing diabetes had an increased risk of both respiratory and cardiovascular morbidity in the post-wildfire period (RR = 1.35, 95% CI: 1.09-1.67; RR = 1.22, 95% CI: 1.01-1.46, respectively).Conclusions: Wildfire-related PM2.5 exposure led to increased respiratory condition-related outpatient physician visits during and after wildfires, particularly for children. An increased risk of physician visits for congestive heart failure and ischaemic heart disease among seniors in the post-wildfire period was also observed. [ABSTRACT FROM AUTHOR]

Background: Little is known about the long-term health impacts of exposures to landscape fire smoke. We aimed to evaluate the association between exposure to coal mine fire-related particulate matter 2.5 μm or less in diameter (PM2.5) and hospitalisation in the 5 years following the 6-week Hazelwood coal mine fire in Australia in 2014.Methods: We surveyed 2725 residents (mean age: 58.3 years; 54.3% female) from an exposed and a comparison town. Individual PM2.5 exposures during the event were estimated using modelled PM2.5 concentrations related to the coal mine fire and self-reported location data. The individual exposure and survey data were linked with hospitalisation records between January 2009 and February 2019. Recurrent event survival analysis was used to evaluate relationships between PM2.5 exposure and hospitalisation following mine fire, adjusting for important covariates.Results: Each 10-µg/m3 increase in mine fire-related PM2.5 was associated with a 9% increased hazard [hazard ratio (HR) = 1.09; 95% confidence interval (CI): 1.01, 1.17] of respiratory hospitalisation over the next 5 years, with stronger associations observed for females (HR = 1.16; 95% CI: 1.06, 1.27) than males (HR = 0.99; 95% CI: 0.89, 1.11). In particular, increased hazards were observed for hospitalisations for asthma (HR = 1.43; 95% CI: 1.19, 1.73) and chronic obstructive pulmonary disease (HR = 1.14; 95% CI: 1.02, 1.28). No such association was found for hospitalisations for cardiovascular diseases, mental illness, injuries, type 2 diabetes, renal diseases or neoplasms.Conclusions: A 6-week exposure to coal mine fire-related PM2.5 was associated with increased hazard of respiratory hospitalisations over the following 5 years, particularly for females. [ABSTRACT FROM AUTHOR]

Background: Exercise may increase the inhalation and deposition of air pollutants, which may counteract its beneficial effects. We thus examined the combined effects of chronic exposure to fine particulate matter (PM2.5) and habitual exercise on the risk of death from cancer in Taiwan.Patients and Methods: A total of 384 128 adults (≥18 years of age) were recruited for a medical screening programme between 2001 and 2016, yielding 842 384 medical-examination records. All participants were followed up until 31 May 2019. Vital data were obtained from the National Death Registry of Taiwan and the ambient PM2.5 exposure was estimated using a satellite-based spatiotemporal model. Information on habitual exercise was collected using a standard self-administered questionnaire. The time-dependent Cox-regression model was used to evaluate the combined effects.Results: A greater amount of habitual exercise was associated with lower risk of death from cancer, whilst a higher level of PM2.5 exposure was associated with a higher risk of death from cancer. The inverse associations of habitual exercise with death from cancer were not modified by chronic exposure to PM2.5. The participants in the group with a high level of exercise and a low level of PM2.5 exposure exhibited a 35% lower risk of death from cancer than those in the group with a low level of exercise and a high level of PM2.5 exposure (95% confidence interval: 28%, 42%).Conclusions: Increased levels of exercise and reduced exposure levels of PM2.5 are associated with a lower risk of death from cancer. Habitual exercise reduces the risk of death from cancer regardless of the levels of chronic PM2.5 exposure. Our results indicate that habitual exercise is a suitable health-promotion strategy even for people who reside in moderately polluted regions. [ABSTRACT FROM AUTHOR]

