Incidence and risk factors of hospital falls on long-term care wards in Japan

2011 ◽  
Vol 18 (3) ◽  
pp. 572-577 ◽  
Author(s):  
Buichi Tanaka ◽  
Mio Sakuma ◽  
Masae Ohtani ◽  
Jinichi Toshiro ◽  
Tadashi Matsumura ◽  
...  
Keyword(s):  
Risk Factors ◽  
Long Term Care ◽  
Term Care

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

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

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

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.

Risk Factors for Depression in Long-Term Care: A Systematic Review

2018 ◽  
Vol 42 (3) ◽  
pp. 224-237 ◽  
Author(s):  
Rebecca Chau ◽  
David W. Kissane ◽  
Tanya E. Davison
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Long Term Care ◽  
Term Care

Risk factors for fecal carriage of IMP-6-producing Enterobacteriaceae at a long-term care hospital in Japan: A follow-up report from the northern Osaka multicentre study group

2018 ◽  
Vol 24 (9) ◽  
pp. 769-772 ◽  
Author(s):  
Hideharu Hagiya ◽  
Norihisa Yamamoto ◽  
Ryuji Kawahara ◽  
Yukihiro Akeda ◽  
Rathina Kumar Shanmugakani ◽  
...  
Keyword(s):  
Risk Factors ◽  
Multicentre Study ◽  
Long Term Care ◽  
Study Group ◽  
Care Hospital ◽  
Term Care ◽  
Fecal Carriage

scholarly journals Risk factors for pneumonia and influenza hospitalizations in long-term care facility residents: a retrospective cohort study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Patience Moyo ◽  
Andrew R. Zullo ◽  
Kevin W. McConeghy ◽  
Elliott Bosco ◽  
Robertus van Aalst ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Long Term Care ◽  
Care Facility ◽  
Term Care ◽  
Term Care Facility ◽  
Long Term Care Facility

Comparing Changes and Transitions of Home Care Clients in Retirement Homes and Private Homes

2019 ◽  
Vol 39 (3) ◽  
pp. 421-431
Author(s):  
Jeffrey Poss ◽  
Chi-Ling Sinn ◽  
Galina Grinchenko ◽  
Lialoma Salam-White ◽  
John Hirdes
Keyword(s):  
Risk Factors ◽  
Home Care ◽  
Long Term Care ◽  
Local Health ◽  
Caregiver Distress ◽  
Retirement Home ◽  
Term Care ◽  
Home Residency ◽  
Private Homes

ABSTRACTLong-stay home care clients mostly reside in private homes or retirement homes, and the type of residence may influence risk factors for long-term care placement. This multi-state analytic study uses RAI-Home Care and administrative data from the Hamilton Niagara Haldimand Brant Local Health Integration Network to model conceptualized states of risk at baseline through a 13-month follow-up period. Modifiable risk factors in these states were client loneliness or depressive symptoms, and caregiver distress. A higher adjusted likelihood of being discharged deceased was found for the lowest-risk clients in retirement homes. Adjusting for client, service, and caregiver characteristics, retirement home residency was associated with higher likelihood of placement in a long-term care home; reduced caregiver distress; and increased client loneliness/depression. As an alternative to private home settings as the location for aging in place among these long-stay home care clients, retirement home residency represents some trade-offs between client and informal caregiver.

scholarly journals 1462. Derivation of a Prediction Model for Risk of Drug-Resistant Urinary Tract Infection

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S534-S534
Author(s):  
Sabeen Ali ◽  
Kimberly C Claeys
Keyword(s):  
Risk Factors ◽  
Urinary Tract ◽  
Long Term Care ◽  
Previous Literature ◽  
Antibiotic Prescribing ◽  
Drug Resistant ◽  
Term Care ◽  
Tract Infections ◽  
Scoring Tool

