Public Health Surveillance Systems in the Eastern Mediterranean Region: Bibliometric Analysis of Scientific Literature

BackgroundThe Eastern Mediterranean Region (EMR) hosts some of the world’s worst humanitarian and health crises. The implementation of health surveillance in this region has faced multiple constraints. New and novel approaches in surveillance are in a constant state of high and immediate demand. Identifying the existing literature on surveillance helps foster an understanding of scientific development and thus potentially supports future development directions.ObjectiveThis study aims to illustrate the scientific production, quantify the scholarly impact, and highlight the characteristics of publications on public health surveillance in the EMR over the past decade.MethodsWe performed a Scopus search using keywords related to public health surveillance or its disciplines, cross-referenced with EMR countries, from 2011 to July 2021. Data were exported and analyzed using Microsoft Excel and Visualization of Similarities Viewer. Quality of journals was determined using SCImago Journal Rank and CiteScore.ResultsWe retrieved 1987 documents, of which 1927 (96.98%) were articles or reviews. There has been an incremental increase in the number of publications (exponential growth, R²=0.80) over the past decade. Publications were mostly affiliated with Iran (501/1987, 25.21%), the United States (468/1987, 23.55%), Pakistan (243/1987, 12.23%), Egypt (224/1987, 11.27%), and Saudi Arabia (209/1987, 10.52%). However, Iran only had links with 40 other countries (total link strength 164), and the biggest collaborator from the EMR was Egypt, with 67 links (total link strength 402). Within the other EMR countries, only Morocco, Lebanon, and Jordan produced ≥79 publications in the 10-year period. Most publications (1551/1987, 78.06%) were affiliated with EMR universities. Most journals were categorized as medical journals, and the highest number of articles were published in the Eastern Mediterranean Health Journal (SCImago Journal Rank 0.442; CiteScore 1.5). Retrieved documents had an average of 18.4 (SD 125.5) citations per document and an h-index of 66. The top-3 most cited documents were from the Global Burden of Diseases study. We found 70 high-frequency terms, occurring ≥10 times in author keywords, connected in 3 clusters. COVID-19, SARS-CoV-2, and pandemic represented the most recent 2020 cluster.ConclusionsThis is the first research study to quantify the published literature on public health surveillance and its disciplines in the EMR. Research productivity has steadily increased over the past decade, and Iran has been the leading country publishing relevant research. Recurrent recent surveillance themes included COVID-19 and SARS-CoV-2. This study also sheds light on the gaps in surveillance research in the EMR, including inadequate publications on noncommunicable diseases and injury-related surveillance.     

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.     

Extracting Surveillance Data from Templated Sections of an Electronic Medical Note: Challenges and Opportunities

Objective

To highlight the importance of templates in extracting surveillance data from the free text of electronic medical records using natural language processing (NLP) techniques.

Introduction

The main stay of recording patient data is the free text of electronic medical records (EMR). While stating the chief complaint and history of presenting illness in the patients ‘own words’, the rest of the electronic note is written by the provider in their words. Providers often use boiler-plate templates from EMR pull-downs to document information on the patient in the form of checklists, check boxes, yes/no and free text responses to questions. When these templates are used for recording symptoms, demographic information or medical, social or travel history, they represent an important source of surveillance data [1]. There is a dearth of literature on the use of natural language processing in extracting data from templates in the EMR.

Methods

A corpus of 1000 free text medical notes from the VA integrated electronic medical record (CPRS) was reviewed to identify commonly used templates. Of these, 500 were enriched for the surveillance domain of interest for this project (homelessness). The other 500 were randomly sampled from a large corpus of electronic notes. An NLP algorithm was developed to extract concepts related to our target surveillance domain. A manual review of the notes was performed by three human reviewers to generate a document-level reference standard that classified this set of documents as either demonstrating evidence of homelessness (H) or not (NH). A rule-based NLP algorithm was developed that used a combination of key word searches and negation based on an extensive lexicon of terms developed for this purpose. A random sample of 50 documents each of H and NH documents were reviewed after each iteration of the NLP algorithm to determine the false positive rate of the extracted concepts.