Background: Infectious disease outbreaks present unique challenges to study designs for vaccine evaluation. Test-negative design (TND) studies have previously been used to estimate vaccine effectiveness and have been proposed for Ebola virus disease (EVD) vaccines. However, there are key differences in how cases and controls are recruited during outbreaks and pandemics of novel pathogens, whcih have implications for the reliability of effectiveness estimates using this design.Methods: We use a modelling approach to quantify TND bias for a prophylactic vaccine under varying study and epidemiological scenarios. Our model accounts for heterogeneity in vaccine distribution and for two potential routes to testing and recruitment into the study: self-reporting and contact-tracing. We derive conventional and hybrid TND estimators for this model and suggest ways to translate public health response data into the parameters of the model.Results: Using a conventional TND study, our model finds biases in vaccine effectiveness estimates. Bias arises due to differential recruitment from self-reporting and contact-tracing, and due to clustering of vaccination. We estimate the degree of bias when recruitment route is not available, and propose a study design to eliminate the bias if recruitment route is recorded.Conclusions: Hybrid TND studies can resolve the design bias with conventional TND studies applied to outbreak and pandemic response testing data, if those efforts collect individuals' routes to testing. Without route to testing, other epidemiological data will be required to estimate the magnitude of potential bias in a conventional TND study. Since these studies may need to be conducted retrospectively, public health responses should obtain these data, and generic protocols for outbreak and pandemic response studies should emphasize the need to record routes to testing. [ABSTRACT FROM AUTHOR]

Background: Urban greening may reduce loneliness by offering opportunities for solace, social reconnection and supporting processes such as stress relief. We (i) assessed associations between residential green space and cumulative incidence of, and relief from, loneliness over 4 years; and (ii) explored contingencies by age, sex, disability and cohabitation status.Methods: Multilevel logistic regressions of change in loneliness status in 8049 city-dwellers between 2013 (baseline) and 2017 (follow-up) in the Household, Income and Labour Dynamics in Australia study. Associations with objectively measured discrete green-space buffers (e.g. parks) (<400, <800 and <1600 m) were adjusted for age, sex, disability, cohabitation status, children and socio-economic variables. Results were translated into absolute risk reductions in loneliness per 10% increase in urban greening.Results: The absolute risk of loneliness rose from 15.9% to 16.9% over the 4 years; however, a 10% increase in urban greening within 1.6 km was associated with lower cumulative incident loneliness [odds ratio (OR) = 0.927, 95% confidence interval (CI) = 0.862 to 0.996; absolute risk reduction = 0.66%]. Stronger association was observed for people living alone (OR = 0.828, 95% CI = 0.725 to 0.944). In comparison to people with <10% green space, the ORs for cumulative incident loneliness were 0.833 (95% CI = 0.695 to 0.997), 0.790 (95% CI = 0.624 to 1.000) and 0.736 (95% CI = 0.549 to 0.986) for 10-20%, 20-30% and >30% green space, respectively. Compared with the <10% green-space reference group with 13.78% incident loneliness over 4 years and conservatively assuming no impact on incident loneliness, associations translated into absolute risk reductions of 1.70%, 2.26% and 2.72% within populations with 10-20%, 20-30% and >30% green space, respectively. These associations were stronger again for people living alone, with 10-20% (OR = 0.608, 95% CI = 0.448 to 0.826), 20-30% (OR = 0.649, 95% CI = 0.436 to 0.966) and >30% (OR = 0.480, 95% CI = 0.278 to 0.829) green space within 1600 m. No age, sex or disability-related contingencies, associations with green space within 400 or 800 m or relief from loneliness reported at baseline were observed.Conclusions: A lower cumulative incidence of loneliness was observed among people with more green space within 1600 m of home, especially for people living alone. Potential biopsychosocial mechanisms warrant investigation. [ABSTRACT FROM AUTHOR]

