Abstract
Background: The overall incidence of melanoma has increased steadily for several years. The relative change in incidence at different ages has not been fully described. Objective: To describe how incidence at different ages has changed over time and to consider what aspects of tumour biology may explain the observed pattern of change in incidence. Methods: The slope of incidence vs age measures the acceleration of cancer incidence with age. We described the pattern of change over time in the overall incidence of melanoma, as well as in acceleration. We used data for males and females from 3 different countries in the 17 sequential 5-year birth-cohort categories from 1895-99 to 1975-79, from which we derived the incidence patterns. Results: Over time, there has been a tendency for the overall incidence of melanoma to increase and for the acceleration (slope) of the age-incidence curves to decline. The changing patterns of melanoma incidence and acceleration differ between males and females and between the countries analysed. Conclusions: The observed pattern in melanoma of rising incidence and declining acceleration occurs in other cancers in response to genetic knockouts of mechanisms that protect against cancer. Perhaps some protective mechanism with respect to melanoma may be less effective now than in the past, possibly because of more intense environmental challenges.
Keywords: melanoma epidemiology, age-period-cohort effects, sun exposure, age-specific incidence
Introduction
The incidence of malignant melanoma has increased steadily over the past 50 years in predominately fair-skinned populations 1. The trends in incidence probably reflect changing prevalence of risk factors such as increased leisure time in sunny destinations, changing fashion and sunbed use, coupled with increased surveillance, early detection and changes in diagnostic criteria 2, 3.
The purpose of this paper is to study the particular ways in which incidence has changed over time. By analysing the 17 sequential 5-year birth cohorts from 1895–99 to 1975–79, we show that incidence has indeed increased steadily over time. Our analysis also shows that the particular patterns of increase in incidence differ between males and females and between different countries.
In addition to the overall increase in incidence, the relationship between age and incidence has also changed over time. We show that more recent cohorts typically have a disproportionate increase in cases at earlier ages.
To quantify the age-incidence relationship and its change over time, we study the rate of change of melanoma incidence with age 4– 6, which is the acceleration of cancer 7. The patterns of acceleration provide interesting information about the forces acting on cancer progression at different ages 8.
Methods
Age-specific incidence data on malignant melanoma (ICD-10; C43) for males and females were obtained for Great Britain 9– 11 for the period 1975–2014, the USA 12 for the period 1975–2013 and Australia 13 for the period 1982–2012. Incidence data for the USA relate to white people only.
Because the incidence of melanoma is increasing over time, age-specific rates are heavily influenced by the year of birth. To allow for this effect, we separated the 17 sequential 5-year birth-cohort categories from 1895–99 to 1975–79. For each cohort, we computed the 5-year average age-specific incidences for males and females aged 25 years and over.
The analyses were done with Microsoft Excel 2003.
Results
Table 1 shows the age-specific incidence for British males born during different time periods. The risk of malignant melanoma within each cohort rises consistently throughout life, as is true for most other cancers 8. Figure 1 shows the age-incidence curves for both genders from Great Britain, the USA, and Australia for successive birth cohorts from 1895–99 to 1975–79.
Figure 1. The age-specific incidence of melanoma in different time periods and different countries.
The plots show the incidence for males (left) and females (right) in Great Britain (top row), USA (middle row), and Australia (bottom row) for the birth cohorts shown in the top legend. The plots do not show the intermediate decadal cohorts because of visual limitations in plotting the data. The plots are based on the summary given in Dataset 1, derived from the data and analyses in Dataset 2– Dataset 5.
Table 1. The age-specific incidence per 100,000 man-years of malignant melanoma in British males averaged in 5-year intervals.
