Ethical collection, storage, and use of public health data: a proposal for a national privacy protection

Lessons learnt from the COVID-19 pandemic in selected countries to inform strengthening of public health systems: a qualitative study

INTRODUCTION

The COVID-19 pandemic has prompted governments internationally to consider strengthening their public health systems. To support the work of Ireland's Public Health Reform Expert Advisory Group, the Health Information and Quality Authority, an independent governmental agency, was asked to describe the lessons learnt regarding the public health response to COVID-19 internationally and the applicability of this response for future pandemic preparedness.

METHODS

Semi-structured interviews with key public health representatives from nine countries were conducted. Interviews were conducted in March and April 2022 remotely via Zoom and were recorded. Notes were taken by two researchers, and a thematic analysis undertaken.

RESULTS

Lessons learnt from the COVID-19 pandemic related to three main themes: 1) setting policy; 2) delivering public health interventions; and 3) providing effective communication. Real-time surveillance, evidence synthesis, and cross-sectoral collaboration were reported as essential for policy setting; it was noted that having these functions established prior to the pandemic would lead to a more efficient implementation in a health emergency. Delivering public health interventions such as testing, contact tracing, and vaccination were key to limiting and or mitigating the spread of the SARS-CoV-2 virus. However, a number of challenges were highlighted such as staff capacity and burnout, delays in vaccination procurement, and reduced delivery of regular healthcare services. Clear, consistent, and regular communication of the scientific evidence was key to engaging citizens with mitigation strategies. However, these communication strategies had to compete with an infodemic of information being circulated, particularly through social media.

CONCLUSIONS

Overall, functions relating to policy setting, public health interventions, and communication are key to pandemic response. Ideally, these should be established in the preparedness phase so that they can be rapidly scaled-up during a pandemic.

Trusting the Government to Do the Right Thing: Data Ethics in Australia's Pandemic Response

After a brief overview of ethical issues in an Australian context catalyzed by the current pandemic, this article focuses on data protection in the light of recent debates about COVID-19 data tracking in Australia and globally. This article looks at the issue of trust as a fundamental principle of effective and ethical COVID-safe measures undertaken by the government. Key to ensuring such trust are Habermasian participatory dialogs, which assume trust as a condition of authentic illocution, and an emphasis on short-term data capture.

Assessment of COVID-19 data reporting in 100+ websites and apps in India

India is among the top three countries in the world both in COVID-19 case and death counts. With the pandemic far from over, timely, transparent, and accessible reporting of COVID-19 data continues to be critical for India's pandemic efforts. We systematically analyze the quality of reporting of COVID-19 data in over one hundred government platforms (web and mobile) from India. Our analyses reveal a lack of granular data in the reporting of COVID-19 surveillance, vaccination, and vacant bed availability. As of 5 June 2021, age and gender distribution are available for less than 22% of cases and deaths, and comorbidity distribution is available for less than 30% of deaths. Amid rising concerns of undercounting cases and deaths in India, our results highlight a patchy reporting of granular data even among the reported cases and deaths. Furthermore, total vaccination stratified by healthcare workers, frontline workers, and age brackets is reported by only 14 out of India's 36 subnationals (states and union territories). There is no reporting of adverse events following immunization by vaccine and event type. By showing what, where, and how much data is missing, we highlight the need for a more responsible and transparent reporting of granular COVID-19 data in India.

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made. This research field inspects the measures and models involved in decision-making and seeks solutions to explain them explicitly. Many of the machine learning algorithms cannot manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use. Consequently, our confidence in AI systems can be hindered by the lack of explainability in these black-box models. The XAI becomes more and more crucial for deep learning powered applications, especially for medical and healthcare studies, although in general these deep neural networks can return an arresting dividend in performance. The insufficient explainability and transparency in most existing AI systems can be one of the major reasons that successful implementation and integration of AI tools into routine clinical practice are uncommon. In this study, we first surveyed the current progress of XAI and in particular its advances in healthcare applications. We then introduced our solutions for XAI leveraging multi-modal and multi-centre data fusion, and subsequently validated in two showcases following real clinical scenarios. Comprehensive quantitative and qualitative analyses can prove the efficacy of our proposed XAI solutions, from which we can envisage successful applications in a broader range of clinical questions.

Trust and The Acquisition and Use of Public Health Information

Information is clearly vital to public health, but the acquisition and use of public health data elicit serious privacy concerns. One strategy for navigating this dilemma is to build 'trust' in institutions responsible for health information, thereby reducing privacy concerns and increasing willingness to contribute personal data. This strategy, as currently presented in public health literature, has serious shortcomings. But it can be augmented by appealing to the philosophical analysis of the concept of trust. Philosophers distinguish trust and trustworthiness from cognate attitudes, such as confident reliance. Central to this is value congruence: trust is grounded in the perception of shared values. So, the way to build trust in institutions responsible for health data is for those institutions to develop and display values shared by the public. We defend this approach from objections, such as that trust is an interpersonal attitude inappropriate to the way people relate to organisations. The paper then moves on to the practical application of our strategy. Trust and trustworthiness can reduce privacy concerns and increase willingness to share health data, notably, in the context of internal and external threats to data privacy. We end by appealing for the sort of empirical work our proposal requires.

