Printer Friendly

Intangible costs of alcohol dependence from the perspective of patients and their relatives: A contingent valuation study/Costes intangibles de la dependencia alcoholica desde la perspectiva de los pacientes y sus familiares: un estudio de valoracion contingente.

The excessive consumption of alcoholic beverages is highly prevalent. It is estimated that about 15% of the European population consumes alcohol excessively (Rehm et al., 2004) and about 1.2-3% suffers from alcohol dependence (Anderson & Baumberg, 2006; Rehm, Rehm, Shield, Gmel & Gual, 2013). The effects of excessive alcohol consumption have innumerable direct as well as indirect economic costs (Anderson et al, 2006; Baumberg, 2010). Direct costs refer to expenditures that could have been put to some other productive use, primarily those resulting from greater medical expenses (Johansson et al., 2006). Indirect costs refer, primarily, to the loss of resources caused by reduced participation in the labour market and the lower productivity of workers with alcohol problems (Petersen et al, 2005).

Alcohol dependence also has numerous intangible, or non-financial, costs, such as lowered life expectancy and reduced quality of life (pain, suffering, physical health problems, etc.), for the dependent person, as well as for the persons around them. "These costs are non-financial because they do not have a monetary value, in the sense that you cannot sell or exchange pain. Nevertheless, individuals and society would be prepared to pay something to avoid them, which means they do have a (non-financial) value" (Baumberg, 2010). Most studies that have analysed intangible costs have focused on the effects on the drinker's health in terms of mortality (Collins & Lapsley, 2008; John et al., 2013) and quality of life. One of the most frequently used quality of life measure is the quality-adjusted life year. This measure has been applied to assess both the impact of alcohol dependence (Kraemer et al., 2005; Maheswaran, Petrou, Rees & Stranges, 2013; Petrie, Doran, Shakeshaft & Sanson-Fisher, 2008; Saarni et al., 2007; Sanderson, Andrews, Corry & Lapsley, 2004; Stouthard, Essink-Bot & Bonsel, 2000) and the benefit of interventions aimed at their treatment or prevention (Chisholm, Rehm, Van Ommerem & Monteiro, 2004; Corry, Sanderson, Issakidis, Andrews & Lapsley, 2004; Mortimer & Segal, 2005; Parrott, Godfrey, Heather, Clark & Ryan, 2006; UKATT Research Team, 2005). In Spain, although the clinical guidelines provide an ample description of the intangible consequences, few studies have focused on measurement of these effects. The recent review by Garcia-Perez et al. (2014) found two studies that quantify the impact of alcoholism on the quality of life (Fernandez et al., 2010; Grandes, Montoya, Arietaleanizbeaskoa, Arce & Sanchez, 2011) and Mosquera & Rodriguez-Miguez (2015) provide new empirical evidence about the effects of alcohol dependence on the quality of life of the dependent and those around them.

However, the intangible effects on well-being caused by alcohol go well beyond direct effects on the drinker's health. Thus, alcohol dependence has additional effects on the drinker such as suffering, isolation, family problems, social exclusion, etc. Moreover, this disease has considerable effects on the drinker's surroundings (Laslett et al., 2010). Although alcohol is considered the addictive substance that inflicts the most damage to others (Nutt, King & Phillips, 2010), few studies have analyzed these intangible effects. Except some studies have estimated the quality of life lost by cohabiting relatives (Jarl et al., 2008; Mosquera et al, 2015), most of the research in this field focuses on the measurement of direct and indirect costs. So, the research has concentrated on the study of foetal alcohol syndrome and the impact of alcohol abuse on victims of crimes and traffic accidents, using the cost of illness as the primary measurement method (for a review of these studies, see Navarro, Doran & Shakeshaft, 2011). Failure to consider the intangible effects of alcohol consumption can result in significant underestimation of the effects of the disease, as well as of the benefits associated with treatment.

Contingent valuation studies using the willingness to pay (WTP) method have proven to be a useful tool for assessing the effects of certain treatments providing benefits extending beyond health. The WTP method allows valuation of the intangible costs of alcohol dependence based on the maximum amount a person is willing to pay to reduce, eliminate, or avoid the situation. This methodology has been widely applied in the valuation of health consequences (Byrne, O'Malley & Suarez-Almazor, 2005; Fautrel et al., 2007; Greenberg, Bakhai, Neumann & Cohen, 2004; Gueylard-Chenevier & Leloier, 2005; Pinto-Prades, Farreras & de Bobadilla, 2008), as well as clinical procedures (Bergmo & Wangberg, 2007; Boonen et al. 2005; He et al., 2007; Jimoh, Sofola, Petu & Okorosobo, 2007; Sadri, Mackeigan, Leite & Einarson, 2005; Walsh & Bartfield, 2006; Whynes, Frew & Wolstenholme, 2003; Yasunaga, Ide, Imamura & Ohe, 2006; Unutzer et al., 2003) (for a review of studies prior to 2002, see Smith, 2003). Application of this methodology to the area of drugs in general (Bishai et al., 2008; Tang, Liu, Chang & Chang, 2007; Zarkin, Cates & Bala, 2000) and to alcohol dependence in particular has been quite limited. To our knowledge, only two published contingent valuation studies have used the WTP method to measure the effects of abusive alcohol consumption. Jeanrenaud and Pellegrini (2008) utilized a sample of 236 subjects from the general Swiss population to determine the WTP for a curative treatment for alcohol dependence of a hypothetical cohabiting relative. Petrie, Doran & Shakeshaft (2011) used a sample from the general Australian population to determine the WTP for 10% and 20% reductions in damages caused by alcohol within the population. However, we do not know any study that had obtained the WTP of the patients themselves or their relatives. It can be important because there is abundant empirical evidence that shows that the preferences of the general population and the persons directly involved can be quite different (Brazier et al., 2005; Gabriel et al., 1999; Mann, Brazier & Tsuchiya, 2009; Ubel, Loewenstein & Jepson, 2003).

The aim of this study is to quantify the intangible costs of alcohol dependence, from the perspective of the patients themselves and their relatives, in 2010 in Spain. In line with the studies mentioned above, our study applies the WTP method to estimate these intangible effects in an ample sense, not just effects on health. However, unlike them our study measures those effects from the perspective of the persons directly involved, who were personally interviewed by the first author.

