Users can access data about cancer statistics in the United States including but not limited to searches by type of cancer and race, sex, ethnicity, age at diagnosis, and age at death. Background Surveillance Epidemiology and End Results (SEER) database’s mission is to provide information on cancer statistics to help reduce the burden of disease in the U.S. population. The SEER database is a project to the National Cancer Institute. The SEER database collects information on incidence, prevalence, and survival from specific geographic areas representing 28 percent of the United States population. User functionality Users can access a variety of reso urces. Cancer Stat Fact Sheets allow users to look at summaries of statistics by major cancer type. Cancer Statistic Reviews are available from 1975-2008 in table format. Users are also able to build their own tables and graphs using Fast Stats. The Cancer Query system provides more flexibility and a larger set of cancer statistics than F ast Stats but requires more input from the user. State Cancer Profiles include dynamic maps and graphs enabling the investigation of cancer trends at the county, state, and national levels. SEER research data files and SEER*Stat software are available to download through your Internet connection (SEER*Stat’s client-server mode) or via discs shipped directly to you. A signed data agreement form is required to access the SEER data Data Notes Data is available in different formats depending on which type of data is accessed. Some data is available in table, PDF, and html formats. Detailed information about the data is available under “Data Documentation and Variable Recodes”.

Context

COVID-19 Cases and Deaths by Race/Ethnicity

Content

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The age-adjusted rates are directly standardized using the 2018 ASRH Connecticut population estimate denominators (available here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Annual-State--County-Population-with-Demographics).

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age-adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

This dataset will be updated on a daily basis. Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differs from the timestamp in DPH's daily PDF reports.

Acknowledgements

Thanks to catalog.data.gov.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

This dataset contains Age-Adjusted Rate, Confidence Interval, Average Annual Count, and Trend field information for US States for the average 5 year span from 2012 to 2016.Data is segmented by sex and age, with fields describing the sex and age group tabulated.For more information, visit statecancerprofiles.cancer.gov Data NotationsState Cancer Registries may provide more current or more local data.† Incidence rates (cases per 100,000 population per year) are age-adjusted to the 2000 US standard population seer.cancer.gov/stdpopulations/stdpop.19ages.html. Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Rates calculated using SEER*Stat. [seer.cancer.gov/seerstat]Population counts for denominators are based on Census populations as modified [seer.cancer.gov/popdata] by NCI. The 1969-2016 US Population Data File [seer.cancer.gov/popdata] is used for SEER and NPCR incidence rates.‡ Incidence data come from different sources. Due to different years of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. Please refer to the source for each area for additional information. Rates and trends are computed using different standards for malignancy. For more information see malignant.html.^ All Stages refers to any stage in the Surveillance, Epidemiology, and End Results (SEER) summary stage [seer.cancer.gov/tools/ssm].Healthy People 2020 Objectives [www.healthypeople.gov]provided by the Centers for Disease Control and Prevention [www.cdc.gov]. Michigan Data do not include cases diagnosed in other states for those states in which the data exchange agreement specifically prohibits the release of data to third parties.Trend Data not available for Nevada.Data Source Field Key:(1) Source: CDC's National Program of Cancer Registries Cancer Surveillance System (NPCR-CSS) November 2018 data submission and SEER November 2018 submission as published in United States Cancer Statistics nccd.cdc.gov/uscs Source: State Cancer Registry and the CDC's National Program of Cancer Registries Cancer Surveillance System (NPCR-CSS) November 2018 data submission. State rates include rates from metropolitan areas funded by SEER [seer.cancer.gov/registries].(6) Source: State Cancer Registry and the CDC's National Program of Cancer Registries Cancer Surveillance System (NPCR-CSS) November 2018 data submission.(7) Source: SEER November 2018 submission.8 Source: Incidence data provided by the SEER Program. [seer.cancer.gov] AAPCs are calculated by the Joinpoint Regression Program [surveillance.cancer.gov/joinpoint] and are based on APCs. Data are age-adjusted to the 2000 US standard population www.seer.cancer.gov/stdpopulations/single_age.html. Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Population counts for denominators are based on Census populations as modified by NCI. The 1969-2017 US Population Data [seer.cancer.gov/popdata] File is used with SEER November 2018 data. Please note that the data comes from different sources. Due to different years [statecancerprofiles.cancer.gov/historicaltrend/differences.html] of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. [seer.cancer.gov/seerstat] Please refer to the source for each graph for additional information. Some data are not available [http://statecancerprofiles.cancer.gov/datanotavailable.html] for combinations of geography, cancer site, age, and race/ethnicity.

