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Implementation Science

Brief Report: Understanding Preferences for HIV Care Among Patients Experiencing Homelessness or Unstable Housing: A Discrete Choice Experiment

Conte, Madellena BA, MSa,b,c; Eshun-Wilson, Ingrid PhDd; Geng, Elvin MD, MPHd; Imbert, Elizabeth MD, MPHa; Hickey, Matthew D. MDa; Havlir, Diane MDa; Gandhi, Monica MD, MPHa; Clemenzi-Allen, Angelo MD, MASa,e

Author Information
JAIDS Journal of Acquired Immune Deficiency Syndromes: December 1, 2020 - Volume 85 - Issue 4 - p 444-449
doi: 10.1097/QAI.0000000000002476

Abstract

INTRODUCTION

Homelessness and unstable housing (HUH) are major barriers in realizing the full benefits of antiretroviral therapy for people living with HIV (PLWH).1–9 In San Francisco, only 33% of PLWH experiencing HUH were virally suppressed in 2018 compared with 75% in those who were housed.10 Moreover, in the context of a worsening homelessness epidemic in light of the COVID-19 pandemic, unstable housing constitutes a major obstacle to achieving the goals of the ending the HIV epidemic initiative.11–15 Multiple strategies exist to enhance retention in care for PLWH-HUH,16–21 such as providing low-barrier care, using financial incentives to promote behavior change, and strengthening patient-centered care.17,19,22 Because multiple individual level and structural barriers to care exist for PLWH-HUH, multicomponent programs are likely to be required to improve care outcomes in this population.12,23–29 However, consensus on program design and component prioritization is lacking, and program implementation may need to adapt to accommodate clinic-level and patient-level characteristics.

Robust methods to elicit patient preferences for program components can guide program design. Discrete choice experiments (DCEs), research tools commonly used in marketing, can be used to quantify patient preferences and evaluate trade-offs between program components, the results of which have been applied to decisions to improve clinic-based care for PLWH.30,31 In this article, we used a DCE among PLWH-HUH to understand patient preferences for, relative utility of, and trade-offs between program components to help design a clinic-based care model to improve retention in care and treatment outcomes among PLWH-HUH.

METHODS

Study Setting, Population, and Sampling

This study was conducted between March and July, 2019 at San Francisco General Hospital's HIV primary care clinic (“Ward 86”), which serves a vulnerable and diverse patient population with approximately one-third experiencing HUH.7 Clinic providers referred patients to this study. Eligible participants (1) reported HUH defined as staying outdoors (eg, on the streets, in parks, or in a vehicle), in emergency housing (eg, shelter, navigation center, or temporary room in an single residency occupancy hotel), “couch surfing” (ie, living temporarily with friends or family or in a place in exchange for sex or drugs), an institution (eg, drug or alcohol treatment program and transitional housing), and nonresidential space (eg, commercial space, office space, or storage unit); (2) had at least 1 viral load measurement > 200 copies/mL in the past 12 months; (3) ≥1 missed primary care visit in the 12 months; and (4) were able to conduct the interviews in English. Patients received a $20 grocery store gift card for their participation. This study was approved by University of California, San Francisco Institutional Review Board.

Selection of Attributes for the Choice Experiment

We performed a literature review to identify interventions that improve retention in care among PLWH experiencing HUH32: (1) providing low-barrier access primary care without the need for clinic appointments,19 (2) using financial incentives for care engagement,17 (3) allowing for direct communication with clinical care team,33–35 and (4) strengthening models of patient-centered care.22 We then conducted 10 semistructured interviews among PLWH experiencing HUH (90% male, 90% living outdoors, or in transitional housing) to inform our attribute selection further. These interviews highlighted personal and structural barriers and enablers of attending visits. The selected attributes, derived from an iterative process reflecting the interview and literature results, were as follows: (1) defining patient-centered care as “providers and staff get to know me as a person” versus “providers and staff do not get to know me as a person”36; (2) having “scheduled visits” by appointment or “unscheduled drop-in visits (Monday–Friday afternoon)”29; (3) receiving gift cards for attending clinic visits in the amount of $10, $15, or $2017; (4) communicating with clinic team through “phone calls directly to a care provider at the clinic during clinic hours” versus “through phone calls to the front desk during clinic hours”37; and (5) the distance from where the patient stays to the clinic equaling “2 city blocks” versus “20 city blocks.”28 Final choice tasks were piloted among 7 patients to confirm general comprehension, refine definitions, and verify readability.

