A time-varying shared frailty model with application to infectious diseases

We propose a new parametric time-varying shared frailty model to represent changes over time in population heterogeneity, for use with bivariate current status data. The model uses a power transformation of a time-invariant frailty U, and is particularly convenient when U is a member of the generalized gamma family. This model avoids some shortcomings of a previously suggested time-varying frailty model, notably time-dependent support. We describe some key properties of the model, including its relative frailty variance function in different settings and how the model can be fitted to data. We describe several applications to shared frailty modeling of bivariate current status data on infectious diseases, in which the frailty represents age-dependent heterogeneity in contact rates or susceptibility to infection

Self-controlled case series with multiple event types

Self-controlled case series methods for events that may be classified as one of several types are described. When the event is non-recurrent, the different types correspond to competing risks. It is shown that, under circumstances that are likely to arise in practical applications, the SCCS multi-type likelihood reduces to the product of the type-specific likelihoods. For recurrent events, this applies whether or not the marginal type-specific counts are dependent. As for the standard SCCS method, a rare disease assumption is required for non-recurrent events. Several forms of this assumption are investigated by simulation. The methods are applied to data on MMR vaccine and convulsions (febrile and non-febrile), and to data on thiazolidinediones and fractures (at different sites)

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

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

Automated Biosurveillance Data from England and Wales, 1991–2011

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

Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems

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

Risk of Injection-Site Abscess among Infants Receiving a Preservative-Free, Two-Dose Vial Formulation of Pneumococcal Conjugate Vaccine in Kenya.

There is a theoretical risk of adverse events following immunization with a preservative-free, 2-dose vial formulation of 10-valent-pneumococcal conjugate vaccine (PCV10). We set out to measure this risk. Four population-based surveillance sites in Kenya (total annual birth cohort of 11,500 infants) were used to conduct a 2-year post-introduction vaccine safety study of PCV10. Injection-site abscesses occurring within 7 days following vaccine administration were clinically diagnosed in all study sites (passive facility-based surveillance) and, also, detected by caregiver-reported symptoms of swelling plus discharge in two sites (active household-based surveillance). Abscess risk was expressed as the number of abscesses per 100,000 injections and was compared for the second vs first vial dose of PCV10 and for PCV10 vs pentavalent vaccine (comparator). A total of 58,288 PCV10 injections were recorded, including 24,054 and 19,702 identified as first and second vial doses, respectively (14,532 unknown vial dose). The risk ratio for abscess following injection with the second (41 per 100,000) vs first (33 per 100,000) vial dose of PCV10 was 1.22 (95% confidence interval [CI] 0.37-4.06). The comparator vaccine was changed from a 2-dose to 10-dose presentation midway through the study. The matched odds ratios for abscess following PCV10 were 1.00 (95% CI 0.12-8.56) and 0.27 (95% CI 0.14-0.54) when compared to the 2-dose and 10-dose pentavalent vaccine presentations, respectively. In Kenya immunization with PCV10 was not associated with an increased risk of injection site abscess, providing confidence that the vaccine may be safely used in Africa. The relatively higher risk of abscess following the 10-dose presentation of pentavalent vaccine merits further study

Mortality risk after COVID-19 vaccination: A self-controlled case series study

Background Although previous studies found no-increased mortality risk after COVID-19 vaccination, residual confounding bias might have impacted the findings. Using a modified self-controlled case series (SCCS) design, we assessed the risk of non-COVID-19 mortality, all-cause mortality, and four cardiac-related death outcomes after primary series COVID-19 vaccination. Methods We analyzed all deaths between December 14, 2020, and August 11, 2021, among individuals from eight Vaccine Safety Datalink sites. Demographic characteristics of deaths in recipients of COVID-19 vaccines and unvaccinated individuals were reported. We conducted SCCS analyses by vaccine type and death outcomes and reported relative incidences (RI). The observation period for death spanned from the dates of emergency use authorization to the end of the study period (August 11, 2021) without censoring the observation period upon death. We pre-specified a primary risk interval of 28-day and a secondary risk interval of 14-day after each vaccination dose. Adjusting for seasonality in mortality analyses is crucial because death rates vary over time. Deaths among unvaccinated individuals were included in SCCS analyses to account for seasonality by incorporating calendar month in the models. Results For Pfizer-BioNTech (BNT162b2), RIs of non-COVID-19 mortality, all-cause mortality, and four cardiac-related death outcomes were below 1 and 95 % confidence intervals (CIs) excluded 1 across both doses and both risk intervals. For Moderna (mRNA-1273), RI point estimates of all outcomes were below 1, although the 95 % CIs of two RI estimates included 1: cardiac-related (RI = 0.78, 95 % CI, 0.58-1.04) and non-COVID-19 cardiac-related mortality (RI = 0.80, 95 % CI, 0.60-1.08) 14 days after the second dose in individuals without pre-existing cancer and heart disease. For Janssen (Ad26.COV2.S), RIs of four cardiac-related death outcomes ranged from 0.94 to 0.98 for the 14-day risk interval, and 0.68 to 0.72 for the 28-day risk interval and 95 % CIs included 1. Conclusion Using a modified SCCS design and adjusting for temporal trends, no-increased risk was found for non-COVID-19 mortality, all-cause mortality, and four cardiac-related death outcomes among recipients of the three COVID-19 vaccines used in the US

Informed choice, balance and the MMR saga

Tom Jefferson states that the three studies he and co-workers included in their review of the evidence on measles, mumps, and rubella (MMR) vaccination and autism contained “fundamental methodological weaknesses”, primarily because the studies did not include an unvaccinated control group. He describes such studies as “uncontrolled” and “always prone to the interference of known and unknown biases and confounders”. He goes on to question the epidemiological and statistical competence of those who published these studies without warning of their “methodological limitations”. We were curious to learn on what evidence he bases these damning statements. On further inspection, one of the three studies he quotes turns out to be a case-control study on the association between MMR and inflammatory bowel disease, not autism, and is therefore not relevant. Conversely, his review ignored several other studies relevant to the association between MMR vaccination and autism. We refute his claims that the two studies he did consider on MMR and autism are uncontrolled and biased. Both studies are controlled, with exposure defined in terms of time since vaccination, and both studies also included unvaccinated children. Studies of this type, incorrectly described by Jefferson as “time series”, or “before and after” studies, have been used for many years in vaccine safety assessment. There is an ample literature on them, including several large cohort and self-controlled case series studies on MMR, which were unaccountably excluded from Jefferson's review (for example, Griffen et al and Farrington et al). The self-controlled case series method is self-matched, and hence less prone to confounding bias than either case-control or cohort studies, for which adjustment is often either not possible, or achieved only imperfectly through proxy variables. Jefferson argues that “every scrap of knowledge” must be put to good use in assessing vaccine safety. We agree. However, his assessment of the epidemiological evidence of MMR involvement in autism, and its safety more widely, was based on studies selected through inappropriately restrictive inclusion criteria, incoherently applied, and subjectively assessed. Such a distorted view of the evidence cannot constitute an appropriate basis on which to draw wider lessons about vaccine safety. In 2002–03 CPF was an expert witness in litigation on MMR and autism, for which he was instructed by the defendants. Since 2003 his department is in receipt of an EPSRC CASE studentship partly funded by GlaxoSmithKline Biologicals. EM's department has received funding from vaccine manufacturers for carrying out clinical trials and for provision of surveillance reports

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