Overview
We used data from a population-based prospective cohort study of adults with COPD. SHS exposure was assessed using both self-report from structured telephone interviews and direct exposure assessment (urine cotinine and personal nicotine badge monitors). Using these exposure and outcomes data, we examined the longitudinal association between SHS exposure and health status among persons with COPD.
Definition of COPD
We used the standard epidemiologic approach to define COPD based on a self-reported physician diagnosis of chronic bronchitis, emphysema, or COPD.[12–14] During the telephone interview, subjects were asked whether they had ever received a physician's diagnosis of any of several chronic respiratory conditions. Those who reported physician diagnoses of chronic bronchitis or emphysema were considered to have COPD, along with those who specifically reported a physician diagnosis of COPD. We included respondents with COPD who had concomitant asthma because they clinically resemble persons with COPD alone.[15] As reported previously, we validated the case definition of COPD using spirometry in a subgroup of 47 participants with COPD whose physicians provided spirometry reports (of 386 subjects).[2]
Study recruitment
The study was approved by the University of California, San Francisco Committee on Human Research. We used data from wave 3 and wave 4 of a population-based multi-wave longitudinal cohort study of U.S. adults to elucidate the impact of SHS exposure on COPD health outcomes. Direct measures of SHS exposure were obtained only in these waves. Initial recruitment was previously reported in detail.[2, 4]. Briefly, 2,113 adults aged 55 to 75 years were initially recruited by random digit dialing among residents of the 48 contiguous U.S. states with random over-sampling of geographic areas that had the highest published COPD mortality rates.[16] The random "hot spot" sample was further enriched by randomly over-sampling subjects with COPD. The initial overall study participation rate was 53% among households with an eligible respondent present and there were 383 subjects with COPD.
Details of the wave 2 follow-up interview have also been previously reported.[17] Approximately 12 months after the initial interview, we attempted to contact all 517 subjects who reported either COPD or asthma at baseline interview. Of these subjects, 352 (68%) completed the follow-up interview, of whom 267 reported COPD at baseline and an additional 19 indicated COPD at wave 2 (total 286) This follow-up and direct SHS monitoring participation (see below) is summarized in Figure 1.
At wave 3, which was approximately 12 months after wave 2, we attempted to contact all 352 subjects with asthma or COPD plus an additional 307 subjects who reported other airways diseases reported at baseline (allergic rhinitis and obstructive sleep apnea). Of the total 659 subjects, 433 completed the wave 3 interview (66%) and 229 subjects had COPD, which included 27 newly reported COPD cases at wave 3. Wave 4 was conducted about 12 months after wave 3, with completion of interviews in 373 of 433 subjects (86%), which included 211 respondents with COPD. There were no statistical differences in sociodemographcic characteristics (age, sex, race, educational attainment), health status (COPD severity score or SF-12 physical component summary score), or smoking status by follow-up status from wave 2 to wave 3 (p > 0.10 in all cases). Subjects who completed wave 4 were more likely to be white, non-hispanic (90% vs. 78%) and to have a college or graduate degree (30 vs. 12%) than were those who did not complete wave 4 follow-up (p < 0.05); there were no other statistical differences (p > 0.10 in all cases).
The current analysis was restricted to subjects who completed wave 3 and who reported no current smoking (either wave 3 or wave 4, if applicable) and had urine cotinine levels that were less than 100 ng/ml, a level which is consistent with direct personal smoking.[18–20] In sum, 152 current non-smoking subjects with COPD were eligible, of whom 77 subjects completed direct SHS monitoring with urine cotinine and personal badge nicotine measurements at baseline (wave 3), which corresponds to a 51% completion rate. Of this group, 68 subjects completed the direct SHS exposure follow-up approximately 1 year later (wave 4) (88% completion rate among direct SHS monitoring group). As shown in Table 1, there were no statistical differences between the 152 current non-smokers with COPD who did and did not complete baseline direct SHS measurement (p > 0.15, all cases). There were also no statistical differences between those who completed baseline direct SHS assessment who did and did not complete the 1 year follow-up assessment (p > 0.05, all cases).
Table 1 Characteristics of adult non-smoking participants with COPD
Structured telephone interviews
Participants completed structured telephone interviews that included health history, cigarette smoking, SHS exposure, sociodemographic characteristics, and health status. Direct personal cigarette smoking was evaluated using standard questions from the National Health Interview Survey. [21] Based on these responses, subjects were defined as current smokers, ex-smokers, and never smokers.
Self-reported SHS exposure
We previously developed and validated a survey instrument that assesses recent ETS exposure.[8] The instrument, which was tailored for adults with asthma living in Northern California, assesses exposure during the past 7 days in 6 microenvironments: the respondent's home, another person's home, traveling in a car or another vehicle, workplace (including dedicated smoking areas), bars and nightclubs, and other locations. In each area, the instrument ascertains the total duration (in hours) of exposure during the past 7 days. Based on the distribution of responses, we defined three categories of exposure: no exposure, lower level exposure (1–3 hours/week), and higher level (≥4 hours/week) exposure.
Direct SHS monitoring
We used a combined approach to conduct direct SHS exposure monitoring based on urine cotinine and personal nicotine badges. Cotinine, a metabolite of nicotine, is a widely used and specific biomarker of SHS exposure.[22] Cotinine has a short half-life of 20 hours and reflects shorter term SHS exposure than the personal nicotine badge that measures average 7-day exposures. Urine samples and completed badges were returned to the investigators by mail (completion rates are provided above).
