Positive Epidemiology?

VanderWeele, Tyler J.; Song, Mingyang; Long, Katelyn N. G.; Kim, Eric S.; Trudel‐Fitzgerald, Claudia; Kubzansky, Laura D.

doi:10.1097/ede.0000000000001147

Cited by 105 publications

(86 citation statements)

References 46 publications

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“…We comprehensively investigated eleven predictors of PWB, that belong to three groups: biological, psychological, and social and economic variables. This is in response to one criticism of research on health and wellbeing where the focus is only on a few isolated variables at a time [12]. Health and well-being are multifaceted constructs with many causes.…”

Section: Introductionmentioning

confidence: 99%

Thriving during COVID-19: Predictors of psychological well-being and ways of coping

2021

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COVID-19 has led to global dramatic shifts in daily life. Following the biopsychosocial model of health, the goal of the current study was to predict people’s psychological well-being (PWB) during the initial lockdown phase of the pandemic and to investigate which coping strategies were most common among people with low and high PWB. Participants were 938 volunteers in the United States who responded to an online survey during the lockdown in April 2020. The main findings were that all three groups of variables, biological, psychological, and socio-economic, significantly contributed to PWB explaining 53% variance. Social loneliness and sense of agency were the strongest predictors. PWB was significantly predicted by physical health (not gender nor age); by spirituality, emotional loneliness, social loneliness, and sense of agency; by job security (not income, nor neighborhood safety, nor hours spent on social media). Comparing the coping strategies of participants, results show more intentional coping in the high-PWB group and more passive coping in the low-PWB group. During this unprecedented pandemic, the findings highlight that ability to sustainably cope with the global shifts in daily life depends on actively and intentionally attending to PWB by being one’s own agent for physical health, spiritual health, and social connection.

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Section: Introductionmentioning

confidence: 99%

Thriving during COVID-19: Predictors of psychological well-being and ways of coping

2021

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“…But we know that in rehabilitation and in other clinical fields, many outcomes are good : recovery of function, improvement in pain, remission from cancer, an undetectable hepatitis C viral load, being alive at 30 days after intracerebral hemorrhage. Studying good health outcomes is vital to informing clinical practice, and the EBP framework applies to the study of both good and bad outcomes 14 . So, although it might feel awkward to talk about the “risk” of improvement in function, or some other good outcome, it is actually OK—this is widely accepted in evidence‐based practice!…”

Section: Rehabilitation Outcomes Can Be “Good” Toomentioning

confidence: 99%

Summary Measures and Measures of Effect: Summarizing and Comparing Outcomes in Rehabilitation Research. Part 2: Binary Outcomes

Speckman

Burnham

2020

PM&R

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“…Further, it may be that the CoOL approach reveals complementary information when studying positive outcomes (high quality of life) in comparison to negative outcomes (diseases and death). [42] Multiple other extensions of the CoOL approach may be possible, e.g. ways to incorporate time, such as time-varying variables, complex confounding scenarios, and censoring, would be of high relevance for epidemiological, and we encourage others to explore these.…”

Section: Modelmentioning

confidence: 99%

Causes of Outcome Learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome

Rieckmann

Dworzyński

Arras

et al. 2020

Preprint

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Nearly all diseases can be caused by different combinations of exposures. Yet, most epidemiological studies focus on the causal effect of a single exposure on an outcome. We present the Causes of Outcome Learning (CoOL) approach, which seeks to identify combinations of exposures (which can be interpreted causally if all causal assumptions are met) that could be responsible for an increased risk of a health outcome in population sub-groups. The approach allows for exposures acting alone and in synergy with others. It involves (a) a pre-computational phase that proposes a causal model; (b) a computational phase with three steps, namely (i) analytically fitting a non-negative additive model, (ii) decomposing risk contributions, and (iii) clustering individuals based on the risk contributions into sub-groups based on the predefined causal model; and (c) a post-computational phase on hypothesis development and validation by triangulation on new data before eventually updating the causal model. The computational phase uses a tailored neural network for the non-negative additive model and Layer-wise Relevance Propagation for the risk decomposition through this model. We demonstrate the approach on simulated and real-life data using the R package ‘CoOL’. The presentation is focused on binary exposures and outcomes but can be extended to other measurement types. This approach encourages and enables epidemiologists to identify combinations of pre-outcome exposures as potential causes of the health outcome of interest. Expanding our ability to discover complex causes could eventually result in more effective, targeted, and informed interventions prioritized for their public health impact.

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Positive Epidemiology?

Cited by 105 publications

References 46 publications

Thriving during COVID-19: Predictors of psychological well-being and ways of coping

Thriving during COVID-19: Predictors of psychological well-being and ways of coping

Summary Measures and Measures of Effect: Summarizing and Comparing Outcomes in Rehabilitation Research. Part 2: Binary Outcomes

Causes of Outcome Learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome

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