Predictors of long-term care placement in persons with dementia: a systematic review and meta-analysis

OBJECTIVE

The main objective of this study was to summarize the effects of various individual, caregiver, and system-related factors on the risk of long-term care (LTC) placement for persons with dementia.

METHODS

We searched electronic databases for longitudinal studies reporting on predictors of LTC placement for persons with dementia residing in the community or supportive care settings. We performed meta-analyses with hazard ratios (HRs) of various predictors using random effects models and stratified the HRs with several study variables. Data on predictors not included in the meta-analyses were summarized descriptively.

RESULTS

Full-text reviews of 360 papers were performed with data from 37 papers used to calculate pooled HRs for LTC placement of select person with dementia (age, sex, race, marital status, type of dementia, living arrangement, and relationship to caregiver) and caregiver (age, sex, and depressive symptoms) characteristics. White race [HR = 1.67, 95% confidence intervals (CI): 1.41-1.99], greater dementia severity (HR = 1.05, 95% CI: 1.03-1.06), and older age (HR = 1.02, 95% CI: 1.01-1.03) increased the risk of LTC placement. Married persons with dementia (HR = 0.38, 95% CI: 0.16-0.86) and living with their caregiver (HR = 0.72, 95% CI: 0.56-0.92) had a lower risk. Behavioral and psychological symptoms of dementia, the degree of functional impairment, and caregiver burden had a consistent effect on the risk of LTC placement in our descriptive review.

CONCLUSION

We quantified the predictive effect of several risk factors for LTC placement. These estimates could be used to more precisely categorize the risk of institutionalization and potentially link those at higher risk to appropriate services. Copyright © 2016 John Wiley & Sons, Ltd.

Dementia case management and risk of long-term care placement: a systematic review and meta-analysis

OBJECTIVES

The objective of our study is to evaluate the effectiveness of dementia case management compared with usual care on reducing long-term care placement, hospitalization, and emergency department visits for adult patients with dementia. We also sought to evaluate the effectiveness of this intervention on delaying time to long-term care placement and hospitalization.

METHODS

We searched electronic databases supplemented by bibliographies and conference proceedings for randomized controlled trials testing the effectiveness of dementia case management in reducing resource utilization in a population of caregiver-care recipient dyads living in the community. We meta-analyzed the risk ratio (RR) and weighted mean differences of long-term care placement and the RR of hospital admissions. Pooled estimates were further stratified by study characteristics and measures of study quality.

RESULTS

Seventeen studies were included in the meta-analysis. The overall pooled RR of long-term care placement was 0.94 (95% confidence interval [0.85, 1.03]; p = 0.227) for dementia case management compared with usual care. Stratification by follow-up duration indicated a statistically significant reduction in risk of long-term care placement when follow-up duration was less than 18 months (RR 0.61, 95% confidence interval [0.41, 0.91], p = 0.015). There was no effect of dementia case management compared with usual care for the other outcomes.

CONCLUSION

Dementia case management demonstrated a short-term positive effect on reducing the risk of long-term care placement among older people with dementia residing in the community. However, other sources of resource utilization and more extended effects of dementia case management on risk of long-term care placement warrant further investigation.

Elevated Hospitalization Risk of Assisted Living Residents With Dementia in Alberta, Canada

OBJECTIVES

Assisted living (AL) is an increasingly used residential option for older adults with dementia; however, lower staffing rates and service availability raise concerns that such residents may be at increased risk for adverse outcomes. Our objectives were to determine the incidence of hospitalization over 1 year for dementia residents of designated AL (DAL) facilities, compared with long-term care (LTC) facilities, and identify resident- and facility-level predictors of hospitalization among DAL residents.

METHODS

Participants were 609 DAL (mean age 85.7 ± 6.6 years) and 691 LTC (86.4 ± 6.9 years) residents with dementia enrolled in the Alberta Continuing Care Epidemiological Studies. Research nurses completed a standardized comprehensive assessment of residents and interviewed family caregivers at baseline (2006-2008) and 1 year later. Standardized administrator interviews provided facility level data. Hospitalization was determined via linkage with the provincial Inpatient Discharge Abstract Database. Multivariable Cox proportional hazards models were used to identify predictors of hospitalization.

RESULTS

The cumulative annual incidence of hospitalization was 38.6% (34.5%-42.7%) for DAL and 10.3% (8.0%-12.6%) for LTC residents with dementia. A significantly increased risk for hospitalization was observed for DAL residents aged 90+ years, with poor social relationships, less severe cognitive impairment, greater health instability, fatigue, high medication use (11+ medications), and 2+ hospitalizations in the preceding year. Residents from DAL facilities with a smaller number of spaces, no chain affiliation, and from specific health regions showed a higher risk of hospitalization.

