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

Research output: Contribution to journalArticleOtherpeer-review

Abstract

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.
Original languageEnglish
Article number18
Number of pages14
JournalInternational Journal of Population Data Science
Volume4
Issue number1
DOIs
Publication statusPublished - 5 Aug 2019

Cite this

@article{82e00419487e4c2290b4073875d2f2bd,
title = "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",
abstract = "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.",
author = "Nadine Andrew and Joosup Kim and Dominique Cadilhac and Vijaya Sundararajan and Amanda Thrift and Leonid Churilov and Lannin, {Natasha A.} and Velandai Srikanth and Monique Kilkenny",
year = "2019",
month = "8",
day = "5",
doi = "10.23889/ijpds.v4i1.1097",
language = "English",
volume = "4",
journal = "International Journal of Population Data Science",
issn = "2399-4908",
publisher = "Swansea University",
number = "1",

}

TY - JOUR

T1 - Protocol for evaluation of enhanced models of primary care in the management of stroke and other chronic disease (PRECISE)

T2 - A data linkage healthcare evaluation study

AU - Andrew, Nadine

AU - Kim, Joosup

AU - Cadilhac, Dominique

AU - Sundararajan, Vijaya

AU - Thrift, Amanda

AU - Churilov, Leonid

AU - Lannin, Natasha A.

AU - Srikanth, Velandai

AU - Kilkenny, Monique

PY - 2019/8/5

Y1 - 2019/8/5

N2 - 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.

AB - 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.

U2 - 10.23889/ijpds.v4i1.1097

DO - 10.23889/ijpds.v4i1.1097

M3 - Article

VL - 4

JO - International Journal of Population Data Science

JF - International Journal of Population Data Science

SN - 2399-4908

IS - 1

M1 - 18

ER -