scholarly journals Bigger and Better? Representativeness of the Influenza A surveillance using one consolidated clinical microbiology laboratory data set as compared to the Belgian Sentinel Network of Laboratories

2018 ◽  
Author(s):  
Sigi Van den Wijngaert ◽  
Nathalie Bossuyt ◽  
Bridget Ferns ◽  
Laurent Busson ◽  
Gabriela Serrano ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Clinical Microbiology ◽  
Public Health Surveillance ◽  
Laboratory Data ◽  
Health Surveillance ◽  
Health Concern ◽  
Clinical Microbiology Laboratory ◽  
Data Set ◽  
Sentinel Network

ABSTRACTInfectious diseases remain a serious public health concern globally, while the need for reliable and representative surveillance systems remains as acute as ever. The public health surveillance of infectious diseases uses reported positive results from sentinel clinical laboratories or laboratory networks, to survey the presence of specific microbial agents known to constitute a threat to public health in a given population. This monitoring activity is commonly based on a representative fraction of the microbiology laboratories nationally reporting to a single central reference point. However in recent years a number of clinical microbiology laboratories (CML) have undergone a process of consolidation involving a shift towards laboratory amalgamation and closer real-time informational linkage. This report aims to investigate whether such merging activities might have a potential impact on infectious diseases surveillance. Influenza data was used from Belgian public health surveillance 2014-2017, to evaluate whether national infection trends could be estimated equally as effectively from only just one centralised CML serving the wider Brussels area (LHUB-ULB). The overall comparison reveals that there is a close correlation and representativeness of the LHUB-ULB data to the national and international data for the same time periods, both on epidemiological and molecular grounds. Notably, the effectiveness of the LHUB-ULB surveillance remains partially subject to local regional variations. These results illustrate that centralised CML-derived data are not only credible but also advantageous to use for future surveillance and prediction purposes, especially for automatic detection systems that might include multiple layers of information and timely implementation of control strategies.

scholarly journals Impact of Changes in Clinical Microbiology Laboratory Location and Ownership on the Practice of Infectious Diseases

2020 ◽  
Vol 58 (5) ◽  
Author(s):  
Michael Pentella ◽  
Melvin P. Weinstein ◽  
Susan E. Beekmann ◽  
Philip M. Polgreen ◽  
Richard T. Ellison
Keyword(s):  
Infectious Diseases ◽  
Clinical Microbiology ◽  
Clinical Microbiology Laboratory ◽  
Microbiology Laboratory ◽  
Emerging Infections ◽  
Clinical Practices ◽  
Sentinel Network ◽  
Effective Implementation ◽  
Serious Infections ◽  
The Impact

ABSTRACT The number of onsite clinical microbiology laboratories in hospitals is decreasing, likely related to the business model for laboratory consolidation and labor shortages, and this impacts a variety of clinical practices, including that of banking isolates for clinical or epidemiologic purposes. To determine the impact of these trends, infectious disease (ID) physicians were surveyed regarding their perceptions of offsite services. Clinical microbiology practices for retention of clinical isolates for future use were also determined. Surveys were sent to members of the Infectious Diseases Society of America’s (IDSA) Emerging Infections Network (EIN). The EIN is a sentinel network of ID physicians who care for adult and/or pediatric patients in North America and who are members of IDSA. The response rate was 763 (45%) of 1,680 potential respondents. Five hundred forty (81%) respondents reported interacting with the clinical microbiology laboratory. Eighty-six percent of respondents thought an onsite laboratory very important for timely diagnostic reporting and ongoing communication with the clinical microbiologist. Thirty-five percent practiced in institutions where the core microbiology laboratory has been moved offsite, and an additional 7% (n = 38) reported that movement of core laboratory functions offsite was being considered. The respondents reported that only 24% of laboratories banked all isolates, with the majority saving isolates for less than 30 days. Based on these results, the trend toward centralized core laboratories negatively impacts the practice of ID physicians, potentially delays effective implementation of prompt and targeted care for patients with serious infections, and similarly adversely impacts infection control epidemiologic investigations.

scholarly journals Use of artificial intelligence for public health surveillance: a case study to develop a machine Learning-algorithm to estimate the incidence of diabetes mellitus in France

2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Romana Haneef ◽  
Sofiane Kab ◽  
Rok Hrzic ◽  
Sonsoles Fuentes ◽  
Sandrine Fosse-Edorh ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Test Data ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Data Sets ◽  
Data Set ◽  
Linear Discriminant ◽  
Final Data ◽  
Selection Of

Abstract Background The use of machine learning techniques is increasing in healthcare which allows to estimate and predict health outcomes from large administrative data sets more efficiently. The main objective of this study was to develop a generic machine learning (ML) algorithm to estimate the incidence of diabetes based on the number of reimbursements over the last 2 years. Methods We selected a final data set from a population-based epidemiological cohort (i.e., CONSTANCES) linked with French National Health Database (i.e., SNDS). To develop this algorithm, we adopted a supervised ML approach. Following steps were performed: i. selection of final data set, ii. target definition, iii. Coding variables for a given window of time, iv. split final data into training and test data sets, v. variables selection, vi. training model, vii. Validation of model with test data set and viii. Selection of the model. We used the area under the receiver operating characteristic curve (AUC) to select the best algorithm. Results The final data set used to develop the algorithm included 44,659 participants from CONSTANCES. Out of 3468 variables from SNDS linked to CONSTANCES cohort were coded, 23 variables were selected to train different algorithms. The final algorithm to estimate the incidence of diabetes was a Linear Discriminant Analysis model based on number of reimbursements of selected variables related to biological tests, drugs, medical acts and hospitalization without a procedure over the last 2 years. This algorithm has a sensitivity of 62%, a specificity of 67% and an accuracy of 67% [95% CI: 0.66–0.68]. Conclusions Supervised ML is an innovative tool for the development of new methods to exploit large health administrative databases. In context of InfAct project, we have developed and applied the first time a generic ML-algorithm to estimate the incidence of diabetes for public health surveillance. The ML-algorithm we have developed, has a moderate performance. The next step is to apply this algorithm on SNDS to estimate the incidence of type 2 diabetes cases. More research is needed to apply various MLTs to estimate the incidence of various health conditions.

scholarly journals Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019

2020 ◽  
Vol 110 (S3) ◽  
pp. S326-S330
Author(s):  
Erika Bonnevie ◽  
Jaclyn Goldbarg ◽  
Allison K. Gallegos-Jeffrey ◽  
Sarah D. Rosenberg ◽  
Ellen Wartella ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Supervised Machine Learning ◽  
Sources Of Information ◽  
Full Data ◽  
Data Set ◽  
Data Collection Period ◽  
Collection Period

Objectives. To report on vaccine opposition and misinformation promoted on Twitter, highlighting Twitter accounts that drive conversation. Methods. We used supervised machine learning to code all Twitter posts. We first identified codes and themes manually by using a grounded theoretical approach and then applied them to the full data set algorithmically. We identified the top 50 authors month-over-month to determine influential sources of information related to vaccine opposition. Results. The data collection period was June 1 to December 1, 2019, resulting in 356 594 mentions of vaccine opposition. A total of 129 Twitter authors met the qualification of a top author in at least 1 month. Top authors were responsible for 59.5% of vaccine-opposition messages. We identified 10 conversation themes. Themes were similarly distributed across top authors and all other authors mentioning vaccine opposition. Top authors appeared to be highly coordinated in their promotion of misinformation within themes. Conclusions. Public health has struggled to respond to vaccine misinformation. Results indicate that sources of vaccine misinformation are not as heterogeneous or distributed as it may first appear given the volume of messages. There are identifiable upstream sources of misinformation, which may aid in countermessaging and public health surveillance.

scholarly journals Clinical Microbiology Laboratories' Adoption of Culture-Independent Diagnostic Tests Is a Threat to Foodborne-Disease Surveillance in the United States

2016 ◽  
Vol 55 (1) ◽  
pp. 10-19 ◽  
Author(s):  
Shari Shea ◽  
Kristy A. Kubota ◽  
Hugh Maguire ◽  
Stephen Gladbach ◽  
Amy Woron ◽  
...  
Keyword(s):  
Public Health ◽  
Diagnostic Tests ◽  
Clinical Microbiology ◽  
Public Health Surveillance ◽  
The United States ◽  
Health Surveillance ◽  
Enteric Pathogens ◽  
Public Health Laboratory ◽  
Content Type ◽  
Culture Independent

INTRODUCTION In November 2015, the Centers for Disease Control and Prevention (CDC) sent a letter to state and territorial epidemiologists, state and territorial public health laboratory directors, and state and territorial health officials. In this letter, culture-independent diagnostic tests (CIDTs) for detection of enteric pathogens were characterized as “a serious and current threat to public health surveillance, particularly for Shiga toxin-producing Escherichia coli (STEC) and Salmonella .” The document says CDC and its public health partners are approaching this issue, in part, by “reviewing regulatory authority in public health agencies to require culture isolates or specimen submission if CIDTs are used.” Large-scale foodborne outbreaks are a continuing threat to public health, and tracking these outbreaks is an important tool in shortening them and developing strategies to prevent them. It is clear that the use of CIDTs for enteric pathogen detection, including both antigen detection and multiplex nucleic acid amplification techniques, is becoming more widespread. Furthermore, some clinical microbiology laboratories will resist the mandate to require submission of culture isolates, since it will likely not improve patient outcomes but may add significant costs. Specimen submission would be less expensive and time-consuming for clinical laboratories; however, this approach would be burdensome for public health laboratories, since those laboratories would need to perform culture isolation prior to typing. Shari Shea and Kristy Kubota from the Association of Public Health Laboratories, along with state public health laboratory officials from Colorado, Missouri, Tennessee, and Utah, will explain the public health laboratories' perspective on why having access to isolates of enteric pathogens is essential for public health surveillance, detection, and tracking of outbreaks and offer potential workable solutions which will allow them to do this. Marc Couturier of ARUP Laboratories and Melissa Miller of the University of North Carolina will explain the advantages of CIDTs for enteric pathogens and discuss practical solutions for clinical microbiology laboratories to address these public health needs.

scholarly journals Establishing a Plan of Action Implementing Integrated Disease Surveillance in Sudan

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Hayat Khogali ◽  
Ngozi A. Erondu ◽  
Betiel H. Haile ◽  
Scott J. McNabb
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Public Health Surveillance ◽  
One Health ◽  
Health Surveillance ◽  
Strategic Approach ◽  
Public Health Surveillance System

A recent assessment of the Sudan public health surveillance system found fragmented and siloed disease programs and an overburdened workforce due to vertical systems and inefficient processes. A plan of action was developed to support improving public health surveillance strengthening by: 1) implementing a strategic approach to achieving IHR (2005), 2) implementing One Health and IDSR aims, and 3) establishing an E-surveillance ICT platform for increasing public health surveillance capacity to safely and rapidly detect and report infectious diseases in Sudan.

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