Prevalence of risk factors for falls among elderly people living in long-term care homes

The aim of this study was to assesses the factors associated with the occurrence of urinary incontinence (UI) in elderly people living in long-term care homes. Reports on Urinary Incontinence coupled to clinical-functional and socio-demographic data were retrieved from the medical records of elderly people. In addition, the application of the protocols: Mini-Mental State Examination, Katz Index, Short Physical Performance Battery. It was considered a significance level of 5%. It was noted that the UI occurred in 80.6% of elderly people, with average age 76.5 years (± 8.3) and average time at the care home reaching 5.2 years (± 6.4). Significant UI association was reported with gender, education and disease. A discrete increase in scores occurred in protocols for elderly people without UI occurrence. It is concluded that sample was characterized by elderly females with less than five years living in homes. The elderly with UI were similar to elderly people in general with regard to protocols.

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Elderly/Long-Term Care: Elderly people who lack prescription drug coverage may not get needed medications for chronic conditions

PsycEXTRA Dataset ◽

10.1037/e556292006-031 ◽

2005 ◽

Keyword(s):

Prescription Drug ◽

Elderly People ◽

Chronic Conditions ◽

Long Term Care ◽

Drug Coverage ◽

Prescription Drug Coverage ◽

Term Care

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Status and Degree of Dysphagia in the Elderly-A Comparison Study of Long-Term Care Elderly People in an Outpatient Clinic

Nihon Kikan Shokudoka Gakkai Kaiho ◽

10.2468/jbes.54.277 ◽

2003 ◽

Vol 54 (4) ◽

pp. 277-284 ◽

Cited By ~ 1

Author(s):

Masanori Komatsu ◽

Kayoko Hirata ◽

Idumi Mochimatsu ◽

Kazuo Matsui ◽

Hajime Hirose ◽

...

Keyword(s):

Elderly People ◽

Outpatient Clinic ◽

Long Term Care ◽

The Elderly ◽

Comparison Study ◽

Term Care

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Design and Development of a Web-based Delirium Preventive Application for Long-term Care Facilities: A Methodological Study (Preprint)

10.2196/preprints.26092 ◽

2020 ◽

Author(s):

Kyoung Ja Moon ◽

Chang-Sik Son ◽

Jong-Ha Lee ◽

Mina Park

Keyword(s):

Risk Factors ◽

Long Term Care ◽

Cloud System ◽

Evidence Based ◽

Methodological Study ◽

Term Care ◽

Web Based ◽

Care Facilities ◽

Delirium Prevention

BACKGROUND Long-term care facilities demonstrate low levels of knowledge and care for patients with delirium and are often not properly equipped with an electronic medical record system, thereby hindering systematic approaches to delirium monitoring. OBJECTIVE This study aims to develop a web-based delirium preventive application (app), with an integrated predictive model, for long-term care (LTC) facilities using artificial intelligence (AI). METHODS This methodological study was conducted to develop an app and link it with the Amazon cloud system. The app was developed based on an evidence-based literature review and the validity of the AI prediction model algorithm. Participants comprised 206 persons admitted to LTC facilities. The app was developed in 5 phases. First, through a review of evidence-based literature, risk factors for predicting delirium and non-pharmaceutical contents for preventive intervention were identified. Second, the app, consisting of several screens, was designed; this involved providing basic information, predicting the onset of delirium according to risk factors, assessing delirium, and intervening for prevention. Third, based on the existing data, predictive analysis was performed, and the algorithm developed through this was calculated at the site linked to the web through the Amazon cloud system and sent back to the app. Fourth, a pilot test using the developed app was conducted with 33 patients. Fifth, the app was finalized. RESULTS We developed the Web_DeliPREVENT_4LCF for patients of LTC facilities. This app provides information on delirium, inputs risk factors, predicts and informs the degree of delirium risk, and enables delirium measurement or delirium prevention interventions to be immediately implemented with a verified tool. CONCLUSIONS This web-based application is evidence-based and offers easy mobilization and care to patients with delirium in LTC facilities. Therefore, the use of this app improves the unrecognized of delirium and predicts the degree of delirium risk, thereby helping initiatives for delirium prevention and providing interventions. This would ultimately improve patient safety and quality of care. CLINICALTRIAL none

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The psychometric properties, feasibility and utility of behavioural-observation methods in pain assessment of cognitively impaired elderly people in acute and long-term care: A systematic review

JBI Library of Systematic Reviews ◽

10.11124/jbisrir-2010-644 ◽

2010 ◽

Vol 8 (Supplement) ◽

pp. 1-33

Keyword(s):

Systematic Review ◽

Psychometric Properties ◽

Elderly People ◽

Pain Assessment ◽

Long Term Care ◽

Cognitively Impaired ◽

Term Care ◽

Behavioural Observation ◽

Observation Methods

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Risk factors for COVID-19 diagnosis, hospitalization, and subsequent all-cause mortality in Sweden: a nationwide study

European Journal of Epidemiology ◽

10.1007/s10654-021-00732-w ◽

2021 ◽

Vol 36 (3) ◽

pp. 287-298

Author(s):

Jonathan Bergman ◽

Marcel Ballin ◽

Anna Nordström ◽

Peter Nordström

Keyword(s):

Risk Factors ◽

Down Syndrome ◽

Long Term Care ◽

Care Facility ◽

Prescription Medication ◽

Term Care ◽

Term Care Facility ◽

Long Term Care Facility ◽

All Cause Mortality

AbstractWe conducted a nationwide, registry-based study to investigate the importance of 34 potential risk factors for coronavirus disease 2019 (COVID-19) diagnosis, hospitalization (with or without intensive care unit [ICU] admission), and subsequent all-cause mortality. The study population comprised all COVID-19 cases confirmed in Sweden by mid-September 2020 (68,575 non-hospitalized, 2494 ICU hospitalized, and 13,589 non-ICU hospitalized) and 434,081 randomly sampled general-population controls. Older age was the strongest risk factor for hospitalization, although the odds of ICU hospitalization decreased after 60–69 years and, after controlling for other risk factors, the odds of non-ICU hospitalization showed no trend after 40–49 years. Residence in a long-term care facility was associated with non-ICU hospitalization. Male sex and the presence of at least one investigated comorbidity or prescription medication were associated with both ICU and non-ICU hospitalization. Three comorbidities associated with both ICU and non-ICU hospitalization were asthma, hypertension, and Down syndrome. History of cancer was not associated with COVID-19 hospitalization, but cancer in the past year was associated with non-ICU hospitalization, after controlling for other risk factors. Cardiovascular disease was weakly associated with non-ICU hospitalization for COVID-19, but not with ICU hospitalization, after adjustment for other risk factors. Excess mortality was observed in both hospitalized and non-hospitalized COVID-19 cases. These results confirm that severe COVID-19 is related to age, sex, and comorbidity in general. The study provides new evidence that hypertension, asthma, Down syndrome, and residence in a long-term care facility are associated with severe COVID-19.

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