Technological Diffusion, Spatial Spillovers And Regional Convergence In Europe

In this paper we study two closely related issues. First, the role of technology heterogeneity and diffusion in the convergence of GDP per worker observed across the European regions, in the absence of data on regional TFP. Second, the spatial pattern of the observed regional heterogeneity in technology and the relevance of this pattern for the econometric analysis of regional convergence in Europe. As for the first issue, our aim is to assess whether the convergence observed across European regions is due to convergence in technology as well as to convergence in capital-labor ratios. We first develop a growth model where technology accumulation in lagging regions depends on their own propensity to innovate and on technology diffusion from the leading region, and convergence in GDP per worker is due to both capital deepening and catch-up. We use data (1978-97) on 131 European regions. Propensities to innovate are computed by assigning each patent collected by the European Patent Office to its region of origin. Our findings are consistent with the hypothesis that technology differs across regions and that convergence is partly due to technological catch-up. As for the second empirical issue, we study to what extent each region's propensity to innovate is correlated with that of the surrounding regions. Our results show, first, that the performance of each region does depend on that of the surrounding areas. Second, that the intensity of such spillovers fades with distance. Taken together, these findings suggest the existence of significant localized spillovers of technological knowledge. Finally, we show that these spillovers are strong enough to play a role that cannot be ignored in the econometric analysis of the convergence process in Europe

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified