The use of syndromic surveillance for decision-making during the H1N1 pandemic: a qualitative study

Describing the indirect impact of COVID-19 on healthcare utilisation using syndromic surveillance systems

Background

Since the end of January 2020, the coronavirus (COVID-19) pandemic has been responsible for a global health crisis. In England a number of non-pharmaceutical interventions have been introduced throughout the pandemic, including guidelines on healthcare attendance (for example, promoting remote consultations), increased handwashing and social distancing. These interventions are likely to have impacted the incidence of non–COVID-19 conditions as well as healthcare seeking behaviour. Syndromic Surveillance Systems offer the ability to monitor trends in healthcare usage over time.

Methods

This study describes the indirect impact of COVID-19 on healthcare utilisation using a range of syndromic indicators including eye conditions, mumps, fractures, herpes zoster and cardiac conditions. Data from the syndromic surveillance systems monitored by Public Health England were used to describe the number of contacts with NHS 111, general practitioner (GP) In Hours (GPIH) and Out-of-Hours (GPOOH), Ambulance and Emergency Department (ED) services over comparable periods before and during the pandemic.

Results

The peak pandemic period in 2020 (weeks 13–20), compared to the same period in 2019, displayed on average a 12% increase in NHS 111 calls, an 11% decrease in GPOOH consultations, and a 49% decrease in ED attendances. In the GP In Hours system, conjunctivitis consultations decreased by 64% and mumps consultations by 31%. There was a 49% reduction in attendance at EDs for fractures, and there was no longer any weekend increase in ED fracture attendances, with similar attendance patterns observed across each day of the week. There was a decrease in the number of ED attendances with diagnoses of myocardial ischaemia.

Conclusion

The COVID-19 pandemic drastically impacted healthcare utilisation for non-COVID-19 conditions, due to a combination of a probable decrease in incidence of certain conditions and changes in healthcare seeking behaviour. Syndromic surveillance has a valuable role in describing and understanding these trends.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12117-5.

Health Systems and Services During COVID-19: Lessons and Evidence From Previous Crises: A Rapid Scoping Review to Inform the United Nations Research Roadmap for the COVID-19 Recovery

This rapid scoping review has informed the development of the November 2020 United Nations Research Roadmap for the COVID-19 Recovery, by providing a synthesis of available evidence on the impact of pandemics and epidemics on (1) essential services and (2) health systems preparedness and strengthening. Emerging findings point to existing disparities in health systems and services being further exacerbated, with marginalized populations and low- and middle-income countries burdened disproportionately. More broadly, there is a need to further understand short- and long-term impacts of bypassed essential services, quality assurance of services, the role of primary health care in the frontline, and the need for additional mechanisms for effective vaccine messaging and uptake during epidemics. The review also highlights how trust—of institutions, of science, and between communities and health systems—remains central to a successful pandemic response. Finally, previous crises had repeatedly foreshadowed the inability of health systems to handle upcoming pandemics, yet the reactive nature of policies and practices compounded by lack of resources, infrastructure, and political will have resulted in the current failed response to COVID-19. There is therefore an urgent need for investments in implementation science and for strategies to bridge this persistent research–practice gap.

Technologies Enabling Situational Awareness During Disaster Response: A Systematic Review

Situational awareness (SA) is critical to mobilizing a rapid, efficient, and effective response to disasters. Limited by time and resources, response agencies must make decisions about rapidly evolving situations, which requires the collection, analysis, and sharing of actionable information across a complex landscape. Emerging technologies, if appropriately applied, can enhance SA and enable responders to make quicker, more accurate decisions. The aim of this systematic review is to identify technologies that can improve SA and assist decision-making across the United States Government and the domestic and international agencies they support during disaster response operations. A total of 1459 articles and 36 after-action reports were identified during literature searches. Following the removal of duplicates and application of inclusion/exclusion criteria, 302 articles and after-action reports were included in the review. Our findings suggest SA is constrained primarily due to unreliable and significantly delayed communications, time-intensive data analysis and visualization, and a lack of interoperable sensor networks and other capabilities providing data to shared platforms. Many of these challenges could be addressed by existing technologies. Bridging the divide between research and development efforts and the operational needs of response agencies should be prioritized.

