Intended for healthcare professionals

General Practice

Self screening for risk of melanoma: validity of self mole counting by patients in a single general practice

BMJ 1995; 310 doi: (Published 08 April 1995) Cite this as: BMJ 1995;310:912
  1. Paul Little, research fellowa,
  2. Martin Keefe, senior lecturerb,
  3. John White, consultant dermatologistb
  1. a Primary Care, Faculty of Medicine, University of Southampton, Aldermoor Health Centre, Southampton SO16 5ST
  2. b Department of Dermatology, Southampton University, Southampton
  1. Health Centre, Thame, Oxfordshire OX9 3JZ Duncan Keeley, general practitioner. Correspondence to: Dr Little.
  • Accepted 27 January 1995


Objective: To validate self screening by patients of high mole counts, assess the within family association of sun protection behaviour and mole counts, and estimate prevalence of risk factors for melanoma.

Setting and subjects: Systematic sample of families from a single affluent general practice population in Wessex.

Design: Subjects completed a questionnaire about risk factors for melanoma and counted their moles. Subsequently a mole count was done by a general practitioner trained at dermatology clinics.

Main outcome measures: Validation of self counts by observer's count. Within family association of sun protection behaviour and mole counts; self reported risk factors.

Results: 199/237 subjects (84%) returned the questionnaire; 212/237 (89%) were examined. High counts by patients on the front of the trunk (>7 moles of >/=2 mm) were reasonably sensitive (79%), predictive (75%), and specific (97%) of the observer's mole counts ((kappa)=0.74), unlike arm or total body counts. Sun protection behaviour correlated between individuals and other family members (Spearman's coefficient r=0.50, P<0.01). In the past three months 15/114 adults (13.2%, 95% confidence interval 7.0% to 19.4%) reported any change in a mole and 6/114 (5.3%, 2.0% to 11.1%) “major” changes; 6/109 adults (5.5%, 2.1% to 11.6%) had both high mole counts and freckling.

Conclusions: Asking patients to count trunk moles could be a feasible way of identifyng patients at high risk of melanoma. Concentrating on reported major changes in moles should avoid considerable workload in general practice. The generalisability of these findings and the adverse effects, net benefit in earlier diagnosis and prevention, and workload implications of such self screening need further research.

Key messages

  • Key messages

  • Simple self screening of trunk moles may be feasible to identify high risk individuals

  • Sun protection behaviour is associated within families

  • Advice about changes in moles should emphasise the need to report only major changes (size, shape, colour), otherwise the assessment workload may increase substantially


Malignant melanoma has a fairly low average annual incidence (about 1 in 10000)1 and has a poor prognosis if identified late.2 Identifying individuals at high risk of melanoma in a primary care setting, in addition to population approaches, is a potentially important part of the strategy to reduce mortality from melanoma.3

The number of benign moles is one of the best simple indicators of risk of melanoma, associated with high relative risks and population attributable risk.3 4 5 6 7 8 9 10 11 A case-control study in Australia used data from one half of the cases to try to predict melanoma in the other half: the number of benign moles was the best predictor of risk, and prediction was not greatly improved by the addition of other risk factors.7 Site specific counts of moles (on limb or trunk) are also associated with high risk of melanoma,4 5 6 7 8 9 10 11 and mole count at one site of the body correlates with the total mole count.6 12 13 The presence of atypical moles or the atypical mole syndrome may further increase risk of melanoma.14 15 16

Is it feasible to identify individuals with many moles (therefore at high risk of melanoma) in general practice? Although total body mole counts are likely to be too time consuming in primary care, site specific counts might be feasible to identify high risk individuals. Site specific self counts of moles by nurses predict risk of melanoma6 and self reported mole counts of “more than average (20)” provide as good an indicator of risk of melanoma as mole counts by trained interviewers on limbs.17 Interviewers' counts of raised moles on one arm, however, do not correlate well with dermatologists' counts.12 These observations have been documented in few studies, none in a British primary care setting, and no evidence exists for the comparative accuracy of untrained mole counting for different body sites.

