3. The Effect of Economic
Fluctuation on Acute
Cardiovascular Syndromes
Iris Rukshin
4. Introduction
Cardiac events in New Jersey after the September 11, 2001, terrorist attack
Long-term effects of the Niigata-Chuetsu earthquake in Japan on acute myocardial
infarctions mortality: an analysis of death certificate data
5. Background
• Psychosocial stress affects the viscosity and clotting
properties of blood.
• The INTERHEART study found stress to account for
more than 30% risk increase for MIs and strokes.
• Logically, among the leading causes for chronic stress
are long-term financial troubles.
• Psychosocial stress, such as anxiety
and depression, is known to elevate
during economic failure or uncertainty.
6. Experimental Design Diagram
Alternative Hypothesis (H1):
Null Hypothesis (H0):
Independent Variable:
Index
Number of Trials:
Dependent Variable:
Operational Definition of
the Dependent Variable:
variable
Constants:
hospitalized.
There is a significant correlation between economic fluctuation
and recorded hospitalization records for myocardial infarction
and stroke-like symptoms.
There is no significant correlation between economic
fluctuation and recorded hospitalization records for myocardial
infarction and stroke-like symptoms.
Economic Fluctuation measured by the Dow Jones
There are no levels for bivariate data.
1637 daily record which were eventually condensed into 78
comprehensive trials.
Note: There is no control for this experiment.
Recorded hospitalization for myocardial infarction and strokelike symptoms
Bivariate data will be collected based on individual
hospitalization cases with the recorded hospitalization for
myocardial infarctions and strokes as the dependent
and the economy as the independent variable.
Country and general region in which patients were
Hospital from which data was obtained.
Differences in age, sex, and comorbidities were negligible.
7. Difficulties
• Issue: Isolating the
variables affecting the Characteristic
incidence of MIs and
strokes.
• Solution: The data pool Mean Age
Female/Male
was tested for standard Ratio
deviations in sex, age, Hypertension
and co-morbidities and
Diabetes
revealed that these
factors contributed no Dyslipidemia
significant difference.
LowHighIncome
Income Area
Area Group
Group
(n = 990)
(n = 477)
P-value
69.5
70.1
0.50
0.86
0.81
0.58
452
(45.7%)
194 (40.7%)
0.07
365
(36.9%)
154 (32.3%)
0.09
378
(38.2%)
161 (33.8%)
0.10
8. Difficulties
• Issue: Accounting for the lag in the
manifestation of stress and the public
awareness of the economy.
• Solution: The data was segregated into
months. These monthly increments would
account for the time it took for the stress to
take effect on the body. They also eliminated
the assumption that each patient monitors
the Dow Jones Index daily.
9. Difficulties
• Issue: Defining a “stable” and “unstable”
economy.
• Solution: By calculating the month-to-month
differences in statistics, the need to define an
economy which causes stress and one that
does not was removed.
10. Difficulties
• Issue: Determining whether the correlation is
truly linear.
• Solution: Residual plot analysis and ANOVA
tests revealed that the data had a linear
correlation and a Pearson’s correlation test
could be used.
11. Methodology
Data Collection:
• Collect deidentified data for stroke patients and myocardial infarctions from the
Raritan Bay.
• Collect the Dow Jones Index for each day from January 3, 2006 to July 2, 2012.
Data Organization:
• The Dow Jones Index data is formatted as a number with 2 columns: Date and Index.
Highlight both columns so that they remain paired and select Data > Sort > Ascending.
This will sort the indexes in chronological order.
• Sort the hospitalization dates in ascending order manually into a column.
• Once all the data is in chronological order, the dates that repeat are those on which
more than one hospitalization occurred. The dates which repeat must be lined up in a
row. The amount of repetitions there are for a certain date is equivalent to the amount
of times that date that should go across.
• After data is sorted as described above, manually go through all of the admission dates
and compare them to the dates for each index. Match the dates and record the
multiplicity of date.
• Finally, segregate the data into separate sheets by months starting with January 3,
2006 to February 3, 2006. This method will provide approximately the same amount of
days per month. On each sheet, there should be one column with the date, another
with the Dow Jones Index, a third with myocardial infarction admissions each day, and
a fourth for stroke admissions each day.
12. Data Analysis
•
•
•
•
•
•
•
•
•
•
Graph the data for each month so that there are 3 graphs per month: one for the chronological
Dow Jones Index and the other 2 for the chronological occurrences of strokes and MIs.
Calculate the average Dow Jones Index, the total amount of MIs, and the total amount of stroke
admissions for each month.
Document the monthly Dow Jones average and MI and stroke sums in a table along with a column
for the month.
Calculate the month to month difference in the Dow Jones Index, myocardial infarction admissions,
and stroke admissions.
