How Much Does A Skin Cancer Screening Cost
J Am Acad Dermatol. Writer manuscript; available in PMC 2019 April 1.
Published in last edited form as:
PMCID: PMC5963718
NIHMSID: NIHMS941588
Estimating the toll of skin cancer detection by dermatology providers in a large healthcare system
Martha Matsumoto
iAcademy of Pittsburgh, School of Medicine, Pittsburgh, PA, United states of america
Aaron Secrest
twoDepartment of Dermatology, University of Utah, Salt Lake City, UT
Alyce Anderson
1University of Pittsburgh, School of Medicine, Pittsburgh, PA, United states
Melissa I. Saul
3Department of Medicine, University of Pittsburgh, School of Medicine, Pittsburgh, PA, Usa
Jonhan Ho
4Department of Dermatology, Academy of Pittsburgh, School of Medicine, Pittsburgh, PA, USA
John M. Kirkwood
5Department of Medicine, Division of Medical Oncology, Academy of Pittsburgh, School of Medicine, Pittsburgh, PA, USA
Laura Thousand. Ferris
4Section of Dermatology, University of Pittsburgh, Schoolhouse of Medicine, Pittsburgh, PA, USA
Abstract
Background
Data on the cost and efficiency of skin cancer detection through total body skin exam (TBSE) are scarce.
Objective
To determine the number needed to screen (NNS) and biopsy (NNB) and cost per pare cancer diagnosed in a large dermatology exercise in patients undergoing TBSE.
Methods
Retrospective observational written report.
Results
From 2011 – 2015, twenty,270 patients underwent 33,647 visits for TBSE; 9956 lesion biopsies were performed yielding 2,763 skin cancers, including 155 melanomas. The NNS to find ane peel cancer was 12.2 (95% CI 11.7–12.6) and i melanoma was 215 (95% CI 185–252). The NNB to find one pare cancer was 3.0 (95% CI two.9–3.ane) and i melanoma was 27.eight (95% CI 23.iii–33.3). In a multivariable model for NNS, age and personal history of melanoma were significant factors. Historic period switches from a protective to a risk factor at 51 years. The estimated cost per melanoma detected was $32,594 (95% CI $27,326–$37,475).
Limitations
Information are from a single healthcare arrangement and based on dr. coding.
Conclusions
Melanoma detection through TBSE is almost efficient in patients over historic period 50 and those with a personal history of melanoma. Our findings will exist helpful in modeling the cost effectiveness of melanoma screening by dermatologists.
Keywords: melanoma, skin cancer, screening, cost, detection, biopsy
INTRODUCTION
Although studies indicate that total body skin test (TBSE) is an constructive means to detect melanoma at an early, treatable phase,1,2 the United States Preventive Services Chore Force (USPSTF) concluded that current show was inadequate to evaluate the residue of benefits and harms of melanoma screening by TBSE.three Quantifying the price of screening to the healthcare system is important for drawing conclusions well-nigh the benefits and harms of screening. Cost effectiveness studies have been used to judge and model societal costs and benefits of population melanoma screening projects, nevertheless these are predominantly in the primary intendance setting, or under the setting of a clinical trial.4–seven Nevertheless, the applied costs of melanoma screening in the setting of dermatology practices has not been well-studied in the United states.
Using visits coded every bit skin cancer screening visits performed by dermatology practitioners at a big healthcare organisation with both academic and community-based providers, we aimed to determine the number needed to screen (NNS) and number needed to biopsy (NNB) to diagnose 1 skin cancer, two standard metrics of screening efficacy,8 equally well as the toll per skin cancer diagnosed during TBSE.
METHODS
We identified all visits occurring at University of Pittsburgh Medical Center (UPMC)-affiliated dermatology offices from January 1, 2011 to December 31, 2015 in which pare cancer screening was performed using International Classification of Diseases (ICD) diagnoses V76.43 (ICD-9) or Z12.83 (ICD-x), which code for "Meet for screening for malignant neoplasm of the peel." To appraise the accuracy of using ICD diagnoses in identifying TBSE visits, we employed published methodology as follows9: 100 eligible visits with and 100 eligible visits without these ICD-9/10 diagnoses were randomly selected and charts were manually reviewed to calculate positive and negative predictive values.
