Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects

Prognostic value of degree and types of anaemia on clinical outcomes for hospitalised older patients

Study objective This study investigated in a large sample of in-patients the impact of mild-moderate-severe anaemia on clinical outcomes such as in-hospital mortality, re-admission, and death within three months after discharge. Methods A prospective multicentre observational study, involving older people admitted to 87 internal medicine and geriatric wards, was done in Italy between 2010 and 2012. The main clinical/laboratory data were obtained on admission and discharge. Based on haemoglobin (Hb), subjects were classified in three groups: group 1 with normal Hb, (reference group), group 2 with mildly reduced Hb (10.0–11.9 g/dL in women; 10.0–12.9 g/dL in men) and group 3 with moderately-severely reduced Hb (<10 g/dL in women and men). Results Patients (2678; mean age 79.2 ± 7.4 y) with anaemia (54.7%) were older, with greater functional impairment and more comorbidity. Multivariable analysis showed that mild but not moderate-severe anaemia was associated with a higher risk of hospital re-admission within three months (group 2: OR = 1.62; 95%CI 1.21–2.17). Anaemia failed to predict in-hospital mortality, while a higher risk of dying within three months was associated with the degree of Hb reduction on admission (group 2: OR = 1.82;95%CI 1.25–2.67; group 3: OR = 2.78;95%CI 1.82–4.26) and discharge (group 2: OR = 2.37;95%CI 1.48–3.93; group 3: OR = 3.70;95%CI 2.14–6.52). Normocytic and macrocytic, but not microcytic anaemia, were associated with adverse clinical outcomes. Conclusions Mild anaemia predicted hospital re-admission of older in-patients, while three-month mortality risk increased proportionally with anaemia severity. Type and severity of anaemia affected hospital re-admission and mortality, the worst prognosis being associated with normocytic and macrocytic anaemia