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SUMMARYThe self-controlled case-series method was originally developed to investigate potential associations between vaccines and adverse events, and is now commonly used for this purpose. This study reviews applications of the method to vaccine safety investigations in the period 1995-2010. In total, 40 studies were reviewed. The application of the self-controlled case-series method in these studies is critically examined, with particular reference to the definition of observation and risk periods, control of confounders, assumptions and potential biases, methodological and presentation issues, power and sample size, and software. Comparisons with other study designs undertaken in the papers reviewed are also highlighted. Some recommendations are presented, with the emphasis on promoting good practice. [ABSTRACT FROM AUTHOR]

We review sources of bias which can affect non-randomized cohort studies of non-specific effects of vaccines on child mortality. Using examples from the literature on non-specific effects, we describe different sources of selection and information bias, and, where possible, outline analysis strategies to mitigate or eliminate such biases. [ABSTRACT FROM AUTHOR]

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. Copyright © 2004 Infectious Diseases Society of America [ABSTRACT FROM AUTHOR]

Background: We undertook an epidemiological study to investigate whether measles, mumps, and rubella (MMR) vaccine may be causally associated with autism.Methods: Children with autism born since 1979 were identified from special needs/disability registers and special schools in eight North Thames health districts, UK. Information from clinical records was linked to immunisation data held on the child health computing system. We looked for evidence of a change in trend in incidence or age at diagnosis associated with the introduction of MMR vaccination to the UK in 1988. Clustering of onsets within defined postvaccination periods was investigated by the case-series method.Findings: We identified 498 cases of autism (261 of core autism, 166 of atypical autism, and 71 of Asperger's syndrome). In 293 cases the diagnosis could be confirmed by the criteria of the International Classification of Diseases, tenth revision (ICD10: 214 [82%] core autism, 52 [31%] atypical autism, 27 [38%] Asperger's syndrome). There was a steady increase in cases by year of birth with no sudden "step-up" or change in the trend line after the introduction of MMR vaccination. There was no difference in age at diagnosis between the cases vaccinated before or after 18 months of age and those never vaccinated. There was no temporal association between onset of autism within 1 or 2 years after vaccination with MMR (relative incidence compared with control period 0.94 [95% CI 0.60-1.47] and 1.09 [0.79-1.52]). Developmental regression was not clustered in the months after vaccination (relative incidence within 2 months and 4 months after MMR vaccination 0.92 [0.38-2.21] and 1.00 [0.52-1.95]). No significant temporal clustering for age at onset of parental concern was seen for cases of core autism or atypical autism with the exception of a single interval within 6 months of MMR vaccination. This appeared to be an artifact related to the difficulty of defining precisely the onset of symptoms in this disorder.Interpretation: Our analyses do not support a causal association between MMR vaccine and autism. If such an association occurs, it is so rare that it could not be identified in this large regional sample. [ABSTRACT FROM AUTHOR]

Background: Unregulated skin-piercing procedures potentially facilitate the transmission of bloodborne pathogens. In February, 1998, a patient who had recently received autohaemotherapy at an alternative medicine clinic in the UK was diagnosed with acute hepatitis B. The autohaemotherapy procedure involved the drawing of 1 mL of the patient's blood, mixing with saline, and reinjection of the autologous blood mixture. We investigated the extent of hepatitis B virus (HBV) infection in patients and staff of the clinic. Methods: Patients who had attended the clinic between January, 1997, and February, 1998, were tested for serological markers of HBV, and for HBV DNA by PCR. HBV DNA was sequenced to assess the relatedness of the virus identified in the cases. We analysed the number and dates of visits with regard to HBV status. Findings: Serum samples were received from 352 patients and four staff members. Serological evidence of exposure to HBV was found in 57 (16%). Of the 33 patients and staff who were positive for hepatitis B surface antigen, 30 (91%) showed complete nucleotide identity in the DNA segments derived from the surface and core genes. Five patients with linked infection had markers of chronic hepatitis B, and one of these was regarded as the likely source of the outbreak. The attack rate was associated with the number of visits (p<0.0001) and the week of visit (p=0.011). Contaminated saline in a repeatedly used bottle was the probable vehicle of transmission. Interpretation: We have described a large community-based outbreak of hepatitis B due to transmission by a single HBV variant. Our findings emphasise the continuing risk of transmission of bloodborne viruses in all health-care settings where skin-piercing procedures are used. [ABSTRACT FROM AUTHOR]

The screening method provides a simple and rapid way of estimating vaccine effectiveness. This paper discusses the validity of the screening method with particular reference to bias and precision. Methods for correcting confounding, adjusting for covariates and over-dispersion, and deriving confidence limits are discussed in a modelling framework. The methods are illustrated using data on measles and pertussis vaccines., (© The Author(s) 2022; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

The self-controlled case series method assumes that adverse outcomes arise according to a non-homogeneous Poisson process. This implies that it is applicable to independent recurrent outcomes. However, the self-controlled case series method may also be applied to unique, non-recurrent outcomes or first outcomes only, in the limit where these become rare. We investigate this rare outcome assumption when the self-controlled case series method is applied to non-recurrent outcomes. We study this requirement analytically and by simulation, and quantify what is meant by 'rare' in this context. In simulations we also apply the self-controlled risk interval design, a special case of the self-controlled case series design. To illustrate, we extract data on the incidence rate of some recurrent and non-recurrent outcomes within a defined study population to check whether outcomes are sufficiently rare for the rare outcome assumption to hold when applying the self-controlled case series method to first or unique outcomes. The main findings are that the relative bias should be no more than 5% when the cumulative incidence over total time observed is less than 0.1 per individual. Inclusion of age (or calendar time) effects will further reduce bias. Designs that begin observation with exposure maximise bias, whereas little or no bias will be apparent when there is no time trend in the distribution of exposures, or when exposure is central within time observed., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

We describe some simple techniques for investigating 2 key assumptions of the self-controlled case series (SCCS) method, namely, that events do not influence subsequent exposures and that events do not influence the length of observation periods. For each assumption, we propose some simple tests based on the standard SCCS model, along with associated graphical displays. The methods also enable the user to investigate the robustness of the results obtained using the standard SCCS model to failure of assumptions. The proposed methods are investigated by simulations and applied to data on measles, mumps and rubella vaccine, and antipsychotics., (Copyright © 2017 John Wiley & Sons, Ltd.)

The self-controlled case series (SCCS) method is an alternative to study designs such as cohort and case control methods and is used to investigate potential associations between the timing of vaccine or other drug exposures and adverse events. It requires information only on cases, individuals who have experienced the adverse event at least once, and automatically controls all fixed confounding variables that could modify the true association between exposure and adverse event. Time-varying confounders such as age, on the other hand, are not automatically controlled and must be allowed for explicitly. The original SCCS method used step functions to represent risk periods (windows of exposed time) and age effects. Hence, exposure risk periods and/or age groups have to be prespecified a priori, but a poor choice of group boundaries may lead to biased estimates. In this paper, we propose a nonparametric SCCS method in which both age and exposure effects are represented by spline functions at the same time. To avoid a numerical integration of the product of these two spline functions in the likelihood function of the SCCS method, we defined the first, second, and third integrals of I-splines based on the definition of integrals of M-splines. Simulation studies showed that the new method performs well. This new method is applied to data on pediatric vaccines. Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is separated into one series giving the baseline (background) disease incidence and a second series giving disease outbreaks. One series is shifted forward by twelve months and the two are then recombined, giving a realistic series in which it is known where outbreaks have been added. The metrics used to evaluate performance include a scoring rule that appropriately balances sensitivity against specificity and is sensitive to variation in probabilities near 1. In the context of disease surveillance, a scoring rule can be adapted to reflect the size of outbreaks and this was done. Results indicate that the two new algorithms are comparable to each other and better than the algorithm they were designed to replace.

