Abstract
Acral lentiginous melanoma (ALM) classically presents in glabrous skin. Some studies suggest an association between mechanical stress and ALM development. People working jobs with long periods of standing/walking may be exposed to near-daily mechanical stress. To investigate the relationship between occupation type and ALM development, we performed a retrospective cohort study using Optum’s Clinformatics Database (2007–2023). We compared the risk of ALM in adults with occupational data (n = 9,270,935). Retired patients had the highest prevalence (0.081) and incidence per 1,000,000 person-years (0.514, 95% confidence interval = 0.487–0.542) of ALM. We performed multivariate logistic regression, adjusted for demographics and insurance, and stratified by age. In patient aged <65 years, homemaker patients had 55.2% increased risk of ALM (P < .001). In patients aged ≥65 years, retired patients had 17% increased risk of ALM (P < .001). Our findings suggest that homemaker and retired patients are at highest risk of ALM, possibly owing to increased mechanical stress on the palmoplantar surfaces associated with work around the home in homemakers. Occupation-related mechanical stress may play a role in the retired population as well, although the previous occupation of retired patients is unreported. Future directions include exploring prior occupations in the retired population.
Keywords: Clinical research, Epidemiology, Melanoma
Introduction
Acral lentiginous melanoma (ALM) is a melanoma subtype that affects the glabrous skin of palmar, plantar, or subungual surfaces. Prior literature suggests an association between mechanical stress and ALM development (Minagawa et al, 2016). Mechanical stress induces mechanotransduction and changes in cellular morphology and survival (Miroshnikova and Wickström, 2022). Cancer cells utilize mechanotransduction to enhance survival in the stiff tumor microenvironment (Liu et al, 2020).
Age-related epithelial and vascular changes cause increased dermal mechanical stress (Ichijo et al, 2022). Occupation may also contribute to mechanical stress, particularly jobs that emphasize manual labor. There is a dearth of literature investigating the relationship between occupational history involving manual labor and ALM risk. However, using population-based databases, we can explore our hypothesis that there is a relationship between occupations involving repeated mechanical stress and ALM.
Results
Of 9,036,631 adults with ≥1 year of continuous enrollment and specified occupations in Optum Clinformatics Data Mart (CDM), 3272 had ALM. Full demographic characteristics are shown in Table 1. The incidence and prevalence of ALM were both highest in retired patients, followed by those in blue-collar patients (Table 2). Compared with manager/owner/professional patients, retired patients were at 21.9% increased risk of developing ALM (P < .001) on multivariable logistic regression adjusted for demographic characteristics (age, sex, race, occupation type) and insurance type (Table 3).
Table 1.
Study Population Demographics
| Variable | Overall (n = 9,036,631) | No ALM Diagnosis (n = 9,033,359) | ALM Diagnosis (n = 3272) |
|---|---|---|---|
| Age | |||
| Median (IQR) | 65 (52–75) | 65 (52–75) | 77 (69–86) |
| Sex, n (%) | |||
| Female | 5,347,491 (59%) | 5,345,902 (59.0%) | 1589 (49.0%) |
| Male | 3,689,140 (41.0%) | 3,687,457 (41.0%) | 1683 (51.0%) |
| Race, n (%) | |||
| Asian | 254,871 (2.8%) | 254,833 (2.8%) | 38 (1.2%) |
| Black | 852,596 (9.4%) | 852,176 (9.4%) | 420 (12.8%) |
| Hispanic | 681,715 (7.5%) | 681,519 (7.5%) | 196 (6.0%) |
| White | 7,247,449 (80.2%) | 7,244,831 (80.2%) | 2618 (80.0%) |
| Occupation type, n (%) | |||
| Manager/owner/professional | 2,705,718 (30.0%) | 2,704,960 (30.0%) | 758 (23.2%) |
| White collar/health/civil service/military | 2,515,466 (27.8%) | 2,514,819 (27.8%) | 647 (19.8%) |
| Blue collar | 1,330,455 (14.7%) | 1,330,013 (14.7%) | 442 (13.5%) |
| Homemaker | 792,600 (8.8%) | 792,540 (8.8%) | 60 (1.8%) |
| Retired | 1,692,392 (18.7%) | 1,691,027 (18.7%) | 1365 (41.7%) |
| Health insurance, n (%) | |||
| Commercial | 5,737,044 (63.5%) | 5,736,639 (63.5%) | 405 (12.4%) |
| Medicare | 3,037,211 (33.6%) | 3,034,489 (33.6%) | 2722 (83.2%) |
| Both | 262,376 (2.9%) | 262,231 (2.9%) | 145 (4.4%) |
Abbreviations: ALM, acral lentiginous melanoma; IQR, interquartile range.
