cohort and case-control studies, predictive studies, archival studies, census studies, monitoring and surveillance studies, implementation tracking, and policy analysis. These designs and methodologies are helpful for answering several “Why,” “What,” and “How” questions that can guide the search for associated evidence. Table 5-4 provides examples of how this type of evidence might be used. See Appendix E for a detailed discussion of economic cost analysis.
Cross-sectional studies, conducted at one point in time, raise the additional problem of identifying whether observed associations are temporal: Did a person become obese because of a low physical activity level, or did physical activity decline as weight increased, or both? Also, many cross-sectional surveys are conducted for general use or for specific administrative purposes and may vary in the completeness of coverage of relevant variables and the quality of data.
Despite these limitations, good-quality observational data are the best evidence sources for answering many questions of potential importance for decision making. They can also be useful as a source of baseline measures in populations.
An experimental study is one in which the investigator has full control over the allocation of subjects to a preventive or treatment intervention versus a control condition, as well as the timing of an intervention. Randomized manipulation and assignment of individuals or groups to an intervention is a defining requirement of an experimental study. By contrast, a quasi-experimental study (e.g., matched cohort, regression-discontinuity, or interrupted time series design) is often described as nonrandomized because the investigator lacks full control over the allocation process and timing of the intervention. A quasi-experimental study design often includes pre–post intervention studies in which outcomes are measured both before and after the intervention is implemented.
Both experimental and quasi-experimental studies are potentially helpful sources of evidence for answering “What” questions about certain categories of interventions. For example, a quasi-natural experimental design was used to estimate the causal impact of physical education classes on overall student physical activity and weight (Cawley et al., 2007), and an interrupted time series design was used to evaluate the effectiveness of a framework designed to increase fruit and water consumption (Laurence et al., 2007). Table 5-5 provides examples of how this type of evidence might be tied to specific questions and applications.
As explained in Chapter 3, quasi-experiments are of critical importance as sources of evidence for obesity prevention interventions as an alternative to randomized controlled trials (RCTs). Experiments simply cannot be conducted with certain environmental or policy variables that influence obesity because they lie outside the control of researchers. Quasi-experimental approaches may be used for evaluating ongoing initiatives. See Appendix E for a detailed discussion of some commonly used