BMJ Open
BMJ Publishing Group
Prevention versus early detection for long-term control of melanoma and keratinocyte carcinomas: a cost-effectiveness modelling study
Volume: 10, Issue: 2
DOI 10.1136/bmjopen-2019-034388
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### Notes

Abstract

ObjectiveTo compare the long-term economic impact of melanoma prevention by sun protection, with the corresponding impact of early detection of melanoma to decrease melanoma deaths.DesignCost-effectiveness analysis using Markov cohort model. Data were primarily from two population-based randomised controlled trials, epidemiological and costing reports, and included flow-on effects for keratinocyte cancers (previously non-melanoma skin cancers) and actinic keratoses.SettingQueensland, Australia.ParticipantsMen and women with a mean age 50 years modelled for 30 years.InterventionsDaily sunscreen use (prevention) compared with annual clinical skin examinations (early detection) and comparing these in turn with the status quo.Primary and secondary outcomesCosts, counts of melanoma, melanoma deaths, keratinocyte cancers, life years and quality-adjusted life years.ResultsPer 100 000 individuals, for early detection, primary prevention and without intervention, there were 2446, 1364 and 2419 new melanomas, 556, 341 and 567 melanoma deaths, 64 452, 47 682 and 64 659 keratinocyte cancers and £493.5, £386.4 and £406.1 million in economic costs, respectively. There were small differences between prevention and early detection in life years saved (0.09%) and quality-adjusted life years gained (0.10%).ConclusionsCompared with early detection of melanoma, systematic sunscreen use at a population level will prevent substantial numbers of new skin tumours, melanoma deaths and save healthcare costs. Primary prevention through daily use of sunscreen is a priority for investment in the control of melanoma.

Keywords
Gordon, Olsen, Whiteman, Elliott, Janda, and Green: Prevention versus early detection for long-term control of melanoma and keratinocyte carcinomas: a cost-effectiveness modelling study
Strengths and limitations of this study
This is the first study to compare the cost-effectiveness of primary prevention and early detection in the context of skin cancer.
Melanoma diagnosis and mortality data from the Queensland Cancer Registry are considered as highly accurate and complete.
Modelling relied on population outcomes from two randomised controlled trials that serve to minimise bias in key model inputs but indirect comparison analyses were undertaken.
Data are scarce for health utilities of multiple keratinocyte carcinomas and benign skin tumours so proxies and small disutility values for these events were used.
Self-reports of skin checks and sunscreen use from the QSKIN study were used and we cannot rule out responder bias.

## Introduction

### Patient and public involvement

The research study did not involve any direct patient and public involvement.

## Results

For the next 30 years where no intervention occurs, it was predicted that for every 100 000 persons, 2419 melanomas, 567 melanoma deaths and 64 659 KCs will occur (table 2). Furthermore, 2.6 million life years, 1.8 million QALYs and £406.1 million in economic costs were predicted (table 2). This compares with the 30-year outcomes of a primary prevention strategy of 1055 fewer melanomas, 226 fewer melanoma deaths, 16 977 fewer KCs, 1736 additional QALYs and £19.7 million savings in societal costs (table 2). Conversely, for the early detection strategy compared with no intervention, there would be an estimated 21 melanomas (previously undetected), 793 additional KCs, 6 fewer QALYs and cost an additional £87.4 million (table 2). With early detection, diagnosing melanomas that were previously undetected is a positive finding leading to lower-risk cancers but health utility decrements accrue for all melanomas and KCs and the higher numbers of early stage cancers and KCs for resulted in slightly fewer overall QALYs than the no intervention strategy. Primary prevention was superior to early detection across most outcomes but at the expense of 21 undetected melanomas per 100 000 and produced small differences in life years (0.09%) and QALYs (0.10%) (table 2, online supplementary figures 3–5). Compared with primary prevention, early detection cost an additional £107.1 million (22%) to society, and there were 531 (47%) more in situ melanomas, 311 (45%) more thin melanomas and 261 (42%) more thicker melanomas per 100 000 persons (table 2).

