Published online Aug 26, 2013. doi: 10.13105/wjma.v1.i2.57
Revised: May 9, 2013
Accepted: August 4, 2013
Published online: August 26, 2013
AIM: To quantify smoking/lung cancer relationships accurately using parametric modelling.
METHODS: Using the International Epidemiological Studies on Smoking and Lung Cancer database of all epidemiological studies of 100+ lung cancer cases published before 2000, we analyzed 97 blocks of data for amount smoked, 35 for duration of smoking, and 27 for age started. Pseudo-numbers of cases and controls (or at risk) estimated from RRs by dose level formed the data modelled. We fitted various models relating loge RR to dose (d), including βd, βdY and βloge (1 + Wd), and investigated goodness-of-fit and heterogeneity between studies.
RESULTS: The best-fitting models for loge RR were 0.833 loge [1 + (8.1c/10)] for cigarettes/d (c), 0.792 (y/10)0.74 for years smoked (y) and 0.176 [(70 - a)/10]1.44 for age of start (a). Each model fitted well overall, though some blocks misfitted. RRs rose from 3.86 to 22.31 between c = 10 and 50, from 2.21 to 13.54 between y = 10 and 50, and from 3.66 to 8.94 between a = 30 and 12.5. Heterogeneity (P < 0.001) existed by continent for amount, RRs for 50 cigarettes/d being 7.23 (Asia), 26.36 (North America) and 22.16 (Europe). Little heterogeneity was seen for duration of smoking or age started.
CONCLUSION: The models describe the dose-relationships well, though may be biased by factors including misclassification of smoking status and dose.
Core tip: This paper, for the first time, meta-analyses smoking/lung cancer dose-relationships. Based on data from 71 studies published before 2000, single parameter models were fitted to summarize how the RR increased with increasing amount smoked, longer duration of smoking, and earlier age of starting to smoke. Overall, the models fitted well. Little heterogeneity was seen for duration of smoking or age of start, but the rise in RR with amount smoked was much steeper in North America and Europe than in Asia. The fitted models can be used to more precisely estimate the lung cancer risk from smoking.