Speed of ethics and governance approvals in Australia before and after the COVID-19 pandemic: a cross-sectional study

Speed of ethics and governance approvals in Australia before and after the COVID-19 pandemic: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Speed of ethics and governance approvals in Australia before and after the COVID-19 pandemic: a cross-sectional study sam patrick crosby, Liping Huang, Stephen Jan, Richard Holden, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4197166/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract Background Multiple Australian jurisdictions reported the introduction of measures to speed the processing of research projects addressing the Severe Acute Respiratory Syndrome 2 SARS-CoV-2 (COVID-19) pandemic. The goal of this study was to compare the times taken for ethics committee and governance approvals of projects before and after the pandemic onset. Methods Data were sought from all 8 Australian states and territories for ethics and governance approvals made between 1/1/2019 and 31/12/2021 and 99% of ethics approvals and 98% of governance approvals fell within this period. Median (range) times from submission to approval were compared for pre-pandemic submissions made before 1/1/2020 and for post-pandemic submissions made after that date. For the post-pandemic period comparisons were also made between projects addressing COVID-19 and those addressing other topics. Differences in median times were tested using non-parametric tests. Results Data were obtained for 4876 ethics approvals from 8 states and for 4604 governance approvals from 5 states. 36% (1751) of ethics approvals and 29% (1313) of governance approvals were pre-pandemic and of the post-pandemic approvals, 6% (174) of ethics approvals and 5% (176) of governance approvals were for COVID-19 projects. The overall median time to ethics approval was 46 days (range 0-1799) pre-pandemic and 42 days (0-829) post-pandemic (p<0.001). The corresponding times for governance were 42 days (0-690) pre-pandemic and 28 days (0-716) post-pandemic (p<0.001). In the post-pandemic period, the median time for ethics approval was 22 days (0 – 804) for COVID-19 projects compared to 44 days (0 – 829) for other projects (<0.001) and the median times for governance approvals of COVID-19 projects was 19 days (0 – 333) compared to 30 days (0 – 716) for other types of projects (p<0.001). Interpretation Ethics and governance approval times were substantially reduced by interventions that targeted COVID-19 projects. For governance approval times, but not for ethics approval times, the interventions appear to have also benefitted non- COVID-19 projects. Figures Figure 1 Figure 2 INTRODUCTION The outbreak of the COVID-19 pandemic in Australia in early 2020 required a rapid clinical and public health response, to address both the large number of infections caused by the virus and to prevent the much larger potential consequences of uncontrolled nationwide transmission. The pandemic put hospitals, primary care, and medical support services under extraordinary pressure, as well as the community at large. 12 In parallel there was an urgent need for medical research to address the new problems presented by the pandemic. Researchers rushed to track the spread of the disease, assess its impact on health systems, find treatments, develop vaccines, and understand the long-term consequences of infection. Governments in Australia, and all around the world, identified the need for the rapid commencement of large new research initiatives and high-profile successes like the United Kingdom RECOVERY trial showed the world what a high-functioning clinical research sector could deliver. 3 Achieving timely approval of clinical research projects is a widely acknowledged challenge and reflects a balance between providing essential research oversight with limited infrastructure, while enabling a thriving research sector. High-profile historical failures of medical ethics in research have highlighted the need for careful ethical review of projects and, in parallel, the institutions responsible for the conduct of research have identified the need for better governance of research done in their organisations. 4 Under normal circumstances, ethics and governance delays are an accepted, if unwelcome, part of the research process though the problem is recognised - “ethics regulation has another, little studied, cost: because it slows, discourages and stops life-saving research and lives are lost that would otherwise have been saved.” 5 During the COVID-19 pandemic these delays had potentially much greater consequences and there were multiple updates to review processes designed to improve the speed of clinical research approvals by governments, hospitals, and ethics panels, including identifying and fast tracking COVID_19 applications. 6 In general, these processes appear to have been targeted at COVID-19 projects and to have been implemented from early 2020. The goal of this project was to quantify the impacts of these changes on times to ethics and governance approvals across the states and territories of Australia. METHODS This was a cross-sectional study of routinely collected meta-data recording the process of ethics and governance approvals from the 8 states and territories of Australia. Our underlying hypothesis was that changes made to ethics and governance processes consequent upon the COVID-19 pandemic altered times to ethics approval and times to governance approval. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist was used to standardize this report. 7 Data sources, included studies and data processing Researchers sought data on times to ethics approval and governance approval from any available source for any type of clinical medical study submitted for review between 1/1/2019 and 31/12/21 and 99% of ethics approvals and 98% of governance approvals fell within this period. In the absence of a national repository of sufficient granularity, data were sought from state and territory governments, from local health districts and from individual hospitals. Data were sought for health and medical research projects submitted for ethics and/or governance approval and the key inclusion criteria for each record were access to data describing the type of submission (ethics or governance), the jurisdiction of submission, submission date and approval date. The different forms of data were synthesized, standardized, and cleaned with de-identification done by replacing project name, ethics committee name, governance body name and jurisdiction name with numeric codes. Outcomes The outcomes were the times from submission to approval for the ethics and governance review processes. The distributions of the approval times were reviewed and observed to be substantially right skewed, so the primary outcomes selected for analysis were median time to ethics approval and median time to governance approval. Analysis The primary analysis done for this project was a comparison between the median times to ethics approval for projects submitted for review pre-pandemic onset (i.e. pre-1 January 2020) compared to projects submitted for review post-pandemic onset. The main analyses were done overall for all states and territories combined, with subsidiary reporting by individual jurisdictions. In addition, for the studies submitted for review post-pandemic, the median approval times for projects addressing COVID-19 were compared against the median approval times for projects addressing other conditions. Once again analyses were done overall and separately for each jurisdiction. All the analyses done assessing median times to ethics approval were then repeated for median times to governance approval (including their range). Differences in median times were tested using non-parametric tests (i.e., Wilcoxon rank sum tests). P-values <0.05 were considered statistically significant. All analyses were done using R version 4.2.3 and RStudio 2023.06.0 Build 421. 