Materials and methods

Samples

The patients and relatives were contacted at an alcohol treatment unit within the National Health Service. This care unit treats patients with alcohol dependence from the sanitary area of Vigo (Spain). The sample of patients, all of whom met the DSM-4R criteria for alcohol dependence, included all those who came in for consultation for two months, starting in January of 2010. Participation in the study was voluntary and anonymous. The exclusion criteria were refusal to participate, undergoing the first consultation at the centre, acute alcohol intoxication or untreated mental disorder at the time of the interview, and sufficient cognitive deterioration to hinder comprehension of the questionnaire (in the therapist's opinion). The sample of relatives included all individuals who accompanied the patients participating in the interview on the day it was conducted. If the patients came in for consultation alone at the time of inclusion in the study, the person who usually accompanied them (if there was one) was contacted by telephone to invite them to participate voluntarily. There were no other exclusionary criteria besides the refusal to participate. During the recruitment period, 161 patients came in for consultation. Two patients were excluded for alcohol intoxication, two for untreated mental illness, and six for cognitive deterioration. In addition, five patients were excluded from the analysis because they did not provide a WTP. One subject declined to participate. In only 66 cases were we able to interview a relative (in the remaining cases, no relative was involved in the treatment process). One relative declined to participate and four were excluded because they did not provide a WTP. The first coauthor interview personally and independently to participants, to address potential problems during the interview.

Questionnaire

In the first part of the interview, the participant was informed regarding its voluntary and anonymous nature and informed consent to participate was obtained. At the same time, the participants were also explained that the proposed scenarios were hypothetical and that the answers given would in no way influence the care received. In the second part of the interview, the following scenario was described to the subject:

"Imagine a hypothetical situation, a situation that is not real. Suppose there is a new treatment to solve the problems related to alcohol. This treatment is not always effective. In 5 out of 10 people (i.e. half of treated patients) is effective, that is, they stop drinking alcohol and have no desire to do so. In the other half of the patients, the treatment is not effective. The effects of the treatment remain for a year. After one year, the subject would have to receive the treatment again with the same probability of success. The treatment is not free, that is, it is not financed by the National Health Service. What is the maximum annual amount you would pay to receive such treatment? Think calmly your answer. You must take into account your level of income. Please note that this payment would mean giving up the consumption of other goods or would reduce their ability to save money".

In addition, as proposed by Blumenschein, Johannesson, Yokoyama & Freeman (2001), a follow-up question was included. After the participants provided the maximum amounts they would be willing to pay for the treatment, they were asked to choose between two answers: "I'm absolutely certain I would pay it" and "I think I would pay but I am not sure". If they chose the second answer, they were asked again to provide an amount they were sure they would pay. Next, another scenario was proposed in which the efficacy of the hypothetical treatment was 100% but the patient would have to continue treatment indefinitely, because otherwise there would be a relapse, reverting to the initial situation. As in the previous scenario, the participants were again asked for the maximum they would be willing to pay per month, followed by the follow-up question.

In the third part of the interview, the participants were asked for a subjective opinion regarding the consequences of their alcohol dependence in four areas: health, family relationships, occupational consequences, and legal problems. The possible answers in each case were 'hardly any', 'moderate/some', and 'severe/many'. We also know the date in which the actual treatment started as well as the level of consumption (measured in standard drink units), in a normal day, at that date. Next, standard sociodemographic questions were asked to the participants. Finally, the patients as well as the relatives were asked to complete the 36-item Short-Form Health Survey (SF-36), a generic health-related quality of life questionnaire (information needed for another study underway).

Statistical analysis

The intangible cost of alcohol dependence was estimated based on the mean and median values provided by participants after the follow-up question for both success scenarios. Next, a linear regression was estimated to identify the variables correlated with the WTP. The independent variable was the WTP provided by the participants after the follow-up question and the explanatory variables were the variables that, a priori, might be related to the WTP. A regression model with random effects was used to take into account that the participants provided two responses, one for the treatment with a 50% probability of success and the other for the treatment with a 100% probability of success.

Validity analysis

There is a consensus that contingent valuation studies, at a minimum, should show a positive correlation between WTP and income level. Therefore, the sign of the regression coefficient for this variable is used as the theoretical validity test. The lack of prior literature regarding an alcohol-dependent population's WTP presents a considerable challenge to our formulation of the hypotheses regarding the remaining variables. In any case, it would seem reasonable that, ceteris paribus, the worse the consequences of dependence are, the greater the WTP for treatment should be. Another expected result is for the WTP to be sensitive to the quantity and/or quality of the good (Arrow et al., 1993), known as sensitivity to scale. In our study, we analyse whether or not the WTP for the treatment with 100% success is significantly greater than that for the treatment with 50% success. Failure to support this hypothesis would raise serious doubt about the validity of the results (Diamond & Haussman, 1994).

Compliance with the two preceding analyses of validity is a necessary but not a sufficient condition to guarantee the validity of the results. Criterion validity is the most important validity test, because it analyses the extent to which the results for a hypothetical scenario match those obtained in a real transaction. Since a hypothetical treatment was proposed in our study as a mechanism for obtaining the intangible costs of alcohol dependence, the criterion validity cannot be tested. The impossibility of testing criterion validity is common to other WTP studies (in fact, the lack of a real market is one of the reasons that justifies performing WTP study). However, this test is relevant because the differences between the WTP in a real and a hypothetical situation can be quite large. The study of Blumenschein et al. (2001) on WTP for an asthma treatment found that the overestimation obtained from the hypothetical scenario (compared to a real purchase scenario) was corrected by asking the interviewees if they were absolutely certain they would make the payment they had mentioned. For this reason, to minimize the potential difference between the real and hypothetical WTP, we asked a follow-up question assessing the certainty with which the interviewees would pay the amounts they initially provided.

