Discussion: 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.

Discussion: 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.

Discussion: 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.

 Discovering Relationships and Building Models: Epidemiological Data

In the current health practice, providers rely heavily on data to make appropriate practice changes. Most changes focus on improving health and ensuring that patients and populations live healthily. Epidemiological data is among the widely used data sources in health care settings. It provides information on the distribution and determinants of health-related events and conditions in specified populations (Fontaine, 2018). Through epidemiological data, health care providers can adequately understand the causes, patterns, and other particulars of diseases and other health-related events. The purpose of this paper is to describe how epidemiological data influences practice changes, examples, and the necessary data for guiding practice change.

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How Epidemiological Data Influences Changes in Health Practices

Health practices in health care organizations are designed to address specified populations’ health needs. For instance, an area where riots and accidents are common compels health care organizations to design special units and programs to respond to related emergencies. In places where people consume a lot of fast foods, health care organizations experience increased visits of children and adults with lifestyle diseases like obesity and diabetes (Singh et al., 2021). In response, facilities design programs such as health education and community visits to reduce such infections.

In field epidemiology, health care professionals collect data from investigations and vital statistics. Such data provides insight regarding health events like diseases, injuries, and environmental hazards count rates and patterns in terms of place, people, and time (Fontaine, 2018). Interpreting patterns and comparing them to the desired situations inform health care professionals about possible changes in the type and numbers of patients expected in hospitals and other facilities. The potential increase in patients triggers preparations such as increasing staff, adopting appropriate technologies, and reorganizing how health care teams function.

Examples of Practice Changes

Since the outbreak of the COVID-19 pandemic, health care professionals and researchers have explored many features of the pandemic, including transmission, distribution in populations, death rates, and prevention measures. One of the common findings was the need for minimal physical contact and travel, leading to the adoption of telehealth to facilitate remote patient monitoring and support (Monaghesh & Hajizadeh, 2020). The pandemic also fueled an unexpected increase in patients in the emergency departments forcing health care facilities to employ temporary staff and assign nurses new roles. Kim and Kim (2021) further observed that health practitioners required adequate provision with protective personal equipment (PPEs) and basic education to reduce transmission and help patients readily and more professionally. The rise in patients and uncertainty about when the pandemic will end increased anxiety, worry, and burnout among nurses. Most organizations responded through psychiatric support, command centers to improve communication, and opportunities for self-care (Rose et al., 2021). The changes have been instrumental in supporting health and improving mental and physical preparedness to cope with the pandemic.

Necessary Data for Practice Changes

Descriptive data is the most appropriate for practice change. It involves organizing, inspecting, and interpreting data to understand current and historical patterns. Through descriptive data, health care providers can accurately dissect a public health concern like the COVID-19 pandemic into its component parts. Other uses include identifying at-risk populations, measuring the progress of control measures and their effectiveness, and generating testable hypotheses (Fontaine, 2018). The data helps health care providers to prepare effectively and make appropriate, data-driven interventions.

Conclusion

Epidemiology involves studying patterns, causes, risk factors, and other crucial elements of health-related events. The data generated guides health care professionals in implementing evidence-based control measures to protect populations from adverse effects. The study and management of the COVID-19 pandemic exemplify the professional application of epidemiological data to protect the public’s health and safety. Control measures dominating the pandemic’s management include telehealth, using protective equipment, and mental health support for nurses and patients.

References

Fontaine, R. E. (2018). Describing epidemiological data. Centers for Disease Control and Prevention. https://www.cdc.gov/eis/field-epi-manual/chapters/Describing-Epi-Data.html

Kim, J., & Kim, S. (2021). Nurses’ Adaptations in Caring for COVID-19 Patients: A Grounded Theory Study. International Journal of Environmental Research and Public Health, 18(19), 10141. https://doi.org/10.3390/ijerph181910141

Monaghesh, E., & Hajizadeh, A. (2020). The role of telehealth during COVID-19 outbreak: A systematic review based on current evidence. BMC Public Health, 20(1), 1-9. https://doi.org/10.1186/s12889-020-09301-4

Rose, S., Hartnett, J., & Pillai, S. (2021). Healthcare worker’s emotions, perceived stressors and coping mechanisms during the COVID-19 pandemic. Plos One, 16(7), e0254252. https://doi.org/10.1371/journal.pone.0254252

Singh, S, A., Dhanasekaran, D., Ganamurali, N., L, P., & Sabarathinam, S. (2021). Junk food-induced obesity- a growing threat to youngsters during the pandemic. Obesity Medicine, 26, 100364. https://doi.org/10.1016/j.obmed.2021.100364