Steps of EBM data analysis Interpretation and calculation of Sensitivity, Specificity, Predictive values, Prevalence and Incidence

1. Analyzing Data in
Health Care
Dr. Majdi N. Al-Jasim
SBFM, ABFM
Consultant Family Medicine
PCFCM - AlAhsa

2. Scenario
56 year-old woman came to you to discuss her
mammogram study result. Her result was
positive and so she asked you “Oh, do I have
breast cancer doctor?”
How will you respond to this question?

3. Continue…
You searched the literature and you found that the
prevalence of breast cancer in KSA in 2015 was 14% among
women age 45-59 years old. Further more, you knew that
the sensitivity of mammogram is about 98% at this age
group while the specificity is 82%.
Can You answer the woman previous question now?
YOU!

4. OBJECTIVES
1. To discuss the general steps of data analysis in health care.
2. To discuss the important fundamental terms used in data
analysis of health problem, lab or imaging studies:
▪Prevalence, Incidence, Sensitivity, Specificity, Predictive
values.
▪Others! (will be covered in research course)

5. Steps of Data
Analysis
1: ASK
Formulate clinical
question
2: ACQUIRE
Searching the data-
bases
3: ORGANIZE
and display
4: ANALYZE
Interpret properly
5: APPLY
Take action

6. Ask
Should be in PICO format
(clinical question).

7. Ask
You were setting in the clinic where a post
bariatric surgery teen girl came to ask
you: “Since I had bariatric surgery, do I
need iron supplement?”
How can you formulate clinical question
(PICO)?

8. Ask
P Population Do teen girls
I Intervention Who had bariatric surgery
C Comparison Compared to those who did not had the surgery
O Outcome Need iron supplementation

9. Mother of 4 years old boy told you;
ibuprofen is better in reducing fever
than paracetamol.
A 55 year-old smoker for more than
10 years request from you to do MRI
chest to check if he has lung cancer,
but you think CT chest will be enough.
Ask
Ask PICO!
Ask PICO!

10. Ask
To identify the important
KEYWORDS in searching.

11. Mother of 4 years old boy told you; ibuprofen is better in
reducing fever than paracetamol.
▪ PICO: In 4 years old boy, does paracetamol compared
to ibuprofen better reduce fever?
▪ KEYWARDS: Boy, paracetamol, ibuprofen, fever
Ask

12. Acquire & Organize
Searching the latest literature
about the medical condition.
This includes pubmed data-
base, Cochrane data-base…

13. Analyze
Interpret the data presented
correctly and understand its
biostatistical purpose.

14. Apply
Apply your analyzed data on
your patient appropriately
(benefit overweight risk, and
cost effective).

15. Data Interpretation

16. Prevalence
What is prevalence?
It is the proportion of a particular population found to be
affected by a medical condition compared to all population
Importance of prevalence:
Gives an estimated magnitude and burden of the medical
problem within the population. This will help for future
health planning and resource allocation.

17. Prevalence
Types of prevalence:
Point Prevalence
A prevalence that occurs on specific time. Example the
prevalence of DM in KSA is 24% in 2015
Period Prevalence
A prevalence that occurs over period of time. Example
the prevalence of MersCoV in KSA is 0.4% from 2014 -
2017

18. Prevalence
Calculation:
Number of all cases
Total population
X 100

19. Incidence
What is incidence?
It is the proportion of occurrence of new medical condition
within population at risk at a given time.
Importance of incidence:
The incidence helps in studying risk factors within the
population.

20. Calculation:
Number of new cases
Population at risk
X 100
Incidence

21. Question #1A:
On October 2017 in a village of 10,000 population, you found that
250 people have HBV infection. You raised a campaign to screen for
HBV infection and you discovered 200 new cases.
Calculate prevalence?
Examples

22. Question #1A:
On October 2017 in a village of 10,000 population, you found that
250 people have HBV infection. You raised a campaign to screen for
HBV infection and you discovered 200 new cases.
Calculate prevalence?
Examples
Number of all cases
Total population
X 100

23. Question #1A:
On October 2017 in a village of 10,000 population, you found that
250 people have HBV infection. You raised a campaign to screen for
HBV infection and you discovered 200 new cases.
Calculate prevalence?
Examples
250 + 200
10,000
X 100 = 4.5%

24. Question #1B:
On October 2017 in a village of 10,000 population, you found that
250 people have HBV infection. You raised a campaign to screen for
HBV infection and you discovered 200 new cases.
Calculate incidence?
Examples

25. Question #1B:
On October 2017 in a village of 10,000 population, you found that
250 people have HBV infection. You raised a campaign to screen for
HBV infection and you discovered 200 new cases.
Calculate incidence?
Examples
Number of new cases
Population at risk
X 100

26. Question #1B:
On October 2017 in a village of 10,000 population, you found that
250 people have HBV infection. You raised a campaign to screen for
HBV infection and you discovered 200 new cases.
Calculate incidence?
Examples
200
10,000 - 250
X 100 = 2.05%

27. Question #2A:
On December 2011 in a city of 100,000 population, you found that
450 people have HIV infection. You raised a campaign to screen for
HIV infection and you discovered 300 new cases.
Calculate prevalence?
Examples

28. Question #2A:
On December 2011 in a city of 100,000 population, you found that
450 people have HIV infection. You raised a campaign to screen for
HIV infection and you discovered 300 new cases.
Calculate prevalence?
Examples
Number of all cases
Total population
X 100