8 ,9 Packages “gtsummary” and “ggplot2” were used for summarizing the data. 10 , 11 RESULTS Information about 5101 ethics submissions was received from 8 jurisdictions. There were 225 with insufficient data leaving a total of 4876 for analysis. Of these, 2860 submissions came from the jurisdiction providing most data and 11 from the jurisdiction providing least. There were 1751 (36%) ethics submissions made pre-pandemic and 3125 (64%) submitted post-pandemic. Amongst the post-pandemic submissions 174 (6%) were for COVID-19 projects (Table 1). For governance, there were data for 6446 submissions received from 5 jurisdictions. Amongst these there were 1842 excluded because of data quality or completeness issues, leaving 4604 for analysis. The jurisdiction with most data provided information on 2202 governance submissions and the jurisdiction with least data provided data on 19 submissions. There were 1313 (or 28%) governance applications submitted pre-pandemic and 3291 (72%) post pandemic. 176 (5%) of the post-pandemic submissions were for COVID-19 projects (Table 1). Ethics approval times The overall median time to ethics approval was 46 days (range 0-1799) pre-pandemic and 42 days (0-829) post pandemic (p<0.001) (Figure 1). Amongst the 8 individual jurisdictions, there were 4 with shorter median approval times post-pandemic and 4 with longer median approval times post-pandemic. However, all three jurisdictions with a statistically significant difference between pre-and post-pandemic approval times had shorter approval times post pandemic. Amongst the post-pandemic submissions there was a clearly shorter median approval time for COVID-19 projects compared to other projects (22 days versus 44 days, p<0.001). COVID-19 median project approval times were shorter for all 7 jurisdictions that reviewed a COVID-19 project. Governance approvals times The overall pre-pandemic median time to governance approval was 42 days (0-690) compared to 28 days (0-716) post pandemic (p<0.001) (Figure 2). There were 4 of the 5 jurisdictions that provided governance data pre- and post-COVID and in all cases median approval times were shorter post-COVID, though in only 3 were the differences separately statistically significant. Amongst the post-pandemic submissions in the 5 jurisdictions there was an overall shorter median approval time for COVID-19 projects compared to other projects (19 days versus 30 days, p<0.001). Median governance approval times were shorter for COVID-19 projects in 4 jurisdictions (significantly in 3) but longer in one. In the latter jurisdiction the times to governance approval were very short for both COVID-19 projects (11 days) and other projects (7 days) (p=0.20). DISCUSSION The underlying hypothesis that changes made to ethics and governance processes because of the COVID-19 pandemic altered times to ethics approval and times to governance approval was proven. Approval times were shorter in the post-pandemic period when compared to the pre-pandemic era and COVID-19 projects had shorter approval times compared to projects addressing other disease areas. The coherence of these observations makes changes to ethics and governance processes resulting from the pandemic by far the most likely explanation. Differences in pre- and post-pandemic times to approval were broadly consistent for both ethics and governance approval processes across the states that contributed data. The variation between states that was present seems more likely to be a consequence of the play of chance than a real difference, with no state observing a statistically significant effect going in the opposite direction to the overall findings. In every state where timeframes appeared to deteriorate rather than improve, there were small numbers included in the sample and the reliability of the results was low. There do, however, appear to be differences between approval times for states for both ethics and governance processes. For example, ethics approval times for state 1 were short both pre- and post-pandemic (26 and 23 days, respectively) whereas they were both much longer in state 5 (142 and 66 days, respectively). These differences are based on fairly large numbers of ethics approvals and are likely to be a consequence of real differences in ethics processes. Likewise, the much shorter governance approval times for state 5 pre- and post-pandemic (17 and 7 days, respectively) compared to state 8 (58 and 41 days, respectively) are also very likely to reflect real differences in the processing of governance applications. This study documented approval times to test the hypothesis that ethics and governance interventions implemented because of the COVID-19 pandemic had delivered changes. It did not seek to explore the reasons why the changes were achieved, and this should be the focus of subsequent work now that the hypothesis has been proven. It is, however, possible to hypothesise some possible reasons for the differences observed. For example, a prior systematic review has identified that triaging of applications by their risk category can result in shorter approval times. The separation of projects into COVID-19 studies versus other studies is a form of triage and this may therefore partly explain the reductions in approval times we saw. Anecdotal reports indicate that COVID-19 projects were prioritised to the head of the agenda by review committees which would also be expected to reduce approval times for those studies. The same overview also identified that using scope guidelines that define (and limit) the breadth of review by committee can speed review though we do not know if this was done. 12 Likewise, mutual acceptance of ethics approval provisions can greatly reduce ethics approval timeframes once the initial review at the first site has been completed. Past literature reviews show that jurisdictions seeking to reduce their ethics and governance approval times most commonly implement several such interventions and disentangling the effects of each can be difficult. 13 Some sites have recently reported use of “integrated models” where trial design and ethics review are done concurrently with members of the ethics review panel attending and contributing to project planning meetings. This was in place in at least one state in Australia during the pandemic years. 