John Jay Participants of the 2012 Epidemiology and
Population Health Summer Institute (EPIC) at Columbia University
Week 1: June 4-8
Advanced Epidemiology: Applications in Causal
Thinking: Three causal frameworks are prominent within epidemiology: Potential Outcomes, Sufficient Component Causes, and Directed Acyclic Graphs. Together they provide a clear definition for “causal effects” and articulate the assumptions necessary for the associations we see in our data to represent the causal effects we desire. This workshop will describe these causal frameworks and the relationships among them, analyze the exchangeability assumption central to causal inference, and compare and contrast traditional tools for confounder control with newer methods deriving from these frameworks. We will cover randomization, propensity scores, instrumental variables, standardization and inverse probability weighting from the perspective of these causal frameworks. The workshop will end with a hands-on demonstration of how inverse probability weighting is used.
Introduction to Biostatistics: The course will give an overview of the basic tools for the collection, analysis, and presentation of data in all areas of public health. The topics covered will include descriptive statistics; hypothesis testing; methods for comparison of discrete and continuous data including ANOVA, t-test,
correlation, chi-squared analysis, and linear and logistic regression. This course will provide a foundation for the skill-building courses and focused
Epidemiological Analysis Using SAS: This course will introduce participants to the basic concepts of using SAS statistical analysis software. The skills learned are intended to be foundational for individuals wishing to perform data manipulations and statistical analysis of epidemiological data. The material builds on concepts from introductory epidemiology and biostatistics. Sessions will be a combination of didactic lecture and hands-on application.
Lifecourse Epidemiology: This course will examine conceptual models and identify study designs
appropriate for a variety of life course research questions, as well as the limitations of these designs. Recent evidence highlights the importance of life
course perspectives to identify behaviors, exposures, and contextual situations which influence health and disease outcomes. Understanding the approaches to the life course, the development and evaluation of epidemiologic research designs related to the life course, and the contextual models and their relevance for the design and evaluation of research studies is necessary for epidemiologists who aspire to study the life course. Participants will further examine problems
in measurement unique to the life course approach and identify key biases in study designs. After an introductory session, this class will take a case studies approach, drawing on major life course studies.
Program Evaluation in Public Health: This course centers around health program evaluation, which is the systematic assessment of the effectiveness of public health policies, initiatives, and activities. Sound program evaluation provides an essential evidence base for informing policymaking and program design. Robust evaluation design and careful measurement are essential for drawing conclusions about program effectiveness. Competence in research design and measurement is useful not only for conducting evaluations but also for critically assessing and applying findings from published evaluations. With growing pressure for accountability in health, these skills are increasingly in demand domestically and internationally.
Johnniersi (Jojo) Harris
Introduction to Observational Epidemiology: This course will introduce basic concepts of descriptive and analytic epidemiology and illustrate their practical
applications. Emphasis is on commonly used observational study designs – cohort and case-control studies – that make up a substantial part of epidemiological literature. This course will also address bias, confounding, and other problems that arise in planning and conducting studies and analyzing and interpreting data. Students are encouraged to bring in their own data or research questions to enrich class discussion and to facilitate individual project
Week 2: June 11-15
Select Topics in Social Epidemiology: This course will familiarize students with the key theories, concepts, methods, and findings in social epidemiology. Through lectures, readings, and discussion we will investigate key social determinants of health, the theories and empirical evidence with respect to how social conditions “get under the skin,” and the methodological challenges involved in measuring social phenomena and making causal inferences about the relationship between social factors and health. This short course will be anchored around current debates and problems in the field to explicate cross-cutting
themes. Individual sessions will cover key conceptual frameworks in social epidemiology, the influence of social stratification (e.g., class, race, and gender) on health, and the role of social relations (e.g., networks).
Nutritional Epidemiology: This course will provide students with an understanding of the methods, including study design and analyses, involved in
determining the role of nutrition in the etiology of disease states. Examples from the literature will be used to illustrate various aspects of nutritional
epidemiology including assessment of dietary intake, biochemical markers of nutritional intake, body composition, and issues in the analysis and
presentation of nutritional data in epidemiological studies. Both the application and implications of these methods will be discussed.
