Summary Multi-sectoral collaborative approaches with strong community engagement are essential for addressing health disparities. A valid tool for assessing organizational research and capacity for community health research stakeholders could help strengthen organizational capacity for engagement in such collaborations. This study was conducted to validate an innovative tool for assessing research activity and capacity of a spectrum of stakeholder organizations to provide support for strengthening community health research capacity in Bhutan. In-person interviews with academics (n = 10), clinicians (n = 10), government staff (n = 10), consultants (n = 2) and management of health-related civil society organizations (CSOs; n = 12 interviews/organizations, 13 individuals) were recorded and transcribed. Questions covered individual and organizational research activity and capacity, research networks and an international version of the Community Research Assessment Tool (CREAT-I). Almost all participants (84%) had participated in community health research projects. Social network analysis showed a large, interconnected cluster with a few key individuals linking across sectors. CREAT-I responses identified the highest capacity in organizational support for research among academic participants, while clinical and CSO participants reported highest capacity in practical research experiences and government participants reported highest capacity in research specific experiences. The CREAT-I tool showed strong internal reliability (Cronbach’s α = 0.91) and validity. Limited money, time and skilled staff were identified as barriers to research. The CREAT-I assesses community health research capacity of organizations, and such a tool could be useful in identifying research capacity needs, monitoring impact of research capacity-building activities and contributing to a greater capacity for multi-sectoral collaborative approaches to community health research in international settings.
BackgroundPediatric cataract is an important cause of blindness and visual impairment in children. A large proportion of pediatric cataracts are inherited, and many genes have been described for this heterogeneous Mendelian disease. Surveys of schools for the blind in Bhutan, Cambodia, and Sri Lanka have identified many children with this condition and we aimed to identify the genetic causes of inherited cataract in these populations.MethodsWe screened, in parallel, 51 causative genes for inherited cataracts in 33 probands by Ampliseq enrichment and sequencing on an Ion Torrent PGM. Rare novel protein coding variants were assessed for segregation in family members, where possible, by Sanger sequencing.ResultsWe identified 24 rare (frequency <1% in public databases) or novel protein coding variants in 12 probands and confirmed segregation of variants with disease in the extended family where possible. Of these, six are predicted to be the cause of disease in the patient, with four other variants also highly likely to be pathogenic.ConclusionThis study found that 20%–30% of patients in these countries have a mutation in a known cataract causing gene, which is considerably lower than the 60%–70% reported in Caucasian cohorts. This suggests that additional cataract genes remain to be discovered in this cohort of Asian pediatric cataract patients.
Consistent with other studies, there is a high rate of blinding disease, which may be prevented, treated, or ameliorated.
I.AbstractThe Coronavirus Disease 2019 (COVID-19) has demonstrated that accurate forecasts of infection and mortality rates are essential for informing healthcare resource allocation, designing countermeasures, implementing public health policies, and increasing public awareness. However, there exist a multitude of modeling methodologies, and their relative performances in accurately forecasting pandemic dynamics are not currently comprehensively understood.In this paper, we introduce the non-mechanistic MIT-LCP forecasting model, and assess and compare its performance to various mechanistic and non-mechanistic models that have been proposed for forecasting COVID-19 dynamics. We performed a comprehensive experimental evaluation which covered the time period of November 2020 to April 2021, in order to determine the relative performances of MIT-LCP and seven other forecasting models from the United States’ Centers for Disease Control and Prevention (CDC) Forecast Hub.Our results show that there exist forecasting scenarios well-suited to both mechanistic and non-mechanistic models, with mechanistic models being particularly performant for forecasts that are further in the future when recent data may not be as informative, and non-mechanistic models being more effective with shorter prediction horizons when recent representative data is available. Improving our understanding of which forecasting approaches are more reliable, and in which forecasting scenarios, can assist effective pandemic preparation and management.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.