Concentrations of cotinine and trans-3'-hydroxycotinine, which is the proximate metabolite of cotinine and the most abundant metabolite of nicotine present in urine, were determined using liquid chromatography-tandem mass spectrometry (LC-MS/MS).[23, 24] The method is similar to a published method for determining cotinine concentrations in serum of non-smokers.[25] Deuterium-labeled cotinine (cotinine-d9) and deuterium-labeled trans-3'-hydroxycotinine (trans-3'-hydroxycotinine-d9) were used as internal standards. The mass spectrometer was operated in the positive ion mode using atmospheric pressure chemical ionization. Quantitation was achieved using selected reaction monitoring (SRM) of the transitions m/z 177 to m/z 80 for cotinine, m/z 193 to m/z 80 for trans-3'-hydroxycotinine and the transitions m/z 186 to m/z 84 and m/z 202 to m/z 84 for the respective internal standards. Limits of quantitation were 0.05 ng/mL for cotinine and 0.1 ng/mL for trans-3'-hydroxycotinine.
The extent of hepatic metabolism of nicotine to cotinine, which occurs primarily via cytochrome P450 2A6 (CYP2A6), and the rate of cotinine metabolism both determine the mathematical relationship between nicotine intake from SHS exposure and cotinine level.[23] There are substantial inter-individual differences in CYP2A6 activity, which could affect cotinine levels for a given nicotine dose. Because cotinine is also converted to trans-3'-hydroxycotinine via CYP2A6, the ratio of trans-3'-hydroxycotinine/cotinine is highly correlated with the clearance of nicotine.[23] Consequently, this marker can be used as a non-invasive marker of nicotine metabolism.
Each subject was instructed to wear the personal nicotine badge monitor during regular activities for 7 days. The passive monitor, which has been previously described, samples nicotine from ambient air.[26, 27] A 4-cm-diameter polystyrene cassette holds a filter treated with sodium bisulfate and a membrane filter functions as a windscreen. Ambient nicotine diffuses to the treated filter, where it is trapped. The collected nicotine is analyzed by gas chromotography with nitrogen selective detection. Based on the amount of nicotine measured on the filter (ug), which represents the total amount of nicotine collected during the monitoring period, the nicotine concentrations were calculated by dividing the nicotine collected by the estimated volume of air sampled (monitoring duration multiplied by sampling rate of 24 ml/minute). The passive monitors have a limit of detection less than 0.01 ug per filter and a coefficient of variability of 0.11 for replicate analysis.[26] Based on the distribution of values, we divided urine cotinine and badge nicotine levels into tertiles for statistical analysis.
Study outcome variables: COPD-related health status
We used a combined approach with disease-specific and generic health status measurements to assess COPD-related health status. To measure disease severity, we previously developed and validated a disease-specific COPD severity score for use in epidemiologic and outcomes research.[28] Based on survey responses, the COPD severity score is comprised of 5 overall aspects of COPD severity: respiratory symptoms, systemic corticosteroid use, other COPD medication use, previous hospitalization or intubation for respiratory disease, and home oxygen use. Each item was weighted based on clinical aspects of the disease and its expected contribution to overall COPD severity. Possible total scores range from 0 to 35, with higher scores reflecting more severe COPD.
Generic physical health status was measured with the SF-12 Physical Component Summary Score. The SF-12 is derived from the Medical Outcomes Study SF-36 instrument, which is the most widely used measure of generic health status. The SF-36 has been extensively validated in the general population[29] and among adults with COPD.[30] Defined from the eight SF-36 subscales by factor analysis, the physical component summary score reflects an underlying physical dimension of physical HRQL.[31] Higher scores reflect more favorable health states.
We used the Airways Questionnaire 20 (AQ-20) to measure disease specific quality of life.[32] This instrument is a short survey that was validated against the St. George's Respiratory Questionnaire, which is a 50 item instrument that has been used extensively in COPD to measure disease-specific QOL.[33, 34] It has excellent psychometric properties for assessing QOL in COPD and asthma and higher scores correspond to poorer QOL.[32, 35] Dyspnea was measured using three of five questions from the modified MRC dyspnea scale.[36] Higher scores indicate greater levels of dyspnea.
Statistical analysis
Statistical analysis was conducted using SAS 9.1 (Cary, NC). Bivariate analysis was conducted using the unpaired t-test for continuous variables and likelihood ratio chi-square test for dichotomous variables. The analysis was conducted both for self-reported SHS exposure and directly measured SHS exposure (urine cotinine and personal badge nicotine). To ensure comparable results, the analysis was restricted to subjects who completed at least baseline direct SHS monitoring. We used linear regression analysis to examine the cross-sectional impact of SHS exposure and health-related COPD status (COPD severity, generic physical health status, disease-specific QOL, and dyspnea). We used multivariate linear regression analysis to control for variables that could confound the relationship between SHS exposure and health outcomes, including age, sex, race, educational attainment, and previous smoking history (all subjects were current non-smokers).[2, 37] Furthermore, we used multivariate linear regression to elucidate the impact of baseline SHS exposure on COPD-related health status at 1 year follow-up. To take into account inter-individual differences in nicotine metabolism, we repeated the multivariate analysis after adding the ratio of trans-3'-hydroxycotinine/cotinine, a non-invasive measure of CYP2A6 activity, to the model.
To express the "clinical significance" of the impact of SHS on COPD-related health status, we expressed the change in each health status variable in terms of proportional change in standard deviation of the score. It has been shown that a one-half standard deviation in health status variables corresponds to the minimally important difference.[38] Consequently, we evaluated the impact of SHS exposure on each health status variable according to this criterion.
As above, participants in the direct SHS monitoring program were similar to non-participants, including socioeconomic status, COPD severity, and physical health status. To further take non-response into account, sampling weights were developed using all the personal characteristics in Table 1. The weighted analysis was not substantively different from the unweighted analysis, so we report the unweighted analysis only.
Role of the funding source
The funding source was not involved in study design, data collection, statistical analysis, or manuscript preparation.