The COVID-19 pandemic: a new challenge for syndromic surveillance

The COVID-19 pandemic is exerting major pressures on society, health and social care services and science. Understanding the progression and current impact of the pandemic is fundamental to planning, management and mitigation of future impact on the population. Surveillance is the core function of any public health system, and a multi-component surveillance system for COVID-19 is essential to understand the burden across the different strata of any health system and the population. Many countries and public health bodies utilise ‘syndromic surveillance’ (using real-time, often non-specific symptom/preliminary diagnosis information collected during routine healthcare provision) to supplement public health surveillance programmes. The current COVID-19 pandemic has revealed a series of unprecedented challenges to syndromic surveillance including: the impact of media reporting during early stages of the pandemic; changes in healthcare-seeking behaviour resulting from government guidance on social distancing and accessing healthcare services; and changes in clinical coding and patient management systems. These have impacted on the presentation of syndromic outputs, with changes in denominators creating challenges for the interpretation of surveillance data. Monitoring changes in healthcare utilisation is key to interpreting COVID-19 surveillance data, which can then be used to better understand the impact of the pandemic on the population. Syndromic surveillance systems have had to adapt to encompass these changes, whilst also innovating by taking opportunities to work with data providers to establish new data feeds and develop new COVID-19 indicators. These developments are supporting the current public health response to COVID-19, and will also be instrumental in the continued and future fight against the disease.

Predicting influenza-like illness-related emergency department visits by modelling spatio-temporal syndromic surveillance data

Predicting the magnitude of the annual seasonal peak in influenza-like illness (ILI)-related emergency department (ED) visit volumes can inform the decision to open influenza care clinics (ICCs), which can mitigate pressure at the ED. Using ILI-related ED visit data from the Alberta Real Time Syndromic Surveillance Net for Edmonton, Alberta, Canada, we developed (training data, 1 August 2004–31 July 2008) and tested (testing data, 1 August 2008–19 February 2014) spatio-temporal statistical prediction models of daily ILI-related ED visits to estimate high visit volumes 3 days in advance. Our Main Model, based on a generalised linear mixed model with random intercept, incorporated prediction residuals over 14 days and captured increases in observed volume ahead of peaks. During seasonal influenza periods, our Main Model predicted volumes within ±30% of observed volumes for 67%–82% of high-volume days and within 0.3%–21% of observed seasonal peak volumes. Model predictions were not as successful during the 2009 H1N1 pandemic. Our model can provide early warning of increases in ILI-related ED visit volumes during seasonal influenza periods of differing intensities. These predictions may be used to support public health decisions, such as if and when to open ICCs, during seasonal influenza epidemics.

Syndromic survey and molecular analysis of influenza viruses at the human-swine interface in two West African cosmopolitan cities suggest the possibility of bidirectional interspecies transmission

