Association Between Melanoma Detected During Routine Skin Checks and Mortality

Caroline G Watts; Kirstie McLoughlin; Chris Goumas; Cathelijne H van Kemenade; Joanne F Aitken; H Peter Soyer; Pablo Fernandez Peñas; Pascale Guitera; Richard A Scolyer; Rachael L Morton; Scott W Menzies; Michael Caruana; Yoon Jung Kang; Graham J Mann; Annette H Chakera; Christine M Madronio; Bruce K Armstrong; John F Thompson; Anne E Cust

doi:10.1001/jamadermatol.2021.3884

. 2021 Nov 3;157(12):1–12. doi: 10.1001/jamadermatol.2021.3884

Association Between Melanoma Detected During Routine Skin Checks and Mortality

Caroline G Watts ^1,², Kirstie McLoughlin ^1,³, Chris Goumas ¹, Cathelijne H van Kemenade ¹, Joanne F Aitken ^4,⁵, H Peter Soyer ^6,⁷, Pablo Fernandez Peñas ^8,⁹, Pascale Guitera ^10,^11,¹², Richard A Scolyer ^10,^11,¹³, Rachael L Morton ^10,¹⁴, Scott W Menzies ^11,¹², Michael Caruana ^1,³, Yoon Jung Kang ^1,³, Graham J Mann ^10,^15,¹⁶, Annette H Chakera ^17,¹⁸, Christine M Madronio ¹⁹, Bruce K Armstrong ²⁰, John F Thompson ^10,¹¹, Anne E Cust ^1,^10,^✉

¹The Daffodil Centre, The University of Sydney, Cancer Council NSW, Sydney, Australia

²Surveillance, Epidemiology and Research Program, Kirby Institute, University of New South Wales, Sydney, Australia

³Cancer Research Division, Cancer Council NSW, Woolloomooloo, Sydney, Australia

⁴School of Public Health, The University of Queensland, Brisbane, Australia

⁵Cancer Council Queensland, Brisbane, Australia

⁶The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Australia

⁷Dermatology Department, Princess Alexandra Hospital, Brisbane, Australia

⁸Sydney Medical School, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

⁹Department of Dermatology, Westmead Hospital, Westmead, Sydney, Australia

¹⁰Melanoma Institute Australia, The University of Sydney, Sydney, Australia

¹¹Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

¹²Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, Australia

¹³Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and New South Wales Health Pathology, Sydney, Australia

¹⁴National Health and Medical Research Council Clinical Trials Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

¹⁵Centre for Cancer Research, Westmead Institute for Medical Research, The University of Sydney, Sydney, Australia

¹⁶John Curtin School of Medical Research, Australian National University, Canberra, Australia

¹⁷Department of Plastic Surgery, Herlev and Gentofte Hospital, Copenhagen, Denmark

¹⁸Department of Clinical Medicine, Copenhagen University, Copenhagen, Denmark

¹⁹Nepean Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

²⁰Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

Accepted for Publication: September 14, 2021.

Published Online: November 3, 2021. doi:10.1001/jamadermatol.2021.3884

^✉

Corresponding Author: Anne E. Cust, PhD, Professor, The University of Sydney, A27 Edward Ford Building, Sydney, NSW 2006, Australia (anne.cust@sydney.edu.au).

Author Contributions: Drs Cust and Goumas had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. These authors contributed equally: Drs Watts and McLoughlin.

Concept and design: Watts, McLoughlin, Morton, Mann, Chakera, Madronio, Armstrong, Thompson, Cust.

Acquisition, analysis, or interpretation of data: Watts, McLoughlin, Goumas, van Kemenade, Aitken, Soyer, Fernandez Peñas, Guitera, Scolyer, Morton, Menzies, Caruana, Kang, Madronio, Armstrong, Thompson, Cust.

Drafting of the manuscript: Watts, McLoughlin, Cust.

Critical revision of the manuscript for important intellectual content: McLoughlin, Goumas, van Kemenade, Aitken, Soyer, Fernandez Peñas, Guitera, Scolyer, Morton, Menzies, Caruana, Kang, Mann, Chakera, Madronio, Armstrong, Thompson, Cust.

Statistical analysis: Goumas, Caruana, Cust.

Obtained funding: Mann, Armstrong, Cust.

Administrative, technical, or material support: Watts, van Kemenade, Fernandez Peñas, Scolyer, Morton, Mann, Chakera, Madronio, Cust.

Supervision: Guitera, Morton, Mann, Cust.

Conflict of Interest Disclosures: Dr Soyer reported receiving fees from MoleMap NZ Limited; serving as a medical consultant, minor shareholder, and receiving nonfinancial support from Canfield Scientific Medical; being a shareholder of E-Derm Consult GmbH; receiving medical reporting and consultant fees from Revenio Research OY Medical, and acting as a medical advisor for First Derm Medical outside the submitted work. Dr Scolyer reported receiving fees for professional services from Qbiotics, Novartis, Merck Sharp & Dohme, NeraCare, AMGEN Inc, Bristol Myers Squibb, Myriad Genetics, and GlaxoSmithKline; being a shareholder of MoleMap NZ Limited and E-Derm Consult GmbH; undertaking regular teledermatological reporting for MoleMap NZ Limited and E-Derm Consult; being a medical consultant for Canfield Scientific Inc and Revenio Research Oy; and being a medical advisor for First Derm. Dr Fernandez Peñas reported having these relationships related to inflammatory skin disease (psoriasis, eczema, etc): advisory board participant for AbbVie, Boehringer Ingelheim, Celgene, Eli Lilly and Company, Roche, Merck Sharp & Dohme, Janssen, Novartis, Sanofi, Leo Pharma, Bristol Myers Squibb, and Pfizer; participant in clinical trials for AbbVie, AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, CSL Behring, Galderma, Eli Lilly and Company, Janssen, Novartis, Oncosec, Pfizer, UCB, Regeneron, and Sun Pharma; and educational lecturer and recipient of honoraria for AbbVie, Amgen, Eli Lilly and Company, Galderma, Janssen, Merck Sharp & Dohme, Merck, Novartis, Pfizer, Roche, Sanofi, and Schering Plough outside the submitted work. Dr Menzies reported receiving personal fees from SciBase AB and from MoleMap NZ Limited outside the submitted work. Dr Chakera reported receiving personal fees as an educational advisor for Sanofi and Merck and receiving grants from Innovation Fund Denmark and the Danish Cancer Society outside the submitted work. Dr Thompson reported receiving honoraria from Bristol Myers Squibb Australia and Merck Sharp & Dohme Australia for advisory board participation; honoraria from GlaxoSmithKline; honoraria and travel support from Provectus; and nonfinancial support from Novartis for meeting attendance outside the submitted work. No other disclosures were reported.

Funding/Support: This work was supported by the Australian National Health and Medical Research Council (grant No. 1135285 from the Centre of Research Excellence in Melanoma and grant No. 1165936 from Project Grant); grant No. 05/POC/1-06 from the Cancer Institute New South Wales; and the New South Wales State Government via a grant to the New South Wales Melanoma Network. Additional financial and in-kind support was provided by the Melanoma Institute Australia and the New South Wales Melanoma Network; grant 1137127 from the National Health and Medical Research Council Next Generation Clinical Researchers Program Practitioner Fellowship (Dr Soyer); a grant from the National Health and Medical Research Council Practitioner Fellowship (Dr Scolyer); investigator grant No. 1194703 from the National Health and Medical Research Council (Dr Morton); a University of Sydney Robinson Fellowship (Dr Morton); grant No. 1093017 from the National Health and Medical Research Council (Drs Scolyer, Mann, and Thompson); and a Career Developmental Fellowship grant No. 1147843 from the National Health and Medical Research Council (Dr Cust).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We thank other investigators from the Melanoma Patterns of Care Study, including Associate Professor Austin Curtin. We thank the Centre for Health Record Linkage, New South Wales Department of Health, Cancer Institute New South Wales, and the New South Wales Cancer Registry for providing linked data. We thank the University of Sydney and the Royal Prince Alfred Hospital for their in-kind support and the physicians who took part in the study. We thank the Cameron Family’s support of the Melanoma Institute Australia Research Database. No one was financially compensated for their contribution.

Additional Information: The Cause of Death Unit Record File is provided by the Australian Coordinating Registry for the Cause of Death Unit Record File on behalf of the New South Wales Registry of Births, Deaths and Marriages; New South Wales Coroner; and the National Coronial Information System. The Cause of Death Unit Record File is held by the New South Wales Ministry of Health Secure Analytics for Population Health Research and Intelligence.

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Corresponding author.

PMCID: PMC8567188 PMID: 34730781

Key Points

Question

Are melanomas diagnosed through routine skin checks (a proxy for skin cancer screening) associated with reduced melanoma-specific and all-cause mortality?

Findings

This population-based cohort study included 2452 patients, each with 1 diagnosed melanoma, 35% of which were detected during a routine skin check. In multivariable analyses, routine skin-check detection was associated with a subhazard ratio of 0.68 for melanoma-specific mortality and 0.75 for all-cause mortality compared with patient-detected melanomas after a mean follow-up of 11.9 years.

Meaning

Melanomas diagnosed through routine skin checks were associated with significantly lower all-cause mortality, but not melanoma-specific mortality, after adjustment for patient, sociodemographic, and clinicopathologic factors.

This cohort study investigates the association between melanomas diagnosed during routine skin checks and melanoma-specific and all-cause mortality.

Abstract

Importance

Early melanoma diagnosis is associated with better health outcomes, but there is insufficient evidence that screening, such as having routine skin checks, reduces mortality.

Objective

To assess melanoma-specific and all-cause mortality associated with melanomas detected through routine skin checks, incidentally or patient detected. A secondary aim was to examine patient, sociodemographic, and clinicopathologic factors associated with different modes of melanoma detection.

Design, Setting, and Participants

This prospective, population-based, cohort study included patients in New South Wales, Australia, who were diagnosed with melanoma over 1 year from October 23, 2006, to October 22, 2007, in the Melanoma Patterns of Care Study and followed up until 2018 (mean [SD] length of follow-up, 11.9 [0.3] years) by using linked mortality and cancer registry data. All patients who had invasive melanomas recorded at the cancer registry were eligible for the study, but the number of in situ melanomas was capped. The treating doctors recorded details of melanoma detection and patient and clinical characteristics in a baseline questionnaire. Histopathologic variables were obtained from pathology reports. Of 3932 recorded melanomas, data were available and analyzed for 2452 (62%; 1 per patient) with primary in situ (n = 291) or invasive (n = 2161) cutaneous melanoma. Data were analyzed from March 2020 to January 2021.

Main Outcomes and Measures

Melanoma-specific mortality and all-cause mortality.

Results

A total of 2452 patients were included in the analyses. The median age at diagnosis was 65 years (range, 16-98 years), and 1502 patients (61%) were men. A total of 858 patients (35%) had their melanoma detected during a routine skin check, 1148 (47%) self-detected their melanoma, 293 (12%) had their melanoma discovered incidentally when checking another skin lesion, and 153 (6%) reported “other” presentation. Routine skin-check detection of invasive melanomas was associated with 59% lower melanoma-specific mortality (subhazard ratio, 0.41; 95% CI, 0.28-0.60; P < .001) and 36% lower all-cause mortality (hazard ratio, 0.64; 95% CI, 0.54-0.76; P < .001), adjusted for age and sex, compared with patient-detected melanomas. After adjusting for prognostic factors including ulceration and mitotic rate, the associations were 0.68 (95% CI, 0.44-1.03; P = .13), and 0.75 (95% CI, 0.63-0.90; P = .006), respectively. Factors associated with higher odds of routine skin-check melanoma detection included being male (female vs male, odds ratio [OR], 0.73; 95% CI, 0.60-0.89; P = .003), having previous melanoma (vs none, OR, 2.36; 95% CI, 1.77-3.15; P < .001), having many moles (vs not, OR, 1.39; 95% CI, 1.10-1.77; P = .02), being 50 years or older (eg, 50-59 years vs <40 years, OR, 2.89; 95% CI, 1.92-4.34; P < .001), and living in nonremote areas (eg, remote or very remote vs major cities, OR, 0.23; 95% CI, 0.05-1.04; P = .003).

Conclusions and Relevance

In this cohort study, melanomas diagnosed through routine skin checks were associated with significantly lower all-cause mortality, but not melanoma-specific mortality, after adjustment for patient, sociodemographic, and clinicopathologic factors.