Data extraction
For each identified visit, visit and patient level information were extracted from the electronic medical tape (EMR). Patient level data included sexual activity, date of nascence, race and ethnicity (self-reported), personal history of melanoma, personal history of whatever skin cancer. Age was adamant as of first dermatology visit. Age was later dichotomized at age 50 based upon preliminary statistical analyses showing melanoma run a risk increased over this age, and for consistency with melanoma screening cost-effectiveness studies.6 Personal history of melanoma and of any skin cancer were adamant using ICD-ix/x codes (V10.82 or Z85.820 for melanoma, V10.83 or Z85.828 for not-melanoma skin cancer, respectively) associated with current or prior visits, pathology reports containing melanoma diagnoses in our arrangement, and EMR health history data. Visit level data included current procedural terminology (CPT) codes. CPT codes were used to determine level of service for each visit and procedures associated with visits, including any codes used to denote lesion removal for pathologic examination (including biopsy, shave, and excision), and preparation of slides and examination by a dermatopathologist. The Medicare physician fee schedule non-facility cost was used to determine cost associated with each CPT code (Supplemental Table ane).10
Pathology reports from all visits in which a skin lesion was removed for pathologic test (on solar day of or up to one month after the role visit) were reviewed to categorize lesions as pigmented or not-pigmented and to obtain the diagnosis of the lesion removed.
Statistical analysis
Poisson regression was used to model counts of any pare cancer, melanoma, and non-melanoma skin cancer (NMSC). NNS was calculated equally the inverse of the absolute risk of skin cancer per patient-visit in univariate regression models. NNB was calculated every bit the changed of the absolute take chances of skin cancer per patient-biopsy in univariate regression models. Only pigmented lesion biopsies were used to calculate NNB for melanoma. Only not-pigmented lesion biopsies were used to calculate NNB for NMSC. All biopsies were used to summate NNB for any skin cancer. Poisson regression was used with one observation per patient for both univariate and multivariable models. To adjust for patients with multiple visits and biopsies, an exposure variable corresponding to number of visits for NNS estimates and number of biopsies for NNB estimates were included. Mixed models were initially attempted; yet, due to high proportion of patients with a single visit and low number of melanomas diagnosed, these models did not converge. Stepwise regression was used to select factors from those significant in univariate models for multivariate models. Stepwise regression models included historic period, sex, and personal history of skin cancer and melanoma as potential covariates for selection in multivariable models, and used a selection p-value of <0.05.
Biopsied patient charge per unit (BPR) was calculated as the number of visits in which a biopsy was performed divided past the total number of screening visits. Biopsy rate (BR) was calculated equally the BPR times the mean number of biopsies per biopsied patient (MBP). Chi-square tests were used to compare BR by age, sexual activity, and personal history of skin cancer. Boilerplate visit cost was broken down into the cost of the office visit and biopsy costs, including dermatopathology costs. Educatee'south t-test was used to compare visit costs by age, sex, and personal history of skin cancer. Normal bootstrap confidence intervals were calculated for cost per cancer detected and p-values for comparisons were calculated by permutation method. Toll per melanoma detection was calculated equally NNS x mean price per visit. All statistical analyses were performed in R 3.3.one (R Core Team, Vienna, Austria).11
Ethical considerations
This study was approved by the University of Pittsburgh Institutional Review Lath (PRO16080018).
RESULTS
Population presenting for skin cancer screening
During the 5-year study menses, 33,647 TBSEs were performed in 20,270 adult patients (historic period≥18 years), with a mean of one.66 TBSEs per patient. Patients with personal history of skin cancer were more than likely to have multiple TBSEs during the report menstruum (p < 0.001) (Table 1).
