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

Patients and Methods

The described study has been reported in accordance with the Reporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement (see Table S1 in the Supporting Information).[12] The study was conducted in two stages. In stage one, a case–control study was performed to assess the relationship between smoking and the development of melanoma. In stage two, a cohort study was conducted to determine the association between smoking and survival within the melanoma cohort (Figure 1).

Figure 1.

Study design. SAIL, Secure Anonymised Information Linkage; WCISU, Welsh Cancer Intelligence and Surveillance Unit; WDS, Welsh Demographic Service; WIMD, Welsh Index of Multiple Deprivation.

Overview of Methods

Analyses of primary and secondary care National Health Service data and national administrative data for 2000–2015 in Wales, U.K. (population 3·1 million) were performed. In instances where relevant data were unavailable from a single source, multiple datasets were linked. Data were retrieved from six national databases (Table 1). In Wales, population-level de-identified person-based health and socioeconomic administrative datasets are collated and linked within the Secure Anonymised Information Linkage (SAIL) databank.[13–15] Robust policies, structures and controls are in place to protect privacy through a reliable matching and anonymization process, achieved in conjunction with the NHS Wales Informatics Service using a split-file multiple-encryption approach described in detail in previous published work.[14]


In Wales, all patients with a diagnosis of melanoma are recorded in the Welsh Cancer Intelligence and Surveillance Unit (WCISU) register. Cases were identified from WCISU using International Classification of Disease 10 (ICD-10) codes C43·0-C43·9 and morphology codes according to the International Classification of Diseases for Oncology (ICDO-3) 8720–8790.[16] Patients with melanoma in situ were not included in the study as either cases or controls. Demographic information was assessed at the date of diagnosis. Melanoma-specific variables (tumour location, stage and morphology) were assessed at the date of diagnosis.


Four sets of general population controls were randomly selected from the Welsh Demographic Service Dataset. Controls were not matched to cases. Both cases and controls needed to be alive and resident in Wales on the date of melanoma diagnosis. To increase the power of the study we aimed to have four controls for every case.[17]

Smoking Status

Self-reported smoking status, for cases and controls were obtained from the Welsh Longitudinal General Practice (WLGP) data, as recorded during patients' consultations with their general practitioner in primary care, using Read codes that have been previously validated[18] (Table S2; see Supporting Information). Patients were defined as either a nonsmoker (for lifelong nonsmokers), ex-smoker (for those that had previously smoked) or current smokers. The smoking assessment window extended from the melanoma diagnosis date to 6 months prior. Where serial assessments were available, the smoking record most recent to the diagnosis was selected. Where 'nonsmoker' was recorded, the WLGP dataset was explored to establish whether the individual had previously been classified as a smoker. In such circumstances, the individual was classed as an ex-smoker.

Socioeconomic Status

Socioeconomic status was measured using the Welsh Index of Multiple Deprivation (WIMD) version 2001, a measure based on the Index of Multiple Deprivation and used as the official measure of socioeconomic status for the Welsh Government.[19] Individual scores are based upon a person's postal address. Wales is divided into 1896 lower-layer super-output areas (LSOAs) following the 2001 Census, each consisting of approximately 1600 people. The WIMD scores for each LSOA are calculated from weighted scores from eight domains of socioeconomic status (income, employment, health, education, access to services, community safety, physical environment and housing socioeconomic status). Each LSOA in Wales has been ranked according to its WIMD score and grouped into quintiles, with quintile five being the highest socioeconomic status and one being the lowest.

Mortality Data

Data relating to mortality, including cause of death for the melanoma cohort were obtained from the Annual District Death Extract dataset, which contains the diagnostic codes listed on patient's death certificates, held within the SAIL Databank.

Charlson Comorbidity Index

The Charlson Comorbidity Index is a widely used measure of comorbidity. An overall score is calculated from a list of conditions, each of which has been allocated a weight of between one and six based upon its adjusted relative risk of 1-year mortality.[20]

Ethical Approval

Study approval was granted by the SAIL Databank independent Information Governance Review Panel (project 0593). Data held within the SAIL Databank are made available to researchers in an anonymized format and are therefore not subject to data protection legislation. SAIL follows all relevant legislative and regulatory frameworks in using population data for research.

Statistical Analysis

Case–Control (Stage One). Descriptive statistics were used to characterize the melanoma cases and controls by smoking status and stage at diagnosis (cases only). An unconditional binary logistic regression model was used to calculate ORs with 95% CIs for the association with melanoma. Sex, socioeconomic status and age at the time of diagnosis (as a continuous variable) were incorporated into the statistical model as confounders.

Cohort Study (Patients With Melanoma Only) (Stage Two). In this stage of the study only those with a diagnosis of melanoma were included (Figure 1). Overall survival was calculated as the time from melanoma diagnosis to the time of death (outcome) or the end of the study (December 2018). Melanoma-specific survival was calculated as the time from melanoma diagnosis to the date of death from melanoma, or the end of the study for patients still alive (December 2018). Cases with missing variables were excluded from this aspect of the study.

Kaplan–Meier curves were generated for smoking status and socioeconomic status, with curves compared using the log-rank test. A Cox hazard proportional regression model was used to determine the association between smoking and mortality in the melanoma cohort. Sex, socioeconomic status, melanoma stage at diagnosis and age at diagnosis as a continuous variable were incorporated into the model as confounders. Both overall survival (deaths from any cause) and melanoma-specific survival (defined on their death registration held within the Annual District Death Extract) were analysed in the melanoma cohort. All data were analysed using IBM SPSS Statistics for Windows (Released 2017, version 25·0, IBM Corp, Armonk, NY, U.S.A.). Statistical significance was assumed with a P < 0·05.