Nine-year follow-up of a survey on smoking habits in Florence (Italy): higher mortality among non-responders

Alessandro Barchielli and Daniela Balzi

Epidemiology Unit, Local Health Unit 10, Viale Michelangelo 41, 50125 Florence, Italy. E-mail: epidemiologia{at}asf.toscana.it

Dr Alessandro Barchielli, Epidemiology Unit, Local Health Unit 10, Viale Michelangelo 41, 50125 Florence, Italy. E-mail: alessandro. barchielli{at}asf.toscana.it


    Abstract
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Background Smoking prevalence is often assessed in random samples of a population. Non-response bias has been rarely investigated.

Methods In 1989 a survey on smoking habits in Florence, Italy, was carried out (response rate: 85%). For responders and non-responders (3621 subjects) the life status as of 1998 was assessed. Poisson regression models were fitted to estimate age-adjusted risks of death (RR) of non-responders for overall mortality and for the most important causes of death, taking the whole series of responders, postal responders and telephone responders as the reference in different analyses. This analysis included 2071 subjects aged >=45 years.

Results Compared to the whole series of responders, mortality from all causes was significantly higher among non-responders in males (RR = 1.74; 95% CI: 1.23–2.44) and females (RR = 2.45; 95% CI: 1.79–3.29). The higher risk was seen for smoking-related and ‘other’ causes of death. Among females the difference was more evident for smoking-related causes (RR = 3.14; 95% CI: 1.66–5.93), among males the higher risk was similar for both groups of causes. The excess of mortality was less evident when telephone responders alone were taken as reference.

Conclusions The follow-up of subjects enrolled in a survey on smoking habits shows high mortality risks among non-responders. The data indirectly suggest that smoking was (or had been) more widespread among non-responders, in particular among females. Therefore, the prevalence of smokers assessed through this survey, focussed on smoking habit, may be underestimated. Telephone contact with non-responders to the postal questionnaire attenuated the selection bias of responders, but even with telephone back-up the response bias persisted.

Keywords Smoking habits, survey, non-response bias, mortality

Accepted 8 May 2002


    Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Cigarette smoking is, as a single factor, the most common cause of mortality in developed countries.1 In many of them, declining trends of cigarette consumption have been observed among men, whereas little change or increasing trends have been observed for women.2 It is important to monitor trends in smoking prevalence in order to obtain indications on present and future adverse health effects and to evaluate the impact of activities aimed at its control. It is therefore useful to know the patterns of smoking habits both at national and local level.

Many studies have investigated the accuracy of self-reported smoking status using biomarkers as a gold standard (i.e. expired carbon monoxide, salivary thiocyanate, concentration of nicotine and cotinine in plasma, saliva or urine).3–10 These show a consistently high validity of self-reported smoking in population-based studies.11 Few studies have been published on non-responder bias in the assessment of smoking habits.12–15

Our study indirectly analyses the effect of non-response bias in a survey on smoking prevalence, comparing the long-term risk of death between responders and non-responders.


    Methods
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
In 1989 a survey on smoking habits in the Municipality of Florence, Italy, was carried out.16 A postal questionnaire on smoking habits and educational level was mailed to a random sample of the population aged >=14 years, recruited from the computerized file of the Registry Office of the town. Non-responders to the postal questionnaire were interviewed by phone.

Of the 3721 subjects selected (1744 males and 1977 females), 100 (2.7%) were excluded from the study due to death, emigration to another municipality, or because they were unknown at the address where the questionnaire had been mailed. Of the remaining 3621 subjects, 2217 (61.2%) responded to the postal questionnaire, 43 (1.2%) of them without answering questions about smoking habits, whereas 893 (24.7%) were interviewed by phone. On this basis, overall 3067 (84.7%) subjects were classified as responders and 554 (15.3%) as non-responders.

For both responders and non-responders (3621 subjects), the life status as of 31 December 1998 was assessed through computerized linkage with the Mortality Registry of Tuscany (RMR). The RMR includes the death certificates, for all causes, of individuals residing in this area who died in Tuscany or other Italian regions. For unlinked cases, the Register Office of the residence municipality was consulted. At the end of the follow-up, 3163 subjects (87.3%) were still alive, 416 (11.5%) were deceased and 42 (1.2%) were lost to follow-up.

Person-years were calculated from the date when the questionnaire was mailed to the end of follow-up (end of study period or death or, for lost to follow-up, when last information was available). Poisson regression models were fitted to estimate the age-adjusted risk of death (RR) of non-responders for overall mortality and for the most important causes of death. The whole series of responders and, separately, mail and telephone responders were considered as the reference category. Statistical analysis was performed using the package STATA 7. To improve the fit of regression models, the analysis was restricted to people aged >=45 years (2071 subjects) at the time of the survey, because death occurs rarely in younger people (14–44 years: 1603 subjects, 11 930 person-years, 9 deaths).

In addition, for both groups, relative survival curves were obtained using the life-table method17 from the date of mailing of the questionnaire to the end of follow-up. Relative survival was calculated by dividing the observed survival rate in the group of subjects under study by that expected for a group of subjects in the general population of the same area, similar in gender, age and period of life. In this context, relative survival rates are around 100% if the risk of death of the studied group is similar to that of the general population. On the other hand, these are >100% if the risk of death is lower, and <100% if the risk of death is higher than in the general population. Relative survival curves show the difference in mortality risk at different intervals of follow-up.


    Results
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Table 1Go shows some characteristics of the sample of subjects interviewed. The proportion of non-responders was similar for both genders. Among males, the mean age at the moment of interview was similar for responders and non-responders. Among females, the non-responders were significantly older than the responders. Considering both genders together, the non-responders were significantly older than responders (mean age: 64.6 versus 61.8 years, P < 0.001). Overall, the 2017 subjects aged >=45 years contributed to the study a total of 19 574 person-years and 407 deaths. The crude mortality rate was 17.8/1000 person-years among responders (306/17 152) and 41.7/1000 person-years among non-responders (101/2421).


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Table 1 Some characteristics of the sample involved in the survey, stratified by gender (age >=45 years)
 
Table 2Go shows the distribution by smoking habit, stratified by gender and modality of response, and demonstrates a different pattern of response between postal and telephone responders. In particular, postal responders were more frequently ex-smokers for both genders, whereas telephone responders were more frequently male current smokers. For ex-smokers, the mean number of cigarettes per day was higher for telephone than postal responders both in males (29.2 and. 24.2, respectively, P = 0.08) and in females (15.6 and 11.0, respectively, P = 0.01).


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Table 2 Smoking habits of responders, stratified by gender and modality of response (age >=45 years)
 
Table 3Go shows the age-adjusted RR of death for non-responders, separately taking as reference: all responders, postal responders, and telephone responders. Compared to the whole series of responders, mortality from all causes was significantly higher among non-responders. The difference was evident in both genders, more markedly among females. The higher risk refers both to the group of smoking-related causes of death and to the group of ‘other’ causes of death. Whereas among females the difference was more evident for smoking-related causes, among males the higher risk was similar for both groups of causes. Among smoking-related causes of death, even if most of the differences were not statistically significant, lung cancer showed a higher RR of death than ischaemic heart disease both in females (RR, respectively: 6.45, 95% CI: 0.90–46.58, and 2.47, 95% CI: 1.12–5.46) and in males (RR, respectively: 2.95, 95% CI: 0.93–9.33, and 1.15, 95% CI: 0.40–3.29). More generally, significantly higher mortality risks were observed for most of the causes of death analysed in females (all cancers, bladder cancer, leukaemias and lymphomas, all circulatory diseases, cardiovascular diseases, coronary heart disease, myocardial infarction, cerebrovascular diseases and respiratory diseases), and for all cancers and for digestive cancers among males (data not reported). The higher risk of death of non-responders persisted when taking postal or telephone responders separately as a reference. In all comparisons the higher risk was less evident when telephone responders were taken as a reference, particularly in females and for smoking-related causes of deaths.


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Table 3 Age-adjusted relative risks (RR) of death of non-responders, stratified by gender and cause of death
 
Figure 1Go shows the relative survival for both responders and non-responders. In the first year of follow-up, the mortality rate among non-responders was 4.5 times higher than responders (54.2/1000 versus 12.4/1000). A difference in mortality risk (and thus survival) was seen for the whole period of follow-up, the absolute difference increasing over time. The pattern of relative survival curves was similar when stratifying the comparison by gender (data not reported).



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Figure 1 Relative survival curves for responders and for non-responders (males + females)

 

    Discussion
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Previous studies have reported response rates to mailed questionnaires, telephone interviews and health interviews of smokers12,14,15 and the presence of non-response bias related to other lifestyle risk factors,13 but few data have been published about health status among non-responders,15 and in particular about mortality.

This study showed, in a 9-year follow-up of subjects randomly sampled for a survey on smoking habits (compliance to the interview: about 85%), a higher mortality risk among non-responders compared to responders. The excess of mortality, persisting for the whole period of follow-up, concerned all causes, smoking-related and ‘other’ causes of death. The difference was more evident for smoking-related causes and among females. The comparison of mortality for diseases with a different level of association with smoking (i.e. lung cancer and ischaemic heart disease)18 suggested there was a higher excess of risk, among non-responders, for the cause more strongly related to smoking. Responders, therefore, had a lower mortality risk than the general population of the survey area.

The study also showed that the excess in mortality risk of non-responders compared to responders to the telephone interview was lower when compared to postal responders. Therefore, the effort to interview non-responders to the postal questionnaire by telephone lowered, even though it did not eliminate, selection bias.

From a general point of view, some reasons might explain the higher mortality rates observed among non-responders. Firstly, a different prevalence of exposure to the major risk factors may explain the difference. Of course, our study did not directly provide information about the prevalence of smoking habits among non-responders, nevertheless the data (i.e. the higher risk of death being more marked for smoking-related causes) indirectly suggest that smoking was (or has been) more widespread among non-responders, in particular among females. In fact, although dividing causes of death into ‘smoking-related’ and ‘other’ diseases may be a rough categorization, smoking generally plays only a minor role in the diseases of the latter group. Therefore, the estimates of smoking prevalence assessed through this survey may be underestimated because of the selection of responders.

The survey was mainly focussed on smoking. Thus some smokers may have been upset by the survey topic and discouraged from responding to the questionnaire. This possible cause of non-response might be avoided if items on smoking are mixed with other questions. On the other hand, the excess of mortality of non-responders also concerned the ‘other’ causes of death, suggesting that other reasons for selection (i.e. the exposure to other lifestyle risk factors) affected the higher mortality risk among non-responders.

With regard to the different pattern by gender in the risk of death, the higher mortality among non-responders in females may be partly explained by the differences in the prevalence of smoking habits among responders (i.e. never smokers were 26% in males and 69% in females). Therefore, the baseline risk of death of the reference group was low in females, due to the low prevalence of smoking.

Secondly, as non-responders had a higher risk of death in the first year of follow-up, the presence of a disease at the time of the survey may be a cause of non-response (i.e. seriously ill people would not respond). Nevertheless, the difference in mortality risk persisted for the whole period of follow-up, as the comparison of relative survival curves between responders and non-responders suggests.

In conclusion, the follow-up of subjects enrolled in a survey on smoking habits shows high mortality risks among non-responders. The data indirectly suggest that smoking was (or had been) more widespread among non-responders, in particular among females. Therefore, the estimates of prevalence of smoking assessed through a survey mainly focussed on smoking habits may be underestimated because of the selection of responders. Telephone contact with non-responders to the postal questionnaire attenuated the selection bias of responders, but even with telephone back-up the response bias persisted.


KEY MESSAGES

  • The follow-up of subjects enrolled in a survey on smoking habits shows high mortality risks among non-responders.
  • The difference was more evident for smoking-related causes and among females.
  • The data indirectly suggest that a survey mainly focussed on smoking may underestimate smoking prevalence because of selection bias among responders.
  • Telephone contact with non-responders to the postal questionnaire attenuated the selection bias, but even with telephone back-up the response bias persisted.

 


    References
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 Abstract
 Introduction
 Methods
 Results
 Discussion
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