As of 2021, non-Hispanic white people in the United States had the highest incidence rates of skin cancer among all races and ethnicities. Skin cancer is one of the most commonly occurring cancers in the world. Furthermore, the United States is among the countries with the highest rates of skin cancer worldwide. Skin cancer in the U.S. There are a few different types of skin cancer and some are more deadly than others. Basal and squamous skin cancers are more common and less dangerous than melanomas. Among U.S. residents, skin cancer has been demonstrated to be more prevalent among men than women. Skin cancer is also more prevalent among older adults. With treatment and early detection, skin cancers have a high survival rate. Fortunately, in recent years the U.S. has seen a reduction in the rate of death from melanoma. Skin cancer prevention Avoiding and protecting exposed skin from the sun (and other sources of UV light) is the primary means of preventing skin cancer. However, a survey of U.S. adults from 2024 found that around ******* never used sunscreen.

Between the years 2012 and 2016, there were 372 invasive melanoma cases recorded in the Black population in the U.S. versus 1,725 such cases for Hispanics. The statistic illustrates the number of invasive melanoma cases in the U.S. between 2012 and 2016, by race/ethnicity.

From 2012 to 2016, there were around ** annual deaths from invasive melanoma among Hispanic females. The statistic illustrates the average annual number of female deaths attributed to invasive melanoma in the U.S. between 2012 and 2016, by race/ethnicity.

From 2012 to 2016, there were around 140 annual deaths among Hispanic males in the U.S. due to invasive melanoma. The statistic illustrates the average annual number of male deaths attributed to invasive melanoma in the U.S. between 2012 and 2016, by race/ethnicity.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated 8/14/2024.

Definition of "All Cancer Sites": ICD-O-3 Topography (Site) Codes C00.0 – C80.9 with histology codes including all invasive cancers of all sites except basal and squamous cell skin cancers, and in situ cancer cases of the urinary bladder. Rates are per 100,000 population and are age-adjusted to 2000 U.S. standard population. Rates based on case counts of 1-15 are suppressed per DHMH/MCR Data Use Policy and Procedures.

Analysis of ‘Age-Adjusted Incidence Rates for All Cancer Sites by Jurisdiction, Gender, and Race, Maryland 2009’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3520f596-6552-4020-b6a2-f55757e1a0a9 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Definition of "All Cancer Sites": ICD-O-3 Topography (Site) Codes C00.0 – C80.9 with histology codes including all invasive cancers of all sites except basal and squamous cell skin cancers, and in situ cancer cases of the urinary bladder. Rates are per 100,000 population and are age-adjusted to 2000 U.S. standard population. Rates based on case counts of 1-15 are suppressed per DHMH/MCR Data Use Policy and Procedures.

--- Original source retains full ownership of the source dataset ---

Definition of "All Cancer Sites": ICD-O-3 Topography (Site) Codes C00.0 – C80.9 with histology codes including all invasive cancers of all sites except basal and squamous cell skin cancers, and in situ cancer cases of the urinary bladder. Total includes cases reported as transexual, hermaphrodite, and unknown gender. Some cells are missing data due to suppression of low cell counts.

This dataset contains the summary data visualizations and clinical data of 65 samples from 36 Melanoma patients. Clinical data includes: Cancer Type, Number of Samples Per Patient, Mutation Count, Fraction Genome Altered, Race, Subtype, Mutation Burden, Purity, BRAF Mutation Status, CKIT Mutation Status, NRAS Mutation Status, Gender, Age, Best response, Administered treatment, Breslow Depth (mm). The plaintext components of the dataset can be downloaded as a tar file. The clinical data can be downloaded as a tsv file. The dataset includes copy-number segment data downloadable as .seg files and viewable via the Integrative Genomics Viewer.

In 2022, Australia had the fourth-highest total number of skin cancer cases worldwide and the highest age-standardized rate, with roughly 37 cases of skin cancer per 100,000 population. The graph illustrates the rate of skin cancer in the countries with the highest skin cancer rates worldwide in 2022.

