Showing papers by "Thierry Christiaens published in 2008"

Home Medication Cabinets and Self-Medication: A Source of Potential Health Threats?

Abstract: BACKGROUND:Data regarding the contents of home medication cabinets (HMCs), the management of leftover medications, and the inclination of patients toward self-initiated treatment using nonprescription drugs are scarce.OBJECTIVE:To evaluate the nature and safety of medication storage and intended self-medication in a general population.METHODS:A cross-sectional study was conducted in 72 Belgian community pharmacies. Pharmacy customers (N = 288, aged 18–80 y) were visited in their homes by pharmacy students. The HMCs were inventoried and the participants were interviewed.RESULTS:A mean of 31 ± 17 (range 6–136) drug packages were identified per household; in one-third of the cases, the packages were not stored safely. Prescription drugs accounted for 34% of the total. The most frequently encountered categories of registered medicines were nonopioid analgesics (7.2%), nonsteroidal antiinflammatory drugs (NSAIDs) (6.9%), nasal decongestants (3.5%) and antinausea agents (3.2%). Despite their high prevalence, NS...

Evolution of bacterial susceptibility pattern of Escherichia coli in uncomplicated urinary tract infections in a country with high antibiotic consumption: a comparison of two surveys with a 10 year interval

Abstract: Methods: Sixty-six general practitioners in the region of the city of Ghent were asked to inoculate a dipslide with midstream urine from every adult female patient with complaints suggestive for cystitis, during a period of 1 year. The dipslides were further processed in a central microbiological laboratory, where counting, identification and susceptibility testing were performed. Results: Three hundred specimens were collected, of which 187 (62.3%) yielded a positive culture of 10 5 cfu/mL. In the age group of 18‐54 years, Escherichia coli was the most frequently isolated uropathogen (77.5%), followed by Staphylococcus saprophyticus (13.5%) and Proteus spp. (2.7%). There were no statistically significant differences when compared with the data from 1996. In 2006, susceptibility of E. coli to nitrofurantoin was 100%, to quinolones 100%, to ampicillin 62.8% and to co-trimoxazole 86%, compared with 99.3%, 99.3%, 73.2% and 83.3%, respectively, in 1996 (no statistically significant differences). Conclusions: Over a period of 10 years, a systematic surveillance of uropathogens in female patients with uncomplicated UTI in general practice could not demonstrate a significant change in species distribution or antimicrobial susceptibility.

Effects of an evidence report and policies lifting reimbursement restrictions for acid suppressants: Analysis of the Belgian national database

Abstract: Purpose: To explore the effect on prescribing of policies that lift reimbursement restrictions on selected H2-antihistamines and proton pump inhibitors (PPI) and of practice recommendations. Methods: Monthly claims based data for proton pump inhibitors (PPIs) and H-antihistamines were obtained from the national health insurance database (Farmanet 1997-2005). Two policies were issued. In March 2001 two H2-antihistamines and in March 2003 two PPIs became available without restrictions. An evidence report was distributed in September 2004. Periods before and after implementation of the interventions were compared. Interrupted time series with segmented regression analysis was used to assess and compare time trends. Results: The first policy resulted in an increase of volume (1.6 million DDDs; 95% CI 1.4-1.8 million; p<.001) and expenditure (€637 744; 95% CI 177 052-1 098 437; p=.026) for H2-antihistamines, but consumption of PPIs continued to grow. After the second policy use of selected PPIs also increased (4.7 million DDDs increase; 95% CI 3.9-5.5 million; p<.001), but more than the desired shift toward selected PPIs. Although total expenditure stabilized at a lower level, there was no significant change of trend. Publication of the evidence report did not have any impact on prescribing. Conclusions: Policies that lift reimbursement restrictions did not result in meaningful changes in utilization or cost saving. They may even have unintended effects. Collaboration between policymakers and guideline developers and linking policies to evidence-based guidelines could be a more effective way to pursue cost-containment and better quality of care.

