Evaluation of a national pharmacy-based syndromic surveillance system

Early Detection and Monitoring of Gastrointestinal Infections Using Syndromic Surveillance: A Systematic Review

The underreporting of laboratory-reported cases of community-based gastrointestinal (GI) infections poses a challenge for epidemiologists understanding the burden and seasonal patterns of GI pathogens. Syndromic surveillance has the potential to overcome the limitations of laboratory reporting through real-time data and more representative population coverage. This systematic review summarizes the utility of syndromic surveillance for early detection and surveillance of GI infections. Relevant articles were identified using the following keyword combinations: 'early warning', 'detection', 'gastrointestinal activity', 'gastrointestinal infections', 'syndrome monitoring', 'real-time monitoring', 'syndromic surveillance'. In total, 1820 studies were identified, 126 duplicates were removed, and 1694 studies were reviewed. Data extraction focused on studies reporting the routine use and effectiveness of syndromic surveillance for GI infections using relevant GI symptoms. Eligible studies (n = 29) were included in the narrative synthesis. Syndromic surveillance for GI infections has been implemented and validated for routine use in ten countries, with emergency department attendances being the most common source. Evidence suggests that syndromic surveillance can be effective in the early detection and routine monitoring of GI infections; however, 24% of the included studies did not provide conclusive findings. Further investigation is necessary to comprehensively understand the strengths and limitations associated with each type of syndromic surveillance system.

Constructing big data prevention and control model for public health emergencies in China: A grounded theory study

Big data technology plays an important role in the prevention and control of public health emergencies such as the COVID-19 pandemic. Current studies on model construction, such as SIR infectious disease model, 4R crisis management model, etc., have put forward decision-making suggestions from different perspectives, which also provide a reference basis for the research in this paper. This paper conducts an exploratory study on the construction of a big data prevention and control model for public health emergencies by using the grounded theory, a qualitative research method, with literature, policies, and regulations as research samples, and makes a grounded analysis through three-level coding and saturation test. Main results are as follows: (1) The three elements of data layer, subject layer, and application layer play a prominent role in the digital prevention and control practice of epidemic in China and constitute the basic framework of the “DSA” model. (2) The “DSA” model integrates cross-industry, cross-region, and cross-domain epidemic data into one system framework, effectively solving the disadvantages of fragmentation caused by “information island”. (3) The “DSA” model analyzes the differences in information needs of different subjects during an outbreak and summarizes several collaborative approaches to promote resource sharing and cooperative governance. (4) The “DSA” model analyzes the specific application scenarios of big data technology in different stages of epidemic development, effectively responding to the disconnection between current technological development and realistic needs.

Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance

Early outbreak detection is a key aspect in the containment of infectious diseases, as it enables the identification and isolation of infected individuals before the disease can spread to a larger population. Instead of detecting unexpected increases of infections by monitoring confirmed cases, syndromic surveillance aims at the detection of cases with early symptoms, which allows a more timely disclosure of outbreaks. However, the definition of these disease patterns is often challenging, as early symptoms are usually shared among many diseases and a particular disease can have several clinical pictures in the early phase of an infection. As a first step toward the goal to support epidemiologists in the process of defining reliable disease patterns, we present a novel, data-driven approach to discover such patterns in historic data. The key idea is to take into account the correlation between indicators in a health-related data source and the reported number of infections in the respective geographic region. In an preliminary experimental study, we use data from several emergency departments to discover disease patterns for three infectious diseases. Our results show the potential of the proposed approach to find patterns that correlate with the reported infections and to identify indicators that are related to the respective diseases. It also motivates the need for additional measures to overcome practical limitations, such as the requirement to deal with noisy and unbalanced data, and demonstrates the importance of incorporating feedback of domain experts into the learning procedure.

The potential roles of pharmacy medication sales data to augment the syndromic surveillance system in response to COVID-19 and preparedness for other future infectious disease outbreaks in Indonesia

BACKGROUND

Indonesia faces a continuous threat from communicable disease outbreaks. The current COVID-19 outbreak, the previous one of SARS, and many other infectious outbreaks encountered in the country warn of the need to develop comprehensive early warning systems to enable timely health responses in the long run. In this article, we argue that over the counter medication sales data at community pharmacies in Indonesia can potentially augment and increase the detection power of the current syndromic surveillance system, particularly in dealing with COVID-19 and other future infectious disease outbreaks in the country.