Results

The corpus consisted of 48% H and 52% NH documents as determined by human review. The NLP algorithm successfully extracted concepts from these documents. The H set had an average of 8 concepts related to homelessness per document (median 8, range 1 to 34). The NH set had an average 2 concepts (median 1, range 1 to 13)”. Thirteen template patterns were identified in this set of documents. The three most common were check boxes with square brackets, Yes/No and free text answer after a question. Several positively and negatively asserted concepts were noted to be in the responses to templated questions such as “Are you currently homeless: Yes or No”; “How many times have you been homeless in the past 3 years: (free text response)”; “Have you ever been in jail? [Y] or [N]”; Are you in need of substance abuse services? Yes or No”. Human review of a random sample of documents at the concept level indicated that the NLP algorithm generated 28% false positives in extracting concepts related to homelessness when templates were ignored among the H documents. When the algorithm was refined to include templates, the false positive rate declined to 22%. For the NH documents, the corresponding false positive rates were 56% and 21%.

Conclusions

To our knowledge, this is one of the first attempts to address the problem of information extraction from templates or templated sections of the EMR. A key challenge of templates is that they will most likely lead to poor performance of NLP algorithms and cause bottlenecks in processing if they are not considered. Acknowledging the presence of templates and refining NLP algorithms to handle them improves information extraction from free text medical notes, thus creating an opportunity for improved surveillance using the EMR. Algorithms will likely need to be customized to the electronic medical record and the surveillance domain of interest. A more detailed analysis of the templated sections is underway.     

The Interplay Between Policy and COVID-19 Outbreaks in South Asia: Longitudinal Trend Analysis of Surveillance Data

BackgroundCOVID-19 transmission rates in South Asia initially were under control when governments implemented health policies aimed at controlling the pandemic such as quarantines, travel bans, and border, business, and school closures. Governments have since relaxed public health restrictions, which resulted in significant outbreaks, shifting the global epicenter of COVID-19 to India. Ongoing systematic public health surveillance of the COVID-19 pandemic is needed to inform disease prevention policy to re-establish control over the pandemic within South Asia.ObjectiveThis study aimed to inform public health leaders about the state of the COVID-19 pandemic, how South Asia displays differences within and among countries and other global regions, and where immediate action is needed to control the outbreaks.MethodsWe extracted COVID-19 data spanning 62 days from public health registries and calculated traditional and enhanced surveillance metrics. We use an empirical difference equation to measure the daily number of cases in South Asia as a function of the prior number of cases, the level of testing, and weekly shifts in variables with a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano–Bond estimator in R.ResultsTraditional surveillance metrics indicate that South Asian countries have an alarming outbreak, with India leading the region with 310,310 new daily cases in accordance with the 7-day moving average. Enhanced surveillance indicates that while Pakistan and Bangladesh still have a high daily number of new COVID-19 cases (n=4819 and n=3878, respectively), their speed of new infections declined from April 12-25, 2021, from 2.28 to 2.18 and 3.15 to 2.35 daily new infections per 100,000 population, respectively, which suggests that their outbreaks are decreasing and that these countries are headed in the right direction. In contrast, India’s speed of new infections per 100,000 population increased by 52% during the same period from 14.79 to 22.49 new cases per day per 100,000 population, which constitutes an increased outbreak.ConclusionsRelaxation of public health restrictions and the spread of novel variants fueled the second wave of the COVID-19 pandemic in South Asia. Public health surveillance indicates that shifts in policy and the spread of new variants correlate with a drastic expansion in the pandemic, requiring immediate action to mitigate the spread of COVID-19. Surveillance is needed to inform leaders whether policies help control the pandemic.     

