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This introductory chapter traces the development of population-based experiments and highlights some of their advantages over traditional experiments and surveys. There is a tendency to think about population-based survey experiments as simply a hybrid methodology that melds certain characteristics of surveys and experiments. But to say this tells nothing about which advantages and disadvantages of each methodology are inherited. The chapter argues that population-based survey experiments are instead more akin to an agricultural hybrid that produces something that was not present in either of the two original plants. To the extent that population-based survey experiments can be implemented with effective treatments and with the same degree of control over random assignment as in the lab, it is the only kind of research design capable of straightforwardly estimating population average treatment effects without complex statistical machinations.

This chapter aims to eliminate the need for others to learn by trial and error. The practical issues addressed here range from the process of explaining a population-based experiment to the Institutional Review Board (IRB), to the issue of maximizing the effectiveness of a treatment in a population-based experiment. Different disciplines had different problems adapting to the idea of a population-based experiment, and the chapter uses various war stories to illustrate the kinds of problems most likely to plague users from different disciplines. The challenge of producing effective treatments involves simultaneously increasing the extent to which the independent variable is varied and reducing measurement error. Consideration of ethics and human subjects comes into play because there are important limits on what investigators can do by way of manipulation in the context of surveys.

This chapter addresses three common problems that emerge in the analysis stage. These include the misguided practice of doing randomization checks, whether and how to use survey weights, and the use and misuse of covariates. Investigators tend to approach the analysis and interpretation of population-based experiments from the perspective of usual practices in whatever their home discipline happens to be. However, as it turns out, some of these choices go hand in hand with common errors and faulty assumptions about the most appropriate way to analyze results from population-based experiments. Usual practices are not always appropriate, particularly with disciplines that favor observational methods. The chapter aims to provide guidance on the best practices and to explain when and why they make a difference.

This chapter illustrates population-based experiments designed to improve measurement. These are descendants of the early split-ballot approach, also geared toward improving measurement of attitudes and behaviors, but the approaches are now far more sophisticated and complex. The experimental treatments discussed here are not designed to test a specific theoretical hypothesis so much as to improve measurement. Although the chapter makes a distinction between testing hypotheses and improving measurement, in many cases the fundamental hypothesis of a study is that a measure is flawed in some systematic way. Three general approaches to improving measurement are evident in population-based experiments: the “item count technique,” alteration of the inferential process, and techniques involving anchoring as a means of improving measurement.

This chapter illustrates the conduct of population-based studies by describing the implementation of a large school-based survey of substance use in several Central American countries. The project's methodology followed an etic approach although considerable work was conducted to validate and harmonize the instrument across countries and sites. The project's implementation involved a number of activities that required collaborators to navigate different cultural and geopolitical situations, some of them of a very sensitive nature, some of which we describe in this chapter. Through these examples, we hope readers will acquire a better understanding of the inner workings of these complicated multi-national projects. We also highlight the importance of building partnerships between country researchers and international organizations, including a discussion of protection issues for human subjects, and we provide an example of power analyses and an analytic strategy of accounting for clustering when conducting statistical analyses with these large studies.

This chapter discusses how population-based survey experiments can be especially valuable to more particularistic research because the target of applicability is known and specified. With particularistic research, population-based survey experiments are important not so much to explore boundaries of generalizability as to establish that the empirical cause and effect relationship works as predicted on the targeted population. By increasing the variety of experimental subjects, the settings in which research is done, and the kinds of treatments and measures that are utilized, population-based survey experiments may produce greater awareness of the boundaries of various social science theories. Whether this kind of activity is welcome or not, it remains an important contribution to social science knowledge.

Environmental modifiers of the effects of genetic variants, or gene-environment interactions, have received increased attention in recent years due to the recognition that genetic variants alone are unlikely to explain most of the recent increases in chronic diseases. Such increases are more likely due to environmental and behavioral changes interacting with a genetic predisposition, suggesting that failing to identify and control environmental modifiers of disease risk could mask important associations with genetic variants or misestimate the magnitude of their effects. Identifying environmental modifiers of these variants may also be essential in mitigating the risk conferred by these variants. Population-based genetic association studies with detailed characterization of environmental exposures are critical and underused resources for identifying potential interacting factors. This chapter explores the substantial and complementary strengths offered by the two main approaches to these studies — case-control and cohort designs — in the search for the genetic and environmental influences on common diseases.

This chapter presents an overview of the latest research on population-based interventions to promote healthful food choices. It begins by providing an overview of the theoretical models and conceptual and intervention issues. Intervention strategies are presented by specific settings: worksites, schools, and other community settings including government food programmes and policies that affect individuals and families. Cross-cutting strategies include individual and environmental-level interventions such as promotion, advertising, and media; food pricing; and availability. The chapter does not aim to present an exhaustive review of the literature. Rather, important ‘state of the science’ research theories and intervention strategies are presented, and illustrative studies are described that provide strong examples of a particular theoretical or methodological approach. The overall aim is to illustrate the most promising intervention strategies and conceptual models to date, and to define the next steps for developing the most effective research-based interventions to promote population-wide, healthful food choices and dietary intake.

