Journal list menu
Cancer statistics, 2019
Abstract
Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data, available through 2015, were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data, available through 2016, were collected by the National Center for Health Statistics. In 2019, 1,762,450 new cancer cases and 606,880 cancer deaths are projected to occur in the United States. Over the past decade of data, the cancer incidence rate (2006‐2015) was stable in women and declined by approximately 2% per year in men, whereas the cancer death rate (2007‐2016) declined annually by 1.4% and 1.8%, respectively. The overall cancer death rate dropped continuously from 1991 to 2016 by a total of 27%, translating into approximately 2,629,200 fewer cancer deaths than would have been expected if death rates had remained at their peak. Although the racial gap in cancer mortality is slowly narrowing, socioeconomic inequalities are widening, with the most notable gaps for the most preventable cancers. For example, compared with the most affluent counties, mortality rates in the poorest counties were 2‐fold higher for cervical cancer and 40% higher for male lung and liver cancers during 2012‐2016. Some states are home to both the wealthiest and the poorest counties, suggesting the opportunity for more equitable dissemination of effective cancer prevention, early detection, and treatment strategies. A broader application of existing cancer control knowledge with an emphasis on disadvantaged groups would undoubtedly accelerate progress against cancer.
Introduction
Cancer is a major public health problem worldwide and is the second leading cause of death in the United States. In this article, we provide the estimated numbers of new cancer cases and deaths in 2019 in the United States nationally and for each state, as well as a comprehensive overview of cancer occurrence based on the most current population‐based data for cancer incidence through 2015 and for mortality through 2016. We also estimate the total number of deaths averted because of the continuous decline in cancer death rates since the early 1990s and analyze cancer mortality rates by county‐level poverty.
Materials and Methods
Incidence and Mortality Data
Mortality data from 1930 to 2016 were provided by the National Center for Health Statistics (NCHS).1-3 Forty‐seven states and the District of Columbia met data quality requirements for reporting to the national vital statistics system in 1930, and Texas, Alaska, and Hawaii began reporting in 1933, 1959, and 1960, respectively. The methods for abstraction and age adjustment of historic mortality data are described elsewhere.3, 4 Five‐year mortality rates (2011‐2015) for Puerto Rico were previously published in volume 3 of the North American Association of Central Cancer Registries’ (NAACCR’s) Cancer in North America: 2011‐2015.5
Population‐based cancer incidence data in the United States have been collected by the National Cancer Institute’s (NCI’s) Surveillance, Epidemiology, and End Results (SEER) Program since 1973 and by the Centers for Disease Control and Prevention's (CDC’s) National Program of Cancer Registries (NPCR) since 1995. The SEER program is the only source for historic population‐based incidence data. Long‐term (1975–2015) incidence and survival trends were based on data from the 9 oldest SEER areas (Connecticut, Hawaii, Iowa, New Mexico, Utah, and the metropolitan areas of Atlanta, Detroit, San Francisco–Oakland, and Seattle–Puget Sound), representing approximately 9% of the US population.6, 7 The lifetime probability of developing cancer and contemporary stage distribution and survival statistics were based on data from all 18 SEER registries (the SEER 9 registries plus Alaska Natives, California, Georgia, Kentucky, Louisiana, and New Jersey), covering 28% of the US population.8 The probability of developing cancer was calculated using NCI’s DevCan software (version 6.7.6).9 Some of the statistical information presented herein was adapted from data previously published in the SEER Cancer Statistics Review 1975‐2015.10
The NAACCR compiles and reports incidence data from 1995 onward for registries that participate in the SEER program and/or the NPCR. These data approach 100% coverage of the US population for the most recent years and were the source for the projected new cancer cases in 2019 and cross‐sectional incidence rates by state and race/ethnicity.11, 12 Some of the incidence data presented herein were previously published in volumes 1 and 2 of Cancer in North America: 2011‐2015.13, 14
All cancer cases were classified according to the International Classification of Diseases for Oncology except childhood and adolescent cancers, which were classified according to the International Classification of Childhood Cancer (ICCC).15, 16 Causes of death were classified according to the International Classification of Diseases.17 All incidence and death rates were age standardized to the 2000 US standard population and expressed per 100,000 population, as calculated by NCI’s SEER*Stat software (version 8.3.5).18 The annual percent change in rates was quantified using NCI’s Joinpoint Regression Program (version 4.6.0).19
Whenever possible, cancer incidence rates were adjusted for delays in reporting, which occur because of a lag in case capture or data corrections. Delay‐adjustment has the largest effect on the most recent data years for cancers that are frequently diagnosed in outpatient settings (eg, melanoma, leukemia, and prostate cancer) and provides the most accurate portrayal of cancer occurrence in the most recent time period.20 For example, the leukemia incidence rate for 2015 in the 9 oldest SEER registries was 12% higher after adjusting for reporting delays (15.2 vs 13.6 per 100,000 population).10
Projected Cancer Cases and Deaths in 2019
The most recent year for which reported incidence and mortality data are available lags 2 to 4 years behind the current year due to the time required for data collection, compilation, quality control, and dissemination. Therefore, we projected the numbers of new cancer cases and deaths in the United States in 2019 to provide an estimate of the contemporary cancer burden.
To calculate the number of invasive cancer cases, a generalized linear mixed model was used to estimate complete counts for each county (or health service area for rare cancers) from 2001 through 2015 using delay‐adjusted, high‐quality incidence data from 48 states and the District of Columbia (96% population coverage) and geographic variations in sociodemographic and lifestyle factors, medical settings, and cancer screening behaviors.21 (Data were unavailable for all years for Kansas and Minnesota, as well as for a few sporadic years for a handful of states.) Modeled counts were aggregated to the national and state level for each year, and a time series projection method (vector autoregression) was applied to all 15 years to estimate cases for 2019. Basal cell and squamous cell skin cancers cannot be estimated because incidence data are not collected by most cancer registries. For complete details of the case projection methodology, please refer to Zhu et al.22
New cases of in situ female breast carcinoma and melanoma of the skin diagnosed in 2019 were estimated by first approximating the number of cases occurring annually from 2006 through 2015 based on age‐specific NAACCR incidence rates (data from 46 states with high‐quality data for all 10 years) and US Census Bureau population estimates obtained via SEER*Stat. Counts were then adjusted for delays in reporting using SEER delay factors for invasive disease (delay factors are unavailable for in situ cases) and projected to 2019 based on the average annual percent change generated by the joinpoint regression model.
The number of cancer deaths expected to occur in 2019 was estimated based on the most recent joinpoint‐generated annual percent change in reported cancer deaths from 2002 through 2016 at the state and national levels as reported to the NCHS. For the complete details of this methodology, please refer to Chen et al.23
Other Statistics
The number of cancer deaths averted in men and women due to the reduction in cancer death rates since the early 1990s was estimated by summing the difference between the annual number of recorded cancer deaths from the number that would have been expected if cancer death rates had remained at their peak. The expected number of deaths was estimated by applying the 5‐year age‐ and sex‐specific cancer death rates in the peak year for age‐standardized cancer death rates (1990 in men and 1991 in women) to the corresponding age‐ and sex‐specific populations in subsequent years through 2016.
Temporal trends in socioeconomic disparities in cancer mortality were examined using county‐level poverty as a proxy for socioeconomic status. Cancer death rates by county‐level poverty quintile were calculated using linked attributes from the US Census Bureau American Community Survey 2012–2016 available through SEER*Stat. The total resident population in each quintile was 73,559,180 persons (1.81%‐10.84% poverty); 62,695,449 persons (10.85%‐14.10% poverty); 74,157,401 persons (14.11%‐17.16% poverty); 76,945,467 persons (17.17%‐21.17% poverty); and 35,770,016 persons (21.18%‐53.95% poverty), respectively. County‐level poverty in the United States has shifted slightly from the South to the West since 1970, although the highest concentration remains in the South.24
Selected Findings
Expected Numbers of New Cancer Cases
Table 1 presents the estimated numbers of new cases of invasive cancer in the United States in 2019 by sex and cancer type. In total, there will be approximately 1,762,450 cancer cases diagnosed, which is the equivalent of more than 4,800 new cases each day. In addition, there will be approximately 62,930 new cases of female breast carcinoma in situ and 95,830 new cases of melanoma in situ of the skin. The estimated numbers of new cases by state are shown in Table 2.
| ESTIMATED NEW CASES | ESTIMATED DEATHS | |||||
|---|---|---|---|---|---|---|
| BOTH SEXES | MALE | FEMALE | BOTH SEXES | MALE | FEMALE | |
| All sites | 1,762,450 | 870,970 | 891,480 | 606,880 | 321,670 | 285,210 |
| Oral cavity & pharynx | 53,000 | 38,140 | 14,860 | 10,860 | 7,970 | 2,890 |
| Tongue | 17,060 | 12,550 | 4,510 | 3,020 | 2,220 | 800 |
| Mouth | 14,310 | 8,430 | 5,880 | 2,740 | 1,800 | 940 |
| Pharynx | 17,870 | 14,450 | 3,420 | 3,450 | 2,660 | 790 |
| Other oral cavity | 3,760 | 2,710 | 1,050 | 1,650 | 1,290 | 360 |
| Digestive system | 328,030 | 186,080 | 141,950 | 165,460 | 97,110 | 68,350 |
| Esophagus | 17,650 | 13,750 | 3,900 | 16,080 | 13,020 | 3,060 |
| Stomach | 27,510 | 17,230 | 10,280 | 11,140 | 6,800 | 4,340 |
| Small intestine | 10,590 | 5,610 | 4,980 | 1,590 | 890 | 700 |
| Colon
†
†
Deaths for colon and rectal cancers are combined because a large number of deaths from rectal cancer are misclassified as colon.
|
101,420 | 51,690 | 49,730 | 51,020 | 27,640 | 23,380 |
| Rectum | 44,180 | 26,810 | 17,370 | |||
| Anus, anal canal, & anorectum | 8,300 | 2,770 | 5,530 | 1,280 | 520 | 760 |
| Liver & intrahepatic bile duct | 42,030 | 29,480 | 12,550 | 31,780 | 21,600 | 10,180 |
| Gallbladder & other biliary | 12,360 | 5,810 | 6,550 | 3,960 | 1,610 | 2,350 |
| Pancreas | 56,770 | 29,940 | 26,830 | 45,750 | 23,800 | 21,950 |
| Other digestive organs | 7,220 | 2,990 | 4,230 | 2,860 | 1,230 | 1,630 |
| Respiratory system | 246,440 | 130,370 | 116,070 | 147,510 | 80,380 | 67,130 |
| Larynx | 12,410 | 9,860 | 2,550 | 3,760 | 3,010 | 750 |
| Lung & bronchus | 228,150 | 116,440 | 111,710 | 142,670 | 76,650 | 66,020 |
| Other respiratory organs | 5,880 | 4,070 | 1,810 | 1,080 | 720 | 360 |
| Bones & joints | 3,500 | 2,030 | 1,470 | 1,660 | 960 | 700 |
| Soft tissue (including heart) | 12,750 | 7,240 | 5,510 | 5,270 | 2,840 | 2,430 |
| Skin (excluding basal & squamous) | 104,350 | 62,320 | 42,030 | 11,650 | 8,030 | 3,620 |
| Melanoma of the skin | 96,480 | 57,220 | 39,260 | 7,230 | 4,740 | 2,490 |
| Other nonepithelial skin | 7,870 | 5,100 | 2,770 | 4,420 | 3,290 | 1,130 |
| Breast | 271,270 | 2,670 | 268,600 | 42,260 | 500 | 41,760 |
| Genital system | 295,290 | 186,290 | 109,000 | 65,540 | 32,440 | 33,100 |
| Uterine cervix | 13,170 | 13,170 | 4,250 | 4,250 | ||
| Uterine corpus | 61,880 | 61,880 | 12,160 | 12,160 | ||
| Ovary | 22,530 | 22,530 | 13,980 | 13,980 | ||
| Vulva | 6,070 | 6,070 | 1,280 | 1,280 | ||
| Vagina & other genital, female | 5,350 | 5,350 | 1,430 | 1,430 | ||
| Prostate | 174,650 | 174,650 | 31,620 | 31,620 | ||
| Testis | 9,560 | 9,560 | 410 | 410 | ||
| Penis & other genital, male | 2,080 | 2,080 | 410 | 410 | ||
| Urinary system | 158,220 | 108,450 | 49,770 | 33,420 | 23,290 | 10,130 |
| Urinary bladder | 80,470 | 61,700 | 18,770 | 17,670 | 12,870 | 4,800 |
| Kidney & renal pelvis | 73,820 | 44,120 | 29,700 | 14,770 | 9,820 | 4,950 |
| Ureter & other urinary organs | 3,930 | 2,630 | 1,300 | 980 | 600 | 380 |
| Eye & orbit | 3,360 | 1,860 | 1,500 | 370 | 200 | 170 |
| Brain & other nervous system | 23,820 | 13,410 | 10,410 | 17,760 | 9,910 | 7,850 |
| Endocrine system | 54,740 | 15,650 | 39,090 | 3,210 | 1,560 | 1,650 |
| Thyroid | 52,070 | 14,260 | 37,810 | 2,170 | 1,020 | 1,150 |
| Other endocrine | 2,670 | 1,390 | 1,280 | 1,040 | 540 | 500 |
| Lymphoma | 82,310 | 45,660 | 36,650 | 20,970 | 12,100 | 8,870 |
| Hodgkin lymphoma | 8,110 | 4,570 | 3,540 | 1,000 | 590 | 410 |
| Non‐Hodgkin lymphoma | 74,200 | 41,090 | 33,110 | 19,970 | 11,510 | 8,460 |
| Myeloma | 32,110 | 18,130 | 13,980 | 12,960 | 6,990 | 5,970 |
| Leukemia | 61,780 | 35,920 | 25,860 | 22,840 | 13,150 | 9,690 |
| Acute lymphocytic leukemia | 5,930 | 3,280 | 2,650 | 1,500 | 850 | 650 |
| Chronic lymphocytic leukemia | 20,720 | 12,880 | 7,840 | 3,930 | 2,220 | 1,710 |
| Acute myeloid leukemia | 21,450 | 11,650 | 9,800 | 10,920 | 6,290 | 4,630 |
| Chronic myeloid leukemia | 8,990 | 5,250 | 3,740 | 1,140 | 660 | 480 |
| Other leukemia
‡
‡
More deaths than cases may reflect a lack of specificity in recording the underlying cause of death on death certificates and/or an undercount in the case estimate.
|
4,690 | 2,860 | 1,830 | 5,350 | 3,130 | 2,220 |
|
Other & unspecified primary sites
‡
‡
More deaths than cases may reflect a lack of specificity in recording the underlying cause of death on death certificates and/or an undercount in the case estimate.
|
31,480 | 16,750 | 14,730 | 45,140 | 24,240 | 20,900 |
- * Rounded to the nearest 10; cases exclude basal cell and squamous cell skin cancers and in situ carcinoma except urinary bladder. Approximately 62,930 cases of carcinoma in situ of the female breast and 95,830 cases of melanoma in situ will be newly diagnosed in 2019.
- † Deaths for colon and rectal cancers are combined because a large number of deaths from rectal cancer are misclassified as colon.
- ‡ More deaths than cases may reflect a lack of specificity in recording the underlying cause of death on death certificates and/or an undercount in the case estimate.
- Note: These are model‐based estimates that should be interpreted with caution and not compared with those for previous years.
| STATE | ALL CASES | FEMALE BREAST | UTERINE CERVIX | COLON & RECTUM | UTERINE CORPUS | LEUKEMIA | LUNG & BRONCHUS | MELANOMA OF THE SKIN | NON‐HODGKIN LYMPHOMA | PROSTATE | URINARY BLADDER |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Alabama | 28,950 | 4,240 | 240 | 2,330 | 760 | 840 | 4,150 | 1,420 | 990 | 4,060 | 1,100 |
| Alaska | 3,090 | 470 |
†
†
Estimate is fewer than 50 cases.
|
290 | 110 | 90 | 400 | 120 | 130 | 460 | 150 |
| Arizona | 37,490 | 5,630 | 250 | 2,840 | 1,200 | 1,110 | 4,290 | 2,340 | 1,420 | 2,800 | 1,780 |
| Arkansas | 16,580 | 2,210 | 140 | 1,440 | 510 | 560 | 2,690 | 760 | 640 | 2,680 | 740 |
| California | 186,920 | 27,700 | 1,590 | 15,360 | 6,230 | 6,030 | 18,990 | 10,710 | 8,230 | 24,550 | 7,780 |
| Colorado | 26,800 | 4,180 | 170 | 1,940 | 830 | 810 | 2,690 | 1,830 | 1,130 | 2,270 | 1,210 |
| Connecticut | 21,950 | 3,490 | 120 | 1,560 | 720 | 670 | 2,580 | 930 | 950 | 1,980 | 1,160 |
| Delaware | 5,870 | 930 |
†
†
Estimate is fewer than 50 cases.
|
440 | 220 | 210 | 840 | 400 | 240 | 700 | 300 |
| Dist. of Columbia | 3,190 | 510 |
†
†
Estimate is fewer than 50 cases.
|
260 | 120 | 80 | 340 | 80 | 120 | 300 | 80 |
| Florida | 131,470 | 19,130 | 1,040 | 11,310 | 4,520 | 4,980 | 18,560 | 8,360 | 5,420 | 11,860 | 6,450 |
| Georgia | 50,450 | 8,000 | 440 | 4,450 | 1,640 | 1,800 | 7,070 | 3,050 | 2,030 | 5,400 | 2,040 |
| Hawaii | 7,120 | 1,280 | 50 | 620 | 310 | 200 | 860 | 490 | 280 | 680 | 280 |
| Idaho | 8,390 | 1,340 | 50 | 630 | 310 | 340 | 1,030 | 670 | 380 | 1,370 | 460 |
| Illinois | 68,560 | 11,560 | 510 | 6,030 | 2,700 | 2,380 | 9,130 | 3,750 | 2,890 | 6,990 | 3,240 |
| Indiana | 35,280 | 5,820 | 270 | 3,360 | 1,330 | 1,230 | 5,500 | 2,120 | 1,550 | 2,530 | 1,710 |
| Iowa | 17,810 | 2,730 | 100 | 1,540 | 660 | 730 | 2,410 | 1,070 | 830 | 1,720 | 890 |
| Kansas | 15,340 | 2,420 | 110 | 1,290 | 520 | 590 | 2,000 | 870 | 650 | 2,070 | 640 |
| Kentucky | 26,400 | 3,670 | 200 | 2,320 | 890 | 940 | 4,960 | 1,310 | 1,050 | 2,190 | 1,130 |
| Louisiana | 26,800 | 3,770 | 230 | 2,340 | 700 | 830 | 3,810 | 1,020 | 1,060 | 3,380 | 1,050 |
| Maine | 8,920 | 1,390 | 50 | 670 | 320 | 310 | 1,400 | 510 | 400 | 660 | 560 |
| Maryland | 33,140 | 5,290 | 230 | 2,620 | 1,280 | 960 | 4,040 | 1,750 | 1,280 | 3,810 | 1,390 |
| Massachusetts | 40,020 | 6,610 | 210 | 2,840 | 1,380 | 1,140 | 5,150 | 1,640 | 1,720 | 2,710 | 2,130 |
| Michigan | 58,360 | 9,310 | 360 | 5,040 | 2,200 | 1,930 | 8,070 | 3,300 | 2,530 | 4,580 | 2,930 |
| Minnesota | 30,560 | 4,740 | 140 | 2,300 | 1,080 | 1,360 | 3,600 | 1,640 | 1,360 | 1,970 | 1,400 |
| Mississippi | 17,050 | 2,370 | 150 | 1,680 | 450 | 520 | 2,520 | 650 | 570 | 1,930 | 630 |
| Missouri | 35,480 | 5,350 | 260 | 3,110 | 1,180 | 1,240 | 5,490 | 1,800 | 1,430 | 3,290 | 1,570 |
| Montana | 5,920 | 890 |
†
†
Estimate is fewer than 50 cases.
|
470 | 220 | 240 | 820 | 390 | 260 | 600 | 340 |
| Nebraska | 9,780 | 1,580 | 70 | 900 | 360 | 420 | 1,290 | 580 | 460 | 750 | 470 |
| Nevada | 14,810 | 2,190 | 140 | 1,340 | 420 | 530 | 1,880 | 850 | 600 | 1,180 | 770 |
| New Hampshire | 8,610 | 1,330 |
†
†
Estimate is fewer than 50 cases.
|
590 | 300 | 260 | 1,140 | 450 | 370 | 1,030 | 500 |
| New Jersey | 53,400 | 8,340 | 410 | 4,250 | 2,130 | 2,070 | 6,070 | 2,850 | 2,330 | 5,710 | 2,580 |
| New Mexico | 9,460 | 1,440 | 80 | 830 | 370 | 360 | 1,070 | 630 | 400 | 520 | 410 |
| New York | 111,870 | 17,490 | 880 | 9,150 | 4,500 | 4,540 | 13,380 | 5,150 | 5,030 | 9,700 | 5,410 |
| North Carolina | 58,690 | 8,870 | 410 | 4,310 | 1,960 | 1,960 | 8,010 | 3,550 | 2,220 | 7,490 | 2,490 |
| North Dakota | 3,940 | 590 |
†
†
Estimate is fewer than 50 cases.
|
350 | 130 | 170 | 430 | 230 | 180 | 360 | 190 |
| Ohio | 67,150 | 10,240 | 430 | 6,200 | 2,600 | 2,100 | 9,680 | 3,750 | 2,850 | 5,340 | 3,210 |
| Oklahoma | 20,540 | 2,980 | 170 | 1,840 | 630 | 780 | 3,220 | 860 | 850 | 1,800 | 910 |
| Oregon | 23,320 | 3,390 | 150 | 1,620 | 810 | 670 | 2,900 | 1,780 | 1,010 | 1,950 | 1,140 |
| Pennsylvania | 79,890 | 12,070 | 540 | 6,520 | 3,280 | 3,040 | 10,380 | 4,340 | 3,430 | 7,470 | 4,230 |
| Rhode Island | 6,540 | 1,010 |
†
†
Estimate is fewer than 50 cases.
|
470 | 210 | 190 | 940 | 310 | 270 | 550 | 360 |
| South Carolina | 29,830 | 4,470 | 210 | 2,370 | 930 | 1,040 | 4,360 | 1,810 | 1,100 | 3,130 | 1,270 |
| South Dakota | 4,770 | 750 |
†
†
Estimate is fewer than 50 cases.
|
430 | 160 | 200 | 580 | 250 | 210 | 400 | 240 |
| Tennessee | 37,350 | 5,580 | 310 | 3,290 | 1,210 | 1,280 | 6,210 | 2,070 | 1,550 | 3,160 | 1,670 |
| Texas | 124,890 | 18,750 | 1,290 | 10,950 | 4,090 | 4,820 | 14,750 | 4,270 | 5,430 | 10,660 | 4,470 |
| Utah | 11,620 | 1,660 | 70 | 770 | 420 | 480 | 780 | 1,160 | 550 | 1,080 | 450 |
| Vermont | 3,920 | 620 |
†
†
Estimate is fewer than 50 cases.
|
280 | 130 | 130 | 510 | 250 | 170 | 210 | 230 |
| Virginia | 45,440 | 7,120 | 310 | 3,540 | 1,650 | 1,400 | 5,950 | 2,810 | 1,760 | 5,440 | 2,010 |
| Washington | 39,160 | 5,840 | 230 | 2,800 | 1,400 | 1,370 | 4,770 | 2,790 | 1,800 | 2,470 | 1,910 |
| West Virginia | 12,440 | 1,540 | 80 | 980 | 450 | 410 | 2,010 | 650 | 470 | 1,010 | 630 |
| Wisconsin | 34,220 | 5,270 | 190 | 2,450 | 1,290 | 1,320 | 4,150 | 1,940 | 1,480 | 5,260 | 1,710 |
| Wyoming | 2,930 | 440 |
†
†
Estimate is fewer than 50 cases.
|
250 | 100 | 110 | 310 | 210 | 130 | 430 | 150 |
| United States | 1,762,450 | 268,600 | 13,170 | 145,600 | 61,880 | 61,780 | 228,150 | 96,480 | 74,200 | 174,650 | 80,470 |
- * Rounded to the nearest 10; excludes basal cell and squamous cell skin cancers and in situ carcinomas except urinary bladder. Estimates for Puerto Rico are not available.
- † Estimate is fewer than 50 cases.
- Note: These are model‐based estimates that should be interpreted with caution and not compared with those for previous years. State estimates may not add to US total due to rounding and the exclusion of states with fewer than 50 cases.
Figure 1 depicts the most common cancers expected to be diagnosed in men and women in 2019. Prostate, lung and bronchus (referred to as lung hereafter), and colorectal cancers (CRCs) account for 42% of all cases in men, with prostate cancer alone accounting for nearly 1 in 5 new diagnoses. For women, the 3 most common cancers are breast, lung, and colorectum, which collectively represent one‐half of all new diagnoses; breast cancer alone accounts for 30% of all new cancer diagnoses in women.

The lifetime probability of being diagnosed with invasive cancer is slightly higher for men (39.3%) than for women (37.7%) (Table 3). The reasons for the excess risk in men are not fully understood, but partly reflect differences in environmental exposures, endogenous hormones, and probably complex interactions between these influences. Recent research suggests that sex differences in immune function and response may also play a role.25 Adult height, which is determined by genetics and childhood nutrition, is positively associated with cancer incidence and mortality in both men and women,26 and has been estimated to account for one‐third of the sex disparity.27
| BIRTH TO 49 | 50 TO 59 | 60 TO 69 | ≥70 | BIRTH TO DEATH | ||
|---|---|---|---|---|---|---|
|
All sites
†
†
All sites excludes basal cell and squamous cell skin cancers and in situ cancers except urinary bladder.
|
Male | 3.4 (1 in 30) | 6.1 (1 in 16) | 13.2 (1 in 8) | 31.9 (1 in 3) | 39.3 (1 in 3) |
| Female | 5.6 (1 in 18) | 6.2 (1 in 16) | 10.0 (1 in 10) | 26.0 (1 in 4) | 37.7 (1 in 3) | |
| Breast | Female | 2.0 (1 in 51) | 2.3 (1 in 43) | 3.5 (1 in 29) | 6.7 (1 in 15) | 12.4 (1 in 8) |
| Colorectum | Male | 0.4 (1 in 272) | 0.7 (1 in 143) | 1.2 (1 in 87) | 3.3 (1 in 30) | 4.4 (1 in 23) |
| Female | 0.3 (1 in 292) | 0.5 (1 in 190) | 0.8 (1 in 123) | 3.0 (1 in 33) | 4.1 (1 in 25) | |
| Kidney & renal pelvis | Male | 0.2 (1 in 440) | 0.4 (1 in 280) | 0.6 (1 in 155) | 1.3 (1 in 73) | 2.1 (1 in 47) |
| Female | 0.2 (1 in 665) | 0.2 (1 in 575) | 0.3 (1 in 319) | 0.7 (1 in 135) | 1.2 (1 in 82) | |
| Leukemia | Male | 0.3 (1 in 396) | 0.2 (1 in 570) | 0.4 (1 in 259) | 1.4 (1 in 72) | 1.8 (1 in 56) |
| Female | 0.2 (1 in 508) | 0.1 (1 in 876) | 0.2 (1 in 434) | 0.9 (1 in 112) | 1.3 (1 in 80) | |
| Lung & bronchus | Male | 0.1 (1 in 719) | 0.6 (1 in 158) | 1.8 (1 in 56) | 6.0 (1 in 16) | 6.7 (1 in 15) |
| Female | 0.1 (1 in 673) | 0.6 (1 in 178) | 1.4 (1 in 72) | 4.7 (1 in 21) | 5.9 (1 in 17) | |
|
Melanoma of the skin
‡
‡
Probabilities for non‐Hispanic whites only.
|
Male | 0.5 (1 in 215) | 0.5 (1 in 186) | 1.0 (1 in 104) | 2.7 (1 in 37) | 3.7 (1 in 27) |
| Female | 0.7 (1 in 150) | 0.4 (1 in 238) | 0.5 (1 in 191) | 1.1 (1 in 87) | 2.5 (1 in 40) | |
| Non‐Hodgkin lymphoma | Male | 0.3 (1 in 382) | 0.3 (1 in 350) | 0.6 (1 in 176) | 1.8 (1 in 54) | 2.4 (1 in 42) |
| Female | 0.2 (1 in 548) | 0.2 (1 in 484) | 0.4 (1 in 247) | 1.4 (1 in 74) | 1.9 (1 in 54) | |
| Prostate | Male | 0.2 (1 in 437) | 1.7 (1 in 59) | 4.6 (1 in 22) | 7.9 (1 in 13) | 11.2 (1 in 9) |
| Thyroid | Male | 0.2 (1 in 513) | 0.1 (1 in 764) | 0.2 (1 in 584) | 0.2 (1 in 417) | 0.6 (1 in 156) |
| Female | 0.8 (1 in 122) | 0.4 (1 in 268) | 0.3 (1 in 286) | 0.4 (1 in 262) | 1.8 (1 in 55) | |
| Uterine cervix | Female | 0.3 (1 in 366) | 0.1 (1 in 835) | 0.1 (1 in 938) | 0.2 (1 in 628) | 0.6 (1 in 162) |
| Uterine corpus | Female | 0.3 (1 in 333) | 0.6 (1 in 164) | 1.0 (1 in 102) | 1.3 (1 in 75) | 2.9 (1 in 35) |
- * For people without a history of cancer at beginning of age interval.
- † All sites excludes basal cell and squamous cell skin cancers and in situ cancers except urinary bladder.
- ‡ Probabilities for non‐Hispanic whites only.
Expected Number of Cancer Deaths
An estimated 606,880 Americans will die from cancer in 2019, corresponding to almost 1,700 deaths per day (Table 1). The greatest number of deaths are from cancers of the lung, prostate, and colorectum in men and the lung, breast, and colorectum in women (Fig. 1). One‐quarter of all cancer deaths are due to lung cancer. Table 4 provides the estimated numbers of cancer deaths in 2019 by state.
| STATE | ALL SITES | BRAIN & OTHER NERVOUS SYSTEM | FEMALE BREAST | COLON & RECTUM | LEUKEMIA | LIVER & INTRAHEPATIC BILE DUCT | LUNG & BRONCHUS | NON‐HODGKIN LYMPHOMA | OVARY | PANCREAS | PROSTATE |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Alabama | 10,630 | 350 | 690 | 930 | 380 | 540 | 2,760 | 290 | 240 | 770 | 510 |
| Alaska | 1,120 |
†
†
Estimate is fewer than 50 deaths.
|
70 | 110 |
†
†
Estimate is fewer than 50 deaths.
|
60 | 260 |
†
†
Estimate is fewer than 50 deaths.
|
†
†
Estimate is fewer than 50 deaths.
|
90 | 50 |
| Arizona | 12,470 | 400 | 890 | 1,050 | 510 | 710 | 2,630 | 410 | 320 | 1,040 | 900 |
| Arkansas | 6,800 | 190 | 410 | 600 | 240 | 310 | 1,960 | 200 | 140 | 440 | 280 |
| California | 60,590 | 1,970 | 4,560 | 5,290 | 2,400 | 4,070 | 10,970 | 2,110 | 1,580 | 4,720 | 4,470 |
| Colorado | 8,120 | 290 | 610 | 660 | 330 | 430 | 1,500 | 250 | 220 | 600 | 540 |
| Connecticut | 6,470 | 210 | 430 | 470 | 270 | 320 | 1,440 | 230 | 160 | 520 | 320 |
| Delaware | 2,140 | 60 | 150 | 150 | 80 | 110 | 540 | 80 | 50 | 180 | 90 |
| Dist. of Columbia | 1,020 |
†
†
Estimate is fewer than 50 deaths.
|
100 | 100 |
†
†
Estimate is fewer than 50 deaths.
|
90 | 180 |
†
†
Estimate is fewer than 50 deaths.
|
†
†
Estimate is fewer than 50 deaths.
|
90 | 70 |
| Florida | 45,000 | 1,240 | 3,000 | 3,700 | 1,740 | 2,300 | 10,880 | 1,500 | 980 | 3,490 | 2,290 |
| Georgia | 17,880 | 530 | 1,350 | 1,630 | 590 | 940 | 4,340 | 530 | 410 | 1,260 | 920 |
| Hawaii | 2,560 | 50 | 160 | 230 | 80 | 190 | 550 | 90 |
†
†
Estimate is fewer than 50 deaths.
|
230 | 120 |
| Idaho | 3,040 | 110 | 220 | 250 | 110 | 160 | 620 | 120 | 90 | 240 | 200 |
| Illinois | 24,410 | 670 | 1,720 | 2,070 | 900 | 1,150 | 5,940 | 770 | 560 | 1,740 | 1,480 |
| Indiana | 13,690 | 360 | 870 | 1,110 | 510 | 580 | 3,690 | 460 | 290 | 950 | 610 |
| Iowa | 6,480 | 200 | 380 | 560 | 240 | 270 | 1,600 | 240 | 150 | 480 | 310 |
| Kansas | 5,550 | 170 | 350 | 470 | 240 | 260 | 1,370 | 190 | 110 | 420 | 270 |
| Kentucky | 10,580 | 290 | 610 | 820 | 370 | 460 | 3,290 | 320 | 190 | 670 | 400 |
| Louisiana | 9,260 | 230 | 620 | 830 | 320 | 580 | 2,390 | 290 | 160 | 740 | 410 |
| Maine | 3,310 | 100 | 180 | 230 | 110 | 120 | 890 | 110 | 60 | 230 | 170 |
| Maryland | 10,780 | 300 | 830 | 880 | 390 | 600 | 2,380 | 340 | 260 | 870 | 550 |
| Massachusetts | 12,420 | 400 | 750 | 870 | 480 | 690 | 2,920 | 380 | 310 | 990 | 620 |
| Michigan | 21,150 | 600 | 1,410 | 1,650 | 770 | 920 | 5,410 | 740 | 490 | 1,650 | 980 |
| Minnesota | 10,020 | 320 | 640 | 790 | 420 | 440 | 2,260 | 380 | 220 | 780 | 530 |
| Mississippi | 6,720 | 190 | 440 | 650 | 210 | 340 | 1,810 | 170 | 110 | 500 | 320 |
| Missouri | 13,080 | 340 | 860 | 1,050 | 480 | 580 | 3,650 | 370 | 250 | 920 | 560 |
| Montana | 2,100 | 70 | 140 | 180 | 80 | 100 | 480 | 70 | 50 | 160 | 140 |
| Nebraska | 3,520 | 120 | 230 | 310 | 150 | 130 | 840 | 120 | 70 | 270 | 180 |
| Nevada | 5,390 | 200 | 400 | 540 | 200 | 250 | 1,280 | 160 | 150 | 380 | 290 |
| New Hampshire | 2,820 | 90 | 180 | 200 | 100 | 120 | 730 | 110 | 60 | 200 | 130 |
| New Jersey | 15,860 | 470 | 1,250 | 1,410 | 590 | 750 | 3,390 | 570 | 380 | 1,290 | 780 |
| New Mexico | 3,720 | 100 | 270 | 340 | 130 | 250 | 700 | 120 | 120 | 270 | 210 |
| New York | 35,010 | 940 | 2,460 | 2,890 | 1,370 | 1,740 | 7,790 | 1,210 | 890 | 2,830 | 1,730 |
| North Carolina | 20,410 | 550 | 1,390 | 1,580 | 720 | 1,110 | 5,370 | 610 | 420 | 1,450 | 960 |
| North Dakota | 1,280 |
†
†
Estimate is fewer than 50 deaths.
|
80 | 120 | 50 |
†
†
Estimate is fewer than 50 deaths.
|
300 | 50 |
†
†
Estimate is fewer than 50 deaths.
|
90 | 70 |
| Ohio | 25,440 | 680 | 1,710 | 2,110 | 920 | 1,100 | 6,690 | 860 | 560 | 1,880 | 1,130 |
| Oklahoma | 8,420 | 220 | 540 | 760 | 340 | 420 | 2,270 | 270 | 180 | 560 | 410 |
| Oregon | 8,270 | 250 | 560 | 650 | 300 | 500 | 1,820 | 280 | 230 | 650 | 470 |
| Pennsylvania | 28,170 | 770 | 1,900 | 2,380 | 1,080 | 1,320 | 6,730 | 960 | 660 | 2,220 | 1,320 |
| Rhode Island | 2,140 | 60 | 130 | 160 | 80 | 120 | 560 | 70 |
†
†
Estimate is fewer than 50 deaths.
|
170 | 100 |
| South Carolina | 10,720 | 300 | 740 | 870 | 380 | 530 | 2,710 | 320 | 220 | 790 | 540 |
| South Dakota | 1,680 | 60 | 110 | 170 | 70 | 70 | 410 | 60 |
†
†
Estimate is fewer than 50 deaths.
|
130 | 90 |
| Tennessee | 14,840 | 360 | 950 | 1,220 | 520 | 730 | 4,190 | 470 | 310 | 980 | 620 |
| Texas | 41,300 | 1,300 | 2,980 | 3,850 | 1,580 | 2,810 | 8,640 | 1,350 | 920 | 3,030 | 1,900 |
| Utah | 3,310 | 140 | 280 | 280 | 160 | 170 | 440 | 130 | 110 | 280 | 230 |
| Vermont | 1,440 | 50 | 80 | 110 | 50 | 50 | 370 | 50 |
†
†
Estimate is fewer than 50 deaths.
|
110 | 70 |
| Virginia | 15,200 | 440 | 1,120 | 1,340 | 520 | 770 | 3,590 | 490 | 360 | 1,140 | 730 |
| Washington | 13,010 | 430 | 890 | 1,000 | 480 | 730 | 2,830 | 450 | 340 | 970 | 710 |
| West Virginia | 4,820 | 120 | 290 | 440 | 190 | 190 | 1,360 | 150 | 90 | 300 | 190 |
| Wisconsin | 11,730 | 380 | 740 | 900 | 490 | 480 | 2,770 | 400 | 260 | 930 | 620 |
| Wyoming | 980 |
†
†
Estimate is fewer than 50 deaths.
|
70 | 80 | 50 | 60 | 200 |
†
†
Estimate is fewer than 50 deaths.
|
†
†
Estimate is fewer than 50 deaths.
|
70 | 50 |
| United States | 606,880 | 17,760 | 41,760 | 51,020 | 22,840 | 31,780 | 142,670 | 19,970 | 13,980 | 45,750 | 31,620 |
- * Rounded to the nearest 10. Estimates for Puerto Rico are not available.
- † Estimate is fewer than 50 deaths.
- Note: These are model‐based estimates that should be interpreted with caution and not compared with those for previous years. State estimates may not add to US total due to rounding and the exclusion of states with fewer than 50 deaths.