The self-controlled case series (SCCS) method, commonly used to investigate the safety of vaccines, requires information on cases only and automatically controls all age-independent multiplicative confounders, while allowing for an age-dependent baseline incidence. Currently, the SCCS method represents the time-varying exposures using step functions with pre-determined cut points. A less prescriptive approach may be beneficial when the shape of the relative risk function associated with exposure is not known a priori, especially when exposure effects can be long-lasting. We therefore propose to model exposure effects using flexible smooth functions. Specifically, we used a linear combination of cubic M-splines which, in addition to giving plausible shapes, avoids the integral in the log-likelihood function of the SCCS model. The methods, though developed specifically for vaccines, are applicable more widely. Simulations showed that the new approach generally performs better than the step function method. We applied the new method to two data sets, on febrile convulsion and exposure to MMR vaccine, and on fractures and thiazolidinedione use., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Population seroprevalence can be estimated from serosurveys by classifying quantitative measurements into positives (past infection/vaccinated) or negatives (susceptible) according to a fixed assay cut-off. The choice of assay cut-offs has a direct impact on seroprevalence estimates. A time-resolved fluorescence immunoassay (TRFIA) was used to test exposure to human parvovirus 4 (HP4). Seroprevalence estimates were obtained after applying the diagnostic assay cut-off under different scenarios using simulations. Alternative methods for estimating assay cut-offs were proposed based on mixture modelling with component distributions for the past infection/vaccinated and susceptible populations. Seroprevalence estimates were compared to those obtained directly from the data using mixture models. Simulation results showed that when there was good distinction between the underlying populations all methods gave seroprevalence estimates close to the true one. For high overlap between the underlying components, the diagnostic assay cut-off generally gave the most biased estimates. However, the mixture model methods also gave biased estimates which were a result of poor model fit. In conclusion, fixed cut-offs often produce biased estimates but they also have advantages compared to other methods such as mixture models. The bias can be reduced by using assay cut-offs estimated specifically for seroprevalence studies.

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., (© The Author 2014. Published by Oxford University Press on behalf of the European Society of Cardiology.)

The self-controlled case series method, commonly used to investigate potential associations between vaccines and adverse events, requires information on cases only and automatically controls all age-independent multiplicative confounders while allowing for an age-dependent baseline incidence. In the parametric version of the method, we modelled the age-specific relative incidence by using a piecewise constant function, whereas in the semiparametric version, we left it unspecified. However, mis-specification of age groups in the parametric version can lead to biassed estimates of exposure effect, and the semiparametric approach runs into computational problems when the number of cases in the study is moderately large. We, thus, propose to use a penalized likelihood approach where the age effect is modelled using splines. We use a linear combination of cubic M-splines to approximate the age-specific relative incidence and integrated splines for the cumulative relative incidence. We conducted a simulation study to evaluate the performance of the new approach and its efficiency relative to the parametric and semiparametric approaches. Results show that the new approach performs equivalently to the existing methods when the sample size is small and works well for large data sets. We applied the new spline-based approach to data on febrile convulsions and paediatric vaccines. Co, (Copyright © 2013 John Wiley & Sons, Ltd.)

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.

The basic reproduction number of an infection in a given population, R0, is inflated by individual heterogeneity in contact rates. Recently, new methods for estimating R0 using social contact data and serological survey data have been proposed. These methods, like most of their predecessors, ignore individual heterogeneity, and are sensitive to perturbation of the contact function. Using a frailty framework, we derive expressions for R0 in the presence of age-varying heterogeneity. In this case, R0 is the spectral radius of a population version of the next generation operator, which involves the variance function of the age-dependent frailty. This variance can be estimated within a shared frailty framework from paired data on two infections transmitted by the same route. We propose two estimators of R0 for infections in endemic equilibrium. We investigate their performance by simulation, and find that one is generally less efficient but more robust than the other to perturbation of the effective contact function. These methods are applied to data on varicella zoster virus infection from two European countries.

