The neglected zoonotic diseases (NZDs) have been all but eradicated in wealthier countries, but remain major causes of ill-health and mortality across Africa, Asia, and Latin America. This neglect is, in part, a consequence of under-reporting, resulting in an underestimation of their global burden that downgrades their relevance to policy-makers and funding agencies. Increasing awareness about the causes of NZDs and how they can be prevented could reduce the incidence of many endemic zoonoses. Addressing NZDs by targeting the animal reservoir can deliver a double benefit, as enhanced animal health means a reduced risk of infection for humans, as well as improved livelihoods through increased animal productivity. Advocacy for NZD control is increasing, but with it comes a growing awareness that NZD control demands activities both in the short term and over a long period of time. Moreover, despite the promise of cheap, effective vaccines or other control tools, these endemic diseases will not be sustainably controlled in the near future without long-term financial commitment, particularly as disease incidence decreases and other health priorities take hold. NZD intervention costs can seem high when compared with the public health benefits alone, but these costs are easily outweighed when a full cross-sector analysis is carried out and monetary/non-monetary benefits--particularly regarding the livestock sector--are taken into account. Public-private partnerships have recently provided advocacy for human disease control, and could prove equally effective in addressing endemic zoonoses through harnessing social impact investments. Evidence of the disease burdens imposed on communities by the NZDs and demonstration of the cost-effectiveness of integrated control can strengthen the case for a One Health approach to endemic zoonotic disease control.
Background Routinely-collected mental health data could deliver novel insights for mental health research. However, patients’ willingness to share their mental health data remains largely unknown. We investigated factors influencing likelihood of sharing these data for research purposes amongst people with and without experience of mental illness. Methods We collected responses from a diverse sample of UK National Health Service (NHS) users (n = 2187) of which about half (n = 1087) had lifetime experience of mental illness. Ordinal logistic regression was used to examine the influence of demographic factors, clinical service experience, and primary mental illness on willingness to share mental health data, contrasted against physical health data. Results There was a high level of willingness to share mental (89.7%) and physical (92.8%) health data for research purposes. Higher levels of satisfaction with the NHS were associated with greater willingness to share mental health data. Furthermore, people with personal experience of mental illness were more willing than those without to share mental health data, once the variable of NHS satisfaction had been controlled for. Of the mental illnesses recorded, people with depression, obsessive-compulsive disorder (OCD), personality disorder or bipolar disorder were significantly more likely to share their mental health data than people without mental illness. Conclusions These findings suggest that positive experiences of health services and personal experience of mental illness are associated with greater willingness to share mental health data. NHS satisfaction is a potentially modifiable factor that could foster public support for increased use of NHS mental health data in research.
Background: Mental health research is commonly affected by difficulties in recruiting and retaining participants, resulting in findings which are based on a sub-sample of those actually living with mental illness. Increasing the use of Big Data for mental health research, especially routinely-collected data, could improve this situation. However, steps to facilitate this must be enacted in collaboration with those who would provide the data - people with mental health conditions.Methods: We used the Delphi method to create a best practice checklist for mental health data science. Twenty participants with both expertise in data science and personal experience of mental illness worked together over three phases. In Phase 1, participants rated a list of 63 statements and added any statements or topics that were missing. Statements receiving a mean score of 5 or more (out of 7) were retained. These were then combined with the results of a rapid thematic analysis of participants' comments to produce a 14-item draft checklist, with each item split into two components: best practice now and best practice in the future. In Phase 2, participants indicated whether or not each item should remain in the checklist, and items that scored more than 50% endorsement were retained. In Phase 3 participants rated their satisfaction with the final checklist.Results: The final checklist was made up of 14 “best practice” items, with each item covering best practice now and best practice in the future. At the end of the three phases, 85% of participants were (very) satisfied with the two best practice checklists, with no participants expressing dissatisfaction.Conclusions: Increased stakeholder involvement is essential at every stage of mental health data science. The checklist produced through this work represents the views of people with experience of mental illness, and it is hoped that it will be used to facilitate trustworthy and innovative research which is inclusive of a wider range of individuals.
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