Other risk factors for melanoma are freckling, tendency to burn, hair colour, probably sunburn in childhood, and possibly sunburn in adulthood and regular sunbathing or beach holiday.10 11 16 18 19 20 21 22 23 24 25 26 The case-control studies in Britain, however, used hospital controls. How common each of these risk factors is in British general practice lists is not known.

What kinds of practice should have greater priority for screening for risk of melanoma? Estimation of risk is especially relevant for young families with children and in higher social classes. Sunburn in childhood may be particularly important,7 8 18 22 24 27 so it is probably vital to identify children who are at additional risk because they have many moles, which are likely to increase in number with age.28 29 Higher social classes have more than twice the risk of melanoma,8 18 probably partly because of sunburn.18

We therefore chose a general practice population that was relatively affluent and young for a validation and prevalence study to estimate the sensitivity, specificity, and positive predictive value of patients' mole counts; to estimate the prevalence of reported changes in moles and of known risk factors for melanoma in an affluent general practice population; and to assess the extent to which current skin protection behaviour and mole counts are associated in families.



In this collaborative study the general practitioner (PL) was trained to count moles for six months at twice weekly dermatology clinics. To check the validity of the training, in 32 subjects at a hospital clinic the right arm and trunk moles (>/=2 mm) were counted by both the general practitioner and the dermatologist (MK) in alternate order and blind to each other's count: the rank correlation was 0.92. The subjects then completed a questionnaire related to risk factors for melanoma (the same questionnaire to be used in the main study).

The reliability of the questionnaire was assessed in 28 of the subjects after one to two weeks: there was perfect agreement for subjects reporting any change in a mole or a major change, and for the reported risk factors (see table I) rank correlations were greater than 0.80 (except childhood beach holidays, r=0.71).


Prevalence of risk factors for melanoma in study population. Values are numbers (percentage; 95% confidence interval) unless stated otherwise

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To estimate the prevalence of risk factors at a 5% (95% confidence interval 1% to 9%), 10% (5% to 15%), and 20% (14% to 26%) level and for the 95% confidence intervals to exclude 50% for 75% sensitivity and 75% positive predictive value for patient versus observer mole counts (for the top 10% of the population), we estimated that a minimum of 200 subjects would be needed (CIA, BMJ Publishing Group).


The subjects all came from the list of a single general practice, which possibly contributed to the high compliance for such an intensive study. A systematic sample ensured uniform coverage of the practice list with relatively few samples: every 125th patient registered with the practice was chosen to generate a minimum of 60 individuals (67 individuals in all). All members of the same family and address who were registered at the same practice were identified, yielding 67 family groups. An introductory letter explaining the rationale of the project was sent to the household, and a questionnaire enclosed for each member of the family to complete. The questionnaire related to risk factors for melanoma, and included questions about “any” change or “major” change in moles (changes in size, shape, or colour). The questionnaire gave the following simplified descriptions: “A mole is a skin mark (usually dark brown) which appears before the age of 35 and does not appear or change colour in the sun. Moles can be either flat (not raised above the skin surface) or raised (felt above the skin surface). (NB A freckle is a light brown mark with an irregular edge that appears or goes darker in the sun. A seborrhoeic wart is a darker coloured skin mark which appears after the age of 35, has small pits on the surface, and appears ‘stuck on’).”

The questionnaire also included instructions to count moles of different sizes (<2 mm, >/=2 mm, >/=4 mm; filled in circles showed sizes) on two sites: (a) the right arm (up to a verticle line up from the armpit) and (b) front of trunk (from the bottom of the neck to the groin), and a request to perform a total body count of moles 2 mm or larger. The count was performed in mid-winter when freckling would be expected to be low. Children under the age of 12 had their moles counted by a parent. Families were contacted after a few days by the observer (PL) to arrange to count the subjects' moles (all sizes, all parts of the body) at home. The observer was blind to the patients' self counts.


