Fall 2020 - HSCI 416 D100

Health Services Research (4)

Class Number: 6306

Delivery Method: Remote

Overview

  • Course Times + Location:

    Mo 9:30 AM – 12:20 PM
    REMOTE LEARNING, Burnaby

  • Prerequisites:

    STAT 302 or STAT 305, and HSCI 307 or HSCI 330.

Description

CALENDAR DESCRIPTION:

An introduction to the fundamental concepts of Health services research. Examination of how people access health care, how much care costs, and what happens to patients as a result of this care. Identification of the most effective ways to organize, manage, finance, and deliver high quality care.

COURSE DETAILS:

Course Description:

Health services research (HSR) is a multidisciplinary field that examines how people get access to health care, how much care costs, and what happens to patients as a result of this care. The main goals of HSR are to identify the most effective ways to organize, manage, finance, and deliver high quality care; reduce medical errors; and improve patient safety.  This foundational course will introduce students to the fundamental concepts of HSR including the measurement and evaluation of health system performance.

Areas of emphasis related to methodology include: theoretical foundations for the evaluation of health services and systems; measurement; study design; threats to validity; data sources commonly used in HSR; and analytic methods for HSR, including multiple regression analysis.

COURSE-LEVEL EDUCATIONAL GOALS:

Learning Objectives:  At the end of this course students should be able to: Describe key themes and research approaches underlying HSR; Identify common research designs used in the study of health systems and programs; Critically appraise the design, analysis, and interpretation of published HSR and be able to identify sources of bias; Demonstrate analytical skills for commonly-used data sources and research questions in HSR using SAS statistical software

Grading

  • Problem-Based Learning Activity 35%
  • Final Empirical Project 45%
  • Participation 20%

NOTES:

Expected Course Schedule:

Lecture 1: Introduction

Lecture 2: Measurement

Lecture 3: Study design in HSR

Lecture 4: Threats to validity

Problem-Based Learning Assignment Presentations

Lecture 5: HSR data sources

Lecture 6: Statistical inference (I)

Lecture 7: Statistical inference (II)

*Note that the course materials and grading distribution will be the same for 416 and 891 enrolees, however additional requirements will be added for the problem-based learning and final empirical assignments for masters students.

**Note also that this class will feature a mix of in-person and asynchronous and synchronous delivery. I expect to hold a weekly one-hour videoconference to review the key concepts of the past week's lecture and provide space for questions and further discussion.

Explanation of Grades:

A+ = Excellent performance. Work and learning exemplifying the highest quality possible.  

A = Superior performance in all elements of the course. Unquestionably prepared for subsequent courses in field.   

B+= Good. High quality performance in all or most elements of the course.  Very good chance of success in subsequent courses.  

B = Good. High quality performance in some of the course; satisfactory in others. Good chance of success in subsequent courses.  

B- = Satisfactory performance in the course. Evidence of sufficient learning to succeed in subsequent courses.  

C+ = Satisfactory performance in most of the course, with the remainder being somewhat substandard. Evidence of sufficient learning to succeed in subsequent courses, with effort.  

C = Evidence of some learning, but generally marginal performance. Marginal chance of success in subsequent courses.  

C- = Poor. Minimal learning and substandard performance throughout the course. Doubtful chance of success in subsequent courses.  

D = Poor. Minimal learning and low quality performance. Doubtful chance of success in subsequent courses.  

F = Failure. Complete absence of evidence of learning. Completely unprepared for subsequent courses.  

The Faculty of Health Sciences includes in our grading policies a few guidelines for expected grade distributions in its courses. Lower division (100- and 200-level) undergraduate courses should, in general, have no more than 5% A+’s, and the median letter grade should be a B-/B. Upper division (300- and 400-level) undergraduate courses should in general have no more than 8% A+’s, and the median letter grade should be a B/B+.   FHS adheres to SFU Academic Honesty and Student Conduct policies. Students in this course are responsible for knowing these policies, at http://www.sfu.ca/policies/Students/. A tutorial on plagiarism is at http://www.lib.sfu.ca/help/writing/plagiarism. If the instructor believes a student has committed an act of academic dishonesty, he/she will submit a form reporting the matter to the SFU Registrar. In this course if you are found to have cheated (whether plagiarism or another type) you will be given a zero for that test or assignment.  

REQUIREMENTS:

Note that STAT 305 and HSCI 307 are both acceptable as pre-requisites for this course.

Materials

MATERIALS + SUPPLIES:

Required Textbooks:   N/A – Readings will be made available electronically through the Canvas system.  Readings will be selected from the following texts, as well as articles in the peer-reviewed literature.  All readings should be considered ‘mandatory’, and are intended to reinforce and complement the lecture material.  

Angrist JD, Pischke J-S.  Mostly Harmless Econometrics – An Empiricist’s Companion.  Princeton University Press, New Jersey, 2009.  

Gordis L.  Epidemiology, 3rd Edition.  Elsevier Inc., Pennsylvania, 2004.  

Harrell FE.  Regression Modeling Strategies: With applications to linear models, logistic regression, and survival analysis.  Springer, New York, 2001.  

Holford TR.  Multivariate Methods in Epidemiology.  Oxford University Press, New York, 2002.  

Rosner BA.  Fundamentals of Biostatistics.  Cengage Learning, New York, 2006.  

Rothman KJ, Greenland S, Lash TL.  Modern Epidemiology, 3rd Edition.  Lippincott Williams and Wilkins, New York, 2008.  

Shi L.  Health Services Research Methods, 2nd Edition.  Delmar Cengage Learning, New York, 2005.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

TEACHING AT SFU IN FALL 2020

Teaching at SFU in fall 2020 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).