Access to routinely collected data for population health research: experiences in Canada and Australia

Researchers' perceptions of the trustworthiness, for reuse purposes, of government health data in Victoria, Australia: Implications for policy and practice

In 2022 the Australian Data Availability and Transparency Act (DATA) commenced, enabling accredited "data users" to access data from "accredited data service providers." However, the DATA Scheme lacks guidance on "trustworthiness" of the data to be utilised for reuse purposes. Objectives: To determine: (i) Do researchers using government health datasets trust the data? (ii) What factors influence their perceptions of data trustworthiness? and (iii) What are the implications for government and data custodians? Method: Authors of published studies (2008-2020) that utilised Victorian government health datasets were surveyed via a case study approach. Twenty-eight trust constructs (identified via literature review) were grouped into data factors, management properties and provider factors. Results: Fifty experienced health researchers responded. Most (88%) believed that Victorian government health data were trustworthy. When grouped, data factors and management properties were more important than data provider factors in building trust. The most important individual trust constructs were: "compliant with ethical regulation" (100%) and "monitoring privacy and confidentiality" (98%). Constructs of least importance were knowledge of "participant consent" (56%) and "major focus of the data provider was research" (50%). Conclusion: Overall, the researchers trusted government health data, but data factors and data management properties were more important than data provider factors in building trust. Implications: Government should ensure the DATA Scheme incorporates mechanisms to validate those data utilised by accredited data users and data providers have sufficient quality (intrinsic and extrinsic) to meet the requirements of "trustworthiness," and that evidentiary documentation is provided to support these "accredited data."

A pilot study examining the impact of a pragmatic process for improving the cultural responsiveness of non-Aboriginal alcohol and other drug treatment services using routinely collected data in Australia

Objective: Routine health data has the potential to identify changes in patient-related outcomes, in close to real time. This pilot project used routine data to explore and compare the impact of changes to cultural responsiveness on service use by Aboriginal and Torres Strait Islander (hereafter, Aboriginal) clients in Australia.Methods: The New South Wales Minimum Data Set (MDS) for alcohol and other drug use treatment services was provided for 11 services for a period of 30 months from March 2019 to September 2021 (four months prior to two years after the intervention; data were analysed between March 2022 to February 2023). Change in cultural responsiveness was assessed via practice audits of services at baseline and two years. The average change in audit rating was analysed using a linear mixed regression model. Generalised Linear Mixed Models were used to identify changes in service use by Aboriginal clients. Results: All 11 services showed increased audit scores at two years, with a statistically significant mean increase of 18.6 (out of 63 points; b = 18.32, 95% CI 12.42-24.22). No statistically significant pre-to post-changes were identified in: (1) the proportion of episodes delivered to Aboriginal versus non-Aboriginal clients (OR = 1.15, 95% CI = 0.94-1.40), (2) the number of episodes of care provided to Aboriginal clients per month (IRR = 1.01, 95% CI = 0.84-1.23), or (3) the proportion of episodes completed by Aboriginal clients (OR = 0.96, 95% CI = 0.82-1.13). Conclusions: The lack of statistically significant impact on service use outcomes using MDS contrasts to the improvements in cultural responsiveness, suggesting further work is needed to identify appropriate outcome measures. This may include patient-reported experience measures. This project showed that routine data has potential as an efficient method for measuring changes in patient-related outcomes in response to health services improvements.

ution of the data and methods in real-world COVID-19 vaccine effectiveness studies on mortality: a oping eview protocol

BACKGROUND

Early evidence on COVID-19 vaccine efficacy came from randomised trials. Many important questions subsequently about vaccine effectiveness (VE) have been addressed using real-world studies (RWS) and have informed most vaccination policies globally. As the questions about VE have evolved during the pandemic so have data, study design, and analytical choices. This scoping review aims to characterise this evolution and provide insights for future pandemic planning-specifically, what kinds of questions are asked at different stages of a pandemic, and what data infrastructure and methods are used?

METHODS AND ANALYSIS

We will identify relevant studies in the Johns Hopkins Bloomberg School of Public Health VIEW-hub database, which curates both published and preprint VE RWS identified from PubMed, Embase, Scopus, Web of Science, the WHO COVID Database, MMWR, Eurosurveillance, medRxiv, bioRxiv, SSRN, Europe PMC, Research Square, Knowledge Hub, and Google. We will include RWS of COVID-19 VE that reported COVID-19-specific or all-cause mortality (coded as 'death' in the 'effectiveness studies' data set).Information on study characteristics; study context; data sources; design and analytic methods that address confounding will be extracted by single reviewer and checked for accuracy and discussed in a small group setting by methodological and analytic experts. A timeline mapping approach will be used to capture the evolution of this body of literature.By describing the evolution of RWS of VE through the COVID-19 pandemic, we will help identify options for VE studies and inform policy makers on the minimal data and analytic infrastructure needed to support rapid RWS of VE in future pandemics and of healthcare strategies more broadly.

