In 1830, a French physician named Louis-Rene Villerme noted that the mortality rate within Paris varied widely among the districts. He tried to correlate mortality with the distance of each district from the Seine river, the relationship of the streets to the prevailing winds-- 

[WIND SOUND] 

-- the district's source of water, and even factors such as soil type, elevation, and exposure to the sun. 

None of these adequately explained differences in mortality rates. Finally, he wondered if the differences might be due to differences in poverty. He collected several years of data on the percentage of people paying taxes in each district, and ranked the districts from poorest to wealthiest on this basis, as shown in this graph that illustrates a striking correlation between poverty and mortality rates. 

Villerme was studying a well-defined population with distinct internal subdivisions. And he obviously had collected a great deal of information on this population of interest. But many of the populations and subsets of populations that we are concerned with are defined by characteristics other than geographic boundaries. 

For example, we might be interested in studying health outcomes in Medicaid recipients, or we might be interested in studying teenaged females. How one studies the determinants of health and disease is often dependent on how the population is defined. In addition, how one selects samples from a population depends on the goals of the research study. 

If our goal is to describe or characterize the population by obtaining accurate estimates of population parameters such as the mean age, or the mean body mass index, or the proportion of adults who smoke, we need to select samples that are representative of the overall population. However, if our goal is to conduct analytic research to identify the determinants of disease, then we might want a sample that is selective, rather than representative. 

For example, if we wanted to determine whether environmental tobacco smoke exposure had adverse effects on respiratory health in children, we might want to select children enrolled in a specific large school system, which enabled us to track absences from school for medical reasons such as respiratory infection, or asthma attacks. 

This module will deal first with concepts focusing on populations and the samples drawn from them to conduct research on the factors-- that is, the exposures that influence health outcomes. Throughout this course, we will consider a wide range of exposures that are potentially relevant to health, including environmental exposures, personal behavior, one's genome, and an array of social determinants. Health outcomes will also be considered broadly, including diseases, disabilities, injuries, and mental health. 

Later in this module, we will begin to explore relationships between exposures and health outcomes, by looking briefly at how monitoring and astute observations identify health problems in a population, and also serve to generate hypotheses that focus subsequent research studies. Finally, we will draw a distinction between exposure disease associations that are causal and those that are not.