Patients with Parkinson's disease are at high risk for developing melanoma, though current literature lacks details on the associated clinicopathologic characteristics. Our retrospective case-control study aimed to guide skin cancer surveillance recommendations for Parkinson's disease patients, focusing on tumor site. Our study included 70 adults with concurrent diagnoses of Parkinson's disease and melanoma from January 1, 2007 to January 1, 2020 at Duke University, and 102 age-, sex-, and race-matched controls. The head/neck region accounted for 39.5% of invasive melanomas in the case group compared to 25.3% in the control group, as well as 48.7% of non-invasive melanomas in the case group compared to 39.1% in the control group. Of note, 50% of metastatic melanomas in Parkinson's disease patients originated on the head and neck (n=3). Logistic regression showed 2.09 times higher odds of having a head/neck melanoma in our case group compared to the control group (odds ratio = 2.09, 95% confidence interval [1.13, 3.86]; p=0.020). Our study is limited by a small sample size, and our case cohort lacked diversity regarding race, ethnicity, sex, and geography. Validation of the reported trends could provide more robust guidance for melanoma surveillance in Parkinson's disease patients. We wish to acknowledge support from the Biostatistics, Epidemiology and Research Design (BERD) Methods Core. This content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. ... [Read More]

As of 2024, the annual wage of dermatologists employed in the United States ranged from around 190.3 thousand U.S. dollars per year to around 490.8 thousand U.S. dollars per year, by state. Washington had the highest annual wage for dermatologists in the United States, whereas West Virginia had the lowest.

BackgroundPost-transplant malignancy (PTM) causes long-term morbidity and mortality in heart transplant (HTx) recipients. However, the detailed characteristics or predictors of PTM are not well-known. We evaluated the incidence, characteristics, long-term outcomes, and predictors of de novo PTM using a single center large-volume database.MethodsWe retrospectively analyzed the types and characteristics of de novo PTM in 989 patients who underwent HTx. Univariate and multivariate logistic regression analyses were used for the PTM prediction model.ResultsTwo hundred and six patients (20.8%) had de novo PTMs (241 cancers) during a median follow-up of 11.5 years. PTM patients were older than non-PTM patients, received immunosuppressive therapy for a longer period, and were more likely to be male and white. Skin cancers were the most frequent types of malignancy (60.6%) followed by prostate (9.5%), lung (7.1%), and breast (4.1%) cancers. Although most cancers (88.8%) were surgically resected at initial presentation, about half (47.3%) recurred or progressed. Patients with skin cancer and non-skin cancer had significantly lower overall survival (P < 0.001) than patients without cancer. Older age (P < 0.001), white race (P = 0.001), and longer time receiving immunosuppressive therapy (P < 0.001) were independent predictors for PTM.ConclusionOlder age, white race, and longer administration of immunosuppressive therapies were independent risk factors for PTM, which was associated with increased mortality. Further research is necessary for the prevention and early detection of PTM in HTx recipients.

ObjectiveFollowing the introduction of highly active antiretroviral therapy (HAART) the risk of AIDS-defining cancers decreased but incidence of many non-AIDS-defining cancers has reportedly increased in those with HIV/AIDS. Whether melanoma risk has also changed in HIV/AIDS patients post-HAART is unknown and therefore we evaluated this in comparison with the risk before HAART.DesignSystematic review and meta-analysis.MethodsWe searched Medline, Embase and ISI science citation index databases to April 2013. All cohort studies of patients diagnosed with HIV/AIDS that permitted quantitative assessment of the association with melanoma were eligible. Detailed quality assessment of eligible studies was conducted, focussing particularly on adjustment for ethnicity, a priori considered essential for an unbiased assessment of melanoma risk. Data were pooled using a random effects model.ResultsFrom 288 articles, we identified 21 that met the inclusion criteria, 13 presenting data for the post-HAART era and 8 for the pre-HAART era. Post-HAART the pooled relative risk (pRR) for the association between HIV/AIDS and melanoma was 1.26 (95% CI, 0.97–1.64) and 1.50 (95% CI 1.12–2.01) among studies that accounted for ethnicity, with evidence of significant heterogeneity (P = 0.004, I2 = 55.5). Pre-HAART pRRs were 1.26 (95% CI 1.11–1.43; Phet = 0.82) and 1.28 (95% CI 1.10–1.49) among studies adjusted for ethnicity.ConclusionsPeople with HIV/AIDS remain at a significantly increased risk of developing melanoma in the post-HAART era. White skinned people with HIV/AIDS should be screened regularly and counselled against excessive sun exposure.

