Using the biomarker cotinine and survey self-report to validate smoking data from United States Veterans Health Administration electronic health records

Relationship between smoking experience and risk of suicide mortality in South Korean adults: A nationwide population-based retrospective cohort study

BACKGROUND

Korea has one of the highest suicide rates in the world. Many factors associated with suicidal thoughts or behaviors are known. This study examines the association between 1) smoking status or intensity (pack-years) and 2) risk of suicide mortality in South Korea.

METHODS

We analyzed data from 3,966,305 individuals aged ≥20 who underwent health examinations conducted by the South Korean National Health Insurance Service in 2009 and were followed until December 2021. Participants were categorized based on their baseline smoking status and intensity. We performed a Multivariate Cox proportional hazards regression analysis with subgroup analysis by age, sex, body mass index, alcohol consumption, regular exercise, and depression.

RESULTS

During an 11.1-year follow-up period, 12,326 individuals died by suicide. Compared with never-smokers, increased hazard ratios of suicide mortality were observed in current smokers (1.64, 95 % CI = 1.56-1.72), but not in ex-smokers. The suicide mortality risk of current smokers increased for all types of smoking intensity without a dose-response relationship. The association between smoking and suicide mortality risk was stronger among women, non-drinkers, adults aged <40 years, non-obese patients, and individuals without depression.

LIMITATION

Given that the study used retrospective data, the causal relationship remains unclear.

CONCLUSION

Current smoking is associated with a significant increased risk of suicide mortality. Smoking cessation is crucial to prevent suicide, especially among young adults, non-obese individuals, non-drinkers, women, and those without depression. Government policies in South Korea should focus on raising awareness about smoking hazards and providing cessation education to reduce the suicide mortality.

COVID-19 Breakthrough Infection after Vaccination and Substance Use Disorders: A Longitudinal Cohort of People with and without HIV Receiving Care in the United States Veterans Health Administration

Research regarding HIV, substance use disorders (SUD), and SARS-CoV-2 infections after COVID-19 vaccination is limited. In the Veterans Aging Cohort Study (VACS)-HIV cohort, we followed vaccinated persons with HIV (PWH) and without HIV (PWoH) from 12/2020 to 3/2022 and linked SARS-CoV-2 test results for laboratory-confirmed breakthrough infection through 9/2022. We examined associations of substance use (alcohol use disorder [AUD], other SUD, smoking status) and HIV status and severity with breakthrough infections, using Cox proportional hazards regression hazard ratios (HR). To test for potential interactions between substance use and HIV, we fit survival models with a multiplicative interaction term. Among 24,253 PWH and 53,661 PWoH, 8.0% of PWH and 7.1% of PWoH experienced COVID-19 breakthrough. AUD (HR 1.42, 95% CI 1.32, 1.52) and other SUD (HR 1.49, 95% CI 1.39, 1.59) were associated with increased risk of breakthrough, and this was similar by HIV status (p-interaction > 0.09). Smoking was not associated with breakthrough. Compared to PWoH, PWH at all HIV severity levels had increased risk of breakthrough ranging from 9% for PWH with CD4 count ≥ 500 cells/µl (HR 1.09, 95% CI 1.02, 1.17) to 59% for PWH with CD4 count < 200 (HR 1.59, 95% CI 1.31, 1.92). Patients with AUD (HR 1.42, 95% CI 1.33, 1.52) and other SUD (HR 1.48, 95% CI 1.38, 1.59) had increased COVID-19 breakthrough risk, regardless of HIV status. HIV was associated with breakthrough; risk was greatest among PWH with lower CD4 count. In addition to inhibiting HIV treatment adherence and increasing HIV progression, AUD and other SUD may increase COVID-19 breakthrough risk.

Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.

