Fall, Winter, Spring, Spring-Summer, Summer term(s) for residential students;
1-4 credit hour(s) for residential students;
Instructor(s):
Prerequisites: None
Description: Practical Projects in the application of theory and principles of Environmental Health Sciences in public health settings. Course requirements include an approved practical work experience related to Environmental Health Sciences in consultation with a faculty advisor. May be elected more than once. Enrollment limited to Environmental Health Sciences majors with at least two full terms of prior registration.
EHS697: Readings
Graduate level
Residential
Fall, Winter, Spring, Summer term(s) for residential students;
1-3 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: Perm. Instr
Description: Supervised study/review of a selected topic in environmental health, occupational health, nutrition and/or toxicology. May be elected more than once for a maximum of six credits.
EHS698: Research
Graduate level
Both Residential and OJOC
Fall, Winter, Spring, Summer term(s) for OJOC and residential students;
1-6 credit hour(s) for OJOC and residential students;
Instructor(s): Staff
Prerequisites: Perm. Instr.
Description: Original research investigation of a special topic in environmental health, occupational health, nutrition and/or toxicology. May be elected more than once for a maximum of six credits.
EHS699: Master's Thesis
Graduate level
Residential
Fall, Winter, Spring, Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: Perm of Thesis Advisor
Description: This course shall be elected by students enrolled in Master's degree programs that require a formal written thesis as a condition of program completion. The thesis shall be defended in front of the student's thesis committee. The course grade will reflect the student's accomplishments relative to the thesis and its defense. The course is to be elected only once.
EHS899: Advanced Research
Graduate level
Residential
Fall, Winter, Spring, Summer term(s) for residential students;
1-6 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: Perm. Instr.
Description: Original investigations of a specific topic in environmental health, occupational health, nutrition and/or toxicology. Designed for doctoral students performing research prior to passing their qualifying exam. May be elected more than once.
EHS990: Dissertation/Pre-Candidacy
Graduate level
Residential
Fall, Winter, Spring, Summer term(s) for residential students;
1-8 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Description: Election for dissertation work by doctoral students not yet admitted to status as candidate.
EHS995: Dissertation Research for Doctorate in Philosophy
Graduate level
Residential
Fall, Winter, Spring, Summer term(s) for residential students;
8 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Description: Election for dissertation work by doctoral students who have been admitted to status as candidate.
EPID565: Research in Hospital and Molecular Epidemiology
Graduate level
Residential
Fall, Winter, Spring, Summer term(s) for residential students;
Description: Investigation of a selected problem planned and carried out by each student. Pertinent literature, investigational approaches, and progress in the investigations are discussed in seminars. May be taken more than once for up to six credits. Usually taken first for one credit. This is the Capstone Course for Hospital and Molecular Epidemiology Students.
Fall, Winter, Spring, Spring-Summer, Summer term(s) for residential students;
1-4 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: Instructor Permission
Description: Application of epidemiological methods and concepts to analysis of data from epidemiological, clinical or laboratory studies. Introduction to independent research and scientific writing under faculty guidance.
Description: Review of literature on selected subjects under guidance of individual faculty members and through scheduled seminars at which reports are presented. May be elected more than once.
Description: This course teaches the statistical methods and principles necessary for understanding and interpreting data used in public health and policy evaluation and formation. Topics include descriptive statistics, graphical data summary, sampling, statistical comparison of groups, correlation, and regression. Students will learn via lecture, group discussions, critical reading of published research, and analysis of data.
Advisory Prerequisites: Required: introduction to statistical inferences, such as likelihood estimations, and regression models; Recommended: correlated and longitudinal data analysis
Undergraduates are allowed to enroll in this course.
Description: This course discusses both statistical theory and methodology aimed at addressing
ongitudinal studies, drop-out, selection model, and pattern-mixture model. Overall,
this course covers both applied and theoretical aspects related to statistical analysis
with missing data.
Learning Objectives: Overall,
this course covers both applied and theoretical aspects related to statistical analysis
with missing data.
Advisory Prerequisites: Experience with modeling or good quantitative background, including statistics and differential equations; familiarity with R software.
