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Yu J, Wang H, Chen M, Han X, Deng Q, Yang C, Zhu W, Ma Y, Yin F, Weng Y, Yang C, Zhang T. A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases. BMC Infect Dis 2024; 24:832. [PMID: 39148009 PMCID: PMC11328433 DOI: 10.1186/s12879-024-09718-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 08/05/2024] [Indexed: 08/17/2024] Open
Abstract
BACKGROUND Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection. METHODS This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China. RESULTS When the spatio-temporal variation was small (time delay coefficient: 0.1-0.2, spatial sparsity:0.1-0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2-0.3, spatial sparsity: 0.6-0.9). CONCLUSION This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators-the time delay coefficient and spatial sparsity-into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.
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Affiliation(s)
- Jie Yu
- West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Huimin Wang
- West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Miaoshuang Chen
- West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Xinyue Han
- West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Qiao Deng
- West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Chen Yang
- West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Wenhui Zhu
- West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Yue Ma
- West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Fei Yin
- West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Yang Weng
- College of Mathematics, Sichuan University, Chengdu, Sichuan Province, China
| | - Changhong Yang
- Sichuan Center for Disease Control and Prevention, Chengdu, Sichuan Province, China
| | - Tao Zhang
- West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan Province, China.
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Gertz A, Rader B, Sewalk K, Varrelman TJ, Smolinski M, Brownstein JS. Decreased Seasonal Influenza Rates Detected in a Crowdsourced Influenza-Like Illness Surveillance System During the COVID-19 Pandemic: Prospective Cohort Study. JMIR Public Health Surveill 2023; 9:e40216. [PMID: 38153782 PMCID: PMC10784978 DOI: 10.2196/40216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/24/2023] [Accepted: 11/14/2023] [Indexed: 12/29/2023] Open
Abstract
BACKGROUND Seasonal respiratory viruses had lower incidence during their 2019-2020 and 2020-2021 seasons, which overlapped with the COVID-19 pandemic. The widespread implementation of precautionary measures to prevent transmission of SARS-CoV-2 has been seen to also mitigate transmission of seasonal influenza. The COVID-19 pandemic also led to changes in care seeking and access. Participatory surveillance systems have historically captured mild illnesses that are often missed by surveillance systems that rely on encounters with a health care provider for detection. OBJECTIVE This study aimed to assess if a crowdsourced syndromic surveillance system capable of detecting mild influenza-like illness (ILI) also captured the globally observed decrease in ILI in the 2019-2020 and 2020-2021 influenza seasons, concurrent with the COVID-19 pandemic. METHODS Flu Near You (FNY) is a web-based participatory syndromic surveillance system that allows participants in the United States to report their health information using a brief weekly survey. Reminder emails are sent to registered FNY participants to report on their symptoms and the symptoms of household members. Guest participants may also report. ILI was defined as fever and sore throat or fever and cough. ILI rates were determined as the number of ILI reports over the total number of reports and assessed for the 2016-2017, 2017-2018, 2018-2019, 2019-2020, and 2020-2021 influenza seasons. Baseline season (2016-2017, 2017-2018, and 2018-2019) rates were compared to the 2019-2020 and 2020-2021 influenza seasons. Self-reported influenza diagnosis and vaccination status were captured and assessed as the total number of reported events over the total number of reports submitted. CIs for all proportions were calculated via a 1-sample test of proportions. RESULTS ILI was detected in 3.8% (32,239/848,878) of participants in the baseline seasons (2016-2019), 2.58% (7418/287,909) in the 2019-2020 season, and 0.27% (546/201,079) in the 2020-2021 season. Both influenza seasons that overlapped with the COVID-19 pandemic had lower ILI rates than the baseline seasons. ILI decline was observed during the months with widespread implementation of COVID-19 precautions, starting in February 2020. Self-reported influenza diagnoses decreased from early 2020 through the influenza season. Self-reported influenza positivity among ILI cases varied over the observed time period. Self-reported influenza vaccination rates in FNY were high across all observed seasons. CONCLUSIONS A decrease in ILI was detected in the crowdsourced FNY surveillance system during the 2019-2020 and 2020-2021 influenza seasons, mirroring trends observed in other influenza surveillance systems. Specifically, the months within seasons that overlapped with widespread pandemic precautions showed decreases in ILI and confirmed influenza. Concerns persist regarding respiratory pathogens re-emerging with changes to COVID-19 guidelines. Traditional surveillance is subject to changes in health care behaviors. Systems like FNY are uniquely situated to detect disease across disease severity and care seeking, providing key insights during public health emergencies.