Trends in Cancer Incidence
Figure 2 illustrates long‐term trends in cancer incidence rates for all cancers combined by sex. Cancer incidence patterns reflect trends in behaviors associated with cancer risk and changes in medical practice, such as the use of cancer screening tests. The volatility in incidence for males reflects rapid changes in prostate cancer incidence rates, which spiked in the late 1980s and early 1990s (Fig. 3) due to a surge in the detection of asymptomatic disease as a result of widespread prostate‐specific antigen (PSA) testing among previously unscreened men.28


Over the past decade of data, the overall cancer incidence rate in men declined by approximately 2% per year (Table 5). This trend reflects accelerated declines during the past 5 data years (2011‐2015) of approximately 3% per year for cancers of the lung and colorectum, and 7% per year for prostate cancer. The sharp drop in prostate cancer incidence has been attributed to decreased PSA testing from 2008 to 2013 in the wake of US Preventive Services Task Force recommendations against the routine use of the test to screen for prostate cancer (Grade D) in men aged 75 years and older in 2008 and in all men in 2011 because of growing concerns about overdiagnosis and overtreatment.29, 30 Although PSA testing prevalence stabilized from 2013 to 2015,31 the effect of the reduction in screening on the occurrence of advanced disease is being watched closely. Based on analysis of cancer registry data covering 89% of the US population, Negoita et al recently reported that the overall decline in prostate cancer incidence masks an increase in distant stage diagnoses since around 2010 across age and race, although improved staging may have contributed to this trend.32 The Task Force has revised their recommendation for men aged 55 to 69 years to informed decision making (Grade C) based on an updated evidence review, noting that “screening offers a small potential benefit” of reduced prostate cancer mortality “in some men.”33-35
| TREND 1 | TREND 2 | TREND 3 | TREND 4 | TREND 5 | TREND 6 | 2006‐2015 | 2011‐2015 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| YEARS | APC | YEARS | APC | YEARS | APC | YEARS | APC | YEARS | APC | YEARS | APC | AAPC | AAPC | ||||
| All sites | |||||||||||||||||
| Overall | 1975‐1989 | 1.2**
The APC or AAPC is significantly different from zero (P < .05).
|
1989‐1992 | 2.8 | 1992‐1995 | −2.4 | 1995‐1998 | 1.1 | 1998‐2009 | −0.3**
The APC or AAPC is significantly different from zero (P < .05).
|
2009‐2015 | −1.2**
The APC or AAPC is significantly different from zero (P < .05).
|
−0.9**
The APC or AAPC is significantly different from zero (P < .05).
|
−1.2**
The APC or AAPC is significantly different from zero (P < .05).
|
|||
| Male | 1975‐1989 | 1.3**
The APC or AAPC is significantly different from zero (P < .05).
|
1989‐1992 | 5.2**
The APC or AAPC is significantly different from zero (P < .05).
|
1992‐1995 | −4.9**
The APC or AAPC is significantly different from zero (P < .05).
|
1995‐1999 | 0.6 | 1999‐2008 | −0.6**
The APC or AAPC is significantly different from zero (P < .05).
|
2008‐2015 | −2.3**
The APC or AAPC is significantly different from zero (P < .05).
|
−1.9**
The APC or AAPC is significantly different from zero (P < .05).
|
−2.3**
The APC or AAPC is significantly different from zero (P < .05).
|
|||
| Female | 1975‐1979 | −0.3 | 1979‐1987 | 1.6**
The APC or AAPC is significantly different from zero (P < .05).
|
1987‐1995 | 0.1 | 1995‐1998 | 1.5 | 1998‐2003 | −0.6 | 2003‐2015 | 0.1 | 0.1 | 0.1 | |||
| Female breast | 1975‐1980 | −0.5 | 1980‐1987 | 4.0**
The APC or AAPC is significantly different from zero (P < .05).
|
1987‐1994 | −0.2 | 1994‐1999 | 1.8**
The APC or AAPC is significantly different from zero (P < .05).
|
1999‐2004 | −2.3**
The APC or AAPC is significantly different from zero (P < .05).
|
2004‐2015 | 0.4**
The APC or AAPC is significantly different from zero (P < .05).
|
0.4**
The APC or AAPC is significantly different from zero (P < .05).
|
0.4**
The APC or AAPC is significantly different from zero (P < .05).
|
|||
| Colorectum | |||||||||||||||||
| Male | 1975‐1985 | 1.1**
The APC or AAPC is significantly different from zero (P < .05).
|
1985‐1991 | −1.2**
The APC or AAPC is significantly different from zero (P < .05).
|
1991‐1995 | −3.2**
The APC or AAPC is significantly different from zero (P < .05).
|
1995‐1998 | 2.1 | 1998‐2015 | −2.9**
The APC or AAPC is significantly different from zero (P < .05).
|
−2.9**
The APC or AAPC is significantly different from zero (P < .05).
|
−2.9**
The APC or AAPC is significantly different from zero (P < .05).
|
|||||
| Female | 1975‐1985 | 0.3 | 1985‐1995 | −1.9**
The APC or AAPC is significantly different from zero (P < .05).
|
1995‐1998 | 1.8 | 1998‐2008 | −2.0**
The APC or AAPC is significantly different from zero (P < .05).
|
2008‐2011 | −4.6**
The APC or AAPC is significantly different from zero (P < .05).
|
2011‐2015 | −0.9 | −2.4**
The APC or AAPC is significantly different from zero (P < .05).
|
−0.9 | |||
| Liver & intrahepatic bile duct | |||||||||||||||||
| Male | 1975‐1984 | 2.2**
The APC or AAPC is significantly different from zero (P < .05).
|
1984‐2011 | 3.9**
The APC or AAPC is significantly different from zero (P < .05).
|
2011‐2015 | 1.0 | 2.6**
The APC or AAPC is significantly different from zero (P < .05).
|
1.0 | |||||||||
| Female | 1975‐1983 | 0.4 | 1983‐1998 | 4.4**
The APC or AAPC is significantly different from zero (P < .05).
|
1998‐2001 | −0.4 | 2001‐2015 | 3.4**
The APC or AAPC is significantly different from zero (P < .05).
|
3.4**
The APC or AAPC is significantly different from zero (P < .05).
|
3.4**
The APC or AAPC is significantly different from zero (P < .05).
|
|||||||
| Lung & bronchus | |||||||||||||||||
| Male | 1975‐1982 | 1.5**
The APC or AAPC is significantly different from zero (P < .05).
|
1982‐1991 | −0.5**
The APC or AAPC is significantly different from zero (P < .05).
|
1991‐2008 | −1.7**
The APC or AAPC is significantly different from zero (P < .05).
|
2008‐2015 | −2.9**
The APC or AAPC is significantly different from zero (P < .05).
|
−2.6**
The APC or AAPC is significantly different from zero (P < .05).
|
−2.9**
The APC or AAPC is significantly different from zero (P < .05).
|
|||||||
| Female | 1975‐1982 | 5.6**
The APC or AAPC is significantly different from zero (P < .05).
|
1982‐1991 | 3.4**
The APC or AAPC is significantly different from zero (P < .05).
|
1991‐2006 | 0.5**
The APC or AAPC is significantly different from zero (P < .05).
|
2006‐2015 | −1.5**
The APC or AAPC is significantly different from zero (P < .05).
|
−1.5**
The APC or AAPC is significantly different from zero (P < .05).
|
−1.5**
The APC or AAPC is significantly different from zero (P < .05).
|
|||||||
| Melanoma of skin | |||||||||||||||||
| Male | 1975‐1986 | 5.4**
The APC or AAPC is significantly different from zero (P < .05).
|
1986‐2005 | 3.1**
The APC or AAPC is significantly different from zero (P < .05).
|
2005‐2015 | 1.8**
The APC or AAPC is significantly different from zero (P < .05).
|
1.8**
The APC or AAPC is significantly different from zero (P < .05).
|
1.8**
The APC or AAPC is significantly different from zero (P < .05).
|
|||||||||
| Female | 1975‐1980 | 5.5**
The APC or AAPC is significantly different from zero (P < .05).
|
1980‐2009 | 2.4**
The APC or AAPC is significantly different from zero (P < .05).
|
2009‐2012 | −1.3 | 2012‐2015 | 5.4**
The APC or AAPC is significantly different from zero (P < .05).
|
2.1 | 3.7**
The APC or AAPC is significantly different from zero (P < .05).
|
|||||||
| Pancreas | |||||||||||||||||
| Male | 1975‐1981 | −1.8**
The APC or AAPC is significantly different from zero (P < .05).
|
1981‐1985 | 1.2 | 1985‐1990 | −2.2**
The APC or AAPC is significantly different from zero (P < .05).
|
1990‐2003 | 0.2 | 2003‐2006 | 3.1 | 2006‐2015 | 0.3 | 0.3 | 0.3 | |||
| Female | 1975‐1984 | 1.4**
The APC or AAPC is significantly different from zero (P < .05).
|
1984‐1996 | −0.5 | 1996‐2015 | 1.0**
The APC or AAPC is significantly different from zero (P < .05).
|
1.0**
The APC or AAPC is significantly different from zero (P < .05).
|
1.0**
The APC or AAPC is significantly different from zero (P < .05).
|
|||||||||
| Prostate | 1975‐1988 | 2.6**
The APC or AAPC is significantly different from zero (P < .05).
|
1988‐1992 | 16.5**
The APC or AAPC is significantly different from zero (P < .05).
|
1992‐1995 | −11.5**
The APC or AAPC is significantly different from zero (P < .05).
|
1995‐2000 | 2.2 | 2000‐2009 | −1.6**
The APC or AAPC is significantly different from zero (P < .05).
|
2009‐2015 | −7.4**
The APC or AAPC is significantly different from zero (P < .05).
|
−5.5**
The APC or AAPC is significantly different from zero (P < .05).
|
−7.4**
The APC or AAPC is significantly different from zero (P < .05).
|
|||
| Thyroid | |||||||||||||||||
| Male | 1975‐1980 | −4.6 | 1980‐1997 | 1.8**
The APC or AAPC is significantly different from zero (P < .05).
|
1997‐2012 | 5.5**
The APC or AAPC is significantly different from zero (P < .05).
|
2012‐2015 | −1.1 | 3.2**
The APC or AAPC is significantly different from zero (P < .05).
|
0.5 | |||||||
| Female | 1975‐1977 | 6.5 | 1977‐1980 | −5.2 | 1980‐1993 | 2.3**
The APC or AAPC is significantly different from zero (P < .05).
|
1993‐1999 | 4.4**
The APC or AAPC is significantly different from zero (P < .05).
|
1999‐2009 | 7.1**
The APC or AAPC is significantly different from zero (P < .05).
|
2009‐2015 | 1.2**
The APC or AAPC is significantly different from zero (P < .05).
|
3.1**
The APC or AAPC is significantly different from zero (P < .05).
|
1.2**
The APC or AAPC is significantly different from zero (P < .05).
|
|||
| Uterine corpus | 1975‐1979 | −6.0**
The APC or AAPC is significantly different from zero (P < .05).
|
1979‐1988 | −1.7**
The APC or AAPC is significantly different from zero (P < .05).
|
1988‐1997 | 0.7**
The APC or AAPC is significantly different from zero (P < .05).
|
1997‐2006 | −0.4**
The APC or AAPC is significantly different from zero (P < .05).
|
2006‐2009 | 3.5**
The APC or AAPC is significantly different from zero (P < .05).
|
2009‐2015 | 0.3 | 1.3**
The APC or AAPC is significantly different from zero (P < .05).
|
0.3 | |||
- AAPC indicates average annual percent change; APC, annual percent change based on delay‐adjusted incidence rates age adjusted to the 2000 US standard population.
- Note: Trends analyzed by the Joinpoint Regression Program, version 4.6, allowing up to 5 joinpoints. Trends are based on Surveillance, Epidemiology, and End Results (SEER) 9 areas.
- * The APC or AAPC is significantly different from zero (P < .05).
The overall cancer incidence rate in women has remained generally stable over the past few decades. Declines have continued for lung cancer, but tapered in recent years for CRC, whereas rates for other common cancers are increasing or stable (Table 5). Breast cancer incidence rates increased from 2006 to 2015 by approximately 0.3% to 0.4% per year among non‐Hispanic white (NHW) and Hispanic women, by 0.7% to 0.8% per year among black (non‐Hispanic) and American Indian/Alaska Native women, and by 1.8% per year among Asian/Pacific Islander women.36 This trend may in part be a consequence of the obesity epidemic, as well as declining parity.37, 38
Lung cancer incidence continues to decline twice as fast in men as in women, reflecting historical differences in tobacco uptake and cessation, as well as upturns in female smoking prevalence in some birth cohorts.39, 40 However, smoking patterns do not appear to explain the higher lung cancer incidence rates recently reported in young women compared with men born around the 1960s.41 In contrast, CRC incidence patterns are generally similar in men and women (Fig. 3), although in the past 5 data years rates have continued to decline by approximately 3% per year in men, but appear to have stabilized in women (Table 5). Reductions in CRC incidence prior to 2000 are attributed equally to changes in risk factors and the use of screening, which allows for the removal of premalignant lesions.42 However, more recent rapid declines are thought to primarily reflect the increased uptake of colonoscopy, which now is the predominant screening test.43, 44 Colonoscopy use among US adults aged 50 years and older tripled from 21% in 2000 to 60% in 2015.45 The rapid declines in overall CRC incidence rates mask an increase in adults aged younger than 55 years of almost 2% per year since the mid‐1990s.7
Incidence rates continue to increase for melanoma and cancers of the liver, thyroid, uterine corpus, and pancreas. Liver cancer incidence is rising faster than that for any other cancer in both men and women.38 Notably, however, the majority (71%) of cases in the United States are potentially preventable because most risk factors are modifiable (eg, obesity, excess alcohol consumption, cigarette smoking, and hepatitis B and C viruses).46 Approximately 24% of cases are caused by chronic hepatitis C virus (HCV) infection, which confers the largest relative risk and is also the most common chronic blood‐borne infection in the United States.47 Although there is exciting potential to avert much of the future burden of HCV‐associated disease because of new, well‐tolerated, antiviral therapies that achieve cure rates of greater than 90%,48 most infected individuals are undiagnosed. One‐time screening has been recommended for baby boomers (those born between 1945 and 1965), who account for three‐fourths of affected individuals,49, 50 since 2012 and is now even mandated in several states.51 However, only 14% of the more than 76 million boomers reported having received HCV testing in 2015.52 Compounding the challenge is a 3‐fold spike in acute HCV infections reported to the CDC from 2010 through 2016, after a decade of stable/declining rates, that is attributed to the opioid epidemic.53, 54 Fewer than 10% of new infections are reported and the CDC estimates the actual number of acute infections in 2016 to be 41,200 (95% confidence interval, 32,600‐140,600), approximately 75% to 85% of which will progress to chronic infection.
Cancer Survival
The 5‐year relative survival rate for all cancers combined diagnosed during 2008 through 2014 was 67% in whites and 62% in blacks.10 Figure 4 shows 5‐year relative survival rates by cancer type, stage at diagnosis, and race. For all stages combined, survival is highest for prostate cancer (98%), melanoma of the skin (92%), and female breast cancer (90%) and lowest for cancers of the pancreas (9%), liver (18%), esophagus (19%), and lung (19%). Black patients have lower survival rates than whites for every cancer type shown in Figure 4 except for cancers of the kidney and pancreas, with the absolute difference being 10% or higher for most. The largest disparities are for melanoma (26%) and cancers of the uterine corpus (21%) and oral cavity and pharynx (18%), in part reflecting a much later stage at diagnosis in black patients (Fig. 5). However, blacks also have lower stage‐specific survival for most cancer types. After adjusting for sex, age, and stage at diagnosis, the relative risk of death after a cancer diagnosis is 33% higher in black patients than in white patients.55 The disparity is even larger for American Indians/Alaska Natives, who are 51% more likely than whites to die from their cancer.


Cancer survival has improved since the mid‐1970s for all of the most common cancers except those of the uterine cervix and uterine corpus,55 although for some cancer types (eg, breast and prostate) this partly reflects lead time bias because of changes in detection practice. Progress has been especially rapid for hematopoietic and lymphoid malignancies due to improvements in treatment protocols, including the discovery of targeted therapies. For example, the 5‐year relative survival rate for chronic myeloid leukemia increased from 22% for patients diagnosed in the mid‐1970s to 69% for those diagnosed during 2008 through 2014,10 and most patients treated with tyrosine kinase inhibitors experience nearly normal life expectancy.56
In contrast to the steady increase in survival for most cancer types, advances have been slow for lung and pancreatic cancers, partly because greater than one‐half of cases are diagnosed at a distant stage (Fig. 5). There is a potential for earlier lung cancer diagnosis through screening with low‐dose computed tomography, which has demonstrated a 20% reduction in lung cancer mortality in current/former smokers with a history of 30 or more pack‐years.57 However, the translation of this benefit from clinical trial participants to the general population remains challenging. In 2015, only 4% of the 6.8 million eligible Americans reported being screened for lung cancer with low‐dose computed tomography.58 Another study found that more individuals who did not meet guideline‐recommended criteria for lung cancer screening had received a recent test than those who did meet criteria.59 Broad implementation of guideline‐recommended lung cancer screening will require new systems to facilitate unique aspects of the process, such as identifying eligible patients and acquainting physicians with information that should be delivered during the shared decision‐making conversation, which is recommended by the American Cancer Society and US Preventive Services Task Force and required by the Centers for Medicare and Medicaid Services. A recent small study suggests stark failure in the practice of shared decision making by primary care and pulmonary physicians.60
Trends in Cancer Mortality
Mortality rates are a better indicator of progress against cancer than incidence or survival rates because they are less affected by biases resulting from changes in detection practices.61 The cancer death rate rose during most of the 20th century, largely driven by rapid increases in lung cancer deaths among men as a consequence of the tobacco epidemic. However, since its peak of 215.1 deaths (per 100,000 population) in 1991, the cancer death rate has dropped steadily by approximately 1.5% per year, resulting in an overall decline of 27% as of 2016 (156.0 per 100,000 population). This translates to an estimated 2,629,200 fewer cancer deaths (1,804,000 in men and 825,200 in women) than what would have occurred if mortality rates had remained at their peak (Fig. 6). The number of averted deaths is larger for men than for women because the total decline in cancer mortality has been steeper for men (34% vs 24%).

The decline in cancer mortality over the past 2 decades is primarily the result of steady reductions in smoking and advances in early detection and treatment, which are reflected in the rapid declines for the 4 major cancers (lung, breast, prostate, and colorectum) (Fig. 7). Specifically, the death rate for lung cancer dropped by 48% from 1990 to 2016 among males and by 23% from 2002 to 2016 among females, whereas the death rate for breast cancer dropped by 40% from 1989 to 2016, that for prostate cancer dropped by 51% from 1993 to 2016, and that for CRC dropped by 53% from 1970 to 2016. During the most recent data years, declines in mortality from lung cancer have accelerated whereas those for CRC have slowed (Table 6). Prostate cancer mortality stabilized during 2013 through 2016 after 2 decades of steep (4% per year) reductions that are attributed to an earlier stage at diagnosis due to PSA testing and advances in treatments.62, 63 The leveling of rates is temporally associated with both declines in PSA testing and an uptick in distant stage disease diagnoses.32 Death rates rose from 2012 through 2016 for cancers of the liver, pancreas, and uterine corpus (Table 6), as well as for cancers of the brain and other nervous system, soft tissue (including heart), and sites within the oral cavity and pharynx associated with the human papillomavirus (HPV).1

| TREND 1 | TREND 2 | TREND 3 | TREND 4 | TREND 5 | TREND 6 | 2007‐2016 | 2012‐2016 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| YEARS | APC | YEARS | APC | YEARS | APC | YEARS | APC | YEARS | APC | YEARS | APC | AAPC | AAPC | |
| All sites | ||||||||||||||
| Overall | 1975‐1984 | 0.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
1984‐1991 | 0.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
1991‐1994 | −0.5 | 1994‐1998 | −1.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
1998‐2001 | −0.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
2001‐2016 | −1.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
−1.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
−1.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
| Male | 1975‐1979 | 1.0aa
The APC or AAPC is significantly different from zero (P <.05).
|
1979‐1990 | 0.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
1990‐1993 | −0.5 | 1993‐2001 | −1.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
2001‐2016 | −1.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
−1.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
−1.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
||
| Female | 1975‐1990 | 0.6aa
The APC or AAPC is significantly different from zero (P <.05).
|
1990‐1994 | −0.2 | 1994‐2002 | −0.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
2002‐2016 | −1.4aa
The APC or AAPC is significantly different from zero (P <.05).
|
−1.4aa
The APC or AAPC is significantly different from zero (P <.05).
|
−1.4aa
The APC or AAPC is significantly different from zero (P <.05).
|
||||
| Female breast | 1975‐1990 | 0.4aa
The APC or AAPC is significantly different from zero (P <.05).
|
1990‐1995 | −1.7aa
The APC or AAPC is significantly different from zero (P <.05).
|
1995‐1998 | −3.4aa
The APC or AAPC is significantly different from zero (P <.05).
|
1998‐2016 | −1.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
−1.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
−1.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
||||
| Colorectum | ||||||||||||||
| Male | 1975‐1979 | 0.6 | 1979‐1987 | −0.0aa
The APC or AAPC is significantly different from zero (P <.05).
|
1987‐2002 | −1.9aa
The APC or AAPC is significantly different from zero (P <.05).
|
2002‐2005 | −4.2aa
The APC or AAPC is significantly different from zero (P <.05).
|
2005‐2016 | −2.4* | −2.4aa
The APC or AAPC is significantly different from zero (P <.05).
|
−2.4aa
The APC or AAPC is significantly different from zero (P <.05).
|
||
| Female | 1975‐1984 | −1.0aa
The APC or AAPC is significantly different from zero (P <.05).
|
1984‐2001 | −1.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
2001‐2012 | −2.9aa
The APC or AAPC is significantly different from zero (P <.05).
