Personnel planning in general practices: development and testing of a skill mix analysis method

Background: General practitioners (GPs) have to match patients’ demands with the mix of their practice staff’s competencies. However, apart from some general principles, there is little guidance on recruiting new staff. The purpose of this study was to develop and test a method which would allow GPs or practice managers to perform a skill mix analysis which would take into account developments in local demand. Methods: The method was designed with a stepwise method using different research strategies. Literature review took place to detect available methods that map, predict, or measure patients’ demands or needs and to fill the contents of the skill mix analysis. Focus groups and expert interviews were held both during the design process and in the first test stage. Both secondary data analysis as primary data collection took place to fill the contents of the tool. A pilot study in general practices tested the feasibility of the newly-developed method. Results: The skill mix analysis contains both a quantitative and a qualitative part which includes the following sections: i) an analysis of the current and the expected future demand; ii) an analysis of the need to adjust skill mix; iii) an overview about the functions of different provider disciplines; and iv) a system to assess the input, assumed or otherwise, of each function concerning the ‘catching up demand’, the connection between supply and demand, and the introduction of new opportunities. The skill mix analysis shows an acceptable face and content validity and appears feasible in practice. Conclusions: The skill mix analysis method can be used as a basis to analyze and match, systematically, the demand for care and the supply of practice staff. (aut. ref.

Population genetic differentiation of height and body mass index across Europe

Across-nation differences in the mean values for complex traits are common, but the reasons for these differences are unknown. Here we find that many independent loci contribute to population genetic differences in height and body mass index (BMI) in 9,416 individuals across 14 European countries. Using discovery data on over 250,000 individuals and unbiased effect size estimates from 17,500 sibling pairs, we estimate that 24% (95% credible interval (CI) = 9%, 41%) and 8% (95% CI = 4%, 16%) of the captured additive genetic variance for height and BMI, respectively, reflect population genetic differences. Population genetic divergence differed significantly from that in a null model (height, P < 3.94 7 10(-8); BMI, P < 5.95 7 10(-4)), and we find an among-population genetic correlation for tall and slender individuals (r = -0.80, 95% CI = -0.95, -0.60), consistent with correlated selection for both phenotypes. Observed differences in height among populations reflected the predicted genetic means (r = 0.51; P < 0.001), but environmental differences across Europe masked genetic differentiation for BMI (P < 0.58)