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Protocol for evaluation of enhanced models of primary care in the management of stroke and other chronic disease (PRECISE): a data linkage healthcare evaluation study


Andrew, NE and Kim, J and Cadilhac, DA and Sundararajan, V and Thrift, AG and Churilov, L and Lannin, NA and Nelson, MR and Srikanth, V and Kilkenny, MF, Protocol for evaluation of enhanced models of primary care in the management of stroke and other chronic disease (PRECISE): a data linkage healthcare evaluation study, International Journal of Population Data Science, 4, (1) pp. 1-14. ISSN 2399-4908 (2019) [Refereed Article]

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DOI: doi:10.23889/ijpds.v4i1.1097


Introduction: The growing burden of chronic diseases means some governments have been providing financial incentives for multidisciplinary care and self-management support delivered within primary care. Currently, population-based evaluations of the effectiveness of these policies are lacking.

Aim: To outline the methodological approach for our study that is designed to evaluate the effectiveness (including cost) of primary care policies for chronic diseases in Australia using stroke as a case study.

Methods: Person-level linkages will be undertaken between registrants from the Australian Stroke Clinical Registry (AuSCR) and (i) Government-held Medicare Australia claims data, to identify receipt or not of chronic disease management and care coordination primary care items; (ii) state government-held hospital data, to define outcomes; and (iii) government-held pharmaceutical and aged care claims data, to define covariates. N=1500 randomly selected AuSCR registrants will be sent surveys to obtain patient experience information. In Australia, unique identifiers are unavailable. Therefore, personal-identifiers will be submitted to government data linkage units. Researchers will merge the de-identified datasets for analysis using a project identifier. An economic evaluation will also be undertaken.

Analysis: The index event will be the first stroke recorded in the AuSCR. Multivariable competing risks Poisson regression for multiple events, adjusted by a propensity score, will be used to test for differences in the rates of hospital presentations and medication adherence for different care (policy) types. Our estimated sample size of 25,000 patients will provide 80% estimated power (α >0.05) to detect a 6-8% difference in rates. The incremental costs per Quality-adjusted life years gained of community-based care following the acute event will be estimated from a health sector perspective.

Conclusion: Completion of this study will provide a novel and comprehensive evaluation of the effectiveness and cost-effectiveness of Australian primary care policies. Its success will enable us to highlight the value of data-linkage for this type of research.

Item Details

Item Type:Refereed Article
Research Division:Health Sciences
Research Group:Public health
Research Field:Community child health
Objective Division:Health
Objective Group:Public health (excl. specific population health)
Objective Field:Health status (incl. wellbeing)
UTAS Author:Nelson, MR (Professor Mark Nelson)
ID Code:150227
Year Published:2019
Deposited By:Medicine
Deposited On:2022-06-03
Last Modified:2022-06-20

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