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Epidemiology and Social

Preferences for linkage to HIV care services following a reactive self-test

discrete choice experiments in Malawi and Zambia

d’Elbée, Marca; Indravudh, Pitchaya P.b; Mwenge, Lawrencec; Kumwenda, Moses M.b; Simwinga, Musondac; Choko, Augustine T.b,d; Hensen, Bernadetted; Neuman, Melissad; Ong, Jason J.e; Sibanda, Euphemia L.f,g; Johnson, Cheryl C.h,i; Hatzold, Karinj; Cowan, Frances M.f,g; Ayles, Helenc,k; Corbett, Elizabeth L.b,h; Terris-Prestholt, Ferna

Author Information
doi: 10.1097/QAD.0000000000001918

Abstract

Introduction

The HIV burden remains highly concentrated in southern Africa, with an estimated adult prevalence of 8.8% in Malawi and 13% in Zambia between 2014 and 2015 [1,2]. Despite substantial progress, there is a significant gap in HIV testing, with just 73 and 67% of HIV-positive individuals know their status in Malawi and Zambia, respectively [3]. HIV self-testing (HIVST), now recommended by the WHO, is defined as the process by which a person collects his/her own specimen, performs a test for HIV and interprets the results [4]. A reactive HIVST needs to be confirmed by a healthcare professional with referral to antiretroviral therapy (ART) services if HIV positive. HIVST has demonstrated high acceptability, though rates of linkage to confirmatory HIV testing and treatment have remained sub-optimal among self-testers [5,6].

Discrete choice experiments (DCEs) are a valuable way of measuring and quantifying user preferences for goods and services, particularly when there is a dearth of data around observed behaviour [7–9] and only limited service configurations are available. DCEs have been used in a myriad of health interventions in sub-Saharan Africa including the investigation of populations’ preferences for the design of sexual and reproductive healthcare services [10–12], including to inform the design of a voluntary medical circumcision programme [13] and guide of HIVST kit distribution [14]. This research uses a DCE to identify drivers of demand for linkage into confirmatory testing and care following a reactive HIVST in Malawi and Zambia.

Materials and methods

Sampling and data collection

The DCEs were nested within household surveys to evaluate the impact of community-based delivery of oral-fluid HIVST kits in Malawi (NCT02718274) and Zambia (NCT02793804) under UNITAID/Population Services International HIVST Africa (STAR) [15]. The DCE design is presented in the supplemental material, https://links.lww.com/QAD/B300. The DCEs were administered between June 2016 and December 2016 before community-based distribution of HIVST kits in rural Malawi (Blantyre, Machinga, Mwanza and Neno districts) and rural Zambia (Choma, Kapiri Mphoshi, Lusaka and Ndola districts). The questionnaires, programmed onto electronic tablets and administered by surveyors, captured data on sociodemographic background, HIVST history, previous HIV service utilisation and beliefs related to HIV. To administer the DCEs, interviewers explained each design attribute and level and provided a demonstration of the oral-fluid self-test. Each choice set included three alternatives: two for linking to care and one opt-out alternative described as ‘I would not link to confirm my HIV self-test results’. An example of a scenario exercise in Malawi and in Zambia is presented in the supplement Figure S1, https://links.lww.com/QAD/B300.

Statistical analysis

Given the similarities in cross-country DCE designs, the Malawi and Zambia datasets were pooled to test for differences in preferences across countries. Random parameter logit (RPL) models estimated the effect of the attributes on the choice made between the sets of alternatives [16]. The outputs coefficients of these models represent relative utility, that is the direction and relative magnitude of preferences for each attribute level. These quantitative utilities also allow for testing for heterogeneity, that is how the strength of preferences varied by observable respondent characteristics [17]. The log likelihood ratio test and Akaike's Information Criterion (AIC) were used for assessing model fit [16,18]. We interacted the attribute on separation of HIV services with the health facility and mobile clinic attribute levels given the applicability of these services only to clinic settings. Price values were converted using purchasing power parity metrics to equalize the value of Malawian and Zambian kwacha [19].

To explore observed heterogeneity in preferences, the model examined interaction effects between the attributes, the countries and sociodemographic and HIV-related variables. Sex and age were included to understand the systematic failure to link and underutilization of HIV services among men, young and old groups [6,20–22]. We evaluated the effect of direct/indirect costs as a demand-side barrier [17,22–24] to linkage to care by including a proxy for household socioeconomic status [25]. HIV status was examined to understand how the salience of the choice affects preferences. Given the policy relevance of treatment as prevention interventions, we assessed beliefs towards ART efficacy and included a five-item Likert scale response to the following statement: ‘I believe that HIV treatment makes people with HIV less infectious’ [26]. Data were analysed in Nlogit 5 software[27].

Results

Participant characteristics

Participants’ characteristics are presented in Table 1. In total, 941 participants completed the survey (59% in Malawi). Malawians had a higher percentage of respondents without formal education (P ≤ 0.001), higher household food insecurity (P < 0.001) and higher unemployment (P < 0.001) than Zambians. Malawi respondents had lower testing rates (P ≤ 0.001) and HIV positivity rate (P = 0.020), but fewer misconceptions around ART effectiveness (P ≤ 0.001) compared with Zambian participants.

T1-15
Table 1:
Study population characteristics in Malawi and Zambia.

Average preferences and differences between Malawi and Zambia

The results from the RPL model are presented in Fig. 1 wherein the bars represent the sample average preferences and the error bars indicate variation between countries. The full model outputs are presented in the supplement table S1, https://links.lww.com/QAD/B300. With high coefficient values and significant variation across countries, waiting time to access healthcare (Malawi: β = −0.50; Zambia: β = −0.34, P < 0.010) and payment of a testing fee (Malawi: β = −1.01; Zambia: β = −0.39, P < 0.010) were the most important factors for uptake of HIV care services. Preferences for separation of HIV services varied considerably by country suggesting the importance of the setting configuration on determining location-related preferences. In Malawi, participants had a stronger preference for separate HIV services at health facilities (β = 0.74, P < 0.050), whereas participants in Zambia preferred inclusive services (β = 0.38, P < 0.050). A similar effect was observed for mobile clinics. Participants also preferred confirming their test results at the counsellor's home (β = 0.15, P < 0.05); no significant result was found for linkage at the participant's home. The preferred method of support for linkage to care was receiving a phone call reminder (β = 0.162, P < 0.010) but, compared with other attributes, linkage support was relatively less important to respondents in both countries. Generally, there was a strong preference in favour of linking to care, estimated as -1*utility of the opt-out: β = −3.58 (P < 0.010) in Zambia and β = −5.57 (P < 0.010) in Malawi (supplement table S1, https://links.lww.com/QAD/B300).

F1-15
Fig. 1:
Random parameter logit - Main effects with differences by country (Model 2).The utility coefficients are averages per country and variation between the countries is in brackets. Instruction leaflet and mobile clinic (separate HIV services) are the omitted categories; therefore, variation by country could not be explored. Health facility and mobile clinic locations have either inclusive (all) or separated (sep) HIV services. *10%, **5%, ***1% level of significance.

Preferences by sociodemographic characteristics and HIV-related indicators

Preferences by subgroups were analysed on the pooled data. The pooled data set allows for testing for differences in preferences between countries. We report here the main effects of interest; the full model outputs can be found in supplement table S2, https://links.lww.com/QAD/B300.

There were no significant differences in preferences by sex. Older participants were less likely to link to care after a reactive self-test result (1-year age increment: β = 0.04, P < 0.010). Compared with HIV-negative respondents, self-reported HIV-positive respondents were more likely to link at health facilities with separate HIV services (β = 0.21, P < 0.100). Finally, participants reporting doubts about the effectiveness of ART were more likely to not link to HIV care services (β = 0.64, P < 0.010).

Discussion

Findings showed high testing fees and long waiting times for services were the most significant barriers to linkage to care in both countries. These two attributes were closely related and represent economic costs to the self-tester, as the time associated with utilizing services often represented opportunity costs. The adverse effect of direct and indirect costs on the utilization of HIV services were widely acknowledged [19,28,29].

Location of HIV care services and how they were provided also mattered. Overall, separated HIV services at health facilities had strong effects on choice of location, albeit in opposite directions in Malawi and Zambia. This suggests that fear of stigma and the need for privacy, which are known barriers to HIV service utilization [19,30,31], could manifest in different ways, through desire for discreetness with either physical separation or integration with other services. A study in Malawi showed an increase in ART initiations following provision of home-based confirmatory testing and ART assessment compared with use of referrals following self-testing [32]. Participants preferred to be followed-up after self-testing by the distributor through phone calls, similar to other HIVST studies [33–35]. Although previous literature demonstrated high rates of linkage and retention into care through an active referral, we found that support for linkage was the least important attribute and would have the smallest effect on encouraging self-testers to link [36–38]. We found a strong willingness to link into HIV care given the proposed service configurations. A community-based implementation study in Malawi reported that 77% of 16 660 participants shared their self-test results with distributors [6]. This underpins the need for HIVST services to provide support for linkage to onward services to facilitate the potentially high demand.

The analysis did not identify variation in preferences by sex; however, our findings show that a long waiting time is a major barrier to linkage, which represents high opportunity costs especially for adult men [29]. Although studies showed adolescents (16–19 years) have lower coverage of ART [19,22], we found higher willingness to link for younger individuals, and this is suggestive of unmet demand within existing services and the need for tailoring services. HIV-positive participants preferred to have separate waiting areas for HIV services at health facilities; issues of HIV-related stigma might resonate more with these participants who expressed a stronger desire for private services. Those sceptical of the health benefits of ART were less willing to link to care. Consistent with studies in Ethiopia, South Africa and Zambia, misbeliefs around ART benefits had a deleterious effect on adherence and led patients to access traditional medicine [39–41]. Future work should explore how to develop effective messages to ensure that patients have a clear understanding of ART benefits, which may enhance their willingness to seek care [42].

The current study has a number of limitations. Although HIVST has a potential to reach populations who are not covered by existing HTS services, the sampling method included the general population and a bigger sample of hard-to-reach groups such as HIV-positive individuals not linked into care could identify specific preferences to inform how to encourage their service uptake. The DCEs were administered to individuals who had not been exposed to HIVST; many had never received a positive HIV result. DCE is a method of stated choices; we acknowledge that willingness to choose one of the linkage services rather than opt-out may be inherently overestimated. Lastly, the variable on the belief of treatment efficacy had not previously been locally validated neither in Malawi nor Zambia [43–45].

To our knowledge, this multicountry study is the first to explore preferences for linkage into care services following a reactive HIV self-test. Results may be generalized to consider more broadly how HIV service characteristics are likely to affect uptake of ART after a reactive self-test. The findings of this study will be used to inform the design of linkage programmes as part of an HIVST delivery strategy in southern Africa.

Acknowledgements

The study was undertaken in collaboration with UNITAID, PSI, WHO and the HIVST Africa Research Consortium institutions. The authors would like to thank all their partners and study participants of Malawi and Zambia. This work was supported by the UNITAID/PSI HIV Self-Testing Africa (STAR) Project; grant number PO # 8477–0-600.

F.T.P. and M.D. were responsible for the conceptual design of the study. M.D., P.I., L.M., A.C., M.K. and M.S. contributed to the initial design of the DCE. M.D., P.I., L.M., E.C. and F.T.P. were involved with the experimental design of the DCE. P.I., M.N. and H.A. oversaw data collection. M.D. drafted the paper; all authors revised and approved the final manuscript.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

The research project has been approved by the College of Medicine Research Ethics Committee in Malawi, the Biomedical Ethics Committee of the University of Zambia and the Research Ethics Committee of the London School of Hygiene and Tropical Medicine.

Informed consent was obtained from all individual participants included in the study. In cases wherein the participants were illiterate, they were asked to give verbal consent along with a witnessed thumb print. Finally, parental consent was required if participants were 16 or 17 years old. The surveyors answered any questions raised by the participant and allowed them sufficient time to respond during the questionnaire.

Conflicts of interest

The authors declare that they have no conflict of interest. E.C. is funded by a Wellcome Trust Senior Research Fellowship in Clinical Science (WT200901/Z/16/Z).

