Practices and patients selection

Data for this study were derived from the routine electronic medical records of GP practices that participate in the Netherlands Information Network of General Practice (LINH), a national network of around 90 general practices, who are representative of all Dutch general practices with respect to geographical distribution and degree of urbanization [7]. All Dutch citizens are enlisted as patients in a family practice, so the population listed in a general practice can be used as the denominator in epidemiological studies. The GP is the first professional to contact for health problems and a referral to the secondary health care system (hospitals and medical specialists) can only be made by the GP. The LINH practice population consists of more than 350,000 registered patients, representative of the Dutch population with respect to age and gender. The database holds longitudinal data on morbidity, prescriptions and referrals. Clinical diagnoses are coded using the ICPC (International Classification of Primary Care) coding system [8]. Drugs are coded according to the Anatomical Therapeutic Chemical (ATC) classification [9]. GP practices were included when they provided data on the registration of claimed services, ICPC-coded diagnoses, and ATC-coded prescriptions for the full calendar year of 2009. Only patients enlisted with the general practice during the whole year 2009 were included. Data collection within the LINH network is carried our according to Dutch legislation on privacy. Each patient is coded with an anonymous administrative number. The key to this coding number is only with the general practitioner. The privacy regulation of the LINH network was approved by the Dutch Data Protection Authority. According to the Dutch Central Committee on Research Involving Human Subjects, obtaining informed consent is not obligatory for observational studies and approval by the Medical Ethical Committee was not necessary.

Data collection

All contacts (claims) of patients with the GPs were considered. First, patients having a prescription of oseltamivir (ATC-code J05AH02) between August 8 and December 31, 2009 were considered. No prescriptions of zanamivir were registered. When there was more than one oseltamivir prescription, only the first one was taken into account. We looked for all diagnoses within 7 days before or after the date of prescription, in order to take into account registration delays. However, in 88% of oseltamivir prescriptions, the diagnosis was registered at the same day. We specifically considered diagnoses of influenza (ICPC-code R80, which will be used by GPs for patients with influenza-like illness in the context of an influenza epidemic, or upon virological confirmation of an influenza virus), another acute respiratory infection (ARI, defined by ICPC-codes R74 - acute upper respiratory infection, R78 - acute bronchitis/bronchiolitis or R81 - pneumonia), and non-specified virus infection (A77, which can be used by GPs for patients with influenza-like illness outside the influenza season).

Patients who did not receive oseltamivir were evaluated for a diagnosis of influenza. When more than one episode was present, the date of the first consultation was taken. Recommended treatment of high risk patients with oseltamivir was based on age (<2 years or ≥60 years), co-morbidity (cardiac disease, respiratory disease, diabetes mellitus, chronic renal insufficiency, reduced resistance against infections and children up to 18 years using salicylates), a complicated course of illness (the occurrence of pneumonia within 7 days before or after the diagnosis of influenza or ARI, or the need to prescribe antibiotics – not for urinary infections – within these days), or a third trimester pregnancy (an oseltamivir prescription or an influenza diagnosis between 20 to 34 weeks after a first contact related to pregnancy, based on the assumption that pregnant women usually have their first pregnancy-related contact with their GP between weeks 6 and 20; a contact indicating the birth of a child had to be no more than 14 weeks after oseltamivir prescription or influenza diagnosis). Details on ICPC- and ATC-codes used to classify high-risk patients have been published elsewhere [10].

Additional patient characteristics comprised gender, month of prescribing, total number of contacts with the practice in 2009, and the total number of prescriptions in 2009 besides oseltamivir. Other underlying chronic health conditions were based on a selection of diseases with a high prevalence, a long-term course and a serious illness, as used in the National Public Health Compass (http://www.nationaalkompas.nl). This selection is based on the list of chronic conditions from the Australian Family Medicine Research Centre (http://www.fmrc.org.au) and adapted from ICPC-2 to ICPC-1. Practice characteristics included practice type, dispensing practice, and level of urbanisation and geographic region of the practice location.