Table 2.
Incidence and Prevalence of ALM by Occupation Group
| Incidence or prevalence | Manager/owner/professional (n = 2,705,718) |
White collar/health/civil service/military (n = 2,515,466) |
Blue collar (n = 1,330,455) |
Homemaker (n = 792,600) |
Retired (n = 1,692,392) |
|---|---|---|---|---|---|
| Incidence per 1,000,000 person-years (95% CI) | 0.223 (0.208–0.240) | 0.220 (0.204–0.238) | 0.279 (0.253–0.306) | 0.079 (0.060–0.102) | 0.514 (0.487–0.542) |
| Prevalence, n (%) | 758 (0.028%) | 647 (0.026%) | 442 (0.033%) | 60 (0.008%) | 1,365 (0.081%) |
Abbreviation: CI, confidence interval.
Table 3.
Multivariate Logistic Regression Analysis of ALM Risk by Occupation Type
| Variable | OR | P-Value |
|---|---|---|
| Occupation type | ||
| Manager/owner/professional | 1 (reference) | — |
| White collar/health/civil service/military | 0.973 | .608 |
| Blue collar | 1.029 | .630 |
| Homemaker | 1.313 | .058 |
| Retired | 1.219 | <.001 |
| Age | 1.011 | <.001 |
| Sex | ||
| Female | 1 (reference) | — |
| Male | 1.796 | <.001 |
| Race | ||
| White | 1 (reference) | — |
| Asian | 0.501 | <.001 |
| Black | 1.356 | <.001 |
| Hispanic | 0.897 | .142 |
| Health insurance type | ||
| Commercial only | 1 (reference) | — |
| Medicare only | 9.561 | <.001 |
Abbreviation: ALM, acral lentiginous melanoma.
To further isolate the relationship between occupation and ALM risk from the confounding effects of age, we stratified the study population by age: <65 years and ≥65 years. Full demographic characteristics for the age-stratified cohorts are shown in Tables 4 and 5. The incidence and prevalence of ALM were highest in retired patients in both the cohort comprising patients aged <65 years and that comprising those aged ≥65 years (Tables 6 and 7). On subgroup analysis of patients aged <65 years, multivariate logistic regression adjusted for sex, race, and insurance type demonstrated that homemaker patients were at 55.2% increased risk of developing ALM (P < .001) compared with manager/owner/professional patients (Table 8). In patients aged ≥65 years, retired patients were at 27.4% increased risk of developing ALM (P = .003) on multivariable logistic regression adjusted for the same covariates (Table 9).
Table 4.
Study Population Demographics, Cohort of Patients Aged <65 Years
| Variable | Overall (n = 4,397,442) | No ALM Diagnosis (n = 4,396,914) | ALM Diagnosis (n = 528) |
|---|---|---|---|
| Age, y | |||
| Median (IQR) | 51 (40–59) | 51 (40–59) | 59 (53–62) |
| Sex, n (%) | |||
| Female | 2,559,375 (58.2%) | 2,559,132 (58.2%) | 243 (46.0%) |
| Male | 1,838,067 (41.8%) | 1,837,782 (41.8%) | 285 (54.0%) |
| Race, n (%) | |||
| Asian | 147,204 (3.3%) | 147,197 (3.3%) | 7 (1.3%) |
| Black | 421,918 (9.6%) | 421,832 (9.6%) | 86 (16.3%) |
| Hispanic | 389,469 (8.9%) | 389,443 (8.9%) | 26 (4.9%) |
| White | 3,438,851 (78.2%) | 3,438,442 (78.2%) | 409 (77.5%) |
| Occupation type, n (%) | |||
| Manager/owner/professional | 1,507,161 (34.3%) | 1,507,021 (34.3%) | 140 (26.5%) |
| White collar/health/civil service/military | 1,319,158 (30.0%) | 1,319,052 (30.0%) | 106 (20.1%) |
| Blue collar | 683,988 (15.6%) | 683,899 (15.6%) | 89 (16.9%) |
| Homemaker | 792,600 (18.0%) | 792,540 (18.0%) | 60 (11.4%) |
| Retired | 94,535 (2.1%) | 94,402 (2.1%) | 133 (25.2%) |
| Health insurance, n (%) | |||
| Commercial | 4,205,178 (95.6%) | 4,204,934 (95.6%) | 244 (46.2%) |
| Medicare | 179,311 (4.1%) | 179,044 (4.1%) | 267 (50.6%) |
| Both | 12,953 (0.3%) | 12,936 (0.3%) | 17 (3.2%) |
Abbreviations: ALM, acral lentiginous melanoma; IQR, interquartile range.