Table 1
Patient Demographics in Screening Skin Examination cohort, 2011–2015.
| Variable | Unique Patients* | Total Screening Visits |
|---|---|---|
| | ||
| N | 20,270 | 33,647 |
| | ||
| Female sex | 12722 (62.8) | 19990 (59.four) |
| | ||
| Male person sex | 7548 (37.2) | 13657 (xl.6) |
| | ||
| Historic period (years) | 52.vii (±17.4) | 55.5 (±17.1) |
| | ||
| Race | ||
| Missing | 493 | 680 |
| American Indian/Alaskan Native | 13 (0.one) | 18 (0.1) |
| Asian/Pacific Islander | 56 (0.3) | 65 (0.2) |
| Blackness | 193 (ane) | 231 (0.7) |
| White | 19515 (98.7) | 32653 (99.0) |
| | ||
| Hispanic/Latino ethnicity | 86 (0.four) | 120 (0.4) |
| | ||
| Personal history of melanoma | 1164 (5.seven) | 3572 (10.half dozen) |
| | ||
| Personal history of whatsoever skin cancer | 4983 (24.six) | 13120 (39) |
| | ||
| Diagnosed with melanoma during study menstruum | 149 (0.74) | 155 (0.46) |
| | ||
| Diagnosed with non-melanoma skin cancer during written report period | 1600 (7.9) | 2024 (vi.0) |
Validation of identifying TBSEs by ICD diagnoses
Of the 100 randomly sampled records with ICD diagnoses V76.43 or Z10.43, a TBSE was performed in 97% of the visits on manual review of the electronic chart (PPV 97%). Of the 100 randomly sampled records without these ICD diagnoses, a TBSE was performed in 17% of the visits on transmission review of the electronic chart (NPV 83%).
All skin cancers
In total, 2763 skin cancers were diagnosed from 9956 biopsies. For all skin cancers, NNS was 12.ii (95% CI 11.vii–12.6) and NNB was 3.0 (95% CI 2.9–iii.ane) (Figures 1 and 2). In univariate analysis, NNS and NNB were lower with increasing age, in males, and in patients with a personal history of any skin cancer (p < 0.001 for all, Figures ane and two). Personal history of melanoma was also associated with lower NNB (p=0.035). In multivariable models for NNS and NNB, age, sexual activity and skin cancer history remained meaning factors afterward stepwise regression (p < 0.001 for all, Table 2).
Number needed to screen for melanoma, all peel cancers, and non-melanoma skin cancers (NMSC)
The number needed to screen (NNS) is displayed as a mean value (bar) and 95% confidence interval (lines betoken lower and upper bound of the interval). NNS values are plotted for melanoma (meridian panel), all skin cancers (middle console), and not-melanoma skin cancers (NMSC, bottom panel). NNS indicates the number of screening visits required to diagnose i cancer of the given type and is displayed for subgroups inside our patient population. MelHx = personal history of melanoma. SCHx = personal history of peel cancer.
Number needed to biopsy for melanoma, all skin cancers, and not-melanoma skin cancers (NMSC)
The number needed to excise or biopsy (NNB) is displayed as a mean value (bar) and 95% confidence interval (lines bespeak lower and upper spring of the interval). NNB values are plotted for melanoma (summit panel), all peel cancers (middle panel), and not-melanoma skin cancers (NMSC, bottom panel). NNB indicates the number of biopsies or excisions required to diagnose 1 cancer of the given type and is displayed for subgroups within our patient population. MelHx = personal history of melanoma. SCHx = personal history of skin cancer
Table 2
Multivariable analysis for positive screen or biopsy of melanoma, non-melanoma pare cancer, and whatever skin cancer
| Positive screen | Positive biopsy | |||
|---|---|---|---|---|
| Relative Adventure (95% CI) | p-value | Relative Risk (95% CI) | p-value | |
| Melanoma * | ||||
| Male Sex | 1.35 (0.98–one.86) | 0.06 | Non selected | |
| Historic period (years) | ane.02 (ane.01–1.03) | < 0.001 | i.04 (1.03–1.05) | < 0.001 |
| Personal history of melanoma | 1.93 (1.27–2.92) | < 0.01 | Not selected | |
| Not-melanoma pare cancer * | ||||
| Male sex | i.91 (1.77–2.08) | < 0.001 | ane.47 (one.33–1.62) | < 0.001 |
| Age (years) | 1.04 (ane.04–1.05) | < 0.001 | i.01 (1.01–1.02) | < 0.001 |
| Personal history of any skin cancer | 1.thirty (ane.20–i.41) | < 0.001 | 1.36 (1.23–i.fifty) | < 0.001 |