Description: Infectious disease modeling is increasingly being used to inform policy, practice, and research. This course will provide an introduction to the epidemiological and mathematical concepts underlying infectious disease modeling as well as the application of these concepts through hands-on model implementation.
Advisory Prerequisites: An entry-level qualitative research course or equivalent experience is helpful. In addition, an introduction to quantitative research such as a course on statistics and research design or equivalent experience is helpful.
Undergraduates are allowed to enroll in this course.
Description: Participants will gain knowledge of the foundations of mixed methods research, mixed methods quality criteria, major mixed methods research designs, the value added of mixed methods research, and legitimation and validation concepts. Through an interactive, problem-based approach, attendees will develop skills in designing a mixed methods study throughout the course.
Learning Objectives: The intent of this course is to provide an overview of mixed methods research to learners who already have some familiarity with quantitative and qualitative research
Prerequisites: EPID 701 or EPID 503 or EPID 600 or EPID 601 AND EPID 709 or BIOSTAT 501 or BIOSTAT 521
Description: This course focuses on the design, analysis, and interpretation of epidemiologic studies addressing diet and health. The course will provide quantitative practical skills to deal with methodological issues around dietary assessment methods, sources of variation in the diet, energy intake, measurement error, anthropometry and body composition, and biomarkers of intake.
EPID708: Machine Learning for Epidemiologic Analysis in the Era of Big Data
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Advisory Prerequisites: Introductory course in statistics as well as courses or working knowledge of basic regressions (linear, logistic, etc.). Having some background in the programming language R preferred.
Description: Course focuses on advances in machine learning and its application to causal inference and prediction via Targeted Learning, which allows the use of machine learning algorithms for prediction and estimating so-called causal parameters, such as average treatment effects, optimal treatment regimes, etc. We will discuss implementation via cloud computing.
EPID712: Epidemiologic Foundations And Applications In Dental, Oral, And Craniofacial Diseases
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s):
Prerequisites: none
Undergraduates are allowed to enroll in this course.
Description: The course addresses the fundamentals of oral and dental epidemiology including the most common conditions such as dental caries, periodontal diseases, and head and neck cancers with emphasis on their population distribution, evolution, multilevel determinants, and contemporary methodological issues on study design, measurement, analysis, and results interpretation and communication.
EPID716: Clinical Epidemiology and Evidence-Based Decision Making
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s):
Offered Every Summer
Prerequisites: Perm. Instr.
Description: With the increasing demand for an evidence-based approach in the delivery of health care services and the economic pressures for a more rational and efficient use of limited health care resources, practitioners and administrators in the health care field need to develop clinical measurement and evaluative skills in order to conduct their work optimally. Clinical Epidemiology and Evidence-Based Decision Making identifies and teaches these skills. The course will cover the basic concepts of clinical epidemiology in the context of appraising the recent medical literature pertaining to issues of causation, diagnosis, management, and economic evaluation. The format will include problem-based learning. Course materials will be provided in advance of the sessions, and should be reviewed before the course begins in order to obtain the maximum benefit from enrollment in the course. All health professionals (clinicians and administrators) who rely on the medical literature to guide their activities are invited to attend the course. No prerequisite.
EPID717: Design and Conduct of Clinical Trials
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s):
Offered Every Summer
Prerequisites: Intro Epidemiology and Perm. Instr
Description: The theoretical and practical challenges to be considered in designing and conducting a clinical trial will be presented. Topics to be discussed include the specification of a primary objective, adherence to accepted ethical guidelines, the role of randomization and the means of its implementation, the choice of design strategy and design strengthening features, and the considerations involved in sample size determination and patient recruitment. Detailed analytic issues will be considered in the complementary one-week course that follows. No prerequisite.
EPID718: Analysis of Clinical Trials
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s):
Offered Every July
Prerequisites: Intro Biostatistics and Perm. Instr.
Undergraduates are allowed to enroll in this course.
Description: Methods of analysis appropriate to various designs, such as cross-over designs, nested designs, factorial designs, and designs with repeated measures will be presented. The use of GLM techniques for analysis will also be illustrated. Topics will include estimation of survival functions, survival comparison between groups of subjects, identification of important covariates, adjustment for covariates, testing for interaction, and understanding the difference between confounding and interaction. Specific tools to be discussed include the Kaplan-Meier estimators, the log-rank (Mantel-Haenszel) statistics, and the Cox proportional hazards model. Instruction will focus on empirical use of methodologies rather than formal algebraic knowledge. Practical applications of manual and software-based analysis will illustrate specific procedures and interpretation of results. Students receive a disk with the data and analysis programs for all examples in the course. Students are advised to bring a scientific calculator.
EPID719: Bioinformatics Analysis Of Epigenomics Data
Undergraduates are allowed to enroll in this course.
Description: This course is to provide students with bioinformatics tools to analyze and interpret epigenomics data in the setting of epidemiological studies. Topics to be included: epigenome-wide association studies (EWAS), differentially methylated region (DMR) analysis, and estimation of different epigenetic clocks. Data management and analyses will be carried out in R.
Description: The course will approach concepts and methods for mediation from the perspective of the counterfactual framework. Mediation analysis quantifies the extent to which the effect of an exposure on some outcome is mediated through a particular intermediate and the extent to which it is direct or through other pathways. Definitions, identification results and statistical techniques related to mediation analysis will be covered. The course will clarify the assumptions required for the estimation of direct and indirect effect and will extend the approach to mediation typically employed in epidemiology and the social sciences to settings with interactions, non-linearities, and time-varying exposures. Prerequisite: Familiarity with regression analysis and potential outcomes.
Learning Objectives: 1.To understand the assumptions of a counterfactual frame in
formulating mediation analyses questions
2.To identify different types of causal effects (e.g. total, direct,
indirect) and their mathematical relations with each other
3.To correctly specify regression models in conducting mediation analyses
2.To master the use of statistical software code to conduct mediation
analyses and the interpretation of output
EPID721: Applied Sensitivity Analyses In Epidemiology
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: Introductory epidemiology. Introductory biostatistics or introduction to generalized linear models. Working knowledge of a general statistical software like SAS, Stata or R
Advisory Prerequisites: An introductory course on causal inference (e.g. EPID 780) is highly recommended
Description: This course introduces how to think about and conduct sensitivity analyses for uncontrolled confounding, selection bias and measurement error in epidemiologic studies. The course will demonstrate the intuition behind the separate and combined consequences of these sources of bias on estimating and inferring causal effects. It will provide practical quantitative skills for assessing the sensitivity of analytical results to these biases in order to aid credible causal modeling and inference using empirical epidemiologic studies
Learning Objectives: 1. Learn to articulate the different of impact of uncontrolled confounding, selection bias and measurement error separately and in combination.
2. Learn to depict visually these sources of bias and understand their impact using causal diagrams.
3. Learn to conduct quantitative bias analyses including multiple-bias modeling.
4. Learn to reason about and obtain bias parameters for sensitivity analyses.
5. Learn to apply and interpret probabilistic sensitivity analyses in epidemiologic studies.
EPID722: Foundations Of Clinical Pharmacology In Pharmacoepidemiology
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Description: This course will explore the application of principles in clinical pharmacology (mechanism of action, drug-drug interactions, pharmacokinetics, pharmacodynamics, etc.) to guide study design, interpretation, and clinical relevance for pharmacoepidemiology research.
EPID724: Leadership and Strategic Planning for Public Health
Description: This course focuses on leadership skills and strategic planning for public health and healthcare professionals with the ultimate goal of readying students for public health 3.0. Students will learn approaches to empower teams and to collaborate across sectors and will practice using systems thinking and policy evaluation as tools for promoting health for individuals and populations. The course will include self-assessment of leadership skills, practice in identifying appropriate leadership and management techniques, and analysis of case studies to understand policy evaluation and systems thinking. Students will be encouraged to bring real-world experience to the class lessons and discussions.
Learning Objectives: 1. Understand the basic structure of the public health system
2. Describe the reasons for and concepts behind Public Health 3.0
3. Discuss systems thinking mindset and utilize tools of systems thinking
4. List steps in policy analysis and evaluation and apply skills of policy analysis
5. Understand the concept of health in all policies
6. Be familiar with leadership styles in public health
7. Understand the differences in public health management and public health leadership
8. State their own leadership style
9. Apply leadership skills in a case-study
10. Be familiar with tools that are available for policy evaluation, systems thinking, and public health leadership
Advisory Prerequisites: Either tobacco epidemiology or tobacco control knowledge, or familiarity with modeling. For those without modeling background, we recommend taking the EPID 793 Complex Systems Modeling for Public Health Research course first (offered the prior week)
Undergraduates are allowed to enroll in this course.
Description: This course will introduce students to the use of simulation modeling to assess the burden of tobacco use on health and project the impact of tobacco control interventions and regulations on use patterns and downstream health effects.
Learning Objectives: This course will introduce students to the use of simulation modeling to assess the burden of tobacco use on health, and project the impact of tobacco control interventions and regulations on use patterns and downstream health effects
Advisory Prerequisites: Quantitative training, familiarity with traditional regression methods, basic epidemiologic principles, and working knowledge of R. The course will be instructed with minimal mathematics formulas and will include comprehensive examples to facilitate a bro
Undergraduates are allowed to enroll in this course.
Description: To gain knowledge of the process of cleaning and abstracting EHR data to create analytic datasets, attain a broader understanding the secondary use of EHR data for research, with a focus on epidemiologic principles including the role of study design, bias, and generalizability
Learning Objectives: This short course will offer an overview of modern analytical methods and research applications using EHR data, with a specific focus on epidemiologic inferences. Upon completion of the course, participants will i) gain knowledge of the process of cleaning and abstracting EHR data to create analytic datasets, ii) attain a broader understanding of the opportunities and challenges of the secondary use of EHR data for research, with a focus on epidemiologic principles including the role of study design, bias, and generalizability, iii) explore and gain hands-on experience using EHRs from Michigan Medicine, and iv) be prepared to generate and further explore new questions and perspectives.
EPID733: Quasi-experimental Methods In Epidemiology
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: No
Advisory Prerequisites: familiarity with introductory epidemiology (e.g., confounding), and introductory biostatistics (e.g., expectation, laws of probability, linear regression); and some background in either Stata or R.
Description: The course will cover the concepts, assumptions, statistical techniques, and empirical applications of these methods in the literature. Upon completion of the course, students will be able to critique the quality of a research paper that uses these methods and be able to conduct basic analyses in Stata or R.
Learning Objectives: Currently, the cluster on causal inference at SSE includes full courses on causal inference fundamentals, mediation analysis, sensitivity analysis, and machine learning. However, there is no systematic coverage on 1) instrumental variable analysis, 2) difference-in-differences methods, and 3) regression discontinuity design. This proposed course will fill in this gap. These tools have found
EPID734: Epidemiologic Data Collection, Management, And Harmonization
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Advisory Prerequisites: The course does not presume any background in data collection, management or harmonization methods; however, prior experience in designing or conducting health research projects is helpful.
Undergraduates are allowed to enroll in this course.
Description: provide an overview of techniques for data collection, management and harmonization to learners who plan to conduct or are already engaged in health research and would like to gain familiarity with methods aimed at generating quality data for hypothesis testing and sharing purposes
Learning Objectives: The intent of this course is to provide an overview of techniques for data collection, management and
harmonization to learners who plan to conduct or are already engaged in health research and would like to
gain familiarity with methods aimed at generating quality data for hypothesis testing and sharing
purposes.
Advisory Prerequisites: Introductory level courses in epidemiology and biostatistics.
Description: Course will cover regression methods for continuous, binary, and count data. The
majority of epidemiologic data involve either binary or count data, and binary data often
arise from underlying continuous data. Therefore, linear , logistic
,and Poisson regression analyses are important
analytic approaches that provide valuable insights into data collected.
Prerequisites: Intro Epidemiology and Biostatistics and Perm. Instr
Description: This course is an introduction to the most powerful analysis technique in statistics: linear regression. This course discusses the applications of linear regression models to medical research and public health data. We will focus on the two major goals of linear models:
(1) Explanation: the estimation of associations, and
(2) Prediction: the use of models to predict subject outcomes, as with diagnostic tests.
Specific topics include graphical exploratory data analysis, assumptions behind simple and multiple linear regression, use of categorical explanatory variables, identification of appropriate transformations of explanatory and/or outcome variables, assessment of predictor/outcome associations through hypothesis testing, identification of confounding and effect modification, assessment of model fit, and model selection techniques.
EPID747: Successful Scientific Writing
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s):
Offered Every Summer
Prerequisites: Perm. Instr.
Description: This course takes an active, participatory approach to help public health and health care professionals learn how to communicate the findings of their research and investigations more effectively and expedite publication of their manuscripts. Working in small groups, students spend much of their class time critiquing actual published and unpublished manuscripts, including their own, and solving a wide range of exercises that exemplify the real-world challenges that authors face. Free-form in-class discussions make it possible for class members to learn from one another's experiences. Major components of the course include the following: basic sections of a scientific article: the purpose, elements and organization of each section; principles of style for writing in public health and epidemiology; systematic approaches to the process of writing and publishing an article in a peer-reviewed journal; effective strategies for dealing with requests of journal editors and reviewers.
EPID761: Social Determinants Of Population Health
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Advisory Prerequisites: Intro Epidemiology and Biostatistics courses
Undergraduates are allowed to enroll in this course.
Description: Introduce how social factors affect health. Survey social determinants of health literature to provide basic substantive knowledge on differential patterns of health across individual and contextual levels. Discuss methodological approaches to systematic assessment of health disparities, conceptualizing and measuring social exposures, examining contextual factors, and estimating policy impacts.
EPID762: Analysis of Complex Sample Survey Data
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: . A first course in survey sampling or research methods and a basic understanding of sampling concepts such as stratification, cluster sampling and weighting is required.
Description: This course will present a practical overview of modern techniques for analyzing survey data in a way that accounts for the complex features of the sample design that gave rise to the sample of units that was ultimately surveyed
EPID766: Analysis of Longitudinal Data from Epidemiologic Studies
Description: It has been popular in epidemiology to conduct longitudinal studies where study participants are followed over time and repeated measurements of interest are obtained. Compared to traditional cross-sectional or case-control studies, longitudinal studies can be more efficient to detect difference of interest, offer more evidence for possible causal inference, etc. However, longitudinal data are likely to be correlated, which presents substantial challenge in analyzing such data. This course will address 1) epidemiologic methods for the design and interpretation of longitudinal studies involving repeated measures and 2) statistical methods appropriate for longitudinal data including generalized estimating equations (GEEs), linear mixed models and generalized linear mixed models. A series of studies will be used to illustrate the major design issues and statistical approaches. Relevant procedures in statistical package SAS will be introduced and appropriate interpretation of results will be emphasized. Prerequisite: Students are expected to have one or two graduate or biostatistics courses on (simple and multiple) linear regression models, categorical data analysis such as logistic regression models, and experience with conducting data analysis using statistical software SAS.
EPID777: Geographic Information Systems for Epidemiology
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s):
Offered Every Summer
Prerequisites: None
Description: Geographic Information Systems (GIS) are used for analyzing and displaying
spatial data. Data from a variety of sources may be compared with overlay
analysis and spatial statistics. Modern tools permit novice GIS users to
perform advanced spatial analysis without extensive training. This course
will introduce students to ArcView, the world's leading GIS analysis
package. Examples of epidemiological applications will give students the
opportunity to see and use this powerful tool. Some of the topics to be
covered are data import/export, layering, table management, classification,
labeling, spatial and attribute queries, buffering, and address geocoding.
No prerequisite.
EPID778: Spatial Statistics for Epidemiological Data
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s):
Prerequisites: None
Advisory Prerequisites: Previous expericne with R is preferred, not required
Description: With the increasing availability of geographic information systems, spatial data have become more frequent in many disciplines, including public health and epidemiology. This course aims to provide an introduction to spatial statistical methods for epidemiological data, covering modeling approaches for the two different types of spatial data: point-referenced data, where the geographical coordinates of the observations have been recorded; and areal-averaged data, where summary statistics (e.g., number of disease cases by county, zip code, etc.) are reported for each areal unit. Topics covered include: exploratory analysis for spatial data, covariance functions, kriging, spatial regression; disease mapping, spatial smoothing; point processes, assessment of clustering, and cluster detection. Each lecture will feature a lab component, during which spatial analyses of datasets, made available to the participants, will be performed using the publically available R statistical software
EPID780: Applied Epidemiologic Analysis For Causal Inference
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Advisory Prerequisites: Students should have at least one basic epidemiology course with a working knowledge of regression and other standard statistical methodology common in basic epidemiological analysis.
Undergraduates are allowed to enroll in this course.
Description: This course introduces concepts and applications of potential outcomes and structural causal models for the estimation of causal parameters in epidemiologic research. The course will familiarize students with the assumptions underpinning modern causal inference methods and provide a conceptual understanding of standardization/g-computation and inverse probability weighting.
EPID784: Survival Analysis Applied To Epidemiologic And Medical Data
Prerequisites: An introductory course in (bio)statistics that covers regression is required. Familiarity with a statistical software package is helpful, but example code will be provided in labs.
Advisory Prerequisites: The mathematical level is completely accessible with knowledge of high school algebra, one semester of calculus, and a one-year course in basic statistical methods.
Description: The primary objectives of this course are to provide participants with the
background required to understand commonly used survival analysis methods
and to apply such methods using standard statistical software. The course
material relies heavily on examples and intuitive explanations of concepts.
EPID787: An Introduction To Multilevel Analysis In Public Health
Graduate level
Residential
Summer term(s) for residential students;
1 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Offered Every Summer
Prerequisites: None
Advisory Prerequisites: Introductory course in epidemiology and an introductory course in statistics (i.e. some familiarity with regression modeling).
Description: This short course will review the rationale for multilevel analysis in public health research, build the statistical theory and practice of these models from the fundamentals of the regression-based approaches and demonstrate a variety of different forms that the models can take, including fixed and random effects, marginal (population average) models and extensions for categorical and survival outcomes. Fitting and interpreting models will be demonstrated using Stata statistical software, and parallel code will also be provided in SAS. Special emphasis will be placed on the strengths and limitations of multilevel analyses in investigating social and group-level determinants of health, and the causal interpretations of estimated parameters.
EPID793: Complex Systems Modeling for Public Health Research
Graduate level
Residential
Summer term(s) for residential students;
2 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Description: This course will provide an introduction to two major complex systems science modeling techniques with wide applicability to public health. We will cover an introductory overview of complex systems modeling in general, and systems dynamics and agent-based modeling in particular. We will discuss model applications, best practices, and more advanced practical topics such as team-building, computation, funding, and publication. We will provide extensive hands-on lab experience during each section of the course. At the completion of the course the student will be able to explain current and potential future roles of complex systems science in public health, describe the respective advantages/disadvantages of each method covered, and will be expected to produce a draft proposal for applying one of the two system science methods to a particular problem. Students will become informed consumers of complex systems research, will be prepared to actively participate in interdisciplinary teams using the modeling techniques, and will be well positioned to incorporate systems science methods into their own research. Prerequisite: Relevant background in public health.
Prerequisites: Introductory level courses in Epidemiology (e.g., EPID 503 or EPID 600) and Biostatistics (e.g., BIOSTAT 503 or BIOSTAT 553). Experience in the use of Windows-based microcomputers. No experience of R is required.
Description: This course will introduce the R statistical programming language for epidemiologic data analysis. R is a freely available, versatile, and powerful program for statistical computing and graphics. This course will focus on core basics of organizing, managing, and manipulating data; basic graphics in R; and descriptive methods and regression models widely used in epidemiology.
Description: This course provides an overview of qualitative research methods that can complement and enhance epidemiologic studies. It is useful for epidemiologists interested in understanding the social, cultural and behavioral aspects of public health issues within communities. Students will learn how to integrate qualitative methods into epidemiology research and how to select appropriate qualitative methods. Sessions will cover: principles of qualitative research, study design, participant recruitment, data collection methods (interviews, group discussion, and observation), writing and presenting qualitative research and assessing research quality. The course uses participatory learning activities to build core skills. The course is valuable for public health professionals, staff at government and non-government agencies focusing on health and disease, graduate students and researchers. Skills learnt in this course will be valuable for conducting epidemiology research and evaluating qualitative research components in funding proposals, projects and publications.
EPID891: Advanced Readings in Epidemiology
Graduate level
Residential
Fall, Winter, Spring, Summer term(s) for residential students;
2 credit hour(s) for residential students;
Instructor(s):
Last offered Winter 2015
Not offered 2024-2025
Prerequisites: Perm. Instr.
Description: Students will review assigned readings on the epidemiology or natural history of specific infections or chronic diseases or on host or environmental factors associated with disease, or on epidemiological methods and their application. May be elected more than once
EPID990: Dissertation Research/Pre-Candidate
Graduate level
Residential
Fall, Winter, Spring, Summer term(s) for residential students;
1-8 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Description: For students who have NOT reached candidacy yet.
EPID995: Dissertation Research/Candidate
Graduate level
Residential
Fall, Winter, Spring, Summer term(s) for residential students;
8 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Description: Election for dissertation work by doctoral student who has been admitted to status as a candidate
HBHEQ578: Practical Projects
Graduate level
Residential
Fall, Winter, Spring, Spring-Summer, Summer term(s) for residential students;
1-3 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Description: Practical projects in the application of theory and principles of Health Behavior and Health Education to individual and community-based public health settings. Course requirements include an approved practical project related to Health Behavior and Health Education in consultation with a faculty advisor. THE EXPERIENCE IS REPORTED IN AN INTEGRATIVE PAPER DEMONSTRATING THE SCIENTIFIC APPLICATION OF HBHE THEORIES AND PRINCIPLES TO THE PRACTICAL PROJECT. May be elected more than once. Enrollment limited to Health Behavior and Health Education majors with at least two full terms of prior registration.
HBHEQ625: Research in Health Behavior
Graduate level
Residential
Fall, Winter, Spring, Spring-Summer, Summer term(s) for residential students;
1-4 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: Perm. Inst.
Description: Individual work on a problem in the area of health behavior relevant to program effectiveness in public health, under the tutorial guidance of an appropriate staff member. Regular conferences are arranged to discuss research designs, proposed problem solutions, methods for data collection and analysis. The investigation is reported in a paper, which may be submitted for publication. May be elected more than once.
HBHEQ644: Readings in Health Behavior and Health Education
Graduate level
Residential
Fall, Winter, Spring, Spring-Summer, Summer term(s) for residential students;
1-6 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: Perm. Instr.
Description: Review of literature on selected topics in health behavior, health education or related areas under guidance of faculty member. Critical analysis; written and oral reports. May be taken more than once for a total not to exceed 6 credit hours.
HBHEQ900: Research in Health Behavior and Health Education
Graduate level
Residential
Fall, Winter, Spring, Spring-Summer, Summer term(s) for residential students;
2-6 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Description: Research work undertaken by doctoral students in collaboration with faculty advisers, including participation in on-going departmental research activities. Open only to doctoral students in Health Behavior and Health Education. May be elected more than once.
HMP815: Readings in Medical Care
Graduate level
Residential
Fall, Winter, Spring, Spring-Summer, Summer term(s) for residential students;
1-4 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: Perm Instr
Description: Directed readings in special areas. May be elected more than once. Primarily for doctoral students in Health Services Organization and Policy.
HMP990: Dissertation/Precandidates
Graduate level
Residential
Fall, Winter, Spring, Spring-Summer, Summer term(s) for residential students;
1-8 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Description: Election for dissertation work by doctoral students not yet admitted to status as candidate.
HMP995: Dissertation Research for Doctorate in Philosophy
Graduate level
Residential
Fall, Winter, Spring, Spring-Summer, Summer term(s) for residential students;
8 credit hour(s) for residential students;
Instructor(s): Staff (Residential);
Prerequisites: None
Description: Election for dissertation work by doctoral students admitted as candidates
