Public health surveillance through community health workers: a scoping review of evidence from 25 low-income and middle-income countries.

BACKGROUND: The last 3 years have witnessed global health challenges, ranging from the pandemics of COVID-19 and mpox (monkeypox) to the Ebola epidemic in Uganda. Public health surveillance is critical for preventing these outbreaks, yet surveillance systems in resource-constrained contexts struggle to provide timely disease reporting. Although community health workers (CHWs) support health systems in low-income and middle-income countries (LMICs), very little has been written about their role in supporting public health surveillance. This review identified the roles, impacts and challenges CHWs face in public health surveillance in 25 LMICs. METHODS: We conducted a scoping review guided by Arksey and O'Malley's framework. We exported 1,156 peer-reviewed records from Embase, Global Health and PubMed databases. After multiple screenings, 29 articles were included in the final review. RESULTS: CHWs significantly contribute to public health surveillance in LMICs including through contact tracing and patient visitation to control major infectious diseases such as HIV/AIDS, malaria, tuberculosis, Ebola, neglected tropical diseases and COVID-19. Their public health surveillance roles typically fall into four main categories including community engagement; data gathering; screening, testing and treating; and health education and promotion. The use of CHWs in public health surveillance in LMICs has been impactful and often involves incorporation of various technologies leading to improved epidemic control and disease reporting. Nonetheless, use of CHWs can come with four main challenges including lack of education and training, lack of financial and other resources, logistical and infrastructural challenges as well as community engagement challenges. CONCLUSION: CHWs are important stakeholders in surveillance because they are closer to communities than other healthcare workers. Further integration and training of CHWs in public health surveillance would improve public health surveillance because CHWs can provide health data on 'hard-to-reach' populations. CHWs' work in public health surveillance would also be greatly enhanced by infrastructural investments.

Using Social Listening for Digital Public Health Surveillance of Human Papillomavirus Vaccine Misinformation Online: Exploratory Study.

Despite challenges related to the data quality, representativeness, and accuracy of artificial intelligence-driven tools, commercially available social listening platforms have many of the attributes needed to be used for digital public health surveillance of human papillomavirus vaccination misinformation in the online ecosystem.

Optimal environmental testing frequency for outbreak surveillance.

Public health surveillance for pathogens presents an optimization problem: we require enough sampling to identify intervention-triggering shifts in pathogen epidemiology, such as new introductions or sudden increases in prevalence, but not so much that costs due to surveillance itself outweigh those from pathogen-associated illness. To determine this optimal sampling frequency, we developed a general mathematical model for the introduction of a new pathogen that, once introduced, increases in prevalence exponentially. Given the relative cost of infection vs. sampling, we derived equations for the expected combined cost per unit time of disease burden and surveillance for a specified sampling frequency, and thus the sampling frequency for which the expected total cost per unit time is lowest.

Developing public health surveillance dashboards: a scoping review on the design principles.

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.

Enhancing the notification system for surveillance of infectious diseases in Qatar during the FIFA World Cup 2022: project overview.

BACKGROUND: In 2022, the Surveillance Department of the Ministry of Public Health in Qatar adopted an integrated project called the Notification Enhancement Project (NEP) to enhance the infectious disease notification system. Efficient surveillance and notification promote early alerts and allow immediate interference in reducing morbidity and mortality from outbreaks. The project was designed to improve the knowledge, attitudes, practices, and notification processes of healthcare workers in Qatar by increasing their reporting rates. METHODS: The strategy for comprehensively enhancing notifications was based on the observation and evaluation of the current notification system, the implementation of interventions, and post-evaluation follow-up. To implement the project, we relied on three aspects: effective methods used in previous relevant studies through a literature review, feedback received from healthcare workers, and suggestions from public health surveillance experts from the Ministry of Public Health, Qatar. A preassessment was conducted through an online survey by the Ministry of Public Health. The effectiveness of the different interventions was assessed by analyzing the data of notified patients reported through the Disease Surveillance and Reporting Electronic System. Pre- and postintervention assessments were performed by comparing the percentage of patients notified by healthcare providers with that of patients confirmed by healthcare providers in the laboratory to compare the notification rates over three time periods between January and December 2022. RESULTS: There was significant improvement in the infectious disease notification process. A comparison before and after the implementation of the interventions revealed an increase in the communicable disease notification rate among healthcare workers. Pre- and postintervention data were compared. Infectious disease notification activities by healthcare workers increased from 2.5% between January and May 2022 to 41.4% between November and December 2022. CONCLUSION: This study highlights the efficiency of different interventions in correcting the underreporting of infectious diseases. Our findings suggest that implementing the Notification Enhancement Project significantly improves notification rates. We recommend continuing interventions through constant education and training, maintaining solid communication with HCWs through regular reminder emails and feedback, periodic assessment of the electronic notification system, and engagement of healthcare workers and other stakeholders to sustain and expand progress achieved through continuous evaluation.

