ORCID Profile
0000-0002-9279-7453
Current Organisation
UNSW
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Publisher: Oxford University Press (OUP)
Date: 23-03-2023
DOI: 10.1093/IJE/DYAD038
Abstract: Directed acyclic graphs (DAGs) are used in epidemiological research to communicate causal assumptions and guide the selection of covariate adjustment sets when estimating causal effects. For any given DAG, a set of graphical rules can be applied to identify minimally sufficient adjustment sets that can be used to adjust for bias due to confounding when estimating the causal effect of an exposure on an outcome. The daggle app is a web-based application that aims to assist in the learning and teaching of adjustment set identification using DAGs. The application offers two modes: tutorial and random. The tutorial mode presents a guided introduction to how common causal structures can be presented using DAGs and how graphical rules can be used to identify minimally sufficient adjustment sets for causal estimation. The random mode tests this understanding by presenting the user with a randomly generated DAG—a daggle. To solve the daggle, users must correctly identify a valid minimally sufficient adjustment set. The daggle app is implemented as an R shiny application using the golem framework. The application builds upon existing R libraries including pcalg to generate reproducible random DAGs, dagitty to identify all valid minimal adjustment sets and ggdag to visualize DAGs. The daggle app can be accessed online at [cbdrh.shinyapps.io/daggle]. The source code is available on GitHub [github.com/CBDRH/daggle] and is released under a Creative Commons CC BY-NC-SA 4.0 licence.
Publisher: British Editorial Society of Bone & Joint Surgery
Date: 09-2022
DOI: 10.1302/0301-620X.104B9.BJJ-2022-0116.R1
Abstract: The aim of this study was to estimate the 90-day periprosthetic joint infection (PJI) rates following total knee arthroplasty (TKA) and total hip arthroplasty (THA) for osteoarthritis (OA). This was a data linkage study using the New South Wales (NSW) Admitted Patient Data Collection (APDC) and the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR), which collect data from all public and private hospitals in NSW, Australia. Patients who underwent a TKA or THA for OA between 1 January 2002 and 31 December 2017 were included. The main outcome measures were 90-day incidence rates of hospital readmission for: revision arthroplasty for PJI as recorded in the AOANJRR conservative definition of PJI, defined by T84.5, the PJI diagnosis code in the APDC and extended definition of PJI, defined by the presence of either T84.5, or combinations of diagnosis and procedure code groups derived from recursive binary partitioning in the APDC. The mean 90-day revision rate for infection was 0.1% (0.1% to 0.2%) for TKA and 0.3% (0.1% to 0.5%) for THA. The mean 90-day PJI rates defined by T84.5 were 1.3% (1.1% to 1.7%) for TKA and 1.1% (0.8% to 1.3%) for THA. The mean 90-day PJI rates using the extended definition were 1.9% (1.5% to 2.2%) and 1.5% (1.3% to 1.7%) following TKA and THA, respectively. When reporting the revision arthroplasty for infection, the AOANJRR substantially underestimates the rate of PJI at 90 days. Using combinations of infection codes and PJI-related surgical procedure codes in linked hospital administrative databases could be an alternative way to monitor PJI rates. Cite this article: Bone Joint J 2022 -B(9):1060–1066.
Publisher: Wiley
Date: 22-11-2017
DOI: 10.1111/PPE.12426
Abstract: Preterm birth and developmental vulnerability are more common in Australian Aboriginal compared with non-Aboriginal children. We quantified how gestational age relates to developmental vulnerability in both populations. Perinatal datasets were linked to the Australian Early Development Census (AEDC), which collects data on five domains, including physical, social, emotional, language/cognitive, and general knowledge/communication development. We quantified the risk of developmental vulnerability on ≥1 domains at age 5, according to gestational age and Aboriginality, for 97 989 children born in New South Wales, Australia, who started school in 2009 or 2012. Seven thousand and seventy-nine children (7%) were Aboriginal. Compared with non-Aboriginal children, Aboriginal children were more likely to be preterm (5% vs. 9%), and developmentally vulnerable on ≥1 domains (20% vs. 36%). Overall, the proportion of developmentally vulnerable children decreased with increasing gestational age, from 44% at ≤27 weeks to 20% at 40 weeks. Aboriginal children had higher risks than non-Aboriginal children across the gestational age range, peaking among early term children (risk difference [RD] 19.0, 95% confidence interval [CI] 16.3, 21.7 relative risk [RR] 1.91, 95% CI 1.77, 2.06). The relation of gestational age to developmental outcomes was the same in Aboriginal and non-Aboriginal children, and adjustment for socio-economic disadvantage attenuated the risk differences and risk ratios across the gestational age range. Although the relation of gestational age to developmental vulnerability was similar in Aboriginal and non-Aboriginal children, Aboriginal children had a higher risk of developmental vulnerability at all gestational ages, which was largely accounted for by socio-economic disadvantage.
