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Impact of the 2005 and 2010 Spanish smoking laws on hospital admissions for tobacco-related diseases in Valencia, Spain
تأثیر قوانین مربوط به استعمال دخانیات 2005 و 2010 اسپانیا بر روی بستری در بیمارستان برای بیماری های مرتبط با دخانیات در والنسیا ، اسپانیا-2020 Objectives: This study aimed to assess the impact of the latest smoke-free legislation on
hospital admission rates due to smoking-related diseases in Spain.
Study design: A retrospective cohort study was conducted to evaluate changes in hospital
admission rates for cardiovascular, respiratory diseases, and smoking-related cancer in
Valencia, Spain, during the period 1995e2013. Law 28/2005 and then law 42/2010 prohibited
smoking in bars and restaurants as well as playgrounds and access points to schools and
hospitals.
Methods: General population data by age and sex were obtained from the National Institute
of Statistics census. Data on hospital admissions were obtained from the Minimum Basic
Data Set. Diagnoses were codified according to the International Classification of Diseases-
9th revision. Data from all hospitals of the Valencian Community from 1995 to 2013 were
analysed. Trend analyses in the periods before and after the approval of the 2005 law were
conducted using least-squares linear regression models.
Results: Adjusted hospital admission rates per 100,000 inhabitants for cardiovascular diseases
significantly decreased after the 2005 Law (from 550.0/100,000 in 2005 to 500.5/100,000
in 2007), with a further decrease (to 434.6/100,000) in 2013, after the 2010 Law. Reductions in
hospital admissions were seen in men and women, although declining trends were more
marked in men. Hospital admission rates for respiratory diseases showed a reduction of a
lower magnitude, whereas for smoking-related cancer admissions there was a slight
decline only among men.
Conclusions: The Spanish comprehensive smoking ban resulted in a remarkable reduction
of the adjusted rate of hospital admissions mainly associated to cardiovascular diseases.
The decrease in the number of persons requiring in-patient care is relevant and may be
viewed as an improvement of the publics health. Keywords: Smoking/prevention and control | Smoke-free policies | Cardiovascular diseases/prevention | and control | Patient admission | Health policy |
مقاله انگلیسی |
2 |
A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis
روش مبتنی بر یادگیری ماشینی برای پیش بینی شیوع بیماریهای قلبی عروقی در بیماران مبتلا به دیالیز-2019 Background and Objective: Patients with End- Stage Kidney Disease (ESKD) have a unique cardiovascular risk. This study aims at predicting, with a certain precision, death and cardiovascular diseases in dialysis patients. Methods: To achieve our aim, machine learning techniques have been used. Two datasets have been taken into consideration: the first is an Italian dataset obtained from the Istituto di Fisiologia Clinica of Consiglio Nazionale delle Ricerche of Reggio Calabria; the second is an American dataset provided by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) repository. From each one we obtained 5 datasets, according to the outcome of interest. We tested different types of algorithm (both linear and non-linear), but the final choice was to use Support Vector Machine. In particular, we obtained the best performances using the non-linear SVC with RBF kernel algorithm, optimizing it with GridSearch. The last is an algorithm useful to search the best combination of hyper-parameters (in our case, to find the best couple (C, γ)), in order to improve the accuracy of the algorithm. Results: The use of non-linear SVC with RBF kernel algorithm, optimized with GridSearch, allowed to obtain an accuracy of 95.25% in the Italian dataset and of 92.15% in the American dataset, in a timeframe of 2.5 years,in the prediction of Ischaemic Heart Disease. A worse performance was obtained for the other outcomes. Conclusions: The machine learning-based approach applied in our study is able to predict, with a high accuracy, the outbreak of cardiovascular diseases in patients on dialysis. Keywords: Machine learning | Cardiovascular outcomes | ESRD | Prognosis |
مقاله انگلیسی |