14 The differences between approval times pre- and post-pandemic were more marked for governance review (42 days versus 28 days) than for ethics review (46 days versus 42 days), though there were large improvements for both when comparing the post-pandemic review of COVID-19 versus other types of studies. The inference is that the governance review processes implemented to speed approval post-pandemic benefited all projects, COVID-19 and others, while the ethics interventions that were implemented benefited the COVID-19 projects alone. The reasons for this are unclear and not easily explained by anecdotal reporting of more frequent ethics committee meetings, increased research funding for COVID-19 projects, 6 or the adoption of the national mutual acceptance for ethics review by two further states during the pandemic period. 15 The variance in approval processes observed in the meta-data collected for this project was also experienced by the researchers doing this project which involved multiple states across Australia. Some states determined that the de-identified meta-data required could be released without an ethics review while some required full ethics committee assessment. Some states were part of a national mutual acceptance of ethics review program, some were not, and some were but still required a secondary approval of their own. Governance processes were similarly varied and while rich data were obtained from some within relatively short time frames it proved very difficult to negotiate the requirements for others. Challenges in accessing data were most substantive in the states that lacked state-wide coordination of processes and had no centralised mechanism for submitting and tracking approvals. Strengths and weaknesses The large size of the overall data set provided good statistical power to minimize random errors and maximise the capacity to test the primary hypothesis. Numbers of approvals were smaller for individual states, which meant that there was more uncertainty about between state comparisons, though the large differences in approval times observed between some mean that they are unlikely to all have arisen by chance. The same is true for the comparisons of approval times between COVID-19 projects versus other project types. The inability to collect all data for all states during the period of interest limits the generalisability of the findings to all Australian institutions though the breadth of data available has provided considerable insight. The evidence of differences between states is of particular interest because it suggests that subsequent research might identify actions taken by one, but not by another, that could explain the differences observed. It is possible that the data may be confounded by the incomplete collection of all data, which could mean that selections of studies with systematically shorter or longer approval times have been preferentially included or excluded. It is also possible that differences in characteristics of sites or study types not captured in the dataset could explain the differences observed. For example, it may be that the phases of clinical trial projects differed between states and that early phase projects had different average timelines than later phase projects. Additionally, it is possible that the volume of applications processed by each site changed considerably during the pandemic years as both by total numbers and by each site. The requirement to anonymise the data will limit the direct interpretation of the data by the states of Australia, but we will work with each to better understand how the findings might support enhanced approval processes. Finally, this study does not examine the quality of the reviews undertaken and how this may have been impacted by the faster times seen during the pandemic. CONCLUSION Australia has a strong reputation for the conduct of high-quality clinical research, though there remain significant practical challenges to the implementation of projects across Australia. Timelines for ethics and governance approvals are widely cited as an area for improvement and this research evaluating upgrades made during the COVID-19 pandemic shows that real improvements can be delivered. The next challenge is to understand more explicitly how timelines were reduced and how the changes that were implemented can be scaled and sustained across the country. It seems highly likely that it will be possible to identify specific interventions that can speed the commencement of research projects, further enhance Australia’s reputation as a global centre of research excellence and make Australia a destination for overseas research investment. Abbreviations COVID-19: Severe acute respiratory syndrome 2 SARS-CoV-2 STROBE: Strengthening the Reporting of Observational Studies in Epidemiology Declarations Ethics approval The project received ethics approval from the UNSW Sydney Human Research Ethics Committee on the 26 th of May 2022 (125 days from submission to approval). The project received further approval from the West Australian Department of Health Human Research Ethics Committee 27 June 2022 (120 days from submission to approval), and National Ethics Approval from the Central Adelaide Local Health Network Human Research Ethics Committee on 17 February 2023 (153 days from submission to approval). An amendment to the national approval to include a greater number of sites was sought and received on 16 March 2023 (2 days from submission to approval). Consent for publication Not required for this publication Availability of data and material The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study did not attract any external funding or grants. Authors Contributions SC, LH, SJ, RH & BN made substantial contributions to the conception and design of the work. SC acquired and processed the data, SC and LH primarily interpreted the data assisted by BN. BN, SC, SJ, RH drafted and substantially revised the work. SC as corresponding author liaised with each author ensuring their input and revisions were accounted for. Acknowledgements The authors would also like to acknowledge the assistance of Ryan Shave 5, the NT Health Research Governance Office 6 , and August Marchesi 7 for their help in procuring and explaining the data. References Admitted Patients, Canberra, 2023, Australian Institute of Health and Welfare, available from https://www.aihw.gov.au/reports-data/myhospitals/sectors/admitted-patients Rafferty, AC., Hewitt, MC., Wright, R., Hogarth, F., Coatsworth, N., Ampt, F., Dougall, S., Alpren, C., Causer, L., Coffey, C., Wakefield, A., Campbell, S., Pingault, N., Harlock, M., Smith, KJ., Kirk MD., COVID-19 in health care workers, Australia, 2021, Communicable Diseases Intelligence 2021 - Volume 45, https://doi.org/10.33321/cdi.2021.45.57 The RECOVERY Trial – two years on, 2022, university of Oxford, available at https://www.ox.ac.uk/news/features/recovery-trial-two-years Rothman DJ., Ethics and Human Experimentation, New England Journal of Medicine, 1987 Volume 317, 1195-1199 Whitney, S. N & Schneider, C. E., Viewpoint: A method to estimate the cost in lives of ethics board review of biomedical research, The Association for the Publication of the Journal of Internal Medicine Journal of Internal Medicine, 2011, Volume 269, 392–406,. Seidler AL, Aberoumand M, Williams JG, Tan A, Hunter KE, Webster A. The landscape of COVID-19 trials in Australia. Med J Aust. 2021 Jul;215(2):58-61.e1. doi: 10.5694/mja2.51148. PMID: 34275134; PMCID: PMC8447014. Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, Strobe Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. International journal of surgery. 2014 Dec 1;12(12):1495-9. R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical. Computing, Vienna, Austria. URL https://www.R-project.org/. Posit team (2023). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/. Sjoberg DD, Whiting K, Curry M, Lavery JA, Larmarange J. Reproducible summary tables with the gtsummary package. The R Journal 2021;13:570–80. https://doi.org/10.32614/RJ-2021-053. H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. Crosby, S., Malavisi, A., Huang, L. Jan, S., Holden, R., Neal, B., Factors influencing the time to ethics and governance approvals for clinical trials: a retrospective cross-sectional survey. Trials 24, 779 (2023). https://doi.org/10.1186/s13063-023-07802-2 Crosby, S., Rajadurai, E., Jan, S., Holden, R., Neal, B., (2022), The effects of government policies targeting ethics and governance processes on clinical trial activity and expenditure: a systematic review, Human and Social Sciences Communication, Volume 266. https://doi.org/10.1057/s41599-022-01269-3 Five ways the COVID-19 pandemic has changed clinical trials, Sydney, NSW Dept Health, 2020, https://www.medicalresearch.nsw.gov.au/five-ways-covid19-pandemic-has-changed-clinical-trials/ National Mutual Acceptance, National system for mutual acceptance of scientific and ethical review of multicentre human research projects, Clinical Trials and Research, Melbourne, VIC Government, 2023, https://www.clinicaltrialsandresearch.vic.gov.au/national-mutual-acceptance Table Table 1: Available data for ethics and governance submissions made pre-and post-pandemic onset overall and for each state and territory. Overall, N = 4,876 State 1, N = 436 State 2, N = 1,015 State 3, N = 11 State 4, N = 86 State 5, N = 399 State 6, N = 16 State 7, N = 233 State 8, N = 2,680 Ethics submissions Submission date Pre-pandemic 1,751 (36%) 144 (33%) 348 (34%) 2 (18%) 28 (33%) 141 (35%) 10 (62%) 91 (39%) 987 (37%) Post-pandemic 3,125 (64%) 292 (67%) 667 (66%) 9 (82%) 58 (67%) 258 (65%) 6 (38%) 142 (61%) 1,693 (63%) Post-pandemic study type COVID-19 174 (6%) 35 (12%) 26 (4%) 1 (11%) 3 (5%) 14 (5%) 0 (0%) 4 (3%) 91 (5%) Other 2,951 (94%) 257 (88%) 641 (96%) 8 (89%) 55 (95%) 244 (95%) 6 (100%) 138 (97%) 1,602 (95%) Overall, N = 4,604 State 1, N = 517 State 2, N = 2202 State 3, N = 19 State 4, N = 0 State 5, N = 646 State 6, N = 0 State 7, N = 0 State 8, N = 1220 Governance submissions Submission date Pre-pandemic 1,313 (29%) 181 (35.0%) 613 (28%) 0 (0%) 159 (25%) 360 (30%) Post-pandemic 3,291 (71%) 336 (65.0%) 1,589 (72%) 19 (100%) 487 (75%) 860 (70%) Post-pandemic study type COVID-19 176 (5%) 39 (12%) 62 (4%) 2 (10%) 22 (5%) 51 (6%) Other 3,115 (95%) 297 (88%) 1,527 (96%) 17 (90%) 465 (95%) 809 (94%) Supplementary Files STROBEchecklistv4combinedcopy.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Reviewers agreed at journal 03 Jul, 2024 Reviewers invited by journal 12 May, 2024 Editor assigned by journal 16 Apr, 2024 First submitted to journal 15 Apr, 2024 Editorial decision: Major revision 08 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Australia in early 2020 required a rapid clinical and public health response, to address both the large number of infections caused by the virus and to prevent the much larger potential consequences of uncontrolled nationwide transmission. The pandemic put hospitals, primary care, and medical support services under extraordinary pressure, as well as the community at large.\u003csup\u003e12\u003c/sup\u003e In parallel there was an urgent need for medical research to address the new problems presented by the pandemic. Researchers rushed to track the spread of the disease, assess its impact on health systems, find treatments, develop vaccines, and understand the long-term consequences of infection. Governments in Australia, and all around the world, identified the need for the rapid commencement of large new research initiatives and high-profile successes like the United Kingdom RECOVERY trial showed the world what a high-functioning clinical research sector could deliver.\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAchieving timely approval of clinical research projects is a widely acknowledged challenge and reflects a balance between providing essential research oversight with limited infrastructure, while enabling a thriving research sector. High-profile historical failures of medical ethics in research have highlighted the need for careful ethical review of projects and, in parallel, the institutions responsible for the conduct of research have identified the need for better governance of research done in their organisations.\u003csup\u003e4\u003c/sup\u003e Under normal circumstances, ethics and governance delays are an accepted, if unwelcome, part of the research process though the problem is recognised - \u0026ldquo;ethics regulation has another, little studied, cost: because it slows, discourages and stops life-saving research and lives are lost that would otherwise have been saved.\u0026rdquo;\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eDuring the COVID-19 pandemic these delays had potentially much greater consequences and there were multiple updates to review processes designed to improve the speed of clinical research approvals by governments, hospitals, and ethics panels, including identifying and fast tracking COVID_19 applications.\u003csup\u003e6\u003c/sup\u003e In general, these processes appear to have been targeted at COVID-19 projects and to have been implemented from early 2020. The goal of this project was to quantify the impacts of these changes on times to ethics and governance approvals across the states and territories of Australia.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis was a cross-sectional study of routinely collected meta-data recording the process of ethics and governance approvals from the 8 states and territories of Australia. Our underlying hypothesis was that changes made to ethics and governance processes consequent upon the COVID-19 pandemic altered times to ethics approval and times to governance approval. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist was used to standardize this report.\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\n\u003ch2\u003eData sources, included studies and data processing\u003c/h2\u003e\n\u003cp\u003eResearchers sought data on times to ethics approval and governance approval from any available source for any type of clinical medical study submitted for review between 1/1/2019 and 31/12/21 and 99% of ethics approvals and 98% of governance approvals fell within this period. In the absence of a national repository of sufficient granularity, data were sought from state and territory governments, from local health districts and from individual hospitals. Data were sought for health and medical research projects submitted for ethics and/or governance approval and the key inclusion criteria for each record were access to data describing the type of submission (ethics or governance), the jurisdiction of submission, submission date and approval date. The different forms of data were synthesized, standardized, and cleaned with de-identification done by replacing project name, ethics committee name, governance body name and jurisdiction name with numeric codes.