1. Acceptability and preferences for HIV self-testing in Zambia: a population-based formative study using a discrete choice experiment
Arianna Zanolini
American Institutes for Research
and Centre for Infectious Disease Research in Zambia
Joint work with Jenala Chipungu, Samuel Bosomprah, Charles Holmes, Mazuba Mafwenko, Michael Vinikoor, Harsha Thirumurthy
Thank you for inviting me here today. I am presenting on behalf of this collaborative team and would like to acknowledge 3IE, who funded the study.

2. Background
In Zambia, only 37% of men and 46% of women tested in the past 12 months. Similar rates in other countries in SSA.
HIV self-testing (HIVST) is gaining consideration in many countries in SSA
Recent research promising results and high acceptability of HIVST
More evidence is needed on optimal delivery models for HIVST
In late 2014, Zambia’s Ministry of Health requested formative local research around acceptability and preferences for HIVST
In Zambia, only 37% of men and 46% of women tested for HIV in the past 12 months-far from our 90% target. To increase testing rates in SSA, HIV Self-testing is gaining more and more consideration especially as research from the region is showing promising results and high acceptabilility. More research, however, is needed around optimal delivery models for HIVST. In this context, the Zambia Ministry of Health requested formative local research around acceptability and preferences for HIV Selftesting-since at that time HIVST was still only being considered by the government, the research could not make use of actual HIVST
(1min)

3. Methods
We conducted a mixed methods study in Zambia’s most populated province to assess preferences for HIVST
Quantitative part of the study
Structured questionnaires were administered to a representative sample of households in Lusaka Province
Two-step sampling procedure was chosen to include urban and peri-urban districts
We selected with probability proportional to size 17 CSA, and in each CSA targeted approximately 100 households randomly.
In each household selected, we randomly chose one household member from the household roster.
Questionnaire included assessment around acceptability, preferences, understanding of instructions, linkage to care
Respondents received basic explanation of what Self-testing is
I am going to be discussing the quantitatyive component of this mixed method study. We administered a survey to Lusaka province by first selecting CSA, then randomly selecting 100 households and (40sec)

4. Discrete choice experiment
A section of the questionnaire included a discrete choice experiment (DCE) to understand preferences for various ways of delivering HIV self-tests
DCE: series of questions where we ask participants to choose between models
DCEs can help us eliciting preferences when we can’t observe the demand curve from real-life choices
Based on revealed preferences: when we choose A over B we “reveal” that we prefer A to B
30 sec

5. DCE- An example question
OPD Pharmacy
Chemist
VCT/ART
No Counseling
Counseling
10 Kwacha
25 Kwacha
Free A B
C-regular
DCE- An example question
Here is an example:
Imagine you had these 3 options available for your HIV testing. Which one would you choose?
Full factorial design, then blocked into 2 versions
Each participant received a version with 9 questions
(1 min)
Non-testers had a “no testing” option as their opt-out rather than regular testing.

6. Results

7. Participant characteristics
All
(N=1617)
Female
(N=970)
Male
(N=647)
Age (mean)
28.9
28.5
29.5
Employment status
Employed for wages
23%
14%
37%
Self-employed with business or farm
28%
29%
Unemployed/not looking for work
49%
59%
33%
Income level
<70US$/month
21%
70-15 US$/month
US$/month
44%
34%
55%
>600US$/month 7% 5% 8%
Education
No secondary school
20%
24%
16%
Jr Secondary school
32%
31%
Sr. Secondary or higher
47%
Rural setting
27%
Positive HIV Status, self-reported
12%
11%
Perceived high or moderate HIV risk
41%
42%
Here some participant characteristics. I will skip the demographic and just highlight that 12% of our sample self-reports being HIV positive, and 40% of our sample considers being at high or medium risk of HIV infection.

8. HIV testing history of participants
The HIV testing statistics are encouragingly very similar to the DHS ones for that province, with only 38% of men and 53% of women having tested in the past 12 months.

9. Acceptability of HIV self-testing
74% of the participants felt very comfortable at the idea of using a self-test and 64% thought that their partner would feel the same, 87% said that a self-test would make them more likely to test,and thought that their friends would feel similar. 76% of those who have not tested in the past year said that they would test if a self-test was given to them for free. These are summary data but when we analyze acceptability for different strata we get fairly similar results.

10. Acceptability-concerns
Any concerns at all with HIV Self-test?
35%
If yes, which one?
Suicide 8%
Lack of post-counseling and mental health 6%
Lack of linkage to care 4%
Validity of the test
Lack of behavioral post-counseling advice
Partners’ violence 3%
Forcing people to test 2%
Cost 1%
Other
How severe is this concern?
No concerns
65%
Relatively minor
Important but can be addressed
25%
Very severe. No self-testing in Zambia
35% of participants had some concerns about self-testing, and when we asked which one without prompting for specific categories, we find that the most common concerns are around suicide and lack of post-counseling mental health and advice. However, only 2% of all of the participants thought that these concerns were too severe to not want self-test in Zambia.

11. DCE main results
Attributes with positive coefficients mean that participants preferred options with this attribute
When we look at these coefficients, it is important to note that these represent a change in utility and since utility is not a cardinal concept, these coefficients do not have a cardinal interpretation but just an ordinal one.
You can see that almost all of the attributes are significant and therefore having an impact on the probability of choosing an alternative. A positive coefficient to a binary variable means that participants prefer the presence of that attribute-for example, they want counseling. The coefficient represent the change in utility of moving from a scenario with for example, no counseling to a scenario with counseling, everything else constant; a negative coefficient means that participants prefer a lower level of that attribute; for example, they want lower cost. The lower cost is an obvious result, but for example counseling is not. Throughout the models and throughout all of the possible available stratifications, participants have a strong preference for counseling. This result was confirmed in the qualitative research too.
In addition to looking at the sign, we can look at the absolute value. Counseling is the coefficient with the highest value, and therefore the one single attribute with the highest impact on utility, everything else being constant.
Since we are considering cost as a continuous variable, there is one more thing we can do with these results. As I said before, each one of these coefficients represent a marginal change in utility- the coefficient for cost represents the change in utility for a 1 kwacha additional cost of the self-test. If we calculate the ratio between the coefficients of any other attribute and of cost, we get the marginal rate at which participants are willing to trade one unit of that attribute with one unit of cost. In other words, we can get their willingness to pay for having that attribue.
In addition, we can see that testers don’t really like the idea of picking up their test at the chemist.
Coefficients show the change in utility in moving from having no information
(i.e. not being screened) to having simple information (b1) or
It is 2.5US$ for rural pops and 5 US$ for high income
Coefficient represent a marginal change in utility of, for example, moving from a scenario with no counseling to an equal scenario with counseling, everything else constant.

12. Willingness to pay for HIVST
Marginal willingness to pay= /-0.07 = 24 Kwacha = 3 USD
5 USD

13. Conclusions
HIV Self-testing highly acceptable in Lusaka Province, Zambia, across different demographics
Promising for non-testers, 75% of whom report willingness to test and who reveal stronger preferences for self-testing than testers
Strong, positive preference for counseling.
In our simple model, we estimated a willingness to pay of 3US$ for regular testers and 5 US$ for non-regular testers.
Ideal to complement these data from other WTP-based on real buying choices to estimate demand
Highly acceptable in Lusaka province, Zambia across differen demographics
Strong preference for counseling-actually the one factor that people care the most
Willlingness to pay for