Introduction

The costs, morbidity, and mortality associated with melanoma diagnosis place a high burden on health care systems in many countries.^1,2,3 Diagnosis of melanoma at an earlier stage is associated with better survival,⁴ and observational studies have shown that whole-body clinical skin examination is associated with lower melanoma mortality and fewer thick (ie, higher stage) melanomas,^5,6,7,8 although the observed mortality decline in the Schleswig-Holstein region of Germany was not replicated after the introduction of population-wide screening in Germany.⁹ Apart from Germany, no country has an organized population-based melanoma screening program because of insufficient evidence that screening reduces melanoma mortality; uncertainty about possible harms, such as overdiagnosis and unnecessary biopsy^10,11,12; and limited evidence for cost-effectiveness.^1,13,14

Over the last decade, there has been renewed interest in melanoma screening,^10,15,16,17 driven by the changing landscape of melanoma care. Changes include the increasing health system costs for new therapies,^18,19 better diagnostic technologies,²⁰ more accurate risk-stratification algorithms,²¹ and better understanding of high-risk melanoma features.^10,16 There may also be benefits from the inevitably associated early detection of the much more numerous nonpigmented skin cancers (keratinocyte carcinomas) and enhanced prevention education.^16,17

In addition to cancer-specific mortality, the effect of cancer screening on all-cause mortality is also of interest, as reducing deaths from a particular cancer type should translate to an improvement in all-cause mortality unless investigation of positive screening results leads to deaths from other causes.²²

Using population-based data, we aimed to assess melanoma-specific and all-cause mortality associated with melanoma identified through routine skin checks (as a proxy for skin cancer screening), incidentally, or through patient detection. A secondary aim was to examine patient, sociodemographic, and clinicopathologic factors associated with different modes of melanoma detection.

Methods

Study Sample

The Melanoma Patterns of Care Study cohort comprised patients diagnosed with a histopathologically confirmed primary in situ or invasive cutaneous melanoma in New South Wales (NSW), Australia, compulsorily reported to the NSW Cancer Registry between October 23, 2006, and October 22, 2007.²³ Based on available resources, the study prioritized the collection of invasive melanomas (for the full year of data collection) over in situ melanomas (limited to the first 450 diagnoses, collected over 2-3 months), as invasive melanomas were most relevant to clinical practice guidelines and patient outcomes. The treating physician at diagnosis was asked to complete a questionnaire about the patient’s risk factors, diagnosis, and treatment. Race and ethnicity were not routinely measured in clinical practice and so were not collected as part of this study. If the patient was referred to another physician, that physician was also sent a questionnaire; otherwise, it was assumed that all of the patient’s care had been provided by the notifying physician. Rates of discrepancy between questionnaires were low and were handled by keeping any value where a characteristic was reported as present (vs absent) or by keeping data from the most appropriate source (eg, pathology data from pathology report, diagnosis-related data from the diagnosing physician). Questionnaires were completed for 2758 of 3869 patients (71%). Trained field workers assisted with the completion of 1298 of 4057 questionnaires (32%).

Ethics approval was obtained from the NSW Population and Health Services Research Ethics Committee and the Australian Capital Territory Health Human Research Ethics Committee, and governance approvals were received for each linked data set. A waiver of patient consent was approved by the ethics committee, in line with national ethical regulations, as data were collected directly from clinicians and administrative sources. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline was used to guide this report.²⁴

Data Collection

The questionnaire asked “How did this melanoma present? (tick one only)” with 5 responses: “patient reported this skin lesion,” “found incidentally when checking another skin lesion,” “found in a routine skin check,” “other (specify),” and “don’t know.” The questionnaire asked if the patient had a personal or family history of melanoma and if they had “lots of moles.” Postcode of residence was used to estimate patients’ socioeconomic status²⁵ and their Accessibility/Remoteness Index.²⁶ Histopathologic data were extracted from pathology reports held by the NSW Cancer Registry. If melanoma subtype was coded as “not otherwise specified” in the Registry, the pathology report was checked for these details. We also cross-checked missing histopathologic details with a linked database from Melanoma Institute Australia, which is the largest melanoma referral and treatment center in NSW.

Patients were followed up for diagnosis of new primary melanomas and death from any cause for a mean (SD) of 11.9 (0.3) years. Dates and cause of death from October 23, 2006, to December 31, 2018, were identified through linkage of patients’ identities with the Cause of Death Unit Record File and Registry of Births, Deaths and Marriages in NSW and the Australian Capital Territory. Data sources for the Australian Capital Territory were used because this geographically small, population-dense area is completely enclosed within NSW. Physicians or coroners completed the death certificates, recording multiple causes of death where applicable. The Australian Bureau of Statistics then checked and coded the information using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision. The incidence of other in situ and invasive melanomas that occurred in the Melanoma Patterns of Care Study cohort both before and after the 2006 to 2007 study period were identified from the NSW and Australian Capital Territory Cancer Registries from 1972 to 2018. Pathological and clinical data were also obtained from the Melanoma Institute Australia research database from October 23, 2006, to December 31, 2018.

Statistical Analysis

The analysis data set included 2452 melanomas (1 per patient), of which 291 (12%) were in situ and 2161 (88%) were invasive (eFigure 1 in the Supplement). To calculate differences in proportions, χ² tests were used, and multinomial logistic regression models were used to estimate multivariable odds ratios (ORs) and 95% CIs. To evaluate potential interactions between patient factors (age, sex, number of moles, family history, and previous melanoma) and different modes of melanoma detection, we tested the inclusion of interaction terms in the multivariable models and stratified analyses.

Hazard ratios for all-cause mortality were estimated from Cox regression models. Subhazard ratios for melanoma-specific mortality were estimated from Fine and Gray competing risk regression models with death from other causes as the competing risk.²⁷ Minimally adjusted models were adjusted for age (continuous) and sex. Multivariable-adjusted model 1 was further adjusted for previous melanoma, family history, moles, socioeconomic status,²⁵ residential remoteness,²⁶ anatomic site, and melanoma subtype; and multivariable-adjusted model 2 was adjusted for these variables plus ulceration and mitotic rate. Explanatory variables were selected a priori based on known or expected confounders. An assessment of confounding is shown in eFigure 2 in the Supplement. We also conducted mortality analyses stratified by Breslow thickness categories.

