Cross-Sectional Study.pptx

CROSS-SECTIONAL STUDY
1
Professor Dr. AB Rajar, MBBS, Dip-Diab, MPH, Ph.D. CPHE
Director of Research and Innovative Center
[IBN-E-SINA UNIVERSITY]
5/18/2023 Professor Dr AB Rajar

LEARNING OBJECTIVES
• At the end of this lecture the participants will be able:
• Define CSS & describe the purpose & types of CSS.
• Discuss in detail the steps of CSS with respective
examples.
• Calculate the Prevalence rate, ratio, Odds ratio
2

Examples of CSS
• We can carry out a cross-sectional survey to estimate
the
 Knowledge and Education about Artificial Intelligence among
Medical & Dental Students at Muhammad Medical & Dental College,
MPK.
 Attitudes Toward Cheating of Self and Others by College Students
and Professors.
 Knowledge and Awareness about Genetic Problems Associated with
Consanguineous Marriages among Medical & Dental Students.
 Associations between empathy and big five personality traits among
undergraduate medical students.
3

CLASSIFICATION
4

HIERARCHY OF EVIDENCE
Systemic
Review
Meta-
analysis
Randomized
Controlled Trials
Analytical Studies
Descriptive Studies
5

CROSS-SECTIONAL STUDY.
• A cross-sectional study is a descriptive study in which
disease and exposure status are measured simultaneously
in a given population.
• It measures
• the prevalence of health outcomes(also called prevalence
study)
• or determinants of health,
• or both,
• In a population at a point in time or over a short period.
6

• 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 disease/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 exposure in the past),
• is called the Cross-sectional analytic study.
7

 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 the outcome of interest
e.g. obesity.
• measuring a range of variables on an individual basis
• including past and current exposure.
Measure the prevalence of disease and thus are often called
prevalence studies.
8

SYNONYMS
• Survey
• Snap-shot
• Prevalence study
9

PURPOSES OF CROSS-SECTIONAL STUDY
1. They provide clues to disease etiology and help in the
formulation of an etiological hypothesis
2. They provide background data for planning, organizing,
and evaluating preventive and curative services by disease
surveillance.
3. They contribute to research by describing variations in
disease occurrence by time, place, and person
4. They provide data regarding the magnitude of disease
load and types of disease problems in the community
regarding morbidity and mortality rates and ratios.
10

CROSS-SECTIONAL STUDY
• May be
– Descriptive
– Analytical or
– Both
• At the descriptive level:
• To describe the frequency and characteristics of the observed health-
related phenomena at a certain point in time or
• It yields information about a single variable or about each of the number
of separate variables in a study population.
• At the analytic level:
• To analyze the relationship between two or more health-related
phenomena
• It provides information about the presence and strength of associations
between variables, permitting testing of the hypothesis.
11

DESCRIPTIVE CSS
• Information about single /Multiple variables
• Estimate – Problem
• Point prevalence [1st April]
• Period prevalence [Year]
• Disease and suspected risk factors population /specified
individuals.
12

DESCRIPTIVE CROSS-SECTIONAL STUDY
• Dimension of positive health such as
• Fitness,
• Level of happiness,
• and life satisfaction.
• Presence of disease, disability, and symptoms of ill health.
• Levels of anxiety, stress, and depression,
• Prevalence of hepatitis B,
• Prevalence of self-centeredness and obsessive-compulsive disorders
• Attributes related to health
• Body measurements ( waist circumference, BMI, Blood pressure)
13

ANALYTICAL CROSS-SECTIONAL STUDY.
• “ An analytic investigation in which subjects are
sampled at a fixed point or period of time, and then the
association between the concurrent presence or
absence of risk factors and diseases are investigated.
(Raymond S, Greenberg et all-1995)
14

ANALYTICAL CROSS-SECTIONAL STUDY.
Strength of
association
between disease
& Risk factors
Determinants of
disease/
conditions
Predictors of
disease/condition
15

DESIGN OF CROSS-SECTIONAL STUDY
16

Exposure and Disease Measures Simultaneously
17

STEPS OF CROSS-SECTIONAL STUDY
1. Step: State your research question, research
hypothesis, objectives, and background significance
of the research question.
2. Step: Define the “Total (whole, reference) /source
population” and sample
3. Step: Specify your study variables and the ‘scales’ of
measurements
4. Step: Calculate the sample size
5. Step: Describe the sampling method
6. Step: Ensure validity and reliability, prevent bias
7. Step: Data Collection
8. Step: Analysis of data
18

Steps in Conducting a Cross-Sectional Study
• Step-1
• State your research question
• ( SMART )
• Specific
• Measurable
• Realistic
• Time bound
• Research hypothesis
• Research Objectives
• Background significance of the research question
19

Steps in conducting Cross-sectional study
• Step-1
• State your research question, research hypothesis, objectives,
and background significance of the research question.
• Research question:
• What is the prevalence of seropositivity of HBV among voluntary blood
donors at a blood bank of MMCH
• Research hypothesis
• Is prevalence of seropositivity of HBV among voluntary blood donors at a
blood bank of MMCH increased or associated with the reuse of injection
• Research Objective
• To study prevalence of seropositivity of HBV among voluntary blood donors
at a blood bank”
• To find out the risk factors associated with increased prevalence of
seropositivity of HBV among voluntary blood donors
20

1. Step: State your research question, research hypothesis,
objectives, and background significance of the research
question.
2. Step: Define the Total (whole, reference) population and
the “actual (study) population from which the sample
will be drawn.
measurements
21

• 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.
22

TARGET POPULATION
• The population to which the main results of the
study will be extrapolated
• For example, if we were planning a study of the IQ level of
secondary school children.
• Next, a suitable source population needs to be
identified
23

SOURCE POPULATION
• For practical and logistic reasons, the source population
is generally more limited than the target population.
• For Example, although our target population comprises all
secondary school children at MPK city, it would be
impossible to include all of them in the study.
• The choice of the source population should be determined by
the definition of the target population and by logistic
constraints.
24

SOURCE POPULATION
• For logistic reasons, we might decide to conduct the
study in MPK, where the study participants as
mentioned are present.
• If this source population is small enough to be studied using
the human and financial resources available, the entire
population can be included.
• If the source population is still too large, a representative
sample has to be selected.
25

SOURCES OF DATA
Target Population
Source
Population
Sample
Study
Participants
Diagram illustrating the relationship between the target population and the
study participants
26

SAMPLE
• A subset of the population derived from the source
population
– If this source population is small enough to be studied using
the human and financial resources available, the entire
population can be included. OR
– If the source population is too large, a representative
sample must be selected.
27

question.
2. Step: Define the Total (whole, reference) population and the
“actual (study) population from which the sample will be
drawn.
measurements
28

• Step-3: Specify your study variables and the
‘scales’ of measurements.
• To study the prevalence of Depression, Anxiety and
Stress among undergraduate medical & dental students
of MMDC at MPK”
29

STUDY VARIABLES
1. Main variable
2. Other variables of secondary interest:
3. What are the variables related to person, place and
time
30

STUDY VARIABLES
• To study the prevalence of depression, anxiety, and stress among
undergraduate medical & dental students at an MMDC”
• 1-Main variables: Name one or two variables that are
most interesting to you, e.g.,
• Depression,
• Anxiety
• & Stress.
31

STUDY VARIABLES
• 2-Variables of secondary interest:
• These are the variables you want to study besides
the main interest variables.
• Try to limit these to not more than 3 to 5 (e.g. other
Psychiatric diseases).
32

STUDY VARIABLES
3. What are the variables related to person, place,
and time according to which you will describe the
“distribution” of the “main” and “secondary interest”
variables, e.g. :
• Person related: as age groups (less than 20, 20 -22, 23 -25,
more than 26); sex; socio-economic status; history of
psychiatric drugs; marital status; frequency of visits to any
psychiatrist; knowledge about depression, anxiety & stress.
• Time-related: month-wise detection of cases out of the total
tested by any scale.
• Place related: as hostler/non-hostler, rural-urban differences
of cases.
33

Specify the ‘scales’ of measurements
• Specify the ‘scales’ of measurements in respect of each
of the main variables & secondary variables and those
person/place/time variables on which “distribution” is
to be described.
• whether on
I. Continuous [3.5,35.6,50.56,100.2]
II. Discrete [Whole numbers like: [3, 23, 35]
III. Ordinal [Mild, Moderate, Severe]
IV. Dichotomous [Male, Female]
V. Polychotomous scales.
34

question.
drawn.
measurements
35

SAMPLE SIZE CALCULATION
4- Step: Calculate the sample size:
n=
𝒛𝟐(𝒑𝒒)
𝒆𝟐
Description:
n= Sample size.
z= Standard Error associated with the chosen level of
confidence ( typically 1.96)
p= Variability / standard Deviation ( It can be taken from
previous studies or Pilot study)
q-1-p
e = Acceptable sample error
36

SAMPLE SIZE CALCULATION
• Assume
• Variability (p) = 0.2
• Confidence level = 95%
• Sampling error =5%
• Yamane's formula
• n=
𝒛𝟐(𝒑𝒒)
𝒆𝟐 =
𝟏.𝟗𝟔 𝟐(𝟎.𝟐)(𝟎.𝟓)
(𝟎.𝟎𝟓)𝟐 =
(𝟑.𝟖𝟒𝟏𝟔ᕁ (𝟎.𝟎𝟏)
𝟎.𝟎𝟎𝟐𝟓
• n=
(𝟎.𝟑𝟖𝟒𝟏)
𝟎.𝟎𝟎𝟐𝟓
= 153
• n=153
37

question.
drawn.
measurements
38

• Step-5
• Describe the sampling method
• For the selection of a sample from the source population, we
need to decide on the sample design/technique. Samples are
sometimes chosen
• There are several methods of sampling. In general, they could be
classified into two major groups:
1. Probability/Random sampling: (preferred)
2. Non-probability sampling –
39

MAJOR TYPES OF SAMPLING
Probability sampling
• Every unit in the population
has a chance (greater than
zero) of being selected in the
sample, and this probability
can be accurately determined
Non-Probability sampling
• Some elements of the
population have no chance of
selection
• Selection of elements based
on assumptions regarding the
population of interest
40

MAJOR TYPES OF SAMPLING
Probability Samples
• Simple random sample (SRS)
• Systematic random sample
• Stratified random sample
• Cluster sample
• Single, double & Multistage
Sampling
Non-Probability Samples
• Convenience sample
• Purposive sample
• Quota
• Snowball
41

question.
drawn.
measurements
42

• Step-6: Ensure validity and reliability, 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: the extent to which repeated measurement of a
stable phenomenon by different people and instruments at
different time and place get similar results.
• Bias: any trend in the collection, analysis, interpretation,
publication, or review of data that can lead to conclusions
that are systematically different from the truth.
43

question.
drawn.
measurements
44

• Step-7: Data Collection
• Collect the data in a predesigned form.
• Ensure that before you start the data collection, you have
undertaken a pilot study on a sample of 10% of the total
required sample (5% in case of extensive sample studies) for
validating and standardizing all your instruments, questionnaire,
and techniques.
45

• Step-7: Data Collection
• During the data collection of the actual study, frequently
examine your Performa for any missing data and get back to the
subjects if there is any missing data, at the earliest.
• If different data collectors collect data, cross-check at least 20%
of the filled Performa independently to ensure data quality
control and reduce observer variations.
• Enter data into the Master chart or computer (SPSS) periodically
and at an early date.
46

TOOLS OF DATA COLLECTION
• According to which tool is used to collect data, there
exist two main types of CSS:
• Health interview surveys (HIS):
• In which collection of data is carried out only using
questionnaires, and
• Health examination surveys (HES):
• which are usually a combination of questionnaires and health
examinations, including diagnostic and laboratory tests.
47

METHODS OF DATA COLLECTION
• In Health interview surveys:
• Questionnaires may be communicated to the randomly
selected study subjects in three ways: mail, personal
interview, or telephone interview.
• In Health examination surveys:
• The contact between participants and research personnel is
personal since the health examination is a part of the CSS.
• In this type of survey, also questionnaires are usually
communicated to the randomly selected study subjects
through personal interview
48

question.
drawn.
measurements
49

• Step-8: Analysis of data
• Getting data ready for analysis in CSS starts with drafting
the questionnaire, where the codes for different answers are
already predefined in respect of individual questions.
• The encoded data are then entered in the data matrix or
spreadsheet.
• Data entry & data analysis should be carried out by
statistical software [SPSS] or Microsoft Excel
50

 Analysis of data
 Prevalence is the measure of the occurrence of a disease,
condition, or characteristic that can primarily be obtained
from cross-sectional surveys.
51

 Analysis of data
• Example:
• Suppose a cross-sectional survey was carried out to assess the
prevalence of breast cysts in a particular female population.
• A sample of 5891 women randomly selected from that population
was examined, and 201 were found to have breast cysts.
52

• Analysis of data
• The prevalence of breast cysts in this population at the
time of the survey was thus: 201 / 5891 = 3.4%.
53

• Analysis of data
• To examine the association between a putative risk factor for
the attribute of interest and the attribute itself, the population is
first subdivided into those exposed and those not exposed to the
factor under study.
• The prevalence of the attribute in each group is calculated and
compared.
• A prevalence ratio can then be computed as the ratio of the
prevalence of the attribute of interest in those exposed to the
putative risk factor relative to the prevalence in those
unexposed.
54

DATA ANALYSIS
• A cross-sectional study was conducted among 5891 females of
reproductive age; among them, 3247 had been using
contraceptives for the last 10 years.
• In the contraceptive user group, 124 were positive for cysts,
while among the non-user, 2567 did not reveal cysts.
• Find out the prevalence ratio?
55

DATA ANALYSIS
Breast Cyst
Lifetime use of oral contraceptives
Total
Ever used Never used
Yes 124
No 2567 5690
Total 3247 5891
Prevalence of breast cysts among ever users =
Prevalence of breast cysts among never users =
Prevalence ratio=
• A cross-sectional study was conducted among 5891; among them,
3247 were using contraceptives for the last 10 years in the
contraceptive user group, 124 were positive for cysts, while among the
non-user, 2567 did not reveal cysts find out the prevalence ratio
56

DATA ANALYSIS
Breast Cyst
Total
Yes 124 77 201
No 3123 2567 5690
Total 3247 2644 5891
Prevalence of breast cysts among ever users =124/3247=3.8%
Prevalence of breast cysts among never users =77/2644=2.9%
Prevalence ratio=ever user /never user=3.8/2.9=1.3
Suppose that in the hypothetical survey described in the above
example, the investigators wished to assess whether the prevalence
of breast cysts was associated with having ever used OC
57

INTERPRETATION
• The prevalence of breast cysts was higher in ever-users of
oral contraceptives compared to never-users.
• It should be noted that the prevalence ratio is a good estimate of
the incidence rate ratio only if the prevalence of the outcome of
interest among those unexposed is low (less than 10%) and the
duration of the disease is the same among those who were
exposed and those who were unexposed to the factor of interest.
58

Prevalence Odd ratio
Breast Cyst
Total
Yes 124 77 201
No 3123 2567 5690
Total 3247 2644 5891
Prevalence of breast cysts among ever users =124/3247=3.8%
Prevalence of breast cysts among never users =77/2644=2.9%
Prevalence ratio=ever user /never user=3.8/2.9=1.3
From this hypothetical study example, We can calculate the odds of having
ever-used oral contraceptives among women with (cases) and without (controls)
breast cysts.
Odds of exposure to oral contraceptives among ‘cases’=124/77=1.61
Odds of exposure to oral contraceptives among ‘controls’=3123/2667=1.22
Odds ratio=1.61/1.22=1.3
59

Prevalence Odd ratio
• From this hypothetical study example, We can calculate
the odds of having ever-used oral contraceptives
among women with (cases) and without (controls)
breast cysts.
• Odds of exposure to oral contraceptives among ‘cases’=124/77=1.61
• Odds of exposure to oral contraceptives among ‘controls’=3123/2667=1.22
• Odds ratio=1.61/1.22=1.3
60

Analysis of Descriptive Cross-sectional Study
• Objective:
• To describe the disease in time, place, and person
• To generate a hypothesis
• Analysis:
• Means & SD
• Median & percentile
• Proportions – Prevalence
• Ratios
• Age, sex, or other group-specific analysis
61

Analysis of Analytical Cross-sectional Study
• Objective:
• Is there any association?
• If “YES”, then what is the strength of the 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)
62

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.
63

Point and Period prevalence
• Point Prevalence:
• P=
Number of individuals with the disease at a specified period of time
Population at that time
• Period prevalence:
• P=
Number of individuals manifestin the disease in the stated time period
Population at risk
64

MEASURE OF ASSOCIATION
• Measures of association: odds ratio
• Odds Ratio- the ratio of one odds to another.
• It is the probability that something is so or will occur to the
probability that it 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
65

Odds Ratio
• Disease OR=
Odds of disease among exposed
Odds of disease among not exposed
• Exposure OR=
Odds of exposure among diseased
Odds of exposure among not diseased
66

• 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 un-exposed group
= 1/prevalence difference = 1/0.0444=22.5
67

Measure of impact
• If the factor is a 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 a population
68

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
69

USES OF CROSS- SECTIONAL STUDY
• Used as a 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)
70

Guidelines for critical appraisal of the
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 an 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 ?
71

Advantages of Cross-Sectional Study
• Cheap and quick studies.
• Data is frequently available through current records or
statistics.
• Ideal for generating new hypotheses.
• Correlation between two continuously distributed
phenomenon can be studied.
• Prevalence of the disease.
• Starting point of the cohort study.
72

Disadvantages of Cross-Sectional Study
• Needs a 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.
73

Thank You
74

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