CONCLUSIONS

DAL residents with dementia had a hospitalization rate almost 4-fold higher than LTC residents with dementia. Our findings raise questions about the ability of some AL facilities to adequately address the needs of cognitively impaired residents and highlight potential clinical, social, and policy areas for targeted interventions to reduce hospitalization risk.

Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review

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.

Visual Analytic Tools and Techniques in Population Health and Health Services Research: Protocol for a Scoping Review

BACKGROUND

Visual analytics (VA) promotes the understanding of data using visual, interactive techniques and using analytic and visual engines. The analytic engine includes machine learning and other automated techniques, whereas common visual outputs include flow maps and spatiotemporal hotspots for studying service gaps and disease distribution. The principal objective of this scoping review is to examine the state of science on VA and the various tools, strategies, and frameworks used in population health and health services research (HSR).

OBJECTIVE

The purpose of this scoping review is to develop an overarching global view of established techniques, frameworks, and methods of VA in population health and HSR. The main objectives are to explore, map, and synthesize the literature related to VA in its application to the two main focus areas of health care.

METHODS

We will use established scoping review methods to meet the study objective. As the use of the term visual analytics is inconsistent, one of the major challenges was operationalizing the concepts for developing the search strategy, based on the three main concepts of population health, HSR, and VA. We included peer reviewed and grey literature sources from 2005 till March 2019 in the search. Independent teams of researchers will screen the titles, abstracts and full text articles, whereas an independent researcher will arbiter conflicts. Data will be abstracted and presented using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist and explanation by two independent researchers.

RESULTS

As of late August 2019, the scoping review is in the full-text screening stage. Data synthesis will follow and the first results are expected to be submitted for publication in December 2019. In this protocol, the methods for undertaking this scoping review are detailed. We present how we operationalized the varied concepts of population health, health services, and VA. The main results of the scoping review will synthesize peer reviewed and grey literature sources on the main methods of VA in the interrelated fields of population health and health services research from January 2005 till March 2019.

CONCLUSIONS

VA is being increasingly used and integrated with emerging technologies to support decision making using large data sets. This scoping review of the VA tools, strategies, and frameworks applied to population health and health services aims to increase awareness of this approach for uptake by decision makers working within and toward developing learning health systems globally.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/14019.

Interactive Visualization Applications in Population Health and Health Services Research: Systematic Scoping Review

BACKGROUND

Simple visualizations in health research data, such as scatter plots, heat maps, and bar charts, typically present relationships between 2 variables. Interactive visualization methods allow for multiple related facets such as numerous risk factors to be studied simultaneously, leading to data insights through exploring trends and patterns from complex big health care data. The technique presents a powerful tool that can be used in combination with statistical analysis for knowledge discovery, hypothesis generation and testing, and decision support.

OBJECTIVE

The primary objective of this scoping review is to describe and summarize the evidence of interactive visualization applications, methods, and tools being used in population health and health services research (HSR) and their subdomains in the last 15 years, from January 1, 2005, to March 30, 2019. Our secondary objective is to describe the use cases, metrics, frameworks used, settings, target audience, goals, and co-design of applications.

METHODS

We adapted standard scoping review guidelines with a peer-reviewed search strategy: 2 independent researchers at each stage of screening and abstraction, with a third independent researcher to arbitrate conflicts and validate findings. A comprehensive abstraction platform was built to capture the data from diverse bodies of literature, primarily from the computer science and health care sectors. After screening 11,310 articles, we present findings from 56 applications from interrelated areas of population health and HSR, as well as their subdomains such as epidemiologic surveillance, health resource planning, access, and use and costs among diverse clinical and demographic populations.

RESULTS

In this companion review to our earlier systematic synthesis of the literature on visual analytics applications, we present findings in 6 major themes of interactive visualization applications developed for 8 major problem categories. We found a wide application of interactive visualization methods, the major ones being epidemiologic surveillance for infectious disease, resource planning, health service monitoring and quality, and studying medication use patterns. The data sources included mostly secondary administrative and electronic medical record data. In addition, at least two-thirds of the applications involved participatory co-design approaches while introducing a distinct category, embedded research, within co-design initiatives. These applications were in response to an identified need for data-driven insights into knowledge generation and decision support. We further discuss the opportunities stemming from the use of interactive visualization methods in studying global health; inequities, including social determinants of health; and other related areas. We also allude to the challenges in the uptake of these methods.

