Data Quality Improvement Activities NIGERIA. Dr. Ifeanyi Okoye September 2017

Transcription

1 Data Quality Improvement Activities NIGERIA Dr. Ifeanyi Okoye September 2017

2 Presentation Outline TX_CURR Validation using the RADET Validation for Other Indicators Impact on Program Plans Conclusion 2

3 TX_CURR Validation 3

4 Background Year 2013 Several APRs Several IPs EMRs Issues 9 years of PEPFAR activities in Nigeria Data quality across board How MANY patients were currently receiving ART in the Program??? 4

5 Reporting TX_CURR New Intakes Existing Patients Patient Exits (Dead, LTFU, TO, Stopped) Tracking the various stages (especially exits) Accurate reporting Validation of reports EMR 5

6 The Solution Easy to use tool Built with data quality in mind Validation rules and formatting Provides line list of patients Provides validated and verifiable results for all participating sites Flexibility for Quarterly Reporting Useful to both IP and USG The RADET Retention and Audit Determination Tool (RADET) 6

7 The RADET Process Tool Dev/Review Send to IPs/Feedback from IPs USGSI Data Validation IP Tool Review Feedback to IPs Result updates Data Cleaning / Analysis Data Collection (IP) Data Collation (USG) 7

8 Variables Collected Patient ID (ART or Hospital Numbers) Sex Age ART Start Date Last Pickup Date Months of ARV Refill Given Current Pregnancy Status (if Female) Viral Load Regimen information A separate sheet for each year data quality 8

9 Data Collection 9

10 Data Collection 10

11 Validation of Current ART Status Uses National Guidelines Facility Reports on Status of Patient e.g Active, LTFU etc. If ACTIVE, RADET validates based data entered Output on Summary Page for Reporting Calculates a validation factor 11

12 Summary Page (with Results Disaggregated by Sex and Age) Further data cleaning and analysis done using STATA 12

13 Why the RADET? Line list of all patients ever on ART Current pregnancy status for all female patients TX_CURR results by age and sex Year Total Started Total Current % Current ,335 1,542 66% ,369 9,391 54% ,409 15,972 49% ,400 26,939 53% ,550 44,285 54% ,804 50,904 58% ,235 53,884 60% Viral load results by age and sex Regimen analysis 13

14 RADET Validation by USGSI Random sample of medium and high volume treatment facilities taking into account agency, partner, and geographic location Random sample of patient charts at each facility reviewed for accuracy of report # Folders Sampled for Review # Patient Folders Reviewed # Verified Active on Treatment # Not Active on Treatment # of Folders Not seen % Verified Active on Treatment 5,172 5,048 4, % Preliminary Report of PEPFAR Nigeria Audit of Patients on ART at PEPFAR-Supported Treatment Sites in Nigeria

15 Number of Patients Initial Results: APR14 700,000 APR14 TX_CURR: Partner Reported Vs RADET Validated 600, , , , ,000 11% 300, , ,000 APR 14 RADET 14 15

16 Validation for Other Indicators 16

17 The Process Done as part of the RADET validation process Develop/review data validation protocol Scope Inclusion and exclusion criteria Sampling methodology for site selection Data collection Conduct of the exercise Develop/review data collection tools Paper based Electronic Get approvals Agency Leads OGAC RSO/Travel/Front Office 17

18 Expanded Scope of Data Validation Additional indicators validated at selected facilities HTS_TST HTS_TST_POS PMTCT_STAT PMTCT_ART TX_NEW Exercise conducted after Q2 reporting Compares report in DATIM to recount of registers at the facility Feedback immediately given to Facility and IP Data entry and analyses done a dedicated instance of DHIS2 18

19 Expanded Scope of Data Validation A sample of sites that failed validation are included in next round of DQA process to assess remediation Done as part of the multi-stage sampling Facilities that fail validation Remediation plan within a stipulated timeline Follow up on plan by IP Follow up on IP by Agency 19

20 Screenshot Data Entry Page for USG DQA System 20

21 Feedback and Remediation 21

22 Impact on the Program Use across several reporting cycles has demonstrated the usefulness of the RADET to both Partners and PEPFAR Nigeria High confidence in the TX_CURR numbers being reported from supported facilities in Nigeria Provides a verifiable means of showing accountability for resources utilized Addition of other indicators to validation exercise strengthens the emphasis on data quality Provides course correction for partners in preparation for APR 22

23 Challenges Coverage of the sites due to security issues Approvals from RSO Sites in some level 3 and all level 4 areas excluded from validation exercise The RADET data collection process is very intensive Data collation for the RADET is time consuming STATA: a blessing or a pain Requires computers with high-end resources (RAM, processing speed etc.) 23

24 Plans Include new age disaggregations on the tool Align in-country data validation plans with OGAC efforts to bolster data quality across country programs Get the GoN to come onboard using the RADET for GoN facilities In discussions with GF on the use of the RADET for GF facilities 24

25 Conclusion The RADET process has been extremely useful for IPs and USG The use of EMRs by all facilities will lead to less dependence on the RADET The overall validation process (TX_CURR and additional indicators) emphasizes the need for data quality with IPs Timing of exercise helps provide course-correction for IPs in terms of reporting 25

26 Questions? 26

27 Thank You 27