Preferences of people living with HIV for differentiated care models in Kenya: A discrete choice experiment

Sagar Dommaraju; Jill Hagey; Thomas A Odeny; Sharon Okaka; Julie Kadima; Elizabeth A Bukusi; Craig R Cohen; Zachary Kwena; Ingrid Eshun-Wilson; Elvin Geng

doi:10.1371/journal.pone.0255650

. 2021 Aug 25;16(8):e0255650. doi: 10.1371/journal.pone.0255650

Preferences of people living with HIV for differentiated care models in Kenya: A discrete choice experiment

Sagar Dommaraju ^1,^*, Jill Hagey ², Thomas A Odeny ^3,⁴, Sharon Okaka ⁴, Julie Kadima ⁴, Elizabeth A Bukusi ⁴, Craig R Cohen ^1,⁵, Zachary Kwena ⁴, Ingrid Eshun-Wilson ⁵, Elvin Geng ⁵

Editor: D M Basavarajaiah⁶

PMCID: PMC8386850 PMID: 34432795

Abstract

Introduction

To improve retention on HIV treatment in Africa, public health programs are promoting a family of innovations to service delivery—referred to as “differentiated service delivery” (DSD) models—which seek to better meet the needs of both systems and patients by reducing unnecessary encounters, expanding access, and incorporating peers and patients in patient care. Data on the relative desirability of different models to target populations, which is currently sparse, can help guide prioritization of specific models during scale-up.

Methods

We conducted a discrete choice experiment to assess patient preferences for various characteristics of treatment services. Clinically stable people living with HIV were recruited from an HIV clinic in Kisumu, Kenya. We selected seven attributes of DSD models drawn from literature review and previous qualitative work. We created a balanced and orthogonal design to identify main term effects. A total of ten choice tasks were solicited per respondent. We calculated relative utility (RU) for each attribute level, a numerical representation of the strength of patient preference. Data were analyzed using a Hierarchical Bayesian model via Sawtooth Software.

Results

One hundred and four respondents (37.5% men, 41.1 years mean age) preferred receiving care at a health facility, compared with home-delivery or a community meeting point (RU = 69.3, -16.2, and -53.1, respectively; p << 0.05); receiving those services from clinicians and pharmacists—as opposed to lay health workers or peers (RU = 21.5, 5.9, -24.5; p < 0.05); and preferred an individual support system over a group support system (RU = 15.0 and 4.2; p < 0.05). Likewise, patients strongly preferred longer intervals between both clinical reviews (RU = 40.1 and -50.7 for 6- and 1-month spacing, respectively; p < 0.05) and between ART collections (RU = 33.6 and -49.5 for 6- and1-month spacing, respectively; p < 0.05).

Conclusion

Although health systems find community- and peer-based DSD models attractive, clinically stable patients expressed a preference for facility-based care as long as clinical visits were extended to biannual. These data suggest that multi-month scripting and fast-track models best align with patient preferences, an insight which can help prioritize use of different DSD models in the region.

Introduction

Despite greatly expanded access to antiretroviral therapy (ART), about 30% of people living with HIV (PLHIV) in Kenya have not achieved consistently suppressed HIV viral load [1–4]. Treatment for HIV in the country over the last decade has been conducted using a “one-size-fits-all model” that was developed during the emergency response phase of the initial HIV epidemic and which focused on treating patients with advanced medical disease [5]. Patients—regardless of their age, socioeconomic status, CD4 levels, and medical stability—are required to visit the clinic every 1–3 months and to collect their medication just as often [5].

DSD models, which vary the timing, frequency, location and cadre of delivering care, have recently arisen as an alternative to the traditional model and have been heralded as a strategic solution to enhance the effectiveness and efficiency of the care cascade [6–9]. Many patients who are clinically stable need not expend considerable opportunity costs to visit facilities monthly or even quarterly to receive high quality care. Reduction of unnecessary visits would also ameliorate overburdened clinic facilities, reduce waiting time, and potentially improve health care worker burnout. DSD models like multi-month scripting, fast track, adherence clubs, and community ART groups all attempt to reduce visits, offset labor from health care workers to peers and patients, and encourage community-based treatment [5].

As DSD models continue to expand and require maintenance, public health implementers may need to select between models, as implementing multiple models may be burdensome. One way to assign prioritization could be to identify which features are most desired by patients, information which is to date relatively poorly understood. There are many variations of innovations in DSD, and identifying features that are preferred can suggest which are most likely to lead to sustained engagement and retention. For example, DSD includes strategies to deliver HIV care outside of the health facility and deliver care with peers or non-clinicians. Do patients prefer community-based care over facility-based care, and by how much? If they come to the facility rarely, do they prefer higher-cadre health workers, and by how much? DSD models are a large and diverse set of practices, and the comparative desirability of each model may influence which to prioritize. While some data exist on comparative effectiveness of different models, these experimental trials have not accounted for patient preferences which could be an important driver of success for a given individual [10, 11].

We propose to advance our understanding of patient preferences for innovations in HIV care through use of a discrete choice experiment. This discrete choice experiment (DCE) asked patients to choose between hypothetical models of care, which differ in certain attributes such as social support, frequency of clinical visits, and location of visits. In doing so, DCEs offer an efficient way to examine the potential effectiveness of DSD by quantifying preferences in a population [12, 13]. Services that are most preferred by patients are most likely to be taken up and are most likely to yield consistent engagement. We aimed to reveal which attributes of treatment programs would be most important to patients, which can yield information relevant to policy makers.

Materials and methods

This study took place in a dedicated HIV clinic in Kisumu County in Western Kenya between April 25, 2017 and June 6, 2017. This HIV clinic is a public health facility operated by the Kisumu County Health Department and supported by Family AIDS Care and Education Services (FACES), a collaboration between the University of California, San Francisco and the Kenya Medical Research Institute [14]. Stable HIV patients were recruited from the facility based on the following criteria: Age greater than or equal to 20 years, BMI greater than 18.5, most recent viral load less than 5,000 copies/mL, on current ART regimen for greater than 12 months, adherent to scheduled clinic visits for the past 6 months, non-pregnant/not breastfeeding, and the primary healthcare team does not have concerns about providing longer follow-up intervals for the patient. These criteria mirror those utilized by the Kenyan Ministry of Health as patients potentially eligible for DSD models [15]. Additionally, the Kenyan Ministry of Health has recently encouraged all patients living with HIV to undergo 6 months of isoniazid prevention therapy; however, this has not been fully implemented at the current HIV clinic and was not included in our inclusion criteria.

Attributes for the discrete choice experiment were identified primarily through a literature search on differentiated care in the region [16–19]. Although not all of these studies were conducted in Kenya, the attributes identified were applicable to HIV care in the country. We confirmed the validity of these attributes by conducting qualitative interviews with the FACES differentiated care team, clinicians at the HIV clinic, and other healthcare personnel who worked with the HIV+ population. Of the original seven attributes that were identified, all seven were ultimately included in our final design. We categorized the levels of each attribute with a similar process. All attributes and their levels are listed below (Table 1).

Table 1. Attributes and levels of differentiated care models for HIV in Kenya.

Attributes	Levels
Location of ART refills	Health facility¹	Community meeting point	Home
Frequency of receiving ART refills	Every month	Every 3 months	Every 6 months
Person providing ART refills	Nurse	Lay health worker	Pharmacist	PLHIV²
Adherence (adh) support provided³	No adh support	Individual adh support	Group adh support
Refill pick-up/delivery times	Weekday during facility hours	Weekday (early morning, evening)	Weekend
Location of viral load sample	Health facility¹	Community meeting point	Home
Frequency of clinical visits	Every month	Every 3 months	Every 6 months	Every 12 months

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¹Either HIV-specific or integrated primary care clinic

²Either participant or other member of the group, community peer

³Individual or group counseling around ART adherence, either by peers or health workers

Questionnaires were produced by using the Choice-based Conjoint feature of Sawtooth Software™, which is widely used for DCE design, administration, and analysis [20]. Choice tasks were generated that maximize balance—meaning each level appears with the same frequency—and orthogonality—meaning each pair of levels appears with the same frequency across each pair of attributes. The experimental design generated 10 choice tasks that were added to the questionnaire, with each choice task asking a patient to choose between three hypothetical care models that have different levels of attributes (Table 2). A forced-choice format (i.e. no option to opt out of all three care models altogether) was used in order to more closely approximate the choice that a patient would have to make in real life, assuming that a stable patient with HIV would necessarily choose any care model over none at all.

Table 2. Example of choice task.

