CROSS
SECTIONAL
STUDY
Framework of presentation
Design options in epidemiological research
Cross sectional study
Design of cross sectional study
Steps of cross sectional study
Analysis of cross sectional study with example
Use of cross sectional study
Advantage & disadvantage
Comparison with other studies
Design options in epidemiological research
•
Observational studies
• Descriptive
• Analytical
• Ecological
• Cross sectional
• Case control
• Cohort
Experimental/
interventional studies
• Randomized controlled
trial
• Field trial
• Community trial
Hierarchy of Evidence
Systematic
Review
&
Meta-analysis
Randomised Controlled
Trials
Analytical Studies
Descriptive Studies
Cross sectional study


when the investigator draws a sample out of the study
population of interest, and examines all the subjects to
detect those having the disease / outcome and those not
having this outcome of interest.
at the same time finds out whether or not they have the
presence of the suspected cause (exposure) (or give a
History of such an exposure in the past), is called the Cross
sectional analytic study.
Cross sectional study


A cross-sectional studies
 a type of observational study
 the investigator has no control over the exposure of
interest.
It involves
 identifying a defined population at a particular point in
time
 At the same time measuring outcome of interest
e. g. obesity.
Measure the prevalence of disease and thus are often
called prevalence studies.
Design of cross sectional study
Cross sectional study
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
When to use cross sectional analytical study
 If cases of the disease are not likely to be
admitted, since the disease is perceived to be a
routine illness.
 If the disease has a wide clinical spectrum.
 When the objective is not to study the cause of a
disease but rather the cause of a health related
phenomena.
 When the objective is to see the correlation
between two continuously distributed variables.
Steps in conducting cross sectional study
 Step 1:
State your research question
( SMART )
Specific
Measurable
Realistic
Time bound
Research hypothesis
Objectives
Background significance of the research
question.
 Step
2:
 Define the Total (whole, reference) population
and the “actual (study) population from which the
sample will be drawn.
 Ensure that the actual population is a
“representative subset” of the total population.
 Step 3 - Specify your study variables and the
‘scales’ of measurements.
``
 Outcome variable:
dichotomous,
polychotomous, continues, ordinal.
 Exposure variable
 Potential confounding factor: make a detailed
list of all the variables that can confound the
exposure - outcome relationship and specify the
scales of their measurement
Calculate the Sample size :
 Sample Size Determination for estimating a Mean

 Sample Size Determination for estimating
Proportion
 Sample size ‘n’ is given by
Step 5 :Sampling methods
 Probability sampling




Simple random sampling
Systematic sampling
Stratified random sampling
Cluster sampling
 Non-probability sampling



Consecutive sampling
Convenience sampling
Purposive (Judgmental) sampling
STEP 6: Ensure Validity, reliability and
prevent Bias

Validity: Validity is an expression of the degree to which a
test is capable of measuring what it is intended to measure.

Reliability : is the extent to which repeated measurement of
a stable phenomenon by different people and instrument at
different time and place get similar results.

Bias: any trend in the collection, analysis ,interpretation,
publication, review of data that can lead to conclusion that
are systematically different from truth.

Fig showing relationship between the true value and
measured values for low and high validity and reliability

Internal validity: is the degree to which the results of an
observation are correct for the particular group of people
being studied.

External validity or generalizability is the extent to which the
results of a study apply to people not in it.

Internal validity is necessary for, but does not guarantee,
external validity, and is easier to achieve.
Internal validity
sample
sample
Measurement
&
confounding
bias
conclusion
External validity
ERRORS IN EPIDEMIOLOGICAL STUDY
 Random error (by chance)
 Individual biological variation
 Sampling error
 Measurement error
Systemic error
Selection bias: occurs when comparison are made between
group of patient that differ in determinant of outcome. EX:
Sample bias
Non response bias
Non participation bias
Berkson’s bias
 Measurement bias: occurs when methods of
measurement /classification of subjects are
dissimilar among groups.
 Interviewers bias
 Recall bias
 Response bias
 Confounding bias: Confounding occurs when the
effects of two exposures (risk factors) have not
been separated and the analysis concludes that
the effect is due to one variable rather than the
other.
fig showing : Confounding : relationship between
coffee drinking (exposure) , heart disease (outcome) ,
and third variable (tobacco use)
Strategies in dealing with systemic error
 Confounding bias:



Restriction
Matching
Stratified analysis/Multivariate analysis
 Misclassification bias:


Blinding
Minimal gap between theoretical and empirical definition
of exposure/disease
 Selection bias:



Population should be defined independently of disease of
interest
All information on the subjects should be secured to
avoid selective loss of information
Prevent loss to follow-up
DATA COLLECTION

pilot study on a sample of 10% of the total required.

sample for validating and standardizing all your instruments,
questionnaire and techniques.

If data collection done by different data collectors, cross
check at least 20% of the filled performae, independently
for ensuring quality control of data and reducing observer
variations.
Analysis of data
 Analysis



plan
Data cleaning
Depending on objective of study
Make dummy table
Analysis of descriptive CS study
 Objective:


To describe the disease in time, place and person
To generate hypothesis
 Analysis





Means & SD
Median & percentile
Proportions – Prevalence
Ratios
Age, sex or other group specific analysis
Analysis of analytical CS study


Objective:
 Is there any association?
 If “YES”, then what is the strength of association?
Analysis:
 Is there any association?
 Chi-square, student-t test, etc
 What is the strength of association?

Odds ratio, Rate ratio , Rate difference, Difference between
mean, Correlation , Regression coefficient.
 Measure of impact
 Risk factor



Attributable fraction (exposed)
Attributable fraction (population)
Protective factor


Prevented fraction (exposed)
Prevented fraction (population)
Measure of prevalence
 Prevalence proportion: Proportion of the
subjects who have the disease at a point in
time
 Example:
 Of
1800 middle aged women 30 had diabetes
on January 1, 2007.
 The prevalence proportion of diabetes was
30/1800 = 0.016 or 1.6%
 Point
prevalence
 Period prevalence
Point & Period prevalence
Point prevalence
Number of individuals with disease at a specified
period of time
P = --------------------------------------------------------------------Population at that time
Period prevalence
Number of individuals manifesting the disease in the
stated time period
P = ----------------------------------------------------Population at risk
Measures of association : odds ratio

OR- is the ratio of one odds to another.
It is the probability that something is so or will occur to the
probability that is not so or will not occur.

Example:

Exposure to
fumes
Headache
present
Headache
absent
total
Factor
present
a=10
b=90
a+b=100
Factor
absent
c=50
d=850
c+d=900
total
a+c=60
b+d=940
n=1000
Odds ratio
Disease OR =
Odds of disease among exposed
------------------------------------Odds of disease among not exposed
Odds of exposure among diseased
Exposure OR = ------------------------------------Odds of exposure among not diseased

Rate ratio

Prevalence ratio = {a/(a+b)}/{c/(c+d)} = 1.8

Exposure ratio = {a/(a+c)}/{b/(b+d)} = 1.74

Rate differences

Prevalence difference = {a/(a+b)} - {c/(c+d)} = 0.0444

Exposure difference = {a/(a+c)} - {b/(b+d)} = 0.07

Number needed to avoid one case in unexposed
group
= 1/prevalence difference = 1/0.0444=22.5
Measure of impact





If the factor is risk factor:
Excess risk among exposed=
= {a/(a+b)} - {c/(c+d)} = 0.0444
Population excess risk =
= (a+c)/n – c/(c+d) = 0.004
Attributable fraction (exposed)=
= [(Prevalence ratio – 1)/Prevalence ratio] *100= 44.4
Attributable fraction (population)=
= [(Prevalence ratio – 1)*E]/{1+[(Prevalence ratio 1)*E]} *100= 7.4. E = exposure rate in population
Measure of impact : protective factor

If the factor is protective factor
Excess risk among unexposed = c/(c+d) – a(a+b)

Population excess risk = (a+c)/n – a(a+b)

Prevented fraction (exposed) =
= {[c/(c+d) – a(a+b)]/[c/(c+d)}*100

Prevented fraction (population) =
={[(a+c)/n – a(a+b)]/[(a+c)/n]}*100

Uses of cross sectional study
 used as tool in community health
care
Community diagnosis
 Health care
 Determinants of health & disease
 Identification of group requiring special care
Surveillance
Community education & community involvement
Evaluation of community health care




 Can contribute
to clinical care (community oriented
primary care)
 Can provide new knowledge (studies on etiology ,
growth & development)
Guideline for critical appraisal of prevalence study
1. Are the study design & sampling method appropriate for the
RQ?
2. Is the sampling frame appropriate?
3. Is the sample size adequate?
4. Are objective, suitable and standard criteria used to
measure the health outcome?
5. Is the health outcome measured in unbiased manner?
6.Is the response rate adequate? Are the refusers described?
7.Are the estimates of prevalence given with CI & in detail by
subgroup – if appropriate?
8.Are the study subjects and the setting described in detail ?
Cross sectional study advantage
Cheap and quick studies.
Data is frequently available through current records or
statistics.
Ideal for generating new hypothesis.
Correlation between two continuously distributed
phenomenon can be studied.
Prevalence of the disease .
Starting point of cohort study.
Cross sectional study Disadvantage

Needs large sample size.

Large number of logistic support needed.
The importance of the relationship between the
cause and the effect cannot be determined.
• Temporal weakness:

– Cannot determine if cause preceded the effect or
the effect was responsible for the cause.
.
Choice of strategy
for administrative
purpose
BEST
Advantage & disadvantage of different
observational study design
Ecological
study
Cross
sectional
Case
control
cohort
Selection bias
NA
medium
High
low
Recall bias
NA
high
high
low
Loss to follow up
NA
NA
low
high
confounding
HIGH
medium
medium
low
Time required
LOW
medium
medium
high
cost
LOW
medium
medium
high
Probability of
Comparison of different study design
case control
cohort
Cross sectional
References






Detels R, Mcewen J, Beaglehole R. Oxford Textbook of Public
health, Fourth Edition, oxford university press.
Beaglehole R, Bonita R, Kjellstrom T. Basic Epidemiology.
World Health Organisation, Geneva: AITBS Publishers;2006.
Fletcher RW, Fletcher SW, Clinical Epidemiology. 4th edition,
Lippincott Williams & Wilkins.
Bhalwar R et al, Text Book of Public Health and Community
Medicine. 1st edition, pune: Department of Community
Medicine, Armed Forces Medical College;2009.
Deshmukh PR . Study design options in epidemiological
research at MGIMS Sevagram 2011.
Abramson JH. Survey Methods in Community Medicine. 4th
edition, Churchill Livingstone.