Additional file 1: of Proximity to mining industry and respiratory diseases in children in a community in Northern Chile: A cross-sectional study

Proximity to mining industries and respiratory diseases in children of a Northern Chilean Community: A Cross-sectional study Supplemental Material. (PDF 3236 kb

Job strain, bullying and violence at work and asthma in Peruvian cleaners—a cross-sectional analysis

<p><i>Objectives</i>: An increased asthma prevalence was found in cleaners. Many of them work in precarious employment conditions, potentially leading to stress, a known risk factor for asthma. We aimed to analyze whether asthma in cleaners might partly be explained by psychosocial working conditions. <i>Methods</i>: The study population of this cross-sectional study included 199 cleaners employed at regional public health services in Puno Province (Peru). They were compared to 79 unexposed workers from Lima, Peru (response 83%). Both groups answered the short version of the European Working Condition Survey and a modified version of the European Community Respiratory Health screening questionnaire. After multiple imputation, the association between psychosocial working conditions and asthma (wheeze without cold or use of asthma medication) was assessed. <i>Results</i>: The 12-months prevalence of asthma was 22% among cleaners versus 5% among unexposed workers (<i>p</i>_Chi² = .001). Cleaners were more likely than unexposed workers to work with temporary or sub-contracts, have a high employment insecurity, high strain working conditions and low social support (all <i>p</i>_Chi² < .05). Twenty-six percent vs. 10% reported a high bullying score; 39% vs. 8% had experienced violence at work (both <i>p</i>_Chi² < .001). High bullying score (adjusted Odds Ratio 5.6; 95% Confidence Interval 1.5–21.4) and violence (2.4; 1.1–5.4) were the main predictors of asthma. Taking these factors into account, being a cleaner was not statistically significantly associated with the outcome (3.5; 0.9–13.8). <i>Conclusions</i>: Poor psychosocial working conditions of cleaners may partly explain the high prevalence of asthma. The underlying mechanism might be a stress-induced inflammatory immune response.</p

List of ICD10-Codes used for comparison of comorbidities and medication.

<p>List of ICD10-Codes used for comparison of comorbidities and medication.</p

Overview on the information used for defining the concordance scores.

<p>Overview on the information used for defining the concordance scores.</p

Distribution of concordance scores for different comorbidities (percentages based on the total number of included patients (n = 2653)).

<p>Distribution of concordance scores for different comorbidities (percentages based on the total number of included patients (n = 2653)).</p

Basic characteristics of the patients stratified according to COPD GOLD categories 0–4.

<p>Basic characteristics of the patients stratified according to COPD GOLD categories 0–4.</p

Diagram showing the logical structure of the combined categorization procedure (Approach I plus Approach II).

<p>A-D indicates the concordance scores, Ø the absence of the disease under study. ATC-Codes refer to the patients‘ medication, and ICD10 matching to the comparison of medication with the revised ICD10-Codes of the disease (for details see text).</p

Examples of different distribution patterns of the concordance scores for the diseases asthma, diabetes, hyperuricemia and GI disorders.

<p>The values are percentages relative to the total number of patients (n = 2653). The blue part (A) represents the concordance between reported disease and specific medication, the red part (C) illustrates self-reports confirmed by non-specific medication. Green parts show the proportion of patients only reporting a disease without any suitable medication (D). The violet part (B) on top presents patients without the report of a disease but identified as likely having the disease due to the intake of a specific medication. The sum of A, C and D represents the prevalence according to self-reports (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163408#pone.0163408.t005" target="_blank">Table 5</a>). The distribution patterns vary widely among the different diseases.</p

ICD10 coding of relevant and/or prevalent comorbidities as used in the analysis of Approach I.

<p>ICD10 coding of relevant and/or prevalent comorbidities as used in the analysis of Approach I.</p