If these differentiated care models (DCMs) were your only options, which one would you choose? Please put an X under that DCM.
Attributes	DCM 1	DCM 2	DCM 3
Location of ART refills	Community meeting point	Home	Health Center
Frequency of receiving ART refills	every 3 months	Every 3 months	Every month
Adherence support provided	No support	Individual support	Group support
Person providing ART refills	Pharmacist	PLHIV	Nurse
		X

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Sawtooth generates the design by sampling from a subset of the full-choice designs for each respondent while ensuring level balance and near-orthogonality within each respondent’s profile. This allows for the generation of up to 999 blocks, and using a unique randomized design for each respondent reduces context effects. We used a fractional factorial design to reduce the number of choice tasks required in the experiment and removed combinations of attributes and levels that would not be feasible or practical (e.g. patient receiving ART refill at clinic but having viral load taken at home). Finally, we used a partial profile design wherein each choice task was limited to four attributes rather than the total seven. Compared to full profile, a partial profile design reduces the cognitive burden on patients and thus lowers response error, producing results with greater predictive validity [21].

Patients were randomly allocated to receive one of twelve different, randomly-generated versions of the questionnaire, which had been translated to Dholuo and Kiswahili and uploaded onto Sawtooth servers. These questionnaires were accessed via Android tablets at the HIV clinic. Patients were given verbal instructions at the start of the interview and before each choice task to ensure understanding of the care models being presented. Each attribute and level was explained, and patients were allowed to complete the survey at their own pace.

To have enough power to reveal main effects, we needed a minimum of 67 patients based on the following sample size calculation for conjoint-based analysis in discrete choice experiments:

\frac{n t a}{c} \geq 500

where n is the number of patients, t is the number of choice tasks per questionnaire (t = 10 in our study), a is the number of options per choice task (a = 3), and c is the number of cells (c = 4) [22, 23]. To have enough power to examine all two-way interactions between attributes, we needed 267 patients based on the same formula as above, setting c = 16 to reflect the largest product of levels of any two attributes.

Patients were introduced to the study during their clinician visit if they met criteria as a stable patient. One of three researchers approached each interested patient to obtain oral consent, and administered the questionnaire containing sociodemographic information and the ten choice tasks in the language of the patient’s choosing. Patient IDs were also collected to obtain additional data from the EMR. Detailed field notes and observations were taken in tandem. Basic demographic information was collected from all participants: age, gender, education level, average monthly income, and average travel time to the clinic.

We used parametric and non-parametric tests to summarize all sociodemographic information using R version 3.2.3 [24]. Significance testing for continuous variables was completed using Student’s t-test or Wilcoxon’s Rank Sum test where appropriate and for categorical variables using Chi Square or Fisher’s exact test where appropriate. Relative utilities and average importance for each of the levels and attributes, respectively, were calculated by Sawtooth Software using Hierarchical Bayesian (HB) analysis and effects coding [22, 24]. Compared to mixed logit or latent class models which may be used to analyze DCE data, HB analysis uses a two-level hierarchical model to generate both relative utilities for the population as well as individual utilities which can be used to identify detailed preference segments in the population. The HB model in Sawtooth has two levels: At the upper level, it is assumed that individuals’ vectors of part-worths are drawn from a multivariate normal distribution [25]. At the lower level, a logit model is assumed for each individual, where the utility of each alternative is the sum of the part-worths of its attribute levels, and the respondent’s probability of choosing each alternative is equal to its utility divided by the sum of utilities for the alternatives in that choice set [25]. Several Markov Chain Monte Carlo (MCMC) simulations of an algorithm using these model estimates generates the part-worths for the individuals, the mean for the population, and variances and covariances [25]. Represented as a formula, the utility function we used is described by Rao et al as follows:

U_{t} (x_{j t}) = D_{t 1} U_{t 1} + D_{t 2} U_{t 2} + \dots + D_{t r_{t}} U_{t r_{t}}

{U t}_{(x j t)} \sim N ({D t}_{i} {U t}_{i})

Where U_t = part-worth function of the t^th attribute; x_jt = level for the j^th profile on the t^th attribute; r_t = number of levels for the t^th attribute; D_tk = 1 if the value x_jt is equivalent to the k^th discrete level of x_t and 0 otherwise; and U_tk = component of the part-worth function for the k^th discrete level of x_t [26]. The effect of each attribute of our DCE was modeled by:

U_{t} (x_{j t}) = D_{i} (\frac{t_{1} + t_{2} + \dots + t_{n}}{k}) * U_{i}

Where D_i = the discrete value assigned for each of i^th attributes; k = the discrete level of x_t; and t₁+t₂+…+t_n is the sum of the attributes at the n^th level.

In addition, average importances are calculated in Sawtooth via standard probability analysis by dividing the utility range for each attribute (i.e. the utility of the highest level minus the utility of the lowest level) by the total sum of utility ranges for all attributes [22]. Average importances are reported as percentages and can be interpreted as how important each attribute is for a patient when making a decision regarding their preferred DSD model [20]. Descriptive statistics were used to describe any differences in relative utilities or importances across various socio demographic groups, although the study was not powered to confidently detect differences between sub-groups. Qualitative data from field notes were summarized using narrative analysis, whereby researchers identified common unifying stories that arose in response to probing questions about patients’ decision-making process [27].

This evaluation was approved by the Kenya Medical Research Institute Ethical Review Committee and the University of California San Francisco Human Research Protection Program as part of routine program evaluation within the Family AIDS Care and Education Services (FACES) program.

Results

Sociodemographic characteristics

A total of 104 stable patients with HIV were recruited from the HIV clinic in Kisumu over a 5-week period beginning May 2017. The mean age of our study sample was 41 years, and 62.5% of the participants were women (Table 3). Women were significantly younger with a lower income than their male counterparts (Table 4).

Table 3. Sociodemographic characteristics of HIV-infected patients (n = 104).

Characteristic	Number (%)	Mean	Std Dev
Gender
Men	39 (37.5)
Women	65 (62.5)
Other	0 (0.0)
Age (yrs)		41.06	10.89
18–34.9	32 (30.8)
35–49.9	47 (45.2)
50–64.9	23 (22.1)
> = 65	2 (1.9)
Survey Language
English	51 (49.0)
Kiswahili	12 (11.5)
Dholuo	41 (39.4)
Education Level ¹
None	1 (1.0)
Primary	50 (48.1)
Secondary	41 (39.4)
Univ/Postgrad	12 (11.5)
Income (KSH/mo)		9161	12966
Below PL²	61 (58.7)
Low income³	36 (34.6)
Middle income+	7 (6.7)
Travel time (min)		48.08	44.9
0–29.9	26 (25.0)
30–59.9	47 (45.2)
> = 60	31 (29.8)

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¹Indicates highest level of education started or completed

²PL = Poverty line, monthly income below KSH 6200

³Low income = Monthly income between KSH 6200 and 26000

Table 4. Comparison of continuous and categorical variables by gender amongst HIV-infected patients (n = 104).

	Gender
Continuous Variable	Men	Women	test stat	p
Age, mean (yrs)	44.64	38.91	t = 2.553	^*0.013
Income, median (KSH/mo)	7000	4000	W = 1590	^*0.03
Travel time, median (min)	30	30	W = 1373	0.466
Categorical Variable	Men	Women	test stat	p
Survey Language			χ² = 5.92	0.052
English, n	22	29
Kiswahili, n	7	5
Dholuo, n	10	31
Education Level			Fisher	0.858
None, n	0	1
Primary, n	17	33
Secondary, n	17	24
Univ/Postgrad, n	5	7

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* significant at p < 0.05.

** significant at p < 0.01.

Histogram of all significant differences provided on the right.

Relative utilities and importances

Relative utilities (RUs) represent patient preference and were calculated for each level of the discrete choice experiment (Table 5).

Table 5. Average utilities of all levels included in our study.

Attributes and Levels	Avg Utilities	95% CI	Std Dev	95% CI	Attributes and Levels	Avg Utilities	95% CI	Std Dev	95% CI
Location of ART Refills					Adherence Support
Health Centre	50.0	(37.45, 62.45)	64.26	(,)	N o support	-19.3	(-23.77, -14.76)	23.15	(,)
Community meeting point	-46.1	(-56.00, -36.14)	51.05	(,)	Individual support	15.0	(10.71, 19.35)	22.19	(,)
Home	-3.9	(-14.41, 6.65)	54.15	(,)	Group support	4.2	(-0.61, 9.08)	24.92	(,)
Location of Clinical Review					Person delivering ART
Health Centre	69.3	(57.95, 80.62)	58.30	(,)	Nurse	-2.8	(-6.88, 1.22)	20.81	(,)
Community meeting point	-53.1	(-61.83, -44.36)	44.93	(,)	Pharmacist	21.4	(17.09, 25.80)	22.39	(,)
Home	-16.2	(-27.80, -4.58)	59.71	(,)	PLHIV (community peer)	-24.5	(-29.04, -19.94)	23.40	(,)
Frequency of ART Refills					Lay health worker	5.9	(1.35, 10.40)	23.27	(,)
Every month	-49.5	(-56.38, -42.67)	35.24	(,)	Refill delivery time
Every 3 months	15.9	(12.08, 19.71)	19.60	(,)	Weekday (regular hours)	16.3	(11.61, 21.02)	24.18	(,)
Every 6 months	33.6	(27.80, 39.46)	29.96	(,)	Weekday (off hours)	-1.8	(-6.45, 2.85)	23.90	(,)
Frequency of Clinical Rev					Weekend	-14.5	(-18.58, -10.44)	20.93	(,)
Every month	-50.7	(-56.85, -44.56)	31.58	(,)
Every 3 months	7.4	(3.80, 10.96)	18.42	(,)
Every 6 months	40.1	(33.52, 46.72)	33.95	(,)
Every 12 months	3.2	(-2.02, 8.44)	26.90	(,)

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A higher magnitude (or darker shade) indicates a stronger preference, while a positive or negative value (green or red) indicates a positive or negative preference.