Johnniersi (Jojo) Harris
Public Health Surveillance: Public health surveillance is the fundamental mechanism that public health agencies use to monitor the health of the communities they serve. It is a core function of public health practice, and its purpose is to provide a factual basis from which agencies can appropriately set priorities, plan programs, and take actions to promote and protect the public’s health. This course will cover the principles of public health surveillance, including historical context, vital registration, disease reporting regulations and notifiable diseases, surveillance registries, surveillance for behaviors and risk factors, administrative data sources in surveillance, epidemiologic uses of surveillance data, legal and ethical issues, and dissemination of surveillance information. The course will use a combination of lecture and hands on data analysis excercises to convey and reinforce key concepts and learning objectives.
Infectious Disease Epidemiology: This course will provide participants with an introduction to methodology specific to infectious disease epidemiology, as well as a review of basic concepts of molecular biology, microbiology, and immunology. Much progress has been made in infectious disease control and prevention, but many challenges remain. We will examine these challenges through discussion of topics including transmission dynamics, outbreak investigation, molecular epidemiology, antimicrobial resistance, nosocomial infection, vaccination and eradication, and emerging infectious diseases. These topics will be explored using a case study approach focused on specific infectious diseases including tuberculosis, HIV, malaria, Lyme disease, polio, MRSA, and influenza.
Longitudinal Data Analysis: This course will provide participants with an introduction to the theoretical,
analytical, and practical issues arising from longitudinal study designs. Longitudinal studies, which include both prospective cohorts and clinical trials, provide an opportunity to examine changes within and between individuals over time. The primary feature of longitudinal data that complicates their analysis is the correlated nature of repeated measurements over time. In this course, participants will explore methods for analyzing continuous and categorical longitudinal data. We will compare the various regression models for longitudinal data, including marginal models (GEE) and generalized linear mixed effect models. Survival analysis will also be discussed. This course will cover how to obtain regression parameters and other outputs from longitudinal data analysis in SAS. We will examine in-depth practical issues regarding the design
and interpretation of longitudinal studies, including sampling procedures, time-dependent confounding, loss to follow-up, missing data, and errors in
measurement / misclassification.
Geographic Information Systems: This course will introduce the key concepts of Geographic Information Systems (GIS) and illustrate how GIS data may be employed in population health research. The course will be comprised of daily lectures and lab sessions. The lectures will feature presentations of basic GIS concepts, GIS data and analysis, how to leverage GIS data to model spatial health-related phenomena, data mining, and how to use GIS output data. The lab sessions will allow course attendees to work hands-on with ESRI’s ArcGIS software package on lab exercises using spatial data, geocoding, preparing data for analysis, geoprocessing with Spatial Analyst, and mapping of public health data.
Week 3: June 18-22
The Ethics of Public Health: This course will permit participants to examine the underlying ethical
challenges of public health and the controversies that inform public health policy. The course will begin with an examination of the emergence of public health ethics in the last three decade, and the way it is distinguished from bioethics, focusing on the clinical relationship. Broad principles of liberty, paternalism, and justice will shape discussions of the ethics of mass behavioral change, as exemplified by tobacco control; the use of compulsory measures, as exemplified by mandatory childhood vaccination; and the impact of HIV/AIDS on the paradigm of infectious disease control. Finally, the course will examine public policies to protect the vulnerable from disease and for claiming a right to access health care.
Systematic Review and Meta-analysis:
Systematic reviews and meta-analyses are
increasingly used for evidence-based clinical and public health practice. Health-care professionals need to understand and critique this research design. This course will present a detailed description of the systematic review and meta-analysis process, discuss the strengths, potential bias and limitations of this design, and provide step-by-step guidance on how to actually perform and report a systematic review and meta-analysis.
Introduction to Multi-Level Modeling: This course will provide an overview of the theory underlying the use of multi-level models, using a parametric framework, as well as teach the basic application of methods necessary to conduct and interpret multi-level analyses of epidemiologic data. After reviewing basic statistical and theoretical principles of linear and logistic regression models using only one level of organization, lecture and lab sessions will be focused on the use of random-effects models and generalized estimating equation (GEE) models for the analysis of data with two levels. Hands-on exercises will use data from an investigation of the influence of NYC neighborhoods on obesity, focusing on the application and interpretation of regression models that account for clustered observations and group-level covariates. SAS code will be provided for all in-class exercises and participants will learn how to implement multi-level
models using both SAS Proc MIXED and SAS Proc GLIMMIX.
Chistine N. Massey
Approaches to Race in Epidemiological Research:
This course will address issues related to
conceptualizing, measuring, and categorizing racial and ethnic groups, including an examination of the implications of racial misclassification on health
outcomes and study results. Additionally, the current impact of racial stratification on health and health inequalities will be discussed and related to current approaches of assessing racial and ethnic disparities in health such as racial discrimination. Finally, there will be an examination of the conceptual framework and measurement problems of racial discrimination in
epidemiologic research utilizing the current literature on stress processes and social determinants of health.
Epidemiologic Analysis Using R: This course will teach public health researchers and epidemiologists how to use the R statistical computing platform to do
epidemiologic analysis. The material is intended for students or practitioners who want to use R to apply the basic epidemiological or biostatistical methods they have learned or are currently learning. Participants will construct, manipulate, and analyze data objects, addressing issues commonly encountered in
epidemiologic research. The overall goal is to introduce students to programming skills so they can (1) develop their own tools to apply epidemiologic methods, and (2) use those tools to answer epidemiologic questions.
Analysis of Complex Survey Data: This course will provide participants with practical skills to analyze data arising from the Continuous National Health and Nutrition Examination Survey (NHANES), a complex epidemiologic sampling design. NHANES provides a wealth of easily accessible, publically available data that can be used to answer important public health questions. However, the magnitude of the project and complex sampling methods are often barriers to analyzing and publishing this data. We will briefly discuss the theory behind complex sampling
strategies and the necessity of applying appropriate statistical techniques to analyze these data. However, the course will primarily focus on applied
operationalization, analysis, and interpretation of NHANES data - this is not intended to be a course in advanced statistical theory. We will demonstrate the
appropriate use of sampling weights in the NHANES data and how the appropriate weight is specific to the research question being asked. We will demonstrate how to obtain basic descriptive statistics, appropriate variance estimates, regression parameters, and survival analysis output in SAS and SAS-callable
Week 4: June 25-29
Genetic Epidemiology: This course will review the basic concepts of molecular biology and genetic principles to focus on the conceptual underpinnings and important design issues for researching genetic aspects of complex disease traits. In particular, the attendees will learn about the fundamentals of conducting a study to map common variants for human diseases and traits, and the potential functional implications of genome-wide association studies. Topics will include study design (case-control and family-based), candidate and whole genome approaches, common genetic databases including Hapmap, data management and quality control issues, and methods to test candidate gene and whole genome associations.
Comparative Effectiveness Research Methods:
This course will provide an overview of methods used in Comparative Effectiveness Research. According to the Institute of Medicine, "CER is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care." Course content will include the main type of CER approaches and methodologies such as randomized controlled trials ( including pragmatic trials), diagnostic studies, observational studies (including registries and administrative databases), meta-analysis, indirect comparisons, network meta-analysis, and methods for translating the results from CER studies into practice. Students will learn to critically interpret the strengths and limitations of each design, and to choose the best design for answering a CER question. Additional topics covered will include the rationale for CER.
Social Media in Population Health Communication:
This course will cover several critical health
communication issues, both conceptually and practically, facing public health professionals in today's rapidly changing media environments. Topics will
include using social media to build and maintain public trust in public health systems, mitigate the growing influence of online anti-science movements, increase the speed and efficieny of vital public health information communication, increase public support for health policy initiatives, and build online health communities aimed at encouraging and maintaining individual health behavior change. Sound health communication practices are essential to maintaining public trust, credibility, and support for scientifically-based population health information, as well as reducing the public health risks associated with anti-scientific information. Social media health communication strategies are becoming recognized as essential tools in public health policy and individual behvior change interventions. Additionally, social and digital media increase the speed and efficieny with which health information is communicated to mass publics during times of crisis.
Place and Health: This course will address substantive issues in research connecting health to place, from nation to neighborhood. Place will be
treated as a complex system that affects population health over the life span. This course will explore tensions between the positive and negative forces place exerts on health across human development, as well as the potential consequences of place-based interventions. The course will: 1) provide a rationale for studying place effects on population health over the life span; 2) synthesize evidence regarding the positive and negative effects of place on physical and mental health over the life span, identify knowledge gaps, and address implications for interventions; 3) survey the theories, methods, and analytic tools used to evaluate place effects in cross-national and neighborhood
comparisons; and 4) stimulate the application of a systems approach to understanding place effects on health through an interactive project focused on
dramatic inequalities between two New York City communities—Harlem and the Upper East Side—and the potential public health interventions to redress