Unknown responses for variables were kept as separate categories in the multivariable models to maintain a constant sample size. Regression models excluded melanomas detected by “other” presentation because of its heterogeneous representation. Analyses for mortality outcomes were conducted with and without the inclusion of patients with in situ melanoma only and also after excluding patients who developed another (thicker or thinner) invasive melanoma (15%) or another thicker invasive melanoma (8%) either before or after the study period (identified using linked data). All analyses were conducted using SAS, version 9.4 (SAS Institute). Figures were made using R, version 4.0.2 (R Foundation). All P values were 2-sided, and P < .05 was considered significant. Data were analyzed from March 2020 to January 2021.

Results

Patient Characteristics

Of 3932 recorded melanomas, data were available and analyzed for 2452 (62%; 1 per patient). Of the 2452 patients included in the analysis, 858 (35%) had their melanoma detected at a routine skin check, 1148 (47%) self-detected their melanoma, 293 (12%) had their melanoma discovered incidentally when checking another skin lesion, and 153 (6%) reported “other” presentation (Table 1). The median patient age at diagnosis was 65 years (range, 16-98 years); 1502 patients were men (61%), and 950 were women (39%).

Table 1. Detection of Melanomas by Demographic, Clinical, and Melanoma Characteristics.

Characteristic	No. (%)					P value^a
	How the melanoma was detected				Total (N = 2452)
	Patient detected (n = 1148 [47%])	Found incidentally (n = 293 [12%])	Routine skin check (n = 858 [35%])	Other (n = 153 [6%])	Total (N = 2452)
Patient characteristics
Sex
Male	636 (42)	199 (13)	579 (39)	88 (6)	1502 (100)	<.001
Female	512 (54)	94 (10)	279 (29)	65 (7)	950 (100)	<.001
Age, y
<40	163 (76)	6 (3)	41 (19)	4 (2)	214 (100)	<.001
40-49	177 (66)	17 (6)	60 (22)	15 (6)	269 (100)
50-59	213 (46)	50 (11)	166 (36)	33 (7)	462 (100)
60-69	236 (43)	81 (15)	206 (38)	26 (5)	549 (100)
70-79	217 (39)	73 (13)	230 (41)	40 (7)	560 (100)
≥80	142 (36)	66 (17)	155 (39)	35 (9)	398 (100)
Socioeconomic status^b
1st Quintile (lowest)	123 (48)	34 (13)	82 (32)	18 (7)	257 (100)	.37
2nd Quintile	233 (51)	54 (12)	147 (32)	25 (5)	459 (100)
3rd Quintile	296 (47)	84 (13)	218 (34)	35 (6)	633 (100)
4th Quintile	199 (47)	50 (12)	145 (34)	31 (7)	425 (100)
5th Quintile (highest)	297 (44)	71 (10)	266 (39)	44 (6)	678 (100)
Residential remoteness^c
Major cities	490 (47)	120 (11)	367 (35)	68 (7)	1045 (100)	.003
Inner regional	439 (44)	125 (12)	386 (38)	58 (6)	1008 (100)
Outer regional	205 (54)	46 (12)	103 (27)	27 (7)	381 (100)
Remote or very remote	14 (78)	2 (11)	2 (11)	0	18 (100)
Previous melanoma
No	1006 (50)	227 (11)	650 (32)	123 (6)	2006 (100)	<.001
Yes	93 (27)	57 (17)	183 (53)	11 (3)	344 (100)	<.001
Family history of melanoma
No	794 (48)	191 (12)	572 (35)	95 (6)	1652 (100)	.25
Yes	99 (50)	15 (8)	75 (38)	8 (4)	197 (100)	.25
Many moles
No	750 (47)	196 (12)	537 (34)	111 (7)	1594 (100)	<.001
Yes	223 (42)	62 (12)	225 (42)	21 (4)	531 (100)	<.001
Clinical characteristics
Change in color, size, elevation or shape of lesion
No	224 (20)	192 (17)	619 (55)	96 (8)	1131 (100)	<.001
Yes	924 (70)	101 (8)	239 (18)	57 (4)	1321 (100)	<.001
Itch
No	1086 (46)	287 (12)	849 (36)	150 (6)	2372 (100)	<.001
Yes	62 (78)	6 (8)	9 (11)	3 (4)	80 (100)	<.001
Bleeding or ulceration
No	1036 (45)	282 (12)	840 (37)	142 (6)	2300 (100)	<.001
Yes	112 (74)	11 (7)	18 (12)	11 (7)	152 (100)	<.001
Observed lesion for a period before biopsy
No	1059 (47)	278 (12)	792 (35)	145 (6)	2274 (100)	.10
Yes	51 (42)	12 (10)	55 (45)	4 (3)	122 (100)	.10
Clinical melanoma
No	228 (53)	48 (11)	129 (30)	23 (5)	428 (100)	<.001
Suspicious	317 (53)	68 (11)	163 (27)	51 (9)	599 (100)
Yes	572 (42)	176 (13)	555 (40)	74 (5)	1377 (100)
Practice setting
General practice	514 (55)	107 (11)	243 (26)	75 (8)	939 (100)	<.001
Skin cancer clinic	146 (33)	35 (8)	252 (57)	6 (1)	439 (100)
Dermatology	188 (30)	113 (18)	283 (46)	36 (6)	620 (100)
Surgery	300 (66)	38 (8)	80 (18)	36 (8)	454 (100)
Melanoma characteristics
Breslow thickness, mm
In situ	103 (35)	31 (11)	132 (45)	25 (9)	291 (100)	<.001
0.01-0.79	452 (39)	158 (14)	492 (42)	62 (5)	1164 (100)
0.80-1.0	125 (49)	31 (12)	85 (33)	15 (6)	256 (100)
1.01-2.00	210 (56)	44 (12)	95 (26)	23 (6)	372 (100)
2.01-4.00	164 (70)	21 (9)	34 (15)	15 (6)	234 (100)
>4.00	89 (71)	8 (6)	17 (13)	12 (10)	126 (100)
Mean (median)	1.5 (0.8)	1.0 (0.6)	0.7 (0.5)	1.3 (0.6)	1.2 (0.6)
Melanoma subtype (invasive)
Superficial spreading	529 (47)	128 (11)	408 (36)	57 (5)	1122 (100)	<.001
Nodular	208 (70)	24 (8)	43 (14)	23 (8)	298 (100)
Lentigo maligna melanoma	67 (31)	38 (18)	92 (43)	17 (8)	214 (100)
Other^d	64 (51)	16 (13)	34 (27)	11 (9)	125 (100)
Not otherwise specified	177 (44)	56 (14)	149 (37)	20 (5)	402 (100)
Melanoma subtype (in situ)
Superficial spreading	28 (40)	5 (7)	29 (41)	8 (11)	70 (100)	.05
Lentigo maligna	41 (32)	14 (11)	65 (50)	10 (8)	130 (100)
Not otherwise specified, or other^d	34 (37)	12 (13)	38 (42)	7 (8)	91 (100)
Anatomic site
Head or neck	254 (51)	66 (13)	141 (28)	37 (7)	498 (100)	<.001
Anterior trunk (front)	91 (51)	20 (11)	55 (31)	11 (6)	177 (100)
Posterior trunk (back)	264 (34)	100 (13)	356 (46)	58 (7)	778 (100)
Upper limb	276 (51)	66 (12)	171 (32)	28 (5)	541 (100)
Lower limb	255 (58)	40 (9)	128 (29)	18 (4)	441 (100)
Ulceration
No	817 (46)	218 (12)	636 (36)	106 (6)	1777 (100)	<.001
Yes	173 (65)	29 (11)	48 (18)	15 (6)	265 (100)	<.001
Mitotic rate per mm
<1	292 (39)	101 (13)	317 (42)	46 (6)	756 (100)	<.001
1-2	249 (51)	64 (13)	156 (32)	20 (4)	489 (100)
3-10	234 (62)	40 (11)	76 (20)	25 (7)	375 (100)
>10	64 (70)	6 (7)	11 (12)	11 (12)	92 (100)