Artificial intelligence and the future of life sciences

Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials; mobile devices can also be used to improve aspects of both the diagnosis and treatment of diseases. In addition, AI can be used in the development of new drugs or for drug repurposing, in faster diagnosis and more efficient treatment for various diseases, as well as to identify data-driven hypotheses for scientists. In this review, we discuss how AI is starting to revolutionize the life sciences sector.

Disparity in the quality of COVID-19 data reporting across India

BACKGROUND

Transparent and accessible reporting of COVID-19 data is critical for public health efforts. Each Indian state has its own mechanism for reporting COVID-19 data, and the quality of their reporting has not been systematically evaluated. We present a comprehensive assessment of the quality of COVID-19 data reporting done by the Indian state governments between 19 May and 1 June, 2020.

METHODS

We designed a semi-quantitative framework with 45 indicators to assess the quality of COVID-19 data reporting. The framework captures four key aspects of public health data reporting - availability, accessibility, granularity, and privacy. We used this framework to calculate a COVID-19 Data Reporting Score (CDRS, ranging from 0-1) for each state.

RESULTS

Our results indicate a large disparity in the quality of COVID-19 data reporting across India. CDRS varies from 0.61 (good) in Karnataka to 0.0 (poor) in Bihar and Uttar Pradesh, with a median value of 0.26. Ten states do not report data stratified by age, gender, comorbidities or districts. Only ten states provide trend graphics for COVID-19 data. In addition, we identify that Punjab and Chandigarh compromised the privacy of individuals under quarantine by publicly releasing their personally identifiable information. The CDRS is positively associated with the state's sustainable development index for good health and well-being (Pearson correlation: r=0.630,p=0.0003).

CONCLUSIONS

Our assessment informs the public health efforts in India and serves as a guideline for pandemic data reporting. The disparity in CDRS highlights three important findings at the national, state, and individual level. At the national level, it shows the lack of a unified framework for reporting COVID-19 data in India, and highlights the need for a central agency to monitor or audit the quality of data reporting done by the states. Without a unified framework, it is difficult to aggregate the data from different states, gain insights, and coordinate an effective nationwide response to the pandemic. Moreover, it reflects the inadequacy in coordination or sharing of resources among the states. The disparate reporting score also reflects inequality in individual access to public health information and privacy protection based on the state of residence.

Digitalization of contact tracing: balancing data privacy with public health benefit

The COVID-19 pandemic has brought the long-standing public health practice of contact tracing into the public spotlight. While contact tracing and case investigation have been carefully designed to protect privacy, the huge volume of tracing which is being carried out as part of the pandemic response in the United States is highlighting potential concerns around privacy, legality, and equity. Contact tracing during the pandemic has gained particular attention for the new use of digital technologies-both on the consumer side in the form of Exposure Notification applications, and for public health agencies as digital case management software systems enable massive scaling of operations. While the consumer application side of digital innovation has dominated the news and academic discourse around privacy, people are likely to interact more intensively with public health agencies and their use of digital case management systems. Effective use of digital case management for contact tracing requires revisiting the existing legal frameworks, privacy protections, and security practices for management of sensitive health data. The scale of these tools and demands of an unprecedented pandemic response are introducing new risks through the collection of huge volumes of data, and expanding requirements for more adept data sharing among jurisdictions. Public health agencies must strengthen their best practices for data collection and protection even in the absence of comprehensive or clear guidance. This requires navigating a difficult balance between rigorous data protection and remaining highly adaptive and agile.

Ethical practice in sharing and mining medical data

Purpose

The purpose of this paper is to review current practice in sharing and mining medical data revealing benefits, costs and ethical issues. Based on stakeholder perspectives and values, the authors create an ethical code to regulate the sharing and mining of medical information.

Design/methodology/approach

The framework is based on a review of academic, practitioner and legal research.

Findings

Owing to the inability of current safeguards to protect consumers from risks related to the disclosure of medical information, the authors develop a framework for ethical sharing and mining of medical data, security, transparency, respect, accountability, community and quality (STRACQ), which espouses security, transparency, respect, accountability, community and quality as the basic tenets of ethical data sharing and mining practice.

Research limitations/implications

The STRACQ framework is an original, previously unpublished contribution that will require modification over time based on discussion and debate within and among the academy, medical community and public policymakers.

Social implications

The framework for sharing borrows from the Fair Credit Reporting Act, allowing the collection and dissemination of identified medical data but placing strict limitations on use. Following this framework, benefits of shared and mined medical data are freely available with appropriate safeguards for consumer privacy.

Originality/value

Mandates for adoption of electronic health-care records require an understanding of medical data mining. This paper presents a review of data mining techniques and reasons for engaging in the practice of identifying benefits, costs and ethical issues. The authors create an original framework, STRACQ, for ethical sharing and mining of medical information, allowing knowledge exploration while protecting consumer privacy.

Disparity in the quality of COVID-19 data reporting across India

BACKGROUND

Transparent and accessible reporting of COVID-19 data is critical for public health efforts. Each state and union territory (UT) of India has its own mechanism for reporting COVID-19 data, and the quality of their reporting has not been systematically evaluated. We present a comprehensive assessment of the quality of COVID-19 data reporting done by the Indian state and union territory governments. This assessment informs the public health efforts in India and serves as a guideline for pandemic data reporting by other governments.

METHODS

We designed a semi-quantitative framework to assess the quality of COVID-19 data reporting done by the states and union territories of India. This framework captures four key aspects of public health data reporting - availability, accessibility, granularity, and privacy. We then used this framework to calculate a COVID-19 Data Reporting Score (CDRS, ranging from 0 to 1) for 29 states based on the quality of COVID-19 data reporting done by the state during the two-week period from 19 May to 1 June, 2020. States that reported less than 10 total confirmed cases as of May 18 were excluded from the study.

FINDINGS

Our results indicate a strong disparity in the quality of COVID-19 data reporting done by the state governments in India. CDRS varies from 0.61 (good) in Karnataka to 0.0 (poor) in Bihar and Uttar Pradesh, with a median value of 0.26. Only ten states provide a visual representation of the trend in COVID-19 data. Ten states do not report any data stratified by age, gender, comorbidities or districts. In addition, we identify that Punjab and Chandigarh compromised the privacy of individuals under quarantine by releasing their personally identifiable information on the official websites. Across the states, the CDRS is positively associated with the state's sustainable development index for good health and well-being (Pearson correlation: r=0.630, p=0.0003).

INTERPRETATION

The disparity in CDRS across states highlights three important findings at the national, state, and individual level. At the national level, it shows the lack of a unified framework for reporting COVID-19 data in India, and highlights the need for a central agency to monitor or audit the quality of data reporting done by the states. Without a unified framework, it is difficult to aggregate the data from different states, gain insights from them, and coordinate an effective nationwide response to the pandemic. Moreover, it reflects the inadequacy in coordination or sharing of resources among the states in India. Coordination among states is particularly important as more people start moving across states in the coming months. The disparate reporting score also reflects inequality in individual access to public health information and privacy protection based on the state of residence.

FUNDING

J.Z. is supported by NSF CCF 1763191, NIH R21 MD012867-01, NIH P30AG059307, NIH U01MH098953 and grants from the Silicon Valley Foundation and the Chan-Zuckerberg Initiative.

Ethics in Health Informatics

Contemporary bioethics was fledged and is sustained by challenges posed by new technologies. These technologies have affected many lives. Yet health informatics affects more lives than any of them. The challenges include the development and the appropriate uses and users of machine learning software, the balancing of privacy rights against the needs of public health and clinical practice in a time of Big Data analytics, whether and how to use this technology, and the role of ethics and standards in health policy. Historical antecedents in statistics and evidence-based practice foreshadow some of the difficulties now faced, but the scope and scale of these challenges requires that ethics, too, be brought to scale in parallel, especially given the size of contemporary data sets and the processing power of new computers. Fortunately, applied ethics affords a variety of tools to help identify and rank applicable values, support best practices, and contribute to standards. The bioethics community can in partnership with the informatics community arrive at policies that promote the health sciences while reaffirming the many and varied rights that patients expect will be honored.

Standalone smartphone apps for mental health-a systematic review and meta-analysis

While smartphone usage is ubiquitous, and the app market for smartphone apps targeted at mental health is growing rapidly, the evidence of standalone apps for treating mental health symptoms is still unclear. This meta-analysis investigated the efficacy of standalone smartphone apps for mental health. A comprehensive literature search was conducted in February 2018 on randomized controlled trials investigating the effects of standalone apps for mental health in adults with heightened symptom severity, compared to a control group. A random-effects model was employed. When insufficient comparisons were available, data was presented in a narrative synthesis. Outcomes included assessments of mental health disorder symptom severity specifically targeted at by the app. In total, 5945 records were identified and 165 full-text articles were screened for inclusion by two independent researchers. Nineteen trials with 3681 participants were included in the analysis: depression (k = 6), anxiety (k = 4), substance use (k = 5), self-injurious thoughts and behaviors (k = 4), PTSD (k = 2), and sleep problems (k = 2). Effects on depression (Hedges’ g = 0.33, 95%CI 0.10–0.57, P = 0.005, NNT = 5.43, I² = 59%) and on smoking behavior (g = 0.39, 95%CI 0.21–0.57, NNT = 4.59, P ≤ 0.001, I² = 0%) were significant. No significant pooled effects were found for anxiety, suicidal ideation, self-injury, or alcohol use (g = −0.14 to 0.18). Effect sizes for single trials ranged from g = −0.05 to 0.14 for PTSD and g = 0.72 to 0.84 for insomnia. Although some trials showed potential of apps targeting mental health symptoms, using smartphone apps as standalone psychological interventions cannot be recommended based on the current level of evidence.

Public Health Data Collection and Implementation of the Revised Common Rule

For the first time, the revised Common Rule specifies that public health surveillance activities are not research. This article reviews the historical development of the public health surveillance exclusion and implications for other foundational public health practices.

