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LITERATURE REVIEW

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Data sets are essential for accurate analysis (Dekker, 2021). Having data to measure against other data enhances one’s ability to understand the information. Data sets are an important component when conducting research (Dekker, 2021). Scientific research is based on the gathering and analysis of measurement data. Scientific datasets are intermediate results in scientific research projects (Dekker, 2021). Computers have brought substantial improvements

to technology that led to the production of data (Zia & Khan, 2017). The healthcare industry contains very large and sensitive data and can benefit society (Zia & Khan, 2017).

Improving the quality of care is a priority and main goal for healthcare organizations. Data comes from patients as well as patients’ health record. The information assists healthcare workers on developing prevention measures for healthcare concerns and enhance quality of care.

The United States has an epidemic of type 2 diabetes (Stevens, 2020). Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces (Chan, 2016). Diabetes is a health care concern (Chan, 2016). Diabetic Mellitus is a set of diseases in which the body is unable to control the quantity of sugar in the blood (Zia & Khan, 2017). It is a group of metabolic diseases which results in high blood sugar level, may be as the body does not produce sufficient insulin, or may because cells do not react to the produced insulin (Zia & Khan, 2017). The number of cases and the prevalence of diabetes have been increasing over the past few decades (Chan, 2016). An estimate of over 50% of the adult population will have diabetes by 2022 (Stevens, 2020). More than 90% of people with prediabetes are unaware and 25% of people with diabetes are unaware (Stevens, 2020).

Over the past decade, diabetes prevalence has risen faster in low- and middle-income countries (Chan, 2016). Diabetes caused 1.5 million deaths in 2012 (Chan, 2016). Increases the risks of cardiovascular disease (Chan, 2016). Many people with diabetes are affected by type 2 diabetes and occur in children now (Chan, 2016).

Diabetes causes a substantial economic loss to people with diabetes and their families (Chan, 2016). It affects the health systems and national economies through direct medical costs and loss of work and wages (Chan, 2016). Hospitals and outpatient care is the major expense factor, however; the rise in cost of insulins is a concern (Chan, 2016).

Even though type 1 diabetes can’t be prevented at this time, type 2 diabetes can with appropriate interventions. This is by implementing and adapting a healthy lifestyle at school, home and in the workplace (Chan, 2016). Exercising regularly, eating healthily, avoiding smoking, and controlling blood pressure and lipids (Chan, 2016). Education is key to success as in many areas. A whole-of-government and whole-of society approach, in which all sectors systematically consider the health impact of policies in trade, agriculture, finance, transport, education and urban planning (Chan, 2016).

The prevalence of diabetes in the United States is increasing rapidly (Grundy, Howard, Smith, Eckel, Redberg, & Bonow, 2020). Individuals with diabetes are at high risk for cardiovascular disorders that affect the heart, brain, and peripheral vessels (Grundy, Howard, Smith, Eckel, Redberg, & Bonow, 2020). The increasing of obesity and sedentary lifestyles, major underlying risk factors for type 2 diabetes in both developed and developing countries, predicts that diabetes will continue to be a growing clinical and public health problem (Grundy, Howard, Smith, Eckel, Redberg, & Bonow, 2020).

Collecting data from electronic health records, claims, and patient-reported outcomes is essential in diabetes (Centers for Disease Control and Prevention, 2018). The dataset contains patient vitals, family history, and laboratory values (Centers for Disease Control and Prevention, 2018). Obtaining information from physician office visits is another data set utilized for diabetes. It contains objective data on selected risk factors (e.g., age, sex, race, ethnicity), laboratory results, ambulatory health care services, pharmaceuticals, diagnoses, and a list of diseases drawn from the physician office electronic health record, including diabetes (Centers for Disease Control and Prevention, 2018). Risk factors to diabetes include family history, obesity, ethnicity, and abnormal lipids. Utilizing this information for data set may mitigate diabetes or enhance prevention measure. Data sets including socioeconomic determinants such as education level, occupation, income, ethnicity, sex, and age within a specific area is essential. Data reflecting the number of individuals diagnosed with type 2 diabetes between the age of 18 and 33. Data reflecting the education level of the individuals with diabetes between the same age group. Also, the education level of the parents of these individuals. The education level will help identify if diabetes may be associated with making healthy lifestyle choices. Data sets of ethnicity and sex will help determine if either groups are effected more than the other.

References

Centers for Disease Control and Prevention. (2018). Novel Approaches to State-level Diabetes

and Prediabetes Surveillance. https://www.cdc.gov/diabetes/research/modeling/index.html

Chan, M. (2016). Global Reports on Diabetes. World Health Organization.

https://apps.who.int/iris/bitstream/handle/10665/204871/9789241565257_eng.pdf;Sequence=1

Grundy, S., Howard, B., Smith, S., Eckel, R., Redberg, R., & Bonow, R. (2020). Diabetes and

Cardiovascular Disease Executive Summary Conference Proceeding for Healthcare Professionals.105:8 (2231-2239). https://doi.org/10.1161/01.CIR.0000013952.86046.DDCirculation

Stevens, S. (2020). The United States of Diabetes: Challenges and opportunities in the decade

ahead. UnitedHealth Group. https://www.unitedhealthgroup.com/content/dam/UHG/PDF/UNH-Working-Paper-5.pdf

Zia, U. & Khan, N. (2017). Predicting Diabetes in Medical Datasets Using Machine Learning

Techniques. International Journal of Scientific & Engineering Research. 8:5. https://www.ijser.org/researchpaper/Predicting-Diabetes-in-Medical-Datasets-Using-Machine-Learning-Techniques.pdf