Protecting the confidentiality and security of personal health information in low- and middle-income countries in the era of SDGs and Big Data
Bibliographic record
Abstract
BackgroundAs increasing amounts of personal information are being collected through a plethora of electronic modalities by statutory and non-statutory organizations, ensuring the confidentiality and security of such information has become a major issue globally. While the use of many of these media can be beneficial to individuals or populations, they can also be open to abuse by individuals or statutory and non-statutory organizations. Recent examples include collection of personal information by national security systems and the development of national programs like the Chinese Social Credit System. In many low- and middle-income countries, an increasing amount of personal health information is being collected. The collection of personal health information is necessary, in order to develop longitudinal medical records and to monitor and evaluate the use, cost, outcome, and impact of health services at facility, sub-national, and national levels. However, if personal health information is not held confidentially and securely, individuals with communicable or non-communicable diseases (NCDs) may be reluctant to use preventive or therapeutic health services, due to fear of being stigmatized or discriminated against. While policymakers and other stakeholders in these countries recognize the need to develop and implement policies for protecting the privacy, confidentiality and security of personal health information, to date few of these countries have developed, let alone implemented, coherent policies. The global HIV response continues to emphasize the importance of collecting HIV-health information, recently re-iterated by the Fast Track to End AIDS by 2030 program and the recent changes in the Guidelines on When to Start Antiretroviral Therapy and on Pre-exposure Prophylaxis for HIV. The success of developing HIV treatment cascades in low- and middle-income countries will require the development of National Health Identification Systems. The success of programs like Universal Health Coverage, under the recently ratified Sustainable Development Goals is also contingent on the availability of personal health information for communicable and non-communicable diseases.DesignGuidance for countries to develop and implement their own guidelines for protecting HIV-information formed the basis of identifying a number of fundamental principles, governing the areas of privacy, confidentiality and security. The use of individual-level data must balance maximizing the benefits from their most effective and fullest use, and minimizing harm resulting from their malicious or inadvertent release.DiscussionThese general principles are described in this paper, as along with a bibliography referring to more detailed technical information. A country assessment tool and user's manual, based on these principles, have been developed to support countries to assess the privacy, confidentiality, and security of personal health information at facility, data warehouse/repository, and national levels. The successful development and implementation of national guidance will require strong collaboration at local, regional, and national levels, and this is a pre-condition for the successful implementation of a range of national and global programs.ConclusionThis paper is a call for action for stakeholders in low- and middle-income countries to develop and implement such coherent policies and provides fundamental principles governing the areas of privacy, confidentiality, and security of personal health information being collected in low- and middle-income countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".