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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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Kostova D, Richter P, Van Vliet G, Mahar M, Moolenaar RL. The Role of Noncommunicable Diseases in the Pursuit of Global Health Security. Health Secur 2021; 19:288-301. [PMID: 33961498 PMCID: PMC8217593 DOI: 10.1089/hs.2020.0121] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Noncommunicable diseases and their risk factors are important for all aspects of outbreak preparedness and response, affecting a range of factors including host susceptibility, pathogen virulence, and health system capacity. This conceptual analysis has 2 objectives. First, we use the Haddon matrix paradigm to formulate a framework for assessing the relevance of noncommunicable diseases to health security efforts throughout all phases of the disaster life cycle: before, during, and after an event. Second, we build upon this framework to identify 6 technical action areas in global health security programs that are opportune integration points for global health security and noncommunicable disease objectives: surveillance, workforce development, laboratory systems, immunization, risk communication, and sustainable financing. We discuss approaches to integration with the goal of maximizing the reach of global health security where infectious disease threats and chronic disease burdens overlap.
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Affiliation(s)
- Deliana Kostova
- Deliana Kostova, PhD, is a Senior Economist; Patricia Richter, PhD, is Branch Chief, Global Noncommunicable Diseases Branch; Michael Mahar, PhD, is a Public Health Advisor; and Ronald L. Moolenaar, MD, is Associate Director for Science; all in the Division of Global Health Protection, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA. Gretchen Van Vliet, MPH, is Senior Public Health Project Director, Global Public Health Impact Center, RTI International, Research Triangle Park, NC
| | - Patricia Richter
- Deliana Kostova, PhD, is a Senior Economist; Patricia Richter, PhD, is Branch Chief, Global Noncommunicable Diseases Branch; Michael Mahar, PhD, is a Public Health Advisor; and Ronald L. Moolenaar, MD, is Associate Director for Science; all in the Division of Global Health Protection, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA. Gretchen Van Vliet, MPH, is Senior Public Health Project Director, Global Public Health Impact Center, RTI International, Research Triangle Park, NC
| | - Gretchen Van Vliet
- Deliana Kostova, PhD, is a Senior Economist; Patricia Richter, PhD, is Branch Chief, Global Noncommunicable Diseases Branch; Michael Mahar, PhD, is a Public Health Advisor; and Ronald L. Moolenaar, MD, is Associate Director for Science; all in the Division of Global Health Protection, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA. Gretchen Van Vliet, MPH, is Senior Public Health Project Director, Global Public Health Impact Center, RTI International, Research Triangle Park, NC
| | - Michael Mahar
- Deliana Kostova, PhD, is a Senior Economist; Patricia Richter, PhD, is Branch Chief, Global Noncommunicable Diseases Branch; Michael Mahar, PhD, is a Public Health Advisor; and Ronald L. Moolenaar, MD, is Associate Director for Science; all in the Division of Global Health Protection, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA. Gretchen Van Vliet, MPH, is Senior Public Health Project Director, Global Public Health Impact Center, RTI International, Research Triangle Park, NC
| | - Ronald L Moolenaar
- Deliana Kostova, PhD, is a Senior Economist; Patricia Richter, PhD, is Branch Chief, Global Noncommunicable Diseases Branch; Michael Mahar, PhD, is a Public Health Advisor; and Ronald L. Moolenaar, MD, is Associate Director for Science; all in the Division of Global Health Protection, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA. Gretchen Van Vliet, MPH, is Senior Public Health Project Director, Global Public Health Impact Center, RTI International, Research Triangle Park, NC
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Salerno J, Ross N, Ghai R, Mahero M, Travis DA, Gillespie TR, Hartter J. Human-Wildlife Interactions Predict Febrile Illness in Park Landscapes of Western Uganda. ECOHEALTH 2017; 14:675-690. [PMID: 29181611 DOI: 10.1007/s10393-017-1286-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Revised: 09/29/2017] [Accepted: 10/06/2017] [Indexed: 06/07/2023]
Abstract
Fevers of unknown origin complicate treatment and prevention of infectious diseases and are a global health burden. We examined risk factors of self-reported fever-categorized as "malarial" and "nonmalarial"-in households adjacent to national parks across the Ugandan Albertine Rift, a biodiversity and emerging infectious disease hotspot. Statistical models fitted to these data suggest that perceived nonmalarial fevers of unknown origin were associated with more frequent direct contact with wildlife and with increased distance from parks where wildlife habitat is limited to small forest fragments. Perceived malarial fevers were associated with close proximity to parks but were not associated with direct wildlife contact. Self-reported fevers of any kind were not associated with livestock ownership. These results suggest a hypothesis that nonmalarial fevers in this area are associated with wildlife contact, and further investigation of zoonoses from wildlife is warranted. More generally, our findings of land use-disease relationships aid in hypothesis development for future research in this social-ecological system where emerging infectious diseases specifically, and rural public health provisioning generally, are important issues.
