The Association of Smoking and Socioeconomic Status on Cutaneous Melanoma

A Population-based, Data-linkage, Case-control Study

J.A.G. Gibson; T.D. Dobbs; R. Griffiths; J. Song; A. Akbari; S. Whitaker; A. Watkins; S.M. Langan; H.A. Hutchings; R.A. Lyons; I.S. Whitaker


The British Journal of Dermatology. 2020;182(5):1136-1147. 

In This Article


We found that smokers were less likely to develop melanoma in this population-based, case–control study, but that their overall survival was reduced. After controlling for age, sex, socioeconomic status, tumour location, morphology and stage, the smoking group had an increased risk of death from all causes as compared with the nonsmoking group. However, when investigating melanoma-specific mortality, no association was observed.

The mechanism responsible for the observed protective association of smoking on the risk of developing melanoma is not yet known, but several plausible hypotheses exist. Some authors hypothesize that the accumulation of nicotine in cells containing melanin suppresses the inflammatory response to ultraviolet B.[21–23] Additionally, as smoking increases elastosis, it has been hypothesized that elastosis formation is protective of melanoma.[24] Alternative explanations include earlier deaths in current and ex-smokers leading to survival bias, whereby those exposed to smoking die before being at risk of developing melanoma.

Melanoma is not the only condition where smoking has been shown to have a favourable association, others include Parkinson disease and ulcerative colitis.[25,26] The protective association in Parkinson disease has been attributed to nicotine's ability to prevent brain damage and dopamine depletion. The depletion of dopamine occurs in the substantia nigra, an area of the brain populated by melanocytes. It is therefore plausible that Parkinson disease and melanoma share similar pathogenesis.[27] Numerous studies have demonstrated an increased risk of melanoma in patients with Parkinson disease and vice versa.[28] The inverse association of smoking and the risk of developing ulcerative colitis is well reported in the literature; however, the pathogenesis is less well understood.[29]

The relationship with smoking status has been investigated for nonmelanoma skin cancers. In a prospective cohort study of over one million participants, current smokers were found to have a reduced risk of developing basal cell carcinoma (BCC). Similar to our study, this 'protective' association was not observed in ex-smokers. Squamous cell carcinoma (SCC) is conversely more common in smokers.[30] The Notch pathway, which functions broadly in specifying cell fates during embryogenesis and adult life, has a key role in linking the control of epidermal differentiation and proliferation.[31] Aberrant Notch signalling leads to skin cancer, although with different associations with different skin cancer types.[31] For melanoma, nodular and superficial BCC, Merkel carcinoma and SCC in sun-protected sites increased Notched signalling has an oncogenic effect. However, for basosquamous BCC and SCC on sun-exposed sites increased signalling has an oncosuppressive effect. The Notch pathway has been found to be downregulated in smokers, which could provide a further explanation on the protective association of smoking on melanoma and nodular BCC and the higher risk of SCC on sun-exposed sites.[31–34]

Although we observed that smokers appeared to be at reduced risk of melanoma, their overall survival was reduced. This finding is not surprising given the strong relationship between smoking and other life-limiting conditions, such as the majority of cancers and cardiorespiratory disease. However, consistent with the potential protective influence of smoking on melanoma development, the risk of death from melanoma was not different between the smokers and nonsmokers after adjusting for age, sex, stage of disease, morphology, socioeconomic status and tumour location. This might imply that smoking does not affect the disease progression of melanoma. This is however, not consistent with the work of Jones et al., who identified that at presentation, smokers had an increased risk of lymph node metastasis.[35] The discrepancy may be explained by the fact that their study did not control for socioeconomic status. In addition, Jones et al. reported an association between smoking status and Breslow thickness at presentation. In our study we did not have data on Breslow thickness; however, smoking status was not associated with stage at presentation.

