The accuracy and trends of smoking history documentation in electronic medical records in a large managed care organization

Trends in cigarette smoking and the risk of incident cardiovascular disease among Asian American, Pacific Islander, and multiracial populations

Background

Cardiovascular disease (CVD) is the leading cause of death in the United States, and rates of CVD incidence vary widely by race and ethnicity. Cigarette smoking is associated with increased risk of CVD. The purpose of the study was: 1) to examine smoking prevalence over time across Asian and Pacific Islander (API) and multi-race API subgroups; 2) to determine whether the CVD risk associated with smoking differed among these subgroups.

Methods

We identified patients belonging to 7 single race/ethnicity groups, 4 multi-race/ethnicity groups, and a non-Hispanic White (NHW) comparison group at two large health systems in Hawaii and California. We estimated annual smoking prevalence from 2011 through 2018 by group and gender. We examined incidence of CVD events by smoking status and race/ethnicity, and computed hazard ratios for CVD events by age, gender, race/ethnicity, census block median household income, census block college degree, and study site using Cox regression.

Results

Of the 12 groups studied, the Asian Indian and Chinese American groups had the lowest smoking prevalence, and the Asian + Pacific Islander multiracial group had the highest smoking prevalence. The prevalence of smoking decreased from 2011 to 2018 for all groups. Multi-race/ethnicity groups had higher risk of CVD than the NHW group. There was no significant interaction between race/ethnicity and smoking in models predicting CVD, but the association between race/ethnicity and CVD incidence was attenuated after adjusting for smoking status.

Conclusions

There is considerable heterogeneity in smoking prevalence and the risk of CVD among API subgroups.

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.

Cannabis smoking, tobacco cigarette smoking, and adenomyosis risk

OBJECTIVE

To investigate cannabis smoking and tobacco cigarette smoking in relation to adenomyosis risk.

DESIGN

We used data from a case-control study of adenomyosis conducted among enrollees ages 18-59 years of an integrated health care system in Washington State. The case-control study used 2 control groups given the challenge of selecting noncases when cases are diagnosed by hysterectomy.

SUBJECTS

Cases (n = 386) were enrollees with incident, pathology-confirmed adenomyosis diagnosed between April 1, 2001, and March 31, 2006. The 2 control groups comprised hysterectomy controls (n = 233) with pathology-confirmed absence of adenomyosis and population controls (n = 323) with an intact uterus selected randomly from the health care system population and frequency matched to cases on age.

EXPOSURE

Detailed data on cannabis and tobacco cigarette smoking history were ascertained through in-person structured interviews, allowing estimation of joint-years of cannabis smoking and pack-years of tobacco cigarette smoking.

MAIN OUTCOME MEASURES

Odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between cannabis smoking, tobacco cigarette smoking, and adenomyosis were estimated using multivariable unconditional logistic regression. Analyses were adjusted for age, reference year, menarche age, education, and pack-years of cigarette smoking (or joint-years of cannabis smoking).

RESULTS

No association was observed between cannabis smoking history and adenomyosis risk. However, we did observe the suggestion of an association between ever tobacco cigarette smoking and adenomyosis risk, comparing cases to hysterectomy controls (OR, 1.3; 95% CI, 0.9-1.9) and population controls (OR, 1.2; 95% CI, 0.8-1.8). Our data suggested a 50% increased odds of adenomyosis with >15 pack-years of smoking (vs. never smoking), comparing cases to hysterectomy controls (OR, 1.5; 95% CI, 0.9-2.6; Ptrend=.135). The suggestion of a 40% increased adenomyosis odds was observed with smoking >5-15 pack-years (vs. never smoking), comparing cases to population controls (OR, 1.4; 95% CI, 0.8-2.4; Ptrend=0.136).

CONCLUSION

In the first study of cannabis smoking and adenomyosis risk, no association was observed. However, our data suggested an increased odds of adenomyosis with history of tobacco cigarette smoking. Further research is warranted to replicate our results given the substantial morbidity with adenomyosis and frequency of cigarette smoking and recreational and medical cannabis use.

Effects of Smoking on SARS-CoV-2 Positivity: A Study of in Northern and Central California

Introduction

COVID-19 continues to impact vulnerable populations disproportionally. Identifying modifiable risk factors could lead to targeted interventions to reduce infections. The purpose of this study is to identify risk factors for testing positive for SARS-CoV-2.

Methods

Using electronic health records collected from a large ambulatory care system in northern and central California, the study identified patients who had a test for SARS-CoV-2 between 2/20/2020 and 3/31/2021. The adjusted effect of active and passive smoking and other risk factors on the probability of testing positive for SARS-CoV-2 were estimated using multivariable logistic regression. Analyses were conducted in 2021.

