Using discrete choice experiments to design interventions for heterogeneous preferences: protocol for a pragmatic randomised controlled trial of a preference-informed, heterogeneity-focused, HIV testing offer for high-risk populations

Using Discrete Choice Experiments to Quantify Patient Preferences

Discrete choice experiments are a robust technique for quantifying preferences. With this method, respondents are presented with a series of hypothetical comparisons described by attributes with varying levels. The aggregated choices from respondents can be used to infer the relative importance of the described attributes and acceptable trade-offs between attributes. The data generated from discrete choice experiments can aid surgeons in aligning patient values with treatment decisions and support the design of research that is responsive to patient preferences. This article summarizes the application of discrete choice experiments to orthopaedics. We share best practices for designing discrete choice experiments and options for reporting study results. Finally, we suggest opportunities for this method within our field.

Divergent preferences for enhanced HIV testing options among high-risk populations in northern Tanzania: a short report

To achieve the UNAIDS target of diagnosing 95% of all persons living with HIV, enhanced HIV testing services with greater attractional value need to be developed and implemented. We conducted a discrete choice experiment (DCE) to quantify preferences for enhanced HIV testing features across two high-risk populations in the Kilimanjaro Region in northern Tanzania. We designed and fielded a survey with 12 choice tasks to systematically recruited female barworkers and male mountain porters. Key enhanced features included: testing availability on every day of the week, an oral test, integration of a general health check or an examination for sexually transmitted infections (STI) with HIV testing, and provider-assisted confidential partner notification in the event of a positive HIV test result. Across 300 barworkers and 440 porters surveyed, mixed logit analyses of 17,760 choices indicated strong preferences for everyday testing availability, health checks, and STI examinations. Most participants were averse to oral testing and confidential partner notification by providers. Substantial preference heterogeneity was observed within each risk group. Enhancing HIV testing services to include options for everyday testing, general health checks, and STI examinations may increase the appeal of HIV testing offers to high-risk populations.Trial registration: ClinicalTrials.gov identifier: NCT02714140.

A modelling framework for translating discrete choice experiment results into cost-effectiveness estimates: an application to designing tailored and scalable HIV and contraceptive services for adolescents in Gauteng, South Africa

INTRODUCTION

South African youth and adolescents face a high burden of (Sexually Transmitted Infections) STIs, HIV and unintended pregnancies, but uptake of services remains low. To address this, tailored and scalable interventions are urgently needed. We developed a framework to fill the gap and translate the impact of facility-level attributes into a cost-effectiveness analysis for increasing HIV/contraceptive service uptake in adolescents using a discrete choice experiment (DCE).

METHODS

We used a DCE (n = 805) conducted in Gauteng, South Africa, which found that staff attitude, confidentiality, Wi-Fi, subsidized food, afternoon hours and youth-only services were preferred attributes of health services. Based on this, we simulated the uptake of services adapted for these preferences. We divided preferences into modifiable attributes that could readily be adapted (e.g. Wi-Fi), and challenging to modify (more nuanced attributes that are more challenging to cost and evaluate): staff attitude and estimated the incremental change in the uptake of services using adapted services. Costs for modifiable preferences were estimated using data from two clinics in South Africa (2019 US$). We determined the incremental cost-effectiveness ratio (ICER) for additional adolescents using services of 15 intervention combinations, and report the results of interventions on the cost-effectiveness frontier.

RESULTS

Greatest projected impact on uptake was from friendly and confidential services, both of which were considered challenging to modify (18.5% 95% CI: 13.0%-24.0%; 8.4% 95% CI: 3.0%-14.0%, respectively). Modifiable factors on their own resulted in only small increases in expected uptake. (Food: 2.3% 95% CI: 4.0%-9.00%; Wi-Fi: 3.0% 95% CI: -4.0% to 10.0%; Youth-only services: 0.3% 95% CI: -6.0% to 7.0%; Afternoon services: 0.8% 95% CI: -6.0% to 7.0%). The order of interventions on the cost-effectiveness frontier are Wi-Fi and youth-only services (ICER US$7.01-US$9.78 per additional adolescent utilizing HIV and contraceptive services), Wi-Fi, youth-only services and food (ICER US$9.32-US$10.45), followed by Wi-Fi, youth-only services and extended afternoon hours (ICER US$14.46-US$43.63).

CONCLUSIONS

Combining DCE results and costing analyses within a modelling framework provides an innovative way to inform decisions on effective resource utilization. Modifiable preferences, such as Wi-Fi provision, youth-only services and subsidized food, have the potential to cost-effectively increase the proportion of adolescents accessing HIV and contraceptive services.

How Poor Is Your Sample? A Simple Approach for Estimating the Relative Economic Status of Small and Nonrepresentative Samples

BACKGROUND

Asset-based indices of living standards, or wealth indices, are widely used proxies for economic status; however, such indices are not readily available for small and nonrepresentative samples.

METHODS

We describe a simple out-of-sample prediction approach that uses estimates from large and representative "reference" samples to calculate measures of relative economic status (e.g., wealth index scores) for small and/or nonrepresentative "target" samples. The method relies on the availability of common variables and assumptions about comparable associations between these variables and the underlying construct of interest (e.g., household wealth). We provide 2 sample applications that use Demographic and Health Surveys (DHS) from 5 countries as reference samples. Using ordinary least squares regression, we estimate associations between household characteristics and the DHS wealth index. We use parameter estimates to predict wealth index scores for small nonrepresentative target samples. Comparisons of wealth distributions in the reference and target samples highlight selection effects.

RESULTS

Applications of the approach to diverse populations, including populations at high risk of HIV infection and households with orphaned and separated children, demonstrate its usefulness for characterizing the economic status of small and nonrepresentative samples relative to existing reference samples. Women and men in northern Tanzania at high risk of HIV infection were concentrated in the upper half of the wealth distribution. By contrast, the relative distribution of household wealth among households with orphaned and separated children varied greatly across countries and rural versus urban settings.

CONCLUSIONS

Public health professionals who implement, manage, and evaluate programs in low- and middle-income countries may find this approach applicable because of the simplicity of the estimation methods, low marginal cost of primary data acquisition, and availability of established measures of relative economic status in many publicly available household surveys (e.g., those administered by the DHS Program, World Bank, International Labour Organization, and UNICEF).

Assessing preferences for HIV pre-exposure prophylaxis (PrEP) delivery services via online pharmacies in Kenya: protocol for a discrete choice experiment

INTRODUCTION

Pre-exposure prophylaxis (PrEP) is highly effective at preventing HIV acquisition, but coverage remains low in high prevalence settings. Initiating and continuing PrEP via online pharmacies is a promising strategy to expand PrEP uptake but little is known about user preferences for this strategy. We describe methods for a discrete choice experiment (DCE) to assess preferences for PrEP delivery from an online pharmacy.

METHODS AND ANALYSIS

This cross-sectional study is conducted in Nairobi, Kenya, in partnership with MYDAWA, a private online pharmacy retailer with a planned sample size of >400 participants. Eligibility criteria are: ≥18 years, not known HIV-positive and interested in PrEP. Initial DCE attributes and levels were developed via literature review and stakeholder meetings. We conducted cognitive interviews to assess participant understanding of the DCE survey and refined the design. The final DCE used a D-efficient design and contained four attributes: PrEP eligibility assessment, HIV test type, clinical consultation type and user support options. Participants are presented with eight scenarios consisting of two hypothetical PrEP delivery services. The survey was piloted among 20 participants before being advertised on the MYDAWA website on pages displaying products indicating HIV risk (eg, HIV self-test kits). Interested participants call a study number and those screened eligible meet a research assistant in a convenient location to complete the survey. The DCE will be analysed using a conditional logit model to assess average preferences and mixed logit and latent class models to evaluate preference heterogeneity among subgroups.

ETHICS AND DISSEMINATION

This study was approved by the University of Washington Human Research Ethics Committee (STUDY00014011), the Kenya Medical Research Institute, Nairobi County (EOP/NMS/HS/128) and the Scientific and Ethics Review Unit in Kenya (KEMRI/RES/7/3/1). Participation in the DCE is voluntary and subject to completion of an electronic informed consent. Findings will be shared at international conferences and peer-reviewed publications, and via engagement meetings with stakeholders.

