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Unit of study_

PUBH5215: Analysis of Linked Health Data

Intensive June, 2023 [Block mode] - Remote

Throughout our lives, information about our health and the care we receive is recorded and stored across various health-related databases. Data linkage is a process that brings together information from different databases about the same individual, family, place or event. This process creates a chronological sequence of health events or individual 'health story' that can be combined into a much larger story about the health of people. This information 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 introduce how data linkage is conducted, illustrate how data linkage can be used for research, highlighting the advantages, dangers, and pitfalls. We describe how to design linked data studies, outline the data management steps required before analysing the data, 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 to develop their programming skills for handling large complex datasets.

Unit details and rules

Unit code PUBH5215
Academic unit Public Health
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
None
Corequisites
? 
(PUBH5010 or BSTA5011 or CEPI5100) and (PUBH5211 or PUBH5217 or BSTA5004)
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

Available to study abroad and exchange students

No

Teaching staff

Coordinator Erin Cvejic, erin.cvejic@sydney.edu.au
Demonstrator(s) James Hedley, james.hedley@sydney.edu.au
Heather Baldwin, heather.baldwin@sydney.edu.au
Joseph Van Buskirk, joseph.vanbuskirk@sydney.edu.au
Lecturer(s) Kylie-Ann Mallitt, kylie-ann.mallitt@sydney.edu.au
Type Description Weight Due Length
Small continuous assessment Module 2 Exercise
Module exercise solutions and associated statistical code.
20% -
Due date: 14 Jun 2023 at 23:59
3-5 pages
Outcomes assessed: LO3 LO7
Assignment Reflective activities
Written assessment
30% -
Due date: 25 Jun 2023 at 23:59
5-6 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment Analysis Report
Written data analysis assessment
50% -
Due date: 01 Jul 2023 at 23:59
6-8 pages plus program code
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7

Assessment summary

  • Students will submit their solutions and associated statistical code for the Module 2 Exercise. Time will be scheduled as part of the block mode workshop to complete this task in-class.
  • Reflective activities will require students to reflect on the topics covered within selected modules and provide a personal reflection statement on their learning, or create multiple choice questions with answers and explanations based on theoretical topics.
  • 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 sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

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 Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
- Day 1: Introduction to data linkage systems Block teaching (8 hr) LO1 LO6
Day 2: Privacy, ethics, and data security; Characterising populations using linked data Block teaching (8 hr) LO1 LO2 LO5 LO6 LO7
Day 3: Measuring health and services using linked data Block teaching (8 hr) LO2 LO3 LO4 LO5 LO6 LO7
Day 4: Health care outcomes research using linked data Block teaching (8 hr) LO3 LO4 LO5 LO6 LO7
Day 5: Risk adjustment using linked data Block teaching (8 hr) LO2 LO3 LO4 LO5 LO6 LO7

Attendance and class requirements

Enrolled students are expected to attend all live lectures and Q&A 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 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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

This course was last delivered in block mode in November 2022. In response to student feedback, the weighting of the final assessment task has been reduced, and the assessment of an in-class Module Exercise included. This will provide the opportunity for some earlier feedback on submitted work prior to completing the final major assessment. A selection of previously recorded lectures will be delivered live, or re-recorded for this delivery.
  • Access to either R or SAS (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 VPN to access data sets for use in practical activities.
  • 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.