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This chapter discusses the concept of risk in epidemiology. Topics covered include quantifying disease frequency, risk factors, and their relationships; incidence and incidence rate; prevalence and prevalence rate; relationship of incidence and prevalence; and choice of incidence or prevalence measures.

This chapter emphasizes a systematic framework of analysis of disease variations, particularly to ensure that observations of variation are real, and not products of data errors and artifacts. The framework applies to all measures of disease frequency. Topics discussed include reasons for analysing disease variations: environment and genetics; introducing human genetic variation and genetic epidemiology; disease clustering and clusters in epidemiology; and applications of observations of disease variation.

This chapter discusses the following topics: What is epidemiology? How do we measure disease? using rates in measuring disease; describing disease in terms of time, place, and person; defining cases and populations; and undertaking health surveys. It also discusses randomized controlled trials and systematic reviews.

This chapter focuses on associations, describing several quantitative measures of excess risk that can be calculated from epidemiologic data. Estimating the relative risk when incidence rates cannot be calculated and estimating excess risk from results of case-control studies are discussed. Exercises are provided at the end of the chapter.

This chapter looks at the epidemiology or health-event patterns associated with venous thromboembolism (VTE). It explains that VTE, comprising deep vein thrombosis (DVT) and pulmonary embolism (PE), is a multifactorial disease, involving interactions between environmental exposures and genetic predispositions. It investigates the disease burden or incidence, outcomes, and risk factors of VTE in the community, the elderly population, and in individuals with advanced diseases.

The two most common types of observational studies conducted by field epidemiologists are cohort studies and case control studies. Conceptually, a cohort study, like an experimental study, begins with exposure and looks for differences in disease incidence among different exposure groups. In a case control study, enrollment is based on the presence (“case”) or absence (“control”) of disease, and the frequency of exposures is compared between the cases and controls. Each type of study has its strengths and limitations, but each has an important place in field investigations. This chapter provides an overview of these two study designs, emphasizing methodological considerations in the field.

This chapter discusses the concept of epidemiology. Topics covered include definition of epidemiology and a statement of its central paradigm; directions in epidemiology and its uses; epidemiology as a science, practice, and craft; the nature of epidemiological variables; and the basic tools of epidemiology.

This chapter deals with the development and use of statistical methods for geographic disease surveillance. The second section discusses some ideas that are commonly used in statistical process control (SPC) and considers their relevance to disease surveillance. The third section addresses the three areas to which the study of the distribution of disease can be applied: disease mapping, disease clustering, and ecological analysis. The fourth section presents models for spatial surveillance of disease maps and considers disease clustering. The fifth section describes monitoring in a fixed time period and in an evolving time frame. The sixth section reviews disease surveillance modeling and presents a simple example of a spatial temporal disease map analysis. Finally the software available for spatial temporal map surveillance is described.

This chapter argues that the concept of population drives epidemiology and its applications. Population thinking emphasizes that disease patterns are the outcome of the interaction between groups of individuals and their environment. It emphasizes that effective understanding of the causes and control of disease must be based on these interactions and not on an analysis of individuals in isolation. Topics discussed include harnessing variety in individual and group level disease and risk factor patterns; disease patterns as an outcome of individuals living in changing social groups; individual and population level epidemiological variables; and applications of the epidemiological population concept.

This chapter discusses the advantages of and disadvantages of mapping mortality data, methods used in producing mortality atlas, and recent research suggesting improvements in map design that can enhance the accurate communication of the underlying patterns. Experiences in mapping mortality data for the Atlas of United States Mortality is used to illustrate the general methods.

Prediction of spatial heterogeneity in disease incidence based on measurable spatial factors is a major goal of spatial epidemiology. There are a number of applied goals of these predictions, including appropriately targeting resources for surveillance and intervention and accurately quantifying disease burden. Although spatial heterogeneity is evident in the epidemiology of many diseases, several aspects of the biology of vector-borne diseases amplify this form of heterogeneity. Here, we review several aspects of this biology, highlighting seven distinct ways in which the biology of vector-borne diseases impacts understanding spatial heterogeneity in disease incidence. Whereas traditional methods place emphasis on spatial regression and other forms of statistical analysis of empirical data, the goal here is to offer a perspective on potential pitfalls of analyses that take data at face value and do not acknowledge the complex, nonlinear, and dynamic relationships between spatial patterns of disease incidence and spatial heterogeneity in transmission.

Age-adjusted mortality rates for ischemic heart disease have fallen for the last thirty years. The reasons for the decline — which include primary prevention of coronary events, secondary prevention, improved outcomes of the events themselves, and changes in event severity — have been the subject of considerable debate. Much of the debate centers on the relative importance of medical technology versus lifestyle changes or other sources of reductions in risk factors. The debate has important implications for priorities in health care research and policymaking: if medical interventions have been relatively unimportant, then the direction of more resources to research and education on preventive care may be worthwhile. Using evidence from the clinical literature and a range of empirical databases, this chapter looks at the impact of particular changes in medical treatment in acute myocardial infarction (AMI) or heart attacks over the last two decades. It first discusses trends in disease incidence, medical treatments, and thirty-day mortality before estimating the overall cost-effectiveness of all AMI treatments combined.

The objective of epidemiology is to understand the determinants of disease occurrence in populations. One set of potentially important determinants includes the policies that societies and organizations follow to achieve their goals. This chapter seeks to illustrate how epidemiologic study designs can be used to evaluate the health effects of such policies. Exercises are provided at the end of the chapter.

The Sinai's Improving Community Health Survey found that Puerto Rican children had the highest asthma prevalence rate of all groups. Twenty-one percent of Puerto Rican children had diagnosed asthma while another 13% had a positive asthma screen for a potential total asthma burden of 34%. This chapter discusses the lack of specific interventions that target Puerto Rican children, establishment of the Greater Humboldt Park Community of Wellness, and the asthma experience in Humboldt Park as a model of community-based participatory research.