Complexity-functioning relationships differ across different environmental conditions.

Habitat complexity is widely considered an important determinant of biodiversity, and enhancing complexity can play a key role in restoring degraded habitats. However, the effects of habitat complexity on ecosystem functioning - as opposed to biodiversity and community structure - are relatively poorly understood for artificial habitats, which dominate many coastlines. With Greening of Grey Infrastructure (GGI) approaches, or eco-engineering, increasingly being applied around the globe, it is important to understand the effects that modifying habitat complexity has on both biodiversity and ecological functioning in these highly modified habitats. We assessed how manipulating physical (primary substrate) and/or biogenic habitat (bivalves) complexity on intertidal artificial substrata affected filtration rates, net and gross primary productivity (NPP and GPP, respectively) and community respiration (CR) - as well as abundance of filter feeders and macro-algae and habitat use by cryptobenthic fish across six locations in three continents. We manipulated both physical and biogenic complexity using 1) flat or ridged (2.5 cm or 5 cm) settlement tiles that were either 2) unseeded or seeded with oysters or mussels. Across all locations, increasing physical and biogenic complexity (5 cm seeded tiles) had a significant effect on most ecological functioning variables, increasing overall filtration rates and community respiration of the assemblages on tiles but decreasing productivity (both GPP and NPP) across all locations. There were no overall effects of increasing either type of habitat complexity on cryptobenthic fish MaxN, total time in frame or macro-algal cover. Within each location, there were marked differences in the effects of habitat complexity. In Hobart, we found higher filtration, filter feeder biomass and community respiration on 5 cm tiles compared to flat tiles. However, at this location, both macro-algae cover and GPP decreased with increasing physical complexity. Similarly in Dublin, filtration, filter feeder biomass and community respiration were higher on 5 cm tiles compared to less complex tiles. In Sydney, filtration and filter feeder biomass were higher on seeded than unseeded tiles, and fish MaxN was higher on 5 cm tiles compared to flat tiles. On unseeded tiles in Sydney, filter feeder biomass also increased with increasing physical complexity. Our findings suggest that GGI solutions via increased habitat complexity are likely to have trade-offs among potentially desired functions, such as productivity and filtration rates, and variable effects on cryptobenthic fish communities. Importantly, our results show that the effects of GGI practices can vary markedly according to the environmental context and therefore should not be blindly and uniformly applied across the globe.

Forecasting national and regional level intensive care unit bed demand during COVID-19: The case of Italy.

Given the pressure on healthcare authorities to assess whether hospital capacity allows properly responding to outbreaks such as COVID-19, there is a need for simple, data-driven methods that may provide accurate forecasts of hospital bed demand. This study applies growth models to forecast the demand for Intensive Care Unit admissions in Italy during COVID-19. We show that, with only some mild assumptions on the functional form and using short time-series, the model fits past data well and can accurately forecast demand fourteen days ahead (the mean absolute percentage error (MAPE) of the cumulative fourteen days forecasts is 7.64). The model is then applied to derive regional-level forecasts by adopting hierarchical methods that ensure the consistency between national and regional level forecasts. Predictions are compared with current hospital capacity in the different Italian regions, with the aim to evaluate the adequacy of the expansion in the number of beds implemented during the COVID-19 crisis.

Care Appropriateness and Health Productivity Evolution: A Non-Parametric Analysis of the Italian Regional Health Systems.

BACKGROUND: There has been increasing interest in measuring the productive performance of healthcare services since the mid-1980s. OBJECTIVE: By applying bootstrapped data envelopment analysis across the 20 Italian Regional Health Systems (RHSs) for the period 2008-2012, we employed a two-stage procedure to investigate the relationship between care appropriateness and productivity evolution in public hospital services. METHODS: In the first stage, we estimated the Malmquist index and decomposed this overall measure of productivity into efficiency and technological change. In the second stage, the two components of the Malmquist index were regressed on a set of variables measuring per capita health expenditure, care appropriateness, and clinical appropriateness. RESULTS: Malmquist analysis shows that no gains in productivity in the health industry have been achieved in Italy despite the sequence of reforms that took place during the 1990s, which were devoted to increasing efficiency and reducing costs. Analysis of the efficiency change index clearly indicates that the source of productivity gain relies on a rationalization of the employed inputs in the Italian RHSs. At the same time, the trend of the technological change index reveals that the health systems in the three macro-areas (North, Central, and South) are characterized by technological regress. CONCLUSION: Overall, our results suggest that productivity increases could be achieved in the Italian health system by reducing the level of inputs, improving care and clinical appropriateness, and by counteracting the 'DRG (diagnosis-related group) creep' phenomenon.

Healthcare reform in Italy: an analysis of efficiency based on nonparametric methods.

Over the past twenty years, important changes in the Italian health system have led to different approaches in organizing, delivering and financing health services throughout the country's regions. In this paper, we assess the impacts that such changes have had on health efficiency. The analysis performed here is in two stages. In the first stage, healthcare efficiency is measured via bootstrapped Data Envelopment Analysis. In the second stage, the impacts of organizational and environmental variables on efficiency are investigated. Our results highlight that the organizational model adopted by the Lombardia region allows for the best results in healthcare efficiency in Italy. A process of administrative decentralization from the regional governments to local health units appears to be a source of inefficiency. Finally, patient mobility has a significant impact on healthcare efficiency.