Effectiveness

In several randomized studies, alcohol brief intervention has been proven to be effective in reducing excessive alcohol use [7], [8], [9], [10], [11]. However, Beich et al. [11] indicate that the numbers of patients needed to screen (NNS) in general practices is quite high to achieve benefits, because only two to three patients per thousand screened benefit from the laborious activities entailed in screening. But, we demonstrated that opportunistic screening is relatively cheap since all patients are already present in the waiting room of the GP.

Cost-effectiveness

Previous research studies investigating the cost-effectiveness of interventions similar to alcohol SBI also considered this type of intervention to be cost-effective [15], [16], [17], [18], [19]. However, these studies did not always express the outcome measure in terms of QALYs. Only Solberg et al. [15], when ranking the health impact and cost effectiveness of alcohol primary care intervention to reduce alcohol misuse did this in terms of QALYs gained. However, Solberg et al. [15] did not take into account the medical costs of diseases unrelated to alcohol in life years gained. Furthermore, they did not take into account the fact that quality of life decreases at older ages [21]. Wutzke et al. [16] expressed the outcome measure only as costs per life year saved by preventing alcohol-related deaths, and did not include the initial costs of screening. Babor et al. [17] took as their outcome measure the intervention costs per reduction in alcohol consumption per patient, again not the amount of QALYs gained.

Assumptions

In this study, we made some assumptions that need to be confirmed by further research. The long term effects of alcohol SBI are debatable. Babor et al. [22] indicated that long term effects on population health had not yet been demonstrated. Wutzke et al. [23] conclude that there is no effect of alcohol SBI without any regular check-ups after 10 years. However, Nilssen [24] conclude that the impact of brief interventions for individuals who are at increased risk but not (yet) meeting the DSM-IV criteria for alcohol dependency appears to be long lasting, up till 9 years after the provision of alcohol SBI. In our study, we took into account the uncertainty regarding the long term persistence of the effect by incorporating a “bandwidth” for the part of the short term decrease in alcohol consumption that is conserved in the long run, including the possibility that no effect remains. It is obvious to note that if there would be no more effect of alcohol SBI in the long term, then the cost-effectiveness ratios of this intervention would increase substantially.

Limitations

One limitation of our analysis is that external effects of implementing the alcohol SBI were ignored because a health care perspective was taken. We have solely focused on health care costs, ignoring broader costs and consequences (like damage done due to violence and accidents induced by drinking) of a reduction of excessive alcohol consumption which fall outside the scope of the health care budget. However, these broader costs seem to be related mostly to drinking patterns [25]. The model we employed only models the relation between average alcohol consumption on quality of life, mortality and health care costs which corresponds well to the outcome measures reported in the brief intervention trials. In case brief intervention would have any effect on alcohol patterns, brief intervention is expected to be more cost-effective, and maybe even cost-saving.

In summary, prevention of excessive alcohol use by implementing the alcohol SBI in a GP setting appears to be cost-effective, with mean incremental costs of €5,400 per QALY gained. The findings of this alcohol SBI economic evaluation implicate that such an intervention is a wise use of health care resources to reduce the public burden of diseases related to excessive alcohol use.

Intervention

SBI for excessive alcohol use in medical settings entails two elements: 1) opportunistic screening in a primary care setting (i.e. a General Practice) to identify excessive drinkers; 2) brief intervention: a low-intensity, short-duration counselling intervention of 10 to 15 minutes, with feedback about drinking, advice and goal setting, and a follow-up contact (one or more discussions lasting 10 to 15 minutes with a primary care physician).

Scenarios

To estimate the effects of SBI, the two following scenarios were compared:

• Current Practice scenario: in this scenario nobody in the Dutch population is screened for alcohol consumption and alcohol consumption patterns remain at their current level;
• SBI scenario: all persons between age 20 and age 65 who visit the GP within one year are screened and those who are identified as excessive drinkers receive the brief intervention. Due to brief intervention alcohol consumption patterns are altered.