Influenza viruses are frequently transmitted between pigs and their handlers, and among pig handlers. However, reports on socio-environmental variables as potential risk factors associated with transmission of influenza in West African swine production facilities are very scarce. Syndromic survey for influenza was therefore conducted in Ibadan, Nigeria, and Kumasi, Ghana, in order to identify and elucidate selected socio-environmental variables that may contribute to the occurrence and distribution of influenza-like illness (ILI) among swine industry workers. In addition, molecular analyses were conducted to elucidate the nature of influenza viruses circulating at the human-swine interface in these cities and better understand the dynamics of their transmission. Influenza viruses were detected by type-specific and subtype-specific RT-PCR. Sequencing and phylogenetic analyses were carried out. Socio-environmental variables were tested by both univariable and multivariable regression methods for significance at p < 0.05. Three risk factors for ILI were identified in each city. These included "frequency of visit of pig handler to pig pen or lairage" (Ibadan: risk ratio [RR] = 1.54, 95% confidence interval [CI] = 1.36-1.79, p = 0.02; Kumasi: RR = 1.28, 95% CI = 1.11-1.71, p = 0.01) and "pig handler's awareness about biosecurity measures" (Ibadan: RR = 7.09, 95% CI = 2.36-21.32, p < 0.001; Kumasi: RR = 4.84, 95% CI = 1.98-11.80, p < 0.001). Influenza A(H1N1)pdm09 viruses, with M genes closely related to those which circulated among pigs in the two cities during the same period, were detected among Nigerian and Ghanaian pig industry workers. These findings suggest the possibility of bidirectional transmission of influenza at the human-swine interface in these cities and underscore the need for more extensive molecular studies. Risk factors identified may assist in the control of human-to-human and human-to-swine transmission of influenza in the West African swine industry.

A Practitioner-Driven Research Agenda for Syndromic Surveillance

Syndromic surveillance has expanded since 2001 in both scope and geographic reach and has benefited from research studies adapted from numerous disciplines. The practice of syndromic surveillance continues to evolve rapidly. The International Society for Disease Surveillance solicited input from its global surveillance network on key research questions, with the goal of improving syndromic surveillance practice. A workgroup of syndromic surveillance subject matter experts was convened from February to June 2016 to review and categorize the proposed topics. The workgroup identified 12 topic areas in 4 syndromic surveillance categories: informatics, analytics, systems research, and communications. This article details the context of each topic and its implications for public health. This research agenda can help catalyze the research that public health practitioners identified as most important.

The burden of seasonal respiratory infections on a national telehealth service in England

Seasonal respiratory illnesses present a major burden on primary care services. We assessed the burden of respiratory illness on a national telehealth system in England and investigated the potential for providing early warning of respiratory infection. We compared weekly laboratory reports for respiratory pathogens with telehealth calls (NHS 111) between week 40 in 2013 and week 29 in 2015. Multiple linear regression was used to identify which pathogens had a significant association with respiratory calls. Children aged <5 and 5-14 years, and adults over 65 years were modelled separately as were time lags of up to 4 weeks between calls and laboratory specimen dates. Associations with respiratory pathogens explained over 83% of the variation in cold/flu, cough and difficulty breathing calls. Based on the first two seasons available, the greatest burden was associated with respiratory syncytial virus (RSV) and influenza, with associations found in all age bands. The most sensitive signal for influenza was calls for 'cold/flu', whilst for RSV it was calls for cough. The best-fitting models showed calls increasing a week before laboratory specimen dates. Daily surveillance of these calls can provide early warning of seasonal rises in influenza and RSV, contributing to the national respiratory surveillance programme.

Evaluation of the ability of standardized supports to improve public health response to syndromic surveillance for respiratory diseases in Canada

Background

Despite widespread implementation of syndromic surveillance systems within public health agencies, previous studies of the implementation and use of these systems have indicated that the functions and responses taken in response to syndromic surveillance data vary widely according to local context and preferences. The objective of the Syndromic Surveillance Evaluation Study was to develop and implement standardized supports in local public health agencies in Ontario, Canada, and evaluate the ability of these supports to affect actions taken as part of public health communicable disease control programs.

Methods

Local public health agencies (LPHA) in Ontario, which used syndromic surveillance based on emergency department visits for respiratory disease, were recruited and randomly allocated to the study intervention or control group. The intervention group health agencies received standardized supports in terms of a standardized aberrant event detection algorithm and a response protocol dictating steps to investigate and assess the public health significance of syndromic surveillance alerts. The control group continued with their pre-existing syndromic surveillance infrastructure and processes. Outcomes were assessed using logbooks, which collected quantitative and qualitative information about alerts received, investigation steps taken, and public health responses. The study was conducted prospectively for 15 months (October 2013 to February 2015).

