Predictors of nursing home admission of individuals without a dementia diagnosis before admission - results from the Leipzig Longitudinal Study of the Aged (LEILA 75+)

<p>Abstract</p> <p>Background</p> <p>In previous decades a substantial number of community-based studies mostly including dementia cases examined predictors of nursing home admission (NHA) among elderly people. However, no one study has analysed predictors of NHA for individuals without developing dementia before NHA.</p> <p>Methods</p> <p>Data were derived from the Leipzig Longitudinal Study of the Aged, a population-based study of individuals aged 75 years and older. 1,024 dementia-free older adults were interviewed six times on average every 1.4 years. Socio-demographic, clinical, and psychometric variables were obtained. Kaplan-Meier estimates were used to determine mean time to NHA. Cox proportional hazards regression was used to examine predictors of long-term NHA.</p> <p>Results</p> <p>Of the overall sample, 7.8 percent of the non-demented elderly (n = 59) were admitted to nursing home (NH) during the study period. The mean time to NHA in the dementia-free sample was 7.6 years. Characteristics associated with a shorter time to NHA were increased age, living alone, functional and cognitive impairment, major depression, stroke, myocardial infarction, a low number of specialist visits and paid home helper use.</p> <p>Conclusions</p> <p>Severe physical or psychiatric diseases and living alone have a significant effect on NHA for dementia-free individuals. The findings offer potentialities of secondary prevention to avoid or delay NHA for these elderly individuals. Further investigation of predictors of institutionalization is warranted to advance understanding of the process leading to NHA for this important group.</p

Perceived neighborhood safety and incident mobility disability among elders: the hazards of poverty

<p>Abstract</p> <p>Background</p> <p>We investigated whether lack of perceived neighborhood safety due to crime, or living in high crime neighborhoods was associated with incident mobility disability in elderly populations. We hypothesized that low-income elders and elders at retirement age (65 – 74) would be at greatest risk of mobility disability onset in the face of perceived or measured crime-related safety hazards.</p> <p>Methods</p> <p>We conducted the study in the New Haven Established Populations for Epidemiologic Studies of the Elderly (EPESE), a longitudinal cohort study of community-dwelling elders aged 65 and older who were residents of New Haven, Connecticut in 1982. Elders were interviewed beginning in 1982 to assess mobility (ability to climb stairs and walk a half mile), perceptions of their neighborhood safety due to crime, annual household income, lifestyle characteristics (smoking, alcohol use, physical activity), and the presence of chronic co-morbid conditions. Additionally, we collected baseline data on neighborhood crime events from the New Haven Register newspaper in 1982 to measure local area crime rates at the census tract level.</p> <p>Results</p> <p>At baseline in 1982, 1,884 elders were without mobility disability. After 8 years of follow-up, perceiving safety hazards was associated with increased risk of mobility disability among elders at retirement age whose incomes were below the federal poverty line (HR 1.56, 95% CI 1.02 – 2.37). No effect of perceived safety hazards was found among elders at retirement age whose incomes were above the poverty line. No effect of living in neighborhoods with high crime rates (measured by newspaper reports) was found in any sub-group.</p> <p>Conclusion</p> <p>Perceiving a safety hazard due to neighborhood crime was associated with increased risk of incident mobility disability among impoverished elders near retirement age. Consistent with prior literature, retirement age appears to be a vulnerable period with respect to the effect of neighborhood conditions on elder health. Community violence prevention activities should address perceived safety among vulnerable populations, such as low-income elders at retirement age, to reduce future risks of mobility disability.</p

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

<p>Abstract</p> <p>Background</p> <p>Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2).</p> <p>Methods</p> <p>A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances.</p> <p>Results</p> <p>The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity.</p> <p>Conclusions</p> <p>Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.</p