The SBI scenario entails the following steps [26]:

1. All patients visiting general practice are approached by a GP-assistant when they are in the waiting room during a regular visit, i.e. not specifically for alcohol related complaints. They are asked to complete the AUDIT-questionnaire, a well-validated alcohol screening instrument developed by the World Health Organization [27];
2. Completed questionnaires are scored by the GP. Those identified as excessive drinkers (an AUDIT-score of 8+) and not meeting the DSM-IV criteria for alcohol dependency are administered the Brief Intervention;
3. The GP provides follow-up sessions at 6 and 12 months.

The RIVM Chronic Disease Model

We estimated incremental health effects and costs by comparing the two scenarios. To extrapolate from decreased alcohol consumption as a result of the intervention to effects on health care costs, life years gained and QALYS gained, we used the RIVM Chronic Disease Model (CDM) [28], [29], [30]. The CDM is a tool that relates (changing) prevalences of risk factors in the general population, such as smoking, overweight and alcohol consumption, to the occurrence of chronic diseases. It has been used for projections into the future, as well as to estimate health adjusted life expectancy and to perform cost effectiveness analysis [21], [31], [32], [33], [34]. The model describes the life course of cohorts in terms of changes between risk factor classes and changes between disease states over time. It allows for co-morbidity and includes epidemiological data on the most important chronic diseases and their risk factors. Risk factors and diseases are linked through relative risks for disease incidence. Alcohol consumption is included as a risk factor that is linked to the following diseases: coronary heart disease, stroke, oesophagus cancer, breast cancer, oral cavity cancer and larynx cancer. Alcohol consumption is divided into the following classes: abstinence (no alcohol consumption), moderate alcohol consumption (less than 2 standard drink units per day, for women, or 4 units for men), excessive alcohol consumption (between 2 and 4 standard drink units per day, respectively 4 and 6 for men) and dangerous alcohol consumption (more than 4, respectively 6 standard drink units per day) [35]. The distribution over these classes of alcohol consumption patterns of the current Dutch population was estimated using data from the annual General Public Health and Lifestyle Survey (Dutch initials: POLS) conducted by Statistics Netherlands [36].

The relative risks for the diseases related to alcohol consumption and all cause mortality employed in the CDM were all derived from the meta-analysis by Holman et al. [35]. This was the only study that included relative risk estimates for the alcohol categories employed in the CDM for both diseases and mortality, and that provided estimates of all-cause mortality. Such a category of all-cause mortality was not used in other studies on relative risks of alcohol consumption. In the simulation model we used these estimates of relative risks on total mortality to estimate the effects of alcohol on mortality through causes of death that are not explicitly in our model.

In essence, our model compared the current distribution of alcohol consumption according to the classes distinguished in the model to the distribution that would result from the intervention. In order to estimate this new “post-intervention” distribution, we again used the POLS data mentioned above, but this time subtracting from the raw data the average decrease in alcohol consumption due to SBI for every individual in the data set having the characteristics of the target population, and then re-estimating the alcohol consumption distribution.

To calculate the decrease in alcohol consumption due to SBI we multiplied the sensitivity of the screening instrument by the decrease in alcohol consumption times the long term maintenance fraction. The long term maintenance fraction is the fraction of the decrease in alcohol consumption that can be sustained in the long run. It is assumed that only this fraction results in health gains.

To compute health effects in terms of QALYs, the CDM couples disability weights from the Dutch Burden of Disease Study to disease prevalence rates [28]. For diseases causally related to alcohol consumption, we used the CDM to estimate diseases prevalence rates as a function of time. To capture the impact on quality of life of diseases not related to alcohol consumption during life years gained we used age and gender specific prevalence rates as reported in the Dutch Burden of Disease Study [37]. Cost of illness (COI) data from the Netherlands for the year 2003 [38] served to estimate health care expenditure conditional on disease status and age [31], [32]. Annual disease costs per patient were multiplied by the projected future prevalence numbers for each alcohol related chronic disease in the model. Costs of all other diseases, incurred during life years gained, were calculated as the product of the numbers of “survivors” and the category of ‘remaining costs’. These latter equal the difference between total health care costs and the costs of the alcohol related diseases incorporated in the model. They include, for instance, the costs of mental and behavioural disorders.

