Are there non-linear relationships between alcohol consumption and long-term health?: a systematic review of observational studies employing approaches to improve causal inference

Background Research has long found ‘J-shaped’ relationships between alcohol consumption and certain health outcomes, indicating a protective effect of moderate consumption. However, methodological limitations in most studies hinder causal inference. This review aimed to identify all observational studies employing improved approaches to mitigate confounding in characterizing alcohol–long-term health relationships, and to qualitatively synthesize their findings. Methods Eligible studies met the above description, were longitudinal (with pre-defined exceptions), discretized alcohol consumption, and were conducted with human populations. MEDLINE, PsycINFO, Embase and SCOPUS were searched in May 2020, yielding 16 published manuscripts reporting on cancer, diabetes, dementia, mental health, cardiovascular health, mortality, HIV seroconversion, and musculoskeletal health. Risk of bias of cohort studies was evaluated using the Newcastle-Ottawa Scale, and a recently developed tool was used for Mendelian Randomization studies. Results A variety of functional forms were found, including reverse J/J-shaped relationships for prostate cancer and related mortality, dementia risk, mental health, and certain lipids. However, most outcomes were only evaluated by a single study, and few studies provided information on the role of alcohol consumption pattern. Conclusions More research employing enhanced causal inference methods is urgently required to accurately characterize alcohol–long-term health relationships. Those studies that have been conducted find a variety of linear and non-linear functional forms, with results tending to be discrepant even within specific health outcomes. Trial registration PROSPERO registration number CRD42020185861.

The effect of livestock density on Trypanosoma brucei gambiense and T. b. rhodesiense : a causal inference-based approach

Domestic and wild animals are important reservoirs of the rhodesiense form of human African trypanosomiasis (rHAT), however quantification of this effect offers utility for deploying non-medical control activities, and anticipating their success when wildlife are excluded. Further, the uncertain role of animal reservoirs—particularly pigs—threatens elimination of transmission (EOT) targets set for the gambiense form (gHAT). Using a new time series of high-resolution cattle and pig density maps, HAT surveillance data collated by the WHO Atlas of HAT, and methods drawn from causal inference and spatial epidemiology, we conducted a retrospective ecological cohort study in Uganda, Malawi, Democratic Republic of Congo (DRC) and South Sudan to estimate the effect of cattle and pig density on HAT risk.

Livestock, pathogens, vectors, and their environment: a causal inference-based approach to estimating the pathway-specific effect of livestock on human African trypanosomiasis risk

Livestock are important reservoirs for many diseases, and investigation of such zoonoses has long been the focus of One Health research. However, the effects of livestock on human and environmental health extend well beyond direct disease transmission. In this retrospective ecological cohort study we use pre-existing data and methods derived from causal inference and spatial epidemiology to estimate three hypothesized mechanisms by which livestock can come to bear on human African trypanosomiasis (HAT) risk: the reservoir effect, by which infected cattle and pigs are a source of infection to humans; the zooprophylactic effect, by which preference for livestock hosts exhibited by the tsetse fly vector of HAT means that their presence protects humans from infection; and the environmental change effect, by which livestock keeping activities modify the environment in such a way that habitat suitability for tsetse flies, and in turn human infection risk, is reduced. We conducted this study in four high burden countries: at the point level in Uganda, Malawi, and Democratic Republic of Congo (DRC), and at the county-level in South Sudan. Our results indicate cattle and pigs play an important reservoir role for the rhodesiense form (rHAT) in Uganda, however zooprophylaxis outweighs this effect for rHAT in Malawi. For the gambiense form (gHAT) we found evidence that pigs may be a competent reservoir, however dominance of the reservoir versus zooprophylactic pathway for cattle varied across countries. We did not find compelling evidence of an environmental change effect.

Polynomial Mendelian Radomization reveals widespread non-linear causal effects in the UK Biobank

Causal inference is a critical step in improving our understanding of biological processes and Mendelian randomisation (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g. a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g. the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels.

Coding, causality, and statistical craft: The emergence and evolutionary drivers of moralistic supernatural punishment remain unresolved

The target article from Turchin et al. assesses the relationship between social complexity and moralistic supernatural punishment. In our evaluation of their project, we argue that each step of its workflow -- from data production and theory to modeling and reporting -- makes it impossible to test the hypothesis that its authors claim they are testing. We focus our discussion on three important classes of issues: problems of data, analysis, and causal inference.

Oliver’s four-factor model: Validation through causality

While there has been a growing interest in sports analysis in recent years, much research first focused on a classical statistical approach and later on an artificial intelligence approach. This article aims instead to propose a causal inference approach to sports analysis. In particular, the present article intends to review the famous four-factor model proposed by Dean Oliver for assessing the winning ability of National Basketball Association (NBA) teams through a causal inference approach. A structural equation model is used to validate Oliver’s model. The present paper considers the winning percentage and the factors’ statistics over entire seasons from [Formula: see text] to [Formula: see text]. The statistics for the [Formula: see text] season are considered only on a subset of the games. This is because the games played in the Orlando bubble under the particular COVID-19 situation have been regarded as outliers compared to the games played in the other NBA seasons, hence they have not been taken into account. The second goal of the article is to analyse if the fitting ability of the four-factor model changes when it is fitted over the pre[Formula: see text] and post[Formula: see text] basketball eras datasets, considering the year [Formula: see text] as the turning point for the NBA playing style.

Identification of causal effects in case-control studies

Background Case-control designs are an important yet commonly misunderstood tool in the epidemiologist’s arsenal for causal inference. We reconsider classical concepts, assumptions and principles and explore when the results of case-control studies can be endowed a causal interpretation. Results We establish how, and under which conditions, various causal estimands relating to intention-to-treat or per-protocol effects can be identified based on the data that are collected under popular sampling schemes (case-base, survivor, and risk-set sampling, with or without matching). We present a concise summary of our identification results that link the estimands to the (distribution of the) available data and articulate under which conditions these links hold. Conclusion The modern epidemiologist’s arsenal for causal inference is well-suited to make transparent for case-control designs what assumptions are necessary or sufficient to endow the respective study results with a causal interpretation and, in turn, help resolve or prevent misunderstanding. Our approach may inform future research on different estimands, other variations of the case-control design or settings with additional complexities.