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This chapter provides a brief introduction to some of the epidemiologic and statistical methods for and challenges to gathering and analyzing the data that underlie the research presented in this volume and in the field of public mental health as a whole. It focuses on some of the data and methodological complexities particularly common in public mental health research. It explores three fundamental types of questions relevant to public mental health: estimating rates of disorders in a population across people, place, and time; examining risk and protective factors associated with particular disorders; and exploring interventions to prevent disorders or to treat them once they emerge.

This chapter reviews some of the historical contributions of epidemiology and describes some of the most pressing current issues encountered in the application of epidemiologic methods. Many of the topics discussed are covered in more detail in later chapters.

An important aim for a first course in epidemiologic methods is to provide a strong working knowledge of the fundamentals of epidemiologic principles. In this chapter the authors discuss a sequence of core topics to broach the teaching of epidemiologic concepts and methods, to cover the essential principles and the main methods for conducting epidemiologic research. The topics addressed include causation and causal inference; epidemiologic measures; types of epidemiologic studies; principles of good study design; principles of epidemiologic data analysis; stratified analysis; analysis of interaction; multivariable analysis; and analysis of multilevel or continuous exposures. For each topic, suggestions are offered for what concepts should be the focus in the classroom, as well as strategies for presenting those concepts. By the end of the course, students should be able to discuss and criticize the design of epidemiologic studies in a balanced way and to demonstrate quantitative regard for various sources of error.

The accuracy of epidemiologic studies includes issues of both precision and validity. This chapter provides an overview of the main themes, along with more detailed presentations of those aspects of precision and validity that are particularly important in occupational epidemiology. It starts by outlining the distinction between issues of precision, which involves random error, and validity, which involves systematic error. The major issues of validity can be grouped into three components—confounding, selection bias, and information bias—which are considered in turn. The “Healthy Worker Effect” is described as characteristic bias of occupational studies that has elements of both confounding and selection bias. The design of epidemiologic studies often involves a tradeoff between these competing study design consideration.

This chapter provides a brief introduction to some of the epidemiologic and statistical methods for, and challenges to, gathering and analyzing the data that underlie the research presented in this volume and in the field of public mental health as a whole. The chapter is not intended as a general introduction to epidemiologic and statistical methods, but focuses more specifically on some of the data and methodological complexities particularly common in public mental health research. Three fundamental types of questions relevant to public mental health are discussed in particular: (1) estimating rates of disorders in a population across people, places, and time; (2) examining risk and protective factors associated with particular disorders; and (3) exploring and understanding the effects of interventions to prevent disorders or to treat them once they emerge.

Cross-sectional studies are particularly well suited for investigating workplace hazards in relation to non-fatal health outcomes, such as disease symptoms. The repeated measures design is an embellishment of the cross-sectional approach in which the original cross-sectional study sample is followed longitudinally, thus becoming a prospective cohort study. This chapter summarizes the design, implementation, and standard data analysis methods for cross-sectional and repeated measures studies. It explains options for selecting study subjects, comparisons populations, fundamental data analysis methods, and characteristic strengths and limitations, including biases that can occur from studies of disease prevalence of these approaches.

The only conceptual difference between a full cohort study and a case-control study based on the same cohort is that the latter involves outcome-based sampling of the source population rather than an analysis of the entire source population. Case-control studies offer considerable savings in time and expense, and are particularly valuable for studying diseases that are rare and/or have long induction times. This chapter presents the basic case-control design. It examines issues in the selection of cases and controls, including sampling strategies, matching, sources of controls, and avoidance of bias. Methods for analyzing case-control data are then presented.

This chapter discusses a new and controversial development in perinatal epidemiology. The chapter begins with an overview of some traditional concepts in perinatology, namely, the calculation of birth weight and gestational age-specific perinatal mortality rates and the use of dual, overlapping time scales for estimating the duration of gestational and chronologic age. This is followed by a review of the conundrums extant in contemporary perinatology and a proposal for an alternative formulation (called the fetuses-at-risk approach) for the calculation of rates in perinatal epidemiology. The focus under this alternate approach is on incidence measures, with gestational age treated as survival time. Latent period considerations for perinatal phenomena that have intrauterine origins are also an integral part of this formulation. Finally, there is reframing of the traditional and fetuses-at-risk models as serving prediction (noncausal) and causal functions, respectively.