Presented at the 2017 American Evaluation Association Conference, Washington, DC, November 8-11, 2017

1. Impact Evaluation of a Project in
Ukraine to Strengthen Tuberculosis
Control
Siân Curtis (UNC-CH), Stephanie Mullen (JSI), Zulfiya
Charyeva (Palladium), Smisha Kaysin Agarwal (UNC-CH)
Presented at the 2017 American Evaluation Association
Conference, Washington, DC, November 8-11, 2017

2. Background
 National protocols exist for
screening for HIV at tuberculosis
(TB) facilities and for TB at HIV
facilities and for treatment of co-
infected patients
 Intervention aimed to improve
systems to implement TB-HIV
screening and treatment protocols
 Evaluation Question: To what
extent does the intervention
decrease time to testing and
treatment and improve survival
outcomes?
Ukraine (2016):
 TB incidence: 87 per
100,000 population
 TB-HIV co-infection:18
per 100,000

3. Evaluation Design
 Study conducted in 3 intervention and 3 comparison
oblasts
 Separate samples of patients initiating intensive
treatment in TB facilities and registering in HIV
treatment facilities
 Over-sampling of TB-HIV co-infected patients
 Extracted data from standard TB and HIV forms in
medical records
 Follow World Health Organization (WHO) basic
management unit TB register
 Baseline data collection in 2012; end line data collection
in 2016

4. Example: Testing and Treatment Cascade
for HIV Patients in 2012

5. Example: Time to ART Initiation among Co-
infected Patients in TB Facilities, 2012
Wald chi-square test: p < 0.001
0.000.250.500.751.00
0 200 400 600 800 1000
Time (days)
Comparison Oblasts Intervention Oblasts
Kaplan-Meier Survival Curves: HIV Testing

6. Lessons Learned Using Routine Data
 Using routine data doesn’t mean that there are no
data collection costs
 HIV and TB systems contain different variables – has
implications for analysis
 Patients can appear in both systems; can’t identify
duplicates
 System changed over time

7. Lessons Learned Using Routine Data
 There will be missing data and inconsistent data,
even in good systems
 Developing and documenting imputation rules and
other decisions on how to handle missing and
inconsistent data is essential
 Data cleaning takes time
 Data were better suited to some analyses (e.g.,
service cascades) than others (e.g., effect of services
on survival)

8. Conclusions
 We were able to use routine data in Ukraine
successfully to address our evaluation questions
 Planning, documentation, and flexibility were
important
 Ability to use routine data in evaluation will depend
on context and the evaluation questions
 Will work better for some kinds of questions than
others─not a substitute for other methods for all
questions

9. This presentation was produced with the support of the United
States Agency for International Development (USAID) under the
terms of MEASURE Evaluation cooperative agreement AID-OAA-
L-14-00004. MEASURE Evaluation is implemented by the
Carolina Population Center, University of North Carolina at
Chapel Hill in partnership with ICF International; John Snow, Inc.;
Management Sciences for Health; Palladium; and Tulane
University. Views expressed are not necessarily those of USAID
or the United States government.
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