Results

Of 556 690 eligible patients in our sample, 70 564 (12.7%) patients tested positive for SARS-CoV-2. Younger age, being male, racial/ethnic minorities, and having mild major comorbidities were significantly associated with a positive SARS-CoV-2 test. Current smokers (adjusted OR: 0.69, 95% CI: 0.66-0.73) and former smokers (adjusted OR: 0.92, 95% CI: 0.89-0.95) were less likely than nonsmokers to be lab-confirmed positive, but no statistically significant differences were found when comparing passive smokers with non-smokers. The patients with missing smoking status (25.7%) were more likely to be members of vulnerable populations with major comorbidities (adjusted OR ranges from severe: 2.52, 95% CI = 2.36-2.69 to mild: 3.28, 95% CI = 3.09-3.48), lower income (adjusted OR: 0.85, 95% CI: 0.85-0.86), aged 80 years or older (adjusted OR: 1.11, 95% CI: 1.07-1.16), have less access to primary care (adjusted OR: 0.07, 95% CI: 0.07-0.07), and identify as racial ethnic minorities (adjusted OR ranges from Hispanic: 1.61, 95% CI = 1.56-1.65 to Non-Hispanic Black: 2.60, 95% CI = 2.5-2.69).

Conclusions

Our findings suggest that the odds of testing positive for SARS-CoV-2 were significantly lower in smokers compared to nonsmokers. Other risk factors include missing data on smoking status, being under 18, being male, being a racial/ethnic minority, and having mild major comorbidities. Since those with missing data on smoking status were more likely to be members of vulnerable populations with higher smoking rates, the risk of testing positive for SARS-CoV-2 among smokers may have been underestimated due to missing data on smoking status. Future studies should investigate the risk of severe outcomes among active and passive smokers, the role that exposure to tobacco smoke constitutes among nonsmokers, the role of comorbidities in COVID-19 disease course, and health disparities experienced by disadvantaged groups.

Outreach to primary care patients in lung cancer screening: A randomized controlled trial

Current guidelines recommend annual lung cancer screening (LCS), but rates are low. The current study evaluated strategies to increase LCS. This study was a randomized controlled trial designed to evaluate the effects of patient outreach and shared decision making (SDM) about LCS among patients in four primary care practices. Patients 50 to 80 years of age and at high risk for lung cancer were randomized to Outreach Contact plus Decision Counseling (OC-DC, n = 314), Outreach Contact alone (OC, n = 314), or usual care (UC, n = 1748). LCS was significantly higher in the combined OC/OC-DC group versus UC controls (5.5% vs. 1.8%; hazard ratio, HR = 3.28; 95% confidence interval, CI: 1.98 to 5.41; p = 0.001). LCS was higher in the OC-DC group than in the OC group, although not significantly so (7% vs. 4%, respectively; HR = 1.75; 95% CI: 0.86 to 3.55; p = 0.123). LCS referral/scheduling was also significantly higher in the OC/OC-DC group compared to controls (11% v. 5%; odds ratio, OR = 2.02; p = 0.001). We observed a similar trend for appointment keeping, but the effect was not statistically significant (86% v. 76%; OR = 1.93; p = 0.351). Outreach contacts significantly increased LCS among primary care patients. Research is needed to assess the additional value of SDM on screening uptake.

OUP accepted manuscript

Objective

Materials and Methods

Results

Discussion

Conclusion

Concordance Between Electronic Health Record and Tumor Registry Documentation of Smoking Status Among Patients With Cancer

PURPOSE

Patients with cancer who use tobacco experience reduced treatment effectiveness, increased risk of recurrence and mortality, and diminished quality of life. Accurate tobacco use documentation for patients with cancer is necessary for appropriate clinical decision making and cancer outcomes research. Our aim was to assess agreement between electronic health record (EHR) smoking status data and cancer registry data.

MATERIALS AND METHODS

We identified all patients with cancer seen at University of Florida Health from 2015 to 2018. Structured EHR smoking status was compared with the tumor registry smoking status for each patient. Sensitivity, specificity, positive predictive values, negative predictive values, and Kappa statistics were calculated. We used logistic regression to determine if patient characteristics were associated with odds of agreement in smoking status between EHR and registry data.

RESULTS

We analyzed 11,110 patient records. EHR smoking status was documented for nearly all (98%) patients. Overall kappa (0.78; 95% CI, 0.77 to 0.79) indicated moderate agreement between the registry and EHR. The sensitivity was 0.82 (95% CI, 0.81 to 0.84), and the specificity was 0.97 (95% CI, 0.96 to 0.97). The logistic regression results indicated that agreement was more likely among patients who were older and female and if the EHR documentation occurred closer to the date of cancer diagnosis.

CONCLUSION

Although documentation of smoking status for patients with cancer is standard practice, we only found moderate agreement between EHR and tumor registry data. Interventions and research using EHR data should prioritize ensuring the validity of smoking status data. Multilevel strategies are needed to achieve consistent and accurate documentation of smoking status in cancer care.