Results

Description of the samples

Table 1 summarizes the characteristics of the 145 patients and 61 relatives selected. Males dominate the patient sample and about half live with a wife or partner. The mean personal income is [euro]766 and 20 subjects gave their income as [euro]0 (in seven cases, the family income was also [euro]0). We compared information from the patient sample for sex, mean age and education, with information provided by the institution for all patients under its care and found no significant differences. More than half of the sample of relatives consists of women, spouses of the dependent person. Table 1 also reports the mood (downhearted and depressed) of the dependent person during the last four weeks (obtained from the SF-36) and the percentage that had family support (patients were considered to have family support if we contacted a family member for inclusion in the study).

With regard to the perception of the interviewees regarding the consequences of alcohol dependence, it seems that patients as well as relatives agreed that family problems, followed by health problems, are the most frequent. However, except for legal problems, relatives perceived significantly greater problems than expressed by patients (this conclusion holds when we compare the sample of relatives to the subsample of 61 patients whose relative was interviewed).

All data are available by request to the corresponding author.

WTP results

Table 2 gives the mean and median WTP and Figure 1 provides the WTP distribution. The mean monthly WTP for a treatment with 50% efficacy was [euro]135 after the first question and [euro]129 after the follow-up question. In 23 cases, the answer was [euro]0. The monthly WTP for the treatment with 100% effectiveness was [euro]168, rejecting the existence of insensitivity to scale. Since only one individual changed the response after the follow-up question, the final WTP was practically the same as before. In 22 cases, the answer was [euro]0.

Among patients who were unwilling to pay anything, there is no evidence that their answers can be considered "protest" responses. To start with, 55% of interviewees with zero WTP for the treatment with 100% success had no personal income and 29% had no family income either (they got by with help from other persons or non-governmental institutions). These percentages are slightly reduced (to 52% and 27%, respectively) when the treatment had a 50% success rate. In addition, if we examine only the participants who did have personal incomes, the mean income is 23% greater among those who had a positive WTP, compared to those who provided a zero WTP response. Finally, the participants who provided a zero WTP mentioned their low level of income as the reason for this response. Therefore, we believe that there is not a clear justification for considering these responses as "protest" responses and they have been included in the analysis.

The WTP for the sample of relatives was significantly greater, with a mean monthly WTP of [euro]307 when the treatment efficacy was 50% and a mean monthly WTP of [euro]420 when the efficacy was 100%. Only four relatives provided a zero WTP. The median is lower than the mean but shows the same pattern, with higher values for the 100% success treatment than for the 50% success treatment and higher valuations from relatives than patients.

Determinants of the WTP

Table 3 shows the results from the regression analysis performed to identify possible determinants of patients' WTP. WTP is positively correlated with treatment efficacy (sensitivity to scale). Accordingly, interviewees were willing to pay an additional [euro]39 for treatment that guaranteed success, compared to one with only a 50% success rate. Personal income is also positively correlated with WTP, supporting the theoretical validity of the results. The WTP is also positively related to having family support (was able to contact a relative involved in treatment) and negatively related to feeling downhearted and depressed during the last four weeks.

[Insert Table 3]

With regard to the effects of alcoholism on health, it was found that persons for whom alcohol dependency had caused moderate health problems were willing to pay [euro]108 more than those who hardly any had health problems. However, when alcohol dependence had caused serious health problems, WTP, although positive, was not significant. In any case, the result that might a priori seem most surprising is the negative correlation between the presence of serious family problems and WTP. This result combined with the fact that WTP is negatively related to feeling downhearted and depressed may be related to the influence of the subject's self-efficacy on his or her expectations. In other words, to pay more for a treatment, there must be some degree of optimism about the possibility of success, which could be less plausible in highly deteriorated family situations. However, these results hold even for the 100% probability of success, which may indicate limited motivation to improve one's life among patients in highly deteriorated situations.

The variable "alcohol intake" (see table 1) has been excluded from regression analysis because it refers to the date in which the actual treatment started, which is not representative of the current situation. In any case, we estimated the regression with this variable and it was not significant, obtaining similar results in the rest of variables. We also estimated the model excluding the patients who had no personal income. Similar results were obtained with regard to the sign and the significance of parameters, except that severe family consequences was not significant (p=0.127).

The results of the regression performed on data from relatives (not shown) indicate that none of the variables examined significantly influences WTP, except for the probability of success and income (both significant at the 5% level). In any case, we must be very cautious with these results, given the small size of the sample of relatives.

Discussion

The objective of this study is to obtain a monetary valuation of the intangible costs of alcohol dependence by means of a contingent valuation study conducted with the patients and their families. Although, to our knowledge, no prior study of these characteristics has been conducted, the study with the greatest similarity to ours is that of Jeanrenaud et al (2007), conducted with a sample of the general Swiss population. The authors found that the mean WTP for a curative treatment for alcohol dependence of a hypothetical cohabiting relative accounts for about 7% of the average monthly household income, a percentage that is significantly lower than that obtained in our sample of relatives (23% of the income). This difference may reflect the discrepancy between assuming one has and actually having an alcoholic relative. Our sample of patients also provides, in relative terms, greater WTP (13% of the family income) than that of the Swiss population.

The lower WTP for the dependents than for their relatives could have different explanations. First, the income of the patients was lower than that of relatives and so it is to be expected that the WTP would be lower. However, we find those differences to be very large. One should consider that, while the personal income of relatives is 11% greater than that of patients, the WTP is more than double for both scenarios. Second, differences in the perception of problems generated by dependence (relatives perceive these problems to be significantly more severe than the dependent persons themselves do) could reflect another important part of these differences. In other words, these differences could be partly motivated by differences in the perceived gain in well-being. Finally, there is evidence that individuals may be willing to pay more to avoid a risk or treat the disease of a relative than to protect their own health (Amin & Khondoker, 2004; Viskusi, Magat & Huber, 1987).

It is arguable whether the WTP obtained is capturing solely intangible costs, as was our objective, or, instead, is also capturing tangible costs (direct or indirect). Since Spain has a public health care system that requires minimal copayment for services, it is assumed that the direct cost incurred by the dependence treatment was not incorporated by the interviewees (or, if so, only marginally). However, the WTP could well be capturing part of the indirect costs resulting from loss of productivity (loss of employment, lower income from absenteeism, premature disability pension, etc.). Although we do not know if participants took these effects into account at the time they provided their WTP, we have information suggesting that any influence they may have had was small. Namely, only 13% of the patients considered that drinking has had severe consequences in their work (although this value increased to 26% if we consider the opinions of relatives).

