The potential impact of media reporting in syndromic surveillance: an example using a possible Cryptosporidium exposure in North West England, August to September 2015

Using a novel 'difference-in-differences' method and syndromic surveillance to estimate the change in local healthcare utilisation during periods of media reporting in the early stages of the COVID-19 pandemic in England

OBJECTIVES

Syndromic surveillance supplements traditional laboratory reporting for infectious diseases monitoring. Prior to widespread COVID-19 community surveillance, syndromic surveillance was one of several systems providing real-time information on changes in healthcare-seeking behaviour. The study objective was to identify changes in healthcare utilisation during periods of high local media reporting in England using 'difference-in-differences' (DiD).

STUDY DESIGN

A retrospective observational study was conducted using five media events in January-February 2020 in England on four routinely monitored syndromic surveillance indicators.

METHODS

Dates 'exposed' to a media event were estimated using Google Trends internet search intensity data (terms = 'coronavirus' and local authority [LA]). We constructed a negative-binomial regression model for each indicator and event time period to estimate a direct effect.

RESULTS

We estimated a four-fold increase in telehealth 'cough' calls and a 1.4-fold increase in emergency department (ED) attendances for acute respiratory illness in Brighton and Hove, when a so-called 'superspreading event' in this location was reported in local and national media. Significant decreases were observed in the Buxton (telehealth and ED attendance) and Wirral (ED attendance) areas during media reports of a returnee from an outbreak abroad and a quarantine site opening in the area respectively.

CONCLUSIONS

We used a novel approach to directly estimate changes in syndromic surveillance reporting during the early phase of the COVID-19 pandemic in England, providing contextual information on the interpretation of changes in health indicators. With careful consideration of event timings, DiD is useful in producing real-time estimates on specific indicators for informing public health action.

Worldwide Surveillance Actions and Initiatives of Drinking Water Quality: A Scoping Review

This study identified and mapped worldwide surveillance actions and initiatives of drinking water quality implemented by government agencies and public health services. The scoping review was conducted between July 2021 and August 2022 based on the Joanna Briggs Institute method. The search was performed in relevant databases and gray literature; 49 studies were retrieved. Quantitative variables were presented as absolute and relative frequencies, while qualitative variables were analyzed using the IRaMuTeQ software. The actions developed worldwide and their impacts and results generated four thematic classes: (1) assessment of coverage, accessibility, quantity, and drinking water quality in routine and emergency situations; (2) analysis of physical-chemical and microbiological parameters in public supply networks or alternative water supply solutions; (3) identification of household water contamination, communication, and education with the community; (4) and investigation of water-borne disease outbreaks. Preliminary results were shared with stakeholders to favor knowledge dissemination.

Investigating novel approaches to tick-borne encephalitis surveillance in Sweden, 2010-2017

Tick-borne encephalitis (TBE) is a vaccine-preventable, high-priority disease in Sweden, with increasing incidence. However, surveillance is limited to case reports. We investigated relationships between reported TBE incidence and syndromic surveillance data to determine if these novel data sources could provide earlier indications of disease activity. We retrospectively compared national, weekly (2010-2017) reported TBE incidence to the percentage of TBE-related a) searches on the main Swedish healthcare information website and b) calls to its telehealth service using Spearman's ρ to determine the most strongly correlated lags. We conducted a sub-analysis (2012-2017) of TBE-related Google Trends queries and compared the number of TBE-related media stories to each novel surveillance dataset. Healthcare website searches for "tbe" and "vaccine" combined, "tbe", "tick", and "tick bite" led case data by 12, 8, 7, and 6 weeks, respectively (ρ = 0.87-0.89); telehealth calls led by 4 weeks (ρ = 0.92; all p < 0.001). Correlations and lags for Google Trends and healthcare website searches were fairly similar to each other. In comparison, correlation between the different syndromic surveillance datasets and the number of media stories was lower (ρ = 0.25-0.56). We observed volume discrepancies between TBE incidence and the novel surveillance datasets during some years, particularly for web searches. Syndromic surveillance data were strongly correlated with and preceded case data by 4-12 weeks. Syndromic data may provide advanced awareness and earlier indications of TBE activity, which can improve timing and specificity of public health communications. The use of these data as supplements to notifiable disease data for national planning and preparedness in real-time should be investigated.

