Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies

Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review

PURPOSE OF REVIEW

The purpose of this scoping review was to summarize literature regarding the use of user-generated digital data collected for non-epidemiological purposes in human immunodeficiency virus (HIV) research.

RECENT FINDINGS

Thirty-nine papers were included in the final review. Four types of digital data were used: social media data, web search queries, mobile phone data, and data from global positioning system (GPS) devices. With these data, four HIV epidemiological objectives were pursued, including disease surveillance, behavioral surveillance, assessment of public attention to HIV, and characterization of risk contexts. Approximately one-third used machine learning for classification, prediction, or topic modeling. Less than a quarter discussed the ethics of using user-generated data for epidemiological purposes. User-generated digital data can be used to monitor, predict, and contextualize HIV risk and can help disrupt trajectories of risk closer to onset. However, more attention needs to be paid to digital ethics and the direction of the field in a post-Application Programming Interface (API) world.

Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models

The COVID-19 pandemic led to unparalleled pressure on healthcare services. Improved healthcare planning in relation to diseases affecting the respiratory system has consequently become a key concern. We investigated the value of integrating sales of non-prescription medications commonly bought for managing respiratory symptoms, to improve forecasting of weekly registered deaths from respiratory disease at local levels across England, by using over 2 billion transactions logged by a UK high street retailer from March 2016 to March 2020. We report the results from the novel AI (Artificial Intelligence) explainability variable importance tool Model Class Reliance implemented on the PADRUS model (Prediction of Amount of Deaths by Respiratory disease Using Sales). PADRUS is a machine learning model optimised to predict registered deaths from respiratory disease in 314 local authority areas across England through the integration of shopping sales data and focused on purchases of non-prescription medications. We found strong evidence that models incorporating sales data significantly out-perform other models that solely use variables traditionally associated with respiratory disease (e.g. sociodemographics and weather data). Accuracy gains are highest (increases in R2 (coefficient of determination) between 0.09 to 0.11) in periods of maximum risk to the general public. Results demonstrate the potential to utilise sales data to monitor population health with information at a high level of geographic granularity.

Establishment of a Public Mental Health Database for Research Purposes in the Ferrara Province: Development and Preliminary Evaluation Study

Background

The immediate use of data exported from electronic health records (EHRs) for research is often limited by the necessity to transform data elements into an actual data set.

Objective

This paper describes the methodology for establishing a data set that originated from an EHR registry that included clinical, health service, and sociodemographic information.

Methods

The Extract, Transform, Load process was applied to raw data collected at the Integrated Department of Mental Health and Pathological Addictions in Ferrara, Italy, from 1925 to February 18, 2021, to build the new, anonymized Ferrara-Psychiatry (FEPSY) database. Information collected before the first EHR was implemented (ie, in 1991) was excluded. An unsupervised cluster analysis was performed to identify patient subgroups to support the proof of concept.

Results

The FEPSY database included 3,861,432 records on 46,222 patients. Since 1991, each year, a median of 1404 (IQR 1117.5-1757.7) patients had newly accessed care, and a median of 7300 (IQR 6109.5-9397.5) patients were actively receiving care. Among 38,022 patients with a mental disorder, 2 clusters were identified; the first predominantly included male patients who were aged 25 to 34 years at first presentation and were living with their parents, and the second predominantly included female patients who were aged 35 to 44 years and were living with their own families.

Conclusions

The process for building the FEPSY database proved to be robust and replicable with similar health care data, even when they were not originally conceived for research purposes. The FEPSY database will enable future in-depth analyses regarding the epidemiology and social determinants of mental disorders, access to mental health care, and resource utilization.

Predicting pulmonary tuberculosis incidence in China using Baidu search index: an ARIMAX model approach

BACKGROUND

Existing researches have established a correlation between internet search data and the epidemics of numerous infectious diseases. This study aims to develop a prediction model to explore the relationship between the Pulmonary Tuberculosis (PTB) epidemic trend in China and the Baidu search index.

METHODS

Collect the number of new cases of PTB in China from January 2011 to August 2022. Use Spearman rank correlation and interaction analysis to identify Baidu keywords related to PTB and construct a PTB comprehensive search index. Evaluate the predictive performance of autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models for the number of PTB cases.

