Epidemiology deals with the determinants of health among the population. Today, clinical epidemiology is defined as the science where predictions about patients and their diseases are made through the analysis of clinical events and group studies (Fletcher, Fletcher, & Fletcher, 2012). The relationship between clinical medicine and epidemiology is based on the fact that clinicians are the founders of epidemiology and the developers of guidelines (Williams & Robertson, 2017). Quantitative data, including numbers and probability, are used in epidemiology to introduce populations, samples, risks, and benefits and reduce the number of negative outcomes connected with death.
Performance measurements vary. Some of them are validity (the degree to which the data is measured), reliability (repeated measurements of stable issues), or variation (several variations related to one act of measurement) (Fletcher et al., 2012). The main reason for variations in data is the existing differences among people. The reports developed by the CDC and the National Center for Health Statistics help follow recent changes and progress. The example is the data about mental illness, its effects, and its presence among 45 million US adults reported by Cherry, Albert, and McCaig (2018). Statistics can demonstrate the achievements in the field of mental health care.
Epidemiological Studies and Risks
Risks are frequent in epidemiological studies. People want to know more about the risks of diseases. Fletcher et al. (2012) define risk as a probability of an untoward event with a list of characteristics. Risk characteristics, also known as risk factors, may be inherited (e.g., diabetes) and acquired (e.g., smoking). The recognition of risks is a life-long process to compare the results between different populations, make distinctions, and understand what kind of treatment may be offered. People want to learn from each other’s mistakes. If the recognition of such factors as smoking or alcohol can save lives, people need to have as much evidence as possible.
Epidemiological studies can impact interventions to reduce risks. Positive results may be achieved through the analysis of past studies and conducting experiences, observations, or cohort studies (Fletcher et al., 2012). The examples are the cohort study by Hinnouho et al. (2014) about the risks of cardiovascular diseases among diabetic patients or a lab-in-field experiment by de Oliveira et al. (2016) to prove the connection between economic preferences and obesity risks. These epidemiological studies can be applied to evidence-based medicine because both of them play a significant role in clinical decision-making using scientifically sound explanations.
Levels of Prevention
Three main levels of prevention exist. Primary prevention aims at preceding diseases and promoting health (Edelman, Kudzma, & Mandle, 2014). The scenario may include free weekly lectures about the importance of healthy eating, regular physical exercises, and tobacco avoidance. Secondary prevention is promoted through screening and treatment at early stages like the prescription of aspirin to decrease the cases of strokes among elderly patients (Zhang et al., 2015). Another scenario of this type of prevention can be regular screenings and consultations with a mammalogist to detect breast cancer at its early stage and be able to save lives through specific interventions. Blood tests, urine tests, and mammography should be recommended among people whose family member have already been diagnosed with cancer or even died because of this disease.
Tertiary prevention usually occurs when it is impossible to avoid disease. However, the minimization of negative effects and the reduction of morbidity are still possible (Jacobsen & Andrykowski, 2015). The creation of diabetic patient support groups is the scenario for this type of prevention. People should have a place to go in order to share their experience, ask for help, and learn how to deal with their current problems using available sources.
Communication and Therapeutic Relationship
The development of trustful therapeutic relationships is an essential part of any care process. However, there are the situations when clinician’s values contradict patients’ beliefs. In these cases, a healthcare worker has to remember that the problem belongs to a patient who should make a final decision (Edelman et al., 2014). The task of a nurse or another clinician is to educate, inform, and offer alternatives but never impose personal views. Such aspects of the communication process as space and flexibility should be promoted to overcome a barrier of ineffective therapeutic relationships. They can provide the patient with comfort and the feeling of personal worthiness even when it is necessary to take treatment and follow prescriptions.
There are also many ways to establish strong patient-clinician relationships. Epidemiological studies prove the effectiveness of any chosen intervention. For example, a working alliance has to be developed so that both parties can share the necessary information (Gelso, 2014). The role of mentalizing cannot be ignored as it is a chance to demonstrate readiness to cooperate and choose appropriate behavior (Fonagy & Allison, 2014). Despite being limited and framed within certain healthcare guidelines and barriers, human choice and the presence of alternatives facilitate communication.
Determinants of Health
The identification of the determinants of health becomes an important part of the Healthy People 2020 program. Multiple personal, environmental, economic, and social factors may contribute to human health (Healthy People 2020, 2018). Recently, much attention has been paid to the determination of social factors in regard to health because they can considerably shape care (Braveman & Gottlieb, 2014; Garg, Toy, Tripodis, Silverstein, & Freeman, 2015). It is better to understand the role of each determinant through the discussion of a particular case.
