Risk Factors Associated with a Dengue Fever Outbreak in Islamabad,Pakistan: Case-Control Study

BackgroundOn October 23, 2016, 79 dengue fever cases were reported from the Union Council Tarlai to Federal Disease Surveillance and Response Unit Islamabad. A team was established to investigate the suspected dengue outbreak.ObjectiveThe aim of this study was to determine the extent of the outbreak and identify the possible risk factors.MethodsActive case finding was performed through a house-to-house survey. A case was defined as an acute onset of fever ≥38℃ in a resident of Tarlai from October 2 to November 11, 2016, with a positive dengue virus (nonstructural protein, NS-1) test and any of the two of following signs and symptoms: retroorbital/ocular pain, headache, rash, myalgia, arthralgia, and hemorrhagic manifestations. A structured questionnaire was used to collect data. Age- and sex-matched controls (1:1) were identified from residents in the same area as cases. Blood samples were taken and sent to the National Institute of Health for genotype identification.ResultsDuring the active case search, 145 cases of dengue fever were identified by surveying 928 houses from October 23 to November 11, 2016. The attack rate (AR) was 17.0/10,000. The mean age was 34.4 (SD 14.4) years. More than half of the cases were male (80/145, 55.2%). Among all cases, 29% belonged to the 25-34 years age group and the highest AR was found in the 35-44 years age group (35.6/10,000), followed by the 55-64 years age group (35.5/10,000). All five blood samples tested positive for NS-1 (genotype DENV-2). The most frequent presenting signs/symptoms were fever and headache (both 100%). Stagnant water around houses (odds ratio [OR] 4.86, 95% CI 2.94-8.01; P<.001), presence of flower pots in the home (OR 2.73, 95% CI 1.67-4.45; P<.001), and open water containers (OR 2.24, 95% CI 1.36-3.60; P<.001) showed higher odds among cases. Conversely, use of bed nets (OR 0.44, 95% CI 0.25-0.77; P=.003), insecticidal spray (OR 0.33, 95% CI 0.22-0.55; P<.001), door screens (OR 0.27, 95% CI 0.15-0.46; P<.001), mosquito coil/mat (OR 0.26, 95% CI 0.16-0.44; P<.001), and cleanliness of the house (OR 0.12, 95% CI 0.05-0.26; P<.001) showed significant protective effects.ConclusionsStagnant water acting as breeding grounds for vectors was identified as the probable cause of spread of the dengue outbreak. Establishment of surveillance and an early reporting system along with use of protective measures against the vector are strongly recommended.     

A Mobile App and Dashboard for Early Detection of Infectious Disease Outbreaks: Development Study

BackgroundOutbreaks of infectious diseases pose great risks, including hospitalization and death, to public health. Therefore, improving the management of outbreaks is important for preventing widespread infection and mitigating associated risks. Mobile health technology provides new capabilities that can help better capture, monitor, and manage infectious diseases, including the ability to quickly identify potential outbreaks.ObjectiveThis study aims to develop a new infectious disease surveillance (IDS) system comprising a mobile app for accurate data capturing and dashboard for better health care planning and decision making.MethodsWe developed the IDS system using a 2-pronged approach: a literature review on available and similar disease surveillance systems to understand the fundamental requirements and face-to-face interviews to collect specific user requirements from the local public health unit team at the Nepean Hospital, Nepean Blue Mountains Local Health District, New South Wales, Australia.ResultsWe identified 3 fundamental requirements when designing an electronic IDS system, which are the ability to capture and report outbreak data accurately, completely, and in a timely fashion. We then developed our IDS system based on the workflow, scope, and specific requirements of the public health unit team. We also produced detailed design and requirement guidelines. In our system, the outbreak data are captured and sent from anywhere using a mobile device or a desktop PC (web interface). The data are processed using a client-server architecture and, therefore, can be analyzed in real time. Our dashboard is designed to provide a daily, weekly, monthly, and historical summary of outbreak information, which can be potentially used to develop a future intervention plan. Specific information about certain outbreaks can also be visualized interactively to understand the unique characteristics of emerging infectious diseases.ConclusionsWe demonstrated the design and development of our IDS system. We suggest that the use of a mobile app and dashboard will simplify the overall data collection, reporting, and analysis processes, thereby improving the public health responses and providing accurate registration of outbreak information. Accurate data reporting and collection are a major step forward in creating a better intervention plan for future outbreaks of infectious diseases.     

