As of 2022, 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, around ****** cases of invasive melanoma were recorded among males in the U.S. from all races, with around ****** cases among White, Non-Hispanic males. The statistic illustrates the number of invasive melanoma cases in U.S. males 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.

From 2012 to 2016, around ****** cases of invasive melanoma were recorded among females in the U.S. from all races.The statistic illustrates the number of invasive melanoma cases in U.S. females between 2012 and 2016, by race/ethnicity.

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.

From 2012 to 2016, there were around 237 annual deaths among Hispanics in the U.S. due to invasive melanoma. The statistic illustrates the average annual number of deaths attributed to invasive melanoma in the U.S. between 2012 and 2016, by race/ethnicity.

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.

Population based cancer incidence rates were abstracted from National Cancer Institute, State Cancer Profiles for all available counties in the United States for which data were available. This is a national county-level database of cancer data that are collected by state public health surveillance systems. All-site cancer is defined as any type of cancer that is captured in the state registry data, though non-melanoma skin cancer is not included. All-site age-adjusted cancer incidence rates were abstracted separately for males and females. County-level annual age-adjusted all-site cancer incidence rates for years 2006–2010 were available for 2687 of 3142 (85.5%) counties in the U.S. Counties for which there are fewer than 16 reported cases in a specific area-sex-race category are suppressed to ensure confidentiality and stability of rate estimates; this accounted for 14 counties in our study. Two states, Kansas and Virginia, do not provide data because of state legislation and regulations which prohibit the release of county level data to outside entities. Data from Michigan does not include cases diagnosed in other states because data exchange agreements prohibit the release of data to third parties. Finally, state data is not available for three states, Minnesota, Ohio, and Washington. The age-adjusted average annual incidence rate for all counties was 453.7 per 100,000 persons. We selected 2006–2010 as it is subsequent in time to the EQI exposure data which was constructed to represent the years 2000–2005. We also gathered data for the three leading causes of cancer for males (lung, prostate, and colorectal) and females (lung, breast, and colorectal). The EQI was used as an exposure metric as an indicator of cumulative environmental exposures at the county-level representing the period 2000 to 2005. A complete description of the datasets used in the EQI are provided in Lobdell et al. and methods used for index construction are described by Messer et al. The EQI was developed for the period 2000– 2005 because it was the time period for which the most recent data were available when index construction was initiated. The EQI includes variables representing each of the environmental domains. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., L. Messer, K. Rappazzo , C. Gray, S. Grabich , and D. Lobdell. County-level environmental quality and associations with cancer incidence#. Cancer. John Wiley & Sons Incorporated, New York, NY, USA, 123(15): 2901-2908, (2017).

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 dataset provides insights into one-year survival rates from all cancers, serving as a key indicator of early cancer outcomes. It measures the proportion of individuals diagnosed with an invasive cancer who survive for at least one year following their diagnosis. The dataset includes all invasive tumours classified under ICD-10 codes C00 to C97, excluding non-melanoma skin cancer (C44). It supports analysis across different population groups and geographies, including ethnicity, deprivation levels, and the Birmingham and Solihull (BSol) area.

Rationale

Improving one-year survival rates is a critical goal in cancer care, as it reflects the effectiveness of early diagnosis and initial treatment. This indicator helps monitor progress in reducing early mortality from cancer and supports targeted interventions to improve outcomes.

Numerator

The numerator includes individuals who were diagnosed with a specific type of cancer and died from the same type of cancer within one year of diagnosis. Only invasive cancers are included, as defined by ICD-10 codes C00 to C97, excluding non-melanoma skin cancer (C44). Data is sourced from the National Cancer Registration and Analysis Service (NCRAS).

Denominator

The denominator comprises all individuals diagnosed with an invasive cancer (ICD-10 codes C00 to C97, excluding C44) within a five-year period. This data is also sourced from the National Cancer Registration and Analysis Service (NCRAS).

Caveats

This dataset uses a simplified methodology that differs from the national calculation of one-year cancer survival. As a result, the figures presented here may not align with nationally published statistics. However, this approach enables the provision of survival data disaggregated by ethnicity, deprivation, and local geographies such as BSol, which is not always possible with national data.

External references

For more information, visit the National Cancer Registration and Analysis Service (NCRAS).

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

aOne or more of the following conditions was reported at study entry: prevalent cancer (except non melanoma skin), heart disease, stroke, respiratory disease (chronic bronchitis, emphysema, asthma), currently sick, or weight loss of ≥10 lbs. in past year.bRate per 100,000 standardized to the age-distribution of the CPS-II men.cCox proportional hazards model, adjusted for age, race, education, physical activity, alcohol use, marital status, aspirin use, fat consumption, and vegetable consumptiondNone of the conditions listed in footnote(a) were reported.Rates and relative risks of death from any cause among men according to BMI, smoking, prevalent disease status and race, CPS-II 1982-2010.

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.

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.
*The COVID-19 pandemic was associated with increases in all-cause mortality. COVID-19 deaths have affected the patterns of mortality including those of Non-Communicable conditions.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS). California Department of Health Care Access and Information (HCAI), Emergency Department Database and Patient Discharge Database, 2020. SANDAG Population Estimates, 2020 (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, February 2023.

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.