CONCLUSIONS

Visualization in health has strong historical roots, with an upward trend in the use of these methods in population health and HSR. Such applications are being fast used by academic and health care agencies for knowledge discovery, hypotheses generation, and decision support.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/14019.

Policy choices in dementia care-An exploratory analysis of the Alberta continuing care system (ACCS) using system dynamics

BACKGROUND

The increase in the incidence of dementia in the aging population and the decrease in the availability of informal caregivers put pressure on continuing care systems to care for a growing number of people with disabilities. Policy changes in the continuing care system need to address this shift in the population structure. One of the most effective tools for assessing policies in complex systems is system dynamics. Nevertheless, this method is underused in continuing care capacity planning.

METHODS

A system dynamics model of the Alberta Continuing Care System was developed using stylized data. Sensitivity analyses and policy evaluations were conducted to demonstrate the use of system dynamics modelling in this area of public health planning. We focused our policy exploration on introducing staff/resident benchmarks in both supportive living and long-term care (LTC).

RESULTS

The sensitivity analyses presented in this paper help identify leverage points in the system that need to be acknowledged when policy decisions are made. Our policy explorations showed that the deficits of staff increase dramatically when benchmarks are introduced, as expected, but at the end of the simulation period, the difference in deficits of both nurses and health care aids are similar between the 2 scenarios tested. Modifying the benchmarks in LTC only versus in both supportive living and LTC has similar effects on staff deficits in long term, under the assumptions of this particular model.

CONCLUSION

The continuing care system dynamics model can be used to test various policy scenarios, allowing decision makers to visualize the effect of a certain policy choice on different system variables and to compare different policy options. Our exploration illustrates the use of system dynamics models for policy making in complex health care systems.

Exploratory analysis using discrete event simulation modelling of the wait times and service costs associated with the maximum wait time guarantee policy applied in a rheumatology central intake clinic

Adherence to wait time benchmark targets for the diagnosis and initiation of interventions for rheumatoid arthritis is crucial in altering the disease trajectory. We analysed the impact of the maximum wait time guarantee (MWTG) policy for routing referrals for the initial rheumatologist consults on the waiting and service costs. We modelled a central intake system for a rheumatology clinic as a discrete event simulation (DES) model. Using data from a central intake and rheumatology clinic as input to the model of the system, we simulated the arrival of referrals and rheumatologist visits of patients. We demonstrated the impact of the referral policy on system performance and compared the system costs in an MWTG policy and first-available-appointment policy scenarios. MWTG policy is an option for a wait time management strategy but comes with essential cost considerations. Healthcare managers and policymakers should consider the DES approach to support referral decision policy choices.

A Simulation Modelling Study of Referral Distribution Policies in a Centralized Intake System for Surgical Consultation

Delays beyond recommended wait times, especially for specialist services, are associated with adverse health outcomes. The Alberta Surgical Initiative aims to improve the referral wait time-the time between a referral is received at the central intake to the time a specialist sees the patient. Using the discrete event simulation modelling approach, we evaluated and compared the impact of four referral distribution policies in a central intake system on three system performance measures (number of consultations, referral wait time and surgeon utilization). The model was co-designed with clinicians and clinic staff to represent the flow of patients through the system. We used data from the Facilitated Access to Surgical Treatment (FAST) centralized intake referral program for General Surgery to parameterize the model. Four distribution policies were evaluated - next-available-surgeon, sequential, "blackjack," and "kanban." A sequential distribution of referrals for surgical consultation among the surgeons resulted in the worst performance in terms of the number of consultations, referral wait time and surgeon utilization. The three other distribution policies are comparable in performance. The "next available surgeon" model provided the most efficient and robust model, with approximately 1,000 more consultations, 100 days shorter referral time and a 14% increase in surgeon utilization. Discrete event simulation (DES) modelling can be an effective tool to illustrate and communicate the impact of the referral distribution policy on system performance in terms of the number of consultations, referral wait time and surgeon utilization.

Intended and unintended consequences of strategies to meet performance benchmarks for rheumatologist referrals in a centralized intake system

RATIONALE

Timely assessment of a chronic condition is critical to prevent long-term irreversible consequences. Patients with inflammatory arthritis (IA) symptoms require diagnosis by a rheumatologist and intervention initiation to minimize potential joint damage. With limited rheumatologist capacity, meeting urgency wait time benchmarks can be challenging. We investigate the impact of the maximum wait time guarantee (MWTG) policy and referral volume changes in a rheumatology central intake (CI) system on meeting this challenge.

