This PowerPoint presentation delves into the fundamentals, methodologies, and critical insights of cross-sectional studies, an essential component in the realm of epidemiological research and beyond. Designed to cater to both novices and seasoned professionals in the field of research, it offers a comprehensive exploration of how cross-sectional studies serve as a pivotal tool in understanding the prevalence and characteristics of health-related states or events in a population at a single point in time.
Starting with a brief introduction, the presentation demystifies the concept of cross-sectional studies, outlining their primary purpose and distinguishing features from other study designs. It emphasizes the non-temporal nature of these studies, highlighting their particular utility in public health and social science research for assessing and making inferences about population health parameters without the need for long-term follow-up.
2. Introduction
• A cross-sectional study is a type of observational
research that analyzes data of variables collected
at one given point in time across a sample
population.
• A cross-sectional study is a study in which disease
and exposure are measured simultaneously in a
given population.
3. • cross-sectional studies are used to describe what
is happening at the present moment.
• This type of research is frequently used to
determine the prevailing characteristics in a
population at a certain point in time.
4. Cohort vs cross-sectional study
• The cross-sectional study has an identical
structure to the cohort study except that the
exposures and outcomes are measured at the
same time, whereas in a cohort study outcomes
are typically measured after the exposure/s has
been measured.
5. • When a study only measures health outcome, it is
known as descriptive cross-sectional study.
• When a study measures both exposure and
health outcome at the same time, it is known as
analytic cross-sectional study.
6. Cross sectional study: Conduction
• Usually conducted by taking a random sample from
the population of interest
• Samples can be taken by
• Simple random sampling;
• Systematic sampling;
• Stratified random sampling and
• Cluster sampling
• Multistage sampling
7. Cross sectional study: Conduction
• Once the sample is selected, data are collected from the
study subjects through-
• Questionnaire interview
• Observation
• Physical examination
• Lab examinations
8. uses
1. To measure the prevalence of health outcomes.
2. Understand determinants of health.
3. Extent and distribution of health problems.
4. To describe the characteristics of a population at a
certain point in time.
5. Compare different groups of people.
6. Evaluate the effectiveness of public health
intervention.
9. 7. Describe the characteristics of a population:
Collect information about a group's demographics,
socioeconomic status, health behaviors, etc.
8. Monitor the effectiveness of a public health
intervention.
• Useful for designing interventions or programs
tailored to specific populations.
10. Here are some specific examples of how cross-
sectional studies are used:
• Public health: Investigating the prevalence of
smoking and its association with lung cancer in a
population.
• Nutrition: Assessing the relationship between
dietary habits and obesity in children.
• Mental health: Studying the prevalence of
depression among adults and its association with
socio-economic
11. advantages
1. Cross-sectional studies are much cheaper to perform
than other options that are available to researchers.
2. Require less time and financial resources.
3. multiple variables can be analyzed at the same time.
4. Cross-sectional studies allow you to collect data from a
large pool of subjects and compare differences between
groups.
12. disadvantages
1. It requires a defined population group to be
successful.
2. It is unable to measure incidence.
3. that it cannot look at changes over time.
4. It does not offer data about casual relationships.
15. • As seen in the 2 × 2 table in the top portion, there will be
a persons, who have been exposed and have the
disease; b persons, who have been exposed but do not
have the disease; c persons, who have the disease but
have not been exposed; and d persons, who have neither
been exposed nor have the disease.
16. • Two kinds of measures can be calculated from
cross-sectional data, prevalence ratio (PR) and
prevalence odds ratio (POR).
• PR = (a/a+b) / (c/c+d)
• POR =(a x d) ÷ (b x c)
17. Example
• To determine the prevalence of sputum positive
tuberculosis (TB) in a community, a random sample of
400 individuals (age more than 15 years) have been
selected who gave history of cough for more than 2
weeks.
• Morning sputum from all of them was collected and was
checked for AFB (acid-fast bacillus).
18. • Out of 400 sputum collected, AFB was found in the
sputum of 32 individuals. Therefore, prevalence of sputum
positive TB in the community is:
Prevalence of sputum +ve TB = (32 ÷400) X 100 or 8%
19. • Data of the survey are cross classified by
sex and is given in the following table.
Gender
AFB in sputum total
+VE -VE
female 21 129 150
male 11 239 250
total 32 368 400
20. • Now we can compute prevalence rates among
males and females as follows:
• Prevalence rate among females = [a ÷(a + b)] X
100, or,
(21 ÷150) X 100 or 14.0%
• Prevalence rate among males = [c ÷(c + d)] X
100, or
(11 ÷250) X 100 or 4.4%
21. • Data clearly indicate that the prevalence of
TB is much higher among thethe females
compared to the males.
22. • However, we can compute the PR as (Prevalence among
females ÷Prevalence among males), which is 3.2. We can
also calculate the POR.
• In this example POR is 3.5. POR 3.5 indicates that
females are at 3.5 times higher risk of developing
tuberculosis (TB) compared to males females compared
to the males.
23. • We can use Chi-square test to find association between
sex and TB. Formula for the Chi-square test is given
below.
χ2 = n (ad - bc) 2 /(a + b) (c + d) (a + c)(b + d)
= 400(21*239 – 129*11)2/ (21 +
129)(11+239)(21+11)(129+239)
χ2 =11.7
24. • In this example, χ2= 11.7, which is much higher than the
tabulated value of 3.841 with df 1.
• df= (c-1)(r-1) = (2-1)(2-1) = 1
• Therefore, we can say that there is association between
sex and sputum positive TB in the community. In the
same manner, association between other factor(s) of
interest and TB can be determined.
