Differences in breast cancer incidence among young women aged 20–49 years by stage and tumor characteristics, age, race, and ethnicity, 2004–2013
A recent CDC study highlights the differences in breast cancer incidence among young women. Although breast cancer is not common among younger women, rates have remained stable in recent years. Breast cancers in young women are more likely to be found at later stages and with more aggressive, larger tumors. Based on data from CDC’s National Program of Cancer Registries (NPCR) and the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program, the study looked at breast cancer rates and trends by stage, grade, and tumor subtype, as well as age and race/ethnicity among women aged 20-49 years. From 2004-2013, the majority of invasive breast cancer cases (77.3%) occurred among women aged 40-49 years. Among women younger than 45 years old, black women had the highest breast cancer incidence. For women aged 45-49 years, white women had higher breast cancer incidence than black women. Across all age groups, incidence rates for triple-negative breast cancer were higher in black women than other races/ethnicities. These differences show that breast cancers in young women are highly diverse and in need of further research into personal and cultural factors. Take a look at our resource for triple-negative breast cancer.
Younger women diagnosed with breast cancer have poorer prognoses and higher mortality compared to older women. Young black women have higher incidence rates of breast cancer and more aggressive subtypes than women of other races/ethnicities. In this study, we examined recent trends and variations in breast cancer incidence among young women in the United States.
Using 2004–2013 National Program of Cancer Registries and Surveillance, Epidemiology, and End Results Program data, we calculated breast cancer incidence rates and trends and examined variations in stage, grade, and tumor subtype by age and race/ethnicity among young women aged 20–49 years.
The majority of breast cancer cases occurred in women aged 40–44 and 45–49 years (77.3%). Among women aged < 45 years, breast cancer incidence was highest among black women. Incidence trends increased from 2004 to 2013 for Asian or Pacific Islander (API) women and white women aged 20–34 years. Black, American Indian or Alaska Native, and Hispanic women had higher proportions of cases diagnosed at later stages than white and API women. Black women had a higher proportion of grade III–IV tumors than other racial/ethnic groups. Across all age groups, incidence rates for triple-negative breast cancer were significantly higher in black women than women of other races/ethnicities, and this disparity increased with age.
Breast cancer among young women is a highly heterogeneous disease. Differences in tumor characteristics by age and race/ethnicity suggest opportunities for further research into personal and cultural factors that may influence breast cancer risk among younger women.
About 1 in 59 children has been identified with autism spectrum disorder (ASD) according to estimates from CDC’s Autism and Developmental Disabilities Monitoring (ADDM) Network
Approximately 1.7% — or 1 in 59 — of 8-year-old kids in 11 diverse communities across the U.S. could be identified as having autism in 2014, according to the CDC report, April 27, 2018. That’s a small but measurable increase over the 2012 estimate of 1.5% — or 1 in 68 kids — and the highest rate ever recorded by the agency’s Autism and Developmental Disabilities Monitoring (ADDM) Network, an extensive tracking system that monitors the prevalence and characteristics of autism spectrum disorder (ASD) among more than 300,000 8-year-old children.
Autism spectrum disorder (ASD).
Description of System
The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system that provides estimates of the prevalence of autism spectrum disorder (ASD) among children aged 8 years whose parents or guardians reside within 11 ADDM sites in the United States (Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin). ADDM surveillance is conducted in two phases. The first phase involves review and abstraction of comprehensive evaluations that were completed by professional service providers in the community. Staff completing record review and abstraction receive extensive training and supervision and are evaluated according to strict reliability standards to certify effective initial training, identify ongoing training needs, and ensure adherence to the prescribed methodology. Record review and abstraction occurs in a variety of data sources ranging from general pediatric health clinics to specialized programs serving children with developmental disabilities. In addition, most of the ADDM sites also review records for children who have received special education services in public schools. In the second phase of the study, all abstracted information is reviewed systematically by experienced clinicians to determine ASD case status. A child is considered to meet the surveillance case definition for ASD if he or she displays behaviors, as described on one or more comprehensive evaluations completed by community-based professional providers, consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) diagnostic criteria for autistic disorder; pervasive developmental disorder–not otherwise specified (PDD-NOS, including atypical autism); or Asperger disorder. This report provides updated ASD prevalence estimates for children aged 8 years during the 2014 surveillance year, on the basis of DSM-IV-TR criteria, and describes characteristics of the population of children with ASD. In 2013, the American Psychiatric Association published the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), which made considerable changes to ASD diagnostic criteria. The change in ASD diagnostic criteria might influence ADDM ASD prevalence estimates; therefore, most (85%) of the records used to determine prevalence estimates based on DSM-IV-TR criteria underwent additional review under a newly operationalized surveillance case definition for ASD consistent with the DSM-5 diagnostic criteria. Children meeting this new surveillance case definition could qualify on the basis of one or both of the following criteria, as documented in abstracted comprehensive evaluations: 1) behaviors consistent with the DSM-5 diagnostic features; and/or 2) an ASD diagnosis, whether based on DSM-IV-TR or DSM-5 diagnostic criteria. Stratified comparisons of the number of children meeting either of these two case definitions also are reported.
