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Vecino-Ortiz AI, Guzman-Tordecilla DN, Maniar V, Agudelo-Londoño S, Franco-Suarez O, Aya Pastrana N, Rodríguez-Patarroyo M, Mejía-Rocha M, Cardona J, Chavez Chamorro M, Gibson D. Mobile Phone Syndromic Surveillance for Respiratory Conditions in an Emergency (COVID-19) Context in Colombia: Representative Survey Design. J Med Internet Res 2024; 26:e50184. [PMID: 39418077 PMCID: PMC11528172 DOI: 10.2196/50184] [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: 06/22/2023] [Revised: 03/03/2024] [Accepted: 03/08/2024] [Indexed: 10/19/2024] Open
Abstract
BACKGROUND Syndromic surveillance for respiratory infections such as COVID-19 is a crucial part of the public health surveillance toolkit as it allows decision makers to detect and prepare for new waves of the disease in advance. However, it is labor-intensive, costly, and increases exposure to survey personnel. This study assesses the feasibility of conducting a mobile phone-based respiratory syndromic surveillance program in a middle-income country during a public health emergency, providing data to support the inclusion of this method in the standard infection control protocols at the population level. OBJECTIVE This study aims to assess the feasibility of a national active syndromic surveillance system for COVID-19 disease in Colombia. METHODS In total, 2 pilots of syndromic mobile phone surveys (MPSs) were deployed using interactive voice response technology in Colombia (367 complete surveys in March 2022 and 451 complete surveys in April and May 2022). Respondents aged 18 years and older were sampled using random digit dialing, and after obtaining consent, they were sent a 10-minute survey with modules on sociodemographic status, respiratory symptoms, past exposure to COVID-19 infection and vaccination status, preferences about COVID-19 vaccination, and information source for COVID-19. Pilot 1 used a nationally representative sample while pilot 2 used quota sampling to yield representative results at the regional level. In this work, we assessed the performance characteristics of the survey pilots and compared the demographic information collected with a nationally representative household survey. RESULTS For both pilots, contact rates were between 1% and 2%, while participation rates were above 80%. The results revealed that younger, female, and higher educated participants were more likely to participate in the syndromic survey. Survey rates as well as demographics, COVID-19 vaccination status, and prevalence of respiratory symptoms are reported for both pilots. We found that respondents of the MPSs are more likely to be younger and female. CONCLUSIONS In a COVID-19 pandemic setting, using an interactive voice response MPS to conduct syndromic surveillance may be a transformational, low-risk, and feasible method to detect outbreaks. This evaluation expects to provide a path forward to the inclusion of MPSs as a traditional surveillance method.
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Affiliation(s)
- Andres I Vecino-Ortiz
- Health Systems Program, International Health Department, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Deivis Nicolas Guzman-Tordecilla
- Health Systems Program, International Health Department, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Vidhi Maniar
- Health Systems Program, International Health Department, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | | | | | | | | | | | | | | | - Dustin Gibson
- Health Systems Program, International Health Department, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
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Greenleaf AR, Francis S, Zou J, Farley SM, Lekhela T, Asiimwe F, Chen Q. Influenza-Like Illness in Lesotho From July 2020 to July 2021: Population-Based Participatory Surveillance Results. JMIR Public Health Surveill 2024; 10:e55208. [PMID: 39378443 PMCID: PMC11479357 DOI: 10.2196/55208] [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: 12/05/2023] [Revised: 08/15/2024] [Accepted: 08/20/2024] [Indexed: 10/10/2024] Open
Abstract
Background Participatory surveillance involves at-risk populations reporting their symptoms using technology. In Lesotho, a landlocked country of 2 million people in Southern Africa, laboratory and case-based COVID-19 surveillance systems were complemented by a participatory surveillance system called "LeCellPHIA" (Lesotho Cell Phone Population-Based HIV Impact Assessment Survey). Objective This report describes the person, place, and time characteristics of influenza-like illness (ILI) in Lesotho from July 15, 2020, to July 15, 2021, and reports the risk ratio of ILI by key demographic variables. Methods LeCellPHIA employed interviewers to call participants weekly to inquire about ILI. The average weekly incidence rate for the year-long period was created using a Quasi-Poisson model, which accounted for overdispersion. To identify factors associated with an increased risk of ILI, we conducted a weekly data analysis by fitting a multilevel Poisson regression model, which accounted for 3 levels of clustering. Results The overall response rate for the year of data collection was 75%, which resulted in 122,985 weekly reports from 1776 participants. ILI trends from LeCellPHIA mirrored COVID-19 testing data trends, with an epidemic peak in mid to late January 2021. Overall, any ILI symptoms (eg, fever, dry cough, and shortness of breath) were reported at an average weekly rate of 879 per 100,000 (95% CI 782-988) persons at risk. Compared to persons in the youngest age group (15-19 years), all older age groups had an elevated risk of ILI, with the highest risk of ILI in the oldest age group (≥60 years; risk ratio 2.6, 95% CI 1.7-3.8). Weekly data were shared in near real time with the National COVID-19 Secretariat and other stakeholders to monitor ILI trends, identify and respond to increases in reports of ILI, and inform policies and practices designed to reduce COVID-19 transmission in Lesotho. Conclusions LeCellPHIA, an innovative and cost-effective system, could be replicated in countries where cell phone ownership is high but internet use is not yet high enough for a web- or app-based surveilance system.
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Affiliation(s)
- Abigail R Greenleaf
- ICAP at Columbia, 60 Haven Ave, New York, NY, 10032, United States, 1 212 342 0505
- Department of Population and Family Health, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Sarah Francis
- Department of Epidemiology, Mailman School of Public Health, Columbia, New York, NY, United States
| | - Jungang Zou
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Shannon M Farley
- ICAP at Columbia, 60 Haven Ave, New York, NY, 10032, United States, 1 212 342 0505
- Department of Population and Family Health, Mailman School of Public Health, Columbia University, New York, NY, United States
| | | | - Fred Asiimwe
- Centers for Disease Control and Prevention, Maseru, Lesotho
| | - Qixuan Chen
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, United States
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Valerio MGP, Laher B, Phuka J, Lichand G, Paolotti D, Leal Neto O. Participatory Disease Surveillance for the Early Detection of Cholera-Like Diarrheal Disease Outbreaks in Rural Villages in Malawi: Prospective Cohort Study. JMIR Public Health Surveill 2024; 10:e49539. [PMID: 39012690 PMCID: PMC11289577 DOI: 10.2196/49539] [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: 06/01/2023] [Revised: 02/16/2024] [Accepted: 05/16/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Cholera-like diarrheal disease (CLDD) outbreaks are complex and influenced by environmental factors, socioeconomic conditions, and population dynamics, leading to limitations in traditional surveillance methods. In Malawi, cholera is considered an endemic disease. Its epidemiological profile is characterized by seasonal patterns, often coinciding with the rainy season when contamination of water sources is more likely. However, the outbreak that began in March 2022 has extended to the dry season, with deaths reported in all 29 districts. It is considered the worst outbreak in the past 10 years. OBJECTIVE This study aims to evaluate the feasibility and outcomes of participatory surveillance (PS) using interactive voice response (IVR) technology for the early detection of CLDD outbreaks in Malawi. METHODS This longitudinal cohort study followed 740 households in rural settings in Malawi for 24 weeks. The survey tool was designed to have 10 symptom questions collected every week. The proxies' rationale was related to exanthematic, ictero-hemorragica for endemic diseases or events, diarrhea and respiratory/targeting acute diseases or events, and diarrhea and respiratory/targeting seasonal diseases or events. This work will focus only on the CLDD as a proxy for gastroenteritis and cholera. In this study, CLDD was defined as cases where reports indicated diarrhea combined with either fever or vomiting/nausea. RESULTS During the study period, our data comprised 16,280 observations, with an average weekly participation rate of 35%. Maganga TA had the highest average of completed calls, at 144.83 (SD 10.587), while Ndindi TA had an average of 123.66 (SD 13.176) completed calls. Our findings demonstrate that this method might be effective in identifying CLDD with a notable and consistent signal captured over time (R2=0.681404). Participation rates were slightly higher at the beginning of the study and decreased over time, thanks to the sensitization activities rolled out at the CBCCs level. In terms of the attack rates for CLDD, we observed similar rates between Maganga TA and Ndindi TA, at 16% and 15%, respectively. CONCLUSIONS PS has proven to be valuable for the early detection of epidemics. IVR technology is a promising approach for disease surveillance in rural villages in Africa, where access to health care and traditional disease surveillance methods may be limited. This study highlights the feasibility and potential of IVR technology for the timely and comprehensive reporting of disease incidence, symptoms, and behaviors in resource-limited settings.