Publisher: Elsevier BV
Date: 04-2022
Publisher: Cold Spring Harbor Laboratory
Date: 07-04-2021
DOI: 10.1101/2021.04.07.21255067
Abstract: COVID-19 population vaccination programs are underway globally. In Australia, the federal government has entered into three agreements for the supply of vaccines, with roll-out beginning for the highest priority groups in February 2021. Expansion of the vaccination program throughout February and March failed to meet government targets and this has been attributed to international supply issues. However, Australia has local capacity to manufacture one million doses of the AstraZeneca vaccine weekly and once fully operational this will greatly increase the national vaccination capacity. Under current plans, these vaccine doses will be distributed primarily through a network of general practices, to be joined in later phases by community pharmacies. It remains unclear whether these small distribution venues have the logistical capacity to administer vaccines at the rate they will become available. To inform this discussion, we applied stochastic queue network models to estimate the capacity of vaccination sites based on assumptions about appointment schedules, service times and available staff numbers. We specified distinct queueing models for two delivery modes: (i) mass vaccination hubs located in hospitals or sports arenas and (ii) smaller clinics situated in general practices or community pharmacies. Based on our assumed service times, the potential daily throughput for an eight hour clinic at a mass vaccination hub ranged from around 500 vaccinations for a relatively small hub to 1,400 vaccinations a day for a relatively large hub. For GP vaccination clinics, the estimated daily throughput ranged from about 100 vaccinations a day for a relatively small practice to almost 300 a day for a relatively large practice. Stress tests showed that for both delivery modes, sites with higher staff numbers were more robust to system pressures, such as increased arrivals or staff absences, and mass vaccination sites were more robust that GP clinics. Our analysis is accompanied by an interactive web-based queue simulation applet, which allows users to explore queue performance under their own assumptions regarding appointments, service times and staff availability. Different vaccine delivery modes offer distinct benefits and may be particularly appealing to specific population segments. A combination of expanded mass vaccination hubs and expanded GP vaccination is likely to achieve mass vaccination faster than either mode alone.
Publisher: Oxford University Press (OUP)
Date: 15-10-2016
DOI: 10.1111/RSSA.12143
Abstract: Sequence analysis is widely used in life course research and more recently has been applied by survey methodologists to summarize complex call record data. However, summary variables derived in this way have proved ineffective for post-survey adjustments, owing to weak correlations with key survey variables. We reflect on the underlying optimal matching algorithm and test the sensitivity of the output to input parameters or ‘costs’, which must be specified by the analyst. The results illustrate the complex relationship between these costs and the output variables which summarize the call record data. Regardless of the choice of costs, there was a low correlation between the summary variables and the key survey variables, limiting the scope for bias reduction. The analysis is applied to call records from the Irish Longitudinal Study on Ageing, which is a nationally representative, face-to-face household survey.
Publisher: Wiley
Date: 11-10-2022
DOI: 10.5694/MJA2.51291
Publisher: Elsevier BV
Date: 2019
Publisher: Public Library of Science (PLoS)
Date: 24-04-2018
Publisher: Cold Spring Harbor Laboratory
Date: 03-02-2021
DOI: 10.1101/2021.02.02.21250979
Abstract: The Australian Government’s COVID-19 vaccine rollout strategy is scheduled to commence in late February 2021 and aims to vaccinate the Australian adult population by the end of October 2021. The task of vaccinating some 20 million people within this timeframe presents considerable logistical challenges. Key to meeting this target is the rate of vaccine delivery: the number of vaccine doses that can be administered per day. In the opening phase, high priority groups will receive the Pfizer/BioNTech vaccine through hospital hubs at an initial rate of 80,000 doses per week. However, pending regulatory approval, the currently announced plan appears to be to distribute the AstraZeneca vaccine to the bulk of the popluation through a combination of general practices and community pharmacies. Here, we run a series of projections to estimate how long it will take to vaccinate the Australian population under different assumptions about the rate of vaccine administration as well as the schedule for second doses and prevalence of vaccine hesitancy. Our analysis highlights the ambitious rate of vaccine administration that will be neccessary to meet the Australian Government completion target of October 2021. A rate of 200,000 doses per day would comfortably meet that target 80,000 doses a day would see roll-out extended until mid-2022. Speed is of the essence when it comes to vaccine rollout: protecting the population quickly will minimise the risk of sporadic and costly lockdowns lockdowns and the potential for small, local clusters getting out of control and sparking new epidemic waves. The government should gather all its resources to maximise the daily vaccination rate, ideally aiming to r up administration to at least 200,000 doses per day as quickly as possible. Quickly achieving and maintaining this pace will likely require dedicated large-scale vaccination sites that are capable of delivering thousands of doses a week in addition to the enthusiastic participation of GP practices and community pharmacies around the country. Lessons on the neccessary logistical planning, including coordination of delivery, ultra-cold-chain storage and staffing, can potentially be learned from Israel, where between 7,000 and 20,000 vaccinations per million population have been delivered daily throughout January.
Publisher: BMJ
Date: 06-2013
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Mark Hanly.