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.
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
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.
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 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. .
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
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.