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Estimation of smoking prevalence in Canada: implications of survey characteristics in the CCHS and CTUMS/CTADS.

Smoking continues to be a primary cause of morbidity and mortality in Canada. As a result of decades of successful public health interventions, Canada enjoyed a large decrease in prevalence with an approximate 60% relative reduction since the 1950s. (1) One of the main activities associated with such a massive endeavour has been surveillance, that is, to measure and follow over time the extent of smoking among the Canadian population. Currently, smoking prevalence measures continue to be a vital tool to assist researchers and policy-makers in better understanding what populations are initiating and persisting in smoking at the national, regional and subregional levels, and deciding which should be our priorities.

This commentary provides insight into a simple yet difficult-to-answer question: What is the prevalence of smoking in Canada? Many data sources can be used to estimate the extent of smoking behaviour in Canada and its provinces (e.g., a 2015 directory updated by the Ontario Tobacco Research Unit lists more than 60 sources (2)). In contrast to other countries, such as New Zealand, our national census does not inquire into Canadians' smoking status. The assessment of the prevalence of smoking in Canada, therefore, requires specific national surveys. To fulfill the mandate of producing representative estimates of smoking prevalence at the national and regional levels, Statistics Canada operates two main surveys: the Canadian Community Health Survey (CCHS) and the Canadian Tobacco, Alcohol and Drugs Survey (CTADS). Upon closer inspection, understanding the estimation of smoking prevalence requires a critical appreciation of these surveys and their idiosyncrasies.

While a definitive answer (or number) cannot be offered here, this commentary proposes to contribute to this issue by reviewing the two main data sources that are used to study smoking prevalence, examining how smoking prevalence is measured in these surveys, and exploring potential reasons for their noticeable discrepancies.

ESTIMATING SMOKING PREVALENCE: THE CANADIAN COMMUNITY HEALTH SURVEY AND THE CANADIAN TOBACCO, ALCOHOL AND DRUGS SURVEY

The CCHS is a repeated cross-sectional survey created in 2000 that collects annually information related to health status, health care utilization and health determinants for the Canadian population. It collected and published information bi-annually from 2000-2001 through to 2007, whereupon it became an annual survey. (3) The CTADS is a repeated cross-sectional survey created in 2013 to provide bi-annual data on tobacco, alcohol and drug use and related issues, with a focus on 15-24 year olds. (4) The CTADS built upon the longstanding Canadian Tobacco Use Monitoring Survey (CTUMS), which repeated annually from 1999 until 2012, and was combined with Statistics Canada's last alcohol and drugs survey, the Canadian Alcohol and Drugs Use Monitoring Survey (CADUMS).

How do these surveys measure smoking? The most common definition of smoking is "current smoking status", which includes daily and non-daily (occasional) smokers based on responses to the question "At the present time do you smoke cigarettes every day, occasionally, or not at all?". This question was developed in the mid-1990s to standardize measures across Statistics Canada surveys. (5) In order to ensure consistency, this question follows no skip patterns and its wording and ordering within the full questionnaire remains almost identical across surveys. Notably, the CTUMS/CTADS questionnaire adds the following preamble: "I am going to start with questions about cigarette smoking. Include cigarettes that are bought ready-made as well as cigarettes that you make yourself."

Figure 1 presents the prevalence estimates in the CCHS and CTUMS/CTADS between 2001 (the first year of the CCHS) and 2013 (the latest available year for CTADS data when this commentary was written) (full estimates are given in Supplementary Table 1, in the ARTICLE TOOLS section on the journal website). During this period, smoking prevalence changed from 25.9% (95% CI: 25.6-26.3) to 19.3% (95% CI: 18.7-19.9) in the CCHS; and from 21.7% (95% CI: 20.6-22.9) to 14.6% (95% CI: 13.5-15.8) according to the CTUMS/ CTADS. The latter reports a systematically lower smoking prevalence estimate than that of the CCHS. Percentage point differences varied from 2.1 (2003) to 4.7 (2013) with an average difference of 3.4 between surveys. Readers can compare the non-overlapping confidence intervals as a conservative test to confirm that the differences in estimates are statistically significant.

EXPLORING POTENTIAL REASONS FOR DISCREPANCIES BETWEEN SURVEYS

Why do these sources produce different smoking prevalence estimates? Statistics Canada's Health Division addresses this concern by providing the following warning:

"Users should be aware of a number of differences between CCHS and CTADS. CCHS collects information from respondents aged 12 and over, CTADS collects information from respondents aged 15 and over; the two surveys use different sampling frames; the annual sample for CTADS is 20,000 compared to 65,000 for CCHS; in CCHS, smoking questions are asked in the context of a wide range of health-related behaviours whereas in CTADS, all questions are related to the use of multiple products and substances with addictive properties." (6)

Table 1 presents and compares the main survey design elements of both surveys (i.e., coverage, sampling, non-response and measurement). Differences suggested here above can be summarized to age range, sampling frame, sample size and survey context. Some of these differences are unlikely to explain the lower prevalence systematically observed in the CTADS. First, the lower age group included in the CCHS (12+ versus 15+) should not produce a higher estimate because smoking rates at ages 12-14 years are significantly lower than those in the rest of the population. Second, the sample sizes used in both surveys are determined based on their statistical ability to provide precise estimates at the national level; therefore, they are unlikely to be at the base of their consistently different results. On the other hand, differences in sampling frame and questionnaire content may contribute to the discrepancies between CCHS and CTUMS/CTADS.

Statistics Canada has been aware of the potential for producing differences in smoking prevalence estimates across its different surveys since the development of the CTUMS and its first results in 2000-2001. A working group was formed to tackle this issue and a summary of findings from an unpublished internal report was made available online. (5) This report explored several hypotheses related to sampling frame, use of proxy respondents, questions' wording and ordering, mode of administration and survey context. Among these factors, two were deemed to be potential sources of bias. First, it was proposed that CCHS used a warmer contact approach: participants were contacted in advance for face-to-face interviews, and questions regarding smoking status were asked only after a bond of confidence was established. On the other hand, CTUMS resorted to phone interviews with no advance contacts, and surveyed smoking status earlier in their questionnaire. Second, the report referenced a study documenting discrepancies in the measurement of disabilities (i.e., an outcome liable to social desirability bias and under-reporting) and proposed that respondents could be more open to report their characteristics when it was done in a general health survey (i.e., CCHS) that did not label them as smokers.

The importance of these two factors may have changed since CTUMS' inception. In particular, the new format of the CTADS may further influence the responses of respondents with the inclusion of alcohol- and drug-related issues. Drugs and alcohol surveys tend to have higher non-response rates, which in turn are known to be associated with smoking behaviour. (7,8) This issue is especially problematic given the relatively strong association between cigarette smoking, alcohol and drug use among Canadians. (9) This inclusion may have resulted in survey avoidance by potential respondents because of their alcohol or drug use. The extent of this influence is, however, unknown. Zhao et al. (2009) compared non-respondents in the 2004 Canadian Addictions Survey (CAS, a past iteration of the CADUMS) with those in the 2002 CCHS and found that their samples were not significantly different in terms of educational attainment, household income and marital status. (10)

Other factors have also changed in importance over time as well. For instance, the CCHS and CTADS use a slightly different sampling frame: the CCHS used a combination of an area frame (40%-50%), lists of telephone numbers (40%-50%) and randomdigit dialing (RDD) ([+ or -] 1%) to cover their sampling frame while the CTUMS/CTADS used an RDD-only strategy. RDD cannot reach potential respondents without landline phones (i.e., those without phones or with only cellphones). This sampling frame can translate into a potential selection bias because these individuals without landline are often younger, have lower incomes and also higher smoking rates. (11) In 2002, the Statistics Canada report comparing CCHS and CTUMS argued that these differences were marginal given that only a small percentage of the population did not have landline phones. This argument is however untenable today: in 2013, 21% of Canadian households and 60% of individuals aged 35 years or younger had a cellphone, but no landline phones. (12) Fortunately, CTADS updated their sampling frame in 2015 to include telephone lists that include cellphones.

DISCUSSION

Canadian public health policy and practice face continued challenges as a result of increasing social inequalities in smoking. (1,13) Notably, this behaviour is increasingly concentrated within specific subpopulations, such as young adults and those who are socio-economically disadvantaged. This necessitates the continual adaptation of surveillance infrastructures, including the use of new survey methodologies, statistical approaches, and information technologies in order to provide the best evidence. There is considerable Canadian research devoted to finding solutions to survey-related issues that arise within the broader context of decreasing response rates and new information technologies. (14) The application of new approaches in survey methodology nonetheless constitutes a considerable hurdle for longstanding surveys, as it hampers their ability to attribute changes in estimates to true behavioural changes rather than to changes in methodology themselves.

