01/09/2023: 5:30 PM - 6:30 PM MST
Posters
Background:
Network analyses describe complex patterns through graphical displays and may help detect novel associations using empirical data. Network science was first used to analyze social relationships, and is increasingly used to examine patterns of co-occurring health conditions or health service use. Networks are particularly useful for populations with complex health status or healthcare needs. Characterizing health condition co-occurrence networks by the number and strengths of connections (i.e., relationships) can aid in better understanding health needs among populations.
Dementia is an incurable neurodegenerative condition that impacts an individual's cognition, independence, and life-expectancy. Persons with dementia (PWD) residing in the community have different access to health resources than PWD in long-term care (LTC). Multimorbidity, the co-occurrence of multiple chronic health conditions, is common among PWD. As a consequence of multimorbidity, many PWD are cared for by multiple healthcare professionals. Understanding profiles of chronic health conditions will help identify areas where coordination between healthcare services is necessary.
Purpose and Objectives:
To compare patterns of multimorbidity among PWD residing in long-term care (LTC) and the community in the province of Manitoba, Canada, using network analysis. Demographic differences in multimorbidity patterns will be evaluated by stratifying networks by sex, age (67-74, ≥75 years).
Methods:
This study will use population-based administrative databases from the Manitoba Population Research Data Repository, including outpatient claims, inpatient records, community-based pharmaceutical dispensations, and LTC records. These data were extensively validated to investigate chronic health conditions and were used to generate multimorbidity networks. This retrospective cohort will consist of PWD ≥67 years with ≥2 other chronic health conditions residing in Manitoba from 2015 to 2020 in LTC and community settings. LTC in Manitoba are regulated residences where healthcare workers provide 24-hour care and are not used for sub-acute care or rehabilitation. Chronic conditions will be identified using the open-source Clinical Classification System, which encompasses 130 clinically relevant chronic condition categories.
Non-directional networks, consisting of nodes (chronic health conditions among PWD) connected by edges (cosine index, a metric that quantifies the strength of association between pairs of health conditions), will be generated based on residence location (LTC, community). Only statistically significant associations (edges) will be included in networks; determined by Pearson chi-square controlling for false discovery rate. Networks will be portrayed through graphical displays. Networks' properties will be described, including the number and distribution of: node, edge, and node degrees (i.e., associations with other diseases for a given condition), will be described. The Louvain community detection algorithm will be used to identify clusters of closely associated health conditions. Modularity (a measure of the extent to which a network divides into clusters) will be calculated.
Significance and Impact:
Our study will use unique visual and inferential techniques to describe the complexity of multimorbidity among PWD in LTC and the community. Characterizing profiles of chronic conditions is an important step towards developing policies or improving services aimed to provide high quality care for PWD. Health services that frequently interact due to co-occurring chronic conditions can be improved to avoid fragmentation and engage collaboratively in patient-centred decision making. Cross-specialty collaborative care models and multidisciplinary teams can be designed to address co-occurring conditions. Community and LTC programs can more efficiently allocate resources to meet intricate health needs.
network analyses
dementia
multimorbidity
long-term care
nursing home
health service utilization
Presenting Author
Samuel Quan
First Author
Samuel Quan
CoAuthor(s)
Barret Monchka, University of Manitoba
Phil St. John, University of Manitoba
Malcolm Doupe, University of Manitoba
Max Turgeon, University of Manitoba
Lisa Lix, University of Manitoba
Target Audience
Mid-Level
Tracks
Knowledge
International Conference on Health Policy Statistics 2023