*Red represents the least preferred attribute level and green represents the most preferred.

*All differences are statistically significant at p < 0.05, except for 3 months vs 12 months for Frequency of Clinical Review

For location of ART refills, patients strongly preferred a model of care where they received ART at the health center, followed by a home-delivery model, followed by a community-point-delivery model (RU = 49.95, -3.88, and -46.07). The same trends were found for location of clinical review (RU = 69.28, -16.19, and -53.10).

For frequency of ART refills, patients preferred a model of care where they only had to collect their drugs every 6 months, followed closely by a model with ART refills every 3 months (RU = 33.63 and 15.89). They overwhelmingly did not prefer a 1-month ART refill system relative to the other two options (RU = -49.53). Similarly, for frequency of clinical review, patients strongly preferred a model of care where they had a 6-month time between clinical appointments (TCA), followed by either a 3-month or 12-month TCA, and a strong preference against a 1-month TCA (RU = 40.12, 7.38, 3.21, and -50.71).

For adherence support provided, patients preferred an individual support system (i.e. one-on-one counseling) over a group support system. However, they preferred either option over having no support at all (RU = 15.03, 4.23, and -19.27). Patients preferred that the person delivering ART be a pharmacist, followed by a lay health worker, followed by a nurse, and finally by a community peer/PLHIV (RU = 21.45, 5.87, -2.83, and -24.49). Finally, for refill pick-up/delivery times, patients preferred to collect their drugs during regular clinic hours—between 8 AM and 4 PM—instead of off-hours or on the weekend (RU = 16.31, -1.80, and -14.51).

The average importance of each attribute of a care model is presented in Table 6. The location of clinical review and the location of ART refills were the most important attributes in the decision-making process when patients were faced with the choice tasks, and both were significantly more important to patients than any other attributes (p < 0.05).

Table 6. Average importances of all attributes included in our study.

Attribute	Avg Importances (%)	Std Dev	95% CI
Location of clinical review	24.1	6.6	(22.8, 25.4)
Location of ART Refills	21.6	7.6	(20.1, 23.1)
Frequency of clinical visits	15.2	6.0	(14.0, 16.3)
Frequency of ART refill	13.5	7.6	(12.0, 15.0)
Person providing ART	9.7	3.9	(8.9, 10.4)
Adherence Support	8.2	4.1	(7.4, 9.0)
Refill pick-up/deliv time	7.8	3.9	(7.0, 8.5)

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The values represent what proportion of a patient’s decision was made based on that attribute. A higher value indicates that the attribute is considered more important by patients when choosing a model of care.

The next most important attributes were the frequency of clinical review and the frequency of ART refill, with average importances of 15.18 and 13.50, respectively. These are followed in importance by the person providing ART, the form of adherence support provided, and the schedule for ART refill delivery (Avg. Importance = 9.65, 8.19, and 7.78, respectively).

Differences in DCE results by gender

Differences in relative utilities between men and women were also assessed (Table 7). Both men and women strongly preferred to receive their ART at a health center than at a community meeting point or at home, but women found home-based delivery more acceptable than did men (RU = 9.76 and -24.47, respectively; p < 0.05). Both genders also preferred to receive their drugs from a pharmacist rather than a nurse, lay health worker, or peer. Women, however, found it more acceptable to receive ART refills from lay health workers than did men (RU = 20.80 and -4.59; p < 0.05).

Table 7. Normalized average utilities of all levels by gender.

	Gender
	Men				Women
Attributes and Levels	Avg Utilities	95% CI	Std Dev	95% CI	Avg Utilities	95% CI	Std Dev	95% CI
Location of ART Refills
Health Centre	62.2	(39.47, 84.99)	70.2	(61.79, 81.31)	45.1	(26.68, 63.56)	74.4	(65.49, 86.17)
Community meeting point	-37.8	(-54.28, -21.26)	50.9	(44.82, 58.97)	-54.9	(-69.68, -40.08)	59.7	(52.57, 69.17)
Home*	-24.5	(-38.24, -10.70)	42.5	(37.38, 49.19)	9.8	(-5.38, 24.90)	61.1	(53.78, 70.77)
Location of Clinical Review
Health Centre	57.0	(37.69, 76.31)	59.6	(52.43, 68.99)	57.0	(41.09, 73.01)	58.3	(51.31, 67.51)
Community meeting point	-43.0	(-54.27, -31.73)	34.8	(30.60, 40.26)	-43.0	(-57.62, -28.29)	59.3	(52.19, 68.67)
Home	-14.0	(-29.89, 1.88)	49.0	(43.12, 56.74)	-14.1	(-33.07, 4.87)	60.3	(53.07, 69.82)
Frequency of ART Refills
Every month	-64.0	(-76.34, -51.76)	37.9	(33.36, 43.90)	-52.6	(-62.29, -42.94)	39.1	(34.37, 45.23)
Every 3 months*	28.1	(21.39, 34.80)	20.7	(18.20, 23.95)	5.2	(-1.46, 11.92)	27.0	(23.76, 31.26)
Every 6 months	36.0	(23.19, 48.71)	39.4	(34.65, 45.60)	47.4	(40.26, 54.50)	28.7	(25.29, 33.28)
Frequency of Clinical Rev
Every month	-62.7	(-75.57, -49.80)	39.8	(34.99, 46.04)	-45.8	(-52.92, -38.67)	28.8	(25.30, 33.29)
Every 3 months	7.0	(0.21, 13.85)	21.0	(18.52, 24.36)	5.8	(-0.48, 12.01)	25.2	(22.19, 29.19)
Every 6 months	37.3	(26.18, 48.45)	34.4	(30.24, 39.79)	54.2	(46.12, 62.29)	32.6	(28.71, 37.78)
Every 12 months*	18.3	(10.05, 26.62)	25.6	(22.49, 29.60)	-14.2	(-21.12, -7.23)	28.0	(24.68, 32.47)
Adherence Support
N o support	-62.5	(-69.84, -55.16)	22.6	(19.93, 26.22)	-43.1	(-49.53, -36.72)	25.8	(22.74, 29.92)
Individual support	37.5	(26.44, 48.55)	34.1	(30.02, 39.50)	56.9	(52.27, 61.48)	18.6	(16.36, 21.52)
Group support*	25.0	(15.87, 34.15)	28.2	(24.81, 32.65)	-13.8	(-21.38, -6.13)	30.8	(27.08, 35.63)
Person delivering ART
Nurse	3.1	(-7.18, 13.42)	31.8	(27.96, 36.79)	-10.9	(-15.40, -6.48)	18.0	(15.84, 20.85)
Pharmacist	50.7	(44.07, 57.40)	20.5	(18.08, 23.80)	45.1	(37.97, 52.18)	28.7	(25.23, 33.20)
PLHIV (community peer)	-49.3	(-59.91, -38.62)	32.9	(28.91, 38.04)	-54.9	(-64.27, -45.59)	37.7	(33.17, 43.64)
Lay health worker*	-4.6	(-18.16, 8.98)	41.9	(36.85, 48.49)	20.8	(15.14, 26.46)	22.9	(20.11, 26.47)
Refill delivery time
Weekday (regular hours)	47.4	(36.57, 58.22)	33.4	(29.40, 38.68)	51.8	(46.20, 57.38)	22.5	(19.85, 26.11)
Weekday (off hours)	5.2	(-3.49, 13.90)	26.8	(23.61, 31.07)	-3.6	(-10.09, 2.93)	26.3	(23.12, 30.42)
Weekend	-52.6	(-60.91, -44.30)	25.6	(22.55, 29.67)	-48.2	(-53.76, -42.66)	22.4	(19.72, 25.94)

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A higher magnitude indicates a stronger preference, while a positive or negative value indicates a positive or negative preference. A star (*) denotes a level where preferences were significantly different between men and women (p < 0.05).

Both men and women strongly disliked receiving their ART refills on a monthly basis, preferring instead to receive them at three, six, or twelve-month intervals. Men, however, had almost equal preference for three and six-monthly refills (RU = 28.1 and 36.0), whereas women highly preferred the latter (RU = 5.2 and 47.4; p < 0.05). Both genders preferred six-monthly clinical review compared to one, three, or twelve-monthly options. Finally, men reported a stronger preference for a group-based support system than did women (RU = 25.01 and -13.75; p < 0.05). Though both genders still believed individual support was the best option, men viewed group support as a close second, whereas women did not.