The lack of an increasing positive correlation between the severity of the consequences of alcohol dependence and the WTP should be emphasized. The results suggest that patients with serious problems provide a significantly lower WTP than those with moderate problems. These results relate to phenomena highly relevant to treating drug dependencies, namely, the perception of self-efficacy (Burling, Reilly, Motzen & Ziff, 1989). Self-efficacy has to do with the perception that the addict has of his or her chances of success and, obviously, the higher those chances are, the more they will pay. This is more likely to come into play for patients with less severe problems (in the very initial phases, with greater control of the situation, etc.) and encouraged than for patients with more problems who may have failed in previous attempts for a cure or for patients who have adapted to their situation. The potential influence of these aspects is apparent in the 50% scenario (patients may perceive their personal probability to be greater or less than that provided). However, secondary regression analyses indicate these results hold when only the answers referring to 100% success scenario are considered. Consequently, factors such as a lower perception of the seriousness of the problem by patients who have more severe problems (and probably a more severe addiction) may have a greater impact on these results. Our study suggests that there is greater willingness to be treated among alcoholic subjects in the less evolved stages of alcohol dependence, with family support, encouraged, and when a large number of secondary problems are not associated.

Our results are subject to several limitations. First, our sample of people with alcohol dependence is small and it is not taken from the general population, which could cause selection bias. If selection bias is present, we do not know in what direction it would alter the composition of the sample. There could be a bias towards subjects with more serious alcohol dependence, as would be the case with those coming into a centre specialized in the treatment of alcoholism. However, the bias could also come from the exclusion of patients with very severe pathology, linked in many cases to situations of social exclusion, who do not come in for treatment. In any case, our sample has some advantages with respect to an extracted sample of the general population. On the one hand, our recruitment method guarantees that all the patients interviewed are alcohol dependent, as diagnosed by a specialist. On the other hand, the type of contact (within an alcoholism treatment unit) and the interview format (direct interview rather than a mail or telephone interview) provided a response rate and valid questionnaire percentage that were very high compared to those ordinarily encountered in this kind of study (Petrie et al., 2008; Saarni et al., 2007), avoiding the bias that a low response rate could cause.

Second, a considerable portion of patients has no relatives committed to the treatment. This resulted in a particularly small sample of relatives and could introduce selection biases that are hard to evaluate. In addition, the small size of the sample of relatives may have contributed to the result that, among the variables measured, only income and probability of treatment success influenced the WTP. Another possible limitation is the question design. Since one of the scenarios proposed a 100% cure rate, it is possible that the WTP values obtained are strongly conditioned by budget constraints. Obviously, any WTP study faces a budget constraint. When participants have to state how much they would pay for a good, this amount is limited by their income and by what they want to consume with the remaining assets. The problem arises when the benefit is so great that the value the participants assign to the good exceeds their income producing an underestimation of the benefit or insensitivity of WTP values to changes in the quantity of the good. To avoid this, the scope of the good being valued is often decreased by introducing, for example, a probability of obtaining the good lower than 100%. In our study, an additional scenario was proposed in which the probability of success was 50%. The result is that participants were willing to pay 30% more to guarantee the success of treatment (37% more in the case of relatives). Since the differences are significant, we believe that, at least in the first question (50% success), the participants' WTP was not exhausted, because in the second question the amount was increased. The constraint imposed by the 100% cure is hard to assess. In any case, our results agree with the literature. The study of Neuman and Johannesson (1994), for example, analysing WTP for an in vitro fertilization treatment, found that participants were willing to pay between 37% and 47% more (depending on the perspective taken) for a program that ensured 100% success than for one that only had a 50% probability of success.

Finally, the WTP obtained could be influenced by the open-ended question format utilized. This format is especially suitable when the sample size is small (Carson & Hanemann, 2005), as in our study. However, there is empirical evidence that the types of elicitation techniques can influence the values estimated. Relevant literature indicates that values obtained with an open-ended or payment card format are often lower than the results from dichotomous choices (Gyrd-Hansen, Jensen & Kjaer, 2014). In addition, in the area of health services, it has been found that the open format, when compared to the payment card format, produces either lower valuations (Whynes et al. 2003; Donaldson, Thomas & Torgerson, 1997) or no significant differences (Gyrd-Hansen et al., 2014). These results suggest that our study should be providing conservative valuations of the intangible costs of alcohol dependence.

The results obtained can be used--with all necessary precautions given the previously mentioned limitations--in the area of economic valuation, specifically in cost--benefit analysis studies. Our study provides a range of values that could be utilized to approximate the benefits derived from programs focused on the prevention, treatment, or cure of the alcohol dependence. However, the selection of a single value is not easy, since one must decide whether to utilize mean or median values, the results from the 100% or the 50% success scenario (in the last scenario, the benefit from curing dependence is assumed to be twice the value provided), or, finally, answers from relatives or patients. Depending on this decision, the annual value for curing one case of alcohol dependence could range from [euro]1200--the median provided by patients for a 100% cure rate--up to [euro]7361, twice the mean WTP provided by relatives for a 50% cure rate. We suggest that the annual benefit of curing (or preventing) a case of alcoholic dependence should initially be approximated by using the mean values from the 50% cure scenario ([euro]3095 from the perspective of the patients and [euro]7361 from the perspective of the relatives), with a subsequent sensitivity analysis using the remaining values. The reason for this choice is that cost--benefit analysis usually utilizes mean values and that we assume that the values estimated for the 100% cure scenario could be strongly restricted by the participants' budget constraints. In any case, these values should be taken with caution. This study shows a methodology to evaluate the intangible costs and provides a first approach to these values, but our findings need to be validated by future studies with larger samples and in other settings.

This study suggests that the contingent valuation approach can be a suitable method for measuring the intangible costs resulting from alcohol dependence, from the perspective of patients and relatives. The results show that the valuations obtained are very different, depending on the perspective taken. Although a vast literature in the area of economic valuation shows disparities between the patients' and the general population's perspectives, these results add new empirical evidence regarding disparities between patients and relatives. In our opinion, future investigations on the measurement of intangible effects of alcohol dependence in particular and of drugs in general should study these differences in greater depth. Since dependent patients may distort the true magnitude of the problem, the perspective of relatives could be especially relevant in that context.

Acknowledgments

We appreciate the help given by the employees of Alcohol Unit of Vigo in the recruitment of patients. We are also particularly grateful to all the patients and their families for their contribution to this study. Financial support from Spanish Ministry of Science and Innovation (ECO2015-69334-R) and Regional Government of Galicia (10SEC300038PR and ECOBAS [AGRUP2015/08]) is gratefully acknowledged.

Conflict of interest

The authors declare that they have no conflicts of interest in the research.

References

Amin, M. & Khondoker, F. (2004). A contingent valuation study to estimate the parental willingness-to-pay for childhood diarrhoea and gender bias among rural households in India. Health Research Policy and Systems, 2, 1359-1386. doi:10.1186/1478-4505-2-3.

Anderson, P. & Baumberg, B. (2006). Alcohol in Europe. London: Institute of Alcohol Studies. Retrieved at http://ec.europa.eu/health-eu/news_alcoholineurope_en.htm.

Arrow, K., Solow, R., Portney, P. R., Leamer, E. E., Radner, R. & Schuman, H. (1993). Report of the NOAA Panel on Contingent Valuation. Federal Register 58, 4602-4614.

Baumberg, B. (2010). Best practice in estimating the costs of alcohol--Recommendations for future studies. WHO Regional Office for Europe. Retrieved at http://www.euro.who.int/_data/assets/pdf_file/0009/112896/E93197.pdf.

Bergmo, T. S. & Wangberg, S. C. (2007). Patients' willingness to pay for electronic communication with their general practitioner. The European Journal of Health Economics, 8, 105-110. doi:10.1007/s10198-006-0014-5.

Bishai, D., Sindelar, J., Ricketts, E. P., Huettner, S., Cornelius, L., Lloyd, J. J.,... Strathdee, S. A. (2008). Willingness to pay for drug rehabilitation: implications for cost recovery. Journal of Health Economics, 27, 959-972. doi:10.1016/j.jhealeco.2007.11.007.

Blumenschein, K., Johannesson, M., Yokoyama, K. & Freeman, P. (2001). Hypothetical versus real willingness to pay in the health care sector: results from a field experiment. Value in Health, 4, 79-79. doi:10.1046/j.1524-4733.2001.40202-36.x.

Boonen, A., Severens, J. L., Van Tubergen, A., Landewe, R., Bonsel, G., Van der Heijde, D. & van der Linden, S. (2005). Willingness of patients with ankylosing spondylitis to pay for inpatient treatment is influenced by the treatment environment and expectations of improvement. Annals of the Rheumatic Diseases, 64, 1650-1652. doi:10.1136/ard.2005.038786.

Brazier, J., Akehurst, R., Brennan, A., Dolan, P., Claxton, K., McCabe, C.,... Tsuchyia, A. (2005). Should patients have a greater role in valuing health states?. Applied Health Economics and Health Policy, 4, 201-208. doi:10.2165/00148365-200504040-00002.

Burling, T. A., Reilly, P. M., Moltzen, J. O. & Ziff, D. C. (1989). Self-efficacy and relapse among inpatient drug and alcohol abusers: a predictor of outcome. Journal of Studies on Alcohol, 50, 354-360. doi:10.15288/jsa.1989.50.354.

Byrne, M. M., O'Malley, K. & Suarez-Almazor, M. E. (2005). Willingness to pay per quality-adjusted life year in a study of knee osteoarthritis. Medical Decision Making, 25, 655-666. doi:10.1177/0272989X05282638.

Carson, R. T. & Hanemann, W. M. (2005). Contingent Valuation. In K. G. Maler & J. R. Vincent (Eds.), the Handbook of Environmental Economics: Valuing Environmental Changes (Vol. 2), pp 821-920. Elsevier.

Chisholm, D., Rehm, J., Van Ommeren, M. & Monteiro, M. (2004). Reducing the global burden of hazardous alcohol use: a comparative cost-effectiveness analysis. Journal of Studies on Alcohol, 65, 782-793. doi:10.15288/jsa.2004.65.782.

Collins, D. J. & Lapsley, H. M. (2008). The avoidable costs of alcohol abuse in Australia and the potential benefits of effective policies to reduce the social costs of alcohol (pp. 1-51). Australian Government Department of Health & Ageing. Retrieved at http://www.nationaldrugstrategy.gov.au/internet/drugstrategy/publishing.nsf/content/mono70.

Corry, J., Sanderson, K., Issakidis, C., Andrews, G. & Lapsley, H. (2004). Evidence-based care for alcohol use disorders is affordable. Journal of Studies on Alcohol. 65, 521-529. doi:10.15288/jsa.2004.65.521.

Diamond, P. A. & Hausman, J. A. (1994). Contingent valuation: Is some number better than no number?. The Journal of Economic Perspectives, 8, 45-64. doi:10.1257/jep.8.4.45.

Donaldson, C., Thomas, R. & Torgerson, D. J. (1997). Validity of open-ended and payment scale approaches to eliciting willingness to pay. Applied Economics, 29, 79-84. doi:10.1080/000368497327425.

Fautrel, B., Clarke, A. E., Guillemin, F., Adam, V., St-Pierre, Y., Panaritis, T.,... Penrod, J. R. (2007). Costs of rheumatoid arthritis: new estimates from the human capital method and comparison to the willingness-to-pay method. Medical Decision Making, 27, 138-150. doi:10.1016/j.healthpol.2008.12.011.

Fernandez, A., Saameno,J. A. B., Pinto-Meza, A., Luciano, J. V., Autonell, J., Palao, D.,... Serrano, A. (2010). Burden of chronic physical conditions and mental disorders in primary care. The British Journal of Psychiatry, 196, 302-309. doi:10.1192/bjp.bp.109.074211.