Systematic review with meta-analysis of the epidemiological evidence in Europe, Israel, America and Australasia on smoking and COVID-19

BACKGROUND

Previous meta-analyses related smoking to death or severe infection from coronavirus disease 2019 (COVID-19) in hospitalized patients, but considered only a few studies, did not adjust for demographics and comorbidities, and inadequately defined smoking.

AIM

To review and meta-analyse epidemiological evidence on smoking and COVID-19, considering a range of endpoints, populations and smoking definitions and the effect of adjustment.

METHODS

Studies were identified from publications in English up to 30 September, 2020 involving at least 100 individuals, carried out in Europe, Israel, America or Australasia, not restricted to those with specific other diseases, and providing information relating smoking to various COVID-related endpoints. Meta-analyses were carried out for combinations of population and endpoint, with variation studied by smoking definition, adjustment level and other factors.

RESULTS

From 96 publications, 74 studies were identified, 37 in the United States, 10 in the United Kingdom, with up to four in the other countries. Three involved over a million individuals, and 37 involved less than a thousand. Adjusted results for smoking were available in 42 studies, with adjustment not considered in 20 studies. Results were considered by endpoint. No significant effect of smoking on COVID-19 positivity was seen in the general population, but there was a reduced risk in those tested. Best-adjusted estimates for current (vs never) smoking were 0.87 (95% confidence interval: 0.52-1.47) in the general population and 0.52 (0.43-0.64) in those tested. For those hospitalized due to COVID-19, unadjusted rates were significantly increased in current smokers (1.20, 1.01-1.42) and ever smokers (1.64, 1.41-1.91), but those adjusted for comorbidities showed no increase for current (0.82, 0.52-1.30) or ever smokers (1.00, 0.76-1.32). There was little evidence to suggest that smoking was associated with intensive care admission. For those hospitalized with COVID-19, best-adjusted estimates were 0.88 (0.72-1.08) for current smokers and 1.10 (0.99-1.22) for ever smokers. In those hospitalized with COVID-19, smoking was not significantly related to subsequent mechanical ventilation, with best-adjusted estimates of 1.12 (0.60-2.09) for current smokers and 1.05 (0.88-1.25) for ever smokers. For those hospitalized with severe COVID-19, best-adjusted estimates were 0.74 (0.49-1.12) for current smokers and 1.15 (0.87-1.51) for ever smokers; few estimates were adjusted for comorbidities. While smoking was associated with increased mortality in unadjusted analyses, the association disappeared after adjustment for comorbidities. For example, in those hospitalized with COVID-19, the unadjusted estimate for ever smokers of 1.59 (1.37-1.83) reduced to 1.07 (0.82-1.38) when adjusted for comorbidities. Studies on those with severe COVID-19 showed that smoking tended to be associated with worsening of the disease. However, no estimate was adjusted, even for demographics. Estimates did not clearly vary by location or study size, and there was too little evidence to usefully study variations by age, amount smoked or years quit.

CONCLUSION

The increased COVID-19 death rate in smokers seen in unadjusted analyses disappears following adjustment for demographics and comorbidities. Among those tested, smoking is associated with lower COVID-19 infection rates.

Brief original report: Does smoking status provide information relevant to screening for other substance use among US adults?

We assessed whether tobacco screening provides clinically meaningful information about other substance use, including alcohol and other drug use, potentially facilitating targeting of screening for substance use. Using data from the Veterans Aging Cohort Study survey sample (VACS; N = 7510), we calculated test performance characteristics of tobacco use screening results for identification of other substance use including sensitivity, specificity, positive-likelihood-ratio (+LR = [sensitivity/(1-specificity)]: increase in odds of substance use informed by a positive tobacco screen), and negative-likelihood-ratio (-LR: [(1-sensitivity)/specificity]: reduction in odds of substance use informed by a negative tobacco screen). The sample was 95% male, 75% minority, and 43% were current and 33% were former smokers. Never smoking, versus any history, indicated an approximate four-fold decrease in the odds of injection drug use (-LR = 0.26), an approximate 2.5-fold decrease in crack/cocaine (-LR = 0.35) and unhealthy alcohol use (-LR = 0.40), an approximate two-fold decrease in marijuana (-LR = 0.51) and illicit opioid use (-LR = 0.48), and an approximate 30% decrease in non-crack/cocaine stimulant use (-LR = 0.75). Never smoking yielded more information than current non-smoking (never/former smoking). Positive results on tobacco screening were less informative than negative results; current smoking, versus former/never smoking, provided more information than lifetime smoking and was associated with a 40% increase in the odds of non-crack/cocaine stimulant use (+LR = 1.40) and opioid use (+LR = 1.44), 50% increase in marijuana use (+LR = 1.52) and injection drug use (+LR = 1.55), and an 80-90% increase in crack/cocaine use (+LR = 1.93) and unhealthy alcohol use (+LR = 1.75). When comprehensive screening for substance use is not possible, tobacco screening may inform decisions about targeting substance use screening.

