Challenges and opportunities for using population health data to investigate cancer survivors’ quality of life in Australia

Abstract

There is a recognised need for reported national data that inform health policy, health professions, and consumers about the wellbeing of Australians with cancer and other chronic conditions. International initiatives have demonstrated the viability and benefits of utilising population-based cancer registries to monitor the prevalence and trajectory of health-related quality of life (HRQOL) outcomes among people with cancer. Establishing a similar level of monitoring in Australia would require timely access to health data collected by publicly funded, population-based cancer registries, and the capacity to link this information across jurisdictions. Combining information from different sources via data linkage is an efficient and cost-effective way to maximise how data are used to inform population health and policy development. However, linking health datasets has historically been highly restricted, resource-intensive, and costly in Australia due to complex and outdated legislative requirements, duplicative approval processes, and differing policy frameworks in each state and territory. This has resulted in significant research waste due to underutilisation of existing data, duplication of research efforts and resources, and data not being translated into decision-making. Recognising these challenges, from 2015 to 2017 the Productivity Commission investigated options for improving data availability and use in Australia, considering factors such as privacy, security, and intellectual property. The inquiry report recommended significant reforms for Australian legislation, including the creation of a data sharing and release structure to improve access to data for research and policy development purposes. This paper discusses (1) opportunities in HRQOL research enabled by data linkage, (2) barriers to data access and use in Australia and the implications for waste in HRQOL research, and (3) proposed legislative reforms for improving data availability and use in Australia.

Introduction to the Special Section Reducing Research Waste in (Health-Related) Quality of Life Research

“…The special section closes with a paper [32] focused on inefficient research regulation and management practices as sources of research waste, and discusses opportunities in (HR)QL/PRO research enabled by data linkage and data registries; barriers to data access and use and the implications for waste in (HR)QL/PRO research; and proposed legislative reforms. The authors argue that rather than investing in (HR)QL/PRO data infrastructure to support multiple studies, there is reliance on collecting data de novo for discrete studies, resulting in research waste that arises "from questions being overlooked or unnecessarily addressed, research being underpowered or done too slowly, and research being too costly" [33].…”

Introduction to the special section "Reducing research waste in (health-related) quality of life research"

“…The special section closes with a paper [33] focused on inefficient research regulation and management practices as sources of research waste, and discusses opportunities in (HR)QL/PRO research enabled by data linkage and data registries; barriers to data access and use and the implications for waste in (HR)QL/PRO research; and proposed legislative reforms. The authors argue that rather than investing in (HR)QL/PRO data infrastructure to support multiple studies, there is reliance on collecting data de novo for discrete studies, resulting in research waste that arises "from questions being overlooked or unnecessarily addressed, research being underpowered or done too slowly, and research being too costly" [34].…”

Improving Clinical Registry Data Quality via Linkage With Survival Data From State-Based Population Registries

“…By matching patient-specific data between two or more established registries with patients in common, DLs may improve the accuracy and completeness of registry data. 8 Previous DL studies have reported the impact data accuracy has on ambulatory care, 9 health outcome reporting, 10 quality-of-life measures, 11 cancer reporting of specific or vulnerable populations, 12 and the development of prospective prognostic models. 13 However, there remains scant literature focused on the impact that improved data accuracy through DL has on survival and other outcome estimates.…”