Supplemental Methods This study utilized a retrospective cohort design leveraging data from the TriNetX research network, which aggregates electronic health records (EHRs) from 97 healthcare organizations (HCOs). The analysis compared two patient cohorts based on cardiovascular disease (CVD) status, both of whom had been diagnosed with skin cancers, including malignant melanoma (ICD-10: C43), squamous cell carcinoma (C44.92), or basal cell carcinoma (C44.91), and underwent Mohs micrographic surgery (CPT: 17311). The CVD cohort included patients with a concurrent diagnosis of cardiovascular disease, including hypertension (ICD-10: I10-I15), hypertensive crisis (I16), or ischemic heart disease (I20-I25), whereas the non-CVD cohort consisted of patients with similar skin cancer diagnoses and Mohs procedures but without any history of these cardiovascular conditions. The study population was limited to patients whose diagnoses and procedures occurred between January 1, 2015, and December 31, 2023. Patients with an index event occurring more than 20 years prior to the study period were excluded. The index event was defined as the date of the first recorded visit (TNX:Visit) associated with both the skin cancer diagnosis and the Mohs micrographic surgery procedure. A follow-up visit (CVD f/u) was required within six months after the initial visit to ensure patients remained engaged with healthcare services and to capture subsequent cardiovascular events. The time window for outcomes assessment began one day after the index event and extended 30 days and 3-months postoperatively. The primary outcomes included acute myocardial infarction (AMI; ICD-10: I21) and stroke (ICD-10: I63.9). Patients who had a documented history of these outcomes before the study time window were excluded from the risk analysis and survival analysis. To minimize confounding, propensity score matching (PSM) was performed at a 1:1 ratio between the CVD and non-CVD cohorts. Matching variables included age, sex, race, ethnicity, comorbid conditions (hypertension, diabetes, chronic kidney disease, hyperlipidemia, obesity), medication use (anticoagulants, beta-blockers, lipid-lowering agents), and laboratory values (e.g., tobacco smoking status). After matching, each cohort consisted of 27,758 patients. The study employed multiple statistical approaches to compare outcomes between the cohorts. Risk analysis was performed to calculate risk differences, risk ratios, and odds ratios for AMI and stroke. Kaplan-Meier survival analysis was conducted to assess differences in survival probability, applying censoring to account for patients lost to follow-up. Number of instances analysis was used to quantify the frequency of AMI and stroke occurrences within the 30-day and 3-month time window.

Supplementary Table 4: Multivariable Analysis of Specialty Clinics and Association with No-Shows. These separate multivariable regression analyses split the patient appointments into specialty clinics such as alopecia, melanoma, patch testing, high risk skin cancer, etc. When stratified by specialty clinics, most patient characteristics were not significantly associated with no-shows, though being of Black race was significantly associated with no-shows in virtual, high-risk skin cancer (HRSC), injection, and urgent visits (P<.01). Having public insurance was significantly associated with higher odds of no-show in HRSC, patch testing, and urgent visits (P<.02).

Data by medical encounter for the following conditions by age, race/ethnicity, and gender:

Acute Myocardial Infarction (AMI) Asthma Bladder Cancer Brain Cancer Coronary Heart Disease (CHD) Colorectal Cancer Chronic Obstructive Pulmonary Disease (COPD)/Chronic Lower Respiratory Diseases Diabetes Female Breast Cancer Female Reproductive Cancer Heart Failure Hyperlipidemia (High Blood Cholesterol) Kidney Cancer Leukemia Liver Cancer Lung Cancer Lupus and Connective Tissue Disorders Melanoma of the Skin Non-Hodgkin's Lymphoma Non-melanoma Skin Cancer Overall Cancer Overall Heart Disease Overall Hypertensive Diseases Pancreatic Cancer Prostate Cancer Stroke Thyroid Cancer

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.
Blank Cells: Rates not calculated for fewer than 11 events. Rates not calculated in cases where zip code is unknown. Geography not reported where there are no cases reported in a given year. SES: Is the median household income by SRA community. Data for SRAs only.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS), 2021. California Department of Health Care Access and Information (HCAI), Emergency Department Discharge Database and Patient Discharge Database, 2021. SANDAG Population Estimates, 2021 (vintage: 09/2022). Population estimates were derived using the 2010 Census and data should be considered preliminary. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, March 2023.

2021 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2021CommunityProfilesDataGuideandDataDictionaryDashboards_16802003011430/HomePage

From the dbGaP study description: "In this study, we performed paired tumor/normal long insert whole genome and exome sequencing and tumor RNA sequencing on primary or metastatic acral melanoma tumors collected from 34 patients. Patients were enrolled from either Vanderbilt University or the Memorial Sloan-Kettering Cancer Center. We report an integrated analysis of DNA and RNA sequencing data to describe genomic and transcriptomic characteristics of acral melanoma. The data includes information about the study, subject phenotype datasets (de-identified subject IDs, disease onset age, subject gender, and subject race), and molecular datasets (SRA run information)."

