Improving the estimation of Cost-of-Illness in rheumatoid arthritis

Hsieh, Ping Hsuan (2022) Improving the estimation of Cost-of-Illness in rheumatoid arthritis. PhD thesis, University of Glasgow.

Abstract

Cost-of-illness (COI) studies measure the economic burden of a disease and estimate the maximum amount that could potentially be saved or gained if a disease were to be eradicated. Estimates of the COI can help appropriately target specific problems and policies on a disease in policy agenda setting. COI studies are particularly useful for chronic diseases that impact heavily on health expenditures and productivity loss for the whole society. It is essential for policymakers to know where costs are incurred.

Consequently, appropriate interventions can be implemented and prioritised. Over the past two decades, the accumulation of coexisting long-term conditions within an individual has been confirmed as the best predictor of sustained high costs. It is now an established priority for both research and clinical practice owing to the high prevalence of coexisting diseases among patients, particularly with ageing populations. Because of this shift in how we approach chronic diseases in medical research, it is pertinent that we also think about how this impacts the way we look at COI.

On the other hand, inconsistencies in the designs and methodologies that COI studies are conducted and a lack of transparency in reporting have made interpretation and comparison difficult and have limited the usefulness of results in health decision making. Variations include data sources, perspectives, cost components, and costing approaches. On the other hand, while standardisation of methodology through the implementation of guidelines is becoming increasingly important, some flexibility may be required for diseases or different contexts with unique characteristics to be adequately described.

Rheumatoid arthritis (RA), as one of the most common chronic diseases, is a leading cause of work disability worldwide. Although numerous COI studies have attempted to quantify the economic burden of RA, the cost estimates vary substantially due to different methodological approaches, perspectives and settings. This thesis aims to improve the estimation of COI. To explore the differences in estimating COI, two case studies were developed in diverse contexts: Scotland and Tanzania. Both studies were complementary to each other in terms of different approaches and contexts to estimating COI. The former was in a high-income country, using secondary data analysis from a RA inception cohort linked to routinely collected health records to estimate the COI. In contrast, the latter was in a low- and middle-income country with limited treatment options. Due to the absence of routinely collected health data and the availability of screening tools for RA, a widening criterion of musculoskeletal (MSK) disorders was adopted. A context-specific questionnaire was developed to collect primary data to estimate the COI of MSK in Tanzania.

This thesis confirms the need for improved estimation of COI studies. Good quality COI studies are not easy to do. Current evidence shows a lack of consistency in taking into account indirect costs, resulting in underestimating COI in RA. Moreover, indirect costs need more attention, with improvements in terms of data collection and costing approaches. Health conditions are complex and multi-dimensional, especially when the way we look at them have evolved over time. It is becoming clear that context is also an influencing factor in estimating COI. These complexities need to be considered in COI. While many systematic reviews for COI studies have urged the need to increase comparability, it is more crucial to be transparent in reporting contexts and methodological clarity, including identifying, measuring, and valuing COI.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: R Medicine > R Medicine (General); R Medicine > RA Public aspects of medicine
• Colleges/Schools: College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Health Economics and Health Technology Assessment
• Supervisor's Name: Wu, Professor Olivia, McIntosh, Professor Emma and Gueu, Dr. Claudia
• Date of Award: 2022
• Depositing User: Theses Team
• Unique ID: glathesis:2022-82985
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 17 Jun 2022 12:40
• Last Modified: 17 Jun 2022 12:40
• Thesis DOI: 10.5525/gla.thesis.82985
• URI: https://theses.gla.ac.uk/id/eprint/82985

Related URLs

• Article DOI