Participatory COVID-19 Surveillance Tool in Rural Appalachia: Real-Time Disease Monitoring and Regional Response

IntroductionFew US studies have examined the usefulness of participatory surveillance during the coronavirus disease 2019 (COVID-19) pandemic for enhancing local health response efforts, particularly in rural settings. We report on the development and implementation of an internet-based COVID-19 participatory surveillance tool in rural Appalachia.MethodsA regional collaboration among public health partners culminated in the design and implementation of the COVID-19 Self-Checker, a local online symptom tracker. The tool collected data on participant demographic characteristics and health history. County residents were then invited to take part in an automated daily electronic follow-up to monitor symptom progression, assess barriers to care and testing, and collect data on COVID-19 test results and symptom resolution.ResultsNearly 6500 county residents visited and 1755 residents completed the COVID-19 Self-Checker from April 30 through June 9, 2020. Of the 579 residents who reported severe or mild COVID-19 symptoms, COVID-19 symptoms were primarily reported among women (n = 408, 70.5%), adults with preexisting health conditions (n = 246, 70.5%), adults aged 18-44 (n = 301, 52.0%), and users who reported not having a health care provider (n = 131, 22.6%). Initial findings showed underrepresentation of some racial/ethnic and non–English-speaking groups.Practical ImplicationsThis low-cost internet-based platform provided a flexible means to collect participatory surveillance data on local changes in COVID-19 symptoms and adapt to guidance. Data from this tool can be used to monitor the efficacy of public health response measures at the local level in rural Appalachia.     

An Urban Population Health Observatory System to Support COVID-19 Pandemic Preparedness,Response, and Management: Design and Development Study

BackgroundCOVID-19 is impacting people worldwide and is currently a leading cause of death in many countries. Underlying factors, including Social Determinants of Health (SDoH), could contribute to these statistics. Our prior work has explored associations between SDoH and several adverse health outcomes (eg, asthma and obesity). Our findings reinforce the emerging consensus that SDoH factors should be considered when implementing intelligent public health surveillance solutions to inform public health policies and interventions.ObjectiveThis study sought to redefine the Healthy People 2030’s SDoH taxonomy to accommodate the COVID-19 pandemic. Furthermore, we aim to provide a blueprint and implement a prototype for the Urban Population Health Observatory (UPHO), a web-based platform that integrates classified group-level SDoH indicators to individual- and aggregate-level population health data.MethodsThe process of building the UPHO involves collecting and integrating data from several sources, classifying the collected data into drivers and outcomes, incorporating data science techniques for calculating measurable indicators from the raw variables, and studying the extent to which interventions are identified or developed to mitigate drivers that lead to the undesired outcomes.ResultsWe generated and classified the indicators of social determinants of health, which are linked to COVID-19. To display the functionalities of the UPHO platform, we presented a prototype design to demonstrate its features. We provided a use case scenario for 4 different users.ConclusionsUPHO serves as an apparatus for implementing effective interventions and can be adopted as a global platform for chronic and infectious diseases. The UPHO surveillance platform provides a novel approach and novel insights into immediate and long-term health policy responses to the COVID-19 pandemic and other future public health crises. The UPHO assists public health organizations and policymakers in their efforts in reducing health disparities, achieving health equity, and improving urban population health.     

Seed grants as a means of stimulating cancer research funding

ObjectiveTo investigate national public health target awareness at two organisational levels in health care comprising public officials and district nurses (DNs). To determine if the targets were incorporated in county council public health policy documents and if DNs worked in accordance with them.MethodTelephone interviews were performed with 21 county council officials and personal interviews were conducted with 54 DNs representing Sweden's 21 health care regions.ResultsSixteen officials reported that their county council had documented public health programs, and in 13, some of the national targets were incorporated. Primary care was given major responsibility for public health. Two programs mentioned DNs’ work. The officials said prevention should focus on all age groups and they emphasised the importance of health promotion. The DNs did not mention the national targets for public health and rarely mentioned targets at the county council level. Their work with prevention included self-care advice, changes in lifestyle, and preventing and relieving consequences of chronic disease. The DNs considered themselves as key persons in this work.ConclusionsThe results reflect difficulties in implementing national targets and the existence of communication problems between political authorities, public officials, and “doers”. Preventive work is nevertheless done in relevant areas.     

