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The Australian Collaboration for Coordinated Enhanced Sentinel Surveillance (ACCESS) was established to monitor national testing and test outcomes for blood-borne viruses (BBVs) and sexually transmissible infections (STIs) in key populations. ACCESS extracts deidentified data from sentinel health services that include general practice, sexual health, and infectious disease clinics, as well as public and private laboratories that conduct a large volume of BBV/STI testing. An important attribute of ACCESS is the ability to accurately link individual-level records within and between the participating sites, as this enables the system to produce reliable epidemiological measures.
The aim of this study was to evaluate the use of GRHANITE software in ACCESS to extract and link deidentified data from participating clinics and laboratories. GRHANITE generates irreversible hashed linkage keys based on patient-identifying data captured in the patient electronic medical records (EMRs) at the site. The algorithms to produce the data linkage keys use probabilistic linkage principles to account for variability and completeness of the underlying patient identifiers, producing up to four linkage key types per EMR. Errors in the linkage process can arise from imperfect or missing identifiers, impacting the system’s integrity. Therefore, it is important to evaluate the quality of the linkages created and evaluate the outcome of the linkage for ongoing public health surveillance.
Although ACCESS data are deidentified, we created two gold-standard datasets where the true match status could be confirmed in order to compare against record linkage results arising from different approaches of the GRHANITE Linkage Tool. We reported sensitivity, specificity, and positive and negative predictive values where possible and estimated specificity by comparing a history of HIV and hepatitis C antibody results for linked EMRs.
Sensitivity ranged from 96% to 100%, and specificity was 100% when applying the GRHANITE Linkage Tool to a small gold-standard dataset of 3700 clinical medical records. Medical records in this dataset contained a very high level of data completeness by having the name, date of birth, post code, and Medicare number available for use in record linkage. In a larger gold-standard dataset containing 86,538 medical records across clinics and pathology services, with a lower level of data completeness, sensitivity ranged from 94% to 95% and estimated specificity ranged from 91% to 99% in 4 of the 6 different record linkage approaches.
This study’s findings suggest that the GRHANITE Linkage Tool can be used to link deidentified patient records accurately and can be confidently used for public health surveillance in systems such as ACCESS.
The Australian Collaboration for Coordinated Enhanced Sentinel Surveillance (ACCESS) of blood-borne viruses (BBVs) and sexually transmissible infections (STIs) monitors diagnostic testing and other episodes of care for priority BBVs and STIs [
A key challenge for ACCESS (and similar sentinel surveillance systems) is that patient outcomes can be inaccurately measured if individuals attend multiple health services, leading to potential reporting bias. For example, markers of testing frequency, an important indicator for BBV/STI prevention and management [
The linkage of deidentified ACCESS records across sites relies on specialized health data extraction software GRHANITE, which is installed at participating clinics and laboratories. GRHANITE interfaces with patient databases, securely extracting line-listed consultation, demographic, BBV and STI clinical and pathology data [
The GRHANITE Linkage Tool has been validated to perform large-scale population-level record linkage [
Typically, when a patient first attends a medical facility, an EMR is created in the facility’s patient database, containing the patient’s identifying information, including the name, date of birth, contact details, and Medicare number (an Australian government–issued health care card number used for Medicare billing). Most clinics will also have recorded other demographic information, such as preferred language, country of birth, and indigenous background in the EMR.
Every individual’s EMR has a unique medical record number generated by the patient database, linking all of a patient’s consultations, tests, and prescription records. Multiple EMRs may be created for one patient at the same facility if the patient’s details change and are not updated, leading to the creation of a new EMR; if the patient uses an alias; or if the patient attends a clinic that allows anonymous or free testing.
Data were extracted from participating ACCESS clinical sites that included an EMR for every patient available in their databases at the time of extraction. GRHANITE generated a new unique record ID and up to four irreversible hash-coded linkage keys for each EMR. Personal identifying information (eg, name, date of birth, Medicare number) in the patient’s EMR was passed through advanced encryption to generate both record ID and linkage keys [
Data extraction in the Australian Collaboration for Coordinated Enhanced Sentinel Surveillance: using GRHANITE to deidentify electronic medical records and create linkage keys.