Risk Factors of Dengue Fever in Urban Areas of Rawalpindi District in Pakistan During 2017: A Case Control Study

BackgroundDuring August 2017, increased numbers of suspected dengue fever cases were reported in the hospitals of Rawalpindi district. A case control study was conducted to determine the risk factors among urban areas, dengue serotype, and recommend preventive measures.ObjectiveThe objective of the investigation was to determine the risk factors among urban areas, dengue serotype, and recommend preventive measures.MethodsA case was defined as having acute febrile illness with one or more of the following symptoms: retro-orbital pain, headache, rash, myalgia, arthralgia, and hemorrhage. The cases were residents of Rawalpindi and were confirmed for dengue fever from August 30, 2017, to October 30, 2017. All NS1 confirmed cases from urban areas of Rawalpindi were recruited from tertiary care hospitals. Age- and sex-matched controls were selected from the same community with a 1:1 ratio. Frequency, univariate, and multivariate analyses were performed at 95% CI with P<.05 considered statistically significant.ResultsTotally 373 cases were recruited. The mean age was 36 (SD 2.9) years (range 10-69 years), and 280 cases (75%) were male. The most affected age group was 21-30 years (n=151, attack rate [AR] 40%), followed by 31-40 years (n=66, AR 23%). Further, 2 deaths were reported (case fatality rate of 0.53%). The most frequent signs or symptoms were fever (n=373, 100%), myalgia and headache (n=320, 86%), and retro-orbital pain (n=272, 73%). Serotype identification was carried out in 322 cases, and DEN-2 was the dominant serotype (n=126, 34%). Contact with a confirmed dengue case (odds ratio [OR] 4.27; 95% CI 3.14-5.81; P<.001), stored water in open containers at home (OR 2.04; 95% CI 1.53-2.73; P<.001), and travel to a dengue outbreak area (OR 2.88; 95% CI 2.12-3.92; P<.001) were the main reasons for the outbreak, whereas use of mosquito repellents (OR 0.12; 95% CI 0.09-0.18; P<.001) and regular water supply at home (OR 0.03; 95% CI 0.02-0.04; P<.001) showed protective effects. The geographical distribution of cases was limited to densely populated areas and all the 5 randomly collected water samples tested positive for dengue larvae.ConclusionsStored water in containers inside houses and subsequent mosquito breeding were the most probable causes of this outbreak. Based on the study findings, undertaking activities to improve the use of mosquito repellents and removing sources of breeding (uncovered water stored indoors) are some recommendations for preventing dengue outbreaks.     

Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE): Overview,Components, and Public Health Applications

