Dataset Information

Algorithm for predicting death among older adults in the home care setting: study protocol for the Risk Evaluation for Support: Predictions for Elder-life in the Community Tool (RESPECT).

ABSTRACT: INTRODUCTION:Older adults living in the community often have multiple, chronic conditions and functional impairments. A challenge for healthcare providers working in the community is the lack of a predictive tool that can be applied to the broad spectrum of mortality risks observed and may be used to inform care planning. OBJECTIVE:To predict survival time for older adults in the home care setting. The final mortality risk algorithm will be implemented as a web-based calculator that can be used by older adults needing care and by their caregivers. DESIGN:Open cohort study using the Resident Assessment Instrument for Home Care (RAI-HC) data in Ontario, Canada, from 1 January 2007 to 31 December 2013. PARTICIPANTS:The derivation cohort will consist of ?437 000 older adults who had an RAI-HC assessment between 1 January 2007 and 31 December 2012. A split sample validation cohort will include ?122 000 older adults with an RAI-HC assessment between 1 January and 31 December 2013. MAIN OUTCOME MEASURES:Predicted survival from the time of an RAI-HC assessment. All deaths (n?245?000) will be ascertained through linkage to a population-based registry that is maintained by the Ministry of Health in Ontario. STATISTICAL ANALYSIS:Proportional hazards regression will be estimated after assessment of assumptions. Predictors will include sociodemographic factors, social support, health conditions, functional status, cognition, symptoms of decline and prior healthcare use. Model performance will be evaluated for 6-month and 12-month predicted risks, including measures of calibration (eg, calibration plots) and discrimination (eg, c-statistics). The final algorithm will use combined development and validation data. ETHICS AND DISSEMINATION:Research ethics approval has been granted by the Sunnybrook Health Sciences Centre Review Board. Findings will be disseminated through presentations at conferences and in peer-reviewed journals. TRIAL REGISTRATION NUMBER:NCT02779309, Pre-results.

SUBMITTER: Hsu AT

PROVIDER: S-EPMC5168641 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Algorithm for predicting death among older adults in the home care setting: study protocol for the Risk Evaluation for Support: Predictions for Elder-life in the Community Tool (RESPECT).

Hsu Amy T AT Manuel Douglas G DG Taljaard Monica M Chalifoux Mathieu M Bennett Carol C Costa Andrew P AP Bronskill Susan S Kobewka Daniel D Tanuseputro Peter P

BMJ open 20161201 12

<h4>Introduction</h4>Older adults living in the community often have multiple, chronic conditions and functional impairments. A challenge for healthcare providers working in the community is the lack of a predictive tool that can be applied to the broad spectrum of mortality risks observed and may be used to inform care planning.<h4>Objective</h4>To predict survival time for older adults in the home care setting. The final mortality risk algorithm will be implemented as a web-based calculator th ...[more]

PMID: 27909039

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Project description:BackgroundMathematical models are needed for the design of breeding programs using genomic prediction. While deterministic models for selection on pedigree-based estimates of breeding values (PEBV) are available, these have not been fully developed for genomic selection, with a key missing component being the accuracy of genomic EBV (GEBV) of selection candidates. Here, a deterministic method was developed to predict this accuracy within a closed breeding population based on the accuracy of GEBV and PEBV in the reference population and the distance of selection candidates from their closest ancestors in the reference population.MethodsThe accuracy of GEBV was modeled as a combination of the accuracy of PEBV and of EBV based on genomic relationships deviated from pedigree (DEBV). Loss of the accuracy of DEBV from the reference to the target population was modeled based on the effective number of independent chromosome segments in the reference population (Me). Measures of Me derived from the inverse of the variance of relationships and from the accuracies of GEBV and PEBV in the reference population, derived using either a Fisher information or a selection index approach, were compared by simulation.ResultsUsing simulation, both the Fisher and the selection index approach correctly predicted accuracy in the target population over time, both with and without selection. The index approach, however, resulted in estimates of Me that were less affected by heritability, reference size, and selection, and which are, therefore, more appropriate as a population parameter. The variance of relationships underpredicted Me and was greatly affected by selection. A leave-one-out cross-validation approach was proposed to estimate required accuracies of EBV in the reference population. Aspects of the methods were validated using real data.ConclusionsA deterministic method was developed to predict the accuracy of GEBV in selection candidates in a closed breeding population. The population parameter Me that is required for these predictions can be derived from an available reference data set, and applied to other reference data sets and traits for that population. This method can be used to evaluate the benefit of genomic prediction and to optimize genomic selection breeding programs.