The data components used by GRHANITE to create the linkage keys include the following patient identifiers: 5 digits of the Medicare number, date of birth, sex, first name, last name, and residential postcode. However, not all EMRs have the same set of patient identifiers recorded in the same way. For example, a patient name may be recorded as
GRHANITE creates up to four linkage keys for each EMR, using combinations of identifying information that is recorded at each site (
Linkage key and components of base identifying information:
Type 1: 5 Medicare digits; date of birth; and sex
Type 2: 5 Medicare digits; postcode; first three characters of first name; and year of birth
Type 3: Last name and first name (either order permitted) and fuzzy matching used; date of birth with day/month (transpositions permitted)
Type 4: Last name and first name (either order permitted) and fuzzy matching used; 5 Medicare digits
There are three steps in the record linkage process in ACCESS when applying the linkage tool. The first step finds pairs of EMRs based on at least one linkage key matching and records the linkage key type/s used to match each record pair. The second step examines the strength of the link using other available data within the matched pair of records to accept or reject linked records as described in
GRHANITE Linkage Tool approaches to accepting matches.
Linkage approach | Description |
Accept all | Accept all record links as determined by the linkage tool |
Year of birth match | Accept only record links if year of birth matches |
Sex match | Accept only record links if sex matches |
Year of birth and sex match | Accept only record links if year of birth and sex match |
Two or more linkage keysa | Accept record links only if matched on two or more linkage key types |
Linkage key type 3 plus sex matchb | Accept only record links that match on linkage key type 3 and match on sex |
aGiven that 3 out of the 4 linkage key types are generated using the Medicare number, this approach requires the Medicare number to be present in the EMR (
bThis approach only relies on linkage key type 3, which does not require the Medicare number to be present in the EMR.
Record linkage in the Australian Collaboration for Coordinated Enhanced Sentinel Surveillance: using the GRHANITE Linkage Tool to identify and accept matches.
To evaluate the record linkage in ACCESS, we generated two gold-standard datasets, using a deterministic record linkage method, where the true match status could be identified [
PrEPX is a population-level intervention study in Victoria in which HIV pre-exposure prophylaxis was made available to eligible individuals, and the study used ACCESS data to monitor participants’ BBV and STI testing [
Data flow of electronic medical records in PrEPX and deterministic linkage for the gold-standard dataset.
A second and much larger gold-standard dataset was generated from the EMRs extracted from 7 clinics and 4 laboratories participating in ACCESS between January 2009 and April 2018. To be included in this dataset, patients had to have at least one specimen sent from one of the ACCESS clinics to one of the ACCESS laboratories. A unique laboratory specimen ID was assigned to the specimen at the laboratory, and when laboratories returned pathology results to the clinic, this specimen ID was also recorded at the clinic. To create the gold-standard dataset, clinic and laboratory records were matched using the laboratory specimen ID, year of birth, and test date. We allowed for a 7-day difference in test dates, as in medical records, the recorded date can commonly vary for the same specimen. Only matched records were included in the gold-standard dataset and linked using an arbitrarily assigned link identifier (
An EMR in the pathology results gold-standard dataset may match to many other EMRs for several reasons, including the following: individuals may have had multiple specimens sent to multiple laboratories for testing, individuals may have attended different clinics and therefore had the same test result sent from the laboratory to more than one clinic, or individuals may have had multiple EMRs at the laboratory or clinic as a result of outdated or incomplete personal identifiers.
Data flow of pathology results in electronic medical records and deterministic linkage for the gold-standard dataset.
The sensitivity was calculated as the number of correctly linked EMRs, as identified using the GRHANITE Linkage Tool, as a percentage of the total number of linked EMRs in the gold standard dataset.
In the PrEPX gold-standard dataset, the specificity was calculated as the number of single EMRs correctly identified as unlinked using the GRHANITE Linkage Tool as a percentage of the total number of unlinked EMRs. The positive predictive value (PPV) and negative predictive value were also calculated to provide probabilities of true matches and missed matches.
Given the deidentified nature of the ACCESS data, it was not possible to include unmatched specimen IDs in the pathology results gold-standard dataset because there was no way to confirm whether they belonged to different individuals (correctly unmatched), making it impossible to calculate specificity. Therefore, to evaluate specificity, we assessed the concordance of chronological HIV and hepatitis C test records to identify EMRs that should not have been linked. By identifying the linked EMRs with discordant results, the PPV (the proportion of linked records with concordant antibody results) could be determined. The specificity was then estimated using the PPV and the sensitivity for each linkage approach as summarized in
Estimating specificity when positive predictive value and sensitivity are known.