Field notes and observations

Field notes were taken during each interview by the researcher conducting the questionnaire. If patients cited a specific reason for their decision in a choice experiment, this was documented in notes that were reviewed at the end of the study period.

A majority of patients interviewed preferred models of care in which ART delivery and clinical review were both performed at the health center, as opposed to a community-meeting point or at home. Stigma against HIV was most commonly cited as the reason for this preference.

Patients almost unanimously preferred models of care with longer periods of time between either clinical reviews or ART deliveries. Work and travel restrictions were cited as the most common reasons for this preference

Most patients strongly preferred to receive their drugs from the pharmacist because they view pharmacists as those with the most training for the task. However, a few patients had interacted with lay health workers before, and were fine with models of care that utilize these individuals to take charge of ART delivery.

Patients unanimously preferred some form of social support over none at all. However, given the choice between an individual support or a group support system, patients chose the former. Patients reported fear of stigma, lack of individualized attention to understand how to adhere to one’s medications, and challenges in managing a group dynamic as reasons they did not want a group support system.

Finally, most patients preferred care models in which ART delivery and clinical review happened during regular clinic hours simply because this is what they were used to. However, a few patients saw value in off-hours care, particularly if it allowed them more flexibility in their work.

Discussion

Our discrete choice experiment revealed clear preferences of certain features of HIV care and treatment among PLHIV in Kenya with immediate implications for public health programming. Patients desired to come to the facility less frequently, with six-month intervals showing the greatest utilities. Patients, however, also valued location of care highly when choosing between models: counter to expectations, they strongly preferred models where clinical review and ART refills were done at a central location (e.g. the health clinic) instead of at home or in the community. Patients strongly preferred to interact with healthcare professionals: they preferred physicians/pharmacists rather than peers, and they preferred individual psychosocial support rather than group therapy.

Our findings support previous literature that suggests that excessive visits to the facility for clinical review or medication refill represents an undesired barrier. At present, standard of care in Kenya has increased visit intervals from monthly to quarterly [14], but these data suggest even longer intervals would be more preferred. Six months appeared to be the optimal period, as patients felt a decreasing utility associated with yearly visits for clinical review [28]. These data also mirror observation studies that suggest that a visit interval of six months was associated with a smaller chance of a missed visit as compared to shorter assigned return intervals. While at present the Kenya Ministry of Health recommends 3 months as an upper limit for ART to accommodate supply chain and stocking [28, 29], these data suggest a potential public health benefit to developing the capability to procure, maintain, and distribute enough ART to dispense 6-months of medications.

Many studies have shown the efficacy of community-based forms of HIV care in Kenya and SSA at large in reducing the burden on the facility, alleviating clinician workload, and reducing frequency of clinical appointments (TCA) [30–32]. Although community-based care seems beneficial from a systems standpoint, we found that patients themselves strongly prefer facility-based care models, and patients would often choose a facility-based care model even if it meant sacrificing other benefits such as reduced travel time, individual counseling, or less frequent TCA. Additionally, other studies have proposed using peer health workers and other lay health providers to help decrease health facility staff workloads. However, our study indicates that the person responsible for delivering care was also an important attribute for patients. Patients strongly preferred to receive care by facility-based workers (i.e. clinicians and pharmacists), and not from PLHIV. One predominant barrier to the implementation of community- or peer-based models of care is the continued high level of stigma and discrimination against patients with HIV in Kenya, which was noted multiple times from field notes during the study [33–35].

Participants in our study prefer to have some form of psychosocial support to help them manage their condition. We observe that many clinics in the region have already led the way in the design and implementation of group support systems for PLHIV, albeit with varying levels of success [36]. However, given the option between an individual support system—such as one-on-one counseling—and a group support system, patients showed a preference towards the former.

Kenya’s ongoing push for universal healthcare must be considered when examining our study results. Barriers to this goal include the gap in healthcare access between rural and urban communities, redundancies in service delivery, and a relative shortage of healthcare workers [37, 38]. In addition to being the model most preferred by the patients in our study, a multi-month scripting, fast-track model of care would also help improve the efficiency of the healthcare system and reduce the amount of time spent by workers on stable patients. As HIV/AIDS remains amongst the leading causes of death and disability in the country, adaptation of this model would aid in Kenya’s efforts to refinance and restructure the healthcare system to provide universal coverage [37, 38].

Our study’s primary strength lies in its design. DCEs provide more useful information than does traditional qualitative research in several ways. First, our study allowed us to not only rank, but also to quantify the magnitude of patient preferences for each attribute and corresponding levels. Such information will be useful for implementors who need to prioritize between several service characteristics when designing a DSD model. Second, our questionnaires simulated choices that patients may have to make in real life, which provides more earnest insight into a patient’s preferences than would a traditional survey. Finally, we used prohibitions to ensure that all randomly generated care models would be feasible, so that patients would not be choosing between options that could never exist. Some of our findings support previous research, but many also challenge conventional approaches to differentiated care in Kenya.

However, our study had a few limitations. First, we did not meet the sample size goal to examine all two-way interactions between attributes. Our sample was large enough to reveal all significant within-attribute differences, but not large enough to analyze how preferences might vary between certain sociodemographic characteristics like income or level of education. Future research should prioritize understanding how preferences vary amongst different groups of patients. In addition, it will be important to explore how a patient’s preference for one aspect of a care model might influence their preference for another (e.g. how does the preference for location of ART delivery interact with preference for type of support system). Second, we recruited from a naïve population of stable patients in Kisumu. None of the patients interviewed had any previous exposure to any form of differentiated care, so all of their responses were based on what they deemed to be the hypothetical best option. However, this may account for some of the differences seen in which differentiated care choices were acceptable in this study versus other studies where patients were assessed for their thoughts on a model after participating in it. For example, one year after implementation of a community ART group in Lesotho, patients reported overall satisfaction with the care model, citing reduced stigma against HIV in their community in addition to the expected benefits of reduced visit time and increased retention [39].

Finally, the COVID-19 pandemic may reshape patient preferences and health systems practices in a way that we cannot predict with our current data. Certain changes to HIV care in Kenya in response to COVID-19 coincide with findings from our study. For example, the Kenyan Ministry of Health was recently able to procure and distribute an additional 3-month supply of ART to all FACES HIV clinics in Kenya, effectively increasing the space between ART refills leading to a 50.7% reduction in average daily clinic attendance [40]. This practice coincides with our finding that patients strongly prefer longer spacing between ART refills. However, other changes may be inconsistent with findings from our study. For example, FACES and the Kenyan Ministry of Health have created a goal to scale-up community distribution of ART in order to further decongest clinics and thus reduce transmission of COVID-19 [36]. It is conceivable that patient preference may shift to community- and home-delivery of ART in light of COVID-19. While we cannot assess this and other potential changes with our current data, we believe that this DCE will be useful to ensure that health systems provide the right mix of choices to meet changing patient preferences.

Conclusions

Differentiated care promises to reduce system inefficiencies—such as unnecessary resources used on stable patients—while simultaneously improving patients’ experiences with and retention in treatment regimens. As new research emerges and clinics throughout the country begin to test early forms of differentiated care, special attention must be paid to consider patient preferences in the design and implementation of these care models. Most importantly, we found that patients strongly prefer to stay in a centralized model of HIV care—one in which care is delivered in a health facility by trained health workers. This data must be taken into account in conjunction with the realities of limitations in care provision, but we must remain diligent in finding ways to improve HIV care in Kenya without sacrificing the needs and desires of patients themselves.

Supporting information

S1 Dataset. Raw data generated by Sawtooth Software based on the questionnaires submitted by the patients in our study.

This data can only be accessed by software that uses SQL programming language, such as Sawtooth.

(XLSX)

Click here for additional data file.^{(3.9MB, xlsx)}

Acknowledgments

Thank you to the directors of the Global Health program at the University of California-San Francisco who supported this research academically. Finally, a special thanks to the clinicians and staff at Lumumba health clinic in Kisumu, Kenya where this research was conducted.

Data Availability

All relevant data are within the paper and its Supporting Information files. We have included the raw data as a .db3 file, which can be accessed by software that uses the SQL programming language. The data was stored this way by the software that we used for our study.

