Empirical evidence of transmission over a school-household network for SARS-CoV-2; exploration of transmission pairs stratified by primary and secondary school.

BACKGROUND: Children play a key role in the transmission of many infectious diseases. They have many of their close social encounters at home or at school. We hypothesized that most of the transmission of respiratory infections among children occur in these two settings and that transmission patterns can be predicted by a bipartite network of schools and households. AIM AND METHODS: To confirm transmission over a school-household network, SARS-CoV-2 transmission pairs in children aged 4-17 years were analyzed by study year and primary/secondary school. Cases with symptom onset between 1 March 2021 and 4 April 2021 identified by source and contact-tracing in the Netherlands were included. In this period, primary schools were open and secondary school students attended class at least once per week. Within pairs, spatial distance between the postcodes was calculated as the Euclidean distance. RESULTS: A total of 4059 transmission pairs were identified; 51.9% between primary schoolers; 19.6% between primary and secondary schoolers; 28.5% between secondary schoolers. Most (68.5%) of the transmission for children in the same study year occurred at school. In contrast, most of the transmission of children from different study years (64.3%) and most primary-secondary transmission (81.7%) occurred at home. The average spatial distance between infections was 1.2 km (median 0.4) for primary school pairs, 1.6 km (median 0) for primary-secondary school pairs and 4.1 km (median 1.2) for secondary school pairs. CONCLUSION: The results provide evidence of transmission on a bipartite school-household network. Schools play an important role in transmission within study years, and households play an important role in transmission between study years and between primary and secondary schools. Spatial distance between infections in a transmission pair reflects the smaller school catchment area of primary schools versus secondary schools. Many of these observed patterns likely hold for other respiratory pathogens.

Occupation- and age-associated risk of SARS-CoV-2 test positivity, the Netherlands, June to October 2020.

High coronavirus incidence has prompted the Netherlands to implement a second lockdown. To elucidate the epidemic's development preceding this second wave, we analysed weekly test positivity in public test locations by population subgroup between 1 June and 17 October 2020. Hospitality and public transport workers, driving instructors, hairdressers and aestheticians had higher test positivity compared with a reference group of individuals without a close-contact occupation. Workers in childcare, education and healthcare showed lower test positivity.

Determination of the time-dependent association between ciprofloxacin consumption and ciprofloxacin resistance using a weighted cumulative exposure model compared with standard models.

OBJECTIVES: To obtain comprehensive insight into the association of ciprofloxacin use at different times in the past with the current risk of detecting resistance. METHODS: This retrospective nested case-control study of ciprofloxacin users used Dutch data from the PHARMO Database Network and one laboratory for the period 2003-14. Cases and controls were selected as patients with an antibiotic susceptibility test (AST) indicating ciprofloxacin resistance or susceptibility, respectively. We performed univariable and multivariable conditional logistic regression analyses, defining time-dependent exposure using standard definitions (current ciprofloxacin use, used 0-30, 31-90, 91-180 and 181-360 days ago) and a flexible weighted cumulative effect (WCE) model with four alternative time windows of past doses (0-30, 0-90, 0-180 and 0-360 days). RESULTS: The study population consisted of 230 cases and 909 controls. Under the standard exposure definitions, the association of ciprofloxacin use with resistance decreased with time [current use: adjusted OR 6.8 (95% CI 3.6-12.4); used 181-360 days ago: 1.3 (0.8-1.9)]. Under the 90 day WCE model (best-fitting model), more recent doses were more strongly associated with resistance than past doses, as was longer or repeated treatment. The 180 day WCE model, which fitted the data equally well, suggested that doses taken 91-180 days ago were also significantly associated with resistance. CONCLUSIONS: The estimates for the association between ciprofloxacin use at different times and resistance show that ciprofloxacin prescribers should consider ciprofloxacin use 0-180 days ago to ensure that patients receive suitable treatment. The OR of ciprofloxacin resistance could be reduced by eliminating repeated ciprofloxacin prescription within 180 days and by treating for no longer than necessary.

Using flexible methods to determine risk factors for ventilator-associated pneumonia in the Netherlands.

Seven hospitals participated in the Dutch national surveillance for ventilator-associated pneumonia (VAP) and its risk factors. We analysed time-independent and time-dependent risk factors for VAP using the standard Cox regression and the flexible Weighted Cumulative Effects method (WCE) that evaluates both current and past exposures. The prospective surveillance of intensive care patients aged ≥16 years and ventilated ≥48 hours resulted in the inclusion of 940 primary ventilation periods, comprising 7872 ventilation days. The average VAP incidence density was 10.3/1000 ventilation days. Independent risk factors were age (16-40 years at increased risk: HR 2.42 95% confidence interval 1.07-5.50), COPD (HR 0.19 [0.04-0.78]), current sedation score (higher scores at increased risk), current selective oropharyngeal decontamination (HR 0.19 [0.04-0.91]), jet nebulizer (WCE, decreased risk), intravenous antibiotics for selective decontamination of the digestive tract (ivSDD, WCE, decreased risk), and intravenous antibiotics not for SDD (WCE, decreased risk). The protective effect of ivSDD was afforded for 24 days with a delay of 3 days. For some time-dependent variables, the WCE model was preferable over standard Cox proportional hazard regression. The WCE method can furthermore increase insight into the active time frame and possible delay herein of a time-dependent risk factor.

Using Multiple Pharmacovigilance Models Improves the Timeliness of Signal Detection in Simulated Prospective Surveillance.

INTRODUCTION: Prospective pharmacovigilance aims to rapidly detect safety concerns related to medical products. The exposure model selected for pharmacovigilance impacts the timeliness of signal detection. However, in most real-life pharmacovigilance studies, little is known about which model correctly represents the association and there is no evidence to guide the selection of an exposure model. Different exposure models reflect different aspects of exposure history, and their relevance varies across studies. Therefore, one potential solution is to apply several alternative exposure models simultaneously, with each model assuming a different exposure-risk association, and then combine the model results. METHODS: We simulated alternative clinically plausible associations between time-varying drug exposure and the hazard of an adverse event. Prospective surveillance was conducted on the simulated data by estimating parametric and semi-parametric exposure-risk models at multiple times during follow-up. For each model separately, and using combined evidence from different subsets of models, we compared the time to signal detection. RESULTS: Timely detection across the simulated associations was obtained by fitting a set of pharmacovigilance models. This set included alternative parametric models that assumed different exposure-risk associations and flexible models that made no assumptions regarding the form/shape of the association. Times to detection generated using a simple combination of evidence from multiple models were comparable to those observed under the ideal, but unrealistic, scenario where pharmacovigilance relied on the single 'true' model used for data generation. CONCLUSIONS: Simulation results indicate that, if the true model is not known, an association can be detected in a more timely manner by first fitting a carefully selected set of exposure-risk models and then generating a signal as soon as any of the models considered yields a test statistic value below a predetermined testing threshold.

Determinants of Rotavirus Transmission: A Lag Nonlinear Time Series Analysis.

Rotavirus is a common viral infection among young children. As in many countries, the infection dynamics of rotavirus in the Netherlands are characterized by an annual winter peak, which was notably low in 2014. Previous study suggested an association between weather factors and both rotavirus transmission and incidence. From epidemic theory, we know that the proportion of susceptible individuals can affect disease transmission. We investigated how these factors are associated with rotavirus transmission in the Netherlands, and their impact on rotavirus transmission in 2014. We used available data on birth rates and rotavirus laboratory reports to estimate rotavirus transmission and the proportion of individuals susceptible to primary infection. Weather data were directly available from a central meteorological station. We developed an approach for detecting determinants of seasonal rotavirus transmission by assessing nonlinear, delayed associations between each factor and rotavirus transmission. We explored relationships by applying a distributed lag nonlinear regression model with seasonal terms. We corrected for residual serial correlation using autoregressive moving average errors. We inferred the relationship between different factors and the effective reproduction number from the most parsimonious model with low residual autocorrelation. Higher proportions of susceptible individuals and lower temperatures were associated with increases in rotavirus transmission. For 2014, our findings suggest that relatively mild temperatures combined with the low proportion of susceptible individuals contributed to lower rotavirus transmission in the Netherlands. However, our model, which overestimated the magnitude of the peak, suggested that other factors were likely instrumental in reducing the incidence that year.

The impact of exposure model misspecification on signal detection in prospective pharmacovigilance.

PURPOSE: Pharmacovigilance monitors the safety of drugs after their approval and marketing. Timely detection of adverse effects is important. The true relationship between time-varying drug use and the adverse event risk is typically unknown. Yet, most current pharmacovigilance studies rely on arbitrarily chosen exposure metrics such as current exposure or use in the past 3 months. The authors used simulations to assess the impact of a misspecified exposure model on the timeliness of adverse effect detection. METHODS: Prospective pharmacovigilance studies were simulated assuming different true relationships between time-varying drug use and the adverse event hazard. Simulated data were analyzed by fitting conventional parametric and more complex spline-based estimation models at multiple, pre-specified testing times. The 'signal' was generated on the basis of the corrected model-specific p-value selected to ensure a 5% probability of incorrectly rejecting the null hypothesis of no association. RESULTS: Results indicated that use of an estimation model that diverged substantially from the true underlying association-reduced sensitivity and increased the time to detection of a clinically important association. CONCLUSIONS: Time to signal detection in pharmacovigilance may depend strongly on the method chosen to model the exposure. No single estimation model performed optimally across different simulated scenarios, suggesting the need for data-dependent criteria to select the model most appropriate for a given study.

Reconciling conflicting clinical studies of antioxidant supplementation as HIV therapy: a mathematical approach.

BACKGROUND: Small, highly reactive molecules called reactive oxygen species (ROS) play a crucial role in cell signalling and infection control. However, high levels of ROS can cause significant damage to cell structure and function. Studies have shown that infection with the human immunodeficiency virus (HIV) results in increased ROS concentrations, which can in turn lead to faster progression of HIV infection, and cause CD4+ T-cell apoptosis. To counteract these effects, clinical studies have explored the possibility of raising antioxidant levels, with mixed results. METHODS: In this paper, a mathematical model is used to explore this potential therapy, both analytically and numerically. For the numerical work, we use clinical data from both HIV-negative and HIV-positive injection drug users (IDUs) to estimate model parameters; these groups have lower baseline concentrations of antioxidants than non-IDU controls. RESULTS: Our model suggests that increases in CD4+ T cell concentrations can result from moderate levels of daily antioxidant supplementation, while excessive supplementation has the potential to cause periods of immunosuppression. CONCLUSION: We discuss implications for HIV therapy in IDUs and other populations which may have low baseline concentrations of antioxidants.