Data were entered and analysed with the SPSS package. Median values, 90th and 95th centile, and Spearman's correlation coefficients were used to avoid assumptions of normality for distributions of mole counts (which are skewed) and other artificial scales—for example, sunscreen use during leisure time. Confidence intervals were calculated with CIA. The Bland-Altman plot30 was used to assess the mean difference between observer and patient measurements and the relation between the difference and measurement level.


Of the initial 237 subjects (from 67 families) initially sent a questionnaire, 219 (92%) took part. In all, 199 (84%) returned the questionnaire, and 212 (89%) agreed to be examined. The study population (113 male, 106 female) had a preponderance of social classes I and II (50.2% compared with 25% nationally) and of subjects under the age of 16 (43% compared with 20% nationally).


Tables I-III summarise the main results. Table I uses a pragmatic definition of the phenotype of the atypical mole syndrome and atypical moles according to previous descriptions.16 Table III uses a cut off of the top 10% of the whole study population, equivalent to the top 15% for the adult group (over 15 years) alone, for pragmatic reasons: the workload implications for self screening are not yet clear, and to counsel more than 10%-15% of the population would become impracticable. The lower correlations for moles of >/=4 mm and the head and neck for all sizes (table II) probably reflect lower numbers of moles.


Individual body site mole count by observer and correlation of site count with total body count. Figures are median values (90th, 95th centile; Spearman's correlation coefficient)

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Patients' self note counts compared with observer's mole counts. Values in parentheses are 95% confidence intervals unless stated otherwise

View this table:


In the adult group six out of 114 subjects (5.3%; 95% confidence interval 2.0 to 11.1%) reported major changes and 15 subjects (13.2%; 7.0 to 19.4%) reported any change in moles in the past three months. Changes in moles in children are more difficult to interpret because of the normal changes associated with the development of moles.


Patients' mole counts for the right arm and the front of the trunk correlated reasonably well with the observer's counts for the same sites (table III). For mole counts of the arm the patient-observer correlation was higher for the individuals with fewer than average freckles (r=0.72) than for the individuals with average or more than average freckles (r=0.56), whereas for trunk counts increased freckling does not reduce the correlation (r=0.76) for average or more than average freckles). For social classes III-V Spearman's rank correlations for self counts compared with the observer's counts for arm and trunk moles of >/=2 mm were 0.63 and 0.67 respectively—that is, comparable to the whole group. With the exception of lower sensitivity, sensitivity, specificity, positive predictive value, and (kappa) in social classes III-V are comparable to those in the whole group for counts of trunk moles of >/=2 mm (56%, 97%, 71%, 0.59 respectively) and trunk moles of >/=4 mm (58%, 100%, 100%, 0.71 respectively). Compared with the whole group the adult group alone (n=107) had better figures for sensitivity, specificity, predictive value, and (kappa) for trunk moles of >/=2 mm (81%, 97%, 81%, 0.78 respectively). If the cut off is reduced for patient and observer—for example, to the 50th centile—the validation characteristics of arm counts of moles of >/=2 mm improve (sensitivity, specificity, and predictive value were all greater than 70% for counts of both arm and trunk moles).


When individuals' mole counts and their family members' counts were corrected for age (by dividing the value by the mean for that five year age band) the subjects' mole counts showed little correlation with the mean for other family members (r=0.11, P=0.06). The correlation between an individual's and other family members' mole counts was still low if the predicted value for age from regression equations was used to correct for age (r=0.20, P<0.01). Such low correlations may partly reflect variability in the timing of development of moles in young children or the difficulty of adequately modelling the effect of age, or both these factors.

Sun protection behaviour in midday summer sun (on a scale of 1 (always use sunscreen) to 6 (never use sunscreen)) correlated significantly with the mean or median value for other family members (r=50, r=0.44 respectively, P<0.002). For individuals who used a sunscreen “less than half the time” in midday summer sun most (70/105 (67%, 95% confidence interval 58% to 76%)) had families whose median sunscreen use was also less than half the time. If only individuals who “usually” or “always” burnt are considered then the correlation between individual and family use of a sunscreen was even stronger (r=0.54 and r=0.60 for median and mean family values respectively), and subjects who use a sunscreen “less than half the time” in midday summer sun were even more likely to have a family whose median use was also less than half the time.


