[Reliability of peer review-like dialogue in the German statutory quality assurance program]. / Reliabilität des Strukturierten Dialogs in der gesetzlich verpflichtenden Qualitätssicherung.

BACKGROUND: Quality measurement in the German statutory program for quality in health care follows a two-step process. For selected areas of health care, quality is measured via performance indicators (first step). Providers failing to achieve benchmarks in these indicators subsequently enter into a peer review process (second step) and are asked by the respective regional authority to provide a written statement regarding their indicator results. The statements are then evaluated by peers, with the goal to assess the provider's quality of care. In the past, similar peer review-based approaches to the measurement of health care quality in other countries have shown a tendency to lack reliability. So far, the reliability of this component of the German statutory program for quality in health care has not been investigated. METHOD: Using logistic regression models, the influence of the respective regional authority on the peer review component of health care quality measurement in Germany was investigated using three exemplary indicators and data from 2016. RESULTS: Both the probability that providers are asked to provide a statement as well as the results produced by the peer review process significantly depend on the regional authority in charge. This dependence cannot be fully explained by differences in the indicator results or by differences in case volume. CONCLUSIONS: The present results are in accordance with earlier findings, which show low reliability for peer review-based approaches to quality measurement. Thus, different results produced by the peer review component of the quality measurement process may in part be due to differences in the way the review process is conducted. This heterogeneity among the regional authorities limits the reliability of this process. In order to increase reliability, the peer review process should be standardized to a higher degree, with clear review criteria, and the peers should undergo comprehensive training for the review process. Alternatively, the future peer review component could be adapted to focus rather on identification of improvement strategies than on reliable provider comparisons.

WHO Global Situational Alert System: a mixed methods multistage approach to identify country-level COVID-19 alerts.

BACKGROUND: Globally, since 1 January 2020 and as of 24 January 2023, there have been over 664 million cases of COVID-19 and over 6.7 million deaths reported to WHO. WHO developed an evidence-based alert system, assessing public health risk on a weekly basis in 237 countries, territories and areas from May 2021 to June 2022. This aimed to facilitate the early identification of situations where healthcare capacity may become overstretched. METHODS: The process involved a three-stage mixed methods approach. In the first stage, future deaths were predicted from the time series of reported cases and deaths to produce an initial alert level. In the second stage, this alert level was adjusted by incorporating a range of contextual indicators and accounting for the quality of information available using a Bayes classifier. In the third stage, countries with an alert level of 'High' or above were added to an operational watchlist and assistance was deployed as needed. RESULTS: Since June 2021, the system has supported the release of more than US$27 million from WHO emergency funding, over 450 000 rapid antigen diagnostic testing kits and over 6000 oxygen concentrators. Retrospective evaluation indicated that the first two stages were needed to maximise sensitivity, where 44% (IQR 29%-67%) of weekly watchlist alerts would not have been identified using only reported cases and deaths. The alerts were timely and valid in most cases; however, this could only be assessed on a non-representative sample of countries with hospitalisation data available. CONCLUSIONS: The system provided a standardised approach to monitor the pandemic at the country level by incorporating all available data on epidemiological analytics and contextual assessments. While this system was developed for COVID-19, a similar system could be used for future outbreaks and emergencies, with necessary adjustments to parameters and indicators.

Hospital profiling using Bayesian decision theory.

For evaluating the quality of care provided by hospitals, special interest lies in the identification of performance outliers. The classification of healthcare providers as outliers or non-outliers is a decision under uncertainty, because the true quality is unknown and can only be inferred from an observed result of a quality indicator. We propose to embed the classification of healthcare providers into a Bayesian decision theoretical framework that enables the derivation of optimal decision rules with respect to the expected decision consequences. We propose paradigmatic utility functions for two typical purposes of hospital profiling: the external reporting of healthcare quality and the initiation of change in care delivery. We make use of funnel plots to illustrate and compare the resulting optimal decision rules and argue that sensitivity and specificity of the resulting decision rules should be analyzed. We then apply the proposed methodology to the area of hip replacement surgeries by analyzing data from 1,277 hospitals in Germany which performed over 180,000 such procedures in 2017. Our setting illustrates that the classification of outliers can be highly dependent upon the underlying utilities. We conclude that analyzing the classification of hospitals as a decision theoretic problem helps to derive transparent and justifiable decision rules. The methodology for classifying quality indicator results is implemented in an R package (iqtigbdt) and is available on GitHub.

Bayesian nowcasting with leading indicators applied to COVID-19 fatalities in Sweden.

The real-time analysis of infectious disease surveillance data is essential in obtaining situational awareness about the current dynamics of a major public health event such as the COVID-19 pandemic. This analysis of e.g., time-series of reported cases or fatalities is complicated by reporting delays that lead to under-reporting of the complete number of events for the most recent time points. This can lead to misconceptions by the interpreter, for instance the media or the public, as was the case with the time-series of reported fatalities during the COVID-19 pandemic in Sweden. Nowcasting methods provide real-time estimates of the complete number of events using the incomplete time-series of currently reported events and information about the reporting delays from the past. In this paper we propose a novel Bayesian nowcasting approach applied to COVID-19-related fatalities in Sweden. We incorporate additional information in the form of time-series of number of reported cases and ICU admissions as leading signals. We demonstrate with a retrospective evaluation that the inclusion of ICU admissions as a leading signal improved the nowcasting performance of case fatalities for COVID-19 in Sweden compared to existing methods.

Epitweetr: Early warning of public health threats using Twitter data.

BackgroundThe European Centre for Disease Prevention and Control (ECDC) systematically collates information from sources to rapidly detect early public health threats. The lack of a freely available, customisable and automated early warning tool using data from Twitter prompted the ECDC to develop epitweetr, which collects, geolocates and aggregates tweets generating signals and email alerts.AimThis study aims to compare the performance of epitweetr to manually monitoring tweets for the purpose of early detecting public health threats.MethodsWe calculated the general and specific positive predictive value (PPV) of signals generated by epitweetr between 19 October and 30 November 2020. Sensitivity, specificity, timeliness and accuracy and performance of tweet geolocation and signal detection algorithms obtained from epitweetr and the manual monitoring of 1,200 tweets were compared.ResultsThe epitweetr geolocation algorithm had an accuracy of 30.1% at national, and 25.9% at subnational levels. The signal detection algorithm had 3.0% general PPV and 74.6% specific PPV. Compared to manual monitoring, epitweetr had greater sensitivity (47.9% and 78.6%, respectively), and reduced PPV (97.9% and 74.6%, respectively). Median validation time difference between 16 common events detected by epitweetr and manual monitoring was -48.6 hours (IQR: -102.8 to -23.7).ConclusionEpitweetr has shown sufficient performance as an early warning tool for public health threats using Twitter data. Since epitweetr is a free, open-source tool with configurable settings and a strong automated component, it is expected to increase in usability and usefulness to public health experts.

Comment "On the role of data, statistics and decisions in a pandemic" by Jahn et al.

We comment the paper by Jahn et al. (On the role of data, statistics and decisions in a pandemic, 2022).

Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection.

As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggests ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. As such, our method could be of wider use for infectious disease surveillance. We illustrate ASMODEE using publicly available data of National Health Service (NHS) Pathways reporting potential COVID-19 cases in England at a fine spatial scale, showing that the method would have enabled the early detection of the flare-ups in Leicester and Blackburn with Darwen, two to three weeks before their respective lockdown. ASMODEE is implemented in the free R package trendbreaker. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points.

We analysed the coronavirus disease 2019 epidemic curve from March to the end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analysed by a trend regression model with change points. The change points are estimated directly from the data. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between 9 and 13 March for the time series of infections: from a strong increase to a decrease. Another change was found between 25 March and 29 March, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the pandemic for the age group 80 + resulting in a turning point at the end of March. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.

Analysis of Asymptomatic and Presymptomatic Transmission in SARS-CoV-2 Outbreak, Germany, 2020.

We determined secondary attack rates (SAR) among close contacts of 59 asymptomatic and symptomatic coronavirus disease case-patients by presymptomatic and symptomatic exposure. We observed no transmission from asymptomatic case-patients and highest SAR through presymptomatic exposure. Rapid quarantine of close contacts with or without symptoms is needed to prevent presymptomatic transmission.

Nowcasting the COVID-19 pandemic in Bavaria.

To assess the current dynamics of an epidemic, it is central to collect information on the daily number of newly diseased cases. This is especially important in real-time surveillance, where the aim is to gain situational awareness, for example, if cases are currently increasing or decreasing. Reporting delays between disease onset and case reporting hamper our ability to understand the dynamics of an epidemic close to now when looking at the number of daily reported cases only. Nowcasting can be used to adjust daily case counts for occurred-but-not-yet-reported events. Here, we present a novel application of nowcasting to data on the current COVID-19 pandemic in Bavaria. It is based on a hierarchical Bayesian model that considers changes in the reporting delay distribution over time and associated with the weekday of reporting. Furthermore, we present a way to estimate the effective time-varying case reproduction number Re(t) based on predictions of the nowcast. The approaches are based on previously published work, that we considerably extended and adapted to the current task of nowcasting COVID-19 cases. We provide methodological details of the developed approach, illustrate results based on data of the current pandemic, and evaluate the model based on synthetic and retrospective data on COVID-19 in Bavaria. Results of our nowcasting are reported to the Bavarian health authority and published on a webpage on a daily basis (https://corona.stat.uni-muenchen.de/). Code and synthetic data for the analysis are available from https://github.com/FelixGuenther/nc_covid19_bavaria and can be used for adaption of our approach to different data.

Parallelized calculation of permutation tests.

MOTIVATION: Permutation tests offer a straightforward framework to assess the significance of differences in sample statistics. A significant advantage of permutation tests are the relatively few assumptions about the distribution of the test statistic are needed, as they rely on the assumption of exchangeability of the group labels. They have great value, as they allow a sensitivity analysis to determine the extent to which the assumed broad sample distribution of the test statistic applies. However, in this situation, permutation tests are rarely applied because the running time of naïve implementations is too slow and grows exponentially with the sample size. Nevertheless, continued development in the 1980s introduced dynamic programming algorithms that compute exact permutation tests in polynomial time. Albeit this significant running time reduction, the exact test has not yet become one of the predominant statistical tests for medium sample size. Here, we propose a computational parallelization of one such dynamic programming-based permutation test, the Green algorithm, which makes the permutation test more attractive. RESULTS: Parallelization of the Green algorithm was found possible by non-trivial rearrangement of the structure of the algorithm. A speed-up-by orders of magnitude-is achievable by executing the parallelized algorithm on a GPU. We demonstrate that the execution time essentially becomes a non-issue for sample sizes, even as high as hundreds of samples. This improvement makes our method an attractive alternative to, e.g. the widely used asymptotic Mann-Whitney U-test. AVAILABILITYAND IMPLEMENTATION: In Python 3 code from the GitHub repository https://github.com/statisticalbiotechnology/parallelPermutationTest under an Apache 2.0 license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Model selection and parameter estimation for dynamic epidemic models via iterated filtering: application to rotavirus in Germany.

Despite the wide application of dynamic models in infectious disease epidemiology, the particular modeling of variability in the different model components is often subjective rather than the result of a thorough model selection process. This is in part because inference for a stochastic transmission model can be difficult since the likelihood is often intractable due to partial observability. In this work, we address the question of adequate inclusion of variability by demonstrating a systematic approach for model selection and parameter inference for dynamic epidemic models. For this, we perform inference for six partially observed Markov process models, which assume the same underlying transmission dynamics, but differ with respect to the amount of variability they allow for. The inference framework for the stochastic transmission models is provided by iterated filtering methods, which are readily implemented in the R package pomp by King and others (2016, Statistical inference for partially observed Markov processes via the R package pomp. Journal of Statistical Software69, 1-43). We illustrate our approach on German rotavirus surveillance data from 2001 to 2008, discuss practical difficulties of the methods used and calculate a model based estimate for the basic reproduction number $R_0$ using these data.