References

1. Central Statistical Office. Zambia demographic and health survey 2013-14. Rockville, Maryland: Central Statistical Office, Ministry of Health, and ICF International; 2014:233.
2. ICF National Statistical Office. Malawi demographic and health survey 2015-16. Zomba, Malawi, and Rockville, Maryland: NSO and ICF; 2017.
3. Population-based HIV impact assessment project [Internet]. 2016. http://phia.icap.columbia.edu/ [Accessed 31 October 2017]
4. WHO. Guidelines on HIV self-testing and partner notification: supplement to consolidated guidelines on HIV testing services. WHO Press. Geneva. 2016. (http://www.who.int/hiv/pub/vct/hiv-self-testing-guidelines/en/)
5. Choko AT, Desmond N, Webb EL, Chavula K, Napierala-Mavedzenge S, Gaydos CA, et al. The uptake and accuracy of oral kits for HIV self-testing in high HIV prevalence setting: a cross-sectional feasibility study in Blantyre, Malawi. PLoS Med 2011; 8:e1001102.
6. Choko AT, MacPherson P, Webb EL, Willey BA, Feasy H, Sambakunsi R, et al. Uptake, accuracy, safety, and linkage into care over two years of promoting annual self-testing for HIV in Blantyre, Malawi: a community-based prospective study. PLoS Med 2015; 12:e1001873.
7. Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user's guide. Pharmacoeconomics 2008; 26:661–677.
8. Lancsar E, Swait J. Reconceptualising the external validity of discrete choice experiments. Pharmacoeconomics 2014; 32:951–965.
9. Louviere JJ, Lancsar E. Choice experiments in health: the good, the bad, the ugly and toward a brighter future. Health Econ Policy Law 2009; 4 (Pt 4):527–546.
10. Michaels-Igbokwe C, Lagarde M, Cairns J, Terris-Prestholt F. Using decision mapping to inform the development of a stated choice survey to elicit youth preferences for sexual and reproductive health and HIV services in rural Malawi. Soc Sci Med 2014; 105:93–102.
11. Ostermann J, Njau B, Mtuy T, Brown DS, Mühlbacher A, Thielman N. One size does not fit all: HIV testing preferences differ among high-risk groups in Northern Tanzania. AIDS Care 2015; 27:595–603.
12. Terris-Prestholt F, Hanson K, MacPhail C, Vickerman P, Rees H, Watts C. How much demand for New HIV prevention technologies can we really expect? Results from a discrete choice experiment in South Africa. PLoS One 2013; 8:e83193.
13. Terris-Prestholt F. Using rapid choice experiments to inform study design: an example from formative research on voluntary medical male circumcision in Tanzania (oral presentation). International Health Economics Association; 12–15 July 2015; Milan, Italy.
14. Indravudh PP, Sibanda EL, d’Elbée M, Kumwenda MK, Ringwald B, Maringwa G, et al. ’I will choose when to test, where I want to test’: investigating young people's preferences for HIV self-testing in Malawi and Zimbabwe. AIDS 2017; 31 (suppl 3):S203–S212.
15. London School of Hygiene and Tropical Medicine. UNITAID/PSI HIV self-testing Africa (STAR): research 2018. http://hivstar.lshtm.ac.uk/ [Accessed 8 June 2018]
16. Hensher D, Rose J, Greene W. Applied choice analysis. 2nd ed.Cambridge: Cambridge University Press; 2005.
17. Govindasamy D, Kranzer K, van Schaik N, Noubary F, Wood R, Walensky RP, et al. Linkage to HIV, TB and noncommunicable disease care from a mobile testing unit in Cape Town, South Africa. PLoS One 2013; 8:e80017.
18. Cambridge University Press, Louviere JJ, Hensher DA, Swait JD. Stated choice methods: analysis and applications. 2000.
19. OECD. Prices and purchasing power parities (PPP) 2017 [updated 29 June 2017]. http://www.oecd.org/std/prices-ppp/ [Accessed 8 June 2018]
20. Govindasamy D, Ford N, Kranzer K. Risk factors, barriers and facilitators for linkage to antiretroviral therapy care: a systematic review. AIDS 2012; 26:2059–2067.
21. Maman D, Ben-Farhat J, Chilima B, Masiku C, Salumu L, Ford N, et al. Factors associated with HIV status awareness and linkage to care following home based testing in rural Malawi. Trop Med Int Health 2016; 21:1442–1451.
22. Naik R, Doherty T, Jackson D, Tabana H, Swanevelder S, Thea DM, et al. Linkage to care following a home-based HIV counselling and testing intervention in rural South Africa. J Int AIDS Soc 2015; 18:19843.
23. Bassett IV, Coleman SM, Giddy J, Bogart LM, Chaisson CE, Ross D, et al. Barriers to care and 1-year mortality among newly diagnosed HIV-infected people in Durban, South Africa. J Acquir Immune Defic Syndr 2017; 74:432–438.
24. Govindasamy D, Meghij J, Kebede Negussi E, Clare Baggaley R, Ford N, Kranzer K. Interventions to improve or facilitate linkage to or retention in pre-ART (HIV) care and initiation of ART in low- and middle-income settings: a systematic review. J Int AIDS Soc 2014; 17:19032.