Graph a scatter plot for myocardial infarctions and another for strokes so that the x-axis will be the
monthly change in the Dow Jones Index and the y-axis will represent the number of cases for
myocardial infarctions
Graph a line of best fit (trend line) by selecting Chart > Add Trend line > Linear.
Display the R² value and equation of the trend line by selecting Chart > Add Trend line > Options >
Display equation on chart > Display R-squared value on chart.
Take the square root of the R² value displayed to obtain the R value of the data.
Compare the R value of the data to the critical value or the R value that is required in order to
dictate a significant difference. This critical value is 0.22 and is located under the Two-Tailed
column for N = 0.05.
Record observations and conclusions.
13. Data Analysis
Pearson’s correlation coefficient was used. This test basically determines the
covariance of two variables divided by the product of their standard deviations. This
is the formula for a correlation test of a population. However, my test was unique in
that I had the sample of a population. So, by substituting covariances and variances
into the population formula, another formula for a sample is derived.
Where:
are the standard score, sample mean, and sample standard deviation.
14. Findings
Change in Dow Jones Index v. Change in MI Admissions
20
Total Monthly Admissions
15
10
5
-1,500.00
-1,000.00
-500.00
0
0.00
-5
500.00
1,000.00
-10
-15
Change in Monthly Dow Jones Index Average
2
R = 0.0528
R = 0.2298
15. Findings
Change in Dow Jones Index v. Change in Stroke Admissions
5
Total Monthly Admissions
4
3
2
1
-1,500.00
-1,000.00
-500.00
0
-10.00
500.00
1,000.00
-2
-3
-4
-5
Change in Monthly Dow Jones Index Average
2
R = 0.0689
R = -0.2625
18. Conclusion
Based on the analysis, a possible correlation may exist. It is
inaccurate to provide a definite conclusion in either direction.
However, it is possible to declare that the data I have collected
and analyzed suggests that there may be a significant
correlation between the fluctuation of Dow Jones Index and the
amount of hospitalization cases for myocardial infarction and
stroke-like symptoms.
19. Applications
This study suggests that there may exist an association between
the economy and cardiovascular health which might indicate a
higher risk of acute myocardial infarctions and strokes during
periods of economic instability. Policy makers and social
program planners should be aware of such an increased risk for
acute MIs and strokes during periods of economic chaos in
order to design interventions to lessen the adverse impact of
economic disorder on cardiovascular diseases.
20. Bibliography
Feng, Jianwei, Daniel J. Lenihan, Marcella M. Johnson, Vandana Karri, and C.V.R. Reddy.
"Clinical Cardiology Volume 29, Issue 1, Article First Published Online: 5 DEC
2006."Cardiac Sequelae in Brooklyn after the September 11 Terrorist Attacks. Clinical
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"Heart Disease and Stroke Statistics—2011 Update."Circ.ahajournals.org. American Heart
Association, 15 Dec. 2010. Web. 19 Mar. 2012. Keefer, Amber.
"Top 10 Causes of Stress." LIVESTRONG.COM. LIVESTRONG.COM, 4 May 2011. Web. 18
May 2012.
Leor, Jonathan, and Robert A. Kloner. "The Northridge Earthquake as a Trigger for Acute
Myocardial Infarction." The American Journal of Cardiology 77.14 (1996): 1230232. Www.sciencedirect.com. The American Journal of Cardiology, 1 June 1996. Web.
19 Mar. 2012.
"Stress and Heart Attack Risk." WebMD. WebMD. Web. 19 Mar. 2012. "Table of Critical
Values for Pearson Correlation – N (not Df) Is in Column 1."N.p., n.d. Web. 17 Dec.
2012.
"The Top Ten Causes of Death." www.who.int. World Health Organization, Feb. 2007. Web.
16 May 2012. Subramanian SV, Kawachi I. Income inequality and health: what have we
learned so far? Epidemiol Rev. 2004;26:78-91.
van Oeffelen AA, Agyemang C, Bots ML, Stronks K, Koopman C, van Rossem L, Vaartjes I.
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Editor's Notes
Cardiovascular disease is the leading cause of death in the United States.
It is estimated that approximately 1.34 million people in this country suffer a fatal or nonfatal myocardial infarction (MI) each year.
Strokes and other cerebrovascular diseases are the second leading cause of death with 0.77 million deaths per year.
Together, myocardial infarctions, strokes, and other diseases of their nature, called Acute Cardiovascular Syndromes, claim approximately 2.11 million lives.
Cardiovascular disease is the leading cause of death in the United States.
It is estimated that approximately 1.34 million people in this country suffer a fatal or nonfatal myocardial infarction (MI) each year.
Strokes and other cerebrovascular diseases are the second leading cause of death with 0.77 million deaths per year.
Together, myocardial infarctions, strokes, and other diseases of their nature, called Acute Cardiovascular Syndromes, claim approximately 2.11 million lives.