RESULTS

Incidence of PTB had shown a fluctuating downward trend. The Spearman rank correlation coefficient between the PTB comprehensive search index and its incidence was 0.834 (P < 0.001). The ARIMA model had an AIC value of 2804.41, and the MAPE value was 13.19%. The ARIMAX model incorporating the Baidu index demonstrated an AIC value of 2761.58 and a MAPE value of 5.33%.

CONCLUSIONS

The ARIMAX model is superior to ARIMA in terms of fitting and predicting accuracy. Additionally, the use of Baidu Index has proven to be effective in predicting cases of PTB.

Exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology

Osteoarthritis is a degenerative joint disease that affects millions of people worldwide. Current guidelines emphasize the importance of regular physical activity as a preventive measure against disease progression and as a valuable strategy for pain and functionality management. Despite this, most patients with osteoarthritis are inactive. Modern technological advances have led to the implementation of digital devices, such as wearables and smartphones, showing new opportunities for healthcare professionals and researchers to monitor physical activity and therefore engage patients in daily exercising. Additionally, digital devices have emerged as a promising tool for improving frequent health data collection, disease monitoring, and supporting public health surveillance. The leveraging of digital data has laid the foundation for developing a new concept of epidemiological study, known as "Digital Epidemiology". Analyzing real-world data can change the way we observe human behavior and suggest health interventions, as in the case of physical exercise and osteoarthritic patients. Furthermore, large-scale data could contribute to personalized and precision medicine in the future. Herein, an overview of recent clinical applications of wearables for monitoring physical activity in patients with osteoarthritis and the benefits of exploiting real-world data in the context of digital epidemiology are discussed.

NextGen Public Health Surveillance and the Internet of Things (IoT)

Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.

COVID-19 in the Twitterverse, from epidemic to pandemic: information-sharing behavior and Twitter as an information carrier

In this study, we defined a Twitter network as an information channel that includes information sources containing embedded messages. We conducted stage-based comparative analyses of Twitter networks during three periods: the beginning of the COVID-19 epidemic, the period when the epidemic was becoming a global phenomenon, and the beginning of the pandemic. We also analyzed the characteristics of scientific information sources and content on Twitter during the sample period. At the beginning of the epidemic, Twitter users largely shared trustworthy news information sources about the novel coronavirus. Widely shared scientific information focused on clinical investigations and case studies of the new coronavirus as the disease became a pandemic while non-scientific information sources and messages illustrated the social and political aspects of the global outbreak, often including emotional elements. Multiple suspicious, bot-like Twitter accounts were identified as a great connector of the COVID-19 Twitterverse, particularly in the beginning of the global crisis. Our findings suggest that the information carriers, which are information channels, sources, and messages were coherently interlocked, forming an information organism. The study results can help public health organizations design communication strategies, which often require prompt decision-making to manage urgent needs under the circumstances of an epidemic.

Validating viral hepatitis B and C diagnosis codes: a retrospective analysis using Ontario's health administrative data

OBJECTIVE

We aimed to determine the criterion validity of using diagnosis codes for hepatitis B virus (HBV) and hepatitis C virus (HCV) to identify infections.

METHODS

Using linked laboratory and administrative data in Ontario, Canada, from January 2004 to December 2014, we validated HBV/HCV diagnosis codes against laboratory-confirmed infections. Performance measures (sensitivity, specificity, and positive predictive value) were estimated via cross-validated logistic regression and we explored variations by varying time windows from 1 to 5 years before (i.e., prognostic prediction) and after (i.e., diagnostic prediction) the date of laboratory confirmation. Subgroup analyses were performed among immigrants, males, baby boomers, and females to examine the robustness of these measures.

RESULTS

A total of 1,599,023 individuals were tested for HBV and 840,924 for HCV, with a resulting 41,714 (2.7%) and 58,563 (7.0%) infections identified, respectively. HBV/HCV diagnosis codes ± 3 years of laboratory confirmation showed high specificity (99.9% HBV; 99.8% HCV), moderate positive predictive value (70.3% HBV; 85.8% HCV), and low sensitivity (12.8% HBV; 30.8% HCV). Varying the time window resulted in limited changes to performance measures. Diagnostic models consistently outperformed prognostic models. No major differences were observed among subgroups.

CONCLUSION

HBV/HCV codes should not be the only source used for monitoring the population burden of these infections, due to low sensitivity and moderate positive predictive values. These results underscore the importance of ongoing laboratory and reportable disease surveillance systems for monitoring viral hepatitis in Ontario.

Power of Big Data in ending HIV

The articles in this special issue of AIDS focus on the application of the so-called Big Data science (BDS) as applied to a variety of HIV-applied research questions in the sphere of health services and epidemiology. Recent advances in technology means that a critical mass of HIV-related health data with actionable intelligence is available for optimizing health outcomes, improving and informing surveillance. Data science will play a key but complementary role in supporting current efforts in prevention, diagnosis, treatment, and response needed to end the HIV epidemic. This collection provides a glimpse of the promise inherent in leveraging the digital age and improved methods in Big Data science to reimagine HIV treatment and prevention in a digital age.

Digital data-based strategies: A novel form of better understanding COVID-19 pandemic and international scientific collaboration

International scientific collaborations have always been regarded as critical actions to address global pandemics, however, there was an obvious uncertainty between international collaboration and the COVID-19 control. We aim to combine digital data-based strategies to produce meaningful and advanced insights into the imbalance between COVID-19 and international collaboration, as well as reveal possible influencing factors, and ultimately enhance global collaboration. We conducted three retrospective cohort studies using respectively COVID-19 data from WHO, a complete dataset of scientific publications on coronavirus-related research from WoS, and daily data from Google Trends (GT). The results of geovisualization and spatiotemporal analysis revealed that the global COVID19 pandemic still remains serious. The global issue of imbalance between international collaborations and pandemic does exit, and the nations with good pandemic control had their own characteristics in above-mentioned correlation. Digital epidemiology provides, at least in part, evidence-based assessment and scientific advice to understand the imbalance between international collaborations and COVID-19. Our investigation demonstrates that transdisciplinary conversation through digital data-based strategies can help us fully understand the complex factors influencing the effectiveness of international scientific collaboration, thus facilitating the global response to COVID-19.

Digital public health surveillance: a systematic scoping review

The ubiquitous and openly accessible information produced by the public on the Internet has sparked an increasing interest in developing digital public health surveillance (DPHS) systems. We conducted a systematic scoping review in accordance with the PRISMA extension for scoping reviews to consolidate and characterize the existing research on DPHS and identify areas for further research. We used Natural Language Processing and content analysis to define the search strings and searched Global Health, Web of Science, PubMed, and Google Scholar from 2005 to January 2020 for peer-reviewed articles on DPHS, with extensive hand searching. Seven hundred fifty-five articles were included in this review. The studies were from 54 countries and utilized 26 digital platforms to study 208 sub-categories of 49 categories associated with 16 public health surveillance (PHS) themes. Most studies were conducted by researchers from the United States (56%, 426) and dominated by communicable diseases-related topics (25%, 187), followed by behavioural risk factors (17%, 131). While this review discusses the potentials of using Internet-based data as an affordable and instantaneous resource for DPHS, it highlights the paucity of longitudinal studies and the methodological and inherent practical limitations underpinning the successful implementation of a DPHS system. Little work studied Internet users' demographics when developing DPHS systems, and 39% (291) of studies did not stratify their results by geographic region. A clear methodology by which the results of DPHS can be linked to public health action has yet to be established, as only six (0.8%) studies deployed their system into a PHS context.

Coronavirus (Covid-19) pandemic: How may communication strategies influence our behaviours?

A novel Corona virus (SARS-CoV-2), started in Wuhan China, caused an outbreak of viral pneumonia to subsequently spread throughout the world. Italy has been one of the most affected countries in the world and the increasing number of cases and deaths has created strong emotional reactions in people. This study has aimed at evaluating public attention to this emerging disease through the use of Google Trends. Public attention, measured as the volume of internet search activity, was correlated with Health Communication Strategies and official COVID-19 data. At the moment of the study analysis, Italy was by far the first country in terms of search volume for "coronavirus" and the highest peak of searches was reached on February 23, 2020. We have found that there was a correlation between public attention to coronavirus disease and communications from Public Health policies: we observed spikes in search volumes on the days of Presidential Decree publications. Furthermore, this attention was also correlated with Case Fatality Rate (CFR). Even if CFR data are continuously updated and can be affected by patient histories, the correlation found suggests that the increase in mortality has generated growing interest in the disease and its risk perception. This study shows that tracking searches through Google Trends as a public focus indicator is a useful tool for decision-makers in guiding communication strategies and should as well stimulate a more transparent media and policy making reporting.

Social media based surveillance systems for healthcare using machine learning: A systematic review

BACKGROUND

Real-time surveillance in the field of health informatics has emerged as a growing domain of interest among worldwide researchers. Evolution in this field has helped in the introduction of various initiatives related to public health informatics. Surveillance systems in the area of health informatics utilizing social media information have been developed for early prediction of disease outbreaks and to monitor diseases. In the past few years, the availability of social media data, particularly Twitter data, enabled real-time syndromic surveillance that provides immediate analysis and instant feedback to those who are charged with follow-ups and investigation of potential outbreaks. In this paper, we review the recent work, trends, and machine learning(ML) text classification approaches used by surveillance systems seeking social media data in the healthcare domain. We also highlight the limitations and challenges followed by possible future directions that can be taken further in this domain.

METHODS

To study the landscape of research in health informatics performing surveillance of the various health-related data posted on social media or web-based platforms, we present a bibliometric analysis of the 1240 publications indexed in multiple scientific databases (IEEE, ACM Digital Library, ScienceDirect, PubMed) from the year 2010-2018. The papers were further reviewed based on the various machine learning algorithms used for analyzing health-related text posted on social media platforms.

FINDINGS

Based on the corpus of 148 selected articles, the study finds the types of social media or web-based platforms used for surveillance in the healthcare domain, along with the health topic(s) studied by them. In the corpus of selected articles, we found 26 articles were using machine learning technique. These articles were studied to find commonly used ML techniques. The majority of studies (24%) focused on the surveillance of flu or influenza-like illness (ILI). Twitter (64%) is the most popular data source to perform surveillance research using social media text data, and Support Vector Machine (SVM) (33%) being the most used ML algorithm for text classification.

CONCLUSIONS

The inclusion of online data in surveillance systems has improved the disease prediction ability over traditional syndromic surveillance systems. However, social media based surveillance systems have many limitations and challenges, including noise, demographic bias, privacy issues, etc. Our paper mentions future directions, which can be useful for researchers working in the area. Researchers can use this paper as a library for social media based surveillance systems in the healthcare domain and can expand such systems by incorporating the future works discussed in our paper.

[Digitalization and Health Sciences - White Paper Digital Public Health]

Potential limits and risk of digitalization in public health will be a core topic of the High profile Research Area Health Sciences at the University of Bremen in coming years. A white paper was developed to support positioning in this dynamic research. The paper describes the Health Sciences viewpoint on core evaluation criteria for digital public health, identifies interfaces and approaches for interdisciplinary cooperation and discusses cross-cutting themes as well as demarcations with respect to digitalization in medicine (digital health). An abbreviated version of the white paper is presented for discussion.

Social Media- and Internet-Based Disease Surveillance for Public Health

Disease surveillance systems are a cornerstone of public health tracking and prevention. This review addresses the use, promise, perils, and ethics of social media- and Internet-based data collection for public health surveillance. Our review highlights untapped opportunities for integrating digital surveillance in public health and current applications that could be improved through better integration, validation, and clarity on rules surrounding ethical considerations. Promising developments include hybrid systems that couple traditional surveillance data with data from search queries, social media posts, and crowdsourcing. In the future, it will be important to identify opportunities for public and private partnerships, train public health experts in data science, reduce biases related to digital data (gathered from Internet use, wearable devices, etc.), and address privacy. We are on the precipice of an unprecedented opportunity to track, predict, and prevent global disease burdens in the population using digital data.