MAIN BODY

This article discusses the experience of other countries in employing pharmacy medication sales data to serve as potential syndromic surveillance platform and contribute to pandemic responses. We argue why it is worth considering utilising medication sales data from pharmacies in Indonesia to support the current surveillance system which enables the provision of early warnings of disease outbreaks. We then discuss the potential challenges of operationalising these data and suggest a way forward for the development and implementation of the syndromic surveillance system at community pharmacy settings in Indonesia.

CONCLUSION

While there are several challenges in developing a workable system in Indonesia that need to be addressed, introducing a syndromic surveillance system using pharmacy-setting medication sales data is worth investigating in the country.

Hawke's Bay pharmacists' activities during a campylobacter contamination of public water supply in Havelock North during 2016

INTRODUCTION In August 2016 contamination of the local water supply resulted in a significant gastroenteritis outbreak in Hawke's Bay. The significance of the initial test result was recognised early, partly as a result of information provided by a Havelock North pharmacist to health authorities about an unusual number of requests for anti-diarrhoeal medication. AIM To describe the breadth of activities undertaken by pharmacists working in Hawke's Bay in August 2016, following Campylobacter jejuni contamination of the public water supply in Havelock North, New Zealand. METHODS All pharmacists and hospital pharmacy management staff working in Hawke's Bay in 2017 were eligible to complete the qualitative online questionnaire. Additionally, information was requested from stakeholders with known relevant experiences. Free-text responses were thematically analysed using a general inductive approach. RESULTS Thirteen pharmacists and two ancillary staff from community pharmacy, hospital pharmacy, general practice, management, emergency response and dispensary management responded to the survey. Analysis of responses revealed three overarching themes and six sub-themes. The first was public wellbeing, with sub-themes of community information, local emergency response and pharmacy operational management. The second was pharmaceutical distribution, with a sub-theme of stock management. The third theme was clinical medicine management, with sub-themes of acute symptom management and medicine management. DISCUSSION The pharmacy profession appears to have played an important role in public wellbeing, pharmaceutical distribution and medicine therapy management during the outbreak. It is likely that through their actions, responding pharmacists reduced demand on other primary care services and prevented hospitalisations. Further research directions include exploring the effectiveness of community pharmacists in public health surveillance and the use of endorsed public health information to ensure consistent delivery of health messages.

Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms

Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes. The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34,000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries.

Big Data and the Global Public Health Intelligence Network (GPHIN)

Background

Globalization and the potential for rapid spread of emerging infectious diseases have heightened the need for ongoing surveillance and early detection. The Global Public Health Intelligence Network (GPHIN) was established to increase situational awareness and capacity for the early detection of emerging public health events.

Objective

To describe how the GPHIN has used Big Data as an effective early detection technique for infectious disease outbreaks worldwide and to identify potential future directions for the GPHIN.

Findings

Every day the GPHIN analyzes over more than 20,000 online news reports (over 30,000 sources) in nine languages worldwide. A web-based program aggregates data based on an algorithm that provides potential signals of emerging public health events which are then reviewed by a multilingual, multidisciplinary team. An alert is sent out if a potential risk is identified. This process proved useful during the Severe Acute Respiratory Syndrome (SARS) outbreak and was adopted shortly after by a number of countries to meet new International Health Regulations that require each country to have the capacity for early detection and reporting. The GPHIN identified the early SARS outbreak in China, was credited with the first alert on MERS-CoV and has played a significant role in the monitoring of the Ebola outbreak in West Africa. Future developments are being considered to advance the GPHIN's capacity in light of other Big Data sources such as social media and its analytical capacity in terms of algorithm development.

Conclusion

The GPHIN's early adoption of Big Data has increased global capacity to detect international infectious disease outbreaks and other public health events. Integration of additional Big Data sources and advances in analytical capacity could further strengthen the GPHIN's capability for timely detection and early warning.

Big Data is changing the battle against infectious diseases

Big Data has traditionally been associated with computer geeks and commercial enterprises, but it has become entrenched in many scientific disciplines including the prevention and control of infectious diseases. The use of Big Data has allowed disease trends to be identified and outbreak origins to be tracked and even predicted. Big Data is not getting smaller. The challenges we face are to hone our analytical capacity to address the huge "signal-to-noise" ratio with adequate computing power and multidisciplinary teams that can handle ever-increasing amounts of data. Big Data will also create the opportunity for future applications of bespoke (or personalized) treatment.