Mpumalanga HIV Data Triangulation and Use 4 Nov 2014
Transcript of Mpumalanga HIV Data Triangulation and Use 4 Nov 2014
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HIV Data Triangulation and Use
Nelspruit, Mpumalanga5-7 Nov 2014
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Welcome
• Please introduce yourself…
– Name
– Affiliation
– Role
– Say one expectation you have for this workshop
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Data Triangulation and Use in South Africa
• Jan 2013: 3 day workshop, 11 participants
– CDC/USAID
• July/Aug 2013: 2.5 day workshop, 18 participants
– 2 PDOH representatives each from: Eastern Cape, Gauteng, KZN, Limpopo, Mpumalanga, Western Cape,
– NDOH, ANOVA Health Institute, USAID, ACTSASA
• Today
– Pilot Data Use and Strategic Planning model for district and facility level audience
• Feb 2015
– TOT for 12 DSPs
• 2015-2016
– DSPs roll-out data triangulation model to 52 districts in South Africa
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Workshop goal & objectives
Goal: To achieve the goals of the National Strategic Plan 2012-2016 by assessing and mapping HIV program coverage and impact at province, district, sub-district and municipality levels in order to improve HIV programs through evidence based strategic planning.
Objectives1. To identify HCT program coverage strengths and gaps2. To identify HIV linkage to care strengths and gaps3. To encourage the use of data in service and resource planning at
the facility, sub-district, district and provincial levels.4. To improve the quality of reporting especially at the facility level
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Agenda
Day 1: – Introduction to data triangulation, Fusion Tables and fact sheets
Day 2: – Use Fusion Tables to answer specific objectives
– Identify strengths and gaps of HCT data and program within district based on data outputs and NSP
Day 3: – Develop evidence –based, actionable recommendations for the district
based on HCT strengths and gaps
– Determine next steps and action items for HCT priorities in the province
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Day 1
1. Introduction to HIV in Mpumalanga
2. Introduction to Data Triangulation and Use
3. Lecture guided work: Google Fusion Tables
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Day 1
1. Introduction to HIV in Mpumalanga
2. Introduction to Data Triangulation and Use
3. Lecture guided work: Google Fusion Tables
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Data Use Website
Resources and workshop materials stored here:
http://datause.ucsf.edu
Please complete the start of workshop survey
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Why Do We Spend So Much Time and Energy Collecting All This Data ?!
Strengthen M&E programs
Use evidence for decision making
Strengthen capacity of staff
Improve program planning and
resource allocation
Gain efficiency and
effectiveness
Improve data quality
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Data Is At The Center of M&E
DATA
Improve coverage, reach,
intensity of services
Improve quality of
data
Priority setting and resource
allocation
Accountability
But…..only if we review, discuss, interpret, and use it regularly! 10
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Why good data is important
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Facility Level • Serves as basis for planning and developing Interventions• Allows providers to identify patients/clients in need of services and/or referrals• Improves efficiency through administrative organization• Inventories resources and determines which supplies and medicines are available and which need to
be ordered when• Monitors and evaluates quality of care
Region/district level• Informs acquisition and distribution of resources
• Provides evidence for construction and/or expansion of facilities
• Explains human resource capabilities and challenges
• Assists with more precise budgeting
• Assists council authorities in planning interventions and monitoring those activities
• Demonstrates trends in calculated indicators used to estimate future changes
• Demonstrates trends in calculated indicators used to estimate future changes
National level• Informs policy • Assists in planning and assessing
various interventions to make strategic decisions about the improvement of those interventions
• Works towards meeting the overall national goal of reducing the burden of poor health
• Provides evidence towards meeting targets
• Provides the basis for M&E
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HIV Data Triangulation and Use Process
• A process for incorporating into program plans
– Where are we now?
• Examine data on the current epidemiology, program coverage and locations and costs
– Where do we want to go?
• Identify or refine program goals
– How do we get there?
• Establish a timeline and action steps for achieving program goals
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HIV Data Triangulation and Use Process
Surveillance
Program data
Surveys
Data Use Tool
Available data
Identify strengths and gaps
Program improvement
Questions of interest
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Day 1
1. Introduction to HIV in Mpumalanga
2. Introduction to Data Triangulation and Use
3. Lecture guided work: Google Fusion Tables
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Data Key and Indicator List
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Group Fact Sheets
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Fusion Tables
Data from: - DHIS - ANC- PIMS
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1. Preparing data for use in Fusion Tables2.1. Data Inputs
2.2.Importing data into Google Fusion Tables
2.3.Editing a dataset
2.4.Merging multiple datasets into one
2.5.Downloading a dataset
2.6.Calculating Formulas
2.7.Filters
2. Visualizing data3.1.Cards
3.2.Charts
3.2.1.Edit chart appearance
3.3.Maps
3.3.1.Edit map appearance
3. Final steps4.1.Creating additional outputs4.2.Accessing saved FusionTables4.3.Sharing Google FusionTables and Charts
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Question 1
• What is the test positivity rate in Mpumalanga by district?
