C. Paddy Farrington

MMR and autism: further evidence against a causal association

The hypothesis that MMR vaccines cause autism was first raised by reports of cases in which developmental regression occurred soon after MMR vaccination. A previous study found no evidence to support this hypothesis. It has recently been suggested that MMR vaccine might cause autism, but that the induction interval need not be short. The data from the earlier study were reanalysed to test this second hypothesis. Our results do not support this hypothesis, and provide further evidence against a causal association between MMR vaccination and autism.

The methodology of self-controlled case series studies.

The self-controlled case series method is increasingly being used in pharmacoepidemiology, particularly in vaccine safety studies. This method is typically used to evaluate the association between a transient exposure and an acute event, using only cases. We present both parametric and semiparametric models using a motivating example on MMR vaccine and bleeding disorders. We briefly describe approaches for interferent events and a sequential version of the method for prospective surveillance of drug safety. The efficiency of the self-controlled case series method is compared to the that of cohort and case control studies. Some further extensions, to long or indefinite exposures and to bivariate counts, are described.

Case series analysis for censored, perturbed, or curtailed post-event exposures

A new method is developed for analyzing case series data in situations where occurrence of the event censors, curtails, or otherwise affects post-event exposures. Unbiased estimating equations derived from the self-controlled case series model are adapted to allow for exposures whose occurrence or observation is influenced by the event. The method applies to transient point exposures and rare nonrecurrent events. Asymptotic efficiency is studied in some special cases. A computational scheme based on a pseudo-likelihood is proposed to make the computations feasible in complex models. Simulations, a validation study, and 2 applications are described.

Self-Controlled Case Series Analysis With Event-Dependent Observation Periods

The self-controlled case series method may be used to study the association between a time-varying exposure and a health event. It is based only on cases, and it controls for fixed confounders. Exposure and event histories are collected for each case over a predefined observation period. The method requires that observation periods should be independent of event times. This requirement is violated when events increase the mortality rate, since censoring of the observation periods is then event dependent. In this article, the case series method for rare nonrecurrent events is extended to remove this independence assumption, thus introducing an additional term in the likelihood that depends on the censoring process. In order to remain within the case series framework in which only cases are sampled, the model is reparameterized so that this additional term becomes estimable from the distribution of intervals from event to end of observation. The exposure effect of primary interest may be estimated unbiasedly. The age effect, however, takes on a new interpretation, incorporating the effect of censoring. The model may be fitted in standard loglinear modeling software; this yields conservative standard errors. We describe a detailed application to the study of antipsychotics and stroke. The estimates obtained from the standard case series model are shown to be biased when event-dependent observation periods are ignored. When they are allowed for, antipsychotic use remains strongly positively associated with stroke in patients with dementia, but not in patients without dementia. Two detailed simulation studies are included as Supplemental Material.

Contact Surface Models for Infectious Diseases: Estimation From Serologic Survey Data

Controlling of infectious diseases requires information about the rates at which individuals make contact. We propose a novel approach to modeling contact rates via a continuous contact surface. This provides a more realistic and flexible representation of contact rates than currently used methods. Our approach allows modeling of sources of heterogeneity due to age, individual effects, and gender. The models are fitted to serologic survey data by maximum likelihood. This involves solving an integral equation linking the contact surface to the infection hazards. The method is illustrated with two datasets, on mumps and rubella and on Epstein–Barr virus and herpes simplex virus type 1 infection. The advantages and shortcomings of the method, particularly the identifiability of the contact surface, are discussed.

Assessment of the Status of Measles Elimination from Reported Outbreaks: United States, 1997–1999

The status of measles elimination is best summarized by evaluation of the effective reproduction number R; maintaining R<1 is necessary and sufficient to achieve elimination. Previously described methods for estimating R from the sizes and durations of chains of measles transmission and the proportion of cases imported were applied to the measles data reported for the United States in 1997-1999. These comprised 338 cases, forming 165 chains of transmission, of which 43 had >1 case. One hundred seven cases were classified as importations. All 3 methods suggested that R was in the range 0.6-0.7. Results were not sensitive to the minimum size and duration of outbreak considered (so long as single-case chains were excluded) or to exclusion of chains without a known imported source. These results demonstrate that susceptibility to measles was beneath the epidemic threshold and that endemic transmission was eliminated.

