Which Factors Are Associated With Comorbid Psychiatric Conditions in Patients Affected by Substance Use Disorders? The Impact of COVID-19 Pandemic on Dual-Diagnosis Subjects.

Objective: To investigate demographic/cinical variables associated to dual diagnosis and the psychological reaction of dual-diagnosis patients to COVID-19 pandemic. Methods: Information was collected at the Addiction Service of Monza, Italy. The Impact of Event Scale-Revised (IES-R), a self-report questionnaire measuring the subjective response to a traumatic event, was administered. Univariate analyses and binary logistic regression were performed. IES-R scores were compared between groups defined by qualitative variables through one-way analyses of variance (ANOVA). Results: 118 outpatients were included, 48.3% with dual diagnosis. Alcohol use disorder and being female were associated to dual diagnosis. IES-R scores were significantly higher in the dual-diagnosis group, especially for personality disorders (PDs). IES-R scores were higher in patients taking treatment for substance use disorder (SUD). Conclusions: Females and alcohol abusers were at-risk subjects for dual diagnosis. Patients with SUD and PDs may benefit from additional support, especially when traumatic life events occur. Trial Registration: ClinicalTrials.gov Identifier: NCT04694482.

A monthly gridded burned area database of national wildland fire data.

We assembled the first gridded burned area (BA) database of national wildfire data (ONFIRE), a comprehensive and integrated resource for researchers, non-government organisations, and government agencies analysing wildfires in various regions of the Earth. We extracted and harmonised records from different regions and sources using open and reproducible methods, providing data in a common framework for the whole period available (starting from 1950 in Australia, 1959 in Canada, 1985 in Chile, 1980 in Europe, and 1984 in the United States) up to 2021 on a common 1° × 1° grid. The data originate from national agencies (often, ground mapping), thus representing the best local expert knowledge. Key opportunities and limits in using this dataset are discussed as well as possible future expansions of this open-source approach that should be explored. This dataset complements existing gridded BA data based on remote sensing and offers a valuable opportunity to better understand and assess fire regime changes, and their drivers, in these regions. The ONFIRE database can be freely accessed at https://zenodo.org/record/8289245 .

Anthropogenic climate change impacts exacerbate summer forest fires in California.

Record-breaking summer forest fires have become a regular occurrence in California. Observations indicate a fivefold increase in summer burned area (BA) in forests in northern and central California during 1996 to 2021 relative to 1971 to 1995. While the higher temperature and increased dryness have been suggested to be the leading causes of increased BA, the extent to which BA changes are due to natural variability or anthropogenic climate change remains unresolved. Here, we develop a climate-driven model of summer BA evolution in California and combine it with natural-only and historical climate simulations to assess the importance of anthropogenic climate change on increased BA. Our results indicate that nearly all the observed increase in BA is due to anthropogenic climate change as historical model simulations accounting for anthropogenic forcing yield 172% (range 84 to 310%) more area burned than simulations with natural forcing only. We detect the signal of combined historical forcing on the observed BA emerging in 2001 with no detectable influence of the natural forcing alone. In addition, even when considering fuel limitations from fire-fuel feedbacks, a 3 to 52% increase in BA relative to the last decades is expected in the next decades (2031 to 2050), highlighting the need for proactive adaptations.

The association between reasons for first using cannabis, later pattern of use, and risk of first-episode psychosis: the EU-GEI case-control study.

BACKGROUND: While cannabis use is a well-established risk factor for psychosis, little is known about any association between reasons for first using cannabis (RFUC) and later patterns of use and risk of psychosis. METHODS: We used data from 11 sites of the multicentre European Gene-Environment Interaction (EU-GEI) case-control study. 558 first-episode psychosis patients (FEPp) and 567 population controls who had used cannabis and reported their RFUC.We ran logistic regressions to examine whether RFUC were associated with first-episode psychosis (FEP) case-control status. Path analysis then examined the relationship between RFUC, subsequent patterns of cannabis use, and case-control status. RESULTS: Controls (86.1%) and FEPp (75.63%) were most likely to report 'because of friends' as their most common RFUC. However, 20.1% of FEPp compared to 5.8% of controls reported: 'to feel better' as their RFUC (χ2 = 50.97; p < 0.001). RFUC 'to feel better' was associated with being a FEPp (OR 1.74; 95% CI 1.03-2.95) while RFUC 'with friends' was associated with being a control (OR 0.56; 95% CI 0.37-0.83). The path model indicated an association between RFUC 'to feel better' with heavy cannabis use and with FEPp-control status. CONCLUSIONS: Both FEPp and controls usually started using cannabis with their friends, but more patients than controls had begun to use 'to feel better'. People who reported their reason for first using cannabis to 'feel better' were more likely to progress to heavy use and develop a psychotic disorder than those reporting 'because of friends'.

The psychopathological impact of the SARS-CoV-2 epidemic on subjects suffering from different mental disorders: An observational retrospective study.

SARS-CoV-2 infection causes a pulmonary disease (COVID-19) which spread worldwide generating fear, anxiety, depression in the general population as well as among subjects affected by mental disorders. Little is known about which different psychopathological changes the pandemic caused among individuals affected by different psychiatric disorders, which represents the aim of the present study. Specific psychometric scales were administered at three time points: T0 as outbreak of pandemic, T1 as lockdown period, T2 as reopening. Descriptive analyses and linear regression models were performed. A total of 166 outpatients were included. Overall, psychometric scores showed a significant worsening at T1 with a mild improvement at T2. Only psychopathology in schizophrenia (SKZ) patients and obsessive-compulsive (OC) symptoms did not significantly improve at T2. Subjects affected by personality disorders (PDs) resulted to be more compromised in terms of general psychopathology than depressed and anxiety/OC ones, and showed more severe anxiety symptoms than SKZ patients. In conclusion, subjects affected by PDs require specific clinical attention during COVID-19 pandemic. Moreover, the worsening of SKZ and OC symptoms should be strictly monitored by clinicians, as these aspects did not improve with the end of lockdown measures. Further studies on larger samples are needed to confirm our results. ClinicalTrials.gov Identifier: NCT04694482.

Human and climate drivers of global biomass burning variability.

Biomass burning is one of the most critical factors impacting vegetation and atmospheric trends, with important societal implications, particularly when extreme weather conditions occur. Trends and factors of burned area (BA) have been analysed at regional and global scales, but little effort has been dedicated to study the interannual variability. This paper aimed to better understand factors explaining this variation, under the assumption that the more human control of fires the more frequently they occur, as burnings will be less dependent of weather cycles. Interannual variability of BA was estimated from the coefficient of variation of the annual BA (BA_CV) estimated from satellite data at 250 m, covering the period from 2001 to 2018. These data and the explanatory variables were resampled at 0.25-degree resolution for global analysis. Relations between this variable and explanatory factors, including human and climate drivers, were estimated using Random Forest (RF) and generalized additive models (GAM). BA_CV was negatively related to BA_Mean, implying that areas with higher average BA have lower variability as well. Interannual BA variability decreased when maximum temperature (TMAX) and actual and potential evapotranspiration (AET, PET) increased, cropland and livestock density increased and the human development index (HDI) values decreased. GAM models indicated interesting links with AET, PET and precipitation, with negative relation with BA_CV for the lower ranges and positive for the higher ones, the former indicating fuel limitations of fire activity, and the latter climate constrains. For the global RF model, TMAX, AET and HDI were the main drivers of interannual variability. As originally hypothesised, BA_CV was more dependent on human factors (HDI) in those areas with medium to large BA occurrence, particularly in tropical Africa and Central Asia, while climatic factors were more important in boreal regions, but also in the tropical regions of Australia and South America.

Population exposure to particulate-matter and related mortality due to the Portuguese wildfires in October 2017 driven by storm Ophelia.

In October 2017, hundreds of wildfires ravaged the forests of the north and centre of Portugal. The fires were fanned by strong winds as tropical storm Ophelia swept the Iberian coast, dragging up smoke (together with Saharan dust from north-western Africa) into higher western European latitudes. Here we analyse the long-range transport of particulate matter (PM10) and study associations between PM10 and short-term mortality in the Portuguese population exposed to PM10 due to the October 2017 wildfires, the worst fire sequence in the country over the last decades. We analysed space- and ground-level observations to track the smoke plume and dust trajectory over Portugal and Europe, and to access PM10 concentrations during the wildfires. The effects of PM10 on mortality were evaluated using satellite data for exposure and Poisson regression models. The smoke plume covered most western European countries (including Spain, France, Belgium and the Netherlands), and reached the United Kingdom, where the population was exposed in average to an additional PM10 level of 11.7 µg/m3 during seven smoky days (three with dust) in relation to the reference days (days without smoke or dust), revealing the impact of the wildfires on distant populations. In Portugal, the population was exposed in average to additional PM10 levels that varied from 16.2 to 120.6 µg/m3 in smoky days with dust and from 6.1 to 20.9 µg/m3 in dust-free smoky days. Results suggest that PM10 had a significant effect on the same day natural and cardiorespiratory mortalities during the month of October 2017. For every additional 10 µg/m3 of PM10, there was a 0.89% (95% confidence interval, CI, 0-1.77%) increase in the number of natural deaths and a 2.34% (95% CI, 0.99-3.66%) increase in the number of cardiorespiratory-related deaths. With rising temperatures and a higher frequency of storms due to climate change, PM from Iberian wildfires together with NW African dust will tend to be more often transported into Northern European countries, which may carry health threats to areas far from the ignition sites.

The requirements of a specialist breast centre.

This article is an update of the requirements of a specialist breast centre, produced by EUSOMA and endorsed by ECCO as part of Essential Requirements for Quality Cancer Care (ERQCC) programme, and ESMO. To meet aspirations for comprehensive cancer control, healthcare organisations must consider the requirements in this article, paying particular attention to multidisciplinarity and patient-centred pathways from diagnosis, to treatment, to survivorship.

Climate drivers of the 2017 devastating fires in Portugal.

A record 500,000 hectares burned in Portugal during the extreme wildfire season of 2017, with more than 120 human lives lost. Here we analyse the climatic factors responsible for the burned area (BA) from June to October series in Portugal for the period 1980-2017. Superposed onto a substantially stationary trend on BA data, strong oscillations on shorter time scales were detected. Here we show that they are significantly affected by the compound effect of summer (June-July-August) drought and high temperature conditions during the fire season. Drought conditions were calculated using the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Index (SSI). Then the extent to which the burned area has diverged from climate-expected trends was assessed. Our results indicate that in the absence of other drivers, climate change would have led to higher BA values. In addition, the 2017 extreme fire season is well captured with the model forced with climate drivers only, suggesting that the extreme fire season of 2017 could be a prelude to future conditions and likewise events. Indeed, the expected further increase of drought and high temperature conditions in forthcoming decades, point at a potential increase of fire risk in this region. The climate-fire model developed in this study could be useful to develop more skilled seasonal predictions capable of anticipating potentially hazardous conditions.

Seasonal prediction of climate-driven fire risk for decision-making and operational applications in a Mediterranean region.

In this paper, we assess and develop a climate service focused on the production of seasonal predictions for summer wildfires in a Mediterranean region through a participatory approach with end-users. We start by building a data-driven model that links a drought indicator (Standardised Precipitation Evapotranspiration Index; SPEI) with a series of burned areas in Catalonia (northeastern Spain). Afterwards, we feed this model with SPEI forecasts obtained through a combination of the antecedent observed conditions and climatology. Finally, we assess the forecasting skill of the system by using cross-validation to evaluate the predictions as if they had been made operationally. Our fire forecasting system reveals an untapped and useful burned area predictive ability. We argue that this source of predictability is mostly attributable to the effect of observed initial conditions on summer drought conditions. This system was conceived with the stakeholders, merging climate-driven predictions with information that is of interests to the users, including the identification of climate variables, thresholds and models. The co-production of this customized system allows fire-risk outlooks to be translated into usable information for fire management. This fire forecasting ability plays a crucial role in developing proactive fire management practices such as long-term fuel assessment and other fire-risk planning, thus minimising the impact of adverse climate conditions on summer burned area.

Microinvasive breast carcinoma: An analysis from ten Senonetwork Italia breast centres.

