The COVID-19 Register combines COVID-19 case data with a range of other data sets, including hospitals and deaths data.
This linked data set can be used to look at the effects of COVID-19 on health outcomes and health service usage.
Researchers can utilise the data set to explore issues such as re-infection rates and the effect of vaccines and treatments.
The COVID-19 (coronavirus) pandemic has led to substantial individual, health system, and broader social and economic effects that will continue to evolve. The pandemic has also demonstrated the need for better national health data infrastructure, such as disease registers, to support evidence- based public health policy decisions.
The AIHW analyses and publishes evidence of the medium-term and long-term effects of many health conditions and diseases, including COVID-19.
In April 2022, the AIHW was funded by the Medical Research Future Fund to establish a national linked data platform using existing health data sets to strengthen evidence-based public health and health system planning and management for current and future pandemics.
Linking COVID-19-related data sets provides new insights into the health outcomes for people who have been diagnosed with the disease, and the effect that COVID-19 has had on the health system and broader community. It also offers researchers the opportunity to explore a range of issues associated with the pandemic.
What is data linkage?
Data linkage is the process of identifying, matching, and merging records that correspond to the same person or entity from several datasets or even within one dataset, to create a new combined data set. The results of the linkage can provide a richer story than would be possible from a single data source. This improves data completeness and provides a rich person-level source of information beyond that available through routine disease surveillance and single data sources. The AIHW uses robust methods to carry out data linkage, and more information can be found at AIHW’s data linkage services.
The COVID-19 Register will enable us to provide a fuller picture of the relationship between COVID-19 and risk factors, the best approaches to prevention or early intervention, and the effectiveness of health and safety interventions. This will give a more holistic picture of the connections between COVID-19 and broader medical and social wellbeing.
Utility of the COVID-19 Register
The COVID-19 Register will provide many benefits, including:
- The public will benefit as the data can be used to identify risk factors for severity, long term effects and re-infection. This can then inform service planning and guidelines for the treatment and management of COVID-19 and improve immediate, medium, and longer-term health outcomes for people who have had a COVID-19 diagnosis.
- The public will also benefit from the publication of research findings through fact sheets and reports that will make robust, accessible information available in the public domain.
- Health service providers are able to gain a better understanding of the service and treatment needs of people who have been diagnosed with COVID-19, and particularly those who develop long term effects of COVID-19 or have had a COVID-19 vaccine, as well as interactions with pre-existing comorbidities.
- Health services providers can understand patterns of service use and medication dispensing by people who have tested positive to COVID-19.
- The research community can benefit from the contribution of knowledge the project will make by filling existing research gaps.
- Existing surveillance systems are being improved through return of linked data at a national level and therefore improving national surveillance reporting.
- The data enables researchers to monitor and evaluate policies and programs implemented throughout pandemics.
- The platform provides a foundation for a wide range of future research purposes without these parties having to start from scratch.
- The data helps Commonwealth, state and territory governments plan and manage health resources, for example in aged care facilities.
What information is included?
Federal, state and local government agencies, as well as health and welfare service providers, hold an enormous amount of administrative and clinical data.
Information on COVID-19 cases are sourced from participating states and territories as well as the Australian Government’s National Notifiable Diseases Surveillance System, and a number of other administrative data sources.
The sources that are currently included are:
State/territory notifiable diseases data: COVID-19 cases
Medicare Benefits Schedule (MBS)
Medicare Consumer Directory (MCD)
Pharmaceutical Benefits Scheme (PBS)
National Notifiable Diseases Surveillance System (NNDSS)
National Hospital Morbidity Database: admitted patient care data (NHMD)
National Death Index (NDI)
Australian Immunisation Register (AIR)
National Aged Care Data Clearinghouse: aged care data (NACDC)
National Non-Admitted Patient Emergency Department Care Database: emergency department presentations (NNAPEDCD)
Australian and New Zealand Intensive Care Society (ANZICS)
The register will soon include the following additional dataset:
National Disability Insurance Scheme (NDIS)
How is your privacy protected?
The AIHW manages this data with respect for its sensitivity, and with privacy and confidentiality assured through legislation, accountability practices and procedures. The AIHW also manages relationships with data custodians to ensure accountability and appropriate use of the relevant data collections.
The AIHW protects the privacy of an individual through a process of de-identification. This involves removing identifying information (for example, a person’s name, address or Medicare number) so that researchers are unable to tell who the information belongs to.
Researchers are only allowed to publish data that has been approved by the data custodian and checked that there is no way a person can be identified.
This project has ethical approval from:
- the AIHW Ethics Committee (EO2021-2-1232)
- the NSW Population and Health Services Research Ethics Committee- as part of the National Mutual Acceptance Scheme (2021_ETH00412) and
- the Northern Territory Department of Health and the Menzies School of Health Research (2021-4106).
The AIHW uses the Five Safes Framework to reinforce management of the privacy and confidentiality of data. Five Safes is an approach to assessing and managing risks associated with data sharing and release. Under the framework, the risk of re-identification is minimised, particularly as data are supplied to researchers in secure access environments where outputs are checked by the AIHW.
How can the data be accessed?
All users who want to access the de-identified research data will be required to submit to AIHW a project proposal including a data analysis plan and a signed Australian Institute of Health and Welfare Act 1987 s29 Undertaking of Confidentiality form. This form protects the privacy of individuals by making it a criminal offence to disclose information about the participants of a study, punishable by fines and/or imprisonment. Data will not be provided to, accessed, or used by another, unauthorised party. Access is strictly controlled within a secure remote access environment, with no access allowed to other project workspaces.
Due to the detailed and sensitive nature of the data, access will only be provided via secure research environments where AIHW can apply appropriate vetting and management processes in line with AIHW’s Five Safes Framework.
Using the data for research
Researchers who wish to use the data will need to ensure their research question/s falls under the approved uses. Examples of approved broad areas of research are described in Table 1.
Researchers will also need to complete a project proposal form, which will be reviewed by the AIHW custodian and approved by the relevant data custodians.
For more information, contact [email protected].
|Epidemiological and statistical research
|Service use and medication dispensing and patient journeys
|Identifying groups or cohorts of interest
|Monitoring, evaluation and data quality improvement