[SOUND] We will be discussing measures of disease occurrence in this module. These include terms that are used to describe who, what, when and where in the population with regards to disease or different kinds of health outcomes. [BLANK_AUDIO] After you have reviewed all of the week two lectures you should be able to complete these learning objectives. They include differentiating the following measures, prevalence, risk, rate and odds. You'll also learn how to calculate each of these measure the prevalence risks rates and odds. Then you'll also learn how to define the concept of person-time and be able to apply it to calculations of rates. You'll also be able to choose the measure of frequency most appropriate for a given situation. Then you'll also be able to interpret the prevalence, odds, risks and rates within the context of public health research. So epidemiologists study diseases in the population. Here is an example of an epidemiologist collecting data with in-person interviews. In addition to diseases, epidemiologists also study health outcomes in the population. What do we mean by health outcomes? It's a broad term. Health outcomes can include diseases, illnesses, conditions, disorders, symptoms, behaviors, risk factors and injuries. Health outcomes can also include healthy behaviors such as consumption of fruits and vegetables or the benefits of moderate physical activity. You may commonly come across articles in the news that use measures of disease occurrence. However, in this course, in order to become more comprehensive and inclusive, we will frequently refer to measures of health outcome occurrences instead of disease occurrence. These occurrence measures can be applied to many health outcomes and not just diseases. So, in order to describe the distribution of health outcomes. We first need to define the population at risk and then measure the occurrence of one or more health outcomes in the population. We need to be able to measure the occurrence of health outcomes in a population in order to monitor changes and plan interventions. For example, this map illustrates differences in malaria transmission among various regions of the world, with parts of Africa and South America having the highest risk. We can use this data to plan and target malaria interventions. The ability to quantify numbers of peoples with some health outcome of interest at a given point in time. Or over a period of time is the foundation for comparing why, where and how health problems affect different populations. We need accurate statistics on the occurrence of diseases and health outcomes in order to identify new health trends and to evaluate whether specific health programs are making a difference. Now we will see four examples illustrating the terms: prevalence, risks, rates and odds. Let's start with the first measure of health outcome occurrence, prevalence. Malaria is a mosquito-borne infectious disease of humans and other animals caused by parasitic protozoa. Malaria infects 10% of the world's population. This statistic about malaria is an example of a prevalence. Human immunodeficiency virus or HIV is a retrovirus that causes acquired immunodeficiency syndrome or AIDS. Infection with HIV gradually destroys the immune system which makes it harder for the body to fight infections. Approximately 50,000 people are newly infected with HIV each year in the United States. Dividing the number of newly infected people by the at risk population yields a statistic which is an example of risk. Cardiovascular disease includes diseases of the blood vessels, heart rhythm problems, heart infections, and heart defects someone is born with. Cardiovascular diseases are the number one cause of death globally. More people die annually from cardiovascular disease than from any other cause. The overall rate of death from cardiovascular disease was 236.1 per 100,000 person-years in 2009 in the United States. This statistic is an example of a rate. You might have also heard of the term odds before. For example you might ask the doctor. Doctor what are the odds of having a baby boy? Out of a hundred births the probability of having a boy is 51%. While the probability of having a girl is 49%. So the odds of having a boy is 51 to 49. Dividing 51 by 49 you get the odds of 1.04. In this segment we have introduced the following terms, prevalence, risk, rates, and odds. These measures are used to describe patterns of health outcomes or disease in the population. These measures are instrumental in being able to describe patterns of disease distribution, changes in health trends, or even being able to evaluate specific programs like interventions or teen smoking. And whether they are effective or not, and make changes in smoking prevalence's for example, in the population. In the next segments, we'll delve into each of these terms in more detail. [MUSIC]
