Last data update: Sep 23, 2024. (Total: 47723 publications since 2009)
Records 1-6 (of 6 Records) |
Query Trace: Thomas MJ [original query] |
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An evaluation of syndromic surveillance-related practices among selected state and local health agencies
Romano S , Yusuf H , Davis C , Thomas MJ , Grigorescu V . J Public Health Manag Pract 2020 28 (2) 109-115 CONTEXT: Syndromic surveillance consists of the systematic collection and use of near real-time data about health-related events for situational awareness and public health action. As syndromic surveillance programs continue to adopt new technologies and expand, it is valuable to evaluate these syndromic surveillance systems and practices to ensure that they meet public health needs. OBJECTIVE: This assessment's aim is to provide recent information about syndromic surveillance systems and practice characteristics among a group of state and local health departments. DESIGN/SETTING: Information was obtained between November 2017 and June 2018 through a telephone survey using an Office of Management and Budget-approved standardized data collection tool. Participants were syndromic surveillance staff from each of 31 state and local health departments participating in the National Syndromic Surveillance Program funded by the Centers for Disease Control and Prevention. Questions included jurisdictional experience, data sources and analysis systems used, syndromic system data processing characteristics, data quality verification procedures, and surveillance activities conducted with syndromic data. MEASURES: Practice-specific information such as types of systems and data sources used for syndromic surveillance, data quality monitoring, and uses of data for public health situational awareness (eg, investigating occurrences of or trends in diseases). RESULTS: The survey analysis revealed a wide range of experiences with syndromic surveillance. Participants reported the receipt of data daily or more frequently. Emergency department data were the primary data source; however, other data sources are being integrated into these systems. All health departments routinely monitored data quality. Syndromes of highest priority across the respondents for health events monitoring were influenza-like illness and drug-related syndromes. However, a wide variety of syndromes were reported as priorities across the health departments. CONCLUSION: Overall, syndromic surveillance was relevantly integrated into the public health surveillance infrastructure. The near real-time nature of the data and its flexibility to monitor different types of health-related issues make it especially useful for public health practitioners. Despite these advances, syndromic surveillance capacity, locally and nationally, must continue to evolve and progress should be monitored to ensure that syndromic surveillance systems and data are optimally able to meet jurisdictional needs. |
Evaluation of syndromic surveillance systems in 6 US state and local health departments
Thomas MJ , Yoon PW , Collins JM , Davidson AJ , Mac Kenzie WR . J Public Health Manag Pract 2017 24 (3) 235-240 OBJECTIVE: Evaluating public health surveillance systems is critical to ensuring that conditions of public health importance are appropriately monitored. Our objectives were to qualitatively evaluate 6 state and local health departments that were early adopters of syndromic surveillance in order to (1) understand the characteristics and current uses, (2) identify the most and least useful syndromes to monitor, (3) gauge the utility for early warning and outbreak detection, and (4) assess how syndromic surveillance impacted their daily decision making. DESIGN: We adapted evaluation guidelines from the Centers for Disease Control and Prevention and gathered input from the Centers for Disease Control and Prevention subject matter experts in public health surveillance to develop a questionnaire. PARTICIPANTS: We interviewed staff members from a convenience sample of 6 local and state health departments with syndromic surveillance programs that had been in operation for more than 10 years. RESULTS: Three of the 6 interviewees provided an example of using syndromic surveillance to identify an outbreak (ie, cluster of foodborne illness in 1 jurisdiction) or detect a surge in cases for seasonal conditions (eg, influenza in 2 jurisdictions) prior to traditional, disease-specific systems. Although all interviewees noted that syndromic surveillance has not been routinely useful or efficient for early outbreak detection or case finding in their jurisdictions, all agreed that the information can be used to improve their understanding of dynamic disease control environments and conditions (eg, situational awareness) in their