Course Description
Foundations of Public Health Computing
UBC School of Population and Public Health September 7 – December 7, 2021 (Thursdays, 2-5PM, B151)
By the end of this course students should be able to:
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Learning objectives
SPPH 553 001 2021W (SAS)
The work of public health professionals is increasingly becoming dependent on large and complex datasets. In Canada, particularly British Columbia, typical sources of these data include Vital Statistics, Medical Services Plan (MSP) billings, the Canadian Institute for Health Information (CIHI) hospital Discharge Abstract Database (DAD), and PharmaNet. Because these data are not collected primarily for research purposes, they are not in a typical format that is ready for statistical analysis. They need to be imported into a software package, checked, cleaned, and linked to other datasets. Handling and working with these massive datasets (e.g. the MSP data alone covers up to 4.8 million of British Columbians with multiple visits to their physicians each year) require a disciplined approach to data preparation and management.
In this course, students will gain practical, hands-on experience in cleaning, preparing, linking, and managing large health data. The specific focus is on ensuring correctness, efficiency, security, and reproducibility when managing and preparing data for analysis.
There are many tools that can be used to work with large data sets. SAS is the software package that will be used in this course.
1. Identify survey and health administrative data that can be used for research
2. Import/load and explore large health datasets using SAS
3. Use common SAS functions
4. Clean,organize,andcreatedatafieldsorcolumns,suchasdates,age,diagnosticcodes and
postal codes
5. Subset, collapse/aggregate, append, and merge datasets
6. Write and execute SQL statements
7. Run descriptive statistics
8. Generate graphs
9. Write loops and macros to automate repetitive tasks
10. Create analytic cohorts using linked health administrative data
SPPH 400 and SPPH 502
Prerequisites
Joseph H. Puyat, PhD, MSc, MA (Psychology)
Assistant Professor (Partner), UBC School of Population & Public Health Scientist & Research Methodologist, CHEOS, Providence Health Care
588 – 1081 Burrard St (Room 583A), St. Paul’s Hospital, Vancouver, BC V6Z 1Y6 Officehoursbyappointment
Instructor
PhD Student
Office hours by appointment
Teaching Assistant
1. A relatively recent laptop/desktop computer with at least 4Gb of RAM and 50Gb of free hard disk space.
Equipment and Software
2. Robust and Hi-speed Internet connection, preferably wired.
3. Free SAS OnDemand account (https://welcome.oda.sas.com/login)
Learning Resources
Recommended Text:
Delwiche LD, J. The Little SAS Book: A Primer, Fifth Edition. SAS Institute, 2012.
Students are also encouraged to use other online resources, such as the SAS tutorials 1 and reference manuals (Base SAS 9.3 Procedures Guide, 2 SAS 9.3 SQL Procedure3).
The course consists of 6 in-person classes (~3 hours each) and 5 modules (~ 2 – 3 hours each) that can be completed offline. There will be lectures and group-based activities in the in-person sessions while the web-based sessions will consist of videos, readings, and written instructions. Optional tutorials(structured)mayalsobescheduled. Themajorityoftimewillbedevotedtodata management and preparation, and to some extent, statistical analysis.
Course Format
Assignments (5) 30% Examinations (3) 60% Participation (discussion board) 10%
Grading/Assessment
Assignments are due at the end of the week a specific module is scheduled to be completed, or at pre-specified deadlines. The three exams are timed and are available within a week. The timed assignments/exams must be taken at a time when they can be completed without interruption as
1 https://support.sas.com/en/software/university-edition-support.html#tutorials 2 BaseSAS 9.3ProceduresGuide
3 SAS 9.3 SQL Procedure
the system starts counting down the moment the first question/item is shown. All work (completed or not) will be submitted automatically at the end of the time limit. Starting an assignment/exam, just to view the questions/items, without working on or completing it could result in receiving the lowest possible score for that assignment/exam.
Participation in online discussions will be marked and assessed following this rubric: 0 No posts or contributions
1 Questions or requests for help or assistance
2 Short responses to questions or requests for assistance 3 Very helpful responses to questions or requests for
assistance; sharing alternate code that facilitates communication and reproducibility
There will be a total of 11 online discussions, one for each module. Students are expected to participate or post in 8 of the 11 discussions. Those who participated in all 11 discussions will have only their top 8 contributions included in the calculation of the final marks for this course. Note that discussions will be open for 2 weeks only and will not be available for further posting once closed.
Academic dishonesty will not be tolerated and students are expected to know what constitutes academic dishonesty. For further information and guidance, please consult the UBC Academic Calendar, particularly the section on Academic Honesty and Standards and Academic Misconduct.
Academic Integrity
Course Schedule
Date Topics/Activities
Introduction to SAS Working with CCHS data
Sep 7 – 12
• SAS statements: DATA step and PROC step
• Subsetting by observation and by variables
• SAS operators: Comparison and Logical Operators • Creating Variables
In-person class (1/6): September 9
Sep 13 – 19 • Generating frequency counts and cross tabulations • Calculating means and standard deviations
• Importing CSV files
In-person class (2/6): September 16
Merging data through DATA step; collapsing data across observations;
Introduction to the BC Vital Statistics data
Sep 20 – 26
Renaming variables, running basic SAS functions, working with DATE/TIME variables, creating and using SAS MACRO variables, and
Sep 27 – Oct 3
generating bar plots.
Automating repetitive tasks using loops and MACROs
Oct 4 – 10 generating high-resolution, scalable scatterplots via PROC SGPLOT (SAS Statistical Graphics); running linear regression analyses
In-person class (3/6): October 7
Oct 11 – 17
First Exam (3 hours)
Oct 18 – 24
In-person class (4/6): October 21
Oct 25 – Oct 31 Generating basic maps in SAS
Nov 1 – 7 handling compressed (gzipped) flat files
Nov 8 – 14
Dec 6 – 12
Introduction to the Structured Query Language
Using SQL to create data sets, define variables, and merge data sets Preparing hospital data (Discharge Abstract Database) for analysis;
Working with prescription drug use data (PharmaNet data) Creating analytic cohorts based on administrative data
Third Exam (4 hours)
Second Exam (3 hours)
Examining hospital length of stay and readmission rates Exploring
Nov 15 – 21
Health Canada’s Drug Product database
In-person class (5/6): November 18
22 – 28 Linking PharmaNet data with Health Canada Drug Product Database
Nov 29 – Dec 5
In-person class (6/6): December 2
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