HLT-362 Topic 5 DQ 1: DESCRIBE HOW EPIDEMIOLOGICAL DATA INFLUENCES CHANGES IN HEALTH PRACTICES

HLT-362 Topic 5 DQ 1: DESCRIBE HOW EPIDEMIOLOGICAL DATA INFLUENCES CHANGES IN HEALTH PRACTICES

Topic 5 DQ 1

Describe how epidemiological data influences changes in health practices. Provide an example and explain what data would be necessary to make a change in practice.

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Epidemiological Data Influences on Health Practices

Epidemiology is the study of the distribution and determinants of health-related states or events within a specified population, its purpose being to inform decisions about the control of health problems (Hannaford & Owen-Smith, 1998). Epidemiological data can be useful in health practices to help promote health and well-being and save lives by collecting data. This data should include: what? How much? When? Where? and among whom? (CDC, 2018). Epidemiological data influences changes in health practices because it gathers and analyzes all aspects of a disease process, allowing for the development of best practices and evidence-based approaches that directly impact people’s lives.

Collecting epidemiological data will affect healthcare policy by revealing how things are connected and whether making improvements results in different outcomes. For example, to stop germs from infecting more people, we must break the chain of infection. The infectious agent, reservoir, portal of exit, mode of transmission, portal of entry, and susceptible host are all part of the chain. Hand hygiene has been described as essential in breaking the chain of infection based on epidemiological evidence.

Example of Epidemiology Data

The COVID-19 pandemic we are currently facing is an example of how epidemiological evidence affects improvements in health practices. Epidemiologists collaborate with other scientists to determine who has been infected, why they have been infected, and what the CDC may do to help (CDC, 2020). Epidemiologists could pinpoint the outbreak’s source, track and control the disease, and determine risk factors, transmission mode, and the most appropriate treatment. They devise strategies for slowing the disease’s spread and reducing its effects. The guidelines include masking, social distancing, personal protective equipment (PPE), and adequate hand washing.

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Reference:

Centers for Disease Control and Prevention (CDC). (2018). Describing Epidemiologic Data. Retrieved from: https://www.cdc.gov/eis/field-epi-manual/chapters/Describing-Epi-Data.html

Centers for Disease Control and Prevention (CDC). (2020). About COVID-19 Epidemiology, Investigating Covid-19: The Science Behind CDC’s Response. https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/about-epidemiology/index.html

Hannaford, P. C., & Owen-Smith, V. (1998). Using epidemiological data to guide clinical practice: review of studies on cardiovascular disease and use of combined oral contraceptives. BMJ (Clinical research ed.), 316(7136), 984–987. https://doi.org/10.1136/bmj.316.7136.984

Torres, Elissa. (2018). Application of Analysis https://lc.gcumedia.com/hlt362v/applied- statistics-for-health-care/v1.1/#/chapter/5

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Claudia Delgado Lugo

replied toIrene Igbinosa

Sep 16, 2022, 3:30 AM

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Irene,

As nurses, we play an important role in educating the public about epidemiology prevention. The latest pandemic of COVID-19 has been challenging for health officials and providers to take control. This present historical account of the past 800 years looks specifically at how some aspects of education were shaped from the early medieval epidemics such as leprosy and the Black Plague to the Spanish Flu and COVID-19 (Spielman, 2021). Such education focuses on hand washing, protecting ones self from the ill, quarantining and vaccination.

Spielman AI, Sunavala-Dossabhoy G. Pandemics and education: A historical review. J Dent Educ. 2021 Jun;85(6):741-746. doi: 10.1002/jdd.12615. Epub 2021 Apr 19. PMID: 33876429

Epidemiology is a field that deals with the study of healthcare problems, how they affect the population and the interventions necessary when dealing with the healthcare issues (CDC, 2012). Not only does the field offer quantitative data about a health care problem using hypothesis in human behavior, biology and physics but also offers proper action plans based on data and research done to help solve the healthcare problem.

When epidemiologists want to study a disease, they focus on all factors contributing to the spread of the disease and conduct a descriptive research that is able to answer the questions of who, how much, when, among who?, of the healthcare problem so that change can be made (CDC, 2020).

An example of a healthcare problem currently being studied is the Covid-19 pandemic, epidemiologist have carried out research on the risk factors, spread and the necessary action plan needed to help curb the pandemic. They have focused on quantitative and descriptive data that has shown high mortality rates are to those with diabetes, hypertension, and heart problems and with those necessary changes in the healthcare system have been made. Not only has the study helped to educate the public but also has helped given the healthcare professionals an idea of the changes needed using the data collected to help fight the pandemic.

