1. NHISSA Data Analysis NATIONAL HEALTH INFORMATION SYSTEM DIRECTORATE 06 July 2016

2. Reporting timelines and roles and responsibilities

3. NATIONAL LEVEL MONTHLY 50 days after reporting period National import, validation & saving on server completed Feedback 60 days after reporting period to provinces PROVINCIAL LEVEL MONTHLY 45 days after reporting period Provincial import, validation & export completed Feedback in 5 days to district level DISTRICT LEVEL MONTHLY 30 th : District level import, validation & export completed Feedback in 5 days down to sub-district level SUB-DISTRICT LEVEL MONTHLY 20 th : Sub-district level capturing, import, validation & export completed Feedback in 5 days down to facility level FACILITY LEVEL DAILY 1. Collect data during each patient/client contact 2. Validate data 3. Calculate sub-totals 4. Capture data (selected facilities) WEEKLY Interim aggregation & validation MONTHLY 1 st : Validated clinician/service point summary to facility manager 5 th : Validated facility summary submitted for capturing 10 th :Facility level capturing, validation and export to sub-district level completed

4. 4 a)Distributed system, Microsoft Access – size limitations b)Different versions of Office in provinces - all not supported c)Effects on Data Quality 1)Human error during importing and exporting of data affects 2)Reporting discrepancies at different levels 3)OrgUnit standardisation and maintenance challenges 4)QR time lines, dataflow 45 days to NDoH – NDoH manager access 50-60days 5)Updates e.g. NIDS to each facility – inconsistent implementation d)Inefficient a)DHIS1.4 recombination, DQI and feedback 50 days / month b)Some instances set up in DHIS2 – HPV campaign (mobile capture), RIPDA, PEC, NTSG – costly to maintain two systems Rest of South Africa is still using DHIS 1.4

5. Data Challenges outlines  Data inconsistence  Gaps / Incompleteness  Outliers  Violation of validation Rules  Performance indicators

6. Data Inconsistency  Data need to be consistence at all times for proper decision making.  There are of course obvious reasons why the data will not be consistence but those reasons need to be accounted for in the form of comments.  Data inconsistency example Data Inconsistency.xlsxData Inconsistency.xlsx  1 st and 2 nd Recombination TROA report Troa Analysis All Facilities.xlsxTroa Analysis All Facilities.xlsx

7. Gaps / Incomplete reporting  This focuses on the data elements that need to be reported over a period of time. In this case April 2015 to December 2015  There is a different between zero (0) reporting and blank reporting.  Incomplete Reporting example Gap Analysis April_15 to Jan_16.xlsxGap Analysis April_15 to Jan_16.xlsx

8. Violation of Validation Rules  DHIS has build in rules to help identify data quality issues. There are two types of rules i.e. absolute validation and statistic validation.  Absolute validation means a number entered against 1 data elements cannot be greater than that data element which is compared with. E.g. ANC 1 st test can never be less than ANC 1 st test positive.  The following links shows the absolute validation for the period of April 2015 to March 2016 Abs_Violation Apri to March 2016.xlsxAbs_Violation Apri to March 2016.xlsx

9. Performance Indicators  Indicators are calculated using a Numerator / Denominator * 100, (1000),(100 000) depending on what is been measured.  In most cases the answer to the above calculations cannot be more than 100% but there are those that are more than 100% especially if the Denominator is population based.  BUT indicator like Antenatal Client initiated on ART CANNOT be more than 100%, if it happens that it is more then validation rule is been broken and needs verifications.  Performance indicator Example Data performance March 2016.xlsxData performance March 2016.xlsx