American College of Physicians Ethics Manual: Seventh Edition

Medicine, law, and social values are not static. Reexamining the ethical tenets of medicine and their application in new circumstances is a necessary exercise. The seventh edition of the American College of Physicians (ACP) Ethics Manual covers emerging issues in medical ethics and revisits older ones that are still very pertinent. It reflects on many of the ethical tensions in medicine and attempts to shed light on how existing principles extend to emerging concerns. In addition, by reiterating ethical principles that have provided guidance in resolving past ethical problems, the Manual may help physicians avert future problems. The Manual is not a substitute for the experience and integrity of individual physicians, but it may serve as a reminder of the shared duties of the medical profession.

Artificial intelligence in healthcare

Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.

Biometrics and public health surveillance in criminalised and key populations: policy, ethics, and human rights considerations

Widespread public health surveillance efforts focused on key populations (men who have sex with men, sex workers, people who inject drugs, and others) gather data on population sizes, HIV prevalence, and other information for planning and resource allocation. Biometric identification might improve this data gathering. However, in the context of extensive criminalisation of these populations, the use of biometrics such as fingerprints raises concerns that are insufficiently addressed in current policies. These concerns include infringing privacy, exposing participants to risks of legal action or violence, biasing surveillance results, and undermining trust in the health system. We set out key ethics and human rights considerations regarding the use of biometrics in HIV surveillance among these populations, and outline a typology of jurisdictions wherein such methods might be considered, based on data about legal, political, and social environments. In this Review, we suggest that the biometrics approach is not currently likely to be appropriate in many jurisdictions.

Big Data in Public Health: Terminology, Machine Learning, and Privacy

The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores several key issues that have arisen around big data. First, we propose a taxonomy of sources of big data to clarify terminology and identify threads common across some subtypes of big data. Next, we consider common public health research and practice uses for big data, including surveillance, hypothesis-generating research, and causal inference, while exploring the role that machine learning may play in each use. We then consider the ethical implications of the big data revolution with particular emphasis on maintaining appropriate care for privacy in a world in which technology is rapidly changing social norms regarding the need for (and even the meaning of) privacy. Finally, we make suggestions regarding structuring teams and training to succeed in working with big data in research and practice.

A 21st-Century Public Health Approach to Abortion

In the United States, groups advocating for and against abortion rights often deploy public health arguments to advance their positions. Recently, these arguments have evolved into state laws that use the government health department infrastructure to increase law enforcement and regulatory activities around abortion. Many major medical and public health associations oppose these new laws because they are not evidence-based and do not protect women's health. Yet state health departments have been defending these laws in court. We propose a 21st-century public health approach to abortion based in an accepted public health framework. Specifically, we apply the Centers for Disease Control and Prevention's 10 Essential Public Health Services framework to abortion to describe how health departments should engage with abortion. With this public health framework as our guide, we argue that health departments should be facilitating women's ability to obtain an abortion in the state and county where they reside, researching barriers to abortion care in their states and counties, and promoting the use of a scientific evidence base in abortion-related laws, policies, regulations, and implementation of essential services.

Personally Identifiable Information in State Laws: Use, Release, and Collaboration at Health Departments

Despite benefits to sharing data among public health programs, confidentiality laws are often presumed to obstruct collaboration or data sharing. We present an overview of the use and release of confidential, personally identifiable information as consistent with public health interests and identify opportunities to align data-sharing procedures with use and release provisions in state laws to improve program outcomes. In August 2013, Centers for Disease Control and Prevention staff and legal researchers from the National Nurse-Led Care Consortium conducted a review of state laws regulating state and local health departments in 50 states and the District of Columbia. Nearly all states and the District of Columbia employ provisions for the general use and release of personally identifiable information without patient consent; disease-specific use or release provisions vary by state. Absence of law regarding use and release provisions was noted. Health departments should assess existing state laws to determine whether the use or release of personally identifiable information is permitted. Absence of direction should not prevent data sharing but prompt an analysis of existing provisions in confidentiality laws.

Orchestrating differential data access for translational research: a pilot implementation

Background

Translational researchers need robust IT solutions to access a range of data types, varying from public data sets to pseudonymised patient information with restricted access, provided on a case by case basis. The reason for this complication is that managing access policies to sensitive human data must consider issues of data confidentiality, identifiability, extent of consent, and data usage agreements. All these ethical, social and legal aspects must be incorporated into a differential management of restricted access to sensitive data.

Methods

In this paper we present a pilot system that uses several common open source software components in a novel combination to coordinate access to heterogeneous biomedical data repositories containing open data (open access) as well as sensitive data (restricted access) in the domain of biobanking and biosample research. Our approach is based on a digital identity federation and software to manage resource access entitlements.

Results

Open source software components were assembled and configured in such a way that they allow for different ways of restricted access according to the protection needs of the data. We have tested the resulting pilot infrastructure and assessed its performance, feasibility and reproducibility.

Conclusions

Common open source software components are sufficient to allow for the creation of a secure system for differential access to sensitive data. The implementation of this system is exemplary for researchers facing similar requirements for restricted access data. Here we report experience and lessons learnt of our pilot implementation, which may be useful for similar use cases. Furthermore, we discuss possible extensions for more complex scenarios.

Online Public Health Intelligence: Ethical Considerations at the Big Data Era

Often times terms such as Big Data, increasing digital footprints in the Internet accompanied with advancing analytical techniques, represent a major opportunity to improve public health surveillance and delivery of interventions. However, early adaption of Big Data in other fields revealed ethical challenges that could undermine privacy and autonomy of individuals and cause stigmatization. This chapter aims to identify the benefits and risks associated with the public health application of Big Data through ethical lenses. In doing so, it highlights the need for ethical discussion and framework towards an effective utilization of technologies. We then discuss key strategies to mitigate potentially harmful aspects of Big Data to facilitate its safe and effective implementation.

Legal assessment tool (LAT): an interactive tool to address privacy and data protection issues for data sharing

BACKGROUND

In an unprecedented rate data in the life sciences is generated and stored in many different databases. An ever increasing part of this data is human health data and therefore falls under data protected by legal regulations. As part of the BioMedBridges project, which created infrastructures that connect more than 10 ESFRI research infrastructures (RI), the legal and ethical prerequisites of data sharing were examined employing a novel and pragmatic approach.

METHODS

We employed concepts from computer science to create legal requirement clusters that enable legal interoperability between databases for the areas of data protection, data security, Intellectual Property (IP) and security of biosample data. We analysed and extracted access rules and constraints from all data providers (databases) involved in the building of data bridges covering many of Europe's most important databases. These requirement clusters were applied to five usage scenarios representing the data flow in different data bridges: Image bridge, Phenotype data bridge, Personalised medicine data bridge, Structural data bridge, and Biosample data bridge. A matrix was built to relate the important concepts from data protection regulations (e.g. pseudonymisation, identifyability, access control, consent management) with the results of the requirement clusters. An interactive user interface for querying the matrix for requirements necessary for compliant data sharing was created.

RESULTS

To guide researchers without the need for legal expert knowledge through legal requirements, an interactive tool, the Legal Assessment Tool (LAT), was developed. LAT provides researchers interactively with a selection process to characterise the involved types of data and databases and provides suitable requirements and recommendations for concrete data access and sharing situations. The results provided by LAT are based on an analysis of the data access and sharing conditions for different kinds of data of major databases in Europe.

CONCLUSIONS

Data sharing for research purposes must be opened for human health data and LAT is one of the means to achieve this aim. In summary, LAT provides requirements in an interactive way for compliant data access and sharing with appropriate safeguards, restrictions and responsibilities by introducing a culture of responsibility and data governance when dealing with human data.

Quality of Big Data in health care

Purpose

– The current trend in Big Data analytics and in particular health information technology is toward building sophisticated models, methods and tools for business, operational and clinical intelligence. However, the critical issue of data quality required for these models is not getting the attention it deserves. The purpose of this paper is to highlight the issues of data quality in the context of Big Data health care analytics.

Design/methodology/approach

– The insights presented in this paper are the results of analytics work that was done in different organizations on a variety of health data sets. The data sets include Medicare and Medicaid claims, provider enrollment data sets from both public and private sources, electronic health records from regional health centers accessed through partnerships with health care claims processing entities under health privacy protected guidelines.

Findings

– Assessment of data quality in health care has to consider: first, the entire lifecycle of health data; second, problems arising from errors and inaccuracies in the data itself; third, the source(s) and the pedigree of the data; and fourth, how the underlying purpose of data collection impact the analytic processing and knowledge expected to be derived. Automation in the form of data handling, storage, entry and processing technologies is to be viewed as a double-edged sword. At one level, automation can be a good solution, while at another level it can create a different set of data quality issues. Implementation of health care analytics with Big Data is enabled by a road map that addresses the organizational and technological aspects of data quality assurance.

Practical implications

– The value derived from the use of analytics should be the primary determinant of data quality. Based on this premise, health care enterprises embracing Big Data should have a road map for a systematic approach to data quality. Health care data quality problems can be so very specific that organizations might have to build their own custom software or data quality rule engines.

Originality/value

– Today, data quality issues are diagnosed and addressed in a piece-meal fashion. The authors recommend a data lifecycle approach and provide a road map, that is more appropriate with the dimensions of Big Data and fits different stages in the analytical workflow.

Enhancing participation to health screening campaigns by group interactions

Improving the prevention efficacy of health screening campaigns by increasing their attendance rate represents a challenge that calls for new strategies. This paper analyzes the response to a Pap test screening campaign of 155,000 women over the last decade. Using a mathematical model of statistical physics origins we derive a quantitative estimate of the mutual influence between participating groups. Different scenarios and possible actions are studied from the cost-benefit perspective. The performance of alternative strategies to improve participation are forecasted and compared. The results show that the standard strategies with incentives concentrated toward the low participating groups are outperformed by those toward pivotal groups with higher influence power. Our method provides a flexible tool useful to support policy maker decisions while complying with ethical regulations on privacy and confidentiality.

