1. Cross-Sectional Studies
Epidemiology and Biostatistics
Xinjiang Medical University
Ferdon Mijit
Nov.24th, 2010

2. Risk factors/Exposure, Outcome/Disease

3. Epidemiological Studies
Some study types
Observational studies
Cross-sectional studies
Cohort studies
Case-control studies
Experimental studies
Randomized, controlled experiments
Interventions

4. Epidemiologic Study Designs
Time o
Time t t
Cross-Section
Exposure & Disease
Prospective Cohort
Disease over time
Case-Control
Past exposure

5. Survey Poll

6. Cross-Sectional Study
Cross-sectional studies involve data collected at a defined time. They are often used to assess the prevalence of acute or chronic conditions, or to answer questions about the causes of disease or the results of medical intervention. They may also be described as censuses or survey. Cross-sectional studies may involve special data collection, including questions about the past, but they often rely on data originally collected for other purposes.

7. Features of Cross-sectional studies
Prevalence: estimate the prevalence of the outcome of interest for a given population, commonly for the purposes of public health planning.
Data Collection: individual characteristics, including exposure to risk factors, alongside information about the outcome.
Snapshot: Provide a 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.

8. Why Cross-sectional Studies?
The purpose of the study is descriptive, often in the form of a survey. Usually there is no hypothesis as such, but the aim is to describe a population or a subgroup within the population with respect to an outcome and a set of risk factors.
To find the prevalence of the outcome of interest
investigate associations between risk factors and the outcome of interest (starting point)
limited: The fact that they are carried out at one time point and give no indication of the sequence of events — whether exposure occurred before, after or during the onset of the disease outcome. impossible to infer causality.
useful in generating hypotheses for future research.

9. Why Cross-sectional Studies?
Repeated cross-sectional studies: as longitudinal study, where the individuals included in the study are either chosen from the same sampling frame or from a different one.
An example: The British Association for the Study of Community Dentistry Survey ,5-year-old children are examined annually, prevalence of caries is recorded.
The prevalence of caries for this age group is monitored over time and this information is used in public health policy planning and in the development of targeting strategies

11. Sample selection and response rates
The sample used in a large cross-sectional study is often taken from the whole population.
The response rate determine how well results can be generalised to the population as a whole.
This is the optimum situation: if the sample is selected using a random technique it is likely that it will be highly representative. In order for the results to be representative of the population, however, not only must the selected sample be representative but so must the responders.
Nonresponse: a common problem in wide-scale surveys; techniques to minimise nonresponse include telephone and mail prompting, second and third mailing of surveys, letters outlining the importance of replying and a range of incentives.

12. Biased response: where a person is more likely to respond when they have a particular characteristic or set of characteristics.
Example: The response rate of a survey conducted by door-to-door interview looking at a particular disease, for example, may be highest in the elderly and unemployed because these groups are more likely to be in their home during the day. These two groups are also more likely to experience higher levels of disease, therefore biasing the results.

13. Measures of outcome and exposure
A lot of information can be collected about potential risk factors in a cross-sectional study. Loss to follow-up is a common concern in longitudinal studies and one of the strategies used to overcome this is to minimise the amount of information collected. This is not a problem in cross-sectional study design.
It is advisable to think carefully about what might be relevant because this is a good opportunity to gain a broad base of knowledge about subjects who have/do not have the outcome of interest, but it is also important to maintain optimum response levels. Associations between outcomes and exposures of long duration are particularly difficult to establish using cross-sectional studies.

14. Cross-sectional: Advantages
Usually use population-based samples, instead of convenient samples. Generalizability.
Conducted over short period of time
Relatively inexpensive

15. Cross-sectional: Disadvantages
Difficult to separate cause from effect, because measurement of exposure and disease is conducted at the same time.
A persons exposure status at the time of the study may have little to do with their exposure status at the time the disease began.
Neyman Bias. Longer-lasting cases.

16. Large Cross-Sectional Studies

17. Cross-sectional study
Cross-sectional study = prevalence survey
Example: prevalence of hypertension survey. How to do?