BACKGROUND AND OBJECTIVES: We studied a large series of ductal carcinoma in situ with microinvasion (MIDC) an infrequent disease whose diagnosis and management are not well defined. METHODS: 17,431 cases of breast carcinoma were treated between 2011 and 2016 by ten Italian Breast Units. Our analysis included diagnostic and clinic-pathological characteristics, surgical management, and the use of adjuvant therapies. RESULTS: 15,091 cases (86.6%) were infiltrating carcinomas (IC), 2107 (12.1%) ductal carcinoma in situ (DCIS), and 233 (1.3%) MIDC. Age at diagnosis did not differ between DCIS and MIDC. MIDC were usually larger and expressed more frequently biologically aggressive features (higher Ki67 values, hormone receptor negativity and HER2/neu over-expression) (p < 0.01). Axillary lymph nodes were involved in 25 MIDC cases (12%), but >3 lymph nodes were involved in two cases only (1%). At multivariable analysis, only lymphovascular invasion (LVI) was associated with lymph node status (p < 0.01). Hormone therapy was prescribed in 388/1462 DCIS cases (26.5%), in 84/200 MIDC cases (42%), and in 11,086/14,188 IC cases (84.7%) (p < 0.01). Chemotherapy was administered in 28/190 MIDC cases (14.7%), and in 4080/11,548 IC cases (35.3%) (p < 0.001). CONCLUSIONS: This is one of the largest studies of MIDC reported in the literature. Approximately 10% of DCIS harbor one or more foci of MIDC, and the latter often expresses aggressive biological features. LVI is a predictor of axillary node involvement, but this is infrequent and usually limited. Conservative surgery is performed less often than in DCIS, and adjuvant chemotherapy is less frequently utilized compared to IC.

Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models.

The observed trend towards warmer and drier conditions in southern Europe is projected to continue in the next decades, possibly leading to increased risk of large fires. However, an assessment of climate change impacts on fires at and above the 1.5 °C Paris target is still missing. Here, we estimate future summer burned area in Mediterranean Europe under 1.5, 2, and 3 °C global warming scenarios, accounting for possible modifications of climate-fire relationships under changed climatic conditions owing to productivity alterations. We found that such modifications could be beneficial, roughly halving the fire-intensifying signals. In any case, the burned area is robustly projected to increase. The higher the warming level is, the larger is the increase of burned area, ranging from ~40% to ~100% across the scenarios. Our results indicate that significant benefits would be obtained if warming were limited to well below 2 °C.

Skilful forecasting of global fire activity using seasonal climate predictions.

Societal exposure to large fires has been increasing in recent years. Estimating the expected fire activity a few months in advance would allow reducing environmental and socio-economic impacts through short-term adaptation and response to climate variability and change. However, seasonal prediction of climate-driven fires is still in its infancy. Here, we discuss a strategy for seasonally forecasting burned area anomalies linking seasonal climate predictions with parsimonious empirical climate-fire models using the standardized precipitation index as the climate predictor for burned area. Assuming near-perfect climate predictions, we obtained skilful predictions of fire activity over a substantial portion of the global burnable area (~60%). Using currently available operational seasonal climate predictions, the skill of fire seasonal forecasts remains high and significant in a large fraction of the burnable area (~40%). These findings reveal an untapped and useful burned area predictive ability using seasonal climate forecasts, which can play a crucial role in fire management strategies and minimise the impact of adverse climate conditions.

Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast.

Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.

Use of bias correction techniques to improve seasonal forecasts for reservoirs - A case-study in northwestern Mediterranean.

In this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been applied on precipitation and temperature from the European Centre for Middle-range Weather Forecasting System 4 (S4). We have used three bias correction strategies: two linear (mean bias correction, BC, and linear regression, LR) and one non-linear (Model Output Statistics analogs, MOS-analog). The results have been compared with climatology and persistence. The volume-anomaly model is a previously computed Multiple Linear Regression that ingests precipitation, temperature and in-flow anomaly data to simulate monthly volume anomalies. The potential utility for end-users has been assessed using economic value curve areas. We have studied the S4 hindcast period 1981-2010 for each month of the year and up to seven months ahead considering an ensemble of 15 members. We have shown that the MOS-analog and LR bias corrections can improve the original S4. The application to volume anomalies points towards the possibility to introduce bias correction methods as a tool to improve water resource seasonal forecasts in an end-user context of climate services. Particularly, the MOS-analog approach gives generally better results than the other approaches in late autumn and early winter.

Low synovial double negative T and γδ T cells predict longer free-disease survival in oligoarticular JIA.

BACKGROUND: Oligoarticular juvenile idiopathic arthritis (oJIA) is the most frequent form of chronic arthritis in children; the clinical course is extremely variable. In this study we have characterized by flow cytometry synovial B and T cells subsets in patients with oJIA in order to identify any parameters that could predict a more aggressive course of disease. METHODS: B and T cells from synovial fluid (SF) of 39 patients with oJIA were characterized by flow cytometry. In 22 patients SF was analysed at the onset of the disease (GroupA), in 17 SF was analysed at articular relapse (Group B). All patients in Group A were followed up for at least for 2 years after SF analysis: 13 patients relapsed during the follow-up period. RESULTS: Comparison of SF from Group A and Group B demonstrated an activated phenotype in relapsed patients, with higher Switched Memory B cells (58.53 vs 36.07% of CD19+, P-value 0.004) and lower Naïve B cells (8.53 vs 25.9 of CD19+, P-value 0.002) in Group B. Furthermore, patients from Group A who did not relapse showed lower percentages of synovial DNT (2.38 vs 1.50% of CD3 + TCRalpha/beta+, P-value 0.025) and γδ T cells (19.1 vs 15.0% of CD3+ cells, P-value 0.004) at the onset, if compared with other Group A patients. CONCLUSIONS: In oJIA relapse SF present an activated B phenotype. Patients at disease onset with DNTs <1.8% and/or γδ T cells <16% of CD3+ in synovial fluid have longer free-disease survival. © 2017 International Clinical Cytometry Society.

Inter-annual and decadal changes in teleconnections drive continental-scale synchronization of tree reproduction.

Climate teleconnections drive highly variable and synchronous seed production (masting) over large scales. Disentangling the effect of high-frequency (inter-annual variation) from low-frequency (decadal trends) components of climate oscillations will improve our understanding of masting as an ecosystem process. Using century-long observations on masting (the MASTREE database) and data on the Northern Atlantic Oscillation (NAO), we show that in the last 60 years both high-frequency summer and spring NAO, and low-frequency winter NAO components are highly correlated to continent-wide masting in European beech and Norway spruce. Relationships are weaker (non-stationary) in the early twentieth century. This finding improves our understanding on how climate variation affects large-scale synchronization of tree masting. Moreover, it supports the connection between proximate and ultimate causes of masting: indeed, large-scale features of atmospheric circulation coherently drive cues and resources for masting, as well as its evolutionary drivers, such as pollination efficiency, abundance of seed dispersers, and natural disturbance regimes.

Quality indicators in breast cancer care: An update from the EUSOMA working group.

In 2010, EUSOMA published a position paper, describing a set of benchmark quality indicators (QIs) that could be adopted by breast centres to allow standardised auditing and quality assurance and to establish an agreed minimum standard of care. Towards the end of 2014, EUSOMA decided to update the paper on QIs to consider and incorporate new scientific knowledge in the field. Several new QIs have been included to address the need for improved follow-up care of patients following primary treatments. With regard to the management of elderly patients, considering the complexity, the expert group decided that, for some specific quality indicators, if centres fail to meet the minimum standard, older patients will be excluded from analysis, provided that reasons for non-adherence to the QI are specified in the clinical chart and are identified at the review of the clinical records. In this way, high standards are promoted, but centres are able to identify and account for the effect of non-standard treatment in the elderly. In the paper, there is no QI for outcome measurements, such as relapse rate or overall survival. However, it is hoped that this will be developed in time as the databases mature and user experience increases. All breast centres are required to record outcome data as accurately and comprehensively as possible to allow this to occur. In the paper, different initiatives undertaken at international and national level to audit quality of care through a set of QIs have been mentioned.

Spatial patterns and broad-scale weather cues of beech mast seeding in Europe.

Mast seeding is a crucial population process in many tree species, but its spatio-temporal patterns and drivers at the continental scale remain unknown . Using a large dataset (8000 masting observations across Europe for years 1950-2014) we analysed the spatial pattern of masting across the entire geographical range of European beech, how it is influenced by precipitation, temperature and drought, and the temporal and spatial stability of masting-weather correlations. Beech masting exhibited a general distance-dependent synchronicity and a pattern structured in three broad geographical groups consistent with continental climate regimes. Spearman's correlations and logistic regression revealed a general pattern of beech masting correlating negatively with temperature in the summer 2 yr before masting, and positively with summer temperature 1 yr before masting (i.e. 2T model). The temperature difference between the two previous summers (DeltaT model) was also a good predictor. Moving correlation analysis applied to the longest eight chronologies (74-114 yr) revealed stable correlations between temperature and masting, confirming consistency in weather cues across space and time. These results confirm widespread dependency of masting on temperature and lend robustness to the attempts to reconstruct and predict mast years using temperature data.