BackgroundThe Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) is a secure web-based tool that enables health care practitioners to monitor health indicators of public health importance for the detection and tracking of disease outbreaks, consequences of severe weather, and other events of concern. The ESSENCE concept began in an internally funded project at the Johns Hopkins University Applied Physics Laboratory, advanced with funding from the State of Maryland, and broadened in 1999 as a collaboration with the Walter Reed Army Institute for Research. Versions of the system have been further developed by Johns Hopkins University Applied Physics Laboratory in multiple military and civilian programs for the timely detection and tracking of health threats.ObjectiveThis study aims to describe the components and development of a biosurveillance system increasingly coordinating all-hazards health surveillance and infectious disease monitoring among large and small health departments, to list the key features and lessons learned in the growth of this system, and to describe the range of initiatives and accomplishments of local epidemiologists using it.MethodsThe features of ESSENCE include spatial and temporal statistical alerting, custom querying, user-defined alert notifications, geographical mapping, remote data capture, and event communications. To expedite visualization, configurable and interactive modes of data stratification and filtering, graphical and tabular customization, user preference management, and sharing features allow users to query data and view geographic representations, time series and data details pages, and reports. These features allow ESSENCE users to gather and organize the resulting wealth of information into a coherent view of population health status and communicate findings among users.ResultsThe resulting broad utility, applicability, and adaptability of this system led to the adoption of ESSENCE by the Centers for Disease Control and Prevention, numerous state and local health departments, and the Department of Defense, both nationally and globally. The open-source version of Suite for Automated Global Electronic bioSurveillance is available for global, resource-limited settings. Resourceful users of the US National Syndromic Surveillance Program ESSENCE have applied it to the surveillance of infectious diseases, severe weather and natural disaster events, mass gatherings, chronic diseases and mental health, and injury and substance abuse.ConclusionsWith emerging high-consequence communicable diseases and other health conditions, the continued user requirement–driven enhancements of ESSENCE demonstrate an adaptable disease surveillance capability focused on the everyday needs of public health. The challenge of a live system for widely distributed users with multiple different data sources and high throughput requirements has driven a novel, evolving architecture design.     

Novel Methods in the Surveillance of Influenza-Like Illness in Germany Using Data From a Symptom Assessment App (Ada): Observational Case Study

BackgroundParticipatory epidemiology is an emerging field harnessing consumer data entries of symptoms. The free app Ada allows users to enter the symptoms they are experiencing and applies a probabilistic reasoning model to provide a list of possible causes for these symptoms.ObjectiveThe objective of our study is to explore the potential contribution of Ada data to syndromic surveillance by comparing symptoms of influenza-like illness (ILI) entered by Ada users in Germany with data from a national population-based reporting system called GrippeWeb.MethodsWe extracted data for all assessments performed by Ada users in Germany over 3 seasons (2017/18, 2018/19, and 2019/20) and identified those with ILI (report of fever with cough or sore throat). The weekly proportion of assessments in which ILI was reported was calculated (overall and stratified by age group), standardized for the German population, and compared with trends in ILI rates reported by GrippeWeb using time series graphs, scatterplots, and Pearson correlation coefficient.ResultsIn total, 2.1 million Ada assessments (for any symptoms) were included. Within seasons and across age groups, the Ada data broadly replicated trends in estimated weekly ILI rates when compared with GrippeWeb data (Pearson correlation—2017-18: r=0.86, 95% CI 0.76-0.92; P<.001; 2018-19: r=0.90, 95% CI 0.84-0.94; P<.001; 2019-20: r=0.64, 95% CI 0.44-0.78; P<.001). However, there were differences in the exact timing and nature of the epidemic curves between years.ConclusionsWith careful interpretation, Ada data could contribute to identifying broad ILI trends in countries without existing population-based monitoring systems or to the syndromic surveillance of symptoms not covered by existing systems.     

Timeliness of Yellow Fever Surveillance,Central African Republic

During January 2007–July 2012, a total of 3,220 suspected yellow fever cases were reported in the Central African Republic; 55 were confirmed and 11 case-patients died. Mean delay between onset of jaundice and case confirmation was 16.6 days. Delay between disease onset and blood collection could be reduced by increasing awareness of the population.     

Early Detection of Nosocomial Outbreaks Caused by Rare Pathogens: A Case Study Employing Score Prediction Interval

ObjectivesNosocomial outbreaks involve only a small number of cases and limited baseline data. The present study proposes a method to detect the nosocomial outbreaks caused by rare pathogens, exploiting score prediction interval of a Poisson distribution.MethodsThe proposed method was applied to three empirical datasets of nosocomial outbreaks in Japan: outbreaks of (1) multidrug-resistant Acinetobacter baumannii (n = 46) from 2009 to 2010, (2) multidrug-resistant Pseudomonas aerginosa (n = 18) from 2009 to 2010, and (3) Serratia marcescens (n = 226) from 1999 to 2000.ResultsThe proposed method successfully detected all three outbreaks during the first 2 months. Both the model-based and empirically derived threshold values indicated that the nosocomial outbreak of rare infectious disease may be declared upon diagnosis of index case(s), although the sensitivity and specificity were highly variable.ConclusionThe findings support the practical notion that, upon diagnosis of index patient(s), one should immediately start the outbreak investigation of nosocomial outbreak caused by a rare pathogen. The proposed score prediction interval can permit easy computation of outbreak threshold in hospital settings among healthcare experts.     

Usefulness of Syndromic Surveillance for Early Outbreak Detection in Small Islands: The Case of Mayotte

Objective

To present the usefulness of syndromic surveillance for the detection of infectious diseases outbreak in small islands, based on the experience of Mayotte.

Introduction

Mayotte Island, a French overseas department of around 374 km2 and 200 000 inhabitants is located in the North of Mozambique Channel in the Indian Ocean (Figure 1).Open in a separate windowFigure 1Map of the western Indian Ocean featuring Mayotte IslandIn response to the threat of the pandemic influenza A(H1N1)2009 virus emergence, a syndromic surveillance system has been implemented in order to monitor its spread and its impact on public health (1). This surveillance system which proved to be useful during the influenza pandemic, has been maintained in order to detect infection diseases outbreaks.

Methods

Data are collected daily directly from patients’ computerized medical files that are filled in during medical consultations at the emergency department (ED) of the hospital Center of Mayotte (2). Among the collected variables, the diagnosis coded according to ICD-10 is used to categorize the syndromes. Several syndromes are monitored including the syndromic grouping for conjunctivitis and unexplained fever.For early outbreak detection, a control chart is used based on an adaptation of the Cusum methods developed by the CDC within the framework of the EARS program (3).

Results

Each week, about 700 patients attend the ED of the hospital. The syndromic surveillance system allowed to detect an outbreak of conjunctivitis from week 10 (Figure 2). During the epidemic peak on week 12, conjunctivitis consultations represented 5% of all consultations. The data of the sentinel practitioner network confirmed this epidemic and the laboratory isolated Enterovirus (4). At the same time, an unusual increase of unexplained fever was detected.Open in a separate windowFigure 2Weekly number of conjonctivitis and unexplained fever consultations and statistical alarms detected

Conclusions

Due to its geographical and socio-demographical situation, the population of Mayotte is widely exposed to infectious diseases. Even on a small island, syndromic surveillance can be useful to detect outbreak early leading to alerts and to mobilize a rapid response in addition to others systems.     

Assessing the Electronic Evidence System Needs of Canadian Public Health Professionals: Cross-sectional Study

BackgroundTrue evidence-informed decision-making in public health relies on incorporating evidence from a number of sources in addition to traditional scientific evidence. Lack of access to these types of data as well as ease of use and interpretability of scientific evidence contribute to limited uptake of evidence-informed decision-making in practice. An electronic evidence system that includes multiple sources of evidence and potentially novel computational processing approaches or artificial intelligence holds promise as a solution to overcoming barriers to evidence-informed decision-making in public health.ObjectiveThis study aims to understand the needs and preferences for an electronic evidence system among public health professionals in Canada.MethodsAn invitation to participate in an anonymous web-based survey was distributed via listservs of 2 Canadian public health organizations in February 2019. Eligible participants were English- or French-speaking individuals currently working in public health. The survey contained both multiple-choice and open-ended questions about the needs and preferences relevant to an electronic evidence system. Quantitative responses were analyzed to explore differences by public health role. Inductive and deductive analysis methods were used to code and interpret the qualitative data. Ethics review was not required by the host institution.ResultsRespondents (N=371) were heterogeneous, spanning organizations, positions, and areas of practice within public health. Nearly all (364/371, 98.1%) respondents indicated that an electronic evidence system would support their work. Respondents had high preferences for local contextual data, research and intervention evidence, and information about human and financial resources. Qualitative analyses identified several concerns, needs, and suggestions for the development of such a system. Concerns ranged from the personal use of such a system to the ability of their organization to use such a system. Recognized needs spanned the different sources of evidence, including local context, research and intervention evidence, and resources and tools. Additional suggestions were identified to improve system usability.ConclusionsCanadian public health professionals have positive perceptions toward an electronic evidence system that would bring together evidence from the local context, scientific research, and resources. Elements were also identified to increase the usability of an electronic evidence system.     

