Disparities in melanoma incidence and mortality in rural versus urban Michigan

Oregon primary care providers as a frontline defense in the War on Melanoma™: improving access to melanoma education

Melanoma is one of the deadliest forms of skin cancer but is typically cured with surgical excision when detected early. As an access point to medical care, primary care providers (PCP) play an integral role in early skin cancer detection. However, limited time for examinations and dermatologic training may present barriers to effective skin examination in the primary care setting. As a facet of Oregon Health & Science University's War on Melanoma™ (WoM), our multi-pronged outreach initiative aims to provide PCPs across Oregon with free, convenient, and effective melanoma education. The WoM PCP education campaign was disseminated starting in May 2019 through primary care networks throughout the state of Oregon to 12,792 PCPs, and education was delivered across several platforms: online multimedia tools, large group didactics, individualized practice-based sessions, and in-person distribution of materials to clinics. To date, 829 PCPs have participated in the online Melanoma Toolkit for Early Detection curriculum, 1,874 providers have attended CME didactics, and 9 clinics have received facilitated meetings by Oregon Rural Practice-based Research Network. Eighty-three clinics (comprising 770 providers) were visited on-site and provided educational materials, and more than 150 PCPs have received a free smartphone dermatoscope to aid in skin examination and e-consultation. OHSU's WoM has successfully implemented a multifaceted approach to provide accessible melanoma education to PCPs across the state of Oregon. As a result, we hope to encourage appropriate skin examination in the primary care setting and improve PCPs' diagnostic accuracy and confidence in pigmented lesion evaluation.

Spatial clusters and temporal trends of cutaneous malignant melanoma mortality in Spain

OBJECTIVE

To analyse time trends in mortality due to cutaneous malignant melanoma (CMM) in Spain between 1980 and 2020, and to identify possible spatial clusters of provinces with an excess risk of CMM mortality during the period 2011-2020.

METHODS

Joinpoint regression models were used to assess temporal trends in data from the Spanish National Institute of Statistics. Crude rates, standardized mortality ratio (SMR), smoothed relative risk (RR), and posterior probabilities (PP) of RR >1 during the period 2011-2020 were calculated. The Global Moran I index was used to assess global spatial autocorrelation.

RESULTS

Two time-periods were detected in women: a significant increase during 1980-1994 (annual percent change [APC], 6.5% per year) and rate stabilization during 1994-2020 (nonsignificant APC, -0.17%). A similar pattern was observed in men, with three periods comprising a significant increase during 1980-1985 (APC, 16.59%), a slowing of the increase during 1985-1998 (APC, 4.40%), and stabilization during 1998-2020 (nonsignificant APC, 0.37%). Spatial analysis showed greater spatial heterogeneity with an east-north pattern in men compared with the pattern in women, which tended to be concentrated in north-western areas.

CONCLUSION

Mortality rates associated with CMM in Spain have remained stable in recent years. There were provincial clusters that exhibited an excess risk of mortality from CMM, with different patterns according to sex.

Patterns and trends in melanoma mortality in the United States, 1999-2020

INTRODUCTION

Melanoma, a deadly form of skin cancer, has witnessed a notable increase in incidence over the past decades. Despite advancements in treatment, it remains a significant cause of cancer mortality. Understanding demographic trends and variations in melanoma mortality is crucial for addressing disparities and implementing effective interventions.

METHODS

Using the Centers for Disease Control Wide Ranging Online Data for Epidemiologic Research (CDC WONDER) database, we analyzed melanoma mortality data in the United States from 1999 to 2020. Data were stratified by demographic and regional variables, and age-adjusted mortality rates were calculated. Descriptive analysis was performed and Joinpoint regression analysis was employed to identify temporal trends.