The importance of meropenem resistance, rather than imipenem resistance, in defining carbapenem-resistant Enterobacterales for public health surveillance: an analysis of national population-based surveillance.

BACKGROUND: In Japan, carbapenem-resistant Enterobacterales (CRE) infections were incorporated into the National Epidemiological Surveillance of Infectious Diseases (NESID) in 2014, necessitating mandatory reporting of all CRE infections cases. Subsequently, pathogen surveillance was initiated in 2017, which involved the collection and analysis of CRE isolates from reported cases to assess carbapenemase gene possession. In this surveillance, CRE is defined as (i) minimum inhibitory concentration (MIC) of meropenem ≥2 mg/L (MEPM criteria) or (ii) MIC of imipenem ≥2 mg/L and MIC of cefmetazole ≥64 mg/L (IPM criteria). This study examined whether the current definition of CRE surveillance captures cases with a clinical and public health burden. METHODS: CRE isolates from reported cases were collected from the public health laboratories of local governments, which are responsible for pathogen surveillance. Antimicrobial susceptibility tests were conducted on these isolates to assess compliance with the NESID CRE definition. The NESID data between April 2017 and March 2018 were obtained and analyzed using antimicrobial susceptibility test results. RESULTS: In total, 1681 CRE cases were identified during the study period, and pathogen surveillance data were available for 740 (44.0%) cases. Klebsiella aerogenes and Enterobacter cloacae complex were the dominant species, followed by Klebsiella pneumoniae and Escherichia coli. The rate of carbapenemase gene positivity was 26.5% (196/740), and 93.4% (183/196) of these isolates were of the IMP type. Meanwhile, 315 isolates were subjected to antimicrobial susceptibility testing. Among them, 169 (53.7%) fulfilled only the IPM criteria (IPM criteria-only group) which were susceptible to meropenem, while 146 (46.3%) fulfilled the MEPM criteria (MEPM criteria group). The IPM criteria-only group and MEPM criteria group significantly differed in terms of carbapenemase gene positivity (0% vs. 67.8%), multidrug resistance rates (1.2% vs. 65.8%), and mortality rates (1.8% vs 6.9%). CONCLUSION: The identification of CRE cases based solely on imipenem resistance has had a limited impact on clinical management. Emphasizing resistance to meropenem is crucial in defining CRE, which pose both clinical and public health burden. This emphasis will enable the efficient allocation of limited health and public health resources and preservation of newly developed antimicrobials.

Early detection of new pandemic waves. Control chart and a new surveillance index.

The COVID-19 pandemic highlights the pressing need for constant surveillance, updating of the response plan in post-peak periods and readiness for the possibility of new waves of the pandemic. A short initial period of steady rise in the number of new cases is sometimes followed by one of exponential growth. Systematic public health surveillance of the pandemic should signal an alert in the event of change in epidemic activity within the community to inform public health policy makers of the need to control a potential outbreak. The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with a new surveillance metric to overcome some of their difficulties in capturing the changing dynamics of the pandemic. At statistically-founded threshold values, the new measure will trigger alert signals giving early warning of the onset of a new pandemic wave. We define a new index, the weighted cumulative incidence index, based on the daily new-case count. We model the infection spread rate at two levels, inside and outside homes, which explains the overdispersion observed in the data. The seasonal component of real data, due to the public surveillance system, is incorporated into the statistical analysis. Probabilistic analysis enables the construction of a Control Chart for monitoring index variability and setting automatic alert thresholds for new pandemic waves. Both the new index and the control chart have been implemented with the aid of a computational tool developed in R, and used daily by the Navarre Government (Spain) for virus propagation surveillance during post-peak periods. Automated monitoring generates daily reports showing the areas whose control charts issue an alert. The new index reacts sooner to data trend changes preluding new pandemic waves, than the standard surveillance index based on the 14-day notification rate of reported COVID-19 cases per 100,000 population.

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review.

BACKGROUND: Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts. OBJECTIVE: This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods. METHODS: A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers. RESULTS: Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses. CONCLUSIONS: The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Pandemic Management: Health Data and Public Health Surveillance.

The inefficiency of the healthcare system in addressing pandemics is highlighted after COVID-19 which is mostly rooted in data availability and accuracy. As it is believed we might witness more pandemics in future, our research's main objective is to propose an integrated health system to support healthcare preparedness for future infectious outbreaks and pandemics. The system could support managers and authorities in healthcare and disaster management, and policymakers through data collection, sharing, and analysis.