| Age Specific Rate (5-y average) in Birth Cohort | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age
Band |
1895–99 | 1900–04 | 1905–09 | 1910–14 | 1915–19 | 1920–24 | 1925–29 | 1930–34 | 1935–39 | 1940–44 | 1945–49 | 1950–54 | 1955–59 | 1960–64 | 1965–69 | 1970–74 | 1975–79 | |
| 25–29 | 1.5 | 2.0 | 2.9 | 3.4 | 4.1 | 4.7 | ||||||||||||
| 30–34 | 2.2 | 2.9 | 4.0 | 4.7 | 5.7 | 6.8 | 7.4 | |||||||||||
| 35–39 | 3.2 | 4.2 | 5.4 | 6.1 | 7.2 | 9.5 | 10.5 | 10.1 | ||||||||||
| 40–44 | 3.7 | 5.2 | 7.4 | 7.7 | 9.4 | 12.4 | 14.4 | 14.2 | ||||||||||
| 45–49 | 4.2 | 5.9 | 8.5 | 10.2 | 11.8 | 14.7 | 17.0 | 20.0 | ||||||||||
| 50–54 | 4.8 | 7.0 | 9.9 | 12.5 | 14.9 | 18.1 | 22.0 | 24.2 | ||||||||||
| 55–59 | 5.0 | 7.4 | 11.4 | 15.4 | 17.9 | 24.3 | 28.7 | 30.8 | ||||||||||
| 60–64 | 6.0 | 8.9 | 13.2 | 16.9 | 22.6 | 31.3 | 41.1 | 44.1 | ||||||||||
| 65–69 | 6.5 | 9.7 | 14.2 | 19.5 | 26.1 | 36.8 | 50.7 | 57.1 | ||||||||||
| 70–74 | 7.3 | 10.4 | 15.4 | 20.9 | 28.8 | 44.3 | 63.6 | 73.8 | ||||||||||
| 75–79 | 8.9 | 11.9 | 18.5 | 24.1 | 33.6 | 47.5 | 71.4 | 90.8 | ||||||||||
| 80–84 | 9.5 | 13.5 | 19.4 | 29.1 | 41.6 | 56.3 | 79.4 | 103.5 | ||||||||||
| 85+ | 17.0 | 24.6 | 33.3 | 43.9 | 63.2 | 90.1 | 114.9 | |||||||||||
From Figure 1, it appears that, over time, there has been a tendency for the acceleration (slope) of the incidence curves to decline. The decline in acceleration over time seems particularly strong for certain datasets shown in Figure 1, for example, for British males. Other datasets, such as Australian females, seem not to show a clear trend. Thus, it is helpful to make a more direct analysis for the changing acceleration patterns between the different datasets.
To describe the tendency for age-specific acceleration to decline over birth cohorts, we calculated the following summary statistics separately for each of the 6 datasets represented by the 6 panels in Figure 2. In each successive pair of the 17 cohorts, we used data only for the common ages shared by the two cohorts. For those common ages, we estimated by linear regression the slope of the log-log age-incidence data, which estimates the age-specific acceleration. We then calculated the ratio of the accelerations for the more recent cohort relative to the prior cohort, and used the logarithm base 2 value of that ratio. A negative value means the more recent cohort has a lower slope.
Figure 2. The age-specific acceleration of melanoma in different time periods and different countries.
The plots show the acceleration for males (left) and females (right) in Great Britain (top row), USA (middle row), and Australia (bottom row) for the birth cohorts shown in the top legend. The plots do not show the intermediate decadal cohorts because of visual limitations in plotting the data. The plots are based on the summary given in Dataset 6, derived from the data and analyses in Dataset 2– Dataset 5.
The average of the logarithms over the successive pairs of cohorts describes the geometric mean of the slopes, capturing the multiplicative tendency of the slope to change over cohorts. A negative value expresses an overall tendency for the slope to decline over time.
To gain a sense of the trend in acceleration over the successive cohorts, Table 2 shows, for each of the 6 datasets, the average logarithm for the ratio of successive slopes, and the standard error of that average. We also calculated the average logarithm divided by the standard error of that average, which gives the deviation from zero in terms of the number of standard errors of the mean.
Table 2. Logarithm to base 2 of the ratio of slope in a given birth cohort to that in the adjacent older cohort determined only across those ages that both cohorts have in common.