In this paper, we propose new methods for investigating the extent of heterogeneity in effective contact rates relevant to the transmission of infections. These methods exploit the correlations between ages at infection for different infections within individuals. The methods are developed for serological surveys, which provide accessible individual data on several infections, and are applied to a wide range of infections. We find that childhood infections are often highly correlated within individuals in early childhood, with the correlations persisting into adulthood only for infections sharing a transmission route. We discuss 2 applications of the methods: 1) to making inferences about routes of transmission when these are unknown or uncertain and 2) to estimating epidemiologic parameters such as the basic reproduction number and the critical immunization threshold. Two examples of such applications are presented: elucidating the transmission route of polyomaviruses BK and JC and estimating the basic reproduction number and critical immunization coverage of varicella-zoster infection in Belgium, Italy, Poland, and England and Wales. We speculate that childhood correlations stem from confounding of different transmission routes and represent heterogeneity in childhood circumstances, notably nursery-school attendance. In contrast, it is suggested that correlations in adulthood are route-specific.

Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991-2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity.

In this paper, a new measure for assessing the temporal variation in the strength of association in bivariate current status data is proposed. This novel measure is relevant for shared frailty models. We show that this measure is particularly convenient, owing to its connection with the relative frailty variance and its interpretability in suggesting appropriate frailty models. We introduce a method of estimation and standard errors for this measure. We discuss its properties and compare it to an existing measure of association applicable to current status data. Small sample performance of the measure in realistic scenarios is investigated using simulations. The methods are illustrated with bivariate serological survey data on a pair of infections, where the time-varying association is likely to represent heterogeneities in activity levels and/or susceptibility to infection.

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., (Copyright © 2010 John Wiley & Sons, Ltd.)

We propose a measure of disassortativeness to summarize contact patterns relevant to the transmission of directly transmitted infections. We discuss the properties of this measure, describe standardization relative to homogeneous mixing, and generalize it to multivariate contact structures. We explore some of its properties and apply our methods to serological surveys of close contact infections and surveys of self-reported social contacts obtained in several European countries.

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.

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.

We adapt the self-controlled case series method for routine surveillance of vaccine safety using cumulative sum (CUSUM) charts. The CUSUM surveillance method we propose is applicable for detecting associations that arise in a short pre-determined risk period following vaccination. The performance of the case series CUSUM is investigated through simulations. We illustrate the method using retrospective analyses of influenza vaccine and Bell's palsy, and MMR vaccine and febrile convulsions.

The hypothesis that hepatitis B vaccination is a risk factor for multiple sclerosis has been discussed at length. The data from an earlier case-control study were reanalyzed using the self-controlled case series method. Using the matched cases from the case-control study, we found a relative incidence of 1.68, 95% CI (0.77-3.68) for the 0-60-day post-vaccination risk period; this compares to an odds ratio of 1.8, 95% CI (0.7-4.6). When an additional 53 unmatched cases not used in the case-control study were included, the relative incidence was 1.35, 95% CI (0.66-2.79). Our results throw further light on the methodological aspects of the case series method. We recommend that, when case-control studies of vaccination and adverse events are planned, case series analyses based on the cases are also undertaken when appropriate.

We derive several formulae for the sample size required for a study designed using the self-controlled case series method without age effects. We investigate these formulae by simulation, and identify one based on the signed root likelihood ratio statistic which performs well. We extend this method to allow for age effects, which can have a big impact on the sample size needed. This more general sample size formula is also found to perform well in a broad range of situations., (Copyright 2006 John Wiley & Sons, Ltd.)

The self-controlled case series method was developed to investigate associations between acute outcomes and transient exposures, using only data on cases, that is, on individuals who have experienced the outcome of interest. Inference is within individuals, and hence fixed covariates effects are implicitly controlled for within a proportional incidence framework. We describe the origins, assumptions, limitations, and uses of the method. The rationale for the model and the derivation of the likelihood are explained in detail using a worked example on vaccine safety. Code for fitting the model in the statistical package STATA is described. Two further vaccine safety data sets are used to illustrate a range of modelling issues and extensions of the basic model. Some brief pointers on the design of case series studies are provided. The data sets, STATA code, and further implementation details in SAS, GENSTAT and GLIM are available from an associated website., (Copyright 2006 John Wiley & Sons, Ltd.)

Background: A 15-fold increased risk of gastrointestinal bleeding has been reported with concurrent use of selective serotonin reuptake inhibitors and non-steroidal anti-inflammatory drugs. Recent guidance cautions against concurrent prescription, particularly in older people., Aim: To quantify the risk of gastrointestinal bleeding associated with current exposure to non-steroidal anti-inflammatory drugs, selective serotonin reuptake inhibitors, and both drugs concurrently., Methods: We conducted a case-control analysis of 11,261 cases with upper gastrointestinal bleeding and 53,156 controls matched by gender, age and general practice from computerized primary care data. We coupled this with self-controlled case series analysis., Results: Both drugs were associated with a twofold increased risk of gastrointestinal bleeding (odds ratio =2.38, 95% confidence interval 2.08-2.72 for selective serotonin reuptake inhibitors and odds ratio = 2.15, 95% confidence interval 2.02-2.28 for non-steroidal anti-inflammatory drugs). This increased risk was marginally higher for concurrent prescription (odds ratio = 2.93, 95% confidence interval 2.25-3.82). The self-controlled analysis showed a greater incidence rate ratio for gastrointestinal bleeding with non-steroidal anti-inflammatory drugs (2.71, 95% confidence interval 2.51-2.91) and lower incidence rate ratio with selective serotonin reuptake inhibitors (1.71, 95% confidence interval 1.48-1.98). The incidence rate ratio when both drugs were combined was 3.25, 95% confidence interval 1.95-5.42. Estimates were similar after restricting to people over 80 years of age. Increased risk of gastrointestinal bleeding was not specifically related to class of non-steroidal anti-inflammatory drugs and was similar when we looked at tricyclic anti-depressants., Conclusions: Our study suggests that the risk of gastrointestinal bleeding is not substantially increased when non-steroidal anti-inflammatory drugs and selective serotonin reuptake inhibitors are prescribed together, compared with their use alone.

Established in 1999, the Global Advisory Committee on Vaccine Safety advises the World Health Organization (WHO) on vaccine-related safety issues and enables WHO to respond promptly, efficiently, and with scientific rigor to issues of vaccine safety with potential global importance. The committee also assesses the implications of vaccine safety for practice worldwide and for WHO policies. We describe the principles on which the committee was established, its modus operandi, and the scope of the work undertaken, both present and future. We highlight its recent recommendations on major issues, including the purported link between the measles-mumps-rubella vaccine and autism and the safety of the mumps, influenza, yellow fever, BCG, and smallpox vaccines as well as that of thiomersal-containing vaccines.

We develop methods for the analysis of infectious disease data when age-specific contact rates vary over time. Our methods are valid when contact rates vary slowly on the time scale of the infection process, and are applicable to a variety of data types including serial seroprevalence surveys and case reports. The methods exploit approximate endemic equilibria, and require numerical solution of an associated integral equation in age and time. We also estimate summary statistics such as time-dependent analogs of the basic reproduction number and critical immunization threshold. We illustrate the methods with data on varicella (chickenpox) in the United Kingdom.

Serological surveys are a useful source of information about epidemiological parameters for infectious diseases. In particular they may be used to estimate contact rates, forces of infection, the reproduction number and the critical vaccination threshold. However, these estimation methods require the assumption that the infection is in endemic equilibrium. Such equilibria seldom exist in practice: for example, many common infections of childhood exhibit regular epidemic cycles. In this paper, we investigate whether ignoring such cycles produces biased estimates. We apply the methods to data on mumps and rubella in the U.K. prior to the introduction of the combined measles, mumps, rubella (MMR) vaccine. We conclude that past epidemics have only a marginal effect on estimates, and that standard methods that do not adjust for regular epidemics are valid.

Epidemiological methods involving only cases are reviewed in the context of vaccine safety studies. These methods include some ecological methods, case-coverage methods, case-crossover and self-controlled case series methods. The properties of the methods are described using examples from the literature. It is argued that such methods, and in particular the self-controlled case series method, are powerful epidemiologic tools meriting the same attention as more traditional cohort and case-control methods.

