OBJECTIVETo provide an overview of the economic aspects of needlestick and sharps injury (NSI) management among healthcare personnel (HCP) within a Health Technology Assessment project to evaluate the impact of safety-engineered devices on health careMETHODSA systematic review of economic analyses related to NSIs was performed in accordance with the PRISMA statement and by searching PubMed and Scopus databases (January 1997–February 2015). Mean costs were stratified by study approach (modeling or data driven) and type of cost (direct or indirect). Costs were evaluated using the CDC operative definition and converted to 2015 International US dollars (Int$).RESULTSA total of 14 studies were retrieved: 8 data-driven studies and 6 modeling studies. Among them, 11 studies provided direct and indirect costs and 3 studies provided only direct costs. The median of the means for aggregate (direct + indirect) costs was Int$747 (range, Int$199–Int$1,691). The medians of the means for disaggregated costs were Int$425 (range, Int$48–Int$1,516) for direct costs (9 studies) and Int$322 (range, Int$152–Int$413) for indirect costs (6 studies). When compared with data-driven studies, modeling studies had higher disaggregated and aggregated costs, but data-driven studies showed greater variability. Indirect costs were consistent between studies, mostly referring to lost productivity, while direct costs varied widely within and between studies according to source infectivity, HCP susceptibility, and post-exposure diagnostic and prophylactic protocols. Costs of treating infections were not included, and intangible costs could equal those associated with NSI medical evaluations.CONCLUSIONSNSIs generate significant direct, indirect, potential, and intangible costs, possibly increasing over time. Economic efforts directed at preventing occupational exposures and infections, including provision of safety-engineered devices, may be offset by the savings from a lower incidence of NSIs.Infect Control Hosp Epidemiol 2016;37:635–646

We study the relationship between gerontocracy and aggregate economic performance in a simple model where growth is driven by human capital accumulation and productive government spending. We show that less patient élites display the tendency to underinvest in public education and productive government services, and thus are harmful for growth. The damage caused by gerontocracy is mainly due to the lack of long-term delayed return on investments, originated by the lower subjective discount factor. An empirical analysis using public investment in Information and Communication Technologies (ICT) is carried out to test theoretical predictions across different countries and different economic sectors. The econometric results confirm our main hypotheses.

We provide aggregate macroeconomic evidence on how, in the long-run, a diverse degree of complexity in production may affect not only the rate of economic growth, but also the correlation between the latter, population growth and the monopolistic (intermediate) markups. For a sample of OECD countries, we find that the impact of population change on economic growth is slightly positive. According to our theoretical model, this implies that the losses due to more complexity in production are lower than the corresponding specialization gains. Using a Finite Mixture Model, we also classify the countries in the sample and verify for each cluster the impact that the population growth rate and the intermediate sector's markups exert on the 5-year average real GDP growth rate.

In this chapter we examine whether the Solow growth model is consistent with the international variation in the standard of living once investments in education and health are explicitly and simultaneously taken into account. Using the sample of OECD countries, we provide evidence that the level of per capita income is positively affected by the population's health level, here proxied by the life expectancy at birth. Public expenditure on health affects indirectly the level of per capita income through its positive effect on life expectancy. Using a Finite Mixture approach, we also show that richer countries are those in which the impact of unobserved factors is stronger in the determination of the level of per capita income.

The empirical analysis based on United States and Italian data corroborates these results.