| Birth Cohort | ||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1900–04 | 1905–09 | 1910–14 | 1915–19 | 1920–24 | 1925–29 | 1930–34 | 1935–39 | 1940–44 | 1945–49 | 1950–54 | 1955–59 | 1960–64 | 1965–69 | 1970–74 | 1975–79 | Mean | SE | Ratio
mean:SE |
||
| Males | ||||||||||||||||||||
| GB | 0.03 | 0.01 | -0.07 | -0.03 | -0.04 | -0.05 | -0.02 | -0.02 | -0.07 | -0.06 | -0.07 | -0.09 | -0.12 | -0.02 | -0.20 | -0.28 | -0.07 | 0.02 | -3.54 | |
| USA | -0.19 | -0.01 | -0.04 | -0.13 | 0.01 | -0.04 | -0.10 | -0.01 | -0.05 | -0.08 | -0.06 | 0.01 | -0.03 | -0.03 | -0.18 | -0.33 | -0.08 | 0.02 | -3.45 | |
| Australia | -1.39 | -0.70 | -0.29 | -0.19 | 0.13 | 0.07 | 0.07 | 0.07 | 0.07 | -0.03 | -0.12 | -0.07 | -0.23 | -0.01 | -0.16 | -0.19 | 0.10 | -1.83 | ||
| Females | ||||||||||||||||||||
| GB | 0.43 | -0.14 | -0.29 | -0.09 | -0.08 | -0.06 | -0.08 | -0.03 | -0.13 | -0.10 | -0.08 | -0.09 | -0.03 | -0.07 | -0.12 | -0.57 | -0.10 | 0.05 | -2.00 | |
| USA | 0.73 | -0.54 | -0.15 | 0.13 | 0.04 | -0.16 | 0.03 | -0.03 | -0.06 | -0.15 | -0.02 | 0.00 | 0.01 | -0.18 | -0.26 | -0.99 | -0.10 | 0.09 | -1.13 | |
| Australia | -0.28 | -1.00 | -0.45 | -0.26 | 0.28 | 0.10 | 0.22 | 0.04 | 0.06 | -0.13 | -0.03 | -0.19 | -0.11 | -0.09 | -0.83 | -0.18 | 0.09 | -1.93 | ||
The overall trends suggest that acceleration has declined over time, consistent with the general visual pattern shown in Figure 2. However, Table 2 shows that there is significant variation in the trends between genders and countries, also apparent from Figure 1 and Figure 2.
In every case the overall tendency over the cohorts has been for incidence to increase and acceleration (slope) to decline.
Copyright: © 2017 Diffey BL and Frank SA
Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
Data obtained from Australian Institute of Health and Welfare (AIHW) 2016, Australian Cancer Incidence and Mortality (ACIM) books: Melanoma of the skin, Canberra: AIHW. Available at http://www.aihw.gov.au/acim-books.
Copyright: © 2017 Diffey BL and Frank SA
Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
Data obtained from (1) Office for National Statistics, Cancer Registration Statistics, England, available at http://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/datasets/cancerregistrationstatisticscancerregistrationstatisticsengland, (2) Welsh Cancer Intelligence and Surveillance Unit, Cancer in Wales, available at: http://www.wcisu.wales.nhs.uk/cancer-in-wales-1, and (3) Information and Statistics Division Scotland, Cancer Statistics, available at: http://www.isdscotland.org/Health-Topics/Cancer/Cancer-Statistics/Skin/
Copyright: © 2017 Diffey BL and Frank SA
Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
Data obtained from Surveillance Research Program of the Division of Cancer Control and Population Sciences, National Cancer Institute, available at: http://seer.cancer.gov/seerstat
Copyright: © 2017 Diffey BL and Frank SA
Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
Copyright: © 2017 Diffey BL and Frank SA
Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
Copyright: © 2017 Diffey BL and Frank SA
Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
Discussion
We analysed the incidence of malignant melanoma in 6 separate datasets representing males and females from Great Britain, the United States, and Australia, locations with large differences in ambient solar ultraviolet radiation, which is regarded as a major aetiological factor in the disease. Because the incidence of melanoma has tended to increase over time, we calculated the patterns of incidence separately for 17 successive 5-year birth cohorts between 1895 and 1979 in each of the 6 datasets.
In our analysis, we calculated the age-specific incidence separately for each cohort. We also calculated the acceleration of cancer incidence with age for each cohort, in which acceleration is the rate of increase in incidence with age described by the slope of the log incidence vs log age plots.
The tendency over the cohorts has been for incidence to increase and acceleration to decline over time. Figure 1 summarizes the incidence patterns, in which the higher position of the curves with the passing of time expresses the rise in incidence. In that figure, one can also see a tendency for the slope to decline with the passing of time, which corresponds to a decline in acceleration. Figure 2 and Table 2 provide a more detailed summary of the way in which acceleration has tended to decline with the passing of time. The variation between sexes and between countries is clear but unexplained.
It is evident that observed incidence data on melanoma over time are subject to the influence of many factors that include period effects and cohort effects.