communities. CONCLUSION: In the jurisdictions studied, syndromic surveillance may be useful for monitoring the spread and intensity of large outbreaks of disease, especially influenza; enhancing public health awareness of mass gatherings and natural disasters; and assessing new, otherwise unmonitored conditions when real-time alternatives are unavailable. Future studies should explore opportunities to strengthen syndromic surveillance by including broader access to and enhanced analysis of text-related data from electronic health records. Health departments may accelerate the development and use of syndromic surveillance systems, including the improvement of the predictive value and strengthening the early outbreak detection capability of these systems. These efforts support getting the right information to the right people at the right time, which is the overarching goal of CDC's Surveillance Strategy. |
Accomplishments and opportunities in biosurveillance
Thomas MJ . J Public Health Manag Pract 2016 22 Suppl 6, Public Health Informatics S81-s82 This commentary discusses the accomplishments and opportunities in biosurveillance to guide public health action, planning, and prioritization. | Surveillance is the cornerstone of public health. Robust surveillance data and their analysis meaningfully impact public health action, planning, and prioritization. However, as Dr Alexander Langmuir stated in his 1962 Cutter Lecture on Preventive Medicine at the Harvard School of Public Health, “Good surveillance does not necessarily ensure making the right decisions, but it reduces the chances of wrong ones.”1(p191) Over the past 50 years, the application of computer and information science has improved efficiency and effectiveness of public health surveillance by changing the way we collect, process, and analyze vast and disparate data for research, decision making, and learning. One example is the development of syndromic surveillance systems. What began as an experiment in a few large cities 20 years ago to track over-the-counter sales of antidiarrhea medications as an early warning of outbreaks of gastrointestinal illness expanded substantially after the terrorist attacks of 2001. Today the model has expanded to include many additional sources of real-time or near real-time data and broader uses of those data. Health departments are using data collected from emergency department visits that they receive from hospitals for monitoring conditions for which there are no surveillance systems (eg, opioid overdoses and carbon monoxide poisonings) and for improving public health situational awareness. These syndromic-based systems can continue to improve as additional text fields from the electronic health record (EHR) become part of the data stream. These additional data along with advanced natural-language processing and statistical learning methods may enhance the use of other coded and free-text contextual information. Over time, adaptive machine learning methods could make possible the detection of syndromes that were not prespecified, which could enhance overall surveillance and improve early event detection. |
Global health security: the wider lessons from the West African Ebola virus disease epidemic
Heymann DL , Chen L , Takemi K , Fidler DP , Tappero JW , Thomas MJ , Kenyon TA , Frieden TR , Yach D , Nishtar S , Kalache A , Olliaro PL , Horby P , Torreele E , Gostin LO , Ndomondo-Sigonda M , Carpenter D , Rushton S , Lillywhite L , Devkota B , Koser K , Yates R , Dhillon RS , Rannan-Eliya RP . Lancet 2015 385 (9980) 1884-901 The Ebola virus disease outbreak in West Africa was unprecedented in both its scale and impact. Out of this human calamity has come renewed attention to global health security--its definition, meaning, and the practical implications for programmes and policy. For example, how does a government begin to strengthen its core public health capacities, as demanded by the International Health Regulations? What counts as a global health security concern? In the context of the governance of global health, including WHO reform, it will be important to distil lessons learned from the Ebola outbreak. The Lancet invited a group of respected global health practitioners to reflect on these lessons, to explore the idea of global health security, and to offer suggestions for next steps. Their contributions describe some of the major threats to individual and collective human health, as well as the values and recommendations that should be considered to counteract such threats in the future. Many different perspectives are proposed. Their common goal is a more sustainable and resilient society for human health and wellbeing. |
The draft genome sequence of the ferret (Mustela putorius furo) facilitates study of human respiratory disease.
Peng X , Alfoldi J , Gori K , Eisfeld AJ , Tyler SR , Tisoncik-Go J , Brawand D , Law GL , Skunca N , Hatta M , Gasper DJ , Kelly SM , Chang J , Thomas MJ , Johnson J , Berlin AM , Lara M , Russell P , Swofford R , Turner-Maier J , Young S , Hourlier T , Aken B , Searle S , Sun X , Yi Y , Suresh M , Tumpey TM , Siepel A , Wisely SM , Dessimoz C , Kawaoka Y , Birren BW , Lindblad-Toh K , Di Palma F , Engelhardt JF , Palermo RE , Katze MG . Nat Biotechnol 2014 32 (12) 1250-5 The domestic ferret (Mustela putorius furo) is an important animal model for multiple human respiratory diseases. It is considered the 'gold standard' for modeling human influenza virus infection and transmission. Here we describe the 2.41 Gb draft genome assembly of the domestic ferret, constituting 2.28 Gb of sequence plus gaps. We annotated 19,910 protein-coding genes on this assembly using RNA-seq data from 21 ferret tissues. We characterized the ferret host response to two influenza virus infections by RNA-seq analysis of 42 ferret samples from influenza time-course data and showed distinct signatures in ferret trachea and lung tissues specific to 1918 or 2009 human pandemic influenza virus infections. Using microarray data from 16 ferret samples reflecting cystic fibrosis disease progression, we showed that transcriptional changes in the CFTR-knockout ferret lung reflect pathways of early disease that cannot be readily studied in human infants with cystic fibrosis disease. |
Integrative deep sequencing of the mouse lung transcriptome reveals differential expression of diverse classes of small RNAs in response to respiratory virus infection.
Peng X , Gralinski L , Ferris MT , Frieman MB , Thomas MJ , Proll S , Korth MJ , Tisoncik JR , Heise M , Luo S , Schroth GP , Tumpey TM , Li C , Kawaoka Y , Baric RS , Katze MG . mBio 2011 2 (6) We previously reported widespread differential expression of long non-protein-coding RNAs (ncRNAs) in response to virus infection. Here, we expanded the study through small RNA transcriptome sequencing analysis of the host response to both severe acute respiratory syndrome coronavirus (SARS-CoV) and influenza virus infections across four founder mouse strains of the Collaborative Cross, a recombinant inbred mouse resource for mapping complex traits. We observed differential expression of over 200 small RNAs of diverse classes during infection. A majority of identified microRNAs (miRNAs) showed divergent changes in expression across mouse strains with respect to SARS-CoV and influenza virus infections and responded differently to a highly pathogenic reconstructed 1918 virus compared to a minimally pathogenic seasonal influenza virus isolate. Novel insights into miRNA expression changes, including the association with pathogenic outcomes and large differences between in vivo and in vitro experimental systems, were further elucidated by a survey of selected miRNAs across diverse virus infections. The small RNAs identified also included many non-miRNA small RNAs, such as small nucleolar RNAs (snoRNAs), in addition to nonannotated small RNAs. An integrative sequencing analysis of both small RNAs and long transcripts from the same samples showed that the results revealing differential expression of miRNAs during infection were largely due to transcriptional regulation and that the predicted miRNA-mRNA network could modulate global host responses to virus infection in a combinatorial fashion. These findings represent the first integrated sequencing analysis of the response of host small RNAs to virus infection and show that small RNAs are an integrated component of complex networks involved in regulating the host response to infection. IMPORTANCE: Most studies examining the host transcriptional response to infection focus only on protein-coding genes. However, mammalian genomes transcribe many short and long non-protein-coding RNAs (ncRNAs). With the advent of deep-sequencing technologies, systematic transcriptome analysis of the host response, including analysis of ncRNAs of different sizes, is now possible. Using this approach, we recently discovered widespread differential expression of host long (>200 nucleotide [nt]) ncRNAs in response to virus infection. Here, the samples described in the previous report were again used, but we sequenced another fraction of the transcriptome to study very short (about 20 to 30 nt) ncRNAs. We demonstrated that virus infection also altered expression of many short ncRNAs of diverse classes. Putting the results of the two studies together, we show that small RNAs may also play an important role in regulating the host response to virus infection. |
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