Courses

The listing of a course description here does not guarantee a course’s being offered in a particular semester. Please refer to the published schedule of classes on the Student Link for confirmation a class is actually being taught and for specific course meeting dates and times.

• SPH PH 737: Geographic Information Systems (GIS) for Public Health Decision Making
  This course is an introductory level mapping class for a novice Geographic Information Systems (GIS) user, applicable to all public health fields. Topics covered include development of geographical datasets (local, national and global), basic mapping and data analysis, and geographical data presentation. A substantial portion of the course will be devoted to computer lab sessions. The course will use ArcGIS software.
• SPH PH 740: Pharmaceuticals in Public Health: An Introductory Course
  This course provides the students with an overview of the role of pharmaceuticals in public health and the basic functions of the pharmaceutical sector in terms of stakeholders,regulations, policies and evaluation. In addition the course has the objective to introduce the students to the pharmaceutical program and provide them with basic knowledge that is necessary to enter other courses. By the end of the course the students will be able to discuss the relevance of pharmaceuticals for public health, identify relevant actors in the pharmaceutical sector and their functions, to identify problems within the pharmaceutical sector that lead to inequity and inefficiencies and the proposal strategies to overcome these problems.
• SPH PH 746: Career P.R.E.P.
  This career development course is made up of 6 sessions, each 90 minutes long, designed to give you the tools and techniques to effectively market yourself during the job search process and advance in your career. It will also enable you to research potential career options and to manage your job searches and careers as proactively and effectively as possible.
• SPH PH 747: Using data for decision making
  More than ever before, managers need to make sound decisions based on data. Robust dashboards are important tools in this process. Build your Excel "toolbox" by learning and applying useful formulas, graphing and dashboarding techniques, and data analysis in a wide range of real-world case study examples, such as costing of standard treatment guidelines, utilization analysis, and performance dashboards to monitor and evaluate health interventions. Students will have the opportunity to build their own dashboards to apply to a health service challenge, either using a data set of their choosing or one supplied by the instructors.
• SPH PH 750: Essentials of Population Health Research
  The goal of this 4-credit course is to introduce students to the data, tools, and methods of population health research. Students will develop the skills necessary to formulate and answer consequential research questions in population health research drawing on theory and methods from epidemiology, biostatistics, and the broader social sciences. Students will prepare an extended research abstract by the end of the semester on a topic of interest in population health research.
• SPH PH 757: Chronic Disease Prevention and Management
  Chronic, non-communicable diseases (NCDs), including cardiovascular disease, cancer, diabetes, and chronic lung disease, are a leading threat to the health of the population. In this course, students will set out to ascertain the background and significance of major chronic diseases affecting population health, and evaluate intervention efforts targeting chronic disease prevention and its long term management. Controversies in current chronic disease prevention efforts will be analyzed. Students are expected to gain skills directly relevant for the development, implementation, and evaluation of interventions directed towards chronic disease prevention and management.
• SPH PH 760: Accelerated Training in Statistical Computing
  This class will introduce students to statistical programming in SAS and the conceptual foundations for biostatistical and epidemiologic data analyses, including descriptive statistics, univariate and multivariable regression, and stratified analyses. The course will also introduce students to analysis of qualitative data, predictive and causal modeling, and data visualization. This is a two week intensive course that includes hands-on exercises and projects designed to build skills in statistical computing in population health research.
• SPH PH 771: Topics in Public Health
  Varies by semester; see printed course descriptions and printed course schedules on School of Public Health web site, http://sph.bu.edu/registrar
• SPH PH 780: Chronic Disease: A Public Health Perspective
  This is the foundational course for the certificate in chronic and non-communicable disease (chronic/NCD). Chronic and non-communicable diseases (Chronic/NCD) are responsible for a large majority of the deaths in the United States and a rapidly rising share of deaths in low- and middle-income countries. In addition to their effect on mortality, these conditions have an enormous impact on disability, quality of life, health care costs, and lost productivity, and are also a major contributor to health disparities. The course provides students with an overview of the public health approach toward chronic/NCD across the continuum of identification of causes, implementation and evaluation of strategies for prevention, and treatment and management of disease to reduce mortality and improve quality of life. Through readings, lectures, in-class exercises, and group work, the course provides a foundation for students to further develop their knowledge and skills in subsequent courses toward their certificate.
• SPH PH 781: Topics in Bst
  