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Affiliation(s)
- Autumn Gertz
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, United States
| | - Benjamin Rader
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, United States
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - Kara Sewalk
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, United States
| | - Tanner J Varrelman
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, United States
| | | | - John S Brownstein
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
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Atkins N, Harikar M, Duggan K, Zawiejska A, Vardhan V, Vokey L, Dozier M, de los Godos EF, Mcswiggan E, Mcquillan R, Theodoratou E, Shi T. What are the characteristics of participatory surveillance systems for influenza-like-illness? J Glob Health 2023; 13:04130. [PMID: 37856769 PMCID: PMC10587643 DOI: 10.7189/jogh.13.04130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023] Open
Abstract
Background Seasonal influenza causes significant morbidity and mortality, with an estimated 9.4 million hospitalisations and 290 000-650 000 respiratory related-deaths globally each year. Influenza can also cause mild illness, which is why not all symptomatic persons might necessarily be tested for influenza. To monitor influenza activity, healthcare facility-based syndromic surveillance for influenza-like illness is often implemented. Participatory surveillance systems for influenza-like illness (ILI) play an important role in influenza surveillance and can complement traditional facility-based surveillance systems to provide real-time estimates of influenza-like illness activity. However, such systems differ in designs between countries and contexts, making it necessary to identify their characteristics to better understand how they fit traditional surveillance systems. Consequently, we aimed to investigate the performance of participatory surveillance systems for ILI worldwide. Methods We systematically searched four databases for relevant articles on influenza participatory surveillance systems for ILI. We extracted data from the included, eligible studies and assessed their quality using the Joanna Briggs Critical Appraisal Tools. We then synthesised the findings using narrative synthesis. Results We included 39 out of 3797 retrieved articles for analysis. We identified 26 participatory surveillance systems, most of which sought to capture the burden and trends of influenza-like illness and acute respiratory infections among cohorts with risk factors for influenza-like illness. Of all the surveillance system attributes assessed, 52% reported on correlation with other surveillance systems, 27% on representativeness, and 21% on acceptability. Among studies that reported these attributes, all systems were rated highly in terms of simplicity, flexibility, sensitivity, utility, and timeliness. Most systems (87.5%) were also well accepted by users, though participation rates varied widely. However, despite their potential for greater reach and accessibility, most systems (90%) fared poorly in terms of representativeness of the population. Stability was a concern for some systems (60%), as was completeness (50%). Conclusions The analysis of participatory surveillance system attributes showed their potential in providing timely and reliable influenza data, especially in combination with traditional hospital- and laboratory led-surveillance systems. Further research is needed to design future systems with greater uptake and utility.
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Affiliation(s)
- Nadege Atkins
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Joint first authorship
| | - Mandara Harikar
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Joint first authorship
| | - Kirsten Duggan
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Agnieszka Zawiejska
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Vaishali Vardhan
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Laura Vokey
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Marshall Dozier
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Emma F de los Godos
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Equal contribution
| | - Emilie Mcswiggan
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Ruth Mcquillan
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Equal contribution
| | - Evropi Theodoratou
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Equal contribution
| | - Ting Shi
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- Equal contribution
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Uchida M, Yamauchi T. Rate of diagnosed seasonal influenza in children with influenza-like illness: A cross-sectional study. PLoS One 2022; 17:e0269804. [PMID: 35687648 PMCID: PMC9187082 DOI: 10.1371/journal.pone.0269804] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 05/31/2022] [Indexed: 11/19/2022] Open
Abstract
Introduction Although influenza surveillance systems have been used to monitor influenza epidemics, these systems generally evaluate diagnostic information obtained from medical institutions and they do not include patients who have not been examined. In contrast, community based epidemiological studies target people with influenza-like illness (ILI) that self-reported influenza-like symptoms whether they have medical examinations or not. Because the criteria for influenza surveillance systems and ILI differ, there is a gap between them. The purpose of this study was to clarify this gap using school-based survey data. Methods Questionnaires about both ILI and the influenza diagnosis history during the 2018/19 season were administered to the guardians of 11,684 elementary schoolchildren in a single city in Japan. Based on their responses, a Bayesian model was constructed to estimate the probability of infection, ILI onset, and diagnosis at medical institutions. Results Responses were obtained from guardians of 10,309 children (88.2%). Of these, 3,380 children (32.8%) had experienced ILI, with 2,380 (23.1%) diagnosed as influenza at a medical institution. Bayesian estimation showed that the probability of influenza cases being diagnosed among ILI symptomatic children was 70% (95% credible interval, 69–71%). Of the infected children, 5% were without ILI symptoms, with 11% of these patients diagnosed with influenza. Conclusions This epidemiological study clarified the proportion gap between ILI and influenza diagnosis among schoolchildren. These results may help to establish epidemic control measures and secure sufficient medical resources.
