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Expanding co-payment for methadone maintenance services in Vietnam: the importance of addressing health and socioeconomic inequalities

Contributed equally
BMC Health Services ResearchBMC series – open, inclusive and trusted201717:480

https://doi.org/10.1186/s12913-017-2405-y

Accepted: 21 June 2017

Published: 12 July 2017

Abstract

Background

Ensuring high enrollment while mobilizing resources through co-payment services is critical to the success of the methadone maintenance treatment (MMT) program in Vietnam. This study assessed the willingness of patients to pay (WTP) for different MMT services delivery models and determined its associated factors.

Methods

A facility based survey was conducted among 1016 MMT patients (98.7% male, 42% aged 35 or less, and 67% living with spouse) in five MMT clinics in Hanoi and Nam Dinh province in 2013. Socioeconomic, HIV and health status, history of drug use and rehabilitation, and MMT experience were interviewed. WTP was assessed using contingent valuation method, including a set of double-bounded binary questions and a follow-up open-ended question. Point and interval data models were used to estimate maximum willingness to pay.

Statistical analysis

Student-t and Chi-square tests were used to examine the differences in opioid use behaviours among MMT patients in different sites. Since WTP was interviewed using both double-bounded and open-ended questions, it included a mixture of censored and uncensored data. The point and interval data models, which consist of interval data model and simple Tobit model, were used to estimate the average WTP for MMT services by different patients groups [37].

In the typical interval model, the WTP is supposed to have lognormal distribution, which is calculated by formula as below [37]:
$${logWTP}_i^{\ast }={x\prime}_i\beta +{\varepsilon}_i$$
Where WTPi represents the true value of WTP of patient i; x’ i denotes a vector of independent variables and ε i represents a random element (with normal distribution, mean zero and standard deviation σ). In the interval model, the interval censoring means that the value of WTP is between the selected bid (denoted t li ) and the next bid in the scale (denoted t ui ). Thus, the logWTP* also lies in (logt li ; logt ui ). The right censoring means that logWTP* is higher than logt ui ; and the left censoring refers that logWTP* is lower than logt li . As a result, the log-likelihood function is constituted by three components (interval censoring, right censoring and left censoring) as below [37]:
$$logL=\sum\limits^{N}_{i=1} \left\{ \begin{array}{ll} I^a_i\ \text{log}\ \left(\upphi\left(\frac{logt_{ui}-x^{\prime}{~}_{i}\beta}{\sigma}\right)-\upphi\left(\frac{logt_{li}-x^{\prime}{~}_{i}\beta}{\sigma}\right)\right)\\ +I^b_i\ \text{log}\ \left(1-\upphi\left(\frac{logt_{ui}-x^{\prime}{~}_{i}\beta_2}{\sigma}\right)\right)+I^c_i\ \text{log}\ \left(\upphi\left(\frac{logt_{ui}-x^{\prime}{~}_{i}\beta}{\sigma}\right)\right) \end{array} \right\}$$

Where logL means Log-likelihood function; σ is the scale parameter; ɸ denotes the cumulative standard normal density function; I a i, I b i and I c i are binary variables (value 0/1 options) that have value 1 if the data are treated as (a) interval censored (I a i = 1 , I b i = 0 and I c i = 0); or (b) right-censored (I a i = 0 , I b i = 1 and I c i = 0); or (c) left-censored (I a i = 0 , I b i = 0 and I c i = 1) [37].