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Affiliation(s)
| | - Beverly Laher
- Kamuzu University of Health Sciences, Lilongwe, Malawi
| | - John Phuka
- Kamuzu University of Health Sciences, Lilongwe, Malawi
| | - Guilherme Lichand
- Graduate School of Education, Stanford University, Stanford, CA, United States
| | | | - Onicio Leal Neto
- Department of Epidemiology and Biostatistics, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, United States
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Nikhab A, Morbey R, Todkill D, Elliot AJ. Using a novel 'difference-in-differences' method and syndromic surveillance to estimate the change in local healthcare utilisation during periods of media reporting in the early stages of the COVID-19 pandemic in England. Public Health 2024; 232:132-137. [PMID: 38776588 DOI: 10.1016/j.puhe.2024.04.022] [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: 12/22/2023] [Revised: 04/11/2024] [Accepted: 04/12/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVES Syndromic surveillance supplements traditional laboratory reporting for infectious diseases monitoring. Prior to widespread COVID-19 community surveillance, syndromic surveillance was one of several systems providing real-time information on changes in healthcare-seeking behaviour. The study objective was to identify changes in healthcare utilisation during periods of high local media reporting in England using 'difference-in-differences' (DiD). STUDY DESIGN A retrospective observational study was conducted using five media events in January-February 2020 in England on four routinely monitored syndromic surveillance indicators. METHODS Dates 'exposed' to a media event were estimated using Google Trends internet search intensity data (terms = 'coronavirus' and local authority [LA]). We constructed a negative-binomial regression model for each indicator and event time period to estimate a direct effect. RESULTS We estimated a four-fold increase in telehealth 'cough' calls and a 1.4-fold increase in emergency department (ED) attendances for acute respiratory illness in Brighton and Hove, when a so-called 'superspreading event' in this location was reported in local and national media. Significant decreases were observed in the Buxton (telehealth and ED attendance) and Wirral (ED attendance) areas during media reports of a returnee from an outbreak abroad and a quarantine site opening in the area respectively. CONCLUSIONS We used a novel approach to directly estimate changes in syndromic surveillance reporting during the early phase of the COVID-19 pandemic in England, providing contextual information on the interpretation of changes in health indicators. With careful consideration of event timings, DiD is useful in producing real-time estimates on specific indicators for informing public health action.
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Affiliation(s)
- A Nikhab
- UK Field Epidemiology Training Programme, UK Health Security Agency (UKHSA), UK; Field Service Midlands, UK Health Security Agency (UKHSA), UK.
