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Shyamali C Dharmage,1 Jennifer L Perret,1,2, John A Burgess,1 Caroline J Lodge,1 David P Johns,3 Paul S Thomas,4 Graham G Giles,1,5 John L Hopper,1,6 Michael J Abramson,7,8 E Haydn Walters,3,9, Melanie C Matheson1 1Allergy and Lung Health Unit, Center for Epidemiology and Biostatistics, The University of Melbourne, 2Institute for Breathing and Sleep (IBAS), Melbourne, VIC, 3“Breathe Well” Center of Research Excellence for Chronic Respiratory Disease and Lung Ageing, School of Medicine, University of Tasmania, Hobart, TAS, 4Inflammation and Infection Research, Faculty of Medicine, University of New South Wales, Sydney, NSW, 5Cancer Epidemiology Center, Cancer Council Victoria, Melbourne, VIC, Australia; 6Department of Public Health, Seoul National University, Seoul, South Korea; 7Allergy, Immunology and Respiratory Medicine, The Alfred Hospital, Melbourne, 8School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 9School of Medicine, University of Tasmania, Hobart, TAS, Australia Background and objective: Personal smoking is widely regarded to be the primary cause of chronic bronchitis (CB) in adults, but with limited knowledge of contributions by other factors, including current asthma. We aimed to estimate the independent and relative contributions to adult CB from other potential influences spanning childhood to middle age.Methods: The population-based Tasmanian Longitudinal Health Study cohort, people born in 1961, completed respiratory questionnaires and spirometry in 1968 (n=8,583). Thirty-seven years later, in 2004, two-thirds responded to a detailed postal survey (n=5,729), from which the presence of CB was established in middle age. A subsample (n=1,389) underwent postbronchodilator spirometry between 2006 and 2008 for the assessment of chronic airflow limitation, from which nonobstructive and obstructive CB were defined. Multivariable and multinomial logistic regression models were used to estimate relevant associations.Results: The prevalence of CB in middle age was 6.1% (95% confidence interval [CI]: 5.5, 6.8). Current asthma and/or wheezy breathing in middle age was independently associated with adult CB (odds ratio [OR]: 6.2 [95% CI: 4.6, 8.4]), and this estimate was significantly higher than for current smokers of at least 20 pack-years (OR: 3.0 [95% CI: 2.1, 4.3]). Current asthma and smoking in middle age were similarly associated with obstructive CB, in contrast to the association between allergy and nonobstructive CB. Childhood predictors included allergic history (OR: 1.3 [95% CI: 1.1, 1.7]), current asthma (OR: 1.8 [95% CI: 1.3, 2.7]), “episodic” childhood asthma (OR: 2.3 [95% CI: 1.4, 3.9]), and parental bronchitis symptoms (OR: 2.5 [95% CI: 1.6, 4.1]).Conclusion: The strong independent association between current asthma and CB in middle age suggests that this condition may be even more influential than personal smoking in a general population. The independent associations of childhood allergy and asthma, though not childhood bronchitis, as clinical predictors of adult CB raise the possibility of some of this burden having originated in childhood. Keywords: nonobstructive chronic bronchitis, obstructive chronic bronchitis, current asthma, personal smoking, allergy history

Dharmage SC, Perret JL, Burgess JA, et al. Int J Chron Obstruct Pulmon Dis. 2016;11:1911–1920. On page 1911, the author list “Shyamali C Dharmage,1 Jennifer L Perret,1,2 John A Burgess,1 Caroline J Lodge,1 David P Johns,3 Paul S Thomas,4 Graham G Giles,1,5 John L Hopper,1,6 Michael J Abramson,7,8 E Haydn Walters,3,9 Melanie C Matheson1” should have read “Shyamali C Dharmage,1,* Jennifer L Perret,1,2,* John A Burgess,1 Caroline J Lodge,1 David P Johns,3 Paul S Thomas,4 Graham G Giles,1,5 John L Hopper,1,6 Michael J Abramson,7,8 E Haydn Walters,3,9 Melanie C Matheson1”, and was missing the following statement “*These authors contributed equally to this work”.Read the original article

Background: Superiority of potent P2Y12 inhibitors over clopidogrel after an acute coronary syndrome (ACS) has been well established, however potent P2Y12 inhibition is responsible for more adverse events, which may influence patient adherence to treatment. Aim of the present study is to investigate the adherence to the prescribed P2Y12 inhibitor (P2Y12i) in patients on dual antiplatelet therapy (DAPT) after an ACS. Methods: In an IDEAL-LDL trial substudy, we included 344 patients after ACS discharged on DAPT. The primary outcome was the difference between potent P2Y12i and clopidogrel in terms of adherence, as well as other predictors of adherence to the antiplatelet regimen. Secondary outcomes included the prevalence of DAPT continuation and its predictors and the antiplatelet regimen selection after DAPT. Results: Adherence to the potent P2Y12i and to clopidogrel was observed in 140/178 (78.7%) and 111/166 (66.9%) patients (p = 0.016), respectively. In the multivariate model, after adjustment for P2Y12i switching during the first year of therapy, there was no difference observed in adherence between potent P2Y12i and clopidogrel (odds ratio [OR] = 0.98, 95% confidence interval [CI] = 0.55–1.74). Significant predictors included history of cardiovascular disease (CVD) (OR = 0.51, 95% CI = 0.31–0.86) and percutaneous coronary intervention (PCI) index event treatment (OR = 2.58, 95% CI = 1.38–4.82). Of patients, 72% continued DAPT >12 months and female gender was a negative predictor of DAPT prolongation (adjusted OR = 0.43, 95% CI = 0.21–0.90). DAPT was continued until the end of follow-up in 42.7%, while 54.6% resumed with single antiplatelet regimen. Conclusions: Adherence to DAPT was not affected by the P2Y12i potency, whereas history of CVD and PCI treatment were associated with reduced and increased adherence, respectively. Clinical Trial Registration: NCT02927808, https://clinicaltrials.gov/ct2/show/NCT02927808.

The scope of this paper is to evaluate the short-term predictive capacity of the stochastic models ARIMA, Transfer Function (TF) and Artificial Neural Networks for water parameters, specifically for 1, 2 and 3 steps forward (m = 1, 2 and 3). The comparison of statistical parameters indicated that ARIMA models could be proposed as short-term prediction models. In some cases that TF models resulted in better predictions, the difference with ARIMA was minimal and since the latter are simpler in their construction, they are proposed for short-term prediction. Artificial Neural Networks didn’t show a good short-term predictive capacity in comparison with the aforementioned models.