Following infection, any HIV or hepatitis C antibody test that subsequently occurs should always return a positive result. Using the pathology results gold-standard dataset provided only a small number of HIV and hepatitis C results; therefore, a dataset of linked EMRs was derived using all available EMRs from the same clinic and laboratory sites used to create the gold-standard dataset. Two datasets were created, one that contained any HIV western blot or antibody result and one that contained any hepatitis C antibody result. EMRs containing discordant results before record linkage were excluded from the sample so as not to confuse it with discordance resulting from record linkage. Records within each dataset were then linked using all six approaches (
To calculate the PPV, the linked EMRs were then inspected for negative antibody results occurring at least seven days after a positive test result, which were then classified as incorrectly matched. Where most subsequent antibody tests were negative, the initial and any subsequent positive results were considered incorrectly matched records.
The PrEPX gold-standard dataset identified 28 joins among 56 EMRs, indicating 28 study participants had attended two different clinical sites during the PrEPX study period. The remaining 3644 EMRs were from participants who only attended a single clinic during the study and therefore did not have any linked records.
Over 99% of EMRs had all four linkage key types present in 8 of the 9 sites, indicating that the patient-identifying information to generate those linkage keys was near fully recorded at the clinics. One site was missing data needed to generate linkage types 1, 2, and 4 (which all require the Medicare number) in 11% (8/76) of their EMRs (
In all linkage approaches, except the approach requiring two or more linkage keys, all pairs of EMRs from the 28 individuals who attended two sites were correctly joined (100% sensitivity). With the approach which required two or more linkage keys for matching, one pair was not identified (96% sensitivity). Specificity was 100% using all linkage approaches, without any of the remaining 3644 EMRs in the dataset being falsely linked (
Percentage of electronic medical records in the PrEPX gold-standard dataset by linkage key type and site.
Site | Number of electronic medical records, N | Percentage of electronic medical records with Linkage Key | |||
|
|
Type 1, n (%) | Type 2, n (%) | Type 3, n (%) | Type 4, n (%) |
Site 1 | 76 | 68 (89) | 68 (89) | 76 (100) | 68 (89) |
Site 2 | 853 | 853 (100.0) | 853 (100.0) | 853 (100.0) | 853 (100.0) |
Site 3 | 1087 | 1087 (100.00) | 1084 (99.72) | 1087 (100.00) | 1087 (100.00) |
Site 4 | 582 | 582 (100.0) | 582 (100.0) | 582 (100.0) | 582 (100.0) |
Site 5 | 40 | 40 (100.0) | 40 (100.0) | 40 (100.0) | 40 (100.0) |
Site 6 | 135 | 135 (100.0) | 135 (100.0) | 135 (100.0) | 135 (100.0) |
Site 7 | 106 | 106 (100.0) | 103 (99.2) | 106 (100.0) | 106 (100.0) |
Site 8 | 314 | 314 (100.0) | 314 (100.0) | 314 (100.0) | 314 (100.0) |
Site 9 | 507 | 507 (100.0) | 507 (100.0) | 507 (100.0) | 507 (100.0) |
Total | 3700 | 3692 (99.78) | 3686 (99.62) | 3700 (100.00) | 3692 (99.78) |
Evaluation measures derived from using the GRHANITE Linkage Tool on the PrEPX gold-standard dataset.
Linkage approach | Sensitivity (N=56), n (%) | Specificity (N=3644), n (%) | Positive predictive value (N=56), n (%) | Negative predictive value (N=3644), n (%) |
Accept all | 56 (100) | 3644 (100.00) | 56 (100) | 3644 (100.00) |
Year of birth match | 56 (100) | 3644 (100.00) | 56 (100) | 3644 (100.00) |
Sex match | 56 (100) | 3644 (100.00) | 56 (100) | 3644 (100.00) |
Year of birth and sex match | 56 (100) | 3644 (100.00) | 56 (100) | 3644 (100.00) |
Two or more linkage keys | 54 (96) | 3644 (100.00) | 54 (100)a | 3644 (99.90)b |
Linkage key type 3 plus sex match | 56 (100) | 3644 (100.00) | 56 (100) | 3644 (100.00) |
aN=54.
bN=3646.
Using the GRHANITE Linkage Tool on the pathology results gold-standard dataset created 50,484 linked records among 86,538 EMRs, with a maximum of six EMRs identified as belonging to the same individual.
A total of 99.69% (86,273/86,538) of EMRs contained at least one linkage key type, and all four linkage key types were present in 73.51% (63,610/86,538) of records, suggesting that the completion of patient-identifying information in the patient database was very high overall. However, 21.62% (18,709/86,538) of EMRs had only linkage key type 3 available for matching. One or more of linkage types 1, 2, and 4 (which all require the Medicare number) was missing in 97.42% (7914/8124) of EMRs from one public laboratory, 53.95% (5967/11,060) of EMRs from the sexual health clinic, 48.25% (1403/2908) of EMRs from a private laboratory, and 23.42% (6134/26,186) of EMRs from another public laboratory (
For the first 4 linkage approaches, the GRHANITE Linkage Tool correctly linked 94% to 95% of EMRs in the pathology results gold-standard dataset, dropping to 66% (57,330/86,538) where two or more linkage keys are needed to form a match (
Percentage of electronic medical records in the pathology gold-standard dataset by linkage key type and site.
Site | Number of electronic medical records, N | Number of electronic medical records with no linkage keys, n (%) | Percentage of electronic medical records with Linkage Key | |||
|
|
|
Type 1, n (%) | Type 2, n (%) | Type 3, n (%) | Type 4, n (%) |
Clinic 1 | 3165 | 0 (0.00) | 3083 (97.41) | 3077 (97.22) | 3165 (100) | 3083 (97.41) |
Clinic 2 | 6342 | 0 (0.00) | 6031 (95.10) | 6015 (94.84) | 6342 (100) | 6031 (95.10) |
Clinic 3 | 2514 | 0 (0.00) | 2493 (99.16) | 2489 (99.01) | 2513 (99.96) | 2492 (99.12) |
Clinic 4 | 9679 | 0 (0.00) | 9351 (96.61) | 9322 (96.31) | 9676 (99.97) | 9350 (96.60) |
Clinic 5 | 1369 | 1 (0.07) | 1357 (99.12) | 1356 (99.05) | 1368 (99.93) | 1357 (99.12) |
Clinic 6 | 2489 | 5 (0.20) | 2315 (93.01) | 2288 (91.92) | 2484 (99.80) | 2315 (93.01) |
Clinic 7 (sexual health) | 11,060 | 9 (0.08) | 5097 (46.08) | 5094 (46.06) | 11,049 (99.90) | 5095 (46.07) |
Lab 1 (public) | 26,186 | 241 (0.92) | 23,705 (90.53) | 20,059 (76.60) | 25,465 (97.25) | 23,227 (88.70) |
Lab 2 (public) | 8124 | 8 (0.10) | 215 (2.65) | 210 (2.58) | 8116 (99.90) | 215 (2.65) |
Lab 3 (private) | 2908 | 1 (0.03) | 1706 (58.67) | 1509 (51.89) | 2907 (99.97) | 1710 (58.80) |
Lab 4 (private) | 12,702 | 0 (0.00) | 12,205 (96.09) | 12,203 (96.07) | 12,700 (99.98) | 12,203 (96.07) |
Total | 86,538 | 265 (0.31) | 67,558 (78.07) | 63,622 (73.52) | 85,785 (99.13) | 67,078 (77.51) |
Evaluation measures derived from using the GRHANITE Linkage Tool on the pathology results gold-standard dataset.