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^{^a}

Nonreported or missing values are excluded from percentage and P value calculations.

^{^b}

Index of Relative Socioeconomic Disadvantage, based on postcode of residence.

^{^c}

Residential remoteness is based on 2006 Census data matched to patients’ postcode of residence.

^{^d}

Other category includes rarer subtypes.

Among the male patients, 579 of 1502 melanomas (39%) were detected on a routine skin check, compared with 279 of 950 (29%) for female patients (Table 1). Female patients’ melanomas were more likely to be patient detected (512 of 950 [54%] vs 94 [10%] found incidentally, 279 [29%] found on routine skin check, or 65 [7%] found by other means). Younger people were more likely to detect their melanomas than older people (eg, for age <40 years, 163 of 214 patients [76%] self-detected melanoma, whereas for age >80 years, 142 of 398 patients [36%] self-detected melanoma). Patients with many moles (yes, 225 of 531 [42%] vs no, 537 of 1594 [34%]) or a previous melanoma (yes, 183 of 344 [53%] vs no, 650 of 2006 [32%]) were more likely to have their melanoma detected at a routine skin check than those without these risk factors, but family history was not associated with mode of detection. People living in outer regional and remote areas were less likely to have their melanoma detected during a routine skin check (outer regional, 103 of 381 [27%]; remote, 2 of 18 [11%]) and more likely to be patient detected (outer regional, 205 of 381 [54%]; remote, 14 of 18 [78%]) compared with people living in major cities (routine skin check, 367 of 1045 [35%]; patient detected, 490 of 1045 [47%]) and inner regional areas (routine skin check, 386 of 1008 [38%]; patient detected, 439 of 1008 [44%]), but mode of detection did not differ by area-based socioeconomic status.

The same associations were apparent in the multivariable multinomial logistic regression model (Table 2). Compared with patient detection, there was a higher odds of melanoma being detected during a routine skin check for men (women vs men, OR, 0.73; 95% CI, 0.60-0.89; P = .003), those aged 50 years or older (eg, 50-59 years vs <40 years, OR, 2.89; 95% CI, 1.92-4.34; P < .001), those with a previous melanoma (vs none, OR, 2.36; 95% CI, 1.77-3.15; P < .001), those with many moles (vs without, OR, 1.39; 95% CI, 1.10-1.77; P = .02), or living in nonremote areas (eg, remote or very remote vs major cities, OR, 0.23; 95% CI, 0.05-1.04; P = .003). Family history of melanoma and socioeconomic status were not significantly associated with melanoma detection. Similar results were found for incidental detection, although the CIs were wider owing to fewer patients in this subgroup. The results were similar in a sensitivity analysis that excluded predictors with “don’t know” or missing values. There were no significant interactions between patient factors and melanoma detection.

Table 2. Multivariable Analysis of Patient Characteristics Associated With Melanoma Detection on a Routine Skin Check or Found Incidentally, Compared With Patient-Detected Presentation^a.

Patient characteristic	Patient detected (referent group), No.	Found incidentally		Routine skin check		P value^b
Patient characteristic	Patient detected (referent group), No.	No.	Odds ratio (95% CI)^c	No.	Odds ratio (95% CI)^c	P value^b
Sex
Male	636	199	1 [Reference]	579	1 [Reference]	.003
Female	512	94	0.74 (0.55-0.98)	279	0.73 (0.60-0.89)	.003
Age, y
<40	163	6	1 [Reference]	41	1 [Reference]	<.001
40-49	177	17	2.50 (0.96-6.53)	60	1.29 (0.81-2.04)
50-59	213	50	5.74 (2.39-13.80)	166	2.89 (1.92-4.34)
60-69	236	81	8.20 (3.47-19.38)	206	3.26 (2.18-4.88)
70-79	217	73	7.56 (3.18-18.00)	230	3.78 (2.52-5.67)
≥80	142	66	10.33 (4.30-24.8)	155	3.94 (2.57-6.05)
Previous melanoma
No	1006	227	1 [Reference]	650	1 [Reference]	<.001
Yes	93	57	2.06 (1.41-3.03)	183	2.36 (1.77-3.15)
Unknown	49	9	0.68 (0.31-1.47)	25	0.78 (0.46-1.34)
Family history of melanoma
No	794	191	1 [Reference]	572	1 [Reference]	.35
Yes	99	15	0.77 (0.42-1.38)	75	1.13 (0.80-1.59)
Unknown	255	87	1.26 (0.91-1.73)	211	1.02 (0.80-1.29)
Many moles
No	750	196	1 [Reference]	537	1 [Reference]	.02
Yes	223	62	1.17 (0.82-1.65)	225	1.39 (1.10-1.77)
Unknown	175	35	0.80 (0.53-1.22)	96	0.81 (0.61-1.09)
Socioeconomic status^d
1st Quintile (lowest)	123	34	1 [Reference]	82	1 [Reference]	.36
2nd Quintile	233	54	0.80 (0.48-1.32)	147	0.90 (0.62-1.30)
3rd Quintile	296	84	0.93 (0.58-1.49)	218	1.00 (0.70-1.41)
4th Quintile	199	50	0.90 (0.54-1.50)	145	1.02 (0.70-1.49)
5th Quintile (highest)	297	71	0.88 (0.53-1.44)	266	1.32 (0.93-1.89)
Residential remoteness
Major cities	490	120	1 [Reference]	367	1 [Reference]	.003
Inner regional	439	125	1.17 (0.86-1.61)	386	1.34 (1.07-1.67)
Outer regional	205	46	0.88 (0.57-1.37)	103	0.79 (0.57-1.09)
Remote or very remote	14	2	0.57 (0.12-2.66)	2	0.23 (0.05-1.04)

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^{^a}

This analysis was based on 2299 patients, as it excluded 153 patients with “other” recorded for how the melanoma presented.

