scholarly journals Time from dementia diagnosis to nursing-home admission and death among persons with dementia: A multistate survival analysis

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243513
Author(s):  
Marit Mjørud ◽  
Geir Selbæk ◽  
Espen Bjertness ◽  
Trine Holt Edwin ◽  
Knut Engedal ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Nursing Home ◽  
Cognitive Impairment ◽  
Sex Education ◽  
Parametric Models ◽  
Nursing Home Admission ◽  
Lower Probability ◽  
Dementia Diagnosis ◽  
Transition Times

Objectives To estimate transition times from dementia diagnosis to nursing-home (NH) admission or death and to examine whether sex, education, marital status, level of cognitive impairment and dementia aetiology are associated with transition times. Design Markov multistate survival analysis and flexible parametric models. Setting Participants were recruited from the Norwegian Registry of Persons Assessed for Cognitive Symptoms (NorCog) in specialist healthcare between 2008 and 2017 and followed until August 2019, a maximum of 10.6 years follow-up time (mean 4.4 years, SD 2.2). Participants’ address histories, emigration and vital status were retrieved from the National Population Registry from time of diagnosis and linked to NorCog clinical data. Participants 2,938 home-dwelling persons with dementia, ages 40–97 years at time of diagnosis (mean 76.1, SD 8.5). Results During follow-up, 992 persons (34%) were admitted to nursing-homes (NHs) and 1,556 (53%) died. Approximately four years after diagnosis, the probability of living in a NH peaked at 19%; thereafter, the probability decreased due to mortality. Median elapsed time from dementia diagnosis to NH admission among those admitted to NHs was 2.28 years (IQR 2.32). The probability of NH admission was greater for women than men due to women´s lower mortality rate. Persons living alone, particularly men, had a higher probability of NH admission than cohabitants. Age, dementia aetiology and severity of cognitive impairment at time of diagnosis did not influence the probability of NH admission. Those with fewer than 10 years of education had a lower probability of NH admission than those with 10 years or more, and this was independent of the excess mortality in the less-educated group. Conclusion Four years after diagnosis, half of the participants still lived at home, while NH residency peaked at 19%. Those with fewer than 10 years of education were less often admitted to NH.

scholarly journals Nursing Home Admission during the First Year after Hospitalization – The Contribution of Cognitive Impairment

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e86116 ◽  
Author(s):  
Anne-Sofie Helvik ◽  
Randi Helene Skancke ◽  
Geir Selbæk ◽  
Knut Engedal
Keyword(s):  
Nursing Home ◽  
Cognitive Impairment ◽  
Nursing Home Admission ◽  
First Year

scholarly journals Predictors of Nursing Home Admission in the Older Population in Belgium

2022 ◽  
Author(s):  
Finaba Berete ◽  
Stefaan Demarest ◽  
Rana Charafeddine ◽  
Karin Ridder ◽  
Johan Vanoverloop ◽  
...  
Keyword(s):  
Health Care ◽  
Nursing Home ◽  
Home Care ◽  
Home Care Services ◽  
Subjective Health ◽  
Living Alone ◽  
Nursing Home Admission ◽  
Care Providers ◽  
Care Services

Abstract BackgroundThis study examines the risk factors associated with nursing home admission (NHA) in Belgium to contribute to a better planning of the future demand for nursing home (NH) services and health care resources.MethodsIndividual level linkage of the 2013 Belgian health interview survey data and health insurance data (2012 to 2018) was done. Only non-institutionalized participants, aged ≥65 years at the time of the survey were included in this study (n=1930). Participants were followed until NHA, death or end of study period, i.e., December 31, 2018. The risk of NHA was calculated using a competing risk analysis.ResultsOver the follow-up period (median 5.29 years), 226 individuals were admitted to a NH and 268 died without admission to a NH. The overall cumulative risk of NHA was 1.4%, 5.7% and 13.1% at, respectively 1 year, 3 years and the end of follow-up. After multivariable adjustment, higher age, low educational attainment, belonging to low income household, living alone, use of home care services and a number of need factor (e.g., history of falls, suffering from urinary incontinence, depression or Alzheimer disease, etc.) were significantly associated with a higher risk of NHA, while female, individuals with multimorbidity and increased contacts with health care providers were significantly associated with a decreased risk of NHA. Subjective health and limitations are both significant determinants of NHA, but subjective health is an effect modifier on the effect of limitations and vice versa.ConclusionsOur findings pinpoint important predictors of NHA in older adults, and offer possibilities of prevention to avoid or delay NHA for this population. The strong impact of need factors on the risk of NHA may indicate equitable access to NHA (i.e., those in need for support have access to NH). Practical implications include prevention of falls and appropriate and timely management of physical chronic conditions and neurodegenerative disorders. Focus should also be on people living alone to provide the appropriate social support and/or home care services. Further investigation of predictors of NHA should include contextual factors such as the availability of nursing-home beds, hospital beds, physicians and waiting lists for NHA.

scholarly journals Predictors of incident malnutrition—a nutritionDay analysis in 11,923 nursing home residents

2021 ◽  
Author(s):  
Gabriel Torbahn ◽  
Isabella Sulz ◽  
Franz Großhauser ◽  
Michael J. Hiesmayr ◽  
Eva Kiesswetter ◽  
...  
Keyword(s):  
Nursing Home ◽  
Cognitive Impairment ◽  
High Risk ◽  
Nursing Home Residents ◽  
Risk Groups ◽  
Final Model ◽  
Severe Cognitive Impairment ◽  
Meal Intake ◽  
Low Bmi

Abstract Background/Objectives Malnutrition (MN) in nursing home (NH) residents is associated with poor outcome. In order to identify those with a high risk of incident MN, the knowledge of predictors is crucial. Therefore, we investigated predictors of incident MN in older NH-residents. Subjects/Methods NH-residents participating in the nutritionDay-project (nD) between 2007 and 2018, aged ≥65 years, with complete data on nutritional status at nD and after 6 months and without MN at nD. The association of 17 variables (general characteristics (n = 3), function (n = 4), nutrition (n = 1), diseases (n = 5) and medication (n = 4)) with incident MN (weight loss ≥ 10% between nD and follow-up (FU) or BMI (kg/m2) < 20 at FU) was analyzed in univariate generalized estimated equation (GEE) models. Significant (p < 0.1) variables were selected for multivariate GEE-analyses. Effect estimates are presented as odds ratios and their respective 99.5%-confidence intervals. Results Of 11,923 non-malnourished residents, 10.5% developed MN at FU. No intake at lunch (OR 2.79 [1.56–4.98]), a quarter (2.15 [1.56–2.97]) or half of the meal eaten (1.72 [1.40–2.11]) (vs. three-quarter to complete intake), the lowest BMI-quartile (20.0–23.0) (1.86 [1.44–2.40]) (vs. highest (≥29.1)), being between the ages of 85 and 94 years (1.46 [1.05; 2.03]) (vs. the youngest age-group 65–74 years)), severe cognitive impairment (1.38 [1.04; 1.84]) (vs. none) and being immobile (1.28 [1.00–1.62]) (vs. mobile) predicted incident MN in the final model. Conclusion 10.5% of non-malnourished NH-residents develop MN within 6 months. Attention should be paid to high-risk groups, namely residents with poor meal intake, low BMI, severe cognitive impairment, immobility, and older age.

scholarly journals NUTRITIONAL RISK PREDICTS HEALTH SERVICES UTILIZATION AND DEATH OVER 1 YEAR: RESULTS FROM THE UAB STUDY OF AGING II

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S347-S347
Author(s):  
David R Buys ◽  
Richard E Kennedy ◽  
Yue Zhang ◽  
Julie Locher ◽  
Cynthia J Brown
Keyword(s):  
Older Adults ◽  
Emergency Department ◽  
Nursing Home ◽  
Health Services ◽  
Community Dwelling ◽  
Emergency Department Visits ◽  
Nutritional Risk ◽  
Nursing Home Admission ◽  
Services Utilization

