Defining antimicrobial prescribing quality indicators: what is a new prescription?

Contains fulltext : 95791.pdf (publisher's version ) (Open Access): Since guidelines on antibiotic drug treatment often focus on appropriate first choice drugs, assessment of guideline adherence should only concentrate on the first drug prescribed, and not on subsequent antibiotics prescribed after failure of the first one. PURPOSE: To determine a valid cut-off point for a definition of "first" or "new" prescription in indicators for the assessment of the quality of antibiotic drug treatment on the basis of pharmaceutical data. METHODS: Three possible definitions for the term "new prescription" were compared, based on three different periods of time, viz. more than 35, 28, or 21 days after starting a previous antibiotic. In an observational study, 1,225 antimicrobial prescriptions from the medical files of five family practices were audited ("clinical classification") and compared with a classification based on the three definitions ("technical classification"). Agreement between these clinical and technical classifications was determined by calculating Cohen's kappa. The technical classification was analyzed as a diagnostic test, using the clinical classification as the gold standard, and sensitivity, specificity, likelihood ratios, and post-test probabilities were calculated. RESULTS: Defining "new prescription" as "more than 35 days after a previous prescription was issued" resulted in a Cohen's kappa of 0.93 (95% CI 0.92-0.98). The diagnostic value of this definition was extremely high, with a sensitivity of 0.976, specificity of 0.987, positive likelihood ratio of 77.7, and negative likelihood ratio of 0.02. CONCLUSION: We recommend using a cut-off value of 35 days since the last antimicrobial prescription as the definition of a "new prescription" when no diagnostic information is available, i.e., when using pharmaceutical data to assess the quality of antibiotic prescribing behavior.01 januari 201

Diabetes mellitus type II as a risk factor for depression: a lower than expected risk in a general practice setting

The aim of the present study was to determine whether a diagnosis of diabetes mellitus (DM) in a primary setting is associated with an increased risk of subsequent depression. A retrospective cohort design was used based on the Registration Network Family Practice (RNH) database. Patients diagnosed with diabetes mellitus at or after the age of 40 and who were diagnosed between 01-01-1980 and 01-01-2007 (N = 6,140), were compared with age-matched controls from a reference group (N = 18,416) without a history of diabetes. Both groups were followed for an emerging first diagnosis of depression (and/or depressive feelings) until January 1, 2008. 2.0% of the people diagnosed with diabetes mellitus developed a depressive disorder, compared to 1.6% of the reference group. After statistical correction for confounding factors diabetes mellitus was associated with an increased risk of developing subsequent depression (HR 1.26; 95% CI: 1.12–1.42) and/or depressive feelings (HR 1.33; 95% CI: 1.18–1.46). After statistical adjustment practice identification code, age and depression preceding diabetes, were significantly related to a diagnosis of depression. Patients with diabetes mellitus are more likely to develop subsequent depression than persons without a history of diabetes. Results from this large longitudinal study based on a general practice population indicate that this association is weaker than previously found in cross-sectional research using self-report surveys. Several explanations for this dissimilarity are discussed

A structured registration program can be validly used for quality assessment in general practice

ABSTRACT: BACKGROUND: Patient information, medical history, clinical outcomes and demographic information, can be registered in different ways in registration programs. For evaluation of diabetes care, data can easily be extracted from a structured registration program (SRP). The usability of data from this source depends on the agreement of this data with that of the usual data registration in the electronic medical record (EMR). Aim of the study was to determine the comparability of data from an EMR and from an SRP, to determine whether the use of SRP data for quality assessment is justified in general practice. METHODS: We obtained 196 records of diabetes mellitus patients in a sample of general practices in the Netherlands. We compared the agreement between the two programs in terms of laboratory and non-laboratory parameters. Agreement was determined by defining accordance between the programs in absent and present registrations, accordance between values of registrations, and whether the differences found in values were also a clinically relevant difference. RESULTS: No differences were found in the occurrence of registration (absent/present) in the SRP and EMR for all the laboratory parameters. Smoking behaviour, weight and eye examination were registered significantly more often in the SRP than in the EMR. In the EMR, blood pressure was registered significantly more often than in the SRP. Data registered in the EMR and in the SRP had a similar clinical meaning for all parameters (laboratory and non-laboratory). CONCLUSIONS: Laboratory parameters showed good agreement and non-laboratory acceptable agreement of the SRP with the EMR. Data from a structured registration program can be used validly for research purposes and quality assessment in general practice

Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty

Background: Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined to estimate incidence and prevalence of chronic diseases and to explore the role of uncertainty when comparing diseases. Methods. Incidence and prevalence counts, specified by gender and age, of 18 chronic diseases from 5 GPRNs in the Netherlands from the year 2007 were used as input. Generalized linear mixed models were fitted with the GPRN identifier acting as random intercept, and age and gender as explanatory variables. Using predictions of the regression models we estimated the incidence and prevalence for 18 chronic diseases and calculated a stochastic ranking of diseases in terms of incidence and prevalence per 1,000. Results: Incidence was highest for coronary heart disease and prevalence was highest for diabetes if we looked at the point estimates. The between GPRN variance in general was higher for incidence than for prevalence. Since uncertainty intervals were wide for some diseases and overlapped, the ranking of diseases was subject to uncertainty. For incidence shifts in rank of up to twelve positions were observed. For prevalence, most diseases shifted maximally three or four places in rank. Conclusion: Estimates of incidence and prevalence can be obtained by combining data from GPRNs. Uncertainty in the estimates of absolute figures may lead to different rankings of diseases and, hence, should be taken into consideration when comparing disease incidences and prevalences

Tailored Print Communication and Telephone Motivational Interviewing Are Equally Successful in Improving Multiple Lifestyle Behaviors in a Randomized Controlled Trial

Background: Computer tailoring and motivational interviewing show promise in promoting lifestyle change, despite few head-to-head comparative studies. Purpose: Vitalum is a randomized controlled trial in which the efficacy of these methods was compared in changing physical activity and fruit and vegetable consumption in middle-aged Dutch adults. Methods: Participants (n?=?1,629) were recruited via 23 general practices and randomly received either four tailored print letters, four motivational telephone calls, two of each type of intervention, or no information. The primary outcomes were absolute change in self-reported physical activity and fruit and vegetable consumption. Results: All three intervention groups (i.e., the tailored letters, the motivational calls, and the combined version) were equally and significantly more effective than the control group in increasing physical activity (hours/day), intake of fruit (servings/day), and consumption of vegetables (grams/day) from baseline to the intermediate measurement (week 25), follow-up 1 (week 47) and 2 (week 73). Effect sizes (Cohen's d) ranged from 0.15 to 0.18. Participants rated the interventions positively; interviews were more positively evaluated than letters. Conclusions: Tailored print communication and telephone motivational interviewing or their combination are equally successful in changing multiple behaviors. © 2010 The Author(s)

Routinely collected general practice data: goldmines for research? A report of the European Federation for Medical Informatics Primary Care Informatics Working Group (EFMI PCIWG) from MIE2006, Maastricht, The Netherlands.

Much of European primary care is computerised and many groups of practices pool data for research. Technology is making pooled general practice data widely available beyond the domain within which it is collected