DCE Design

We used Lighthouse Studio Version 9.6.1 (Sawtooth Software, Provo, UT) to construct the survey. The DCE included 5 attributes as listed above, 4 with 2 levels and 1 with 3 levels, yielding a total of 48 (eg, 2 × 2 × 2 × 2 × 3) potential combinations. Because it is not feasible to evaluate the total number of comparisons [(47 × 48)/2 or 1128], we constructed a fractional factorial design to limit the number of choice pairings presented to respondents.38 We used an orthogonal main effect plan38 to construct choice tasks that prioritized understanding trade-offs and preferences of having the first attribute (patient-centered care) compared with other attributes.

We considered balance (ie, each attribute level was presented to the respondents the same number of times), orthogonality (ie, each pair of attribute levels appeared with the same frequency across all pairs of attributes),39,40 and efficiency when constructing the DCE design. In this way, the correlation among attributes was zero. We tested efficiency using SAS software. One hundred percent efficiency is achieved when D-error (the average variance of all attributes) is equal to 1/number of choice tasks. The final design was balanced, orthogonal, and fully efficient with a D-efficiency of 100% relative to the hypothetical optimal design.

Participants completed 12 choice tasks, which maximized the design's efficiency and minimized cognitive burden.38 The survey displayed pictorial examples alongside attributes to improve understanding (see Figure, Supplemental Digital Content 1, https://links.lww.com/QAI/B523 which demonstrates the attributes and pictorial examples in the DCE). Each choice task presented participants with 2 models of clinic programs that differed from each other across one or more characteristics. The participant was asked for their clinic preference: “Do you prefer going to Clinic A, Clinic B, or would you rather not go to either one?”

Sample Size

We calculated that 63 patients would be sufficient using a rule suggested by Johnson and Orme41 that a sample size required for the main effects depends on the number of choice tasks (t), the number of alternatives per task, not including the none alternative (a), and the largest number of levels for any one attribute (c) according to the following equation: N > 500c/(t × a).

Data Collection

We used tablets to collect sociodemographic information (using REDCap) and conduct the DCE. The first author screened for eligibility, obtained consent, guided the participants through an example question, and remained available to answer clarifying questions during the survey.

Analysis

Using STATA, we tabulated patient demographics and clinical characteristics. We used a mixed logit regression model to estimate the relative utility (ie, preference) of each attribute level in the cohort which, assuming independence of attributes, presents the relative mean preference weights (β-coefficients), standard deviations of effects across the sample, and captures the heterogeneity across participants.38,39 The gift card attribute was treated as a continuous variable and the remaining attributes were treated as dichotomous variables. The study was not powered to evaluate differences between baseline characteristics. We conducted a willingness to pay analysis, in which we standardized the relative utility from visiting a clinic with a given attribute against that of financial incentives. We divided the coefficient of each variable by the coefficient of gift card amount to determine the amount in dollars that would be required to have an equivalent preference for that attribute.38

RESULTS

Demographics

Two hundred forty-two patients were referred for participation from social workers, primary care physicians, and urgent care providers at the time of clinical visits, of whom 192 did not meet enrollment criteria, yielding 65 eligible patients, all of whom completed the DCE. Of the 65 respondents (Table 1), 40 (61%) were older than 50 years of age, 50 (77%) were men, 36 (55%) were non-White, 16 (25%) identified as heterosexual, 46 (71%) reported receiving some form of government financial assistance, and 47 (72%) reported daily or weekly substance use. For housing status, 36 (56%) reported staying outdoors or in emergency housing and 29 (44%) reported living in temporary housing.

TABLE 1. - Patient Baseline Demographic and Clinical Information (N = 65)
Patient Characteristics N (%)
Age, yrs
 <30 9 (14)
 31–40 16 (25)
 41–50 18 (28)
 >50 22 (33)
Self-reported gender
 Male 50 (77)
 Female 9 (13)
 Male-to-female transgendered/transgendered woman 3 (5)
 Other gender not listed 3 (5)
Highest level of schooling completed
 Some college or more 33 (51)
Race
 White 29 (45)
 Black/African American 20 (31)
 Latino 12 (18)
 Others 4 (6)
Sexual identity
 Lesbian, gay, or homosexual 29 (45)
 Straight or heterosexual 16 (25)
 Bisexual 10 (15)
 Do not know 5 (8)
 Others 5 (8)
Receives government financial assistance (any form) 46 (71)
 GA or CAAP 20 (31)
 Disability income 24 (37)
 Social security 11 (17)
 Temporary assistance for needy families 4 (6)
Substance use (daily or weekly use) 47 (72)
 Opiates 7 (11)
 Stimulants 40 (62)
 Others 18 (28)
Viral load <200 copies/mL in 12 mo before survey 25 (38)
Psychiatric diagnosis
 Depression or anxiety 39 (60)
 Schizophrenia or bipolar disorder 24 (37)
 None 15 (23)
Current living arrangement
 Outdoors 14 (22)
 Emergency housing 22 (34)
 “Couch surfing” or “housing sitting” 16 (25)
 An institution 10 (15)
 A nonresidential space that you rent, own, or occupy 3 (4)
Outdoors = on the streets, in parks, or in a vehicle; emergency housing = (shelter, navigation center, or temporary room in a single residency occupancy/hotel); couch surfing (eg, temporarily with friends or family or in a place in exchange for sex or drugs); an institution = drug or alcohol treatment program and transitional housing; nonresidential space = commercial space, office space, or storage unit.
CAAP, County Adult Assistance Program; GA, general assistance.