Abstract Nutritional risk has been demonstrated to be associated with poor health outcomes, increased risk of health services utilization (HSU), and mortality among older adults. The aim of this study was to assess the prospective relationship between nutritional risk; HSU focusing separately on emergency department visits, hospitalization, and nursing home admission; and mortality. Using the University of Alabama-Birmingham Study of Aging II, we examined this relationship among 419 community-dwelling older Alabamians (75+years). We used the Mini-Nutrition Assessment (MNA), a well-validated nutritional risk assessment, which classifies individuals as either well-nourished, at-risk or malnourished, collected at baseline. We assessed HSU by asking about healthcare encounters since the last monthly follow-up call for 12 months and verified death with family reports and official documents. We completed univariate, bivariate, and Cox proportional hazards regression analyses with one-year of follow-up data, adjusting for social support, social isolation, comorbidities, and demographic variables. Accounting for covariates, being either at-risk or malnourished, relative to well-nourished, was associated with emergency department visits (HR: 1.30, 95% CI:1.14,1.48), hospitalization (HR: 1.58, 95% CI:1.37,1.82), nursing home admission (HR: 8.94, 95% CI:3.99,20.02), and mortality (HR: 1.90, 95% CI:1.25,2.88). These findings underscore the growing awareness that nutritional risk, particularly for older adults, is a significant factor affecting their well-being and particularly their ability to continue living in the community. Nutrition assessment, interventions, and services for community-dwelling older adults may lead to a reduction in health care utilization, particularly nursing home placement, and ultimately to reduced healthcare costs to families and taxpayers.

scholarly journals Diagnostic Failure of Cognitive Impairment in Nursing Home Residents May Lead to Impaired Medical Care

2019 ◽  
Vol 47 (4-6) ◽  
pp. 209-218 ◽  
Author(s):  
Björn Westerlind ◽  
Carl Johan Östgren ◽  
Patrik Midlöv ◽  
Jan Marcusson
Keyword(s):  
Nursing Home ◽  
Cognitive Impairment ◽  
Medical Care ◽  
Nursing Home Residents ◽  
Control Group ◽  
Cognitively Impaired ◽  
Dementia Diagnosis ◽  
Laboratory Findings ◽  
Group A ◽  
The Impact

Background/Objectives: Dementia and cognitive impairment are common in nursing homes. Few studies have studied the impact of unnoted cognitive impairment on medical care. This study aimed to estimate the prevalence of diagnostic failure of cognitive impairment in a sample of Swedish nursing home residents and to analyze whether diagnostic failure was associated with impaired medical care. Method: A total of 428 nursing home residents were investigated during 2008–2011. Subjects without dementia diagnosis were grouped by result of the Mini Mental State Examination (MMSE), where subjects with <24 points formed a possible dementia group and the remaining subjects a control group. A third group consisted of subjects with diagnosed dementia. These three groups were compared according to baseline data, laboratory findings, drug use, and mortality. Results: Dementia was previously diagnosed in 181 subjects (42%). Among subjects without a dementia diagnosis, 72% were cognitively impaired with possible dementia (MMSE <24). These subjects were significantly older, did not get anti-dementia treatment, and had higher levels of brain natriuretic peptide compared to the diagnosed dementia group, but the risks of malnutrition and pressure ulcers were similar to the dementia group. Conclusions: Unnoted cognitive impairment is common in nursing home residents and may conceal other potentially treatable conditions such as heart failure. The results highlight a need to pay increased attention to cognitive impairment among nursing home residents.

5226The majority of 30-day survivors of in-hospital cardiac arrest are alive one-year post-arrest without anoxic brain damage, admission to nursing home or need of in-home care

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
H Yonis ◽  
K Bundgaard ◽  
R Noermark Mortensen ◽  
M Wissenberg ◽  
G Gislason ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Nursing Home ◽  
Home Care ◽  
Brain Damage ◽  
Nursing Home Admission ◽  
Risk Of Death ◽  
Study Population ◽  
One Year ◽  
Hospital Cardiac Arrest

Abstract Background Survivors of in-hospital cardiac arrest are at risk of anoxic brain damage that can lead to admission to nursing home or need of in-home care. However, studies on long-term outcomes after in-hospital cardiac arrest are scarce with previous research focusing on short term measures such as survival-to-discharge. Purpose This study aimed to investigate the composite endpoint of nursing home admission or anoxic brain damage among 30-day survivors of in-hospital cardiac arrest within the first-year post-arrest. As a sub analysis, we also investigated the additional need of in-home care. Methods All in-hospital cardiac arrests in 13 Danish hospitals during 2013–2015 were identified from the DANARREST register. Inclusion criteria were indication for a resuscitation attempt and survival to day 30. Patients who, prior to arrest, already lived in a nursing home, and/or had anoxic brain damage were excluded. In the sub analysis patients who received in-home care prior to arrest were also excluded. The DANARREST data was linked to nationwide registries including the National Patient Register and administrative nursing home and home care registries using the Danish Civil Registration Number, a unique personal identification number that is given to every citizen in Denmark. Results The primary study population comprised of 454 (26.3%) 30 day-survivors out of 1723 eligible patients. Median age was 67 (Q1-Q3 57–75); 301 (66.9%) were men. In this group, the 1-year risk of anoxic brain damage or nursing home admission was 4.6% (95% CI 2.7%- 6.6%) with a competing risk of death of 15.6% (95% CI 12.3%-19.0%), leaving 79.8% alive without anoxic brain damage or nursing home admission at one-year follow-up (see Figure 1A). The sub study population comprised of 343 30-day survivors with a 1-year risk of anoxic brain damage, nursing home admission or need of in-home care of 23.6% (95% CI 19.1%-28.1%). The competing risk of death was 7.6% (95% CI 4.8%-10.4%), leaving 68.8% alive without anoxic brain damage, nursing home admission or need of in-home care at one-year follow-up (see Figure 1B). Figure 1 Conclusion The majority of 30-day survivors of in-hospital cardiac arrest were alive at one-year follow-up without being diagnosed with anoxic brain damage, admitted to nursing home or without need of in-home care.

Conditional Hazard Estimating Neural Networks

2011 ◽  
pp. 390-395
Author(s):  
Antonio Eleuteri ◽  
Azzam Taktak ◽  
Bertil Damato ◽  
Angela Douglas ◽  
Sarah Coupland
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Survival Analysis ◽  
Survival Time ◽  
Failure Time ◽  
Population Level ◽  
Parametric Models ◽  
Qualitative Description ◽  
Survival Times

Survival analysis is used when we wish to study the occurrence of some event in a population of subjects and the time until the event of interest. This time is called survival time or failure time. Survival analysis is often used in industrial life-testing experiments and in clinical follow-up studies. Examples of application include: time until failure of a light bulb, time until occurrence of an anomaly in an electronic circuit, time until relapse of cancer, time until pregnancy. In the literature we find many different modeling approaches to survival analysis. Conventional parametric models may involve too strict assumptions on the distributions of failure times and on the form of the influence of the system features on the survival time, assumptions which usually extremely simplify the experimental evidence, particularly in the case of medical data (Cox & Oakes, 1984). In contrast, semiparametric models do not make assumptions on the distributions of failures, but instead make assumptions on how the system features influence the survival time (the usual assumption is the proportionality of hazards); furthermore, these models do not usually allow for direct estimation of survival times. Finally, non-parametric models usually only allow for a qualitative description of the data on the population level. Neural networks have recently been used for survival analysis; for a survey on the current use of neural networks, and some previous attempts at neural network survival modeling we refer to (Bakker & Heskes, 1999), (Biganzoli et al., 1998), (Eleuteri et al., 2003), (Lisboa et al., 2003), (Neal, 2001), (Ripley & Ripley, 1998), (Schwarzer et al. 2000). Neural networks provide efficient parametric estimates of survival functions, and, in principle, the capability to give personalised survival predictions. In a medical context, such information is valuable both to clinicians and patients. It helps clinicians to choose appropriate treatment and plan follow-up efficiently. Patients at high risk could be followed up more frequently than those at lower risk in order to channel valuable resources to those who need them most. For patients, obtaining information about their prognosis is also extremely valuable in terms of planning their lives and providing care for their dependents. In this article we describe a novel neural network model aimed at solving the survival analysis problem in a continuous time setting; we provide details about the Bayesian approach to modeling, and a sample application on real data is shown.

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