Reasons for Nonuse,Discontinuation of Use,and Acceptance of Additional Functionalities of a COVID-19 Contact Tracing App: Cross-sectional Survey Study

BackgroundIn several countries, contact tracing apps (CTAs) have been introduced to warn users if they have had high-risk contacts that could expose them to SARS-CoV-2 and could, therefore, develop COVID-19 or further transmit the virus. For CTAs to be effective, a sufficient critical mass of users is needed. Until now, adoption of these apps in several countries has been limited, resulting in questions on which factors prevent app uptake or stimulate discontinuation of app use.ObjectiveThe aim of this study was to investigate individuals’ reasons for not using, or stopping use of, a CTA, in particular, the Coronalert app. Users’ and nonusers’ attitudes toward the app’s potential impact was assessed in Belgium. To further stimulate interest and potential use of a CTA, the study also investigated the population’s interest in new functionalities.MethodsAn online survey was administered in Belgium to a sample of 1850 respondents aged 18 to 64 years. Data were collected between October 30 and November 2, 2020. Sociodemographic differences were assessed between users and nonusers. We analyzed both groups’ attitudes toward the potential impact of CTAs and their acceptance of new app functionalities.ResultsOur data showed that 64.9% (1201/1850) of our respondents were nonusers of the CTA under study; this included individuals who did not install the app, those who downloaded but did not activate the app, and those who uninstalled the app. While we did not find any sociodemographic differences between users and nonusers, attitudes toward the app and its functionalities seemed to differ. The main reasons for not downloading and using the app were a perceived lack of advantages (308/991, 31.1%), worries about privacy (290/991, 29.3%), and, to a lesser extent, not having a smartphone (183/991, 18.5%). Users of the CTA agreed more with the potential of such apps to mitigate the consequences of the pandemic. Overall, nonusers found the possibility of extending the CTA with future functionalities to be less acceptable than users. However, among users, acceptability also tended to differ. Among users, functionalities relating to access and control, such as digital certificates or “green cards” for events, were less accepted (358/649, 55.2%) than functionalities focusing on informing citizens about the spread of the virus (453/649, 69.8%) or making an appointment to get tested (525/649, 80.9%).ConclusionsOur results show that app users were more convinced of the CTA’s utility and more inclined to accept new app features than nonusers. Moreover, nonusers had more CTA-related privacy concerns. Therefore, to further stimulate app adoption and use, its potential advantages and privacy-preserving mechanisms need to be stressed. Building further knowledge on the forms of resistance among nonusers is important for responding to these barriers through the app’s further development and communication campaigns.     

Development and Implementation of a Surveillance Network System for Emerging Infectious Diseases in the Caribbean (ARICABA)

Dengue fever, including dengue hemorrhagic fever, has become a re-emerging public health threat in the Caribbean in the absence of a comprehensive regional surveillance system. In this deficiency, a project entitled ARICABA, strives to implement a pilot surveillance system across three islands: Martinique, St. Lucia, and Dominica. The aim of this project is to establish a network for epidemiological surveillance of infectious diseases, utilizing information and communication technology. This paper describes the system design and development strategies of a “network of networks” surveillance system for infectious diseases in the Caribbean. Also described are benefits, challenges, and limitations of this approach across the three island nations identified through direct observation, open-ended interviews, and email communications with an on-site IT consultant, key informants, and the project director. Identified core systems design of the ARICABA data warehouse include a disease monitoring system and a syndromic surveillance system. Three components comprise the development strategy: the data warehouse server, the geographical information system, and forecasting algorithms; these are recognized technical priorities of the surveillance system. A main benefit of the ARICABA surveillance system is improving responsiveness and representativeness of existing health systems through automated data collection, process, and transmission of information from various sources. Challenges include overcoming technology gaps between countries; real-time data collection points; multiple language support; and “component-oriented” development approaches.     