BackgroundMelanoma is the fourth leading cause of cancer-related death worldwide. The continuous exploration and reporting of risk factors of melanoma is important for standardizing and reducing the incidence of the disease. Calcium signaling is a promising therapeutic target for melanoma; however, the relationship between total serum calcium levels and melanoma development remains unclear.MethodsIn this study, we included patients with melanoma from the National Health and Nutrition Examination Survey (NHANES) database from 2003 to 2006 and from 2009 to 2016. The baseline clinical characteristics of the participants were analyzed using the chi-square and rank-sum tests. Subsequently, a fitted model was constructed to evaluate the relationship between total serum calcium levels and melanoma development. The performance of total serum calcium levels and covariates in predicting the risk of melanoma was assessed based on ROC curves. Finally, LASSO regression analysis was performed using the “glmnet” R package to identify clinical characteristics associated with melanoma.ResultsA total of 13,432 participants were included in this study. Age, race, household poverty-to-income ratio, response of the skin to sunlight after a certain period of non-exposure, wearing long-sleeved shirts, frequency of sunscreen use, and arthritis were significantly correlated with the development of melanoma. The p-values of total serum calcium levels in three fitted models were 

Data by medical encounter for the following conditions by age, race/ethnicity, and sex (gender):

Acute Myocardial Infarction (AMI) Asthma Bladder Cancer Brain Cancer Coronary Heart Disease (CHD) Colorectal Cancer Chronic Kidney Disease (CKD) Chronic Obstructive Pulmonary Disease (COPD)/Chronic Lower Respiratory Diseases Diabetes Female Breast Cancer Female Reproductive Cancer Heart Failure Hyperlipidemia (High Blood Cholesterol) Kidney Cancer Leukemia Liver Cancer Lung Cancer Lupus and Connective Tissue Disorders Melanoma of the Skin Non-Hodgkin's Lymphoma Non-melanoma Skin Cancer Overall Cancer Overall Heart Disease Overall Hypertensive Diseases Pancreatic Cancer Prostate Cancer Stroke Thyroid Cancer

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population. Blank Cells: Events less than 11 are suppressed. Starting with data year 2022, geographies with less than 20,000 population contain no age-adjusted rates and all rates based on events <20 are suppressed due to statistical instability. Rates not calculated in cases where zip code is unknown. SES: Is the median household income by Subregional Area (SRA) community. Data for SRA only.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS), 2022. California Department of Health Care Access and Information (HCAI), Emergency Department Discharge Database and Patient Discharge Database, 2022. SANDAG Population Estimates, 2022 (v11/23). 2022 population estimates were derived from the 2020 decennial census. Comparison of rates to prior years may not be appropriate. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, May 2024.

2022 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2022COREDataGuideandDataDictionary/Home

Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as "Do you have serious difficulty walking or climbing stairs?" or "Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]".

To improve the efficiency and relevance of our analysis, we removed certain attributes from the original BRFSS dataset. Many of the 279 original attributes included administrative codes, metadata, or survey-specific variables that do not contribute meaningfully to heart disease prediction—such as respondent IDs, timestamps, state-level identifiers, and detailed lifestyle questions unrelated to cardiovascular health. By focusing on a carefully selected subset of 18 attributes directly linked to medical, behavioral, and demographic factors known to influence heart health, we streamlined the dataset. This not only reduced computational complexity but also improved model interpretability and performance by eliminating noise and irrelevant information. All predicting variables could be divided into 4 broad categories:

1. Demographic factors: sex, age category (14 levels), race, BMI (Body Mass Index)

2. Diseases: weather respondent ever had such diseases as asthma, skin cancer, diabetes, stroke or kidney disease (not including kidney stones, bladder infection or incontinence)

3. Unhealthy habits:

   • Smoking - respondents that smoked at least 100 cigarettes in their entire life (5 packs = 100 cigarettes)
   • Alcohol Drinking - heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week

4. General Health:

   • Difficulty Walking - weather respondent have serious difficulty walking or climbing stairs
   • Physical Activity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job
   • Sleep Time - respondent’s reported average hours of sleep in a 24-hour period
   • Physical Health - number of days being physically ill or injured (0-30 days)
   • Mental Health - number of days having bad mental health (0-30 days)
   • General Health - respondents declared their health as ’Excellent’, ’Very good’, ’Good’ ,’Fair’ or ’Poor’

Below is a description of the features collected for each patient: