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Health economic evaluation of childhood hearing screening using real-world data

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posted on 2022-07-28, 01:49 authored by Rajan Sharma


The market for healthcare is imperfect. This results in increased governmental intervention in the healthcare market compared to other markets. Many governments use economic evaluation to make decisions regarding funding healthcare interventions since there are competing demands for scarce resources. Data for economic evaluations can be drawn from randomised controlled trials (RCTs) or real-world studies. RCTs are considered to have high internal validity, but their generalisability and feasibility from an economic perspective are often limited. For example, they may have a short follow-up period and small sample size. Furthermore, randomisation may not always be possible with interventions involving children or when universally implemented. Real-world data could provide an alternative solution, particularly in terms of long term outcomes, resource use and transition probabilities and its strength in external validity. However, using real-world data in economic evaluations is not without challenges. The objective of this thesis is to demonstrate the value of real-world data to address methodological challenges related to screening programs, using Australia’s Universal Newborn Hearing Screening Program (UNHS) as a case study. 

While UNHS has been widely accepted and implemented, its value for money is not clear. Incorporation of long-term costs and outcomes in economic evaluations of UNHS has not been previously undertaken. The value and challenges of using real-world data in economic evaluations, especially in screening programs, such as the UNHS, are also less understood.

This thesis identifies methodological limitations in the existing economic evaluations. It shows that addressing these limitations is not a single-step approach. Thus multiple steps, including a mapping exercise and regression analysis, are adopted to address the lack of utility data based on hearing loss characteristics. Two economic evaluations are conducted – first to compare the cost-effectiveness of UNHS with targeted screening, and second to compare the costeffectiveness of UNHS with no screening. Both evaluations address several methodological limitations identified earlier by incorporating long-term costs and outcomes, probabilities related to transition and remission based on severity levels, and probabilities related to diagnosis and treatments, and sensitivity analyses. 

The analysis undertaken in this thesis demonstrates that economic evaluation using real-world data may be valuable to inform decision makers regarding the cost-effectiveness of a program. VI Additionally, it also demonstrates the challenges of using real-world data in economic modelling. 


Table of Contents

Chapter 1. Introduction -- Chapter 2. Economic evaluations of childhood hearing loss screening programmes: a systematic review and critique -- Chapter 3. Mapping the strengths and difficulties questionnaire onto the Child Health Utility 9D in a large study of children -- Chapter 4. Utilities among children with hearing loss based on the comparative assessment of mapped CHU9D and observed HUI3 utilities -- Chapter 5. An economic evaluation of Australia’s newborn hearing screening program: a within-study cost-effectiveness analysis -- Chapter 6. Evaluating a universal newborn hearing screening program using real-world data: tackling several methodological challenges -- Chapter 7. Discussion and conclusion


A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Macquarie University Centre for the Health Economy (MUCHE) Thesis by publication

Awarding Institution

Macquarie University

Degree Type

Thesis PhD


Thesis (PhD), Macquarie Business School, Department of Economics, 2020

Department, Centre or School

Department of Economics

Year of Award


Principal Supervisor

Bonny Parkinson

Additional Supervisor 1

Yuanyuan Gu

Additional Supervisor 2

Kompal Sinha


Copyright disclaimer: Copyright Rajan Sharma 2020.



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