METHODS

We applied a system simulation approach to model a high-volume CI rheumatology clinic. Model parameters were based on the referral and triage data from the CI and clinic appointment data. We compare the wait time performance of the current distribution policy MWTG and when referral volumes change.

RESULTS

The MWTG policy ensures 100% of new patients see a rheumatologist within their urgency wait time benchmark. However, the average wait time for new patients increased by 51% (178-269 days). A 10% decrease in referrals resulted in a 76% decrease on average wait times (178-43 days) for new patients and an increase in the number of patients seen by a rheumatologist within 1 year of the initial visit.

CONCLUSION

An MWTG policy can result in intended and unintended consequences-ensuring that all patients meet the wait time benchmarks but increasing wait times overall. Relatively small changes in referral volume significantly impact wait times. These relationships can assist clinic managers and policymakers decide on the best approach to manage referrals for better system performance.

A multi-step approach to developing a health system evaluation framework for community-based health care

Background

Community-based health care (CBHC) is a shift towards healthcare integration and community services closer to home. Variation in system approaches harkens the need for a conceptual framework to evaluate outcomes and impacts. We set out to develop a CBHC-specific evaluation framework in the context of a provincial ministry of health planning process in Canada.

Methods

A multi-step approach was used to develop the CBHC evaluation framework. Modified Delphi informed conceptualization and prioritization of indicators. Formative research identified evaluation framework elements (triple aim, global measures, and impact), health system levels (tiers), and potential CBHC indicators (n = 461). Two Delphi rounds were held. Round 1, panelists independently ranked indicators on CBHC relevance and health system tiering. Results were analyzed by coding agreement/disagreement frequency and central tendency measures. Round 2, a consensus meeting was used to discuss disagreement, identify Tier 1 indicators and concepts, and define indicators not relevant to CBHC (Tier 4). Post-Delphi, indicators and concepts were refined, Tier 1 concepts mapped to the evaluation framework, and indicator narratives developed. Three stakeholder consultations (scientific, government, and public/patient communities) were held for endorsement and recommendation.

Results

Round 1 Delphi results showed agreement for 300 and disagreement for 161 indicators. Round 2 consensus resulted in 103 top tier indicators (Tier 1 = 19, Tier 2 = 84), 358 bottom Tier 3 and 4 indicators, non-CBHC measure definitions, and eight Tier 1 indicator concepts—Mortality/Suicide; Quality of Life, and Patient Reported Outcome Measures; Global Patient Reported Experience Measures; Cost of Care, Access to Integrated Primary Care; Avoidable Emergency Department Use; Avoidable Hospitalization; and E-health Penetration. Post Delphi results refined Tier 3 (n = 289) and 4 (n = 69) indicators, and identified 18 Tier 2 and 3 concepts. When mapped to the evaluation framework, Tier 1 concepts showed full coverage across the elements. ‘Indicator narratives’ depicted systemness and integration for evaluating CBHC. Stakeholder consultations affirmed endorsement of the approach and evaluation framework; refined concepts; and provided key considerations to further operationalize and contextualize indicators, and evaluate CBHC as a health system approach.

Conclusions

This research produced a novel evaluation framework to conceptualize and evaluate CBHC initiatives. The evaluation framework revealed the importance of a health system approach for evaluating CBHC.

Criteria for Referring Patients With Outpatient Gastroenterological Disease for Specialist Consultation: A Review of the Literature

Background

Demands on gastroenterology are growing, as a result of the high prevalence of digestive diseases, the impact of colon cancer screening programs and an aging population. Prioritizing referrals to gastroenterology would assist in managing wait times. Our objectives were (1) to assess whether there were consistent criteria to guide referrals from family physicians for gastroenterological outpatient consultation and (2) to determine if there were different levels of urgency or priority in referral criteria.

Methods

We conducted a scoping review, searching Medline, Embase and Cochrane databases from 1997 to 2009, using the terms referral, triage, consultation and at least one from a list of gastroenterology-specific search terms. Of 2978 initial results, 51 papers were retrieved, and 20 were retained after review by two reviewers. Additional publications were identified through hand searches of retained papers, website searches and nomination by a panel of specialists.

Results

Thirty-four papers, reports or websites were retained. No referral criteria covered the spectrum of disorders that might be referred by family physicians to gastroenterologists. Criteria for referral were most commonly listed for suspected colorectal cancer, followed by suspected upper GI cancer, hepatitis, and functional disorders.

Conclusions

A clinical panel comprised of gastroenterologists and primary care providers, informed by this literature review, are completing the work of formulating a Gastroenterology Priority Referral Score, and plan to test the reliability and validity of the tool for determining the relative urgency for referral from primary care to gastroenterology.