| Any pare cancer * | ||||
| Male Sex | one.88 (1.74–ii.03) | < 0.001 | 1.threescore (ane.45–1.75) | < 0.001 |
| Age (years) | 1.04 (1.04–i.04) | < 0.001 | i.04 (ane.03–ane.04) | < 0.001 |
| Personal history of any peel cancer | 1.26(1.17–1.37) | < 0.001 | i.43 (1.thirty–1.58) | < 0.001 |
Melanoma peel cancers
In total, 155 melanomas were diagnosed from 4930 biopsies of pigmented lesions. Review of all melanoma cases showed that 81/156 (51.9%) were not identified equally suspicious by the patient per chart history, and thus were truly detected through TBSE.. Overall, the NNS was 215 (95% CI 185–252) and NNB was 27.8 (95% CI 23.three–33.3) to detect i melanoma (Figures 1 and 2). In univariate models, NNS was lower with increasing age (p < 0.001), in males (p < 0.01) and in patients with a personal history of melanoma (p < 0.001), only non in patients with a personal history of any skin cancer (Effigy one). In univariate modeling, age switches from a protective to a risk factor at 51 years old. Increasing age (p < 0.001) and male sex (p < 0.01) were associated with lower NNB (Figure 2). However, after controlling for historic period, male sex activity lost significance, and age was the but gene included in the regression model for NNB by stepwise selection. In a multivariable model for NNS, age (p < 0.001) and personal history of melanoma (p < 0.001) were significant factors (Table 2).
Non-melanoma skin cancers
In total, 2607 NMSC were diagnosed in 5026 biopsies for non-pigmented lesions. For NMSC, NNS was 12.ix (95% CI 12.four–13.4) and NNB was ane.6 (95% CI 1.5–1.7) (Figures ane and 2). In the univariate model, both NNS and NNB were lower with increasing age, in males, and in patients with a personal history of any pare cancer (p < 0.001 for all, Figures one and 2). Personal history of melanoma was also associated with lower NNB (p < 0.01). In the multivariable model, sex activity, age, and any peel cancer history remained meaning factors (p < 0.001 for all, Table 2).
Costs of screening
The overall mean visit cost for a skin cancer screening visit was $150, consisting of $105 (70%) for office visit costs and $45 (30%) for biopsy and dermatopathology costs (a value that accounts for the fact that a biopsy was not performed at every visit) (Table 3). Biopsy charge per unit (BR) per visit for all lesions was higher in males, patients over age 50, and patients with a personal history of skin cancer (Table 3). Pigmented lesion BR was highest in patients with a personal history of melanoma and patients nether age 50.
Tabular array 3
Biopsy rates, costs per visit, and cost per any peel cancer diagnosis and melanoma diagnosis
| Number of TBSEs | Biopsy Charge per unit | Pigmented lesion Biopsy Rate | Boilerplate visit Cost† | Percent of costs from biopsy | Cost (95% CI) per melanoma (in USD) † | Cost (95% CI) per NMSC (in USD) † | |
|---|---|---|---|---|---|---|---|
| All Patients | 33647 | 0.22 | 0.12 | 150 | xxx% | 32555 (27032,37548) | 2493 (2397,2590) |
| | |||||||
| Historic period < 50 | 11401 | 0.20 | 0.sixteen | 146 | 28% | 51990 (30483,70030) | 9141 (7784,10406) |
| Age 50+ | 22246 | 0.23 | 0.098 | 152 | 32% | 27498 (22399,32003)* | 1836 (1763,1910)* |
| | |||||||
| Male | 13657 | 0.25 | 0.12 | 156 | 34% | 27013 (20718,32878) | 1718 (1638,1800) |
| | |||||||
| Historic period < 50 | 3360 | 0.20 | 0.fifteen | 146 | 28% | 81668 (0,200592) | 6282 (4728,7593) |
| Age fifty+ | 10297 | 0.27 | 0.11 | 160 | 35% | 22521 (16960,27519) | 1412 (1338,1482)* |
| | |||||||
| Female | 19990 | 0.twenty | 0.12 | 146 | 28% | 38315 (28176,47261) # | 3724 (3484,3965)# |
| | |||||||
| Age < 50 | 8041 | 0.twenty | 0.17 | 146 | 28% | 45142 (23462,63482) | 11285 (8911,13444) |
| Age 50+ | 11949 | 0.20 | 0.092 | 146 | 28% | 34766 (23821,44405) | 2564 (2393,2731)* |
| | |||||||
| Personal history of melanoma | |||||||
| | |||||||
| Yeah | 3572 | 0.25 | 0.xvi | 158 | 34% | 15697 (9310,20871) | 2307 (2028,2560) |
| No | 30075 | 0.21 | 0.12 | 149 | 30% | 37701 (30340,44323)* | 2522 (2415,2623) |
