Curriculum

Introduces the field of spatial analysis for public health. Concepts are examined through the use of ArcGIS Geographic Information System (GIS) mapping software as a tool for integrating, manipulating, and displaying public health related spatial data. GIS topics covered include mapping, geocoding, and manipulations related to data structures and topology. Introduces the spatial science paradigm: Spatial Data, GIS, and Spatial Statistics. Selected case studies are used to demonstrate concepts along the paradigm. Focus is on using GIS to generate and refine hypotheses about public health related spatial data in preparation for follow up analyses. 

Introduces the basic principles of the practice of public health at the local, regional, national, and international levels. Uncovers relevant public health topics through a combination of presentations by experts, discussions, and lectures. Focuses on the core competencies required for the effective assessment and improvement of the health and well-being of communities. Explores the public health approach to describing the health of a population, including the importance of understanding the social and cultural context surrounding every community. Covers a broad spectrum of population-based, prevention-oriented issues relevant to public health in the private and public sectors of both domestic and international communities, including global health promotion, disease prevention, health care delivery systems, environmental issues, and the spectrum of factors influencing the health status of populations and communities.

Examines technologies for collecting, obtaining and creating spatial data. Technologies considered include, but are not limited to: GPS, tablets, tracking devices, cell phones, mHealth, Google Earth, remote sensing applications, and the internet. Integrates spatial data from the aforementioned technologies into ArcGIS for spatial analysis. Other GIS related software applications such as QGIS, ERDAS, and Rare introduced. Covers relevant properties of spatial data such as metadata, confidentiality/disclosure and spatial data accuracy. Covers additional topics and concepts that reinforce the spatial science paradigm: Spatial Data, GIS, and Spatial Statistics. 

Introduces principles and methods of epidemiologic investigation of diseases. Illustrates methods by which studies of the distribution and transmission of diseases in populations (including disease outbreaks and epidemics) can contribute to an understanding of etiologic factors and modes of transmission. Covers various study designs, including randomized trials, case-control and cohort studies, as well as risk estimation and causal inference. The course also discusses applications of Epidemiology to solving public health problems, such as identifying sources and strategies for control of disease outbreaks, applying research findings to policy and practice, and program evaluation. Quantitative and analytic methods covered during the course include life tables, disease surveillance, measures of morbidity and mortality, and measures of diagnostic test accuracy. 

Provides students with a broad overview of Biostatistical methods and concepts used in the public health sciences. Emphasizes the interpretation and conceptual foundations of statistical estimation and inference. Covers summary measures, measures of association, confidence intervals, p-values, and statistical power. The statistical software package R will be introduced in the class and utilized to demonstrate the concepts and methods with data. 

Expands on GIS concepts and skills previously learned with more hands-on practice with epidemiological applications. Focuses on translating an epidemiological problem or getting into a set of spatial objectives that align with our spatial science paradigm. Surveys and summarizes the literature on spatial applications in public health. Prepares students to design a protocol to help identify a public health problem and accompanying data for their MAS Integrative Activity.

Prerequisite: Spatial Analysis for Public Health, Spatial Data Technologies for Mapping, and Introduction to Epidemiology. Prior or concurrent enrollment in Public Health Statistics 1.

Employs a conceptual framework to highlight the similarities and differences between linear, logistic, Poisson and Cox Proportional Hazards methods, in terms of usage and the interpretations of results from such models. Provides details for these regression approaches in the “simple” scenario, involving relating an outcome to single predictor. Following this overview of simple regression, explores the use of multiple regression models to compare and contrast confounding and effect modification, produce adjusted and stratum-specific estimates, and allow for better prediction of an outcome via the use of multiple predictors. Students will learn to use the statistical software package R to fit linear, logistic and Poisson regression models. 

Would you like to be able to quickly and efficiently find reliable, high quality evidence to support your work?

Introduces and explores online sources for finding high-quality, full-text research articles. Also prepares students to use advanced search techniques efficiently within these sources and to manage references using tools such as RefWorks, EndNote, Zotero and Mendeley. Finally, students learn about tools available to use to stay current on topics related to the public health field.

Prerequisite: None

Expands knowledge beyond introductory level epidemiologic concepts and methods material, using examples from the published literature. Emphasizes interpretation and the ability to critically evaluate issues related to populations/study design, measurement, population comparisons and inference, including: modern cohort study designs; advanced nested designs; novel techniques for exposure assessment; interpretation and utility of measures of impact; sources of bias and methods for their prevention; descriptive and analytical goals for observational study inference; the counterfactual model for defining exchangeability, cause, and confounding; and synthesis of inferences from observational studies as compared with randomized clinical trials. 

Introduces statistical techniques used to describe, analyze and interpret public health related spatial data. Included will be methods for characterizing clustering, cluster detection and spatial variation in health related outcomes and events. Three well known types of spatial data, geostatistical data, point event data, and area-level data, will be defined and used to motivate presented material. Regression methods previously learned will be adapted to the spatial setting. The statistical software package R will be used for analysis. Covers additional topics and concepts reinforcing the spatial science paradigm: Spatial Data, GIS, and Spatial Statistics. 

Provides an opportunity for students to gain a working knowledge of resources for conducting spatial analysis (i.e., literature, software, and data). Introduces new and relevant topics in GIS, spatial data technologies and spatial statistics not previously covered in the OPAL spatial analysis series. Expands students’ abilities to design and conduct spatial analysis by applying knowledge and tools learned from the previous three OPAL spatial analysis courses by providing data for reproduction, and in some cases extension, of analyses from existing studies.. Focuses on further developing and integrating components of the spatial science paradigm: Spatial Data, GIS and Spatial Statistics. 

This course will involve the research, analysis and writing of a complete and independent spatial analysis project.  Intermediate outlines, hypotheses and objectives produced in previous classes will be finalized.   No new material will be covered. The finalized project will follow journal article format including an abstract, and introduction/background, methods, results and conclusion sections.   The final project will represent an integrated and synthesized assessment of the spatial science paradigm (Spatial Data, GIS, Spatial Statistics) applied to a relevant public health problem.