A response playbook for early detection and population surveillance of new SARS-CoV-2 variants in a regional public health laboratory.

BACKGROUND: Timely genomic surveillance is required to inform public health responses to new SARS-CoV-2 variants. However, the processes involved in local genomic surveillance introduce inherent time constraints. The Regional Innovative Public Health Laboratory in Chicago developed and employed a genomic surveillance response playbook for the early detection and surveillance of emerging SARS-CoV-2 variants. METHODS: The playbook outlines modifications to sampling strategies, laboratory workflows, and communication processes based on the emerging variant's predicted viral characteristics, observed public health impact in other jurisdictions and local community risk level. The playbook outlines procedures for implementing and reporting enhanced and accelerated genomic surveillance, including supplementing whole genome sequencing (WGS) with variant screening by quantitative PCR (qPCR). RESULTS: The ability of the playbook to improve the response to an emerging variant was tested for SARS-CoV-2 Omicron BA.1. Increased submission of clinical remnant samples from local hospital laboratories enabled detection of a new variant at an average of 1.4% prevalence with 95% confidence rather than 3.5% at baseline. Genotyping qPCR concurred with WGS lineage assignments in 99.9% of 1541 samples with results by both methods, and was more sensitive, providing lineage results in 90.4% of 1833 samples rather than 85.1% for WGS, while significantly reducing the time to lineage result. CONCLUSIONS: The genomic surveillance response playbook provides a structured, stepwise, and data-driven approach to responding to emerging SARS-CoV-2 variants. These pre-defined processes can serve as a template for other genomic surveillance programs to streamline workflows and expedite the detection and public health response to emerging variants. Based on the processes piloted during the Omicron BA.1 response, this method has been applied to subsequent Omicron subvariants and can be readily applied to future SARS-CoV-2 emerging variants and other public health surveillance activities.

Rapidly scalable and low-cost public health surveillance reporting system for COVID-19.

OBJECTIVE: Data-driven innovations are essential in strengthening disease control. We developed a low-cost, open-source system for robust epidemiological intelligence in response to the COVID-19 crisis, prioritising scalability, reproducibility and dynamic reporting. METHODS: A five-tiered workflow of data acquisition; processing; databasing, sharing, version control; visualisation; and monitoring was used. COVID-19 data were initially collated from press releases and then transitioned to official sources. RESULTS: Key COVID-19 indicators were tabulated and visualised, deployed using open-source hosting in October 2022. The system demonstrated high performance, handling extensive data volumes, with a 92.5% user conversion rate, evidencing its value and adaptability. CONCLUSION: This cost-effective, scalable solution aids health specialists and authorities in tracking disease burden, particularly in low-resource settings. Such innovations are critical in health crises like COVID-19 and adaptable to diverse health scenarios.

Addressing bias in artificial intelligence for public health surveillance.

Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the timeliness and robustness of health data. NLP techniques have been implemented to analyse large volumes of text from social media platforms to gain insights on disease symptoms, understand barriers to care and predict disease outbreaks. However, AI-based decisions may contain biases that could misrepresent populations, skew results or lead to errors. Bias, within the scope of this paper, is described as the difference between the predictive values and true values within the modelling of an algorithm. Bias within algorithms may lead to inaccurate healthcare outcomes and exacerbate health disparities when results derived from these biased algorithms are applied to health interventions. Researchers who implement these algorithms must consider when and how bias may arise. This paper explores algorithmic biases as a result of data collection, labelling and modelling of NLP algorithms. Researchers have a role in ensuring that efforts towards combating bias are enforced, especially when drawing health conclusions derived from social media posts that are linguistically diverse. Through the implementation of open collaboration, auditing processes and the development of guidelines, researchers may be able to reduce bias and improve NLP algorithms that improve health surveillance.

COVID-19 public health surveillance and response in Taiwan: From containment to mitigation.

Taiwan learned from its 2003 SARS experience and established multiple surveillance systems to be able to detect and respond to COVID-19. With the find, test, trace, isolate, and support (FTTIS) strategy, Taiwan was successful in containing SARS-CoV-2 from spreading for two years. During the surge of the Omicron variant in the community, COVID-19 control strategy shifted from containment to mitigation in April 2022, to reduce morbidity and mortality. Lessons learned from COVID-19 response re-emphasizes the importance of having sensitive public health surveillance and linking surveillance with public health actions.

Assessing the role of social determinants of health in health disparities: The need for data on work.