Evaluation of the National Tuberculosis Surveillance System in Sana’a,Yemen, 2018: Observational Study

BackgroundTuberculosis remains a public problem that is considered one of the top causes of morbidity and mortality worldwide. The National Tuberculosis Control Program in Yemen was established in 1970 and included in the national health policy under the leadership of the Ministry of Public Health and Population to monitor tuberculosis control. The surveillance system must be evaluated periodically to produce recommendations for improving performance and usefulness.ObjectiveThis study aims to assess the usefulness and the performance of the tuberculosis surveillance system attributes and to identify the strengths and weaknesses of the system.MethodsA quantitative and qualitative evaluation of the national tuberculosis surveillance system was conducted using the Centers for Disease Control and Prevention’s updated guidelines. The study was carried out in 10 districts in Sana’a City. A total of 28 public health facilities providing tuberculosis services for the whole population in their assigned catchment areas were purposively selected. All participants were interviewed based on their involvement with key aspects of tuberculosis surveillance activities.ResultsThe tuberculosis surveillance system was found to have an average performance in usefulness (57/80, 71%), flexibility (30/40, 75%), acceptability (174/264, 66%), data quality (4/6, 67%), and positive predictive value (78/107, 73%), and poor performance in simplicity (863/1452, 59%) and stability (15%, 3/20). In addition, the system also had a good performance in sensitivity (78/81, 96%).ConclusionsThe tuberculosis surveillance system was found to be useful. The flexibility, positive predictive value, and data quality were average. Stability and simplicity were poor. The sensitivity was good. The main weaknesses in the tuberculosis surveillance system include a lack of governmental financial support, a paper-based system, and a lack of regular staff training. Developing an electronic system, securing governmental finances, and training the staff on tuberculosis surveillance are strongly recommended to improve the system performance.     

The Global Infectious Diseases Epidemic Information Monitoring System: Development and Usability Study of an Effective Tool for Travel Health Management in China

BackgroundObtaining comprehensive epidemic information for specific global infectious diseases is crucial to travel health. However, different infectious disease information websites may have different purposes, which may lead to misunderstanding by travelers and travel health staff when making accurate epidemic control and management decisions.ObjectiveThe objective of this study was to develop a Global Infectious Diseases Epidemic Information Monitoring System (GIDEIMS) in order to provide comprehensive and timely global epidemic information.MethodsDistributed web crawler and cloud agent acceleration technologies were used to automatically collect epidemic information about more than 200 infectious diseases from 26 established epidemic websites and Baidu News. Natural language processing and in-depth learning technologies have been utilized to intelligently process epidemic information collected in 28 languages. Currently, the GIDEIMS presents world epidemic information using a geographical map, including date, disease name, reported cases in different countries, and the epidemic situation in China. In order to make a practical assessment of the GIDEIMS, we compared infectious disease data collected from the GIDEIMS and other websites on July 16, 2019.ResultsCompared with the Global Incident Map and Outbreak News Today, the GIDEIMS provided more comprehensive information on human infectious diseases. The GIDEIMS is currently used in the Health Quarantine Department of Shenzhen Customs District (Shenzhen, China) and was recommended to the Health Quarantine Administrative Department of the General Administration of Customs (China) and travel health–related departments.ConclusionsThe GIDEIMS is one of the most intelligent tools that contributes to safeguarding the health of travelers, controlling infectious disease epidemics, and effectively managing public health in China.     