Clinician Variation in Ordering and Completion of Low-Dose Computed Tomography for Lung Cancer Screening in a Safety-Net Medical System

BACKGROUND

Less than 5% of eligible individuals in the United States undergo lung cancer screening. Variation in clinicians' participation in lung cancer screening has not been determined.

PATIENTS AND METHODS

We studied medical providers who ordered ≥ 1 low-dose computed tomography (LDCT) for lung cancer screening from February 2017 through February 2019 in an integrated safety-net healthcare system. We analyzed associations between provider characteristics and LDCT orders and completion using chi-square, Fisher exact, and Student t tests, as well as ANOVA and multinomial logistic regression.

RESULTS

Among an estimated 194 adult primary care physicians, 144 (74%) ordered at least 1 LDCT, as did 39 specialists. These 183 medical providers ordered 1594 LDCT (median, 4; interquartile range, 2-9). In univariate and multivariate models, family practice providers (P < .001) and providers aged ≥ 50 years (P = .03) ordered more LDCT than did other clinicians. Across providers, the median proportion of ordered LDCT that were completed was 67%. The total or preceding number of LDCT ordered by a clinician was not associated with the likelihood of LDCT completion.

CONCLUSION

In an integrated safety-net healthcare system, most adult primary care providers order LDCT. The number of LDCT ordered varies widely among clinicians, and a substantial proportion of ordered LDCT are not completed.

Assessing Different Approaches to Leveraging Historical Smoking Exposure Data to Better Select Lung Cancer Screening Candidates: A Retrospective Validation Study

INTRODUCTION

There is mounting interest in the use of risk prediction models to guide lung cancer screening. Electronic health records (EHRs) could facilitate such an approach, but smoking exposure documentation is notoriously inaccurate. While the negative impact of inaccurate EHR data on screening practices reliant on dichotomized age and smoking exposure-based criteria has been demonstrated, less is known regarding its impact on the performance of model-based screening.

AIMS AND METHODS

Data were collected from a cohort of 37 422 ever-smokers between the ages of 55 and 74, seen at an academic safety-net healthcare system between 1999 and 2018. The National Lung Cancer Screening Trial (NLST) criteria, PLCOM2012 and LCRAT lung cancer risk prediction models were validated against time to lung cancer diagnosis. Discrimination (area under the receiver operator curve [AUC]) and calibration were assessed. The effect of substituting the last documented smoking variables with differentially retrieved "history conscious" measures was also determined.

RESULTS

The PLCOM2012 and LCRAT models had AUCs of 0.71 (95% CI, 0.69 to 0.73) and 0.72 (95% CI, 0.70 to 0.74), respectively. Compared with the NLST criteria, PLCOM2012 had a significantly greater time-dependent sensitivity (69.9% vs. 64.5%, p < .01) and specificity (58.3% vs. 56.4%, p < .001). Unlike the NLST criteria, the performances of the PLCOM2012 and LCRAT models were not prone to historical variability in smoking exposure documentation.

CONCLUSIONS

Despite the inaccuracies of EHR-documented smoking histories, leveraging model-based lung cancer risk estimation may be a reasonable strategy for screening, and is of greater value compared with using NLST criteria in the same setting.

IMPLICATIONS

EHRs are potentially well suited to aid in the risk-based selection of lung cancer screening candidates, but healthcare providers and systems may elect not to leverage EHR data due to prior work that has shown limitations in structured smoking exposure data quality. Our findings suggest that despite potential inaccuracies in the underlying EHR data, screening approaches that use multivariable models may perform significantly better than approaches that rely on simpler age and exposure-based criteria. These results should encourage providers to consider using pre-existing smoking exposure data with a model-based approach to guide lung cancer screening practices.

The Incidence and Associated Factors of Early Neurological Deterioration After Thrombolysis

Background and purpose:

Early neurological deterioration (END) after stroke onset may predict severe outcomes. Estimated rates of END after intravenous thrombolysis among small patient samples have been reported up to 29.8%. We studied the incidence and factors associated with END among patients following intravenous thrombolysis.

Methods:

We analyzed SITS-International Stroke Thrombolysis registry patients with known outcomes enrolled in 2010 to 2017. END was defined as an increase in National Institutes of Health Stroke Scale score ≥4 or death within 24 hours from baseline National Institutes of Health Stroke Scale. We determined the incidence of END and used logistic regression models to inspect its associated factors. We adjusted for variables found significant in univariate analyses ( P <0.05). Main outcomes were incidence of END, associated predictors of END, ordinal day-90 mRS, and day-90 mortality.