29. Question #2A:
On December 2011 in a city of 100,000 population, you found that
450 people have HIV infection. You raised a campaign to screen for
HIV infection and you discovered 300 new cases.
Calculate prevalence?
Examples
450 + 300
100,000
X 100 = 0.75%

30. Question #2B:
On December 2011 in a city of 100,000 population, you found that
450 people have HIV infection. You raised a campaign to screen for
HIV infection and you discovered 300 new cases.
Calculate incidence?
Examples

31. Question #2B:
On December 2011 in a city of 100,000 population, you found that
450 people have HIV infection. You raised a campaign to screen for
HIV infection and you discovered 300 new cases.
Calculate incidence?
Examples
Number of new cases
Population at risk
X 100

32. Question #2B:
On December 2011 in a city of 100,000 population, you found that
450 people have HIV infection. You raised a campaign to screen for
HIV infection and you discovered 300 new cases.
Calculate incidence?
Examples
300
100,000 - 450
X 100 = 0.3%

33. During the previous HIV
screening campaign, you
noticed some negative results.
Can you safely tell these
patients that they are free
from HIV infection?

35. Sensitivity
The probability of the test to be positive in those
who have the disease [TRUE POSITIVE] to all
diseased cases.
Specificity
The probability of the test to be negative in those
who are healthy [TRUE NEGATIVE] to all healthy
cases.

36. Gold standard
(disease)
AbsentPresent
Positive
Index
test Negative
2 X 2 Table
True Positive (TP)
False Negative (FN)
False Positive (FP)
True Negative (TN)

37. 2 X 2 Table
Total
Gold standard
(disease)
AbsentPresent
a + bb (FP)a (TP)PositiveIndex
test c + dd (TN)c (FN)Negative
a+b+c+db + da + cTotal
SENSITIVITY
Sensitivity =
𝑎
𝑎 + 𝑐

38. 2 X 2 Table
Total
Gold standard
(disease)
AbsentPresent
a + bb (FP)a (TP)PositiveIndex
test c + dd (TN)c (FN)Negative
a+b+c+db + da + cTotal
SPECIFICITY
Specificity =
𝑑
𝑏 + 𝑑

39. SnNout SpPin

40. SnNout
In HIGHLY Sensitive test, a Negative result rules Out the diagnosis;
i.e. False Negative is minimal or null.
Total
Gold standard
(disease)
AbsentPresent
2057198PositiveIndex
test 1971952Negative
402202200Total
Sensitivity =
𝑎
𝑎 + 𝑐
=
198
200
= 0.99 𝑡ℎ𝑎𝑡 𝑖𝑠 99%

41. SpPin
In HIGHLY Specific test, a Positive result rules In the diagnosis; i.e.
False Positive is minimal or null.
Total
Gold standard
(disease)
AbsentPresent
2057198PositiveIndex
test 1971952Negative
402202200Total
Specificity =
195
202
= 0.965 𝑡ℎ𝑎𝑡 𝑖𝑠 96.5%=
𝑑
𝑏 + 𝑑

42. The probability of being diseased in individual with
positive test, compared to all positive tests.
Positive predictive valuePPV
The probability of being healthy in individual with
negative test, compared to all negative tests.
Negative predictive valueNPV

43. 2 X 2 Table
Total
Gold standard
(disease)
AbsentPresent
a + bb (FP)a (TP)PositiveIndex
test c + dd (TN)c (FN)Negative
a+b+c+db + da + cTotal
PPV
PPV =
𝑎
𝑎 + 𝑏

44. 2 X 2 Table
Total
Gold standard
(disease)
AbsentPresent
a + bb (FP)a (TP)PositiveIndex
test c + dd (TN)c (FN)Negative
a+b+c+db + da + cTotal
NPV
NPV =
𝑑
𝑐 + 𝑑

45. Total
Gold standard
(disease)
AbsentPresent
a + bb (FP)a (TP)PositiveIndex
test c + dd (TN)c (FN)Negative
a+b+c+db + da + cTotal
Sn Sp
PPV
NPV

46. Let’s go back to the scenario
56 year-old woman came to you to discuss her
mammogram study result. Her result was
positive and so she asked you “Oh, do I have
breast cancer doctor?”
How will you respond to this question?
PPV or SpPin

47. Continue…
You searched the literature and you found that the
prevalence of breast cancer in KSA in 2015 was 14% among
women age 45-59 years old. Further more, you knew that
the sensitivity of mammogram is about 98% at this age
group while the specificity is 82%.
Can You answer the woman previous question now?
Direct proportion
to PPV

48. During the previous HIV
screening campaign, you
noticed some negative results.
Can you safely tell these
patients that they are free
from HIV infection?
NPV or SnNout

49. Summary
Prevalence
The proportion of a particular population found to be
affected by a medical condition to all population.
Incidence
It is the proportion of occurrence of new medical
condition within population at risk at a given time.

50. Sensitivity
The probability of the test to be positive in those who
have the disease to all diseased cases.
Specificity
The probability of the test to be negative in those who
are healthy to all healthy cases.
Summary

51. PPV
The probability of being diseased in individual with
positive test, compared to all positive tests.
NPV
The probability of being healthy in individual with
negative test, compared to all negative tests.
Summary