^{^b}

P value from Wald χ² test for overall association between predictor variable and 3-level outcome, ie, compares the odds ratios across the 3 modes of detection.

^{^c}

From multinomial logistic regression model, adjusted for all other variables in the table.

^{^d}

Index of Relative Socioeconomic Disadvantage, based on postcode of residence.

Clinical Characteristics

A total of 1321 of the 2452 melanomas (54%) were reported to have a change in color, size, elevation or shape, and these were more likely to be patient detected (n = 924 [70%]), whereas melanomas without these changes were more likely to be detected during a routine skin check (619 of 1131 [55%]) (Table 1). Other symptoms included itch (n = 80 [3%]) and bleeding or ulceration (n = 152 [6%]); melanomas with these symptoms were also more likely to be patient detected (itch, 62 of 80 [78%]; bleeding or ulceration, 112 of 152 [74%]). Four hundred twenty-eight melanomas (17%) did not appear clinically suspicious, and this was more common for patient-detected melanomas (228 of 428 [53%]). Only 122 lesions (5%) were observed clinically for a period before biopsy, and this was more likely to occur for melanomas detected during a routine skin check (55 of 122 [45%]).

Melanomas detected in general practice (community-based primary care) were more likely to be patient detected (514 of 939 [55%]), but those detected in dedicated skin cancer clinics (run by general practitioners) and specialist dermatology clinics were more likely to be detected on a routine skin check (252 of 439 [57%] and 283 of 620 [46%], respectively). Of the 858 melanomas detected on a routine skin check, 243 (28%) were in general practice settings, 252 (29%) in skin cancer clinics, 283 (33%) in dermatology clinics, and 80 (9%) in surgical settings.

Melanoma Characteristics

In situ and thin invasive melanomas were more frequently detected at routine skin checks (in situ, 132 of 291 [45%]; thin invasive 0.01-0.79 mm, 492 of 1164 [42%]), than thicker melanomas (>4.00 mm, 17 of 126 [13%]) (Table 1). Routine skin checks detected a higher proportion of all lentigo maligna melanomas (92 of 214 [43%]) than nodular melanomas (43 of 298 [14%]). A total of 208 of 298 nodular melanomas (70%) were patient detected. Melanomas detected during a routine skin check were more frequently located on the back (356 of 858 [41%]) and more likely to have good prognostic features (nonulcerated, 636 of 858 [74%]; low mitotic rate <1 per mm², 317 of 858 [37%]).

Associations Between Mode of Melanoma Detection and Melanoma-Specific and All-Cause Mortality

Over a mean (SD) follow-up of 11.9 (0.3) years, there were 162 deaths (7%) from melanoma and 604 deaths (26%) from other causes from the 2299 patients who were included in the survival analysis. Detection of invasive melanoma at a routine skin check was associated with 59% lower melanoma-specific mortality (subhazard ratio, 0.41; 95% CI, 0.28-0.60) and 36% lower all-cause mortality (hazard ratio, 0.64; 95% CI, 0.54-0.76) compared with patient-detected melanomas in regression models adjusted for age and sex (Table 3 and Figure). The results were similar when the analyses were stratified by patient characteristics. Incidentally found melanomas were associated with lower melanoma-specific mortality (subhazard ratio, 0.49; 95% CI, 0.29-0.84) and lower all-cause mortality (hazard ratio, 0.83; 95% CI, 0.67-1.03) in the minimally adjusted models (Table 3).

Table 3. Hazard Ratios for All-Cause Mortality and Subhazard Ratios for Melanoma Mortality Associated With Different Modes of Melanoma Detection.

Melanoma detection^a	Alive	Died from melanoma	Died from other causes	Melanoma-specific mortality, subhazard ratio (95% CI)^b			All-cause mortality, hazard ratio (95% CI)^c
Melanoma detection^a	Alive	Died from melanoma	Died from other causes	Minimally adjusted^d	Multivariable adjusted 1^e	Multivariable adjusted 2^f	Minimally adjusted^d	Multivariable adjusted 1^e	Multivariable adjusted 2^f
Invasive melanoma only (excludes patients with in situ primary melanoma only)^g
Patient detected	707	103	235	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Found incidentally	152	16	94	0.49 (0.29-0.84)	0.58 (0.34-1.00)	0.70 (0.41-1.22)	0.83 (0.67-1.03)	0.88 (0.71-1.09)	0.94 (0.76-1.16)
Routine skin check	496	36	194	0.41 (0.28-0.60)	0.49 (0.32-0.74)	0.68 (0.44-1.03)	0.64 (0.54-0.76)	0.69 (0.57-0.82)	0.75 (0.63-0.90)
P value	NA	NA	NA	<.001	.001	.13	<.001	<.001	.006
All patients (with either invasive or in situ primary melanoma)^h
Patient detected	778	103	267	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Found incidentally	171	17	105	0.53 (0.31-0.89)	0.62 (0.36-1.05)	0.74 (0.44-1.27)	0.83 (0.68-1.01)	0.88 (0.72-1.08)	0.94 (0.77-1.15)
Routine skin check	584	42	232	0.45 (0.32-0.65)	0.55 (0.37-0.82)	0.76 (0.51-1.14)	0.65 (0.55-0.75)	0.70 (0.59-0.82)	0.76 (0.64-0.90)
P value	NA	NA	NA	.01	.007	.30	<.001	<.001	.02
Single or thickest invasive melanoma (excludes patients with in situ melanoma only or with another thicker invasive melanoma diagnosed before or after 2006-2007 study)ⁱ
Patient detected	682	97	220	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Found incidentally	134	12	85	0.42 (0.23-0.78)	0.53 (0.28-0.99)	0.67 (0.36-1.24)	0.83 (0.67-1.04)	0.88 (0.70-1.10)	0.94 (0.75-1.18)
Routine skin check	439	25	175	0.33 (0.21-0.52)	0.43 (0.26-0.69)	0.62 (0.38-1.02)	0.64 (0.54-0.76)	0.68 (0.56-0.82)	0.75 (0.62-0.91)
P value	NA	NA	NA	<.001	.001	.11	<.001	<.001	.01
Single invasive melanoma only (excludes patients with in situ melanoma only or with another invasive melanoma of any thickness diagnosed before or after 2006-2007 study)^j
Patient detected	652	92	205	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Found incidentally	123	11	72	0.43 (0.23-0.82)	0.56 (0.29-1.07)	0.75 (0.39-1.44)	0.81 (0.64-1.03)	0.85 (0.67-1.08)	0.94 (0.74-1.20)
Routine skin check	393	22	160	0.32 (0.20-0.51)	0.41 (0.25-0.67)	0.62 (0.37-1.03)	0.66 (0.55-0.79)	0.67 (0.56-0.82)	0.76 (0.62-0.92)
P value	NA	NA	NA	<.001	.001	.16	<.001	<.001	.02

