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Kim Roberts Freedom Group

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The absolute rate of infections per 1000 person-days in the first month to first year was 1.7 (95% CI 1.6 to 1.9) for children with measles and 1.33 (95% CI 1.29 to 1.36) for children free of measles. The adjusted HR for non-measles infectious disease over the full follow-up period starting 30 days after measles diagnosis was 1.20 (95% CI 1.13 to 1.28) (see online Supplementary file S6).

Despite smaller sample sizes, the analysis on hospitalisations also showed increased IRRs, although these were significant during the first period only (figure 5). In the Cox proportional hazards model, confounder selection using either backward selection, or minimisation of the AIC resulted in the same model, namely control for the hospitalisation rate prior to the index date, the GP consultation rate in the year prior to index date and history of cardiac malformation. The absolute rate of hospitalisations per 1000 person days in the first month to first year was equal at 0.2 (95% CI 0.1 to 0.2) for children with measles and children free of measles. The adjusted HR of hospitalisation for measles versus non-measles subjects was 1.12 (95% CI 0.96 to 1.31).

To be considered a new event, prescriptions only had to be given on a different day. Acknowledging that a prescription can be changed if there is poor response or allergy to the first drug, we also examined the effect of anti-infective prescription, considering a 14-day interval between anti-infective prescriptions. This did not change the significance or direction of any result (results not shown). Both groups revealed similar trends with the rate of consultation before index date higher than after index date.20 21 This is most likely related to age. Although measles is a statutory notifiable infectious disease under EU legislation,22 an under-reporting of (severe) cases, who might have by-passed the GP and gone directly to the hospital, cannot be ruled out. Also, it is possible that a mild measles infection would not have prompted a visit to the GP and may have gone undetected as well.23 24 This means that we may have missed some children with measles. It should be noted though that laboratory confirmation for most of the identified cases is lacking. As a result, some patients may have been misclassified. Validation studies to accurately identify measles using this type of database are also lacking. In case of non-differential misclassification, the direction of the bias is likely to be towards the null value, so one would expect to see a larger estimate if misclassification was absent. Differential misclassification however can inflate or attenuate the effect estimates. To minimise the impact of differential misclassification, we examined the consultation rate in both measles and non-measles groups. To provide additional assurance that controls were children truly free of measles, controls had to have at least one measles-containing vaccination prior to the index date. An advantage of this type of observational study is that it is not necessary to identify all outcomes in all children in order to obtain an unbiased estimate. A key assumption, however, is comparability of children with measles and children free of measles. In order to ensure that the children with measles and the children free of measles were comparable, we matched them on confounding factors such as age, sex, GP practice and calendar time. We also considered including experiencing an excluding event (ie, an immune-compromising condition or immune suppressive treatment) as a censoring variable but determined this was not consistent with our matching strategy: the groups were matched to be comparable at index. Nevertheless, we acknowledge that it is possible that confounding due to differences in underlying health status, social background, lifestyle, health seeking behaviour and healthcare utilisation between children with measles and children free of measles may have occurred. The complexity of these factors makes them difficult to control. We attempted to overcome the confounding effect of underlying health status by excluding children with a history of immune-compromising conditions, and controlling for comorbidities such as cardiovascular disease, and respiratory disease. We assessed social background and lifestyle by testing for differences in social deprivation within a particular zip code and matching on practice. Certain children may have had a lower threshold for visiting the GP and therefore may have had a higher likelihood of receiving a diagnosis of measles (particular during an outbreak) and may also have been diagnosed more frequently with other infectious diseases and/or may have received a prescription for anti-infectives more frequently. To investigate this, we included GP consultation rate in the year prior to cohort entry as a covariate in each of our models. In the unmatched Poisson analyses, we did not control for all potential confounders. Because 472 unique practices were represented in the cohort, it was impossible to control for practice. Similarly, the 25 years included in the study period make control for calendar year infeasible unless calendar year is treated as a continuous variable, which would require the assumption of a linear relationship between year and log(events). To address the potential effect of calendar time, we have conducted analyses stratified by calendar period (before 2005 and after 2004) and included these results in supplementary material (see online Supplementary file S9 table). 041b061a72


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