Smooth Risk Functions for Self-Controlled Case Series Models

The self-controlled case series (SCCS) method is commonly used to investigate associations between vaccine exposures and adverse events (side effects). It is an alternative to cohort and case control study designs. It requires information only on cases, individuals who have experienced the adverse event at least once, and automatically controls all fixed confounders that could modify the true association between exposure and adverse event. However, time-varying confounders (age, season) are not automatically controlled. The SCCS method has parametric and semi-parametric versions in terms of controlling the age effect. The parametric method uses piecewise constant functions with a priori chosen age groups and the semi-parametric method leaves the age effect unspecified. Mis-specification of age groups in the parametric version may lead to biased estimates of the exposure effect, and the semi-parametric approach runs into computational problems when the sample size is moderately large. Moreover, both versions of SCCS represent 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. This thesis focuses on extending the SCCS method to avoid the aforementioned limitations by modelling the age and exposure effects using flexible smooth functions. Specifically, we used penalised regression splines based on cubic M-splines, which are piecewise polynomials of degree 3. We developed three new extensions: a method that represents only the age effect with splines, a method that uses splines to model only the exposure effect and a non-parametric SCCS method that represents both effects by splines. Simulation studies showed that these new methods outperformed the parametric and semi-parametric methods. The new methods are illustrated using large data sets. Review of SCCS vaccine studies and directions on how to use the method are also given

Investigating the assumptions of the self-controlled case series method.

We describe some simple techniques for investigating two 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

Spline-based self-controlled case series method

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

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)

Effectiveness of pre-pregnancy lifestyle in preventing gestational diabetes mellitus—a systematic review and meta-analysis of 257,876 pregnancies

Background: Gestational Diabetes Mellitus (GDM) is hyperglycaemia first detected during pregnancy. Globally, GDM affects around 1 in 6 live births (up to 1 in 4 in low- and middle-income countries- LMICs), thus, urgent measures are needed to prevent this public health threat. Objective: To determine the effectiveness of pre-pregnancy lifestyle in preventing GDM. Methods: We searched MEDLINE, Web of science, Embase and Cochrane central register of controlled trials. Randomized control trials (RCTs), case-control studies, and cohort studies that assessed the effect of pre-pregnancy lifestyle (diet and/or physical activity based) in preventing GDM were included. Random effects model was used to calculate odds ratio (OR) with 95% confidence interval. The Cochrane ROB-2 and the Newcastle-Ottawa Scale were used for assessing the risk of bias. The protocol was registered in PROSPERO (ID: CRD42020189574) Results: Database search identified 7935 studies, of which 30 studies with 257,876 pregnancies were included. Meta-analysis of the RCTs (N = 5; n = 2471) in women who received pre-pregnancy lifestyle intervention showed non-significant reduction of the risk of developing GDM (OR 0.76, 95% CI: 0.50–1.17, p = 0.21). Meta-analysis of cohort studies showed that women who were physically active pre-pregnancy (N = 4; n = 23263), those who followed a low carbohydrate/low sugar diet (N = 4; n = 25739) and those women with higher quality diet scores were 29%, 14% and 28% less likely to develop GDM respectively (OR 0.71, 95% CI: 0.57, 0.88, p = 0.002, OR 0.86, 95% CI: 0.68, 1.09, p = 0.22 and OR 0.72, 95% CI 0.60–0.87, p = 0.0006). Conclusion: This study highlights that some components of pre-pregnancy lifestyle interventions/exposures such as diet/physical activity-based preparation/counseling, intake of vegetables, fruits, low carbohydrate/low sugar diet, higher quality diet scores and high physical activity can reduce the risk of developing gestational diabetes. Evidence from RCTs globally and the number of studies in LMICs are limited, highlighting the need for carefully designed RCTs that combine the different aspects of the lifestyle and are personalized to achieve better clinical and cost effectiveness

Self-controlled case series studies: just how rare does a rare non-recurrent outcome need to be?

