The School of Nursing and Midwifery

1. Cross-Sectional Studies The School of Nursing and Midwifery • Professor Catherine Comiskey

2. Learning Outcomes • Develop an understanding of: • What a cross-sectional study is • Point Prevalence Rates • Basic methodology • Advantages and disadvantages

3. Cross-sectional Studies Means of describing distributions of health characteristics, and the frequency of distributions, in populations Determine associations of health characteristics with other variables Determine which groups experience more or less of a particular disorder, event or health behaviour

4. Observational or descriptive study - the researcher has no control over the exposure of interest (e.g. diet, smoking, alcohol intake) • 3 important questions to consider: • Definition of a case • Definition of the population • Are cases and non-cases from an unbiased sample of the population

5. CS studies can’t show WHY health differences exist, but are valuable in showing that they are there Useful in generating hypotheses about causes of disease Those hypotheses can inform further studies that investigate causality CS data can be used in planning effective health services and programmes

6. Cross-sectional Studies – Snapshot Studies Provides description of prevalence at a particular point in time Each participant is assessed only once (face–to-face interview, telephone interview, postal questionnaire) Theoretically completed within a short period of time, but in reality can take place over months or years

7. CS studies are studies of prevalence: Prevalence = Proportion with an attribute or disease Number of cases CS studies provide Point Prevalence Rates (PPV) specifically PPV = Number of cases of a disease/condition x K Number in the population at risk Where K is a factor of 10

8. Example • Researchers examined a representative sample of 1,000 adults living in a community, to determine risk factors for CHD • Each participant was assessed for (in a single evaluation): • BP • Blood cholesterol • Body composition • Aerobic fitness level • Over the course of 6 months, researchers determined the prevalence of hyperlipidemia, hypertension, obesity and PA at a particular point in time

9. Example contd. In this CS study, researchers could also determine whether participants with observable CHD symptoms differed from those who were symptom-free, with regard to the prevalence of those risk factors While also adjusting for possible confounders (age, gender, ethnicity) Observing higher prevalence of risk factors in those with disease suggests certain risk factors are causes of CHD, but more intensive research is needed to show cause-and-effect

10. Why are PPRs useful • Indicate which groups to look at for cases for care • Tailoring care for specific subgroups • Priority groups for care • Baseline indicators for evaluations of services or programmes • PPRs are probability statements – the likelihood of cases existing in certain groups • e.g. if the PPR of a disease is 2% and you want to carry out a study on 100 cases of that disease, you would have to sample at least 5,000 people to be reasonably sure of finding 100 cases

11. Factors that affect PPRs • Rates increased by: • Immigration of cases • Emigration of healthy individuals • Immigration of susceptible or potential cases • e.g. increased aging population • Prolonged life of cases – increase in duration of the disease • Increase of new cases

12. Factors that affect PPRs • Rates decreased by: • Immigration of healthy individuals • Emigration of cases • Improved cure rates • Increased death rates • Decrease in occurrence of new cases • Shorter duration of the disease

13. Populations and samples The purpose of a questionnaire is to gain important knowledge about a population Almost never feasible (or necessary) to administer the questionnaire to everyone in the population Instead methods of sampling and statistics are used in epidemiological studies Statistical methods depend crucially on how data are gathered, and statistical inferences are only as good as the sampling procedures

14. When researchers perform a sample survey, usually a statistician is consulted for expert assistance To be able to generalize results from a sample to a population, a probability-based sample must be taken The population from which the sample is drawn is called the sampling frame

15. Common Sampling Techniques • Simple Random Sample (SRS): • A SRS is a sample taken in such a way that each combination of n individuals in the population has an equal chance of being selected • The SRS is the simplest sampling plan to use if you have a list of the population • e.g. you have a population of 50 nurses and you want to survey 20 of them on their work stress levels • You number each nurse 1-50 and randomly pick 20 numbers using a random number generator or tables

16. Stratified Random Sampling: • When doing a cross-sectional study, important subgroups of people may have different views or health-related behaviours • A refinement of the SRS is stratified random sampling – the population is divided into groups, or strata, on the basis of certain characteristics e.g. age, sex, SES • A SRS is then carried out for each stratum and the results combined for the total sample • The objective is to ensure that each stratum in the population is represented in fixed proportions

17. e.g. to determine the smoking habits of a national population, age and sex are important factors Desirable to select a sample whose age and sex exactly reflects the composition of the whole population Some strata may be over or under-represented, however the important point is that the sample proportions are predetermined Weighting techniques are used to address over or under-representation

18. Sampling Bias • Bad sampling techniques: • Convenience/presenting sampling • Over/under-representation • Selection bias • e.g. sampling diabetic patients in a hospital; not generalizable to all diabetics • Self-selection bias: • When participants have control over whether they participate or not • e.g. people with strong opinions are more likely to respond and participate than those who are apathetic

19. Non-response Bias: • To conduct a survey, the individuals sampled have to be eventually contacted • If some are uncooperative or impossible to trace, these exclusions may affect the representativeness of the sample • Non-response rates higher than 15-20% can cast doubts on the conclusions drawn from a particular study

20. Advantages of Cross-Sectional Studies • Quick – a one-time interview/examination • Less expensive than other study designs • Helpful in indicating case loads, priorities for care, programme planning and designing, budgeting for and allocating resources • Useful for determining associations between variables of interest and generating hypotheses • Descriptions of the relative distributions of health and disease can direct case-finding priorities

21. Disadvantages of Cross-Sectional Studies • Don’t separate cause-effect relationships in associations • Only a single point in time – deals only with surviving cases • Not useful when rare cases are being considered • If the PPR is small e.g. 5 per 100,000…at least 200,000 people would have to be examined to find just 10 cases • Not useful for describing the load of cases or health events • Don’t identify the future risk of cases from particular characteristics • Not useful for short duration health events

22. Data Reporting in CS Studies • Sample size • Response rate • Measures of centrality • Mean, median, mode, variance, standard deviation etc. • Confidence intervals • Range of values within which the true prevalence is believed to be • Cross-tabulations • The prevalence of risk factor variables by demographic characteristics

23. Example: Comiskey & Larkan (2010) paper

24. Critical Appraisal of the Cross-Sectional Method See CASP for CS Studies Handout

25. Thank YouCatherine.Comiskey@tcd.ie