CROSS SECTIONAL STUDY

Framework of presentationDesign options in epidemiological researchCross sectional studyDesign of cross sectional studySteps of cross sectional studyAnalysis of cross sectional study with exampleUse of cross sectional studyAdvantage & disadvantageComparison 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 intereste. 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

MeasurableRealisticTime bound

Research hypothesis ObjectivesBackground 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.

sampleMeasurement

& confounding

bias

conclusion

sample

Internal validity

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 biasNon response bias Non participation biasBerkson’s bias

Measurement bias: occurs when methods of measurement /classification of subjects are dissimilar among groups.

Interviewers biasRecall biasResponse 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 prevalencePrevalence 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 prevalencePeriod prevalence

Point & Period prevalence

Point prevalence Number of individuals with disease at a specified

period of timeP = ---------------------------------------------------------------------

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

Odds of disease among exposed Disease OR = -------------------------------------

Odds of disease among not exposed

Odds of exposure among diseasedExposure 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

Probability of

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

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.