Is there a duty to participate in digital epidemiology?

Emerging data inputs for infectious diseases surveillance and decision making

Infectious diseases create a significant health and social burden globally and can lead to outbreaks and epidemics. Timely surveillance for infectious diseases is required to inform both short and long term public responses and health policies. Novel data inputs for infectious disease surveillance and public health decision making are emerging, accelerated by the COVID-19 pandemic. These include the use of technology-enabled physiological measurements, crowd sourcing, field experiments, and artificial intelligence (AI). These technologies may provide benefits in relation to improved timeliness and reduced resource requirements in comparison to traditional methods. In this review paper, we describe current and emerging data inputs being used for infectious disease surveillance and summarize key benefits and limitations.

Feasibility of mail-based biospecimen collection in an online preconception cohort study

Background

Prospective cohort studies that enroll participants before conception are crucial for deepening scientific understanding of how the preconception environment influences reproductive outcomes. While web-based research methods provide efficient and effective strategies to collect questionnaire-based data, few of these studies incorporate biospecimen collection, which can enhance the validity of exposure assessment. There is limited literature on the feasibility and cost-effectiveness of collecting biospecimens in web-based preconception cohort studies.

Methods

We evaluated the feasibility and cost-effectiveness of in-clinic and mail-based biospecimen collection in Pregnancy Study Online (PRESTO), a North American web-based preconception cohort study. Both members of the couple were eligible to participate if their conception attempt time was ≤3 months at enrollment. We invited study participants from the Boston, MA and Detroit, MI metropolitan areas to attend a study visit and provide urine and blood (hereafter "in-clinic protocol"). We invited all other participants to complete mail-based collection of urine and blood spots (hereafter "mail-based protocol"). We compared overall consent and protocol completion rates, demographic characteristics of those who consented and completed either of the protocols, and costs between mail-based and in-clinic protocols for biospecimen collection. Finally, we described logistical challenges pertaining to reliance on mail-based delivery of time-sensitive biospecimens compared with in-clinic methods.

Results

During January 2022-July 2022, 69% of female participants (134/195) and 42% of male participants (31/74) consented to participate in the mail-based protocol. Consent rates for the in-clinic protocol were 39% for female participants (289/739 during March 2014-July 2022) and 25% for male participants (40/157 during March 2017-July 2022). Participants who consented to participate were generally of higher socioeconomic position than non-participants. Deviations from the protocol occurred more frequently within the mail-based protocol but were easily corrected. The cost per participant enrolled was similar across protocols (mail-based: $276.14 vs. in-clinic: $270.38).

Conclusions

Our results indicate that mail-based collection of biospecimens may create opportunities to recruit a larger and more geographically diverse participant population at a comparable cost-per-participant enrolled to in-clinic methods.

Ethical principles for infodemiology and infoveillance studies concerning infodemic management on social media

Big data originating from user interactions on social media play an essential role in infodemiology and infoveillance outcomes, supporting the planning and implementation of public health actions. Notably, the extrapolation of these data requires an awareness of different ethical elements. Previous studies have investigated and discussed the adoption of conventional ethical approaches in the contemporary public health digital surveillance space. However, there is a lack of specific ethical guidelines to orient infodemiology and infoveillance studies concerning infodemic on social media, making it challenging to design digital strategies to combat this phenomenon. Hence, it is necessary to explore if traditional ethical pillars can support digital purposes or whether new ones must be proposed since we are confronted with a complex online misinformation scenario. Therefore, this perspective provides an overview of the current scenario of ethics-related issues of infodemiology and infoveillance on social media for infodemic studies.

The application of artificial intelligence in hepatology: A systematic review

The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.

Using Shopping Data to Improve the Diagnosis of Ovarian Cancer: Survey Study (Preprint)

BACKGROUND

Shopping data can be analyzed using machine learning techniques to study population health. It is unknown if the use of such methods can successfully investigate prediagnosis purchases linked to self-medication of symptoms of ovarian cancer.

OBJECTIVE

The aims of this study were to gain new domain knowledge from women's experiences, understand how women's shopping behavior relates to their pathway to the diagnosis of ovarian cancer, and inform research on computational analysis of shopping data for population health.

METHODS

A web-based survey on individuals' shopping patterns prior to an ovarian cancer diagnosis was analyzed to identify key knowledge about health care purchases. Logistic regression and random forest models were employed to statistically examine how products linked to potential symptoms related to presentation to health care and timing of diagnosis.

RESULTS

Of the 101 women surveyed with ovarian cancer, 58.4% (59/101) bought nonprescription health care products for up to more than a year prior to diagnosis, including pain relief and abdominal products. General practitioner advice was the primary reason for the purchases (23/59, 39%), with 51% (30/59) occurring due to a participant's doctor believing their health problems were due to a condition other than ovarian cancer. Associations were shown between purchases made because a participant's doctor believing their health problems were due to a condition other than ovarian cancer and the following variables: health problems for longer than a year prior to diagnosis (odds ratio [OR] 7.33, 95% CI 1.58-33.97), buying health care products for more than 6 months to a year (OR 3.82, 95% CI 1.04-13.98) or for more than a year (OR 7.64, 95% CI 1.38-42.33), and the number of health care product types purchased (OR 1.54, 95% CI 1.13-2.11). Purchasing patterns are shown to be potentially predictive of a participant's doctor thinking their health problems were due to some condition other than ovarian cancer, with nested cross-validation of random forest classification models achieving an overall in-sample accuracy score of 89.1% and an out-of-sample score of 70.1%.

