Question 1: Define and discuss health determinants and disease distribution. How are they related? Are determinants always clearly defined in disease distribution?
Health determinants are the environmental, personal, social, and economic factors that affect an individuals or a communitys health. Disease distribution, on the other hand, is the frequency and pattern with which an illness spreads out in a specified population (Macera, Shaffer, & Shaffer, 2013). Determinants of health and disease distribution are related in that environmental, social, personal, and economic factors not only contribute to a persons or a populations well-being; but also characterize a population. For instance, a population can be selected according to their geographical location. One goes further to describe such a population according to their economic status. High-income levels are associated with better health and such a geographic area is likely to have a lower frequency of disease distribution.
In most cases, health determinants are defined in disease distribution. The prevalence of a disease in a particular population is often described by at least one of the determinants (Macera, Shaffer, & Shaffer, 2013). For instance, the risk of contracting malaria varies from region to region; implying that the environmental aspect comes into play when one intends to determine the pattern with which the disease spreads out. Also, a study of the distribution of cholera indicates that it flourishes among the malnourished. This category of people often resides in famine-prone areas or refugee camps.
Question 2: Choose an infectious disease and a chronic disease. Name three approaches for prevention (a primary, secondary, and tertiary) for both.
i. Infectious disease - HIV/AIDS
ii. Chronic illness - Asthma (Engelkirk, Duben-Engelkirk, & Burton, 2011).
Strategies for prevention of HIV/AIDS:
i. Primary - Engaging in safe sex which entails using a condom.
ii. Secondary - Treating STIs to limit disease progression.
iii. Tertiary - Administering ARVs to stop the disabling aspects of AIDS and maximizing the living potential of a victim.
Approaches for prevention of asthma:
i. Primary - Steering clear of tobacco smoke.
ii. Secondary - Pulmonary rehabilitation.
iii. Tertiary - Avoidance of triggers and allergens.
Question 3: Describe a natural experiment that has contributed to the knowledge base of epidemiology. It may be historic or current. Specifically state why this is considered a natural experiment.
In June 2002, all public spaces in Helena, Montana were declared to be non-smoking zones. These included bars, casinos, and shopping centers. The ban was implemented for six months with the aim of observing the number of victims of a heart attack as admitted to the citys single hospital. Between June and December of 2002, there was a significant drop (about 60%) of the recorded cases of heart attack. When the ban was eventually lifted, the hospital recorded an increase in the number of patients with heart attack. Regardless of the fact that these statistics could have arisen by chance, the odds are in favor of the natural experiment. It goes to show that active and passive smoking predispose one to hear attack. Understandably, the smoke from tobacco contains chemicals that are damaging to the blood vessels resulting in atherosclerosis and eventually causing a heart attack. Since the ban lessened the number of people exposed to tobacco smoke, fewer cases of heart attack were registered.
Such is an example of a natural experiment since the study objects were randomly assigned in a bid to answer a particular question. Rothman (2012) documents that in natural experiments; the researcher has no control over the subjects that receive the exposure; as was the Helena case. Also, natural experiments often aim at understanding the health impacts of policies such as the ban on smoking in public places to a particular population.
Question 4: The morbidity and mortality of disease may change over time. Discuss the 4 trends of disorders and give an example of a disease for each trend (that is not given in your text).
The four patterns of diseases are as explained below.
Disappearing disorders are those that were initially caused morbidity and mortality, especially in developed countries (Friis & Sellers, 2014). However, they are presently not considered as epidemics. Some of these disorders are controlled through immunization or the use of antibiotics. An example is smallpox which has been eradicated.
The major contributors of residual diseases are recognized, but the methods for controlling such illnesses are yet to prove effective. This ineffectiveness is largely attributed to non-efficient methods of implementation (Friis & Sellers, 2014). An example is sexually transmitted diseases.
According Friis and Sellers (2014), diseases whose cures remain unknown are described as persisting disorders. These illnesses thrive under the fact that researchers are unable to determine the possible preventive or curative measures. Breast cancer befits this definition.
iv. New epidemic
New epidemic disorders are those that manifest themselves more frequently than they previously were. It is increasingly common to come across patients suffering from these diseases than before. These new epidemics are mostly caused by new environmental exposures or the standard practices that define the contemporary lifestyle (Friis & Sellers, 2014). An example is obesity.
Question 5: Describe Bradford Hills criteria for causality.
According to Bradford Hill, the conclusions about causation ought to have the following aspects:
i. Strength of the association
There is a need to establish a robust relationship between the cause and the effect. An association not only needs to be valid but also strong. A close relationship is likely to have a causal component (Delogu, 2016). Hill invoked the example of lung cancer and indicated that exposure levels of smoking significantly influence the incidences of the chronic illness.
The impact of replication in building ones confidence in a causal relationship cannot be undermined. Distinct investigators, utilizing varied study designs, and belonging to diverse populations ought to repeatedly observe a cause and effect relationship before branding a causal association. This way, it is possible to rule out the probability of a relationship that arises due to a fallacy or a constant error in a similar study design. However, if different studies share flaws, then these will result in a replication of similar wrong conclusions. Hill observes that the inclusion of a healthy control group kills alternative therapy efficacy (Delogu, 2016).
Hill opines that a specific cause leads to a particular outcome, and a clear outcome results from a single cause. For a valid causality, a factor is expected to influence precisely a particular population or outcome. Diseases may have multiple causes, and it is possible that one factor may cause different types of diseases. Thus, the more particular the association between an effect and a factor, the more likely that it is causal.
Apart from the instances where there is an expected delay, the cause should precede the effect. However, Hill observes that it would be cumbersome to establish a temporal direction for a slowly developing disease whose initial forms were hard to measure (Delogu, 2016).
v. Biological gradient
Little exposure equals little effect while significant exposure results in a large effect. For example, there exists a linear relationship between alcohol intake and liver cirrhosis. However, the biological gradient may not be the most accurate measure for causality since some casual relationships often display nonlinear relationships to the outcomes risk.
A credible mechanism between cause and effect capable of attracting substantive matter explanations befits a causal relationship (Delogu, 2016). Biological explanations should exist to explain how an exposure contributes to a particular outcome. Also, these occurrences should not break the laws of the universe.
A casual conclusion need not contradict present substantive knowledge. When epidemiological and laboratory findings are in tandem, then there is an increased likelihood of an effect.
Tests are critical to the understanding and treatment of diseases. Evidence from randomized experiments results in the probability of the determination of causation (Delogu, 2016). Eventually, decisions are made based on experimental evidence since interventions have predictable effects on the occurrence of the disease.
When an effect has been indicated, it is possible to identify similar exposures and outcomes due to a similarity in the cause-effect relationship. Hill suggests that judging from analogy is acceptable. For instance, if some virus can cause a disease, one is justified to think that a second infection can cause a similar illness.
Delogu, B. (2016). Risk analysis and governance in EU policy making and regulation. Switzerland: Springer.
Engelkirk, P. G., Duben-Engelkirk, J. L., & Burton, G. R. W. (2011). Burton's microbiology for the health sciences. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins.
Friis, R. H., & Sellers, T. A. (2014). Epidemiology for public health practice. Burlington, Mass: Jones & Bartlett Learning.
Macera, C. A., Shaffer, R. A., & Shaffer, P. M. (2013). Introduction to epidemiology: Distribution and determinants of disease in humans. Clifton Park, N.Y: Delmar, Cengage Learning.
Rothman, K. J. (2012). Epidemiology: An introduction. New York, NY: Oxford University Press.
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