|
2012‐2016 | −1.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
−2.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
−1.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
||||
| Liver & intrahepatic bile duct | ||||||||||||||
| Male | 1975‐1985 | 1.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
1985‐1996 | 3.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
1996‐1999 | 0.3 | 1999‐2013 | 2.7aa
The APC or AAPC is significantly different from zero (P <.05).
|
2013‐2016 | 0.7 | 2.0aa
The APC or AAPC is significantly different from zero (P <.05).
|
1.2aa
The APC or AAPC is significantly different from zero (P <.05).
|
||
| Female | 1975‐1978 | −1.5 | 1978‐1988 | 1.4aa
The APC or AAPC is significantly different from zero (P <.05).
|
1988‐1995 | 3.9aa
The APC or AAPC is significantly different from zero (P <.05).
|
1995‐2000 | 0.4 | 2000‐2008 | 1.6aa
The APC or AAPC is significantly different from zero (P <.05).
|
2008‐2016 | 2.6aa
The APC or AAPC is significantly different from zero (P <.05).
|
2.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
2.6aa
The APC or AAPC is significantly different from zero (P <.05).
|
| Lung & bronchus | ||||||||||||||
| Male | 1975‐1978 | 2.4aa
The APC or AAPC is significantly different from zero (P <.05).
|
1978‐1984 | 1.2aa
The APC or AAPC is significantly different from zero (P <.05).
|
1984‐1991 | 0.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
1991‐2005 | −1.9aa
The APC or AAPC is significantly different from zero (P <.05).
|
2005‐2012 | −2.9aa
The APC or AAPC is significantly different from zero (P <.05).
|
2012‐2016 | −4.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
−3.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
−4.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
| Female | 1975‐1983 | 5.9aa
The APC or AAPC is significantly different from zero (P <.05).
|
1983‐1992 | 3.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
1992‐2002 | 0.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
2002‐2007 | −0.7aa
The APC or AAPC is significantly different from zero (P <.05).
|
2007‐2014 | −2.0aa
The APC or AAPC is significantly different from zero (P <.05).
|
2014‐2016 | −4.2aa
The APC or AAPC is significantly different from zero (P <.05).
|
−2.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
−3.1aa
The APC or AAPC is significantly different from zero (P <.05).
|
| Melanoma of skin | ||||||||||||||
| Male | 1975‐1989 | 2.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
1989‐2013 | 0.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
2013‐2016 | −6.9aa
The APC or AAPC is significantly different from zero (P <.05).
|
−2.2aa
The APC or AAPC is significantly different from zero (P <.05).
|
−5.2aa
The APC or AAPC is significantly different from zero (P <.05).
|
||||||
| Female | 1975‐1988 | 0.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
1988‐2014 | −0.6aa
The APC or AAPC is significantly different from zero (P <.05).
|
2014‐2016 | −9.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
−2.6aa
The APC or AAPC is significantly different from zero (P <.05).
|
−5.0aa
The APC or AAPC is significantly different from zero (P <.05).
|
||||||
| Pancreas | ||||||||||||||
| Male | 1975‐1986 | −0.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
1986‐2000 | −0.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
2000‐2016 | 0.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
0.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
0.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
||||||
| Female | 1975‐1984 | 0.8aa
The APC or AAPC is significantly different from zero (P <.05).
|
1984‐2003 | 0.1 | 2003‐2006 | 1.0 | 2006‐2016 | 0.0 | 0.0 | 0.0 | ||||
| Prostate | 1975‐1987 | 0.9aa
The APC or AAPC is significantly different from zero (P <.05).
|
1987‐1991 | 3.0aa
The APC or AAPC is significantly different from zero (P <.05).
|
1991‐1994 | −0.5 | 1994‐1998 | −4.2aa
The APC or AAPC is significantly different from zero (P <.05).
|
1998‐2013 | −3.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
2013‐2016 | −0.0 | −2.3aa
The APC or AAPC is significantly different from zero (P <.05).
|
−0.9 |
| Uterine corpus | 1975‐1993 | −1.5aa
The APC or AAPC is significantly different from zero (P <.05).
|
1993‐2008 | 0.1 | 2008‐2016 | 2.1aa
The APC or AAPC is significantly different from zero (P <.05).
|
1.9aa
The APC or AAPC is significantly different from zero (P <.05).
|
2.1aa
The APC or AAPC is significantly different from zero (P <.05).
|
||||||
- AAPC indicates average annual percent change; APC, annual percent change based on mortality rates age adjusted to the 2000 US standard population.
- Note: Trends analyzed by the Joinpoint Regression Program, version 4.6, allowing up to 5 joinpoints.
- a The APC or AAPC is significantly different from zero (P <.05).
Recorded Number of Deaths in 2016
A total of 2,744,248 deaths were recorded in the United States in 2016, 22% of which were from cancer (Table 7). Cancer is the second leading cause of death after heart disease in both men and women nationally, but is the leading cause of death in many states,64 in Hispanic and Asian Americans,65, 66 and in people younger than 80 years. However, those 80 years and older are nearly 2 times more likely to die from heart disease than from cancer. Among females, cancer is the first or second leading cause of death for every age group shown in Table 8, whereas among males, accidents, assault, and suicide predominate before age 40 years.
| 2015 | 2016 | RELATIVE CHANGE IN RATE | ||||||
|---|---|---|---|---|---|---|---|---|
| NO. | PERCENT | RATE | NO. | PERCENT | RATE | |||
| RANK (2016) | All causes | 2,712,630 | 733.0 | 2,744,248 | 729.1 | −0.5% | ||
| 1 | Heart disease | 633,842 | 23% | 168.3 | 635,260 | 23% | 165.5 | −1.7% |
| 2 | Cancer | 595,930 | 22% | 158.7 | 598,038 | 22% | 156.0 | −1.7% |
| 3 | Accidents (unintentional injuries) | 146,571 | 5% | 43.1 | 161,374 | 6% | 47.3 | 9.7% |
| 4 | Chronic lower respiratory diseases | 155,041 | 6% | 41.8 | 154,596 | 6% | 40.8 | −2.4% |
| 5 | Cerebrovascular disease | 140,323 | 5% | 37.6 | 142,142 | 5% | 37.4 | −0.5% |
| 6 | Alzheimer disease | 110,561 | 4% | 29.4 | 116,103 | 4% | 30.3 | 3.1% |
| 7 | Diabetes mellitus | 79,535 | 3% | 21.3 | 80,058 | 3% | 21.0 | −1.4% |
| 8 | Influenza and pneumonia | 57,062 | 2% | 15.2 | 51,537 | 2% | 13.6 | −10.5% |
| 9 | Nephritis, nephrotic syndrome, & nephrosis | 49,959 | 2% | 13.4 | 50,046 | 2% | 13.2 | −1.5% |
| 10 | Intentional self‐harm (suicide) | 44,193 | 2% | 13.3 | 44,965 | 2% | 13.4 | 0.8% |
- Death counts include unknown age.
- Rates are per 100,000 population and age adjusted to the 2000 US standard population. Rank is based on number of deaths.
- Source: National Center for Health Statistics, Centers for Disease Control and Prevention.
| ALL AGES | AGES 1 TO 19 | AGES 20 TO 39 | AGES 40 TO 59 | AGES 60 TO 79 | AGES ≥80 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MALE | FEMALE | MALE | FEMALE | MALE | FEMALE | MALE | FEMALE | MALE | FEMALE | MALE | FEMALE | |
| All Causes | All Causes | All Causes | All Causes | All Causes | All Causes | All Causes | All Causes | All Causes | All Causes | All Causes | All Causes | |
| 1,400,232 | 1,344,016 | 13,110 | 7,250 | 79,366 | 35,290 | 230,142 | 148,035 | 573,327 | 437,947 | 491,323 | 705,160 | |
| 1 | Heart disease | Heart disease | Accidents (unintentional injuries) | Accidents (unintentional injuries) | Accidents (unintentional injuries) | Accidents (unintentional injuries) | Heart disease | Cancer | Cancer | Cancer | Heart disease | Heart disease |
| 339,265 | 295,995 | 4,674 | 2,373 | 33,073 | 11,808 | 52,128 | 48,075 | 172,243 | 140,971 | 141,049 | 188,116 | |
| 2 | Cancer | Cancer | Assault (homicide) | Cancer | Intentional self‐harm (suicide) | Cancer | Cancer | Heart disease | Heart disease | Heart disease | Cancer | Cancer |
| 314,571 | 283,467 | 1,886 | 789 | 11,593 | 4,653 | 49,227 | 22,669 | 140,213 | 82,175 | 87,954 | 88,944 | |
| 3 | Accidents (unintentional injuries) | Cerebro‐vascular disease | Intentional self‐harm (suicide) | Intentional self‐harm (suicide) | Assault (homicide) | Intentional self‐harm (suicide) | Accidents (unintentional injuries) | Accidents (unintentional injuries) | Chronic lower respiratory diseases | Chronic lower respiratory diseases | Chronic lower respiratory diseases | Alzheimer disease |
| 103,864 | 82,787 | 1,873 | 687 | 9,042 | 2,976 | 31,493 | 14,091 | 37,398 | 36,278 | 29,525 | 69,826 | |
| 4 | Chronic lower respiratory diseases | Chronic lower respiratory diseases | Cancer | Assault (homicide) | Heart disease | Heart disease | Intentional self‐harm (suicide) | Chronic lower respiratory diseases | Cerebro‐vascular disease | Cerebro‐vascular disease | Cerebro‐vascular disease | Cerebro‐vascular disease |
| 73,045 | 81,551 | 1,064 | 555 | 5,362 | 2,621 | 12,002 | 6,166 | 23,494 | 21,359 | 28,254 | 55,642 | |
| 5 | Cerebro‐vascular disease | Alzheimer disease | Congenital anomalies | Congenital anomalies | Cancer | Assault (homicide) | Chronic liver disease & cirrhosis | Chronic liver disease & cirrhosis | Diabetes mellitus | Diabetes mellitus | Alzheimer disease | Chronic lower respiratory diseases |
| 59,355 | 80,731 | 519 | 460 | 4,044 | 1,610 | 11,157 | 5,823 | 22,078 | 15,952 | 27,841 | 38,684 | |
| 6 | Diabetes mellitus | Accidents (unintentional injuries) | Heart disease | Heart disease | Chronic liver disease & cirrhosis | Pregnancy, childbirth & puerperium | Diabetes mellitus | Cerebro‐vascular disease | Accidents (unintentional injuries) | Accidents (unintentional injuries) | Accidents (unintentional injuries) | Accidents (unintentional injuries) |
| 43,763 | 57,510 | 341 | 258 | 1,255 | 864 | 8,545 | 5,075 | 19,864 | 10,771 | 14,032 | 17,948 | |
| 7 | Alzheimer disease | Diabetes mellitus | Chronic lower respiratory diseases | Influenza & pneumonia | Diabetes mellitus | Chronic liver disease & cirrhosis | Cerebro‐vascular disease | Diabetes mellitus | Chronic liver disease & cirrhosis | Alzheimer disease | Influenza & pneumonia | Influenza & pneumonia |
| 35,372 | 36,295 | 169 | 111 | 1,029 | 818 | 6,621 | 5,039 | 11,656 | 10,667 | 12,434 | 16,333 | |
| 8 | Intentional self‐harm (suicide) | Influenza & pneumonia | Influenza & pneumonia | Chronic lower respiratory diseases | Cerebro‐vascular disease | Diabetes mellitus | Chronic lower respiratory diseases | Intentional self‐harm (suicide) | Nephritis, nephrotic syndrome & nephrosis | Nephritis, nephrotic syndrome & nephrosis | Diabetes mellitus | Diabetes mellitus |
| 34,727 | 26,526 | 133 | 105 | 784 | 722 | 5,517 | 4,243 | 10,604 | 9,076 | 12,062 | 14,541 | |
| 9 | Chronic liver disease & cirrhosis | Nephritis, nephrotic syndrome & nephrosis | Cerebro‐vascular disease | Septicemia | HIV disease | Cerebro‐vascular disease | Assault (homicide) | Septicemia | Influenza & pneumonia | Septicemia | Nephritis, nephrotic syndrome & nephrosis | Nephritis, nephrotic syndrome & nephrosis |
| 25,818 | 24,647 | 120 | 87 | 727 | 576 | 3,373 | 2,586 | 9,197 | 8,294 | 11,596 | 13,191 | |
| 10 | Nephritis, nephrotic syndrome & nephrosis | Septicemia | Septicemia | Cerebro‐vascular disease | Influenza & pneumonia | Influenza & pneumonia | Septicemia | Influenza & pneumonia | Septicemia | Influenza & pneumonia | Parkinson disease | Hypertension & hypertensive renal diseaseaa
Includes primary and secondary hypertension.
|
| 25,399 | 20,935 | 96 | 85 | 519 | 422 | 3,012 | 2,140 | 8,875 | 7,460 | 11,342 | 12,241 | |
- HIV indicates human immunodeficiency virus.
- Note: Deaths within each age group do not sum to all ages combined due to the inclusion of unknown ages. In accordance with the National Center for Health Statistics’ cause‐of‐death ranking, "Symptoms, signs, and abnormal clinical or laboratory findings" and categories that begin with "Other" and "All other" were not ranked.
- Source: US Final Mortality Data, 2016, National Center for Health Statistics, Centers for Disease Control and Prevention, 2018.
- a Includes primary and secondary hypertension.
Table 9 presents the number of deaths in 2016 for the 5 leading cancer types by age and sex. Brain and other nervous system tumors are the leading cause of cancer death among men aged younger than 40 years and women aged younger than 20 years, whereas breast cancer leads among women aged 20 to 59 years. Lung cancer leads in cancer deaths among men aged 40 years and older and women aged 60 years and older, causing more deaths in 2016 than breast cancer, prostate cancer, CRC, and leukemia combined. There were approximately 20% more lung cancer deaths in men (80,775) than in women (68,095) in 2016, but this pattern is projected to reverse by 2045 if current smoking trends continue.67 Cervical cancer continues to be the second leading cause of cancer death in women aged 20 to 39 years, causing 9 deaths per week in this age group. This finding underscores the need for increased HPV vaccination uptake in adolescents and guideline‐adherent screening in young women. Notably, the percentage of women aged 22 to 30 years who had never been screened for cervical cancer increased between 2000 and 2010.68 In addition, an estimated 14 million screening‐aged women (ages 21‐65 years) had not been tested in the past 3 years in 2015.69
| ALL AGES | <20 | 20 TO 39 | 40 TO 59 | 60 TO 79 | ≥ 80 |
|---|---|---|---|---|---|
| MALE | |||||
| ALL SITES | ALL SITES | ALL SITES | ALL SITES | ALL SITES | ALL SITES |
| 314,571 | 1,100 | 4,044 | 49,227 | 172,243 | 87,954 |
| Lung & bronchus | Brain & ONS | Brain & ONS | Lung & bronchus | Lung & bronchus | Lung & bronchus |
| 80,775 | 314 | 538 | 11,588 | 49,877 | 19,095 |
| Prostate | Leukemia | Leukemia | Colorectum | Colorectum | Prostate |
| 30,370 | 280 | 526 | 5,888 | 14,010 | 15,535 |
| Colorectum | Bones & joints | Colorectum | Liver**
Includes intrahepatic bile duct.
|
Prostate | Colorectum |
| 27,642 | 120 | 487 | 4,001 | 13,447 | 7,250 |
| Pancreas | Soft tissue (including heart) | Non‐Hodgkin lymphoma | Pancreas | Pancreas | Urinary bladder |
| 21,899 | 87 | 237 | 3,747 | 12,926 | 5,621 |
| Liver**
Includes intrahepatic bile duct.
|
Non‐Hodgkin lymphoma | Soft tissue (including heart) | Brain & ONS | Liver**
Includes intrahepatic bile duct.
|
Pancreas |
| 17,843 | 42 | 232 | 2,562 | 10,961 | 5,099 |
| FEMALE | |||||
| ALL SITES | ALL SITES | ALL SITES | ALL SITES | ALL SITES | ALL SITES |
| 283,467 | 820 | 4,653 | 48,075 | 140,971 | 88,944 |
| Lung & bronchus | Brain & ONS | Breast | Breast | Lung & bronchus | Lung & bronchus |
| 68,095 | 238 | 1,158 | 10,405 | 39,029 | 19,199 |
| Breast | Leukemia | Uterine cervix | Lung & bronchus | Breast | Breast |
| 41,488 | 219 | 469 | 9,676 | 18,922 | 11,002 |
| Colorectum | Bone & joints | Colorectum | Colorectum | Pancreas | Colorectum |
| 24,644 | 80 | 417 | 4,328 | 10,971 | 9,637 |
| Pancreas | Soft tissue (including heart) | Brain & ONS | Ovary | Colorectum | Pancreas |
| 20,858 | 80 | 371 | 2,777 | 10,259 | 7,074 |
| Ovary | Liver**
Includes intrahepatic bile duct.
|
Leukemia | Pancreas | Ovary | Leukemia |
| 14,223 | 28 | 342 | 2,725 | 7,669 | 4,135 |
- ONS indicates other nervous system.
- Note: Ranking order excludes category titles that begin with the word “Other.”
- * Includes intrahepatic bile duct.
Cancer Disparities by Socioeconomic Status
Lower socioeconomic status (SES), whether measured at the individual or area level, is associated with numerous health disadvantages and higher mortality across race and ethnicity.70-72 A recent study estimated that approximately one‐third (34%) of cancer deaths in Americans aged 25 to 74 years could be averted with the elimination of socioeconomic disparities.72 Notably, socioeconomic deprivation was associated with lower cancer mortality prior to the mid‐1980s because of the later development of effective treatment and the historically elevated risk of lung and colorectal cancers among individuals with high SES.73, 74
County‐level SES indicators only indirectly reflect individual SES, but are valuable because the county is the smallest geographic unit for which policy is legislated. In addition, county‐level indicators potentially capture some of the complex environmental influences on health. Figure 8 depicts the distribution of county‐level poverty by quintile across the United States during 2012‐2016, when the overall cancer death rate was approximately 20% higher among residents of the poorest compared with the most affluent counties. Socioeconomic inequalities in cancer mortality widened over the past 3 decades overall, but there is substantial variation by cancer type. Consistent with socioeconomic inequalities for cancer incidence,75 the largest gaps are for the most preventable cancers. For example, cervical cancer mortality among women in poor counties is twice that of women in affluent counties, and lung and liver cancer mortality among men is >40% higher (Table 10). The most striking socioeconomic shift occurred for CRC mortality; rates in men in the poorest counties were approximately 20% lower than those in affluent counties in the early 1970s, but are now 35% higher (Fig. 9). This reversal reflects changes in dietary and smoking patterns that influence CRC risk,73 as well as the slower dissemination of screening and treatment advances among disadvantaged populations.76 A similar crossover occurred earlier for male lung cancer mortality because historically, men of higher SES were much more likely to smoke.73

| 1970 TO 1974 | 2012 TO 2016 | |||||
|---|---|---|---|---|---|---|
| RATE RATIO (95% CI) | RATE RATIO (95% CI) | |||||
| POOR | AFFLUENT | POOR VS AFFLUENT | POOR | AFFLUENT | POOR VS AFFLUENT | |
| All cancers | ||||||
| Both sexes | 199.7 | 198.8 | 1.00 (1.00‐1.01) | 176.7 | 149.7 | 1.18 (1.18‐1.19) |
| Male | 259.0 | 250.4 | 1.03 (1.03‐1.04) | 217.5 | 177.3 | 1.23 (1.22‐1.23) |
| Female | 157.5 | 164.4 | 0.96 (0.95‐0.97) | 147.6 | 130.2 | 1.13 (1.13‐1.14) |
| Brain & ONS | ||||||
| Both sexes | 3.7 | 4.1 | 0.90 (0.86‐0.93) | 4.0 | 4.6 | 0.89 (0.86‐0.91) |
| Male | 4.6 | 4.9 | 0.93 (0.88‐0.97) | 4.9 | 5.6 | 0.87 (0.84‐0.91) |
| Female | 3.0 | 3.4 | 0.87 (0.82‐0.92) | 3.4 | 3.7 | 0.91 (0.87‐0.95) |
| Breast (female) | ||||||
| All races | 29.0 | 34.0 | 0.85 (0.84‐0.87) | 22.5 | 19.5 | 1.16 (1.14‐1.17) |
| White | 28.8 | 34.4 | 0.84 (0.82‐0.85) | 20.9 | 19.7 | 1.06 (1.04‐1.09) |
| Black | 30.1 | 30.8 | 0.97 (0.90‐1.05) | 28.8 | 25.7 | 1.12 (1.08‐1.16) |
| Colorectum | ||||||
| Both sexes | 25.5 | 30.9 | 0.83 (0.81‐0.84) | 16.5 | 12.7 | 1.30 (1.28‐1.32) |
| Male | 28.6 | 35.6 | 0.81 (0.79‐0.82) | 20.2 | 14.9 | 1.35 (1.33‐1.38) |
| Female | 23.3 | 27.8 | 0.84 (0.82‐0.86) | 13.6 | 10.9 | 1.25 (1.23‐1.28) |
| Esophagus | ||||||
| Both sexes | 3.9 | 3.3 | 1.19 (1.14‐1.24) | 4.0 | 3.9 | 1.02 (0.99‐1.04) |
| Male | 6.7 | 5.5 | 1.20 (1.14‐1.26) | 7.1 | 6.9 | 1.03 (1.00‐1.06) |
| Female | 1.8 | 1.6 | 1.15 (1.06‐1.25) | 1.5 | 1.5 | 0.99 (0.93‐1.05) |
| Leukemia | ||||||
| Both sexes | 8.1 | 8.3 | 0.97 (0.94‐1.00) | 6.4 | 6.4 | 1.00 (0.97‐1.02) |
| Male | 10.5 | 11.1 | 0.94 (0.91‐0.98) | 8.6 | 8.7 | 0.99 (0.97‐1.02) |
| Female | 6.3 | 6.4 | 0.99 (0.95‐1.04) | 4.8 | 4.8 | 1.00 (0.97‐1.04) |
| Liver & intrahepatic bile duct | ||||||
| Both sexes | 3.5 | 2.8 | 1.27 (1.21‐1.33) | 7.7 | 5.6 | 1.37 (1.35‐1.40) |
| Male | 4.8 | 3.8 | 1.29 (1.21‐1.37) | 11.5 | 8.2 | 1.41 (1.37‐1.44) |
| Female | 2.5 | 2.0 | 1.22 (1.13‐1.31) | 4.5 | 3.5 | 1.31 (1.27‐1.36) |
| Lung & bronchus | ||||||
| Both sexes | 41.2 | 37.3 | 1.11 (1.09‐1.12) | 47.7 | 37.2 | 1.28 (1.27‐1.29) |
| Male | 76.3 | 66.8 | 1.14 (1.13‐1.16) | 63.0 | 44.2 | 1.42 (1.41‐1.44) |
| Female | 14.2 | 14.7 | 0.96 (0.94‐0.99) | 36.1 | 32.0 | 1.13 (1.12‐1.14) |
| Myeloma | ||||||
| Both sexes | 2.8 | 2.8 | 1.00 (0.96‐1.05) | 3.7 | 3.1 | 1.17 (1.14‐1.21) |
| Male | 3.4 | 3.4 | 1.00 (0.93‐1.07) | 4.6 | 4.0 | 1.14 (1.09‐1.19) |
| Female | 2.3 | 2.3 | 1.01 (0.94‐1.08) | 3.0 | 2.5 | 1.22 (1.17‐1.28) |
| Non‐Hodgkin lymphoma | ||||||
| Both sexes | 4.9 | 6.0 | 0.83 (0.80‐0.85) | 5.6 | 5.5 | 1.02 (1.00‐1.05) |
| Male | 6.2 | 7.3 | 0.85 (0.81‐0.89) | 7.2 | 7.1 | 1.02 (0.98‐1.05) |
| Female | 4.0 | 5.0 | 0.80 (0.76‐0.84) | 4.4 | 4.2 | 1.03 (0.99‐1.06) |
| Ovary | ||||||
| All races | 9.0 | 10.6 | 0.84 (0.81‐0.87) | 7.0 | 7.0 | 1.00 (0.97‐1.03) |
| White | 9.3 | 10.8 | 0.86 (0.83‐0.90) | 7.3 | 7.3 | 1.00 (0.97‐1.04) |
| Black | 7.6 | 8.5 | 0.89 (0.77‐1.04) | 6.4 | 6.1 | 1.05 (0.97‐1.14) |
| Pancreas | ||||||
| Both sexes | 10.8 | 10.5 | 1.03 (1.01‐1.06) | 11.4 | 10.8 | 1.06 (1.04‐1.07) |
| Male | 14.3 | 13.3 | 1.07 (1.04‐1.11) | 13.0 | 12.5 | 1.04 (1.02‐1.07) |
| Female | 8.3 | 8.4 | 0.98 (0.95‐1.02) | 10.1 | 9.4 | 1.07 (1.05‐1.10) |
| Prostate | ||||||
| All races | 32.6 | 30.2 | 1.08 (1.05‐1.11) | 22.5 | 17.9 | 1.26 (1.23‐1.28) |
| White | 28.1 | 29.9 | 0.94 (0.91‐0.97) | 18.2 | 17.7 | 1.03 (1.00‐1.05) |
| Black | 51.4 | 52.8 | 0.97 (0.90‐1.06) | 42.9 | 33.7 | 1.27 (1.21‐1.34) |
| Urinary bladder | ||||||
| Both sexes | 5.2 | 5.9 | 0.87 (0.84‐0.91) | 4.2 | 4.3 | 0.96 (0.94‐0.99) |
| Male | 8.5 | 10.4 | 0.82 (0.78‐0.86) | 7.2 | 7.6 | 0.95 (0.92‐0.98) |
| Female | 2.9 | 3.0 | 0.97 (0.91‐1.04) | 2.2 | 2.1 | 1.03 (0.98‐1.08) |
| Uterine corpus | ||||||
| All races | 5.9 | 5.5 | 1.08 (1.04‐1.13) | 5.3 | 4.6 | 1.15 (1.11‐1.19) |
| White | 5.2 | 5.4 | 0.96 (0.92‐1.01) | 4.3 | 4.5 | 0.96 (0.92‐1.00) |
| Black | 9.0 | 8.6 | 1.04 (0.90‐1.22) | 8.9 | 8.2 | 1.08 (1.01‐1.16) |
| Uterine cervix | ||||||
| All races | 8.9 | 5.1 | 1.73 (1.66‐1.80) | 3.2 | 1.6 | 2.00 (1.90‐2.10) |
| White | 6.7 | 4.9 | 1.36 (1.30‐1.43) | 2.9 | 1.6 | 1.86 (1.75‐1.98) |
| Black | 16.9 | 12.4 | 1.37 (1.22‐1.54) | 4.3 | 2.4 | 1.76 (1.57‐1.99) |
- 95% CI indicates 95% confidence interval; ONS, other nervous system.
- "Poor" and "affluent" refer to extreme county‐level poverty categories: 21.18% to 53.95% and 1.81% to 10.84%, respectively.
- Rates are per 100,000 population and age adjusted to the 2000 US standard population. Rate ratio is the unrounded rate in poor counties divided by the corresponding unrounded rate in affluent counties.

In contemporary times, the prevalence of behaviors that increase cancer incidence and mortality are vastly higher among residents of the poorest counties, including double the prevalence of smoking and obesity compared to residents of the wealthiest counties.70 Poverty is also associated with lower cancer screening prevalence,77 later stage diagnosis,78 and a lower likelihood of optimal treatment. Although lack of health care capacity in economically challenged areas likely contributes to these disparities, some states are home to both the poorest and most affluent counties, suggesting an opportunity for improvement in the distribution of services. Increasing access to care weakens the link between SES and health.79 Numerous states have reduced inequalities through various strategies that removed barriers to prevention, early detection, and treatment.80-82
Socioeconomic inequalities in cancer mortality are small or absent for malignancies that are less amenable to prevention or treatment. For example, mortality for leukemia and non‐Hodgkin lymphoma was equivalent across poverty levels, despite a higher incidence in more affluent counties,75 likely reflecting survival disparities.83-85 Inferior survival among those with low SES is predominantly driven by a later stage of disease at diagnosis and less aggressive treatment.86 Disparities are also minimal or nonexistent for pancreatic and ovarian cancers, for which early detection is lacking and even optimal treatment has a nominal influence on survival. The inequality for prostate cancer mortality was largely confined to black men, even after accounting for Hispanic ethnicity among whites (data not shown). This finding is consistent with previous studies showing a stronger association between SES and prostate cancer mortality among blacks.87, 88 The slight excess mortality for brain/other nervous system tumors and urinary bladder cancer in affluent counties is in agreement with incidence studies and may partly reflect detection bias.75, 89
Cancer Disparities by Race/Ethnicity
Cancer occurrence and outcomes vary considerably between racial and ethnic groups, largely because of inequalities in wealth that lead to differences in risk factor exposures and barriers to high‐quality cancer prevention, early detection, and treatment,90, 91 as discussed in the previous section. Cancer incidence and mortality are generally highest among non‐Hispanic blacks (NHBs) and lowest among Asian/Pacific Islanders (Table 11). The overall cancer incidence rate in NHB men during 2011 through 2015 was 84% higher than that in Asian/Pacific Islander men and 9% higher than that in NHW men. Notably, NHB women had 7% lower cancer incidence than NHW women (because of lower rates of breast and lung cancer), but 13% higher cancer mortality. In men and women combined, the black‐white disparity in overall cancer mortality has declined from a peak of 33% in 1993 (279.0 vs 210.5 per 100,000 population) to 14% in 2016 (183.6 vs 160.7 per 100,000 population). This progress is largely due to the steep drop in smoking prevalence unique among black teens from the late 1970s through the early 1990s.92
| ALL RACES COMBINED | NON‐HISPANIC WHITE | NON‐HISPANIC BLACK | ASIAN/PACIFIC ISLANDER |
AMERICAN INDIAN/
ALASKA NATIVE
**
Data based on Indian Health Service Contract Health Service Delivery Areas (CHSDA) counties.
|
HISPANIC | |
|---|---|---|---|---|---|---|
| Incidence, 2011‐2015 | ||||||
| All sites | 449.8 | 465.3 | 463.9 | 291.7 | 398.5 | 346.6 |
| Male | 494.8 | 505.5 | 549.1 | 298.9 | 418.4 | 377.6 |
| Female | 419.3 | 438.4 | 407.0 | 290.3 | 386.9 | 329.9 |
| Breast (female) | 124.7 | 130.1 | 126.5 | 92.9 | 100.9 | 93.0 |
| Colon & rectum | 39.3 | 39.0 | 46.6 | 30.7 | 44.4 | 34.4 |
| Male | 45.2 | 44.6 | 55.2 | 36.1 | 49.8 | 41.7 |
| Female | 34.3 | 34.2 | 40.7 | 26.4 | 40.1 | 28.8 |
| Kidney & renal pelvis | 16.4 | 16.6 | 18.4 | 7.8 | 23.2 | 16.2 |
| Male | 22.2 | 22.5 | 25.4 | 11.1 | 29.9 | 21.1 |
| Female | 11.4 | 11.4 | 13.1 | 5.1 | 17.4 | 12.2 |
| Liver & intrahepatic bile duct | 8.1 | 6.7 | 10.7 | 13.0 | 14.8 | 13.3 |
| Male | 12.5 | 10.3 | 17.6 | 19.9 | 20.9 | 19.7 |
| Female | 4.3 | 3.6 | 5.2 | 7.4 | 9.5 | 7.8 |
| Lung & bronchus | 60.5 | 64.7 | 63.8 | 34.9 | 61.5 | 30.7 |
| Male | 71.3 | 74.3 | 85.4 | 44.5 | 69.3 | 39.2 |
| Female | 52.3 | 57.4 | 49.2 | 27.8 | 55.7 | 24.6 |
| Prostate | 109.2 | 101.7 | 179.2 | 56.0 | 73.1 | 91.6 |
| Stomach | 6.6 | 5.4 | 10.3 | 10.5 | 8.4 | 9.7 |
| Male | 9.1 | 7.8 | 14.1 | 13.7 | 11.2 | 12.5 |
| Female | 4.6 | 3.5 | 7.7 | 8.0 | 6.1 | 7.7 |
| Uterine cervix | 7.6 | 7.1 | 9.2 | 6.0 | 9.2 | 9.6 |
| Mortality, 2012‐2016 | ||||||
| All sites | 161.0 | 165.4 | 190.6 | 100.4 | 148.8 | 113.6 |
| Male | 193.1 | 197.3 | 239.8 | 119.1 | 178.8 | 138.2 |
| Female | 137.7 | 141.8 | 160.4 | 87.0 | 126.8 | 96.4 |
| Breast (female) | 20.6 | 20.6 | 28.9 | 11.3 | 14.5 | 14.3 |
| Colon & rectum | 14.2 | 14.0 | 19.4 | 9.9 | 15.9 | 11.2 |
| Male | 16.9 | 16.6 | 24.5 | 11.7 | 19.5 | 14.4 |
| Female | 11.9 | 11.9 | 16.0 | 8.4 | 13.1 | 8.8 |
| Kidney & renal pelvis | 3.8 | 3.9 | 3.7 | 1.8 | 5.8 | 3.5 |
| Male | 5.5 | 5.7 | 5.6 | 2.7 | 8.2 | 5.0 |
| Female | 2.3 | 2.4 | 2.3 | 1.1 | 3.8 | 2.3 |
| Liver & intrahepatic bile duct | 6.5 | 5.7 | 8.6 | 9.4 | 10.8 | 9.3 |
| Male | 9.6 | 8.3 | 13.6 | 13.9 | 14.6 | 13.3 |
| Female | 3.9 | 3.4 | 4.8 | 5.8 | 7.5 | 6.0 |
| Lung & bronchus | 41.9 | 45.0 | 45.6 | 22.8 | 35.4 | 18.3 |
| Male | 51.6 | 54.1 | 63.9 | 30.3 | 42.7 | 25.3 |
| Female | 34.4 | 37.9 | 33.3 | 17.4 | 29.9 | 13.1 |
| Prostate | 19.2 | 18.1 | 39.8 | 8.6 | 19.1 | 15.9 |
| Stomach | 3.1 | 2.4 | 5.7 | 5.3 | 5.2 | 5.1 |
| Male | 4.2 | 3.3 | 8.4 | 6.8 | 7.0 | 6.5 |
| Female | 2.3 | 1.7 | 3.9 | 4.2 | 3.7 | 4.0 |
| Uterine cervix | 2.3 | 2.1 | 3.6 | 1.7 | 2.8 | 2.6 |
- Rates are per 100,000 population and age adjusted to the 2000 US standard population. Nonwhite and nonblack race categories are not mutually exclusive of Hispanic origin.
- * Data based on Indian Health Service Contract Health Service Delivery Areas (CHSDA) counties.
Geographic Variation in Cancer Occurrence
Tables 12 and 13 show cancer incidence and mortality rates for selected cancers by state. State variation in cancer incidence results from differences in medical detection practices and the prevalence of risk factors, such as smoking, obesity, and other health behaviors. For example, up‐to‐date HPV vaccination coverage among adolescent (ages 13‐17 years) boys and girls ranged widely in 2017, from just 29% in Mississippi to 78% in Rhode Island and the District of Columbia.93 This variation may contribute to future differential patterns in HPV‐associated cancers across states.94, 95 Geographic health disparities, which have increased over time,96, 97 often reflect the national distribution of poverty.98 This trend may be exacerbated by widening inequalities in access to health care because of state/territory differences in Medicaid expansion and other initiatives to improve insurance coverage.99, 100
| STATE | ALL SITES | BREAST | COLORECTUM | LUNG & BRONCHUS | NON‐HODGKIN LYMPHOMA | PROSTATE | URINARY BLADDER | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MALE | FEMALE | FEMALE | MALE | FEMALE | MALE | FEMALE | MALE | FEMALE | MALE | MALE | FEMALE | ||
| Alabama | 518.5 | 392.8 | 120.9 | 51.5 | 37.1 | 89.0 | 51.6 | 19.7 | 13.6 | 123.4 | 33.4 | 7.5 | |
| Alaska | 420.2 | 401.2 | 124.1 | 45.7 | 38.6 | 65.3 | 50.1 | 20.9 | 13.5 | 79.6 | 34.6 | 9.7 | |
| Arizona | 403.6 | 368.6 | 112.9 | 38.6 | 29.1 | 54.7 | 45.0 | 18.3 | 13.3 | 78.6 | 32.3 | 7.9 | |
| Arkansas | 520.6 | 401.2 | 114.7 | 50.8 | 37.5 | 98.7 | 61.6 | 20.9 | 14.6 | 115.9 | 34.6 | 7.4 | |
| California | 438.2 | 382.2 | 121.6 | 41.5 | 31.8 | 49.2 | 39.0 | 22.6 | 15.2 | 101.2 | 30.5 | 7.2 | |
| Colorado | 424.4 | 380.7 | 123.5 | 37.8 | 30.3 | 46.9 | 40.7 | 20.9 | 14.2 | 101.0 | 32.1 | 7.9 | |
| Connecticut | 507.6 | 448.5 | 140.2 | 42.9 | 33.4 | 67.9 | 56.2 | 26.1 | 17.3 | 112.8 | 46.6 | 12.0 | |
| Delaware | 552.2 | 451.8 | 133.8 | 42.7 | 32.8 | 82.7 | 62.8 | 24.8 | 17.5 | 136.1 | 43.2 | 10.4 | |
| Dist. of Columbia**
Data for these states are not included in the US combined rates because either the registry did not consent or high‐quality incidence data were not available for all years during 2011 through 2015 according to the North American Association of Central Cancer Registries (NAACCR).