A modified self‐controlled case series method to examine association between multidose vaccinations and death

The self-controlled case series method (SCCS) was developed to analyze the association between a time-varying exposure and an outcome event. We consider penta- or hexavalent vaccination as the exposure and unexplained sudden unexpected death (uSUD) as the event. The special situation of multiple exposures and a terminal event requires adaptation of the standard SCCS method. This paper proposes a new adaptation, in which observation periods are truncated according to the vaccination schedule. The new method exploits known minimum spacings between successive vaccine doses. Its advantage is that it is very much simpler to apply than the method for censored, perturbed or curtailed post-event exposures recently introduced. This paper presents a comparison of these two SCCS methods by simulation studies and an application to a real data set. In the simulation studies, the age distribution and the assumed vaccination schedule were based on real data. Only small differences between the two SCCS methods were observed, although 50 per cent of cases could not be included in the analysis with the SCCS method with truncated observation periods. By means of a study including 300 uSUD, a 16-fold risk increase after the 4th dose could be detected with a power of at least 90 per cent. A general 2-fold risk increase after vaccination could be detected with a power of 80 per cent. Reanalysis of data from cases of the German case-control study on sudden infant death (GeSID) resulted in slightly higher point estimates using the SCCS methods than the odds ratio obtained by the case-control analysis.

Automated Biosurveillance Data from England and Wales, 1991–2011

Twenty years of data provide valuable insights for the design of large automated outbreak detection systems.

Antipsychotic drugs and risks of myocardial infarction: a self-controlled case series study

Aim Antipsychotics increase the risk of stroke. Their effect on myocardial infarction remains uncertain because people prescribed and not prescribed antipsychotic drugs differ in their underlying vascular risk making between-person comparisons difficult to interpret. The aim of our study was to investigate this association using the self-controlled case series design that eliminates between-person confounding effects. Methods and results All the patients with a first recorded myocardial infarction and prescription for an antipsychotic identified in the Clinical Practice Research Datalink linked to the Myocardial Ischaemia National Audit Project were selected for the self-controlled case series. The incidence ratio of myocardial infarction during risk periods following the initiation of antipsychotic use relative to unexposed periods was estimated within individuals. A classical case–control study was undertaken for comparative purposes comparing antipsychotic exposure among cases and matched controls. We identified 1546 exposed cases for the self-controlled case series and found evidence of an association during the first 30 days after the first prescription of an antipsychotic, for first-generation agents [incidence rate ratio (IRR) 2.82, 95% confidence interval (CI) 2.0–3.99] and second-generation agents (IRR: 2.5, 95% CI: 1.18–5.32). Similar results were found for the case–control study for new users of first- (OR: 3.19, 95% CI: 1.9–5.37) and second-generation agents (OR: 2.55, 95% CI: 0.93–7.01) within 30 days of their myocardial infarction. Conclusion We found an increased risk of myocardial infarction in the period following the initiation of antipsychotics that was not attributable to differences between people prescribed and not prescribed antipsychotics.

Self-Controlled Case Series and Misclassification Bias Induced by Case Selection from Administrative Hospital Databases: Application to Febrile Convulsions in Pediatric Vaccine Pharmacoepidemiology

Vaccine safety studies are increasingly conducted by using administrative health databases and self-controlled case series designs that are based on cases only. Often, several criteria are available to define the cases, which may yield different positive predictive values, as well as different sensitivities, and therefore different numbers of selected cases. The question then arises as to which is the best case definition. This article proposes new methodology to guide this choice based on the bias of the relative incidence and the power of the test. We apply this methodology in a validation study of 4 nested algorithms for identifying febrile convulsions from the administrative databases of 10 French hospitals. We used a sample of 695 children aged 1 month to 3 years who were hospitalized in 2008-2009 with at least 1 diagnosis code of febrile convulsions. The positive predictive values of the algorithms ranged from 81% to 98%, and their sensitivities were estimated to be 47%-99% in data from 1 large hospital. When applying our proposed methods, the algorithm we selected used a restricted diagnosis code and position on the discharge abstract. These criteria, which resulted in the selection of 502 cases with a positive predictive value of 95%, provided the best compromise between high power and low relative bias.