Background: The epidemiological association between type 2 diabetes and cataract has been well established. However, it remains unclear whether the two diseases share a genetic basis, and if so, whether this reflects a putative causal relationship.Methods: We used East Asian population-based genome-wide association studies (GWAS) summary statistics of type 2 diabetes (Ncase = 36 614, Ncontrol = 155 150) and cataract (Ncase = 24 622, Ncontrol = 187 831) to comprehensively investigate the shared genetics between the two diseases. We performed: (i) linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) to estimate the genetic correlation and local genetic correlation pattern between type 2 diabetes and cataract; (ii) multiple Mendelian randomization (MR) analyses to infer the putative causality between type 2 diabetes and cataract; and (iii) summary-data-based Mendelian randomization (SMR) to identify candidate risk genes underling the putative causality. Moreover, to investigate the extent of the population-specific genetic effect size underlying the shared genetics between type 2 diabetes and cataract, we applied the same analytical pipeline to perform a comparative analysis on European population-based GWAS of type 2 diabetes (Ncase = 62 892, Ncontrol = 596 424) and cataract (Ncase = 5045, Ncontrol = 356 096).Results: Using East Asian population-based GWAS summary data, we observed a strong genetic correlation [rg = 0.58, 95% confidence interval (CI) = 0.33, 0.83), P-value = 5.60 × 10-6] between type 2 diabetes and cataract. Both ρ-HESS and multiple MR methods consistently showed a putative causal effect of type 2 diabetes on cataract, with estimated liability-scale MR odds ratios (ORs) at around 1.10 (95% CI = 1.06, 1.17). In contrast, no evidence supports a causal effect of cataract on type 2 diabetes. SMR analysis identified two novel genes MIR4453HG (βSMR = -0.34, 95% CI = -0.46, -0.22, P-value = 6.41 × 10-8) and KCNK17 (βSMR = -0.07, 95% CI = -0.09, -0.05, P-value = 2.49 × 10-10), whose expression levels were likely involved in the putative causality of type 2 diabetes on cataract. On the contrary, our comparative analysis on European population provided universally weak evidence on the genetic correlation and causal relationship between the two diseases.Conclusions: Our results provided robust evidence supporting a putative causal effect of type 2 diabetes on the risk of cataract in East Asians, and revealed potential genetic heterogeneity in the shared genetics underlying type 2 diabetes and cataract between East Asians and Europeans. These findings posed new paths on guiding the prevention and early-stage diagnosis of cataract in type 2 diabetes patients. [ABSTRACT FROM AUTHOR]

Background: US preterm-birth rates are 1.6 times higher for Black mothers than for White mothers. Although traffic-related air pollution (TRAP) may increase the risk of preterm birth, evaluating its effect on preterm birth and disparities has been challenging because TRAP is often measured inaccurately. This study sought to estimate the effect of TRAP exposure, measured at the street level, on the prevalence of preterm birth by race/ethnicity.Methods: We linked birth-registry data with TRAP measured at the street level for singleton births in sampled communities during 2013-2015 in Oakland and San Jose, California. Using logistic regression and marginal standardization, we estimated the effects of exposure to black carbon, nitrogen dioxide and ultrafine particles on preterm birth after confounder adjustment and stratification by race/ethnicity.Results: There were 8823 singleton births, of which 760 (8.6%) were preterm. Shifting black-carbon exposure from the 10th to the 90th percentile was associated with: 6.8%age point higher risk of preterm birth (95% confidence interval = 0.1 to 13.5) among Black women; 2.1%age point higher risk (95% confidence interval = -1.1 to 5.2) among Latinas; and inconclusive null findings among Asian and White women. For Latinas, there was evidence of a positive association between the other pollutants and risk of preterm birth, although effect sizes were attenuated in models that co-adjusted for other TRAP.Conclusions: Exposure to TRAP, especially black carbon, may increase the risk of preterm birth for Latina and Black women but not for Asian and White women. [ABSTRACT FROM AUTHOR]

Directed acyclic graphs (DAGs) are increasingly used in epidemiology to identify and address different types of bias. The present work aims to demonstrate how DAG-based data simulation can be used to understand bias and compare data analytical strategies in an educational context. Examples based on classical confounding situations and an M-DAG are examined and used to introduce basic concepts and demonstrate some important features of regression analysis, as well as the harmful effect of adjusting for a collider variable. Other potential uses of DAG-based data simulation include systematic comparisons of data analytical strategies or the evaluation of the role of uncertainties in a hypothesized DAG structure, including other types of bias such as information bias. DAG-based data simulations, like those presented here, should facilitate the exploration of several key epidemiological concepts, DAG theory and data analysis. Some suggestions are also made on how to further expand the ideas from this study. [ABSTRACT FROM AUTHOR]

Background: Acute respiratory infections have been associated with a transient increase in cardiovascular risk. However, whether such an association persists beyond 1 month and the potential modifying effect of cardiovascular risk factors on such an association are less well established.Methods: The China Kadoorie Biobank enrolled 512 726 participants aged 30-79 years from 10 areas across China during 2004-2008. By the end of 2017, a total of 5444 participants with new-onset ischaemic heart disease (IHD) and 4846 with ischaemic stroke (IS) who also had at least a record of hospitalization for pneumonia during follow-up were included. We used a self-controlled case-series method and calculated the age- and season-adjusted relative incidences (RIs) and 95% confidence intervals (CIs) for ischaemic cardiovascular disease (CVD) after pneumonia.Results: The risk of ischaemic CVD increased during days 1-3 after pneumonia hospitalization, with an RI (95% CI) of 4.24 (2.92-6.15) for IHD and 1.85 (1.02-3.35) for IS. The risk gradually reduced with longer duration since pneumonia hospitalization but remained elevated until days 92-365 for IHD (1.23, 1.12-1.35) and days 29-91 for IS (1.25, 1.05-1.48). Pre-existing cardiovascular risk factors amplified the associations between pneumonia and ischaemic CVD risks, such as chronic obstructive pulmonary disease for both IHD and IS, and diabetes and smoking for IHD (all Pinteraction < 0.05). Besides, the risk of ischaemic CVD was also higher among the participants aged ≥70 years (Pinteraction < 0.001 for IHD and 0.033 for IS).Conclusion: Among middle-aged and older Chinese adults, pneumonia hospitalization was associated with both short- and long-term increases in ischaemic CVD risk for ≤1 year. [ABSTRACT FROM AUTHOR]

Background: With genome-wide association data for many exposures and outcomes now available from large biobanks, one-sample Mendelian randomization (MR) is increasingly used to investigate causal relationships. Many robust MR methods are available to address pleiotropy, but these assume independence between the gene-exposure and gene-outcome association estimates. Unlike in two-sample MR, in one-sample MR the two estimates are obtained from the same individuals, and the assumption of independence does not hold in the presence of confounding.Methods: With simulations mimicking a typical study in UK Biobank, we assessed the performance, in terms of bias and precision of the MR estimate, of the fixed-effect and (multiplicative) random-effects meta-analysis method, weighted median estimator, weighted mode estimator and MR-Egger regression, used in both one-sample and two-sample data. We considered scenarios differing by the: presence/absence of a true causal effect; amount of confounding; and presence and type of pleiotropy (none, balanced or directional).Results: Even in the presence of substantial correlation due to confounding, all two-sample methods used in one-sample MR performed similarly to when used in two-sample MR, except for MR-Egger which resulted in bias reflecting direction and magnitude of the confounding. Such bias was much reduced in the presence of very high variability in instrument strength across variants (IGX2 of 97%).Conclusions: Two-sample MR methods can be safely used for one-sample MR performed within large biobanks, expect for MR-Egger. MR-Egger is not recommended for one-sample MR unless the correlation between the gene-exposure and gene-outcome estimates due to confounding can be kept low, or the variability in instrument strength is very high. [ABSTRACT FROM AUTHOR]

Background: Life-course epidemiology studies people's health over long periods, treating repeated measures of their experiences (usually risk factors) as predictors or causes of subsequent morbidity and mortality. Three hypotheses or models often guide the analyst in assessing these sequential risks: the accumulation model (all measurement occasions are equally important for predicting the outcome), the critical period model (only one occasion is important) and the sensitive periods model (a catch-all model for any other pattern of temporal dependence).Methods: We propose a Bayesian omnibus test of these three composite models, as well as post hoc decompositions that identify their best respective sub-models. We test the approach via simulations, before presenting an empirical example that relates five sequential measurements of body weight to an RNAseq measure of colorectal-cancer disposition.Results: The approach correctly identifies the life-course model under which the data were simulated. Our empirical cohort study indicated with >90% probability that colorectal-cancer disposition reflected a sensitive process, with current weight being most important but prior body weight also playing a role.Conclusions: The Bayesian methods we present allow precise inferences about the probability of life-course models given the data and are applicable in realistic scenarios involving causal analysis and missing data. [ABSTRACT FROM AUTHOR]

Background: Compared with those with a higher socio-economic position (SEP), individuals with a lower SEP have higher cancer morbidity and mortality. However, the contribution of modifiable risk factors to these inequities is not known. This study aimed to quantify the mediating effects of modifiable risk factors to associations between SEP and cancer morbidity and mortality.Methods: This study used a prospective observational cohort design. We combined eight cycles of the Canadian Community Health Survey (2000/2001-2011) as baseline data to identify a cohort of adults (≥35 years) without cancer at the time of survey administration (n = 309 800). The cohort was linked to the Discharge Abstract Database and the Canadian Mortality Database for cancer morbidity and mortality ascertainment. Individuals were followed from the date they completed the Canadian Community Health Survey until 31 March 2013. Dates of individual first hospitalizations for cancer and deaths due to cancer were captured during this time period. SEP was operationalized using a latent variable combining measures of education and household income. Self-reported modifiable risk factors, including smoking, excess alcohol consumption, low fruit-and-vegetable intake, physical inactivity and obesity, were considered as potential mediators. Generalized structural equation modelling was used to estimate the mediating effects of modifiable risk factors in associations between low SEP and cancer morbidity and mortality in the total population and stratified by sex.Results: Modifiable risk factors together explained 45.6% of associations between low SEP and overall cancer morbidity and mortality. Smoking was the most important mediator in the total population and for males, accounting for 15.5% and 40.2% of the total effect, respectively. For females, obesity was the most important mediator.Conclusions: Modifiable risk factors are important mediators of socio-economic inequities in cancer morbidity and mortality. Nevertheless, more than half of the variance in these associations remained unexplained. Midstream interventions that target modifiable risk factors may help to alleviate inequities in cancer risk in the short term. However, ultimately, upstream interventions that target structural determinants of health are needed to reduce overall socio-economic inequities in cancer morbidity and mortality. [ABSTRACT FROM AUTHOR]

Background: Many studies have reported an inferred causal association of income poverty with physical health among children; but making causal inference is challenging due to multiple potential sources of systematic error. We quantified the short-run effect of changes in household poverty status on children's health (asthma and ear infections) and service use (visits to the doctor and parent-reported hospital admissions), using a national longitudinal study of Australian children, with particular attention to potential residual confounding and selection bias due to study attrition.Methods: We use four modelling approaches differing in their capacity to reduce residual confounding (generalized linear, random effects (RE), hybrid and fixed effects (FE) regression modelling) to model the effect of income poverty (<60% of median income) on health for 10 090 children surveyed every 2nd year since 2004. For each method, we simulate the potential impact of selection bias arising due to attrition related to children's health status.Results: Of the 10 090 children included, 20% were in families in poverty at survey baseline (2004). Across subsequent years, ∼25% experienced intermittent and <2% persistent poverty. No substantial associations between poverty and child physical health and service use were observed in the FE models least prone to residual confounding [odds ratio (OR) 0.94, 95% confidence interval (CI) 0.81-1.10 for wheeze], in contrast to RE models that were positive (consistent with previous studies). Selection bias causing null findings was unlikely.Conclusions: While poverty has deleterious causal effects on children's socio-behavioural and educational outcomes, we find little evidence of a short-run causal effect of poverty on asthma, ear infections and health service use in Australia. [ABSTRACT FROM AUTHOR]

Background: At the individual level, it is well known that pregnancies have a short-term effect on a woman's cardiovascular system and blood pressure. The long-term effect of having children on maternal blood pressure, however, is unknown. We thus estimated the causal effect of having children on blood pressure among mothers in India, a country with a history of high fertility rates.Methods: We used nationally representative cross-sectional data from the 2015-16 India National Family and Health Survey (NFHS-4). The study population comprised 444 611 mothers aged 15-49 years. We used the sex of the first-born child as an instrumental variable (IV) for the total number of a woman's children. We estimated the effect of an additional child on systolic and diastolic blood pressure in IV (two-stage least squares) regressions. In additional analyses, we stratified the IV regressions by time since a mother last gave birth. Furthermore, we repeated our analyses using mothers' husbands and partners as the regression sample.Results: On average, mothers had 2.7 children [standard deviation (SD): 1.5], a systolic blood pressure of 116.4 mmHg (SD: 14.4) and diastolic blood pressure of 78.5 mmHg (SD: 9.4). One in seven mothers was hypertensive. In conventional ordinary least squares regression, each child was associated with 0.42 mmHg lower systolic [95% confidence interval (CI): -0.46 to -0.39, P < 0.001] and 0.13 mmHg lower diastolic (95% CI: -0.15 to -0.11, P < 0.001) blood pressure. In the IV regressions, each child decreased a mother's systolic blood pressure by an average of 1.00 mmHg (95% CI: -1.26 to -0.74, P < 0.001) and diastolic blood pressure by an average of 0.35 mmHg (95% CI: -0.52 to -0.17, P < 0.001). These decreases were sustained over more than a decade after childbirth, with effect sizes slightly declining as the time since last birth increased. Having children did not influence blood pressure in men.Conclusions: Bearing and rearing a child decreases blood pressure among mothers in India. [ABSTRACT FROM AUTHOR]

Background: Individuals who are obese in childhood have an elevated risk of disease in adulthood. However, whether childhood adiposity directly impacts intermediate markers of this risk, independently of adult adiposity, is unclear. In this study, we have simultaneously evaluated the effects of childhood and adulthood body size on 123 systemic molecular biomarkers representing multiple metabolic pathways.Methods: Two-sample Mendelian randomization (MR) was conducted to estimate the causal effect of childhood body size on a total of 123 nuclear magnetic resonance-based metabolic markers using summary genome-wide association study (GWAS) data from up to 24 925 adults. Multivariable MR was then applied to evaluate the direct effects of childhood body size on these metabolic markers whilst accounting for adult body size. Further MR analyses were undertaken to estimate the potential mediating effects of these circulating metabolites on the risk of coronary artery disease (CAD) in adulthood using a sample of 60 801 cases and 123 504 controls.Results: Univariable analyses provided evidence that childhood body size has an effect on 42 of the 123 metabolic markers assessed (based on P < 4.07 × 10-4). However, the majority of these effects (35/42) substantially attenuated when accounting for adult body size using multivariable MR. We found little evidence that the biomarkers that were potentially influenced directly by childhood body size (leucine, isoleucine and tyrosine) mediate this effect onto adult disease risk. Very-low-density lipoprotein markers provided the strongest evidence of mediating the long-term effect of adiposity on CAD risk.Conclusions: Our findings suggest that childhood adiposity predominantly exerts its detrimental effect on adult systemic metabolism along a pathway that involves adulthood body size. [ABSTRACT FROM AUTHOR]

Background: Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables.Methods: We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR.Results: In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index.Conclusions: Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution. [ABSTRACT FROM AUTHOR]

Background: Healthy lifestyle interventions offered at points of care, including support groups, may improve chronic disease management, especially in low-resource populations. We assessed the effectiveness of an educational intervention in type 2 diabetes (T2D) support groups to reduce cardiovascular disease (CVD) risk.Methods: We recruited 518 participants to a parallel, two-arm, cluster-randomized, behavioural clinical trial across 22 clinics in Sonora, Mexico, between August 2016 and October 2018. We delivered a 13-week secondary prevention intervention, Meta Salud Diabetes (MSD), within the structure of a support group (GAM: Grupo de Ayuda Mutua) in government-run (community) Health Centres (Centros de Salud). The primary study outcomes were difference in Framingham CVD risk scores and hypertension between intervention (GAM+MSD) and control (GAM usual care) arms at 3 and 12 months.Results: CVD risk was 3.17% age-points lower in the MSD arm versus control at 3 months [95% confidence interval (CI): -5.60, -0.75, P = 0.013); at 12 months the difference was 2.13% age-points (95% CI: -4.60, 0.34, P = 0.088). There was no evidence of a difference in hypertension rates between arms. Diabetes distress was also lower at 3 and 12 months in the MSD arm. Post-hoc analyses showed greater CVD risk reduction among men than women and among participants with HbA1c < 8.Conclusions: MSD contributed to a positive trend in reducing CVD risk in a low-resource setting. This study introduced an evidence-based curriculum that provides T2D self-management strategies for those with controlled T2D (i.e. HbA1c < 8.0) and may improve quality of life. [ABSTRACT FROM AUTHOR]