Predicting nursing home admission in the U.S: a meta-analysis

Background: While existing reviews have identified significant predictors of nursing home admission, this meta-analysis attempted to provide more integrated empirical findings to identify predictors. The present study aimed to generate pooled empirical associations for sociodemographic, functional, cognitive, service use, and informal support indicators that predict nursing home admission among older adults in the U.S. Methods: Studies published in English were retrieved by searching the MEDLINE, PSYCINFO, CINAHL, and Digital Dissertations databases using the keywords: "nursing home placement," "nursing home entry," "nursing home admission," and "predictors/institutionalization." Any reports including these key words were retrieved. Bibliographies of retrieved articles were also searched. Selected studies included sampling frames that were nationally- or regionally-representative of the U.S. older population. Results: Of 736 relevant reports identified, 77 reports across 12 data sources were included that used longitudinal designs and community-based samples. Information on number of nursing home admissions, length of follow-up, sample characteristics, analysis type, statistical adjustment, and potential risk factors were extracted with standardized protocols. Random effects models were used to separately pool the logistic and Cox regression model results from the individual data sources. Among the strongest predictors of nursing home admission were 3 or more activities of daily living dependencies (summary odds ratio [OR] = 3.25; 95% confidence interval [CI], 2.56–4.09), cognitive impairment (OR = 2.54; CI, 1.44–4.51), and prior nursing home use (OR = 3.47; CI, 1.89–6.37). Conclusion: The pooled associations provided detailed empirical information as to which variables emerged as the strongest predictors of NH admission (e.g., 3 or more ADL dependencies, cognitive impairment, prior NH use). These results could be utilized as weights in the construction and validation of prognostic tools to estimate risk for NH entry over a multi-year period

Growing old at home – A randomized controlled trial to investigate the effectiveness and cost-effectiveness of preventive home visits to reduce nursing home admissions: study protocol [NCT00644826]

<p>Abstract</p> <p>Background</p> <p>Regarding demographic changes in Germany it can be assumed that the number of elderly and the resulting need for long term care is increasing in the near future. It is not only an individual's interest but also of public concern to avoid a nursing home admission. Current evidence indicates that preventive home visits can be an effective way to reduce the admission rate in this way making it possible for elderly people to stay longer at home than without home visits. As the effectiveness and cost-effectiveness of preventive home visits strongly depends on existing services in the social and health system existing international results cannot be merely transferred to Germany. Therefore it is necessary to investigate the effectiveness and cost-effectiveness of such an intervention in Germany by a randomized controlled trial.</p> <p>Methods</p> <p>The trial is designed as a prospective multi-center randomized controlled trial in the cities of Halle and Leipzig. The trial includes an intervention and a control group. The control group receives usual care. The intervention group receives three additional home visits by non-physician health professionals (1) geriatric assessment, (2) consultation, (3) booster session.</p> <p>The nursing home admission rate after 18 months will be defined as the primary outcome. An absolute risk reduction from a 20% in the control-group to a 7% admission rate in the intervention group including an assumed drop out rate of 30% resulted in a required sample size of N = 320 (n = 160 vs. n = 160).</p> <p>Parallel to the clinical outcome measurement the intervention will be evaluated economically. The economic evaluation will be performed from a society perspective.</p> <p>Discussion</p> <p>To the authors' knowledge for the first time a trial will investigate the effectiveness and cost-effectiveness of preventive home visits for people aged 80 and over in Germany using the design of a randomized controlled trial. Thus, the trial will contribute to the existing evidence on preventive home visits especially in Germany.</p

A methodological framework to distinguish spectrum effects from spectrum biases and to assess diagnostic and screening test accuracy for patient populations: Application to the Papanicolaou cervical cancer smear test

<p>Abstract</p> <p>Background</p> <p>A spectrum effect was defined as differences in the sensitivity or specificity of a diagnostic test according to the patient's characteristics or disease features. A spectrum effect can lead to a spectrum bias when subgroup variations in sensitivity or specificity also affect the likelihood ratios and thus post-test probabilities. We propose and illustrate a methodological framework to distinguish spectrum effects from spectrum biases.</p> <p>Methods</p> <p>Data were collected for 1781 women having had a cervical smear test and colposcopy followed by biopsy if abnormalities were detected (the reference standard). Logistic models were constructed to evaluate both the sensitivity and specificity, and the likelihood ratios, of the test and to identify factors independently affecting the test's characteristics.</p> <p>Results</p> <p>For both tests, human papillomavirus test, study setting and age affected sensitivity or specificity of the smear test (spectrum effect), but only human papillomavirus test and study setting modified the likelihood ratios (spectrum bias) for clinical reading, whereas only human papillomavirus test and age modified the likelihood ratios (spectrum bias) for "optimized" interpretation.</p> <p>Conclusion</p> <p>Fitting sensitivity, specificity and likelihood ratios simultaneously allows the identification of covariates that independently affect diagnostic or screening test results and distinguishes spectrum effect from spectrum bias. We recommend this approach for the development of new tests, and for reporting test accuracy for different patient populations.</p