Outline

Overview

Throughout our lives, information about our health and the care we receive is recorded and stored across various health-related databases, e.g., hospital admissions, cancer registry. Data linkage is a process that brings together information from various different sources about the same individual, family, place, or event. This process creates a chronological sequence of events that can be combined into a much larger story about the health of people, which can be used for research or to improve health services. This unit is suitable for health services researchers, policy makers, clinical practitioners, biostatisticians, and data managers. We explain how data linkage is conducted, illustrate how data linkage can be used for research, while highlighting the advantages, dangers, and pitfalls. We describe how to design linked data studies, outline the data management steps required before analysis, and discuss some of the methods and issues of analysing linked data. Students will have access to data from a real data linkage and will gain hands-on experience developing their programming skills in R for handling large complex datasets.

Unit details and rules

Academic unit	Public Health
Credit points	6
Prerequisites ?	None
Corequisites ?	(PUBH5010 or BSTA5011 or CEPI5100) and (PUBH5211 or PUBH5217 or BSTA5004)
Prohibitions ?	None
Assumed knowledge ?	The unit assumes introductory-level programming skills in SAS or R, assumes introductory-level knowledge in epidemiology, e.g., PUBH5010 or CEPI5100, and introductory-level knowledge in biostatistics or statistics, e.g., PUBH5018 or FMHU5002.
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Nicole De La Mata, nicole.delamata@sydney.edu.au

Assessment

The census date for this unit availability is 8 August 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Out-of-class quiz	Module Quizzes MCQs assessing theoretical concepts covered in each module	20%	Multiple weeks	Up to 5 MCQs per module	AI allowed
Outcomes assessed:
Presentation	Recorded Presentation Recorded presentation on a specified topic and accompanying slide deck	30%	Week 03 Due date: 03 Aug 2025 at 23:59	5-6 mins (recorded)	AI allowed
Outcomes assessed:
Data analysis	Analysis Report Written data analysis assessment	50%	Week 04 Due date: 09 Aug 2025 at 23:59	6-8 pages plus program code	AI allowed
Outcomes assessed:

Assessment summary

Students will complete a small (up to 5 multiple choice question) time-limited quiz for each of Modules 1 to 4, covering theoretical concepts. Each module quiz will be worth 5%, for a cumulative total of 20%.
The recorded presentation will require students to prepare and record a short presentation (along with the slide deck) for submission on a specified conceptual topic.
The analysis report will require students to perform a data analysis using linked data and report on their findings, including providing the relevant statistical code used to generate the responses.

Detailed information for each assessment will be provided on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Grade	Mark Range	Description
AF Absent fail	Range from 0 to 49	To be awarded to students who fail to demonstrate the learning outcomes for the unit at an acceptable standard through failure to submit or attend compulsory assessment tasks or to attend classes to the required level.
FA Fail	Range from 0 to less than 50	To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty.
PS Pass	Range from 50 to less than 65	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard
CR Credit	Range from 65 to less than 75	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard
D Distinction	Range from 75 to less than 85	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard
HD High distinction	Range from 85 to 100 inclusive	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
-	Day 1: Introduction to data linkage systems	Block teaching (8 hr)
	Day 2: Privacy, ethics, and data security; Characterising populations using linked data	Block teaching (8 hr)
	Day 3: Measuring health and services using linked data	Block teaching (8 hr)
	Day 4: Health care outcomes research using linked data	Block teaching (8 hr)
	Day 5: Risk adjustment using linked data	Block teaching (8 hr)

Attendance and class requirements

Enrolled students are expected to attend all live sessions, either in-person or online, unless prior arrangements have been made with the unit of study coordinator.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. understand the theory of data linkage methods and features of comprehensive data linkage systems, sufficient to know the sources and limitations of linked health data sets
LO2. apply epidemiological principles to the design of studies using linked data
LO3. construct numerators and denominators for the analysis of disease trends and health care utilisation and outcomes
LO4. assess the accuracy and reliability of data sources
LO5. check data linkages and assure the quality of the study process, e.g. consistency of definitions, missing data
LO6. list the issues to be considered when analysing large linked data files
LO7. write syntax to prepare linked data files for analysis, derive exposure and outcome variables, relate numerators and denominators, and produce results from statistical procedures.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This course was last delivered in block mode in June 2024. Additional resources have been added to the unit, including a redesign of some of the data wrangling components.

Additional information

Access to R (installed locally, or via remote access), and a stable internet connection, is required to complete this unit of study.
Offsite students will be required to connect to the university virtual private network (VPN) to access data sets for use in practical sessions.
Before gaining access to learning resources, students will be required to agree to a confidentiality agreement
It is prohibited to copy and locally store the data sets used in this unit of study.

Further information will be provided on Canvas.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

PUBH5215: Analysis of Linked Health Data

Intensive July - August, 2025 [Online] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links