HLT 362 Topic 5 DQ 1 Describe how epidemiological data influences changes in health practices

Topic 5 DQ 1

Feb 27-Mar 1, 2023

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.

Submitted on:

Feb 28, 2023, 1:10 PM

Amber Jones

Mar 1, 2023, 8:36 PM

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

Mirna Garcia

replied toAmber Jones

Mar 2, 2023, 6:23 AM

Hello Amber!

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

 

• Elda Pierre

replied toMirna Garcia

Mar 2, 2023, 8:36 PM

Amber Good job,

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.

 

• · Paula Hemmings

replied toAmber Jones

Mar 2, 2023, 10:52 AM

Hi Amber,

Although epidemiologists and direct health-care providers (clinicians) are both concerned with occurrence and control of disease, they differ greatly in how they view “the patient.” The clinician is concerned about the health of an individual; the epidemiologist is concerned about the collective health of the people in a community or population. In other words, the clinician’s “patient” is the individual; the epidemiologist’s “patient” is the community. Therefore, the clinician and the epidemiologist have different responsibilities when faced with a person with illness. For example, when a patient with diarrheal disease presents, both are interested in establishing the correct diagnosis. However, while the clinician usually focuses on treating and caring for the individual, the epidemiologist focuses on identifying the exposure or source that caused the illness; the number of other persons who may have been similarly exposed; the potential for further spread in the community; and interventions to prevent additional cases or

 

 

References

• 1. Last JM, editor. Dictionary of epidemiology. 4th ed. New York: Oxford University Press; 2001. p. 61.
  2. Cates W. Epidemiology: Applying principles to clinical practice. Contemp Ob/Gyn 1982;20:147–61.
  3. Greenwood M.Epidemics and crowd-diseases: an introduction to the study of epidemiology, Oxford University Press; 1935.

 

• Kristine Rivera

replied toAmber Jones

Mar 2, 2023, 8:18 PM

Hi Amber,

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

Epidemiology is the method used to find the causes of health outcomes and diseases in populations. In epidemiology, the patient is the community and individuals are viewed collectively

Epidemiologic data are paramount to targeting and implementing evidence-based control measures to protect the public’s health and safety. Nowhere are data more important than during a field epidemiologic investigation to identify the cause of an urgent public health problem that requires immediate intervention. Many of the steps to conducting a field investigation rely on identifying relevant existing data or collecting new data that address the key investigation objectives. In today’s information age, the challenge is not the lack of data but rather how to identify the most relevant data for meaningful results and how to combine data from various sources that might not be standardized or interoperable to enable analysis. Epidemiologists need to determine quickly whether existing data can be analyzed to inform the investigation or whether additional data need to be collected and how to do so most efficiently and expeditiously.

Epidemiologists working in applied public health have myriad potential data sources available to them. Multiple factors must be considered when identifying relevant data sources for conducting a field investigation. These include investigation objectives and scope, whether requisite data exist and can be accessed, to what extent data from different sources can be practically combined, methods for and feasibility of primary data collection, and resources (e.g., staff, funding) available. Sources of data and approaches to data collection vary by topic. Although public health departments have access to notifiable disease case data (primarily for communicable diseases) through mandatory reporting by providers and laboratories, data on chronic diseases and injuries might be available only through secondary sources, such as hospital discharge summaries. Existing data on health risk behaviors might be available from population-based surveys, but these surveys generally are conducted only among a small proportion of the total population and are de-identified. Although some existing data sources (e.g., death certificates) cover many disease outcomes, others are more specific (e.g., reportable disease registries).

Accessing or collecting clean, valid, reliable, and timely data challenges most field epidemiologic investigations. New data collected in the context of field investigations should be evaluated for attributes similar to those for surveillance data, such as quality, definitions, timeliness, completeness, simplicity, generalizability, validity, and reliability. Epidemiologists would do well to remember GIGO (garbage in, garbage out) when delineating their data collection plans.

References

 

Centers for Disease Control and Prevention. (2018, December 13). Describing epidemiologic data. Centers for Disease Control and Prevention. Retrieved August 24, 2022, from https://www.cdc.gov/eis/field-epi-manual/chapters/Describing-Epi-Data.html

 

Gozzi, N., Perrotta, D., Paolotti, D., & Perra, N. (2020). Towards a data-driven characterization of behavioral changes induced by the seasonal flu. Ploss computational biology, 16(5), e1007879. https://doi.org/10.1371/journal.pcbi.1007879

 

Fujino,Y., Gulis G.,(2015). Epidemiology, Population Health, and Health Impact Assessment https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4340993/#!po=70.8333