Identifying the source of food-borne disease outbreaks: An application of Bayesian variable selection.

Early identification of contaminated food products is crucial in reducing health burdens of food-borne disease outbreaks. Analytic case-control studies are primarily used in this identification stage by comparing exposures in cases and controls using logistic regression. Standard epidemiological analysis practice is not formally defined and the combination of currently applied methods is subject to issues such as response misclassification, missing values, multiple testing problems and small sample estimation problems resulting in biased and possibly misleading results. In this paper, we develop a formal Bayesian variable selection method to account for misclassified responses and missing covariates, which are common complications in food-borne outbreak investigations. We illustrate the implementation and performance of our method on a Salmonella Thompson outbreak in the Netherlands in 2012. Our method is shown to perform better than the standard logistic regression approach with respect to earlier identification of contaminated food products. It also allows relatively easy implementation of otherwise complex methodological issues.

A statistician's perspective on digital epidemiology.

We address the question "does digital epidemiology represent an epistemic shift in infectious disease epidemiology" from a statistician's viewpoint. Our main argument is that infectious disease epidemiology has not changed fundamentally as it always has been data-driven. However, as the data aspect has become more prominent, we discuss the statistical toolbox of the modern epidemiologist and argue that problem solving in the digital age, more than ever requires an interdisciplinary quantitative approach.

Are People Living Near Modern Swine Production Facilities at Increased Risk of Influenza Virus Infection?

BACKGROUND: Swine can harbor influenza viruses that are pathogenic to humans. Previous studies support an increased risk of human influenza cases among individuals with swine contact. North Carolina has the second-largest swine industry in the United States. METHODS: We investigated the spatiotemporal association between influenza-like illnesses (ILIs) and licensed swine operations from 2008 to 2012 in North Carolina. We determined the week in which ILI cases peaked and statistically estimated their week of onset. This was performed for all 100 North Carolina counties for 4 consecutive influenza seasons. We used linear models to correlate the number of permitted swine operations per county with the weeks of onset and peak ILI activity. RESULTS: We found that during the 2009-2010 and 2010-2011 influenza seasons, both seasons in which the pandemic 2009 H1N1 influenza A virus circulated, ILI peaked earlier in counties with a higher number of licensed swine operations. We did not observe this in 2008-2009 or 2011-2012, nor did we observe a relationship between ILI onset week and number of swine operations. CONCLUSIONS: Our findings suggest that concentrated swine feeding operations amplified transmission of influenza during years in which H1N1 was circulating. This has implications for vaccine strategies targeting swine workers, as well as virologic surveillance in areas with large concentrations of swine.

A system for automated outbreak detection of communicable diseases in Germany.

We describe the design and implementation of a novel automated outbreak detection system in Germany that monitors the routinely collected surveillance data for communicable diseases. Detecting unusually high case counts as early as possible is crucial as an accumulation may indicate an ongoing outbreak. The detection in our system is based on state-of-the-art statistical procedures conducting the necessary data mining task. In addition, we have developed effective methods to improve the presentation of the results of such algorithms to epidemiologists and other system users. The objective was to effectively integrate automatic outbreak detection into the epidemiological workflow of a public health institution. Since 2013, the system has been in routine use at the German Robert Koch Institute.

Bayesian outbreak detection in the presence of reporting delays.

One use of infectious disease surveillance systems is the statistical aberration detection performed on time series of counts resulting from the aggregation of individual case reports. However, inherent reporting delays in such surveillance systems make the considered time series incomplete, which can be an impediment to the timely detection and thus to the containment of emerging outbreaks. In this work, we synthesize the outbreak detection algorithms of Noufaily et al. (2013) and Manitz and Höhle (2013) while additionally addressing right truncation caused by reporting delays. We do so by considering the resulting time series as an incomplete two-way contingency table which we model using negative binomial regression. Our approach is defined in a Bayesian setting allowing a direct inclusion of all sources of uncertainty in the derivation of whether an observed case count is to be considered an aberration. The proposed algorithm is evaluated both on simulated data and on the time series of Salmonella Newport cases in Germany in 2011. Altogether, our method aims at allowing timely aberration detection in the presence of reporting delays and hence underlines the need for statistical modeling to address complications of reporting systems. An implementation of the proposed method is made available in the R package surveillance as the function "bodaDelay".

