Is new drug prescribing in primary care specialist induced?

<p>Abstract</p> <p>Background</p> <p>Medical specialists are often seen as the first prescribers of new drugs. However, the extent to which specialists influence new drug prescribing in primary care is largely unknown.</p> <p>Methods</p> <p>This study estimates the influence of medical specialists on new drug prescribing in primary care shortly after market introduction. The influence of medical specialists on prescribing of five new drugs was measured in a cohort of 103 GPs, working in 59 practices, over the period 1999 until 2003. The influence of medical specialists on new drug prescribing in primary care was assessed using three outcome measures. Firstly, the proportion of patients receiving their first prescription for a new or reference drug from a specialist. Secondly, the proportion of GPs prescribing new drugs before any specialist prescribes to their patients. Thirdly, we compared the time until the GP's first own prescribing between GPs who waited for prescriptions from specialists and those who did not.</p> <p>Results</p> <p>The influence of specialists showed considerable differences among the new drugs studied. The proportion of patients receiving their first prescription from a specialist was greatest for the combination salmeterol/fluticasone (60.2%), and lowest for rofecoxib (23.0%). The proportion of GPs prescribing new drugs before waiting for prescriptions from medical specialists ranged from 21.1% in the case of esomeprazole to 32.9% for rofecoxib. Prescribing new drugs by specialists did not shorten the GP's own time to prescribing.</p> <p>Conclusion</p> <p>This study shows that the influence of medical specialists is clearly visible for all new drugs and often greater than for the existing older drugs, but the rapid uptake of new drugs in primary care does not seem specialist induced in all cases. GPs are responsible for a substantial amount of all early prescriptions for new drugs and for a subpopulation specialist endorsement is not a requisite to initiate in new drug prescribing. This contradicts with the idea that the diffusion of newly marketed drugs always follows a two-step model, with medical specialists as the innovators and GPs as the followers.</p

Distinguishing patterns in the dynamics of long-term medication use by Markov analysis: beyond persistence

<p>Abstract</p> <p>Background</p> <p>In order to accurately distinguish gaps of varying length in drug treatment for chronic conditions from discontinuation without resuming therapy, short-term observation does not suffice. Thus, the use of inhalation corticosteroids (ICS) in the long-term, during a ten-year period is investigated. To describe medication use as a continuum, taking into account the timeliness and consistency of refilling, a Markov model is proposed.</p> <p>Methods</p> <p>Patients, that filled at least one prescription in 1993, were selected from the PHARMO medical record linkage system (RLS) containing >95% prescription dispensings per patient originating from community pharmacy records of 6 medium-sized cities in the Netherlands.</p> <p>The probabilities of continuous use, the refilling of at least one ICS prescription in each year of follow-up, and medication free periods were assessed by Markov analysis. Stratified analysis according to new use was performed.</p> <p>Results</p> <p>The transition probabilities of the refilling of at least one ICS prescription in the subsequent year of follow-up, were assessed for each year of follow-up and for the total study period.</p> <p>The change of transition probabilities in time was evaluated, e.g. the probability of continuing ICS use of starters in the first two years (51%) of follow-up increased to more than 70% in the following years. The probabilities of different patterns of medication use were assessed: continuous use (7.7%), cumulative medication gaps (1–8 years 69.1%) and discontinuing (23.2%) during ten-year follow-up for new users. New users had lower probability of continuous use (7.7%) and more variability in ICS refill patterns than previous users (56%).</p> <p>Conclusion</p> <p>In addition to well-established methods in epidemiology to ascertain compliance and persistence, a Markov model could be useful to further specify the variety of possible patterns of medication use within the continuum of adherence. This Markov model describes variation in behaviour and patterns of ICS use and could also be useful to investigate continuous use of other drugs applied in chronic diseases.</p

Assessing the impact of prescribed medicines on health outcomes

This paper reviews methods that can be used to assess the impact of medicine use on population health outcomes. In the absence of a gold standard, we argue that a convergence of evidence from different types of studies using multiple methods of independent imperfection provides the best bases for attributing improvements in health outcomes to the use of medicines. The major requirements are: good evidence that a safe and effective medicine is being appropriately prescribed; covariation between medicine use and improved health outcomes; and being able to discount alternative explanations of the covariation (via covariate adjustment, propensity analyses and sensitivity analyses), so that medicine use is the most plausible explanation of the improved health outcomes. The strongest possible evidence would be provided by the coherence of the following types of evidence: (1) individual linked data showing that patients are prescribed the medicine, there are reasonable levels of patient compliance, and there is a relationship between medicine use and health improvements that is not explained by other factors; (2) ecological evidence of improvements in these health outcomes in the population in which the medicine is used. Confidence in these inferences would be increased by: the replication of these results in comparable countries and consistent trends in population vital statistics in countries that have introduced the medicine; and epidemiological modelling indicating that changes observed in population health outcomes are plausible given the epidemiology of the condition being treated

Use of nonsteroidal anti-inflammatory drugs and risk of fractures.

In animal models, prostaglandin synthesis has been found to mediate bone metabolism. Nonsteroidal anti-inflammatory drugs (NSAIDs), given their inhibitory effects of prostaglandin synthesis, may play a role in the prevention of osteoporosis. The primary objective of this study is to describe and quantify the fracture risks of patients exposed to NSAIDs in a representative general medical practice setting. A retrospective cohort study was conducted in a general medical practice setting in the UK (using data from the General Practice Research Database). Regular NSAID users (who received three or more NSAID prescriptions), aged 18 years or older, were compared with matched control patients and incidental NSAID users. The study comprised 214,577 regular NSAID users, 286,850 incidental NSAID users, and 214,577 control patients. The relative rate of nonvertebral fractures during regular NSAID treatment compared with control was 1.47 (95% confidence interval [CI] 1.42-1. 52) and that of hip fracture 1.08 (0.98-1.19). No differences in nonvertebral fractures were found between the regular and incidental NSAID users (RR = 1.04; 95% CI 0.99-1.09). The rate of nonvertebral fractures among users of diclofenac (RR = 1.00; 95% CI 0.93-1.08) and naproxen (RR = 0.91; 95% CI 0.82-1.00) was similar to that of ibuprofen. The results of this study are not supportive of clinically significant effects of NSAIDs on bone metabolism