Methods
A web-based survey was developed including the CHU9D and HUI2 instruments and administered to a community-based sample of consenting adolescents (n = 710) aged 11-17 years. The practicality, face and construct validity of the CHU9D was examined. The relationship between the CHU9D and HUI2 instruments was assessed by a comparison of responses to similar dimensions and the utility scores derived from the two instruments.
Results The CHU9D demonstrated high completion rates. CHU9D was able to discriminate between respondents according to their self-reported general health (Kruskal-Wallis P value <0.001). The mean CHU9D adolescent population utilities were similar to those generated from the HUI2 [Mean (SD) CHU9D utility 0.844 (0.102) and HUI2 utility 0.872 selleckchem (0.138)], and the intra-class correlation coefficient indicated good levels of agreement HIF inhibitor overall (ICC = 0.742).
Conclusion The findings from this study provide support for the practicality, face and construct validity of the CHU9D for application with adolescents aged 11-17 years.”
“Background: Clinical malaria has
proven an elusive burden to enumerate. Many cases go undetected by routine disease recording systems. Epidemiologists have, therefore, frequently defaulted to actively measuring malaria in population cohorts through time. Measuring the clinical incidence of malaria longitudinally is labour-intensive and impossible to undertake universally. There is a need, therefore, to define a relationship between clinical incidence and the easier and more commonly measured index of infection prevalence: the “”parasite rate”". This relationship can help provide an informed basis to define malaria burdens in areas where health statistics are inadequate.
Methods: Caspase inhibitor Formal literature searches were conducted for Plasmodium falciparum malaria incidence surveys undertaken prospectively
through active case detection at least every 14 days. The data were abstracted, standardized and geo-referenced. Incidence surveys were time-space matched with modelled estimates of infection prevalence derived from a larger database of parasite prevalence surveys and modelling procedures developed for a global malaria endemicity map. Several potential relationships between clinical incidence and infection prevalence were then specified in a non-parametric Gaussian process model with minimal, biologically informed, prior constraints. Bayesian inference was then used to choose between the candidate models.
Results: The suggested relationships with credible intervals are shown for the Africa and a combined America and Central and South East Asia regions. In both regions clinical incidence increased slowly and smoothly as a function of infection prevalence. In Africa, when infection prevalence exceeded 40%, clinical incidence reached a plateau of 500 cases per thousand of the population per annum.