| | - R Morbey
- Real-time Syndromic Surveillance Team, UK Health Security Agency (UKHSA), UK; National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Emergency Preparedness and Response, King's College London, UK
| | - D Todkill
- Real-time Syndromic Surveillance Team, UK Health Security Agency (UKHSA), UK; Warwick Medical School, The University of Warwick, Coventry, UK
| | - A J Elliot
- Real-time Syndromic Surveillance Team, UK Health Security Agency (UKHSA), UK; National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Emergency Preparedness and Response, King's College London, UK
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Wittwer S, Paolotti D, Lichand G, Leal Neto O. Participatory surveillance for COVID-19 trends detection in Brazil: Cross-section study. JMIR Public Health Surveill 2023; 9:e44517. [PMID: 36888908 PMCID: PMC10138922 DOI: 10.2196/44517] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/25/2023] [Accepted: 03/07/2023] [Indexed: 03/09/2023] Open
Abstract
BACKGROUND The ongoing COVID-19 pandemic has emphasized the necessity of a well-functioning surveillance system to detect and mitigate disease outbreaks. Traditional surveillance (TS) usually relies on healthcare providers and generally suffers from reporting lags that prevent immediate response plans. Participatory surveillance (PS), an innovative digital approach whereby individuals voluntarily monitor and report on their own health status via Web-based surveys, has emerged in the past decade to complement traditional data collections approaches. OBJECTIVE This study compares novel PS data on COVID-19 infection rates across nine Brazilian cities with official TS data to examine the opportunities and challenges of using the former, and the potential advantages of combining the two approaches. METHODS The traditional surveillance data for Brazil, prospectively called the TS data, is publicly accessible on GitHub. The participatory surveillance data was collected through the Brazil Sem Corona - a Colab platform. To gather information on an individual's health status, each participant was asked to fill out a daily questionnaire into the Colab app on symptoms as well as exposure. RESULTS We find that high participation rates are key for PS data to adequately mirror TS infection rates. Where participation was high, we document a significant trend correlation between lagged PS data and TS infection rates, suggesting that the former could be used for early detection. In our data, forecasting models integrating both approaches increased accuracy up to 3% relative to a 14-day forecast horizon model based exclusively on TS data. Furthermore, we show that the PS data captures a population that significantly differs from the traditional observation. CONCLUSIONS In the traditional system, the new recorded COVID-19 cases per day are aggregated based on positive lab-confirmed tests. In contrast, the PS data shows a significant share of reports categorized as potential COVID-19 case that are not lab-confirmed. Quantifying the economic value of a PS system implementation remains hard. But scarce public funds as well as persisting constraints to the TS system motivate for a PS system, making it an important avenue for future research. The decision to set up a PS system requires careful evaluation of its expected benefits, relative to the costs of setting up platforms and incentivizing engagement to increase both coverage and consistent reporting over time. The ability to compute such economic trade-offs might be key to have PS become a more integral part of policy toolkits moving forward. These results corroborate previous studies when it comes to the benefits of an integrated and comprehensive surveillance system, but also shed lights on its limitations, and on the need for additional research to improve future implementations of PS platforms. CLINICALTRIAL
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Affiliation(s)
- Salome Wittwer
- Department of Economics, University of Zurich, Schönberggasse 1, Zurich, CH
| | - Daniela Paolotti
- Data Science for Social Impact and Sustainability, ISI Foundation, Turin, IT
| | - Guilherme Lichand
- Department of Economics, University of Zurich, Schönberggasse 1, Zurich, CH
| | - Onicio Leal Neto
- Department of Computer Science, ETH Zürich, Universitätstrasse 6, Zurich, CH
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Saha P, Gulshan J. Systematic Assessment of COVID-19 Pandemic in Bangladesh: Effectiveness of Preparedness in the First Wave. Front Public Health 2021; 9:628931. [PMID: 34746068 PMCID: PMC8567082 DOI: 10.3389/fpubh.2021.628931] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 08/02/2021] [Indexed: 12/23/2022] Open
Abstract
Background: To develop an effective countermeasure and determine our susceptibilities to the outbreak of COVID-19 is challenging for a densely populated developing country like Bangladesh and a systematic review of the disease on a continuous basis is necessary. Methods: Publicly available and globally acclaimed datasets (4 March 2020-30 September 2020) from IEDCR, Bangladesh, JHU, and ECDC database are used for this study. Visual exploratory data analysis is used and we fitted a polynomial model for the number of deaths. A comparison of Bangladesh scenario over different time points as well as with global perspectives is made. Results: In Bangladesh, the number of active cases had decreased, after reaching a peak, with a constant pattern of death rate at from July to the end of September, 2020. Seventy-one percent of the cases and 77% of the deceased were males. People aged between 21 and 40 years were most vulnerable to the coronavirus and most of the fatalities (51.49%) were in the 60+ population. A strong positive correlation (0.93) between the number of tests and confirmed cases and a constant incidence rate (around 21%) from June 1 to August 31, 2020 was observed. The case fatality ratio was between 1 and 2. The number of cases and the number of deaths in Bangladesh were much lower compared to other countries. Conclusions: This study will help to understand the patterns of spread and transition in Bangladesh, possible measures, effectiveness of the preparedness, implementation gaps, and their consequences to gather vital information and prevent future pandemics.