The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis

BACKGROUND

Smoking is a risk factor for many chronic diseases. Multiple smoking status ascertainment algorithms have been developed for population-based electronic health databases such as administrative databases and electronic medical records (EMRs). Evidence syntheses of algorithm validation studies have often focused on chronic diseases rather than risk factors. We conducted a systematic review and meta-analysis of smoking status ascertainment algorithms to describe the characteristics and validity of these algorithms.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. We searched articles published from 1990 to 2022 in EMBASE, MEDLINE, Scopus, and Web of Science with key terms such as validity, administrative data, electronic health records, smoking, and tobacco use. The extracted information, including article characteristics, algorithm characteristics, and validity measures, was descriptively analyzed. Sources of heterogeneity in validity measures were estimated using a meta-regression model. Risk of bias (ROB) in the reviewed articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

RESULTS

The initial search yielded 2086 articles; 57 were selected for review and 116 algorithms were identified. Almost three-quarters (71.6%) of algorithms were based on EMR data. The algorithms were primarily constructed using diagnosis codes for smoking-related conditions, although prescription medication codes for smoking treatments were also adopted. About half of the algorithms were developed using machine-learning models. The pooled estimates of positive predictive value, sensitivity, and specificity were 0.843, 0.672, and 0.918 respectively. Algorithm sensitivity and specificity were highly variable and ranged from 3 to 100% and 36 to 100%, respectively. Model-based algorithms had significantly greater sensitivity (p = 0.006) than rule-based algorithms. Algorithms for EMR data had higher sensitivity than algorithms for administrative data (p = 0.001). The ROB was low in most of the articles (76.3%) that underwent the assessment.

CONCLUSIONS

Multiple algorithms using different data sources and methods have been proposed to ascertain smoking status in electronic health data. Many algorithms had low sensitivity and positive predictive value, but the data source influenced their validity. Algorithms based on machine-learning models for multiple linked data sources have improved validity.

Multi-ancestry meta-analysis of tobacco use disorder prioritizes novel candidate risk genes and reveals associations with numerous health outcomes

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviors, and although strides have been made using genome-wide association studies (GWAS) to identify risk variants, the majority of variants identified have been for nicotine consumption, rather than TUD. We leveraged five biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records, EHR) in 898,680 individuals (739,895 European, 114,420 African American, 44,365 Latin American). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviors in children, and hundreds of medical outcomes, including HIV infection, heart disease, and pain. This work furthers our biological understanding of TUD and establishes EHR as a source of phenotypic information for studying the genetics of TUD.

Agreement between nicotine metabolites in blood and self-reported smoking status: The Netherlands Epidemiology of Obesity study

Introduction

Self-report and nicotine detection are methods to measure smoking exposure and can both lead to misclassification. It is important to highlight discrepancies between these two methods in the context of epidemiological research.

Objective

The aim of this cross-sectional study is to assess the agreements between self-reported smoking status and nicotine metabolite detection.

Methods

Data of 599 participants from the Netherlands Epidemiology of Obesity study were used to compare serum metabolite levels of five nicotine metabolites (cotinine, hydroxy-cotinine, cotinine N-Oxide, norcotinine, 3-hydroxy-cotinine-glucuronide) between self-reported never smokers (n = 245), former smokers (n = 283) and current smokers (n = 71). We assessed whether metabolites were absent or present and used logistic regression to discriminate between current and never smokers based on nicotine metabolite information. A classification tree was derived to classify individuals into current smokers and non/former smokers based on metabolite information.

Results

In 94% of the self-reported current smokers, at least one metabolite was present, versus in 19% of the former smokers and in 10% of the never smokers. In none of the never smokers, cotinine-n-oxide, 3-hydroxy-cotinine-n-glucorinide or norcotinine was present, while at least one of these metabolites was detected in 68% of the self-reported current smokers. The classification tree classified 95% of the participants in accordance to their self-reported smoking status. All self-reported smokers who were classified as non-smokers according to the metabolite profile, had reported to be occasional smokers.

Conclusion

The agreement between self-reported smoking status and metabolite information was high. This indicates that self-reported smoking status is generally reliable.