ETHICS AND DISSEMINATION

As data is in the public domain, ethical approval is not required. Findings of this study will be disseminated through peer-reviewed publications, conference presentations, and working-papers to policy makers.

REGISTRATION

https://doi.org/10.17605/OSF.IO/ZHDKR.

Harnessing Real-World Evidence to Advance Cancer Research

Randomized controlled trials (RCTs) form a cornerstone of oncology research by generating evidence about the efficacy of therapies in selected patient populations. However, their implementation is often resource- and cost-intensive, and their generalisability to patients treated in routine practice may be limited. Real-world evidence leverages data collected about patients receiving clinical care in routine practice outside of clinical trial settings and provides opportunities to identify and address gaps in clinical trial evidence. This review outlines the strengths and limitations of real-world and RCT evidence and proposes a framework for the complementary use of the two bodies of evidence to advance cancer research. There are challenges to the implementation of real-world research in oncology, including heterogeneity of data sources, timely access to high-quality data, and concerns about the quality of methods leveraging real-world data, particularly causal inference. Improved understanding of the strengths and limitations of real-world data and ongoing efforts to optimise the conduct of real-world evidence research will improve its reliability, understanding and acceptance, and enable the full potential of real-world evidence to be realised in oncology practice.

Nation-Wide Routinely Collected Health Datasets in China: A Scoping Review

Objectives: The potential for using routinely collected data for medical research in China remains unclear. We sought to conduct a scoping review to systematically characterise nation-wide routinely collected datasets in China that may be of value for clinical research.

Methods: We searched public databases and the websites of government agencies, and non-government organizations. We included nation-wide routinely collected databases related to communicable diseases, non-communicable diseases, injuries, and maternal and child health. Database characteristics, including disease area, data custodianship, data volume, frequency of update and accessibility were extracted and summarised.

Results: There were 70 databases identified, of which 46 related to communicable diseases, 20 to non-communicable diseases, 1 to injury and 3 to maternal and child health. The data volume varied from below 1000 to over 100,000 records. Over half (64%) of the databases were accessible for medical research mostly comprising communicable diseases.

Conclusion: There are large quantities of routinely collected data in China. Challenges to using such data in medical research remain with various accessibility. The potential of routinely collected data may also be applicable to other low- and middle-income countries.

The Medicines Intelligence Centre of Research Excellence: Co-creating real-world evidence to support the evidentiary needs of Australian medicines regulators and payers

Regulators and payers play a pivotal role in facilitating timely and affordable access to safe and efficacious medicines. They use evidence generated from randomised clinical trials (RCTs) to support decisions to register and subsidise medicines. However, at the time of registration and subsidy approval, regulators and payers face uncertainty about how RCT outcomes will translate to real-world clinical practice. In response to this situation, medicines policy agencies worldwide have endorsed the use of real-world data (RWD) to derive novel insights on the use and outcomes of prescribed medicines. Recent reforms around data availability and use in Australia are creating unparalleled data access and opportunities for Australian researchers to undertake large-scale research to generate evidence on the safety and effectiveness of medicines in the real world. Highlighting the critical importance of research in this area, Quality Use of Medicines and Medicine Safety was announced as Australia's 10th National Health Priority in 2019. The National Health and Medical Research Council, Medicines Intelligence Centre of Research Excellence (MI-CRE) has been formed to take advantage of the renewed focus on quality use of medicines and the changing data landscape in Australia. It will generate timely research supporting the evidentiary needs of Australian medicines regulators and payers by accelerating the development and translation of real-world evidence on medicines use and outcomes. MI-CRE is developing a coordinated approach to identify, triage and respond to priority questions where there are significant uncertainties about medicines use, (cost)-effectiveness, and/or safety and creating a data ecosystem that will streamline access to Australian data to enable researchers to generate robust evidence in a timely manner. This paper outlines how MI-CRE will partner with policy makers, clinicians, and consumer advocates to leverage real-world data to co-create real-world evidence, to improve quality use of medicines and reduce medicine-related harm.

Generating Real-World Evidence on the Quality Use, Benefits and Safety of Medicines in Australia: History, Challenges and a Roadmap for the Future

Australia spends more than $20 billion annually on medicines, delivering significant health benefits for the population. However, inappropriate prescribing and medicine use also result in harm to individuals and populations, and waste of precious health resources. Medication data linked with other routine collections enable evidence generation in pharmacoepidemiology; the science of quantifying the use, effectiveness and safety of medicines in real-world clinical practice. This review details the history of medicines policy and data access in Australia, the strengths of existing data sources, and the infrastructure and governance enabling and impeding evidence generation in the field. Currently, substantial gaps persist with respect to cohesive, contemporary linked data sources supporting quality use of medicines, effectiveness and safety research; exemplified by Australia's limited capacity to contribute to the global effort in real-world studies of vaccine and disease-modifying treatments for COVID-19. We propose a roadmap to bolster the discipline, and population health more broadly, underpinned by a distinct capability governing and streamlining access to linked data assets for accredited researchers. Robust real-world evidence generation requires current data roadblocks to be remedied as a matter of urgency to deliver efficient and equitable health care and improve the health and well-being of all Australians.

Is there an agreement between self-reported medical diagnosis in the CARTaGENE cohort and the Québec administrative health databases?

BACKGROUND

Population health studies often use existing databases that are not necessarily constituted for research purposes. The question arises as to whether different data sources such as in administrative health data (AHD) and self-report questionnaires are equivalent and lead to similar information.

OBJECTIVES

The main objective of this study was to assess the level of agreement between self-reported medical conditions and medical diagnosis captured in AHD. A secondary objective was to identify predictors of agreement among medical conditions between the two data sources. Therefore, the purposes of the study were to explore the extent to which these two methods of commonly used public health data collection provide concordant records and identify the main predictors of statistical variations.

METHODS

Data were extracted from CARTaGENE, a population-based cohort in Québec, Canada, which was linked to the provincial health insurance records of the same individuals, namely the MED-ÉCHO database from the Régie de l'assurance maladie du Québec (RAMQ) and the fee-for-service billing records provided by the physician, for the time period 1998-2012. Agreement statistics (kappa coefficient) along with sensitivity, specificity and predictive positive value were calculated for 19 chronic conditions and 12 types of cancers. Logistic regressions were used to identify predictors of concordance between self-report and AHD from significant covariates (sex, age groups, education, region, income, heavy utilization of health care system and Charlson comorbidity index).

RESULTS

Agreement between self-reported data and AHD across diseases ranged from kappa of 0.09 for chronic renal failure to 0.86 for type 2 diabetes. Sensitivity of self-reported data was higher than 50% for 14 out of the 31 medical conditions studied, especially for myocardial infarction (88.62%), breast cancer (86.28%), and diabetes (85.06%). Specificity was generally high with a minimum value of 89.70%. Lower concordance between data sources was observed for higher frequency of health care utilization and higher comorbidity scores.

CONCLUSION

Overall, there was moderate agreement between the two data sources but important variations were found depending on the type of disease. This suggests that CARTaGENE's participants were generally able to correctly identify the kind of diseases they suffer from, with some exceptions. These results may help researchers choose adequate data sources according to specific study objectives. These results also suggest that Québec's AHD seem to underestimate the prevalence of some chronic conditions, which might result in inaccurate estimates of morbidity with consequences for public health surveillance.

Personal health information in research: Perceived risk, trustworthiness and opinions from patients attending a tertiary healthcare facility

BACKGROUND

Personal health information is a valuable resource to the advancement of research. In order to achieve a comprehensive reform of data infrastructure in Australia, both public engagement and building social trust is vital. In light of this, we conducted a study to explore the opinions, perceived risks and trustworthiness regarding the use of personal health information for research, in a sample of the public attending a tertiary healthcare facility.

METHODS

The Consumer Opinions of Research Data Sharing (CORDS) study was a questionnaire-based design with 249 participants who were attending a public tertiary healthcare facility located on the Gold Coast, Australia. The questionnaire was designed to explore opinions and evaluate trust and perceived risk in research that uses personal health information. Concept analysis was used to identify key dimensions of perceived risk.

RESULTS

Overall participants were supportive of research, highly likely to participate and mostly willing to share their personal health information. However, where the perceived risk of data misuse was high and trust in others was low, participants expressed hesitation to share particular types of information. Performance, physical and privacy risks were identified as key dimensions of perceived risk.

CONCLUSION

This study highlights that while participant views on the use of personal health information in research is mostly positive, where there is perceived risk in an environment of low trust, support for research decreases. The three key findings of this research are that willingness to share data is contingent upon: (i) data type; (ii) risk perception; and (iii) trust in who is accessing the data. Understanding which factors play a key role in a person's decision to share their personal health information for research is vital to securing a social license.