Gabriel, S. E., Kneeland, T. S., Melton, L. J., Moncur, M. M., Ettinger, B. & Tosteson, A. N. (1999). Health-related Quality of Life in Economic Evaluations for Osteoporosis Whose Values Should We Use?. Medical Decision Making, 19, 141-148. doi:10.1007/s00198-008-0743-7.

Garcia-Perez, L., Aguiar-Ibanez, R., Pinilla-Dominguez, P., Arvelo-Martin, A., Linertova, R. & Rivero-Santana, A. (2014). Revision sistematica de utilidades relacionadas con la salud en Espana: el caso de la salud mental. Gaceta Sanitaria, 28, 77-83.

Grandes, G., Montoya, I., Arietaleanizbeaskoa, M. S., Arce, V. & Sanchez, A. (2011). The burden of mental disorders in primary care. European Psychiatry, 26, 428-435.

Greenberg, D., Bakhai, A., Neumann, P. J. & Cohen, D. J. (2004). Willingness to pay for avoiding coronary restenosis and repeat revascularization: results from a contingent valuation study. Health Policy, 70, 207-216. do?:10.1016/j.healthpol.2004.03.002.

Gueylard-Chenevier, D. G. & LeLorier, J. (2005). A willingness-to-pay assessment of parents' preference for shorter duration treatment of acute otitis media in children. Pharmacoeconomics, 23, 1243-1255. doi:10.1186/1477-7525-8-75.

Gyrd-Hansen, D., Jensen, M. L. & Kjaer, T. (2014). Framing the willingness-to-pay question: Impact on response patterns and mean willingness to pay. Health Economics, 23, 550-563. doi:10.1002/hec.2932.

He, M., Chan, V., Baruwa, E., Gilbert, D., Frick, K. D. & Congdon, N. (2007). Willingness to pay for cataract surgery in rural Southern China. Ophthalmology 114, 411-416. doi:10.1016/j.ophtha.2006.09.012.

Jarl, J., Johansson, P., Eriksson, A., Eriksson, M., Gerdtham, U. G., Hemstrom, O.,... Room, R. (2008). The societal cost of alcohol consumption: an estimation of the economic and human cost including health effects in Sweden, 2002. The European Journal of Health Economics, 9, 351-360. doi:10.1007/S10198-007-0082-1

Jeanrenaud, C. & Pellegrini, S. (2007). Valuing intangible costs of alcohol dependence: A contingent valuation study. Revue d'Economie Politique, 117, 813-825. doi:10.3917/redp.175.0813.

Jimoh, A., Sofola, O., Petu, A. & Okorosobo, T. (2007). Quantifying the economic burden of malaria in Nigeria using the willingness to pay approach. Cost Effectiveness and Resource Allocation, 5, 1. doi:10.1186/1478-7547-5-6.

Johansson, P., Jarl, J., Eriksson, A., Eriksson, M., Gerdtham, U. G., Hemstrom, O.,... Room, R. (2006). The social costs of alcohol in Sweden 2002. Stockholms Universitet, Centrum for socialvetenskaplig alkohol-och drogforskning (SoRAD). doi:10.1007/S10198-007-0082-1.

John, U., Rumpf, H. J., Bischof, G., Hapke, U., Hanke, M. & Meyer, C. (2013). Excess Mortality of Alcohol-Dependent Individuals After 14 Years and Mortality Predictors Based on Treatment Participation and Severity of Alcohol Dependence. Alcoholism: Clinical and Experimental Research, 37, 156-163. doi:10.1111/j.1530-0277.2012.01863.x.

Kraemer, K. L., Roberts, M. S., Horton, N. J., Palfai, T., Samet, J. H., Freedner, N.,... Saitz, R. (2005). Health utility ratings for a spectrum of alcohol-related health states. Medical Care, 43, 541-550. doi:10.1097/01.mlr.0000163644.97251.14.

Laslett, A. M., Catalano, P., Chikritzhs, T., Dale, C., Doran, C., Ferris, J.,... Room, R. (2010). The range and magnitude of alcohol's harm to others. AER Centre for Alcohol Policy Research, Turning Point Alcohol and Drug Centre, Eastern Health: Fitzroy, Victoria.

Maheswaran, H., Petrou, S., Rees, K. & Stranges, S. (2013). Estimating EQ-5D utility values for major health behavioural risk factors in England. Journal of Epidemiology and Community Health, 67, 172-180. doi:10.1136/jech-2012-201019.

Mann, R., Brazier, J. & Tsuchiya, A. (2009). A comparison of patient and general population weightings of EQ-5D dimensions. Health Economics, 18, 363-372. doi:10.1002/hec.1362.

Mortimer, D. & Segal, L. (2005). Economic evaluation of interventions for problem drinking and alcohol dependence: cost per QALY estimates. Alcohol and Alcoholism, 40, 549-555. doi:10.1093/alcalc/agh192

Mosquera J. & Rodriguez-Miguez, E. (2015). Using the SF-6D to measure the impact of alcohol dependence on health-related quality of life. The European Journal of Health Economics, 16, 347-356. doi:10.1007/s10198-014-0627-z.

Navarro, H. J., Doran, C. M. & Shakeshaft, A. P. (2011). Measuring costs of alcohol harm to others: A review of the literature. Drug and alcohol dependence, 114, 87-99. doi:10.1016/j.drugalcdep.2010.11.009.

Neumann, P. J. & Johannesson, M. (1994). The willingness to pay for in vitro fertilization: a pilot study using contingent valuation. Medical Care, 32, 686-699. doi:10.1186/1472-6963-8-261.

Nutt, D. J., King, L. A. & Phillips, L. D. (2010). Drug harms in the UK: a multicriteria decision analysis. The Lancet, 376, 1558-1565. doi:10.1016/S0140-6736(10)61462-6.

Parrott, S., Godfrey, C., Heather, N., Clark, J. & Ryan, T. (2006). Cost and outcome analysis of two alcohol detoxification services. Alcohol and Alcoholism, 4, 84-91 doi:10.1093/alcalc/agh236.

Petersen, S., Peto, V., Rayner, M., Leal, J., Luengo-Fernandez, R. & Gray, A. (2005). European cardiovascular disease statistics: 2005 edition. (pp 70-72). British Heart Foundation. Department of Public Health, University of Oxford.