\u003c/p\u003e\n\u003ch2\u003eOutcomes\u003c/h2\u003e\n\u003cp\u003eThe outcomes were the times from submission to approval for the ethics and governance review processes. The distributions of the approval times were reviewed and observed to be substantially right skewed, so the primary outcomes selected for analysis were median time to ethics approval and median time to governance approval.\u003c/p\u003e\n\u003ch2\u003eAnalysis\u003c/h2\u003e\n\u003cp\u003eThe primary analysis done for this project was a comparison between the median times to ethics approval for projects submitted for review pre-pandemic onset (i.e. pre-1 January 2020) compared to projects submitted for review post-pandemic onset. The main analyses were done overall for all states and territories combined, with subsidiary reporting by individual jurisdictions. In addition, for the studies submitted for review post-pandemic, the median approval times for projects addressing COVID-19 were compared against the median approval times for projects addressing other conditions. Once again analyses were done overall and separately for each jurisdiction. All the analyses done assessing median times to ethics approval were then repeated for median times to governance approval (including their range). Differences in median times were tested using non-parametric tests (i.e., Wilcoxon rank sum tests). P-values \u0026lt;0.05 were considered statistically significant. All analyses were done using R version 4.2.3 and RStudio 2023.06.0 Build 421.\u003csup\u003e8\u003c/sup\u003e\u003csup\u003e,9\u003c/sup\u003e Packages \u0026ldquo;gtsummary\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo; were used for summarizing the data.\u003csup\u003e10\u003c/sup\u003e\u003csup\u003e, 11\u003c/sup\u003e\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eInformation about 5101 ethics submissions was received from 8 jurisdictions. There were 225 with insufficient data leaving a total of 4876 for analysis. Of these, 2860 submissions came from the jurisdiction providing most data and 11 from the jurisdiction providing least. There were 1751 (36%) ethics submissions made pre-pandemic and 3125 (64%) submitted post-pandemic. Amongst the post-pandemic submissions 174 (6%) were for COVID-19 projects (Table 1).\u003c/p\u003e\n\u003cp\u003eFor governance, there were data for 6446 submissions received from 5 jurisdictions. Amongst these there were 1842 excluded because of data quality or completeness issues, leaving 4604 for analysis. The jurisdiction with most data provided information on 2202 governance submissions and the jurisdiction with least data provided data on 19 submissions. There were 1313 (or 28%) governance applications submitted pre-pandemic and 3291 (72%) post pandemic. 176 (5%) of the post-pandemic submissions were for COVID-19 projects (Table 1).\u003c/p\u003e\n\u003ch2\u003eEthics approval times\u003c/h2\u003e\n\u003cp\u003eThe overall median time to ethics approval was 46 days (range 0-1799) pre-pandemic and 42 days (0-829) post pandemic (p\u0026lt;0.001) (Figure 1). Amongst the 8 individual jurisdictions, there were 4 with shorter median approval times post-pandemic and 4 with longer median approval times post-pandemic. However, all three jurisdictions with a statistically significant difference between pre-and post-pandemic approval times had shorter approval times post pandemic. Amongst the post-pandemic submissions there was a clearly shorter median approval time for COVID-19 projects compared to other projects (22 days versus 44 days, p\u0026lt;0.001). COVID-19 median project approval times were shorter for all 7 jurisdictions that reviewed a COVID-19 project.\u003c/p\u003e\n\u003ch2\u003eGovernance approvals times\u003c/h2\u003e\n\u003cp\u003eThe overall pre-pandemic median time to governance approval was 42 days (0-690) compared to 28 days (0-716) post pandemic (p\u0026lt;0.001) (Figure 2). There were 4 of the 5 jurisdictions that provided governance data pre- and post-COVID and in all cases median approval times were shorter post-COVID, though in only 3 were the differences separately statistically significant. Amongst the post-pandemic submissions in the 5 jurisdictions there was an overall shorter median approval time for COVID-19 projects compared to other projects (19 days versus 30 days, p\u0026lt;0.001). Median governance approval times were shorter for COVID-19 projects in 4 jurisdictions (significantly in 3) but longer in one. In the latter jurisdiction the times to governance approval were very short for both COVID-19 projects (11 days) and other projects (7 days) (p=0.20).\u003c/p\u003e"},{"header":"DISCUSSION ","content":"\u003cp\u003eThe underlying hypothesis that changes made to ethics and governance processes because of the COVID-19 pandemic altered times to ethics approval and times to governance approval was proven. Approval times were shorter in the post-pandemic period when compared to the pre-pandemic era and COVID-19 projects had shorter approval times compared to projects addressing other disease areas. The coherence of these observations makes changes to ethics and governance processes resulting from the pandemic by far the most likely explanation.\u003c/p\u003e\n\u003cp\u003eDifferences in pre- and post-pandemic times to approval were broadly consistent for both ethics and governance approval processes across the states that contributed data. The variation between states that was present seems more likely to be a consequence of the play of chance than a real difference, with no state observing a statistically significant effect going in the opposite direction to the overall findings. In every state where timeframes appeared to deteriorate rather than improve, there were small numbers included in the sample and the reliability of the results was low. There do, however, appear to be differences between approval times for states for both ethics and governance processes. For example, ethics approval times for state 1 were short both pre- and post-pandemic (26 and 23 days, respectively) whereas they were both much longer in state 5 (142 and 66 days, respectively). These differences are based on fairly large numbers of ethics approvals and are likely to be a consequence of real differences in ethics processes. Likewise, the much shorter governance approval times for state 5 pre- and post-pandemic (17 and 7 days, respectively) compared to state 8 (58 and 41 days, respectively) are also very likely to reflect real differences in the processing of governance applications.