Defining Public Health Situation Awareness – Outcomes and Metrics for Evaluation

Objective

Review concept of situation awareness (SA) as it relates to public health surveillance, epidemiology and preparedness [1]. Outline hierarchical levels and organizational criteria for SA [2]. Initiate consensus building process aimed at developing a working definition and measurable outcomes and metrics for SA as they relate to syndromic surveillance practice and evaluation.

Introduction

A decade ago, the primary objective of syndromic surveillance was bioterrorism and outbreak early event detection (EED) [3]. Syndromic systems for EED focused on rapid, automated data collection, processing and statistical anomaly detection of indicators of potential bioterrorism or outbreak events. The paradigm presented a clear and testable surveillance objective: the early detection of outbreaks or events of public health concern. Limited success in practice and limited rigorous evaluation, however, led to the conclusion that syndromic surveillance could not reliably or accurately achieve EED objectives. At the federal level, the primary rationale for syndromic surveillance shifted away from bioterrorism EED, and towards all-hazards biosurveillance and SA [4–6]. The shift from EED to SA occurred without a clear evaluation of EED objectives, and without a clear definition of the scope or meaning of SA in practice. Since public health SA has not been clearly defined in terms of operational surveillance objectives, statistical or epidemiological methods, or measurable outcomes and metrics, the use of syndromic surveillance to achieve SA cannot be evaluated.

Methods

This session is intended to provide a forum to discuss SA in the context of public health disease surveillance practice. The roundtable will focus on defining SA in the context of public health syndromic and epidemiologic surveillance. While SA is often noted in federal level documents as a primary rationale for biosurveillance [1, 4–6], it is rarely defined or described in operational detail. One working definition presents SA as “real-time analysis and display of health data to monitor the location, magnitude, and spread of an outbreak”, yet it does not elaborate on the methods, systems or evaluation requirements for SA in public health or biosurveillance [3]. In terms of translating SA into public health surveillance practice [1], we will discuss and define the requirements of public health SA based on its development and practice in other areas [2]. The proposed theoretical framework and evaluation criteria adapted and applied to public health SA [2] follow:

• - Level 1: Perceive relevant surveillance data and epidemiological information.
• - Level 2: Integrate surveillance and non-surveillance data in conjunction with operator goals to provide understanding of the meaning of the information.
• - Level 3: Through perceiving (Level 1) and integrating and understanding (Level 2) provide prediction of future events and system states to allow for timely and effective public health decision making.

Results

Sample questions for discussion: What is the relevance of syndromic surveillance and biosurveillance in the SA framework? Where does it fit within the current public health surveillance environment? To achieve the roundtable discussion objectives, the participants will work towards a consensus definition of SA for public health, and will outline measureable outcomes and metrics for evaluation of syndromic surveillance for public health SA.     

Assessment of Arbovirus Surveillance 13 Years after Introduction of West Nile Virus,United States

Before 1999, the United States had no appropriated funding for arboviral surveillance, and many states conducted no such surveillance. After emergence of West Nile virus (WNV), federal funding was distributed to state and selected local health departments to build WNV surveillance systems. The Council of State and Territorial Epidemiologists conducted assessments of surveillance capacity of resulting systems in 2004 and in 2012; the assessment in 2012 was conducted after a 61% decrease in federal funding. In 2004, nearly all states and assessed local health departments had well-developed animal, mosquito, and human surveillance systems to monitor WNV activity and anticipate outbreaks. In 2012, many health departments had decreased mosquito surveillance and laboratory testing capacity and had no systematic disease-based surveillance for other arboviruses. Arboviral surveillance in many states might no longer be sufficient to rapidly detect and provide information needed to fully respond to WNV outbreaks and other arboviral threats (e.g., dengue, chikungunya).     