Table 2
Projected health and economic outcomes over 30 years (mean per 100 000 persons) by strategy
 Early detection Primary prevention No intvn Primary versus ED difference ED versus no intvn difference Primary versus no intvn difference Number of melanomas All* 2446 1364 2419 −1082 27 −1055 −44.2% 1.1% −43.6% In situ melanomas* 1133 601 1074 −531 59 −473 −46.9% 5.5% −44.0% Thin melanomas (0≤1 mm)* 690 379 676 −311 14 −298 −45.1% 2.0% −44.0% Thick melanomas (>1 mm)* 623 362 647 −261 −24 −285 −41.8% −3.7% −44.0% Undetected melanomas* 0 21 21 21 −21 0 100% −100% 0% Number of deaths from melanoma* 556 341 567 −215 −11 −226 −38.7% −1.9% −39.9% Number of excised keratinocyte cancers* 65 452 47 682 64 659 −17 770 793 −16 977 −27.2% 1.2% −26.3% Societal costs (£million) £493.5 £386.4 £406.1 −£107.1 £87.4 −£19.7 −21.7% 21.5% −4.9% QALYs 1 821 195 1 822 937 1 821 201 1742 −6 1736 0.10% 0.00% 0.10% Life years* 2 635 444 2 637 734 2 635 396 2290 49 2338 0.09% −0.00% 0.09%
*Undiscounted.
ED, early detection; intvn, intervention; QALYs, quality-adjusted life years.

Regarding incremental cost per QALY ratios, one-way sensitivity analyses showed the most important model inputs were unit cost of skin examinations (range £27.52–£83.78); probability of a melanoma being >1 mm (0.21–0.29); rate reduction of benign lesions (0.66–0.86) and health utility of KCs (0.95–0.99) (table 3). These variables changed the ‘base-case’ incremental cost per QALY ratio for primary prevention versus early detection between −£185 000 and −£31 000 but the overall finding that primary prevention incurred lower costs but higher QALYs than early detection remained unchanged. If the mortality probability of thick melanoma (>1 mm) at 5 years (0.233) and 10 years (0.301) was lowered to 0.19 and 0.26, respectively, effect was small and more in favour of primary prevention (4% improvement) (table 3).