Project description:The emergence of mobile technology offers new opportunities to improve clinical guideline adherence in resource-limited settings. We conducted a clinical pilot study in rural Bangladesh to evaluate the impact of a smartphone adaptation of the World Health Organization (WHO) diarrheal disease management guidelines, including a modality for age-based weight estimation. Software development was guided by end-user input and evaluated in a resource-limited district and sub-district hospital during the fall 2015 cholera season; both hospitals lacked scales which necessitated weight estimation. The study consisted of a 6 week pre-intervention and 6 week intervention period with a 10-day post-discharge follow-up. Standard of care was maintained throughout the study with the exception that admitting clinicians used the tool during the intervention. Inclusion criteria were patients two months of age and older with uncomplicated diarrheal disease. The primary outcome was adherence to guidelines for prescriptions of intravenous (IV) fluids, antibiotics and zinc. A total of 841 patients were enrolled (325 pre-intervention; 516 intervention). During the intervention, the proportion of prescriptions for IV fluids decreased at the district and sub-district hospitals (both p < 0.001) with risk ratios (RRs) of 0.5 and 0.2, respectively. However, when IV fluids were prescribed, the volume better adhered to recommendations. The proportion of prescriptions for the recommended antibiotic azithromycin increased (p < 0.001 district; p = 0.035 sub-district) with RRs of 6.9 (district) and 1.6 (sub-district) while prescriptions for other antibiotics decreased; zinc adherence increased. Limitations included an absence of a concurrent control group and no independent dehydration assessment during the pre-intervention. Despite limitations, opportunities were identified to improve clinical care, including better assessment, weight estimation, and fluid/ antibiotic selection. These findings demonstrate that a smartphone-based tool can improve guideline adherence. This study should serve as a catalyst for a randomized controlled trial to expand on the findings and address limitations.

Project description:BACKGROUND:The definition and understanding of elder abuse and neglect in nursing homes can vary in different jurisdictions as well as among health care staff, researchers, family members and residents themselves. Different understandings of what constitutes abuse and its severity make it difficult to compare findings in the literature on elder abuse in nursing homes and complicate identification, reporting, and managing the problem. Knowledge about nursing home leaders' perceptions of elder abuse and neglect is of particular interest since their understanding of the phenomenon will affect what they signal to staff as important to report and how they investigate adverse events to ensure residents' safety. The aim of the study was to explore nursing home leaders' perceptions of elder abuse and neglect. METHODS:A qualitative exploratory study with six focus group interviews with 28 nursing home leaders in the role of care managers was conducted. Nursing home leaders' perceptions of different types of abuse within different situations were explored. The constant comparative method was used to analyse the data. RESULTS:The results of this study indicate that elder abuse and neglect are an overlooked patient safety issue. Three analytical categories emerged from the analyses: 1) Abuse from co-residents: 'A normal part of nursing home life'; resident-to-resident aggression appeared to be so commonplace that care leaders perceived it as normal and had no strategy for handling it; 2) Abuse from relatives: 'A private affair'; relatives with abusive behaviour visiting nursing homes residents was described as difficult and something that should be kept between the resident and the relatives; 3) Abuse from direct-care staff: 'An unthinkable event'; staff-to-resident abuse was considered to be difficult to talk about and viewed as not being in accordance with the leaders' trust in their employees. CONCLUSIONS:Findings in the present study show that care managers lack awareness of elder abuse and neglect, and that elder abuse is an overlooked patient safety issue. The consequence is that nursing home residents are at risk of being harmed and distressed. Care managers lack knowledge and strategies to identify and adequately manage abuse and neglect in nursing homes.

Dataset Information

Algorithm for predicting death among older adults in the home care setting: study protocol for the Risk Evaluation for Support: Predictions for Elder-life in the Community Tool (RESPECT).

Publications

Algorithm for predicting death among older adults in the home care setting: study protocol for the Risk Evaluation for Support: Predictions for Elder-life in the Community Tool (RESPECT).

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