The effective reproduction number of an infection, denoted Re, may be used to monitor the impact of a vaccination programme. If Re is maintained below 1, then sustained endemic transmission of the infection cannot occur. In this paper we discuss methods for estimating Re from serological survey data, allowing for age and individual heterogeneity. We describe semi-parametric and parametric models, and obtain an upper bound on Re when vaccine coverage and efficacy are not known. The methods are illustrated using data on mumps and rubella in England and Wales.

We consider the impact of a vaccination programme on the transmission potential of the infection in large populations. We define a measure of vaccine efficacy against transmission which combines the possibly random effect of the vaccine on individual susceptibility and infectiousness. This definition extends some previous work in this area to arbitrarily heterogeneous populations with one level of mixing, but leads us to question the usefulness of the concept of vaccine efficacy against infectiousness. We derive relationships between vaccine efficacy against transmission, vaccine coverage and reproduction numbers, which generalize existing results. In particular we show that the projected reproduction number RV does not depend on the details of the vaccine model, only on its overall effect on transmission. Explicit expressions for RV and the basic reproduction number R0 are obtained in a variety of settings. We define a measure of projected effectiveness of a vaccination programme PE=1-(RV/R0) and investigate its relationship with efficacy against transmission and vaccine coverage. We also study the effective reproduction number Re(t) at time t. Monitoring Re(t) over time is an important aspect of programme surveillance. Programme effectiveness PE is less sensitive than RV or the critical vaccination threshold to model assumptions. On the other hand Re(t) depends on the details of the vaccine model.

Mass vaccination programmes aim to maintain the effective reproduction number R of an infection below unity. We describe methods for monitoring the value of R using surveillance data. The models are based on branching processes in which R is identified with the offspring mean. We derive unconditional likelihoods for the offspring mean using data on outbreak size and outbreak duration. We also discuss Bayesian methods, implemented by Metropolis-Hastings sampling. We investigate by simulation the validity of the models with respect to depletion of susceptibles and under-ascertainment of cases. The methods are illustrated using surveillance data on measles in the USA.

Capture-recapture studies in epidemiology are frequently undertaken to adjust underlying rates for underreporting. This contrasts with applications in ecology, in which the aim is to estimate population size. In consequence, the Poisson model provides a more appropriate framework than the multinomial in some epidemiological applications. A method for constructing profile Poisson likelihood interval estimates using log-linear modelling, avoiding the need for constrained optimization, is described for several relevant parameters, including the unobserved disease rate, the total disease rate, and the case ascertainment probability. Asymptotic properties of these estimates are studied and contrasted with those derived from the multinomial model. The method is illustrated with a published data set on spina bifida., (Copyright 2002 John Wiley & Sons, Ltd.)

Aim: To study changes in the seroprevalence of varicella zoster virus (VZV) antibody over the past 25 years with a view to determining the target age group for any future vaccination strategy., Methods: Stored sera collected from different age groups over a period of 25 years were tested by a commercial VZV IgG enzyme immunoassay at a four year time interval. Data were analysed by logistic regression to investigate the evidence for changes in incidence and hence seroprevalence over that period., Results: There was a significant rise in VZV antibody prevalence in the 1-4 year age group during the study period., Conclusions: A universal childhood VZV vaccination strategy will need to take account of the increase in incidence of VZV infection in children under the age of 4 years; hence, the suggested target age would be between 12 and 18 months---soon after the disappearance of maternal antibody.

In the case-crossover design, only cases are sampled and the hazard ratio is estimated from within-subject comparisons of exposures at the event time and in M control periods prior to the event. We consider the effect of within-subject dependence of exposures in successive time periods. We show that estimates obtained from the conditional logistic model are biased. This bias disappears if the distribution of exposures in the M+1 successive time intervals is exchangeable. In contrast, the Mantel-Haenszel estimator for the odds ratio is approximately unbiased provided that exposures are stationary. Suitable methods of analysis of case-crossover designs using maximum likelihood may be derived from cohort rather than case-control models., (Copyright 2001 John Wiley & Sons, Ltd.)