Petrie, D., Doran, C. & Shakeshaft, A. (2011). Willingness to pay to reduce alcohol-related harm in Australian rural communities. Expert Review of Pharmacoeconomics & Outcomes Research, 11, 351-363. doi:10.1586/erp.11.28.

Petrie, D., Doran, C., Shakeshaft, A. & Sanson-Fisher, R. (2008). The relationship between alcohol consumption and self-reported health status using the EQ5D: evidence from rural Australia. Social Science & Medicine, 67, 1717-1726. doi:10.1016/j.socscimed.2008.09.017.

Pinto-Prades, J. L., Farreras, V. & de Bobadilla, J. F. (2008). Willingness to pay for a reduction in mortality risk after a myocardial infarction: an application of the contingent valuation method to the case of eplerenone. The European Journal of Health Economics, 9, 69-78. doi:10.1007/sl0198-007-0041-x.

Rehm, J., Room, R., Monteiro, M., Gmel, G., Graham, K., Rehn, T.,... Jernigan, D. (2004). Comparative quantification of health risks: global and regional burden of disease due to selected major risk factors, World Health Organization. Retrieved at: www.who.int/publications/cra/chapters/volume1/0959-1108.pdf.

Rehm, J., Rehm, M., Shield, K., Gmel, G. & Gual, T. (2013). Alcohol consumption, alcohol dependence and related harms in Spain, and the effect of treatment-based interventions on alcohol dependence. Adicciones, 25, 11-18.. doi:10.1186/1747-597X-8-21.

Saarni, S. I., Suvisaari, J., Sintonen, H., Pirkola, S., Koskinen, S., Aromaa, A. & Lonnqwist, J. (2007). Impact of psychiatric disorders on health-related quality of life: general population survey. The British Journal of Psychiatry, 190, 326-332. doi:10.1016/j.comppsych.2008.06.003.

Sadri, H., MacKeigan, L. D., Leiter, L. A. & Einarson, T. R. (2005). Willingness to pay for inhaled insulin. Pharmacoeconomics, 23, 1215-1227. doi:10.1016/j.sapharm. 2008.10.001.

Sanderson, K., Andrews, G., Corry, J. & Lapsley, H. (2004). Using the effect size to model change in preference values from descriptive health status. Quality of Life Research, 13, 1255-1264. doi:10.1023/B:QURE.0000037482.92757.82.

Smith, R. D. (2003). Construction of the contingent valuation market in health care: a critical assessment. Health Economics, 12, 609-628. doi:10.1002/hec.755.

Stouthard, M. E., Essink-Bot, M. L. & Bonsel, G. L. (2000). Disability weights for diseases: a modified protocol and results for a Western European region. The European Journal of Public Health, 10, 24-30. doi:10.1093/eurpub/10.1.24 24-30.

Tang, C. H., Liu, J. T., Chang, C. W. & Chang, W. Y. (2007). Willingness to pay for drug abuse treatment: results from a contingent valuation study in Taiwan. Health Policy, 82, 251-262. doi:10.1016/j.healthpol.2006.09.007.

Ubel, P. A., Loewenstein, G. & Jepson, C. (2003). Whose quality of life? A commentary exploring discrepancies between health state evaluations of patients and the general public. Quality of Life Research, 12, 599-607. doi:10.1023/A:1025119931010.

UKATT Research Team. (2005). Cost effectiveness of treatment for alcohol problems: findings of the randomised UK alcohol treatment trial (UKATT). The British Medical Journal, 331, 544. doi:10.1093/alcalc/agn112.

Unutzer, J., Katon, W. J., Russo, J., Simon, G., Von Korff, M., Lin, E.,... Bush, T. (2003). Willingness to pay for depression treatment in primary care. Psychiatric Services, 54, 340-345. doi:10.1176/ps.54.3.340.

Viscusi, W. K., Magat, W. A. & Huber, J. (1987). An investigation of the rationality of consumer valuations of multiple health risks. The RAND Journal of Economics, 18, 465-479.

Walsh, B. M. & Bartfield, J. M. (2006). Survey of parental willingness to pay and willingness to stay for" painless" intravenous catheter placement. Pediatric Emergency Care, 22, 699-703. doi:10.1097/01.pec.0000238743.96606.69.

Whynes, D. K., Frew, E. & Wolstenholme, J. L. (2003). A comparison of two methods for eliciting contingent valuations of colorectal cancer screening. Journal of Health Economics, 22, 555-574. doi:10.1016/S0167-6296(03)00006-7.

Yasunaga, H., Ide, H., Imamura, T. & Ohe, K. (2006). Benefit evaluation of mass screening for prostate cancer: willingness-to-pay measurement using contingent valuation. Urology, 68, 1046-1050. doi:10.1111/j.1442-2042.2009.02293.x.

Zarkin, G. A., Cates, S. C. & Bala, M. V. (2000). Estimating the willingness to pay for drug abuse treatment: A pilot study. Journal of Substance Abuse Treatment, 18, 149-159. doi:10.1016/S0740-5472(99)00030-6

JACINTO MOSQUERA NOGUEIRA (*), EVA RODRIGUEZ-MIGUEZ (**)

(*) Physician. Alcohol Unit of Vigo (Galician Health Service). Spain. (**) Economist. Departament of Applied Economics. University of Vigo. Spain.