Modelling the epidemiological impact of rotavirus vaccination in Germany--a Bayesian approach.

BACKGROUND: Rotavirus (RV) infection is the primary cause of severe gastroenteritis in children aged <5 years in Germany and worldwide. In 2013 the German Standing Committee on Vaccination (STIKO) developed a national recommendation for routine RV-immunization of infants. To support informed decision-making we predicted the epidemiological impact of routine RV-vaccination in Germany using statistical modelling. METHODS: We developed a population-based model for the dynamic transmission of RV-infection in a vaccination setting. Using data from the communicable disease reporting system and survey records on the vaccination coverage from the eastern federal states, where the vaccine was widely used before recommended at national level, we first estimated RV vaccine effectiveness (VE) within a Bayesian framework utilizing adaptive Markov Chain Monte Carlo inference. The calibrated model was then used to compute the predictive distribution of RV-incidence after achieving high vaccination coverage with the introduction of routine vaccination. RESULTS: Our model estimated that RV-vaccination provides high protection against symptomatic RV-infection (VE=96%; 95% credibility interval (CI): 91-99%) that remains at its maximum level for three years (95% CI: 1.43-5.80 years) and is fully waned after twelve years. At population level, routine vaccination at 90% coverage is predicted to reduce symptomatic RV-incidence among children aged <5 years by 84% (95% prediction interval (PI): 71-90%) including a 2.5% decrease due to herd protection. Ten years after vaccine introduction an increase in RV incidences of 12% (95% PI: -16 to 85%) among persons aged 5-59 years and 14% (95% PI: -6 to 109%) within the age-group >60 years was predicted. CONCLUSION: Routine infant RV-vaccination is predicted to considerably reduce RV-incidence in Germany among children <5 years. Our work generated estimates of RV VE in the field and predicted the population-level impact, while adequately addressing the role of model and prediction uncertainty when making statements about the future.

OutbreakTools: a new platform for disease outbreak analysis using the R software.

The investigation of infectious disease outbreaks relies on the analysis of increasingly complex and diverse data, which offer new prospects for gaining insights into disease transmission processes and informing public health policies. However, the potential of such data can only be harnessed using a number of different, complementary approaches and tools, and a unified platform for the analysis of disease outbreaks is still lacking. In this paper, we present the new R package OutbreakTools, which aims to provide a basis for outbreak data management and analysis in R. OutbreakTools is developed by a community of epidemiologists, statisticians, modellers and bioinformaticians, and implements classes and methods for storing, handling and visualizing outbreak data. It includes real and simulated outbreak datasets. Together with a number of tools for infectious disease epidemiology recently made available in R, OutbreakTools contributes to the emergence of a new, free and open-source platform for the analysis of disease outbreaks.

Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011.

A Bayesian approach to the prediction of occurred-but-not-yet-reported events is developed for application in real-time public health surveillance. The motivation was the prediction of the daily number of hospitalizations for the hemolytic-uremic syndrome during the large May-July 2011 outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 in Germany. Our novel Bayesian approach addresses the count data nature of the problem using negative binomial sampling and shows that right-truncation of the reporting delay distribution under an assumption of time-homogeneity can be handled in a conjugate prior-posterior framework using the generalized Dirichlet distribution. Since, in retrospect, the true number of hospitalizations is available, proper scoring rules for count data are used to evaluate and compare the predictive quality of the procedures during the outbreak. The results show that it is important to take the count nature of the time series into account and that changes in the delay distribution occurred due to intervention measures. As a consequence, we extend the Bayesian analysis to a hierarchical model, which combines a discrete time survival regression model for the delay distribution with a penalized spline for the dynamics of the epidemic curve. Altogether, we conclude that in emerging and time-critical outbreaks, nowcasting approaches are a valuable tool to gain information about current trends.