For 2014, the overall prevalence of ASD among the 11 ADDM sites was 16.8 per 1,000 (one in 59) children aged 8 years. Overall ASD prevalence estimates varied among sites, from 13.1–29.3 per 1,000 children aged 8 years. ASD prevalence estimates also varied by sex and race/ethnicity. Males were four times more likely than females to be identified with ASD. Prevalence estimates were higher for non-Hispanic white (henceforth, white) children compared with non-Hispanic black (henceforth, black) children, and both groups were more likely to be identified with ASD compared with Hispanic children. Among the nine sites with sufficient data on intellectual ability, 31% of children with ASD were classified in the range of intellectual disability (intelligence quotient [IQ] <70), 25% were in the borderline range (IQ 71–85), and 44% had IQ scores in the average to above average range (i.e., IQ >85). The distribution of intellectual ability varied by sex and race/ethnicity. Although mention of developmental concerns by age 36 months was documented for 85% of children with ASD, only 42% had a comprehensive evaluation on record by age 36 months. The median age of earliest known ASD diagnosis was 52 months and did not differ significantly by sex or race/ethnicity. For the targeted comparison of DSM-IV-TR and DSM-5 results, the number and characteristics of children meeting the newly operationalized DSM-5 case definition for ASD were similar to those meeting the DSM-IV-TR case definition, with DSM-IV-TR case counts exceeding DSM-5 counts by less than 5% and approximately 86% overlap between the two case definitions (kappa = 0.85).
Findings from the ADDM Network, on the basis of 2014 data reported from 11 sites, provide updated population-based estimates of the prevalence of ASD among children aged 8 years in multiple communities in the United States. The overall ASD prevalence estimate of 16.8 per 1,000 children aged 8 years in 2014 is higher than previously reported estimates from the ADDM Network. Because the ADDM sites do not provide a representative sample of the entire United States, the combined prevalence estimates presented in this report cannot be generalized to all children aged 8 years in the United States. Consistent with reports from previous ADDM surveillance years, findings from 2014 were marked by variation in ASD prevalence when stratified by geographic area, sex, and level of intellectual ability. Differences in prevalence estimates between black and white children have diminished in most sites, but remained notable for Hispanic children. For 2014, results from application of the DSM-IV-TR and DSM-5 case definitions were similar, overall and when stratified by sex, race/ethnicity, DSM-IV-TR diagnostic subtype, or level of intellectual ability.
Public Health Action
Beginning with surveillance year 2016, the DSM-5 case definition will serve as the basis for ADDM estimates of ASD prevalence in future surveillance reports. Although the DSM-IV-TR case definition will eventually be phased out, it will be applied in a limited geographic area to offer additional data for comparison. Future analyses will examine trends in the continued use of DSM-IV-TR diagnoses, such as autistic disorder, PDD-NOS, and Asperger disorder in health and education records, documentation of symptoms consistent with DSM-5 terminology, and how these trends might influence estimates of ASD prevalence over time. The latest findings from the ADDM Network provide evidence that the prevalence of ASD is higher than previously reported estimates and continues to vary among certain racial/ethnic groups and communities. With prevalence of ASD ranging from 13.1 to 29.3 per 1,000 children aged 8 years in different communities throughout the United States, the need for behavioral, educational, residential, and occupational services remains high, as does the need for increased research on both genetic and nongenetic risk factors for ASD.