DCE Results

The strongest preferences (Fig. 1) were for patient-centered providers [β = 3.80; 95% confidence interval (CI): 2.57 to 5.02] and drop-in (rather than scheduled) clinic visits (β = 1.33; 95% CI: 0.85 to 1.80) (see Table, Supplemental Digital Content 2, https://links.lww.com/QAI/B523). A weaker preference was expressed for receiving gift cards for coming to clinic visits (β = 0.60 per $5 incentive; 95% CI: 0.30 to 0.90). No statistically significant preference was observed for having the ability to make direct phone calls to the care provider versus phone calls to the front desk (β = 0.29; 95% CI: −0.001 to 0.57) and staying 20 versus 2 city blocks away from the clinic (β = −0.18; 95% CI: −0.49 to 0.13).

FIGURE 1.
FIGURE 1.:
Relative utilities (ie, preferences) of clinic attributes (results from the mixed logit regression model). CI, confidence interval.

In the willingness to pay analysis, participants were willing to trade a hypothetical $32.79 (95% CI: 14.75 to 50.81) in gift cards per visit to have a care team that gets to know them as a person, $11.45 (95% CI: 2.95 to 19.96) for having drop-in versus scheduled appointments, and $2.46 (95% CI: 0.46 to 4.47; P = 0.016) for having direct communication with care providers versus front-desk staff.

DISCUSSION

We performed the first DCE reported in the literature to elicit preferences for a clinical care program for PLWH experiencing homelessness and housing instability. We observed that participants most strongly preferred patient-centered providers and hypothetically were willing to trade almost $33 for this preference. Other statistically significant findings included a strong patient preference for drop-in, rather than scheduled, appointments. Surprisingly, there was no preference for shorter travel distance to clinic, suggesting that the flexible clinic design superseded the clinic location for attributes that would encourage retention in care.

Numerous qualitative studies support the importance of positive patient–provider relationships,27,42,43 which has been characterized as having a provider “who cares about you.”44 In a choice experiment conducted in Zambia among PLWH who were lost to follow-up, patients were willing to travel longer distances and attend a clinic with shorter operating hours to interact with “nice” providers.30 Another choice experiment among patients with HIV/hepatitis C virus coinfection in a safety net clinic indicated that receiving treatment from a patient's current regular provider was the single most important attribute in making treatment decisions.31 Positive experiences with HIV providers and clinic staff has been associated with improved retention in HIV care45 and having a patient-centered provider has been associated with a 32% higher odds of adhering to antiretroviral therapy.36 Our study adds information to the current literature by quantifying the degree to which PLWH experiencing HUH may be willing to forego other interventions to receive care from a provider with whom they have a positive relationship.

A strong preference for drop-in, rather than scheduled, appointments was another statistically significant finding from our study, which has been supported by qualitative studies.46–48 Indeed, walk-in access and same-day appointments have been adopted by other clinics that serve PLWH experiencing homelessness and who are poorly retained in care.19

Limitations to this study should be considered. First, DCEs collect data based on stated preferences, not on actual behavior or care outcomes. Second, we assumed model linearity in the willingness to pay analysis. Third, in this convenience sample of participants, results are limited to those who are somewhat engaged with clinic staff and excluded those who could not complete the survey in English. The study was underpowered to calculate preferences between comparison groups, which limits generalizability of the findings and is an area of future research. Fourth, our evaluation emphasized only one dimension of patient centeredness (“providers and staff get to know me as a person”) but did not elicit preferences in other dimensions important to this construct.50–53

CONCLUSIONS

We report on the first DCE to help design a novel clinical care program in an urban safety net HIV clinic. PLWH experiencing HUH strongly preferred having providers who know them as a person and having a model of care with drop-in, rather than scheduled, appointments. These preferences helped our HIV clinic (Ward 86) design a novel model of care for PLWH-HUH called the “POP-UP” program.53 Further research is needed to refine our understanding on how patients define “a provider that knows me” and the role of patient centeredness in improving retention in care among PLWH experiencing HUH.

ACKNOWLEDGMENTS

The authors thank Dr. Hae-Young Kim and Dr. David Glidden for assistance with biostatistical analysis.

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Keywords:

HIV; homelessness and unstable housing; retention in care; discrete choice experiment

Supplemental Digital Content

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