Funding Statement

EG received a grant from the National Institutes of Health under the terms of K24 AI134413 (https://www.nih.gov/). SD received a travel grant from the University of California-San Francisco to cover international transportation and housing costs for the primary author. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.National AIDS and STI Control Programme (NASCOP), Preliminary KENPHIA 2018 report. Nairobi: NASCOP; 2020. [Google Scholar]
2.Cherutich P, Kim AA, Kellogg TA, et al. Detectable HIV Viral Load in Kenya: Data from a Population-Based Survey. PLoS One 2016; 11: 1–14. doi: 10.1371/journal.pone.0154318 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Ministry of Health, National AIDS Control Council, Kenya. 2016. Kenya AIDS Response Progress Report 2016.
4.Joint United Nations Programme on HIV/AIDS (UNAIDS). 2014. 90-90-90: An ambitious treatment target to help end the AIDS epidemic.
5.Broder M. Differentiated Care: A New Concept in the Worldwide Effort to End HIV. MedPage Today. 2016. [Google Scholar]
6.World Health Organization. Consolidated guidelines on the use of antiretroviral drugs for treating and preventing HIV infection: recommendations for a public health approach. WHO, Geneva 2016; 2nd Edition: 237–68. [PubMed]
7.Maman D, Zeh C, Mukui I, et al. 2015. Cascade of HIV care and population viral suppression in a high-burden region of Kenya. AIDS, 29:1557–65. doi: 10.1097/QAD.0000000000000741 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Duncombe C, Rosenblum S, Hellmann N, et al. 2015. Reframing HIV care: putting people at the centre of antiretroviral delivery. Trop Med Int Health, 20(4):430–47. doi: 10.1111/tmi.12460 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.World Health Organization (WHO). 2015. HIV Treatment and Care: What’s New in Service Delivery.
10.Hanrahan CF, Schwartz SR, Mudavanhu M, et al. The impact of community- versus clinic-based adherence clubs on loss from care and viral suppression for antiretroviral therapy patients: Findings from a pragmatic randomized controlled trial in South Africa. 2019; PLOS Medicine: 1–15. doi: 10.1371/journal.pmed.1002808 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Geng EH, Holmes CB (2019) Research to improve differentiated HIV service delivery interventions: Learning to learn as we do. PLOS Medicine 16(5): e1002809 doi: 10.1371/journal.pmed.1002809 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Lindsay J Mangham, Kara Hanson, Barbara McPake; How to do (or not to do) … Designing a discrete choice experiment for application in a low-income country, Health Policy and Planning, Volume 24, Issue 2, 1 March 2009, Pages 151–158 [DOI] [PubMed]
13.Terris-Prestholt F., Neke N., Grund J.M. et al. Using discrete choice experiments to inform the design of complex interventions. Trials 20, 157 (2019) doi: 10.1186/s13063-019-3186-x [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Kulzer JL, Penner JA, Marima R, et al. 2012. Family model of HIV care and treatment: a retrospective study in Kenya. J Int AIDS Soc, 15:8. doi: 10.1186/1758-2652-15-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Ministry of Health, Kenya. 2016. Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection in Kenya.
16.National AIDS and STI Control Programme (NASCOP), Kenya. 2016. Improving the Quality and Efficiency of Health Services in Kenya: A Practical Handbook for HIV Managers and Service Providers on Differentiated Care.
17.Geng E. 2014. Adaptive Strategies for Preventing and Treating Lapses of Retention in Care (AdaPT-R). [Google Scholar]
18.Larson E, Vail D, Mbaruku GM, et al. 2015. Moving toward patient-centered care in Africa: A discrete choice experiment of preference for delivery care among 3,003 Tanzanian women. Plos One, 10:1–12. doi: 10.1371/journal.pone.0135621 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Mangham LJ, Hanson K, Mcpake B. 2009. How to do (or not to do)… Designing a discrete choice experiment for application in a low-income country. Health Policy and Planning, 24(2):151–8. doi: 10.1093/heapol/czn047 [DOI] [PubMed] [Google Scholar]
20.Sawtooth Software. The CBC/HB System for Hierarchical Bayes Estimation. Tech Pap Ser, 1–30.
21.Chrzan K. Using partial profile choice experiments to handle large numbers of attributes. 2010; International Journal of Market Research 52(6). [Google Scholar]
22.Orme B. 2010. Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research. Madison, WI: Research Publishers LLC. [Google Scholar]
23.Johnson FR, Lancsar E, Marshall D, et al. Constructing Experimental Designs for Discrete-Choice Experiments: Report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. 2013; 6: 3–13. [DOI] [PubMed] [Google Scholar]
24.The R Foundation. 2017. The R Project for Statistical Computing.
25.Johnson RM. Understanding HB: An Intuitive Approach. Sawtooth Softw Res Pap Ser 2000. [Google Scholar]
26.Rao VR, Sheth JN, Malhotra NK. (2010). Conjoint Analysis. Wiley International Encyclopedia of Marketing.
27.Allen M. 2017, The sage encyclopedia of communication research methods, vol. 4, SAGE Publications, Inc, Thousand Oaks, CA [Google Scholar]
28.National AIDS and STI AIDS and STI Control Programme (NASCOP), Kenya. 2017. Differentiated Care: Operational Guide.
29.Larson C, Burn R. Minnick-Sakal A, et al. 2014. Strategies to reduce risks in ARV supply chains in the developing world. Glob Health Sci Pract, 2(4):395–402. doi: 10.9745/GHSP-D-14-00105 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Decroo T, Rasschaert F, Telfer B, et al. 2013. Community-based antiretroviral therapy programs can overcome barriers to retention of patients and decongest health services in sub-Saharan Africa: a systematic review. Int Health, 5(3):169–79. doi: 10.1093/inthealth/iht016 [DOI] [PubMed] [Google Scholar]
31.Wouters E, Van Damme W, Van Rensburg D, et al. 2012. Impact of community-based support services on antiretorivral treatment program delivery and outcomes in resource-limited countries: a synthetic review. BMC Health Serv Res, 12(1):194. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Medecins Sans Frontieres (MSF): MSF Analysis & Advocacy Unit Brussels with Southern Africa Medical Unit. 2013. Reaching Closer to Home: Progress implementing community-based and other adherence strategies supporting people on HIV treatment.
33.Brown L, Macintyre K, Trujillo L. 2003. Interventions to reduce HIV stigma: What have we learned? AIDS Educ Prev, 15:49–69. doi: 10.1521/aeap.15.1.49.23844 [DOI] [PubMed] [Google Scholar]
34.National Empowerment Network of People Living with HIV and AIDS in Kenya (NEPHAK). 2011. The People Living with HIV Stigma Index–Kenya.
35.Dlamini PS, Wantland D, Makoae LN, et al. 2009. HIV Stigma and Missed Medications in HIV-Positive People in Five African Countries. AIDS Patient Care and STDs, 23(5):377–87. doi: 10.1089/apc.2008.0164 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Khabala KB, Edwards JK, Baruani B, et al. 2015. Medication Adherence Clubs: A potential solution to managing large number of stable patients with multiple chronic diseases in informal settlements. Trop Med Int Heal, 20:1265–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Okech TC, Lelegwe SL. Analysis of Universal Health Coverage and Equity on Health Care in Kenya. Glob J Health Sci 2016; 8: 218–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Mbau R, Kabia E, Honda A, Hanson K, Barasa E. Examining purchasing reforms towards universal health coverage by the National Hospital Insurance Fund in Kenya. Int J Equity Health. 2020Feb3;19(1):19. doi: 10.1186/s12939-019-1116-x; PMCID: PMC6998279. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Vandendyck M, Motsamai M, Mubanga M, et al. Community-based ART resulted in excellent retention and can leverage community empowerment in rural Lesotho, a mixed method study. HIV/AIDS Res Treat Open J. 2015; 2(2): 44–50. [Google Scholar]
40.Odhiambo F, Mulwa E, Ayieko J et al. 2020. Implementation of HIV care in Western Kenya during corona virus disease 2019 response. AIDS 2020, 33:000–000. doi: 10.1097/QAD.0000000000002576 [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0255650.r001

Decision Letter 0

Matthew Quaife

15 Sep 2020

PONE-D-20-24745

Preferences of People Living with HIV for Differentiated Care Models in Kenya: A Discrete Choice Experiment

PLOS ONE

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Reviewer #1: The authors have addressed the above questions. A couple of comments for the manuscript.

I would like to acknowledge that I have reviewed your paper titled “Preferences of People Living with HIV for Differentiated Care Models in Kenya: A Discrete Choice Experiment.” It is a very important topic as countries aim for patient-centered care as well as adherence to ART among PLHIV.

Below are a few comments that the authors can consider.

1.Although the attribute identification for the DCE is supported by evidence (Literature review and qualitative interviews), the authors do not justify how the final seven attributes and their levels were arrived at. For instance, its not clear how many attributes were identified from the literature review, how many (more or less) attributes were identified from the qualitative interviews and what process was involved in perhaps reducing the number of attributes or having a consensus about the attribute levels. These are important aspects of reporting conjoint analyses (Bridges et al., 2011). Please clarify

2.It is not clear to me why the example choice task, Table 2, has four attributes instead of the seven. Does this mean the “twelve different, randomly generated” questionnaire versions had a different number/set of attributes? (If I interpret this correctly, 12 blocks were generated each with 10 choice tasks – If this is the case then each task, irrespective of the block would have all the seven attributes and three alternatives). On the other hand, the authors might have opted for partial profiles, however, this has not been described anywhere. Please explain.