Surveillance for Hepatitis C

Hepatitis C is a global public health problem. Globally, an estimated 170 million persons (3 % of the world’s population) have been infected with the hepatitis C virus, and an estimated 350,000 persons die annually from complications of chronic hepatitis C. Furthermore, an increasing trend in hepatitis C mortality in the USA was observed over the last decade; in 2007, mortality associated with hepatitis C surpassed mortality associated with HIV. As the hepatitis C epidemic continues, it is increasingly important to accurately measure hepatitis C-related morbidity and mortality in order to inform public health programs and policies and prioritize and evaluate prevention efforts. This chapter provides an overview of hepatitis C surveillance and methods used in the USA with some examples from other countries.

A systematic review of barriers to data sharing in public health

Background

In the current information age, the use of data has become essential for decision making in public health at the local, national, and global level. Despite a global commitment to the use and sharing of public health data, this can be challenging in reality. No systematic framework or global operational guidelines have been created for data sharing in public health. Barriers at different levels have limited data sharing but have only been anecdotally discussed or in the context of specific case studies. Incomplete systematic evidence on the scope and variety of these barriers has limited opportunities to maximize the value and use of public health data for science and policy.

Methods

We conducted a systematic literature review of potential barriers to public health data sharing. Documents that described barriers to sharing of routinely collected public health data were eligible for inclusion and reviewed independently by a team of experts. We grouped identified barriers in a taxonomy for a focused international dialogue on solutions.

Results

Twenty potential barriers were identified and classified in six categories: technical, motivational, economic, political, legal and ethical. The first three categories are deeply rooted in well-known challenges of health information systems for which structural solutions have yet to be found; the last three have solutions that lie in an international dialogue aimed at generating consensus on policies and instruments for data sharing.

Conclusions

The simultaneous effect of multiple interacting barriers ranging from technical to intangible issues has greatly complicated advances in public health data sharing. A systematic framework of barriers to data sharing in public health will be essential to accelerate the use of valuable information for the global good.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2458-14-1144) contains supplementary material, which is available to authorized users.

An integrative review of information systems and terminologies used in local health departments

OBJECTIVE

The purpose of this integrative review based on the published literature was to identify information systems currently being used by local health departments and to determine the extent to which standard terminology was used to communicate data, interventions, and outcomes to improve public health informatics at the local health department (LHD) level and better inform research, policy, and programs.

MATERIALS AND METHODS

Whittemore and Knafl's integrative review methodology was used. Data were obtained through key word searches of three publication databases and reference lists of retrieved articles and consulting with experts to identify landmark works. The final sample included 45 articles analyzed and synthesized using the matrix method.

RESULTS

The results indicated a wide array of information systems were used by LHDs and supported diverse functions aligned with five categories: administration; surveillance; health records; registries; and consumer resources. Detail regarding specific programs being used, location or extent of use, or effectiveness was lacking. The synthesis indicated evidence of growing interest in health information exchange groups, yet few studies described use of data standards or standard terminology in LHDs.

DISCUSSION

Research to address these gaps is needed to provide current, meaningful data that inform public health informatics research, policy, and initiatives at and across the LHD level.

CONCLUSIONS

Coordination at a state or national level is recommended to collect information efficiently about LHD information systems that will inform improvements while minimizing duplication of efforts and financial burden. Until this happens, efforts to strengthen LHD information systems and policies may be significantly challenged.

Shifting the paradigm: using HIV surveillance data as a foundation for improving HIV care and preventing HIV infection

CONTEXT

Reducing HIV incidence in the United States and improving health outcomes for people living with HIV hinge on improving access to highly effective treatment and overcoming barriers to continuous treatment. Using laboratory tests routinely reported for HIV surveillance to monitor individuals' receipt of HIV care and contacting them to facilitate optimal care could help achieve these objectives. Historically, surveillance-based public health intervention with individuals for HIV control has been controversial because of concerns that risks to privacy and autonomy could outweigh benefits. But with the availability of lifesaving, transmission-interrupting treatment for HIV infection, some health departments have begun surveillance-based outreach to facilitate HIV medical care.

METHODS

Guided by ethics frameworks, we explored the ethical arguments for changing the uses of HIV surveillance data. To identify ethical, procedural, and strategic considerations, we reviewed the activities of health departments that are using HIV surveillance data to contact persons identified as needing assistance with initiating or returning to care.

FINDINGS

Although privacy concerns surrounding the uses of HIV surveillance data still exist, there are ethical concerns associated with not using HIV surveillance to maximize the benefits from HIV medical care and treatment. Early efforts to use surveillance data to facilitate optimal HIV medical care illustrate how the ethical burdens may vary depending on the local context and the specifics of implementation. Health departments laid the foundation for these activities by engaging stakeholders to gain their trust in sharing sensitive information; establishing or strengthening legal, policy and governance infrastructure; and developing communication and follow-up protocols that protect privacy.

CONCLUSIONS

We describe a shift toward using HIV surveillance to facilitate optimal HIV care. Health departments should review the considerations outlined before implementing new uses of HIV surveillance data, and they should commit to an ongoing review of activities with the objective of balancing beneficence, respect for persons, and justice.

Health Information in the Background: Justifying Public Health Surveillance Without Patient Consent

Often we think of collecting, storing, and using health data without patient consent as unethical and illegal. However, there are situations where the collection of health information without consent is not only ethical and legal, it is essential for community and public health. Public health surveillance – the ongoing, systematic collection, analysis, and interpretation of health-related data with the a priori purpose of preventing or controlling disease or injury, or identifying unusual events of public health importance, followed by the dissemination and use of information for public health action – allows the government to meet its ethical obligation to protect the health of the population. By adhering to public health ethics principles, public health surveillance systems, including pervasive information and computing technology (PICT), can be designed and implemented in ways that both honor individuals and protect communities.

Big bad data: law, public health, and biomedical databases

The accelerating adoption of electronic health record (EHR) systems will have far-reaching implications for public health research and surveillance, which in turn could lead to changes in public policy, statutes, and regulations. The public health benefits of EHR use can be significant. However, researchers and analysts who rely on EHR data must proceed with caution and understand the potential limitations of EHRs. Because of clinicians' workloads, poor user-interface design, and other factors, EHR data can be erroneous, miscoded, fragmented, and incomplete. In addition, public health findings can be tainted by the problems of selection bias, confounding bias, and measurement bias. These flaws may become all the more troubling and important in an era of electronic "big data," in which a massive amount of information is processed automatically, without human checks. Thus, we conclude the paper by outlining several regulatory and other interventions to address data analysis difficulties that could result in invalid conclusions and unsound public health policies.

A framework for understanding cancer comparative effectiveness research data needs

OBJECTIVES

Randomized controlled trials remain the gold standard for evaluating cancer intervention efficacy. Randomized trials are not always feasible, practical, or timely and often don't adequately reflect patient heterogeneity and real-world clinical practice. Comparative effectiveness research can leverage secondary data to help fill knowledge gaps randomized trials leave unaddressed; however, comparative effectiveness research also faces shortcomings. The goal of this project was to develop a new model and inform an evolving framework articulating cancer comparative effectiveness research data needs.

STUDY DESIGN AND SETTING

We examined prevalent models and conducted semi-structured discussions with 76 clinicians and comparative effectiveness research researchers affiliated with the Agency for Healthcare Research and Quality's cancer comparative effectiveness research programs.

RESULTS

A new model was iteratively developed and presents cancer comparative effectiveness research and important measures in a patient-centered, longitudinal chronic care model better reflecting contemporary cancer care in the context of the cancer care continuum, rather than a single-episode, acute-care perspective.

CONCLUSION

Immediately relevant for federally funded comparative effectiveness research programs, the model informs an evolving framework articulating cancer comparative effectiveness research data needs, including evolutionary enhancements to registries and epidemiologic research data systems. We discuss elements of contemporary clinical practice, methodology improvements, and related needs affecting comparative effectiveness research's ability to yield findings clinicians, policy makers, and stakeholders can confidently act on.

Public health surveillance and knowing about health in the context of growing sources of health data

The past decade has brought substantial changes in how data related to a community's health are collected, stored, and used to inform decisions about health interventions. Despite these changes, the purpose of public health surveillance has remained constant for more than a century. Public health surveillance is the ongoing, systematic collection, analysis, and interpretation of health-related data with the a priori purpose of preventing or controlling disease or injury, or of identifying unusual events of public health importance, followed by the dissemination and use of information for public health action. Surveillance is an important and necessary contributor to knowledge of a community's health. The public health system is responsible for ensuring that public health surveillance is conducted with appropriate practices and safeguards in order to maintain the public's trust.

Ethical justification for conducting public health surveillance without patient consent

Public health surveillance by necessity occurs without explicit patient consent. There is strong legal and scientific support for maintaining name-based reporting of infectious diseases and other types of public health surveillance. We present conditions under which surveillance without explicit patient consent is ethically justifiable using principles of contemporary clinical and public health ethics. Overriding individual autonomy must be justified in terms of the obligation of public health to improve population health, reduce inequities, attend to the health of vulnerable and systematically disadvantaged persons, and prevent harm. In addition, data elements collected without consent must represent the minimal necessary interference, lead to effective public health action, and be maintained securely.

"Who owns your poop?": insights regarding the intersection of human microbiome research and the ELSI aspects of biobanking and related studies

Background

While the social, ethical, and legal implications of biobanking and large scale data sharing are already complicated enough, they may be further compounded by research on the human microbiome.

Discussion

The human microbiome is the entire complement of microorganisms that exists in and on every human body. Currently most biobanks focus primarily on human tissues and/or associated data (e.g. health records). Accordingly, most discussions in the social sciences and humanities on these issues are focused (appropriately so) on the implications of biobanks and sharing data derived from human tissues. However, rapid advances in human microbiome research involve collecting large amounts of data on microorganisms that exist in symbiotic relationships with the human body. Currently it is not clear whether these microorganisms should be considered part of or separate from the human body. Arguments can be made for both, but ultimately it seems that the dichotomy of human versus non-human and self versus non-self inevitably breaks down in this context. This situation has the potential to add further complications to debates on biobanking.

Summary

In this paper, we revisit some of the core problem areas of privacy, consent, ownership, return of results, governance, and benefit sharing, and consider how they might be impacted upon by human microbiome research. Some of the issues discussed also have relevance to other forms of microbial research. Discussion of these themes is guided by conceptual analysis of microbiome research and interviews with leading Canadian scientists in the field.

Informational privacy, public health, and state laws

Developments in information technology that make it possible to rapidly transmit health information also raise questions about the possible inappropriate use and protection of identifiable (or potentially identifiable) personal health information. Despite efforts to improve state laws, adoption of provisions has lagged. We found that half of states have no statutes addressing nondisclosure of personally identifiable health information generally held by public health agencies. Exceptional treatment of HIV, sexually transmitted infections, or tuberculosis-related information was common. Where other provisions were found, there was little consistency in the laws across states. The variation in state laws supports the need to build consensus on the appropriate use and disclosure of public health information among public health practitioners.

The Bio-PIN: a concept to improve biobanking

A new biobanking method is proposed, wherein samples and associated data would be deposited anonymously and labelled using a PIN code that is produced on the basis of personal biological characteristics, such as single nucleotide polymorphisms. The code would be the 'Bio-PIN' to uniquely distinguish the sample depositors, plus their samples and data. This method could help to diminish several long-discussed ethical, legal and societal problems in biobanking regarding privacy, informed consent, autonomy, data security and public trust.

Secondary use of electronic health record data: spontaneous triggered adverse drug event reporting

PURPOSE

Physicians in the United States report fewer than 1% of adverse drug events (ADEs) to the Food and Drug Administration (FDA), but frequently document ADEs within electronic health records (EHRs). We developed and implemented a generalizable, scalable EHR-based system to automatically send electronic ADE reports to the FDA in real-time.

METHODS

Proof-of-concept study involving 26 clinicians given access to EHR-based ADE reporting functionality from December 2008 to May 2009.

MEASUREMENTS

Number and content of ADE reports; severity of adverse reactions (clinician and computer algorithm defined); clinician survey.

RESULTS

During the study period, 26 clinicians submitted 217 reports to the FDA. The clinicians defined 23% of the ADEs as serious and a computer algorithm defined 4% of the ADEs as serious. The most common drug classes were cardiovascular drugs (40%), central nervous system drugs (19%), analgesics (13%), and endocrine drugs (7%). The reports contained information, pre-filled from the EHR, about comorbid conditions (207 reports [95%] listed 1899 comorbid conditions), concurrent medications (193 reports [89%] listed 1687 concurrent medications), weight (209 reports [96%]), and laboratory data (215 reports [99%]). It took clinicians a mean of 53 seconds to complete and send the form. In the clinician survey, 21 of 23 respondents (91%) said they had submitted zero ADE reports to the FDA in the prior 12 months.

CONCLUSIONS

EHR-based, triggered ADE reporting is efficient and acceptable to clinicians, provides detailed clinical information, and has the potential to greatly increase the number and quality of spontaneous reports submitted to the FDA.

Data governance and stewardship: designing data stewardship entities and advancing data access

U.S. health policy is engaged in a struggle over access to health information, in particular, the conditions under which information should be accessible for research when appropriate privacy protections and security safeguards are in place. The expanded use of health information-an inevitable step in an information age-is widely considered be essential to health system reform. Models exist for the creation of data-sharing arrangements that promote proper use of information in a safe and secure environment and with attention to ethical standards. Data stewardship is a concept with deep roots in the science and practice of data collection, sharing, and analysis. Reflecting the values of fair information practice, data stewardship denotes an approach to the management of data, particularly data that can identify individuals. The concept of a data steward is intended to convey a fiduciary (or trust) level of responsibility toward the data. Data governance is the process by which responsibilities of stewardship are conceptualized and carried out. As the concept of health information data stewardship advances in a technology-enabled environment, the question is whether legal barriers to data access and use will begin to give way. One possible answer may lie in defining the public interest in certain data uses, tying provider participation in federal health programs to the release of all-payer data to recognized data stewardship entities for aggregation and management, and enabling such entities to foster and enable the creation of knowledge through research.

Data governance and stewardship: designing data stewardship entities and advancing data access

U.S. health policy is engaged in a struggle over access to health information, in particular, the conditions under which information should be accessible for research when appropriate privacy protections and security safeguards are in place. The expanded use of health information-an inevitable step in an information age-is widely considered be essential to health system reform. Models exist for the creation of data-sharing arrangements that promote proper use of information in a safe and secure environment and with attention to ethical standards. Data stewardship is a concept with deep roots in the science and practice of data collection, sharing, and analysis. Reflecting the values of fair information practice, data stewardship denotes an approach to the management of data, particularly data that can identify individuals. The concept of a data steward is intended to convey a fiduciary (or trust) level of responsibility toward the data. Data governance is the process by which responsibilities of stewardship are conceptualized and carried out. As the concept of health information data stewardship advances in a technology-enabled environment, the question is whether legal barriers to data access and use will begin to give way. One possible answer may lie in defining the public interest in certain data uses, tying provider participation in federal health programs to the release of all-payer data to recognized data stewardship entities for aggregation and management, and enabling such entities to foster and enable the creation of knowledge through research.