Enhancing Bayesian risk prediction for epidemics using contact tracing

Contact tracing data collected from disease outbreaks has received relatively little attention in the epidemic modelling literature because it is thought to be unreliable: infection sources might be wrongly attributed, or data might be missing due to resource contraints in the questionnaire exercise. Nevertheless, these data might provide a rich source of information on disease transmission rate. This paper presents novel methodology for combining contact tracing data with rate-based contact network data to improve posterior precision, and therefore predictive accuracy. We present an advancement in Bayesian inference for epidemics that assimilates these data, and is robust to partial contact tracing. Using a simulation study based on the British poultry industry, we show how the presence of contact tracing data improves posterior predictive accuracy, and can directly inform a more effective control strategy.Comment: 40 pages, 9 figures. Submitted to Biostatistic

Clustering of equine grass sickness cases in the United Kingdom: a study considering the effect of position-dependent reporting on the space-time K-function

Equine grass sickness (EGS) is a largely fatal, pasture-associated dysautonomia. Although the aetiology of this disease is unknown, there is increasing evidence that Clostridium botulinum type C plays an important role in this condition. The disease is widespread in the United Kingdom, with the highest incidence believed to occur in Scotland. EGS also shows strong seasonal variation (most cases are reported between April and July). Data from histologically confirmed cases of EGS from England and Wales in 1999 and 2000 were collected from UK veterinary diagnostic centres. The data did not represent a complete census of cases, and the proportion of all cases reported to the centres would have varied in space and, independently, in time. We consider the variable reporting of this condition and the appropriateness of the space–time K-function when exploring the spatial-temporal properties of a ‘thinned’ point process. We conclude that such position-dependent under-reporting of EGS does not invalidate the Monte Carlo test for space–time interaction, and find strong evidence for space–time clustering of EGS cases (P<0.001). This may be attributed to contagious or other spatially and temporally localized processes such as local climate and/or pasture management practices

Advances in spatiotemporal models for non-communicable disease surveillance

Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public

Estimating stellar oscillation-related parameters and their uncertainties with the moment method

The moment method is a well known mode identification technique in asteroseismology (where `mode' is to be understood in an astronomical rather than in a statistical sense), which uses a time series of the first 3 moments of a spectral line to estimate the discrete oscillation mode parameters l and m. The method, contrary to many other mode identification techniques, also provides estimates of other important continuous parameters such as the inclination angle alpha, and the rotational velocity v_e. We developed a statistical formalism for the moment method based on so-called generalized estimating equations (GEE). This formalism allows the estimation of the uncertainty of the continuous parameters taking into account that the different moments of a line profile are correlated and that the uncertainty of the observed moments also depends on the model parameters. Furthermore, we set up a procedure to take into account the mode uncertainty, i.e., the fact that often several modes (l,m) can adequately describe the data. We also introduce a new lack of fit function which works at least as well as a previous discriminant function, and which in addition allows us to identify the sign of the azimuthal order m. We applied our method to the star HD181558, using several numerical methods, from which we learned that numerically solving the estimating equations is an intensive task. We report on the numerical results, from which we gain insight in the statistical uncertainties of the physical parameters involved in the moment method.Comment: The electronic online version from the publisher can be found at http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9876.2005.00487.

Approximate Bayesian Computation: a nonparametric perspective

Approximate Bayesian Computation is a family of likelihood-free inference techniques that are well-suited to models defined in terms of a stochastic generating mechanism. In a nutshell, Approximate Bayesian Computation proceeds by computing summary statistics s_obs from the data and simulating summary statistics for different values of the parameter theta. The posterior distribution is then approximated by an estimator of the conditional density g(theta|s_obs). In this paper, we derive the asymptotic bias and variance of the standard estimators of the posterior distribution which are based on rejection sampling and linear adjustment. Additionally, we introduce an original estimator of the posterior distribution based on quadratic adjustment and we show that its bias contains a fewer number of terms than the estimator with linear adjustment. Although we find that the estimators with adjustment are not universally superior to the estimator based on rejection sampling, we find that they can achieve better performance when there is a nearly homoscedastic relationship between the summary statistics and the parameter of interest. To make this relationship as homoscedastic as possible, we propose to use transformations of the summary statistics. In different examples borrowed from the population genetics and epidemiological literature, we show the potential of the methods with adjustment and of the transformations of the summary statistics. Supplemental materials containing the details of the proofs are available online

