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School of Medicine. Department of Population and Quantitative Health Sciences. Michelle B. Jackson and Gwendolyn Garth point to opportunities to address food systems challenges in Cleveland. From research concept to influencing public policy in just six weeks Mendel Singer, PhD, MPH, and colleagues released a paper through medRxiv in late July that was prompted by news reports about vaccine side effects —.

Open Faculty and Staff Positions. View Open Positions. Our Education Programs. Study With Us. Learn About Our Affiliations. Our Research. Learn About Our Research. Our Faculty. Using a biopsychosocial perspective, an overview of the measurement and modeling of behavioral, social, psychological, and environmental factors related to disease prevention, disease management, and health promotion is provided. PQHS Data Management and Statistical Programming.

This is an online course that offers no in-person meetings. This course serves as a general introduction to the use of computer systems in epidemiologic investigations and biostatistical applications. Students will develop a conceptual understanding of data types, basic data structures, relational database systems and data normalization, data warehousing, control statements, and programming logic.

Further, students will develop basic scripting skills and will learn to read in, manipulate, and perform basic descriptive analyses on research data using the SAS programming language. Primary emphasis in this course is on developing the knowledge and familiarity required to work with data in a statistical programming context. Basic familiarity with statistics is beneficial, as this course does not teach inferential statistical analysis in detail, but it is not vital to learning the course material.

Statistical Computing and Data Analytics. Statistical computing is an essential part of modern statistical training. This course emphasizes on statistical and data analytic problem solving skills, covers elements of statistical computing, and special topics in modern data analytics.

Some Matlab, Mathematica, and graphviz will be used for symbolic and graphical computation. Computing in Biomedical Health Informatics. This course introduces students to computational techniques and concepts that underpin biomedical and health informatics data management and analysis. In particular, the course will focus on the three topics of: 1 Biomedical terminologies and formal logic used in building knowledge models such as ontologies; 2 Natural language processing NLP , and 3 Big Data technologies, including components of Hadoop stack and Apache Spark.

This is a lecture-based course that relies on both materials covered in class and out-of-class readings of published literature. Students will be assigned reading assignments, homework exercise assignments and they are expected to complete homework assignment for each class. The students will be involved in a team project and they will be expected to prepare a project report at the end of the semester.

Topics in Urban Health in the United States. The focus of this course is on designing sustainable urban policies and programs for advancing health equity in Greater Cleveland. The course builds on recent declarations of racism as a public health crisis in Cuyahoga County and the City of Cleveland and ongoing work in applying system dynamics to addressing structural racism for advancing regional equity.

The course introduces the use of system dynamics for understanding urban health inequities and designing sustainable social policies and programs for advancing health equity.

The course will cover model structure and its relationships to prior knowledge and assumptions, measurable quantities, and ultimate use in solving problems. Application areas focus on social issues of equity in health, education, and general wellbeing emphasizing transdisciplinary integration of systems vertically from cells to society and horizontality across systems. Model verification is discussed, along with the basic theory and practice of system dynamics.

Quantitative methods are emphasized including the formulation and testing of mathematical models of feedback systems and the use of numeric data and estimation of parameters. Special attention will be given to understanding the dynamics of social and economic justice, value and ethical issues, as well as issues related to race, ethnicity, culture, gender, sexual orientation, religion, physical or mental disability or illness, age, and national origin.

This course is designed to give students a first exposure to understanding how GIS is integral to understanding a wide variety of public health problems. It introduces students to current spatial approaches in health research and provides a set of core skills that will allow students to apply these techniques toward their own interests. Subject matter will include chronic diseases, infectious diseases, and vectored diseases examples.

Other topics related to social determinants of health and current events e. Students will be exposed to different types of data and different applications of these data for example, hospitals, police departments , enabling them to think "outside the box" about how GIS can be utilized to solve real-world problems.

Students will learn classic mapping and hotspot techniques. In addition, they will be introduced to novel ways to collect geospatial field data using online sources Google Street View , primary data collection spatial video and mixed method approaches spatial video geonarratives , all of which represent the cutting edge of spatial epidemiology. Application of statistical techniques with particular emphasis on problems in the biomedical sciences. Basic probability theory, random variables, and distribution functions.

Point and interval estimation, regression, and correlation. Problems whose solution involves using packaged statistical programs. First part of year-long sequence. Methods of analysis of variance, regression and analysis of quantitative data. Emphasis on computer solution of problems drawn from the biomedical sciences. Design of experiments, power of tests, and adequacy of models. Community Interventions and Program Evaluation.

This course prepares students to design, conduct, and assess community-based health interventions and program evaluation. Basic concepts of survival analysis including hazard function, survival function, types of censoring; non-parametric models; extended Cox models: time dependent variables, piece-wise Cox model, etc. Introduction to Population Health. Introduces graduate students to the multiple determinants of health including the social, economic and physical environment, health services, individual behavior, genetics and their interactions.

It aims to provide students with the broad understanding of the research development and design for studying population health, the prevention and intervention strategies for improving population health and the disparities that exist in morbidity, mortality, functional and quality of life.

Format is primarily group discussion around current readings in the field; significant reading is required. Communicating in Population Health Science Research. Doctoral seminar on writing journal articles to report original research, and preparing and making oral and poster presentations. The end products are ready-to-submit manuscripts and related slide and poster presentations for the required first-year research project in the PhD program in the Department of Epidemiology and Biostatistics.

While this course provides a nucleus for this endeavor, students work intensively under the supervision of their research mentors, who guide all stages of the work including providing rigorous editorial support.

Seminar sessions are devoted to rigorous peer critiques of every stage of the projects and to in-depth discussions of assigned readings. Fluency in English writing e. Research Ethics in Population Health Sciences. Students will complete a short written assignment due at the end of the semester. Clinical Trials and Intervention Studies. Issues in the design, organization, and operation of randomized, controlled clinical trials and intervention studies. Emphasis on long-term multicenter trials.

Topics include legal and ethical issues in the design; application of concepts of controls, masking, and randomization; steps required for quality data collection; monitoring for evidence of adverse or beneficial treatment effects; elements of organizational structure; sample size calculations and data analysis procedures; and common mistakes.

This course introduces the foundational concepts of genomics and genetic epidemiology through four key principles: 1 Teaching students how to query relational databases using Structure Query Language SQL ; 2 Exposing students to the most current data used in genomics and bioinformatics research, providing a quantitative understanding of biological concepts; 3 Integrating newly learned concepts with prior ones to discover new relationships among biological concepts; and 4 providing historical context to how and why data were generated and stored in the way they were, and how this gave rise to modern concepts in genomics.

Statistical Methods for Genetic Epidemiology. Analytic methods for evaluating the role of genetic factors in human disease, and their interactions with environmental factors. Statistical methods for the estimation of genetic parameters and testing of genetic hypotheses, emphasizing maximum likelihood methods. Models to be considered will include such components as genetic loci of major effect, polygenic inheritance, and environmental, cultural and developmental effects.

Topics will include familial aggregation, segregation and linkage analysis, ascertainment, linkage disequilibrium, and disease marker association studies. Categorical data are often encountered in many disciplines including in the fields of clinical and biological sciences. Analysis methods for analyzing categorical data are different from the analysis methods for continuous data. There is a rich a collection of methods for categorical data analysis.

The elegant "odds ratio" interpretation associated with categorical data is a unique one. This online course will cover cross-sectional categorical data analysis theories and methods. This logo can be used with or without the tagline. The tagline-free version is preferred for signage. When it appears between 1 and 1. This provides an anchor or sign off to all university communications. When using another version of the logo, such as a school or center logo, it is acceptable to put the school or center logo on the cover of the piece, always anchored at the bottom, and use the formal university logo as a sign off on the back cover.

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