In British Columbia (BC) Home and Community Care (HCC) services provide a range of health care and support services for British Columbians with acute, chronic, palliative or rehabilitative health care needs. Although it is not surprising that almost 80% of HCC clients are senior citizens (aged 65 and older), this does lead to some concern. In particular, the most recent population projections suggest that the senior population of BC will double in the next twenty years. Predicting how this will impact HCC is of high importance in preparing for future years. In this paper we discuss the development of a deterministic multistate Markov model of the HCC system, its validation, and its predictions for future client counts for various HCC client groupings. This model was originally developed for the BC Ministry of Health Services, and is currently being used as a first step to developing a strategic direction plan for BC's HCC sector. The model makes several notable steps forward in terms of research and modelling of HCC. First, past literature regarding models of HCC appears to be only concerned with publicly funded (government run) residential care environments. Our model advances this in two directions by including at home care and non-publicly funded care. Second, our model considers both the predicted changes in the age demographics of British Columbia, as well as the predicted changes in the relationship between age and health status.

Realizing the full individual and population-wide benefits of antiretroviral therapy for human immunodeficiency virus (HIV) infection requires an efficient mechanism of HIV-related health service delivery. We developed a system dynamics model of the continuum of HIV care in Vancouver, Canada, which reflects key activities and decisions in the delivery of antiretroviral therapy, including HIV testing, linkage to care, and long-term retention in care and treatment. To measure the influence of operational interventions on population health outcomes, we incorporated an HIV transmission component into the model. We determined optimal resource allocations among targeted and routine testing programs to minimize new HIV infections over five years in Vancouver. Simulation scenarios assumed various constraints informed by the local health policy. The project was conducted in close collaboration with the local health care providers, Vancouver Coastal Health Authority and Providence Health Care.

BackgroundThe structure of a social network as well as peer behaviours are thought to affect personal substance use. Where substance use may create health risks, understanding the contribution of social networks to substance use may be valuable for the design and implementation of harm reduction or other interventions. We examined the social support network of people living in precarious housing in a socially marginalized neighborhood of Vancouver, and analysed associations between social network structure, personal substance use, and supporters’ substance use.MethodsAn ongoing, longitudinal study recruited 246 participants from four single room occupancy hotels, with 201 providing social network information aligned with a 6-month observation period. Use of tobacco, alcohol, cannabis, cocaine (crack and powder), methamphetamine, and heroin was recorded at monthly visits. Ego- and graph-level measures were calculated; the dispersion and prevalence of substances in the network was described. Logistic mixed effects models were used to estimate the association between ego substance use and peer substance use. Permutation analysis was done to test for randomness of substance use dispersion on the social network.ResultsThe network topology corresponded to residence (Hotel) with two clusters differing in demographic characteristics (Cluster 1 –Hotel A: 94% of members, Cluster 2 –Hotel B: 95% of members). Dispersion of substance use across the network demonstrated differences according to network topology and specific substance. Methamphetamine use (overall 12%) was almost entirely limited to Cluster 1, and absent from Cluster 2. Different patterns were observed for other substances. Overall, ego substance use did not differ over the six-month period of observation. Ego heroin, cannabis, or crack cocaine use was associated with alter use of the same substances. Ego methamphetamine, powder cocaine, or alcohol use was not associated with alter use, with the exception for methamphetamine in a densely using part of the network. For alters using multiple substances, cannabis use was associated with lower ego heroin use, and lower ego crack cocaine use. Permutation analysis also provided evidence that dispersion of substance use, and the association between ego and alter use was not random for all substances.ConclusionsIn a socially marginalized neighborhood, social network topology was strongly influenced by residence, and in turn was associated with type(s) of substance use. Associations between personal use and supporter’s use of a substance differed across substances. These complex associations may merit consideration in the design of interventions to reduce risk and harms associated with substance use in people living in precarious housing.

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