HIV, Stigma, and Rates of Infection: A Rumour without Evidence: Authors' Reply

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub

An estimate of the prevalence of epilepsy in Sub-Saharan Africa:A systematic analysis

Epilepsy is a leading serious neurological condition worldwide and has particularly significant physical, economic and social consequences in Sub–Saharan Africa. This paper aims to contribute to the understanding of epilepsy prevalence in this region and how this varies by age and sex so as to inform understanding of the disease characteristics as well as the development of infrastructure, services and policies

Setting health research priorities using the CHNRI method:III. Involving stakeholders

Setting health research priorities is a complex and value–driven process. The introduction of the Child Health and Nutrition Research Initiative (CHNRI) method has made the process of setting research priorities more transparent and inclusive, but much of the process remains in the hands of funders and researchers, as described in the previous two papers in this series. However, the value systems of numerous other important stakeholders, particularly those on the receiving end of health research products, are very rarely addressed in any process of priority setting. Inclusion of a larger and more diverse group of stakeholders in the process would result in a better reflection of the system of values of the broader community, resulting in recommendations that are more legitimate and acceptable

Setting health research priorities using the CHNRI method: V. Quantitative properties of human collective knowledge.

INTRODUCTION: The CHNRI method for setting health research priorities has crowdsourcing as the major component. It uses the collective opinion of a group of experts to generate, assess and prioritize between many competing health research ideas. It is difficult to compare the accuracy of human individual and collective opinions in predicting uncertain future outcomes before the outcomes are known. However, this limitation does not apply to existing knowledge, which is an important component underlying opinion. In this paper, we report several experiments to explore the quantitative properties of human collective knowledge and discuss their relevance to the CHNRI method. METHODS: We conducted a series of experiments in groups of about 160 (range: 122-175) undergraduate Year 2 medical students to compare their collective knowledge to their individual knowledge. We asked them to answer 10 questions on each of the following: (i) an area in which they have a degree of expertise (undergraduate Year 1 medical curriculum); (ii) an area in which they likely have some knowledge (general knowledge); and (iii) an area in which they are not expected to have any knowledge (astronomy). We also presented them with 20 pairs of well-known celebrities and asked them to identify the older person of the pair. In all these experiments our goal was to examine how the collective answer compares to the distribution of students' individual answers. RESULTS: When answering the questions in their own area of expertise, the collective answer (the median) was in the top 20.83% of the most accurate individual responses; in general knowledge, it was in the top 11.93%; and in an area with no expertise, the group answer was in the top 7.02%. However, the collective answer based on mean values fared much worse, ranging from top 75.60% to top 95.91%. Also, when confronted with guessing the older of the two celebrities, the collective response was correct in 18/20 cases (90%), while the 8 most successful individuals among the students had 19/20 correct answers (95%). However, when the system in which the students who were not sure of the correct answer were allowed to either choose an award of half of the point in all such instances, or withdraw from responding, in order to improve the score of the collective, the collective was correct in 19/20 cases (95%), while the 3 most successful individuals were correct in 17/20 cases (85%). CONCLUSIONS: Our experiments showed that the collective knowledge of a group with expertise in the subject should always be very close to the true value. In most cases and under most assumption, the collective knowledge will be more accurate than the knowledge of an "average" individual, but there always seems to be a small group of individuals who manage to out-perform the collective. The accuracy of collective prediction may be enhanced by allowing the individuals with low confidence in their answer to withdraw from answering

Estimating the burden of non–communicable diseases in low– and middle–income countries

Non-communicable causes of death and disability will dominate global health agenda for the foreseeable future. The progress in addressing their burden and achieving measurable reduction in low– and middle– income countries (LMICs) will likely require similar steps that were effective in reducing maternal and child mortality globally: (i) defining the size of the burden and the main causes responsible for the majority of the burden; (ii) understanding the most important risk factors and their importance in different contexts; (iii) systematically assessing the effectiveness and cost of the interventions that are feasible and available in LMICs; and (iv) formulating evidence–based health policies that will define appropriate health care and health research priorities to tackle the burden in the most cost–effective way

Estimating the burden of non–communicable diseases in low– and middle–income countries

Non-communicable causes of death and disability will dominate global health agenda for the foreseeable future. The progress in addressing their burden and achieving measurable reduction in low– and middle– income countries (LMICs) will likely require similar steps that were effective in reducing maternal and child mortality globally: (i) defining the size of the burden and the main causes responsible for the majority of the burden; (ii) understanding the most important risk factors and their importance in different contexts; (iii) systematically assessing the effectiveness and cost of the interventions that are feasible and available in LMICs; and (iv) formulating evidence–based health policies that will define appropriate health care and health research priorities to tackle the burden in the most cost–effective way

A case study of the counterpart technical support policy to improve rural health services in Beijing

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-0420Background There is, globally, an often observed inequality in the health services available in urban and rural areas. One strategy to overcome the inequality is to require urban doctors to spend time in rural hospitals. This approach was adopted by the Beijing Municipality (population of 20.19 million) to improve rural health services, but the approach has never been systematically evaluated. Methods Drawing upon 1.6 million cases from 24 participating hospitals in Beijing (13 urban and 11 rural hospitals) from before and after the implementation of the policy, changes in the rural–urban hospital performance gap were examined. Hospital performance was assessed using changes in six indices over-time: Diagnosis Related Groups quantity, case-mix index (CMI), cost expenditure index (CEI), time expenditure index (TEI), and mortality rates of low- and high-risk diseases. Results Significant reductions in rural–urban gaps were observed in DRGs quantity and mortality rates for both high- and low-risk diseases. These results signify improvements of rural hospitals in terms of medical safety, and capacity to treat emergency cases and more diverse illnesses. No changes in the rural–urban gap in CMI were observed. Post-implementation, cost and time efficiencies worsened for the rural hospitals but improved for urban hospitals, leading to a widening rural–urban gap in hospital efficiency. Conclusions The strategy for reducing urban–rural gaps in health services adopted, by the Beijing Municipality shows some promise. Gains were not consistent, however, across all performance indicators, and further improvements will need to be tried and evaluated.https://doi.org/10.1186/1472-6963-12-48212pubpub