Reference:

Center for Disease Control and Prevention (2012-2020) Epidemic intelligence Service, retrieved from https://www.cdc.gov/eis/field-epi-manual/chapters/Describing-Epi-Data.html

I agree with you, Covid-19 is a great example and the most recent event that epidemiologists continue conducting public health surveillance, collecting, analyzing, and interpreting health data. Epidemiology data plays a big role during the Covid-19 pandemic, one of the epidemiologist’s roles is to estimate the impact of the disease or other health outcomes on the population (CDC.org, n.d.). 

And as you mentioned, epidemiologists are focusing on quantitative and descriptive data that allows them to calculate, incidence (number of new cases reported over a specific period of time), Prevalence (number of cases at one specific point in time), hospitalizations (number of cases resulting in hospitalization), deaths (number of cases resulting in death). Also, it is important to mention that public surveillance is also helpful and it uses to create epidemiological models to predict where, how long, and how far a disease will spread (CDC.org, n.d.) Very interesting post. Thank you for sharing.

 References

Coronavirus Disease 2019 (COVID-19). (2020, February 11). Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/science/about-epidemiology/monitoring-and-tracking.html

Study limitations include the retrospective design with a potential interviewer or recall bias and uncertain validity of the data regarding the type and duration of symptoms. Moreover, the data were collected during the heyday of the first pandemic wave as part of infection control and containment measures, precluding a thorough planning of the interviews.

Also, the follow-up interview could not be conducted within a fixed time frame for each individual but was performed if at least 6 weeks had passed since the reported onset of symptoms, potentially resulting in variation in the timing of the data collected on symptom duration and state of recovery. The data on symptom duration may not be entirely generalizable because mild cases may have been more likely to be contacted than severe cases. As in other studies relying on reported infections, an uncertain number of a- or oligosymptomatic cases may have been missed.

Furthermore, exposure patterns and testing modalities might have changed during the course of the outbreak, such that hospitalizations were more likely to occur at the beginning of the pandemic even in mild cases, whereas PCR testing was initially more restrictive due to a lack of laboratory capacities.

In conclusion, the Regensburg outbreak was characterized by relatively low numbers of cases and fatalities, particularly in elderly patients and those with COVID-19 risk factors. By comparison, the outbreak affected a relatively large proportion of younger individuals. COVID-19 showed a variety of symptoms and varying symptom duration, some of them lasting for weeks. Further prospective research is needed to clarify and confirm the presented data.

References

WHO Coronavirus disease (COVID-19) – Weekly Epidemiological Update. 6 September 2020: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200907-weekly-epi-update-4.pdf?sfvrsn=f5f607ee_2. Accessed 10 Sept 2020

Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395(10223):507–13. https://doi.org/10.1016/S0140-6736(20)30211-7.

Covid-19 data is and will continue to be a very interesting topic for researchers and data collectors.  The epidemic produced such a large response at local, state, and federal levels which produced a large amount of data not only on mortality and infection rates but on disparities among various communities.  A May 2020 article noted conducted a retrospective cohort analysis using California’s Sutter Health’s integrated EHR and noted that from January 1, 2020-April 8, 2020 non-Hispanic African Americans were 2.7 % times more likely to be hospitalized when compared with non-Hispanic White patients.  (Azar et al., 2020)

This EHR data is very interesting because, at that time, we were still in the early stages of the Covid-19 pandemic and shutdown.  It would be interesting to see the trend of this data using the same EHR system and if the recognition of disparities was addressed. (Azar et al., 2020)

On a larger scale, an article published in 2022 also conducted a retrospective data analysis using Kaiser Permanente’s western region (Colorado, Northwest, Washington) to follow up on March-Sept 2020 data which indicated continued US health inequities among Asians, Black /African Americans, Hispanic, Indigenous American and Alaskan Natives.  (Shortreed et al., 2022)

This information and analyzed EHR data expose the continued challenges with epidemiology in the healthcare system requiring more changes in healthcare practice.  

References:

Azar, K. M. J., Shen, Z., Romanelli, R. J., Lockhart, S. H., Smits, K., Robinson, S., Brown, S., & Pressman, A. R. (2020). Disparities In Outcomes Among COVID-19 Patients In A Large Health Care System In California. Health Affairs, 39(7), 10.1377/hlthaff. https://doi.org/10.1377/hlthaff.2020.00598

Shortreed, S. M., Gray, R., Akosile, M. A., Walker, R. L., Fuller, S., Temposky, L., Fortmann, S. P., Albertson-Junkans, L., Floyd, J. S., Bayliss, E. A., Harrington, L. B., Lee, M. H., & Dublin, S. (2022). Increased COVID-19 Infection Risk Drives Racial and Ethnic Disparities in Severe COVID-19 Outcomes. Journal of Racial and Ethnic Health Disparities, 10. https://doi.org/10.1007/s40615-021-01205-2