A Model-Based Economic Evaluation of Improved Primary Care Management of Patients with Type 2 Diabetes in Australia
Abstract
Background There are few studies investigating the economic value of the Australian practice nurse workforce on the management of chronic conditions. This is particularly important in Australia, where the government needs evidence to inform decisions on whether to maintain or redirect current financial incentives that encourage practices to recruit practice nurses.Objective The objective of this study was to estimate the lifetime costs and quality-adjusted life-years (QALYs) associated with two models of practice nurse involvement in clinical-based activities (high and low level) in the management of type 2 diabetes within the primary care setting.Methods A previously validated state transition model (the United Kingdom Prospective Diabetes Study Outcomes Model) was adapted, which uses baseline prognostic factors (e.g. gender, haemoglobin A1c [HbA1c]) to predict the risk of occurrence of diabetes-related complications (e.g. stroke). The model was populated by data from Australian and UK observational studies. Costs and utility values associated with complications were summed over patients' lifetimes to estimate costs and QALY gains from the perspective of the health care system. All costs were expressed in 2011 Australian dollars (AU$). The base-case analysis assumed a 40-year time horizon with an annual discount rate of 5 %.Results Relative to low-level involvement of practice nurses in the provision of clinical-based activities, the high-level model was associated with lower mean lifetime costs of management of complications (-AU$8,738; 95 % confidence interval [CI] -AU$12,522 to -AU$4,954), and a greater average gain in QALYs (0.3; 95 % CI 0.2-0.4). A range of sensitivity analyses were performed, in which the high-level model was dominant in all cases.Conclusion Our results suggest that the high-level model is a dominant management strategy over the low-level model in all modelled scenarios. These findings indicate the need for effective primary care-based incentives to encourage general practices not only to employ practice nurses, but to better integrate them into the provision of clinical services.
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From Use of health economic evaluation in the implementation and improvement science fields—a systematic literature review (2019) · cites this paper as [33]
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References 37 · Cited by 10
- Authors
- Haji Ali Afzali, H., Gray, J., Beilby, J., Holton, C., & Karnon, J.
- Affiliations
- The University of Adelaide (AU)
- Venue
- Applied Health Economics and Health Policy
- Published
- 2013-11-16
- Volume
- 11
- Issue
- 6
- Pages
- 661-670
- ISSN
- 1175-5652 (ISSN-L)
- 1179-1896
- OpenAlex
- W2057846965
- Cited by
- 10
- References
- 37
- Citations by year
- 2026: 2 · 2024: 1 · 2020: 1 · 2019: 2 · 2016: 1 · 2015: 2 · 2014: 1
- FWCI
- 3.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Applied Health Economics and Health Policy (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Health Sciences › Health Professions › General Health Professions › Nursing Roles and Practices
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- type 2 diabetes, practice nurses, primary care, economic evaluation, quality-adjusted life years, lifetime costs, diabetes complications, state transition model, financial incentives, general practice workforce
- MeSH terms
- Australia, Cost-Benefit Analysis, Diabetes Mellitus, Type 2, Female, Humans, Male, Middle Aged, Primary Health Care, Prognosis, Models, Nursing, Treatment Outcome, Health Care Costs, Models, Economic, Quality-Adjusted Life Years, Diabetes Complications, Propensity Score
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