RESULTS

Between 1999 and 2020, there were 184,416 melanoma-related deaths in the United States Overall, the age-adjusted mortality rate declined from 2.7 to 2.0 per 100,000 people at a rate of -1.3% annually, with significant variations across demographic groups and regions. Men, non-Hispanic White individuals, and those aged > 65 experienced higher mortality rates. Non-Hispanic White individuals noted the steepest decrease in AAMR after 2013 at a rate of -6.1% annually. Disparities were seen by geographic density, with rural populations exhibiting higher mortality compared to their urban and suburban counterparts.

CONCLUSION

The study highlights a significant reduction in melanoma mortality in the U.S. since 2013, potentially attributed to advancements in diagnostic techniques such as dermoscopy and the introduction of immune checkpoint inhibitors. Disparities persist, particularly among rural populations. Targeted interventions focusing on increased screening and education are warranted to further mitigate melanoma mortality and address demographic disparities.

Rural Melanoma Patients Have Less Surgery and Higher Melanoma-Specific Mortality

BACKGROUND

Melanoma causes most skin cancer-related deaths, and disparities in mortality persist. Rural communities, compared to urban, face higher levels of poverty and more barriers to care, leading to higher stage at presentation and shorter survival in melanoma. To further evaluate these disparities, we sought to assess the association between rurality and melanoma cause-specific mortality and receipt of recommended surgery in a national cohort.

METHODS

Patients with primary non-ocular, cutaneous melanoma from the SEER database, 2000-2017, were included. Outcomes included melanoma-specific survival and receipt of recommended surgery. Rurality was based on Rural-Urban Continuum Codes. Variables included age, sex, race, ethnicity, income, and stage. Multivariate regression models assessed the effect of rurality on survival and receipt of recommended surgery.

RESULTS

103,606 patients diagnosed with non-ocular cutaneous primary melanoma met criteria during this period. 93.3% (n = 96620) were in urban areas and 6.7% (n = 6986) were in rural areas. On multivariate regression controlling for age, sex, race, ethnicity, and stage patients living in a rural area were less likely to receive recommended surgery (aOR .52, 95% CI: .29-.90, P = .02) and had increased hazard of melanoma-specific mortality (aHR 1.19, 95% CI: 1.02-1.40, P = .03) even after additionally controlling for surgery receipt.

CONCLUSION

Using a large national cohort, our study found that rural patients were less likely to receive recommended surgery and had shorter melanoma cause-specific survival. Our findings highlight the importance of access to cancer care in rural areas and how this ultimately effects survival for these patients.

The influence of rurality on melanoma diagnosis in Indiana: A retrospective cohort study

BACKGROUND

Research from across the United States has shown that rurality is associated with worse melanoma outcomes. In Indiana, nearly a quarter of all residents live in rural counties and an estimated 2180 cases of melanoma will be diagnosed in 2023.

AIMS

This study examines how geographical location affects the stage of melanoma diagnosis in Indiana, aiming to identify and address rural health disparities to ultimately ensure equitable care.

METHODS AND RESULTS

Demographics and disease characteristics of patients diagnosed with melanoma at Indiana University Health from January 2017 to September 2022 were compared using Students t-tests, Wilcoxon tests, chi-squared or Fisher's exact tests. Patients from rural areas presented with more pathological stage T3 melanomas (15.0% vs. 3.5%, p < 0.001) in contrast to their urban counterparts. Additionally, rural patients presented with fewer clinical stage I melanomas (80.8% vs. 89.3%) and more clinical stage II melanomas (19.2% vs. 8.1%), compared to urban patients, with no stage III (p = 0.028). Concerningly, a significantly higher percentage of the rural group (40.7%) had a personal history of BCC compared to the urban group (22.6%) (p = 0.005) and fewer rural patients (78.0%) compared to urban patients (89.4%) received surgical treatment (p = 0.016).

CONCLUSION

Patients from rural counties in Indiana have higher pathological and clinical stage melanoma at diagnosis compared to patients from urban counties. Additionally fewer rural patients receive surgical treatment and may be at higher risk of developing subsequent melanomas.

Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review

BACKGROUND

Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma.

OBJECTIVE

The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma.

METHODS

A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives.

RESULTS

We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%.

CONCLUSIONS

Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability.