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CROSS SECTIONAL STUDY

SAPTAWATI BARDOSONO

W H AT I S CR OSS-SE CTI ONAL ?

Cross-sectional study is a research that use an observational study design with characteristics as follows:

A ll measurements of the variables (dependent and independent) under study is done at the same timeThere is no follow-up period

Example

o A survey on onchocerciasis showed that blind persons were of lower nutritional status than non-blind.

o There are two possible explanations for this association:o Poor nutritional status have lower resistance and are

therefore more likely to become blind from onchocerciosis

o Poor nutritional status is a consequence rather than a cause of the blindness, since blind persons are not as able to provide for themselves


Cross-sectional study: o is carried out at just one point in time or over a

short period of timeo is suitable for measuring prevalence


It is also known as prevalence study:

Subjects are classified at the same time as exposed and non-exposed group and diseased and non-diseasedPrevalence rate is by comparing those exposed and non-exposed related to the disease under study

Diseased Non-diseased Total

Exposed a b a+b

Non-exposed c d c+d

Total a+c b+d N

Prevalence rate = a / cPrevalence rate = a / c

W H E N CH OOSI NG CR OSS-SE CTI ONAL ?

Whenever the study objective is to determine variables (dependent and independent) and its distribution patterns

Whenever we want to estimate the disease’s prevalence

W H E N CH OOSI NG CR OSS-SE CTI ONAL ?

Example:

A researcher wishes to estimate the prevalence rate of low iron status among pubertal girls in relation to their living areas

CONCEPTUAL FRAM EWORK

AGENTS ENVIRONMENT

HOST

HEALTH PROBLEM


HEALTH PHENOMENA

HOST AGENTS ENVIRONMENT

AGESEX

PHYSIOLOGYMETABOLISM

PHYSICAL ACTIVITYOTHERS

BIOLOGICCHEMICALPHYSICALOTHERS

GEOGRAPHYCLIMITEHOUSINGOTHERS


MANIFESTATION

IMMEDIATE CAUSES IMMEDIATE CAUSES IMMEDIATE CAUSES

UNDERLYING CAUSES UNDERLYING CAUSES GUNDERLYING CAUSES

BASIC CAUSES

W H E R E CAN W E D O A CR OSS-SE CTI ONAL STUD Y ?

It can be cone anywhere in accordance to its objectives and its subjects

Community : urban, ruralInstitutions : school, officeClinics, etc : hospital, puskesmas

W H Y CH OOSI NG CR OSS-SE CTI ONAL ?

Advantages:1) Easy 2) Get instant results3) Describe the relationship between health phenomena

under study with its related factors (permanent characteristics)

4) As a preliminary study of a case-control or cohort study design

W H Y CH OOSI NG CR OSS-SE CTI ONAL ?

Disadvantages:1) Only the prevalence case and/or those non-diseased

can be studied 2) Cannot conclude cause-effect relationship because the

time sequence is not determined3) Not suitable for a rare cases 4) Needs a well planned sampling scheme to provide

equal chances to any subject to be studied5) Problem for a non-respond subjects

H OW TO P L AN A CR OSS-SE CTI ONAL D E SI GN?

FORM ULATE RESEARH QUESTION(S)SELECTION OF POPULATION AND SAM PLE PREPARING RESEARCH INSTRUM ENTS (VALIDITY )DATA COLLECTION (RESPONSE RATE & QUALITY CONTROL)DATA ANALYSIS

FOR M UL ATE R E SE AR CH QUE STI ON

What is the health phenomena interested to study (situation)What factors are related to the health phenomena (problems)Clarify the relationship between the health phenomena and its related factors (question and respond)

SE L E CTI ON OF P OP UL ASI AND SAM P L E

Target population is population having certain demography and clinical criteria

Eligible population is target population for a certain place and time

SE L E CTI ON OF P OP UL ATI ON AND SAM P L E

Sample comes form population fulfilled the selection criteria as follows:

Inclusion criteria (to get eligible population):1) clinical characteristics (diagnostic, and

prognostic)2) demographic characteristics (age, gender) 3) geographic characteristics (location)4) time (research duration)


Exclusion criteria:1) Contra indication for study measurements2) Ethical issues (infant, child, pregnancy, etc)3) Special care (elderly, etc)4) Non-participant


Drop-out criteria/cannot continue the study:

1) die, forced home, cannot be contacted or refuse to

participate

2) worsening of organ function or other complication

3) not co-operative during the data collection

M I N I M AL SAM P L E SI ZE

To determine the proportion of health problem in the population:

{ (Z1-α)2 * p * (1-p)} / d2

Ecample:

A district medical officer seeks to estimate the proportion of pregnant women in the district suffering from iron deficiency (data: nominal-ordinal scale)


To determine the average of variable parameter in the population:

{(Z1-α)2 * δ2} / d2

Example:

A researcher is desired of the average calorie intake of 20 obese subjects (data: interval-ratio scale)


To compare the proportion of the subjects to its population:

{ (Z1-α) * V (p0*q0) + (Z1-β) * V (p1*q1)} 2 / (p1 – p0)2

Example:

Health officials in another county are interested in comparing their success at providing prenatal care with the published data


To compare the average of subjects variable parameter to its population:

δ2 (Z1-α + Z1-β)2 / (x - μ)2

Example:

Average calorie intake of mothers has not changed versus the alternative that the average calorie intake has changed, and that a difference of 500 kcal would be considered important


To compare the proportion between two different subjects :

{Z1-α V (2*p*q) + Z1-β V (p1*q1+ p2*q2)} 2 / (p1 – p2)2

Example:

Supposed it has been estimated that the rate of iron deficiency is 800 per-1000 pregnant women in one district and 600 per-1000 in another district. A researcher wants to determine whether this difference is significant at the 10% level if he wishes to have an 80% chance of detecting the difference if it is real


To determine the correlation between two related variable parameters:

{ (Zα + Zβ) / (0,5 ln [(1+r) / (1-r)])} 2

Example:

A study aims to determine the correlation between calorie intake and BM I of mothers

P R E P AR I NG R E SE AR CH I NSTR UM E NTS (VAL I D I TY )

Variable is a variation of the characteristic under study

Parameter is certain value of a variable represents as numeric or categorical data scale

Indicator is a criteria or certain operational definition of a variable value

P R E P AR I NG R E SE AR CH I NSTR UM E NT (VAL I D I TY )

No Variable Indicator M ethod Reference

1 Nutritional status of mothers

BM I Anthropometric X X , 2004

2 Dietary intake Nutrient intake level

Recall Y Y , 2000

3 M orbidity Prevalence rate

Interview AA, 2005


In preparing an instrument then there are certain things to be considered:

Validity

Feasibility:a. budgetb. instrumentsc. methods:

1) accuracy: sensitivity, specificity2) precision: reliability, reproducibility,

repeatability


Validity:Measuring what to be measured (example: measuring body weight using standardized balance scale)

Accuracy:Degree of accuracy of variable measurement (example: 50 kg = 50 kg±0.1 kg)


Sensitivity:The ability of a cut-off point to identify and classified the subject who really ill (example: obesity if the BMI is >30)

Specificity: The ability of a cut-off point to identify and classified the subject who really not ill (example: normal nutritional status if the BMI is between 18.5-25.0)


Precision:Degree of accuracy of a variable will have similar results after underwent repeated measures:

o Instrument precision, related to measuring the same subject in different times

o Biological precision, related to measuring the same subject in different situation and condition


o Observer precision, related to the same observer measuring the same subject in different situation, condition or time

o Between observers precision, related to different observers measuring the same subject at the same condition


o Reliability:Numbers of repeated measurements to have similar

results (example: measurement of body weight as compared to behavior measurement)

o Repeatability:Get similar results for a repeated measurements

o Reproducibility:Repeat the measurements after a certain time

interval


Data is valid if its measurement was relatively free from any error

Any instrument with inconsistent results will never provide a meaningful data

Any data is valid if it is also reliable

♥

♥

♥

♥♥

♥♥♥♥

Not reliable and not valid

Not reliable but somehow valid

Highly reliable and

valid

♥♥♥

Highly reliable but not valid

D ATA COL L E CTI ON

RESPONSE RATE:

Number of participant of those invited

QUALITY CONTROL

Controlling the quality of data (twice, trice, etc)

D ATA ANAL I SI S

PREVALENCE RATE (and OR)

ASSOCIATION (relationship or correlation)

DATA ANALY SIS

o Descriptive to get prevalence rateo For association analysis, the statistic tests are depending

to:1) number of variables under study2) scale of data3) sampling technique4) sample distribution

ASSOCIATION ANALY SIS

o Is the association significant:Parametric and non-parametric test

o How strong is the relationship and does it has a clinical importance or application

Exercise:

A researcher wishes to estimate the prevalence rate of low iron status among pubertal girls in relation to their living areas

Evaluation of the study result:

Chi-square test, can be used to evaluate the significance of the relationship between factors and the disease under study statistically

Odds ratio, measuring the power of relationship between dichotomous variable of factor and the disease under study

Living areas Iron statusLow

Iron statusNormal

Total

Urban a b a+b

Rural c d c+d

Total a+c b+d N

XX22 = N(|ad-bc| – 1/2N) = N(|ad-bc| – 1/2N)22 / (a+b) (c+d) (a+c) (b+d) / (a+b) (c+d) (a+c) (b+d)

OR = ad / bc

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