Real-world Surveillance Data Research Articles

MRNA vaccine effectiveness against hospitalisation due to severe acute respiratory infection (SARI) COVID-19 during Omicron variant predominance estimated from real-world surveillance data, Slovenia, February to March 2022.

For the period of predominance of SARS-CoV-2 Omicron variant in Slovenia, February to March 2022, we estimated mRNA vaccine effectiveness (VE) against severe acute respiratory infection (SARI) COVID-19 using surveillance data. In the most vulnerable age group comprising individuals aged 65 years and more, VE against SARI COVID-19 was 95% (95% CI: 95–96%) for those vaccinated with three doses, in comparison to 82% (95% CI: 79–84%) for those vaccinated with two doses. Such levels of protection were maintained for at least 6 months.

Vaccine effectiveness against severe acute respiratory infections (SARI) COVID-19 hospitalisations estimated from real-world surveillance data, Slovenia, October 2021.

We estimated vaccine effectiveness (VE) against severe COVID-19 during October 2021, using Slovenian surveillance data. For people fully vaccinated with any vaccine in age groups 18–49, 50–64, ≥ 65 years, VE was 86% (95% CI: 79–90), 89% (85–91), and 77% (74–81). Among ≥ 65 year-olds fully vaccinated with mRNA vaccines, VE decreased from 93% (95% CI: 88–96) in those vaccinated ≤ 3 months ago to 43% (95% CI: 30–54) in those vaccinated ≥ 6 months ago, suggesting the need for early boosters.

Methods for Rapid Mobility Estimation to Support Outbreak Response.

When pressed for time, outbreak investigators often use homogeneous mixing models to model infectious diseases in data-poor regions. But recent outbreaks such as the 2014 Ebola outbreak in West Africa have shown the limitations of this approach in an era of increasing urbanization and connectivity. Both outbreak detection and predictive modeling depend on realistic estimates of human and disease mobility, but these data are difficult to acquire in a timely manner. This is especially true when dealing with an emerging outbreak in an under-resourced nation. Weighted travel networks with realistic estimates for population flows are often proprietary, expensive, or nonexistent. Here we propose a method for rapidly generating a mobility model from open-source data. As an example, we use road and river network data, along with population estimates, to construct a realistic model of human movement between health zones in the Democratic Republic of the Congo (DRC). Using these mobility data, we then fit an epidemic model to real-world surveillance data from the recent Ebola outbreak in the Nord Kivu region of the DRC to illustrate a potential use of the generated mobility estimation. In addition to providing a way for rapid risk estimation, this approach brings together novel techniques to merge diverse GIS datasets that can then be used to address issues that pertain to public health and global health security.

Estimating the Clinical and Economic Impact of Switching from the 13-Valent Pneumococcal Conjugate Vaccine (PCV13) to the 10-Valent Pneumococcal Conjugate Vaccine (PCV10) in Italy

Background: Invasive and non-invasive pneumococcal diseases are significant health and economic burdens, especially in children and the elderly. Italy included the 7-valent (PCV7) and 13-valent pneumococcal conjugate vaccine (PCV13) in the National Immunization Program in 2007 and 2010, respectively, allowing a dramatic reduction in the burden of pneumococcal disease. In the era of budget constraints, decision-makers may consider switching from the higher-valent, more costly PCV13, to the lower-cost PCV10. This study estimated the potential public health and economic impact of changing vaccine programs from PCV13 to PCV10 in Italy. Methods: A decision-analytic forecasting model estimated the impact of PCV programs. Real-world surveillance data were used to forecast serotype distribution and disease incidence among children and the elderly over a specified 5-year time horizon. Costs and outcomes included estimates of cases and deaths avoided, quality-adjusted life years (QALYs) gained, and total costs from a payer perspective, discounted at an assumed rate of 3.0%, and robustness validated through several scenarios and sensitivity analyses. Results: A switch from PCV13 to PCV10 would increase invasive pneumococcal disease (IPD) cases by 59.3% (4317 cases) over a 5-year horizon, primarily due to serotypes 3 and 19A. Pneumonia increased by 8.3% and acute otitis media (AOM) by 96.1%. Maintaining a PCV13 program would prevent a total incremental 531,435 disease cases (1.02M over a 10-year time horizon) and 641 deaths due to invasive pneumococcal disease (IPD), with €23,642 per QALY gained over 5 years versus PCV10. One-way and probabilistic sensitivity analyses showed that a PCV13-based program remained cost-effective in 99.7% of the simulations in Italy as parameters varied within their plausible range; percent vaccinated had the most impact. Conclusions: Maintaining the PCV13 strategy would provide substantial public health and economic benefits in Italy and is cost-effective. Switching from PCV13 to PCV10 would increase the incidence of pneumococcal disease primarily linked to re-emergence of serotypes 3 and 19A.