BackgroundColorectal cancer (CRC) incidence rates have increased in younger individuals worldwide. We examined the most recent early- and late-onset CRC rates for the US.MethodsAge-standardized incidence rates (ASIR, per 100,000) of CRC were calculated using the US Cancer Statistics Database’s high-quality population-based cancer registry data from the entire US population. Results were cross-classified by age (20-49 [early-onset] and 50-74 years [late-onset]), race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, American Indian/Alaskan Native, Asian/Pacific Islander), sex, anatomic location (proximal, distal, rectal), and histology (adenocarcinoma, neuroendocrine).ResultsDuring 2001 through 2018, early-onset CRC rates significantly increased among American Indians/Alaskan Natives, Hispanics, and Whites. Compared to Whites, early-onset CRC rates are now 21% higher in American Indians/Alaskan Natives and 6% higher in Blacks. Rates of early-onset colorectal neuroendocrine tumors have increased in Whites, Blacks, and Hispanics; early-onset colorectal neuroendocrine tumor rates are 2-times higher in Blacks compared to Whites. Late-onset colorectal adenocarcinoma rates are decreasing, while late-onset colorectal neuroendocrine tumor rates are increasing, in all racial/ethnic groups. Late-onset CRC rates remain 29% higher in Blacks and 15% higher in American Indians/Alaskan Natives compared to Whites. Overall, CRC incidence was higher in men than women, but incidence of early-onset distal colon cancer was higher in women.ConclusionsThe early-onset CRC disparity between Blacks and Whites has decreased, due to increasing rates in Whites—rates in Blacks have remained stable. However, rates of colorectal neuroendocrine tumors are increasing in Blacks. Blacks and American Indians/Alaskan Natives have the highest rates of both early- and late-onset CRC.ImpactOngoing prevention efforts must ensure access to and uptake of CRC screening for Blacks and American Indians/Alaskan Natives.

Analysis of ‘COVID-19 Cases and Deaths by Race/Ethnicity’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3fdc6593-c708-4a6a-8073-5ca862caa279 on 27 January 2022.

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

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf

Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More infor

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

This dataset contains Cancer Incidence data for Colorectal Cancer (All Stages^) including: Age-Adjusted Rate, Confidence Interval, Average Annual Count, and Trend field information for US States for the average 5 year span from 2016 to 2020.Data are segmented by sex (Both Sexes, Male, and Female) and age (All Ages, Ages Under 50, Ages 50 & Over, Ages Under 65, and Ages 65 & Over), with field names and aliases describing the sex and age group tabulated.For more information, visit statecancerprofiles.cancer.govData NotationsState Cancer Registries may provide more current or more local data.TrendRising when 95% confidence interval of average annual percent change is above 0.Stable when 95% confidence interval of average annual percent change includes 0.Falling when 95% confidence interval of average annual percent change is below 0.† Incidence rates (cases per 100,000 population per year) are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84, 85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used for SEER and NPCR incidence rates.‡ Incidence Trend data come from different sources. Due to different years of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. Please refer to the source for each area for additional information.Rates and trends are computed using different standards for malignancy. For more information see malignant.^ All Stages refers to any stage in the Surveillance, Epidemiology, and End Results (SEER) summary stage.Data Source Field Key(1) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(5) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(6) Source: National Program of Cancer Registries SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention (based on the 2022 submission).(7) Source: SEER November 2022 submission.(8) Source: Incidence data provided by the SEER Program. AAPCs are calculated by the Joinpoint Regression Program and are based on APCs. Data are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84,85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used with SEER November 2022 data.Some data are not available, see Data Not Available for combinations of geography, cancer site, age, and race/ethnicity.Data for the United States does not include data from Nevada.Data for the United States does not include Puerto Rico.