3.Given that the study took place in one facility, it is surprising that the study questionnaire was not piloted. How can we guarantee that respondents could easily understand the attributes and levels, the number of attributes and alternatives in each task and generally the number of choice tasks per questionnaire did not result in respondent cognitive burden? Please clarify.

4.Table 4, I suggest the units (years, KSH/month and minutes) be bracketed to avoid confusion. For example, the travel time unit could be interpreted as presenting the minimum value (which can be abbreviated as min).

5.The authors state that they did not meet their original sample size goal. This is not true. They actually surpassed their target minimum sample size of 67 respondents, however, this sample size was only optimized to examine main effects but not all possible two-way interactions. To have enabled an examination of these, ‘c’ should have been equal to the largest product of levels of any two attributes (in this case 12). Rephrase this limitation.

Reviewer #2: I have the following comments

1.In the introduction section, provide a brief context of the Kenyan health system and how the current HIV care is delivered. This will help explain why DSD models in Kenya are important. Just a paragraph would do to help any readers to understand the context, understand why a DCE was needed, and understand the policy context. State why the DCE was better than any other method out there.

2.The methodology section is not clear. It omits a lot of crucial information that helps the reader understand what was done.

3.“Patients were introduced to the study during their clinician visit if they met criteria as a stable patient. One of three researchers approached each interested patient to obtain oral consent, and administered the questionnaire containing sociodemographic information and the ten choice tasks in the language of the patient’s choosing. Patient IDs were also collected to obtain additional data from the EMR. Detailed field notes and observations were taken in tandem. Basic demographic information was collected from all participants: age, gender, education level, average monthly income, and average travel time to the clinic.” – This piece of text should be put in the right place which describes data collection. This paragraph should come after sample size calculation because you talk about 10 choice tasks being administered while you haven’t described attributes, levels, experimental design, questionnaire format, and sampling. Therefore, it should be placed further down the methodology section preferably after sampling/sample size calculation.

4.I can see that from Table 2, the DCE was unlabelled with three hypothetical alternatives without an opt-out. You need to state this in the text before the sentence “Questionnaires were produced by using the Choice-based Conjoint feature of Sawtooth Software™. Justify in the text why you opted for a forced-choice format i.e. why was the opt-out not needed.

5.In the construction of choice tasks, did you use partial profiles? Because Table 1 shows 7 attributes while Table 2 shows only 4 attributes. If so, state this in the text why partial profiles were used instead of full profiles.

6.Why was an orthogonal design appropriate for this study while there are far better designs such as efficient and Bayesian efficient designs? State this in the text. Also clearly state that the orthogonal design was a main effects model.

7.The experimental design generated 10 choice tasks which were put into the questionnaire.

8.Were the attributes and levels clearly explained to participants? State this in the text

9.“used parametric and non-parametric tests to summarize all sociodemographic information using R version 3.2.3 [19]”. State these tests?

10.“importances” Relative importance would be a better?

11.“In addition, average importances are calculated and represent the relative importance of each attribute within the experiment. Average importances are presented as percentages and can be interpreted as how important each attribute is for a patient when making a decision regarding their preferred DSD model [17].” Clearly state in the text in the methodology section the method was used to calculate relative importance estimates.

12.Write out the utility functions so we can see the model structure and coding of the attributes. What coding was used? Dummy coding or effects coding? Mention this in the text. It’s not clear at all.

13.What distributional assumptions did you make in your Hierarchical Bayes model for each attribute? Normal, lognormal, uniform? State this in text

14.What did the constant (alternative specific constant (asc)) represent in your utility function since your DCE adopted a forced choice format?

15.Tables 5 and 7: Could you clearly indicate which were the base levels (omitted categories/reference category) of the attributes? I can see you have used effects coding if I assume the base levels are first levels that appear for each attribute as the coefficient of the base level will be the negative sum of the included-category coefficients. However, you have not stated anywhere in the manuscript whether effects coding was used. You have to state this to make it easier for the reader.

16.In Tables 5 and &, provide the confidence intervals or standard errors for both the coefficients (relative utility) and standard deviations. There is only one confidence interval. Furthermore, what is the coefficient and standard deviation of your alternative specific constant (asc) and what does its coefficient represent?

17.Table 6. Which method was used to calculate the relative importance estimates (what you call “average importances”) probability analysis? Or Partial log likehood analysis? State this in the methods section and what it means.

18.The wording of the manuscript needs to be improved to reflect the fact that you are reporting a DCE.

19.Also place your study results in the context of Universal health coverage reforms in Kenya.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2021 Aug 25;16(8):e0255650. doi: 10.1371/journal.pone.0255650.r002

Author response to Decision Letter 0

16 Mar 2021

For the editors:

1. We have edited our manuscript to match PLOS One's style guidelines, including the naming of our files.

2. We have removed supporting information files that were deemed non-essential for publication.

For reviewer 1:

1. All seven attributes in our study were identified from the literature. These attributes were confirmed as key components of a differentiated service delivery model with informants (researchers, healthcare workers) in Kenya who work with the HIV+ population. Of the original seven attributes that were identified, all were included in the final study. We did not identify any additional attributes that were later excluded. This explanation has been added to the text to clarify the process.

2. Thank you for this comment. We opted for a partial profile design based on findings from Chrzan et al wherein respondents had difficulty cognitively processing choice tasks with more than six attributes. We have added an explanation for this design decision in the methods section.

3. We addressed this very real concern by having the researcher present each choice task verbally and then allowing the patient to read it. This was done in a standardized way. For example, a researcher might present a choice task as follows: “If these care models were your only options, which one would you choose? The first option is a clinic with ART refills every 3 months, viral load samples at a health facility, and an individual support system. The second option is…” We have added an explanation for this process in the methods section.

4. I’ve added brackets to the units in Table 4 to improve clarity, as you suggested.

5. This is a great point. I have changed the wording in the methods section to reflect that the n=67 sample size calculation is for main effects. I also rephrased the limitation at the end of our discussion section as you suggested.

For reviewer 2:

1. I added a paragraph to the introduction section describing how HIV care has been managed over the past decade in Kenya. Later in the introduction, I also added a few sentences to explain why a DCE is the preferred method for this study.

2. In addressing the comments below, we hope to have clarified our methodology and provided important context on how the study was designed and how the data was analyzed.

3. This paragraph was moved as you suggested.

4. It has now been clearly stated that our DCE uses a forced-choice format. Rationale for doing so has been included in the paragraph that you indicated.

5. Thank you for this comment. We opted for a partial profile design based on findings from Chrzan et al wherein respondents had difficulty cognitively processing choice tasks with more than six attributes. We have added an explanation for this design decision in the methods section.

6. We used sawtooth software to generate the choice experiment design. Sawtooth generates the design by sampling from a subset of the full-choice designs for each respondent while ensuring level balance and near-orthogonality within each respondent’s profile, this allows for the generation of up to 999 blocks, and using a unique randomized design for each respondent reduces context effects (Reference Reed Johnson. Constructing Experimental Designs for Discrete-Choice Experiments: Report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force). Sawtooth software is widely used for DCE design, administration and analysis, as a result we chose this approach for our experiment design. We have added these details to the manuscript.

7. This phrasing has been added to the text.

8. We addressed this concern by having the researcher present each choice task verbally and then allowing the patient to read it. This was done in a standardized way. For example, a researcher might present a choice task as follows: “If these care models were your only options, which one would you choose? The first option is a clinic with ART refills every 3 months, viral load samples at a health facility, and an individual support system. The second option is…” We have added an explanation for this process in the methods section.

9. We listed the exact tests used to summarize the sociodemographic variables.

10. The wording has been changed as you suggested.

11. Per Orme et al, in standard probability analysis the average importance of an attribute is calculated by dividing its utility range (i.e. the utility of the highest level minus the utility of the lowest level) by the sum total lof utility ranges of all attributes. These calculations are done automatically by Sawtooth Software. This explanation has been added to the text.

12. The HB model in Sawtooth has two levels: At the upper level it is assumed that individuals’ vectors of part-worths are drawn from a multivariate normal distribution. At the lower level, a logit model is assumed for each individual, where the utility of each alternative is the sum of the part-worths of its attribute levels, and the respondent’s probability of choosing each alternative is equal to its utility divided by the sum of utilities for the alternatives in that choice set. Several Markov chain Monte Carlo (MCMC) simulations of an algorithm using these model estimates generates the part-worths for the individual, the mean for the population and variances and covariances. We have now included these details in the manuscript and added a reference to sawtooth HB analyses methods and formulae for readers to review.

13. This is addressed above in details regarding sawtooth analysis methods.

14. This is addressed above in details regarding sawtooth analysis methods.

15. We have now stated in the methods that effects coding was used.

16. Ninety-five percent confidence intervals have been added to Tables 5 and 7 for the standard deviations. The alternative specific constant has been addressed above in details regarding sawtooth analysis methods.

17. Average importances are calculated as indicated above, using estimated from HB analysis (Reference: Sawtooth software Interpreting Conjoint Analysis Data series: Interpreting Conjoint Analysis Data)

18. In addressing the comments above, we hope to have clarified our methodology and to now better reflect that we are reporting a DCE.

19. We have added a paragraph in the discussion section to highlight how the fast-track, multi-month scripting model preferred by patients in our study may also help alleviate system inefficiencies, which would be beneficial for Kenya’s goal of providing universal healthcare to patients.

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PLoS One. doi: 10.1371/journal.pone.0255650.r003

Decision Letter 1

Matthew Quaife

20 Apr 2021

PONE-D-20-24745R1

Preferences of People Living with HIV for Differentiated Care Models in Kenya: A Discrete Choice Experiment

PLOS ONE

Dear Dr. Dommaraju,

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Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: (No Response)

Reviewer #2: I am happy with the corrections. I will just request the authors to quickly write down the utility functions and insert it in the main text, after the paragraph explaining the HB model. Then submit the manuscript to the editor for publishing.

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Reviewer #1: Yes: Jacob Kazungu

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PLoS One. 2021 Aug 25;16(8):e0255650. doi: 10.1371/journal.pone.0255650.r004

Author response to Decision Letter 1

28 Apr 2021

In addition to describing in words the functions for relative utility and attribute importance, we added the utility function used in our model which was derived from a textbook chapter on conjoint analysis by Rao et al. This is the utility function used by Sawtooth to calculate the relative utilities in our study. We hope that adding this function clarifies our approach and completes the revision of our methodology.

The additional reference mentioned above has been added to our reference list.

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PLoS One. doi: 10.1371/journal.pone.0255650.r005

Decision Letter 2

D M Basavarajaiah

9 May 2021

PONE-D-20-24745R2

Preferences of People Living with HIV for Differentiated Care Models in Kenya: A Discrete Choice Experiment

PLOS ONE

Dear Dr. Dommaraju,

Please submit your revised manuscript by Jun 23 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

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Academic Editor

PLOS ONE

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Additional Editor Comments (if provided):

Dear author

Below are a few comments that the authors can consider.

(i)The novelty of the research paper is very excellent; selection of the variables and attributes is patrsimonial state model. As per the literature, any model would be constructed or formulated by the author; the model should be expressive form and define the state variables of our objective of interest. I have carefully examined your model, you are unable to define the state variables in the model structure .Although, your formulated model will not be substantiate the state variables because not enough to propagate the state variable attributes for estimation of likelihoods based on numerical simulation. During the process of review, the following mathematical eqn affixed for your information and requested to include in your research paper

() = 11 + 22 + ⋯ + (1.1)

Ut_((xjt) ~) N (〖Dt〗_i 〖Ut〗_i )

The effect of each attributed of DCE was modeled by

Ut_xjt=Di((t1+t2..tn)/k)*Ui (1.2)

Where Di= The discrete value assigned for each of ith attributes

‘K’ is the discrete level of Xt

t1+t2..tn is the sum of the attributes at nth level

(ii) Table 7 represented the Normalized average utilities of all levels by gender. A higher magnitude indicates a stronger preference, while a positive or negative value .plz estimate the likelihood on each attributes based on the DCE state variables

iii) Table 3 Please correlate the selected attributes from weighting time for receiving ART drugs ( weighting time is the state variable)

The optimization of your model is reached maximum epoch, but interaction effect of the attributes is not mentioned in any ware either in result and discussion part.

Requested the Author,plz Rephrase the above limitation and comments .

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PLoS One. 2021 Aug 25;16(8):e0255650. doi: 10.1371/journal.pone.0255650.r006

Author response to Decision Letter 2

20 Jul 2021

Thank you for considering our manuscript for publication in your journal. Each comment from the editor in the third round of editing was helpful, especially in clarifying the methodology for our study. We addressed each comment and incorporated changes to our manuscript. Please find our responses below. Thank you.

Reviewer 1:

1. The novelty of the research paper is very excellent; selection of the variables and attributes is patrsimonial state model. As per the literature, any model would be constructed or formulated by the author; the model should be expressive form and define the state variables of our objective of interest. I have carefully examined your model, you are unable to define the state variables in the model structure .Although, your formulated model will not be substantiate the state variables because not enough to propagate the state variable attributes for estimation of likelihoods based on numerical simulation. During the process of review, the following mathematical eqn affixed for your information and requested to include in your research paper

𝑈(𝑥𝑗𝑡) = 𝐷𝑡1𝑈𝑡1 + 𝐷𝑡2𝑈𝑡2 + ⋯ + 𝐷𝑡𝑟𝑡𝑈𝑡 (1.1)

Ut_((xjt) ~) N (〖Dt〗_i 〖Ut〗_i )

The effect of each attributed of DCE was modeled by

Ut_xjt=Di((t1+t2..tn)/k)*Ui (1.2)

Where Di= The discrete value assigned for each of ith attributes

‘K’ is the discrete level of Xt

t1+t2..tn is the sum of the attributes at nth level

The above equations defining our state variables have been added to our Methods section as requested.

2. Table 7 represented the Normalized average utilities of all levels by gender. A higher magnitude indicates a stronger preference, while a positive or negative value .plz estimate the likelihood on each attributes based on the DCE state variables

It is unclear what the reviewer is suggesting in this comment. If by “likelihood on each attribute” he is referring to the importance of each attribute by gender, there were no significant differences between genders when it comes to weight given to each attribute when choosing between care models. That is, the importance of each attribute for men and women were not significantly different from the importances of the cohort at large (listed in Table 6). As this was not relevant to our discussion, it was not included in our results section. I hope this clarifies the issue.

3. Table 3 Please correlate the selected attributes from weighting time for receiving ART drugs ( weighting time is the state variable)

Table 3 represents the sociodemographic information and baseline characteristics of our study population; there is no variable called “weighting time” in this table. I believe you are referring to Table 5, and specifically the attribute “frequency of ART refills.” If so, the weight of all attributes—and thus the corresponding relative utilities assigned to each level—are relative to the attribute with the highest importance. In our study, the most important attribute for patients when deciding between care models would be “location of clinical review,” not “frequency of ART refills.” I hope this clarifies your question.

4. The optimization of your model is reached maximum epoch, but interaction effect of the attributes is not mentioned in any ware either in result and discussion part.

When describing limitations of our study in our discussion section, we specifically mention that our study was not powered to examine two-way interactions between attributes. I have added an additional sentence to this paragraph to clarify, and I hope this addresses your comment.

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PLoS One. doi: 10.1371/journal.pone.0255650.r007

Decision Letter 3

D M Basavarajaiah

22 Jul 2021

Preferences of People Living with HIV for Differentiated Care Models in Kenya: A Discrete Choice Experiment

PONE-D-20-24745R3

Dear Author

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Reviewers' comments:

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PLoS One. doi: 10.1371/journal.pone.0255650.r008

Acceptance letter

D M Basavarajaiah

9 Aug 2021

PONE-D-20-24745R3

Preferences of People Living with HIV for Differentiated Care Models in Kenya: A Discrete Choice Experiment

Dear Dr. Dommaraju:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

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on behalf of

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PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Dataset. Raw data generated by Sawtooth Software based on the questionnaires submitted by the patients in our study.

This data can only be accessed by software that uses SQL programming language, such as Sawtooth.

(XLSX)

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Data Availability Statement

[pone.0255650.ref001] 1.National AIDS and STI Control Programme (NASCOP), Preliminary KENPHIA 2018 report. Nairobi: NASCOP; 2020. [Google Scholar]

[pone.0255650.ref002] 2.Cherutich P, Kim AA, Kellogg TA, et al. Detectable HIV Viral Load in Kenya: Data from a Population-Based Survey. PLoS One 2016; 11: 1–14. doi: 10.1371/journal.pone.0154318 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref003] 3.Ministry of Health, National AIDS Control Council, Kenya. 2016. Kenya AIDS Response Progress Report 2016.

[pone.0255650.ref004] 4.Joint United Nations Programme on HIV/AIDS (UNAIDS). 2014. 90-90-90: An ambitious treatment target to help end the AIDS epidemic.

[pone.0255650.ref005] 5.Broder M. Differentiated Care: A New Concept in the Worldwide Effort to End HIV. MedPage Today. 2016. [Google Scholar]

[pone.0255650.ref006] 6.World Health Organization. Consolidated guidelines on the use of antiretroviral drugs for treating and preventing HIV infection: recommendations for a public health approach. WHO, Geneva 2016; 2nd Edition: 237–68. [PubMed]

[pone.0255650.ref007] 7.Maman D, Zeh C, Mukui I, et al. 2015. Cascade of HIV care and population viral suppression in a high-burden region of Kenya. AIDS, 29:1557–65. doi: 10.1097/QAD.0000000000000741 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref008] 8.Duncombe C, Rosenblum S, Hellmann N, et al. 2015. Reframing HIV care: putting people at the centre of antiretroviral delivery. Trop Med Int Health, 20(4):430–47. doi: 10.1111/tmi.12460 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref009] 9.World Health Organization (WHO). 2015. HIV Treatment and Care: What’s New in Service Delivery.

[pone.0255650.ref010] 10.Hanrahan CF, Schwartz SR, Mudavanhu M, et al. The impact of community- versus clinic-based adherence clubs on loss from care and viral suppression for antiretroviral therapy patients: Findings from a pragmatic randomized controlled trial in South Africa. 2019; PLOS Medicine: 1–15. doi: 10.1371/journal.pmed.1002808 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref011] 11.Geng EH, Holmes CB (2019) Research to improve differentiated HIV service delivery interventions: Learning to learn as we do. PLOS Medicine 16(5): e1002809 doi: 10.1371/journal.pmed.1002809 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref012] 12.Lindsay J Mangham, Kara Hanson, Barbara McPake; How to do (or not to do) … Designing a discrete choice experiment for application in a low-income country, Health Policy and Planning, Volume 24, Issue 2, 1 March 2009, Pages 151–158 [DOI] [PubMed]

[pone.0255650.ref013] 13.Terris-Prestholt F., Neke N., Grund J.M. et al. Using discrete choice experiments to inform the design of complex interventions. Trials 20, 157 (2019) doi: 10.1186/s13063-019-3186-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref014] 14.Kulzer JL, Penner JA, Marima R, et al. 2012. Family model of HIV care and treatment: a retrospective study in Kenya. J Int AIDS Soc, 15:8. doi: 10.1186/1758-2652-15-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref015] 15.Ministry of Health, Kenya. 2016. Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection in Kenya.

[pone.0255650.ref016] 16.National AIDS and STI Control Programme (NASCOP), Kenya. 2016. Improving the Quality and Efficiency of Health Services in Kenya: A Practical Handbook for HIV Managers and Service Providers on Differentiated Care.

[pone.0255650.ref017] 17.Geng E. 2014. Adaptive Strategies for Preventing and Treating Lapses of Retention in Care (AdaPT-R). [Google Scholar]

[pone.0255650.ref018] 18.Larson E, Vail D, Mbaruku GM, et al. 2015. Moving toward patient-centered care in Africa: A discrete choice experiment of preference for delivery care among 3,003 Tanzanian women. Plos One, 10:1–12. doi: 10.1371/journal.pone.0135621 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref019] 19.Mangham LJ, Hanson K, Mcpake B. 2009. How to do (or not to do)… Designing a discrete choice experiment for application in a low-income country. Health Policy and Planning, 24(2):151–8. doi: 10.1093/heapol/czn047 [DOI] [PubMed] [Google Scholar]

[pone.0255650.ref020] 20.Sawtooth Software. The CBC/HB System for Hierarchical Bayes Estimation. Tech Pap Ser, 1–30.

[pone.0255650.ref021] 21.Chrzan K. Using partial profile choice experiments to handle large numbers of attributes. 2010; International Journal of Market Research 52(6). [Google Scholar]

[pone.0255650.ref022] 22.Orme B. 2010. Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research. Madison, WI: Research Publishers LLC. [Google Scholar]

[pone.0255650.ref023] 23.Johnson FR, Lancsar E, Marshall D, et al. Constructing Experimental Designs for Discrete-Choice Experiments: Report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. 2013; 6: 3–13. [DOI] [PubMed] [Google Scholar]

[pone.0255650.ref024] 24.The R Foundation. 2017. The R Project for Statistical Computing.

[pone.0255650.ref025] 25.Johnson RM. Understanding HB: An Intuitive Approach. Sawtooth Softw Res Pap Ser 2000. [Google Scholar]

[pone.0255650.ref026] 26.Rao VR, Sheth JN, Malhotra NK. (2010). Conjoint Analysis. Wiley International Encyclopedia of Marketing.

[pone.0255650.ref027] 27.Allen M. 2017, The sage encyclopedia of communication research methods, vol. 4, SAGE Publications, Inc, Thousand Oaks, CA [Google Scholar]

[pone.0255650.ref028] 28.National AIDS and STI AIDS and STI Control Programme (NASCOP), Kenya. 2017. Differentiated Care: Operational Guide.

[pone.0255650.ref029] 29.Larson C, Burn R. Minnick-Sakal A, et al. 2014. Strategies to reduce risks in ARV supply chains in the developing world. Glob Health Sci Pract, 2(4):395–402. doi: 10.9745/GHSP-D-14-00105 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref030] 30.Decroo T, Rasschaert F, Telfer B, et al. 2013. Community-based antiretroviral therapy programs can overcome barriers to retention of patients and decongest health services in sub-Saharan Africa: a systematic review. Int Health, 5(3):169–79. doi: 10.1093/inthealth/iht016 [DOI] [PubMed] [Google Scholar]

[pone.0255650.ref031] 31.Wouters E, Van Damme W, Van Rensburg D, et al. 2012. Impact of community-based support services on antiretorivral treatment program delivery and outcomes in resource-limited countries: a synthetic review. BMC Health Serv Res, 12(1):194. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref032] 32.Medecins Sans Frontieres (MSF): MSF Analysis & Advocacy Unit Brussels with Southern Africa Medical Unit. 2013. Reaching Closer to Home: Progress implementing community-based and other adherence strategies supporting people on HIV treatment.

[pone.0255650.ref033] 33.Brown L, Macintyre K, Trujillo L. 2003. Interventions to reduce HIV stigma: What have we learned? AIDS Educ Prev, 15:49–69. doi: 10.1521/aeap.15.1.49.23844 [DOI] [PubMed] [Google Scholar]

[pone.0255650.ref034] 34.National Empowerment Network of People Living with HIV and AIDS in Kenya (NEPHAK). 2011. The People Living with HIV Stigma Index–Kenya.

[pone.0255650.ref035] 35.Dlamini PS, Wantland D, Makoae LN, et al. 2009. HIV Stigma and Missed Medications in HIV-Positive People in Five African Countries. AIDS Patient Care and STDs, 23(5):377–87. doi: 10.1089/apc.2008.0164 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref036] 36.Khabala KB, Edwards JK, Baruani B, et al. 2015. Medication Adherence Clubs: A potential solution to managing large number of stable patients with multiple chronic diseases in informal settlements. Trop Med Int Heal, 20:1265–70. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref037] 37.Okech TC, Lelegwe SL. Analysis of Universal Health Coverage and Equity on Health Care in Kenya. Glob J Health Sci 2016; 8: 218–27. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref038] 38.Mbau R, Kabia E, Honda A, Hanson K, Barasa E. Examining purchasing reforms towards universal health coverage by the National Hospital Insurance Fund in Kenya. Int J Equity Health. 2020Feb3;19(1):19. doi: 10.1186/s12939-019-1116-x; PMCID: PMC6998279. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0255650.ref039] 39.Vandendyck M, Motsamai M, Mubanga M, et al. Community-based ART resulted in excellent retention and can leverage community empowerment in rural Lesotho, a mixed method study. HIV/AIDS Res Treat Open J. 2015; 2(2): 44–50. [Google Scholar]

[pone.0255650.ref040] 40.Odhiambo F, Mulwa E, Ayieko J et al. 2020. Implementation of HIV care in Western Kenya during corona virus disease 2019 response. AIDS 2020, 33:000–000. doi: 10.1097/QAD.0000000000002576 [DOI] [PubMed] [Google Scholar]

PERMALINK

Preferences of people living with HIV for differentiated care models in Kenya: A discrete choice experiment

Sagar Dommaraju

Jill Hagey

Thomas A Odeny

Sharon Okaka

Julie Kadima

Elizabeth A Bukusi

Craig R Cohen

Zachary Kwena

Ingrid Eshun-Wilson

Elvin Geng

Roles

Abstract

Introduction

Methods

Results

Conclusion

Introduction

Materials and methods

Table 1. Attributes and levels of differentiated care models for HIV in Kenya.

Table 2. Example of choice task.

Results

Sociodemographic characteristics

Table 3. Sociodemographic characteristics of HIV-infected patients (n = 104).

Table 4. Comparison of continuous and categorical variables by gender amongst HIV-infected patients (n = 104).

Relative utilities and importances

Table 5. Average utilities of all levels included in our study.

Table 6. Average importances of all attributes included in our study.

Differences in DCE results by gender

Table 7. Normalized average utilities of all levels by gender.

Field notes and observations

Discussion

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Matthew Quaife

Roles

Author response to Decision Letter 0

Decision Letter 1

Matthew Quaife

Roles

Author response to Decision Letter 1

Decision Letter 2

D M Basavarajaiah

Roles

Author response to Decision Letter 2

Decision Letter 3

D M Basavarajaiah

Roles

Acceptance letter

D M Basavarajaiah

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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