This chapter deals with the measurement of psychopathology, with an emphasis on population-based life course approaches. Assessment of mental and behavioral disorders is more difficult than measurement in many areas of medicine, because of the lack of simple and valid biological markers for the presence of mental and behavioral disorders. In effect, the measurement must always involve talking to individuals or observing their behaviors in ways that can be described to others and replicated by them if necessary, consistent with the scientific method.

This chapter begins with a discussion of Mendelian versus complex (non-Mendelian) neurological disorders, followed by an overview of the structure of DNA and the molecular basis of disease. It considers the methods of genetic epidemiology, differentiating family-based studies from population-based studies of unaffected individuals. The options for genetic epidemiology study designs are presented, with a discussion of strengths and limitations of each approach. Recent technological developments are discussed, as are the important ethical and social implications of genetic epidemiologic research in the study of neurological disorders.

This chapter focuses on four key topics related to sexual orientation and mental health. First, it highlights several methodological issues that are relevant to understanding research on the associations between sexual orientation and mental health morbidity and its treatment. Second, it reviews findings from primarily population-based or systematically sampled surveys for evidence of sexual orientation-related disparities in mental health morbidity. The emphasis is on those studies that either included heterosexual comparison groups or used sophisticated research designs to minimize uncontrolled selection bias. Third, it presents preliminary findings from several population-based studies investigating sexual orientation-related differences in patterns of mental health services use. Finally, the chapter closes with some discussion of important future directions for research on sexual orientation and mental health, particularly in the context of large-scale population-based surveys.

In a population-based prospective study in Ullensaker, Norway (The Ullensaker Study), researchers used a generic approach to measuring musculoskeletal pain. This chapter summarizes the results from The Ullensaker Study, compares the results to some other studies which have used similar approaches, and considers the application of findings to a public health view of chronic pain in the general population. It also discusses some limitations in the approach and makes suggestions for future research.

This chapter provides a real-life perspective on systematic reviews, including some of the challenges involved and some of the areas in which there are differences of opinion among experts about potential solutions, and with particular focus on population-based studies. The general steps in conducting a review include selecting appropriate team members to participate, developing a conceptual model and well-constructed key questions, defining and selecting interventions for review, searching for the relevant information, evaluating the quality of the studies and body of literature, and summarizing the data in a clear and useful manner. Each of these steps has a set of methods that have been developed over time as systematic review methodology has grown, and each is meant to maintain rigor and reduce bias in the final conclusions. Ultimately, systematic reviews should be useful for supporting public health and health care decision making.

The quality of exposure data is one of the primary determinants of research validity and informativeness in occupational epidemiology. This chapter describes approaches for assessing exposures to hazardous chemicals, physical agents, and other workplace conditions. Most emphasis is placed on chemical agents. The concepts of dose, and various dose surrogates, especially cumulative exposure and its dimensions of intensity and duration, are introduced. Methods are described for estimating workplace exposure types, intensities, and durations in two broad classes of studies: industry-based studies and population-based studies. Historical, concurrent, and prospective exposure assessment methods are described, and their relative advantages and limitations are presented in reference to health outcomes of particular concern, feasibility, and approximation to true doses.

This chapter examines the impact of palliative care services at the wider-population level. It analyses population-based indicators, such as place of death and patterns of service utilization, and suggests that such indicators are sensitive to the impact of palliative care services. The chapter also contends that it is important to look at total costs when undertaking an overall cost assessment because even charitable funding has an opportunity cost.

Using examples from the field of physical activity and health, this chapter illustrates: designing for the dissemination of population-based environmental and policy approaches; and designing for the dissemination of broad-reach programs for groups with significant health disparities—adults living with chronic disease. These two approaches are complementary, reflecting Geoffrey Rose's perspective on the strategy of preventive medicine: that overall health is improved by both population-wide and high-risk approaches. While the examples in the chapter focus mainly on physical activity, the principles may be applied to other health behaviors, and to other topics in public health.

This chapter provides an overview of the ways to model experimental evolution. There are three approaches to modeling the evolution of quantitative traits: (1) population-based models, (2) Mendelian-based models, and (3) variance-components models. The chapter focuses on variance-components models and discusses how to implement them for both single- and multiple-trait cases. To illustrate how multiple traits can be dealt with in a laboratory evolution model, it presents a case study of the laboratory evolution in the sand cricket, Gryllus firmus.

This chapter describes methods for changing individual behavior through population-based interventions. The main concern is how behavior can be used to improve health-system performance and promote public-health goals. The chapter begins by introducing the basic concepts of behavior change in relation to health-system performance. It then explores four categories of individual behavior that are important to that performance. It presents advice about how to achieve behavior change, through a discussion of the basic elements of social marketing. It concludes with a discussion of the strengths and weaknesses of different behavior-change strategies in the context of health-sector reform.

This chapter addresses the first of the three domains of public health practice — improving health — and is concerned with the broader influences on health. It uses case studies to demonstrate that good health is achieved by the efforts of many, amongst whom primary care health services play a key role not only in providing good quality clinical care but also through providing support to the wider community. This underlines the relevance of the population-based approach to primary care. Improving health is part and parcel of everyday care and integral to providing the package of services within the community, taking account not only of prevention but of care and support.