MSPEI Recommended Electives

MPH3002 Climate Change, Atmospheric Environment and Global Health (Formerly MPH 0721)

Credits: 3 Offered: Spring 2 The atmospheric environment is sensitive to climate change and has significant implications for global health. This course explores how pressuring global atmospheric environmental issues, including heat wave, long-term temperature change, ambient air pollution, and wildfire smoke, affect human health in the context of a changing climate. In addition to acquiring theoretical knowledge, students will apply modern statistical and epidemiologic techniques to real-world datasets using R to model the health effects of climate change-related exposures. This course focuses on developing practical knowledge and skills for climate change and global environmental health research, and follows a learn-by-doing approach, which combines lectures with structured labs.

Pre-requisites: MPH 1004 (formerly MPH 0400) Introduction to Epidemiology MPH 1002 (formerly MPH 0300) Introduction to Biostatistics

MPH4002 Environmental & Occupational Epidemiology (Formerly MPH0419)

Credits: 3 Offered: Spring 1 This course focuses on the fundamentals of epidemiological methods specific to environmental and occupational health research. The course will provide students with an insight to appropriate study designs and methodologies to investigate health effects of environmental and occupational exposures in different settings. These include essential concepts involved in generating research hypotheses, as well as environmental and occupational health specific issues such as use of exposure biomarkers, exposure sampling and modeling of exposures, study design issues, confounding and other types of bias, and phenotyping issues as they relate to environmental and occupational factors. We will also review novel data analytic strategies unique to environmental and occupational health (e.g. exposure mixtures), the nascent field of exposomics, and the interpretation of the study findings and public health implications for environmental and occupational epidemiological research. The students will also learn the techniques for critical appraisal of environmental and occupational epidemiological studies. These are achieved through lectures with in-depth discussion of current research status on environmental and occupational epidemiology, readings, homework assignments, mid-term exam, hands-on statistical analysis workshops, and a final project.

Pre-requisites: MPH 1004 (formerly MPH 0400) Introduction to Epidemiology MPH 1002 (formerly MPH 0300) Introduction to Biostatistics

MPH4003 Implementation Science (Formerly MPH0020)

Credits: 3 Offered: Spring 2 This course provides a comprehensive introduction to implementation science—the study of methods and strategies to promote the adoption and integration of evidence-based interventions, practices, and policies in public health and healthcare settings. The course explores foundational theories, models, and frameworks used in implementation research and practice, emphasizing real-world application to bridge the gap between research and effective population health impact. Students will engage with case studies, current literature, and applied exercises to develop the skills necessary to design, evaluate, and sustain implementation strategies across diverse settings and populations.

MPH5001 Introduction to Epidemiology Data Analysis with R and Python (Formerly MPH0413)

Credits: 3 Offered: Spring 1 R and Python are both open-source languages widely used by epidemiologists to manage and clean data, carry out statistical analyses of epidemiologic data, and produce high-quality figures for research communications. This course will give students a solid foundation in the most important tools for performing epidemiology data analyses using R and Python. Students will learn how to import data, merge datasets, clean and transform variables, visualize, and model population data. Emphasis will be given to modeling approaches for association estimates calculation such as beta coefficients, relative risks, and odds ratios using R as well as data wrangling and exploratory data analysis with Python. Students will also learn about the similarities and differences between R and Python, and how to strategically leverage the strengths of each language depending on the task at hand. Students will be given hands-on training during class and work on an epidemiologic project using R and Python. A key learning goal of this course is to help students familiarize with R and Python and build basic coding skills primarily in R, and extending to Python, while recognizing each unique strengths and complementary utility. Prior programming experience is helpful but not necessary.