Intelligent and Energy-Efficient Data Prioritization in Green Smart Cities: Current Challenges and Future Directions

The excessive use of digital devices such as cameras and smartphones in smart cities has produced huge data repositories that require automatic tools for efficient browsing, searching, and management. Data prioritization (DP) is a technique that produces a condensed form of the original data by analyzing its contents. Current DP studies are either concerned with data collected through stable capturing devices or focused on prioritization of data of a certain type such as surveillance, sports, or industry. This necessitates the need for DP tools that intelligently and cost-effectively prioritize a large variety of data for detecting abnormal events and hence effectively manage them, thereby making the current smart cities greener. In this article, we first carry out an in-depth investigation of the recent approaches and trends of DP for data of different natures, genres, and domains of two decades in green smart cities. Next, we propose an energy-efficient DP framework by intelligent integration of the Internet of Things, artificial intelligence, and big data analytics. Experimental evaluation on real-world surveillance data verifies the energy efficiency and applicability of this framework in green smart cities. Finally, this article highlights the key challenges of DP, its future requirements, and propositions for integration into green smart cities.

2016 International Society for Disease Surveillance Conference New Frontiers in Surveillance: Data Science and Health Security

The International Society for Disease Surveillance (ISDS) held itsfifteenth annual conference in Atlanta, GA, from December 6-8, 2016.Since 2001, individuals interested in sharing and learning emergingtrends in surveillance research and practice have found the ISDSAnnual Conference a unique forum to advance their knowledge in thediscipline of disease surveillance. The 15th ISDS conference receiveda total of 233 abstracts from 23 countries. From the submissions, 189(81%) were accepted for presentation at the conference as an oralpresentation (N=96) or poster (N=93).The theme for the 15th annual conference was New Frontiers inSurveillance: Data Science and Health Security. The theme unitedtwo dominant trends in public health surveillance: 1) a growing desireto extract knowledge from increasing volumes of structured andunstructured data available from health information systems; and 2)increased pressure on nations to strengthen their capacity for diseasesurveillance and response to outbreaks when and where they occuracross the globe. In addition to the major themes of the conference,abstracts were accepted in additional tracks that remain important tothe practice of public health around the world: One Health unitinganimal and human health; Methodological advances in appliedepidemiology; Public health informatics; Public health policy; andBiosurveillance practice.As usual, accepted abstracts for the 2016 ISDS Conferencespan the breadth of surveillance practice around the globe. Thereare timely abstracts on the detection and response to vector-bornediseases such as Zika virus and chikungunya across the Americas,as well as abstracts on the surveillance of opioid abuse observedin many parts of the U.S. Other abstracts cover the surveillance ofnon-communicable diseases that are now the leading causes of deathglobally. Additionally, some abstracts focus on capacity buildingwithin low resource settings on multiple continents to enhance globalhealth security. While other abstracts describe the impact of healthinformation technology (or eHealth) policies on surveillance practiceat local, national, or regional levels. And still other abstracts containemerging, novel methods that advance our understanding of howto analyze “Big” data or reduce the messiness associated with realworldsurveillance data. Together these abstracts represent the broad,diverse and interesting nature of surveillance practice. Furthermore,the abstracts represent important work being done in high incomecountries like the U.S., Canada and the U.K. as well as critical workbeing done in low-and-middle income nations such as Nigeria,Pakistan, and Sierra Leone.I wish to thank the dedicated members of the Scientific ProgrammingCommittee (SPC) and ISDS staff who helped to manage the process ofselecting this year’s abstracts for presentation. These individuals aredomain experts across the spectrum of tracks and themes representedin the program, and their service is much appreciated. The SPC helpedto recruit dozens of public health researchers and practitioners whoalso spent time reviewing abstracts. I also thank these volunteersfor contributing to the richness and diversity of this year’s program.Finally, I wish to thank the Track Chairs who reviewed abstracts andrecruited peers to perform reviews, and whom helped me organizepresentations into meaningful sessions for the final conferenceprogram. Their names are listed in the proceedings to recognize theirselfless service to ISDS and the field of public health surveillance.I hope that these proceedings help to advance scientificunderstanding and the practice of surveillance in public health. Pleaseuse the knowledge herein to improve how you practice or evaluatesurveillance in your jurisdiction. Or you may find ways to apply theknowledge elsewhere in population health. However you use it, I askthat you document your lessons or findings and submit to ISDS inthe future to share the outcomes with others. Together we can reducethe burden of disease and improve health outcomes for populationsglobally.

