Dataset Information

Multinational corporations and infectious disease: Embracing human rights management techniques.

ABSTRACT:

Background

Global health institutions have called for governments, international organisations and health practitioners to employ a human rights-based approach to infectious diseases. The motivation for a human rights approach is clear: poverty and inequality create conditions for infectious diseases to thrive, and the diseases, in turn, interact with social-ecological systems to promulgate poverty, inequity and indignity. Governments and intergovernmental organisations should be concerned with the control and elimination of these diseases, as widespread infections delay economic growth and contribute to higher healthcare costs and slower processes for realising universal human rights. These social determinants and economic outcomes associated with infectious diseases should interest multinational companies, partly because they have bearing on corporate productivity and, increasingly, because new global norms impose on companies a responsibility to respect human rights, including the right to health.

Methods

We reviewed historical and recent developments at the interface of infectious diseases, human rights and multinational corporations. Our investigation was supplemented with field-level insights at corporate capital projects that were developed in areas of high endemicity of infectious diseases, which embraced rights-based disease control strategies.

Results

Experience and literature provide a longstanding business case and an emerging social responsibility case for corporations to apply a human rights approach to health programmes at global operations. Indeed, in an increasingly globalised and interconnected world, multinational corporations have an interest, and an important role to play, in advancing rights-based control strategies for infectious diseases.

Conclusions

There are new opportunities for governments and international health agencies to enlist corporate business actors in disease control and elimination strategies. Guidance offered by the United Nations in 2011 that is widely embraced by companies, governments and civil society provides a roadmap for engaging business enterprises in rights-based disease management strategies to mitigate disease transmission rates and improve human welfare outcomes.

SUBMITTER: Salcito K

PROVIDER: S-EPMC4323175 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multinational corporations and infectious disease: Embracing human rights management techniques.

Salcito Kendyl K Singer Burton H BH Weiss Mitchell G MG Winkler Mirko S MS Krieger Gary R GR Wielga Mark M Utzinger Jürg J

Infectious diseases of poverty 20141103 1

<h4>Background</h4>Global health institutions have called for governments, international organisations and health practitioners to employ a human rights-based approach to infectious diseases. The motivation for a human rights approach is clear: poverty and inequality create conditions for infectious diseases to thrive, and the diseases, in turn, interact with social-ecological systems to promulgate poverty, inequity and indignity. Governments and intergovernmental organisations should be concern ...[more]

PMID: 25671119

Similar Datasets

Project description:BACKGROUND:With the global spread of multidrug resistance in pathogenic microbes, infectious diseases emerge as a key public health concern of the recent time. Identification of host genes associated with infectious diseases will improve our understanding about the mechanisms behind their development and help to identify novel therapeutic targets. RESULTS:We developed a machine learning techniques-based classification approach to identify infectious disease-associated host genes by integrating sequence and protein interaction network features. Among different methods, Deep Neural Networks (DNN) model with 16 selected features for pseudo-amino acid composition (PAAC) and network properties achieved the highest accuracy of 86.33% with sensitivity of 85.61% and specificity of 86.57%. The DNN classifier also attained an accuracy of 83.33% on a blind dataset and a sensitivity of 83.1% on an independent dataset. Furthermore, to predict unknown infectious disease-associated host genes, we applied the proposed DNN model to all reviewed proteins from the database. Seventy-six out of 100 highly-predicted infectious disease-associated genes from our study were also found in experimentally-verified human-pathogen protein-protein interactions (PPIs). Finally, we validated the highly-predicted infectious disease-associated genes by disease and gene ontology enrichment analysis and found that many of them are shared by one or more of the other diseases, such as cancer, metabolic and immune related diseases. CONCLUSIONS:To the best of our knowledge, this is the first computational method to identify infectious disease-associated host genes. The proposed method will help large-scale prediction of host genes associated with infectious-diseases. However, our results indicated that for small datasets, advanced DNN-based method does not offer significant advantage over the simpler supervised machine learning techniques, such as Support Vector Machine (SVM) or Random Forest (RF) for the prediction of infectious disease-associated host genes. Significant overlap of infectious disease with cancer and metabolic disease on disease and gene ontology enrichment analysis suggests that these diseases perturb the functions of the same cellular signaling pathways and may be treated by drugs that tend to reverse these perturbations. Moreover, identification of novel candidate genes associated with infectious diseases would help us to explain disease pathogenesis further and develop novel therapeutics.

Dataset Information

Multinational corporations and infectious disease: Embracing human rights management techniques.

Background

Methods

Results

Conclusions

Publications

Multinational corporations and infectious disease: Embracing human rights management techniques.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure