Abstract Exposure to risks throughout life results in a wide variety of outcomes. Objectively judging the relative impact of these risks on personal and population health is fundamental to individual survival and societal prosperity. Existing mechanisms to quantify and rank the magnitude of these myriad effects and the uncertainty in their estimation are largely subjective, leaving room for interpretation that can fuel academic controversy and add to confusion when communicating risk. We present a new suite of meta-analyses—termed the Burden of Proof studies—designed specifically to help evaluate these methodological issues objectively and quantitatively. Through this data-driven approach that complements existing systems, including GRADE and Cochrane Reviews, we aim to aggregate evidence across multiple studies and enable a quantitative comparison of risk–outcome pairs. We introduce the burden of proof risk function (BPRF), which estimates the level of risk closest to the null hypothesis that is consistent with available data. Here we illustrate the BPRF methodology for the evaluation of four exemplar risk–outcome pairs: smoking and lung cancer, systolic blood pressure and ischemic heart disease, vegetable consumption and ischemic heart disease, and unprocessed red meat consumption and ischemic heart disease. The strength of evidence for each relationship is assessed by computing and summarizing the BPRF, and then translating the summary to a simple star rating. The Burden of Proof methodology provides a consistent way to understand, evaluate and summarize evidence of risk across different risk–outcome pairs, and informs risk analysis conducted as part of the Global Burden of Diseases, Injuries, and Risk Factors Study. 

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The Burden of Proof studies: assessing the evidence of risk

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Studies linking diet with health must get a whole lot better

Linear Mixed-Effects (LME) models are a fundamental tool for modeling correlated data, including cohort studies, longitudinal data analysis, and meta-analysis. Design and analysis of variable selection methods for LMEs is more difficult than for linear regression because LME models are nonlinear. In this work we propose a relaxation strategy and optimization methods that enable a wide range of variable selection methods for LMEs using both convex and nonconvex regularizers, including $\ell_1$, Adaptive-$\ell_1$, CAD, and $\ell_0$. The computational framework only requires the proximal operator for each regularizer to be available, and the implementation is available in an open source python package pysr3, consistent with the sklearn standard. The numerical results on simulated data sets indicate that the proposed strategy improves on the state of the art for both accuracy and compute time. The variable selection techniques are also validated on a real example using a data set on bullying victimization.

A Relaxation Approach to Feature Selection for Linear Mixed Effects Models

Little is known about contemporary lifestyle choices in patients with bladder cancer (BC). These choices include carcinogenic risk factors and could affect fitness to receive treatments.To evaluate the contemporary lifestyle choices in BC patients.Self-reported surveys from participants diagnosed with BC in the previous 10 yr captured smoking patterns, e-cigarette use, physical activity using the GODIN Leisure-Time Exercise Questionnaire, long-term conditions (LTCs), relationship status, sociodemographics, and body mass index (BMI; height and weight).Findings were compared with the general population and men with prostate cancer.Completed surveys were received from 2092 participants. Most respondents were ex-smokers (61% vs 10% current vs 29% never). The use of e-cigarettes was uncommon (9%) and at lower rates than the age-equivalent general population. Passive smoke exposure was frequent (48%). Most participants (68%) were "insufficiently active" using the GODIN criteria and less physically active than the age-equivalent general population. Most respondents (44%) were classified as overweight (BMI 25-29.99) or obese (22%, BMI >30). Lifestyle factors varied with age, sex, socioeconomic deprivation, and LTCs. Younger participants were less likely to smoke (p < 0.001), more likely to have used e-cigarettes (p < 0.001), but more likely to have had passive smoke exposure (p = 0.008). Those from less affluent areas were more likely to smoke (p < 0.001), have used e-cigarettes (p < 0.001), and have had passive smoke exposure (p = 0.02). Females were less likely to be smokers (p < 0.001) but more likely to have been exposed to passive smoke (p < 0.001).Persons affected by BC often have smoking exposures and high BMI, and are insufficiently active. Rates of e-cigarette use were lower than in the general population. Efforts to improve quality of life in this cohort should include wider advocation of smoking cessation, perhaps including the use of e-cigarettes, and programmes to increase exercise and reduce BMI.We looked at the lifestyle choices, such as smoking, e-cigarette use, physical activity levels, and obesity, of patients following a bladder cancer diagnosis. We conclude that this population would benefit from healthy lifestyle interventions. 

Lifestyle Factors in Patients with Bladder Cancer: A Contemporary Picture of Tobacco Smoking, Electronic Cigarette Use, Body Mass Index, and Levels of Physical Activity.

https://www.nature.com/articles/s41591-023-02294-8.pdf

Concerns about the Burden of Proof studies

As a leading behavioral risk factor for numerous health outcomes, smoking is a major ongoing public health challenge. Although evidence on the health effects of smoking has been widely reported, few attempts have evaluated the dose-response relationship between smoking and a diverse range of health outcomes systematically and comprehensively. In the present study, we re-estimated the dose-response relationships between current smoking and 36 health outcomes by conducting systematic reviews up to 31 May 2022, employing a meta-analytic method that incorporates between-study heterogeneity into estimates of uncertainty. Among the 36 selected outcomes, 8 had strong-to-very-strong evidence of an association with smoking, 21 had weak-to-moderate evidence of association and 7 had no evidence of association. By overcoming many of the limitations of traditional meta-analyses, our approach provides comprehensive, up-to-date and easy-to-use estimates of the evidence on the health effects of smoking. These estimates provide important information for tobacco control advocates, policy makers, researchers, physicians, smokers and the public. 

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Matthew J Malloy

Papers

Health effects associated with smoking: a Burden of Proof study