| | |||||||
| Personal history of any skin cancer | |||||||
| | |||||||
| Yes | 13120 | 0.25 | 0.xi | 149 | 34% | 30545 (23083,37793) | 1595 (1524,1669) |
| No | 20527 | 0.20 | 0.xiii | 149 | 28% | 34085 (26496,40862) | 4048 (3761,4323)* |
The toll per melanoma detected was estimated to be $32,594 and $2,496 per NMSC diagnosis (Table 3). For melanoma, the point estimates of the price of detection was higher for females than males (p=0.043) and higher in patients under age 50 (p=0.002). The lowest cost per melanoma detected was observed in patients with a personal history of melanoma at $15,714 which was significantly less than the price of detection for patients without a history of melanoma (p<0.05) (Tabular array three). Given these findings, we looked specifically at cost of melanoma detection in men over age l and found costs were significantly lower than in younger males and females (p<0.001 and p=0.018, respectively). Cost per melanoma detection did not differ significantly in women under fifty versus 50 and older (p=0.258), in males versus females nether historic period 50 years (p=0.113), or in patients with versus without a personal history of any skin (p=0.495).
Give-and-take
Our data from over 33,000 TBSEs performed in dermatology offices in a large healthcare system show that screening is most efficient and least costly in patients at higher risk of melanoma due to age and history of a previous melanoma. In our study population, the overall NNS was 215 and NNB was 27.8 to find 1 melanoma, while the NNS was 12.9 and NNB was 1.6 for NMSC. The NNS and NNB to detect one melanoma dropped higher up the age of 50 in our population, suggesting that screening is likely to be highest yield starting at this age. TBSE in patients with a personal history of melanoma is high-yield with a toll per melanoma detected of less than half of that seen in the overall report population.
Our findings likely reflect the higher gamble population evaluated in a dermatology versus primary intendance office. Data from fifteen years of American Academy of Dermatology screenings by dermatologists found an NNS of 668 for each melanoma detected.12 About 14% of those screened reported a history of skin cancer versus 25% in our population.12 The German SCREEN (Peel Cancer Inquiry to Provide Evidence for Effectiveness of Screening in Northern Germany) plan, in which 360,288 people were screened, primarily by primary care physicians, reported the NNS to find one melanoma was 620; fewer than three% of participants reported a skin cancer history, and the NNB was 28.13
Few studies have evaluated cost at the patient-level. Gordon et al. performed a patient-level toll analysis using data from an Australian clinical trial of skin screening.14 Patients attended complimentary screening clinics and only those with suspicious lesions were referred for biopsy. A cost of $12,152 per melanoma detected can be derived from their data, but this does not include the costs of all screening visits. Extrapolation from the data of Hoorens et al from 1668 screened patients show that the NNS and NNB to find one melanoma were 208.5 and 3.25, respectively, yielding a cost per melanoma diagnosed of $5,346.15 However, as these information were collected in the setting of a study on screening efficiency and not routine practice, biopsies performed at patient asking were likely reduced.
Other studies using Markov models to effort to quantify the price-effectiveness of screening have constitute that screening strategies such as a single screening of the general population or surveillance of high-risk patients in a specialized clinic are cost effective.six,vii,16 Data such as ours can exist useful for hereafter modeling past providing valuable data from a real-earth clinical setting in the Usa. For example, in one Belgian model of the cost effectiveness of screening, a cost of $5.30 per screening was used.7 Using the cost of office visits observed in our accomplice would result in significantly different findings.
