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      Effects of ambient air pollution on outpatient visits for psoriasis in Wuhan, China: a time-series analysis

      , , , , , , , , ,
      British Journal of Dermatology
      Oxford University Press (OUP)

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          Abstract

          Background

          Psoriasis can be provoked by both external and internal factors. The effects of environmental factors on psoriasis remain unclear.

          Objectives

          To investigate the effects of air pollution on outpatient visits for psoriasis.

          Methods

          A distributed lag nonlinear model following quasi-Poisson regression was used to evaluate the lag effects of air pollutants on psoriasis outpatient visits, adjusting for potential confounders. Stratified analyses were performed to identify potential effect modifications by sex, age and season.

          Results

          In total, 13 536 outpatient visits for psoriasis were recorded in Wuhan, China from 1 January 2015 to 31 December 2019. In the single-pollutant model, exposures to particulate matter (PM) smaller than 2.5 μm (PM2.5), PM smaller than 10 μm (PM10), NO2 and SO2 were found to be significantly associated with increased daily psoriasis outpatient visits. For the largest effects, a 10-μg m−3 increase in concentrations of PM2.5 (lag1), PM10 (lag1), NO2 (lag0) and SO2 (lag3) corresponded to 0.32% [95% confidence interval (CI) 0.01–0.63], 0.26% (95% CI 0.05–0.48), 0.98% (95% CI 0.01–1.96) and 2.73% (95% CI 1.01–4.47) increases in psoriasis outpatient visits, respectively. In the two-pollutant model, only NO2 showed significant and stable effects on the outpatient visits for psoriasis.

          Conclusions

          Ambient air pollution, especially NO2, appears to be significantly associated with an increased risk of outpatient visits for psoriasis in Wuhan, China. Air pollution control and exposure prevention could be effective measures to relieve the symptoms of psoriasis among these patients.

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          Most cited references52

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          Pathophysiology, Clinical Presentation, and Treatment of Psoriasis: A Review

          Approximately 125 million people worldwide have psoriasis. Patients with psoriasis experience substantial morbidity and increased rates of inflammatory arthritis, cardiometabolic diseases, and mental health disorders.
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            Psoriasis

            Psoriasis is a common, chronic papulosquamous skin disease occurring worldwide, presenting at any age, and leading to a substantial burden for individuals and society. It is associated with several important medical conditions, including depression, psoriatic arthritis, and cardiometabolic syndrome. Its most common form, chronic plaque or psoriasis vulgaris, is a consequence of genetic susceptibility, particularly in the presence of the HLA-C*06:02 risk allele, and of environmental triggers such as streptococcal infection, stress, smoking, obesity, and alcohol consumption. There are several phenotypes and research has separated pustular from chronic plaque forms. Immunological and genetic studies have identified IL-17 and IL-23 as key drivers of psoriasis pathogenesis. Immune targeting of these cytokines and of TNFα by biological therapies has revolutionised the care of severe chronic plaque disease. Psoriasis cannot currently be cured, but management should aim to minimise physical and psychological harm by treating patients early in the disease process, identifying and preventing associated multimorbidity, instilling lifestyle modifications, and employing a personalised approach to treatment.
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              Is Open Access

              Distributed lag non-linear models

              Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure–response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure–response dependencies and delayed effects. This methodology is based on the definition of a ‘cross-basis’, a bi-dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987–2000. Copyright © 2010 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                British Journal of Dermatology
                Oxford University Press (OUP)
                0007-0963
                1365-2133
                April 01 2023
                March 30 2023
                December 19 2022
                April 01 2023
                March 30 2023
                December 19 2022
                : 188
                : 4
                : 491-498
                Article
                10.1093/bjd/ljac124
                36641781
                badea64c-43e2-484c-96f3-3cacb840d1ba
                © 2022

                https://academic.oup.com/pages/standard-publication-reuse-rights

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