Epidemiology of frequent attenders: a 3-year historic cohort study comparing attendance, morbidity and prescriptions of one-year and persistent frequent attenders

BACKGROUND: General Practitioners spend a disproportionate amount of time on frequent attenders. So far, trials on the effect of interventions on frequent attenders have shown negative results. However, these trials were conducted in short-term frequent attenders. It would be more reasonable to target intervention at persistent frequent attenders. Typical characteristics of persistent frequent attenders, as opposed to 1-year frequent attenders and non-frequent attenders, may generate hypotheses regarding modifiable factors on which new randomized trials may be designed. METHODS: We used the data of all 28,860 adult patients from 5 primary healthcare centers. Frequent attenders were patients whose attendance rate ranked in the (age and sex adjusted) top 10 percent during 1 year (1-year frequent attenders) or 3 years (persistent frequent attenders). All other patients on the register over the 3-year period were referred to as non-frequent attenders. The lists of medical problems coded by the GP using the International Classification of Primary Care (ICPC) were used to assess morbidity.First, we determined which proportion of 1-year frequent attenders was still a frequent attender during the next two consecutive years and calculated the GPs' workload for these patients. Second, we compared morbidity and number of prescriptions for non-frequent attenders, 1-year frequent attenders and persistent frequent attenders. RESULTS: Of all 1-year frequent attenders, 15.4% became a persistent frequent attender equal to 1.6% of all patients. The 1-year frequent attenders (3,045; 10.6%) were responsible for 39% of the face-to-face consultations; the 470 patients who would become persistent frequent attenders (1.6%) were responsible for 8% of all consultations in 2003. Persistent frequent attenders presented more social problems, more psychiatric problems and medically unexplained physical symptoms, but also more chronic somatic diseases (especially diabetes). They received more prescriptions for psychotropic medication. CONCLUSION: One out of every seven 1-year-frequent attenders (15.4%) becomes a persistent frequent attender. Compared with non-frequent attenders, and 1-year frequent attenders, persistent frequent attenders consume more health care and are diagnosed not only with more somatic diseases but especially more social problems, psychiatric problems and medically unexplained physical symptoms

Defining frequent attendance in general practice

Background: General practitioners (GPs) or researchers sometimes need to identify frequent attenders (FAs) in order to screen them for unidentified problems and to test specific interventions. We wanted to assess different methods for selecting FAs to identify the most feasible and effective one for use in a general (group) practice. Methods: In the second Dutch National Survey of General Practice, data were collected on 375 899 persons registered with 104 practices. Frequent attendance is defined as the top 3% and 10% of enlisted patients in each one-year age-sex group measured during the study year. We used these two selections as our reference standard. We also selected the top 3% and 10% FAs (90 and 97 percentile) based on four selection methods of diminishing preciseness. We compared the test characteristics of these four methods. Results: Of all enlisted patients, 24 % did not consult the practice during the study year. The mean number of contacts in the top 10% FAs increased in men from 5.8 (age 15-24 years) to 17.5 (age 64-75 years) and in women from 9.7 to 19.8. In the top 3% of FAs, contacts increased in men from 9.2 to 24.5 and in women from 14 to 27.8. The selection of FAs becomes more precise when smaller age classes are used. All selection methods show acceptable results (kappa 0.849 - 0.942) except the three group method. Conclusion: To correctly identify frequent attenders in general practice, we recommend dividing patients into at least three age groups per se

Relapse prevention for addictive behaviors

The Relapse Prevention (RP) model has been a mainstay of addictions theory and treatment since its introduction three decades ago. This paper provides an overview and update of RP for addictive behaviors with a focus on developments over the last decade (2000-2010). Major treatment outcome studies and meta-analyses are summarized, as are selected empirical findings relevant to the tenets of the RP model. Notable advances in RP in the last decade include the introduction of a reformulated cognitive-behavioral model of relapse, the application of advanced statistical methods to model relapse in large randomized trials, and the development of mindfulness-based relapse prevention. We also review the emergent literature on genetic correlates of relapse following pharmacological and behavioral treatments. The continued influence of RP is evidenced by its integration in most cognitive-behavioral substance use interventions. However, the tendency to subsume RP within other treatment modalities has posed a barrier to systematic evaluation of the RP model. Overall, RP remains an influential cognitive-behavioral framework that can inform both theoretical and clinical approaches to understanding and facilitating behavior change

Computational Models of Classical Conditioning guest editors’ introduction

In the present special issue, the performance of current computational models of classical conditioning was evaluated under three requirements: (1) Models were to be tested against a list of previously agreed-upon phenomena; (2) the parameters were fixed across simulations; and (3) the simulations used to test the models had to be made available. These requirements resulted in three major products: (a) a list of fundamental classical-conditioning results for which there is a consensus about their reliability; (b) the necessary information to evaluate each of the models on the basis of its ordinal successes in accounting for the experimental data; and (c) a repository of computational models ready to generate simulations. We believe that the contents of this issue represent the 2012 state of the art in computational modeling of classical conditioning and provide a way to find promising avenues for future model development