Welcome to Introduction to Statistics & Data Analysis in Public Health! This course will teach you the core building blocks of statistical analysis - types of variables, common distributions, hypothesis testing - but, more than that, it will enable you to take a data set you've never seen before, describe its keys features, get to know its strengths and quirks, run some vital basic analyses and then formulate and test hypotheses based on means and proportions. You'll then have a solid grounding to move on to more sophisticated analysis and take the other courses in the series. You'll learn the popular, flexible and completely free software R, used by statistics and machine learning practitioners everywhere. It's hands-on, so you'll first learn about how to phrase a testable hypothesis via examples of medical research as reported by the media. Then you'll work through a data set on fruit and vegetable eating habits: data that are realistically messy, because that's what public health data sets are like in reality. There will be mini-quizzes with feedback along the way to check your understanding. The course will sharpen your ability to think critically and not take things for granted: in this age of uncontrolled algorithms and fake news, these skills are more important than ever. Prerequisites Some formulae are given to aid understanding, but this is not one of those courses where you need a mathematics degree to follow it. You will need only basic numeracy (for example, we will not use calculus) and familiarity with graphical and tabular ways of presenting results. No knowledge of R or programming is assumed.

Professional epidemiologists are often called on to investigate outbreaks and epidemics. This course serves as an introduction to the essentials of investigation, identifying pathogens, figuring out what's going on, reporting, and responding. You'll learn how to ask precise epidemiologic questions and apply epidemiologic tools to uncover the answers. You'll also learn about basic epidemic dynamics and the terrible law that cause them to grow, as well as the reasons why they recede and eventually go away. The course concludes with deep dives into some real outbreaks from Ebola, in West Africa, to the opioid epidemic in the United States.

Welcome to Linear Regression in R for Public Health! Public Health has been defined as “the art and science of preventing disease, prolonging life and promoting health through the organized efforts of society”. Knowing what causes disease and what makes it worse are clearly vital parts of this. This requires the development of statistical models that describe how patient and environmental factors affect our chances of getting ill. This course will show you how to create such models from scratch, beginning with introducing you to the concept of correlation and linear regression before walking you through importing and examining your data, and then showing you how to fit models. Using the example of respiratory disease, these models will describe how patient and other factors affect outcomes such as lung function. Linear regression is one of a family of regression models, and the other courses in this series will cover two further members. Regression models have many things in common with each other, though the mathematical details differ. This course will show you how to prepare the data, assess how well the model fits the data, and test its underlying assumptions – vital tasks with any type of regression. You will use the free and versatile software package R, used by statisticians and data scientists in academia, governments and industry worldwide.

This course provides an introduction to systems thinking and systems models in public health. Problems in public health and health policy tend to be complex with many actors, institutions and risk factors involved. If an outcome depends on many interacting and adaptive parts and actors the outcome cannot be analyzed or predicted with traditional statistical methods. Systems thinking is a core skill in public health and helps health policymakers build programs and policies that are aware of and prepared for unintended consequences. An important part of systems thinking is the practice to integrate multiple perspectives and synthesize them into a framework or model that can describe and predict the various ways in which a system might react to policy change. Systems thinking and systems models devise strategies to account for real world complexities. This work was coordinated by the Alliance for Health Policy and Systems Research, the World Health Organization, with the aid of a grant from the International Development Research Centre, Ottawa, Canada. Additional support was provided by the Department for International Development (DFID) through a grant (PO5467) to Future Health Systems research consortium. © World Health Organization 2014 All rights reserved. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. All reasonable precautions have been taken by the World Health Organization to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the World Health Organization be liable for damages arising from its use. Johns Hopkins University Bloomberg School of Public Health has a non-exclusive license to use and reproduce the material.

This course, Public Health in Humanitarian Crises 2, addresses public health issues of people affected by disasters, both natural or conflict-related. It discusses the many changes that occur in people’s lives when they are uprooted by a disaster, including many important topics related to humanitarian crises, such as when there is an epidemic a public health emergency; what do we mean with the humanitarian development nexus; what are the basics of disaster epidemiology and surveillance; humanitarian principles; and other very relevant topics. We will explore what humanitarian interventions could look like if we want to mitigate the effects of disasters. This course is a follow-up to Public Health in Humanitarian Crises 1, which dealt with changes in disease patterns, access to health care, livelihoods, shelter, sanitary conditions, nutritional status, and other issues. The course content is a mix of theoretical knowledge and many practical examples from recent disasters. We think this course is unique because it contains so many practical ‘real-life’ examples and is taught be instructors and guest lecturers who together have over 200 years of experience in this field. The course consists of 10 modules totaling approximately 14-16 hours of delivered content with an additional 2-3 hours of self-work (quizzes and writing and evaluating a short peer-review assignment) as well as lively discussions forums. The first course, Public Health in Humanitarian Crises 1 (PHHC1), has been designed in a way that each module builds on the lessons of previous modules. However, the modules from this second course, Public Health in Humanitarian Crises 2 (PHHC2) can be accessed in any order and some can stand alone. You do not necessarily need to do PHHC1 before PHHC2, but it might be helpful to take some or all of PHHC1, as some of the basic or fundamental issues are covered in this course. PHHC2 contains a somewhat more diverse set of topics than the previous course as it deals with more complicated issues. You do not have to pay for this course if you choose to enroll without a certificate. Sometimes referred to as auditing, enrolling without a certificate means that you will have access to all of the videos and discussions. The only difference is that you will not be able to take the quizzes to earn a certificate upon completion. Click the Audit or Enroll Without A Certificate link to sign up and begin the course. Visit the Learner Help Center for more details about your enrollment options.

