Peqod :: Classes - Columbia University Catalog of Classes

Mental, neurological, and substance use (MNS) disorders are substantial drivers of the global burden of disease. The burden is particularly high in low-and-middle-income countries (LMIC) where over 80% of persons in need of MNS services go untreated. Yet for decades, attention to MNS epidemiologic research in LMIC was scarce relative to both psychiatric epidemiology studies conducted in high-income countries as well as infectious disease epidemiology studies in LMIC. Recently, however, the emerging field of global mental health has been recognized by international agencies, including the United Nations (via the Sustainable Development Goals) and the World Health Organization (via the Mental Health Action Plan) as major funding agencies, including NIH, CDC, and the UK MRC have followed suit in prioritizing global mental health research.

As the field has emerged, challenges in how to appropriately conduct public mental health research in LMIC contexts have surfaced. Such challenges require the appropriate application of epidemiologic methods in order to accurately measure and describe MNS problems in LMIC and evaluate and implement intervention approaches. Epidemiologic methods to be discussed in this course include: complex survey designs to measure MNS prevalence in humanitarian and emergency settings; validation of mental health screening tools in the absence of a gold standard criterion among culturally diverse populations; evaluation of MNS intervention effectiveness using experimental and non-experimental designs; novel methods for assessing clinical competency and intervention fidelity of lay mental health providers in LMIC; and implementation science tools, designs, and analysis approaches for translating evidence-based interventions into practice in LMIC.

The course is designed to complement Priorities in Global Mental Health (P6813), which provides a broad overview of priority issues in global mental health, and epidemiologic methods series courses (e.g., Quant Core Module / P6400, and Epidemiology II). The course is also designed to be practical in the sense that the intent is for students to learn the ‘how to’ of conducting global mental health epidemiologic studies in the field. Each lecture will apply a core epidemiologic method or concept (e.g., information and selection bias; survey, cohort, case-control, and RCT study designs; effect modification; and causal inference) to the field of global mental health. Through lectures, int

Department: Epidemiology
W 10 a.m.-11:20 a.m.
Instructor: Jeremy C Kane
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3 points

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EPID P8479

Seminar in Social Determinants of Health

SEMINAR IN SOCIAL DETERM

Department: Epidemiology
Instructor: Lisa Bates
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0-1 points

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EPID P8481

Generative AI for Epidemiologists

Generative AI tools, such as Large Language Models (LLMs), represent a subset of artificial intelligence technologies capable of generating new content, including text, images, audio, and video, that mimics human-generated content. Unlike earlier AI approaches, such as machine learning, which are designed to recognize or classify data, generative AI can create novel data outputs by leveraging the patterns, styles, or information it has learned during its training process. This ability to recognize and generate patterns shares many similarities with the goals of Epidemiology, which focuses on identifying patterns of health and disease in populations.

Although these generative AI models are relatively new, their adoption in the research environment, including in Epidemiology, is rapidly increasing. The introduction of LLMs has the potential to revolutionize scientific research by providing unprecedented speed, innovation, and efficiency. This is achieved by enabling novel and compelling ways to explore data. However, the complexity of the neural networks behind these models' decision-making processes and lack of transparency in data training sources, can make it challenging to understand how they arrive at specific outcomes and biases. The opacity of these models, coupled with the necessity of training them on potentially biased data sets, underscores the need for responsible use. Epidemiologists, therefore, face the challenge of ensuring that research findings generated by AI are real, ethical, and reliable.

This course is designed to introduce LLMs, highlighting their potential to enhance epidemiological research. It aims to explore innovative ways LLMs can be utilized, understand the myriad ethical considerations involved, investigate the potential public health concerns raised by AI, learn how to conduct basic analyses, and examine firsthand applications of LLMs within the field of epidemiology. The class will include informational lectures with in-class applied discussions and laboratory learning exercises using LLMs.

Department: Epidemiology
R 1 p.m.-3:50 p.m.
Instructor: Allison E Aiello
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3 points

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EPID P8491