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Affiliation(s)
- Priom Saha
- Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh
| | - Jahida Gulshan
- Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh
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Greenleaf A, Mwima G, Lethoko M, Conkling M, Keefer G, Chang C, McLeod N, Maruyama H, Chen Q, Farley S, Low A. Participatory surveillance of COVID-19 in Lesotho via weekly calls: Protocol for cell phone data collection. JMIR Res Protoc 2021; 10:e31236. [PMID: 34351866 PMCID: PMC8478051 DOI: 10.2196/31236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/01/2021] [Accepted: 08/01/2021] [Indexed: 11/17/2022] Open
Abstract
Background The increase in cell phone ownership in low- and middle-income countries (LMIC) has created an opportunity for low-cost, rapid data collection by calling participants on their cell phones. Cell phones can be mobilized for a myriad of data collection purposes, including surveillance. In LMIC, cell phone–based surveillance has been used to track Ebola, measles, acute flaccid paralysis, and diarrheal disease, as well as noncommunicable diseases. Phone-based surveillance in LMIC is a particularly pertinent, burgeoning approach in the context of the COVID-19 pandemic. Participatory surveillance via cell phone could allow governments to assess burden of disease and complements existing surveillance systems. Objective We describe the protocol for the LeCellPHIA (Lesotho Cell Phone PHIA) project, a cell phone surveillance system that collects weekly population-based data on influenza-like illness (ILI) in Lesotho by calling a representative sample of a recent face-to-face survey. Methods We established a phone-based surveillance system to collect ILI symptoms from approximately 1700 participants who had participated in a recent face-to-face survey in Lesotho, the Population-based HIV Impact Assessment (PHIA) Survey. Of the 15,267 PHIA participants who were over 18 years old, 11,975 (78.44%) consented to future research and provided a valid phone number. We followed the PHIA sample design and included 342 primary sampling units from 10 districts. We randomly selected 5 households from each primary sampling unit that had an eligible participant and sampled 1 person per household. We oversampled the elderly, as they are more likely to be affected by COVID-19. A 3-day Zoom training was conducted in June 2020 to train LeCellPHIA interviewers. Results The surveillance system launched July 1, 2020, beginning with a 2-week enrollment period followed by weekly calls that will continue until September 30, 2022. Of the 11,975 phone numbers that were in the sample frame, 3020 were sampled, and 1778 were enrolled. Conclusions The surveillance system will track COVID-19 in a resource-limited setting. The novel approach of a weekly cell phone–based surveillance system can be used to track other health outcomes, and this protocol provides information about how to implement such a system. International Registered Report Identifier (IRRID) DERR1-10.2196/31236
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Affiliation(s)
- Abigail Greenleaf
- ICAP at Columbia University, Mailman School of Public Health, Columbia University, 60 Haven Ave, New York, US
| | - Gerald Mwima
- ICAP at Columbia University - Lesotho, Mailman School of Public Health, Columbia University, Maseru, LS
| | - Molibeli Lethoko
- ICAP at Columbia University - Lesotho, Mailman School of Public Health, Columbia University, Maseru, LS
| | - Martha Conkling
- Division of Global HIV/AIDS, Center for Global Health, US Centers for Disease Control and Prevention, Atlanta, US
| | - George Keefer
- ICAP at Columbia University, Mailman School of Public Health, Columbia University, 60 Haven Ave, New York, US
| | - Christiana Chang
- ICAP at Columbia University, Mailman School of Public Health, Columbia University, 60 Haven Ave, New York, US
| | - Natasha McLeod
- ICAP at Columbia University, Mailman School of Public Health, Columbia University, 60 Haven Ave, New York, US
| | - Haruka Maruyama
