Dataset Information

Determinants of Smoking and Quitting in HIV-Infected Individuals.

ABSTRACT:

Background

Cigarette smoking is widespread among HIV-infected patients, who confront increased risk of smoking-related co-morbidities. The effects of HIV infection and HIV-related variables on smoking and smoking cessation are incompletely understood. We investigated the correlates of smoking and quitting in an HIV-infected cohort using a validated natural language processor to determine smoking status.

Method

We developed and validated an algorithm using natural language processing (NLP) to ascertain smoking status from electronic health record data. The algorithm was applied to records for a cohort of 3487 HIV-infected from a large health care system in Boston, USA, and 9446 uninfected control patients matched 3:1 on age, gender, race and clinical encounters. NLP was used to identify and classify smoking-related portions of free-text notes. These classifications were combined into patient-year smoking status and used to classify patients as ever versus never smokers and current smokers versus non-smokers. Generalized linear models were used to assess associations of HIV with 3 outcomes, ever smoking, current smoking, and current smoking in analyses limited to ever smokers (persistent smoking), while adjusting for demographics, cardiovascular risk factors, and psychiatric illness. Analyses were repeated within the HIV cohort, with the addition of CD4 cell count and HIV viral load to assess associations of these HIV-related factors with the smoking outcomes.

Results

Using the natural language processing algorithm to assign annual smoking status yielded sensitivity of 92.4, specificity of 86.2, and AUC of 0.89 (95% confidence interval [CI] 0.88-0.91). Ever and current smoking were more common in HIV-infected patients than controls (54% vs. 44% and 42% vs. 30%, respectively, both P<0.001). In multivariate models HIV was independently associated with ever smoking (adjusted rate ratio [ARR] 1.18, 95% CI 1.13-1.24, P <0.001), current smoking (ARR 1.33, 95% CI 1.25-1.40, P<0.001), and persistent smoking (ARR 1.11, 95% CI 1.07-1.15, P<0.001). Within the HIV cohort, having a detectable HIV RNA was significantly associated with all three smoking outcomes.

Conclusions

HIV was independently associated with both smoking and not quitting smoking, using a novel algorithm to ascertain smoking status from electronic health record data and accounting for multiple confounding clinical factors. Further research is needed to identify HIV-related barriers to smoking cessation and develop aggressive interventions specific to HIV-infected patients.

SUBMITTER: Regan S

PROVIDER: S-EPMC4839777 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Determinants of Smoking and Quitting in HIV-Infected Individuals.

Regan Susan S Meigs James B JB Grinspoon Steven K SK Triant Virginia A VA

PloS one 20160421 4

<h4>Background</h4>Cigarette smoking is widespread among HIV-infected patients, who confront increased risk of smoking-related co-morbidities. The effects of HIV infection and HIV-related variables on smoking and smoking cessation are incompletely understood. We investigated the correlates of smoking and quitting in an HIV-infected cohort using a validated natural language processor to determine smoking status.<h4>Method</h4>We developed and validated an algorithm using natural language processi ...[more]

PMID: 27099932

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Project description:Cardiovascular disease and non-AIDS malignancies have become major causes of death among HIV-infected individuals. The relative impact of lifestyle and HIV-related factors are debated.We estimated associations of smoking with mortality more than 1 year after antiretroviral therapy (ART) initiation among HIV-infected individuals enrolled in European and North American cohorts. IDUs were excluded. Causes of death were assigned using standardized procedures. We used abridged life tables to estimate life expectancies. Life-years lost to HIV were estimated by comparison with the French background population.Among 17,995 HIV-infected individuals followed for 79,760 person-years, the proportion of smokers was 60%. The mortality rate ratio (MRR) comparing smokers with nonsmokers was 1.94 [95% confidence interval (95% CI) 1.56-2.41]. The MRRs comparing current and previous smokers with never smokers were 1.70 (95% CI 1.23-2.34) and 0.92 (95% CI 0.64-1.34), respectively. Smokers had substantially higher mortality from cardiovascular disease, non-AIDS malignancies than nonsmokers [MRR 6.28 (95% CI 2.19-18.0) and 3.31 (95% CI 1.80-5.45), respectively]. [corrected]. Among 35-year-old HIV-infected men, the loss of life-years associated with smoking and HIV was 7.9 (95% CI 7.1-8.7) and 5.9 (95% CI 4.9-6.9), respectively. The life expectancy of virally suppressed, never-smokers was 43.5 years (95% CI 41.7-45.3), compared with 44.4 years among 35-year-old men in the background population. Excess MRRs/1000 person-years associated with smoking increased from 0.6 (95% CI -1.3 to 2.6) at age 35 to 43.6 (95% CI 37.9-49.3) at age at least 65 years.Well treated HIV-infected individuals may lose more life years through smoking than through HIV. Excess mortality associated with smoking increases markedly with age. Therefore, increases in smoking-related mortality can be expected as the treated HIV-infected population ages. Interventions for smoking cessation should be prioritized.

Project description:Mobile health technologies are being developed for personal lifestyle and medical healthcare support, of which a growing number are designed to assist smokers to quit. The potential impact of these technologies in the fight against smoking addiction and on improving quitting rates must be systematically evaluated. The aim of this report is to identify and appraise the most promising smoking detection and quitting technologies (e.g., smartphone apps, wearable devices) supporting smoking reduction or quitting programs. We searched PubMed and Scopus databases (2008-2019) for studies on mobile health technologies developed to assist smokers to quit using a combination of Medical Subject Headings topics and free text terms. A Google search was also performed to retrieve the most relevant smartphone apps for quitting smoking, considering the average user's rating and the ranking computed by the search engine algorithms. All included studies were evaluated using consolidated criteria for reporting qualitative research, such as applied methodologies and the performed evaluation protocol. Main outcome measures were usability and effectiveness of smoking detection and quitting technologies supporting smoking reduction or quitting programs. Our search identified 32 smoking detection and quitting technologies (12 smoking detection systems and 20 smoking quitting smartphone apps). Most of the existing apps for quitting smoking require the users to register every smoking event. Moreover, only a restricted group of them have been scientifically evaluated. The works supported by documented experimental evaluation show very high detection scores, however the experimental protocols usually lack in variability (e.g., only right-hand patients, not natural sequence of gestures) and have been conducted with limited numbers of patients as well as under constrained settings quite far from real-life use scenarios. Several recent scientific works show very promising results but, at the same time, present obstacles for the application on real-life daily scenarios.

Dataset Information

Determinants of Smoking and Quitting in HIV-Infected Individuals.

Background

Method

Results

Conclusions

Publications

Determinants of Smoking and Quitting in HIV-Infected Individuals.

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