Cardiovascular risk tables

Abstract: In the linked study, Hippisley-Cox and colleagues develop and validate the second version of the QRISK cardiovascular disease risk algorithm (QRISK2), an attempt to more accurately estimate cardiovascular risk in patients from different ethnic groups in England and Wales.1 The advent of the first Framingham risk tables in the early 1990s was a challenge for most doctors. Since the second world war the management of cardiovascular risk has been part of the core business of general practice, but the single risk model dominated. Hypertension, diabetes, and hypercholesterolaemia were islands, each with its own experts fighting for bigger kingdoms by pushing for ever stricter boundaries and demanding more attention. Framingham taught us to look at the different risk factors, and provided a major lesson: a cumulative average risk could be more important than one peak. Yet soon the extrapolation of these US tables to European populations seemed to overshoot the real risk in these groups.2 3 The SCORE tables used the same risk factors to calculate corrected European cardiovascular mortality.4 More recently the ASSIGN5 and now the QRISK tables tried to incorporate some other known risk factors, especially deprivation and family history. Again, a major step: for several decades the medical community has had to face the troubling fact that cardiovascular morbidity and mortality are strongly and independently related to deprivation.6 7 If we ignore this we overestimate the risk for rich people (and overtreat them) and underestimate that for poor people. It’s probably naive to think that we can close the gap in cardiovascular risk just by giving more statins to poor people. If epidemiologists could estimate cardiovascular risk accurately, would it solve our problems in managing our patients? Not at all. Risk calculation itself is based on evidence. However, using risk calculation in managing patientsrelies on consensus. When does a “risk” become a “high risk”? At what moment does a high risk justify starting lifelong drug treatment? The SCORE tables are useful, but when the European Guidelines tried to implement these tables and defined a 5% risk of death within the coming 10 years as high risk (comparable to a 20% risk in the Framingham tables),8 it led to an enormous medicalisation of many healthy elderly people, as proved by the Nordic Risk Group.9 Nearly all Norwegian men aged 60 years and older and allwomen aged 65 years and older were classified as at “high risk”—in a population with one of the highest life expectancies in the world. To use an absolute risk score as a threshold for starting drugs is dangerous and not evidence based. It is therefore surprising that the recent NICE guidelines strongly recommend statins for anyone with a cardiovascular risk score of 20 or more in the Framingham tables.10 Age is such an important risk factor for developing cardiovascular problems within the next 10 years that all risk tables are misleading. Becoming older is by far the strongest predictor for morbidity and mortality—this is a biological fact. By looking at the risk tables, anyone can see what happens: by age 65, a large group has reached the 20% risk threshold, and lipid lowering drugs are prescribed for the rest of their lives. A non-smoking man of 70 with a systolic blood pressure of 130 mm Hg and a total cholesterol concentration of 5 mmol (far below the median cholesterol concentration in most European countries) is at high risk according to the SCORE criteria. Unfortunately most of the trials of statins include only a few people older than 70.11 The PROSPER trial, which specifically looked at this elderly population, showed that the primary composite endpoint (cardiovascular death or non-fatal infarction orcerebrovascular accident) was lowered by only 15% (48 people have to be given statins for three years to prevent one event), a marginally significant gain for cardiovascular death (relative risk 0.76, 95% confidence interval 0.58 to 0.99; NNT 112 for three years) and no effect at all on total mortality.12 In contrast, a male smoker aged 50 with a systolic blood pressure of 145 mm Hg and a total cholesterol of 6.5 mmol/l is at low risk on SCORE. A better way of using risk tables would be to compare the risk of an individual with the minimal risk of people of the same sex and age. Treatment should be considered when he or she has, for example, three times that minimal risk for his or her age and sex. This will prevent overtreatment of elderly people whose high risk is related to age and undertreatment of younger people who are at high risk. For our two examples the treatment options would be totally different. All attempts to make risk tables more accurate, as done by Hippisley-Cox and colleagues in the QRISK2 algorithm,1 are necessary and should be welcomed. However, this is not the key problem. We have to fundamentally rethink how to use risk tables when making treatment decisions in practice, taking into consideration the medicalisation of healthy older people and the correct use of drugs.