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Affiliation(s)
- Mitsuo Uchida
- Department of Public Health, Graduate School of Medicine, Gunma University, Maebashi, Gunma, Japan
- * E-mail:
| | - Takenori Yamauchi
- Department of Hygiene, Public Health and Preventive Medicine, Faculty of Medicine, Showa University, Tokyo, Japan
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Lu FS, Nguyen AT, Link NB, Molina M, Davis JT, Chinazzi M, Xiong X, Vespignani A, Lipsitch M, Santillana M. Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches. PLoS Comput Biol 2021; 17:e1008994. [PMID: 34138845 PMCID: PMC8241061 DOI: 10.1371/journal.pcbi.1008994] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 06/29/2021] [Accepted: 04/22/2021] [Indexed: 12/20/2022] Open
Abstract
Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the usefulness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.3 to 4.8 million, with possibly as many as 7.6 million cases, up to 25 times greater than the cumulative confirmed cases of about 311,000. Extending our methods to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 4.9 to 10.1 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.
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Affiliation(s)
- Fred S. Lu
- Department of Statistics, Stanford University, Stanford, California, United States of America
| | - Andre T. Nguyen
- University of Maryland, Baltimore County, Baltimore, Maryland, United States of America
- Booz Allen Hamilton, Columbia, Maryland, United States of America
| | - Nicholas B. Link
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
| | - Mathieu Molina
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
| | - Jessica T. Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Xinyue Xiong
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Marc Lipsitch
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Mauricio Santillana
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
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6
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Lu FS, Nguyen AT, Link NB, Davis JT, Chinazzi M, Xiong X, Vespignani A, Lipsitch M, Santillana M. Estimating the Cumulative Incidence of COVID-19 in the United States Using Four Complementary Approaches. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.04.18.20070821. [PMID: 32587997 PMCID: PMC7310656 DOI: 10.1101/2020.04.18.20070821] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the useful-ness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.2 to 4.9 million, with possibly as many as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 10.3 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.
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Affiliation(s)
- Fred S. Lu
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA
- Department of Statistics, Stanford University, Stanford, CA
| | - Andre T. Nguyen
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA
- University of Maryland, Baltimore County, Baltimore, MD
- Booz Allen Hamilton, Columbia, MD
| | - Nicholas B. Link
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA
| | - Jessica T. Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA
| | - Xinyue Xiong
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA
| | - Marc Lipsitch
- Department of Epidemiology, Harvard T.H. Chan School of Public Health
| | - Mauricio Santillana
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health
- Department of Pediatrics, Harvard Medical School, Boston, MA
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Baltrusaitis K, Reed C, Sewalk K, Brownstein JS, Crawley AW, Biggerstaff M. Health-care seeking behavior for respiratory illness among Flu Near You participants in the United States during the 2015-16 through 2018-19 influenza season. J Infect Dis 2020; 226:270-277. [PMID: 32761050 PMCID: PMC9400452 DOI: 10.1093/infdis/jiaa465] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 07/27/2020] [Indexed: 11/14/2022] Open
Abstract
Background Flu Near You (FNY) is an online participatory syndromic surveillance system that collects health-related information. In this article, we summarized the healthcare-seeking behavior of FNY participants who reported influenza-like illness (ILI) symptoms. Methods We applied inverse probability weighting to calculate age-adjusted estimates of the percentage of FNY participants in the United States who sought health care for ILI symptoms during the 2015–2016 through 2018–2019 influenza season and compared seasonal trends across different demographic and regional subgroups, including age group, sex, census region, and place of care using adjusted χ 2 tests. Results The overall age-adjusted percentage of FNY participants who sought healthcare for ILI symptoms varied by season and ranged from 22.8% to 35.6%. Across all seasons, healthcare seeking was highest for the <18 and 65+ years age groups, women had a greater percentage compared with men, and the South census region had the largest percentage while the West census region had the smallest percentage. Conclusions The percentage of FNY participants who sought healthcare for ILI symptoms varied by season, geographical region, age group, and sex. FNY compliments existing surveillance systems and informs estimates of influenza-associated illness by adding important real-time insights into healthcare-seeking behavior.
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Affiliation(s)
- Kristin Baltrusaitis
- Center for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Carrie Reed
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Kara Sewalk
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, United States
| | - John S Brownstein
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, 02115 United States; Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, United States; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Matthew Biggerstaff
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
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