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

Flexible modelling of vaccine effect in self-controlled case series models

The self-controlled case-series method (SCCS), 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

Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes

Objective The aim of the present study was to identify the factors associated with non-attendance of immediate postpartum glucose test using a machine learning algorithm following gestational diabetes mellitus (GDM) pregnancy. Method: A retrospective cohort study of all GDM women (n = 607) for postpartum glucose test due between January 2016 and December 2019 at the George Eliot Hospital NHS Trust, UK. Results Sixty-five percent of women attended postpartum glucose test. Type 2 diabetes was diagnosed in 2.8% and 21.6% had persistent dysglycaemia at 6–13 weeks post-delivery. Those who did not attend postpartum glucose test seem to be younger, multiparous, obese, and continued to smoke during pregnancy. They also had higher fasting glucose at antenatal oral glucose tolerance test. Our machine learning algorithm predicted postpartum glucose non-attendance with an area under the receiver operating characteristic curve of 0.72. The model could achieve a sensitivity of 70% with 66% specificity at a risk score threshold of 0.46. A total of 233 (38.4%) women attended subsequent glucose test at least once within the first two years of delivery and 24% had dysglycaemia. Compared to women who attended postpartum glucose test, those who did not attend had higher conversion rate to type 2 diabetes (2.5% vs 11.4%; p = 0.005). Conclusion Postpartum screening following GDM is still poor. Women who did not attend postpartum screening appear to have higher metabolic risk and higher conversion to type 2 diabetes by two years post-delivery. Machine learning model can predict women who are unlikely to attend postpartum glucose test using simple antenatal factors. Enhanced, personalised education of these women may improve postpartum glucose screening

Prevalence of prediabetes and type 2 diabetes mellitus in south and southeast Asian women with history of gestational diabetes mellitus: Systematic review and meta-analysis

Background The burden of Gestational Diabetes Mellitus (GDM) is very high in south Asia (SA) and southeast Asia (SEA). Thus, there is a need to understand the prevalence and risk factors for developing prediabetes and type 2 diabetes mellitus (T2DM) postpartum, in this high-risk population. Aim To conduct a systematic review and meta-analysis to estimate the prevalence of prediabetes and T2DM among the women with history of GDM in SA and SEA. Methods A comprehensive literature search was performed in the following databases: Medline, EMBASE, Web of Knowledge and CINHAL till December 2021. Studies that had reported greater than six weeks of postpartum follow-up were included. The pooled prevalence of diabetes and prediabetes were estimated by random effects meta-analysis model and I2 statistic was used to assess heterogeneity. Results Meta-analysis of 13 studies revealed that the prevalence of prediabetes and T2DM in post-GDM women were 25.9% (95%CI 18.94 to 33.51) and 29.9% (95%CI 17.02 to 44.57) respectively. Women with history of GDM from SA and SEA seem to have higher risk of developing T2DM than women without GDM (RR 13.2, 95%CI 9.52 to 18.29, p Conclusion The conversion to T2DM and prediabetes is very high among women with history of GDM in SA and SEA. This highlights the need for follow-up of GDM women for early identification of dysglycemia and to plan interventions to prevent/delay the progression to T2DM

Cardiovascular outcomes associated with use of clarithromycin: population based study

Study question What is the association between clarithromycin use and cardiovascular outcomes? Methods In this population based study the authors compared cardiovascular outcomes in adults aged 18 or more receiving oral clarithromycin or amoxicillin during 2005-09 in Hong Kong. Based on age within five years, sex, and calendar year at use, each clarithromycin user was matched to one or two amoxicillin users. The cohort analysis included patients who received clarithromycin (n=108 988) or amoxicillin (n=217 793). The self controlled case series and case crossover analysis included those who received Helicobacter pylori eradication treatment containing clarithromycin. The primary outcome was myocardial infarction. Secondary outcomes were all cause, cardiac, or non-cardiac mortality, arrhythmia, and stroke. Study answer and limitations The propensity score adjusted rate ratio of myocardial infarction 14 days after the start of antibiotic treatment was 3.66 (95% confidence interval 2.82 to 4.76) comparing clarithromycin use (132 events, rate 44.4 per 1000 person years) with amoxicillin use (149 events, 19.2 per 1000 person years), but no long term increased risk was observed. Similarly, rate ratios of secondary outcomes increased significantly only with current use of clarithromycin versus amoxicillin, except for stroke. In the self controlled case analysis, there was an association between current use of H pylori eradication treatment containing clarithromycin and cardiovascular events. The risk returned to baseline after treatment had ended. The case crossover analysis also showed an increased risk of cardiovascular events during current use of H pylori eradication treatment containing clarithromycin. The adjusted absolute risk difference for current use of clarithromycin versus amoxicillin was 1.90 excess myocardial infarction events (95% confidence interval 1.30 to 2.68) per 1000 patients. What this study adds Current use of clarithromycin was associated with an increased risk of myocardial infarction, arrhythmia, and cardiac mortality short term but no association with long term cardiovascular risks among the Hong Kong population