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Affiliation(s)
- Jonathan Salerno
- Environmental Studies Program, Sustainability, Energy and Environment Community, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO, 80303, USA
| | - Noam Ross
- EcoHealth Alliance, New York, NY, USA
| | - Ria Ghai
- Department of Environmental Sciences and Program in Population Biology, Ecology and Evolution, Emory University, Atlanta, GA, USA
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Michael Mahero
- Department of Veterinary Population Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Dominic A Travis
- Department of Veterinary Population Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Thomas R Gillespie
- Department of Environmental Sciences and Program in Population Biology, Ecology and Evolution, Emory University, Atlanta, GA, USA
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Joel Hartter
- Environmental Studies Program, Sustainability, Energy and Environment Community, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO, 80303, USA.
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Olson D, Lamb M, Lopez MR, Colborn K, Paniagua-Avila A, Zacarias A, Zambrano-Perilla R, Rodríguez-Castro SR, Cordon-Rosales C, Asturias EJ. Performance of a Mobile Phone App-Based Participatory Syndromic Surveillance System for Acute Febrile Illness and Acute Gastroenteritis in Rural Guatemala. J Med Internet Res 2017; 19:e368. [PMID: 29122738 PMCID: PMC5701088 DOI: 10.2196/jmir.8041] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 09/13/2017] [Accepted: 09/13/2017] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND With their increasing availability in resource-limited settings, mobile phones may provide an important tool for participatory syndromic surveillance, in which users provide symptom data directly into a centralized database. OBJECTIVE We studied the performance of a mobile phone app-based participatory syndromic surveillance system for collecting syndromic data (acute febrile illness and acute gastroenteritis) to detect dengue virus and norovirus on a cohort of children living in a low-resource and rural area of Guatemala. METHODS Randomized households were provided with a mobile phone and asked to submit weekly reports using a symptom diary app (Vigilant-e). Participants reporting acute febrile illness or acute gastroenteritis answered additional questions using a decision-tree algorithm and were subsequently visited at home by a study nurse who performed a second interview and collected samples for dengue virus if confirmed acute febrile illness and norovirus if acute gastroenteritis. We analyzed risk factors associated with decreased self-reporting of syndromic data using the Vigilant-e app and evaluated strategies to improve self-reporting. We also assessed agreement between self-report and nurse-collected data obtained during home visits. RESULTS From April 2015 to June 2016, 469 children in 207 households provided 471 person-years of observation. Mean weekly symptom reporting rate was 78% (range 58%-89%). Households with a poor (<70%) weekly reporting rate using the Vigilant-e app during the first 25 weeks of observation (n=57) had a greater number of children (mean 2.8, SD 1.5 vs mean 2.5, SD 1.3; risk ratio [RR] 1.2, 95% CI 1.1-1.4), were less likely to have used mobile phones for text messaging at study enrollment (61%, 35/57 vs 76.7%, 115/150; RR 0.6, 95% CI 0.4-0.9), and were less likely to access care at the local public clinic (35%, 20/57 vs 67.3%, 101/150; RR 0.4, 95% CI 0.2-0.6). Parents of female enrolled participants were more likely to have low response rate (57.1%, 84/147 vs 43.8%, 141/322; RR 1.4, 95% CI 1.1-1.9). Several external factors (cellular tower collapse, contentious elections) were associated with periods of decreased reporting. Poor response rate (<70%) was associated with lower case reporting of acute gastroenteritis, norovirus-associated acute gastroenteritis, acute febrile illness, and dengue virus-associated acute febrile illness (P<.001). Parent-reported syndromic data on the Vigilant-e app demonstrated agreement with nurse-collected data for fever (kappa=.57, P<.001), vomiting (kappa=.63, P<.001), and diarrhea (kappa=.61, P<.001), with decreased agreement as the time interval between parental report and nurse home visit increased (<1 day: kappa=.65-.70; ≥2 days: kappa=.08-.29). CONCLUSIONS In a resource-limited area of rural Guatemala, a mobile phone app-based participatory syndromic surveillance system demonstrated a high reporting rate and good agreement between parental reported data and nurse-reported data during home visits. Several household-level and external factors were associated with decreased syndromic reporting. Poor reporting rate was associated with decreased syndromic and pathogen-specific case ascertainment.
