Approximately 5 million adults are treated annually for skin cancer in the United States costing $8.1 billion.1 Several studies estimate expenses incurred for skin cancer treatment; however, information regarding skin cancer screening costs is lacking. The International Classification of Diseases Ninth or Tenth Revision (ICD-9 or ICD-10) contains diagnoses codes for screening for malignant neoplasm of the skin (V76.53 or Z12.83) that have a 97% positive predictive value (PPV).2 However, the codes are underutilized, reducing the sensitivity when estimating national costs, likely related to lack of reimbursement for skin cancer screening visits.3,4 In 2016, the U.S. Preventive Services Task Force (USPSTF) concluded there is insufficient data to recommend skin cancer screenings.5 This neutral stance poses risk for insurance coverage, necessitating attainment of national expenditure estimates for cost-effective analyses. This study sought to develop an algorithm to identify screening visits utilizing claims data.
To develop the algorithm, Medicare patients evaluated by dermatology at Brigham and Women’s Hospital (BWH) and Massachusetts General Hospital (MGH) were identified from 1/1/2015-12/31/2015 using the Research Patient Data Registry (RPDR). Six hundred encounters were randomly selected excluding Mohs surgery, excisions, cosmetic procedures, and suture removals. Encounters were reviewed for Current Procedural Terminology (CPT) codes, ICD-9/ICD-10 diagnoses, E&M codes, and whether the patient received a total body skin examination (TBSE), which served as a surrogate for skin cancer screening visits. Diagnoses used for chart review (herein referred to as relevant diagnoses) are listed in table 1.
Table 1.
Descriptive statistics evaluating which of the relevant diagnoses and procedures are associated with TBSE.
| Variable | Yes TBSE n (%) |
No TBSE n (%) |
p valuea |
|---|---|---|---|
| Total | 340 (66) | 177 (34) | |
| Procedures | |||
| Biopsy | 53 (78) | 15 (22) | 0.02 |
| Cryotherapy | 125 (82) | 27 (18) | <0.0001 |
| Diagnoses | |||
| Neoplasms of Uncertain Behavior | 40 (74) | 14 (26) | 0.2 |
| Melanocytic Nevi | 34 (83) | 7 (17) | 0.02 |
| Nevi | 31 (84) | 6 (16) | 0.02 |
| Other Benign Lesions | 109 (86) | 18 (14) | <0.0001 |
| Actinic Keratoses | 139 (79) | 36 (21) | <0.0001 |
| Basal Cell Carcinoma | 20 (80) | 5 (20) | 0.1b |
| Squamous Cell Carcinoma | 12 (63) | 7 (37) | 0.8 |
| Seborrheic Keratoses | 178 (77) | 52 (23) | <0.0001 |
| Hemangiomas/Cherry Angiomas | 17 (85) | 3 (15) | 0.05b |
| Other Actinic Damage | 106 (88) | 14 (12) | <0.0001 |
| Melanin Hyperpigmentation | 40 (93) | 3 (7) | <0.0001b |
| Personal Skin Cancer History | 124 (84) | 24 (16) | <0.0001 |
| Unrelated Diagnosescd | 30 (29) | 75 (71) | <0.0001 |
Data was analyzed using Chi2 Test unless otherwise specified.
Data was analyzed using Fisher’s Exact Test
Not considered a relevant diagnosis
Encounters that contained only unrelated diagnoses correlated with not having a TBSE
Abbreviations: total body skin exam (TBSE); International Classification of Diseases Ninth Revision (ICD-9); International Classification of Diseases Tenth Revision (ICD-10)
After applying exclusion criteria, 517 encounters underwent review, of which 340 had a documented TBSE. Chi-square and Fischer’s exact tests were used to determine the relevant diagnoses associated with receiving a TBSE (table 1). Sensitivity, specificity, PPV, and negative predictive value (NPV) were calculated using combinations of relevant diagnoses, high-E&M codes (99203–99205, 99213–99215), and/or cryotherapy/biopsies. The c-statistic was calculated to determine the percentage that the algorithm correctly discriminates whether a TBSE was performed. The best model included high-E&M codes and at least one relevant diagnosis (sensitivity 86%, specificity 58%, PPV 81%, NPV 68%, c-statistic 0.73) (Table 2). Of note, the sensitivity, specificity, PPV, and NPV of codes V76.53 and Z12.83 were 38%, 86%, 84%, and 41%, respectively, indicating the codes accurately capture TBSEs, but are highly underutilized.
Table 2.
Model to identify TBSE encounters based on E&M code and relevant diagnoses.
| Yes TBSE | No TBSE | Total | ||
|---|---|---|---|---|
| Relevant Diagnosis + High E&M | 175 | 42 | 217 | PPV=81% |
| No Relevant Diagnosis + High E&M | 28 | 59 | 87 | NPV=68% |
| Total | 203 | 101 | ||
| Sensitivity=86% | Specificity=58% |
Abbreviations: evaluation and management, E&M; total body skin exam, TBSE; positive predictive value, PPV; negative predictive value, NPV
To validate the algorithm, the RPDR was queried for dermatology visits from 1/1/2015-12/31/2015 at BWH and MGH with high-E&M codes, a relevant diagnosis, and Medicare insurance. Five hundred different visits were randomly selected and reviewed for documented TBSE. Eighty-two percent had a TBSE (i.e. PPV), similar to the original model’s (81%). The c-statistic of 0.73 indicated a good model, but not a strong model. Analyses were conducted using SAS version 9.4 (Cary, NC) and STATA version 14.2 (College Station, TX). Reported p-values were two-sided with type I error (α) of <0.05 considered to be statistically significant.
The study highlights the coding variation for skin cancer screening visits, which limits cost estimates. Without this data, cost-effectiveness studies cannot be performed. Increased utilization of code Z12.83 could improve identification of screening visits to better inform the USPSTF.
Acknowledgments
Funding Sources: Mr. Karia is supported by a Cancer Epidemiology, Prevention, and Control Training Grant (NCI T32 CA009314).
Footnotes
IRB approval status: Approved by the Partners Human Research Office.
Conflicts of Interest: The authors have no conflict of interest to declare.
References
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