PUB 540 Discuss the strengths and weaknesses of cross-sectional studies and provide examples of how they can be “descriptive” or “analytic” study designs
PUB 540 Discuss the strengths and weaknesses of cross-sectional studies and provide examples of how they can be “descriptive” or “analytic” study designs
PUB 540 Discuss the strengths and weaknesses of cross-sectional studies and provide examples of how they can be descriptive or analytic study designs
Case-control studies can be classified as analytical and observational studies. Case-control studies are notorious to bias due to the backward approach (Aigner et al., 2018). A type of observational research that uses the measurement of outcome and exposure is known as a cross sectional study. Furthermore, a cross sectional study can be descriptive or observational (Setia, 2016). With any given study design there are strengths and weakness. Cross-sectional studies are inexpensive and can be conducted quicky. Cross sectional studies measure the outcome and exposure of participants at the same time. Also, cross-sectional studies can prove or disprove hypothesis in cases with multiple variables. However, a weakness lies in the difficulty or uncertainty that lies in determining if the exposure or outcome came first. Also, the use of questionaries does not reflect emotions and the responses tend to be fewer because of the length of completing.
Analytical:
The number of people in a population with diabetes who are obese and the number of people in a population with diabetes who are not obsessed (Cross Sectional Studies).
Descriptive:
Study conducted on men and women with a specific age range to reveal similarities and differences in spending trends related to gender.
References:
Aigner, A., Grittner, U., & Becher, H. (2018). Bias due to differential participation in case-control studies and review of available approaches for Adjustment. PLOS ONE, 13(1).
Cross Sectional Studies – UNC gillings school of global public health. (n.d.). Retrieved June 2, 2022, from https://sph.unc.edu/wp-content/uploads/sites/112/2015/07/nciph_ERIC8.pdf
Setia M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian journal of dermatology, 61(3), 261–264. https://doi.org/10.4103/0019-5154.182410
Click here to ORDER an A++ paper from our Verified MASTERS and DOCTORATE WRITERS PUB 540 Discuss the strengths and weaknesses of cross-sectional studies and provide examples of how they can be “descriptive” or “analytic” study designs:
According to Setia (2016) cross-sectional studies are used for surveys directed at the population, primarily used to determine the prevalence of disease at the clinic level. Advantages of the cross-sectional study is that than can be conducted quickly and inexpensively. The selection process for this regarding participant is either for inclusion or exclusion of criteria while measuring for exposure and outcome at the same time. Another advantage is that the data can be used to figure out surveillance, planning for public health, and evaluation.
Cross-sectional studies can be both descriptive and analytical. An example is when it may be descriptive is, according to Kumar (n.d.) when the investigator just wants to know how many school-aged children from ages 12 to 14 have asthma. An example of how this study design can be analytical is when the investigator is trying to determine the association with a risk factor and an outcome. This type of study is easy to put together, it doesn’t cost much, and prevalence can be measured for more than just one thing. However, there are shortcomings such as whether the exposure happened before the exposure and vice versa. Incidence cannot be measured. Also, associations can be very hard to figure out between the outcome and exposure. An example would be trying to determine the amount of sun exposure for people that developed skin cancer at 30.
References
Setia, M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian journal of dermatology, 61(3), 261–264. https://doi.org/10.4103/0019-5154.182410 Kumar (n.d.) Introduction to Study Designs – Cross-sectional Studies. Health Knowledge Website. https://www.healthknowledge.org.uk/e-learning/epidemiology/practioners/introduction-study-design-ccs
Case-control studies can be classified as analytical and observational studies. Case-control studies are notorious to bias due to the backward approach (Aigner et al., 2018). A type of observational research that uses the measurement of outcome and exposure is known as a cross sectional study. Furthermore, a cross sectional study can be descriptive or observational (Setia, 2016). With any given study design there are strengths and weakness. Cross-sectional studies are inexpensive and can be conducted quicky. Cross sectional studies measure the outcome and exposure of participants at the same time. Also, cross-sectional studies can prove or disprove hypothesis in cases with multiple variables. However, a weakness lies in the difficulty or uncertainty that lies in determining if the exposure or outcome came first. Also, the use of questionaries does not reflect emotions and the responses tend to be fewer because of the length of completing.
Analytical:
The number of people in a population with diabetes who are obese and the number of people in a population with diabetes who are not obsessed (Cross Sectional Studies).
Descriptive:
Study conducted on men and women with a specific age range to reveal similarities and differences in spending trends related to gender.