Table 5.
Study Population Demographics, Cohort of Patients Aged ≥65 Years
| Variable | Overall (n = 4,639,189) | No ALM Diagnosis (n = 4,636,445) | ALM Diagnosis (n = 2744) |
|---|---|---|---|
| Age, y | |||
| Median (IQR) | 75 (70–82) | 75 (70–82) | 80 (73–87) |
| Sex, n (%) | |||
| Female | 2,788,116 (60.0%) | 2,786,770 (60.1%) | 1346 (49.1%) |
| Male | 1,851,073 (40.0%) | 1,849,675 (39.9%) | 1398 (50.9%) |
| Race (n, %) | |||
| Asian | 107,667 (2.3%) | 107,636 (2.3%) | 31 (1.1%) |
| Black | 430,678 (9.3%) | 430,344 (9.3%) | 334 (12.2%) |
| Hispanic | 292,246 (6.3%) | 292,076 (6.3%) | 170 (6.2%) |
| White | 3,808,598 (82.1%) | 3,806,389 (82.1%) | 2209 (80.5%) |
| Occupation type, n (%) | |||
| Manager/owner/professional | 1,198,557 (25.8%) | 1,197,939 (25.8%) | 618 (22.5%) |
| White collar/health/civil service/military | 1,196,308 (25.8%) | 1,195,767 (25.8%) | 541 (19.7%) |
| Blue collar | 646,467 (13.9%) | 646,114 (13.9%) | 353 (12.9%) |
| Homemaker | — | — | — |
| Retired | 1,597,857 (34.4%) | 1,596,625 (34.4%) | 1,232 (44.9%) |
| Health insurance, n (%) | |||
| Commercial | 1,531,866 (33.0%) | 1,531,705 (63.5%) | 161 (5.9%) |
| Medicare | 2,857,900 (61.6%) | 2,855,445 (33.0%) | 2455 (89.5%) |
| Both | 249,423 (5.4%) | 249,295 (5.4%) | 128 (4.7%) |
Abbreviations: ALM, acral lentiginous melanoma; IQR, interquartile range.
Table 6.
Incidence and Prevalence of ALM by Occupation Group, Cohort of Patients Aged <65 Years
| Incidence or prevalence | Manager/owner/professional (n = 2,705,718) | White collar/health/civil service/military (n = 2,515,466) | Blue collar (n = 1,330,455) | Homemaker (n = 792,600) | Retired (n = 1,692,392) |
|---|---|---|---|---|---|
| Incidence per 1,000,000 person-years (95% CI) | 0.084 (0.071–0.010) | 0.083 (0.068–0.010) | 0.129 (0.104–0.159) | 0.079 (0.060–0.102) | 1.214 (1.017–1.439) |
| Prevalence, n (%) | 140 (0.009%) | 106 (0.008%) | 89 (0.013%) | 60 (0.008%) | 133 (1.410%) |
Abbreviations: ALM, acral lentiginous melanoma; CI, confidence interval.
Table 7.
Incidence and Prevalence of ALM by Occupation Group, Cohort of Patients Aged ≥65 Years
| Incidence or prevalence | Manager/owner/professional (n = 2,705,718) | White collar/health/civil service/military (n = 2,515,466) | Blue collar (n = 1,330,455) | Homemaker (n = 792,600) | Retired (n = 1,692,392) |
|---|---|---|---|---|---|
| Incidence per 1,000,000 person-years (95% CI) | 0.356 (0.328–0.385) | 0.327 (0.300–0.355) | 0.394 (0.354–0.438) | — | 0.484 (0.457–0.512) |
| Prevalence, n (%) | 618 (0.052%) | 541 (0.045%) | 353 (0.055%) | — | 1232 (0.077%) |
Abbreviations: ALM, acral lentiginous melanoma; CI, confidence interval.
Table 8.
Multivariate Logistic Regression Analysis of ALM Risk by Occupation Type, Cohort of Patients Aged <65 Years
| Variable | OR | P-Value |
|---|---|---|
| Occupation type | ||
| Manager/owner/professional | 1 (reference) | — |
| White collar/health/civil service/military | 0.973 | .833 |
| Blue collar | 1.090 | .527 |
| Homemaker | 1.552 | <.001 |
| Retired | 1.004 | .978 |
| Sex | ||
| Female | 1 (reference) | — |
| Male | 2.084 | <.001 |
| Race | ||
| White | 1 (reference) | — |
| Asian | 0.595 | .174 |
| Black | 1.189 | .148 |
| Hispanic | 0.565 | .005 |
| Health Insurance type | ||
| Commercial only | 1 (reference) | — |
| Medicare only | 29.398 | <.001 |
| Both | 25.944 | <.001 |
Abbreviation: ALM, acral lentiginous melanoma.
