Introduction: Polypharmacy is commonly associated with adverse health outcomes. There are currently no meta-analyses of the prevalence of polypharmacy or factors associated with polypharmacy. We aimed to estimate the pooled prevalence of polypharmacy and factors associated with polypharmacy in a systematic review and meta-analysis. Methods: MEDLINE, EMBASE, and Cochrane databases were searched for studies with no restrictions on date. We included observational studies that reported on the prevalence of polypharmacy among individuals over age 19. Two reviewers extracted study characteristics including polypharmacy definitions, study design, setting, geography, and participant demographics. The risk of bias was assessed using the Newcastle-Ottawa Scales. The main outcome was the prevalence of polypharmacy and factors associated with polypharmacy prevalence. The pooled prevalence estimates of polypharmacy with 95% confidence intervals were determined using random effects meta-analysis. Subgroup analyses were undertaken to evaluate factors associated with polypharmacy such as polypharmacy definitions, study setting, study design and geography. Meta-regression was conducted to assess the associations between polypharmacy prevalence and study year. Results: 106 full-text articles were identified. The pooled estimated prevalence of polypharmacy in the 54 studies reporting on polypharmacy in all medication classes was 37% (95% CI: 31-43%). Differences in polypharmacy prevalence were reported for studies using different numerical thresholds, study setting, and publication year. Sex, study geography, study design and geographical location were not associated with differences in polypharmacy prevalence. Discussion: Our review highlights that polypharmacy is common particularly among older adults and those in inpatient settings. Clinicians should be aware of populations who have an increased likelihood of experiencing polypharmacy and efforts should be made to review the appropriateness of prescribed medications and occurrence of adverse effects potentially associated with polypharmacy. Conclusions and implications: Clinicians should be aware of the common occurrence of polypharmacy and undertake efforts to minimize inappropriate polypharmacy whenever possible.

Background: With rural population aging there are growing numbers of people with dementia in rural and remote settings. The role of primary health care (PHC) is critical in rural locations, yet there is a lack of rural-specific PHC models for dementia, and little is known about factors influencing the development, implementation, and sustainability of rural PHC interventions. Using a community-based participatory research approach, researchers collaborated with a rural PHC team to co-design and implement an evidence-based interdisciplinary rural PHC memory clinic in the Canadian province of Saskatchewan. This paper reports barriers and facilitators to developing, implementing, and sustaining the intervention.Methods: A qualitative longitudinal process evaluation was conducted over two and half years, from pre- to post-implementation. Data collection and analyses were guided by the Consolidated Framework for Implementation Research (CFIR) which consists of 38 constructs within five domains: innovation characteristics, outer setting, inner setting, individual characteristics, and process. Data were collected via focus groups with the PHC team and stakeholders, smaller team workgroup meetings, and team member interviews. Analysis was conducted using a deductive approach to apply CFIR codes to the data and an inductive analysis to identify barriers and facilitators.Results: Across all domains, 14 constructs influenced development and implementation. Three domains (innovation characteristics, inner setting, process) were most important. Facilitators were the relative advantage of the intervention, ability to trial on a small scale, tension for change, leadership engagement, availability of resources, education and support from researchers, increased self-efficacy, and engagement of champions. Barriers included the complexity of multiple intervention components, required practice changes, lack of formal incentive programs, time intensiveness of modifying the EMR during iterative development, lack of EMR access by all team members, lack of co-location of team members, workload and busy clinical schedules, inability to justify a designated dementia care manager role, and turnover of PHC team members.Conclusions: The study identified key factors that supported and hindered the development and implementation of a rural-specific strategy for dementia assessment and management in PHC. Despite challenges related to the rural context, the researcher-academic partnership was successful in developing and implementing the intervention.

This paper describes a pluralistic framework to inform transformative change across community and healthcare domains to optimize the mental health of older adults in support of healthy ageing. An extensive review and analysis of the literature informed the creation of a framework that contextualizes the priority areas of the WHO Decade of Health Ageing (ageism, age-friendly environments, long-term care, and integrated care) with respect to older adult mental health. The framework additionally identifies barriers, facilitators, and strategies for action at macro (social/system), meso (services/supports), and micro (older adults) levels of influence. This conceptual (analytical) framework is intended as a tool to inform planning and decision-making across policy, practice, education and training, research, and knowledge mobilization arenas. The framework described in this paper can be used by countries around the globe to build evidence, set priorities, and scale up promising practices (both nationally and sub-nationally) to optimize the mental health and healthy ageing trajectories of older adults as a population.

Introduction: Electronic health records (EHR) and administrative healthcare data (AHD) are frequently used in geriatric mental health research to answer various health research questions. However, there is an increasing amount and complexity of data available that may lend itself to alternative analytic approaches using machine learning (ML) or artificial intelligence (AI) methods. We performed a systematic review of the current application of ML or AI approaches to the analysis of EHR and AHD in geriatric mental health.Methods: We searched MEDLINE, Embase, and PsycINFO to identify potential studies. We included all articles that used ML or AI methods on topics related to geriatric mental health utilizing EHR or AHD data. We assessed study quality either by Prediction model Risk OF Bias ASsessment Tool (PROBAST) or Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist.Results: We initially identified 391 articles through an electronic database and reference search, and 21 articles met inclusion criteria. Among the selected studies, EHR was the most used data type, and the datasets were mainly structured. A variety of ML and AI methods were used, with prediction or classification being the main application of ML or AI with the random forest as the most common ML technique. Dementia was the most common mental health condition observed. The relative advantages of ML or AI techniques compared to biostatistical methods were generally not assessed. Only in three studies, low risk of bias (ROB) was observed according to all the PROBAST domains but in none according to QUADAS-2 domains. The quality of study reporting could be further improved.Conclusion: There are currently relatively few studies using ML and AI in geriatric mental health research using EHR and AHD methods, although this field is expanding. Aside from dementia, there are few studies of other geriatric mental health conditions. The lack of consistent information in the selected studies precludes precise comparisons between them. Improving the quality of reporting of ML and AI work in the future would help improve research in the field. Other courses of improvement include using common data models to collect/organize data, and common datasets for ML model validation.