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Providing an overview of disease surveillance, this text frames a roadmap of how newer technologies may allow all countries of the world to reach compliance with the IHR (International Health Regulations) established by the World Health Organization as it pertains to disease detection.

Data System. The Pregnancy Risk Assessment Monitoring System (PRAMS) is an ongoing state-based surveillance system of maternal behaviors, attitudes, and experiences before, during, and shortly after pregnancy. PRAMS is conducted by the Centers for Disease Control and Prevention’s Division of Reproductive Health in collaboration with state health departments. Data Collection/Processing. Birth certificate records are used in each participating jurisdiction to select a sample representative of all women who delivered a live-born infant. PRAMS is a mixed-mode mail and telephone survey. Annual state sample sizes range from approximately 1000 to 3000 women. States stratify their sample by characteristics of public health interest such as maternal age, race/ethnicity, geographic area of residence, and infant birth weight. Data Analysis/Dissemination. States meeting established response rate thresholds are included in multistate analytic data sets available to researchers through a proposal submission process. In addition, estimates from selected indicators are available online. Public Health Implications. PRAMS provides state-based data for key maternal and child health indicators that can be tracked over time. Stratification by maternal characteristics allows for examinations of disparities over a wide range of health indicators.

Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases. The COVID-19 pandemic has resulted in an accelerated development of applications for digital health, including symptom monitoring and contact tracing. Their potential is wide ranging and must be integrated into conventional approaches to public health for best effect.

Background. Researchers have used traditional databases to study public health for decades. Less is known about the use of social media data sources, such as Twitter, for this purpose. Objectives. To systematically review the use of Twitter in health research, define a taxonomy to describe Twitter use, and characterize the current state of Twitter in health research. Search methods. We performed a literature search in PubMed, Embase, Web of Science, Google Scholar, and CINAHL through September 2015. Selection criteria. We searched for peer-reviewed original research studies that primarily used Twitter for health research. Data collection and analysis. Two authors independently screened studies and abstracted data related to the approach to analysis of Twitter data, methodology used to study Twitter, and current state of Twitter research by evaluating time of publication, research topic, discussion of ethical concerns, and study funding source. Main results. Of 1110 unique health-related articles mentioning Twitter, 137 met eligibility criteria. The primary approaches for using Twitter in health research that constitute a new taxonomy were content analysis (56%; n = 77), surveillance (26%; n = 36), engagement (14%; n = 19), recruitment (7%; n = 9), intervention (7%; n = 9), and network analysis (4%; n = 5). These studies collectively analyzed more than 5 billion tweets primarily by using the Twitter application program interface. Of 38 potential data features describing tweets and Twitter users, 23 were reported in fewer than 4% of the articles. The Twitter-based studies in this review focused on a small subset of data elements including content analysis, geotags, and language. Most studies were published recently (33% in 2015). Public health (23%; n = 31) and infectious disease (20%; n = 28) were the research fields most commonly represented in the included studies. Approximately one third of the studies mentioned ethical board approval in their articles. Primary funding sources included federal (63%), university (13%), and foundation (6%). Conclusions. We identified a new taxonomy to describe Twitter use in health research with 6 categories. Many data elements discernible from a user’s Twitter profile, especially demographics, have been underreported in the literature and can provide new opportunities to characterize the users whose data are analyzed in these studies. Twitter-based health research is a growing field funded by a diversity of organizations. Public health implications. Future work should develop standardized reporting guidelines for health researchers who use Twitter and policies that address privacy and ethical concerns in social media research.

Artificial intelligence (AI) has the potential to improve public health’s ability to promote the health of all people in all communities. To successfully realize this potential and use AI for public health functions it is important for public health organizations to thoughtfully develop strategies for AI implementation. Six key priorities for successful use of AI technologies by public health organizations are discussed: 1) Contemporary data governance; 2) Investment in modernized data and analytic infrastructure and procedures; 3) Addressing the skills gap in the workforce; 4) Development of strategic collaborative partnerships; 5) Use of good AI practices for transparency and reproducibility, and; 6) Explicit consideration of equity and bias.

With 87% of providers using electronic health records (EHRs) in the United States, EHRs have the potential to contribute to population health surveillance efforts. However, little is known about using EHR data outside syndromic surveillance and quality improvement. We created an EHR-based population health surveillance system called the New York City (NYC) Macroscope and assessed the validity of diabetes, hyperlipidemia, hypertension, smoking, obesity, depression, and influenza vaccination indicators. The NYC Macroscope uses aggregate data from a network of outpatient practices. We compared 2013 NYC Macroscope prevalence estimates with those from a population-based, in-person examination survey, the 2013–2014 NYC Health and Nutrition Examination Survey. NYC Macroscope diabetes, hypertension, smoking, and obesity prevalence indicators performed well, but depression and influenza vaccination estimates were substantially lower than were survey estimates. Ongoing validation will be important to monitor changes in validity over time as EHR networks mature and to assess new indicators. We discuss NYC’s experience and how this project fits into the national context. Sharing lessons learned can help achieve the full potential of EHRs for population health surveillance.

Background Antibiotic resistance (ABR) has emerged as a major threat to health. Properly informed decisions to mitigate this threat require surveillance systems that integrate information on resistant bacteria and antibiotic use in humans, animals, and the environment, in line with the One Health concept. Despite a strong call for the implementation of such integrated surveillance systems, we still lack a comprehensive overview of existing organizational models for integrated surveillance of ABR. To address this gap, we conducted a scoping review to characterize existing integrated surveillance systems for ABR. Methods The literature review was conducted using the PRISMA guidelines. The selected integrated surveillance systems were assessed according to 39 variables related to their organization and functioning, the socio-economic and political characteristics of their implementation context, and the levels of integration reached, together with their related outcomes. We conducted two distinct, complementary analyses on the data extracted: a descriptive analysis to summarize the characteristics of the integrated surveillance systems, and a multiple-correspondence analysis (MCA) followed by a hierarchical cluster analysis (HCA) to identify potential typology for surveillance systems. Results The literature search identified a total of 1330 records. After the screening phase, 59 references were kept from which 14 integrated surveillance systems were identified. They all operate in high-income countries and vary in terms of integration, both at informational and structural levels. The different systems combine information from a wide range of populations and commodities -in the human, animal and environmental domains, collection points, drug-bacterium pairs, and rely on various diagnostic and surveillance strategies. A variable level of collaboration was found for the governance and/or operation of the surveillance activities. The outcomes of integration are poorly described and evidenced. The 14 surveillance systems can be grouped into four distinct clusters, characterized by integration level in the two dimensions. The level of resources and regulatory framework in place appeared to play a major role in the establishment and organization of integrated surveillance. Conclusions This study suggests that operationalization of integrated surveillance for ABR is still not well established at a global scale, especially in low and middle-income countries and that the surveillance scope is not broad enough to obtain a comprehensive understanding of the complex dynamics of ABR to appropriately inform mitigation measures. Further studies are needed to better characterize the various integration models for surveillance with regard to their implementation context and evaluate the outcome of these models.

When we were unable to identify an electronic data capture (EDC) package that supported our requirements for clinical research in resource-limited regions, we set out to build our own reusable EDC framework. We needed to capture data when offline, synchronize data on demand, and enforce strict eligibility requirements and complex longitudinal protocols. Based on previous experience, the geographical areas in which we conduct our research often have unreliable, slow internet access that would make web-based EDC platforms impractical. We were unwilling to fall back on paper-based data capture as we wanted other benefits of EDC. Therefore, we decided to build our own reusable software platform. In this paper, we describe our customizable EDC framework and highlight how we have used it in our ongoing surveillance programs, clinic-based cross-sectional studies, and randomized controlled trials (RCTs) in various settings in India and Ecuador. This paper describes the creation of a mobile framework to support complex clinical research protocols in a variety of settings including clinical, surveillance, and RCTs. We developed ConnEDCt, a mobile EDC framework for iOS devices and personal computers, using Claris FileMaker software for electronic data capture and data storage. ConnEDCt was tested in the field in our clinical, surveillance, and clinical trial research contexts in India and Ecuador and continuously refined for ease of use and optimization, including specific user roles; simultaneous synchronization across multiple locations; complex randomization schemes and informed consent processes; and collecting diverse types of data (laboratory, growth measurements, sociodemographic, health history, dietary recall and feeding practices, environmental exposures, and biological specimen collection). ConnEDCt is customizable, with regulatory-compliant security, data synchronization, and other useful features for data collection in a variety of settings and study designs. Furthermore, ConnEDCt is user friendly and lowers the risks for errors in data entry because of real time error checking and protocol enforcement.

The National Center for Health Statistics’ (NCHS’s) National Vital Statistics System (NVSS) collects, processes, codes, and reviews death certificate data and disseminates the data in annual data files and reports. With the global rise of COVID-19 in early 2020, the NCHS mobilized to rapidly respond to the growing need for reliable, accurate, and complete real-time data on COVID-19 deaths. Within weeks of the first reported US cases, NCHS developed certification guidance, adjusted internal data processing systems, and stood up a surveillance system to release daily updates of COVID-19 deaths to track the impact of the COVID-19 pandemic on US mortality. This report describes the processes that NCHS took to produce timely mortality data in response to the COVID-19 pandemic. (Am J Public Health. 2021;111(12):2133–2140. https://doi.org/10.2105/AJPH.2021.306519 )

Background Public Health Dashboards (PHDs) facilitate the monitoring and prediction of disease outbreaks by continuously monitoring the health status of the community. This study aimed to identify design principles and determinants for developing public health surveillance dashboards. Methodology This scoping review is based on Arksey and O'Malley's framework as included in JBI guidance. Four databases were used to review and present the proposed principles of designing PHDs: IEEE, PubMed, Web of Science, and Scopus. We considered articles published between January 1, 2010 and November 30, 2022. The final search of articles was done on November 30, 2022. Only articles in the English language were included. Qualitative synthesis and trend analysis were conducted. Results Findings from sixty-seven articles out of 543 retrieved articles, which were eligible for analysis, indicate that most of the dashboards designed from 2020 onwards were at the national level for managing and monitoring COVID-19. Design principles for the public health dashboard were presented in five groups, i.e., considering aim and target users, appropriate content, interface, data analysis and presentation types, and infrastructure. Conclusion Effective and efficient use of dashboards in public health surveillance requires implementing design principles to improve the functionality of these systems in monitoring and decision-making. Considering user requirements, developing a robust infrastructure for improving data accessibility, developing, and applying Key Performance Indicators (KPIs) for data processing and reporting purposes, and designing interactive and intuitive interfaces are key for successful design and development.