Data collection Types

1. Indicators, Data
Sources and Data
Quality for TB M&E

2. Criteria of good indicators
 Valid: actually measure the phenomenon
it is intended to measure
 Reliable: produce the same results when
used more than once to measure precisely
the same phenomenon
 Specific: measure only the phenomenon it
is intended to measure
 Sensitive: reflect changes in the state of
the phenomenon under study
 Operational: measured with developed
and tested definitions and reference
standards

3. Qualitative vs. Quantitative
 Qualitative: answer questions about how
well the program elements are being
carried out.
 Quantitative: measures how much and
how many.

4. Factors in Indicator Selection
 What national district and local levels need
to know
 Availability of the data
 Availability of human and financial
resources to manage the data
 Program needs
 Lender requirements

5. TB data collection methods
and sources
 Routinely collected data
 Process monitoring and evaluation
 Program evaluation/reviews
 Global TB reporting
 Special surveys

6. Routine Recording
 TB Register
 TB-Treatment Card
 Laboratory Register
 Cough register

7. Routine Reporting
 District TB Register
 Quarterly report of new cases and
relapses of TB
 Quarterly report on results of treatment of
pulmonary-TB patients registered 12-15
months earlier

8. Process Monitoring and
Evaluation
 Analysis of recording and reporting
 Supervision
 Records of trainings held, meetings held,
events, etc…

9. Program Evaluation/Review
 Comprehensive review of the entire
program
 Conducted every 2 to 5 years
 External and internal experts break up into
groups and cover a representative sample
of the country
 Usually provides input for developing or
revising the medium term development
plan

11. Special studies
 Prevalence survey
 Population-based survey
 Facility surveys
 Vital-registration surveys
 Tuberculin surveys
 Drug-resistance surveys

12. Example of a national-level
data-collection system
2000 2002 2004 2006
Routine information system and surveillance
Facility
survey
Facility
survey
Prevalence
Survey
Prevalence
Survey
DRS DRS
External-Monitoring Visits
Facility
Survey

13. Why is data quality important?
 The primary function of health information
systems is to provide data that enhance
decision-making in the provision of health
services.
 By ensuring high-quality data, the health
information-system attempts to guarantee
that decision-makers have access to both
unbiased and complete information

14. Standards for good quality data
 Validity: Do the data clearly and directly measure
what was intended to measure?
 Integrity: Are mechanisms in place to reduce the
possibility that data are intentionally
manipulated?
 Precision: Are the data at the appropriate level of
detail?
 Reliability: Would you come to the same finding
if the data collection and analysis were
repeated?
 Timeliness: Are data available frequently enough
to inform decisions?

15. Impediments to good data
quality
 Inappropriate data-collection instruments
and procedures
 Poor reporting and recording
 Errors in processing data (editing, coding,
data entry, tabulating)

16. What can be done to improve and
ensure data quality?
 Keep the design of the information system as simple as
possible
 Involve users in the design of the system
 Standardize procedures and definitions
 Pre-test data collection instruments to make sure they
are useful and user friendly
 Ensure that data collected are useful to the data collector
 Regular supervision and feedback from supervisors
 Plan for effective checking procedures (such as cross-
checking)
 Training (data-collection instruments, data-processing,
analysis, and decision-making based on evidence)

17. Data-quality assessments
Example at district level:
 Step 1: Interview appropriate individual to
obtain understanding of data collection,
analysis, and maintenance process
 Step 2: Review reports to determine
whether they are consistent

18. Data quality assessments (con’t)
 Step 3: Periodically sample and review data for
completeness, accuracy, and consistency
 Indicator definitions are consistent with NTP
guidelines
 Data collection is consistent from year to year
 Data are complete in coverage
 Formula used to calculate indicator (if any) is applied
correctly
 Step 4: Compare central office records with
district or district with facility for consistency and
accuracy

19. Data quality assessments (con’t)
Possible data quality limitations
 Validity: The reported data do not accurately represent
the population. For example, records may over-report or
under-report certain parts of the population
 Integrity: The data could be manipulated for a variety of
reasons
 Timeliness: If reporting is not up to date, then decisions
not based on the most recent evidence
 Reliability: Implementation of data collection may be
irregular or mistimed

20. Conclusion
needed based on context/region
and what the biggest issue is