BACKGROUND: Work is a key social determinant of health. Without the collection of work-related information in public health data systems, the role of social determinants in creating and reinforcing health disparities cannot be fully assessed. METHODS: The Centers for Disease Control and Prevention (CDC) maintains or supports a number of public health surveillance and health monitoring systems, including surveys, case-based disease and exposure systems, vital status records, and administrative data systems. We evaluated a convenience sample of these systems for inclusion of information in three work-related domains: employment status, industry and occupation, and working conditions. RESULTS: While 12 of 39 data systems were identified as collecting work-related data, this information was often minimal (e.g., only employment status), restricted to a subset of respondents, or only gathered periodically. Information on working conditions was particularly sparse. CONCLUSION: Historically, the limited and inconsistent collection of work-related information in public health data systems has hindered understanding of the role work plays in health disparities. Current CDC data modernization efforts present opportunities to enhance the identification and mitigation of health disparities by prioritizing inclusion of an expanded set of work-related data elements.

Comparison of statistical approaches to predicting norovirus laboratory reports before and during COVID-19: insights to inform public health surveillance.

Social distancing interrupted transmission patterns of contact-driven infectious agents such as norovirus during the Covid-19 pandemic. Since routine surveillance of norovirus was additionally disrupted during the pandemic, traditional naïve forecasts that rely only on past public health surveillance data may not reliably represent norovirus activity. This study investigates the use of statistical modelling to predict the number of norovirus laboratory reports in England 4-weeks ahead of time before and during Covid-19 pandemic thus providing insights to inform existing practices in norovirus surveillance in England. We compare the predictive performance from three forecasting approaches that assume different underlying structure of the norovirus data and utilized various external data sources including mobility, air temperature and relative internet searches (Time Series and Regularized Generalized Linear Model, and Quantile Regression Forest). The performance of each approach was evaluated using multiple metrics, including a relative prediction error against the traditional naive forecast of a five-season mean. Our data suggest that all three forecasting approaches improve predictive performance over the naïve forecasts, especially in the 2020/21 season (30-45% relative improvement) when the number of norovirus reports reduced. The improvement ranged from 7 to 22% before the pandemic. However, performance varied: regularized regression incorporating internet searches showed the best forecasting score pre-pandemic and the time series approach achieved the best results post pandemic onset without external data. Overall, our results demonstrate that there is a significant value for public health in considering the adoption of more sophisticated forecasting tools, moving beyond traditional naïve methods, and utilizing available software to enhance the precision and timeliness of norovirus surveillance in England.

Improving estimates of pertussis burden in Ontario, Canada 2010-2017 by combining validation and capture-recapture methodologies.

An underestimation of pertussis burden has impeded understanding of transmission and disallows effective policy and prevention to be prioritized and enacted. Capture-recapture analyses can improve burden estimates; however, uncertainty remains around incorporating health administrative data due to accuracy limitations. The aim of this study is to explore the impact of pertussis case definitions and data accuracy on capture-recapture estimates. We used a dataset from March 7, 2010 to December 31, 2017 comprised of pertussis case report, laboratory, and health administrative data. We compared Chao capture-recapture abundance estimates using prevalence, incidence, and adjusted false positive case definitions. The latter was developed by removing the proportion of false positive physician billing code-only case episodes after validation. We calculated sensitivity by dividing the number of observed cases by abundance. Abundance estimates demonstrated that a high proportion of cases were missed by all sources. Under the primary analysis, the highest sensitivity of 78.5% (95% CI 76.2-80.9%) for those less than one year of age was obtained using all sources after adjusting for false positives, which dropped to 43.1% (95% CI 42.4-43.8%) for those one year of age or older. Most code-only episodes were false positives (91.0%), leading to considerably lower abundance estimates and improvements in laboratory testing and case report sensitivity using this definition. Accuracy limitations can be accounted for in capture-recapture analyses using different case definitions and adjustment. The latter enhanced the validity of estimates, furthering the utility of capture-recapture methods to epidemiological research. Findings demonstrated that all sources consistently fail to detect pertussis cases. This is differential by age, suggesting ascertainment and testing bias. Results demonstrate the value of incorporating real time health administrative data into public health surveillance if accuracy limitations can be addressed.

A laboratory framework for ongoing optimization of amplification-based genomic surveillance programs.

IMPORTANCE: This study provides a laboratory framework to ensure ongoing relevance and performance of amplification-based whole genome sequencing to strengthen public health surveillance during extended outbreaks or pandemics. The framework integrates regular reviews of the performance of a genomic surveillance system and highlights the importance of ongoing monitoring and the identification and implementation of improvements to whole genome sequencing methods to enhance public health responses to pathogen outbreaks.