This document provides an overview of data collection tools and reporting requirements for primary health centers (PHCs) in Nigeria. It discusses common data collection tools used at PHCs like daily reporting forms for antenatal care, postnatal care, family planning, and immunizations. It also lists the expected frequency and person responsible for various data reports. The document reviews concepts of data quality assurance including data cleansing, dimensions of data quality, and common data quality issues. It provides tips for validating routine data and best practices for data reporting, as well as examples of bad reporting practices. Contact information is given for a data reporting helpdesk.

1. DATA COLLECTION
TOOLS TRAINING
BY SAMINU LEWI
(DATABASE OFFICER )

2. OUTLINE
Data Capturing
Tools & PHCs’
Data reporting
requirements
Data Quality
Assurance
(DQA)
M&E best
practices/
Discussions

3. DATA COLLECTION
TOOLS (DCTS)

4. Data Collection Tools
• Data Collection tools devices/instruments
used to collect data, such as a
paper questionnaire or computer-
assisted interviewing system
• Data collection techniques include interviews,
observations (direct and participant),
questionnaires, and relevant documents.
• Relevant documents includes forms, registers,
summary sheets, etc.

5. Some MdM Documents
• Ante Natal Care Cards
• Ante Natal Care Daily Reporting Form
• Post-Natal Care Cards
• Post-Natal Care Daily Reporting Form
• Family Planning Cards
• Family Planning Daily Reporting Form
• Immunization Daily Data Collection Form
• MUAC Screening Daily Reporting Form
• Health Cards
• Prescription Booklet
• OPD Register
• ANC/PNC Register
• Family Planning Register
• PHC & Morbidity Daily Reporting Form
• Immunization Register
• Referral Booklet

6. Let try to list some of the
Data Collection Tools used in
our PHCs…..

7. Expected data reports from PHCs
Data reports Frequency Person(s) Responsible
ANC daily report ??? ???
PNC daily report ??? ???
FP daily report ??? ???
MUAC Screening daily
report
Weekly ???
PHC & Morbidity daily
report
Weekly ???
Immunization daily report Weekly ???

8. Ante Natal Care Daily
Reporting Form

9. Ante Natal Care Daily Reporting Form

10. Post Natal Care Daily
Reporting Form

11. Post Natal Care Daily Reporting Form

12. Family Planning Daily
Reporting Form

14. MUAC Screening Daily
Reporting Form

16. PHC & Morbidity Daily
Reporting Form

18. Immunization Daily Data Collection Form

19. Data Quality
Assurance (DQA)

20. • Data Quality Assurance is the process of data
profiling to discover inconsistencies and other
anomalies in the data, as well as performing data
cleansing activities (e.g. removing outliers, missing data
interpolation) to improve the data quality.
• Data Cleansing is the process identifying incomplete,
incorrect, inaccurate or irrelevant parts of the data and
then replacing, modifying, or deleting the corrupt or
inaccurate records.
DQA

21. • Helps make efficient and effective use of resources
• Helps improve accountability
• Helps improve program results
• Increases trust in data and in their use for decision
making
• Helps to be prepared for audit
Why Data Quality Matters

22. • Completeness data is considered “complete” when it
fulfills expectations of comprehensiveness.
• Validity is a data quality dimension that refers to
information that doesn’t conform to a specific format or
doesn’t follow business rules.
• Consistency If that information matches, it’s considered
“consistent.” For example, if your human resources
information systems say an employee doesn’t work there
anymore, yet your payroll says he’s still receiving a check,
that’s inconsistent.
• Timeliness Is your information available right when it’s
needed?
• Accuracy refers to the degree to which information
accurately reflects an event or object described. For
example, if a client’s age is 32, but the system says she’s
34, that information is inaccurate.
Dimensions of Data Quality

23. Common Data Quality Issues
• Incomplete reports

26. • Invalid entries

27. • Misplaced datasets

28. • Out of range entries
• Invalid entries
• Inconsistent reports
• Wrong date and epidemiological week entries
Common Data Quality Issues

29. Tips for Validating routine daily/weekly data
• Review data report after data entries (one dataset at a
time)
• Compare different sources of the same data for
consistently
• Ensure data subsets add up to the Universal total

30. Data reporting best practices
• Summarizing data reports

31. • Clear and readable writings
• Set time apart for data entry
Data Reporting best practices

32. Data reporting bad practices
• Invalid entries

34. • Invalid entries
• Skipping the 25th row
• Adding a 51st – 53rd entry on the second page of
a sheet
• Age entry with no denomination specified
Data reporting bad practices

35. Data Reporting Helpdesk
Falnyi Jesse Dauda
Data officer
08020733388
Ijasini Amos
Jesse
Data Officer
09026145353
Saminu Lewi
Data Officer (Team Leader)
09013665593

36. THANK YOU ALL
???