Background: Some pesticides, used in large quantities in current agricultural practices all over Europe, are suspected of adverse effects on human reproductive health (breast and prostate cancers), through mechanisms of endocrine disruption and possible carcinogenic properties, as observed in agricultural settings. However, evidence on dietary pesticide exposure and breast cancer (BC) is lacking for the general population. We aimed to assess the associations between dietary exposure to pesticides and BC risk among postmenopausal women of the NutriNet-Santé cohort.Methods: In 2014, participants completed a self-administered semi-quantitative food-frequency questionnaire distinguishing conventional and organic foods. Exposures to 25 active substances used in EU plant-protection products were estimated using a pesticide-residue database accounting for farming practices, from Chemisches und Veterinäruntersuchungsamt Stuttgart, Germany. Non-negative matrix factorization (NMF), adapted for data with excess zeros, was used to establish exposure profiles. The four extracted NMF components' quintiles were introduced into Cox models estimating hazard ratio (HR) and 95% confidence interval (95% CI), adjusted for known confounding factors.Results: A total of 13 149 postmenopausal women were included in the analysis (169 BC cases, median follow-up = 4.83 years). Negative associations between Component 3, reflecting low exposure to synthetic pesticides, and postmenopausal BC risk were found [HRQ5 = 0.57; 95% CI (0.34; 0.93), p-trend = 0.006]. Positive association between Component 1 score (highly correlated to chlorpyrifos, imazalil, malathion, thiabendazole) and postmenopausal BC risk was found specifically among overweight and obese women [HRQ5 = 4.13; 95% CI (1.50; 11.44), p-trend = 0.006]. No associations were detected for the other components.Conclusion: These associations suggest a potential role of dietary pesticide exposure on BC risk. Further research is needed to investigate the mechanisms and confirm these results in other populations. [ABSTRACT FROM AUTHOR]

Background: We aimed to evaluate the association of adiposity rebound (AR) timing on cardiometabolic health in childhood.Methods: Participants were part of the Generation XXI birth cohort, enrolled in 2005/2006 in Porto. All measurements of the child's weight and height performed by health professionals as part of routine healthcare were collected. Individual body mass index (BMI) curves were fitted for 3372 children, using mixed-effects models with smooth spline functions for age and random effects. The AR was categorized into very early (<42 months), early (42-59 months), normal (60-83 months) and late (≥84 months). At age 10 years, cardiometabolic traits were assessed and age- and sex-specific z-scores were generated. Adjusted regression coefficients and 95% confidence intervals [β (95% CI)] were computed.Results: The mean age at AR was 61.9 months (standard deviations 15.7). Compared with children with normal AR, children with very early or early AR had higher z-scores for BMI [β = 0.40 (95% CI: 0.28; 0.53); β = 0.21 (95% CI: 0.12; 0.30)], waist circumference [β = 0.33 (95% CI: 0.23; 0.43); β = 0.18 (95% CI: 0.10; 0.25)], waist-height ratio [β = 0.34 (95% CI: 0.24; 0.44); β = 0.14 (95% CI: 0.07; 0.22)], fat mass index [β = 0.24 (95% CI: 0.15; 0.33); β = 0.14 (95% CI: 0.08; 0.21)], fat-free mass index [β = 0.25 (95% CI: 0.14; 0.35); β = 0.11 (95% CI: 0.03; 0.19)], systolic blood pressure [β = 0.10 (95% CI: 0.01; 0.20); β = 0.08 (95% CI: 0.01; 0.15)], insulin [β = 0.16 (95% CI: 0.04; 0.29); β = 0.10 (95% CI: 0.01; 0.19)], HOMA-IR [β = 0.17 (95% CI: 0.04; 0.29); β = 0.10 (95% CI: 0.03; 0.19)] and C-reactive protein [β = 0.14 (95% CI: 0.02; 0.26); β = 0.10 (95% CI: 0.01; 0.19)]. Children with very early AR also had worse levels of diastolic blood pressure [β = 0.09 (95% CI: 0.02; 0.16)], triglycerides [β = 0.21 (95% CI: 0.08; 0.34)] and high-density lipoprotein cholesterol [β=-0.18 (95% CI: -0.31; -0.04)]. When analysed continuously, each additional month of age at the AR was associated with healthier cardiometabolic traits.Conclusion: The earlier the AR, the worse the cardiometabolic health in late childhood, which was consistently shown across a wide range of outcomes and in the categorical and continuous approach. [ABSTRACT FROM AUTHOR]

Background: People with type 1 diabetes often live for many years with different combinations of diabetes-related complications. We aimed to quantify how complication duration and total complication burden affect mortality, using data from national registers.Methods: This study included 33 396 individuals with type 1 diabetes, registered in the Swedish National Diabetes Register at any time between 2001 and 2012. Each individual was followed and classified according to their time-updated diabetes-related complication status. The main outcomes were all-cause mortality, cardiovascular (CV) mortality and non-CV mortality. Poisson models were used to estimate the rate of these outcomes as a function of the time-updated complication duration.Results: Overall, 1748 of the 33 396 individuals died during 198 872 person-years of follow-up. Overall, the time-updated all-cause mortality rate ratio (MRR) was 2.25 [95% confidence interval (CI): 1.99-2.54] for patients with diabetic kidney disease, 0.98 (0.82-1.18) for patients with retinopathy and 4.00 (3.56-4.50) for patients with cardiovascular disease relative to individuals without complications. The excess rate was highest in the first period after a diagnosis of CVD, with an 8-fold higher mortality rate, and stabilized after some 5 years. After diagnosis of diabetic kidney disease, we observed an increase in all-cause mortality with an MRR of around 2 compared with individuals without diabetic kidney disease, which stabilized after few years.Conclusions: In this cohort we show that duration of diabetes-related complications is an important determinant of mortality in type 1 diabetes, for example the MRR associated with CVD is highest in the first period after diagnosis of CVD. A stronger focus on time-updated information and thorough consideration of complication duration may improve risk stratification in routine clinical practice. [ABSTRACT FROM AUTHOR]

Background: It is commonly assumed that high oestradiol levels in women are cardioprotective. We assessed the association between oestradiol and the risk of incident myocardial infarction (MI) in women.Methods: We used data from 263 295 female UK Biobank participants [mean age 56.2; standard deviation (SD) 8.0 years] without previous cardiovascular disease (CVD). Associations of oestradiol with age and other cardiovascular risk factors were assessed. Cox proportional hazards models estimated crude, ag- and multiple-adjusted hazard ratios (HR) for MI associated with oestradiol levels.Results: After a mean follow-up of 9 years, 2206 incident cases of MI had been recorded, including 230 events among the 57 204 women (mean age 48) with detectable oestradiol levels. In the unadjusted analyses, a unit higher in log-transformed oestradiol was associated with an HR [95% confidence interval (CI) for MI of 0.73 (0.58; 0.92)]. After adjusting for age, this HR became 0.94 (0.75; 1.17), and after further adjusting for classical CVD risk factors, it was 1.05 (0.83; 1.31. Results were similar in subgroup analyses defined by age, menopausal status, socioeconomic status, contraceptive pill use and the use of hormone replacement therapy. The multivariable-adjusted HR for the 171 431 women (mean age 59) with undetectable levels of oestradiol, compared with those with detectable levels, was 0.97 (0.92; 1.02).Conclusions: Higher levels of oestradiol were not associated with a decreased risk of MI. The presumed cardioprotective effects of oestradiol seem to be largely confounded by age and further confounded by other cardiovascular risk factors. [ABSTRACT FROM AUTHOR]