For example, a patient selected the highest bid in WTP scale (i.e. US$200) and he states US$ 220 as a maximum point. In this case, the data are coded as interval censored if we assume WTP to be between t li  = 200 and t ui  = 220. The observation is treated as right-censored if we assume WTP to be more than t li  = 220. Otherwise, if this patient is not willing to pay even the lowest bid in WTP scale (i.e. US$12.5), the observation is considered left-censored with WTP being inferior to t ui = 12.5. Zero responses from patients who are not willing to pay are considered left-censored data [38]. Meanwhile, in the point and interval models, if the patients state the points (below the lowest bid or above the highest bid), these observations are coded as uncensored data; while the others are considered censored data. In this case, the model includes four components: three components (censored data) from the interval data model and one component (uncensored data) from the simple Tobit model: $$logL=\sum\limits^{N}_{i=1} \left\{ \begin{array}{ll} I^a_i\ \text{log}\ \left(\upphi\left(\frac{logt_{ui}-x^{\prime}{~}_{i}\beta}{\sigma}\right)-\upphi\left(\frac{logt_{li}-x^{\prime}{~}_{i}\beta}{\sigma}\right)\right)\\ +I^b_i\ \text{log}\ \left(1-\upphi\left(\frac{logt_{ui}-x^{\prime}{~}_{i}\beta_2}{\sigma}\right)\right)+I^c_i\ \text{log}\ \left(\upphi\left(\frac{logt_{ui}-x^{\prime}_{i}\beta}{\sigma}\right)\right)\\ +I^{d}_i \left(- log{\sigma}_{i} + log\phi\left(\upphi\left(\frac{logWTP^{*}_{i}-x^{\prime}_{i}\beta}{\sigma}\right)\right)\right) \end{array} \right\}$$ Where φ denotes the probability density function of the standard normal distribution. When we ask: “What is the maximum price you would be willing to pay per month for the MMT?”, if a patient states his WTP of US$ 220 after selecting the highest bid, this observation is considered uncensored (WTP* = 220, I a i  = 0 ,  I b i  = 0, I c i  = 0 and I d i  = 1).

In the point and interval data models, because we know the threshold (t li ; t ui ), we can estimate the coefficient (β) and the scale parameter (σ). In addition, the marginal effects of independent variables on increasing or decreasing WTP value can also be computed [38].

The mean WTP is calculated by using formula with the intercept of the models:
$$Mean\ WTP= \exp \left({\beta}_0+\frac{\sigma^2}{2}\right)$$

In multivariable analysis, determinants of patients’ WTP were examined, including an “a priori” defined set of candidate variables: 1) socio-demographics: sex, age, education, marital status, employment, 2) economic status: household’s income and capacity-to-pay, 3) opioid use behaviours: current use, experienced drug rehabilitation, length since first opioid use, frequency of opioid use, opioid expenses, 2) clinical characteristics: HIV stages, CD4 cell count, length of ART, and currently in MMT 3) health status: reported having problems in each EQ-5D dimension. The reduced model was constructed using a stepwise forward selection strategy, which included variables based on the log-likelihood ratio test at a p-value <0.1, and excluded variables at p-values >0.2 [39].

Results

Characteristics of respondents in each site are presented in Table 2. The majority the study sample was male (98.7%), about two-thirds (67.5%) were living with spouse or partners; 44.7% completed high school or above, and 53.4% were self-employed. The proportion of MMT patients living with spouse was lowest at Nam Dinh PAC (54.8%), and unemployment was highest at Tu Liem DHC (31.8%). The differences among clinics were found in marital status, education attainment, employment and religion (p < 0.05).
In Table 3, we compared drug use history of patient across settings. In general, 8.6% patients were HIV-positive and 73.4% ever injected drug. The types of previous drug rehabilitation were diverse across settings. At Tu Liem, Long Bien and Ha Dong clinics, more than half of respondents had experience with private voluntary centers, and this was higher than at Nam Dinh and Xuan Truong clinics (p < 0.05). In Xuan Truong DHC, the rural setting, the proportion of having drug rehabilitation at voluntary and compulsory centers was the lowest as of 36.8% and 9.6%, respectively compared to other clinics (p < 0.05). In rural and suburban areas, Xuan Truong DHC and Ha Dong PRC, patients had fewer number of previous drug rehabilitation episodes in comparison with other clinics (p < 0.05). On average, patients reported spending 295,000 Vietnamese Dong (15 USD) per day for opiates use.
Figure 2 reveals the proportion of participants willing to pay for different bids. There was willingness to pay for MMT services data on 95.5% patients. They reported a WTP average price of 639,000 Vietnamese Dong (32 USD) per month. Patients with HIV/AIDS or not-yet-on MMT or never experienced other drug rehabilitation reported lower price of WTP. Patients in Hanoi were willing to pay more for MMT than those in Nam Dinh Province (Table 4).
In Table 5, we determined factors associated with WTP for MMT with focus on different service delivery models. We found that the WTP of patients taking MMT at urban DHC or suburban RPC was significantly lower than those attending rural DHC or in facility without comprehensive HIV or general health care. Socioeconomic status, health status and drug-related characteristics were found to be predictors of WTP for MMT. Higher WTP was associated with higher level of educational attainment, higher income, male sex, and had high expenses on opiates prior to MMT. HIV status did not remain significant in the reduced model, rather, those patients who reported having any problem in Pain/Discomfort, and who did not have outpatient care last year were willing to pay less for MMT than others. Duration on MMT, number of years of addiction, and times of previous drug rehabilitation episodes were excluded in the reduced model.