18. Goals To describe the distribution of certain health problem
For policy making
To explore the possible relationship between two variables

19. Design

21. Types of cross-sectional study
Census
Sampling survey

22. Census
A census is the process of obtaining information about every member of a population
Investigation in special time and special extend
object
To discovery and cure patients in early stage
To understand distribution of disease and health conditon

23. Census principal need to have organize and high level census team
need to have time request
data need to put together
According to specific cycle

24. Census
Merit
investigated object is easy
data is detailed and has high accuracy
To help research epidemic factors of diseases
weakness
amount of work is big,cost is high and organized work is different
investigated content is limited
repetition and leakage
Investigated accuracy controlled difficultly

25. Sampling survey
To research the subset which of a target population that is chosen for investigation
Prevalence survey often adopts method of sampling survey
principal
Sampling survey must abide by randomization
Sample size must be big enough to represent totality

26. Sampling survey Merit weakness abide by randomization
save labor power , material resources and time
Error may be evaluated and controlled in advance
Accuracy is high
weakness
A non-total investigation method
sampling error and bias
Can not be used in disease of low-prevalence rate

27. Sampling survey sampling methods ： simple random sampling
systematic sampling
stratified sampling
cluster sampling
multi-stage sampling

28. Simple random sampling
Here each sample of size n from the population of size N has an equal chance of selection. In practice ``each unit in the population is numbered 1 to N and n units are randomly drawn from the N''.
A simple random sample gives each member of the population an equal chance of being chosen. 
It is not a haphazard sample as some people think! 
One way of achieving a simple random sample is to number each element in the sampling frame (e.g. give everyone on the Electoral register a number) and then use random numbers to select the required sample. 
Random numbers can be obtained using your calculator, a spreadsheet, printed tables of random numbers, or by the more traditional methods of drawing slips of paper from a hat, tossing coins or rolling dice.

29. Simple random sampling
Advantages
- ideal for statistical purposes
Disadvantages
- hard to achieve in practice
- requires an accurate list of the whole
population
- expensive to conduct as those sampled
may be scattered over a wide area

30. Systematic sampling
At first sight this is very different. Suppose that the N units in the population are numbered 1 to N in some order. To select a systematic sample of n units, if then every k-th unit is selected commencing with a randomly chosen number between 1 and k. Hence the selection of the first unit determines the whole sample, e.g., N = 5,000, n = 250 therefore k = 5000/250 = 20. Therefore, select every 20th item commencing with (say) 6.

31. For example, suppose you want to sample 8 houses from a street of 120 houses.
120/8=15, so every 15th house is chosen after a random starting point between 1 and 15. If the random starting point is 11, then the houses selected are 11, 26, 41, 56, 71, 86, 101, and 116.

33. Systematic sampling Advantages
(i) easier to draw, without mistakes (cards in file)
(ii) more precise than simple random sampling as more evenly spread over population
Disadvantages
(i) if list has periodic arrangement then it can fare very badly

34. Stratified sampling
In a stratified sample the sampling frame is divided into non-overlapping groups or strata, e.g. geographical areas, age-groups, genders. A sample is taken from each stratum, and when this sample is a simple random sample it is referred to as stratified random sampling.
In stratified sampling the population of N units is first divided into non-overlapping subpopulations called strata. If sampling from the strata is simple random sampling then whole procedure is called stratified random sampling.

35. population Strata 3 Strata 2 Strata 1
age、sex、income、education
Strata 3
Strata 2
Strata 1

36. Cluster sampling
Suppose that a survey is to be done in a large town and that the unit of enquiry is the individual household. Suppose further that the town contains 20,000 households, all listed on convenient records, and a sample of 200 is needed. A simple random sample of 200 could well spread over the whole town incurring high costs and much inconvenience. However one might decide to concentrate the sample in a few parts of the town. Suppose for simplicity the town can be divides into 400 areas with 50 households in each then one could select at random 4 areas (1/100) and include all households in these areas.

37. Advantages
(i) reduced field costs
(ii) applicable where no complete list of units is available (speciallists only need be formed for clusters).
Disadvantages
clusters may not be representative of whole population but may be too alike
analysis more complicated than for simple random sampling.

38. Multistage sampling
With ``large'' populations it is often necessary to carry out the sampling in 2 or more stages. For example, to survey the attitudes of school children ;
(i) sample of education authorities
(ii) sample of towns in each local authority
(iii) sample of schools in each town
(iv) sample of classes in each school
(v) sample of children in each class
The advantages and disadvantages are as for cluster sampling.

39. ……
Stage 1
Stage 2

40. Bias in cross-sectional studies
Selection Bias (eg, NSSP study)
Is study population representative of target population?
Is there systematic increase or decrease of RF?
Measurement Bias
Outcome
Misclassified (dead, misdiagnosed, undiagnosed)
Length-biased sampling
Cases overrepresented if illness has long duration and are underrepresented if short duration.(Prev = k x I x duration)
Risk Factor
Recall bias
Prevalence-incidence bias
RF affects disease duration not incidence eg, HLA-A2

41. Feature of prevalence survey
Detailed,rapid and cheap
According to cross-sectional study
Only put forword to hypothesis of pathogenesis
Often be used to research chronic disease
Be more applicable to the research of disease whose exposure factor dose not change easily

42. To describe distribution of disease
To discovery clue of pathogenesis
Be used in secondary prevention
To evaluate prevention and cure effect
surveillance of disease
health demand,health project and health policy decision

43. Methods of prevalence survey
visit face to face
visit by message
visit by telephone
questionnaire
physical examination and lab examination
investigation with IE

44. Types of prevalence survey
Investigation methods of sensitive problem
sensitive problem
Two kinds
attributive sensitive problem
Quantitive sensitive problem

45. Sample size ①quantitative data： d - error S - sd ②enumeration data：
P-prevalence
Q = 1 - P

46. Steps of prevalence survey
1. Determine research object
To describe distribution, explore pathogenesis and build normal value
To reflect practice,creativity,science and advance
2. To grasp data of background
Experience of own,consulting with expert and looking up reference data

47. 3. Determine research object and methods
objects：
persons at risk、occupational population、representative population、population carried out prevention or cure measurement。
census、screening、sampling survey。
Research methods
sample size：

48. 4.Determine research types and methods
According to object
considering specialty of data
thinking over feature of object
5.collection of data
1.Determine research variable：
including data of demographic, disease measurements and relative factors
2.questionnaire

49. 6.Data analysis and explanation of result
To grasp data of background
disease measure
exposure measure
demand of investigator
6.Data analysis and explanation of result
data examination：
program examination
data examination
logic examination

50. (1)analysis measurement：
data analysis
(1)analysis measurement：
numerical variable：arithmetic mean、standard deviation、95%confidence interval。
categorical variable：rate、proportion。
(2)analysis methods：
descriptive distribution
relative analysis
one factor comparison analysis
multiple factors analysis

51. Explanation of result
To state representative sample, sample reliability and estimate the source 、size 、direction and method of adjustment

52. Ecologic Studies Ecologic study (correlation study)
To develop hypotheses about possible causes of disease occurrence, the presence of a suspected risk factor can be measured in different populations and compared with the incidence of a particular disease. This type of comparison is referred to as an ecologic study.
The analysis is at the level of an entire population rather than at the level of individual person.

53. Ecologic Studies: Data collection
Exposure data and disease data are often collected at different times for different reasons.
Environmental measures/ Global measures.
Incidence and mortality data vs working in a factory.
Ecologic Fallacy is an important factor.

56. Ecologic Studies: Disadvantages
Ecological Fallacy. Inappropriate conclusions regarding relationships at the individual level based on ecological data/aggregate data.
Inappropriate conclusions about causation.
No causal conclusions can be drawn. Temporal ambiguity.
Lack of adequate data
Additional studies do and don’t support ecological conclusions.

57. Ecologic Studies: Advantages
Hypothesis generating.
Low cost and not time consuming.
Limited data for individuals (environmental studies).
Achieves substantial variation.
If inferences are to be made about groups.
Useful for social scientists as well as epidemiologists. Evaluation of new policies.