\u003c/p\u003e\n\u003cp\u003eThis study documented approval times to test the hypothesis that ethics and governance interventions implemented because of the COVID-19 pandemic had delivered changes. It did not seek to explore the reasons why the changes were achieved, and this should be the focus of subsequent work now that the hypothesis has been proven. It is, however, possible to hypothesise some possible reasons for the differences observed. For example, a prior systematic review has identified that triaging of applications by their risk category can result in shorter approval times. The separation of projects into COVID-19 studies versus other studies is a form of triage and this may therefore partly explain the reductions in approval times we saw. Anecdotal reports indicate that COVID-19 projects were prioritised to the head of the agenda by review committees which would also be expected to reduce approval times for those studies. The same overview also identified that using scope guidelines that define (and limit) the breadth of review by committee can speed review though we do not know if this was done.\u003csup\u003e12\u003c/sup\u003e Likewise, mutual acceptance of ethics approval provisions can greatly reduce ethics approval timeframes once the initial review at the first site has been completed. Past literature reviews show that jurisdictions seeking to reduce their ethics and governance approval times most commonly implement several such interventions and disentangling the effects of each can be difficult.\u003csup\u003e13\u003c/sup\u003e Some sites have recently reported use of \u0026ldquo;integrated models\u0026rdquo; where trial design and ethics review are done concurrently with members of the ethics review panel attending and contributing to project planning meetings. This was in place in at least one state in Australia during the pandemic years.\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe differences between approval times pre- and post-pandemic were more marked for governance review (42 days versus 28 days) than for ethics review (46 days versus 42 days), though there were large improvements for both when comparing the post-pandemic review of COVID-19 versus other types of studies. The inference is that the governance review processes implemented to speed approval post-pandemic benefited all projects, COVID-19 and others, while the ethics interventions that were implemented benefited the COVID-19 projects alone. The reasons for this are unclear and not easily explained by anecdotal reporting of more frequent ethics committee meetings, increased research funding for COVID-19 projects,\u003csup\u003e6\u003c/sup\u003e or the adoption of the national mutual acceptance for ethics review by two further states during the pandemic period.\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe variance in approval processes observed in the meta-data collected for this project was also experienced by the researchers doing this project which involved multiple states across Australia. Some states determined that the de-identified meta-data required could be released without an ethics review while some required full ethics committee assessment. Some states were part of a national mutual acceptance of ethics review program, some were not, and some were but still required a secondary approval of their own. Governance processes were similarly varied and while rich data were obtained from some within relatively short time frames it proved very difficult to negotiate the requirements for others. Challenges in accessing data were most substantive in the states that lacked state-wide coordination of processes and had no centralised mechanism for submitting and tracking approvals.\u003c/p\u003e\n\u003ch2\u003eStrengths and weaknesses\u003c/h2\u003e\n\u003cp\u003eThe large size of the overall data set provided good statistical power to minimize random errors and maximise the capacity to test the primary hypothesis. Numbers of approvals were smaller for individual states, which meant that there was more uncertainty about between state comparisons, though the large differences in approval times observed between some mean that they are unlikely to all have arisen by chance. The same is true for the comparisons of approval times between COVID-19 projects versus other project types. The inability to collect all data for all states during the period of interest limits the generalisability of the findings to all Australian institutions though the breadth of data available has provided considerable insight. The evidence of differences between states is of particular interest because it suggests that subsequent research might identify actions taken by one, but not by another, that could explain the differences observed.\u003c/p\u003e\n\u003cp\u003eIt is possible that the data may be confounded by the incomplete collection of all data, which could mean that selections of studies with systematically shorter or longer approval times have been preferentially included or excluded. It is also possible that differences in characteristics of sites or study types not captured in the dataset could explain the differences observed. For example, it may be that the phases of clinical trial projects differed between states and that early phase projects had different average timelines than later phase projects. Additionally, it is possible that the volume of applications processed by each site changed considerably during the pandemic years as both by total numbers and by each site. The requirement to anonymise the data will limit the direct interpretation of the data by the states of Australia, but we will work with each to better understand how the findings might support enhanced approval processes. Finally, this study does not examine the quality of the reviews undertaken and how this may have been impacted by the faster times seen during the pandemic.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eAustralia has a strong reputation for the conduct of high-quality clinical research, though there remain significant practical challenges to the implementation of projects across Australia. Timelines for ethics and governance approvals are widely cited as an area for improvement and this research evaluating upgrades made during the COVID-19 pandemic shows that real improvements can be delivered. The next challenge is to understand more explicitly how timelines were reduced and how the changes that were implemented can be scaled and sustained across the country. It seems highly likely that it will be possible to identify specific interventions that can speed the commencement of research projects, further enhance Australia\u0026rsquo;s reputation as a global centre of research excellence and make Australia a destination for overseas research investment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCOVID-19: Severe acute respiratory syndrome 2 SARS-CoV-2\u003c/p\u003e\n\u003cp\u003eSTROBE: Strengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project received ethics approval from the UNSW Sydney Human Research Ethics Committee on the 26\u003csup\u003eth\u003c/sup\u003e of May 2022 (125 days from submission to approval). The project received further approval from the West Australian Department of Health Human Research Ethics Committee 27 June 2022 (120 days from submission to approval), and National Ethics Approval from the Central Adelaide Local Health Network Human Research Ethics Committee on 17 February 2023 (153 days from submission to approval). An amendment to the national approval to include a greater number of sites was sought and received on 16 March 2023 (2 days from submission to approval).