Cross-Sectional Studies

1. Cross-Sectional Studies Emily Howard March 12, 2008

2. THE LINE UP • Research methodology and designs for description • Cross-sectional design definition • Key features • Advantages and Disadvantages • How it has been used • Case study: Breast feeding and obesity: cross-sectional study • Questions

3. RESEARCH METHODOLOGY • Steps in Research Methodology: • • Deciding when and how often to collect data • • Constructing measures • • Identifying a sample or test population • • Choosing a strategy for contacting subjects • • Selecting statistical tools • • Presenting the findings • The quality of a set of data is determined by the research methodology

4. RESEARCH DESIGNThe presentation of the plan for the study’s methodology • Research Design: the design should indicate the purpose of the study and demonstrate that the plan is consistent with the study’s purpose. • Research designs are plans that guide decisions about: • • When and how often to collect data • • What data to gather • • From whom and how to collect data • • How to analyze data

5. DESIGNS FOR DESCRIPTIONCan provide a wealth of easy to understand and interpret information • Designs are frequently used for planning, monitoring, and evaluating • Can provide leads and eliminate untenable explanations • They can suggest causality A wealth of information = information overload

6. CROSS SECTIONAL DESIGN:Used to collect data on all relevant variables at one time • Process: • Decide how to measure each variable in a model • Data for each variable are collected closely enough to the same time to be considered contemporaneous • After measurements have been completed, statistical models are used to examine the relationships between the variables

7. CROSS SECTIONAL DESIGN: KEY POINTS • Key Point: data represent a set of people or other cases at one point in time • Analogies to describe the method: • Can be viewed as a physical “cross section” of the population of interest • Can be seen as a “snapshot” of something at a particular point in time • Both underscore the static, time bound nature of the design

8. CROSS SECTIONAL DESIGN: WHAT THEY CAN DO • Well-suited to studies that collect data on: • Many variables • From a large group of subjects • Gather information on people’s attitudes and behaviors • Answer questions of how much, how many, who, and what happened • Begin exploratory research and identify hypotheses for future research

9. CROSS SECTIONAL DESIGNS: ADVANTAGES • Uncovers relationships that can be studied further in experimental studies • Researchers with different interests and models can often work with data from a single cross sectional study • Using multivariate analyses allow researchers to obtain information on the influence of variables on one another

10. CROSS SECTIONAL DESIGN: DISADVANTAGES • Cannot measure change in variables over time • Inappropriate for demonstrating causal relationships • Researchers have little to no control over environment • Researchers may be unable to rule out alternative explanations • Weak implementation can ruin a good design and produce poor data

11. CROSS SECTIONAL DESIGN: CONSIDERATIONS • A large number of variables can help with identifying strong direct or inverse relationships for closer study • Data is not cheap; more data means higher costs for collecting, coding, compiling, and storing • Chance of error increase as data is coded, transcribed, and computerized • Lengthy questionnaires may discourage participation

12. CROSS SECTIONAL DESIGN: WHERE YOU’VE SEEN THEM BEFORE • Cross sectional studies can be found in everyday places • Often used in conjunction with surveys • Used for demographics information and analyses • Mass media use cross sectional studies on current issues • Newspaper articles • U.S. Decennial Census of Population and Housing (although repeated every 10 years)

13. CASE STUDY • Breast feeding and obesity: cross sectional case study • Institute for Social Pediatrics and Adolescent Medicine, Ludwing Maximillians University • Rüdiger von Kries, Berthold Koletzko, Thorsten Sauerwalkd, Erika von Mutius, Dietmar Barnert, Veit Grunert, Hubertus von Voss

14. CASE STUDY: OVERVIEW • Objective: to assess the impact of breast feeding on the risk of obesity and the risk of being overweight in children at the time of entry to school • Design: cross sectional survey • Setting: Bavaria, southern Germany • Methods: Routine data were collected on the height and weight of 134,577 children from obligatory health exams for school entry • In a subsample of 13,345 children, early feeding, diet, and lifestyle factors were assessed using response to a questionnaire completed by the parents

15. CASE STUDY: DEFINITIONS • Definition of overweight and obese: • Overweight: having a body mass index (BMI) above the 90th centile • Obesity: having a BMI above the 97th centile of all enrolled German children • Exclusive breast feeding was defined as a child being fed no other food

16. CASE STUDY: POPULATION AND DATA COLLECTION • Questionnaires were given to parents of 13,345 children in two rural regions • Collected data on: risk factors for atopic diseases, for how long children were exclusively breast fed, if at all, family history, parents’ education level, and diet • Response rate was 76% (9,357 questionnaires) for 5 and 6 year olds

17. CASE STUDY: ANAYLSIS AND RESULTS • Prevalence of overweight and obese children were calculated according to duration of breast feeding • Logistic regression models used to assess impacts of variables that were significantly associated with both breast feeding and being overweight/obese • Variables that were significantly associated with a child being overweight/obese and independent risk factors in the logistic regression model included: • parental education, maternal smoking during pregnancy, birth weight, own bedroom, consuming margarine, butter, full/low fat milk, full/low fat yogurt, whipped cream, breakfast cereals, and sweet desserts 3 times per week or more

18. CASE STUDY: RESULTS • Relationships among variables: • Higher levels of parental education (10 years or more), premature birth, and low birth weight were inversely associated with being overweight/obese • Maternal smoking during pregnancy and having own bedroom positively correlated • Overweight children consumed full fat products, butter, breakfast cereals, and sweet desserts less frequently than the other children • Children who had been breast fed for at least 6 months or more had reduced risk of being overweight by more than 30% and 40%, respectively

19. CASE STUDY: CAVEATS • Some relevant questions were not asked in questionnaire and could not be accounted for: • Did not address “catch-up weight gain” in children with low birth weight • Did not address family history of being overweight/obesity • The final variables in logistic regression model were breast feeding, parental education, maternal smoking during pregnancy, birth weight, own bedroom, and consumes butter 3 times per week or more

20. CASE STUDY: CONCLUSIONS • Taking biological studies into account, it is plausible that breast feeding might have a programming effect in preventing obesity or becoming overweight later in life • Conclusion: prolonged exclusive breast feeding reduced the risk of being obese or overweight among school age children in Bavaria who were born in the early 1990s. This effect is more likely to be related to the composition of breast milk than to lifestyle factors associated with breast feeding. • Preventing obesity and its consequences may be an important argument in the drive to encourage breast feeding in industrialized countries

21. CASE STUDY: THE USE OF CROSS SECTIONAL DESIGN • Pros: • the use of a questionnaire coupled with existing population data provided a lot of information on thousands of participants • The number of variables allowed for a regression model of statistical analysis • The researchers were able to make a strong enough relationship between breast feeding and obesity to come to some conclusions • Cons: • There were a number of variables there were not accounted for in the questionnaire and study • Alternative explanations for their results could not be ruled out • Results need to be more fully explored in an experimental study

22. CROSS SECTIONAL DESIGN: QUESTIONS?