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.

Machine learning to refine decision making within a syndromic surveillance service

Background

Worldwide, syndromic surveillance is increasingly used for improved and timely situational awareness and early identification of public health threats. Syndromic data streams are fed into detection algorithms, which produce statistical alarms highlighting potential activity of public health importance. All alarms must be assessed to confirm whether they are of public health importance. In England, approximately 100 alarms are generated daily and, although their analysis is formalised through a risk assessment process, the process requires notable time, training, and maintenance of an expertise base to determine which alarms are of public health importance. The process is made more complicated by the observation that only 0.1% of statistical alarms are deemed to be of public health importance. Therefore, the aims of this study were to evaluate machine learning as a tool for computer-assisted human decision-making when assessing statistical alarms.

Methods

A record of the risk assessment process was obtained from Public Health England for all 67,505 statistical alarms between August 2013 and October 2015. This record contained information on the characteristics of the alarm (e.g. size, location). We used three Bayesian classifiers- naïve Bayes, tree-augmented naïve Bayes and Multinets - to examine the risk assessment record in England with respect to the final ‘Decision’ outcome made by an epidemiologist of ‘Alert’, ‘Monitor’ or ‘No-action’. Two further classifications based upon tree-augmented naïve Bayes and Multinets were implemented to account for the predominance of ‘No-action’ outcomes.

Results

The attributes of each individual risk assessment were linked to the final decision made by an epidemiologist, providing confidence in the current process. The naïve Bayesian classifier performed best, correctly classifying 51.5% of ‘Alert’ outcomes. If the ‘Alert’ and ‘Monitor’ actions are combined then performance increases to 82.6% correctly classified. We demonstrate how a decision support system based upon a naïve Bayes classifier could be operationalised within an operational syndromic surveillance system.

Conclusions

Within syndromic surveillance systems, machine learning techniques have the potential to make risk assessment following statistical alarms more automated, robust, and rigorous. However, our results also highlight the importance of specialist human input to the process.

Involving citizens in priority setting for public health research: Implementation in infection research

BACKGROUND

Public sources fund the majority of UK infection research, but citizens currently have no formal role in resource allocation. To explore the feasibility and willingness of citizens to engage in strategic decision making, we developed and tested a practical tool to capture public priorities for research.

METHOD

A scenario including six infection themes for funding was developed to assess citizen priorities for research funding. This was tested over two days at a university public festival. Votes were cast anonymously along with rationale for selection. The scenario was then implemented during a three-hour focus group exploring views on engagement in strategic decisions and in-depth evaluation of the tool.

RESULTS

188/491(38%) prioritized funding research into drug-resistant infections followed by emerging infections(18%). Results were similar between both days. Focus groups contained a total of 20 citizens with an equal gender split, range of ethnicities and ages ranging from 18 to >70 years. The tool was perceived as clear with participants able to make informed comparisons. Rationale for funding choices provided by voters and focus group participants are grouped into three major themes: (i) Information processing; (ii) Knowledge of the problem; (iii) Responsibility; and a unique theme within the focus groups (iv) The potential role of citizens in decision making. Divergent perceptions of relevance and confidence of "non-experts" as decision makers were expressed.

CONCLUSION

Voting scenarios can be used to collect, en-masse, citizens' choices and rationale for research priorities. Ensuring adequate levels of citizen information and confidence is important to allow deployment in other formats.

Analysis of Parasitic Protozoa at the Single-cell Level using Microfluidic Impedance Cytometry

At present, there are few technologies which enable the detection, identification and viability analysis of protozoan pathogens including Cryptosporidium and/or Giardia at the single (oo)cyst level. We report the use of Microfluidic Impedance Cytometry (MIC) to characterise the AC electrical (impedance) properties of single parasites and demonstrate rapid discrimination based on viability and species. Specifically, MIC was used to identify live and inactive C. parvum oocysts with over 90% certainty, whilst also detecting damaged and/or excysted oocysts. Furthermore, discrimination of Cryptosporidium parvum, Cryptosporidium muris and Giardia lamblia, with over 92% certainty was achieved. Enumeration and identification of (oo)cysts can be achieved in a few minutes, which offers a reduction in identification time and labour demands when compared to existing detection methods.