Identifying the PECO: A framework for formulating good questions to explore the association of environmental and other exposures with health outcomes

Morgan, Rebecca L.; Whaley, Paul; Thayer, Kristina A.; Schünemann, Holger J.

doi:10.1016/j.envint.2018.07.015

Cited by 705 publications

(397 citation statements)

References 12 publications

Supporting

Mentioning

385

Contrasting

Unclassified

Order By: Relevance

“…The Population, Exposure, Comparator, and Outcomes (PECO) framework was used to develop the eligibility criteria (see Table 1 ). 26 Physical disabilities, defined with the ICF, 24 included impairments of sensory functions and pain, voice and speech functions, or neuromusculoskeletal and movement-related functions; or of the structures of the nervous system, the eye, ear and related structures, the structures involved in voice and speech, or the structures related to movement. Studies focussing on patients with chronic conditions such as diabetes and hypertension only or with unspecified chronic conditions that cannot ensure a specific diagnosis in the methodology or analysis were excluded.…”

Section: Methodsmentioning

confidence: 99%

Impact of COVID-19 on people with physical disabilities: A rapid review

Lebrasseur

Fortin-Bédard

Lettre

et al. 2021

Disability and Health Journal

210

138

View full text Add to dashboard Cite

Background The COVID-19 pandemic caused drastic changes in the lives of the general population. People with physical disabilities, who commonly encounter daily challenges such as barriers to community mobility, reduced access to healthcare services and higher risk of suffering from depression, may face additional challenges in the context of the pandemic. Objective This study aims to review the impact of the COVID-19 pandemic, and associated isolation and protective measures, among people with physical disabilities. Methods A rapid review of the published literature was conducted on August 10, 2020 through a search in six online databases to synthesize results from original studies regarding the impact of the COVID-19 pandemic on people with physical disabilities. The International Classification of Functioning, Disability and Health was used to describe the population and the personal and environmental factors with a unified and standard health language. Results Eleven records were extracted from 1621 individual papers retrieved from the search strategy. Various impacts on daily functioning such as a decrease in access to healthcare have been noted during the pandemic. Changes in social and lifestyle habits, mood changes and decreased levels of physical activity were also noted. Conclusions Our results highlighted the lack of early research about the impacts of COVID-19 experienced by people with physical disabilities. Future studies should focus on specific consequences and needs of this vulnerable population to ensure their inclusion in public health recommendations and consideration by policy makers.

show abstract

Section: Methodsmentioning

confidence: 99%

Impact of COVID-19 on people with physical disabilities: A rapid review

Lebrasseur

Fortin-Bédard

Lettre

et al. 2021

Disability and Health Journal

210

138

View full text Add to dashboard Cite

show abstract

“…Inclusion and exclusion criteria, grouped according to the population-exposure-comparator-outcome (PECO) framework (Morgan et al 2018), were as follows. We required studies to meet all of the following criteria to be eligible for inclusion:…”

Section: Systematic Review and Selection Criteriamentioning

confidence: 99%

Air Pollution (Particulate Matter) Exposure and Associations with Depression, Anxiety, Bipolar, Psychosis and Suicide Risk: A Systematic Review and Meta-Analysis

Braithwaite

Zhang

Kirkbride

et al. 2019

Environ Health Perspect

438

220

View full text Add to dashboard Cite

BACKGROUND: Particulate air pollution's physical health effects are well known, but associations between particulate matter (PM) exposure and mental illness have not yet been established. However, there is increasing interest in emerging evidence supporting a possible etiological link. OBJECTIVES: This systematic review aims to provide a comprehensive overview and synthesis of the epidemiological literature to date by investigating quantitative associations between PM and multiple adverse mental health outcomes (depression, anxiety, bipolar disorder, psychosis, or suicide). METHODS: We undertook a systematic review and meta-analysis. We searched Medline, PsycINFO, and EMBASE from January 1974 to September 2017 for English-language human observational studies reporting quantitative associations between exposure to PM <1:0 lm in aerodynamic diameter (ultrafine particles) and PM <2:5 and <10 lm in aerodynamic diameter (PM 2:5 and PM 10 , respectively) and the above psychiatric outcomes. We extracted data, appraised study quality using a published quality assessment tool, summarized methodological approaches, and conducted metaanalyses where appropriate. RESULTS: Of 1,826 citations identified, 22 met our overall inclusion criteria, and we included 9 in our primary meta-analyses. In our meta-analysis of associations between long-term (>6 months) PM 2:5 exposure and depression (n = 5 studies), the pooled odds ratio was 1.102 per 10-lg=m 3 PM 2:5 increase (95% CI: 1.023, 1.189; I 2 = 0:00%). Two of the included studies investigating associations between long-term PM 2:5 exposure and anxiety also reported statistically significant positive associations, and we found a statistically significant association between short-term PM 10 exposure and suicide in meta-analysis at a 0-2 d cumulative exposure lag. DISCUSSION: Our findings support the hypothesis of an association between long-term PM 2:5 exposure and depression, as well as supporting hypotheses of possible associations between long-term PM 2:5 exposure and anxiety and between short-term PM 10 exposure and suicide. The limited literature and methodological challenges in this field, including heterogeneous outcome definitions, exposure assessment, and residual confounding, suggest further high-quality studies are warranted to investigate potentially causal associations between air pollution and poor mental health.

show abstract

“…Similar themes to that of historical issues with clinical research are beginning to present in ML/AI based research, such as using outcome variables are predictors, paying little attention to causal pathways, insufficiently detailed descriptions of the conceptualisation of an inception cohort, and documenting exactly what sort of patients made their way into the analysis 21. The PECO principles of epidemiological study design (that is, defining a study population, the exposures used, the key comparators, and the clinical outcomes) have an important role in some these issues when they originally arose in health research, and have now become a useful guide for assessments of the quality and relevance of research evidence 22. Although developed in the clinical domain, these principles are still highly relevant to ML/AI research, especially in providing a framework on which to ground large scale projects involving electronic health records.…”

Section: Critical Questionsmentioning

confidence: 99%

Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

Vollmer

Mateen

Bohner

et al. 2020

BMJ

297

257

View full text Add to dashboard Cite

Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing sources of data for patient benefit. Whilst there is a lot of promising research currently being undertaken, the literature as a whole lacks: transparency; clear reporting to facilitate replicability; exploration for potential ethical concerns; and, clear demonstrations of effectiveness. There are many reasons for why these issues exist, but one of the most important that we provide a preliminary solution for here is the current lack of ML/AIspecific best practice guidance. Although there is no consensus on what best practice looks in this field, we believe that interdisciplinary groups pursuing research and impact projects in the ML/AI for health domain would benefit from answering a series of questions based on the important issues that exist when undertaking work of this nature. Here we present 20 questions that span the entire project life cycle, from inception, data analysis, and model evaluation, to implementation, as a means to facilitate project planning and post-hoc (structured) independent evaluation. By beginning to answer these questions in different settings, we can start to understand what constitutes a good answer, and we expect that the resulting discussion will be central to developing an international consensus framework for transparent, replicable, ethical and effective research in artificial intelligence (AI-TREE) for health.

show abstract

Identifying the PECO: A framework for formulating good questions to explore the association of environmental and other exposures with health outcomes

Cited by 705 publications

References 12 publications

Impact of COVID-19 on people with physical disabilities: A rapid review

Impact of COVID-19 on people with physical disabilities: A rapid review

Air Pollution (Particulate Matter) Exposure and Associations with Depression, Anxiety, Bipolar, Psychosis and Suicide Risk: A Systematic Review and Meta-Analysis

Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

Contact Info

Product

Resources

About