Results

Fifteen LPHAs participated in the study (n = 9 intervention group, n = 6 control group). A total of 1,969 syndromic surveillance alerts were received by all LPHAs. Variations in the types and amount of responses varied by LPHA, in particularly differences were noted by the size of the health unit. Smaller health units had more challenges to both detect and mount a response to any alerts. LPHAs in the control group were more likely to declare alerts to have public health significance and to initiate any action. Regression models using repeated measures showed an interaction between the year (Year 1 versus Year 2) and the intervention as well as an interaction between year and sustained nature of the alert. Both of these were linked to the control health units reporting more “watchful waiting”.

Conclusions

This study raises questions about the effectiveness of using standardized protocols to improve the performance of syndromic surveillance in a decentralized public health system. Despite efforts to create standardized protocols and engage public health agencies in the process, no significant differences in the effective use of syndromic alerts were observed beyond year 1. It also raises questions about the minimum capacity of the agency and minimum population size that are required for an effective response.

Missouri K-12 school collection and reporting of school-based syndromic surveillance data: a cross sectional study

BACKGROUND

School participation in collecting and reporting syndromic surveillance (SS) data to public health officials and school nurses' attitudes regarding SS have not been assessed.

METHODS

An online survey was sent to Missouri Association of School Nurses members during the 2013/2014 school year to assess whether K-12 schools were collecting and reporting SS data. Z-scores were used to assess collection versus reporting of SS indicators. Logistic regressions were used to describe factors predicting nurses' collection and reporting of SS indicators: all-cause absenteeism, influenza-like illness and gastrointestinal illness. Univariate predictors were assessed with Chi-Squares.

RESULTS

In total, 133 school nurses participated (33.6 % response rate). Almost all (90.2 %, n = 120) collect at least one SS indicator; half (49.6 %, n = 66) report at least one. Schools are collecting more SS data than they are reporting to the health department (p < .05 for all comparisons). Determinants of school nurses' collection of SS data included perceived administrative support, and knowledge of collecting and analyzing SS data. The strongest predictive factors for reporting SS data were the perception that the health department was interested in SS data and being approached by the health department to collect SS data.

CONCLUSION

Schools are collecting SS indicators at a relatively high rate, yet less than half of the data is reported to public health officials. Findings from this study indicate that public health officials can increase access to school-based SS data by approaching schools about collecting and reporting this important data.

A practical approach to designing syndromic surveillance systems for livestock and poultry

The field of animal syndromic surveillance (SyS) is growing, with many systems being developed worldwide. Now is an appropriate time to share ideas and lessons learned from early SyS design and implementation. Based on our practical experience in animal health SyS, with additions from the public health and animal health SyS literature, we put forward for discussion a 6-step approach to designing SyS systems for livestock and poultry. The first step is to formalise policy and surveillance goals which are considerate of stakeholder expectations and reflect priority issues (1). Next, it is important to find consensus on national priority diseases and identify current surveillance gaps. The geographic, demographic, and temporal coverage of the system must be carefully assessed (2). A minimum dataset for SyS that includes the essential data to achieve all surveillance objectives while minimizing the amount of data collected should be defined. One can then compile an inventory of the data sources available and evaluate each using the criteria developed (3). A list of syndromes should then be produced for all data sources. Cases can be classified into syndrome classes and the data can be converted into time series (4). Based on the characteristics of the syndrome-time series, the length of historic data available and the type of outbreaks the system must detect, different aberration detection algorithms can be tested (5). Finally, it is essential to develop a minimally acceptable response protocol for each statistical signal produced (6). Important outcomes of this pre-operational phase should be building of a national network of experts and collective action and evaluation plans. While some of the more applied steps (4 and 5) are currently receiving consideration, more emphasis should be put on earlier conceptual steps by decision makers and surveillance developers (1-3).

Description of a school nurse visit syndromic surveillance system and comparison to emergency department visits, New York City

OBJECTIVES

We compared school nurse visit syndromic surveillance system data to emergency department (ED) visit data for monitoring illness in New York City schoolchildren.

METHODS

School nurse visit data recorded in an electronic health record system are used to conduct daily surveillance of influenza-like illness, fever-flu, allergy, asthma, diarrhea, and vomiting syndromes. We calculated correlation coefficients to compare the percentage of syndrome visits to the school nurse and ED for children aged 5 to 14 years, from September 2006 to June 2011.

RESULTS

Trends in influenza-like illness correlated significantly (correlation coefficient = 0.89; P < .001) and 72% of school signals occurred on days that ED signaled. Trends in allergy (correlation coefficient = 0.73; P < .001) and asthma (correlation coefficient = 0.56; P < .001) also correlated and school signals overlapped with ED signals on 95% and 51% of days, respectively. Substantial daily variation in diarrhea and vomiting visits limited our ability to make comparisons.

CONCLUSIONS

Compared with ED syndromic surveillance, the school nurse system identified similar trends in influenza-like illness, allergy, and asthma syndromes. Public health practitioners without school-based surveillance may be able to use age-specific analyses of ED syndromic surveillance data to monitor illness in schoolchildren.

Assessing the relative timeliness of Ontario's syndromic surveillance systems for early detection of the 2009 influenza H1N1 pandemic waves

OBJECTIVES

Building on previous research noting variations in the operation and perceived utility of syndromic surveillance systems in Ontario, the timeliness of these different syndromic systems for detecting the onset of both 2009 H1N1 pandemic (A(H1N1)pdm09) waves relative to laboratory testing data was assessed using a standardized analytic algorithm.

METHODS

Syndromic data, specifically local emergency department (ED) visit and school absenteeism data, as well as provincial Telehealth (telephone helpline) and antiviral prescription data, were analyzed retrospectively for the period April 1, 2009 to January 31, 2010. The C2-MEDIUM aberration detection method from the US Centers for Disease Control and Prevention's EARS software was used to detect increases above expected in syndromic data, and compared to laboratory alerts, defined as notice of confirmed A(H1N1)pdm09 cases over two consecutive days, to assess relative timeliness.

RESULTS

In Wave 1, provincial-level alerts were detected for antiviral prescriptions and Telehealth respiratory calls before the laboratory alert. In Wave 2, Telehealth respiratory calls similarly alerted in advance of the laboratory, while local alerts from ED visit, antiviral prescription and school absenteeism data varied in timing relative to the laboratory alerts. Alerts from syndromic data were also observed to coincide with external factors such as media releases.

CONCLUSIONS

Alerts from syndromic surveillance systems may be influenced by external factors and variation in system operations. Further understanding of both the impact of external factors on surveillance data and standardizing protocols for defining alerts is needed before the use of syndromic surveillance systems can be optimized.

Relevance of Rift Valley fever to public health in the European Union

Rift Valley fever (RVF), a vector-borne zoonotic disease caused by a phlebovirus (family Bunyaviridae), is considered to be one of the most important viral zoonoses in Africa. It is also a potential bioterrorism agent. Transmitted by mosquitoes or by direct contact with viraemic products, RVF affects both livestock and humans, causing abortion storms in pregnant ruminants and sudden death in newborns. The disease provokes flu syndrome in most human cases, but also severe encephalitic or haemorrhagic forms and death. There is neither a treatment nor a vaccine for humans. The disease, historically confined to the African continent, recently spread to the Arabian Peninsula and Indian Ocean. Animal movements, legal or illegal, strongly contribute to viral spread, threatening the Mediterranean basin and Europe, where competent vectors are present. Given the unpredictability of virus introduction and uncertainties about RVF epidemiology, there is an urgent need to fill the scientific gaps by developing large regional research programmes, to build predictive models, and to implement early warning systems and surveillance designs adapted to northern African and European countries.