† † Rates are based on cases diagnosed during 2011 through 2014. |
527.8 | 444.3 | 144.6 | 50.1 | 38.7 | 65.4 | 49.5 | 22.6 | 12.9 | 154.1 | 23.2 | 8.5 | |
| Florida | 462.2 | 389.9 | 116.0 | 42.3 | 32.1 | 69.3 | 51.9 | 20.9 | 14.5 | 97.4 | 32.9 | 8.1 | |
| Georgia | 519.5 | 409.8 | 125.2 | 49.3 | 35.9 | 82.9 | 51.7 | 22.3 | 14.7 | 123.3 | 32.7 | 7.7 | |
| Hawaii | 429.2 | 399.5 | 136.1 | 49.8 | 35.7 | 56.8 | 37.6 | 21.3 | 14.0 | 86.9 | 23.6 | 5.7 | |
| Idaho | 463.0 | 408.6 | 122.2 | 39.6 | 33.2 | 56.2 | 46.5 | 22.4 | 15.7 | 112.2 | 36.4 | 8.9 | |
| Illinois | 508.1 | 435.7 | 131.7 | 51.6 | 37.6 | 77.8 | 57.5 | 23.6 | 16.3 | 114.9 | 37.5 | 9.6 | |
| Indiana | 485.4 | 423.1 | 121.7 | 48.3 | 38.3 | 88.1 | 61.4 | 22.6 | 16.0 | 92.7 | 37.6 | 9.2 | |
| Iowa | 513.0 | 433.3 | 123.4 | 51.2 | 39.3 | 77.1 | 53.4 | 26.5 | 17.8 | 108.0 | 38.3 | 8.7 | |
| Kansas**
Data for these states are not included in the US combined rates because either the registry did not consent or high‐quality incidence data were not available for all years during 2011 through 2015 according to the North American Association of Central Cancer Registries (NAACCR).
|
— | — | — | — | — | — | — | — | — | — | — | — | |
| Kentucky | 570.2 | 468.8 | 125.0 | 58.0 | 42.4 | 112.8 | 79.0 | 24.5 | 16.5 | 108.8 | 39.5 | 10.2 | |
| Louisiana | 557.2 | 415.6 | 124.1 | 54.9 | 40.0 | 87.6 | 54.4 | 23.9 | 16.6 | 137.4 | 32.9 | 7.6 | |
| Maine | 496.6 | 448.4 | 125.7 | 41.5 | 33.9 | 82.5 | 64.8 | 23.2 | 17.7 | 93.6 | 47.1 | 11.9 | |
| Maryland | 488.4 | 418.6 | 131.7 | 42.0 | 33.2 | 65.2 | 51.8 | 20.4 | 14.7 | 125.7 | 37.5 | 9.3 | |
| Massachusetts | 485.3 | 445.1 | 137.6 | 41.9 | 33.1 | 69.3 | 60.2 | 23.4 | 16.3 | 106.4 | 40.4 | 11.2 | |
| Michigan | 492.8 | 419.7 | 123.4 | 42.8 | 33.5 | 75.2 | 58.5 | 24.1 | 16.6 | 117.6 | 38.6 | 10.0 | |
| Minnesota* | 507.5 | 438.7 | 131.5 | 43.0 | 34.1 | 61.6 | 50.5 | 26.9 | 17.9 | 113.8 | 37.9 | 9.5 | |
| Mississippi | 543.4 | 401.6 | 116.0 | 57.5 | 41.1 | 99.8 | 56.3 | 20.3 | 14.3 | 130.6 | 30.8 | 7.0 | |
| Missouri | 489.7 | 424.0 | 128.2 | 48.8 | 35.9 | 87.9 | 63.9 | 22.7 | 15.3 | 98.0 | 33.9 | 8.4 | |
| Montana | 467.4 | 415.3 | 123.2 | 43.8 | 33.0 | 58.6 | 53.7 | 21.8 | 16.4 | 111.1 | 35.8 | 10.2 | |
| Nebraska | 493.3 | 415.4 | 124.1 | 49.5 | 37.4 | 70.6 | 50.1 | 24.7 | 16.8 | 114.3 | 36.4 | 8.7 | |
| Nevada**
Data for these states are not included in the US combined rates because either the registry did not consent or high‐quality incidence data were not available for all years during 2011 through 2015 according to the North American Association of Central Cancer Registries (NAACCR).
|
412.2 | 377.7 | 109.4 | 42.5 | 32.7 | 59.0 | 53.8 | 17.2 | 12.6 | 91.7 | 32.7 | 9.2 | |
| New Hampshire | 511.4 | 459.2 | 143.9 | 42.5 | 33.9 | 70.6 | 62.9 | 24.8 | 17.5 | 116.1 | 47.0 | 12.2 | |
| New Jersey | 525.2 | 447.6 | 133.4 | 47.9 | 37.0 | 64.3 | 52.6 | 26.0 | 18.2 | 134.7 | 41.7 | 10.5 | |
| New Mexico**
Data for these states are not included in the US combined rates because either the registry did not consent or high‐quality incidence data were not available for all years during 2011 through 2015 according to the North American Association of Central Cancer Registries (NAACCR).
|
394.1 | 364.3 | 112.4 | 38.2 | 28.9 | 46.1 | 35.6 | 17.2 | 13.6 | 82.4 | 25.8 | 6.3 | |
| New York | 528.1 | 445.5 | 131.3 | 46.0 | 35.0 | 69.1 | 54.1 | 26.5 | 17.8 | 131.7 | 41.1 | 10.6 | |
| North Carolina | 514.6 | 418.4 | 131.0 | 43.3 | 32.9 | 86.3 | 56.5 | 21.3 | 14.3 | 120.9 | 35.0 | 8.8 | |
| North Dakota | 492.8 | 412.6 | 123.7 | 53.0 | 38.9 | 68.4 | 50.7 | 21.7 | 17.0 | 121.0 | 36.3 | 8.1 | |
| Ohio | 497.9 | 429.5 | 126.2 | 48.3 | 36.4 | 82.7 | 59.4 | 23.1 | 15.6 | 108.0 | 38.7 | 9.3 | |
| Oklahoma | 489.8 | 409.8 | 118.4 | 48.1 | 36.9 | 85.7 | 58.7 | 22.0 | 15.1 | 101.1 | 33.8 | 7.8 | |
| Oregon | 453.8 | 412.4 | 124.9 | 39.8 | 30.4 | 61.3 | 52.4 | 21.8 | 15.6 | 95.4 | 37.1 | 8.9 | |
| Pennsylvania | 524.3 | 455.2 | 131.0 | 49.5 | 37.0 | 76.5 | 56.3 | 25.9 | 17.9 | 111.1 | 43.2 | 10.9 | |
| Rhode Island | 505.5 | 458.1 | 135.3 | 40.4 | 32.5 | 78.2 | 64.2 | 27.0 | 18.3 | 104.1 | 45.6 | 12.7 | |
| South Carolina | 512.3 | 407.5 | 128.3 | 44.6 | 33.7 | 84.4 | 53.5 | 20.2 | 13.9 | 119.4 | 34.6 | 8.5 | |
| South Dakota | 484.6 | 422.2 | 134.3 | 48.9 | 36.8 | 67.4 | 51.7 | 23.6 | 15.4 | 114.6 | 35.3 | 9.1 | |
| Tennessee | 514.8 | 415.2 | 122.2 | 46.3 | 35.6 | 94.3 | 61.7 | 21.6 | 14.5 | 114.4 | 34.2 | 8.1 | |
| Texas | 445.9 | 370.5 | 111.7 | 45.7 | 31.8 | 65.5 | 43.5 | 21.3 | 14.6 | 95.4 | 26.9 | 6.2 | |
| Utah | 439.1 | 371.4 | 115.1 | 34.2 | 27.6 | 32.4 | 23.7 | 22.6 | 14.9 | 121.0 | 29.6 | 6.1 | |
| Vermont | 472.4 | 434.8 | 130.4 | 38.7 | 33.5 | 69.9 | 58.3 | 26.2 | 18.4 | 92.0 | 37.7 | 10.7 | |
| Virginia | 444.4 | 395.6 | 127.9 | 40.3 | 32.3 | 69.8 | 50.6 | 20.4 | 14.2 | 102.8 | 31.1 | 8.1 | |
| Washington | 476.5 | 425.7 | 135.3 | 40.0 | 32.0 | 62.8 | 52.1 | 24.9 | 16.3 | 106.8 | 37.2 | 9.1 | |
| West Virginia | 511.0 | 442.5 | 116.3 | 53.2 | 41.6 | 98.4 | 66.2 | 22.0 | 15.9 | 94.7 | 39.4 | 10.6 | |
| Wisconsin | 497.0 | 430.7 | 129.7 | 42.6 | 33.1 | 68.0 | 54.1 | 25.5 | 17.2 | 111.6 | 39.7 | 9.9 | |
| Wyoming | 428.1 | 375.1 | 112.6 | 39.2 | 27.9 | 46.6 | 43.3 | 19.8 | 13.9 | 103.0 | 36.8 | 9.7 | |
| Puerto Rico
‡
‡
Data for Puerto Rico are not included in the US combined rates for comparability to previously published US rates.
|
404.9 | 319.3 | 93.2 | 52.5 | 35.1 | 24.7 | 12.3 | 17.0 | 12.8 | 146.6 | 16.9 | 4.7 | |
| United States | 494.8 | 419.3 | 124.7 | 45.2 | 34.3 | 71.3 | 52.3 | 22.8 | 15.6 | 109.2 | 35.5 | 8.8 | |
- Rates are per 100,000 population and age adjusted to the 2000 US standard population.
- — Data unavailable.
- * Data for these states are not included in the US combined rates because either the registry did not consent or high‐quality incidence data were not available for all years during 2011 through 2015 according to the North American Association of Central Cancer Registries (NAACCR).
- † Rates are based on cases diagnosed during 2011 through 2014.
- ‡ Data for Puerto Rico are not included in the US combined rates for comparability to previously published US rates.
| STATE | ALL SITES | BREAST | COLORECTUM | LUNG & BRONCHUS | NON‐HODGKIN LYMPHOMA | PANCREAS | PROSTATE | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MALE | FEMALE | FEMALE | MALE | FEMALE | MALE | FEMALE | MALE | FEMALE | MALE | FEMALE | MALE | |
| Alabama | 226.0 | 144.9 | 21.8 | 19.8 | 13.0 | 70.7 | 37.7 | 6.9 | 4.4 | 13.4 | 9.9 | 21.7 |
| Alaska | 189.5 | 145.9 | 19.6 | 17.2 | 14.1 | 50.7 | 37.9 | 6.4 | 4.1 | 11.2 | 11.1 | 18.3 |
| Arizona | 167.8 | 122.8 | 19.3 | 15.1 | 10.6 | 39.7 | 29.2 | 6.3 | 4.0 | 11.8 | 8.8 | 17.7 |
| Arkansas | 229.4 | 151.5 | 21.6 | 20.5 | 13.7 | 75.5 | 43.0 | 7.2 | 4.3 | 12.4 | 9.6 | 19.3 |
| California | 171.0 | 126.5 | 19.8 | 15.1 | 11.1 | 36.4 | 26.4 | 6.8 | 4.2 | 11.8 | 9.1 | 19.7 |
| Colorado | 162.6 | 120.8 | 19.0 | 14.0 | 10.5 | 32.4 | 26.7 | 6.4 | 3.7 | 10.8 | 8.2 | 21.4 |
| Connecticut | 175.3 | 128.4 | 18.1 | 13.5 | 10.0 | 42.0 | 32.0 | 7.1 | 4.1 | 12.1 | 9.7 | 17.7 |
| Delaware | 202.1 | 145.9 | 21.4 | 16.6 | 10.7 | 57.1 | 39.8 | 8.3 | 4.7 | 14.2 | 9.7 | 17.5 |
| Dist. of Columbia | 200.2 | 155.6 | 28.3 | 18.4 | 13.5 | 44.3 | 30.7 | 6.3 | 3.3 | 15.8 | 11.8 | 31.0 |
| Florida | 182.0 | 128.5 | 19.4 | 15.7 | 11.0 | 49.7 | 33.2 | 6.8 | 4.1 | 12.2 | 8.9 | 16.7 |
| Georgia | 206.6 | 137.0 | 21.9 | 19.1 | 12.1 | 59.8 | 33.3 | 7.0 | 4.1 | 12.7 | 9.1 | 22.2 |
| Hawaii | 162.3 | 113.0 | 16.2 | 15.7 | 10.6 | 39.6 | 23.9 | 6.3 | 3.5 | 12.4 | 9.9 | 13.9 |
| Idaho | 180.1 | 132.7 | 20.4 | 15.4 | 10.9 | 40.1 | 30.1 | 7.7 | 5.1 | 12.9 | 9.5 | 23.2 |
| Illinois | 203.0 | 146.7 | 21.9 | 18.7 | 12.8 | 55.2 | 37.6 | 7.4 | 4.4 | 13.0 | 9.6 | 20.4 |
| Indiana | 217.8 | 150.2 | 21.1 | 18.2 | 13.1 | 66.7 | 41.6 | 8.4 | 4.9 | 13.4 | 9.7 | 20.0 |
| Iowa | 200.9 | 139.7 | 19.1 | 17.4 | 13.1 | 55.7 | 35.9 | 8.4 | 4.8 | 12.9 | 9.5 | 19.6 |
| Kansas | 194.4 | 141.7 | 20.3 | 17.5 | 12.4 | 53.3 | 37.3 | 7.1 | 4.9 | 12.8 | 10.2 | 18.4 |
| Kentucky | 243.7 | 165.0 | 21.6 | 20.2 | 13.9 | 84.5 | 52.2 | 8.8 | 4.6 | 12.8 | 10.0 | 19.9 |
| Louisiana | 227.6 | 151.2 | 23.2 | 21.0 | 14.2 | 67.6 | 39.3 | 8.2 | 4.5 | 15.1 | 11.2 | 21.1 |
| Maine | 207.8 | 148.7 | 18.4 | 15.0 | 11.7 | 61.6 | 41.8 | 7.5 | 5.0 | 11.8 | 10.7 | 20.1 |
| Maryland | 190.7 | 140.0 | 22.2 | 16.9 | 11.9 | 48.6 | 34.3 | 6.8 | 4.1 | 13.6 | 10.0 | 20.2 |
| Massachusetts | 187.2 | 135.4 | 18.0 | 14.4 | 10.9 | 47.6 | 35.7 | 6.6 | 4.2 | 12.8 | 9.9 | 18.7 |
| Michigan | 202.4 | 147.8 | 21.3 | 16.8 | 12.1 | 56.6 | 39.8 | 8.4 | 4.9 | 13.5 | 10.6 | 19.0 |
| Minnesota | 181.2 | 132.8 | 18.1 | 14.4 | 11.2 | 44.0 | 33.3 | 7.9 | 4.7 | 12.5 | 9.2 | 19.5 |
| Mississippi | 245.5 | 155.8 | 23.4 | 23.1 | 15.3 | 78.3 | 39.9 | 7.1 | 4.0 | 15.6 | 11.2 | 24.7 |
| Missouri | 210.8 | 150.2 | 21.7 | 18.2 | 12.7 | 65.1 | 43.2 | 7.0 | 4.2 | 12.8 | 9.7 | 17.8 |
| Montana | 176.5 | 135.4 | 20.0 | 16.2 | 11.1 | 41.5 | 36.1 | 7.0 | 4.3 | 10.9 | 9.3 | 21.0 |
| Nebraska | 190.1 | 136.9 | 20.3 | 17.6 | 13.1 | 50.4 | 34.3 | 7.4 | 4.3 | 12.8 | 9.3 | 18.9 |
| Nevada | 184.3 | 142.9 | 21.9 | 19.4 | 14.0 | 47.6 | 39.6 | 6.5 | 3.8 | 11.4 | 9.0 | 20.2 |
| New Hampshire | 192.0 | 141.1 | 19.5 | 13.9 | 11.9 | 50.3 | 39.9 | 7.1 | 4.5 | 12.3 | 9.0 | 19.3 |
| New Jersey | 181.4 | 136.9 | 21.8 | 17.5 | 12.2 | 43.6 | 32.0 | 7.3 | 4.2 | 12.6 | 10.1 | 18.2 |
| New Mexico | 170.4 | 122.6 | 18.8 | 16.5 | 10.9 | 35.1 | 25.7 | 5.8 | 4.0 | 10.9 | 8.5 | 19.8 |
| New York | 180.5 | 133.8 | 19.9 | 15.9 | 11.5 | 45.6 | 31.7 | 7.1 | 4.2 | 12.9 | 9.9 | 18.3 |
| North Carolina | 206.0 | 138.9 | 20.9 | 16.7 | 11.5 | 62.4 | 36.5 | 7.1 | 4.2 | 12.9 | 9.5 | 20.3 |
| North Dakota | 178.8 | 128.0 | 17.5 | 16.2 | 11.9 | 47.3 | 31.2 | 6.8 | 4.7 | 11.3 | 8.4 | 17.8 |
| Ohio | 212.9 | 151.9 | 22.5 | 18.9 | 13.2 | 62.7 | 41.1 | 8.0 | 4.8 | 13.2 | 10.4 | 19.0 |
| Oklahoma | 221.5 | 154.6 | 22.6 | 20.9 | 14.2 | 67.0 | 43.3 | 8.0 | 4.9 | 12.5 | 9.7 | 20.4 |
| Oregon | 189.4 | 140.9 | 20.4 | 15.6 | 11.4 | 46.1 | 35.9 | 7.9 | 4.6 | 13.2 | 9.7 | 20.8 |
| Pennsylvania | 203.6 | 145.0 | 21.6 | 18.2 | 13.0 | 55.2 | 35.6 | 7.8 | 4.7 | 13.8 | 10.1 | 18.9 |
| Rhode Island | 201.0 | 140.4 | 18.2 | 15.9 | 11.3 | 56.4 | 40.4 | 6.5 | 4.5 | 13.3 | 9.8 | 17.6 |
| South Carolina | 213.9 | 141.3 | 21.8 | 17.7 | 12.2 | 61.9 | 35.5 | 6.8 | 4.3 | 13.1 | 9.8 | 22.2 |
| South Dakota | 192.8 | 132.7 | 19.2 | 19.9 | 13.2 | 51.7 | 33.5 | 6.9 | 4.0 | 12.3 | 9.4 | 19.3 |
| Tennessee | 227.7 | 151.5 | 22.1 | 19.1 | 13.2 | 73.1 | 42.6 | 8.2 | 4.8 | 12.7 | 9.7 | 19.8 |
| Texas | 187.0 | 129.1 | 20.0 | 17.8 | 11.4 | 47.5 | 29.4 | 7.0 | 4.3 | 11.7 | 9.0 | 17.9 |
| Utah | 148.5 | 109.5 | 20.1 | 13.1 | 9.6 | 23.4 | 15.6 | 6.7 | 4.3 | 10.9 | 8.7 | 20.5 |
| Vermont | 194.0 | 141.6 | 18.1 | 16.2 | 12.6 | 49.8 | 38.1 | 7.9 | 4.6 | 12.5 | 9.9 | 19.2 |
| Virginia | 194.0 | 137.4 | 21.4 | 16.8 | 11.5 | 53.0 | 34.0 | 6.9 | 4.3 | 12.8 | 9.5 | 19.9 |
| Washington | 183.6 | 135.9 | 19.6 | 14.5 | 10.6 | 44.9 | 34.1 | 7.9 | 4.5 | 12.2 | 9.3 | 20.0 |
| West Virginia | 227.1 | 161.7 | 21.9 | 20.9 | 16.0 | 72.6 | 45.1 | 7.8 | 4.9 | 12.0 | 9.4 | 17.4 |
| Wisconsin | 193.9 | 139.1 | 19.5 | 15.5 | 11.5 | 49.6 | 34.8 | 7.8 | 4.4 | 13.3 | 10.2 | 20.6 |
| Wyoming | 166.2 | 128.2 | 18.1 | 15.5 | 10.3 | 37.3 | 31.1 | 7.0 | 4.4 | 10.5 | 9.2 | 16.5 |
| Puerto Ricoaa
Rates for Puerto Rico are for 2011 through 2015 and are not included in the overall US combined rates.
|
152.7 | 94.6 | 17.9 | 19.7 | 12.2 | 19.8 | 8.9 | 4.7 | 2.6 | 7.9 | 5.8 | 26.7 |
| United States | 193.1 | 137.7 | 20.6 | 16.9 | 11.9 | 51.6 | 34.4 | 7.3 | 4.4 | 12.6 | 9.6 | 19.2 |
- Rates are per 100,000 population and age adjusted to the 2000 US standard population.
- a Rates for Puerto Rico are for 2011 through 2015 and are not included in the overall US combined rates.
The largest geographic variation in cancer occurrence by far is for lung cancer, reflecting the large historical and continuing differences in smoking prevalence between states.101 For example, lung cancer incidence rates during 2011 through 2015 in Kentucky (113 per 100,000 population in men and 79 per 100,000 population in women), where smoking prevalence continues to be highest, were approximately 3.5 times higher than those in Utah (32 per 100,000 population in men and 24 per 100,000 population in women), where smoking prevalence is lowest. In 2016, 1 in 4 residents of Kentucky and West Virginia were current smokers compared with 1 in 10 in Utah, Puerto Rico, and California.102
Cancer in Children and Adolescents
Cancer is the second most common cause of death among children aged 1 to 14 years in the United States, surpassed only by accidents. In 2019, an estimated 11,060 children (birth to 14 years) will be diagnosed with cancer and 1,190 will die from the disease. Benign and borderline malignant brain tumors are not included in the 2019 case estimates because the calculation method requires historical data and these tumors were not required to be reported to cancer registries until 2004.
Leukemia is the most common childhood cancer, accounting for 28% of cases (including benign and borderline malignant brain tumors). Brain and other nervous system tumors, approximately one‐quarter of which are benign/borderline malignant, are second most common (26%) (Table 14). The distribution of cancers that occur in adolescents (aged 15 to 19 years) differs somewhat from that in children. For example, brain and other nervous system tumors (21%), greater than one‐half of which (58%) are benign/borderline malignant, and lymphoma (20%) are the most common cancers, whereas leukemia accounts for just 13% of cases. Thyroid carcinoma and melanoma of the skin account for 11% and 4%, respectively, of cancers in adolescents, but only 2% and 1%, respectively, in children.
by Age and ICCC Type, Ages Birth to 19 Years, United States
| BIRTH TO 14 | 15 TO 19 | |||
|---|---|---|---|---|
| PERCENTAGE OF CASES | 5‐YEAR SURVIVAL, % | PERCENTAGE OF CASES | 5‐YEAR SURVIVAL, % | |
| All ICCC groups combined | 83.4 | 84.6 | ||
| Lymphoid leukemia | 22% | 90.8 | 7% | 73.8 |
| Acute myeloid leukemia | 4% | 66.4 | 4% | 64.2 |
| Hodgkin lymphoma | 3% | 97.8 | 12% | 96.1 |
| Non‐Hodgkin lymphoma (including Burkitt lymphoma) | 5% | 90.2 | 7% | 89.1 |
| Central nervous system neoplasms | 26% | 72.9 | 21% | 77.9 |
| Neuroblastoma & other peripheral nervous cell tumors | 6% | 80.2 | <1% | 54.1
†
†
The standard error of the survival rate is between 5 and 10 percentage points.
|
| Retinoblasoma | 2% | 95.2 | <1% | — |
| Nephroblastoma & other nonepithelial renal tumors | 5% | 92.7 | <1% | — |
| Hepatic tumors | 2% | 80.4 | <1% | 52.4
†
†
The standard error of the survival rate is between 5 and 10 percentage points.
|
| Hepatoblastoma | 1% | 84.6 | <1% | — |
| Osteosarcoma | 2% | 69.6 | 3% | 65.7 |
| Ewing tumor & related bone sarcomas | 1% | 77.7 | 2% | 64.3 |
| Rhabdomyosarcoma | 3% | 70.3 | 1% | 46.2 |
| Germ cell & gonadal tumors | 3% | 91.6 | 11% | 92.6 |
| Thyroid carcinoma | 2% | 99.7 | 11% | 99.2 |
| Malignant melanoma | 1% | 94.9 | 4% | 94.0 |
- ICCC indicates International Classification of Childhood Cancer.
