1. Oral Health Training & Calibration Programme
Epidemiology-Calibration
WHO Collaborating Centre for Oral Health Services Research

2. Oral Health Clinical Survey
Oral Health Clinical Examination Tool
Dentate Status
Prosthetic Status and Prosthetic Treatment Needs
Mucosal Status
Occlusal Status
Orthodontic Treatment Status
Fluorosis Dean’s Index
Gingival Index
Debris and Calculus Indices
Attachment Loss and Probing Score
Tooth Status Chart
Count of Tooth Surfaces with Amalgam
Trauma Index
Treatment and Urgent Needs

3. Training and Calibration
Training for:
Dentate Status
Prosthetic Status
Mucosal Status
Fluorosis
Orthodontic Status
Orthodontic Treatment Status
Periodontal Assessments
Tooth Status
Amalgam Count
Traumatic Injury
Treatment Needs
Calibration for:
Fluorosis
Occulsal Status
Periodontal Assessments
Tooth Status
Amalgam Count
Magnification is not allowed for examinations

4. Calibration Objectives
Define Epidemiology - Index
Discuss Validity and Reliability
Examiner Comparability
Statistics
Calibration Inter and Intra Examiner

5. Suggested 4 Day Calibration Training
Day 2
Time
Chair 1
Chair 2
Chair 3
9:00-12:00
Classroom Session Presentations/Fluorosis Training
9:00-10:15
Statistics/Fluorosis
10:15-10:30
Break
10:30-11:45
Patient 4
Patient 5
Patient 6
11:45-12:00
Discussion/questions
12:00-1:00
Lunch
1:00-2:00
Patient 1
Patient 2
Patient 3
Patient 7
Patient 8
Patient 9
2:00-3:00
Discussion/Questions
3:00-3:15
Patient 10
Patient 11
Patient 12
4:15-5:00
Discussion/Fluorosis training
3:15-5:00
Discussion Fluorosis
This design is based on using the entire Oral Health Module and all of its indices.

6. Suggested 4 Day Calibration Training cont.
Day 3
Day 4
Time
Chair 1
Chair 2
Chair 3
9:00-10:15
Statistics Review
10:15-10:30
Break
10:30-11:45
Patient 13
Patient 14
Patient 15
Repeat*** 1
Repeat 2
Repeat 3
11:45-12:00
Discussion/Questions
Lunch
1:00-2:00
Patient 16
Patient 17
Patient 18
Repeat 4
Repeat 5
Repeat 6
2:00-3:00
Patient 19
Patient 20
Patient 21
2:00-2:30
2:30-3:15
Final Fluorosis Testing Statistical Review
3:00-3:15
3:15-5:00
Discussion Fluorosis as necessary
Discussion Questions Finish

7. ‘science upon the people’
Epidemiology
The study of the distribution and determinants of health related states or events in specified populations and the application of this study to the control of health problems.
‘Epi demos logos’ Greek:
‘science upon the people’

8. Measurement of Oral Disease
We use indices:
as a numerical expression to give a group’s relative position on a graded scale with a defined upper and lower limit.
as a standardised method of measurement that allows comparisons to be drawn with others measured with the same index.
to define the stage of disease; not absolute presence or absence.

9. Desirable characteristics of an index
Valid
Reliable
Acceptable
Easy to use
Amenable to statistical analysis

10. Prevalence
is the number of cases in a defined population at a particular point in time
describes a group at a certain point in time
similar to a snapshot in time
is expressed as a rate -x per 1000 population

11. Simple description of the health status of a population or community.
Descriptive study
Simple description of the health status of a population or community.
No effort to link exposures and effects.
For example:
% with caries
% with periodontal disease

12. Uses of a Prevalence Study
Planning
Targeting
Monitoring
Comparing
International
Regional

13. Validity and Reliability
Valid Yes
Reliable Yes
Valid No
Reliable No
Unbiased
Valid No
Reliable Yes
Valid No
Reliable No
Biased

14. Validity Success in measuring what you set out to measure
Being trained by a Gold Standard trainer ensures validity by
Training on what is proposed to be measured
Confirming that everyone is measuring the same thing -“singing out of the same hymn book”

15. Reliability
The extent to which the clinical examination yields the same result on repeated inspection.
Inter-examiner reliability:
reproducibility between examiners
Intra-examiner reliability:
reproducibility within examiners

16. Reliability
Calibration ensures inter and intra examiner reliability and allows:
International comparisons
Regional comparisons
Temporal comparisons
Without calibration
Are any differences real or due to examiner variability?

17. Examiner Reliability Statistics
Used when:
Training and calibrating examiners in a new index against a Gold Standard Examiner
Re-calibrating examiners against a Gold Standard Examiner

18. Examiner Reliability Statistics
Two measures used:
Percentage Agreement
Kappa Statistic

19. Percentage Agreement
Percentage agreement is one method to measure Examiner Reliability.
It means:
the percentage of judgements where the two examiners have agreed compared to the total number of judgements made

20. Example – Percentage Agreement
Percentage Agreement is equal to the sum of the diagonal values divided by the overall total and multiplied by 100.
Example – Percentage Agreement
Ex 2 A U E M
Total
Ex 1 18 15 4 5 24 2 12 9 23 7 16 35 28 30
100

21. Example – Percentage Agreement
Number of agreements = sum of diagonals
= 61
Total number of cases = overall total
= 100
Percentage agreement = 61%

22. Kappa Statistic
The Kappa Statistic measures the agreement between the evaluations of two examiners when both are rating the same objects.
It describes agreement achieved beyond chance, as a proportion of that agreement which is possible beyond chance.

23. Kappa Statistic
Interpreting Kappa
The value of the Kappa Statistic ranges from , with larger values indicating better reliability.
A value of 1 indicates perfect agreement.
A value of 0 indicates that agreement is no better than chance.
Generally, a Kappa > 0.60 is considered satisfactory.

24. Interpreting Kappa 0.00 Agreement is no better than chance
Slight agreement
Fair agreement
Moderate agreement
Substantial agreement
Almost perfect agreement
Perfect agreement

25. Kappa Statistic
The formula for calculating the Kappa Statistic is:

26. Example – Kappa Statistic
PO is the sum of the diagonals divided by the overall total.
Ex 2 A B C D
Total
Ex 1 18 15 4 5 24 2 12 9 23 7 16 35 28 30
100

27. Example - Kappa Statistic
PE is the sum of each row total multiplied by the corresponding column total divided by square of the overall total
Ex 2 A B C D
Total
Ex 1 18 15 4 5 24 2 12 9 23 7 16 35 28 30
100

28. Example - Kappa Statistic
Number of agreements = sum of diagonals = 61
Total number of cases = overall total = 100
PO = 0.61

29. Example - Kappa Statistic

30. References
Cohen J. A coefficient for nominal scales. Educational and Psychological Measurement 1960; 20:
Cohen J. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin 1968; 70: