Discussion
In a large sample of elderly men and women, we have found that smoking is independently and inversely associated with undergoing a THR or TKR due to osteoarthritis in both men and women. This inverse association was observed after controlling for major confounders and also after accounting for possible misclassification biases.
A link between smoking and a protective mechanism against osteoarthritis has been previously reported. Two in-vitro studies have shown that this inverse relationship may be associated with glycosaminoglycans (GAG) synthesis, and with increased anabolic activity of the chondrocytes in joint cartilage. Nicotine was reported to upregulate glycosaminoglycan and collagen synthesis in a dose–response manner among smokers, thus inhibiting the degeneration of joint structures. Using magnetic resonance imaging, a third study found a positive dose–response between pack-years of smoking and tibial cartilage volume in healthy volunteers. Our analysis is the largest cohort study to report inverse associations between smoking and risk of either THR or TKR due to osteoarthritis in both males and females. This reduced risk persisted after adjusting for major confounders and also after accounting for possible misclassification in the smoking exposure variable. Our sensitivity analyses showed that under a wide range of uncertainty levels in the misclassification of smoking, the significant inverse association between smoking and TJR persisted. Only when the sensitivity of exposure to smoking was as low as 0.50, the simulation produced a non-significant result. A sensitivity of 0.50 would increase the study's observed number of smokers at baseline from 3,535 to 24,075 smokers; a seven-fold increase which seems very unlikely.
In a meta-analysis that assessed the association of smoking with osteoarthritis, Hui et al. evaluated 48 cross-sectional, case–control and cohort studies and found an overall significant inverse association between smoking and risk of OA (OR: 0.87, 95% CI 0.80–0.94). Nonetheless, these authors concluded that such a negative association was most probably false resulting from a possible selection bias of controls. The authors reported that the negative association was predominantly demonstrated in the case–control studies and suggested that the results could have been biased by the inclusion of controls from hospital settings where patients, in general, were more likely to have higher exposure to smoking than the general population. We agree that many of the conditions for which patients are hospitalized may be associated with smoking and the selection of controls from such a population can bias results of case–control studies of tobacco-related diseases as reported by Morabia et al. However, unlike most of the studies assessed by Hui et al., our study samples (both ANBP2 and our previous HIMS study) were community-based, and such selection biases were unlikely. Moreover, out of the 48 studies included in the Hui et al. meta-analysis, 40 (83%) were cross-sectional or case–control, and such study designs are less appropriate to investigate any temporal relationship between a certain exposure and outcome. In contrast, our study is longitudinal and exposure to smoking preceded the event of interest.
Utilisation disparities of total joint replacement by various socioeconomic groups have been reported. In addition to differences in co-morbidities and level of education, those belonging to a low SES status may be less willing to undergo this procedure compared with the more affluent. Similarly, disparities in undergoing a TJR procedure may be associated with socio-economic access factors, and expectations about the process and outcomes of the procedure. Unlike these reports, our longitudinal study did not find significant differences in the utilisation rates of TJR by various socio-economic groups. Our study population was relatively old and, possibly, those coming from lower socio-economic groups were under-represented. This possible selective loss to follow-up may have resulted in biased estimates of socio-economic inequalities in the utilisation of TJR that may occur in a population with a wider age range. Similarly, the SEIFA indices ranked socio-economic well-being of the populations within areas rather than individuals themselves. Any area can include both relatively advantaged and disadvantaged people. Using the postcode may have introduced some misclassifications; however, since the postcode was provided by the participants, any misclassifications were minimized which was also supported by the probabilistic sensitivity analysis.
Strengths and Limitations
This study has several strengths including its longitudinal follow-up design, its large sample of participants of both males and females, and the many years of past exposure to smoking in our elderly participants. The linkage of the participants' records to the national mortality index data allowed us to account for all deaths in our study population. Moreover, death which may be more common among the elderly and especially among the smokers was accounted for as a competing risk. To our knowledge, this study is the first to assess risk of the study outcome while accounting for uncertainty in the exposure to smoking.
However, the study has limitations. Our retrospective cohort study, which is the highest possible level of evidence to investigate the relationship between smoking and long-term outcomes, is not a randomised controlled trial, thus confounding from other unaccounted factors is always possible. Available data did not permit us to control for duration or intensity of smoking, nor for past history of traumatic injury or past stressful physical work. Information on the physical activity of the participants was self-reported and not validated. The relatively advanced age of the participants at baseline enabled us to account for the co-morbidities and also various measures of obesity present at baseline. Nonetheless, we could not account for change in weight or co-morbidities that may have occurred over the mean 8.6 (SD 3.4) years of follow-up. Notwithstanding, age, which is often considered the simplest co-morbidity score, was accounted for over the follow-up period. Our study considered TJR as a surrogate indicator of severe osteoarthritis (OA). We therefore excluded all those who had had a TJR procedure in the past. We could not exclude those who had had hip or knee OA at baseline. The complete national capture of all lower limb joint replacements by the AOA NJRR was 2.3 (SD 0.6) years (mean) after the recruitment of the ANBP2 participants. However, there is no evidence to indicate that the missed procedures were more likely to be among smokers. We used co-morbidities reported by the GPs, research nurses and study participants to calculate the Charlson Index. We had no access to medical charts and therefore these co-morbidities were not validated. If co-morbidity were underestimated, the risk of TJR among non-smokers could have been overestimated (given that the current smokers had more co-morbidities than the non-smokers). Nonetheless, we constructed this co-morbidity score (that is based on 17 co-morbid conditions including various chronic pulmonary and other diseases) using a similar approach as demonstrated by Chaudhry et al. who found high agreement levels between reported co-morbid conditions and those recorded in administrative datasets. Another explanation is the possibility of selection biases prior to surgery. Heavy smokers may have a lower chance of being put forward for surgery because of medical concerns regarding worse outcomes in such patients. However, a survey that sought to find indications for THR or TKR as perceived by orthopaedic surgeons showed that the decision against surgery was mainly affected by patient age, co-morbidity, obesity, alcohol use, technical difficulties and lack of motivation among the patients. Smoking was not indicated as a factor that would sway the decision against TKR or THR. Finally, the study population was relatively old and our findings may not be generalizable to younger patient populations.