A database based on a random sample of the noninstitutionalized population of the United States, developed for the purpose of studying the effects of demographic and socio-economic characteristics on differentials in mortality rates. It consists of data from 26 U.S. Current Population Surveys (CPS) cohorts, annual Social and Economic Supplements, and the 1980 Census cohort, combined with death certificate information to identify mortality status and cause of death covering the time interval, 1979 to 1998. The Current Population Surveys are March Supplements selected from the time period from March 1973 to March 1998. The NLMS routinely links geographical and demographic information from Census Bureau surveys and censuses to the NLMS database, and other available sources upon request. The Census Bureau and CMS have approved the linkage protocol and data acquisition is currently underway. The plan for the NLMS is to link information on mortality to the NLMS every two years from 1998 through 2006 with research on the resulting database to continue, at least, through 2009. The NLMS will continue to incorporate data from the yearly Annual Social and Economic Supplement into the study as the data become available. Based on the expected size of the Annual Social and Economic Supplements to be conducted, the expected number of deaths to be added to the NLMS through the updating process will increase the mortality content of the study to nearly 500,000 cases out of a total number of approximately 3.3 million records. This effort would also include expanding the NLMS population base by incorporating new March Supplement Current Population Survey data into the study as they become available. Linkages to the SEER and CMS datasets are also available. Data Availability: Due to the confidential nature of the data used in the NLMS, the public use dataset consists of a reduced number of CPS cohorts with a fixed follow-up period of five years. NIA does not make the data available directly. Research access to the entire NLMS database can be obtained through the NIA program contact listed. Interested investigators should email the NIA contact and send in a one page prospectus of the proposed project. NIA will approve projects based on their relevance to NIA/BSR''s areas of emphasis. Approved projects are then assigned to NLMS statisticians at the Census Bureau who work directly with the researcher to interface with the database. A modified version of the public use data files is available also through the Census restricted Data Centers. However, since the database is quite complex, many investigators have found that the most efficient way to access it is through the Census programmers. * Dates of Study: 1973-2009 * Study Features: Longitudinal * Sample Size: ~3.3 Million Link: *ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00134

This dataset contains Cancer Incidence data for Breast Cancer (Late Stage^) including: Age-Adjusted Rate, Confidence Interval, Average Annual Count, and Trend field information for US States for the average 5 year span from 2016 to 2020.Data are for females segmented by age (All Ages, Ages Under 50, Ages 50 & Over, Ages Under 65, and Ages 65 & Over), with field names and aliases describing the sex and age group tabulated.For more information, visit statecancerprofiles.cancer.govData NotationsState Cancer Registries may provide more current or more local data.TrendRising when 95% confidence interval of average annual percent change is above 0.Stable when 95% confidence interval of average annual percent change includes 0.Falling when 95% confidence interval of average annual percent change is below 0.† Incidence rates (cases per 100,000 population per year) are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84, 85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used for SEER and NPCR incidence rates.‡ Incidence Trend data come from different sources. Due to different years of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. Please refer to the source for each area for additional information.Rates and trends are computed using different standards for malignancy. For more information see malignant.^ Late Stage is defined as cases determined to be regional or distant. Due to changes in stage coding, Combined Summary Stage (2004+) is used for data from Surveillance, Epidemiology, and End Results (SEER) databases and Merged Summary Stage is used for data from National Program of Cancer Registries databases. Due to the increased complexity with staging, other staging variables maybe used if necessary.Data Source Field Key(1) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(5) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(6) Source: National Program of Cancer Registries SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention (based on the 2022 submission).(7) Source: SEER November 2022 submission.(8) Source: Incidence data provided by the SEER Program. AAPCs are calculated by the Joinpoint Regression Program and are based on APCs. Data are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84,85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used with SEER November 2022 data.Some data are not available, see Data Not Available for combinations of geography, cancer site, age, and race/ethnicity.Data for the United States does not include data from Nevada.Data for the United States does not include Puerto Rico.

This dataset contains Cancer Incidence data for Prostate Cancer(All Stages^) including: Age-Adjusted Rate, Confidence Interval, Average Annual Count, and Trend field information for US States for the average 5 year span from 2016 to 2020.Data are for males segmented age (All Ages, Ages Under 50, Ages 50 & Over, Ages Under 65, and Ages 65 & Over), with field names and aliases describing the sex and age group tabulated.For more information, visit statecancerprofiles.cancer.govData NotationsState Cancer Registries may provide more current or more local data.TrendRising when 95% confidence interval of average annual percent change is above 0.Stable when 95% confidence interval of average annual percent change includes 0.Falling when 95% confidence interval of average annual percent change is below 0.† Incidence rates (cases per 100,000 population per year) are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84, 85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used for SEER and NPCR incidence rates.‡ Incidence Trend data come from different sources. Due to different years of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. Please refer to the source for each area for additional information.Rates and trends are computed using different standards for malignancy. For more information see malignant.^ All Stages refers to any stage in the Surveillance, Epidemiology, and End Results (SEER) summary stage.Data Source Field Key(1) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(5) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(6) Source: National Program of Cancer Registries SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention (based on the 2022 submission).(7) Source: SEER November 2022 submission.(8) Source: Incidence data provided by the SEER Program. AAPCs are calculated by the Joinpoint Regression Program and are based on APCs. Data are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84,85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used with SEER November 2022 data.Some data are not available, see Data Not Available for combinations of geography, cancer site, age, and race/ethnicity.Data for the United States does not include data from Nevada.Data for the United States does not include Puerto Rico.

