Analyzing Data Improve Public Health

Running head: HEALTH STUDY 1

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HEALTH STUDY 2

Analyzing Data Improve Public Health: SARS

The success of understanding the epidemiology of a given disease outbreak depends on the

data and the analysis that the responsible authorities apply. When Data from a reliable and valid

public health research is properly analyzed and applied, the relevant specialists can effectively

estimate the disease epidemiology. The findings from the data provide information on the regions

affected, the rate of disease spread, the factors favoring disease spread, and the appropriate

interventions (Khoury & Ioannidis, 2014). Besides, the data helps the public health sector to

understand the factors that can hinder effective treatment, as well as the public response. Therefore,

using the case study of China and Toronto, analyzing data improves public health on severe

respiratory syndrome (SARS).

In the case of China, the Guangdong Province experienced a shocking outbreak of SARS

that infected nearly 8,000 citizens and killed about 774 in November 2002 (Yang et al., 2017). The

disease was fully contained by July 2003 after the intervention of World Health Organization, U.S

Centers for Disease Control and Prevention (CDC), the Chinese public health authority and other

external stakeholders. However, before the containment, the relevant authorities could not

evaluate exact epidemiology features of SARS, leading to a faster spread of the disease to Hong

Kong, Singapore, Vietnam, and Toronto (Yang et al., 2017). The quick involvement of the various

organizations and governments in sourcing for data and analyzing it to determine the solution for

SARS in China was the best in history.

Analyzing data was the primary factor for the finding of a quick solution to the disease. In

the early months of detection, the Chinese government refused to share vital data with the

international community. When the disease became severe and quick to spread, the WHO through

HEALTH STUDY 3

Global Outbreak Alert and Response network, influenced the CDC to create an emergency

response center. Immediately, CDC SARS website was created for the public to report cases of

the outbreak. Besides, CDC gathered data through media, press releases, live briefings, news

conferences, phone inquiries, and quarantine officers (Zhang & Wen, 2017). Therefore, through

the data gathered CDC quickly evaluated the areas affected, the rate of spread, the ways of

spreading SARS, the patients' progress, appropriate solutions to SARS, and the program impact.

Data analysis enabled the CDC, WHO and relevant authorities to reveal that SARS was

spread through contact with respiratory droplets or close person-to-person contact with an infected

person. The data analysis enabled CDC and WHO to detect the spread of the disease to Hong

Kong, Vietnam, and Singapore. Since the disease originated from China, the authorities were quick

to conclude the need to travel alerts and emergency response centers for quick quarantine of the

persons affected (Zhang & Wen, 2017). Therefore, from the data analysis, the appropriate public

health interventions were designed resulting in curbing the quick spread of SARS.

Analyzing data also enables quick detection of the factors that influence the spread of

disease. Through the data that CDC gathered from the public using varied platforms, the authorities

were quick to declare that SCoV, the SARS virus, could survive on plastic surfaces and human

wastes for 2-4 days (Zhang & Wen, 2017). The finding implied that sharing any material with an

infected person without close contact with the individual also spread the SARS. Such additional

information helped in improving public health since the healthcare workers were informed to take

additional precautions that sealed the remaining avenues of spreading the disease. Therefore,

through analyzing the data, the public health authorities managed to improvise effective measures

to curb the spread of SARS while the research teams were producing the medication.

HEALTH STUDY 4

In Toronto, the similar data from the public were applied to estimate the ways and rate of

disease spread, the areas affected, and the effective control measures. In the public health

intervention on SARS, the healthcare workers and authorities had to determine the demographics

of the persons affected. The age, places of residence, income, and gender of the persons affected

is crucial in improving public health. CDC analyzed the data on SARS and estimated that most

deaths were on children, while persons living in populated regions and with low-middle incomes

were the most affected (Zhang & Wen, 2017). Consequently, the precautionary responses and

treatment were prioritized on children and areas with high population. Further, the data findings

on incomes of the SARS victims enabled the governments and health organizations to estimate the

cost of treatment. Therefore, analyzing data played a significant role in improving public health as

evident in the case of SARS outbreak in China and Toronto; data was used to identify the regions

affected, the rate and ways of spreading, the preventing measures, and cost of action.

Analyzing Data Improve Public Health: Diabetes

Diabetes features in the top ten leading causes of death in the United States despite the

significant improvements made in the prevention and control. According to the CDC, Diabetes is

an endocrine system disorder that occurs when the body fails to generate sufficient insulin that

breaks down blood sugar. Despite Diabetes itself being manageable, it influences other fatal

diseases such as blindness, heart disease, kidney failure, and amputations, and early mortality. The

Type 1 Diabetes is the most severe since it majorly occurs in children and young adults, but the

management is costly with daily insulin injections and monitoring of blood sugar levels. Type 2

Diabetes, on the other hand, occurs mostly in adults but easy to manage through proper diets,

exercise, and occasional insulin injections. In the case of United States, the mortality rates due to

diabetes rose to 23 percent between 1990 and 2002 (Center, 2017). However, the state and

HEALTH STUDY 5

healthcare organizations invested in modern data technologies and analytics, which contributed to

improvements in public health; as a result, the mortality rates declined by 19 percent between 2002

and 2010.

Joslin Diabetes Center invested in Electronic Medical Record, laboratory reports,

pharmacy reports, and media platforms to gather large data on diabetes. The facility created a Big

Data Analytics system that collected and analyzed the data based on diverse patient variables and

disease epidemiology. Currently, Joslin Diabetes Center, the largest diabetes research center, and

the clinics have contributed to significant improvement in public health and control of diabetes.

Through analyzing data, the facility accurately determined that diabetes is prevalent in people aged

45-74 (Draznin et al., 2018). Similarly, the organization examined data and revealed that Blacks

and Hispanics are at the highest risks while Whites and Asians have lower cases. The data from

diabetic patients also revealed that the burden of the disease has increased by almost 15 percent

within the past two decades. Therefore, analyzing data improves the public health in diabetes since

it enables proper prediction of the risks and diagnosis.

Since analyzing data provides vital information for prediction of risks and prompt

diagnosis, the cost of public health on diabetes is reduced. Through the findings from the data, the

public health can easily identify vulnerable persons, perform early diagnosis and implement

control measures before the severe stages occur. For instance, U.S has reduced mortality rates of

diabetes by nearly 25% between 2005 and 2015 due to information from the data analytics and

technology (Center, 2017). Therefore, analyzing data on age, blood glucose, blood sugar,

cholesterol levels, and family history of chronic illnesses has improved the control of diabetes

through easy detection of risks and diagnosis.

HEALTH STUDY 6

Additionally, analyzing data helps in the clinical decision support and practitioners

performance. In the case of Joslin Diabetes Center, the existing data on diabetes from the medical

tests, patients, and lab reports are applied when making decisions on the treatment and control

measures. Practitioners efficiently use the data analytics to categorize patient, detect current

medications, predict the best control measure, and the outcome (Draznin et al., 2018). Therefore,

the data analysis has enhanced decision making, treatment, and prescriptions to patients with

diabetes; this has lowered the mortality rates of the illness. Similarly, analyzing data resulted in

improved patient wellness in Joslin Diabetes Center.

The facility uses the findings from the existing data to determine the outcomes of the

treatment and implement appropriate measures to prevent the unwanted results. For instance, the

data on causes of deaths of persons with Type 1 diabetes helped the facility to categorize patients

and prescribe improved measures to prevent severe states. Public health has also improved as

diabetic patients have access to data that helps them optimize resources and predict future

outcomes (Center, 2017). In the case of Joslin Diabetes Center, patients use existing data to

determine effective control measures, diets, exercises, and the signs and symptoms that require

further attention. Therefore, analyzing data has improved public health on diabetes as evident in

the case of Joslin Diabetes Center.

References

Center, J. D. (2017). Joslin Clinic. (2016). Clinical nutrition guideline for overweight and obese

adults with type 2 diabetes, prediabetes or those at high risk for developing type 2 diabetes.

HEALTH STUDY 7

Draznin, B., Kahn, P. A., Wagner, N., Hirsch, I. B., Korytkowski, M., Harlan, D. M., & Gabbay,

R. A. (2018). Clinical Diabetes Centers of Excellence–A Model for Future Adult Diabetes

Care. The Journal of Clinical Endocrinology & Metabolism.

Khoury, M. J., & Ioannidis, J. P. (2014). Big data meets public health. Science, 346(6213), 1054-

1055.

Yang, S., Wu, J., Ding, C., Cui, Y., Zhou, Y., Li, Y., & Ruan, B. (2017). Epidemiological features

of and changes in incidence of infectious diseases in China in the first decade after the

SARS outbreak: an observational trend study. The Lancet Infectious Diseases, 17(7), 716-

725.

Zhang, M., & Wen, J. (2017). SARS Time Series Modeling and Spatial Data Analysis. DEStech

Transactions on Computer Science and Engineering, (case).

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