Beyond comparisons between CCHS and CTUMS/CTADS, another legitimate question is the extent to which the current utilization of these surveys produces unbiased estimates. The standard approach used to produce prevalence estimates (e.g., descriptive statistics with survey weighting) can also benefit from recent innovations that adjust for sources of error such as nonresponse and under-reporting in order to obtain stronger estimates of smoking prevalence. For instance, Zhao et al. (2009) used predictive models to correct for non-response associated with socio-demographic characteristics (e.g., marital status, educational attainment, household income) and obtained higher prevalence estimates of alcohol and drug use in the Canadian Addiction Survey, with percentage point differences ranging from 0.9 to 2.6.10 Likewise, Land et al. (2014) examined predictors of smoking status under-reporting during pregnancy and used a predictive model integrating socio-demographic correlates to produce more accurate smoking prevalence estimates among pregnant women (in their case, under-reporting lowered self-report estimates by values ranging from 1.1 to 1.9 percentage points). (15)

CONCLUSION

This commentary aimed to present how smoking prevalence is reported and measured in Canada and to introduce some of the challenges associated with its estimation. Researchers should be more critical of prevalence estimates taken at face value, especially when comparing sources with different methodologies and/or from other countries. Although these challenges influence single-point prevalence estimates, the trends produced by both surveys are extremely consistent over time. Rather than comparing smoking rates produced from the two surveys, Statistics Canada advises users to choose a single source, based on their objectives, and to use that source consistently.

Researchers need to acknowledge the uncertainty over the true value of smoking prevalence in order to address it. To this end, tobacco surveillance experts are encouraged to use multiple data sources and more precise estimation approaches whenever applicable, to systematically discuss their limitations when reporting on prevalence and its change over time, and to continue to explore how potential sources of error should guide the development and utilization of available data sources.

doi: 10.17269/CJPH.108.5895

REFERENCES

(1.) Corsi DJ, Boyle MH, Lear SA, Chow CK, Teo KK, Subramanian SV. Trends in smoking in Canada from 1950 to 2011: Progression of the tobacco epidemic according to socioeconomic status and geography. Cancer Causes Control 2014; 25(1):45-57. PMID: 24158778. doi: 10.1007/s10552-013-0307-9.

(2.) Ottawa Tobacco Research Unit (OTRU). Directory of Public Use Data on Tobacco Use in Canada. 2015. Available at: http://otru.org/wp-content/uploads/2015/ 08/DirectoryJune2015.pdf (Accessed January 31, 2017).

(3.) Statistics Canada. Canadian Community Health Survey (CCHS). Available at: http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=3226 (Accessed August 15, 2016).

(4.) Statistics Canada. Canadian Tobacco Alcohol and Drugs Survey (CTADS). Available at: http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey &SDDS=4440 (Accessed August 15, 2016).

(5.) Gilmore J. Report on Smoking in Canada 1985 to 2001. Statistics Canada, 2002. Available at: http://publications.gc.ca/collections/Collection/Statcan/82- 0077X/82F0077XIE2001001.pdf (Accessed August 15, 2016).

(6.) Statistics Canada. Table 105-0503. Health Indicator Profile, Age- Standardized Rate, Annual Estimates, by Sex, Canada, Provinces and Territories. Statistics Canada, 2015. Available at: http://www5.statcan.gc.ca/cansim/a26?lang= eng&id=1050503 (Accessed January 31, 2017).

(7.) Hill A, Roberts J, Ewings P, Gunnell D. Non-response bias in a lifestyle survey. J Public Health Med 1997; 19(2):203-7. PMID: 9243437.

(8.) Lahaut V, Jansen H, van de Mheen D, Garretsen H. Non-response bias in a sample survey on alcohol consumption. Alcohol Alcohol 2002; 37(3):256-60. PMID: 12003914.

(9.) Kirst M, Mecredy G, Chaiton M. The prevalence of tobacco use co-morbidities in Canada. Can J Public Health 2013; 104(3):210-15. PMID: 23823884.

(10.) Zhao J, Stockwell T, Macdonald S. Non-response bias in alcohol and drug population surveys. Drug Alcohol Rev 2009; 28(6):648-57. PMID: 19930019. doi: 10.1111/j.1465-3362.2009.00077.x.

(11.) Livingstone M, Dietze P, Ferris J, Pennay D, Hayes L, Lenton S. Surveying alcohol and other drug use through telephone sampling: A comparison of landline and mobile phone samples. BMC Med Res Methodol 2013; 13:41. PMID: 23497161. doi: 10.1186/1471-2288-13-41.

(12.) Statistics Canada. Residential Telephone Service Survey (RTSS), 2013. 2014. Available at: http://www.statcan.gc.ca/daily-quotidien/140623/dq140623aeng.htm (Accessed August 15, 2016).

(13.) Smith P, Frank J, Mustard C. Trends in educational inequalities in smoking and physical activity in Canada: 1974-2005. J Epidemiol Community Health 2009; 63(4):317-23. PMID: 19147632. doi: 10.1136/jech.2008.078204.

(14.) Stephens T, Brown KS, Ip D, Ferrence R. The Future of Household Telephone Surveys on Tobacco: Methodological and Contextual Issues. Special Report Series. Toronto, ON: The Ontario Tobacco Research Unit, 2009. Available at: http://otru.org/wp-content/uploads/2012/06/special_future_phone_surveys.pdf (Accessed August 9, 2016).

(15.) Land TG, Landau AS, Manning SE, Purtill JK, Pickett K, Wakschlag L, et al. Who underreports smoking on birth records: A Monte Carlo predictive model with validation. PLoS ONE 2012; 7(4):e34853. PMID: 22545091. doi: 10.1371/ journal.pone.0034853.

(16.) Wong SL, Shield M, Leatherdale S, Malaison R, Hammond D. Assessment of validity of self-reported smoking status. Health Rep 2012; 23(1):47-53. PMID: 22590805.

Received: September 27, 2016

Accepted: February 19, 2017

Thierry Gagne, MSc [1,2]

Author Affiliations

[1.] Institut de recherche en sante publique de l'Universite de Montreal (IRSPUM), Montreal, QC

[2.] Ecole de sante publique de l'Universite de Montreal (ESPUM), Montreal, QC Correspondence: Thierry Gagne, IRSPUM, 7101 av. du Parc, office 3139, Montreal, QC H3N 1X9, Tel: 514-343-6111, ext. 4565, E-mail: thierry.gagne@umontreal.ca Acknowledgements: The author thanks the Interdisciplinary Study of Inequalities in Smoking (ISIS) research group, Jennifer O'Loughlin, Benoit Lasnier and Gerry Veenstra for their help, and acknowledges funding support from a PhD scholarship from the Fonds de Recherche du Quebec - Sante (FrQS).

Conflict of Interest: None to declare.

Caption: Figure 1. CCHS and CTUMS/CTADS estimates of current smoking prevalence. Prevalence estimates include both daily and occasional smoking.
Table 1. Survey characteristics of CCHS and CTUMS/CTADS

Survey elements     How can it introduce      How does this manifest
                    error?                    in the CCHS and CTUMS/
                                              CTADS?

Coverage            Coverage issues, either   CCHS uses this
Target population   due to over-or under-     operational definition
Sampling frame      coverage, may result in   for non-eligible
                    estimates inferring to    individuals: "Excluded
                    a different population    from these surveys'
                    than the one targeted.    coverage are: persons
                    Over-coverage is          living on reserves and
                    defined as the            other Aboriginal
                    systematic sampling of    settlements in the
                    individuals outside the   provinces; full-time
                    target population.        members of the Canadian
                    Under-coverage is         Forces; the
                    defined as the            institutionalized
                    systematic inability to   population and persons
                    sample certain            living in the Quebec
                    individuals within the    health regions of
                    target population.        Region du Nunavik and
                                              Region des Terres-
                                              Cries-de-la-Baie-
                                              James. Altogether,
                                              these exclusions
                                              represent less than 3%
                                              of the Canadian
                                              population aged 12 and
                                              over." CCHS changed its
                                              sampling frame in 2013
                                              to cover a higher
                                              proportion of Canadians
                                              in Nunavut (from 71% to
                                              92%). Notably,
                                              CTUMS/CTADS targets
                                              Canadians ages 15 and
                                              up, CTUMS/CTADS also
                                              does not sample
                                              residents of the Yukon,
                                              Northwest Territories
                                              and Nunavut.

                                              CCHS was developed to
                                              produce reliable
                                              estimates at the
                                              subprovincial (health
                                              region) level;
                                              CTUMS/CTADS was
                                              developed to produce
                                              reliable estimates at
                                              the provincial level.
                                              This should not
                                              introduce error unless
                                              sampling at the
                                              provincial level
                                              provides significantly
                                              different sampling
                                              probabilities to
                                              certain subregions and
                                              these probabilities are
                                              associated with smoking
                                              prevalence.

Sampling            Sample sizes have a       CCHS surveyed
Sample size         higher chance of          approximately 130 000
                    providing unreliable      individuals (i.e., 120
                    prevalence estimates      000 adults and 10 000
                    due to random sampling    children between 12 and
                    error as they become      17 years of age) in the
                    smaller, especially if    2001, 2003 and 2005
                    the outcome is rare.      cycles, and 65 000
                    Statistics Canada often   individuals in annual
                    reports coefficients of   cycles since 2007;
                    variation to help         CTUMS/CTADS survey
                    interpret the             approximately 20 000
                    variability of            individuals in annual
                    estimates.                cycles. Both sample
                                              sizes were chosen by
                                              Statistics Canada based
                                              on their ability to
                                              produce reliable
                                              estimates at the
                                              mandated level
                                              (provincial or
                                              subprovincial).

Non-response        Non-response refers to    CCHS and CTUMS/CTADS
Response rate       eligible respondents      use a two-step approach
                    who cannot be reached     to measure response
                    or who, upon contact,     rates because they
                    refused to participate.   initially contact
                    The influence of non-     potential participants
                    response on estimation    by reaching households
                    depends on the            randomly, and then
                    distribution of non-      participants randomly
                    response. Non-response    within that household.
                    that is completely at     This produces two
                    random (MCAR) should      response rates (RR):
                    not bias prevalence       household and person.
                    estimates; non-           Multiplying these,
                    response that is          CCHS' overall RRs
                    associated with           decreased from 84.0% in
                    correlates of smoking     2001 to 66.8% in 2013;
                    but not smoking (MAR)     CTUMS/CTADS' overall
                    introduces bias that      RRs decreased from
                    can be mitigated if       77.5% in 2001 to 63.1%
                    researchers have          in 2013. Results in
                    information on these      Zhao et al. (2009)
                    correlates' true          suggest that both
                    distribution; non-        surveys are liable to
                    response directly         non- response bias as
                    associated with the       participants in the
                    outcome (e.g., smoking)   2002 CCHS and 2004 CAS
                    is non-random (MNAR)      were more often
                    and introduces bias       married, educated and
                    that cannot be            financially better off
                    corrected.                than the general
                                              population. (10)
                                              Complete information on
                                              RRs is available online
                                              (see Supplementary
                                              Table 2, in the ARTICLE
                                              TOOLS section on the
                                              journal website).

Measurement         Measurement error         Both surveys use the
Questionnaire       refers to systematic      same standardized
Respondent          bias due to instrument    question and ordering
Mode of             design (e.g., survey      within the full
administration      questionnaire), the       questionnaire. A recent
Mode of contact     administrator (e.g.,      study examined
                    phone interviewer, data   participants of another
                    entry software) and       Statistics Canada
                    other elements of the     study, the Canadian
                    contact experience        Health Measures Survey
                    (e.g., respondent's       (CHMS), and found that
                    capacity to complete      self-reported smoking
                    the survey, mode of       status was a very
                    administration, mode of   strong surrogate of
                    contact, etc.).           smoking status when
                                              compared with urinary
                                              cotinine, a biomarker
                                              associated with smoking
                                              (sensitivity = 92%,
                                              specificity = 98%).
                                              (16) This study found
                                              that sensitivity could
                                              be lower among certain
                                              participants,
                                              especially young men
                                              (76%), but differences
                                              were not statistically
                                              significant. Gilmore
                                              (2002) argued that mode
                                              of administration
                                              (including face-
                                              to-face interviews) and
                                              contact (providing
                                              advance contacts) in
                                              the CCHS could have
                                              lowered social
                                              desirability bias in
                                              contrast to the
                                              CTUMS/CTADS. (5)
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Title Annotation:COMMENTARY
Author:Gagne, Thierry
Publication:Canadian Journal of Public Health
Article Type:Survey
Geographic Code:1CANA
Date:May 1, 2017
Words:3374
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