Analyzing Data Improve Public Health

Running head: HEALTH STUDY 1
Analyzing Data Improve Public Health
Student’s Name
Institutional Affiliation
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
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.
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
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.
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.
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.
Draznin, B., Kahn, P. A., Wagner, N., Hirsch, I. B., Korytkowski, M., Harlan, D. M., & Gabbay,
R. A. (2018). Clinical Diabetes Centers of ExcellenceA 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-
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-
Zhang, M., & Wen, J. (2017). SARS Time Series Modeling and Spatial Data Analysis. DEStech
Transactions on Computer Science and Engineering, (case).

Place new order. It's free, fast and safe

550 words

Our customers say

Customer Avatar
Jeff Curtis
USA, Student

"I'm fully satisfied with the essay I've just received. When I read it, I felt like it was exactly what I wanted to say, but couldn’t find the necessary words. Thank you!"

Customer Avatar
Ian McGregor
UK, Student

"I don’t know what I would do without your assistance! With your help, I met my deadline just in time and the work was very professional. I will be back in several days with another assignment!"

Customer Avatar
Shannon Williams
Canada, Student

"It was the perfect experience! I enjoyed working with my writer, he delivered my work on time and followed all the guidelines about the referencing and contents."

  • 5-paragraph Essay
  • Admission Essay
  • Annotated Bibliography
  • Argumentative Essay
  • Article Review
  • Assignment
  • Biography
  • Book/Movie Review
  • Business Plan
  • Case Study
  • Cause and Effect Essay
  • Classification Essay
  • Comparison Essay
  • Coursework
  • Creative Writing
  • Critical Thinking/Review
  • Deductive Essay
  • Definition Essay
  • Essay (Any Type)
  • Exploratory Essay
  • Expository Essay
  • Informal Essay
  • Literature Essay
  • Multiple Choice Question
  • Narrative Essay
  • Personal Essay
  • Persuasive Essay
  • Powerpoint Presentation
  • Reflective Writing
  • Research Essay
  • Response Essay
  • Scholarship Essay
  • Term Paper
We use cookies to provide you with the best possible experience. By using this website you are accepting the use of cookies mentioned in our Privacy Policy.