While our arroyo immune united states of america to collect encounter-level information for a large number of visits, the use of this type of data has several limitations. Our assay is limited to visits coded every bit visits for skin cancer screening and approximately 17% of TBSEs performed during the study menstruation were not identified by this strategy, although the NPV of 83% is inside the range reported by other EMR studies (67.7–100%).ix We besides do not know the degree of clinical suspicion for each biopsy performed, and if some were performed due to patient asking rather than strong suspicion for skin cancer. Our analysis did non account for additional downstream screening costs such equally treatment costs for skin cancers or other lesions, such as actinic keratoses, diagnosed during screening. Also, our data are cogitating of TBSE for early detection and cannot necessarily be translated to population-based screening, every bit our dermatology-based population would be considered college take chances due to the large percent of patients with a personal history of skin cancer. Screening of the asymptomatic general population would likely be more expensive due to lower disease prevalence.
In our study, office visits contributed to the bulk of screening costs, and merely 0.46% of these visits result in a melanoma diagnosis. One way to reduce the cost per melanoma detected is to increase melanoma prevalence in the screened population. This tin can be accomplished by selective screening past specific criteria: 1) age and sexual activity; ii) personal history of melanoma; or 3) pre-screening by strategies such as cocky-test, trained partner examination, or community-based screening.1,6,8,15,17,18 Increasing biopsy sensitivity will also reduce the cost per melanoma diagnosed. Strategies shown to reach this include provider training programs and routine use of dermoscopy.18,nineteen
Few studies take attempted to measure the cost per cancer detected. In one study of mammography in Medicare beneficiaries, the cost per breast cancer diagnosed was $16,524 among women ages 66–74, a number similar to our cost per melanoma diagnosis in our highest risk patient groups (age 50 and older and patients with a personal history of melanoma).20
Our information support targeting high-take a chance populations for screening by dermatologists. Population-based screening in a primary care setting with subsequent referral to dermatology for suspicious lesions, may offer a more than cost-effective alternative for screening of lower risk patients21,22 by improving the pre-test probability of melanoma in the population examined past dermatologists while leveraging the higher diagnostic accuracy among dermatologists, reducing both missed melanomas and unnecessary biopsies of benign lesions.
Supplementary Cloth
Acknowledgments
Funding/Support: This study was supported by the National Institutes of Health through Grants Number 2P50CA121973-06 (SPORE in Skin Cancer) and Number UL1-TR-001857 (Clinical and Translational Scientific discipline Honour). Alyce Anderson is supported by an NIH training grant (TL1TR001858, PI: Kapoor).
The authors thank Drs. Tao Sun and Abraham Apfel of the Clinical and Translational Science Institute of the University of Pittsburgh for statistical consulting services.
ABBREVIATIONS
| BPR | Biopsied patient rate |
| BR | Biopsy rate |
| CI | Confidence interval |
| CPT | Current procedural terminology |
| EMR | Electronic medical record |
| ICD | International Nomenclature of Diseases |
| MBP | Mean number of biopsies per biopsied patient |
| MelHx | Personal history of melanoma |
| NMSC | Non-melanoma skin cancer |
| NNB | Number needed to biopsy |
| NNS | Number needed to screen |
| NPV | Negative predictive value |
| PPV | Positive predictive value |
| SCHx | Personal history of pare cancer |
| SD | Standard divergence |
| TBSE | Full trunk skin test(s) |
| USPSTF | Us Preventive Services Job Force |
| USD | United States Dollar |
Footnotes
This study was approved past the University of Pittsburgh Institutional Review Board
Conflicts of involvement: Dr. Kirkwood has served every bit a consultant for Bristol Myers Squibb, Merck, Novartis, Roche, Genentch, EMD Serano, and Array Biopharma and has received grants from Prometheus and Merck. Dr. Ferris has served equally a consultant for DermTech International. The remaining authors have no relevant fiscal interests to report.
Author Contributions:
Dr. Laura Ferris and Martha Matsumoto had full access to all of the data in the report and take responsibility for the integrity of the information and the accuracy of the data analysis. Report concept and design: Ferris, Matsumoto. Acquisition, analysis, and interpretation of data: Ferris, Ho, Matsumoto, Saul, Secrest, Anderson. Drafting of manuscript: Ferris, Matsumoto, Secrest Anderson. Critical revision of the manuscript for important intellectual content: Kirkwood, Ho, Saul. Statistical analysis: Matsumoto, Secrest, Anderson. Obtained funding: Ferris, Kirkwood. Study supervision: Ferris.
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