Use of Stated Preference Methods in HIV Treatment and Prevention Research in the United States: A Systematic Review

Stated preference (SP) methods are increasingly being applied to HIV-related research and continuously provide researchers with health utility scores of select healthcare products or services that populations consider important. Following PRISMA guidelines, we sought to understand how SP methods have been applied in HIV-related research. We conducted a systematic review to identify studies meeting the following criteria: SP method is clearly stated, conducted in the United States, was published between 01/01/2012 and 02/12/2022, and included adults aged 18 and over. Study design and SP method application were also examined. We identified six SP methods (e.g., Conjoint Analysis, Discrete Choice Experiment) across 18 studies, which were categorized into one of two groups: HIV prevention and HIV treatment-care. Categories of attributes used in SP methods largely focused on: administration, physical/health effects, financial, location, access, and external influences. SP methods are innovative tools capable of informing researchers on what populations consider most beneficial when deciding on treatment, care, or prevention options for HIV.

Feasibility, Acceptability, and Potential Cost-Effectiveness of a Novel Mobile Phone Intervention to Promote Human Immunodeficiency Virus Testing Within Social Networks in Tanzania

ABSTRACT

Among 145 individuals surveyed in Tanzania, 42% indicated willingness to test for human immunodeficiency virus in response to a confidential, phone-based text message. On average, participants were likely to provide contact information for 1.5 members of their social networks, suggesting high feasibility and moderate acceptability of a novel mHealth human immunodeficiency virus testing intervention.

Factors associated with treatment escalation among MS specialists and general neurologists: Results from an International cojoint study

BACKGROUND

Previous studies in multiple sclerosis (MS) showed that therapeutic inertia (TI) affects 60-90% of neurologists and up to 25% of daily treatment decisions. The objective of this study was to determine the most common factors and attribute levels associated with decisions to treatment escalation in an international study in MS care.

METHODS

300 neurologists with MS expertise from 20 countries were invited to participate. Participants were presented with 12 pairs of simulated MS patient profiles described by 13 clinically relevant factors. We used disaggregated discrete choice experiments to estimate the weight of factors and attributes affecting physicians' decisions when considering treatment selection. Participants were asked to select the ideal candidate for treatment escalation from modest to higher-efficacy therapies.

RESULTS

Overall, 229 neurologists completed the study (completion rate: 76.3%). The top 3 weighted factors associated with treatment escalation were: previous relapses (20%), baseline expanded disability status scale [EDSS] (18%), and MRI activity (13%). Patient demographics and desire for pregnancy had a modest influence (≤ 3%). We observed differences in the weight of factors associated with treatment escalation between MS specialists and non-MS specialists.

CONCLUSIONS

Our results provide critical information on factors influencing neurologists' treatment decisions and should be applied to continuing medical education strategies.

Optical Biosensors for Diagnostics of Infectious Viral Disease: A Recent Update

The design and development of biosensors, analytical devices used to detect various analytes in different matrices, has emerged. Biosensors indicate a biorecognition element with a physicochemical analyzer or detector, i.e., a transducer. In the present scenario, various types of biosensors have been deployed in healthcare and clinical research, for instance, biosensors for blood glucose monitoring. Pathogenic microbes are contributing mediators of numerous infectious diseases that are becoming extremely serious worldwide. The recent outbreak of COVID-19 is one of the most recent examples of such communal and deadly diseases. In efforts to work towards the efficacious treatment of pathogenic viral contagions, a fast and precise detection method is of the utmost importance in biomedical and healthcare sectors for early diagnostics and timely countermeasures. Among various available sensor systems, optical biosensors offer easy-to-use, fast, portable, handy, multiplexed, direct, real-time, and inexpensive diagnosis with the added advantages of specificity and sensitivity. Many progressive concepts and extremely multidisciplinary approaches, including microelectronics, microelectromechanical systems (MEMSs), nanotechnologies, molecular biology, and biotechnology with chemistry, are used to operate optical biosensors. A portable and handheld optical biosensing device would provide fast and reliable results for the identification and quantitation of pathogenic virus particles in each sample. In the modern day, the integration of intelligent nanomaterials in the developed devices provides much more sensitive and highly advanced sensors that may produce the results in no time and eventually help clinicians and doctors enormously. This review accentuates the existing challenges engaged in converting laboratory research to real-world device applications and optical diagnostics methods for virus infections. The review's background and progress are expected to be insightful to the researchers in the sensor field and facilitate the design and fabrication of optical sensors for life-threatening viruses with broader applicability to any desired pathogens.