Diabetes and obesity are growing health problems in rich and poor countries alike. With this course you will get updated on cutting-edge diabetes and obesity research including biological, genetic and clinical aspects as well as prevention and epidemiology of diabetes and obesity. All lectures are provided by high-profile scientists from one the world's leading universities in diabetes research. This course is part of the EIT Health Campus programme. We hope you will enjoy our course. Best Wishes Jens Juul Holst, Signe Sørensen Torekov and Nicolai Wewer Albrechtsen Department of Biomedical Sciences Novo Nordisk Foundation Center for Basic Metabolic Research Faculty of Health and Medical Sciences University of Copenhagen

Welcome to the Community Engagement in Population Health course! As you will learn, the health system is in the midst of a critical transition. The current system is not sustainable with escalating costs, mediocre health outcomes, and unacceptable disparities. This course will first discuss the current system, including definitions of population health and social determinants of health, and how the US compares to other countries on the triple aim –lower cost, better care, and a healthier population. Section 2 will review resources for both population health data and evidence-based public health interventions. Now more than ever, hospitals are addressing community needs through community benefits spending, community health improvement planning, and problem-based research networks. In the final section, the course describes community engagement in practical terms with a discussion of benefits and barriers. Community-based participatory research is presented as an effective way to engage the community in developing solutions to address problems in the health system.

How can we organise care in such a way that we optimize experience, quality and costs? What type of governance is needed? And how should healthcare organisations collaborate? These are central questions in this course. In answering them, we depart from a recognition of the impact of three crucial global shifts in health care governance: 1) the change in focus from cure to population health, 2) the increased attention to social determinants of health, and 3) the stronger involvement of non-governmental healthcare organisations in service delivery networks. The course is unique in combining the Population Health perspective with theories of healthcare system governance. It takes a ‘multi-level’ perspective: the participant will look at healthcare governance from different angles in the healthcare system as a whole: from the global and national level, to networks of organisations, to individual care organisations, and finally to the healthcare professional. All these actors should function well to optimize accessible, affordable and high quality of care. To attain these goals, specific managerial network and leadership skills are required.

As the last of four courses on Curanderismo, the art of Hispanic/Latino traditional medicine, this course focuses specifically on traditional healing using different techniques of many countries around the world. As an educational and cultural platform, this course will share a number of traditional global perspectives. Learners will not become certified traditional healers at the completion of this course but will be able apply basic principles or traditional medicine for health and illnesses. They will become familiar specifically with African traditional medicine from Uganda, African healing through music from Gabon, Afro-Cuban and Afro Puerto-Rican healing techniques, medicinal plants for women, Native American feather healing and other topics. REQUIRED TEXTS Curanderismo: The Art of Traditional Medicine without Borders by Eliseo Torres Curandero: Traditional Healers of Mexico and the Southwest by Eliseo Torres with Imanol Miranda Where to buy: https://he.kendallhunt.com/product/curanderismo-art-traditional-medicine-without-borders https://he.kendallhunt.com/product/curandero-traditional-healers-mexico-and-southwest OPTIONAL TEXTS Curandero: A life in Mexican Folk Healing by Eliseo Torres & Tim Sawyer Healing with Herbs & Rituals: A Mexican Tradition, Eliseo Torres, edited by Tim Sawyer Where to buy: https://www.barnesandnoble.com/w/curandero-torres-eliseo-cheo/1120135382?ean=9780826336415&st=PLA&sid=BNB_1341481610&sourceId=PLAGoNA&dpid=tdtve346c&2sid=Google_c&gclid=EAIaIQobChMI3_6LmYev3gIViuNkCh3IPgUyEAQYASABEgLYXfD_BwE https://www.barnesandnoble.com/w/healing-with-herbs-and-rituals-torres-eliseo-cheo/1120135381?ean=9780826339621&st=PLA&sid=BNB_825204424&sourceId=PLAGoNA&dpid=tdtve346c&2sid=Google_c&gclid=EAIaIQobChMIrrvswYev3gIVBsRkCh3BXQCHEAQYASABEgLnl_D_BwE PODCAST: Blubrry podcast - https://www.blubrry.com/normallol/41068835/normal-lol-63-depersonalization-mental-wellness-curanderismomexican-traditional-healing-with-eliseo-cheo-torres/ Itunes Episode 63 - https://itunes.apple.com/us/podcast/normal-lol-depersonalization-derealization-anxiety/id1065740418?mt=2

Health care professionals increasingly have to make clinical decisions in aging and diverse populations. Also, they have to deal with rising health care costs, fragmented health care supply and advancing medical technologies and IT systems. These developments go beyond every day practice and will require new skills. In this course we will walk you through key steps in designing a research study, from formulating the research question to common pitfalls you might encounter when interpreting your results. We will focus primarily on analytical studies used in etiological research, which aims to investigate the causal relationship between putative risk factors (or determinants) and a given disease or other outcome. However, the principles we will discuss hold true for most research questions, and you will also encounter these study designs in prognostic and diagnostic research settings. This course is part of a Master's program Population Health Management at Leiden University (currently in development), which includes nine courses on Coursera (including this one). If you are interested in learning more about the Population Health Management approach follow the course "Population Health: Fundamentals of Population Health Management" on Coursera.