This article is based on the lecture for the 2014 American Geriatrics Society Outstanding Scientific Achievement for Clinical Investigation Award. Elder abuse is a global public health and human rights problem. Evidence suggests that elder abuse is prevalent, predictable, costly, and sometimes fatal. This review will highlight the global epidemiology of elder abuse in terms of its prevalence, risk factors, and consequences in community populations. The global literature in PubMed, MEDLINE, PsycINFO, BIOSIS, Science Direct, and Cochrane Central was searched. Search terms included elder abuse, elder mistreatment, elder maltreatment, prevalence, incidence, risk factors, protective factors, outcomes, and consequences. Studies that existed only as abstracts, case series, or case reports or recruited individuals younger than 60; qualitative studies; and nonEnglish publications were excluded. Tables and figures were created to highlight the findings: the most-detailed analyses to date of the prevalence of elder abuse according to continent, risk and protective factors, graphic presentation of odds ratios and confidence intervals for major risk factors, consequences, and practical suggestions for health professionals in addressing elder abuse. Elder abuse is common in community-dwelling older adults, especially minority older adults. This review identifies important knowledge gaps, such as a lack of consistency in definitions of elder abuse; insufficient research with regard to screening; and etiological, intervention, and prevention research. Concerted efforts from researchers, community organizations, healthcare and legal professionals, social service providers, and policy-makers should be promoted to address the global problem of elder abuse.

Community-Dwelling PopulationElder Self-neglect and Abuse and Mortality Risk in a Correction Contact me if this article is corrected. CitationsContact me when this article is cited. This article has been cited 2 times. Topic collectionsContact me when new articles are published in these topic areas.

Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

Elder abuse is associated with increased mortality risk. However, the relationship between elder abuse and health care services utilization remains unclear.Objective: To examine the relationship between overall elder abuse and specific subtypes of elder abuse and rate of hospitalization in a community-dwelling population of older adults.

Background: Elder abuse is a pervasive human right and public health issue. Objectives: We aimed to examine the mortality associated with elder abuse across levels of psychological and social factors. Methods: The Chicago Health and Aging Project (CHAP) is a prospective population-based cohort study that began in 1993. A subset of these participants enrolled between 1993 and 2005 had elder abuse reported to social services agencies (n = 113). Mortality was ascertained during follow-up and with the National Death Index. Psychosocial factors (depression, social network and social engagement) were assessed during the CHAP interview. Cox proportional hazard models were used to assess the mortality of elder abuse across levels of psychosocial factors using time-varying covariate analyses. Results: The median follow-up time for the cohort (n = 7,841) was 7.6 years (interquartile range 3.8–12.4 years). In multivariate analyses, those with highest (hazard ratio (HR) 2.60, 95% CI 1.58–4.28) and middle levels (HR 2.18, 95% CI 1.19–3.99) of depressive symptoms had an increased mortality risk associated with elder abuse. For social network, those with lowest (HR 2.50, 95% CI 1.62–3.87) and middle levels (HR 2.65, 95% CI 1.52–4.60) of social network had increased mortality risk associated with elder abuse. For social engagement, those with lowest (HR 2.32, 95% CI 1.47–3.68) and middle levels (HR 2.59, 95% CI 1.65–5.45) of social engagement had increased mortality risk associated with elder abuse. Among those with lowest levels of depressive symptoms, highest levels of social network and social engagement, there was no significant effect of reported or confirmed elder abuse on mortality risk. Conclusion: Mortality risk associated with elder abuse was most prominent among those with higher levels of depressive symptoms and lower levels of social network and social engagement.