Long-term care (LTC) residents, isolated because of the COVID-19 pandemic, are at increased risk for negative mental health outcomes. The purpose of our article is to demonstrate how the LTCF can inform clinical care and evaluate the effect of strategies to mitigate worsening mental health outcomes during the COVID-19 pandemic. We present a supporting analysis of the effects of lockdown in homes without COVID-19 outbreaks on depression, delirium, and behaviour problems in a network of seven LTC homes in New Brunswick, Canada where mitigative strategies were deployed to minimize poor mental health outcomes (e.g., virtual visits, increased student volunteers). This network meets regularly to review performance on risk-adjusted quality of care indicators from the interRAI LTCF and share learning through a community of practice model. We included 4209 assessments from 765 LTC residents between January 2017 to June 2020 and modelled the change within and between residents for depression, delirium, and behavioural problems over time with longitudinal generalized estimating equations. Though the number of residents who had in-person visits with family decreased from 73.2% before to 17.9% during lockdown (chi square, p<0.0001), the number of residents experiencing delirium (4.5% to 3.5%, p=0.51) and behavioural problems (35.5% to 30.2%, p=0.19) did not change. The proportion of residents with indications of depression decreased from 19.9% before to 11.5% during lockdown (p<0.002). The final multivariate models indicate that the effect of lockdown was not statistically significant on depression, delirium, or behavioural problems. Our analyses demonstrate poor mental health outcomes associated with lockdown can be mitigated with thoughtful intervention and ongoing evaluation with clinical information systems. Policy makers can use outputs to guide resource deployment and researchers can examine the data to identify better management strategies for when pandemic strikes again.
Background Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots. Objective This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR). Methods Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences. Results After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard. Conclusions With the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage. International Registered Report Identifier (IRRID) RR2-10.2196/14019
Outcomes of adverse events in home care are varied and multifactorial. This study tested a framework combining two health measures to identify home care recipients at higher risk of long-term care placement or death within one year. Both measures come from the Resident Assessment Instrument-Home Care (RAI-HC), a standardized comprehensive clinical assessment. Persons scoring high in the Method for Assigning Priority Levels (MAPLe) algorithm and Changes in Health, End-stage disease, Signs and Symptoms (CHESS) scale were at the greatest risk of placement or death and more than twice as likely to experience either outcome earlier than others. The target group was more likely to trigger mood, social relationship, and caregiver distress issues, suggesting mental health and psychosocial interventions might help in addition to medical care and/or personal support services. Home care agencies can use this framework to identify home care patients who may require a more intensive care coordinator approach.
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