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2024 Alzheimer's disease facts and figures. Alzheimers Dement 2024; 20:3708-3821. [PMID: 38689398 PMCID: PMC11095490 DOI: 10.1002/alz.13809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
This article describes the public health impact of Alzheimer's disease (AD), including prevalence and incidence, mortality and morbidity, use and costs of care and the ramifications of AD for family caregivers, the dementia workforce and society. The Special Report discusses the larger health care system for older adults with cognitive issues, focusing on the role of caregivers and non-physician health care professionals. An estimated 6.9 million Americans age 65 and older are living with Alzheimer's dementia today. This number could grow to 13.8 million by 2060, barring the development of medical breakthroughs to prevent or cure AD. Official AD death certificates recorded 119,399 deaths from AD in 2021. In 2020 and 2021, when COVID-19 entered the ranks of the top ten causes of death, Alzheimer's was the seventh-leading cause of death in the United States. Official counts for more recent years are still being compiled. Alzheimer's remains the fifth-leading cause of death among Americans age 65 and older. Between 2000 and 2021, deaths from stroke, heart disease and HIV decreased, whereas reported deaths from AD increased more than 140%. More than 11 million family members and other unpaid caregivers provided an estimated 18.4 billion hours of care to people with Alzheimer's or other dementias in 2023. These figures reflect a decline in the number of caregivers compared with a decade earlier, as well as an increase in the amount of care provided by each remaining caregiver. Unpaid dementia caregiving was valued at $346.6 billion in 2023. Its costs, however, extend to unpaid caregivers' increased risk for emotional distress and negative mental and physical health outcomes. Members of the paid health care and broader community-based workforce are involved in diagnosing, treating and caring for people with dementia. However, the United States faces growing shortages across different segments of the dementia care workforce due to a combination of factors, including the absolute increase in the number of people living with dementia. Therefore, targeted programs and care delivery models will be needed to attract, better train and effectively deploy health care and community-based workers to provide dementia care. Average per-person Medicare payments for services to beneficiaries age 65 and older with AD or other dementias are almost three times as great as payments for beneficiaries without these conditions, and Medicaid payments are more than 22 times as great. Total payments in 2024 for health care, long-term care and hospice services for people age 65 and older with dementia are estimated to be $360 billion. The Special Report investigates how caregivers of older adults with cognitive issues interact with the health care system and examines the role non-physician health care professionals play in facilitating clinical care and access to community-based services and supports. It includes surveys of caregivers and health care workers, focusing on their experiences, challenges, awareness and perceptions of dementia care navigation.
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Chen Y, Shi Y, Liang C, Min Z, Deng Q, Yu R, Zhang J, Chang K, Chen L, Yan K, Wang C, Tan Y, Wang X, Chen J, Hua Q. MicrobeTCM: A comprehensive platform for the interactions of microbiota and traditional Chinese medicine. Pharmacol Res 2024; 201:107080. [PMID: 38272335 DOI: 10.1016/j.phrs.2024.107080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/10/2024] [Accepted: 01/21/2024] [Indexed: 01/27/2024]
Abstract
Thanks to the advancements in bioinformatics, drugs, and other interventions that modulate microbes to treat diseases have been emerging continuously. In recent years, an increasing number of databases related to traditional Chinese medicine (TCM) or gut microbes have been established. However, a database combining the two has not yet been developed. To accelerate TCM research and address the traditional medicine and micro ecological system connection between short board, we have developed the most comprehensive micro-ecological database of TCM. This initiative includes the standardization of the following advantages: (1) A repeatable process achieved through the standardization of a retrieval strategy to identify literature. This involved identifying 419 experiment articles from PubMed and six authoritative databases; (2) High-quality data integration achieved through double-entry extraction of literature, mitigating uncertainties associated with natural language extraction; (3) Implementation of a similar strategy aiding in the prediction of mechanisms of action. Leveraging drug similarity, target entity similarity, and known drug-target entity association, our platform enables the prediction of the effects of a new herb or acupoint formulas using the existing data. In total, MicrobeTCM includes 171 diseases, 725 microbes, 1468 herb-formulas, 1032 herbs, 15780 chemical compositions, 35 acupoint-formulas, and 77 acupoints. For further exploration, please visit https://www.microbetcm.com.
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Affiliation(s)
- Yufeng Chen
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China
| | - Yu Shi
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China
| | - Chengbang Liang
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China
| | - Zhuochao Min
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Zoology, The George S. Wise Faculty of Life Sciences Tel Aviv Tel Aviv University, Tel Aviv 69978, Israel
| | - Qiqi Deng
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China
| | - Rui Yu
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China
| | - Jiani Zhang
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China
| | - Kexin Chang
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China
| | - Luyao Chen
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China
| | - Ke Yan
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China
| | - Chunxiang Wang
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China
| | - Yan Tan
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China
| | - Xu Wang
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China
| | - Jianxin Chen
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China.
| | - Qian Hua
- School of Traditional Chinese Medicine, School of Life Science, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese medicine, Beijing 100029, China.
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Marion C, Manji S, Podlosky L, MacGillivray H, L’Heureux T, Anderson S, Parmar J. Family Involvement Training for Staff and Family Caregivers: Case Report on Program Design and Mixed Methods Evaluation. Healthcare (Basel) 2024; 12:523. [PMID: 38470633 PMCID: PMC10930910 DOI: 10.3390/healthcare12050523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 03/14/2024] Open
Abstract
The COVID-19 pandemic underscored the imperative for meaningful family involvement in long-term care, aligning with policy and safety standards while enhancing outcomes for caregivers, residents, and staff. The objectives of this article are as follows: (1) a case study report on implementing a family involvement intervention designed to facilitate the formal and safe engagement of family caregivers in resident care and (2) the pilot evaluation of the intervention. We used Knapp's six-step implementation science model to guide and describe intervention development to provide insight for others planning family involvement projects. We employed sequential mixed methods, including surveys with quantitative and qualitative questions before and after program implementation for providers, and surveys and interviews with family caregivers a year after. We used the Mann-Whitney U test (p < 0.05) to assess differences in health providers' perceptions pre- and post-education. Families and staff perceived that the Family Involvement Program was important for improving the quality of care, residents' quality of life and family/staff relationships. Providers' perceptions of the program's positive impact on residents' quality of life (p = 0.020) and quality of care (p = 0.010), along with their satisfaction with working relationships with families (p = 0.039), improved significantly after the program. Qualitative data confirmed improvements in family-staff relationships. In conclusion, we documented the design of this family involvement initiative to encourage family caregivers and staff to work together in residents' care. Youville's Family Involvement Program gives families and family caregivers an explicit role as partners in long-term care. The mixed methods pilot evaluation documented improvements in staff and family relationships.
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Affiliation(s)
- Cecilia Marion
- Covenant Health Canada, Youville Home, St. Albert, AB T8N 1K1, Canada; (C.M.); (S.M.); (H.M.)
| | - Shazmin Manji
- Covenant Health Canada, Youville Home, St. Albert, AB T8N 1K1, Canada; (C.M.); (S.M.); (H.M.)
| | - Linda Podlosky
- Family Caregiver, University of Alberta, Edmonton, AB T6G 2T4, Canada;
| | - Heather MacGillivray
- Covenant Health Canada, Youville Home, St. Albert, AB T8N 1K1, Canada; (C.M.); (S.M.); (H.M.)
| | - Tanya L’Heureux
- Department of Family Medicine, University of Alberta, Edmonton, AB T6G 2T4, Canada; (T.L.); (J.P.)
| | - Sharon Anderson
- Department of Family Medicine, University of Alberta, Edmonton, AB T6G 2T4, Canada; (T.L.); (J.P.)
| | - Jasneet Parmar
- Department of Family Medicine, University of Alberta, Edmonton, AB T6G 2T4, Canada; (T.L.); (J.P.)
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