Evaluation of a national pharmacy‐based syndromic surveillance system

Muchaal, PK; Parker, Stephen; Meganath, K; Landry, Lisa; Aramini, Jeff

doi:10.14745/ccdr.v41i09a01

Cited by 10 publications

(12 citation statements)

References 4 publications

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“…Previous works have also focused on detecting gastrointestinal symptoms circulating among the general population through digital unstructured data [33, 63–65] from participatory surveillance, big data, such as Twitter, as well as national pharmacy sales data. These examples show how crowdsourced digital health-related data, as well as passive digital traces generated on the web by individuals from the general population, can complement traditional and syndromic surveillance systems to estimate the circulation of gastrointestinal syndromes.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2019

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“…First is the 2015 study by Muchaal et al in Canada. 27 Muchaal et al compared (i) the trend of sales of over-the-counter (OTC) respiratory products to laboratory reports of respiratory pathogen detections, and (ii) antiviral prescriptions for confirmed cases of influenza and cases of influenza-like-illness.…”

Section: The Potential Of Pharmacies To Provide Useful Data For Syndromic Surveillancementioning

confidence: 99%

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

Wulandari

Sawitri

Hermansyah

2021

Health Planning & Management

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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.

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“…In 2013, PHAC began evaluating the use of over‐the‐counter sales and dispensing data from pharmacies for disease surveillance, with an initial focus on trends in dispensing and over‐the‐counter sales of antiviral medication as a means for understanding changes in respiratory and enteric illness. Given the promising results from Muchaal et al (), a decision was made to examine the utility of these data for estimating the prevalence of diseases, such as ADHD. Drug dispensing records have been used to examine temporal trends in infectious diseases, such as influenza (Papenburg et al, 2016), but we are not aware of such data being used to measure the prevalence of a chronic disease such as ADHD.…”

Section: A Real Data Examplementioning

confidence: 99%

Estimating prevalence using indirect information and Bayesian evidence synthesis

2018

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We focus on the analysis of health count data, aggregated over disjoint geographical locations, by combining information from data sources in a coherent fashion using a Bayesian hierarchical model. The overall objective is to estimate prevalence of a medical condition in the population given that the sampled counts arise from a subset of all cases, and when there is no additional information available from the data. We develop a hierarchical model to predict the overall prevalence using an external data set for calibration. We demonstrate that the Bayesian methodology can account fully for the uncertainty, variability and spatial dependence for the estimate. We apply our model to dispensing data obtained by the Public Health Agency of Canada for 2014, and assess the prevalence of treated Attention Deficit Hyperactivity Disorder (ADHD) from records of drugs dispensed to treat the condition. We demonstrate that our final model fits the data well in an out‐of‐sample assessment. We estimate the prevalence of treated ADHD in Canada to be 1.14% with 95% credible interval (0.86%, 1.27%), with prevalence noted to be higher in the eastern part of Canada, most notably in Nova Scotia. The Canadian Journal of Statistics 46: 673–689; 2018 © 2018 Société statistique du Canada

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Evaluation of a national pharmacy‐based syndromic surveillance system

Cited by 10 publications

References 4 publications

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

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

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

Estimating prevalence using indirect information and Bayesian evidence synthesis

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