Adults Who Did Not Take Prescribed Medication In Order To Reduce Costs: U.S. 2021
Data in June 2023 released from the National Center for Health Statistics show that in 2021, 8.2% of adults aged 18–64 who took prescription medication in the past 12 months reported not taking medication as prescribed due to cost. Women (9.1%) were more likely than men (7.0%) to not take prescribed medication. Adults with disabilities (20.0%) were more likely than adults without disabilities (7.1%) to not take medication as prescribed to reduce costs. Adults without prescription drug coverage were more likely to not take medication as prescribed to reduce costs compared with adults with public or private prescription drug coverage. Cost-saving measures included skipping doses, taking less medication than prescribed, or delaying filling a prescription. Failure to adhere to treatment protocols can pose significant risks that may result in more serious illness and require additional highly costly care.
Prevalence Of Disability By Occupation Group—-U.S. 2016-2020
Approximately 21.5 million employed U.S. adults aged 18–64 years had some form of disability in 2020. Although 75.8% of noninstitutionalized persons without disability aged 18–64 were employed, only 38.4% of their counterparts with disability were employed. According to the May 19, 2023 issue of Morbidity and Mortality Weekly Reports, individuals with disability have job preferences similar to persons without disability, but might encounter barriers (e.g., lower average training or education levels, discrimination, or limited transportation options) that affect the types of jobs they hold. The highest adjusted disability prevalences were among workers in three of the 22 major occupation groups: food preparation and serving-related (19.9%); personal care and service (19.4%); and arts, design, entertainment, sports, and media (17.7%). Occupation groups with the lowest adjusted disability prevalences were business and financial operations (11.3%), health care practitioners and technicians (11.1%), and architecture and engineering (11.0%).
HEALTH TECHNOLOGY CORNER
How Caregiver Speech Can Shape The Infant Brain
Decades of research have established that the home language environment, especially quality of caregiver speech, supports language acquisition during infancy. The neural mechanisms behind this phenomenon remain under studied. An investigation by researchers at the University of Texas at Dallas that was reported in the June 2023 issue of the journal Developmental Cognitive Neuroscience examined associations between the home language environment and structural coherence of white matter tracts in 52 typically developing infants from English speaking homes in a western society. MRI and audio recordings demonstrated that caregiver speech is associated with infant brain development in ways that improve long-term language progress. This study is one of the first to report significant associations between caregiver speech collected in the home and white matter structural coherence in the infant brain and is in line with prior work showing that protracted white matter development during infancy confers a cognitive advantage.
Enabling Analysis Of Electrocardiograms As Language By A New Deep Learning Approach
Mount Sinai researchers developed an innovative artificial intelligence (AI) model for electrocardiogram (ECG) analysis that allows for the interpretation of ECGs as language. This approach can enhance the accuracy and effectiveness of ECG-related diagnoses, especially for cardiac conditions where limited data are available on which to train. A study published in the June 6 online issue of npj Digital Medicine indicates that this new deep learning model called HeartBEiT forms a foundation upon which specialized diagnostic models can be created. Researchers pretrained HeartBEiT on 8.5 million ECGs from 2.1 million patients collected over four decades within the Mount Sinai Health System. In comparison tests, models created using HeartBEiT surpassed established methods for ECG analysis. HeartBEiT has significantly higher performance at lower sample sizes compared to other models. HeartBEiT also improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. standard CNNs.