1. School of Health Systems and Public Health Monitoring & Evaluation of HIV and AIDS Programs INDICATORS Wednesday March 2, Win Brown USAID/South Africa
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2. Objective:
Introduce the 4 stages in the data handling (information management) process & explain indicators.
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3. Learning Outcomes: Explain the concepts of data and information
Analyze the data handling process
Evidence-based decision making
Select indicators & critique indicators
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4. What’s Our Raw Material?
Data:
Information:
Knowledge:
Data Information Knowledge
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5. Types of Data Quantitative: Qualitative: Routine: Non-Routine:
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6. Routine Data Collection
Patient or Client Data: Let’s talk about the lowest level . . .
“Data Capture”
Who
How
When
Where
How are these data aggregated?
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7. Routine Health Information Management?
Monitoring & Evaluation of Routine vs. Non-Routine Information . . .
Which is more useful?
Why?
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8. Information Management Process
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9. Current Problems and the Way Forward
The Deadly Sin of Creating Parallel Structures in Reporting Systems
The “H” Word . . .
Alignment with Government Systems
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10. Ready for Indicators?
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11. What’s an Indicator?
A quantitative or qualitative variable (something that changes) that provides a simple and reliable measurement of one aspect of performance, achievement or change in a program or project.
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12. Why Indicators? Directing resources to areas where needs are greatest
Reducing large amount of data down to its simplest form
Measuring program or project towards targets and desired outcomes
Measuring trends over time
Providing a yardstick whereby organizations, facilities etc. can compare themselves to others doing similar work
Providing evidence for achievement (or lack of) of results and activities
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13. Slide # 13: The Evil Eye
Don’t say anything!
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14. Indicator Structure Numerator Denominator Both . . .
Numerators are the things we count: number of clients, infants immunised, new cases of TB, number of doctors.
Activities/interventions/events
a count of the event being measured
How many occurrences are there:
morbidity (health problem, disease)
mortality (death)
resources (manpower, money, materials)
Generally raw data (numbers)
Denominators are the group with which the thins we count are compared: total population, all births in a year, number of adults or clinics, total miles travelled, number of beds in the hospital
What group do they belong to:
general population (catchments / at risk)
gender population (male / female)
age group population (<1, >18, 15-44)
cases / events – per (still births, children weighed)
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15. Example 1 ? ________________
Percent of Individuals Initiating ART with a CD4 Count of Less than 50. ?
________________
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16. Example 2 ? ________________ ?
Total Number of OVC Reached During the Reporting Period.
? ________________ ?
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17. Example 3 ? ________________ ?
Total Number of Individuals Provided with Cotrimoxazole during the Reporting Period.
? ________________ ?
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18. Example 4 ? ________________ ?
Total Number of Family Members Reached with HIV-Related Palliative Care during the Reporting Period.
? ________________ ?
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19. Example 5 ? ________________ ?
Total Number of Individuals Counseled and Tested as a Couple.
? ________________ ?
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20. Don’t Let Indicators . . . Drive the Programme!
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21. SMART Indicators Specific Measurable Achievable Relevant Time-bound
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22. OPUDATA Objective Practical Useful for management Direct
Attributable to your project’s efforts
Timely
Adequate
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23. How Does An Indicator Get Selected?
Good Luck!
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24. Why Aren’t Managers Using Data?1. 2. 3. 4. 5.
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25. To Be Useful, Data Should Be…1. 2. 3. 4. 5.
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