CONCLUSIONS

Women in the survey were 7 times more likely to have had a duration of more than a year of health problems prior to a diagnosis of ovarian cancer if they were self-medicating based on advice from a doctor rather than having made the decision to self-medicate independently. Predictive modelling indicates that women in such situations, who are self-medicating because their doctor believes their health problems may be due to a condition other than ovarian cancer, exhibit distinct shopping behaviors that may be identifiable within purchasing data. Through exploratory research combining women sharing their behaviors prior to diagnosis and computational analysis of these data, this study demonstrates that women's shopping data could potentially be useful for early ovarian cancer detection.

Toward a Working Definition of eCohort Studies in Health Research: Narrative Literature Review

Background

The wide availability of internet-connected devices and new sensor technologies increasingly infuse longitudinal observational study designs and cohort studies. Simultaneously, the costly and time-consuming nature of traditional cohorts has given rise to alternative, technology-driven designs such as eCohorts, which remain inadequately described in the scientific literature.

Objective

The aim of this study was to outline and discuss what may constitute an eCohort, as well as to formulate a first working definition for health researchers based on a review of the relevant literature.

Methods

A two-staged review and synthesis process was performed comparing 10 traditional cohorts and 10 eCohorts across the six core steps in the life cycle of cohort designs.

Results

eCohorts are a novel type of technology-driven cohort study that are not physically linked to a clinical setting, follow more relaxed and not necessarily random sampling procedures, are primarily based on self-reported and digitally collected data, and systematically aim to leverage the internet and digitalization to achieve flexibility, interactivity, patient-centeredness, and scalability. This approach comes with some hurdles such as data quality, generalizability, and privacy concerns.

Conclusions

eCohorts have similarities to their traditional counterparts; however, they are sufficiently distinct to be treated as a separate type of cohort design. The novelty of eCohorts is associated with a range of strengths and weaknesses that require further exploration.

Balancing Personal Privacy and Public Safety During COVID-19: The Case of South Korea

There has been vigorous debate on how different countries responded to the COVID-19 pandemic. To secure public safety, South Korea actively used personal information at the risk of personal privacy whereas France encouraged voluntary cooperation at the risk of public safety. In this article, after a brief comparison of contextual differences with France, we focus on South Korea's approaches to epidemiological investigations. To evaluate the issues pertaining to personal privacy and public health, we examine the usage patterns of original data, de-identification data, and encrypted data. Our specific proposal discusses the COVID index, which considers collective infection, outbreak intensity, availability of medical infrastructure, and the death rate. Finally, we summarize the findings and lessons for future research and the policy implications.

Digital epidemiological and citizen science methodology to capture prospective physical activity in free-living conditions: a SMART Platform study

OBJECTIVES

The purpose of this study was to develop a replicable methodology of mobile ecological momentary assessments (EMAs) to capture prospective physical activity (PA) within free-living social and physical contexts by leveraging citizen-owned smartphones running on both Android and iOS systems.

DESIGN

Data were obtained from the cross-sectional pilots of the SMART Platform, a citizen science and mobile health initiative.

SETTING

The cities of Regina and Saskatoon, Canada.

PARTICIPANTS

538 citizen scientists (≥18 years) provided PA data during eight consecutive days using a custom-built smartphone application, and after applying a rigid inclusion criteria, 89 were included in the final analysis.

OUTCOME MEASURES

EMAs enabled reporting of light, moderate, and vigorous PA, as well as physical and social contexts of PA. Retrospective PA was reported using International Physical Activity Questionnaire (IPAQ). For both measures, PA intensities were categorised into mean minutes of light and moderate-to-vigorous PA per day. Wilcoxon signed ranks tests and Spearman correlation procedures were conducted to compare PA intensities reported via EMAs and IPAQ.

RESULTS

Using EMAs, citizen scientists reported 140.91, 87.16 and 70.38 mean min/day of overall, light and moderate-to-vigorous PA, respectively, whereas using IPAQ they reported 194.39, 116.99 and 98.42 mean min/day of overall, light and moderate-to-vigorous PA, respectively. Overall (ρ=0.414, p<0.001), light (ρ=0.261, p=0.012) and moderate-to-vigorous PA (ρ=0.316, p=0.009) were fairly correlated between EMA and IPAQ. In comparison with EMAs, using IPAQ, citizen scientists reported significantly greater overall PA in active transportation (p=0.002) and recreation, sport and leisure-time domains (p=0.003).

CONCLUSIONS

This digital epidemiological and citizen science methodology adapted mobile EMAs to capture not only prospective PA, but also important physical and social contexts within which individuals accumulate PA. Ubiquitous tools can be leveraged via citizen science to capture accurate active living patterns of large populations in free-living conditions through innovative EMAs.

Remote monitoring of medication adherence and patient and industry responsibilities in a learning health system

A learning health system (LHS) seeks to establish a closer connection between clinical care and research and establishes new responsibilities for healthcare providers as well as patients. A new set of technological approaches in medication adherence monitoring can potentially yield valuable data within an LHS, and raises the question of the scope and limitations of patients' responsibilities to use them. We argue here that, in principle, it is plausible to suggest that patients have a prima facie obligation to use novel adherence monitors. However, the strength of the obligations depends considerably on the extent to which data that adherence monitors generate are, in fact, used to further the goals of LHSs. The way in which data ownership is structured in the USA poses a considerable challenge here, while the European Union framework offers a more promising alternative.

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.