Scraping the Web for Public Health Gains: Ethical Considerations from a 'Big Data' Research Project on HIV and Incarceration

Non-empirical methods for ethics research on digital technologies in medicine, health care and public health: a systematic journal review

Bioethics has developed approaches to address ethical issues in health care, similar to how technology ethics provides guidelines for ethical research on artificial intelligence, big data, and robotic applications. As these digital technologies are increasingly used in medicine, health care and public health, thus, it is plausible that the approaches of technology ethics have influenced bioethical research. Similar to the "empirical turn" in bioethics, which led to intense debates about appropriate moral theories, ethical frameworks and meta-ethics due to the increased use of empirical methodologies from social sciences, the proliferation of health-related subtypes of technology ethics might have a comparable impact on current bioethical research. This systematic journal review analyses the reporting of ethical frameworks and non-empirical methods in argument-based research articles on digital technologies in medicine, health care and public health that have been published in high-impact bioethics journals. We focus on articles reporting non-empirical research in original contributions. Our aim is to describe currently used methods for the ethical analysis of ethical issues regarding the application of digital technologies in medicine, health care and public health. We confine our analysis to non-empirical methods because empirical methods have been well-researched elsewhere. Finally, we discuss our findings against the background of established methods for health technology assessment, the lack of a typology for non-empirical methods as well as conceptual and methodical change in bioethics. Our descriptive results may serve as a starting point for reflecting on whether current ethical frameworks and non-empirical methods are appropriate to research ethical issues deriving from the application of digital technologies in medicine, health care and public health.

Developing the DIGIFOOD Dashboard to Monitor the Digitalization of Local Food Environments: Interdisciplinary Approach

BACKGROUND

Online food delivery services (OFDS) enable individuals to conveniently access foods from any deliverable location. The increased accessibility to foods may have implications on the consumption of healthful or unhealthful foods. Concerningly, previous research suggests that OFDS offer an abundance of energy-dense and nutrient-poor foods, which are heavily promoted through deals or discounts.

OBJECTIVE

In this paper, we describe the development of the DIGIFOOD dashboard to monitor the digitalization of local food environments in New South Wales, Australia, resulting from the proliferation of OFDS.

METHODS

Together with a team of data scientists, we designed a purpose-built dashboard using Microsoft Power BI. The development process involved three main stages: (1) data acquisition of food outlets via web scraping, (2) data cleaning and processing, and (3) visualization of food outlets on the dashboard. We also describe the categorization process of food outlets to characterize the healthfulness of local, online, and hybrid food environments. These categories included takeaway franchises, independent takeaways, independent restaurants and cafes, supermarkets or groceries, bakeries, alcohol retailers, convenience stores, and sandwich or salad shops.

RESULTS

To date, the DIGIFOOD dashboard has mapped 36,967 unique local food outlets (locally accessible and scraped from Google Maps) and 16,158 unique online food outlets (accessible online and scraped from Uber Eats) across New South Wales, Australia. In 2023, the market-leading OFDS operated in 1061 unique suburbs or localities in New South Wales. The Sydney-Parramatta region, a major urban area in New South Wales accounting for 28 postcodes, recorded the highest number of online food outlets (n=4221). In contrast, the Far West and Orana region, a rural area in New South Wales with only 2 postcodes, recorded the lowest number of food outlets accessible online (n=7). Urban areas appeared to have the greatest increase in total food outlets accessible via online food delivery. In both local and online food environments, it was evident that independent restaurants and cafes comprised the largest proportion of food outlets at 47.2% (17,437/36,967) and 51.8% (8369/16,158), respectively. However, compared to local food environments, the online food environment has relatively more takeaway franchises (2734/16,158, 16.9% compared to 3273/36,967, 8.9%) and independent takeaway outlets (2416/16,158, 14.9% compared to 4026/36,967, 10.9%).

CONCLUSIONS

The DIGIFOOD dashboard leverages the current rich data landscape to display and contrast the availability and healthfulness of food outlets that are locally accessible versus accessible online. The DIGIFOOD dashboard can be a useful monitoring tool for the evolving digital food environment at a regional scale and has the potential to be scaled up at a national level. Future iterations of the dashboard, including data from additional prominent OFDS, can be used by policy makers to identify high-priority areas with limited access to healthful foods both online and locally.

Extracting big data from the internet to support the development of a new patient-reported outcome measure for breast implant illness: a proof of concept study

PURPOSE

Individuals with health conditions often use online patient forums to share their experiences. These patient data are freely available and have rarely been used in patient-reported outcomes (PRO) research. Web scraping, the automated identification and coding of webpage data, can be employed to collect patient experiences for PRO research. The objective of this study was to assess the feasibility of using web scraping to support the development of a new PRO measure for breast implant illness (BII).

METHODS

Nine publicly available BII-specific web forums were chosen post-consultation with two prominent BII advocacy leaders. The Python Selenium and Pandas packages were used to automate extraction of de-identified text from the individual posts/comments into a spreadsheet. Data were coded using a line-by-line approach and constant comparison was used to create top-level domains and sub-domains.

RESULTS

6362 unique codes were identified and organized into four top-level domains of information needs, symptom experiences, life impact of BII, and care experiences. Information needs of women included seeking/sharing information pre-breast implant surgery, post-breast implant surgery, while contemplating explant surgery, and post-explant surgery. Symptoms commonly described by women included fatigue, brain fog, and musculoskeletal symptoms. Many comments described BII's impact on daily activities and psychosocial wellbeing. Lastly, some comments described negative care experiences and experiences related to advocating for themselves to providers.

CONCLUSION

This proof-of-concept study demonstrated the feasibility of employing web scraping as a cost-effective, efficient method to understand the experiences of women with BII. These data will be used to inform the development of a BII-specific PROM.

Few Prison Systems Release Individual Death Data: Death in Custody Reporting Act Completeness, Speed, and Compliance

The United States has one of the largest incarcerated populations per capita. Prisons are dangerous environments, with high in-prison and postrelease mortality. The Death in Custody Reporting Acts (DCRAs) of 2000 and 2013 require deaths of people in correctional custody or caused by law enforcement to be reported to the Bureau of Justice Assistance. These deaths must be reported within 3 months of the death and include 10 required fields (eg, age, cause of death). There is no public reporting requirement. Our Third City Mortality project tracks near-real-time data about individual deaths released publicly and prison system metadata, including data completeness and release speed, across (N = 54) US state, federal (N = 2; Bureau of Prisons, Immigration and Customs Enforcement), Washington, District of Columbia, and Puerto Rico prison systems. Twenty-one (38%) systems release no individual death data; 13 systems release incomplete data slower than 1 year; 19 release timely, but incomplete, death data; and only one system (Iowa) releases complete and timely data. Incomplete, untimely, public prison mortality data limit protective community responses and epidemiology.

Spatio-temporal evaluation of social media as a tool for livestock disease surveillance

Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.

Profiling UK injectable aesthetic practitioners: A national cohort analysis

INTRODUCTION

The United Kingdom (UK) injectables market has been growing rapidly with a lack of robust regulation and to date, no information regarding the profile of practitioners has been published.

AIM

We aim to provide a descriptive and qualitative analysis of the advertised practitioners in the United Kingdom.

METHODS

We performed a systematic search using the internet search engine Google to perform a qualitative descriptive analysis of aesthetic practitioners in the UK. For each contiguous country in the UK: England, Scotland, and Wales, five searches were performed. The list of practitioners was then cross-referenced with professional regulatory bodies, with extraction of registration number, date of registration and presence or absence from the Specialist Register or General Practitioner Register.

RESULTS

3000 websites were visited and evaluated. 1224 independent clinics with 4405 practitioners were identified. 738 were identified as those in business support functions and the remaining 3667 practitioners were undertaking injectable practice. The profile of professions were doctors 32%, nurses 13%, dentists 24% and dental nurses 8%. Of the 1163 doctors identified 481 were on the specialist register (41%) and 219 were on the GP register (19%). 27 specialties were represented in this cohort analysis. Plastic Surgery formed the majority of those who were on the specialist register at 37%, followed by Dermatology at 18%.

CONCLUSION

This paper is the first to describe the range of practitioners, their professional backgrounds and experience who perform non-surgical aesthetic interventions. The range of backgrounds may have an impact on the potential risks to patients and will be an important consideration in proposed legislation to introduce licensing to the industry.

J, Perumalla K. Design of a Novel Information System for Semi-Automated Management of Cybersecurity in Industrial Control Systems

There is an urgent need in many critical infrastructure sectors, including the energy sector, for attaining detailed insights into cybersecurity features and compliance with cybersecurity requirements related to their Operational Technology (OT) deployments. Frequent feature changes of OT devices interfere with this need, posing a great risk to customers. One effective way to address this challenge is via a semi-automated cyber-physical security assurance approach, which enables verification and validation of the OT device cybersecurity claims against actual capabilities, both pre- and post-deployment. To realize this approach, this paper presents new methodology and algorithms to automatically identify cybersecurity-related claims expressed in natural language form in ICS device documents. We developed an identification process that employs natural language processing (NLP) techniques with the goal of semi-automated vetting of detected claims against their device implementation. We also present our novel NLP components for verifying feature claims against relevant cybersecurity requirements. The verification pipeline includes components such as automated vendor identification, device document curation, feature claim identification utilizing sentiment analysis for conflict resolution, and reporting of features that are claimed to be supported or indicated as unsupported. Our novel matching engine represents the first automated information system available in the cybersecurity domain that directly aids the generation of ICS compliance reports.

Advancing a Data Justice Framework for Public Health Surveillance

BACKGROUND

Bioethical debates about privacy, big data, and public health surveillance have not sufficiently engaged the perspectives of those being surveilled. The data justice framework suggests that big data applications have the potential to create disproportionate harm for socially marginalized groups. Using examples from our research on HIV surveillance for individuals incarcerated in jails, we analyze ethical issues in deploying big data in public health surveillance.

METHODS

We conducted qualitative, semi-structured interviews with 24 people living with HIV who had been previously incarcerated in county jails about their perspectives on and experiences with HIV surveillance, as part of a larger study to characterize ethical considerations in leveraging big data techniques to enhance continuity of care for incarcerated people living with HIV.

RESULTS

Most participants expressed support for the state health department tracking HIV testing results and viral load data. Several viewed HIV surveillance as a violation of privacy, and several had actively avoided contact from state public health outreach workers. Participants were most likely to express reservations about surveillance when they viewed the state's motives as self-interested. Perspectives highlight the mistrust that structurally vulnerable people may have in the state's capacity to act as an agent of welfare. Findings suggest that adopting a nuanced, context-sensitive view on surveillance is essential.

CONCLUSIONS

Establishing trustworthiness through interpersonal interactions with public health personnel is important to reversing historical legacies of harm to racial minorities and structurally vulnerable groups. Empowering stakeholders to participate in the design and implementation of data infrastructure and governance is critical for advancing a data justice agenda, and can offset privacy concerns. The next steps in advancing the data justice framework in public health surveillance will be to innovate ways to represent the voices of structurally vulnerable groups in the design and governance of big data initiatives.

The Challenges of Big Data for Research Ethics Committees: A Qualitative Swiss Study

Big data trends in health research challenge the oversight mechanism of the Research Ethics Committees (RECs). The traditional standards of research quality and the mandate of RECs illuminate deficits in facing the computational complexity, methodological novelty, and limited auditability of these approaches. To better understand the challenges facing RECs, we explored the perspectives and attitudes of the members of the seven Swiss Cantonal RECs via semi-structured qualitative interviews. Our interviews reveal limited experience among REC members with the review of big data research, insufficient expertise in data science, and uncertainty about how to mitigate big data research risks. Nonetheless, RECs could strengthen their oversight by training in data science and big data ethics, complementing their role with external experts and ad hoc boards, and introducing precise shared practices.

Early days of the pandemic-The association of economic and socio-political country characteristics with the development of the COVID-19 death toll

This article examines cross-national differences in growth of deaths by COVID-19 over time in the first phase of the pandemic, during the time period of 31st December 2019 to 2nd April 2020. We seek to understand and explain country level reaction in the initial period of the pandemic. We explore socio-economic and socio-political country characteristics as determinants of deaths per day and we examine whether country characteristics act as moderating factors for different growth patterns of deaths per day over time. The country characteristics include variables about economy, globalization, health care and demography. We examine data published by the European Center of Disease Prevention and Control (ECDC) in combination with World Bank data and a webscraping approach. Using a conditional growth model specified as a multilevel regression model with deaths by COVID-19 per day as the outcome variable, we show that economic variables are not significantly associated with decrease or increase of deaths by COVID-19. In contrast, variables about national health care mitigate the impact of the pandemic. Demography shows expected effects with an increase of growth of deaths in countries with a higher percentage of people older than 65 years. Globalization predicts the death toll as well: Social interaction between people is deadly on a short-term scale (in the form of tourism). Our results mirror frequent demands for global investment in national health systems.

COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records

In 2019, COVID-19 quickly spread across the world, infecting billions of people and disrupting the normal lives of citizens in every country. Governments, organizations, and research institutions all over the world are dedicating vast resources to research effective strategies to fight this rapidly propagating virus. With virus testing, most countries publish the number of confirmed cases, dead cases, recovered cases, and locations routinely through various channels and forms. This important data source has enabled researchers worldwide to perform different COVID-19 scientific studies, such as modeling this virus's spreading patterns, developing prevention strategies, and studying the impact of COVID-19 on other aspects of society. However, one major challenge is that there is no standardized, updated, and high-quality data product that covers COVID-19 cases data internationally. This is because different countries may publish their data in unique channels, formats, and time intervals, which hinders researchers from fetching necessary COVID-19 datasets effectively, especially for fine-scale studies. Although existing solutions such as John's Hopkins COVID-19 Dashboard and 1point3acres COVID-19 tracker are widely used, it is difficult for users to access their original dataset and customize those data to meet specific requirements in categories, data structure, and data source selection. To address this challenge, we developed a toolset using cloud-based web scraping to extract, refine, unify, and store COVID-19 cases data at multiple scales for all available countries around the world automatically. The toolset then publishes the data for public access in an effective manner, which could offer users a real time COVID-19 dynamic dataset with a global view. Two case studies are presented about how to utilize the datasets. This toolset can also be easily extended to fulfill other purposes with its open-source nature.

Rural Disparities in Early Childhood Well Child Visit Attendance

PURPOSE

Children should attend well child visits (WCVs) during early childhood so that developmental disorders may be identified as early as possible, so treatment can begin. The aim of this research was to determine if rurality impacts access to WCV during early childhood, and if altering rurality measurement methods impacts outcomes.

DESIGN AND METHODS

We utilized a longitudinal correlational design with early childhood data gathered from the Virginia All Payer Claims Database, which contains claims data from Medicaid and the majority of Virginia commercial insurance payers (n = 6349). WCV attendance was evaluated against three rurality metrics: a traditional metric using Rural-Urban Commuting Area codes, a developed land variable, and a distance to care variable, at a zip code level.

RESULTS

Two of the rurality methods revealed that rural children attend fewer WCVs than their urban counterparts, (67% vs. 50% respectively, using a traditional metric; and a 0.035 increase in WCV attendance for every percent increase in developed land). Differences were attenuated by insurance payer; children with Medicaid attend fewer WCVs than those with private insurance.

CONCLUSIONS

Young children in rural Virginia attend fewer WCVs than their non-rural counterparts, placing them at higher risk for missing timely developmental disorder screenings. The coronavirus disease pandemic has been associated with an abrupt and significant reduction in vaccination rates, which likely indicates fewer WCVs and concomitant developmental screenings. Pediatric nurses should encourage families of young children to develop a plan for continued WCVs, so that early identification of developmental disorders can be achieved.