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Abbreviation: NA, not applicable.

^{^a}

Excludes patients with “other” recorded for how the melanoma was detected.

^{^b}

Subhazard ratios for melanoma-specific mortality are estimated from competing risk regression models with death from other causes as the competing risk.

^{^c}

Hazard ratios for all-cause mortality are estimated from Cox regression models.

^{^d}

Adjusted for age (continuous) and sex.

^{^e}

Adjusted for age (continuous), sex, previous melanoma, family history, moles, socioeconomic status, residential remoteness, anatomic site, and melanoma subtype.

^{^f}

Additionally adjusted for ulceration and mitotic rate.

^{^g}

Analysis based on 2033 patients.

^{^h}

Analysis based on 2299 patients.

^ⁱ

Analysis based on 1869 patients.

^{^j}

Analysis based on 1730 patients.

Figure. — Subhazard ratios for melanoma-specific mortality (A) and hazard ratios for all-cause mortality (B). Minimally adjusted analyses were adjusted for age (continuous) and sex. Multivariable-adjusted model 1 was further adjusted for previous melanoma, family history, moles, socioeconomic status, residential remoteness, anatomic site, and melanoma subtype; and multivariable-adjusted model 2 was adjusted for these variables plus ulceration and mitotic rate.

The melanoma-specific and all-cause mortality associations with routine skin check were slightly attenuated but remained statistically significant after adjusting for patient, sociodemographic, and some clinicopathologic prognostic factors (multivariable-adjusted model 1). After further adjustment for mitotic rate and ulceration, the association was no longer statistically significant for melanoma-specific mortality (subhazard ratio, 0.68; 95% CI, 0.44-1.03) but remained statistically significant for all-cause mortality (hazard ratio, 0.75; 95% CI, 0.63-0.90) (multivariable-adjusted model 2). Adjustment for mitotic rate led to the largest change to the hazard ratios, followed by melanoma subtype and ulceration.

The subhazard ratios for melanoma-specific mortality were higher when the analyses also included patients with in situ melanoma and were lower after excluding patients who developed another invasive melanoma either before or after the study period (Table 3). When the multivariable-adjusted mortality analyses were stratified by Breslow thickness categories, the subhazard ratios for melanoma-specific mortality and routine skin-check detection appeared greater for lower Breslow thickness (subhazard ratio, 0.37; 95% CI, 0.15-0.91 for thickness 0.01-0.79 mm; 0.88; 95% CI, 0.41-1.93 for 0.8-2.0 mm; 1.56; 95% CI, 0.61-3.96 for 2.01-4.0 mm; and 0.97; 95% CI, 0.27-3.46 for thickness >4.0 mm), but the association was not significant.

Discussion

In this population-based prospective cohort study with a mean 11.9 years follow-up, we found that melanomas discovered in asymptomatic individuals through routine skin checks were thinner, less likely to have been noted to exhibit recent change, and associated with lower all-cause mortality than thicker lesions discovered initially by patients. Melanoma-specific mortality was 59% lower for routine skin-check detection of invasive melanoma in minimally adjusted models, and this was attenuated to a nonsignificant 32% lower (with CIs that slightly overlapped with 1) after multivariable adjustment including for mitotic rate and ulceration, suggesting that the association was partly explained by patient, sociodemographic, and clinicopathologic prognostic factors associated with routine skin-check detection. The fact that routine skin-check melanoma detection was significantly associated with lower all-cause mortality but not melanoma-specific mortality in the fully adjusted analyses may reflect residual or unmeasured confounding from sociodemographic characteristics (such as education level), medical access, and health-seeking behaviors (such as physical activity) that are independently associated with both routine skin checks and overall mortality.²⁸ Organized cancer screening programs for breast, bowel, and lung cancer have been shown to reduce cancer-specific mortality by approximately 20% to 30%,^29,30,31 but there is limited evidence of an association with all-cause mortality owing to sample size constraints, possible harms, and length of follow-up.^32,33 In addition, the lower number of melanoma deaths than overall deaths would have contributed to wider CIs for melanoma-specific mortality.

Approximately half (47%) of melanomas were physician detected, either by a routine skin check (35%) or incidentally (12%), compared with 47% patient detected and 6% “other.” In previous Australian³⁴ and international^35,36,37 studies, 16% to 27% of melanomas were physician detected, 27% to 44% were patient detected, and 15% to 31% were detected by a partner or family member. We did not ask separately about partner or family member detection in our study questionnaire. Physician-detected melanomas were more likely to be thinner, which is consistent with previous studies.^7,34,35

Slower-growing melanomas (low mitotic rate and lentigo subtype) were more likely to be detected at a routine skin check and be thinner at diagnosis, whereas faster-growing melanomas (high mitotic rate and nodular subtype) were more likely to be patient detected and thicker. This phenomenon (a type of length-time bias) is a common limitation of cancer screening programs³⁸ and increases concerns about overdiagnosis; it also helps to explain the observed lack of screening-associated reduction in population melanoma mortality rates.^{11,12,39,40,41} A rapid patient-driven diagnosis (eg, mobile phone referral) may be a useful adjunct to a melanoma screening program.