Results:

We excluded 53 539 patients and included 50 726 patients. The incidence of END was 3415/50 726 (6.7% [95% CI, 6.5%–7.0%]). Factors independently associated with END on multivariate analysis were intracerebral hemorrhage (OR, 3.23 [95% CI, 2.96–3.54], P <0.001), large vessel disease (LVD) with carotid stenosis (OR, 2.97 [95% CI, 2.45–3.61], P <0.001), other LVD (OR, 2.41 [95% CI, 2.03–2.88], P <0.001), and ischemic stroke versus transient ischemic attack (TIA)/stroke mimics (OR, 16.14 [95% CI, 3.99–65.3], P <0.001). END was associated with worse outcome on ordinal mRS: adjusted OR 2.48 (95% CI, 2.39–2.57, P <0.001) by day-90 compared with no END. The adjusted OR for day-90 mortality was 9.70 (95% CI, 8.36–11.26, P <0.001).

Conclusions:

The routinely observed rate of END reflected by real-world data is low, but END greatly increases risk of disability and mortality. Readily identifiable factors predict END and may help with understanding causal mechanisms to assist prevention of END.

A Comparison of Smoking History in the Electronic Health Record With Self-Report

INTRODUCTION

Knowing patients' smoking history helps guide who may benefit from preventive services such as lung cancer screening. The accuracy of smoking history electronic health records remains unclear.

METHODS

This was a secondary analysis of data collected from a portal-based lung cancer screening decision aid. Participants of an academically affiliated health system, aged 55-76 years, completed an online survey that collected a detailed smoking history including years of smoking, years since quitting, and smoking intensity. Eligibility for lung cancer screening was defined using the Centers for Medicare and Medicaid Services criteria. Data analysis was performed May-December 2018, and data collection occurred between November 2016 and February 2017.

RESULTS

A total of 336 participants completed the survey and were included in the analysis. Of 175 participants with self-reported smoking intensity, 72% had packs per day and 62% had pack-years recorded in the electronic health record. When present, smoking history in the electronic health records correlated well with self-reported years of smoking (r =0.78, p≤0.0001) and years since quitting (r =0.94, p≤0.0001). Self-reported smoking intensity, including pack-years (r =0.62, p<0.0001) and packs per day (r =0.65, p≤0.0001), was less correlated. Of those participants eligible for lung cancer screening by self-report, only 35% met criteria for screening by electronic health records data alone. Others were either incorrectly classified as ineligible (23%) or had incomplete data (41%).

CONCLUSIONS

The electronic health records frequently misses critical elements of a smoking history, and when present, it often underestimates smoking intensity, which may impact who receives lung cancer screening.

Randomized Electronic Promotion of Lung Cancer Screening: A Pilot

PURPOSE

Screening for lung cancer with low-dose computed tomography is endorsed by the US Preventive Services Task Force, but many eligible patients have yet to be offered screening. Major barriers to the implementation of screening are physician and system related-the requirement for a detailed smoking history, including pack-years, to determine eligibility. We conducted this pilot to determine the feasibility of lung cancer screening (LCS) promotion that would offer screening to eligible persons and patient completion of smoking history to estimate the size of the population of former smokers who may be eligible for LCS in a single health care system.

PATIENTS AND METHODS

Two hundred participants were randomly selected from former smokers who were seen at the University of Minnesota Health in the past 2 years and assigned to control (usual care) and electronic promotion, stratified by age. Electronic messages to promote LCS were sent to an intervention group, including a link to complete a detailed smoking history in the electronic health record.

RESULTS

Of 99 participants, 66 (67%) in the intervention group read the message, 24 (36%) of 66 responded, and 19 (79%) of 24 respondents completed the smoking history. Ten intervention participants and 13 usual care participants were eligible for screening on the basis of pack-year history. Four eligible participants underwent screening in the intervention group compared with one participant in the usual care group.

CONCLUSION

Electronic promotion may help identify patients who are eligible for LCS but will not reliably reach all patients because of low response rates. In this sample of former smokers, the majority are ineligible for LCS on the basis of pack-year history. Electronic methods can improve documentation of smoking history.

DNA Damage, Mutagenesis and Cancer

A large number of chemicals and several physical agents, such as UV light and γ-radiation, have been associated with the etiology of human cancer. Generation of DNA damage (also known as DNA adducts or lesions) induced by these agents is an important first step in the process of carcinogenesis. Evolutionary processes gave rise to DNA repair tools that are efficient in repairing damaged DNA; yet replication of damaged DNA may take place prior to repair, particularly when they are induced at a high frequency. Damaged DNA replication may lead to gene mutations, which in turn may give rise to altered proteins. Mutations in an oncogene, a tumor-suppressor gene, or a gene that controls the cell cycle can generate a clonal cell population with a distinct advantage in proliferation. Many such events, broadly divided into the stages of initiation, promotion, and progression, which may occur over a long period of time and transpire in the context of chronic exposure to carcinogens, can lead to the induction of human cancer. This is exemplified in the long-term use of tobacco being responsible for an increased risk of lung cancer. This mini-review attempts to summarize this wide area that centers on DNA damage as it relates to the development of human cancer.