Discussion

This study assessed the willingness of drug users to pay for MMT services. Involving a large number of patients in two epicenters of injection-driven HIV epidemics in Vietnam, we were interested to examine if the integration of MMT with other HIV/AIDS or general health care services may influence patients’ WTP. We found that almost all patients were willing to pay for MMT service at an average price of 31 USD per month, approximately the unit cost for providing the service [20]. The availability of other health care services did not increase the WTP of patients. However, those with better physical health status were willing to pay less for MMT. Better socioeconomic status and great level of drug addiction, measured by reported money spent on opiates, significantly predicted higher price that patients were willing to pay for MMT.

To date, this is the largest health facility survey to examine the WTP for MMT services. In the literature, there were few studies in developed countries that showed the willingness of patients to pay for drug rehabilitations [19, 21, 22]. For example, Bishai et al. estimated a WTP a greater monthly amount for drug rehabilitation in Baltimore, Maryland (US$29–64) [22]. Zarkin estimated a WTP US$37 for substance abuse treatment among drug users in North Carolina and New York [19]. This is an advancement of previous research in assessing the WTP for MMT services in Vietnam that only involved a small number of HIV positive drug users in HIV outpatient clinics [20]. In the current study, we conducted the survey at various MMT service models including both HIV-negative and HIV-positive patients. Comparing to a prior assessment, respondents in this study sample reported less expenses related to opiates use and more WTP for the MMT services [20]. However, those patients with physical health problems had a lower WTP for MMT similar to patients with HIV/AIDS in the previous study. Findings of this study also support previous work that income, educational attainment, and better health status predicted a lower amount of WTP for MMT [20]. In addition, we further explored that expenses on opiates use previously and having outpatient care predicted higher amount of WTP. It appeared that those patients who had better socioeconomic status or more sufferings from addiction in the past have greater demand for the service than others.

Notably, this study found that patients attending to the MMT clinics with comprehensive care services (comprises MMT, ART, VCT, GH) were willing to pay less amount than those in the MMT clinic with only VCT. The reason for this phenomenon is still unclear and should be elucidated in further studies. However, we assume several explanations based on previous literatures. First, the clinic providing MMT and VCT is located in provincial level, while other clinics are placed in district level. A previous study in Vietnam suggested that the amount of WTP for MMT among patients in provincial clinics were higher than patients in district clinics and even central clinics [20]. Second, patients in the comprehensive clinics had to experience a higher out-of-pocket payment for health services than those in their counterpart, especially patients in the rural clinic [28]. Another analysis in general Vietnamese population from 2002 to 2010 shows that people in rural area were more likely to suffer catastrophic expenditure and impoverishment than those in urban setting [40].

While both availability of comprehensive health care services and duration on MMT did not clearly predict WTP for MMT, implications of this study’s findings mainly focus on individual factors. First, given the high WTP, scaling up co-payment MMT clinics is feasible and can be an effective strategy to mobilize resources to sustain the MMT program as well as the HIV/AIDS system in Vietnam. Second, policies on co-payment MMT services should acknowledge the differences in socioeconomic status of target population; using the average income per capita as a reliable basis for justifying the user fee in each setting. Finally, there should be continuing financial supports for those patients living with HIV/AIDS and who had poor health status. These patient groups are not only economically vulnerable to health care costs but also less motivated to take MMT. It is surprising that those with Pain/ Discomfort were less willing to pay for MMT. Perhaps the MMT was not at a sufficiently high dose or that they didn’t perceive that MMT is adequately helping them. These results suggest the need for pain management for some MMT patients. As it has been known that WTP is associated with medical care retention and compliance, therefore, better case management and support for severe patients is critical, especially in integrative MMT models where patients had complicated health care demand [41]. In a longitudinal study in Vietnam, HIV disease stage and drug interaction between antiretroviral or TB drugs and MMT predict patients’ ongoing drug use during MMT [42] and should be taken into account in any programs that required co-payments.

The strengths of this study include a large sample size of MMT patients in various settings in two epicenters of Vietnam. However, there are several limitations should be acknowledged. First, convenient sampling technique might limit the representativeness of the sample and the capacity to generalize the findings to all MMT patients [43, 44]. In addition, our limitation is that we only selected one clinic at the provincial level; therefore, further research should be warranted in a larger scale with more provincial sites to increase the representativeness of this level of health system. Second, we were not able to collect the MMT dose and cost data. Nonetheless, as the first assessment in different MMT facilities, findings of this study are helpful for developing co-payment policies for MMT services in Vietnam.

Conclusion

In conclusion, co-payment policies can be applied to MMT services as a strategy to mobilize resources for the program. Also, it is necessary to ensure equalities across patient groups by acknowledging socioeconomic status of different settings and providing financial supports for disadvantaged patients with poor health status. Given the economic vulnerability of drug users, future research may focus on household’s capacity- and willingness-to pay, and interventions to economically empower them.

Abbreviations

AIDS:

Acquired Immune Deficiency Syndrome

ART

Antiretroviral therapy

CV

Contingent valuation

DHC

District Health Center

EQ-5D-5 L

EuroQol – 5 dimensions – 5 levels

GH

General Healthcare

HIV

Human Immunodeficiency Virus

IDU

Injecting drug use

MMT

PAC

Provincial AIDS Center

PWID

People who inject drugs

VAS

Visual analogue scale

VCT

Voluntary counselling and testing services

WTP

Willingness to pay

Declarations

Acknowledgements

The authors would like to acknowledge supports by the Vietnam Authority of HIV/AIDS Control for the implementation of the study.

Not applicable.

Funding

There was no funding for this analysis.

Availability of data and materials

The data that support the findings of this study are available from the Vietnam Authority of HIV/AIDS Control but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Vietnam Authority of HIV/AIDS Control.

Authors’ contributions

BXT, QLN, LHN, HTTP, HTL, TDT, VTMT, CAL conceived of the study, and participatedin its design and implementation and wrote the manuscript. BXT and QLNanalyzed the data. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study’s protocol was approved by the IRB of Vietnam Authority of HIV/AIDS Control. Data collection procedures were also approved by the directors of the MMT clinics. Written informed consent was obtained from all participants.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Authors’ Affiliations

(1)
Institute for Preventive Medicine and Public Health, Hanoi Medical University
(2)
Johns Hopkins Bloomberg School of Public Health
(3)
Authority of HIV/AIDS Control, Ministry of Health
(4)
Institute for Global Health Innovations, Duy Tan University
(5)
School of Medicine and Pharmacy, Vietnam National University
(6)
Department of Hepatobiliary Surgery, Viet-Duc Hospital
(7)
Department of Immunology and Allergy, National Otolaryngology Hospital

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