Transparent Development of the WHO Rapid Advice Guidelines

Emerging health problems require rapid advice. We describe the development and pilot testing of a systematic, transparent approach used by the World Health Organization (WHO) to develop rapid advice guidelines in response to requests from member states confronted with uncertainty about the pharmacological management of avian influenza A (H5N1) virus infection. We first searched for systematic reviews of randomized trials of treatment and prevention of seasonal influenza and for non-trial evidence on H5N1 infection, including case reports and animal and in vitro studies. A panel of clinical experts, clinicians with experience in treating patients with H5N1, influenza researchers, and methodologists was convened for a two-day meeting. Panel members reviewed the evidence prior to the meeting and agreed on the process. It took one month to put together a team to prepare the evidence profiles (i.e., summaries of the evidence on important clinical and policy questions), and it took the team only five weeks to prepare and revise the evidence profiles and to prepare draft guidelines prior to the panel meeting. A draft manuscript for publication was prepared within 10 days following the panel meeting. Strengths of the process include its transparency and the short amount of time used to prepare these WHO guidelines. The process could be improved by shortening the time required to commission evidence profiles. Further development is needed to facilitate stakeholder involvement, and evaluate and ensure the guideline's usefulness

A modeling approach for uncertainty assessment of register-based small area statistics

Statistical registers have great potentials when it comes to producing statistics at detailed spatial-demographic levels. However, population totals based on statistical registers are subjected to random variations that exist in the target population as well as errors that are associated with the registration (or measurement) process. While the former counts for heterogeneity across the areas (or domains), i.e. genuine signals of interest, the latter ones are merely noises in measurement. We propose a model-based sensitivity analysis approach, which allows us to distinguish between the different sources of randomness in the data, by which means the strength of the signals can be assessed against the noises. The data from the Norwegian Employer/Employee register are used to demonstrate the existence of measurement noises in administrative data sources, and to illustrate the proposed approach. We believe that both the conceptualization of the random nature of the register data and the sensitivity analysis approach can be useful for assessing detailed statistics produced from statistical registers on various subjects