HLT 362 Describe how epidemiological data influences changes in health practices

HLT 362 Describe how epidemiological data influences changes in health practices

HLT 362 Describe how epidemiological data influences changes in health practices

The study of the causes and root causes of disease within a society is referred to as epidemiology. Within the realm of health practices, this kind of data is put to use in order to trace the genesis of an epidemic or other form of health problem. In order to find a solution to the problem, epidemiologists collect data on symptoms, the results of medical exams, laboratory testing, and recent therapies, and they review patient medical records. The data revolutionizes medical practice by providing an explanation of the health status, identifying risk factors, and analyzing relationships between health and a variety of potentially harmful substances (Gulis & Fujino, 2015). Take, for instance, an infection that occurs in a hospital. determining the source of the infection by doing qualitative and quantitative research in order to achieve a high level of precision. In this study, the effects of the hospital population as a whole are investigated as opposed to the illness triangle’s component parts—the agent, the host, and the environment. The data collected by the epidemiologist are then translated into therapies that seek to cure the condition or, at the very least, slow down the course of the illness. The information and the intervention are the primary components used in the process of transforming healthcare practices for the purpose of improving the quality of patient outcomes and maintaining their safety.

Reference

Gulis, G., & Fujino, Y. (2015). Epidemiology, population health, and health impact assessment. Journal of epidemiology25(3), 179-180. https://www.jstage.jst.go.jp/article/jea/25/3/25_JE20140212/_article/-char/ja/

Per the CDC, epidemiology is the study of the causes and frequency of health-related states and events within a specified population (What is epidemiology? 2016). Research produces epidemiological data when it examines health related information and produces results which are considered generalizable to the general public, or at least categories of the public. Since this data can be applied to the general public, it is used to guide health care professionals when considering risk management and treatment options. However, because this data is considered generalizable, it is only meant to serve as an informational foundation; it is critical for health care professionals to still consider the specific risks and benefits that each individual may experience when considering treatment options for patients (Hannaford & Owen-Smith, 1998).

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A link to an example epidemiology research study is provided below. This study, Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis, reviewed 8,131 ICD-9 codes from hospital events, along with the patient demographic data and clinical variables, and developed algorithms capable of learning how to predict stoke events and stoke subtypes. The algorithms’ basis for this capability is the analysis of epidemiological data. The algorithms were strengthened by analyzing such a broad spectrum of patient data. The performance of the algorithm was verified and determined by stoke physicians through comparison of the algorithms’ results with the stroke classifications of ICD-9 codes. Through analysis of epidemiological data, the outcomes of this study will allow the algorithm to be used to predict stroke diagnosis in patients through genetic and genomic studies (Ni et al., 2018). The change in practice that would come about from this data, is that patients who meet certain epidemiological criteria would be encouraged to participate in genetic screening/testing to assess their risk for stroke.

Link to study: Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis | PLOS ONE

 

References

Centers for Disease Control and Prevention. (2016, June 17). What is epidemiology? Centers for Disease Control and Prevention. Retrieved August 23, 2022, from https://www.cdc.gov/careerpaths/k12teacherroadmap/epidemiology.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. BMJ316(7136), 984–987. https://doi.org/10.1136/bmj.316.7136.984

Ni, Y., Alwell, K., Moomaw, C. J., Woo, D., Adeoye, O., Flaherty, M. L., Ferioli, S., Mackey, J., De Los Rios La Rosa, F., Martini, S., Khatri, P., Kleindorfer, D., & Kissela, B. M. (2018). Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis. PLOS ONE13(2). https://doi.org/10.1371/journal.pone.0192586

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

HLT 362 Describe how epidemiological data influences changes in health practices
HLT 362 Describe how epidemiological data influences changes in health practices

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 biology16(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