Table 3
One-way sensitivity analyses* of model parameters (mean per 100 000 persons) by strategy
 Costs (£ millions) Quality-adjusted life years Primary prevention versus early detection Primary prevention versus no intvn Early detection Primary prevention No intvn Early detection Primary prevention No intvn Incremental cost (£m) Incremental QALYs Incremental cost (£m) Incremental QALYs Base case (30 years duration, age 50, 3% discounting costs and QALYs) 493.5 386.4 406.1 1 821 195 1 822 937 1 821 201 −107.1 1742 −19.7 1736 Cost of wbCSE in high users(base AU$112.80) Cost of wbCSE in high users=$55.58 441.3 386.4 406.1 1 821 195 1 822 937 1 821 201 −54.9 1742 −19.7 1736 Cost of wbCSE in high users=169.20 545.4 386.4 406.1 1 821 195 1 822 937 1 821 201 −159.0 1742 −19.7 1736 Prob thick melanoma (base 0.25) Prob thick melanoma=0.21 488.2 386.4 406.1 1 821 686 1 822 937 1 821 201 −101.8 1251 −19.7 1736 Prob thick melanoma=0.29 497.7 386.4 406.1 1 820 811 1 822 937 1 821 201 −111.3 2126 −19.7 1736 Rate reduction in benign lesions from sunscreen (base 0.76) RR 0.66 493.5 381.1 406.1 1 821 195 1 822 822 1 821 201 −112.4 1627 −25.1 1621 RR 0.86 493.5 392.1 406.1 1 821 195 1 823 055 1 821 201 −101.5 1860 −14.0 1854 KC utility (base 0.98) Utility 0.95 493.5 386.4 406.1 1 819 784 1 821 928 1 819 831 −107.1 2145 −19.8 2097 Utility 0.99 493.5 386.4 406.1 1 821 408 1 823 086 1 821 408 −107.1 1677 −19.8 1677 Rate reduction in melanoma from sunscreen (base 0.50) RR 0.45 493.5 384.2 406.1 1 821 195 1 823 158 1 821 201 −109.3 1963 −21.9 1957 RR 0.55 493.5 388.5 406.1 1 821 195 1 822 720 1 821 201 −105.0 1525 −17.6 1519 Mortality prob of thick—5 years:0.233;10 years:0.301 Prob 0.27 to 0.34 494.3 386.8 407.0 1 821 065 1 822 867 1 821 069 −107.5 1802 20.1 1799 Prob 0.19 to 0.26 492.4 385.8 405.0 1 821 376 1 823 035 1 821 387 −106.6 1659 19.2 1648 Bold values are the main analysis results to compare with the other sensitivity analyses result. *Analyses were performed by changing the parameter of interest and rerunning the model with 5000 Monte Carlo simulations. KC, keratinocyte cancers; QALYs, quality-adjusted life years; RR, relative risk; wbCSE, whole-body clinical skin examination. When the model duration was shortened to 10 years, or separately increased to 40 years, incremental cost savings (per 100 000) for primary prevention versus early detection were £52.9 million and £116.5 million, respectively (table 4). Reducing the starting age to 30 years and raising it to 60 years produced cost savings of £112.0 million and £93.1 million, respectively, and discounting or not, also produced large differences in costs and QALYs (table 4). The probability that primary prevention was cost effective compared with early detection was 100% (online supplementary figures 3-5). Per person mean incremental cost savings for primary prevention versus early detection were £1071 (95% credible interval: £679 to £1490) and mean QALYs were 0.0174 (95% credible interval: 0.0069 to 0.0365). Model validation indicated high external validity (online supplementary file). Table 4 Scenarios* of different structural model parameters (mean per 100 000 persons) by strategy  Costs (£ millions) QALYs Primary prevention versus early detection Primary prevention versus no intvn Early detection Primary prevention No intvn Early detection Primary prevention No intvn Incremental cost (£m) Incremental QALYs Incremental cost (£m) Incremental QALYs Base case (30 years duration, age 50, 3% discounting costs & QALYs) 493.5 386.4 406.1 1 821 195 1 822 937 1 821 201 −107.1 1742 −19.7 1736 Duration of model 10 years 166.5 113.6 121.3 860 698 860 985 860 726 −52.9 287 −7.7 259 20 years 327.5 240.5 256.0 1 453 667 1 454 612 1 453 701 −87.0 945 −15.5 910 40 years 639.4 522.9 544.3 2 003 133 2 005 477 2 003 101 −116.5 2344 −21.4 2376 Starting age (mean of distribution) 30 years 306.7 194.6 211.9 1 978 959 1 979 806 1 978 953 −112.0 848 −17.3 854 40 years 370.5 257.2 278.0 1 933 075 1 934 407 1 933 072 −113.3 1332 −20.8 1335 60 years 674.1 581.0 596.8 1 603 495 1 605 381 1 603 524 −93.1 1886 −15.8 1857 Discounting† Costs 3%, QALYs 0% 493.5 386.4 406.1 2 633 348 2 636 361 2 633 329 −107.1 3013 −19.7 3031 Costs 0%, QALYs 0% 786.6 631.0 659.9 2 633 348 2 636 361 2 633 329 −155.7 3013 −28.9 3031 *Analyses were performed by changing the parameter of interest and rerunning the model with 5000 Monte Carlo simulation. †In general, people value instant benefits, for example, a golden tan versus avoiding skin cancers in future. This time preference for seeking benefits now rather than later is dealt with through discounting, giving future benefits a lower present value than immediate benefits. However, this makes prevention initiatives more unfavourable than without discounting. In prevention analyses therefore, discounting is controversial and warrants scenario analyses. QALYs, quality-adjusted life years. ## Discussion In mid-aged people followed up for 30 years, systematic improvements in sunscreen use would prevent skin cancers and benign skin tumours and bring significant cost savings. Melanoma deaths after 30 years of regular sunscreen use would be one-third less of that after 30 years of increased clinical skin examinations. As expected with an early detection intervention, higher numbers of detected melanomas, KCs and all other skin lesions would be diagnosed and treated than with either the primary prevention or the no intervention scenario. Early detection was favourable in detecting early stage, treatable skin cancers; however, these higher proportions of thin melanomas presenting for medical attention did not offset the economic and quality of life burdens incurred by concurrently detecting higher numbers of KCs and benign skin lesions. Conversely, primary prevention has the dual benefits by avoiding skin cancers altogether and reducing quality of life decrements and costs relative to early detection, although some melanomas would be undetected. With the majority of melanomas routinely detected at early stages in the general population without dedicated surveillance, there is minimal impact on mortality (and therefore life years). We compared primary prevention with early detection and a ‘no intervention’ baseline group but it is important to stress that the interventions were based on pragmatic trials that meant individuals could engage in their normal skin behaviours in Queensland. In earlier iterations of the model, we considered alternative behavioural scenarios but chose the current status quo (or mixed behavioural scenario reflecting real-life) with alternative best possible strategies. Although it may be unrealistic to achieve 100% compliance with protective behaviours, within a pragmatic trial with multiple behaviours possible, the strategies more correctly align with the data inputs on outcomes after these behaviours. Behaviours were not explicitly modelled but rather their consequences through rate ratios of skin cancers or thick melanomas and costs. Our use of distributions around the key estimates in the probabilistic sensitivity analyses will implicitly cover the variations in skin behaviours, costs and health-related quality of life. For example, decrements in quality of life associated with being diagnosed with a skin cancer is likely to be directly related to both treatment and psychological concerns but varies from person to person. Eight previous economic evaluations of melanoma early detection programmes have been reported: three Australian,35 three US35 and one Belgian36 and UK.37 All studies used long-term Markov or decision-analytic models as we did, and all showed early detection producing a downshift of melanoma stage and improved survival.35 Several studies included the costs for increased case-finding of KCs and benign tumours but only recent studies recognised the importance of quality of life and used QALYs as a primary outcome.36 38 39 Economic evaluations of primary prevention of skin cancer have varied in intervention type and duration but all have shown favourable economic and health benefits.35 In our ageing populations, mortality from melanoma competes with all-cause mortality. At a population level, the deaths of a small proportion (~5%) of people with melanoma that have advanced melanoma are somewhat diluted by deaths from other common diseases and so the gains in life years from population health strategies are elusive. Instead of population screening, screening those at known high risk of melanoma based, for example, on high numbers of nevi, or fair skin type, has been proposed.39 Developments in imaging technologies may bring improved diagnostic accuracy given that a proportion of difficult-to-diagnose melanomas and KCs40 are liable to misdiagnosis. We have previously shown that primary prevention via regular sunscreen use reduces economic burden.41 In England, it is estimated that £180 million will be spent by the NHS on skin cancer by 2020.42 In the USA, the annual economic burden of treating melanoma and KCs is US8.1 billion, increasing each year.2 Between 2002–2006 and 2007–2011, growth in costs of melanoma and KCs alone was fivefold higher than for all other cancers and would be even higher if the full cost of treated benign skin tumours were also counted. For example, over 35.6 million actinic keratoses were treated in the US Medicare population in 2015, increasing from 29.7 million in 2007.43 Consequently, the scope for future cost savings to health economies through investment in skin cancer campaigns is considerable.35

The generalisability of these findings will be limited to settings resembling this study’s, although the expected relativities of cost, intervention and quality of life effects should be proportional to country-specific skin cancer incidence. Some assumptions were necessary in our models, particularly regarding melanoma mortality rates in the early detection arm, since to date no relevant trials have been adequately powered to detect melanoma deaths. Reports of melanoma mortality after population screening for melanoma in Germany have been mixed.44 A further issue is the optimal frequency of skin checks since even annual checks may miss rapidly-growing nodular melanomas at a curable stage. For health utilities of multiple KCs and benign skin tumours where data are scarce, we used proxies and small disutility values for these events. We relied on self-reports of skin checks and sunscreen use from the QSKIN study and cannot rule out responder bias. Similarly, self-reported skin checks after melanoma diagnosis in a case control study6 may have been prone to recall bias and random misclassification, though the association between physician skin checks and thinner melanomas has been reported by others.7 Clinical skin examination frequency and outcomes were based on Janda et al ’s 13-month follow-up data but may vary in practice and therefore produce different numbers of skin malignancies in the early detection arm.16 Although the Nambour trial used SPF15+ sunscreen and our estimates may be conservative compared with sunscreen SPF30+, the effects of the very small difference in per cent UV filtered by SPF15+ vs 30+ sunscreens45 would have been covered by our sensitivity analyses. Similarly, categorising thin melanomas as <0.8 mm8 rather than ≤1 mm, would not have changed relative differences observed across strategies. Finally, no indirect comparison analysis was undertaken between the randomised trials by Green et al 4 and Janda et al. 16 At baseline, study participants had similar proportions with fair skin (56% in Green vs 62% in Janda) and sunscreen use (35% in Green vs 33% in Janda). However, Green and Janda study participants differed by age (included 20–69 year olds vs over 50 year olds), gender (men 43% vs 100%) and previous history of skin cancers (25% vs 71%), respectively.