- Survival rates are adjusted for normal life expectancy and are based on follow‐up of patients through 2015.
- — Statistic could not be calculated due to fewer than 25 cases diagnosed during 2008 to 2014.
- * Benign and borderline brain tumors were excluded from survival calculations, but were included in the denominator for case distribution.
- † The standard error of the survival rate is between 5 and 10 percentage points.
The overall cancer incidence rate in children and adolescents has been increasing slightly (by 0.7% per year) since 1975. In contrast, death rates have declined continuously for many decades, from 6.5 per 100,000 population in 1970 to 2.3 per 100,000 population in 2016, an overall reduction of 65% (65% in children and 61% in adolescents). Much of this progress reflects the dramatic 78% decline in leukemia mortality, from 2.7 per 100,000 children and adolescents in 1970 to 0.6 in 2016. Improved remission rates of 90% to 100% for childhood acute lymphocytic leukemia over the past 4 decades have been achieved primarily through the optimization of established chemotherapeutic agents as opposed to the development of new therapies.103 The 5‐year relative survival rate for all cancers combined improved from 58% during the mid‐1970s to 83% during 2008 through 2014 for children and from 68% to 85% for adolescents.10 However, survival varies substantially by cancer type and age at diagnosis (Table 14).
Limitations
Although the estimated numbers of new cancer cases and deaths expected to occur in 2019 provide a reasonably accurate portrayal of the contemporary cancer burden, they are model‐based, 3‐year‐ and 4‐year‐ahead projections that should be interpreted with caution and not be used to track trends over time. First, the estimates may be affected by changes in methodology as we take advantage of improvements in modeling techniques and cancer surveillance coverage. Second, although the models are robust, they can only account for trends through the most recent data year (currently 2015 for incidence and 2016 for mortality) and cannot anticipate abrupt fluctuations for cancers affected by changes in detection practice (eg, PSA testing and prostate cancer). Third, the model can be oversensitive to sudden or large changes in observed data. The most informative metrics for tracking cancer trends are age‐standardized or age‐specific cancer death rates from the NCHS and cancer incidence rates from SEER, NPCR, and/or NAACCR.
Errors in reporting race/ethnicity in medical records and on death certificates may result in underestimates of cancer incidence and mortality in nonwhite and nonblack populations, particularly American Indian/Alaska Native populations. It is also important to note that cancer data in the United States are primarily reported for broad, heterogeneous racial and ethnic groups, masking important differences in the cancer burden within these populations. For example, lung cancer incidence is equivalent in Native Hawaiian and NHW men, but approximately 50% lower in Asians/Pacific Islanders overall.66
Conclusions
The continuous decline in cancer death rates since 1991 has resulted in an overall drop of 27%, translating to approximately 2.6 million fewer cancer deaths. Although the racial gap in cancer mortality is slowly narrowing, socioeconomic inequalities are widening, with residents of the poorest counties experiencing an increasingly disproportionate burden of the most preventable cancers. These counties are low‐hanging fruit for locally focused cancer control efforts, including increased access to basic health care and interventions for smoking cessation, healthy living, and cancer screening programs. A broader application of existing cancer control knowledge with an emphasis on disadvantaged groups would undoubtedly accelerate progress against cancer.
Disclosures
All authors are employed by the American Cancer Society, which receives grants from private and corporate foundations, including foundations associated with companies in the health sector for research outside of the submitted work. The authors are not funded by or key personnel for any of these grants and their salary is solely funded through American Cancer Society funds.
References
Citing Literature
Number of times cited according to CrossRef: 6811
- Manvi Sharma, Itika Arora, Trygve O. Tollefsbol, Gut Microbiota-Derived Epigenetic Alterations During Onset of Diseases, Reference Module in Food Science, 10.1016/B978-0-12-819265-8.00007-3, (2022).
- Eden Romm, Jeremy Li, Valentina L. Kouznetsova, Igor F. Tsigelny, Machine Learning Strategies to Distinguish Oral Cancer from Periodontitis Using Salivary Metabolites, Intelligent Systems and Applications, 10.1007/978-3-030-55190-2_38, (511-526), (2021).
- Xiaoqing Yu, Farnoosh Abbas-Aghababazadeh, Y. Ann Chen, Brooke L. Fridley, Statistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments, Translational Bioinformatics for Therapeutic Development, 10.1007/978-1-0716-0849-4_9, (143-175), (2021).
- Ahmed M. Shabana, Suleyman Akocak, Patent survey on chemosensitizers (2015–2019), pH-Interfering Agents as Chemosensitizers in Cancer Therapy, 10.1016/B978-0-12-820701-7.00003-8, (129-146), (2021).
- N. Ershadi, R. Safaiee, M.M. Golshan, Functionalized (4,0) or (8,0) SWCNT as novel carriers of the anticancer drug 5-FU; A first-principle investigation, Applied Surface Science, 10.1016/j.apsusc.2020.147718, 536, (147718), (2021).
- Patrick Mille, Janice Carsello, Metastatic Bladder Cancer and the Use of Cisplatin Chemotherapy, Chemotherapy and Immunotherapy in Urologic Oncology, 10.1007/978-3-030-52021-2, (193-200), (2021).
- Sanyog Jain, Kaisar Raza, Ashish Kumar Agrawal, Ankur Vaidya, 2D and 3D cell culture: Getting close to mimicking the tumor microenvironment in vitro, Nanotechnology Applications for Cancer Chemotherapy, 10.1016/B978-0-12-817846-1.00027-8, (599-609), (2021).
- Trianth Das, Rong Zhong, Michael T. Spiotto, Notch Signaling and Human Papillomavirus–Associated Oral Tumorigenesis, Notch Signaling in Embryology and Cancer, 10.1007/978-3-030-55031-8_8, (105-122), (2021).
- Roland B. Walter, Selection of Patients for Individual Acute Myeloid Leukemia Therapies, Acute Leukemias, 10.1007/978-3-030-53633-6_4, (69-75), (2021).
- Pedro Enrique Guerrero, Adrià Duran, Maria Rosa Ortiz, Ernesto Castro, Adelaida Garcia-Velasco, Esther Llop, Rosa Peracaula, Microfibril associated protein 4 (MFAP4) is a carrier of the tumor associated carbohydrate sialyl-Lewis x (sLex) in pancreatic adenocarcinoma, Journal of Proteomics, 10.1016/j.jprot.2020.104004, 231, (104004), (2021).
- Ankit Grover, Nitesh Pradhan, Prashant Hemrajani, Comparison-Based Study to Predict Breast Cancer: A Survey, Innovations in Computational Intelligence and Computer Vision, 10.1007/978-981-15-6067-5_61, (543-550), (2021).
- Felipe Boff Maegawa, Yazan Ashouri, Mohammad Hamidi, Chiu-Hsieh Hsu, Taylor Sohn Riall, Gallbladder Cancer Surgery in the United States: Lymphadenectomy Trends and Impact on Survival, Journal of Surgical Research, 10.1016/j.jss.2020.08.041, 258, (54-63), (2021).
- Andrea Cheville, Sean Smith, Touré Barksdale,, Arash Asher, Cancer Rehabilitation, Braddom's Physical Medicine and Rehabilitation, 10.1016/B978-0-323-62539-5.00029-1, (568-593.e7), (2021).
- Bastian Czogalla, Alexandra Partenheimer, Susann Badmann, Elisa Schmoeckel, Doris Mayr, Thomas Kolben, Susanne Beyer, Anna Hester, Alexander Burges, Sven Mahner, Udo Jeschke, Fabian Trillsch, Nuclear Enolase-1/ MBP-1 expression and its association with the Wnt signaling in epithelial ovarian cancer, Translational Oncology, 10.1016/j.tranon.2020.100910, 14, 1, (100910), (2021).
- Stefan P. Haider, Kariem Sharaf, Tal Zeevi, Philipp Baumeister, Christoph Reichel, Reza Forghani, Benjamin H. Kann, Alexandra Petukhova, Benjamin L. Judson, Manju L. Prasad, Chi Liu, Barbara Burtness, Amit Mahajan, Seyedmehdi Payabvash, Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET/CT radiomics, Translational Oncology, 10.1016/j.tranon.2020.100906, 14, 1, (100906), (2021).
- Nagendra Kumar Chandrawanshi, Shekhar Verma, Recent Research and Development in Stem Cell Therapy for Cancer Treatment, Handbook of Research on Advancements in Cancer Therapeutics, 10.4018/978-1-7998-6530-8.ch018, (514-533), (2021).
- Changsong Wu, Yihan Wu, Xiaohui Zhu, Jing Zhang, Jinliang Liu, Yong Zhang, Near-infrared-responsive functional nanomaterials: the first domino of combined tumor therapy, Nano Today, 10.1016/j.nantod.2020.100963, 36, (100963), (2021).
- Lu Yang, Jing Zhang, Yane Song, Guangjian Yang, Haiyan Xu, Junling Li, Lei Guo, Xin Li, Xinying Shi, Beibei Mao, Ying Yang, Lijia Wu, Jiyu Wei, Henghui Zhang, Jianming Ying, Yan Wang, Genomic profile and immune microenvironment in patients with relapsed stage IA lung adenocarcinoma, Translational Oncology, 10.1016/j.tranon.2020.100942, 14, 1, (100942), (2021).
- Eduardo Pérez, Oscar Reyes, Sebastián Ventura, Convolutional neural networks for the automatic diagnosis of melanoma: An extensive experimental study, Medical Image Analysis, 10.1016/j.media.2020.101858, 67, (101858), (2021).
- K. Lankester, T. Hughes, Perioperative Anaesthetic Considerations for the Whipple Procedure and Other Pancreatic Surgeries, Anesthesia for Hepatico-Pancreatic-Biliary Surgery and Transplantation, 10.1007/978-3-030-51331-3, (389-412), (2021).
- Angela L. Mazul, Akash N. Naik, Kevin Y. Zhan, Katelyn O. Stepan, Matthew O. Old, Stephen Y. Kang, Erik R. Nakken, Sidharth V. Puram, Gender and race interact to influence survival disparities in head and neck cancer, Oral Oncology, 10.1016/j.oraloncology.2020.105093, 112, (105093), (2021).
- Ebru Aydin, Arzu Kart, Health Promoting Activities of Nigella sativa Seeds, Black cumin (Nigella sativa) seeds: Chemistry, Technology, Functionality, and Applications, 10.1007/978-3-030-48798-0_11, (153-177), (2021).
- Ronnie M. Ibrahim, Daniel J. Pak, Gastric Cancer Pain, Interventional Management of Chronic Visceral Pain Syndromes, 10.1016/B978-0-323-75775-1.00001-5, (95-104), (2021).
- Shuai Zhou, Chao Zhu, Qing Pang, Hui Chun Liu, MicroRNA-217: A regulator of human cancer, Biomedicine & Pharmacotherapy, 10.1016/j.biopha.2020.110943, 133, (110943), (2021).
- Lingxiang Wang, Tao Sun, Shumei Li, Zhengmao Zhang, Jingde Jia, Baoen Shan, Protein anabolism is key to long-term survival in high-grade serous ovarian cancer, Translational Oncology, 10.1016/j.tranon.2020.100885, 14, 1, (100885), (2021).
- Junmei Jin, Meirong Zhou, Xun Wang, Min Liu, Huilian Huang, Fei Yan, Zhenlong Yu, Xiaohong Shu, Xiaokui Huo, Lei Feng, Baojing Zhang, Shanshan Huang, Sa Deng, Chao Wang, Xiaochi Ma, Triptolidenol, isolated from Tripterygium wilfordii, disrupted NF-κB/COX-2 pathway by targeting ATP-binding sites of IKKβ in clear cell renal cell carcinoma, Fitoterapia, 10.1016/j.fitote.2020.104779, 148, (104779), (2021).
- Rebecca L. Siegel, Kimberly D. Miller, Hannah E. Fuchs, Ahmedin Jemal, Cancer Statistics, 2021, CA: A Cancer Journal for Clinicians, 10.3322/caac.21654, 71, 1, (7-33), (2021).
- Colleen H. Neal, Mark A. Helvie, Overdiagnosis and Risks of Breast Cancer Screening, Radiologic Clinics of North America, 10.1016/j.rcl.2020.09.005, 59, 1, (19-27), (2021).
- Faizah Alotaibi, Exosomal microRNAs: Potential Biomarkers for Cancer Diagnosis, Treatment Response and Prognosis, Role of Exosomes in Biological Communication Systems, 10.1007/978-981-15-6599-1, (321-336), (2021).
- Mattia Sibona, Giancarlo Marra, Paolo Gontero, Salvage Prostatectomy for Radio-Recurrent Prostate Cancer, Salvage Therapy for Prostate Cancer, 10.1007/978-3-030-57181-8, (21-35), (2021).
- Chunxiao Zhao, Lizu Lai, Lin Zhang, Zhihui Cai, Zhihong Ren, Congrong Shi, Wenjun Luo, Yifei Yan, The effects of acceptance and commitment therapy on the psychological and physical outcomes among cancer patients: A meta-analysis with trial sequential analysis, Journal of Psychosomatic Research, 10.1016/j.jpsychores.2020.110304, 140, (110304), (2021).
- Amirhossein Alaghmandfard, Omid Sedighi, Nima Tabatabaei Rezaei, Amir Abbas Abedini, Adrine Malek Khachatourian, Muhammet S. Toprak, Alexander Seifalian, Recent advances in the modification of carbon-based quantum dots for biomedical applications, Materials Science and Engineering: C, 10.1016/j.msec.2020.111756, 120, (111756), (2021).
- Yuanzhang Zou, Binghai Chen, Long non-coding RNA HCP5 in cancer, Clinica Chimica Acta, 10.1016/j.cca.2020.11.015, 512, (33-39), (2021).
- Amit K. Patel, Craig G. Rogers, Anna Johnson, Sabrina L. Noyes, Ji Qi, David Miller, Edward Shervish, Benjamin Stockton, Brian R. Lane, Initial Observation of a Large Proportion of Patients Presenting with Clinical Stage T1 Renal Masses: Results from the MUSIC-KIDNEY Statewide Collaborative, European Urology Open Science, 10.1016/j.euros.2020.11.002, 23, (13-19), (2021).
- Haiyang Wang, Chao Feng, Meixin Lu, Biao Zhang, Yingchen Xu, Quan Zeng, Jiafei Xi, Junnian Zhou, Xiaomin Ying, Jian Zhang, Wen Yue, Xuetao Pei, Integrative single-cell transcriptome analysis reveals a subpopulation of fibroblasts associated with favorable prognosis of liver cancer patients, Translational Oncology, 10.1016/j.tranon.2020.100981, 14, 1, (100981), (2021).
- Matthew August Odenwald, Robert T. Kavitt, Is Routine Upper Endoscopy and H. pylori Testing Indicated in Advance of Bariatric Surgery?, Difficult Decisions in Bariatric Surgery, 10.1007/978-3-030-55329-6_14, (131-142), (2021).
- Vanessa Tran, John Slavin, Molecular Genetics in the Multidisciplinary Management of Sarcoma, Sarcoma, 10.1007/978-981-15-9414-4, (135-152), (2021).
- I-Hsuan Yang, Yo-Shen Chen, Jia-Jing Li, Ya-Jyun Liang, Tzu-Chieh Lin, Subhaini Jakfar, Minal Thacker, Shinn-Chih Wu, Feng-Huei Lin, The development of laminin-alginate microspheres encapsulated with Ginsenoside Rg1 and ADSCs for breast reconstruction after lumpectomy, Bioactive Materials, 10.1016/j.bioactmat.2020.11.029, 6, 6, (1699-1710), (2021).
- Lorenzo Gerratana, Andrew A. Davis, Maurizio Polano, Qiang Zhang, Ami N. Shah, Chenyu Lin, Debora Basile, Giuseppe Toffoli, Firas Wehbe, Fabio Puglisi, Amir Behdad, Leonidas C. Platanias, William J. Gradishar, Massimo Cristofanilli, Understanding the organ tropism of metastatic breast cancer through the combination of liquid biopsy tools, European Journal of Cancer, 10.1016/j.ejca.2020.11.005, 143, (147-157), (2021).
- Larissa Costa de Almeida, Felipe Antunes Calil, João Agostinho Machado-Neto, Leticia Veras Costa-Lotufo, DNA damaging agents and DNA repair: From carcinogenesis to cancer therapy, Cancer Genetics, 10.1016/j.cancergen.2020.12.002, 252-253, (6-24), (2021).
- Feng Pan, Shifei Kang, Yanfeng Zhao, Lei Dai, Qi Shao, Yang Yang, Qiankun Chen, Junjie Zhu, Lifeng Cui, Effect of β-nicotinamide mononucleotide on tumor formation and growth in a lung cancer mouse model, Materials Chemistry Frontiers, 10.1039/D0QM00897D, (2021).
- Tarek Assi, Axel Le Cesne, Olivier Mir, Is there a role for immune checkpoint inhibitors in selected rare subtypes of soft tissue sarcoma?, Immunotherapy, 10.2217/imt-2020-0301, 13, 2, (91-93), (2021).
- Qinrui Han, Chun Zhang, Yongbin Zhang, Yuan Li, Liyi Wu, Xuegang Sun, Bufarenogin induces intrinsic apoptosis via Bax and ANT cooperation, Pharmacology Research & Perspectives, 10.1002/prp2.694, 9, 1, (2021).
- Zhou Jiang, Yiqi Jiang, Ingrid Nielsen, Thriving and career outcomes: The roles of achievement orientation and resilience, Human Resource Management Journal, 10.1111/1748-8583.12287, 31, 1, (143-164), (2020).
- Kate A. Harrington, Amita Shukla‐Dave, Ramesh Paudyal, Richard K.G. Do, MRI of the Pancreas, Journal of Magnetic Resonance Imaging, 10.1002/jmri.27148, 53, 2, (347-359), (2020).
- Robert J. Macielak, Michelle T. Ziebarth, Daniel L. Price, 3D Printed Fistula Plug: A Novel Bridge to Definitive Reconstruction, The Laryngoscope, 10.1002/lary.28563, 131, 1, (111-114), (2020).
- Xiao Zhang, Zhengyu Ju, Yue Zhu, Kenneth J. Takeuchi, Esther S. Takeuchi, Amy C. Marschilok, Guihua Yu, Multiscale Understanding and Architecture Design of High Energy/Power Lithium‐Ion Battery Electrodes, Advanced Energy Materials, 10.1002/aenm.202000808, 11, 2, (2020).
- Andrew L. O'Brien, Erin Jadallah, Albert H. Chao, Reconstruction of a radical total vulvectomy defect with a single split anterolateral thigh perforator flap: A case report and review of the literature, Microsurgery, 10.1002/micr.30592, 41, 1, (70-74), (2020).
- Bing Guo, Yingdi Zhang, Najiaowa Yu, Yang Liu, Impacts of conductive materials on microbial community during syntrophic propionate oxidization for biomethane recovery, Water Environment Research, 10.1002/wer.1357, 93, 1, (84-93), (2020).
