centre for big data research unsw

Centre for Big Data

144 units of credit.

Research Capability & Technology Portfolio

Big data analytics.

Applying advanced analytics to big data—including the millions of records generated routinely by health services, clinical data captured at the point-of-care, and data generated by the population at large through technologies such as wearable devices and social media—in order to enhance health and wellbeing.

Competitive advantage

• Internationally recognised expertise across the following capability areas:
• Data science, data analytics, biostatistics, epidemiology and bioinformatics
• Ethical, legal and policy frameworks relevant to using health big data
• Secure management, sharing, curation and stewardship of health big data
• Developing and delivering training in health data analytics
• Communicating complex research findings through multiple modalities, including data visualisation, technical reports, plain-English briefings, multimedia products and social media

Bringing together researchers, consumers, clinicians, industry and government to frame relevant research questions, generate research designs that apply to real-world contexts, and commit to implementing the research and its findings.

Successful outcomes

• Analysis of Healthdirect telephone helpline records, to identify whether people followed advice and the outcomes
• Analysis of In-Vitro Fertilisation (IVF) treatment data to determine chances of success
• Analysis of data for NSW children showing a later school start has clear developmental benefits

Capabilities and facilities

E-Research Institutional Cloud Architecture (ERICA) secure cloud-based platform for research using sensitive microdata.

Our partners

• Australian Commission on Safety and Quality in Health Care
• Australian Government Department of Health
• Department of Veteran’s Affairs
• Healthdirect Australia
• Australian Institute of Health and Welfare
• NSW Ministry of Health
• Department of Health and Human Services Victoria
• New Zealand Ministry of Health
• NPS MedicineWise
• Prospection Pty Ltd
• Janssen Pty Ltd

Dr Mark Joseph Hanly

Mark is a lecturer and applied social statistician based at the Centre for Big Data Research in Health, UNSW Sydney. His research focuses on the analysis of large, linked administrative datasets to answer research questions related to the health and social welbeing of populations. Topics of interest include early childhood health and development, health service utilisation, Indigenous health and outcomes for vulnerable populations. As an... view more

Mark is a lecturer and applied social statistician based at the Centre for Big Data Research in Health, UNSW Sydney. His research focuses on the analysis of large, linked administrative datasets to answer research questions related to the health and social welbeing of populations. Topics of interest include early childhood health and development, health service utilisation, Indigenous health and outcomes for vulnerable populations. As an applied social statistician, he is interested in statistical methods for answering causal questions using observational data;  summarising and modelling complex longitudinal data; and innovative approaches for  visualising data and communicating research. Mark convenes and teaches the course HDAT9700 Statistical Modelling II as part of the MSc in Health Data Science.

Mark joined the Centre for Big Data Research in Health from the University of Bristol, where he completed his PhD in Advanced Quantitative Methods for the Health and Social Sciences. His doctoral research focused on novel approaches to correct for nonresponse bias in large household surveys. He also holds an MSc in Applied Social Research from Trinity College Dublin and a BSc (Hons) in Mathematics and Statistics from the National University of Ireland, Maynooth.  

$50,000 CRE REACH The type and timing of first child protection service contacts in Aboriginal children in New South Wales, 2002-2019 (CIA)

$655,934 National Health and Medical Research Council Ideas Grant (2021) Hospital trajectories for 15 million Australians (CIE)

$50,000 Triple I Clinical Academic Group Seed Grant (2020) Open source modelling tools to support policy decision-making throughout the COVID-19 post-pandemic phase (CIA)

$1,248,388 National Health and Medical Research Council Transforming the health system response to child maltreatment: the need for cross-jurisdictional e-cohorts (CID)

$26,000 Australian Research Data Council Disseminating Research in the 21st Century (CIA)

$30K NSW Department of Family and Community Services (FACS) Commissioned Project Analysis report for the ‘Family is Culture’ review of Aboriginal children in Out-of-Home care in NSW. (CIB)

$40,000 UNSW Neuroscience, Mental Health and Addictions and SPHERE Clinical Academic Group Seed Funding Scheme Neurodevelopmental, physical and mental health consequences of early life adversity: cross-sectoral population data linkage to inform health and social policy. (AI)

$23,685 NSW FACS Commissioned Project Their Futures Matter – Trial Cohort (Children in residential care) (CIB)

$10,849 NSW FACS Commissioned Project Their Futures Matter – Early life pathways and outcomes for children in contact with child protection (CIB)

My Qualifications

• PhD Advanced Quantitative Mehtods, University of Bristol 2015
• MSc Applied Social Research, Trinity College Dublin 2011
• BSc Mathematics and Statistics, National University of Ireland Maynooth 2006
• mso-list:l0 level1 lfo1;tab-stops:list 36.0pt"> "Times New Roman";mso-ansi-language:EN-IE">CovidR Contest Winner, European R-Users Meeting (2020) mso-fareast-font-family:"Times New Roman"">
• mso-list:l0 level1 lfo1;tab-stops:list 36.0pt"> "Times New Roman";mso-ansi-language:EN-IE">Datathon winner, National University of Singapore - National University Health System - Massachusetts Institute of Technology (MIT) Healthcare Artificial Intelligence (AI) Datathon (2018) "Times New Roman",serif;mso-fareast-font-family:"Times New Roman"">
• mso-list:l0 level1 lfo1;tab-stops:list 36.0pt"> "Times New Roman";mso-ansi-language:EN-IE">UNSW Medicine and Teaching and Research, Excellence in the Design and Development of Programs, Courses or Other Initiatives Award (2018) mso-fareast-font-family:"Times New Roman"">
• mso-list:l0 level1 lfo1;tab-stops:list 36.0pt"> "Times New Roman";mso-ansi-language:EN-IE">European Survey Research Association, Early career researcher award: runner-up (2013)
• mso-list:l0 level1 lfo1;tab-stops:list 36.0pt"> "Times New Roman";mso-ansi-language:EN-IE">ESRC Advanced Quantitative Methods Studentship £16,590 PA for 3 years (+ Tuition fees) (2011)

My Research Supervision

Supervision keywords, areas of supervision.

ILP/Honours/Masters/PhD

Potential supervision areas

• Analysis of linked data
• Causal inference using observational data
• Innovative data communication (interactive dashboards and apps)

My Engagement

Blog posts and articles in the conversation.

Which families delay sending their child to school and why? We crunched the numbers . (The Conversation, April 2019)

Preschool benefits Indigenous children more than other types of preschool care . (The Conversation, December 2020)

Evaluating COVID-19 exit strategies using open-source modelling tools . (The UK Public Health Rapid Support Team Blog Post, August 2020)

Australia must vaccinate 200,000 adults a day to meet October target: new modelling . (The Conversation, February 2021)

Australian vaccine rollout needs all hands on deck after laterst Astra Zeneca news, mass vaccination hubs included . (The Conversation, April 2021)

Using Shiny in Healthcare: Examples from the 2021 Shiny Contest  (R Studio blog, August 2021)

In the media

How big data can change intensive care . (UNSW Newsroom, July 2018)

'A gift of time': Children who start school later fare better, study finds . (Sydney Morning Herald, April 2019)

1 in 4 families delay thier child's school entry -- and older children are more schol-ready: big data study . (UNSW Newsroom, April 2019)

Should kids start school later?  (The Educator magazine, June 2019)

UNSW wins international COVID-19 data science competition . (thesphere.com.au, June 2020)

Dr Mark Hanly talks COVOID R package (eRUM 2020) . (UNSW Newsroom, June 2020)

'What's surprising is the scale of the problem': One in seven children at risk of harm by five years old . (The Sydney Morning Herald, June 2020)

Nurses accuse Commonwealth of slowing vaccine rollout and treating them as handmaidens  (ABC AM, February 2021)

Emailed COVID-19 vaccine certificate part of rollout plan, as Australia gets closer to first vaccinations  (ABC News, February 2021)

Is Australia's goal of vaccinating the entire adult population by October achievable? (The Guardian, February 2021)

Mass vaccination could rescue the snail-paced rollout (The Medical Republic, April 2021)

My Teaching

mso-fareast-font-family:"Times New Roman";mso-ansi-language:EN-IE;mso-fareast-language: EN-GB;mso-bidi-language:AR-SA"> HDAT9700 Statistical Modelling II . Lead course developer, convenor and lecturer.

Publications

• Journal articles 28
• Conference Papers 4
• Conference Presentations 2
• Conference Abstracts 6
• Preprints 1

ORCID as entered in ROS

Research Activities

Funded by NHMRC Project Grant.

Promoting positive early childhood development is fundamental to improving life opportunities and outcomes for Aboriginal Australians. However, national data show that a significant proportion of Aboriginal children have markers of developmental vulnerability at school entry and this tracks through to poor literacy and numeracy outcomes across all schooling years. We currently lack information about the key drivers of positive early childhood development in Aboriginal children, and the features of local communities and early childhood service…

UNSW Sydney NSW 2052 Australia Telephone +61 2 93851000 Authorised by Deputy Vice-Chancellor (Research) UNSW CRICOS Provider Code: 00098G ABN: 57 195 873 179

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Cardiovascular

Using big data to improve patient prediction, management, outcomes and care

At the Centre for Big Data Research in Health, we source big data for our research into cardiovascular diseases, including coronary heart disease, stroke and heart failure. These diseases affect around four million Australians and cost the Australian health system $12 billion per year. 

Our cardiovascular disease research uses multiple sources of big data, including hospital, Medicare and pharmaceutical records to tackle questions including:

Can we use routinely collected primary care and mammography data for early prediction of increased risk of heart attack or stroke? 

Can we use detailed electronic medical records to predict which cardiovascular patients will be readmitted to hospital?

Are there sex differences in the pre-hospital, hospital and post-discharge management of heart attack and stroke?

How do pathways of care and outcomes following heart attack vary between urban and rural patients?

What are the comparative outcomes of medication management versus atrial ablation for patients with atrial fibrillation? 

What are the comparative long-term outcomes of open surgical versus minimally invasive heart valve replacement?

Novel deep learning methods for large-scale cardiovascular risk screening

Through two synergistic studies we will use deep learning methods to deliver highly novel, low-cost approaches to cardiovascular risk screening to achieve unparalleled population reach using health service data already available for millions of Australians.