Table 9.
Multivariate Logistic Regression Analysis of ALM Risk by Occupation Type, Cohort of Patients Aged ≥65 Years
| Variable | OR | P-Value |
|---|---|---|
| Occupation type | ||
| Manager/owner/professional | 1 (reference) | — |
| White collar/health/civil service/military | 0.986 | .640 |
| Blue collar | 0.998 | .869 |
| Retired | 1.274 | .003 |
| Sex | ||
| Female | 1 (reference) | — |
| Male | 1.778 | <.001 |
| Race | ||
| White | 1 (reference) | — |
| Asian | 0.496 | <.001 |
| Black | 1.310 | <.001 |
| Hispanic | 0.962 | .725 |
| Health insurance type | ||
| Commercial only | 1 (reference) | — |
| Medicare only | 7.935 | <.001 |
| Both | 4.769 | <.001 |
Abbreviation: ALM, acral lentiginous melanoma.
Discussion
Although not definitive, our findings from a large population-based dataset suggest that repeated mechanical stress on palmoplantar surfaces may be one potential factor contributing to the development of ALM, as evidenced by the increased risk of homemakers in developing ALM. Although the original dataset combined “homemaker” and “retired” patients into 1 group, we were able to derive separate working definitions of “homemaker” patients and “retired” patients using age and insurance status with reasonable face validity. Homemakers were those aged <65 years with commercial insurance, whereas retired patients were those aged ≥65 years and those aged <65 years with Medicare. Although there is variation in the age of retirement, the average age of retirement in the United States is 64 years in men and 62 years in women (Munnell, 2015). Therefore, although there may be a mix of retired and homemakers in either subdivided cohort, one would expect a majority of the “homemaker/retired” group aged ≥65 years to be retired, and conversely, one would also expect a majority of the “homemaker/retired” group aged <65 years with commercial insurance to remain active as homemakers and not retired, essentially representing the risk of ALM in homemakers.
The increased risk of homemaker patients developing ALM may be due in part to the increased mechanical stress from long hours of strenuous daily work such as cleaning, cooking, and family care duties (Osinuga et al, 2021; Reisine et al, 1987). These domestic labor tasks may involve repetitive hand movements, long hours of standing, and awkward positioning with poor ergonomics, extending for longer hours than a traditional workday owing to the lack of formal working hours for homemakers (Habib et al, 2012, 2010). Ultimately, these activities translate into prolonged physical stress, as evidenced by the prevalence of musculoskeletal disorders affecting homemakers (Habib et al, 2012, 2010; Ojukwu et al, 2017; Osinuga et al, 2021), and thus high amounts of mechanical stress on the palmoplantar surfaces, which may be a contributing factor to the increased risk of ALM.
Our analysis also suggested that retired patients have an increased prevalence, incidence, and risk of ALM, especially given the advanced age of our retired population. The official United States Social Security full retirement age starts from 66 years (Social Security Administration, 2024), and the mean age of our retired population was 79 years. New literature has demonstrated that age-related loss of dermal extracellular matrix proteins leads to dermal matrix stiffening, which contributes to melanoma tumorigenesis and metastasis by signaling through mechanosensitive receptors (Marino-Bravante et al, 2024). Our analysis of a large, nationally representative dataset provides human data that supports the mechanistic findings of Marino-Bravante et al (2024). Clinically, although subtypes of melanoma, including lentigo maligna and desmoplastic melanoma, are known to affect primarily elderly patients on the head and neck (Long et al, 2023), our findings highlight the additional importance of carefully examining the glabrous and subungual surfaces in elderly patients for ALM. One key limitation to note with regard to the retired population is the fact that the previous occupations of retired patients are unreported in Optum CDM. Thus, we cannot parse out the attribution of the increased risk of ALM to occupational mechanical stress versus age-related changes in the dermal matrix. Another limitation in the retired cohort to consider is the possibility of detection bias. Patients with more flexibility in their daytime schedules may have improved ability to access health care compared with employed individuals, whose schedules may not allow for more frequent engagement with the healthcare system. This increased healthcare access may facilitate the ability to find ALM.
One potential argument against our hypothesis of the relationship between mechanical stress and ALM is the fact that we did not demonstrate an increased ALM risk in blue-collar workers. However, although this group of workers have traditionally been defined as those using physical labor outside the office, years of technology with machinery and computers have reduced the need for manual labor. Robots and automation have eased the physical demands of these jobs.
A limitation of our work is inherent in our retrospective study design using a database. In addition to the lack of information regarding the previous occupation of retired individuals or specific tasks of the blue-collar workers, Optum CDM relies on billing data to define the diagnosis of ALM and does not include pathology report data. Thus, we needed to rely on diagnostic codes for melanoma of the limb and anatomically specific excision codes to defining a diagnosis of ALM.
Despite these limitations, our findings suggest that the underlying etiology of ALM may be multifactorial, including the effect of aging skin on the dermal matrix. Moreover, we cannot exclude the effect of repeated mechanical stress on palmoplantar surfaces as a potential factor contributing to the development of ALM. Future studies are needed that include investigating detailed occupational exposure in those patients with an ALM diagnosis.
Materials and Methods
We conducted a population-based, retrospective cohort study using the Optum CDM. Optum CDM consists of deidentified data from health claims of United States individuals with Medicare Advantage and commercial insurance plans. We defined ALM cases as patients with ≥1 visit(s) with diagnostic codes for melanoma of the limb and ≥1 visit(s) with procedure codes for excision of hand skin, feet skin, fingernail, or toenail. Occupations are classified in Optum as “manager/owner/professional,” “white collar/health/civil service/military,” “blue collar,” and “homemaker/retired.” We subdivided the “homemaker/retired” group into 2 cohorts: “homemaker” (patients aged <65 years with commercial insurance) and “retired” (patients aged ≥65 years and patients aged <65 years old with Medicare).
Patient characteristics, including race (Asian, Black, Hispanic, White) were compared using Welch’s 2-sample t-test and chi-square analysis. The classification source for racial groups was predefined racial/ethnic variables set by Optum CDM and race was self-reported. Race and other patient characteristics were collected on the basis of predefined classifications within Optum CDM to evaluate demographic differences between study groups. Prevalence of ALM was calculated as the ratio of ALM diagnoses over the study population. Incidence rate of ALM per 1,000,000 person-years was calculated. Index date was defined as the earliest continuous enrollment start date. End date was defined as the earliest continuous enrollment end date or the study entry date. The OR of ALM diagnosis was calculated using multivariable logistic regression analysis adjusted for age, sex, race, and insurance type (ie, commercial, Medicare Advantage, or both).
Ethics Statement
This study was deemed exempt by the University of California, Los Angeles Institutional Review Board.
Data Availability Statement
The data analyzed in this study were made available through Optum Clinformatics Data Mart by a contractual agreement. Restrictions apply to the availability of these data according to the Optum Clinformatics Data Mart policies for licensed access. To access Optum Clinformatics Data Mart, Optum may be contacted at https://business.optum.com/en/data-analytics/life-sciences/real-world-data/claims-data.html for licensing requests.
ORCIDs
Lily Guo: http://orcid.org/0000-0002-3777-0039
Robin Kikuchi: http://orcid.org/0000-0002-8706-7309
April W. Armstrong: http://orcid.org/0000-0003-0064-8707
Suephy C. Chen: http://orcid.org/0000-0002-0678-7380
Conflict of Interest
AWA has served as a research investigator, scientific advisor, or speaker to AbbVie, Amgen, Almirall, Arcutis, ASLAN, Beiersdorf, BI, BMS, EPI, Incyte, Leo, UCB, Janssen, Lilly, Mindera, Nimbus, Novartis, Ortho, Sun, Dermavant, Dermira, Sanofi, Takeda, Organon, Regeneron, Pfizer, and Ventyx. The remaining authors state no conflict of interest.
Acknowledgments
Author Contributions
Conceptualization: SCC; Formal Analysis: LG; Resources: RK, AWA; Writing – Original Draft Preparation: LG; Writing –Review and Editing: LG, RK, AWA, SCC
Declaration of Generative Artificial Intelligence (AI) or Large Language Models (LLMs)
The author(s) did not use AI/LLM in any part of the research process and/or manuscript preparation.
Accepted manuscript published online XXX; corrected proof published online XXX
Footnotes
Cite this article as: JID Innovations 2026.100473
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data analyzed in this study were made available through Optum Clinformatics Data Mart by a contractual agreement. Restrictions apply to the availability of these data according to the Optum Clinformatics Data Mart policies for licensed access. To access Optum Clinformatics Data Mart, Optum may be contacted at https://business.optum.com/en/data-analytics/life-sciences/real-world-data/claims-data.html for licensing requests.