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

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

• 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.

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.

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.

• 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.

SYNONYMS

• Instantaneous study

• Prevalence study

• Simultaneous study

STEPS IN CROSS-SECTIONAL STUDIES

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.

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.

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.

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

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

• 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

• 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

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.

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)]}

Thank you