Online Public Health Intelligence: Ethical Considerations at the Big Data Era
Ontology highlight
ABSTRACT:
SUBMITTER: Shaban-Nejad A
PROVIDER: S-EPMC7121834 | biostudies-literature | 2017 Jan
REPOSITORIES: biostudies-literature
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Project description:The field of public health includes a wide scope of activities and professional disciplines, ranging from sanitation, health protection, epidemiology, environmental health, financing, health promotion, including supervision, or the provision of clinical care. Each of these disciplines works in systems that face ethical dilemmas, making it important that public health workers have motivation to understand and practice within the ethical guidelines of their profession, thus making ethics an important component of training and practice. The dangers of ethical lapses are overwhelmingly apparent in the case of the Eugenics movement of the early 20th century which metamorphosed from forced sterilizations in many liberal democratic countries into mass murder of physically and mentally handicapped children and adults in Nazi Germany. Between 1939 and 1941, 180 thousand psychiatric patients along with an equivalent number of handicapped children and adults were killed in an organized extermination program in Germany by lethal gassing. This method was then applied to the industrialized murder or Holocaust of six million Jews and millions of other “untermenschen” (sub human) in the greatest genocide in human history. Shortly after World War II ended the Nuremberg Trials of Nazi war criminals were conducted including medical doctors, and some were executed for crimes against humanity. This was followed by the 1948 United Nations Declaration on Human Rights and by the World Medical Association’s Helsinki Declaration. Both are widely accepted as cornerstone documents—the latter specifically governing ethical standards related to human experimentation—and are revised regularly since being issued in 1964. But genocide has not disappeared, nor has unscrupulous experimentation such as the Tuskegee experiment on black Americans infected with syphilis and left untreated even after the availability of a cure, penicillin. Ethical standards are now required by “Helsinki Committees”—ethical review boards—in most medical facilities worldwide. Ethical frameworks have evolved in part due to bitter experience of ethical failures later recognized and affecting public health standards of practice. Future generations of public health leaders and staff will face many ethical issues such as mandatory immunization of health workers and school children, and assisted death of terminally ill patients.
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Project description:Big data trends in biomedical and health research enable large-scale and multi-dimensional aggregation and analysis of heterogeneous data sources, which could ultimately result in preventive, diagnostic and therapeutic benefit. The methodological novelty and computational complexity of big data health research raises novel challenges for ethics review. In this study, we conducted a scoping review of the literature using five databases to identify and map the major challenges of health-related big data for Ethics Review Committees (ERCs) or analogous institutional review boards. A total of 1093 publications were initially identified, 263 of which were included in the final synthesis after abstract and full-text screening performed independently by two researchers. Both a descriptive numerical summary and a thematic analysis were performed on the full-texts of all articles included in the synthesis. Our findings suggest that while big data trends in biomedicine hold the potential for advancing clinical research, improving prevention and optimizing healthcare delivery, yet several epistemic, scientific and normative challenges need careful consideration. These challenges have relevance for both the composition of ERCs and the evaluation criteria that should be employed by ERC members when assessing the methodological and ethical viability of health-related big data studies. Based on this analysis, we provide some preliminary recommendations on how ERCs could adaptively respond to those challenges. This exploration is designed to synthesize useful information for researchers, ERCs and relevant institutional bodies involved in the conduction and/or assessment of health-related big data research.
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Project description:Technological advances in big data (large amounts of highly varied data from many different sources that may be processed rapidly), data sciences and artificial intelligence can improve health-system functions and promote personalized care and public good. However, these technologies will not replace the fundamental components of the health system, such as ethical leadership and governance, or avoid the need for a robust ethical and regulatory environment. In this paper, we discuss what a robust ethical and regulatory environment might look like for big data analytics in health insurance, and describe examples of safeguards and participatory mechanisms that should be established. First, a clear and effective data governance framework is critical. Legal standards need to be enacted and insurers should be encouraged and given incentives to adopt a human-centred approach in the design and use of big data analytics and artificial intelligence. Second, a clear and accountable process is necessary to explain what information can be used and how it can be used. Third, people whose data may be used should be empowered through their active involvement in determining how their personal data may be managed and governed. Fourth, insurers and governance bodies, including regulators and policy-makers, need to work together to ensure that the big data analytics based on artificial intelligence that are developed are transparent and accurate. Unless an enabling ethical environment is in place, the use of such analytics will likely contribute to the proliferation of unconnected data systems, worsen existing inequalities, and erode trustworthiness and trust.
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Project description:Public health disasters reflect a class of global problems that generate moral quandaries and challenges. As such, they demand a global bioethical response involving an approach that is sufficiently nuanced at the local, trans-national, and global domains. Using the overlapping ethical issues engendered by Ebola and pandemic influenza outbreaks, atypical drug-resistant tuberculosis, and earthquakes, this chapter develops a global ethical framework for engaging PHDs. This framework exhibits sufficient responsiveness to local, global, microbial, and metaphysical realities as well as scientific concerns.
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Project description:AIM:Massive digitalization of natural history collections is now leading to a steep accumulation of publicly available species distribution data. However, taxonomic errors and geographical uncertainty of species occurrence records are now acknowledged by the scientific community - putting into question to what extent such data can be used to unveil correct patterns of biodiversity and distribution. We explore this question through quantitative and qualitative analyses of uncleaned versus manually verified datasets of species distribution records across different spatial scales. LOCATION:The American tropics. METHODS:As test case we used the plant tribe Cinchoneae (Rubiaceae). We compiled four datasets of species occurrences: one created manually and verified through classical taxonomic work, and the rest derived from GBIF under different cleaning and filling schemes. We used new bioinformatic tools to code species into grids, ecoregions, and biomes following WWF's classification. We analysed species richness and altitudinal ranges of the species. RESULTS:Altitudinal ranges for species and genera were correctly inferred even without manual data cleaning and filling. However, erroneous records affected spatial patterns of species richness. They led to an overestimation of species richness in certain areas outside the centres of diversity in the clade. The location of many of these areas comprised the geographical midpoint of countries and political subdivisions, assigned long after the specimens had been collected. MAIN CONCLUSION:Open databases and integrative bioinformatic tools allow a rapid approximation of large-scale patterns of biodiversity across space and altitudinal ranges. We found that geographic inaccuracy affects diversity patterns more than taxonomic uncertainties, often leading to false positives, i.e. overestimating species richness in relatively species poor regions. Public databases for species distribution are valuable and should be more explored, but under scrutiny and validation by taxonomic experts. We suggest that database managers implement easy ways of community feedback on data quality.
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Project description:Imputation is a powerful in silico approach to fill in those missing values in the big datasets. This process requires a reference panel, which is a collection of big data from which the missing information can be extracted and imputed. Haplotype imputation requires ethnicity-matched references; a mismatched reference panel will significantly reduce the quality of imputation. However, currently existing big datasets cover only a small number of ethnicities, there is a lack of ethnicity-matched references for many ethnic populations in the world, which has hampered the data imputation of haplotypes and its downstream applications. To solve this issue, several approaches have been proposed and explored, including the mixed reference panel, the internal reference panel and genotype-converted reference panel. This review article provides the information and comparison between these approaches. Increasing evidence showed that not just one or two genetic elements dictate the gene activity and functions; instead, cis-interactions of multiple elements dictate gene activity. Cis-interactions require the interacting elements to be on the same chromosome molecule, therefore, haplotype analysis is essential for the investigation of cis-interactions among multiple genetic variants at different loci, and appears to be especially important for studying the common diseases. It will be valuable in a wide spectrum of applications from academic research, to clinical diagnosis, prevention, treatment, and pharmaceutical industry.
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