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Affiliation(s)
- Daniel Olson
- Section of Pediatric Infectious Diseases, University of Colorado School of Medicine, Aurora, CO, United States.,Center for Global Health, Colorado School of Public Health, Aurora, CO, United States.,Children's Hospital of Colorado, Aurora, CO, United States.,Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
| | - Molly Lamb
- Center for Global Health, Colorado School of Public Health, Aurora, CO, United States.,Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
| | - Maria Renee Lopez
- Centro de Estudios en Salud,, Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | - Kathryn Colborn
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States.,Division of Health Care Policy and Research, University of Colorado School of Medicine, Aurora, CO, United States
| | - Alejandra Paniagua-Avila
- Fundacion para la Salud Integral de los Guatemaltecos, Center for Human Development, Coatepeque, Guatemala.,Center for Public Health Initiatives, Perelman School of Medicine, Philadelphia, PA, United States
| | - Alma Zacarias
- Fundacion para la Salud Integral de los Guatemaltecos, Center for Human Development, Coatepeque, Guatemala
| | | | | | - Celia Cordon-Rosales
- Centro de Estudios en Salud,, Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | - Edwin Jose Asturias
- Section of Pediatric Infectious Diseases, University of Colorado School of Medicine, Aurora, CO, United States.,Center for Global Health, Colorado School of Public Health, Aurora, CO, United States.,Children's Hospital of Colorado, Aurora, CO, United States.,Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
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Brownstein JS, Chu S, Marathe A, Marathe MV, Nguyen AT, Paolotti D, Perra N, Perrotta D, Santillana M, Swarup S, Tizzoni M, Vespignani A, Vullikanti AKS, Wilson ML, Zhang Q. Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches. JMIR Public Health Surveill 2017; 3:e83. [PMID: 29092812 PMCID: PMC5688248 DOI: 10.2196/publichealth.7344] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 04/06/2017] [Accepted: 10/09/2017] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. OBJECTIVE Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. METHODS We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You). RESULTS WISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information. CONCLUSIONS While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world.
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Affiliation(s)
- John S Brownstein
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.,Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Shuyu Chu
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, VA, United States
| | - Achla Marathe
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, VA, United States
| | - Madhav V Marathe
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, VA, United States
| | - Andre T Nguyen
- Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, MA, United States.,Booz Allen Hamilton, Boston, MA, United States
| | - Daniela Paolotti
- Computational Epidemiology Laboratory, Institute for Scientific Interchange, Turin, Italy
| | - Nicola Perra
- Centre for Business Networks Analysis, University of Greenwich, London, United Kingdom
| | - Daniela Perrotta
- Computational Epidemiology Laboratory, Institute for Scientific Interchange, Turin, Italy
| | - Mauricio Santillana
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.,Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Samarth Swarup
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, VA, United States
| | - Michele Tizzoni
- Computational Epidemiology Laboratory, Institute for Scientific Interchange, Turin, Italy
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, United States
| | - Anil Kumar S Vullikanti
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, VA, United States
| | - Mandy L Wilson
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, VA, United States
| | - Qian Zhang
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, United States
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