A cross-sectional study involves identifying a population at a single point in time and measuring variables like exposures and outcomes simultaneously. It can be descriptive by measuring single variables or analytical by examining associations between variables. While able to estimate disease prevalence and exposure proportions quickly, cross-sectional studies cannot determine causation due to issues like recall bias and inability to assess temporal sequence. Analysis uses contingency tables to calculate prevalence rates and measures of association like prevalence rate ratios.

1. PADMA SHREE SCHOOL OF PUBLIC HEALTH
CROSS-SECTIONAL
STUDY DESIGN
B Y -
D R . R A H U L S H R I V A S T A V A
B D S , M P H

2. INTRODUCTION
A cross-sectional studies
• A type of observational study
• The investigator has no control over the exposure of
interest (eg. diet).
It involves
• Identifying a defined population at a particular point in
time
• Measuring a range of variables on an individual basis
eg. include past and current dietary intake
• At the same time measuring outcome of interest
eg. obesity

3. • Measurement of exposure of interest and outcome of
interest is carried out at the same time (e.g. Obesity
and Hypertension).
• There is no in-built directionality as both exposure and
outcome are present in the study subject for quite
some time .
• Deals with the situation existing at a given time (or
during a given period) in a group or population.

4. These may be concerned with:
• The presence of disorders such as diseases,
disabilities and symptoms of ill health.
• Dimensions of positive health, such as physical
fitness.
• Other attributes relevant to health such as blood
pressure and body measurements.
• Determining the workload of personnel in a health
program as given by prevalence.

5. TYPES
• May be
• Descriptive
• Analytical or
• Both
• At descriptive level, it yields information about a single
variable, or about each of number of separate variables in a
study population.
• At analytic level, it provides information about the presence
and strength of associations between variables, permitting
testing of hypothesis.

6. • Essential feature of cross-sectional studies -They
collect information relating to a single specified
time.
• But, often extended to include historical information
which leads to demonstration of statistical
associations with past experience e.g. investigation
of an epidemic.

7. SYNONYMS
• Instantaneous study
• Prevalence study
• Simultaneous study

8. STEPS IN CROSS-SECTIONAL STUDIES

10. CROSS-SECTIONAL STUDY DESIGN
• Cross-Sectional Studies measure existing disease
and current exposure levels.
• They provide some indication of the relationship
between the disease and exposure or non-
exposure.
• Sample without knowledge of Exposure or Disease.
• Sample at one point in time.
• Mostly prevalence studies/surveys.

11. ADVANTAGES
• Good design for hypothesis generation.
• Can estimate overall and specific disease prevalence and
sometimes rates.
• Can estimate exposure proportions in the population.
• Can study multiple exposures or multiple outcomes or diseases.
• Relatively easy, quick and inexpensive.
• No issue of subjecting any animals or producers to particular
treatments.
• Best suited to studying permanent factors (breed, sex, blood-
type).
• Often good first step for new study issue.

12. DISADVANTAGES
• Impractical for rare diseases.
• Not a useful type of study for establishing causal relationships.
• Confounding is difficult to control.
• No control over sample size for each exposure by disease subclass.
• Problems with temporal sequence of data.
• Hard to decide when disease was actually acquired.
• Disease may cure the exposure.
• Miss diseases still in latent period.
• Recall of previous exposure may be faulty.

13. WHAT TYPE OF STUDY TO CHOSE DEPENDS ON:
• what is the research question/ objective
• Time available for study
• Resources available for the study
• Common/rare disease or production problem
• Type of outcome of interest
• Quality of data from various sources
• Often there are multiple approaches which will all
work
• Choosing an established design gives you a huge
head start in design, analysis and eliminating
biases

14. ANALYSIS & INTERPRETATION
• The results can be analyzed using a simple 2 *2
contingency table.
• Firstly, place the frequencies of exposed and
unexposed subjects in this table according to
whether the outcome is present or absent.
Outcome status
Exposed status present absent
Exposed a+b
Unexposed c+d
a+c b+d
a b
c d

15. • The frequencies a,b,c & d represents the no. of
exposed person with disease , the no. of exposed
person without disease, the no. of unexposed person
with disease, and the no. of unexposed person
wihtout disease, respectively.
• These values helps to calculate the prevalence rate
and measures the association.
• Crude prevalence rate (overall prevalence rate) is
calculated as,
PR = [(a+c) / n] * 10ⁿ

16. • Prevalence Rate among exposed subjects
PRe = [a / (a+b) ] * 10ⁿ
• Prevalence Rate among unexposed subjects
PRue = [c / (c+d) ] *10ⁿ
Now, these rates can be used to calculate the Prevalence
rate ratio (Prevalence ratio) and Prevalence rate
difference as under,
PRR = PRe / PRue
PRD = PRe - PRue

17. If,
• PRR = 1.0 , NO association between exposure and
outcome.
• PRR= 2.0, outcome is 2 times more common in exposed
group vs unexposed group.
• PRR=0.5, outcome is only half as common in exposed group
compared to unexposed group.
This association can be positive, when PRR >1, can be
negative, when PRR<1. because PRR is based on prevalence
rates, the interpretation of the measure is restricted to
statements about the frequency / prevalence of the outcome in
the exposed group relative to unexposed group.

18. The statistical significance of PRR can be determined by the
chi square test of independence (X²), which for a 2*2
contingency table can be calculated by,
X² = n (ad - bc) ²
(a+b) (c+d) (a+c) (b+d)
If value of X²,
• From 3.84 to 6.63, association is considered as statistically
significant at p <=0.5
• From 6.64 to 10.82, association is significant at p <=0.01
• >=10.83, association is significant at p <=0.001
A 95%confidence intervall for PRR can be estimated using a
formula developed by D.Katz and associates.
95%CI = exp {ln(PRR) ± 1.96 √ [(b/a) / (a+b)] + [(d/c) (c+d)]}

19. Thank you