Statistical analysis

In the group of patients with a diagnosis of influenza, we evaluated the proportion of patients whom had been prescribed oseltamivir and whom should have been prescribed it. Univariate logistic regression analyses were performed on the association between the prescription of oseltamivir and a recommendation for a prescription, as well as other patients’ and practices’ characteristics. Multivariable regression analyses were performed to assess potential determinants independently associated with the prescription of oseltamivir. Likewise, we evaluated whether oseltamivir was prescribed according to the guidelines among all patients receiving oseltamivir. Because of the recommendation to prescribe oseltamivir to both very young (under two years of age) and old (60 years or above) patients, we added age-squared in the multivariable models to take into account the u-shaped association when using the continuous age variable. Only variables that significantly improved the model fit, based on Likelihood-ratio tests (p-value < 0.05), were included. Since patients were clustered within practices, we used multilevel logistic regression. All analyses were performed in Stata version 11.2 (StataCorp LP, College Station, TX, USA).

Strengths and limitations of this study

This study was conducted using a large database of patients, representative for the general Dutch population, providing a complete picture of morbidity and prescriptions. A limitation of this study was the lacking information on specific details on presented symptoms. More severely ill patients could have been hospitalized, but information on care provided outside the general practice was lacking in our data. This could have resulted in an underestimation in the number of patients with a more severe illness, causing a negative association with the prescription of oseltamivir.

Co-morbidity at high risk of complications was defined by ICPC- and ATC-codes that are used in general practices to select patients eligible for influenza vaccination [10, 11]. This method has been used to monitor the Dutch influenza vaccination rate in different high risk groups for several years, using the LINH database, with stable and representative results [12, 13]. We assumed these patients had at least one contact (claim) with their GP, resulting in at least one record with the specified codes. We think it is very unlikely that a patient with a certain chronic condition did not contact his or her general practitioner for an entire year, nor was prescribed any medication for this chronic condition during that year. Misclassification of patients with co-morbidity is therefore unlikely.

Comparison with other studies

Our observations are in line with those from Fietjé and colleagues [14], who found that 85% of Dutch patients with an oseltamivir prescription belonged to the high risk groups. Their study was based on telephone interviews with patients who filled a prescription for oseltamivir through community pharmacists. Results from other countries on prescriptions of oseltamivir during the A(H1N1)pdm09 pandemic are scarce. In a study by Hersh and colleagues, antivirals were prescribed in 58% of influenza visits to US ambulatory physicians [15]. The prescription rate for patients younger than 2 years was 47% and 68% for patients 65 years or older. Information on underlying medical conditions was not available. A study from the UK focusing on 90 pregnant women presenting with influenza-like illness in primary care, 61% were prescribed antiviral drugs [16]. Forty-three women were in their third trimester, of whom 26 (60%) received antivirals. In a Chicago hospital, 65% of high-risk patients with influenza-like illness received oseltamivir treatment [17]. These figures are much higher than in our study.

The results of our study show that if oseltamivir was prescribed, it was in accordance with the guidelines in most cases. A study by Grol et al. showed that compliance to guidelines was rather good in Dutch primary care; recommendations were followed in, on average, 61% of the decisions [18]. Based on our research it can therefore be said that when general practitioners in the Netherlands prescribed oseltamivir, they followed the recommendations on the prescribing fairly well. But they often did not prescribe, even when oseltamivir was recommended. The availability of antiviral drugs was no problem, since they were available from the national stockpile.

A possible explanation for withholding oseltamivir to patients at higher risk of complications might have been a late presentation of the patient. From the Dutch influenza-like illness surveillance we know that patients wait on average 3 days before contacting a doctor for respiratory complaints [19], while treatment with antiviral drugs should start preferably within 48 hours of the onset of illness. We had no information on time since onset of symptoms, but awareness and anxiety about the on-going pandemic may have urged patients to contact their GP earlier than usually.