Lung Cancer Screening and Smoking Cessation Clinical Trials. SCALE (Smoking Cessation within the Context of Lung Cancer Screening) Collaboration

National recommendations for lung cancer screening for former and current smokers aged 55-80 years with a 30-pack-year smoking history create demand to implement efficient and effective systems to offer smoking cessation on a large scale. These older, high-risk smokers differ from participants in past clinical trials of behavioral and pharmacologic interventions for tobacco dependence. There is a gap in knowledge about how best to design systems to extend reach and treatments to maximize smoking cessation in the context of lung cancer screening. Eight clinical trials, seven funded by the National Cancer Institute and one by the Veterans Health Administration, address this gap and form the SCALE (Smoking Cessation within the Context of Lung Cancer Screening) collaboration. This paper describes methodological issues related to the design of these clinical trials: clinical workflow, participant eligibility criteria, screening indication (baseline or annual repeat screen), assessment content, interest in stopping smoking, and treatment delivery method and dose, all of which will affect tobacco treatment outcomes. Tobacco interventions consider the "teachable moment" offered by lung cancer screening, how to incorporate positive and negative screening results, and coordination of smoking cessation treatment with clinical events associated with lung cancer screening. Unique data elements, such as perceived risk of lung cancer and costs of tobacco treatment, are of interest. Lung cancer screening presents a new and promising opportunity to reduce morbidity and mortality resulting from lung cancer that can be amplified by effective smoking cessation treatment. SCALE teamwork and collaboration promise to maximize knowledge gained from the clinical trials.

Generalizability of Indicators from the New York City Macroscope Electronic Health Record Surveillance System to Systems Based on Other EHR Platforms

Introduction:

The New York City (NYC) Macroscope is an electronic health record (EHR) surveillance system based on a distributed network of primary care records from the Hub Population Health System. In a previous 3-part series published in eGEMS, we reported the validity of health indicators from the NYC Macroscope; however, questions remained regarding their generalizability to other EHR surveillance systems.

Methods:

We abstracted primary care chart data from more than 20 EHR software systems for 142 participants of the 2013–14 NYC Health and Nutrition Examination Survey who did not contribute data to the NYC Macroscope. We then computed the sensitivity and specificity for indicators, comparing data abstracted from EHRs with survey data.

Results:

Obesity and diabetes indicators had moderate to high sensitivity (0.81–0.96) and high specificity (0.94–0.98). Smoking status and hypertension indicators had moderate sensitivity (0.78–0.90) and moderate to high specificity (0.88–0.98); sensitivity improved when the sample was restricted to records from providers who attested to Stage 1 Meaningful Use. Hyperlipidemia indicators had moderate sensitivity (≥0.72) and low specificity (≤0.59), with minimal changes when restricting to Stage 1 Meaningful Use.

Discussion:

Indicators for obesity and diabetes used in the NYC Macroscope can be adapted to other EHR surveillance systems with minimal validation. However, additional validation of smoking status and hypertension indicators is recommended and further development of hyperlipidemia indicators is needed.

Conclusion:

Our findings suggest that many of the EHR-based surveillance indicators developed and validated for the NYC Macroscope are generalizable for use in other EHR surveillance systems.

Validation of Veterans Affairs Electronic Medical Record Smoking Data Among Iraq- and Afghanistan-Era Veterans

BACKGROUND

Research using the Veterans Health Administration (VA) electronic medical records (EMR) has been limited by a lack of reliable smoking data.

OBJECTIVE

To evaluate the validity of using VA EMR "Health Factors" data to determine smoking status among veterans with recent military service.

DESIGN

Sensitivity, specificity, area under the receiver-operating curve (AUC), and kappa statistics were used to evaluate concordance between VA EMR smoking status and criterion smoking status.

PARTICIPANTS

Veterans (N = 2025) with service during the wars in Iraq/Afghanistan who participated in the VA Mid-Atlantic Post-Deployment Mental Health (PDMH) Study.

MAIN MEASURES

Criterion smoking status was based on self-report during a confidential study visit. VA EMR smoking status was measured by coding health factors data entries (populated during automated clinical reminders) in three ways: based on the most common health factor, the most recent health factor, and the health factor within 12 months of the criterion smoking status data collection date.

KEY RESULTS

Concordance with PDMH smoking status (current, former, never) was highest when determined by the most commonly observed VA EMR health factor (κ = 0.69) and was not significantly impacted by psychiatric status. Agreement was higher when smoking status was dichotomized: current vs. not current (κ = 0.73; sensitivity = 0.84; specificity = 0.91; AUC = 0.87); ever vs. never (κ = 0.75; sensitivity = 0.85; specificity = 0.90; AUC = 0.87). There were substantial missing Health Factors data when restricting analyses to a 12-month period from the criterion smoking status date. Current smokers had significantly more Health Factors entries compared to never or former smokers.

CONCLUSIONS

The use of computerized tobacco screening data to determine smoking status is valid and feasible. Results indicating that smokers have significantly more health factors entries than non-smokers suggest that caution is warranted when using the EMR to select cases for cohort studies as the risk for selection bias appears high.

Can Electronic Health Records Be Used for Population Health Surveillance? Validating Population Health Metrics Against Established Survey Data

INTRODUCTION

Electronic health records (EHRs) offer potential for population health surveillance but EHR-based surveillance measures require validation prior to use. We assessed the validity of obesity, smoking, depression, and influenza vaccination indicators from a new EHR surveillance system, the New York City (NYC) Macroscope. This report is the second in a 3-part series describing the development and validation of the NYC Macroscope. The first report describes in detail the infrastructure underlying the NYC Macroscope; design decisions that were made to maximize data quality; characteristics of the population sampled; completeness of data collected; and lessons learned from doing this work. This second report, which addresses concerns related to sampling bias and data quality, describes the methods used to evaluate the validity and robustness of NYC Macroscope prevalence estimates; presents validation results for estimates of obesity, smoking, depression and influenza vaccination; and discusses the implications of our findings for NYC and for other jurisdictions embarking on similar work. The third report applies the same validation methods described in this report to metabolic outcomes, including the prevalence, treatment and control of diabetes, hypertension and hyperlipidemia.

METHODS

NYC Macroscope prevalence estimates, overall and stratified by sex and age group, were compared to reference survey estimates for adult New Yorkers who reported visiting a doctor in the past year. Agreement was evaluated against 5 a priori criteria. Sensitivity and specificity were assessed by examining individual EHR records in a subsample of 48 survey participants.

RESULTS

Among adult New Yorkers in care, the NYC Macroscope prevalence estimate for smoking (15.2%) fell between estimates from NYC HANES (17.7 %) and CHS (14.9%) and met all 5 a priori criteria. The NYC Macroscope obesity prevalence estimate (27.8%) also fell between the NYC HANES (31.3%) and CHS (24.7%) estimates, but met only 3 a priori criteria. Sensitivity and specificity exceeded 0.90 for both the smoking and obesity indicators. The NYC Macroscope estimates of depression and influenza vaccination prevalence were more than 10 percentage points lower than the estimates from either reference survey. While specificity was > 0.90 for both of these indicators, sensitivity was < 0.70.

DISCUSSION

Through this work we have demonstrated that EHR data from a convenience sample of providers can produce acceptable estimates of smoking and obesity prevalence among adult New Yorkers in care; gained a better understanding of the challenges involved in estimating depression prevalence from EHRs; and identified areas for additional research regarding estimation of influenza vaccination prevalence. We have also shared lessons learned about how EHR indicators should be constructed and offer methodologic suggestions for validating them.

CONCLUSIONS

This work adds to a rapidly emerging body of literature about how to define, collect and interpret EHR-based surveillance measures and may help guide other jurisdictions.

Cardiovascular Disease Risk Profiles in Survivors of Adolescent and Young Adult (AYA) Cancer: The Kaiser Permanente AYA Cancer Survivors Study

PURPOSE

To describe the epidemiology and risk factors for cardiovascular disease (CVD) in survivors of adolescent and young adult (AYA) cancer.

METHODS

We identified a retrospective cohort of 2-year survivors of AYA cancer who were diagnosed between the ages of 15 to 39 years (1998 to 2009) at Kaiser Permanente Southern California. A comparison group without cancer was selected and matched 10:1 to cancer survivors on the basis of age, sex, Kaiser Permanente Southern California membership, and calendar year. Patients were followed through December 31, 2012, for coronary artery disease, heart failure, and stroke. Time-dependent Poisson regression was used to evaluate the effect that cancer survivorship had on the risk of developing CVD, adjusted for cardiovascular risk factors (CVRFs; ie, diabetes, hypertension, and dyslipidemia), ethnicity, smoking, and overweight/obesity. Among cancer survivors, mortality risk by CVD status was examined using Cox regression.

RESULTS

A total of 5,673 2-year survivors of AYA cancer and 57,617 comparison patients were included, representing 24,839 and 239,073 person-years of follow-up, respectively. Overall, cancer survivors had more than two-fold risk of developing CVD (adjusted incidence rate ratio, 2.37; 95% CI, 1.93 to 2.93) when compared with patients without cancer; survivors of leukemia and breast cancer were at the highest risk (adjusted incidence rate ratio, 4.23; 95% CI, 1.73 to 10.31; and 3.63; 95% CI, 2.41 to 5.47, respectively) of developing CVD. Having any of the CVRFs increased the risk of CVD in cancer survivors. Cancer survivors who developed CVD had an 11-fold increased overall mortality risk (hazard ratio, 10.9; 95% CI, 8.1 to 14.8) when compared with survivors without CVD.

CONCLUSION

Survivors of AYA cancer are at increased risk for developing CVD. Survival after CVD onset is compromised, and CVRFs are independent modifiers of CVD risk. These data form the basis for identifying high-risk individuals and proactive management of CVRFs.

Cardiovascular Disease Among Survivors of Adult-Onset Cancer: A Community-Based Retrospective Cohort Study

PURPOSE

Cardiovascular diseases (CVDs), including ischemic heart disease, stroke, and heart failure, are well-established late effects of therapy in survivors of childhood and young adult (< 40 years at diagnosis) cancers; less is known regarding CVD in long-term survivors of adult-onset (≥ 40 years) cancer.

METHODS

A retrospective cohort study design was used to describe the magnitude of CVD risk in 36,232 ≥ 2-year survivors of adult-onset cancer compared with matched (age, sex, and residential ZIP code) noncancer controls (n = 73,545) within a large integrated managed care organization. Multivariable regression was used to examine the impact of cardiovascular risk factors (CVRFs; hypertension, diabetes, dyslipidemia) on long-term CVD risk in cancer survivors.

RESULTS

Survivors of multiple myeloma (incidence rate ratio [IRR], 1.70; P < .01), carcinoma of the lung/bronchus (IRR, 1.58; P < .01), non-Hodgkin lymphoma (IRR, 1.41; P < .01), and breast cancer (IRR, 1.13; P < .01) had significantly higher CVD risk when compared with noncancer controls. Conversely, prostate cancer survivors had a lower CVD risk (IRR, 0.89; P < .01) compared with controls. Cancer survivors with two or more CVRFs had the highest risk of CVD when compared with noncancer controls with less than two CVRFs (IRR, 1.83 to 2.59; P < .01). Eight-year overall survival was significantly worse among cancer survivors who developed CVD (60%) when compared with cancer survivors without CVD (81%; P < .01).

CONCLUSION

The magnitude of subsequent CVD risk varies according to cancer subtype and by the presence of CVRFs. Overall survival in survivors who develop CVD is poor, emphasizing the need for targeted prevention strategies for individuals at highest risk of developing CVD.

Passive cigarette smoke exposure and other risk factors for invasive pneumococcal disease in children: a case-control study

OBJECTIVE

To investigate whether passive cigarette smoke exposure increases the risk of invasive pneumococcal disease in children.

METHODS

In a population-based case-control study, 171 children aged 0 to 12 years with culture-confirmed invasive pneumococcal disease during the years 1994 to 2004 were identified. Two controls were matched to each case on age and patterns of Health Plan membership. We reviewed medical records of subjects and family members for information on household cigarette smoke exposure within 2 years of the diagnosis of invasive pneumococcal disease. We collected information on sex, race, pneumococcal vaccination, selected medical conditions, and medications in the 3 months before the diagnosis.

RESULTS

Similar proportions of cases (25%) and controls (30%) had definite or probable passive smoke exposure (odds ratio [OR] = 0.76, 95% confidence interval [CI] = 0.47-1.2). Cases of invasive pneumococcal disease were more likely to be nonwhite than controls (OR = 4.4, 95% CI = 2.3-8.2). Elevated risk of invasive pneumococcal disease was found in subjects with recent pulmonary diagnoses (OR = 2.2, 95% CI = 1.2-4.0) and recent antibiotic use (OR = 1.6, 95% CI = 1.1-2.3).

CONCLUSIONS

Passive cigarette smoke exposure was not associated with invasive pneumococcal disease in this pediatric population. Invasive pneumococcal disease was associated with recent pulmonary diagnoses and recent antibiotic use.

Hidradenitis suppurativa is a systemic disease with substantial comorbidity burden: a chart-verified case-control analysis

BACKGROUND

Hidradenitis suppurativa (HS) is a chronic inflammatory disease involving intertriginous skin. Previous epidemiologic studies have been limited by small sample size.

OBJECTIVE

We sought to describe the prevalence and comorbidities of HS in a large patient care database.

METHODS

In this retrospective case-control study, we chart-validated all patients within a hospital database who received at least 1 billing code for HS between 1980 and 2013. Verified cases were matched with controls based on age, gender, and race. Prevalences of a priori selected comorbidities were compared between HS and control groups.

RESULTS

A total of 2292 patients at Massachusetts General Hospital received at least 1 code for HS. A total of 1776 cases had a validated diagnosis of HS, yielding a prevalence of 0.08%. In unadjusted analysis, all comorbidities were diagnosed significantly more in HS compared with control including (in rank order of likelihood): smoking, arthropathies, dyslipidemia, polycystic ovarian syndrome, psychiatric disorders, obesity, drug dependence, hypertension, diabetes, thyroid disease, alcohol dependence, and lymphoma (all P < .01).

LIMITATIONS

Control subjects were not validated for absence of HS and comorbidity validation was not performed for either group.

CONCLUSIONS

Our results highlights the high comorbidity burden of patients with HS compared with matched control subjects.