Pre-requisite: MPH 1002 (formerly MPH 0300) Introduction to Biostatistics

MPH5002 Introduction to Geoinformatics in Public Health (Formerly MPH0601)

Credits: 3 Offered: Spring 1 This course introduces students to the foundational tools and concepts of geoinformatics as applied to public health. Students will learn how to analyze, visualize, and interpret spatial health data using open- source GIS platforms such as QGIS and R. Through weekly labs and assignments, students will gain hands-on experience in mapping environmental exposures, identifying geographic patterns in health disparities,and conducting spatial epidemiologic analysis. Geoinformatics has become a crucial methodology in understanding and addressing public health challenges that vary by place, such as access to care, environmental risks, and disease outbreaks. By equipping students with these skills, this course supports the growing need for spatial thinking and data science in public health research, planning, and policy.

Recommended Pre-requisite: MPH 1004 (formerly MPH 0400) Introduction to Epidemiology

MPH5003 Machine Learning in Public Health (Formerly MPH0603)

Credits: 3 Offered: Spring 2 This course provides a comprehensive overview of unsupervised and supervised machine learning algorithms for analysis of continuous and categorical (binary) data, with a focus on applications for public health and epidemiology research. Topics discussed include hierarchical clustering, principal component analysis, factor analysis, LASSO, ridge and elastic net regressions, random forest algorithm, combined with hands-on training using public health datasets. The emphasis is on machine-learning concepts and applications in public health, rather than underlying theory. As mathematical results are presented without proof, students are not required to be proficient in calculus or matrix algebra to take this introductory course.

Pre-requisites: MPH 1002 (formerly MPH 0300) Introduction to Biostatistics MPH 5000 (formerly MPH 0602) Introduction to Public Health Data Modeling or MPH 2002 (Formerly MPH 0812) Applied Linear Models I

MPH6020 Nutritional Epidemiology (Formerly MPH0401)

Credits: 3 Offered: Spring I This course provides an overview of the principles and methods used to assess dietary intake and patterns and nutritional status in epidemiology research. Students will learn to identify and apply rigorous methods for assessing diet and nutritional status in adult and children study populations. Topics covered include methods of dietary assessment and nutritional status in adults and children, methods for controlling for measurement error, misclassification, and bias in nutritional epidemiology studies, and modern nutritional epidemiology applications. Through group class assignments, homework, and a final nutritional epidemiology project, students will also obtain practical skills in collecting, analyzing, and interpreting nutritional data for epidemiologic and clinical research. Pre-requisite: MPH1004 (formerly MP 0400) Introduction to Epidemiology

MPH6022 Epidemiology of Infectious Diseases (Formerly MPH0410)

Credits: 3 Offered: Spring 2 Epidemiology of Infectious Diseases builds upon the concepts presented in Introduction to Epidemiology (P400), stressing the importance of considering the host, environment and disease agent in transmission dynamics. The nineteenth and twentieth centuries witnessed advances in prevention, treatment, and study of infectious diseases and the misconception that infectious diseases were disappearing. The study of infectious diseases leads to the continual development of vaccines, antibiotics, and technology, prompting epidemiologists to develop more advanced methods to monitor disease, investigate patterns of disease transmission, and evaluate innovative prevention modalities. The past thirty years have brought to light both new and re-emerging problems in the epidemiology of infectious diseases, including HIV, SARS, avian influenza, arboviruses, antimicrobial resistance, and the threat of bioterrorism. This course will enable students to gain an understanding of the principles of infectious disease epidemiology, including modes of transmission, quantification of occurrence and risk, and methods for preventing disease at the population level. Students will receive a number of disease-specific lectures from public health practitioners who conduct surveillance for and epidemiologic studies on various infectious diseases. Students will also participate in classroom exercises, during which they will investigate an outbreak, create surveillance plans, present evidence of a disease threat, and recommend prevention and control measures. Pre-requisites: MPH1004 (formerly MPH0400) Introduction to Epidemiology MPH1002 (formerly MPH0300) Introduction to Biostatistics

MPH6024 Epidemiology of Cancer & Chronic Diseases (Formerly MPH0416)

Credits: 3 Offered: Fall The course will cover substantive and methodological issues in the epidemiology of chronic diseases, including cancer, chronic respiratory diseases, neurodegenerative diseases, and aging. Students will be presented with examples of descriptive and analytical epidemiology studies in each of these areas; aspects such as disease registration and its contribution to epidemiology research, estimates of attributable fractions, and preventive strategies will be also addressed. The course will complement the series of methodological courses offered within the epidemiology track, by providing a framework to incorporate research in chronic disease etiology and control.

