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Oxford University Press makes no representation, express or implied, that the drug dosages in this book are correct. Readers must therefore always check the product information and clinical procedures with the most up to date published product information and data sheets provided by the manufacturers and the most recent codes of conduct and safety regulations. The authors and the publishers do not accept responsibility or legal liability for any errors in the text or for the misuse or misapplication of material in this work. Except where otherwise stated, drug dosages and recommendations are for the non-pregnant adult who is not breastfeeding.

Cross-sectional studies 

Chapter:
Cross-sectional studies
Author(s):

Manolis Kogevinas

and Leda Chatzi

DOI:
10.1093/med/9780199218707.003.0028

Cross-sectional studies examine the relationship between diseases (or other health-related characteristics) and other variables of interest as they exist in a defined population at a particular point in time (Last 2001). They could be defined as ‘studies taking a snapshot of a society’. Synonyms used for cross-sectional include prevalence and disease-frequency studies.

The principal characteristic of cross-sectional studies is that they provide information on the prevalence of disease; that is, they include prevalent cases. In these studies, exposure and disease are measured at the same point in time, but this characteristic is shared by other epidemiologic designs; for example, case–control studies. In many cross-sectional studies, information on past exposures is not collected, but this should not be regarded as a characteristic defining these studies. The outcome measured in cross-sectional studies can be a continuous variable such as blood pressure or FEV1, as compared to a dichotomous outcome measured in case–control studies and, on most occasions, in cohort studies.

As is frequently the case in epidemiology, studies may use mixed designs; for example, a cross-sectional study may measure a biomarker referring to current exposure (e.g. vitamin E), may request information for the past (e.g. use of health services in the last year), may identify older and recent cases (e.g. subjects who had asthma in childhood or those who had their first attack of asthma in the last few months), and may convert into a cohort study if subjects included in the cross-sectional studies are followed up. The statistical analysis of cross-sectional studies depends on their hybrid design, and is frequently similar to that of a case–control study using logistic regression and calculating (prevalence) odds ratios. Cross-sectional studies are extensively used to measure the prevalence of disease and exposures or other health-related variables. On these occasions, the representativeness of the studied sample is a prerequisite.

In this chapter, we will first describe the uses of cross-sectional studies in epidemiological and public health research, then discuss methodological issues concerning the design, the main biases of these studies, including response rates, and how to improve participation in the studies. We will finally discuss issues related to the statistical analysis of cross-sectional studies. Many of these issues are also relevant to other epidemiological designs.

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