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot required for this publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not attract any external funding or grants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSC, LH, SJ, RH \u0026amp; BN made substantial contributions to the conception and design of the work. SC acquired and processed the data, SC and LH primarily interpreted the data assisted by BN. BN, SC, SJ, RH drafted and substantially revised the work. SC as corresponding author liaised with each author ensuring their input and revisions were accounted for.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would also like to acknowledge the assistance of Ryan Shave\u003csup\u003e5,\u0026nbsp;\u003c/sup\u003ethe NT Health Research Governance Office\u003csup\u003e6\u003c/sup\u003e, and August Marchesi\u003csup\u003e7\u003c/sup\u003e for their help in procuring and explaining the data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdmitted Patients, Canberra, 2023, Australian Institute of Health and Welfare, available from https://www.aihw.gov.au/reports-data/myhospitals/sectors/admitted-patients\u003c/li\u003e\n\u003cli\u003eRafferty, AC., Hewitt, MC., Wright, R., Hogarth, F., Coatsworth, N., Ampt, F., Dougall, S., Alpren, C., Causer, L., Coffey, C., Wakefield, A., Campbell, S., Pingault, N., Harlock, M., Smith, KJ., Kirk MD., COVID-19 in health care workers, Australia, 2021, Communicable Diseases Intelligence 2021 - Volume 45, https://doi.org/10.33321/cdi.2021.45.57\u003c/li\u003e\n\u003cli\u003eThe RECOVERY Trial \u0026ndash; two years on, 2022, university of Oxford, available at https://www.ox.ac.uk/news/features/recovery-trial-two-years\u003c/li\u003e\n\u003cli\u003eRothman DJ., Ethics and Human Experimentation, New England Journal of Medicine, 1987 Volume 317, 1195-1199\u003c/li\u003e\n\u003cli\u003eWhitney, S. N \u0026amp; Schneider, C. E., Viewpoint: A method to estimate the cost in lives of ethics board review of biomedical research, The Association for the Publication of the Journal of Internal Medicine Journal of Internal Medicine, 2011, Volume 269, 392\u0026ndash;406,.\u003c/li\u003e\n\u003cli\u003eSeidler AL, Aberoumand M, Williams JG, Tan A, Hunter KE, Webster A. The landscape of COVID-19 trials in Australia. Med J Aust. 2021 Jul;215(2):58-61.e1. doi: 10.5694/mja2.51148. PMID: 34275134; PMCID: PMC8447014.\u003c/li\u003e\n\u003cli\u003eVon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP, Strobe Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. International journal of surgery. 2014 Dec 1;12(12):1495-9.\u003c/li\u003e\n\u003cli\u003eR Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical. Computing, Vienna, Austria. URL https://www.R-project.org/. \u003c/li\u003e\n\u003cli\u003ePosit team (2023). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/. \u003c/li\u003e\n\u003cli\u003eSjoberg DD, Whiting K, Curry M, Lavery JA, Larmarange J. Reproducible summary tables with the gtsummary package. The R Journal 2021;13:570\u0026ndash;80. https://doi.org/10.32614/RJ-2021-053.\u003c/li\u003e\n\u003cli\u003eH. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.\u003c/li\u003e\n\u003cli\u003eCrosby, S., Malavisi, A., Huang, L. Jan, S., Holden, R., Neal, B., Factors influencing the time to ethics and governance approvals for clinical trials: a retrospective cross-sectional survey. \u003cem\u003eTrials\u003c/em\u003e 24, 779 (2023). https://doi.org/10.1186/s13063-023-07802-2 \u003c/li\u003e\n\u003cli\u003eCrosby, S., Rajadurai, E., Jan, S., Holden, R., Neal, B., (2022), The effects of government policies targeting ethics and governance processes on clinical trial activity and expenditure: a systematic review, Human and Social Sciences Communication, Volume 266. https://doi.org/10.1057/s41599-022-01269-3\u003c/li\u003e\n\u003cli\u003eFive ways the COVID-19 pandemic has changed clinical trials, Sydney, NSW Dept Health, 2020, https://www.medicalresearch.nsw.gov.au/five-ways-covid19-pandemic-has-changed-clinical-trials/ \u003c/li\u003e\n\u003cli\u003eNational Mutual Acceptance, National system for mutual acceptance of scientific and ethical review of multicentre human research projects, Clinical Trials and Research, Melbourne, VIC Government, 2023, https://www.clinicaltrialsandresearch.vic.gov.au/national-mutual-acceptance \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1: Available data for ethics and governance submissions made pre-and post-pandemic onset overall and for each state and territory.\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 4,876\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 1,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 436\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 2,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 1,015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 3,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 4,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 5,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 399\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 6,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 7,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 233\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 8,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 2,680\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthics submissions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003eSubmission date\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003ePre-pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,751 (36%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e144 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e348 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e2 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e28 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e141 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e10 (62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e91 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e987 (37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003ePost-pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3,125 (64%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e292 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e667 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e9 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e58 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e258 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e6 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e142 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e1,693 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003ePost-pandemic study type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003eCOVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e174 (6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e35 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e26 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e1 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e3 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e14 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e4 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e91 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,951 (94%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e257 (88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e641 (96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e8 (89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e55 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e244 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e6 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e138 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e1,602 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 4,604\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 1,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 517\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 2,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 2202\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 3,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 4,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 5,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 646\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 6,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 7,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState 8,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 1220\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovernance submissions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003eSubmission date\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003ePre-pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,313 (29%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e181 (35.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e613 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e159 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e360 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003ePost-pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3,291 (71%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e336 (65.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e1,589 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e19 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e487 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e860 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003ePost-pandemic study type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003eCOVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e176 (5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e39 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e62 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e2 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e22 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e51 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3,115 (95%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e297 (88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e1,527 (96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e17 (90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e465 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e809 (94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"research-integrity-and-peer-review","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ripr","sideBox":"Learn more about [Research Integrity and Peer Review](http://researchintegrityjournal.biomedcentral.com)","snPcode":"41073","submissionUrl":"https://submission.nature.com/new-submission/41073/3","title":"Research Integrity and Peer Review","twitterHandle":"@RIPRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4197166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4197166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eMultiple Australian jurisdictions reported the introduction of measures to speed the processing of research projects addressing the Severe Acute Respiratory Syndrome 2 SARS-CoV-2 (COVID-19) pandemic. The goal of this study was to compare the times taken for ethics committee and governance approvals of projects before and after the pandemic onset.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eData were sought from all 8 Australian states and territories for ethics and governance approvals made between 1/1/2019 and 31/12/2021 and 99% of ethics approvals and 98% of governance approvals fell within this period. Median (range) times from submission to approval were compared for pre-pandemic submissions made before 1/1/2020 and for post-pandemic submissions made after that date. For the post-pandemic period comparisons were also made between projects addressing COVID-19 and those addressing other topics. Differences in median times were tested using non-parametric tests.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eData were obtained for 4876 ethics approvals from 8 states and for 4604 governance approvals from 5 states. 36% (1751) of ethics approvals and 29% (1313) of governance approvals were pre-pandemic and of the post-pandemic approvals, 6% (174) of ethics approvals and 5% (176) of governance approvals were for COVID-19 projects. The overall median time to ethics approval was 46 days (range 0-1799) pre-pandemic and 42 days (0-829) post-pandemic (p\u0026lt;0.001). The corresponding times for governance were 42 days (0-690) pre-pandemic and 28 days (0-716) post-pandemic (p\u0026lt;0.001). In the post-pandemic period, the median time for ethics approval was 22 days (0 – 804) for COVID-19 projects compared to 44 days (0 – 829) for other projects (\u0026lt;0.001) and the median times for governance approvals of COVID-19 projects was 19 days (0 – 333) compared to 30 days (0 – 716) for other types of projects (p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eInterpretation\u003c/p\u003e\n\u003cp\u003eEthics and governance approval times were substantially reduced by interventions that targeted COVID-19 projects. For governance approval times, but not for ethics approval times, the interventions appear to have also benefitted non- COVID-19 projects.\u003c/p\u003e","manuscriptTitle":"Speed of ethics and governance approvals in Australia before and after the COVID-19 pandemic: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-22 16:12:23","doi":"10.21203/rs.3.rs-4197166/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-07-03T20:29:33+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-12T14:54:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T13:45:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Research Integrity and Peer Review","date":"2024-04-16T01:48:25+00:00","index":"","fulltext":""},{"type":"decision","content":"Major revision","date":"2024-04-08T14:37:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"research-integrity-and-peer-review","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ripr","sideBox":"Learn more about [Research Integrity and Peer Review](http://researchintegrityjournal.biomedcentral.com)","snPcode":"41073","submissionUrl":"https://submission.nature.com/new-submission/41073/3","title":"Research Integrity and Peer Review","twitterHandle":"@RIPRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ffec6e30-63d1-4dae-a0e5-4dd73aa2a28c","owner":[],"postedDate":"May 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2024-09-13T13:49:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-22 16:12:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4197166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4197166","identity":"rs-4197166","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}