Why Are So Few Kids Getting the HPV Vaccine?, pewtrusts, April 07, 2016.
Despite media marketing – claiming it might save lives since it is targeting cervical cancer – and medical efforts to raise vaccination rates, public health officials say that for a variety of reasons, parents and doctors have not embraced the HPV vaccine.
Some states have much lower rates ; in Tennessee, for example, the vaccination rate for girls was 20 percent. The higher rate reported is North Carolina with 54%.
” The HPV vaccine is extremely controversial. There are hundreds of reports of injury and even death from this vaccine. This vaccine has never been proven safe or effective. The trials for it never ran long enough to prove that it actually works. The other problem is it only supposedly protects you from 2 of the 6 viruses that can cause HPV. “
Deaths from drug overdoses have hit an alarming record high in the US
Increases in Drug and Opioid Overdose Deaths — United States, 2000–2014, CDC Early Release,
December 18, 2015. PDF.
The United States is experiencing an epidemic of drug overdose (poisoning) deaths. Since 2000, the rate of deaths from drug overdoses has increased 137%, including a 200% increase in the rate of overdose deaths involving opioids (opioid pain relievers and heroin). CDC analyzed recent multiple cause-of-death mortality data to examine current trends and characteristics of drug overdose deaths, including the types of opioids associated with drug overdose deaths. During 2014, a total of 47,055 drug overdose deaths occurred in the United States, representing a 1-year increase of 6.5%, from 13.8 per 100,000 persons in 2013 to 14.7 per 100,000 persons in 2014. The rate of drug overdose deaths increased significantly for both sexes, persons aged 25–44 years and ≥55 years, non-Hispanic whites and non-Hispanic blacks, and in the Northeastern, Midwestern, and Southern regions of the United States. Rates of opioid overdose deaths also increased significantly, from 7.9 per 100,000 in 2013 to 9.0 per 100,000 in 2014, a 14% increase. Historically, CDC has programmatically characterized all opioid pain reliever deaths (natural and semisynthetic opioids, methadone, and other synthetic opioids) as “prescription” opioid overdoses. Between 2013 and 2014, the age-adjusted rate of death involving methadone remained unchanged; however, the age-adjusted rate of death involving natural and semisynthetic opioid pain relievers, heroin, and synthetic opioids, other than methadone (e.g., fentanyl) increased 9%, 26%, and 80%, respectively. The sharp increase in deaths involving synthetic opioids, other than methadone, in 2014 coincided with law enforcement reports of increased availability of illicitly manufactured fentanyl, a synthetic opioid; however, illicitly manufactured fentanyl cannot be distinguished from prescription fentanyl in death certificate data. These findings indicate that the opioid overdose epidemic is worsening. There is a need for continued action to prevent opioid abuse, dependence, and death, improve treatment capacity for opioid use disorders, and reduce the supply of illicit opioids, particularly heroin and illicit fentanyl.
The National Vital Statistics System multiple cause-of-death mortality files were used to identify drug overdose deaths.* Drug overdose deaths were classified using the International Classification of Disease, Tenth Revision (ICD-10), based on the ICD-10 underlying cause-of-death codes X40–44 (unintentional), X60–64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent) (2). Among the deaths with drug overdose as the underlying cause, the type of opioid involved is indicated by the following ICD-10 multiple cause-of-death codes: opioids (T40.0, T40.1, T40.2, T40.3, T40.4, or T40.6); natural and semisynthetic opioids (T40.2); methadone (T40.3); synthetic opioids, other than methadone (T40.4); and heroin (T40.1). Some deaths involve more than one type of opioid; these deaths were included in the rates for each category (e.g., a death involving both a synthetic opioid and heroin would be included in the rates for synthetic opioid deaths and in the rates for heroin deaths). Age-adjusted death rates were calculated by applying age-specific death rates to the 2000 U.S standard population age distribution (3). Significance testing was based on the z-test at a significance level of 0.05.
The opioid epidemic is devastating American families and communities.