This dataset contains Cancer Incidence data for Lung Cancer (All Stages^) including: Age-Adjusted Rate, Confidence Interval, Average Annual Count, and Trend field information for US States for the average 5 year span from 2016 to 2020.Data are segmented by sex (Both Sexes, Male, and Female) and age (All Ages, Ages Under 50, Ages 50 & Over, Ages Under 65, and Ages 65 & Over), with field names and aliases describing the sex and age group tabulated.For more information, visit statecancerprofiles.cancer.govData NotationsState Cancer Registries may provide more current or more local data.TrendRising when 95% confidence interval of average annual percent change is above 0.Stable when 95% confidence interval of average annual percent change includes 0.Falling when 95% confidence interval of average annual percent change is below 0.† Incidence rates (cases per 100,000 population per year) are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84, 85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used for SEER and NPCR incidence rates.‡ Incidence Trend data come from different sources. Due to different years of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. Please refer to the source for each area for additional information.Rates and trends are computed using different standards for malignancy. For more information see malignant.^ All Stages refers to any stage in the Surveillance, Epidemiology, and End Results (SEER) summary stage.Data Source Field Key(1) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(5) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(6) Source: National Program of Cancer Registries SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention (based on the 2022 submission).(7) Source: SEER November 2022 submission.(8) Source: Incidence data provided by the SEER Program. AAPCs are calculated by the Joinpoint Regression Program and are based on APCs. Data are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84,85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used with SEER November 2022 data.Some data are not available, see Data Not Available for combinations of geography, cancer site, age, and race/ethnicity.Data for the United States does not include data from Nevada.Data for the United States does not include Puerto Rico.

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Age-Adjusted Incidence Rate (AAIR)Age-adjustment is a statistical method that allows comparisons of incidence rates to be made between populations with different age distributions. This is important since the incidence of most cancers increases with age. An age-adjusted cancer incidence (or death) rate is defined as the number of new cancers (or deaths) per 100,000 population that would occur in a certain period of time if that population had a 'standard' age distribution. In the California Health Maps, incidence rates are age-adjusted using the U.S. 2000 Standard Population.Cancer incidence ratesIncidence rates were calculated using case counts from the California Cancer Registry. Population data from 2010 Census and SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators. Yearly SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators for 5-year incidence rates (2013-2017)According to California Department of Public Health guidelines, cancer incidence rates cannot be reported if based on <15 cancer cases and/or a population <10,000 to ensure confidentiality and stable statistical rates.Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity

Each data set consists of 19 standard age groups based on 5-year intervals. For female breast and female brain cancer, six racial/ethnic groups (Hispanic (reference), White, Black, Asian, Asian Pacific Islander, and Unknown) were used. For cervical cancer and lung cancer, two ethnic groups (Spanish Hispanic Latino and Non-Spanish Hispanic Latino (reference)) and ten groups based on household deciles income in 2008 (the lowest decile group is the reference) were used, respectively.

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Age-Adjusted Incidence Rate (AAIR)Age-adjustment is a statistical method that allows comparisons of incidence rates to be made between populations with different age distributions. This is important since the incidence of most cancers increases with age. An age-adjusted cancer incidence (or death) rate is defined as the number of new cancers (or deaths) per 100,000 population that would occur in a certain period of time if that population had a 'standard' age distribution. In the California Health Maps, incidence rates are age-adjusted using the U.S. 2000 Standard Population.