For example, an elderly woman moves from an urban area to a rural area because of her asthma complications and the necessity to change the environment and breathe in more fresh air. She cannot get used to new living conditions and continue confusing the roads, which leads to increased falls and associated traumas. In addition, she is not aware of how to use modern mobile GPS navigator to facilitate her walks and learn the surroundings. The solution to this problem depends on several determinants of health. Such social factors as the age group of a person, social interactions, and support should be mentioned to promote the creation of new policies. The discussion of the environmental factors like poor air quality and limited rural infrastructure can help identify the risks of falls and depression. The global factor like technological literacy of the elderly must be analyzed.
Epidemiological Studies’ Impact
Epidemiological studies influence diagnosis, prognosis, and clinical treatment in different ways. Sensitivity, specificity, predictive values, and likelihood ratios are the types of data that may be taken from epidemiological studies. For example, likelihood ratios describe the performance of diagnostic tests (Fletcher et al., 2012). It shows the possibility of a disease regarding the obtained test results. Its main benefit is the summary of test results at different levels. Prognosis’ peculiar feature is the direct connection with risk factors. The example can be the study of Di et al. (2016) about the risks of insulin resistance and its impact on polycystic ovary syndrome prognosis. Restrictions and stratifications reduce the number of biases in cohorts and observations.
Clinical treatments depend on epidemiological data. It becomes possible to test ideas and identify treatment effects beforehand to make a right choice. Treatment disparities cannot be ignored because it is hard to know all health details about any ethnic group. Migration and globalization cannot be stopped, and epidemiology is a good way to gather general information and introduce it in a clear and meaningful way (de Kock, Decorte, Vanderplasschen, Derluyn, & Sacco, 2017). The worth of this information is impressive for health care and medicine.
References
Braveman, P., & Gottlieb, L. (2014). The social determinants of health: It’s time to consider the causes of the causes. Public Health Reports, 129(2), 19-31.
Cherry, D., Albert, M., & McCaig, L. F. (2018). Mental health-related physician office visits by adults aged 18 and over: United States, 2012-2014. Web.
de Kock, C., Decorte, T., Vanderplasschen, W., Derluyn, I., & Sacco, M. (2017). Studying ethnicity, problem substance use and treatment: From epidemiology to social change. Drugs: Education, Prevention and Policy, 24(3), 230-239.
de Oliveira, A. C., Leonard, T. C., Shuval, K., Skinner, C. S., Eckel, C., & Murdoch, J. C. (2016). Economic preferences and obesity among a low-income African American community. Journal of Economic Behavior & Organization, 131(B), 196-208.
Di, N., Chen, Y., Chen, X., Li, L., Zhao, X., Pan, P.,… Huang, H. (2016). Prognosis and risk factors of insulin resistance and abnormal glucose metabolism in patients with polycystic ovary syndrome: A single-centre, retrospective, database study. The Lancet Diabetes & Endocrinology, 4, 33.
Edelman, C., Kudzma, E. C., & Mandle, C. L. (2014). Health promotion throughout the life span (8th ed.). St. Louis, MO: Elsevier.
Fletcher, R. H., Fletcher, S. W., & Fletcher, G. S. (2012). Clinical epidemiology: The essentials (5th ed.). New York, NY: Lippincott Williams & Wilkins.
Fonagy, P., & Allison, E. (2014). The role of mentalizing and epistemic trust in the therapeutic relationship. Psychotherapy, 51(3), 372-380.
Garg, A., Toy, S., Tripodis, Y., Silverstein, M., & Freeman, E. (2015). Addressing social determinants of health at well child care visits: A cluster RCT. Pediatrics, 135(2), 296-304.
Gelso, C. (2014). A tripartite model of the therapeutic relationship: Theory, research, and practice. Psychotherapy Research, 24(2), 117-131.
Healthy People 2020. (2018). Determinants of health. Web.
Hinnouho, G. M., Czernichow, S., Dugravot, A., Nabi, H., Brunner, E. J., Kivimaki, M., & Singh-Manoux, A. (2014). Metabolically healthy obesity and the risk of cardiovascular disease and type 2 diabetes: The Whitehall II cohort study. European Heart Journal, 36(9), 551-559.
Jacobsen, P. B., & Andrykowski, M. A. (2015). Tertiary prevention in cancer care: Understanding and addressing the psychological dimensions of cancer during the active treatment period. American Psychologist, 70(2), 134-145.
Williams, G. H., & Robertson, D. (Eds.). (2017). Clinical and translational science: Principles of human research (2nd ed.). San Diego, CA: Academic Press.
Zhang, Q., Wang, C., Zheng, M., Li, Y., Li, J., Zhang, L.,… Yan, C. (2015). Aspirin plus clopidogrel as secondary prevention after stroke or transient ischemic attack: A systematic review and meta-analysis. Cerebrovascular Diseases, 39(1), 13-22.