The Controversy Over Environmental Tobacco Smoke Exposure
There is convincing evidence that active smoking is a cause of lung cancer and heart disease. Many studies done in a wide variety of circumstances have consistently demonstrated a strong association and also indicate that the risk of lung cancer and cardiovascular disease (i.e.., heart attacks) increases in a dose-related way. These studies have led to the conclusion that active smoking is causally related to lung cancer and cardiovascular disease. Studies in active smokers have had the advantage that the lifetime exposure to tobacco smoke can be quantified with reasonable accuracy, since the unit dose is consistent (one cigarette) and the habitual nature of tobacco smoking makes it possible for most smokers to provide a reasonable estimate of their total lifetime exposure quantified in terms of cigarettes per day or packs per day. Frequently, average daily exposure (cigarettes or packs) is combined with duration of use in years in order to quantify exposure as "pack-years".
It has been much more difficult to establish whether environmental tobacco smoke (ETS) exposure is causally related to chronic diseases like heart disease and lung cancer, because the total lifetime exposure dosage is lower, and it is much more difficult to accurately estimate total lifetime exposure. In addition, quantifying these risks is also complicated because of confounding factors. For example, ETS exposure is usually classified based on parental or spousal smoking, but these studies are unable to quantify other environmental exposures to tobacco smoke, and inability to quantify and adjust for other environmental exposures such as air pollution makes it difficult to demonstrate an association even if one existed. As a result, there continues to be controversy over the risk imposed by environmental tobacco smoke (ETS). Some have gone so far as to claim that even very brief exposure to ETS can cause a myocardial infarction (heart attack), but a very large prospective cohort study by Enstrom and Kabat
was unable to demonstrate significant associations between exposure to spousal ETS and coronary heart disease, chronic obstructive pulmonary disease, or lung cancer. (It should be noted, however, that the report by Enstrom and Kabat has been widely criticized for methodological problems, and these authors also had financial ties to the tobacco industry.)
Correlation analysis provides a useful tool for thinking about this controversy. Consider data from the British Doctors Cohort. They reported the annual mortality for a variety of disease at four levels of cigarette smoking per day: Never smoked, 1-14/day, 15-24/day, and 25+/day. In order to perform a correlation analysis, I rounded the exposure levels to 0, 10, 20, and 30 respectively.
|
Cigarettes Smoked Per Day |
CVD Mortality Per 100,000 Men Per Year |
Lung Cancer Mortality Per 100,000 Men Per Year |
|---|---|---|
|
0 |
572 |
14 |
|
10 (actually 1-14) |
802 |
105 |
|
20 (actually 15-24) |
892 |
208 |
|
30 (actually >24) |
1025 |
355 |
The figures below show the two estimated regression lines superimposed on the scatter diagram. The correlation with amount of smoking was strong for both CVD mortality (r= 0.98) and for lung cancer (r = 0.99). Note also that the Y-intercept is a meaningful number here; it represents the predicted annual death rate from these disease in individuals who never smoked. The Y-intercept for prediction of CVD is slightly higher than the observed rate in never smokers, while the Y-intercept for lung cancer is lower than the observed rate in never smokers.
The linearity of these relationships suggests that there is an incremental risk with each additional cigarette smoked per day, and the additional risk is estimated by the slopes. This perhaps helps us think about the consequences of ETS exposure. For example, the risk of lung cancer in never smokers is quite low, but there is a finite risk; various reports suggest a risk of 10-15 lung cancers/100,000 per year. If an individual who never smoked actively was exposed to the equivalent of one cigarette's smoke in the form of ETS, then the regression suggests that their risk would increase by 11.26 lung cancer deaths per 100,000 per year. However, the risk is clearly dose-related. Therefore, if a non-smoker was employed by a tavern with heavy levels of ETS, the risk might be substantially greater.
Finally, it should be noted that some findings suggest that the association between smoking and heart disease is non-linear at the very lowest exposure levels, meaning that non-smokers have a disproportionate increase in risk when exposed to ETS due to an increase in platelet aggregation.
Summary
Correlation and linear regression analysis are statistical techniques to quantify associations between an independent, sometimes called a predictor, variable (X) and a continuous dependent outcome variable (Y). For correlation analysis, the independent variable (X) can be continuous (e.g., gestational age) or ordinal (e.g., increasing categories of cigarettes per day). Regression analysis can also accommodate dichotomous independent variables.
The procedures described here assume that the association between the independent and dependent variables is linear. With some adjustments, regression analysis can also be used to estimate associations that follow another functional form (e.g., curvilinear, quadratic). Here we consider associations between one independent variable and one continuous dependent variable. The regression analysis is called simple linear regression - simple in this case refers to the fact that there is a single independent variable. In the next module, we consider regression analysis with several independent variables, or predictors, considered simultaneously.