2) 3) are asked to complete the AUDIT-questionnaire, a well- validated alcohol screening instrument developed by the World Health Organization [27]; 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; 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 consump- tion, 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 Excessive Alcohol Use 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 PLoS ONE | www.plosone.org 5 May 2009 | Volume 4 | Issue 5 | e5696