Neighborhood characteristics and the risk of psoriasis: A systematic review
Why this work is in the frame
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Bibliographic record
Abstract
To the Editor: Psoriasis affects 1% to 5% of the North American population.1Boehncke W.-H. Schön M.P. Psoriasis.Lancet. 2015; 386: 983-994Abstract Full Text Full Text PDF PubMed Scopus (1627) Google Scholar The association between lifestyle (eg, physical activity, diet, alcohol, and smoking) and psoriasis/its comorbidities is well established.2Debbaneh M. Millsop J.W. Bhatia B.K. Koo J. Liao W. Diet and psoriasis, part I: impact of weight loss interventions.J Am Acad Dermatol. 2014; 71: 133-140Abstract Full Text Full Text PDF PubMed Scopus (85) Google Scholar,3Zhao S.S. Bellou E. Verstappen S.M.M. et al.Association between psoriatic disease and lifestyle factors and comorbidities: cross-sectional analysis and Mendelian randomisation.Rheumatology. 2022; 62: 1272-1285https://doi.org/10.1093/rheumatology/keac403Crossref Scopus (8) Google Scholar However, it is increasingly recognized that behavioral risk factors arise in a larger context of socioeconomic, cultural, and environmental determinants of health.4Biskup M. Macek P. Gozdz S. et al.Two-year follow-up cohort study focused on gender-specific associations between socioeconomic status and body weight changes in overweight and obese middle-aged and older adults.BMJ Open. 2021; 11e050127Crossref PubMed Scopus (1) Google Scholar Research in chronic diseases (eg, diabetes mellitus, metabolic syndrome) highlighted the importance of the living environment (LE) (ie, physical and socioeconomic conditions in which people live, work, and play) as a critical element to address population-level health differences. LE contributes to health inequity (ie, unjust and potentially avoidable differences in health outcomes among different populations). We aimed to conduct a systematic review to understand the impact of LE on psoriasis. MEDLINE, EMBASE, Web of Science, and CINAHL databases were searched on September 20, 2022, by ODL and ZR for studies exploring the association between LE and the prevalence, incidence, or severity of psoriasis. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Search strategy is detailed in Supplementary Tables I-III, available via Mendeley at https://doi.org/10.17632/vk94dhftw5.1. Studies’ quality was appraised using the Quality Assessment Tool for Quantitative Studies.5den Braver N.R. Lakerveld J. Rutters F. et al.Built environmental characteristics and diabetes: a systematic review and meta-analysis.BMC Med. 2018; 16: 12Crossref PubMed Scopus (135) Google Scholar Of 8 studies included (Fig 1 and Supplementary Table IV, available via Mendeley at https://doi.org/10.17632/vk94dhftw5.1 summarize the PRISMA flow diagram and studies’ details, respectively), 2 ascertained the association between urban versus rural residence and psoriasis risk with conflicting results. Two articles investigated the association between neighborhood socioeconomic conditions and psoriasis. People residing in high- and medium-deprivation neighborhoods (ie, deprivation of essential resources and/or goods) were more likely to have psoriasis whereas patients from the lowest income quartiles had a more severe disease. Four studies researched the association between air quality and psoriasis exacerbations. Particulate matter (PM2.5 and PM10) and NO2 were associated with a modest increase in outpatient visits and hospital admissions in South Korea and China. Italian studies demonstrated higher concentrations of all air pollutants (eg, PM2.5, PM10, CO, NO2, other nitrogen oxides, benzene) prior to psoriasis flares versus regular outpatient visits as well as daily increases of 10 μg/m3 in air pollutants were associated with therapeutic decisions such as dose increments or treatment changes. The available evidence suggests that neighborhoods with socioeconomic deprivation may be associated with a higher psoriasis risk and severity, whereas communities with worse air quality may increase the risk of psoriasis flare. However, this data should be interpreted with caution due to limited number of studies on the topic and at least moderate risk of bias identified across studies included (Supplementary Table V, available via Mendeley at https://doi.org/10.17632/vk94dhftw5.1) owing to data source, study design, patients’ number, and/or statistical analyses. Despite psoriasis disproportionately affecting North American, Western European, and Australasian populations, we identified no studies from these regions. Studying LE characteristics such as environmental (eg, air/noise/light pollution, greenness), built environment-related (eg, man-made buildings and spaces), and socioeconomic neighborhood characteristics (eg, material, social instability, and deprivation) as determinants of psoriasis incidence/severity is important to advance our understanding of population-level determinants of psoriatic disease spectrum. This is essential to reduce health disparities in chronic skin disease such as psoriasis and reduce the individual, societal and economic burden of this common and morbid disease. None disclosed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it