Pre-requisites: MPH1004 (formerly MPH0400) Introduction to Epidemiology MPH2000 (formerly MPH0412) Epidemiology II

MPH6025 Mental Health in the Modern Age (Formerly MPH0417)

Credits: 3 Offered: Spring 1 Mental health is a critical component for high quality of life. Poor mental health is in and of itself aversive, and can lead to poor physical health and in some cases even death. The purpose of this course is to develop understanding modern conceptualizations of mental health on a population level. This will be accomplished by: studying mental health within the context of its historical perspectives, providing foundational learning on the major classifications of mental health disorders and their impact on society, and providing insights into what is, and what factors lead to, “good” or positive mental health.

Pre-requisite: MPH1004 Introduction to Epidemiology

MPH6026 Reproductive & Perinatal Epidemiology (Formerly MPH0418)

Credits: 3 Offered: Spring 1 In this course we will study the epidemiology of human reproductive function, pregnancy and pregnancy outcomes and the methodologic issues involved in studying these. Topics include: basic biology of male and female reproduction, male and female infertility, pregnancy outcomes, assisted reproduction, and factors (environmental, social and occupational) that impact reproductive function and pregnancy outcomes.

Pre-requisite: MPH1004 (formerly MPH0400) Introduction to Epidemiology

MPH6027 Big Data Epidemiology: Introduction to OMICS Research (Formerly MPH0422)

Credits: 3 Offered: Spring 2 Omics is an emerging multidisciplinary and rapidly evolving field that has started to impact both clinical practice and public health and holds promise to significantly improve precision medicine. Omics encompasses many molecular biology domains including genomics epigenomics transcriptomics proteomics metabolomics and exposomics. These molecular domains can offer a more nuanced perspective on how multiple exposures (e.g. environmental lifestyle social factors) affect health compared with traditional research approaches. However omics datasets are large (tens of thousands of variables or more) resulting in analytical challenges that require adaptation of existing epidemiology designs and methods. This course will provide an overview of omics research areas and applications latest omics epidemiology advances and hands-on training in big omics data analysis.

Pre-requisites: MPH2000 (formerly MPH0412) Epidemiology II MPH1002 (formerly MPH0300) Introduction to Biostatistics MPH2002 (formerly MPH0812) Applied Linear Models I

Recommended: BIO6300 Introduction to R Programming

MPH6030 Statistical Computing with SAS (Formerly MPH0802)

Credits: 2 Offered: Fall This course provides students with the skills needed to utilize SAS systems for data management in order to prepare datasets for statistical analysis. In addition, procedures that are used to conduct basic statistical analyses and produce graphical output will be covered. Students will be given hands-on training using sample data provided by the instructor as well as (optionally) data from their own work. Recommended Pre-requisite: MPH1002 (formerly MPH0300) Introduction to Biostatistics

MPH6033 AI in Public Health (Formerly MPH0022)

Credits: 3 Offered: Fall This course will use a journal club-style to introduce students to the rapidly evolving applications of artificial intelligence (AI) in public health research and practice. Through critical reading, presentation and discussion of state-of-the-art peer-reviewed articles of original studies and reviews, students will critically evaluate and discuss the opportunities, limitations, risks, ethical implications, and current and future applications of AI methods and tools such as machine learning and natural language processing in public health research, disease surveillance, global health strategies, public health data analysis, and public health education and communication.