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Descriptive and Analytic Epidemiology

Descriptive and Analytic Epidemiology

Part 1

Malaria disease continues to be one of the major problems and a public health issue affecting the world. Particularly, this disease is highly prevalent in tropical areas, with around 1,382 at risk of infection (Cáceres Carrera et al., 2019). Some of the risk factors include environmental risk constructs since this provides environmental conditions that the malaria vectors can breed. In this case, the disease is more prevalent during rainy seasons and is mostly found in warm and wet climates that provide breeding grounds for malaria vectors. On the other hand, socio-demographic factors or environment are crucial in influencing the disease pattern. Recent data shows that around 91 countries reportedly have autochthonous malaria infections (Moonen & Shiff, 2017). In America, the infections are reported in around 21 countries as the continent experiences a risky factor of around 132 million possibly in danger of contracting the infection. On the other hand, 21 million people in the Americas reside in high-risk areas (Cáceres Carrera et al., 2019). The World Health Organization has given estimates and projections of the disease epidemiology, noting that there has not been any significant progress made in the reduction of overall malaria cases.

Malaria is predominantly common in tropical and subtropical regions of Central and South America, Africa, Oceania, and Asia (Moonen & Shiff, 2017). Where the disease is prevalent, there are overall variations in its intensity on risk and transmission of the infections. Particularly, over 90% of all clinical deaths and infections are reported in sub-Saharan Africa (Cáceres Carrera et al., 2019). Furthermore, the risk is considerably varied. For instance, arid and highland areas are less prone to malaria, although they can become hotspots of the disease when climatic conditions favor mosquito development. Despite the urban regions and areas being less exposed, there has been an emerging concern over explosive and unplanned growth in population, which continues to make the peri-urban and urban regions new areas or regions for malaria transmission (Moonen & Shiff, 2017). Different parasites cause malaria, and this is based on geographical location or distribution. For instance, P. falciparum species is associated with the riskiest and most fatal malaria, accounting for over 90% of sub-Saharan Africa’s malaria infections, 100% of Haitian infections, and two-thirds of the malarial cases in Southeast Asia (Cáceres Carrera et al., 2019). The Vivax species is commonly found in South America, Central America, and the Indian subcontinent regions (Moonen & Shiff, 2017).

In 2018, for instance, malaria cases were around 228 million worldwide, with an estimated death of around 405,000 (WHO, 2020). Children are the most affected, as those below five years are reportedly the most vulnerable, accounting for around 67% of worldwide malaria deaths (WHO, 2020). The World Health Organization has flagged African Region as the worst-hit or prone to malaria infections contributing to around 93% of the cases and 94% of the total worldwide malaria deaths (WHO, 2020).

Part 2

Descriptive epidemiology is used to identify the inherent patterns among cases, including an exploration of a population by place, time, and person (Kelsey & Gold, 2016). Observational information or insight provides the opportunity to develop hypotheses regarding the causes leading to identified patterns, including associated factors increasing the risk of a particular disease. This can be used in studying the prevalence, patterns, and risk factors as causal agents of Tuberculosis within a population. This will include a description of demographic and clinical characteristics of the conditions and diseases, including those who are at risk, while providing clues on what is causing Tuberculosis.

One of the models to use in this case would be an experimental epidemiological design, used by investigators in determining, using a controlled process the specific individual exposure, which is the clinical trial or the community trial (Kelsey & Gold, 2016). The insight is then used in tracking communities or individuals through a time period to detect how the exposure is associated with some effects. For instance, Tuberculosis can be studied with respect to exploring a new vaccine, to which randomized control trials are performed. In this case, a treatment group will receive a new vaccine while others (the control group) will only receive a placebo treatment. This will involve the investigator tracking all the participants and observing those who are getting the disease to which the new vaccine or treatment has been recommended or intended to address (Kelsey & Gold, 2016). The data is used in comparing the two groups (placebo and new vaccine) to identify or determine whether the vaccine treatment has triggered a low disease rate.

An observational epidemiological study can also be used in studying Tuberculosis prevalence, whereby an epidemiologist engages in simple observation of exposure as well as the status of the disease or condition in every study participant (Conley, 2018). This can be studied by introducing behavioral changes or lifestyle changes, including hygiene and precautionary measures. After introducing the intervention, the population will be observed for a while, and incident rates of the disease will be observed or documented to evaluate the efficacy or success of the precisely recommended intervention (lifestyle changes on hygiene and precautionary measures). The two methods, therefore, differ in that an experimental design is about a treatment intervention while observation is about surveillance to understand the disease pattern based on a recommended intervention, without case controls or randomized treatment intervention.

A case control will be used when the disease induction or latent period is long, including the longer time taken between exposure and eventual disease manifestation (Pearce, 2016). This is also used when the population under study has a specific dynamism. Besides, this will involve targeting people with the disease condition and comparing it with a control group or individuals who do not have the disease. The basis for using a control group is to estimate expected or baseline exposure (Kelsey & Gold, 2016). In this case, when the disease rates or levels are higher in the case group in comparison to the baseline or control group, then the disease, Tuberculosis, in this case, shall have been aligned or associated with the specific exposure. This would have been different from a cohort study, whereby the researcher observes the population for exposure and not determining the individual’s status of exposure, whereby the rate of the disease within the exposed group is compared with the rate among the unexposed group or cohort.


Cáceres Carrera, L., Victoria, C., Ramirez, J. L., Jackman, C., Calzada, J. E., & Torres, R. (2019). Study of the epidemiological behavior of malaria in the Darien Region, Panama. 2015–2017. PloS one14(11), e0224508.

Conley, A. (2018). Methods: Data analysis for disaster epidemiology. In Disaster Epidemiology (pp. 135-141). Academic Press.

Kelsey, J. L., & Gold, E. B. (2016). Observational epidemiology. In International Encyclopedia of Public Health (pp. 295-307). Elsevier Inc.

Moonen, B., & Shiff, C. (2017). Should we commit to eradicating malaria worldwide?. BMJ, 356, j916.

Pearce, N. (2016). Analysis of matched case-control studies. BMJ, 352.

World Health Organization, (2020 Jan, 14). Malaria. Retrieved from room/fact-sheets/detail/malaria


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Descriptive and Analytic Epidemiology

Module 3 – Case

Case Assignment

This assignment has two parts.

Part 1: Write a descriptive epidemiological analysis of the following diseases: Lyme disease or Malaria. Describe the basic epidemiological patterns of this health condition by person, place, and time.

Part 2: You are a Health Officer for a rural county health department and have been assigned to study the frequency of an infectious disease such as tuberculosis or HIV/AIDS. Write an essay in which you do the following:

Descriptive and Analytic Epidemiology

Descriptive and Analytic Epidemiology

  1. Describe observational and experimental epidemiology as the two (2) study designs apply to either of the diseases you have chosen. Be sure to identify differences between these two types of analytic epidemiology.
  2. Under what conditions will you conduct a case-control study as opposed to a cohort study?

Be sure to support your opinion with evidence from the literature. Please research from the module materials and any credible, scholarly and professional source including the University’s online library in providing your response.

Length: 3-4 pages, excluding title page and references.

Assignment Expectations

Assessment and Grading: Your paper will be assessed based on the performance assessment rubric. You can view it under Assessments at the top of the page. Review it before you begin working on the assignment. Your work should also follow these Assignment Expectations.

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