These limitations should be set against the major strengths of this work, namely that we used data from two randomised controlled trials, thus minimising internal bias. We also relied on epidemiological and economic studies in the same general population, with the same ambient UV radiation levels and the same health system. We used melanoma diagnosis and mortality data from the Queensland Cancer Registry that is considered highly accurate and complete.

## Conclusion

We have shown that primary prevention through daily use of sunscreen emerges as the priority for investment in the control of melanoma, and secondarily of KC and actinic keratoses in high-risk populations like Queensland’s. As a corollary, there would be no long-term economic benefit in moving to implement whole body clinical skin examinations of people aged over 50 years to reduce the impact of melanoma. Further research is required to assess relative cost-benefit of early detection of melanoma in high risk subgroups versus prevention.

## Acknowledgements

We gratefully thank Professor Andrew Searles from Hunter Medical Research Institute who reviewed an earlier draft of the manuscript.

## Notes

Contributors:: LG and AG conceived the study aim and purpose. LG undertook the main analyses with assistance from TME. CO, DCW, MJ and AG provided critical review of the study, contributed to drafting the paper and provided subject matter expertise. AG provided clinical and scientific expertise. All authors contributed to drafting the manuscript and reviewed the final version.
Funding:: The Skin Awareness Study was funded by National Health and Medical Research Council (NHMRC) Project grant #497200. Follow-up of the Nambour Study was by NHMRC Grants #199600 and #974009. MJ is funded by a NHMRC TRIP Fellowship #1151021.
Competing interests:: Author LG received conference travel, registration and accommodation for presenting a preliminary version of these findings at the 4th International UV and Skin Cancer Prevention Conference in May 2018 Toronto.
Patient consent for publication:: Not required.
Provenance and peer review:: Not commissioned; externally peer reviewed.
Data availability statement:: Data are available on reasonable request. Modelling files are available in TreeAge Pro software and are available on request to the authors.

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