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
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Abstract |
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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: 1.232.44) and females (RR = 2.45; 95% CI: 1.793.29). 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: 1.665.93), 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
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Introduction |
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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).310 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.1215
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.
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Methods |
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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 (1444 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.
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Results |
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Discussion |
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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
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References |
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