Nonparametric directionality measures for time series and point process data

The need to determine the directionality of interactions between neural signals is a key requirement for analysis of multichannel recordings. Approaches most commonly used are parametric, typically relying on autoregressive models. A number of concerns have been expressed regarding parametric approaches, thus there is a need to consider alternatives. We present an alternative nonparametric approach for construction of directionality measures for bivariate random processes. The method combines time and frequency domain representations of bivariate data to decompose the correlation by direction. Our framework generates two sets of complementary measures, a set of scalar measures, which decompose the total product moment correlation coefficient summatively into three terms by direction and a set of functions which decompose the coherence summatively at each frequency into three terms by direction: forward direction, reverse direction and instantaneous interaction. It can be undertaken as an addition to a standard bivariate spectral and coherence analysis, and applied to either time series or point-process (spike train) data or mixtures of the two (hybrid data). In this paper, we demonstrate application to spike train data using simulated cortical neurone networks and application to experimental data from isolated muscle spindle sensory endings subject to random efferent stimulation

Vaccine uptake and SARS-CoV-2 antibody prevalence among 207,337 adults during May 2021 in England: REACT-2 study

Background The programme to vaccinate adults in England has been rapidly implemented since it began in December 2020. The community prevalence of SARS-CoV-2 anti-spike protein antibodies provides an estimate of total cumulative response to natural infection and vaccination. We describe the distribution of SARS-CoV-2 IgG antibodies in adults in England in May 2021 at a time when approximately 7 in 10 adults had received at least one dose of vaccine. Methods Sixth round of REACT-2 (REal-time Assessment of Community Transmission-2), a cross-sectional random community survey of adults in England, from 12 to 25 May 2021; 207,337 participants completed questionnaires and self-administered a lateral flow immunoassay test producing a positive or negative result. Results Vaccine coverage with one or more doses, weighted to the adult population in England, was 72.9% (95% confidence interval 72.7-73.0), varying by age from 25.1% (24.5- 25.6) of those aged 18 to 24 years, to 99.2% (99.1-99.3) of those 75 years and older. In adjusted models, odds of vaccination were lower in men (odds ratio [OR] 0.89 [0.85-0.94]) than women, and in people of Black (0.41 [0.34-0.49]) compared to white ethnicity. There was higher vaccine coverage in the least deprived and highest income households. People who reported a history of COVID-19 were less likely to be vaccinated (OR 0.61 [0.55-0.67]). There was high coverage among health workers (OR 9.84 [8.79-11.02] and care workers (OR 4.17 [3.20-5.43]) compared to non-key workers, but lower in hospitality and retail workers (OR 0.73 [0.64-0.82] and 0.77 [0.70-0.85] respectively) after adjusting for age and key covariates

The use of continuous electronic prescribing data to infer trends in antimicrobial consumption and estimate the impact of stewardship interventions in hospitalized children.

BACKGROUND: Understanding antimicrobial consumption is essential to mitigate the development of antimicrobial resistance, yet robust data in children are sparse and methodologically limited. Electronic prescribing systems provide an important opportunity to analyse and report antimicrobial consumption in detail. OBJECTIVES: We investigated the value of electronic prescribing data from a tertiary children's hospital to report temporal trends in antimicrobial consumption in hospitalized children and compare commonly used metrics of antimicrobial consumption. METHODS: Daily measures of antimicrobial consumption [days of therapy (DOT) and DDDs] were derived from the electronic prescribing system between 2010 and 2018. Autoregressive moving-average models were used to infer trends and the estimates were compared with simulated point prevalence surveys (PPSs). RESULTS: More than 1.3 million antimicrobial administrations were analysed. There was significant daily and seasonal variation in overall consumption, which reduced annually by 1.77% (95% CI 0.50% to 3.02%). Relative consumption of meropenem decreased by 6.6% annually (95% CI -3.5% to 15.8%) following the expansion of the hospital antimicrobial stewardship programme. DOT and DDDs exhibited similar trends for most antimicrobials, though inconsistencies were observed where changes to dosage guidelines altered consumption calculation by DDDs, but not DOT. PPS simulations resulted in estimates of change over time, which converged on the model estimates, but with much less precision. CONCLUSIONS: Electronic prescribing systems offer significant opportunities to better understand and report antimicrobial consumption in children. This approach to modelling administration data overcomes the limitations of using interval data and dispensary data. It provides substantially more detailed inferences on prescribing patterns and the potential impact of stewardship interventions

Self-hypnosis for intrapartum pain management in pregnant nulliparous women: a randomised controlled trial of clinical effectiveness

Objective: (Primary) To establish the effect of antenatal group self-hypnosis for nulliparous women on intra-partum epidural use. Design: Multi-method randomised control trial (RCT). Setting: Three NHS Trusts. Population: Nulliparous women not planning elective caesarean, without medication for hypertension and without psychological illness. Methods: Randomisation at 28–32 weeks’ gestation to usual care, or to usual care plus brief self-hypnosis training (two × 90-minute groups at around 32 and 35 weeks’ gestation; daily audio self-hypnosis CD). Follow up at 2 and 6 weeks postnatal. Main outcome measures: Primary: epidural analgesia. Secondary: associated clinical and psychological outcomes; cost analysis. Results: Six hundred and eighty women were randomised. There was no statistically significant difference in epidural use: 27.9% (intervention), 30.3% (control), odds ratio (OR) 0.89 [95% confidence interval (CI): 0.64–1.24], or in 27 of 29 pre-specified secondary clinical and psychological outcomes. Women in the intervention group had lower actual than anticipated levels of fear and anxiety between baseline and 2 weeks post natal (anxiety: mean difference −0.72, 95% CI −1.16 to −0.28, P = 0.001); fear (mean difference −0.62, 95% CI −1.08 to −0.16, P = 0.009) [Correction added on 7 July 2015, after first online publication: ‘Mean difference’ replaced ‘Odds ratio (OR)’ in the preceding sentence.]. Postnatal response rates were 67% overall at 2 weeks. The additional cost in the intervention arm per woman was £4.83 (CI −£257.93 to £267.59). Conclusions: Allocation to two-third-trimester group self-hypnosis training sessions did not significantly reduce intra-partum epidural analgesia use or a range of other clinical and psychological variables. The impact of women's anxiety and fear about childbirth needs further investigation

Asthma exacerbation and proximity of residence to major roads: a population-based matched case-control study among the pediatric Medicaid population in Detroit, Michigan

<p>Abstract</p> <p>Background</p> <p>The relationship between asthma and traffic-related pollutants has received considerable attention. The use of individual-level exposure measures, such as residence location or proximity to emission sources, may avoid ecological biases.</p> <p>Method</p> <p>This study focused on the pediatric Medicaid population in Detroit, MI, a high-risk population for asthma-related events. A population-based matched case-control analysis was used to investigate associations between acute asthma outcomes and proximity of residence to major roads, including freeways. Asthma cases were identified as all children who made at least one asthma claim, including inpatient and emergency department visits, during the three-year study period, 2004-06. Individually matched controls were randomly selected from the rest of the Medicaid population on the basis of non-respiratory related illness. We used conditional logistic regression with distance as both categorical and continuous variables, and examined non-linear relationships with distance using polynomial splines. The conditional logistic regression models were then extended by considering multiple asthma states (based on the frequency of acute asthma outcomes) using polychotomous conditional logistic regression.</p> <p>Results</p> <p>Asthma events were associated with proximity to primary roads with an odds ratio of 0.97 (95% CI: 0.94, 0.99) for a 1 km increase in distance using conditional logistic regression, implying that asthma events are less likely as the distance between the residence and a primary road increases. Similar relationships and effect sizes were found using polychotomous conditional logistic regression. Another plausible exposure metric, a reduced form response surface model that represents atmospheric dispersion of pollutants from roads, was not associated under that exposure model.</p> <p>Conclusions</p> <p>There is moderately strong evidence of elevated risk of asthma close to major roads based on the results obtained in this population-based matched case-control study.</p