Preferences for transitional HIV care among people living with HIV recently released from prison in Zambia: a discrete choice experiment

Introduction

No studies from sub‐Saharan Africa have attempted to assess HIV service delivery preferences among incarcerated people living with HIV as they transition from prisons to the community (“releasees”). We conducted a discrete choice experiment (DCE) to characterize releasee preferences for transitional HIV care services in Zambia to inform the development of a differentiated service delivery model to promote HIV care continuity for releasees.

Methods

Between January and October 2019, we enrolled a consecutive sample of 101 releasees from a larger cohort prospectively following 296 releasees from five prisons in Zambia. We administered a DCE eliciting preferences for 12 systematically designed choice scenarios, each presenting three hypothetical transitional care options. Options combined six attributes: (1) clinic type for post‐release HIV care; (2) client focus of healthcare workers; (3) transitional care model type; (4) characteristics of transitional care provider; (5) type of transitional care support; and (6) HIV status disclosure support. We analysed DCE choice data using a mixed logit model, with coefficients describing participants’ average (“mean”) preferences for each option compared to the standard of care and their distributions describing preference variation across participants.

Results

Most DCE participants were male (n = 84, 83.2%) and had completed primary school (n = 54, 53.5%), with 29 (28.7%) unemployed at follow‐up. Participants had spent an average of 8.2 months in the community prior to the DCE, with 18 (17.8%) reporting an intervening episode of re‐incarceration. While we observed significant preference variation across participants (p < 0.001 for most characteristics), releasees were generally averse to clinics run by community‐based organizations versus government antiretroviral therapy clinics providing post‐release HIV care (mean preference = –0.78, p < 0.001). On average, releasees most preferred livelihood support (mean preference = 1.19, p < 0.001) and HIV care support (mean preference = 1.00, p < 0.001) delivered by support groups involving people living with HIV (mean preference = 1.24, p < 0.001).

Conclusions

We identified preferred characteristics of transitional HIV care that can form the basis for differentiated service delivery models for prison releasees. Such models should offer client‐centred care in trusted clinics, provide individualized HIV care support delivered by support groups and/or peer navigators, and strengthen linkages to programs providing livelihood support.

What factors influence HIV testing? Modeling preference heterogeneity using latent classes and class-independent random effects

Efforts to eliminate the HIV epidemic will require increased HIV testing rates among high-risk populations. To inform the design of HIV testing interventions, a discrete choice experiment (DCE) with six policy-relevant attributes of HIV testing options elicited the testing preferences of 300 female barworkers and 440 male Kilimanjaro mountain porters in northern Tanzania. Surveys were administered between September 2017 and July 2018. Participants were asked to complete 12 choice tasks, each involving first- and second-best choices from 3 testing options. DCE responses were analyzed using a random effects latent class logit (RELCL) model, in which the latent classes summarize common participant preference profiles, and the random effects capture additional individual-level preference heterogeneity with respect to three attribute domains: (a) privacy and confidentiality (testing venue, pre-test counseling, partner notification); (b) invasiveness and perceived accuracy (method for obtaining the sample for the HIV test); and (c) accessibility and value (testing availability, additional services provided). The Bayesian Information Criterion indicated the best model fit for a model with 8 preference classes, with class sizes ranging from 6% to 19% of participants. Substantial preference heterogeneity was observed, both between and within latent classes, with 12 of 16 attribute levels having positive and negative coefficients across classes, and all three random effects contributing significantly to participants' choices. The findings may help identify combinations of testing options that match the distribution of HIV testing preferences among high-risk populations; the methods may be used to systematically design heterogeneity-focused interventions using stated preference methods.