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Question 2
• What is the HCT coverage in Mpumalanga by sub-district?
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National AIDS Control Program, Republic of Tanzaniahttp://www.nacptz.org/
Mapping data
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Mapping process
Geographic coordinate
data
Mapping software links geographic
data and coordinates
HIV indicator database aggregated by geographic level
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Produce a shape file or KMLdata file
Map is produced
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Mapping small group work
Please break up into groups of 3-4 people. Use
Google Fusion Tables to answer Questions 1-2
on your handout. All groups will present their
outputs to the larger group.
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Day 2
1. Results, interpretation and conclusions
2. Practice using Google Fusion Tables to answer specific workshop questions
3. Use data to identify strengths and gaps of data and program within district
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BASICS OF VISUALLY PRESENTING DATA
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Key Definitions
• Results: Simple description/observations of your results (who, what, where, when, magnitude, trend).
• Interpretation: Explanation of why your results may have occurred.
• Conclusion: the key message of your results, implications and the “action-plan” that you recommend based on your results.– The “Take Away”
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Nine elephants damaged storefronts on Market St
in Joburg in 2010, one elephant damaged a
store in 2013.
The number of elephants on in Pretoria has decreased
since 2010 because an elephant lover has started laying a trail of peanuts to
Kruger Natl Park
Citizens should be sensitized to encourage
elephants to play in Kruger Natl Park instead
of in Pretoria
Result Interpretation Conclusion
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RESULTS
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Presenting Data In Tables
• Tables may be the only presentation format needed when the data are few, relationships are straightforward and when display of exact values is important.
Table X. PEPFAR annual progress reporting, PMTCT indicators, FY12-13, Namibia
Indicator Estimate
Number of pregnant women that are tested or know their HIV status at ANC and L&D 62,142
Number of pregnant women with known positive status at entry to ANC or L&D 7,546
Number of pregnant women newly tested positive 4,251
Source: PEPFAR Annual Progress Report, Namibia 201328
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Bar Charts Are Useful to Show Simple Comparisons, Esp. Differences in Quantity.
55,097 57,219
70,025
2,659 (4.8%) 2,490 (4.4%) 2,546 (3.6%)
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
2009 -10 2010 -11 2011-12
# o
f w
om
en
or
par
tne
rs
Year
Fig. 7. Partner HIV testing among pregnant women attending ANC, Country X, 2009-10 to 2011-12.
Pregnant women attending ANC Partner tested for HIV
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Line Charts Are Good for Showing Change Over Time (Trend)
77%
87%91% 92% 91%
88% 88% 88% 87%
82%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
100%
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
% a
live
on
AR
T
Initiation cohort year
Fig. 8. Percentage of patients alive on ART at 12 months after initiation in Country X, by initiation cohort year.
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Bar and Line Charts Can Be Used Together to
Show Trends Of Several Related Indicators
0
5,000
10,000
15,000
20,000
25,000
0
5
10
15
20
25
30
35
2005 2006 2007 2008 2009 2010 2011 2012
# in
fan
ts e
xpo
sed
% in
fan
ts in
fect
ed
Year
Fig. 9. Estimated MTCT rate at 6 weeks and MTCT rate at 6 weeks including breastfeeding, Country X, 2005-2012
Number infants exposed MTCT rate (excluding breastfeeding infants)
MTCT rate including breastfeeding infants
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Est. no. HIV + per sq km
Maps show geographic relationships
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Figure title
• Be sure to include:
What (the indicator)• HIV prevalence • % circumcised • % alive on ART
Who• pregnant women age 15-49 • adults males age 15-49• pediatric ART patients
Where• in Namibia
• in Ohangwena region • at Engela Hospital Clinic
When• in 2012
• from 2008 to 2012
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0%
10%
20%
30%
40%
50%
60%
70%
2009-10 2010-11 2011-12
% d
istr
ibu
tio
n o
f A
RV
typ
e
Fig. 11. Distribution of ARV prophylaxes used for PMTCT among HIV positive pregnant women attending antenatal care in Namibia, 2009-10 to 2011-12.
Single-dose NVP Combination ARV HAART
Source: Namibia MOHSS (2012) Annual Implementation Progress Report for the National Strategic Framework (NSF) 2011/12.
What ?
When ?
Where ?
Who ?
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Presenting Data Tips (2)• All relevant information needed to interpret the table,
figure, or map should be included so that the reader can understand without reference to text (i.e. in a report)
• Clearly label your X and Y axes, format consistently (font, font size, style, position)
• Use data series legends /labels
• Make the scale appropriate for the findings you want to convey.
• Reference the source of your data
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2009-10 2010-11 2011-12
% d
istr
ibu
tio
n o
f A
RV
typ
e
Reporting period
Fig. 12. Distribution of ARV prophylaxes used for PMTCT among HIV positive pregnant women attending antenatal care in Namibia, 2009-10 to 2011-12.
Single-dose NVP Combination ARV HAART
Source: Namibia MOHSS (2012) Annual Implementation Progress Report for the National Strategic Framework (NSF) 2011/12.
Clear chart title
X-axis label
Y-axis label
Series legend
Data source reference
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X-axis label
Scale spans to 100% to display
complete picture
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Stratification of Data
• What is stratification?
– Dividing into subgroups
• What are common levels of data stratification?
– Year, age, sex, geographic region, facility
• Why do we stratify?
– Let’s look at stratification within the indicator:
• % of patients alive on ART 12 months after initiation
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What Do You Think About This Figure?
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Fig. 13. Percentage of patients alive on ART at 12 months after ART initiation.
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We Can Stratify By Time, e.g. Initiation Cohort…
77%
87%91% 92% 91%
88% 88% 88% 87%
82%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
100%
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
% a
live
on
AR
T
Initiation cohort year
Fig. 14. Percentage of patients alive on ART at 12 months after initiation in Country X, by initiation cohort year.
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We Can Stratify by Age Group
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
100%
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
% a
live
on
AR
T
Initiation cohort year
Fig. 15. Percentage of patients alive on ART at 12 months after initiation in Country X, by cohort year and adult vs. pediatric patients.
Adults Children
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We Can Stratify By Geographic Area
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
100%
2004 2005 2006 2007 2008 2009 2010 2011 2012
% a
live
on
AR
T
Initiation cohort year
Fig.19. Percentage of adult patients alive on ART at 12 months after initiation by cohort year and selected districts in Country X.
District A District B District C
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We Can Stratify By Facilities WithinGeographic Areas
0.87 0.910.89
0.81
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
100%
2009 2010 2011 2012
% a
live
on
AR
T
Initiation cohort year
Fig. 20.Percentage of adult patients alive on ART at 12 months after initiation by selected facilities within District Q in Country X.
Q: Health Centre 1 Q: Health Centre 2Q: District Hospital District Q overall
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Females
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Three indicators for HIV testing by sex and province. Zambia. 2007
Males
We Can Stratify By Sex and Geography …
Source: DHS 2007
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INTERPRETATION
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Magnitude and Trend (1)
• Magnitude :
– the amount of coverage
– The size of the difference between sub-groups or time points
• Trend:
– the direction of change over time (i.e. increasing, decreasing, or remaining stable)
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Magnitude and Trend Statements (2)
“ From 1992 to 2002, HIV prevalence among pregnant women increased (trend) from 4.2% to 22% (magnitude).
After peaking at 22% in 2002 (magnitude), HIV prevalence has remained fairly stable from 2004-2012 (trend) at around 18-20% (magnitude).”
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Fig. 23. HIV prevalence among pregnant women receiving antenatal care at public facilities in Country X, 1992-2012
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Interpretation of Results
• Descriptive results are what you see, interpretation is how you see it.
• Why do you think your results are what they are? What are 1-2 possible programmatic explanations: – Programmatic/guidelines changes? (e.g. CD4 ART eligibility,
Option B+)– Increased/decreased access to services at facilities within
district/region?– Staff reductions? Staff trained in new areas (e.g. IMAI)– Are data missing from some time points, facilities, sub-groups?– Are there facilities or districts that are not reporting,
underreporting for this time period, or reporting data differently?
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Interpretation Statement (3)“ Retention in District A is declining much more rapidly compared to the national average. These declines may be related to the higher than average loss of ART doctors within this district, which may have effected access and quality of care. Alternatively, the observed trend in District A may be a result of incomplete data reported in the ePMS.
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50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
100%
2004 2005 2006 2007 2008 2009 2010 2011 2012
% a
live
on
AR
T
Initiation cohort year
Fig. 28. Percentage of adult patients alive on ART at 12 months after initiation by cohort year and selected districts in Country X.
District A District B District C
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CONCLUSIONS
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Drawing Conclusions (1)• Conclusions are the “take-away” message, i.e. what you want
your audience to remember and do after the presentation.
• Especially relating to programmatic implications of results.
• Conclusion can include the presenter’s recommendations for: • Program improvement
• Additional data verification/quality checks
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Conclusion Statement (2)“Patient and facility level factors predictive of patient loss that are unique to District A should be identified and corrected. Best practices from higher performing districts should be shared. Failure to do so may result in increased AIDS mortality and drug resistance in this district. The completeness of data from this district should also be confirmed to validate our results.
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50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
100%
2004 2005 2006 2007 2008 2009 2010 2011 2012
% a
live
on
AR
T
Initiation cohort year
Fig.31. Percentage of adult patients alive on ART at 12 months after initiation by cohort year and selected districts in country X.
District A District B District C
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Day 3
1. Present HIV program outputs and strengths and gaps identified
2. Develop evidence –based, actionable recommendations based on strengths and gaps
3. Determine next steps and action items for:
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DSP Roll-out
• Demonstrated ability of district-level HIV program and strategic information staff to: – Develop and interpret graphs and maps to identify HCT
program coverage and HIV linkage to care strengths and gaps
– Appreciate and use data for HIV service and resource planning at the facility, sub-district, and district levels
– Report higher quality data, especially at the facility level
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Closing
THANK YOU! 54