This study concerns assessment of risk factors for melanoma in a relatively young and affluent British general practice population. The comparability with other studies of the adult median mole count and correlations between site counts and total count6 12 28 29 31 32 and the comparability of the validation data for the lower social classes and for adults alone with the data for the whole group suggest that it may be possible to extrapolate these findings to other practices and settings. To demonstrate the generalisability of these findings and for tighter confidence intervals further studies are needed.

Risk factors for melanoma include high mole counts, freckling, high social class, fair or red hair, tendency to burn, frequent severe sunburn, and probably atypical moles.4 5 6 7 8 9 10 11 18 19 21 22 23 24 25 26 33 On the basis of the nearest categories to a British study that demonstrated freckling, high mole count, and high social class as independent risk factors,8 6/109 (5.5%) adults in the present study have both many moles and freckles, and 28/109 (25.7%) have either factor. Individuals with both factors (odds ratio 21, 25/187 of the cases8) or either factor (120/187 of the cases8) had a conservatively estimated 0.5%-1% and 0.1%-0.2% 10 year chance respectively of developing melanoma.8 These estimates are probably increased twofold to threefold by either high social class or severe sunburn,8 18 a further twofold to threefold increase if the population incidence is 1-2 per 10000 per year,1 2 3 4 5 6 7 8 and a further 10-fold increase with less conservative British estimates of risk associated with moles.4 We need further research to see how much reinforcement by health professionals (if any) is needed to change the behaviour of high risk individuals after self screening, to assess if screening results in earlier diagnosis and reduced mortality, and to document costs such as increased workload and patient anxiety.34

Individuals commonly report changes in moles: 13% of the adult study population had reported some change in a mole during the previous three months and 5% a major change. If patients are asked to attend the surgery if they see changes in moles doctors should be aware of the workload implications and perhaps suggest that major changes only should be reported.

The validity of mole counts by untrained patients on different body sites has been documented in this study. High mole counts by patients on the front of the trunk (but not the arm) were reasonably sensitive and predictive of the observer's mole counts, which suggests that high risk individuals could assess their own melanoma risk. The difference between arm and trunk counts at high values may relate to the difficulty of seeing moles on the back of the arm and to freckling. At lower cut offs—for example, the 50th centile—the validation characteristics for arm mole counts improve. Further research is needed to confirm these observations in different settings and to assess possible costs and benefits of self screening. We also need to assess other screening methods—for example, arm mole counts when measuring blood pressure.

The within family correlation of sunscreen use during leisure time indicates that sun protection behaviour is associated within families. For individuals who do not protect their skin against the sun, it would be worth while emphasising that other family members should also be careful about sun protection.

The prevalence of the phenotype of atypical mole syndrome and atypical moles in this study is comparable to that in other populations.16 35 Clinical definitions of atypical moles or the atypical mole syndrome are limited by the subjective nature and number of features defining the term atypical, the number of atypical moles and other features defining the atypical mole syndrome, and changing criteria—for example, since the study began.16 36 Furthermore, most studies have not estimated the risk associated with atypical moles or the atypical mole syndrome. Before atypical moles or the associated syndrome can be useful in primary care screening, the issues of definition, risk, and training requirements need clarifying.

In conclusion, individuals with multiple risk factors for melanoma or who report changes in moles are likely to be common in practices with affluent patients, and sun protection behaviour is associated within families. Self screening with trunk mole counts may help to identify high risk individuals. Further research is needed to confirm the validity, adverse effects, and net benefits of screening for risk factors for melanoma in primary care.

We are most grateful to Professors David Mant and Ann-Louise Kinmonth for many comments on the manuscript, Professor Rona Mackie for encouragement and discussion of the project, Julia Newton for initial correspondence, to Clive Osmond for statistical advice, the staff and patients of Nightingale Surgery, Romsey, and the Royal Southants Hospital for its help and support. This project was supported by a small grant from the Wessex Dermatology Research Fund.


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