Linkage approach | Gold standard (N=86,538) | HIV results | Hepatitis C results | ||||
|
Sensitivity, n (%) | N | Positive predictive value, n (%) | Estimated specificity, (%) | N | Positive predictive value, n (%) | Estimated specificity, (%) |
Accept all | 82,345 (95.15) | 1427 | 1245 (87.25) | 90.52 | 3908 | 3866 (98.93) | 99.32 |
Year of birth match | 82,212 (95.00) | 1412 | 1234 (87.39) | 90.71 | 3817 | 3777 (98.95) | 99.34 |
Sex match | 81,689 (94.40) | 1257 | 1143 (90.93) | 93.20 | 3810 | 3775 (99.08) | 99.42 |
Year of birth and sex match | 81,560 (94.25) | 1263 | 1152 (91.21) | 93.42 | 3775 | 3741 (99.10) | 99.43 |
Two or more linkage keys | 57,330 (66.25) | 257 | 256 (99.6) | 99.74 | 2809 | 2795 (99.50) | 99.67 |
Linkage key type 3 plus sex match | 76,928 (88.90) | 1090 | 984 (90.28) | 92.98 | 3626 | 3596 (99.17) | 99.49 |
In the derived HIV dataset, the number of linked EMRs containing an initial positive Western blot result ranged from 1090 to 1427 with all linkage approaches except when two or more linkage keys are needed. The linkage approach which requires two or more linkage keys to match resulted in 257 linked EMRs. The PPV was between 87% and 91% for the first 4 linkage approaches and estimated specificity ranged from 90% to 93%. When fewer EMRs were linked because of the different linkage approaches, both PPV and specificity improved (
In the derived hepatitis C dataset, with the first 4 linkage approaches, in excess of 3700 linked EMRs contained an initial positive hepatitis C antibody result, with a drop to 2809 records when two or more linkage keys are needed. The PPV was greater than 98.9% and an estimated specificity was over 99% for all six linkage approaches (
This paper describes a comprehensive evaluation of a system of probabilistic record linkage using a privacy-preserving software tool within a large-scale health surveillance system. The results showed that this software provides a highly reliable and accurate system for linking routinely collected EMRs through the generation of linkage keys reliant on available identifying information. Optimizing the record linkage involves an appropriate balance between the sensitivity (correctly identifying records belonging to the same person) and specificity (ensuring records that belong to different people are not linked) as well as what will best suit the study design objectives and populations under study without impeding the interpretation of study results.
The high performance of the linkage tool when applied to the relatively small PrEPX gold-standard dataset was related to the data completeness for EMRs in the PrEPX trial compared with the completeness of data in the pathology results gold-standard dataset (
When the linkage tool was applied to the larger pathology results gold-standard dataset, sensitivity ranged between 89% and 95% where the linkage approach relied on a single linkage key matching. However, with the approach that requires records to link on two or more linkage key types, sensitivity was reduced to 66%. This is attributable to 22% of EMRs only having a single linkage key type available for linkage, which is mostly because of the Medicare number not being available. The inclusion of laboratory records in the pathology results gold-standard dataset may contribute to a lower sensitivity as a result of patient identifier errors such as mislabeling and recording of laboratory samples [
The main challenge in evaluating the GRHANITE Linkage Tool was the development of gold-standard datasets given the deidentified nature of EMRs in ACCESS. Researchers rarely have access to gold-standard datasets on which to perform linkage validation outside large administrative health data sources, and our gold-standard dataset of 86,538 records was comparable with other published studies [
Beyond the false-positive record linkages identified by examining the concordance of linked test results for HIV and hepatitis C, there is potential for other false-positives to occur in cases where individuals share common patient identifiers, such as twins. Given the deidentified nature of ACCESS data, without the actual identifying demographic values, these niche cases cannot be identified. The small impact of these false-positives is not expected to impact the main purpose of public health surveillance using ACCESS. For other research projects that require a lower level of false-positive record linkage, particularly if it is known to contain a high proportion of individuals sharing common patient identifiers, then using a linkage approach that only accepts linkage based on a match of multiple linkage keys would minimize false-positives. In addition, ensuring concordance of other extracted data, such as sex, year of birth, HIV, and hepatitis C testing history, can reduce the level of false-positive record linkages to acceptable levels.
Evaluating record linkage is an important part of assessing the utility of surveillance and research systems for answering key population-level research questions or for accurately describing population-level trends using linked data. Our findings suggest that the GRHANITE Linkage Tool is appropriate for accurately linking individuals’ episodes of care and underpins the ability for ACCESS to perform privacy-preserving linkage of patient medical records.
Australian Collaboration for Coordinated Enhanced Sentinel Surveillance
blood-borne virus
electronic medical record
New South Wales
positive predictive value
sexually transmissible infection
Treatment with Antiretrovirals and their Impact on Positive And Negative men
University of New South Wales
The authors would like to acknowledge the contribution of the ACCESS Team members who are not coauthors of this study including: Lisa Bastian, WA Health; Deborah Bateson, Family Planning New South Wales (NSW); Scott Bowden, Doherty Institute; Mark Boyd, University of Adelaide; Allison Carter, Kirby Institute, University of New South Wales (UNSW) Sydney; Aaron Cogle, National Association of People with HIV Australia; Jane Costello, Positive Life NSW; Wayne Dimech, National Serology Reference Laboratory; Jennifer Dittmer, Burnet Institute; Jeanne Ellard, Australian Federation of AIDS Organisations; Christopher Fairley, Melbourne Sexual Health Centre; Lucinda Franklin, Victorian Department of Health; Jane Hocking, University of Melbourne; Jules Kim, Scarlet Alliance; Scott McGill, Australasian Society for HIV Medicine; David Nolan, Royal Perth Hospital; Prital Patel, Kirby Institute, UNSW Sydney; Stella Pendle, Australian Clinical Laboratories; Victoria Polkinghorne, Burnet Institute; Thi Nguyen, Burnet Institute; Catherine O’Connor, Kirby Institute, UNSW; Philip Reed, Kirkton Road Centre; Norman Roth, Prahran Market Clinic; Nathan Ryder, NSW Sexual Health Service Directors; Christine Selvey, NSW Ministry of Health; Michael Traeger, Burnet Institute; Toby Vickers, Kirby Institute, UNSW Sydney; Melanie Walker, Australian Injecting and Illicit Drug Users League; Lucy Watchirs-Smith, Kirby Institute, UNSW Sydney; Michael West, Victorian Department of Health.
The authors also acknowledge all clinics participating in ACCESS, PrEPX or both, and their site investigators for the provision of data to support these analyses: David Baker, East Sydney Doctors; Susan Boyd, Prahran Market Clinic; Mike Catton, Victorian Infectious Diseases Reference Laboratory; Danielle Collins, The Alfred; Vincent Cornelisse, Melbourne Sexual Health Centre; Pauline Cundill, PRONTO!; Philip Cunningham, SydPath, St Vincent’s Pathology; Sian Edwards, Prahran Market Clinic; Christopher K Fairley and, Melbourne Sexual Health Centre; Robert Finlayson; Taylor Square; George Forgan-Smith, Era Health; John Gall, Era Health; Helan Lau, Prahran Market Clinic; Peter Locke, PRONTO! clinic; Anna McNulty, Sydney Sexual Health Centre; Richard Moore, Northside Clinic; Emma Paige, The Alfred; Matthew Penn, PRONTO! clinic; Claire Pickett, Ballarat Community Health Centre; William Rawlinson, South Eastern Area Laboratory Services, NSW Health Pathology; Norman Roth, Prahran Market Clinic; Hans-Gerhard Schneider, Alfred Health; BK Tee, The Centre Clinic; Amanda Wade, Geelong Hospital; Jeff Willcox, Northside Clinic.
The work contributed to this paper was in partnership with Treatment with Antiretrovirals and their Impact on Positive And Negative men (TAIPAN) and we would like to acknowledge members of the TAIPAN study who are not coauthors of this paper including: Andrew Carr, St Vincent’s Hospital Sydney; Jennifer Hoy, Alfred Health; Kathy Petoumenos, Kirby Institute, UNSW; Julian Elliot, Alfred Health; David Templeton, Kirby Institute; Teng Liaw, School of Public Health and Community Medicine, UNSW; David Wilson, Burnet Institute; Andrew Grulich, Kirby Institute, UNSW; David Cooper, Kirby Institute, UNSW; Alisa Pedrana, Monash University; James McMahon, Alfred Health; Garrett Prestage, Kirby Institute; Martin Holt, Centre for Social Research in Health, UNSW; Christopher K. Fairley, Melbourne Sexual Health Centre; Neil McKellar-Stewart, ACON Health Sydney; Simon Ruth, Thorne Harbour Health, Phillip Keen, Kirby Institute; Craig Cooper, Positive Life NSW; Brent Allan, Living Positive Victoria; John Kaldor, Kirby Institute, UNSW.
The authors would also like to acknowledge the PrEPX Study team including the Principal Investigator Edwina Wright, Alfred Health and study coordinators: Brian Price; Luxi Lal; John T. Lockwood; Anne Mak; Christina Chang; Judith Armishaw, Alfred Health.
ACCESS is a partnership between the Burnet Institute, Kirby Institute and National Reference Laboratory. ACCESS is funded by the Australia Department of Health. ACCESS also receives funding from specific studies, including EC Victoria, EC Australia, and PrEPX. The Burnet Institute gratefully acknowledges support from the Victorian Operational Infrastructure Support Program.
MH and MS have received investigator-initiated funding from Gilead Sciences, AbbVie, and Bristol Myers Squibb for research unrelated to this work. MH, RG, MS, and BD are supported by fellowships from the National Health and Medical Research Council.