Cross-Sectional Studies Narges Khanjani, MD, PhD, Fellowship in Environ Epi

Research methods ObservationalDescriptive Case series, case reports, CS, cohort Analytical Ecological Cross- sectional Cohort Case control ExperimentalControlledUncontrolled

Definition  A cross-sectional studies  a type of observational or descriptive study  the research has no control over the exposure of interest (e.q. diet).  It involves  identifying a defined population at a particular point in time  measuring a range of variables on an individual basis

Definition  Cross-sectional studies are studies of prevalence. Proportion with an attribute or disease / Number of subjects = Prevalence.  a type of observational or descriptive study  the research has no control over the exposure of interest (e.q. diet).  3 important questions to consider:  Definition of Case  Definition of the Population  Are cases and non-cases from an unbiased sample of the population?

Definition  “ Snapshot Studies ” (Paffenbarger, 1988)  Observations at a single hypothetical point in time  Each subject assessed once at point in time.  Point Prevalence Studies

Definition  also called a Prevalence survey  A study that is quick and inexpensive to complete.  Designed to determine “ what is happening ? right now”

Basic features  “ Snapshot ” of a population, a “ still life ”  Assesses both the exposure and outcome simultaneously, at a single point in time  Calculates prevalence, but not incidence  A study that is quick and inexpensive to complete.  The first step in testing associations

Uses  Prevalence used in planning  Individual: Pre-treament probability for Dx  Population: Health care services  Examine associations among variables  Hypothesis generating for causal links

Uses  Identify and describe a problem  Collect information for planning e.g. surveys of immunisation, antenatal care, coverage  Evaluate utilisation rates of services  Monitoring health status of a community by regular repeated surveys

Uses  Hypothesis generating for causal links  Method of Difference: If frequency of a disease is markedly different between two groups then it is likely to be caused by a particular factor that differs between them.  Method of Agreement: If a factor commonly occurs in which a disease occurs with high frequency then the factor is very likely associated with the disease.  Concomitant variation: Frequency of a factor varies in proportion to frequency of disease.

Uses  Prevalence survey: The studies are commonly used to describe the burden of disease in the community and its distribution.  Describe population characteristics: They are also commonly used to describe population characteristics, often in terms of person (who?) and place (where?)  The British National Diet and Nutrition Survey  Nutrition and Health Survey in Taiwan  To describe various age groups in the population in terms of food and nutrient intake and range of other personal and lifestyle characteristics.

 Migrant study : Some migrant studies may full into the classification of cross-sectional studies. These studies give clues as to association between genetic background and environmental exposures on the risk of disease.  e.q. A study of the prevalence (percentage) of coronary heart disease  among men of Japanese ancestry living in Japan, Honolulu and the San Francisco Bay area  showed the highest rates among those who had migrated to the United States.

 KAP (knowledges, attitudes, and practices ) study:  KAP studies are purely descriptive and help to build up a better understanding of the behavior of the population, without necessarily relating this to any disease or health outcome.  Management tool: health service managers and planners may make use of cross-sectional survey to assess utilization and effectiveness of service.  Development of hypothesis: Hypotheses on the causes of disease may be developed using data from cross-sectional study survey.

Design of cross-sectional survey  The problem to be studied must be clearly described and a thorough literature review undertaken before starting the data collection.  Specific objectives need to be formulated.  The information has to be collected and data collection techniques need to be decided.  Sampling is a particularly important issue to ensure that the objectives can be met in the most efficient way.

 Fieldwork needs planning:  Who is available to collect the data ?  Do they need training ?  If more than one is to collect the data then it is necessary to assess between-observer variation.  The collection, coding and entry of data need planning.  A pilot study is essential to test the proposed methods and make any alternations as necessary.

Measure: Prevalence  Measure exposure and outcome variables at one point in time.  Main outcome measure is prevalence P = Number of people with disease x at time t Number of people at risk for disease x at time t Prevalence=k x Incidence x Duration

Measure: Prevalence Example: RQ: What is the prevalence of chronic pain after hernia surgery? Exposure of interest: Hernia surgery Outcome of interest: Chronic pain (lasting for more than 3 months) Methods: questionnaire survey Sample: All patients who had a hernia procedure between n=350 Results: Period prevalence chronic pain = 30% (CI 95% %) Point prevalence chronic pain = 25% (on day of survey)