Importance de la surveillance en santé publique et utilité des données administratives

BackgroundPublic health surveillance constitutes an important activity since it helps in identifying health needs through data collection, and contributes to decision making and actions by analyzing and interpreting data and communicating key results.MethodsThis paper presents a discussion on the concept of public health surveillance, its objectives and its historical evolution. It deals with the importance of surveillance systems while describing their components and challenges. In addition, the authors point out the importance of administrative data as a relevant source for the surveillance of public health problems, particularly chronic diseases and risk factors.ResultsThis theoretical discussion leads to the proposal of a conceptual model for surveillance systems, which integrates implementation and evaluation.ConclusionThis article provides a summary of the concept of public health surveillance and underlines the general aspects to be considered by the managers of surveillance systems. It also discusses the use of administrative data for surveillance.     

Syndromic Surveillance from a Local Perspective – A Review of the Literature

Objective

Review of the origins and evolution of the field of syndromic surveillance. Compare the goals and objectives of public health surveillance and syndromic surveillance in particular. Assess the science and practice of syndromic surveillance in the context of public health and national security priorities. Evaluate syndromic surveillance in practice, using case studies from the perspective of a local public health department.

Introduction

Public health disease surveillance is defined as the ongoing systematic collection, analysis and interpretation of health data for use in the planning, implementation and evaluation of public health, with the overarching goal of providing information to government and the public to improve public health actions and guidance [1,2]. Since the 1950s, the goals and objectives of disease surveillance have remained consistent [1]. However, the systems and processes have changed dramatically due to advances in information and communication technology, and the availability of electronic health data [2,3]. At the intersection of public health, national security and health information technology emerged the practice of syndromic surveillance [3].

Methods

To better understand the current state of the field, a review of the literature on syndromic surveillance was conducted: topics and keywords searched through PubMed and Google Scholar included biosurveillance, bioterrorism detection, computerized surveillance, electronic disease surveillance, situational awareness and syndromic surveillance, covering the areas of practice, research, preparedness and policy. This literature was compared with literature on traditional epidemiologic and public health surveillance. Definitions, objectives, methods and evaluation findings presented in the literature were assessed with a focus on their relevance from a local perspective, particularly as related to syndromic surveillance systems and methods used by the New York City Department of Health and Mental Hygiene in the areas of development, implementation, evaluation, public health practice and epidemiological research.

Results

A decade ago, the objective of syndromic surveillance was focused on outbreak and bioterrorism early-event detection (EED). While there have been clear recommendations for evaluation of syndromic surveillance systems and methods, the original detection paradigm for syndromic surveillance has not been adequately evaluated in practice, nor tested by real world events (ie, the systems have largely not ‘detected’ events of public health concern). In the absence of rigorous evaluation, the rationale and objectives for syndromic surveillance have broadened from outbreak and bioterrorism EED, to include all causes and hazards, and to encompass all data and analyses needed to achieve “situational awareness”, not simply detection. To evaluate current practices and provide meaningful guidance for local syndromic surveillance efforts, it is important to understand the emergence of the field in the broader context of public health disease surveillance. And it is important to recognize how the original stated objectives of EED have shifted in relation to actual evaluation, recommendation, standardization and implementation of syndromic systems at the local level.

Conclusions

Since 2001, the field of syndromic surveillance has rapidly expanded, following the dual requirements of national security and public health practice. The original objective of early outbreak or bioterrorism event detection remains a core objective of syndromic surveillance, and systems need to be rigorously evaluated through comparison of consistent methods and metrics, and public health outcomes. The broadened mandate for all-cause situation awareness needs to be focused into measureable public health surveillance outcomes and objectives that are consistent with established public health surveillance objectives and relevant to the local practice of public health [2].     