Period effects can be regarded as resulting from external factors that affect equally all age groups at a particular calendar time and could be a consequences of economic, environmental or social factors; for example, educational awareness and prevention campaigns or depletion of the ozone layer resulting in higher levels of ambient ultraviolet radiation. Also, methodological changes in outcome definitions, classifications, or method of data collection, such as increased surveillance, early detection and changes in diagnostic criteria, could also lead to period effects in data.
Cohort effects, on the other hand, result from the unique experience/exposure of a particular group, or cohort, of subjects as they move across time leading to differences in the risk of outcome based on birth year. For example, following the widespread introduction of sunbeds for cosmetic tanning in the 1980s and their popularity amongst younger people, it would be expected that cohorts born after 1960 would be greater users of this form of UV exposure than cohorts born in earlier years.
We suggest here another possible contributory factor to the observed higher incidence and lower acceleration over time. In other cancer types, genetic mutations that predispose to cancer tend to cause that same coupling of rising incidence and declining acceleration 8, 14. In the multistage theory of cancer progression, a genetic mutation causes a rise in incidence and decline in acceleration because disease arises only after a certain number of restraining processes have broken down. By that theory, an inherited mutation moves an individual ahead one step at birth, reducing the number of restraining processes that must break down before disease develops 15, 16.
Fewer restraining processes mean faster progression to disease and a rise in incidence. Additionally, multistage theory predicts that the rise in incidence with age (acceleration) goes up with the number of restraining steps. Thus, a reduction in the number of restraining steps after mutation leads to a lower acceleration.
In the case of melanoma, it would be interesting to study whether a particular restraining process has become less effective over time, perhaps because of a change in environmental exposure patterns. The abrogation of a protective process would, in theory, lead to the observed rise in incidence and decline in acceleration.
A contributory factor to the uncertainties highlighted by our analysis could be linked to the limitations of the study. For example, information on body site, tumour thickness and stage, and histological subtype was absent. Although we selected three countries with well-established cancer registries, we cannot exclude the impact of long-term melanoma prevention strategies, especially in Australia, on incidence trends and, as acknowledged above, the major variation in acceleration found between countries could be the result of environmental and social influences rather tumour biology.
Data availability
The data referenced by this article are under copyright with the following copyright statement: Copyright: © 2017 Diffey BL and Frank SA
Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication). http://creativecommons.org/publicdomain/zero/1.0/
Dataset 1. Summary data for Figure 1, age-specific incidence of melanoma in different time periods and different countries. doi, 10.5256/f1000research.10491.d148748 17
Dataset 2. Raw age-specific incidence data for Australia for different age groups in different years. Data obtained from Australian Institute of Health and Welfare (AIHW) 2016, Australian Cancer Incidence and Mortality (ACIM) books: Melanoma of the skin, Canberra: AIHW. Available at http://www.aihw.gov.au/acim-books. doi, 10.5256/f1000research.10491.d148749 18
Dataset 3. Raw age-specific incidence data for Great Britain for different age groups in different years. Data obtained from (1) Office for National Statistics, Cancer Registration Statistics, England, available at http://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/datasets/cancerregistrationstatisticscancerregistrationstatisticsengland, (2) Welsh Cancer Intelligence and Surveillance Unit, Cancer in Wales, available at: http://www.wcisu.wales.nhs.uk/cancer-in-wales-1, and (3) Information and Statistics Division Scotland, Cancer Statistics, available at: http://www.isdscotland.org/Health-Topics/Cancer/Cancer-Statistics/Skin/. doi, 10.5256/f1000research.10491.d148750 19
Dataset 4. Raw age-specific incidence data for USA for different age groups in different years. Data obtained from Surveillance Research Program of the Division of Cancer Control and Population Sciences, National Cancer Institute, available at: http://seer.cancer.gov/seerstat. doi, 10.5256/f1000research.10491.d148751 20
Dataset 5. Transformation of raw data in Dataset 2– Dataset 4 into summary statistics used in the figures and analyses and in Table 1 and Table 2. doi, 10.5256/f1000research.10491.d148752 21
Dataset 6. Summary data for Figure 2, age-specific acceleration of melanoma in different time periods and different countries. doi, 10.5256/f1000research.10491.d148753 22
Funding Statement
This work was supported by National Science Foundation (USA) grant DEB-1251035 to SAF.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
[version 1; referees: 2 approved with reservations]
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