Interpretation  Measures prevalence – if incidence is our real interest, prevalence is often not a good surrogate measure  Studies only “ survivors ” and “ stayers ”  May be difficult to determine whether a “ cause ” came before an “ effect ” (exception: genetic factors)

Study Design Exposure (Risk Factor) Disease (Outcome) Disease (Outcome) _ _

Things to consider when designing a cross-sectional study (survey)  What is your research question?  Is the design appropriate for your study?  Who are you going to study?  How are you going to obtain your sample?  Everyone who is eligible should have an equal chance of being invited to take part  Is there a risk of ‘selection bias’?  E.g. taking people attending a specialist clinic; might not be ‘representative’ of all patients with that condition  Selection bias is a threat  How you will collect your exposure/outcome data  Think about analysis (proportion %, denominator)

 In Cross-sectional studies think of:  Sampling Procedures.  Clear definition of Target Population.  Clear definition of outcome.  Clear definition of risk factors.  Remember Confounders. Things to consider when designing a cross- sectional study (survey)

Sampling  A sample is a subset of the population  Can be random or non-random; can be representative or non- representative  Different types of sampling  This is major challenge when doing cross-sectional studies

 face to face interview  mail questionnaire  telephone interview  Self-administrated questionnaire  Medical examination  Laboratory test Methods for collecting data

 To sure what data shall be obtained  To sure which index will be used  Methods for collecting data  Criteria of disease diagnosis  Definition of variables  Training investigators Issues in collecting data

Variable assessment in cross-sectional studies  assessment methods for cross-sectional studies  Measures an individual ’ s intake at one point in time.  Does not require long-term follow up or repeat measures  Valid  Reproducible  Suitable  Cost within study budget

Dietary method application  Food records using household measures have been used in cross-sectional studies.  The recall method attempts to quantify diet over a defined period in the past usually 24 hours.  The most commonly used dietary assessment method which attempts to measure usual intake is the food frequency questionnaire (FFQ).

Analysis  Before starting any formal analysis, the data should be checked for any errors and outlines.  Obvious error must be corrected.  The records of outliners should be examined excluded  Checking normality of data distribution.  e.q. using the Kolmogorov-Smirnov Goodness of Fit Test.

Analysis  Descriptive analyses  Analysis of differences  Analysis of association / relationship  Multivariable analysis

 Standard descriptive statistics can then be used: mean, median, quartiles, and mode; measure of dispersion or variability such as : standard deviation; measure precision such as: standard error, and confidence intervals.  Mean can be compared using t-tests or analysis of variance (ANOVA).  More complex multivariate analysis can be carried out such as multiple and logistic regression. Analysis

(52%) (19%) Grape Tomato Prevalence ratio = 52%/19% = 2.6 (+) (–) DZ = Rash

Analysis  Instead of looking at a ratio of prevalences, we can also look at a ratio of odds.  Odds are not intuitively appealing: they are the likelihood of an event occurring divided by the likelihood of the event not occurring.

Analysis u 35 u8u8 u 88 u 95Grape Tomato (+) - DZ = Rash 95/183 PR= =2.6 8/43 Odds of grape work in rash pts: 95/8=11.9 Odds of grape work in healthy: 88/35=2.5 Odds ratio=(95/8)/(88/35)=11.9/2.5=

Bias Selection Bias Is study population representative of target population? Measurement Bias Outcome  Misclassified (dead, misdiagnosed, undiagnosed)  Length-biased sampling  Cases overrepresented if illness has long duration and are underrepresented if short duration.(Prev = k x I x duration) Risk Factor  Recall bias  Prevalence-incidence bias  RF affects disease duration not incidence

Bias  The selection bias classic for cross-sectional studies is “ the healthy worker effect. ” I.e., only “ healthy workers ” are available for study, distorting your findings.  Example: Low asthma rates in animal handlers (because persons contracting asthma quit and are not available for study).

Advantages  Quick, cheap  Easy to obtain prevalence  Outcome  Exposure

Disadvantages  Prone to selection bias  Recall bias  Cannot measure disease onset  Problem of temporality (not a problem if exposure is constant)  Not suitable for rare disease

Limitation of cross-sectional study  It is not possible to say exposure or disease/outcome is cause and which effect  Confounding factors may not be equally distributed between the groups being compared and this unequal distribution may lead to bias and subsequent misinterpretation.  Cross-sectional studies within dietary survey, may measure current diet in a group of people with a disease. Current diet may be altered by the presence of disease.  A further limitation of cross-sectional studies may be due to errors in reporting of the exposure and possibly outcome.