Physical Activity,Sedentary Behavior,and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research

BackgroundAdvances in automated data processing and machine learning (ML) models, together with the unprecedented growth in the number of social media users who publicly share and discuss health-related information, have made public health surveillance (PHS) one of the long-lasting social media applications. However, the existing PHS systems feeding on social media data have not been widely deployed in national surveillance systems, which appears to stem from the lack of practitioners and the public’s trust in social media data. More robust and reliable data sets over which supervised ML models can be trained and tested reliably is a significant step toward overcoming this hurdle. The health implications of daily behaviors (physical activity, sedentary behavior, and sleep [PASS]), as an evergreen topic in PHS, are widely studied through traditional data sources such as surveillance surveys and administrative databases, which are often several months out-of-date by the time they are used, costly to collect, and thus limited in quantity and coverage.ObjectiveThe main objective of this study is to present a large-scale, multicountry, longitudinal, and fully labeled data set to enable and support digital PASS surveillance research in PHS. To support high-quality surveillance research using our data set, we have conducted further analysis on the data set to supplement it with additional PHS-related metadata.MethodsWe collected the data of this study from Twitter using the Twitter livestream application programming interface between November 28, 2018, and June 19, 2020. To obtain PASS-related tweets for manual annotation, we iteratively used regular expressions, unsupervised natural language processing, domain-specific ontologies, and linguistic analysis. We used Amazon Mechanical Turk to label the collected data to self-reported PASS categories and implemented a quality control pipeline to monitor and manage the validity of crowd-generated labels. Moreover, we used ML, latent semantic analysis, linguistic analysis, and label inference analysis to validate the different components of the data set.ResultsLPHEADA (Labelled Digital Public Health Dataset) contains 366,405 crowd-generated labels (3 labels per tweet) for 122,135 PASS-related tweets that originated in Australia, Canada, the United Kingdom, or the United States, labeled by 708 unique annotators on Amazon Mechanical Turk. In addition to crowd-generated labels, LPHEADA provides details about the three critical components of any PHS system: place, time, and demographics (ie, gender and age range) associated with each tweet.ConclusionsPublicly available data sets for digital PASS surveillance are usually isolated and only provide labels for small subsets of the data. We believe that the novelty and comprehensiveness of the data set provided in this study will help develop, evaluate, and deploy digital PASS surveillance systems. LPHEADA will be an invaluable resource for both public health researchers and practitioners.     

A Preparedness Model for Mother–Baby Linked Longitudinal Surveillance for Emerging Threats

Introduction

Public health responses often lack the infrastructure to capture the impact of public health emergencies on pregnant women and infants, with limited mechanisms for linking pregnant women with their infants nationally to monitor long-term effects. In 2019, the Centers for Disease Control and Prevention (CDC), in close collaboration with state, local, and territorial health departments, began a 5-year initiative to establish population-based mother–baby linked longitudinal surveillance, the Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET).

Objectives

The objective of this report is to describe an expanded surveillance approach that leverages and modernizes existing surveillance systems to address the impact of emerging health threats during pregnancy on pregnant women and their infants.

Methods

Mother–baby pairs are identified through prospective identification during pregnancy and/or identification of an infant with retrospective linking to maternal information. All data are obtained from existing data sources (e.g., electronic medical records, vital statistics, laboratory reports, and health department investigations and case reporting).

Results

Variables were selected for inclusion to address key surveillance questions proposed by CDC and health department subject matter experts. General variables include maternal demographics and health history, pregnancy and infant outcomes, maternal and infant laboratory results, and child health outcomes up to the second birthday. Exposure-specific modular variables are included for hepatitis C, syphilis, and Coronavirus Disease 2019 (COVID-19). The system is structured into four relational datasets (maternal, pregnancy outcomes and birth, infant/child follow-up, and laboratory testing).

Discussion

SET-NET provides a population-based mother–baby linked longitudinal surveillance approach and has already demonstrated rapid adaptation to COVID-19. This innovative approach leverages existing data sources and rapidly collects data and informs clinical guidance and practice. These data can help to reduce exposure risk and adverse outcomes among pregnant women and their infants, direct public health action, and strengthen public health systems.

     

Impact of Firearm Surveillance on Gun Control Policy: Regression Discontinuity Analysis

BackgroundPublic mass shootings are a significant public health problem that require ongoing systematic surveillance to test and inform policies that combat gun injuries. Although there is widespread agreement that something needs to be done to stop public mass shootings, opinions on exactly which policies that entails vary, such as the prohibition of assault weapons and large-capacity magazines.ObjectiveThe aim of this study was to determine if the Federal Assault Weapons Ban (FAWB) (1994-2004) reduced the number of public mass shootings while it was in place.MethodsWe extracted public mass shooting surveillance data from the Violence Project that matched our inclusion criteria of 4 or more fatalities in a public space during a single event. We performed regression discontinuity analysis, taking advantage of the imposition of the FAWB, which included a prohibition on large-capacity magazines in addition to assault weapons. We estimated a regression model of the 5-year moving average number of public mass shootings per year for the period of 1966 to 2019 controlling for population growth and homicides in general, introduced regression discontinuities in the intercept and a time trend for years coincident with the federal legislation (ie, 1994-2004), and also allowed for a differential effect of the homicide rate during this period. We introduced a second set of trend and intercept discontinuities for post-FAWB years to capture the effects of termination of the policy. We used the regression results to predict what would have happened from 1995 to 2019 had there been no FAWB and also to project what would have happened from 2005 onward had it remained in place.ResultsThe FAWB resulted in a significant decrease in public mass shootings, number of gun deaths, and number of gun injuries. We estimate that the FAWB prevented 11 public mass shootings during the decade the ban was in place. A continuation of the FAWB would have prevented 30 public mass shootings that killed 339 people and injured an additional 1139 people.ConclusionsThis study demonstrates the utility of public health surveillance on gun violence. Surveillance informs policy on whether a ban on assault weapons and large-capacity magazines reduces public mass shootings. As society searches for effective policies to prevent the next mass shooting, we must consider the overwhelming evidence that bans on assault weapons and/or large-capacity magazines work.     

Identification and Assessment of Public Health Surveillance Gaps under the IHR (2005)

Objective

To conceive and develop a model to identify gaps in public health surveillance performance and provide a toolset to assess interventions, cost, and return on investment (ROI).

Introduction

Under the revised International Health Regulations (IHR [2005]) one of the eight core capacities is public health surveillance. In May 2012, despite a concerted effort by the global community, the World Health Organization (WHO) reported out that a significant number of member states would not achieve targeted capacity in the IHR (2005) surveillance core capacity.Currently, there is no model to identify and measure these gaps in surveillance performance. Likewise, there is no toolset to assess interventions by cost and estimate the ROI.We developed a new conceptual framework that: (1) described the work practices to achieve effective and efficient public health surveillance; (2) could identify impediments or gaps in performance; and (3) will assist program managers in decision making.

Methods

Published articles and grey-literature reports, manuals and logic model examples were gathered through a literature review of PubMed, Web of Science, Google Scholar, and other databases. Logic models were conceived by categorizing discrete surveillance inputs, activities, outputs, and outcomes. Indicators were selected from authoritative sources or developed and then mapped to the logic model elements. These indicators will be weighted using the principle component analysis (PCA), a method for enhanced precision of statistical analysis. Finally, on the front end of the tool, indicators will graphically measure the surveillance gap expressed through the tool’s architecture and provide information using an integrated cost-impact analysis.

Results

We developed five public health surveillance logic models: for IHR (2005) compliance; event-based; indicator-based; syndromic; and predictive surveillance domains. The IHR (2005) domain focused on national-level functionality, and the others described the complexities of their specific surveillance work practices. Indicators were then mapped and linked to all logic model elements.

Conclusions

This new framework, intended for self-administration at the national and subnational levels, measured public health surveillance gaps in performance and provided cost and ROI information by intervention. The logic model framework and PCA methodology are tools that both describe work processes and define appropriate variables used for evaluation. However, both require real-world data. We recommend pilot testing and validation of this new framework. Once piloted, the framework could be adapted for the other IHR (2005) core capacities.