A Cross-sectional study design is the design that is carried out in one point of time. Cross-sectional study is also known as “prevalence study”. In cross-sectional study, prevalence equals the number of cases in a population at a given point of time where data is collected on both outcome and exposure status of the individuals under study. In this study, we know that participants are neither exposed or treated, nor not treated so there are never ethical difficulties. For instance, cross-sectional study can be carried out to know the prevalence of hepatitis B infection, the prevalence of NCDs in a village at the time of urbanization.
In a cross-sectional study the participants are selected on the basis of inclusion and exclusion criteria for study. These types of design are used for population-based surveys. This type of study design is useful to describe characteristics of study population and can generate new ethological hypothesis. Cross-sectional study may be conducted either before planning a cohort study or baseline in a cohort.
Types of cross-sectional study
Descriptive: This study is used to know the frequency and distribution of a particular disease in a defined population. This characterizes person’s characteristics like age, sex, occupation; place and time occurred of the affected individual. For example a random sample of colleges across London can be used to know the burden or prevalence of asthma among 12-14 year old.
Analytical: Cross-sectional studies are also used to investigate the association between a risk factor and a health outcome. Valid conclusion about the association cannot be studied well. Sometimes it may be difficult to know whether the disease or the exposure came first.
Fig. Process of Study
While talking about the uses of cross-sectional study, they are frequently performed while researchers are waiting for follow-up data to become available. Cross-sectional study provides a snapshot of the frequency of a disease or other health related problems in a population at a given point of time. This can be used to calculate the burden of disease or the needs of the population regarding health. It’s better to use cross-sectional study for chronic diseases than in short-lived.
Design and Analysis of Cross-Sectional Study
While conducting Cross-sectional study, firstly the population must be defined. The presence or absence of exposure and disease are determined for each individual in the defined population at the same point of time.
The data collected can be categorized in the 2×2 contingency table as given below:
Prevalence of Disease –
Prevalence of Disease is determined when prevalence of disease in persons with the exposure is compared with the prevalence of disease in persons without the exposure.
Prevalence of disease in exposed and Prevalence of disease in non-exposed can be calculated as follows:
Prevalence of disease in exposed= a/(a+b)
Population with disease that have been exposed is denoted as ‘a’
Population without disease that have been exposed is denoted as ‘b’
Population that are exposed is denoted as ‘a + b’
Prevalence of disease in non exposed= c/(c+d)
Populations that are not exposed but have disease is denoted as ‘c’
Population that are not exposed and do not have the disease is denoted as ‘b’
Population that are not exposed is denoted as ‘c + d’
Prevalence of exposure –
Prevalence of exposure is determined when prevalence of exposure in persons with the disease is compared to the prevalence of exposure in persons without the disease
Prevalence of exposure in diseased and Prevalence of exposure in non-diseased can be calculated as follows:
Prevalence of exposure in diseased= a/(a+c)
Population of exposed with disease is denoted as ‘a’
Population of exposed but do not have disease is denoted as ‘c’
Population that are diseased is denoted as ‘a + c’
Prevalence of exposure in non diseased= b/(b+d)
Population of exposed and not diseased is denoted as ‘b’
Population of non-exposed and do not have disease is denoted as‘d’
Population that is not diseased is denoted as ‘b + d’
Advantages and Disadvantages of the study design
There are many advantages and disadvantages of cross-sectional studies.
- It is relatively quick and easy to conduct.
- They are cheap.
- Data on all variables is collected once.
- Multiple outcomes and exposures can be studied.
- We can’t differentiate cause and effect from simple association with the use of this study design.
- Not suitable for studying rare diseases or short duration diseases.
- Susceptible to bias due to low response and there can be misclassification due to recall bias.
- Descriptive and Analytic Studies. Department of Health and Human Services.
- Setia MS. Methodology series module 3: Cross-sectional studies. Indian J Dermatol. 2016;61(3):261.
- Kanchanaraksa S. Cross-Sectional Studies. Johns Hopkings Bloomberg School of Public Health,2008.
- Park K. Park’s Textbook of Preventive and Social Medicine. 23rd edition. Banarasidas Bhanot Publisher; 2015. 67-71 p.
- Hennekens CH et al. Cross-sectional Studies.
- Dr. Hashemi B. Sayed. Research Methods and Analysis 2016 Dec 20.
- Chapter 4.Study Design. In Oxford University Press; [Feb 2017] Available from: http://www.oxfordjournals.org/our_journals/tropej/online/ce_ch4.pdf
- Gordis L. Case-Control and Other Study Designs. In: Epidemiology. Fifth. Elsevier Saunders; p. 189–214.
- Levin KA. Study design III: Cross-sectional studies. Evid Based Dent. 2006;7(1):24–5.
- Mann CJ. Observational research methods. Research design II: cohort, cross-sectional, and case-control studies. Emerg Med J. 2003;20(1):54–60.
- Neiderud C-J. How urbanization affects the epidemiology of emerging infectious diseases. Infect Ecol Epidemiol. 2015 Jan;5(1):27060.
- Cyril S, Oldroyd JC, Renzaho A. Urbanisation, urbanicity, and health: a systematic review of the reliability and validity of urbanicity scales. BMC Public Health. 2013;13(1):1.
- Satterthwaite D, McGranahan G, Tacoli C. Urbanization and its implications for food and farming. Philos Trans R Soc B Biol Sci. 2010 Sep 27;365(1554):2809–20.
- Leon DA. Cities, urbanization and health. Int J Epidemiol. 2008 Feb 1;37(1):4–8.
- Kodaman N, Aldrich MC, Sobota R, Asselbergs FW, Poku KA, Brown NJ, et al. Cardiovascular Disease Risk Factors in Ghana during the Rural-to-Urban Transition: A Cross-Sectional Study. Tayo BO, editor. PLOS ONE. 2016 Oct 12;11(10):e0162753.