- Yue Li, Hui‐Chan He, Da‐Lei Zhou, Qing Liu, Xiao Zhang, Xin‐Hua Yang, Zu‐lu Ye, Jun‐Ling Peng, Tao Tang, Xuan Su, Cai‐Yun He, Associations between lncRNA‐related polymorphisms and hepatocellular carcinoma risk: A two‐stage case–control study, Journal of Gastroenterology and Hepatology, 10.1111/jgh.15118, 36, 1, (233-239), (2020).
- Niki Kougioumtsidou, Eleftherios Vavoulidis, Maria Nasioutziki, Marianthi Symeonidou, Georgios Chrysostomos Pratilas, Evangelia Mareti, Stamatios Petousis, Anthoula Chatzikyriakidou, Gregorios Grimbizis, Theodoros Theodoridis, Dimosthenis Miliaras, Konstantinos Dinas, Leonidas Zepiridis, DNA methylation patterns of RAR‐β2 and RASSF1A gene promoters in FNAB samples from Greek population with benign or malignant breast lesions, Diagnostic Cytopathology, 10.1002/dc.24513, 49, 1, (153-164), (2020).
- Chen Sheng, Guoxiong Wu, Yiqiong Tang, Bian He, Yongkun Xie, Tingting Ma, Ting Ma, Jinxiao Li, Qing Bao, Yimin Liu, Characteristics of the potential vorticity and its budget in the surface layer over the Tibetan plateau, International Journal of Climatology, 10.1002/joc.6629, 41, 1, (439-455), (2020).
- Yanhong Ni, Tami Yap, Natasha Silke, John Silke, Michael McCullough, Antonio Celentano, Lorraine A. O’Reilly, Loss of NF‐kB1 and c‐Rel accelerates oral carcinogenesis in mice, Oral Diseases, 10.1111/odi.13508, 27, 2, (168-172), (2020).
- Francisco Javier Maza, Julieta Sztarker, Maria Eugenia Cozzarin, Maria Grazia Lepore, Alejandro Delorenzi, A crabs' high‐order brain center resolved as a mushroom body‐like structure, Journal of Comparative Neurology, 10.1002/cne.24960, 529, 3, (501-523), (2020).
- Yunping Zhu, Xiaoyan Zhao, Xiaowei Zhang, Hongkai Liu, Qiang Ao, Amino acid, structure and antioxidant properties of Haematococcus pluvialis protein hydrolysates produced by different proteases, International Journal of Food Science & Technology, 10.1111/ijfs.14618, 56, 1, (185-195), (2020).
- Sarah McGarrol, Alex Kaley, Rachael Eastham, Margaret Whitehead, Mark Limmer, Disgusting disruptions: Capturing the everyday experience and burden of managing gastrointestinal infections in the home, Health & Social Care in the Community, 10.1111/hsc.13091, 29, 1, (284-293), (2020).
- Zhenjiang Zheng, Ya Liu, Jie Yang, Chunlu Tan, Li Zhou, Xing Wang, Li Xiao, Shu Zhang, Yonghua Chen, Xubao Liu, Diabetes mellitus induced by immune checkpoint inhibitors, Diabetes/Metabolism Research and Reviews, 10.1002/dmrr.3366, 37, 1, (2020).
- Yujie Zhang, Yilin Sun, Yanfei Jia, Qian Zhang, Ping Zhu, Xiaoli Ma, α5‐nAChR and survivin: Two potential biological targets in lung adenocarcinoma, Journal of Cellular Physiology, 10.1002/jcp.29956, 236, 3, (1787-1797), (2020).
- Young Mi Kim, Sung‐Hwa Ko, Yong‐Il Shin, Yeonye Kim, Taehyung Kim, Jaehoon Jung, Sang‐Yull Lee, Nam Gyun Kim, Kyoung‐Jun Park, Ji Hyeon Ryu, Light‐emitting diode irradiation induces AKT/mTOR‐mediated apoptosis in human pancreatic cancer cells and xenograft mouse model, Journal of Cellular Physiology, 10.1002/jcp.29943, 236, 2, (1362-1374), (2020).
- Elizabeth A. Penix, Joshua K. Swift, Kelley A. Russell, Wilson T. Trusty, Client and therapist agreement in moment‐to‐moment helpfulness ratings in psychotherapy: A microprocess approach, Journal of Clinical Psychology, 10.1002/jclp.23030, 77, 1, (36-48), (2020).
- He Huang, Xiao‐Yan Qing, Qiong Zhou, Han‐Dan Li, Zhu‐Yun Hu, Silencing of microRNA‐3175 represses cell proliferation and invasion in prostate cancer by targeting the potential tumor‐suppressor , The Kaohsiung Journal of Medical Sciences, 10.1002/kjm2.12292, 37, 1, (20-26), (2020).
- Valentina Barcherini, Joana Almeida, Elizabeth A. Lopes, Mi Wang, Diogo Magalhães e Silva, Mattia Mori, Shaomeng Wang, Lucília Saraiva, Maria M. M. Santos, Potency and Selectivity Optimization of Tryptophanol‐Derived Oxazoloisoindolinones: Novel p53 Activators in Human Colorectal Cancer, ChemMedChem, 10.1002/cmdc.202000522, 16, 1, (250-258), (2020).
- Jun Gao, Chao Dai, Xin Yu, Xiang‐Bao Yin, Fan Zhou, Long noncoding RNA LEF1‐AS1 acts as a microRNA‐10a‐5p regulator to enhance MSI1 expression and promote chemoresistance in hepatocellular carcinoma cells through activating AKT signaling pathway, Journal of Cellular Biochemistry, 10.1002/jcb.29833, 122, 1, (86-99), (2020).
- Joseph Turnbull, Ryan Szukalo, Dmitrij Zagidulin, Mark Biesinger, David Shoesmith, The kinetics of copper corrosion in nitric acid, Materials and Corrosion, 10.1002/maco.202011707, 72, 1-2, (348-360), (2020).
- Stoyan Stoyanov, Weilun Sun, Henning Peter Düsedau, Carla Cangalaya, Ilseob Choi, Hadi Mirzapourdelavar, David Baidoe‐Ansah, Rahul Kaushik, Jens Neumann, Ildiko Rita Dunay, Alexander Dityatev, Attenuation of the extracellular matrix restores microglial activity during the early stage of amyloidosis, Glia, 10.1002/glia.23894, 69, 1, (182-200), (2020).
- Paul Zolkind, Jake J. Lee, Ryan S. Jackson, Patrik Pipkorn, Sean T. Massa, Untreated head and neck cancer: Natural history and associated factors, Head & Neck, 10.1002/hed.26460, 43, 1, (89-97), (2020).
- Lindsey M. Charo, Ramez N. Eskander, Ryosuke Okamura, Sandip P. Patel, Mina Nikanjam, Richard B. Lanman, David E. Piccioni, Shumei Kato, Michael T. McHale, Razelle Kurzrock, Clinical implications of plasma circulating tumor DNA in gynecologic cancer patients, Molecular Oncology, 10.1002/1878-0261.12791, 15, 1, (67-79), (2020).
- Xing Quan, Jiang Luo, A 27‐33 GHz compact medium power amplifier with pole‐tuning technique in 130 nm CMOS technology, Microwave and Optical Technology Letters, 10.1002/mop.32590, 63, 1, (139-145), (2020).
- Ari J. Rosenberg, Everett E. Vokes, Optimizing Treatment De‐Escalation in Head and Neck Cancer: Current and Future Perspectives, The Oncologist, 10.1634/theoncologist.2020-0303, 26, 1, (40-48), (2020).
- Eric Assenat, Laurent Mineur, Caroline Mollevi, Evelyne Lopez‐Crapez, Catherine Lombard‐Bohas, Emmanuelle Samalin, Fabienne Portales, Thomas Walter, Hélène Forges, Marie Dupuy, Florence Boissière‐Michot, Alexandre Ho‐Pun‐Cheung, Marc Ychou, Thibaut Mazard, Phase II study evaluating the association of gemcitabine, trastuzumab and erlotinib as first‐line treatment in patients with metastatic pancreatic adenocarcinoma (GATE 1), International Journal of Cancer, 10.1002/ijc.33225, 148, 3, (682-691), (2020).
- Garry R. Russ, Justin R. Rizzari, Rene A. Abesamis, Angel C. Alcala, Coral cover a stronger driver of reef fish trophic biomass than fishing, Ecological Applications, 10.1002/eap.2224, 31, 1, (2020).
- Wasim Akhtar, Akranth Marella, Mohammad Mumtaz Alam, Mohemmed F. Khan, Mymoona Akhtar, Tariq Anwer, Farah Khan, Md. Naematullah, Faizul Azam, Moshahid A. Rizvi, Mohammad Shaquiquzzaman, Design and synthesis of pyrazole–pyrazoline hybrids as cancer‐associated selective COX‐2 inhibitors, Archiv der Pharmazie, 10.1002/ardp.202000116, 354, 1, (2020).
- Blayton Padasdao, Bardia Konh, Shape Memory Alloy Actuators in an Active Needle—Modeling, Precise Assembly, and Performance Evaluation, Journal of Manufacturing Science and Engineering, 10.1115/1.4047737, 143, 2, (2020).
- Rodrigo Herrán, Fabricio N. Molinari, Emanuel Bilbao, Leandro N. Monsalve, Javier I. Amalvy, Fabrication of electrospun fibers from a waterborne soy‐based polyurethane employing polyethylene oxide as a coformer, Journal of Applied Polymer Science, 10.1002/app.49815, 138, 6, (2020).
- Xiang Gao, Long Wang, Tao Liu, Jianhua Xiao, Hongbin Wang, Effect of agro‐ecological landscape on the distribution of in northeast China, Pest Management Science, 10.1002/ps.6062, 77, 2, (693-696), (2020).
- Daniel D. Sharbel, Mary Abkemeier, James Sullivan, Zach Zimmerman, William G. Albergotti, Umamaheswar Duvvuri, James Kenneth Byrd, Transcervical arterial ligation for prevention of postoperative hemorrhage in transoral oropharyngectomy: Systematic review and meta‐analysis, Head & Neck, 10.1002/hed.26480, 43, 1, (334-344), (2020).
- Elumalai Kowsalya, Kithiyon MosaChristas, Chinna Rani Inbaraj Jaquline, Pannerselvam Balashanmugam, Thiyagarajan Devasena, Gold nanoparticles induced apoptosis via oxidative stress and mitochondrial dysfunctions in MCF‐7 breast cancer cells, Applied Organometallic Chemistry, 10.1002/aoc.6071, 35, 1, (2020).
- Shaoran Song, Bixia Tian, Miao Zhang, Xiaoqian Gao, Liu Jie, Peijun Liu, Juan Li, Diagnostic and prognostic value of thymidylate synthase expression in breast cancer, Clinical and Experimental Pharmacology and Physiology, 10.1111/1440-1681.13415, 48, 2, (279-287), (2020).
- Aude Beyens, Annekatrien Boel, Sofie Symoens, Bert Callewaert, Cutis laxa: A comprehensive overview of clinical characteristics and pathophysiology, Clinical Genetics, 10.1111/cge.13865, 99, 1, (53-66), (2020).
- Olatunji B. Alese, Katerina Zakka, Xingyue Huo, Renjian Jiang, Walid L. Shaib, Mehmet Akce, Madhusmita Behera, Patrick Sullivan, Christina Wu, Bassel F. El‐Rayes, Perioperative therapy in metastatic colorectal cancer: Pattern of use and survival outcomes, Journal of Surgical Oncology, 10.1002/jso.26278, 123, 2, (596-605), (2020).
- Fa‐Kun Huang, Cheng‐Ying Zheng, Long‐Kai Huang, Chang‐Qing Lin, Jun‐Feng Zhou, Jia‐Xing Wang, Long non‐coding RNA MCF2L‐AS1 promotes the aggressiveness of colorectal cancer by sponging miR‐874‐3p and thereby up‐regulating CCNE1, The Journal of Gene Medicine, 10.1002/jgm.3285, 23, 1, (2020).
- Hiroaki Iwamoto, Kouji Izumi, Takashi Shimada, Hiroshi Kano, Suguru Kadomoto, Tomoyuki Makino, Renato Naito, Hiroshi Yaegashi, Kazuyoshi Shigehara, Yoshifumi Kadono, Atsushi Mizokami, Androgen receptor signaling‐targeted therapy and taxane chemotherapy induce visceral metastasis in castration‐resistant prostate cancer, The Prostate, 10.1002/pros.24082, 81, 1, (72-80), (2020).
- ZeWen Xiao, Wendy Wu, Chunlong Wu, Man Li, Fuming Sun, Lu Zheng, Gaojing Liu, Xiaoling Li, Zhiyuan Yun, Jiebing Tang, Yang Yu, Shengnan Luo, Wenji Sun, Xiaohong Feng, Qian Cheng, Xue Tao, Shuangxiu Wu, Ji Tao, 5‐Hydroxymethylcytosine signature in circulating cell‐free DNA as a potential diagnostic factor for early‐stage colorectal cancer and precancerous adenoma, Molecular Oncology, 10.1002/1878-0261.12833, 15, 1, (138-150), (2020).
- Yi Wang, Kiara F. Bruggeman, Stephanie Franks, Vini Gautam, Stuart I. Hodgetts, Alan R. Harvey, Richard J. Williams, David R. Nisbet, Is Viral Vector Gene Delivery More Effective Using Biomaterials?, Advanced Healthcare Materials, 10.1002/adhm.202001238, 10, 1, (2020).
- Zhaoshi Bai, Nianyang Ding, Jianjuan Ge, Yue Wang, Lei Wang, Nan Wu, Qing Wei, Silu Xu, Xiaolin Liu, Guoren Zhou, Esomeprazole overcomes paclitaxel‐resistance and enhances anticancer effects of paclitaxel by inducing autophagy in A549/Taxol cells, Cell Biology International, 10.1002/cbin.11481, 45, 1, (177-187), (2020).
- Vishesh Agrawal, Kimberly Thomas Benjamin, Eric C. Ko, Radiotherapy and Immunotherapy Combinations for Lung Cancer, Current Oncology Reports, 10.1007/s11912-020-00993-w, 23, 1, (2020).
- Xiaoli Wang, Hua Li, Dong Li, Yudi Bai, Yao Zhang, Xue Yan, Jin Li, Ri Zhao, Jiahui Liu, Wei Liu, Maolin Shi, Cheng Xu, Tai Yang, Tao Zhang, Sorafenib and CuB exert synergistic antitumor effects against hepatocellular carcinoma cells via inhibition of STAT3 phosphorylation, FEBS Open Bio, 10.1002/2211-5463.13035, 11, 1, (133-145), (2020).
- Longhao Wang, Miaomiao Li, Beibei Sha, Xuanyu Hu, Yaxin Sun, Mingda Zhu, Yan Xu, Pingping Li, Yating Wang, Yanyan Guo, Jiangfeng Li, Jianxiang Shi, Pei Li, Tao Hu, Ping Chen, Inhibition of deubiquitination by PR‐619 induces apoptosis and autophagy via ubi‐protein aggregation‐activated ER stress in oesophageal squamous cell carcinoma, Cell Proliferation, 10.1111/cpr.12919, 54, 1, (2020).
- Michael Weber, Taukeer A. Khan, Lukas J. Patalag, Mariano Bossi, Marcel Leutenegger, Vladimir N. Belov, Stefan W. Hell, Photoactivatable Fluorophore for Stimulated Emission Depletion (STED) Microscopy and Bioconjugation Technique for Hydrophobic Labels, Chemistry – A European Journal, 10.1002/chem.202004645, 27, 1, (451-458), (2020).
- Yawen Liu, Dawei Wang, Meng Zhou, Hui Chen, Huizhi Wang, Jingyu Min, Jiaxi Chen, Shuhui Wu, Xiufan Ni, Youli Zhang, Aihua Gong, Min Xu, The KRAS/Lin28B axis maintains stemness of pancreatic cancer cells via the let‐7i/TET3 pathway, Molecular Oncology, 10.1002/1878-0261.12836, 15, 1, (262-278), (2020).
- Bonnie Jerome-D’Emilia, Patricia D. Suplee, Evelyn Robles-Rodriguez, Wyatt D’Emilia, The Impact of Delays in Low-Income Women’s Breast Cancer Experiences, Cancer Nursing, 10.1097/NCC.0000000000000878, 44, 1, (E43-E52), (2020).
- Zhengqing Guo, Hui He, Yi Zhang, Jiaming Rao, Tao Yang, Ting Li, Lu Wang, Mengke Shi, Mengya Wang, Shihong Qiu, Xue Song, Hengte Ke, Huabing Chen, Heavy‐Atom‐Modulated Supramolecular Assembly Increases Antitumor Potency against Malignant Breast Tumors via Tunable Cooperativity, Advanced Materials, 10.1002/adma.202004225, 33, 2, (2020).
- Biao Zhang, Hai‐Yang Wang, De‐Xi Zhao, Dong‐Xing Wang, Quan Zeng, Jia‐Fei Xi, Xue Nan, Li‐Juan He, Jun‐Nian Zhou, Xue‐Tao Pei, Wen Yue, The splicing regulatory factor hnRNPU is a novel transcriptional target of c‐Myc in hepatocellular carcinoma, FEBS Letters, 10.1002/1873-3468.13943, 595, 1, (68-84), (2020).
- Rui Sun, Zhong Zheng, Li Wang, Shu Cheng, Qing Shi, Bin Qu, Di Fu, Christophe Leboeuf, Yan Zhao, Jing Ye, Anne Janin, Wei‐Li Zhao, A novel prognostic model based on four circulating miRNA in diffuse large B‐cell lymphoma: implications for the roles of MDSC and Th17 cells in lymphoma progression, Molecular Oncology, 10.1002/1878-0261.12834, 15, 1, (246-261), (2020).
- Weiqiang Lu, Weiwei Yu, Jiacheng He, Wenjuan Liu, Junjie Yang, Xianhua Lin, Yuanjin Zhang, Xin Wang, Wenhao Jiang, Jian Luo, Qiansen Zhang, Huaiyu Yang, Shihong Peng, Zhengfang Yi, Shancheng Ren, Jing Chen, Stefan Siwko, Ruth Nussinov, Feixiong Cheng, Hankun Zhang, Mingyao Liu, Reprogramming immunosuppressive myeloid cells facilitates immunotherapy for colorectal cancer, EMBO Molecular Medicine, 10.15252/emmm.202012798, 13, 1, (2020).
- Liju Zong, Shuangni Yu, Shengwei Mo, Yuncan Zhou, Yang Xiang, Zhaohui Lu, Jie Chen, High VISTA Expression Correlates With a Favorable Prognosis in Patients With Colorectal Cancer, Journal of Immunotherapy, 10.1097/CJI.0000000000000343, 44, 1, (22-28), (2020).
- Yu Wakabayashi, Takeshi Masuda, Kazunori Fujitaka, Taku Nakashima, Joe Okumoto, Kiyofumi Shimoji, Yoshifumi Nishimura, Kakuhiro Yamaguchi, Shinjiro Sakamoto, Yasushi Horimasu, Shintaro Miyamoto, Hiroshi Iwamoto, Shinichiro Ohshimo, Hironobu Hamada, Noboru Hattori, Clinical significance of BIM deletion polymorphism in chemoradiotherapy for non‐small cell lung cancer, Cancer Science, 10.1111/cas.14711, 112, 1, (369-379), (2020).
- Yi Ding, MinJae Lee, Yan Gao, Ping Bu, Christian Coarfa, Brian Miles, Arun Sreekumar, Chad J. Creighton, Gustavo Ayala, Neuropeptide Y nerve paracrine regulation of prostate cancer oncogenesis and therapy resistance, The Prostate, 10.1002/pros.24081, 81, 1, (58-71), (2020).
- Yini Zhu, Jiling Wen, Gang Huang, Jackson Mittlesteadt, Xiaofei Wen, Xin Lu, CHD1 and SPOP synergistically protect prostate epithelial cells from DNA damage, The Prostate, 10.1002/pros.24080, 81, 1, (81-88), (2020).
- Lihong Li, Pinli Yue, Qianqian Song, Ting‐Tai Yen, Shiho Asaka, Tian‐Li Wang, Anna L Beavis, Amanda N Fader, Yuchen Jiao, Guangwen Yuan, Ie‐Ming Shih, Yan Song, Genome‐wide mutation analysis in precancerous lesions of endometrial carcinoma, The Journal of Pathology, 10.1002/path.5566, 253, 1, (119-128), (2020).
- See more