Consistent with the published literature we found that the risk of developing melanoma was positively associated with socioeconomic status in our study.[1] The underlying explanation is poorly understood and likely to be complex and multifactorial. Socioeconomic status is closely linked with lifestyle factors such as travel, sunbed use and hobbies that are also associated with sunlight exposure, with the literature supporting the notion that those that are more affluent have greater exposure to lifestyle factors that increase melanoma incidence.[1,8] Our study also demonstrated that those in the highest socioeconomic status group were less likely to smoke.

Despite higher socioeconomic status being associated with an increased risk of melanoma development, lower socioeconomic status is associated with poorer survival once diagnosed. This relationship was observed in both overall and disease-specific survival rates. This is consistent with the broader health literature where it has been shown that lower socioeconomic status is associated with premature mortality from a number of conditions such as cardiovascular disease, respiratory disease and some malignancies.[36] In previous studies, low socioeconomic status has been associated with later stage of melanoma diagnosis; however, this was not observed in this study. Our results may be explained by the measure used to classify socioeconomic status, the WIMD score. One of the seven domains used to determine the WIMD quintile is health, which is determined by the number of limiting long-term illnesses, the all-cause death rate, cancer incidence and birthweight. Patients within the low socioeconomic status group may therefore have other attributable factors influencing survival.

Limitations of this study included missing data, the lack of information available on ethnicity and ultraviolet light exposure. As with any population-based study, missing data prevented analysis on the total cohort. Data were missing for some of the cohort on smoking status and stage of disease. Smoking status was obtained from the WLGP, as recorded during patient's consultations with their general practitioner. To date, the WLGP covers 80% of general practices across Wales. Of the 2512 patients for whom smoking data were absent, 2431 (96·8%) belonged to general practices not contributing data to the SAIL Databank. It is therefore assumed that data for this variable were missing at random and would not bias the results.

Additionally, information was not available on the quantity of tobacco smoked by participants. The Read codes listed in the Table S2 (see Supporting Information) do capture some information on the amount of smoking. In practice, these codes were rarely utilized by general practitioners, with the majority simply recording 137R (current smoker) and therefore we were unable to provide meaningful results. This is a substantial limitation as the cumulative exposure to tobacco was not assessed, thus it was not possible to calculate a dose–response relationship.

When stage of melanoma was not recorded in the WCISU data, and could not be obtained from other linked data, these data were missing. To assess the effect of this missingness, a sensitivity analysis was performed. Missing data were incorporated into the regression model as a separate category for stage. This was found not to affect the statistical significances outlined in the results section.

A further limitation of population-based studies using routinely collected data is incomplete control of confounding, that of data that are not specified, incompletely captured or misclassified, namely tumour location (relating to ICD-10 Code C43·9 melanoma unspecified) and tumour morphology [M7203 – MM NOS (melanoma – not otherwise specified)]. The classification codes used to extract smoking status from general practitioner data have shown to classify 8·6% ex-smokers as never smokers. Any misclassification would not significantly bias the results.

Ethnicity is only available on special request within the SAIL Databank and was therefore not incorporated into the statistical model. In Wales, population statistics reveal that 95% of the population are white and therefore the significance of ethnicity on the results would be minimal.[37]

In conclusion, this is the largest study to date indicating that smoking has an inverse relationship on the risk of developing melanoma. While the detrimental repercussions of smoking are well documented, further work is required to uncover the mechanism underlying this relationship, including further assessment about survival bias. If a biological association seems likely, this could lead to the development of novel prevention and treatment options, opening up a new wave of medical therapy for melanoma. Furthermore, this work reinforces the ongoing association between melanoma and socioeconomic status. Despite numerous public health strategies, higher socioeconomic groups continue to have a higher incidence of melanoma, however, lower socioeconomic status is related to poor survival once melanoma is diagnosed. The implications of these results, in a country such as the U.K. where health care is free to all, are significant. Further work is required to investigate the barriers to care that may exist for the lowest socioeconomic status group so that policies can be implemented to prevent healthcare inequality and improve melanoma outcomes for all.