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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 |
Development of machine learning algorithms for prediction of mortality in spinal epidural abscess
توسعه الگوریتم های یادگیری ماشین برای پیش بینی مرگ و میر در آبسه اپیدورال ستون فقرات-2019 BACKGROUND CONTEXT: In-hospital and short-term mortality in patients with spinal epidural
abscess (SEA) remains unacceptably high despite diagnostic and therapeutic advancements.
Forecasting this potentially avoidable consequence at the time of admission could improve patient
management and counseling. Few studies exist to meet this need, and none have explored methodologies
such as machine learning.
PURPOSE: The purpose of this study was to develop machine learning algorithms for prediction
of in-hospital and 90-day postdischarge mortality in SEA.
STUDY DESIGN/SETTING: Retrospective, case-control study at two academic medical centers
and three community hospitals from 1993 to 2016.
PATIENTS SAMPLE: Adult patients with an inpatient admission for radiologically confirmed
diagnosis of SEA.
OUTCOME MEASURES: In-hospital and 90-day postdischarge mortality.
METHODS: Five machine learning algorithms (elastic-net penalized logistic regression, random
forest, stochastic gradient boosting, neural network, and support vector machine) were developed
and assessed by discrimination, calibration, overall performance, and decision curve analysis.
RESULTS: Overall, 1,053 SEA patients were identified in the study, with 134 (12.7%) experiencing
in-hospital or 90-day postdischarge mortality. The stochastic gradient boosting model achieved the best
performance across discrimination, c-statistic=0.89, calibration, and decision curve analysis. The variables
used for prediction of 90-day mortality, ranked by importance, were age, albumin, platelet count,
neutrophil to lymphocyte ratio, hemodialysis, active malignancy, and diabetes. The final algorithm was
incorporated into a web application available here: https://sorg-apps.shinyapps.io/seamortality/.
CONCLUSIONS: Machine learning algorithms show promise on internal validation for prediction
of 90-day mortality in SEA. Future studies are needed to externally validate these algorithms inindependent populations. Keywords: Artificial intelligence | Healthcare | Machine learning | Mortality | Spinal epidural abscess | Spine surgery |
مقاله انگلیسی |