Top Global Causes Of Adolescent Mortality And Morbidity, 2019
According to an Infographic that was published in the October 2021 issue of the Journal of Adolescent Health, the causes of mortality among adolescents are different than the causes of morbidity. Injuries such as road injury, drowning, and self-harm; communicable diseases including diarrheal diseases, tuberculosis, and lower respiratory infections; and maternal conditions are the main causes of mortality among adolescents. Road injury is the leading cause of mortality among adolescent boys. Mental disorders including childhood behavioral, anxiety, and depressive disorders are among the leading causes of morbidity among adolescents of both sexes and across age groups. Iron-deficiency anemia is an important cause of morbidity among younger adolescents of both sexes. Another paper in that issue discusses how adverse health-related outcomes such as greater substance use, mental health difficulties, and higher BMI appear to be more likely to cluster together in the more recent cohort, with public health implications.
Adult Day Services Center Participant Characteristics: U.S., 2018
A Data Brief published on September 2, 2021 by the National Center for Health Statistics indicates that an estimated 251,100 participants were enrolled in adult day services centers (ADSCs) in the United States in 2018. Compared with users of other long-term care services, ADSC participants were younger and more racially and ethnically diverse. ADSC participants have a diverse set of needs, with many of them requiring assistance with activities of daily living (ADLs) and having chronic health conditions. About 57% of adult day services center (ADSC) participants were female, 45% were non-Hispanic white, and 39% were under age 65. Most ADSC participants were Medicaid beneficiaries (72%) while about 85% of participants under age 65 were Medicaid beneficiaries. About 64% of participants needed assistance with three or more activities of daily living. Most ADSC participants had two or three chronic conditions. Just over one-half of participants were diagnosed with high blood pressure.
HEALTH TECHNOLOGY CORNER
Persistence Of COVID-19 After Mild Infection
Uncertainty exists whether mild COVID-19 confers immunity to reinfection and questions also remain about the persistence of antibodies against SARS-CoV-2 after mild infection. A study published in the September/October 2021 issue of the journal Microbiology Spectrum reveals that approximately 90% of participants produced spike and nucleocapsid antibody responses, and all but one had persistent antibody levels at follow up. University of Michigan researchers analyzed nearly 129 subjects with PCR-confirmed COVID-19 illness between three and six months after initial infection. The prospective study’s participants either were Michigan Medicine health care workers or patients with a high risk of exposure to COVID-19. The results show that individuals who have mild COVID-19 illnesses and produce antibodies are protected from reinfection for up to six months afterward. Reinfection was not observed among individuals with mild clinical COVID-19, while infections continued in a group without known prior infection.
Shared Movement Disorders Walking Patterns Among Different Species
Neurodegenerative disorders including Parkinson’s disease, Alzheimer’s disease, and schizophrenia are conditions characterized by motor dysfunctions. Since the variables inherent to such diseases cannot be controlled directly in humans, behavioral dysfunctions and their neural underpinnings have been examined in model organisms. An article published on September 17, 2017 in Nature Communications describes how machine learning was used to obtain patterns from locomotion data created by worm, beetle, mouse, and human subjects that were independent of the species. Researchers at Osaka University trained a deep learning algorithm and used animal location tracking along with artificial intelligence to detect walking behaviors of movement disorders that are shared across species. By automatically removing species-specific features from walking data, the resulting information can be used to understand neurological disorders better, such as Parkinson’s disease that affect movement.