Population Health Surveillance Using Mobile Phone Surveys in Low- and Middle-Income Countries: Methodology and Sample Representativeness of a Cross-sectional Survey of Live Poultry Exposure in Bangladesh

BackgroundPopulation-based health surveys are typically conducted using face-to-face household interviews in low- and middle-income countries (LMICs). However, telephone-based surveys are cheaper, faster, and can provide greater access to hard-to-reach or remote populations. The rapid growth in mobile phone ownership in LMICs provides a unique opportunity to implement novel data collection methods for population health surveys.ObjectiveThis study aims to describe the development and population representativeness of a mobile phone survey measuring live poultry exposure in urban Bangladesh.MethodsA population-based, cross-sectional, mobile phone survey was conducted between September and November 2019 in North and South Dhaka City Corporations (DCC), Bangladesh, to measure live poultry exposure using a stratified probability sampling design. Data were collected using a computer-assisted telephone interview platform. The call operational data were summarized, and the participant data were weighted by age, sex, and education to the 2011 census. The demographic distribution of the weighted sample was compared with external sources to assess population representativeness.ResultsA total of 5486 unique mobile phone numbers were dialed, with 1047 respondents completing the survey. The survey had an overall response rate of 52.2% (1047/2006) and a co-operation rate of 89.0% (1047/1176). Initial results comparing the sociodemographic profile of the survey sample to the census population showed that mobile phone sampling slightly underrepresented older individuals and overrepresented those with higher secondary education. After weighting, the demographic profile of the sample population matched well with the latest DCC census population profile.ConclusionsProbability-based mobile phone survey sampling and data collection methods produced a population-representative sample with minimal adjustment in DCC, Bangladesh. Mobile phone–based surveys can offer an efficient, economic, and robust way to conduct surveillance for population health outcomes, which has important implications for improving population health surveillance in LMICs.     

Identifying COVID-19 Outbreaks From Contact-Tracing Interview Forms for Public Health Departments: Development of a Natural Language Processing Pipeline

BackgroundIn Wisconsin, COVID-19 case interview forms contain free-text fields that need to be mined to identify potential outbreaks for targeted policy making. We developed an automated pipeline to ingest the free text into a pretrained neural language model to identify businesses and facilities as outbreaks.ObjectiveWe aimed to examine the precision and recall of our natural language processing pipeline against existing outbreaks and potentially new clusters.MethodsData on cases of COVID-19 were extracted from the Wisconsin Electronic Disease Surveillance System (WEDSS) for Dane County between July 1, 2020, and June 30, 2021. Features from the case interview forms were fed into a Bidirectional Encoder Representations from Transformers (BERT) model that was fine-tuned for named entity recognition (NER). We also developed a novel location-mapping tool to provide addresses for relevant NER. Precision and recall were measured against manually verified outbreaks and valid addresses in WEDSS.ResultsThere were 46,798 cases of COVID-19, with 4,183,273 total BERT tokens and 15,051 unique tokens. The recall and precision of the NER tool were 0.67 (95% CI 0.66-0.68) and 0.55 (95% CI 0.54-0.57), respectively. For the location-mapping tool, the recall and precision were 0.93 (95% CI 0.92-0.95) and 0.93 (95% CI 0.92-0.95), respectively. Across monthly intervals, the NER tool identified more potential clusters than were verified in WEDSS.ConclusionsWe developed a novel pipeline of tools that identified existing outbreaks and novel clusters with associated addresses. Our pipeline ingests data from a statewide database and may be deployed to assist local health departments for targeted interventions.