Some studies have shown that population-based skin cancer screening programs would not be cost-effective for the health system,^13,42,43 whereas other studies suggest that screening may be cost-effective as a single intervention⁴⁴ or for high-risk groups.^14,45 One US study⁴⁶ of a large-scale melanoma screening program found increased melanoma diagnoses but little impact on subsequent skin surgeries or dermatology visits. The cost-effectiveness of a population melanoma screening program should be reassessed in light of (1) the availability of new and effective, but expensive, therapies for advanced and high-risk melanomas, (2) new diagnostic technologies including artificial intelligence algorithms, and (3) the ability to tailor screening to high-risk groups.^{2,10,16,17,20,45,47}

In the organized skin cancer screening program in Schleswig-Holstein, Germany, the participation rate was only 19%.⁴⁸ In the absence of a structured national melanoma screening program, the prevalence of clinical skin checks conducted opportunistically in Australia is relatively high, with a 2016 to 2017 national survey finding that 37% of Australians aged 45 to 69 years reported a whole-body skin check in the previous 12 months.⁴⁹ This might be due to Australia’s high melanoma incidence rates and widespread skin cancer prevention and awareness campaigns.^50,51,52 We observed a higher odds of melanoma being detected at a routine skin check among men, those with a previous melanoma, those with many moles, patients aged 50 years or older, or those living in nonremote areas. The national survey found similarly higher prevalence of whole-body skin checks in these subgroups, except that skin checks were slightly more prevalent among women.⁴⁹ A Spanish study found that more melanomas were self-detected among women but that men had more melanomas on locations that were not easily visible (such as the back) than women.³⁵ Worse outcomes for people living in rural areas have been noted previously⁵³ and may reflect barriers to health service access, such as distance from medical services, higher out-of-pocket costs, and fewer dermatology services.⁵⁴ Although training and confidence in melanoma detection and management varies by clinical practice setting,⁵⁵ many melanomas in Australia are detected in primary care (56% in this study); therefore, awareness of typical and atypical melanoma features⁵⁶ is essential for both specialist and generalist physicians as well as the community.

Strengths and Limitations

Although it is known from other studies that melanomas identified on routine skin checks tend to be thinner and result in a better prognosis, the novel aspects of our study are the prospective population-based cohort design, large sample size, and more than 10 years of linked follow-up data that included other primary melanomas and melanoma-specific and all-cause mortality outcomes. The prevalence of risk factors such as many moles, family history, and previous melanoma in our study population of patients with melanoma are similar to other Australian studies^57,58,59 and reflect Australia’s high skin cancer incidence.^3,60 Another strength is the comprehensive collection of patient, clinical, and histopathologic information enabling adjustment for important confounders. Compared with single-site studies, population-based studies are more generalizable and less subject to referral bias.⁶¹

Our study had some limitations. First, our results may be less generalizable to countries with lower skin cancer incidence. Second, because of the observational design of our study, causality could not be confirmed. Further, for people who developed multiple primary melanomas during the follow-up period, we did not have information on how the other melanomas were detected. We could not distinguish whether any patient-detected or incidentally detected melanomas were interval cancers in patients undergoing routine skin checks. Follow-up data for some patients may not have been captured if they moved interstate or overseas.^62,63 Because several patient characteristics were associated with mode of detection, and because other factors were associated with each other (such as socioeconomic status with residential remoteness and mitotic rate with Breslow thickness), it is possible that the multivariable models were overadjusted, leading to more conservative estimates of effect and wider CIs.

Conclusions

In this population-based cohort study, we found that melanomas diagnosed through routine skin checks were associated with significantly lower all-cause mortality, but not melanoma-specific mortality, after adjustment for patient, sociodemographic, and clinicopathologic factors. A large randomized clinical trial is needed to provide definitive evidence that screening for skin cancer reduces melanoma-specific and all-cause mortality among people invited (vs not invited) to screen,³⁸ but there are concerns about feasibility.^15,64 Our findings could be used to estimate the sample size for a future trial.

Supplement.

eFigure 1. Flowchart Showing Creation of the Analysis Data Set

eFigure 2. Directed Acyclic Graph (DAG) Showing Relationships Between Variables and Assessment of Confounding

eReferences

Click here for additional data file.^{(214.7KB, pdf)}

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eFigure 1. Flowchart Showing Creation of the Analysis Data Set

eFigure 2. Directed Acyclic Graph (DAG) Showing Relationships Between Variables and Assessment of Confounding

eReferences

Click here for additional data file.^{(214.7KB, pdf)}

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PERMALINK

Association Between Melanoma Detected During Routine Skin Checks and Mortality

Caroline G Watts, PhD

Kirstie McLoughlin, DPhil

Chris Goumas, MPH

Cathelijne H van Kemenade, MPH

Joanne F Aitken, PhD

H Peter Soyer, MD

Pablo Fernandez Peñas, MD

Pascale Guitera, MD, PhD

Richard A Scolyer, MD

Rachael L Morton, PhD

Scott W Menzies, MBBS, PhD

Michael Caruana, DPhil

Yoon Jung Kang, PhD

Graham J Mann, MBBS, PhD

Annette H Chakera, MD, PhD

Christine M Madronio, MPhil, MPH

Bruce K Armstrong, MBBS, PhD

John F Thompson, MD

Anne E Cust, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Study Sample

Data Collection

Statistical Analysis

Results

Patient Characteristics

Table 1. Detection of Melanomas by Demographic, Clinical, and Melanoma Characteristics.

Table 2. Multivariable Analysis of Patient Characteristics Associated With Melanoma Detection on a Routine Skin Check or Found Incidentally, Compared With Patient-Detected Presentationa.

Clinical Characteristics

Melanoma Characteristics

Associations Between Mode of Melanoma Detection and Melanoma-Specific and All-Cause Mortality

Table 3. Hazard Ratios for All-Cause Mortality and Subhazard Ratios for Melanoma Mortality Associated With Different Modes of Melanoma Detection.

Figure. Subhazard and Hazard Ratios for Mortality Associated With Different Modes of Detection of Invasive Melanoma.

Discussion

Strengths and Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 2. Multivariable Analysis of Patient Characteristics Associated With Melanoma Detection on a Routine Skin Check or Found Incidentally, Compared With Patient-Detected Presentation^a.