- ICAP at Columbia University - Tanzania, Mailman School of Public Health, Columbia University, Dar es Salaam, TZ
| | - Qixuan Chen
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, US
| | - Shannon Farley
- ICAP at Columbia University, Mailman School of Public Health, Columbia University, 60 Haven Ave, New York, US
| | - Andrea Low
- ICAP at Columbia University, Mailman School of Public Health, Columbia University, 60 Haven Ave, New York, US
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Papadomanolakis-Pakis N, Maier A, van Dijk A, VanStone N, Moore KM. Development and assessment of a hospital admissions-based syndromic surveillance system for COVID-19 in Ontario, Canada: ACES Pandemic Tracker. BMC Public Health 2021; 21:1230. [PMID: 34174852 PMCID: PMC8233625 DOI: 10.1186/s12889-021-11303-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/14/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic has continued to pose a major global public health risk. The importance of public health surveillance systems to monitor the spread and impact of COVID-19 has been well demonstrated. The purpose of this study was to describe the development and effectiveness of a real-time public health syndromic surveillance system (ACES Pandemic Tracker) as an early warning system and to provide situational awareness in response to the COVID-19 pandemic in Ontario, Canada. METHODS We used hospital admissions data from the Acute Care Enhanced Surveillance (ACES) system to collect data on pre-defined groupings of symptoms (syndromes of interest; SOI) that may be related to COVID-19 from 131 hospitals across Ontario. To evaluate which SOI for suspected COVID-19 admissions were best correlated with laboratory confirmed admissions, laboratory confirmed COVID-19 hospital admissions data were collected from the Ontario Ministry of Health. Correlations and time-series lag analysis between suspected and confirmed COVID-19 hospital admissions were calculated. Data used for analyses covered the period between March 1, 2020 and September 21, 2020. RESULTS Between March 1, 2020 and September 21, 2020, ACES Pandemic Tracker identified 22,075 suspected COVID-19 hospital admissions (150 per 100,000 population) in Ontario. After correlation analysis, we found laboratory-confirmed hospital admissions for COVID-19 were strongly and significantly correlated with suspected COVID-19 hospital admissions when SOI were included (Spearman's rho = 0.617) and suspected COVID-19 admissions when SOI were excluded (Spearman's rho = 0.867). Weak to moderate significant correlations were found among individual SOI. Laboratory confirmed COVID-19 hospital admissions lagged in reporting by 3 days compared with suspected COVID-19 admissions when SOI were excluded. CONCLUSIONS Our results demonstrate the utility of a hospital admissions syndromic surveillance system to monitor and identify potential surges in severe COVID-19 infection within the community in a timely manner and provide situational awareness to inform preventive and preparatory health interventions.
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Affiliation(s)
- Nicholas Papadomanolakis-Pakis
- Knowledge Management Division, Kingston, Frontenac and Lennox & Addington Public Health, 221 Portsmouth Avenue, Kingston, Ontario, K7M 1V5, Canada.
| | - Allison Maier
- Knowledge Management Division, Kingston, Frontenac and Lennox & Addington Public Health, 221 Portsmouth Avenue, Kingston, Ontario, K7M 1V5, Canada
| | - Adam van Dijk
- Knowledge Management Division, Kingston, Frontenac and Lennox & Addington Public Health, 221 Portsmouth Avenue, Kingston, Ontario, K7M 1V5, Canada
| | - Nancy VanStone
- Knowledge Management Division, Kingston, Frontenac and Lennox & Addington Public Health, 221 Portsmouth Avenue, Kingston, Ontario, K7M 1V5, Canada
| | - Kieran Michael Moore
- Office of the Medical Officer of Health, Kingston, Frontenac and Lennox & Addington Public Health, 221 Portsmouth Avenue, Kingston, Ontario, K7M 1V5, Canada
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