Cigarette smoking and risk of substance use and substance use disorder relapse among adults in recovery

(HAP) of many formulations. These concerns, along with regulatory–authority directives, have led to the development of opioid formulations expected to have abuse-deterrent properties. The attractiveness of opioids for abuse have been quantified using validated measures of subjective pharmacodynamic (PD) effects (e.g., drug liking, drug high), as well as select pharmacokinetic (PK) characteristics of opioids, such aspeakplasmaconcentration (Cmax) and time to achieveCmax expressed as tmax. For example, tampering with opioid formulations (e.g., altering themode of administration) may increase PD effects by increasing Cmax and/or shortening tmax. Given this PK relationship, whichmay foster a drug’s attractiveness for abuse, the Cmax/tmax ratio has been called the abuse quotient (AQ). This investigation examined the putative validity of using AQ as a surrogate clinical measure to characterize the HAP of opioid analgesic formulations. Methods: PK and PD data were retrospectively gathered from a series of randomized, double-blind, controlled clinical trials (RCTs) exploring the HAP of proposed abuse-deterrent extended-release opioid formulations in comparison with control opioid formulations with high abuse potential and, in some cases, placebo. Correlations of AQ, Cmax, and tmax with subjective PD effects influencing drug abuse were assessed within and between RCTs. Results: In all instances, there were statistically significant and high correlations of AQ and PD effects, with higher AQ denoting greater abuse potential. Overall, these associations were stronger for AQ than for Cmax or tmax individually. Conclusions: The AQ appears to be an essential parameter of HAP when developing abuse-deterrent opioid formulations. Further analyses using real-world surveillance data for comparison are recommended. Financial support: Funded internally by PRA Health Sciences, Salt Lake City, UT, USA.

An Efficient and Robust System for Multiperson Event Detection in Real-World Indoor Surveillance Scenes

Due to the popularity of security cameras in public places, it is of interest to design an intelligent system that can efficiently detect events automatically. This paper proposes a novel algorithm for multiperson event detection. To ensure greater than real-time performance, features are extracted directly from compressed MPEG video. A novel histogram-based feature descriptor that captures the angles between extracted particle trajectories is proposed, which allows us to capture motion patterns for multiperson events in the video. To alleviate the need for fine-grained annotation, we propose the use of labeled latent Dirichlet allocation, a weakly supervised method that allows the use of coarse temporal annotations, which are much simpler to obtain. This novel system is able to run at $\sim 10$ times real time, while preserving state-of-the-art detection performance for multiperson events on a 100-h real-world surveillance data set (TRECVid surveillance event detection).

A Bayesian spatio-temporal approach for real-time detection of disease outbreaks: a case study.

BackgroundFor researchers and public health agencies, the complexity of high‐dimensional spatio‐temporal data in surveillance for large reporting networks presents numerous challenges, which include low signal‐to‐noise ratios, spatial and temporal dependencies, and the need to characterize uncertainties. Central to the problem in the context of disease outbreaks is a decision structure that requires trading off false positives for delayed detections.MethodsIn this paper we apply a previously developed Bayesian hierarchical model to a data set from the Indiana Public Health Emergency Surveillance System (PHESS) containing three years of emergency department visits for influenza‐like illness and respiratory illness. Among issues requiring attention were selection of the underlying network (Too few nodes attenuate important structure, while too many nodes impose barriers to both modeling and computation.); ensuring that confidentiality protections in the data do not impede important modeling day of week effects; and evaluating the performance of the model.ResultsOur results show that the model captures salient spatio‐temporal dynamics that are present in public health surveillance data sets, and that it appears to detect both “annual” and “atypical” outbreaks in a timely, accurate manner. We present maps that help make model output accessible and comprehensible to public health authorities. We use an illustrative family of decision rules to show how output from the model can be used to inform false positive–delayed detection tradeoffs.ConclusionsThe advantages of our methodology for addressing the complicated issues of real world surveillance data applications are three‐fold. We can easily incorporate additional covariate information and spatio‐temporal dynamics in the data. Second, we furnish a unified framework to provide uncertainties associated with each parameter. Third, we are able to handle multiplicity issues by using a Bayesian approach. The urgent need to quickly and effectively monitor the health of the public makes our methodology a potentially plausible and useful surveillance approach for health professionals.