Data Use Community Meeting - July 21, 2020
HIV/AIDS Treatment Retention Outcomes: Field Perspective on Data Use
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Introductions
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HIV/AIDS Treatment Retention Outcomes: Experience from Kenya ���
Dr. Violet Oramisi
Strategic Information Team lead
National AIDS and STI Control Program, Kenya
The National Data Warehouse (DWH)��A data repository & analytics platform
As a data repository,
Stores monthly uploaded data from the EMRs in secure data marts located at the NASCOP servers
Has dashboards accessible openly to Program teams
Access controlled access to underlying data
As a data analytic & visualization platform,
Presents program data in interactive dashboards
Users can generate & run custom queries on the database to generate datasets for further analysis.
Has been used for preliminary analysis of MOH & PEPFAR priorities
A data source for HIV case based surveillance
HTS data
EMR
Testing
Care
Laboratory
Pharmacy
HTS app
HTS app
Key:
Data warehouse application interface client tool
DW
Encrypted PKV database
DWH Web Portal
Web Portal
HTS paper
Encrypted HIV clinical data
Contains both HIV- and HIV+
Data synchronization
Data entry from paper records
Central NDWH database
Records linking & merging
Data Analysis and Display Portal
High level data flow to DWH
Overall care & treatment reporting rate to the DWH
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EMR Sites�A total of 1,114 sites with EMRs�
eHTS Sites ��Total sites with eHTS 220
�
Coverage of PLHIV as at December 2019
52% (548,822) of all ART Patients have records in DWH
72% (835,644) of all ART Patients were seen at a site with an EMR
73% of ART Patients seen at EMR Sites had records in DWH
�TWELVE MONTHS RETENTION: THE 2018 COHORT ANALYSIS
Twelve months HAART outcomes, 2018 (N=110,657)
Retention by county (N=110,657)
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Overall retention (N=110,657)
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Retention by Diagnosis to HAART initiation (N=110,657)
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*Missing date of diagnosis (n=28,851 [26.1%])
HIV/AIDS CARE AND TREATMENT OUTCOMES �15 YEARS OF PEPFAR PROGRAM: THE KENYAN EXPERIENCE�
2004 – 2018
15 year Retention: Treatment outcomes, 2004-2018 (N=1,233,450)
*Outcomes as at 31st Dec 2018
Overall attrition (N= 1,233,450)*
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*Outcomes as at 31st Dec 2018
Rate: 11.2/100 pyos
Follow up Time | Beginning Total | Attrition | Survivor function |
0 | 1233450 | 0 | 1.000 |
3 | 644129 | 397289 | 0.660 |
6 | 351100 | 104871 | 0.5331 |
9 | 158440 | 0.438 | 0.429 |
12 | 45751 | 20101 | 0.3544 |
15 | 1766 | 2796 | 0.3125 |
Using a Sampling-Based Approach to Ascertain True Outcomes of Lost to Follow-Up Patients and Reasons for Patient Dropout among HIV Patients Enrolled in HIV Care and Treatment in Kenya��
2004 – 2018
Project Objectives
Method
Overall Tracing Outcomes (n=3,203)
Disengagement Category and Top Five Reasons (n=481)
Silent Transfer Category and Top Five Reasons (n=999)
Summary and Recommendations
Summary relevant to webinar….
Acknowledgement
Nigeria Data Use Cases July 21, 2020
Background to NDR
facility for the Government of Nigeria with support from PEPFAR
data push from EMRs
on the NDR
Primary EMR Data Sources for the NDR
Nigeria Medical Records System (NMRS)
Lafiya Management Information System (LAMIS)
Data Use Cases
Data Quality Assurance
Timeliness
Weekly NDR Upload Tracker
Consistency
Key variables on client level line-list from NDR against line-list from IPs EMR across randomly selected health facilities.
Validity
Monthly Treatment and Retention
Indicators concurrence check using external source (SAS etc)
Completeness
Monthly NDR error analysis.
Inbuilt data validation checks on NDR platform
Monthly NDR Data Quality Assessment across different dimensions of data quality
*This slide is using Font Awesome
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Data Use Case: Retention Analysis
Kaplan Meier curve: Cohort analysis of patients that started ART Q4FY19
Figure 1: Kaplan Meier curve showing Time to LTFU by Age group (September 2019)
Figure 2: Kaplan Meier curve showing Time to LTFU by Age group (October 2019)
Figure 3: Kaplan Meier curve showing Time to LTFU by Age group (November 2019)
Figure 4: Kaplan Meier curve showing Time to LTFU by Age group (December 2019)
Data Use Case: Retention Analysis
Data Use Case: Regimen Analysis
Data Use Case: Regimen Analysis - TLD optimization
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
January 2020
May 2020
June 2020
February 2020
TLD
March 2020
Adult 1st Line
April 2020
Adult 2nd Line
Data Use Case: HIV Recent Infection Surveillance
HIV recent infection surveillance aims to achieve epidemic control
and strategies
Data Use Case: Case-based Surveillance (CBS)
HIV Infection
-1st Positive confidential Confirmatory Test
1st CD4+ T-
Cell Count
1st Viral Load
Test
1st AIDS
defining Lab (<200 CD4+
Cell Count or
OI)
ART Regimen
CD4+and VL monitoring according to regulations
Death
Sentinel events
=
Case-based Surveillance
+
HIV diagnosis
“HIV case-based surveillance, is the ongoing, systematic collection, analysis, interpretation, and dissemination of information about persons in whom HIV and AIDS are diagnosed.”
Data Use Case: Mortality Surveillance
ART Clients
Missed Appointment/LTFU
Dead
Conduct Verbal Autopsy
Analyze VA
Instrument
Determine/Assign Cause of Death and enter into EMR/NDR
Analyze: Determine proportion HIV related mortality et. al
Report
HIV Infection
AIDS
Death
Mortality Surveillance
EMR
Viral Load Data
Care/case management Data
Linkage ART data
HTS data
Recency
Surveillance
95
95 95
Diagnosis Treatment Viral Load
Data integration into Data-to-Care activities:
NDR/CBS
Unique ID
95
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Acknowledgements
Special thanks to:
The NDR is supported by #NU2GGH001976
THANKS
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Field Perspectives on Data Use
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