Abstract Background Urinary tract infections (UTIs) are among the most common indications for antibiotic therapy. As antibiotic resistance continues to grow, it is critical to identify those at higher risk for drug-resistant (DR) UTIs to guide empiric therapy, improve clinical outcomes, and limit costs of care. The aim of this study was to identify risk factors for DR UTI and develop a risk scoring tool which could aid in empiric antibiotic prescribing. Methods Single-center retrospective pilot study of adult patients treated for UTI from August 1, 2015 to August 31, 2016. Patients who had asymptomatic bacteriuria, were pregnant within 4 months of admission, or had improperly collected urine cultures were excluded. DR was defined as phenotypic resistance to at least 1 agent in 3 or more antibiotic classes commonly used to treat UTIs. Risk factors for DR UTI were derived from previously published literature and multivariable logistic regression of individual patient data (IPD). Adjusted odds ratios (aORs) were developed by combining ORs from previous literature and IPD. A scoring tool was derived from weight-proportional integer-adjusted coefficients of the predictive model aORs. Results Risk factors were derived from 9 previously published studies and adapted using IPD (N = 77) and included: long-term care (aOR = 4.31), prior hospitalization (aOR = 1.8), previous antibiotics (aOR = 4.33), advanced age (aOR = 1.12), urinary catheterization (aOR = 2.2), immune suppression (aOR = 1.6), and male sex (aOR = 2.56). Previous DR UTI was forced into the model (OR = 1.1). Baseline incidence of DR UTI was 28.7%. A risk score from 1 to 20 was developed and applied to IPD and demonstrated an area under the receiver operator curve (AUROC) of 0.625 (95% CI 0.484–0.767). Removing sex from the score produced an AUROC of 0.64 (95% CI 0.497–783). A sensitivity analysis applying the score to only urinary isolates that exhibited resistance to third-generation cephalosporins (13.8%) produced similar results. Conclusion Residence in long-term care and previous antibiotics were among the risk factors most closely associated with DR UTI. Considering cumulative risk scores may be useful in predicting DR UTI however the current study was hindered by a large degree of heterogeneity in previous literature. Disclosures All authors: No reported disclosures.

Importance of Bacterial Burden Among Methicillin-Resistant Staphylococcus aureus Carriers in a Long-Term Care Facility

2008 ◽  
Vol 29 (2) ◽  
pp. 143-148 ◽  
Author(s):  
Nimalie D. Stone ◽  
Donna R. Lewis ◽  
H. K. Lowery ◽  
Lyndsey A. Darrow ◽  
Catherine M. Kroll ◽  
...  
Keyword(s):  
Risk Factors ◽  
Odds Ratio ◽  
Nasal Swab ◽  
Long Term Care ◽  
Care Facility ◽  
Term Care ◽  
Long Term Care Facility ◽  
Culture Positive ◽  
Epidemiologic Characteristics

Objective.To evaluate the prevalence and transmission of methicillin-resistant Staphylococcus aureus (MRSA) nasal colonization, as well as risk factors associated with MRSA carriage, among residents of a long-term care facility (LTCF).Design.Prospective, longitudinal cohort study.Setting.A 100-bed Veterans Administration LTCFParticipants.All current and newly admitted residents of the LTCF during an 8-week study period.Methods.Nasal swab samples were obtained weekly and cultured on MRSA-selective media, and the cultures were graded for growth on a semiquantitative scale from 0 (no growth) to 6 (heavy growth). Epidemiologic data for the periods before and during the study were collected to assess risk factors for MRSA carriage.Results.Of 83 LTCF residents, 49 (59%) had 1 or more nasal swab cultures that were positive for MRSA; 34 (41%) were consistently culture-negative (designated “noncarriers”). Of the 49 culture-positive residents, 30 (36% of the total of 83 residents) had all cultures positive for MRSA (designated “persistent carriers”), and 19 (23% of the 83 residents) had at least 1 culture, but not all cultures, positive for MRSA (designated “intermittent carriers”). Multivariate analysis showed that participants with at least 1 nasal swab culture positive for MRSA were likely to have had previous hospitalization (odds ratio, 3.9) or wounds (odds ratio, 8.2). Persistent carriers and intermittent carriers did not differ in epidemiologic characteristics but did differ in mean MRSA growth score (3.7 vs 0.7; P < .001).Conclusions.Epidemiologic characteristics differed between noncarriers and subjects with at least 1 nasal swab culture positive for MRSA. However, in this LTCF population, only the degree of bacterial colonization (as reflected by mean MRSA growth score) distinguished persistent carriers from intermittent carriers. Understanding the burden of colonization may be important when determining future surveillance and control strategies.

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