PUB 540 What are the two main types of analytic studies?
PUB 540 What are the two main types of analytic studies?
Analytic epidemiology compares using a control group of people that were not affected. This allows the epidemiologist to root out certain characteristics that are specifically associated with the disease as opposed to not being associated with the disease. Just like in the church outbreak, the people that belonged to the church, but had not had dinner on that night were excluded from the pool to be investigated. When characteristics can be identified that are associated with the disease, it gives public health the chance to start working on interventions. It can also lead to determining what a specific cause may be. This is done by studying the associated and non-associated groups by either an experimental study or observational study. Observational studies can then be either: cohort studies, case-control studies, and cross-sectional studies
Hypothesis testing is part of the analytical epidemiology but is involved more so with interventional study. That part that helps create a cure. As discussed by Friis and Sellers (2020) a statistical test is done to determine the validity of the results. Is that data from the interventional group significantly different from the results of the non-interventional group. In other words, those that got the vaccine and those that did not. We saw this play out in the phase 4 trials of the COVID-19 clinical trials. Can those results be arrived at under the same circumstances with similar study participants? If yes, then your study is significantly important. This is what Pfizer, Moderna, and Johnson & Johnson did. Of course, this is simplified, but this is the gist of hypothesis testing. This is a type is a cohort study.
Dicker et al. (2012). Principles of Epidemiology in Public Health Practice: An Introduction to Applied
Epidemiology and Biostatistics. Centers for Disease Control and
Friis, R. H., & Sellers, T. (2020). Epidemiology for public health practice (6th ed.). Jones & Bartlett Learning.
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Analytical studies help identify and quantify associations. In order to test hypotheses and answer the how and why analytical studies are categorized by two types. Most importantly the key feature of analytical is the comparison of groups (Provost, 2011). The two types are known as observational and experimental. According to the CDC studies the experimental component is made up of random control trials while the observational utilizes cross-section, case-control, and cohort. Investigators began to determine through a controlled environment with a clinical or community trial exposure and record over time the effects of the effects of the exposure (CDC, 2019). The trial is conducted depending on the hypothesis that’s being tested which was established by the epidemiologist. Moreover, the observational studies utilize data that has been gathered from community, induvial, and populations as a whole thru questionnaire.
Previously learned through scientific projects that hypotheses are nothing more than educated guest. To specify hypothesis are tested against data that has been gathered for acceptance or decline. Within analytical studies hypotheses are utilized to evaluated relationship between given variables and samples. Hypothesis testing remains the common approach and is known to be beneficial through medical science. An example would be the following scenario: Research intends to identify relationship between height and gender. Furthermore, an assumption or hypothesis will be developed based on one’s knowledge about human physiology. Hypothesis could Men on average have taller height than women. Next, you’ll collect information and then execute statistical testing and determine if the data gather support or refute the hypothesis.
CDC. (2019). Principle of epidemiology. Center for Disease Control and Prevention. https://www.cdc.gov/csels/dsepd/ss1978/lesson1/section7.html
Provost L. P. (2011). Analytical studies: a framework for quality improvement design and analysis. BMJ quality & safety, 20 Suppl 1(Suppl_1), i92–i96. https://doi.org/10.1136/bmjqs.2011.051557
Once again, I appreciate the way you crisply lay out your description of the types of analytical studies with the example of hypothesis testing being based the assumptions based on knowledge about human physiology. And, it is through this type of testing that lead us to vaccines for the COVID-19 virus. Analytical epidemiology test effects of data overtime through surveillance. In this article” COVID-19 Surveillance Data: A Primer for Epidemiology and Data Science discussed by Tarantola & Dasgupta (2021), they offer suggestions on how surveillance could improve in order improve the quality of what’s collected by correctly interpreting the data. In a digital society, cumulative counts are talked about in the news, perhaps because it’s easier for the lay public to understand, but incidence-based by date works better for revealing transmission patterns better. They discuss that we as health care professional need to stay vigilant to our traditional methods of assessment, determinants, potential of spread, along with social determinants of health in tracking disease and protecting the public. We must stand true to the norms and standards. Thought this was interesting to remember as we go forward with our careers.
Tarantola, D., & Dasgupta, N. (2021). COVID-19 Surveillance Data: A Primer for Epidemiology and Data Science. American Journal of Public Health, 111(4), 614–619. https://doi-org.lopes.idm.oclc.org/10.2105/AJPH.2020.306088
Analytical studies quantify the relationship between disease exposure/cause and disease outcome). These studies
test hypotheses to identify the root cause of diseases. According to Ranganathan et al. (2019), the two main types of analytical studies include observational and experimental studies. Observational analytical studies help determine exposure naturally compared to experiment analytical studies, which involve exposure variance between a study population and a control group. Experiment analytical studies include randomized clinical trials. Therefore, experiment analytical studies minimize bias through randomization. According to Thakur & Shah (2021), experiment analytical studies apply the principles of sequence generation and allocation concealment. Participants have equal chances of allocation to exposure or non-exposure group. Additionally, participants remain unaware of the allocated group until intervention administration.
Observational studies include cohort (prospective and retrospective) and case-control. Cohort studies involve a group of people with shared characteristics. In essence, epidemiologists select a group of people with varying exposure levels and monitor their progress to evaluate the onset of outcomes (Ranganathan et al., 2019). Participants are free of disease outcomes at baseline. Therefore, several exposures and one or more disease outcomes are studied simultaneously. According to Ranganathan et al. (2019), case-control studies involve two groups of participants exposed to disease causative-agent. That is a group presenting with disease outcomes and one without disease outcomes at baseline. These studies are retrospective, thus allowing epidemiologists to elicit a history of exposure. Most importantly, case-control studies enable epidemiologists to identify the relationship between an outcome and several levels of disease exposure.
Hypothesis testing helps verify the plausibility of the hypothesis by examining data samples from random populations. A hypothesis can involve preventive interventions or disease exposure. For instance, one hypothesis involving preventive interventions is maternal adherence to healthy lifestyle practices reduces the risk of obesity in offspring. The researchers conducted a prospective cohort study (Dhana et al., 2018). One hypothesis involving disease exposure is air pollution shortens lung cancer survival (Eckel et al., 2016). The researchers used a cohort study to evaluate the time of death of participants. A descriptive statistical method evaluated survival and exposure to air pollution.
Dhana, K., Haines, J., Liu, G., Zhang, C., Wang, X., Field, A. E., … & Sun, Q. (2018). Association between maternal adherence to healthy lifestyle practices and risk of obesity in offspring: Results from two prospective cohort studies of mother-child pairs in the United States. BMJ, 362.doi: https://doi.org/10.1136/bmj.k2486
Eckel, S. P., Cockburn, M., Shu, Y. H., Deng, H., Lurmann, F. W., Liu, L., & Gilliland, F. D. (2016). Air pollution affects lung cancer survival. Thorax, 71(10), 891- 898.http://dx.doi.org/10.1136/thoraxjnl-2015-207927
Ranganathan, P., & Aggarwal, R. (2019). Study designs: Part 3 – Analytical observational studies. Perspectives in Clinical Research, 10(2), 91–94. https://doi.org/10.4103/picr.PICR_35_19
Thakur N & Shah D. (2021 Dec 15). Interventional Study Designs. Indian Pediatr, 58(12):1171- 1181. Epub 2021 Sep 22. PMID: 34553689.