To curb these trends and save lives, we must help prevent addiction and provide support and treatment to those who suffer from opioid use discords. CDC Director, Dr. Tom Frieden
During 2014, 47,055 drug overdose deaths occurred in the United States. Since 2000, the age-adjusted drug overdose death rate has more than doubled, from 6.2 per 100,000 persons in 2000 to 14.7 per 100,000 in 2014 . The overall number and rate of drug overdose deaths increased significantly from 2013 to 2014, with an additional 3,073 deaths occurring in 2014, resulting in a 6.5% increase in the age-adjusted rate. From 2013 to 2014, statistically significant increases in drug overdose death rates were seen for both males and females, persons aged 25–34 years, 35–44 years, 55–64 years, and ≥65 years; non-Hispanic whites and non-Hispanic blacks; and residents in the Northeast, Midwest and South Census Regions . In 2014, the five states with the highest rates of drug overdose deaths were West Virginia (35.5 deaths per 100,000), New Mexico (27.3), New Hampshire (26.2), Kentucky (24.7) and Ohio (24.6).† States with statistically significant increases in the rate of drug overdose deaths from 2013 to 2014 included Alabama, Georgia, Illinois, Indiana, Maine, Maryland, Massachusetts, Michigan, New Hampshire, New Mexico, North Dakota, Ohio, Pennsylvania, and Virginia.
In 2014, 61% (28,647, data not shown) of drug overdose deaths involved some type of opioid, including heroin. The age-adjusted rate of drug overdose deaths involving opioids increased significantly from 2000 to 2014, increasing 14% from 2013 (7.9 per 100,000) to 2014 (9.0). From 2013 to 2014, the largest increase in the rate of drug overdose deaths involved synthetic opioids, other than methadone (e.g., fentanyl and tramadol), which nearly doubled from 1.0 per 100,000 to 1.8 per 100,000. Heroin overdose death rates increased by 26% from 2013 to 2014 and have more than tripled since 2010, from 1.0 per 100,000 in 2010 to 3.4 per 100,000 in 2014. In 2014, the rate of drug overdose deaths involving natural and semisynthetic opioids (e.g., morphine, oxycodone, and hydrocodone), 3.8 per 100,000, was the highest among opioid overdose deaths, and increased 9% from 3.5 per 100,000 in 2013. The rate of drug overdose deaths involving methadone, a synthetic opioid classified separately from other synthetic opioids, was similar in 2013 and 2014.
Relates Press Releases
Bold bid to rein in painkiller prescriptions hits roadblocks, cnbc, 19 Dec 2015.
CDC: child autism rate now one in 45 after survey method changes
Estimated Prevalence of Autism and Other Developmental Disabilities Following Questionnaire Changes in the 2014 National Health Interview Survey
National Health Statistics Reports 2015 Abstract
The developmental disabilities questions in the 2014 National Health Interview Survey (NHIS) were changed from previous years, including question reordering and a new approach to asking about autism spectrum disorder (ASD). This report examines survey-based estimates of the lifetime prevalence of ASD, intellectual disability (ID), and any other developmental delay (other DD) following the inclusion of a standalone ASD question, the inclusion of specific diagnoses in the ASD question, and the ASD question preceding the other DD question, and compares them with estimates from previous years.
In NHIS, one child is randomly selected from each family to be the subject of detailed questions on health conditions, functional limitations, and health care utilization. Parents are asked if a doctor or health professional had ever told them that their child had each of a series of developmental disabilities. Prevalence estimates of ASD, ID, and other DD for children aged 3–17 years were calculated using data collected in 2011–2014.
The estimated prevalence of ASD based on 2014 data was 2.24%, (1 in 45) in 2014, while averaging 1.25% (1 in 80) from 2011 through 2013. In contrast, the prevalence of other DD declined significantly from 4.84% based on 2011–2013 data to 3.57% based on 2014 data. The prevalence of ID did not significantly change from 2011–2013 (1.27%) to 2014 (1.10%). The prevalence of having any of the three conditions was constant across survey years.
The revised question ordering and new approach to asking about developmental disabilities in the 2014 NHIS likely affected the prevalence estimates of these conditions. In previous years, it is likely that some parents of children diagnosed with ASD reported this developmental disability as other DD instead of, or in addition to, ASD. Following these changes, the 2014 ASD estimate was more similar to ASD prevalence estimates from other sources.
CDC: child autism rate now one in 45 after survey method changes, medicalxpress, November 13, 2015.
Estimated Prevalence of Autism and Other Developmental Disabilities Following Questionnaire Changes in the 2014 National Health Interview Survey, CDC, November 13, 2015.
Prevalence of Autism Spectrum Disorders — Autism and Developmental Disabilities Monitoring Network, 14 Sites, United States, 2008, CDC, 61(SS03);1-19, March 2012.
Community Report From the Autism and Developmental Disabilities Monitoring, CDC, ADDM Network 2012.
Are we reaching an inflection point toward precision medicine?
Each year at the annual American Society of Human Genetics (ASHG) meeting I follow certain rituals. During the first “poster session”, I quickly peruse all of the vendor booths on the floor to assess something of the overall flavor of the commercial space’s focus. During the next two poster sessions I cruise all of the aisles of the scientific posters and scan the titles. This is sort of a daunting challenge requiring months of aerobic training as the number of posters presented at ASHG is huge -more than 3,000 this year. From this, I gain some insights on where genomic science is focused. I find that this ritual provides a valuable overall snapshot of the state of the field of human genetics. Over two decades of watching, broad trends have included linkage studies for gene discovery using microsatellites, mapping of the anatomy of the genome, cataloging of human genetic variation, SNP genotyping/genome wide association studies, and the explosion in sequencing technologies.
This year, at least by my eye, there was a qualitative difference in both the commercial and scientific offerings. ASHG, which has typically focused on discovery science, had much more of a feel of clinical application. Numerous vendors from academic and private industry were promoting clinical sequencing services, microarray technologies, and health informatics software relevant to managing genomics data. Posters covered a very wide spectrum of topics but many dealt either with interrogating clinical data for genomic discovery or the clinical application of genomic technology in the context of ongoing health care delivery. In short, ASHG had the feel that the genomics community has reached an inflection point in the trajectory towards a vision of precision medicine.
There are several underlying factors that could have facilitated this sea change. First, inexpensive genotyping and sequencing have made studies of genotype/phenotype correlations accessible to the scientific masses. Second, health informatics systems have matured to the point (I say this with some hesitation as at least my home institution’s electronic health record [EHR] system is hardly as mature as I would like it to be) that useful information might be gleaned from EHRs about large numbers of properly consented individuals. Finally, and I think most importantly, the climate of fear regarding the integration of genomic discovery research into mainstream clinical care has abated. The center of gravity of discussions around genomics ethical, legal, and social issues has shifted from “Should the genomics research enterprise integrate with clinical care?” to “How can genomics research synergize with clinical care to benefit the widest population possible while minimizing harm?” Also, it seems that the boundaries of public tolerance for personal information sharing in electronic media have changed largely because of the penetration of social media in society. Additionally, at least in the U.S., there seems to be a strong trend towards interest in learning about personal determinants of health (think fitness trackers that follow your every move) that is likely benefiting genomics research participation.
More directly, programs like the National Human Genome Research Institute’s eMERGE, CSER and IGNITE have funded projects that are informing the field with preliminary data across a variety of domains relevant to a future where medicine becomes more individualized. On the regulatory front, the FDA has embarked on a new initiative to overhaul processes for evaluating genomic technologies. Several large health systems, such Geisinger and Kaiser have invested heavily in population scale sequencing initiatives to learn how such information might be used to improve the well-being of their customers. Finally, the announcement regarding NIH’s Precision Medicine Initiative seems to have crystallized the realization across academic health systems that genomics is about to come to main street, and not just to a few enclaves of genomics expertise. Genomics has a long way to go on the trajectory towards application in population health, however these projects are helping to tackle some of the more challenging issues facing the integration of genomics into mainstream clinical care.
Remarkable as this sea change is, it is incumbent on those of us at the interface of public health, health care, and genomics to continue to demand that the genomics community not lose the trees for the forest. In order to benefit the widest number of individuals possible, precision medicine must be predicated on a firm foundation of evidence of health benefit for each application and intended use.
About the vaccines mercury-containing preservative Thimerosal …
Growing numbers of Americans are refusing to vaccinate their children because they think vaccines are causing autism. But it’s not the vaccines that appear to be one cause of neurological disorders, it’s something else.