References:
Aigner, A., Grittner, U., & Becher, H. (2018). Bias due to differential participation in case-control studies and review of available approaches for Adjustment. PLOS ONE, 13(1).
Cross Sectional Studies – UNC gillings school of global public health. (n.d.). Retrieved June 2, 2022, from https://sph.unc.edu/wp-content/uploads/sites/112/2015/07/nciph_ERIC8.pdf
Setia M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian journal of dermatology, 61(3), 261–264. https://doi.org/10.4103/0019-5154.182410
The strength of the cross-sectional study is: Relatively quick, cheap and easy to conduct (no long periods of follow-up); data on all variables is only collected once; able to measure prevalence for all factors under investigation; multiple outcomes and exposures can be studied. The prevalence of disease or other health – related characteristics are important in public health for assessing the burden of disease in a specified population and in planning and allocating health resources; good for descriptive analyses and for generating hypotheses. (Healthknowledge, 2017)
The weakness of the cross-sectional study is: Difficult to determine whether the exposure or outcome came first; not suitable for studying rare diseases or diseases with a short duration; as cross-sectional studies measure prevalent rather than incident cases, the data will always reflect determinants of survival as well as etiology; Unable to measure incidence; associations identified may be difficult to interpret; susceptible to biases such as responder bias, recall bias, interviewer bias and social acceptability bias. (Healthknowledge, 2017).
An example of descriptive cross-sectional study: A random sample of hospitals across Fairfield County, CT are chosen for a prevalence survey to assess the number of night nurses positive for c. diff due to exposure on the job.
An example of analytic cross-sectional study: Analyzing the persons in the community that identify as an “alcoholic” or a severe drinking problem, whether they successfully used Alcoholic Anonymous for help.
An example of a measured disease: A cross-sectional prevalence survey was conducted on a community to identify persons that purchased a raw beef sold at the local grocery store, may have an exposure to a possible e. coli bacteria.
Resources
Healthknowledge. (2017). Design, applications, strengths and weakness of cross-sectional, analytical studies
(including cohort, case-control, and nested case-control studies), and interventions studies (including
randomized control trials).
Simkus, J. (2021). Cross-sectional study. https://www.simplypsychology.org/what-is-a-cross-sectional-study.html
Cross-sectional studies are observational studies under analytical research design, that measure disease outcome and exposure in a population. This type of study is conducted relatively faster since there are no interventions applied to alter the exposure status or outcomes at baseline. Cross-sectional studies are inexpensive and less time-consuming. Epidemiologists take measurements at a single time point, thus reducing need for follow-up. According to Setia (2016), cross-sectional studies are easily applicable in population-based surveys. Cross-sectional studies are also advantageous by providing information regarding disease prevalence. Despite the above advantages, cross-sectional studies have few disadvantages. Cross-sectional studies are associated with difficulties in derivation of causal relationships due to a one-time measurement (Setia, 2016). These studies are prone to bias and limitations of insufficient data regarding disease trends since they solely rely on disease incidence and length of survival. Therefore, researchers strive to overcome the weaknesses while conducting cross-sectional studies.
Reference
Setia M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian Journal of Dermatology, 61(3), 261–264. https://doi.org/10.4103/0019-5154.182410
A cross-sectional study is an observational research that inspects the relationship between illness and variables of concern as they transpire in a given community. The research is measured by the outcome and exposure of the participants at the same time. Questionnaires also enable in reaching to many people. The advantage of a cross-sectional study is that its very affordable. The major disadvantage is that it impossible to study rare diseases that require a different type of study and is more vulnerable to recall biases. According to Simkus (2021), a cross- sectional study cannot determine cause or effect. A good example would be the association of past smoking habits with lung cancer.
References:
Simkus, Julia (2021). How Does Cross-Sectional Research Work. Simply Psychology.
Cross-sectional study also known as prevalence study is an observational study which is often use to measure the prevalence of health outcomes, help understand determinants of health, and identify the features of the population (Wang & Cheng, 2020). The advantage of cross-sectional studies is that it is quicker to complete the study and less expensive. The major disadvantage of cross-sectional study is that it is not very help in concluding the disease etiology (Friis & Sellers, 2021). Cross-sectional study is an efficient tool for descriptive epidemiologic study (Friis & Sellers, 2021). An example would be the prevalence of breast cancer in the state of California. It becomes descriptive study because it describes the characteristics of breast cancer or the health outcomes. Another example of cross-sectional study is the prevalence of PTSD among military personnel who are exposed to combat zone or were deployed.
Reference
Friis, R.H. & Sellers, T.A. (2021). Epidemiology for public health practice (6th ed.). Burlington,
MA: Jones & Bartlett Learning.
Wang, X., & Cheng, Z. (2020). Cross-sectional studies: strengths, weaknesses, and recommendations. Chest, 158(1), S65-S71.
Cross-sectional studies are research designs that allow the researcher to arrange data at a single point in time which is usually collected from participants of a particular study, however, they have different strengths and weaknesses. The major strength of cross-sectional studies is that they are relatively fast to conduct and less expensive as compared to other research designs such as experimental design. Moreover, cross-sectional studies can be able to identify outcomes and multiple exposures from different study associations (Mark et al., 2020). Another strength of cross-sectional studies is that they can be used to disprove or prove assumptions since multiple variables of data can be collected at a time (Zangirolami-Raimundo et al., 2021). In spite of that, cross-sectional studies have various weaknesses which include: the inability to make a causal inference, inability to analyze cause and effect, and the inability to study rare diseases such as Stoneman Syndrome (Zangirolami-Raimundo et al., 2021). Cross-sectional studies require selection of participants of the study from a heterogeneous study population unlike other studies which chose subjects of the study from a series of patients (Mark et al., 2020). However, the biggest weakness of cross-sectional studies is that they usually result in non-response due to bias of the measures of outcome, such that the traits of the responders and non-responders quite differ.
Cross-sectional study designs can be either analytic or descriptive depending on the prevalence of a certain health outcome. Descriptive cross-sectional studies provide data that characterizes the prevalence of various health outcomes at a given period within a specified population. Analytic cross-sectional studies obtain the data of the health outcome and prevalence of exposure of the disease to compare the difference between individuals who are exposed and those who are not exposed (Mark et al., 2020). An example of a disease that can be considered to enroll the characteristics of an analytical cross-sectional research design is Lyme disease (Zangirolami-Raimundo et al., 2021). During a pandemic of Lyme disease, the number of individuals who make up the survival group, are usually not directly exposed but rather get the disease through secondary infection.
References
Mark, E., Ian, F., Wendy, O., & Maria, P. (2020). cross-sectional study design. Oxford
University Press.
Zangirolami-Raimundo, J., Echeimberg, J. O., & Leone, C. (2021). Research methodology
topics: Cross-sectional studies. Journal of Human Growth and Development, 28, (3),
356-360. http://dx.doi.org/10.7322/jhgd.152198
Cross-sectional studies are a type of observational research, which involves looking at data from populations collected at one specific point in time, (Friis & Li, 2021). Which examines and measures the relation between disease or other health-related states or other variables between population groups, (Hennekens & Buring, 1987). The strengths of cross-sectional studies, they can be conducted faster, are inexpensive, gives information about the prevalence of outcomes or exposures, and may be useful for public health planning, monitoring, and evaluation. Cross-sectional limitations and or weaknesses are it’s susceptible to bias, it’s a 1-time measurement, not suitable for studying rare diseases, and measures prevalent rather than incident cases, the data will always reflect determinants of survival as well as etiology, (Setia, 2016).
Cross-sectional may be purely descriptive and used to assess the burden of a particular disease in a defined population. For example, a random sample of schools across London may be used to assess the prevalence of asthma among 12-14-year-olds. Analytical cross-sectional surveys may also be used to investigate the association between a putative risk factor and a health outcome. For example, HIV and male sex workers (Shinde et al., 2009). A cross-sectional analysis to assess the prevalence of HIV and risk behaviors in male sex workers, (Setia, 2016).
Cross-sectional designs measure the outcome and the exposures in the study participants at the same time, an example of a disease where survival influences the association, like HIV and male sex workers. The prevalence of an outcome depends on the incidence of the disease as well as the length of survival following the outcome, (Setia, 2016). For example, even if the incidence of HIV (number of new cases) goes down in one particular community, the prevalence (total number of cases – old as well as new) may increase. This may be due to cumulative HIV-positive cases over a period. Thus, just performing cross-sectional surveys may not be sufficient to understand disease trends in this situation, (Setia, 2016).
References
Friis, R. H., & Sellers, T. A. (2021). Epidemiology for public health practice (6th ed.). Jones and Bartlett Learning. ISBN-13: 9781284175431
Hennekens CH, Buring JE. Epidemiology in Medicine, Lippincott Williams & Wilkins, 1987. Introduction to study designs, case-control studies. Retrieved From: https://www.healthknowledge.org.uk/
Setia M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian journal of dermatology, 61(3), 261–264. https://doi.org/10.4103/0019-5154.182410