Cancer incidence rates

Incidence rates were calculated using case counts from the California Cancer Registry. Population data from 2010 Census and SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators. Yearly SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators for 5-year incidence rates (2013-2017)According to California Department of Public Health guidelines, cancer incidence rates cannot be reported if based on <15 cancer cases and/or a population <10,000 to ensure confidentiality and stable statistical rates.Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity

PurposeAnaplastic thyroid carcinoma (ATC) and primary squamous cell carcinoma of the thyroid (PSCCTh) have similar histological findings and are currently treated using the same approaches; however, the characteristics and prognosis of these cancers are poorly researched. The objective of this study was to determine the differences in characteristics between ATC and PSCCTh and establish prognostic models.Patients and MethodsAll variables of patients with ATC and PSCCTh, diagnosed from 2004–2015, were retrieved from the Surveillance, Epidemiology, and End Results Program (SEER) database. Percentage differences for categorical data were compared using the Chi-square test. Kaplan-Meier curves, log-rank test, and Cox-regression for survival analysis, and C-index value was used to evaluate the performance of the prognostic models.ResultsAfter application of the inclusion and exclusion criteria, a total of 1164 ATC and 124 PSCCTh patients, diagnosed from 2004 to 2015, were included in the study. There were no differences in sex, ethnicity, age, marital status, or percentage of proximal metastases between the two cancers; however, radiotherapy, chemotherapy, incidence of surgical treatment, and presence of multiple primary tumors were higher in patients with ATC than those with PSCCTh. Further cancer-specific survival (CSS) of patients with PSCCTh was better than that of patients with ATC. Prognostic factors were not identical for the two cancers. Multivariate Cox model analysis indicated that age, sex, radiotherapy, chemotherapy, surgery, multiple primary tumors, marital status, and distant metastasis status are independent prognostic factors for CSS in patients with ATC, while for patients with PSCCTh, the corresponding factors are age, radiotherapy, multiple primary tumors, and surgery. The C-index values of the two models were both > 0.8, indicating that the models exhibited good discriminative ability.ConclusionPrognostic factors influencing CSS were not identical in patients with ATC and PSCCTh. These findings indicate that different clinical treatment and management plans are required for patients with these two types of thyroid cancer.

BackgroundDue to individualized conditions of lymph node metastasis (LNM) and distant metastasis (DM), the following therapeutic strategy and diagnosis of T1–2 esophageal cancer (ESCA) patients are varied. A prediction model for identifying risk factors for LNM, DM, and overall survival (OS) of high-risk T1–2 ESCA patients is of great significance to clinical practice.MethodsA total of 1,747 T1–2 ESCA patients screened from the surveillance, epidemiology, and end results (SEER) database were retrospectively analyzed for their clinical data. Univariate and multivariate logistic regression models were established to screen out risk factors for LNM and DM of T1-2 ESCA patients, while those of OS were screened out using the Cox regression analysis. The identified risk factors for LNM, DM, and OS were then subjected to the establishment of three nomograms, respectively. The accuracy of the nomograms was evaluated by depicting the calibration curve, and the predictive value and clinical utility were evaluated by depicting the clinical impact curve (CIC) and decision curve analysis (DCA), respectively.ResultsThe age, race, tumor grade, tumor size, and T-stage were significant factors for predicting LNM of T1–2 ESCA patients (p < 0.05). The age, T-stage, tumor grade, and tumor size were significant factors for predicting DM of T1–2 ESCA patients (p < 0.05). The age, race, sex, histology, primary tumor site, tumor size, N-stage, M-stage, and surgery were significant factors for predicting OS of T1–2 ESCA patients (p < 0.05). The C-indexes of the three nomograms constructed by these factors were 0.737, 0.764, and 0.740, respectively, suggesting that they were clinically effective.ConclusionsThe newly constructed nomograms can objectively and accurately predict the LNM, DM, and OS of T1–2 ESCA patients, which contribute to the individualized decision making before clinical management.

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Age-Adjusted Incidence Rate (AAIR)Age-adjustment is a statistical method that allows comparisons of incidence rates to be made between populations with different age distributions. This is important since the incidence of most cancers increases with age. An age-adjusted cancer incidence (or death) rate is defined as the number of new cancers (or deaths) per 100,000 population that would occur in a certain period of time if that population had a 'standard' age distribution. In the California Health Maps, incidence rates are age-adjusted using the U.S. 2000 Standard Population.Cancer incidence ratesIncidence rates were calculated using case counts from the California Cancer Registry. Population data from 2010 Census and SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators. Yearly SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators for 5-year incidence rates (2013-2017)According to California Department of Public Health guidelines, cancer incidence rates cannot be reported if based on <15 cancer cases and/or a population <10,000 to ensure confidentiality and stable statistical rates.Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Age-Adjusted Incidence Rate (AAIR)Age-adjustment is a statistical method that allows comparisons of incidence rates to be made between populations with different age distributions. This is important since the incidence of most cancers increases with age. An age-adjusted cancer incidence (or death) rate is defined as the number of new cancers (or deaths) per 100,000 population that would occur in a certain period of time if that population had a 'standard' age distribution. In the California Health Maps, incidence rates are age-adjusted using the U.S. 2000 Standard Population.Cancer incidence ratesIncidence rates were calculated using case counts from the California Cancer Registry. Population data from 2010 Census and SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators. Yearly SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators for 5-year incidence rates (2013-2017)According to California Department of Public Health guidelines, cancer incidence rates cannot be reported if based on <15 cancer cases and/or a population <10,000 to ensure confidentiality and stable statistical rates.Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity

PurposeTo analyze the potential prognostic factors of epithelial ovarian cancer (EOC) in women aged under 35 compared to those aged 60–79.MethodsCases were retrospectively obtained from SEER database. Clinical characteristics, such as race, histological type, AJCC stage, laterality of tumors, CA125 results, and surgical strategies, were analyzed in < 35 years group and 60–79 years group. Kaplan-Meier survival curves were used to evaluate overall survival (OS) and cause-specific survival (CSS). Cox proportional hazard model was used to identify the predictors for CSS.ResultsSixteen thousand eight hundred forty-seven EOC patients diagnosed in 2004–2015 were identified from SEER database, with 1,015 aged under 35 and 15,833 aged 60–79. In < 35 years group, mucinous (32.2%) was the most common histological type, followed by high-grade serous (26.6%) and endometrioid (18.3%), while in 60–79 years group, high-grade serous (68.3%) represented the leading histological type. Most young women were diagnosed at stage I (57.7%), while most old women were diagnosed at stage (48.1%). Both 5-year OS and 5-year CSS were higher in < 35 years group (5-year OS: 76.00% vs 40.18%, p < 0.001; 5-year CSS: 83.56% vs 55.18%, p < 0.001). The multivariate analysis identified histological type and stage as prognostic factors for CSS in both groups. Endometrioid represented a positive predictor for CSS, while carcinosarcoma and malignant Brenner were related to a worse CSS. (< 35 years group: carcinosarcoma vs endometrioid: HR 5.630, p=0.024; malignant Brenner vs endometrioid: HR 4.005, p < 0.001; 60–79 years group: carcinosarcoma vs endometrioid: HR 3.606, p < 0.001; malignant Brenner vs endometrioid: HR 2.291, p < 0.001). Tumors laterality, CA125 levels, surgery and lymphadenectomy failed to be associated with the CSS in < 35 years group, while found to be independent risk factors in 60–79 years group.ConclusionEOC women aged under 35 had a better survival outcome over EOC women aged 60–79, owing to high proportion of endometrioid and mucinous types in histology, as well as early-stage diagnosis. Identification of histological types and gene profiles should be underscored in young EOC patients.

Background and aimExtranodal NK/T-cell lymphoma (ENKTL) is a rare and aggressive subtype of non-Hodgkin’s lymphoma that most commonly affects the nasal cavity and nasopharynx. The lung is a rare site for ENKTL involvement, and its clinical behavior and prognostic factors are not well understood. This study aimed to analyze survival outcomes and identify prognostic factors in patients with primary lung ENKTL.MethodsA retrospective analysis was conducted using data from 20 cases of primary lung ENKTL, including four patients who were treated at Peking University Third Hospital in Beijing and 16 patients were extracted from the Surveillance, Epidemiology, and End Results Program database. Clinical characteristics, treatment modalities, and survival data were collected and analyzed using Kaplan–Meier and Cox regression models to identify potential prognostic factors.ResultsThe study cohort included 13 male (65%) and 7 female (35%) patients with a median age of 57 years. Sex was a significant predictor of survival (P = 0.030), with female patients having lower survival rates. Other factors, including age, race, and disease stage, were not significantly associated with survival. Most patients received chemotherapy (45%) or a combination of chemotherapy and radiotherapy (5%), but treatment data were incomplete for 40% of the cohort. The median overall survival was poor, reflecting the aggressive nature of primary lung ENKTL.ConclusionsPrimary lung ENKTL is a rare, aggressive malignancy with limited available data. In this cohort, sex was a significant prognostic factor, while other demographic and clinical variables did not show significant associations with survival. Future research should focus on understanding the molecular and immunological drivers of this disease, with an emphasis on discovering novel therapeutic approaches. Large-scale multicenter studies are needed to improve diagnostic and treatment strategies for primary lung ENKTL.