25. Deitchler M, Ballard T, Swindale A, Coates J. Validation of a measure of household hunger for cross-cultural use. Washington, DC: Food and Nutrition Technical Assistance II Project (FANTA-2), FHI 360; 2010.
26. WHO. HIV testing, treatment and prevention: generic tools for operational research. Geneva: WHO Press; 2009. p. 54.
27. Greene W. Nlogit 5 econometric software. http://www.limdep.com/
28. Bogart LM, Chetty S, Giddy J, Sypek A, Sticklor L, Walensky RP, et al. Barriers to care among people living with HIV in South Africa: contrasts between patient and healthcare provider perspectives. AIDS Care 2013; 25:843–853.
29. Sande L, Mangenah C, Mwenge L, Maheswaran H, Neuman M, Johnson C, et al. A tobit analysis of user costs for HIV testing among rural communities in Malawi. International Health Economics Association Conference; 10 July 2017; Boston, Massachusetts.
30. Figueroa C, Johnson C, Verster A, Baggaley R. Attitudes and acceptability on HIV self-testing among key populations: a literature review. AIDS Behav 2015; 19:1949–1965.
31. Wachira J, Naanyu V, Genberg B, Koech B, Akinyi J, Kamene R, et al. Health facility barriers to HIV linkage and retention in Western Kenya. BMC Health Serv Res 2014; 14:646.
32. MacPherson P, Lalloo DG, Webb EL, Maheswaran H, Choko AT, Makombe SD, et al. Effect of optional home initiation of HIV care following HIV self-testing on antiretroviral therapy initiation among adults in Malawi: a randomized clinical trial. JAMA 2014; 312:372–379.
33. Choko AT, Kumwenda MK, Johnson CC, Sakala DW, Chikalipo MC, Fielding K, et al. Acceptability of woman-delivered HIV self-testing to the male partner, and additional interventions: a qualitative study of antenatal care participants in Malawi. J Int AIDS Soc 2017; 20:21610.
34. Martinez Perez G, Cox V, Ellman T, Moore A, Patten G, Shroufi A, et al. ’I know that I do have HIV but nobody saw me’: oral HIV self-testing in an informal settlement in South Africa. PLoS One 2016; 11:e0152653.
35. Mokgatle MM, Madiba S. High acceptability of HIV self-testing among technical vocational education and training college students in Gauteng and north west province: what are the implications for the scale up in South Africa?. PLoS One 2017; 12:e0169765.
36. Hatcher AM, Turan JM, Leslie HH, Kanya LW, Kwena Z, Johnson MO, et al. Predictors of linkage to care following community-based HIV counseling and testing in rural Kenya. AIDS Behav 2012; 16:1295–1307.
37. Knight LC, Van Rooyen H, Humphries H, Barnabas RV, Celum C. Empowering patients to link to care and treatment: qualitative findings about the role of a home-based HIV counselling, testing and linkage intervention in South Africa. AIDS Care 2015; 27:1162–1167.
38. Tso LS, Best J, Beanland R, Doherty M, Lackey M, Ma Q, et al. Facilitators and barriers in HIV linkage to care interventions: a qualitative evidence review. AIDS 2016; 30:1639–1653.
39. Appelbaum Belisle H, Hennink M, Ordonez CE, John S, Ngubane-Joye E, Hampton J, et al. Concurrent use of traditional medicine and ART: perspectives of patients, providers and traditional healers in Durban, South Africa. Glob Public Health 2015; 10:71–87.
40. Tymejczyk O, Hoffman S, Kulkarni SG, Gadisa T, Lahuerta M, Remien RH, et al. HIV care and treatment beliefs among patients initiating antiretroviral treatment (ART) in Oromia, Ethiopia. AIDS Behav 2016; 20:998–1008.
41. Nozaki I, Kuriyama M, Manyepa P, Zyambo MK, Kakimoto K, Barnighausen T. False beliefs about ART effectiveness, side effects and the consequences of nonretention and nonadherence among ART patients in Livingstone, Zambia. AIDS Behav 2013; 17:122–126.
42. Derksen L, Van Oosterhout J. Love in the time of HIV: testing as a signal of risk, Natural Field Experiments 00550, The Field Experiments Website. (https://ideas.repec.org/p/feb/natura/00550.html)
43. Elford J, Bolding G, Sherr L. High-risk sexual behaviour increases among London gay men between 1998 and 2001: what is the role of HIV optimism?. AIDS 2002; 16:1537–1544.
44. Van de Ven P, Kippax S, Knox S, Garret P, Crawford J. HIV treatments optimism and sexual behaviour among gay men in Sydney and Melbourne. AIDS 1999; 13:2289–2294.
45. Vanable PA, Ostrow DG, McKirnan DJ. Viral load and HIV treatment attitudes as correlates of sexual risk behavior among HIV-positive gay men. J Psychosom Res 2003; 54:263–269.
Keywords:

discrete choice experiments; HIV self-testing; linkage to care; preferences, Malawi; Zambia

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