Discounting

To calculate cost-effectiveness ratios, yearly differences in model outcomes between intervention and current practice scenario were discounted and added over the time horizon to find net present values for incremental life years gained, QALYs gained, and health care costs. Future costs and effects were discounted at the annual percentages of 4% for costs and 1.5% for effects which are recommended in the Dutch guidelines [39]. The time horizon chosen was 100 years since by then the cohorts that experienced the intervention will have become extinct (that is, the cohort was “followed to extinction”). We used consumer price indices to adjust all cost to a 2008 price level.

Derivation of the input parameters for the RIVM CDM

To quantify the costs and effects of the SBI, we needed to make some assumptions and determine several parameters. Here we describe how we determined the parameters which we used as input for the RIVM CDM.

The effectiveness of the Screening and Brief Intervention

First, to estimate the numbers of patients willing to undergo screening (screening uptake) we calculated a pooled estimate from all studies described in Solberg et al. [15], [40], [41], [42]. We fitted a logistic random effects regression model with only a constant to obtain the estimate and the uncertainty surrounding it. Mean screening uptake was 86%. Second, for the sensitivity and specificity of the screening instrument, employing the same methodology we calculated a pooled estimate from all studies described in Fiellin et al. [12]. They analyzed the AUDIT questionnaire as the screening instrument with a cut-off score of 8+, which indicates that a person drinks excessively. Mean estimate for the sensitivity of the screening instrument was 69%; and for specificity 94%. Third, as an estimate of the decrease in alcohol consumption as a result of the brief intervention, we took the values provided by Bertholet et al. [10] in their systematic review, namely on average a reduction in weekly ethanol intake of 38 gram per person. With regard to the persistence of the effects of SBI in the long run, three studies were found showing contrasting results. Babor et al. [22] indicated that brief intervention can reduce alcohol use for at least 12 months in non-dependent heavy drinkers, but long term effects on population health had not yet been demonstrated. Wutzke et al. [23] concluded that there is no remaining effect after a period of 10 years when there are no regular check-ups. However, Nilssen [24] concluded that the impact of a brief intervention appears to be long lasting, up till 9 years after the provision of the intervention.

In our study, we assumed that health effects only accrue if behavioural changes are maintained lifelong. The fraction of the initial decrease in alcohol consumption that is maintained in the long run was drawn from a uniform distribution with minimum 0 and maximum 1.

Costs of the SBI intervention

The costs of opportunistic screening and subsequent intervention were calculated by using a so-called bottom-up method as advocated in the Dutch Pharmacoeconomic guidelines [43]. All elements of the screening and intervention were identified and thereby the resources needed. These units and unit prices are displayed in Table 3.

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Table 3. Costs of the SBI intervention, per person.

https://doi.org/10.1371/journal.pone.0005696.t003

Altogether, the screening process costs about €14,- per person. Providing brief intervention costs about €22,- per person, and follow-ups costs are €44,- per person.

Target population

In this modelling study, the target population consisted of all people between age 20 and 65 who visit the GP in a particular year, which according to Statistics Netherlands (CBS), is approximately 7.2 million [6].

Sensitivity analysis

We used the technique of Probabilistic Sensitivity Analysis (PSA) to account for uncertainty in the model input parameters. PSA lets uncertainty in the input parameters be reflected in the model output (the ICER). We identified and addressed the following sources of uncertainty in our base-case ICER estimate: the uncertainty around the relative risk values [35], the values of screening uptake [15], the sensitivity and specificity of the screening instrument [12], the follow-up rate of the brief intervention [10], decrease in alcohol consumption [10], and, the fraction of decrease in alcohol consumption maintained in the long run [22], [23], [24]. Table 4 summarizes the assumptions and the distributions for the input parameters used in the different scenarios. In order to perform the analysis the model was run 1000 times. For each run of the model, at random a number is drawn for each of the “uncertainty” parameters from the distribution specified for that parameter (see table 4). This leads to a different output of the model for each run. The variability in outcome is then a reflection of the uncertainty due to the input parameters.

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Table 4. Summary of assumptions and input data.

https://doi.org/10.1371/journal.pone.0005696.t004