• SPH PH 782: Topics in Chs
  
• SPH PH 783: Topics in EH: Local and Global Public Health Impact of the COVID-19 Pandemic
  This course provides a high-level overview of how the COVID-19 pandemic has and is affecting global and domestic health and changing civilization. It is co-taught by faculty from the BU Schools of Public Health and Medicine and other professionals actively confronting the COVID-19 pandemic on the frontlines. Initial sessions provide students with background on the emergence and natural history of SARS-CoV-2, epidemiologic and clinical aspects of COVID-19, and treatment options and prevention strategies including non-pharmacological approaches and vaccines. Subsequently, sessions will examine political, social, and economic factors influencing the COVID-19 pandemic, with particular focus on the disease vulnerabilities of certain subpopulations and health disparities that have been aggravated by the disease. Future prevention and control of COVID-19 will be considered throughout the course as well as strategies to optimize preparedness and response to future respiratory diseases of pandemic potential that might subsequently emerge. This course should be of interest to MPH students across all certificates and interested undergraduates. Students will receive current information on the impact of the pandemic on health and wellbeing, and they will engage in discussions with professionals who have ongoing experience with COVID-19.
• SPH PH 784: Topics in EP: Science in a Pandemic
  The SARS-CoV-2 outbreak has put pressure on science the likes of which few epidemiologists have ever experienced. Timelines are compressed, science is under scrutiny, the integrity of scientists are being questioned, the public has mixed trust in science and science itself is confronting its own limitations. The purpose of this course is to use the SARS-CoV-2 outbreak as a case study in how scientists should react to a massive public health emergency and to identify the lessons we can learn from how we have engaged so far in response to COVID-19.
• SPH PH 785: Topics in Gh
  
• SPH PH 786: Topics in HLPM: Implementation Science: Linking Research to Practice
  Implementation science is commonly defined as the study of methods and strategies to promote the uptake of interventions that have proven effective in routine practice, with the aim of improving population health. Often interventions tested in traditional research studies that are found to be effective do not translate into positive outcomes in practice or cannot be practically applied. Alternatively, other interventions that have potential to improve care will not the effectively implemented without practical tools to aid the implementation. Integrating research into practice is a major challenge, both during the period of a study and beyond. This class provides an introduction to what is often referred to as translational, dissemination, or implementation research, as well as the broad field of Implementation Science. Students will learn about the significance and major initiatives associated with moving research into practice and will be introduced to conceptual and analytic tools (e.g., theories, frameworks) to support their emerging work in this area.
• SPH PH 791: Special Topics in Biostatistics: Applied Causal Inference in Health Research
  This is an advanced statistics and epidemiology course, focused on application of causal inference methods in medical research. Topics covered include counterfactual outcomes, causal diagrams, mediation analysis, instrumental variable, and g-methods to deal with time-varying confounding. This course includes lectures, computer instructions, and discussion of reading material.
• SPH PH 792: Topics in Chs
  
• SPH PH 793: Topics in Eh
  
• SPH PH 794: Topics in EP
  
• SPH PH 795: Topics in GH: Applications of Machine Learning in Global Health
  Every day, people from all over the world use digital devices to generate large amounts of text, image, video, and biological data. Researchers typically use machine learning algorithms to process these large datasets to identify patterns that could inform decisions about our health. In this course, we will study how researchers and institutions are using machine learning for public health purposes. Can your digital footprints be used to predict when you will die? Can machine learning algorithms determine the quality of care you receive at a hospital? Can your interactions with a social media platform indicate whether you have insomnia? These and similar questions will be explored in this course using real world examples and data. We will also learn how bias imbedded in the data (e.g., due to a lack of representation of certain populations) and algorithms can worsen existing health inequalities. Students will be introduced to machine learning algorithms in R and have many opportunities to apply these algorithms to various datasets. Students are required to have some familiarity with R but are not expected to be experts. Please reach out to Dr. Nsoesie at onelaine@bu.edu, if you have any questions.

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