Send correspondence to: Jacinto Mosquera Nogueira. C/Escultor Gregorio Fernandez, 8 bajo. 36204 Vigo. jacinto.mosquera.nogueira@sergas.es

Received: Abril 2015; Accepted: Junio 2016.
Table 1. Description of samples of patients and relatives




Sex (% males)
                                     18 to 29 years old
Age distribution (%)                 30 to 44 years old
                                     45 to 59 years old
                                     60 years old and older
Mean personal income ([euro]/month)
Mean family income ([euro]/month)
                                     Elementary or less
Level of education (%)               Secondary
                                     Higher
Living with a partner (%)
Downhearted and depressed (%)        None/a little of the time
                                     Some/most/all of the time
                                     Hardly any
Family consequences (%)              Moderate/some problems
                                     Severe/many problems
                                     Hardly any
Health consequences (%)              Moderate/some problems
                                     Severe/many problems
                                     Hardly any
Legal consequences (%)               Moderate/some problems
                                     Severe/many problems
                                     Hardly any
Occupational consequences (%)        Moderate/some problems
                                     Severe/many problems
                                     <4 units/day (men)/ <3 (women)
Alcohol intake before treatment (%)  >4 and <8 (men) / >3 and <6 (women)
                                     >8 units/day (men)/ >6 (women)
                                     < 4
                                     4 - 6
Duration of treatment (months)       7 - 12
                                     12 - 24
                                     > 24
Has family support (%)
                                     Spouse
                                     Son/daughter
Relationship with dependent (%)      Sibling
                                     Parents
                                     Others

                                     Patients   Relatives
                                     (n = 145)   (n = 61)

Sex (% males)                           69.66      18.03
                                         5.59       6.56
Age distribution (%)                    30.34      31.15
                                        48.28      39.34
                                        15.86      22.95
Mean personal income ([euro]/month)    765.93     854.16
Mean family income ([euro]/month)     1301.03    1826.57
                                        66.9       68.85
Level of education (%)                  25.52      16.39
                                         7.59      14.75
Living with a partner (%)               45.52      85.24
Downhearted and depressed (%)           37.93      50.82
                                        62.07      49.18
                                        17.93       8.2
Family consequences (%)                 36.55      31.15
                                        45.52      60.66
                                        31.03      19.67
Health consequences (%)                 40.69      44.26
                                        28.28      36.07
                                        69.66      78.69
Legal consequences (%)                  15.86       8.2
                                        14.48      13.11
                                        69.66      52.46
Occupational consequences (%)           17.24      22.95
                                        13.10      24.59
                                        12.41
Alcohol intake before treatment (%)     18.62
                                        68.97
                                        12.41
                                         7.59
Duration of treatment (months)          15.17
                                        35.86
                                        28.97
Has family support (%)                  42.76
                                                   67.7
                                                    4.6
Relationship with dependent (%)                    10.8
                                                   12.3
                                                    4.6

Table 2. Mean and median monthly willingness to pay (WTP) values for
patients and relatives

                                       Patients (n = 145)
                          Mean (stand. error)  Median (min, max)


Initial WTP 50% success     135.41 (14.06)       100 (0-1000)
Final WTP 50% success       128.95 (14.01)        90 (0-1000)
Initial WTP 100% success    167.59 (18.05)       100 (0-1000)
Final WTP 100% success      167.53 (18.05)       100 (0-1000)

                          Patients (n = 145)  Relatives (n = 61)
                            Percentiles       Mean (stand. error)
                             25 and 75

Initial WTP 50% success        30-200            322.95 (48.70)
Final WTP 50% success          30-150            306.72 (48.87)
Initial WTP 100% success       30-200            420.25 (65.21)
Final WTP 100% success         30-200            420.25 (65.21)

                                 Relatives (n = 61)
                          Median (min, max)  Percentiles
                                              25 and 75

Initial WTP 50% success      200 (0-2000)       80-400
Final WTP 50% success        200 (0-2000)       55-300
Initial WTP 100% success     300 (0-2000)      100-475
Final WTP 100% success       300 (0-2000)      100-475

Table 3. Determinants of monthly willingness to pay (WTP)

                                                Coefficient  p-value

Treatment efficacy (ref. 50%)                      38.59       .001
Sex (ref. male)                                     1.90       .955
Age                                                -1.23       .430
Education (ref. elementary school or less)
 Secondary school                                  -2.55       .944
 University                                         5.43       .927
Monthly personal income                             0.10       .001
Health consequences (ref. hardly any)
 Moderate/some                                    108.29       .002
 Severe/many                                       23.98       .562
Family consequences (ref. hardly any)
 Moderate/some                                    -64.27       .138
 Severe/many                                      -70.97       .099
Legal consequences (ref. hardly any)
 Moderate/some                                     -8.42       .855
 Severe/many                                       25.05       .591
Occupational consequences (ref. hardly any)
 Moderate/some                                    -24.80       .580
 Severe/many                                       -7.02       .888
Downhearted and depressed (ref. none/a little)    -74.56       .015
Duration of treatment                              -0.46       .769
Has family support (ref. no support)               54.34       .077
Constant                                          151.06       .173

                                                95% Conf. Interval

Treatment efficacy (ref. 50%)                       22.67 - 54.50
Sex (ref. male)                                    -63.86 - 67.65
Age                                                 -4.29 - 1.83
Education (ref. elementary school or less)
 Secondary school                                  -73.26 - 68.16
 University                                       -110.97 - 121.82
Monthly personal income                              0.04 - 0.15
Health consequences (ref. hardly any)
 Moderate/some                                      40.71 - 175.87
 Severe/many                                       -57.08 - 105.03
Family consequences (ref. hardly any)
 Moderate/some                                    -149.23 - 20.69
 Severe/many                                      -155.42 - 13.47
Legal consequences (ref. hardly any)
 Moderate/some                                     -98.78 - 81.94
 Severe/many                                       -66.32 - 116.42
Occupational consequences (ref. hardly any)
 Moderate/some                                    -112.77 - 63.14
 Severe/many                                      -104.96 - 90.92
Downhearted and depressed (ref. none/a little)    -134.63 - -14.50
Duration of treatment                               -3.54 - 2.62
Has family support (ref. no support)                -5.90 - 114.58
Constant                                           -66.08 - 368.20

Note. R-sq= 0.265. Number of participants, 145; number of observations,
290
COPYRIGHT 2018 Socidrogalcohol
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2018 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:original
Author:Nogueira, Jacinto Mosquera; Rodriguez-Miguez, Eva
Publication:Adicciones
Date:Jun 1, 2018
Words:8145
Previous Article:Academic outcomes and cognitive performance in problematic Internet users/Rendimiento academico y cognitivo en el uso problematico de Internet.
Next Article:Relationship between the rs1414334 C/G polymorphism in the HTR2C gene and smoking in patients treated with atypical antipsychotics/Relacion entre el...
Topics:

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |