Alternatives to sharing COVID-19 data with law enforcement: Recommendations for stakeholders

Molldrem, Stephen; Hussain, Mustafa; McClelland, Alexander

doi:10.1016/j.healthpol.2020.10.015

Cited by 13 publications

(11 citation statements)

References 16 publications

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“…The country is thus a unique example of efficient COVID-19 control without lockdown. However, in many European and North American countries, involving police in matters of public health combined together with data sharing is seen as an act of criminalizing illnesses and, therefore, highly critical [ 93 ]. SVN: The country with the least inhabitants in the whole dataset also had the lowest input values for the average health spending per capita.…”

Section: Resultsmentioning

confidence: 99%

COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach

2022

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The COVID-19 pandemic is a global challenge to humankind. To improve the knowledge regarding relevant, efficient and effective COVID-19 measures in health policy, this paper applies a multi-criteria evaluation approach with population, health care, and economic datasets from 19 countries within the OECD. The comparative investigation was based on a Data Envelopment Analysis approach as an efficiency measurement method. Results indicate that on the one hand, factors like population size, population density, and country development stage, did not play a major role in successful pandemic management. On the other hand, pre-pandemic healthcare system policies were decisive. Healthcare systems with a primary care orientation and a high proportion of primary care doctors compared to specialists were found to be more efficient than systems with a medium level of resources that were partly financed through public funding and characterized by a high level of access regulation. Roughly two weeks after the introduction of ad hoc measures, e.g., lockdowns and quarantine policies, we did not observe a direct impact on country-level healthcare efficiency, while delayed lockdowns led to significantly lower efficiency levels during the first COVID-19 wave in 2020. From an economic perspective, strategies without general lockdowns were identified as a more efficient strategy than the full lockdown strategy. Additionally, governmental support of short-term work is promising. Improving the efficiency of COVID-19 countermeasures is crucial in saving as many lives as possible with limited resources.

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Section: Resultsmentioning

confidence: 99%

COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach

2022

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“…This points to a problematic overlay of health and criminal legal systems and institutionalized forms of coordination that meld care and control ( Goetz & Mitchell, 2006 ). The emergence of novel health data sharing protocols between public health and law enforcement in the context of COVID-19 containment efforts provides further illustration of the movement of health data between criminal legal and health systems with troublesome effects ( Luscombe & McClelland, 2021 ; Molldrem et al, 2021 ).…”

Section: Potential Implications For Pwudmentioning

confidence: 99%

Between Care and Control: Examining Surveillance Practices in Harm Reduction

Michaud

Meulen

Guţă

2022

Contemporary Drug Problems

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As harm reduction programs and services proliferate, people who use drugs (PWUD) are increasingly subjected to surveillance through the collection of their personal information, systematic observation, and other means. The data generated from these practices are frequently repurposed across various institutional sites for clinical, evaluative, epidemiological, and administrative uses. Rationales provided for increased surveillance include the more effective provision of care, service optimization, risk stratification, and efficiency in resource allocation. With this in mind, our reflective essay draws on empirical analysis of work within harm reduction services and movements to reflect critically on the impacts and implications of surveillance expansion. While we argue that many surveillance practices are not inherently problematic or harmful, the unchecked expansion of surveillance under a banner of health and harm reduction may contribute to decreased uptake of services, rationing and conditionalities tied to service access, the potential deepening of health disparities amongst some PWUD, and an overlay of health and criminal-legal systems. In this context, surveillance relies on the enlistment of a range of therapeutic actors and reflects the permeable boundary between care and control. We thus call for a broader critical dialogue within harm reduction on the problems and potential impacts posed by surveillance in service settings, the end to data sharing of health information with law enforcement and other criminal legal actors, and deference to the stated need among PWUD for meaningful anonymity when accessing harm reduction and health services.

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“…Data about the exposed/infected people can be gathered from the relevant clinics/diagnostic-centers. It can be shared with different entities to find the contacts of infected people [46]. COVID-19 symptoms and other related data can be gathered with the help of the wearable sensors, ambient tools, and smart phone technology [47,48].…”

Section: Perspective 1: Epidemic Containment Strategies and Ai Rolementioning

confidence: 99%

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

Majeed

Hwang

2021

Symmetry

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This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.

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Alternatives to sharing COVID-19 data with law enforcement: Recommendations for stakeholders

Cited by 13 publications

References 16 publications

COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach

COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach

Between Care and Control: Examining Surveillance Practices in Harm Reduction

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

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