Differences in experiences of care between patients diagnosed with metastatic cancer of known and unknown primaries: mixed-method findings from the 2013 cancer patient experience survey in England.

OBJECTIVES: To explore differences in experiences of care reported in the Cancer Patient Experience Survey (CPES) between patients with cancer of unknown primary (CUP) and those with metastatic disease of known primary (non-CUP); to determine insights pertaining to the experiences of care for CUP respondents from free-text comments. DESIGN: Two separate, but related, studies, involving secondary analysis of existing data. Using frequency matching of CUP and non-CUP patients, statistical comparisons of responses to CPES questions were conducted. Free-text comments from CUP respondents were analysed thematically. SETTING AND PARTICIPANTS: The CPES questionnaire comprises 63 closed questions measuring 8 areas that relate to experience of care and 3 free-text questions. Questionnaires were mailed to all adult patients (aged ≥16 years) in England with cancer admitted to hospital between 1 September 2013 and 30 November 2013. RESULTS: Matched analysis of closed response items from 2992 patients found significant differences between CUP (n=1496) and non-CUP patients (n=1496): CUP patients were more likely to want more written information about their type of cancer and tests received, to receive their diagnosis from a general practitioner (GP) and have seen allied health professionals, but less likely to have understood explanations of their condition or had surgery. Freetext responses (n=3055) were coded into 17 categories and provided deeper insight regarding patient information and interactions with GPs. CPES data may include a preponderance of patients with favourable CUP subtypes and patients initially identified as CUP but whose primary was subsequently identified. CONCLUSIONS: These are the first large-scale studies to explore the experiences of care of CUP patients. The significant differences identified between the experiences of CUP and non-CUP patients suggest CUP patients require more psychosocial support and specific interventions to manage diagnostic uncertainty and the multiple investigations many CUP patients face. Substantial limitations were identified with the CPES data, emphasising the need for prospective studies

Influence of the 2015–2016 El Niño on the record‑breaking mangrove dieback along northern Australia coast

This study investigates the underlying climate processes behind the largest recorded mangrove dieback event along the Gulf of Carpentaria coast in northern Australia in late 2015. Using satellite derived fractional canopy cover (FCC), variation of the mangrove canopies during recent decades are studied, including a severe dieback during 2015–2016. The relationship between mangrove FCC and climate conditions is examined with a focus on the possible role of the 2015–2016 El Niño in altering favorable conditions sustaining the mangroves. The mangrove FCC is shown to be coherent with the low-frequency component of sea level height (SLH) variation related to the El Niño Southern Oscillation (ENSO) cycle in the equatorial Pacific. The SLH drop associated with the 2015–2016 El Niño is identified to be the crucial factor leading to the dieback event. A stronger SLH drop occurred during austral autumn and winter, when the SLH anomalies were about 12% stronger than the previous very strong El Niño events. The persistent SLH drop occurred in the dry season of the year when SLH was seasonally at its lowest, so potentially exposed the mangroves to unprecedented hostile conditions. The influence of other key climate factors is also discussed, and a multiple linear regression model is developed to understand the combined role of the important climate variables on the mangrove FCC variation

Probabilistic estimation of multivariate streamflow using independent component analysis and climate information

A statistical estimation approach is presented and applied to multiple reservoir inflow series that form part of Sydney’s water supply system. The approach involves first identifying sources of interannual and interdecadal climate variability using a combination of correlation- and wavelet-based methods, then using this information to construct probabilistic, multivariate seasonal estimates using a method based on independent component analysis (ICA). The attraction of the ICA-based approach is that, by transforming the multivariate dataset into a set of independent time series, it is possible to maintain the parsimony of univariate statistical methods while ensuring that both the spatial and temporal dependencies are accurately captured. Based on a correlation analysis of the reservoir inflows with the original sea surface temperature anomaly data, the principal sources of variability in Sydney’s reservoir inflows appears to be a combination of the El Niño–Southern Oscillation (ENSO) phenomenon and the Pacific decadal oscillation (PDO). A multivariate ICA-based estimation model was then used to capture this variability, and it was shown that this approach performed well in maintaining the temporal dependence while also accurately maintaining the spatial dependencies that exist in the 11-dimensional historical reservoir inflow dataset.Seth Westra and Ashish Sharm

Dominant modes of interannual variability in Australian rainfall analyzed using wavelets

One of the key aspects to better managing water resources in Australia is to understand the causes of medium- to long-term rainfall variability, which results in both droughts and periods of above average rainfall and flooding. Much of the research on this variability has focused on the El Niño–Southern Oscillation (ENSO) phenomenon, using methods that assume the relationships between ENSO and Australian rainfall are both linear and stationary. In this paper we present an alternative approach based on wavelets to analyze the dominant modes of variability in three rainfall characteristics: (1) the total annual rainfall, (2) the annual number of wet days, and (3) the maximum annual daily rainfall. We then use a wavelet regression approach to examine the extent of the variability that can be associated with ENSO. The results show that time series of total annual rainfall and annual number of wet days exhibit significant variability at periods of 2.6, 4.6, 7 and 13 years in various locations throughout the country and that these periodicities are not caused directly by the ENSO phenomenon. While maintaining that ENSO still plays a significant role in influencing rainfall variability in Australia, these results highlight the importance of looking beyond ENSO to identify dominant sources of variability in the characteristics of annual Australian rainfall that were studied. In contrast, no coherent modes of variability could be found for the maximum annual daily rainfall time series, highlighting the greater level of random behavior in the intensity of larger rainfall events compared with the long-term averages.Seth Westra and Ashish Sharm

An Upper limit to seasonal rainfall predictability?

The asymptotic predictability of global land surface precipitation is estimated empirically at the seasonal time scale with lead times from 0 to 12 months. Predictability is defined as the unbiased estimate of predictive skill using a given model structure assuming that all relevant predictors are included, thus representing an upper bound to the predictive skill for seasonal forecasting applications. To estimate predictability, a simple linear regression model is formulated based on the assumption that land surface precipitation variability can be divided into a component forced by low-frequency variability in the global sea surface temperature anomaly (SSTA) field and that can theoretically be predicted one or more seasons into the future, and a “weather noise” component that originates from nonlinear dynamical instabilities in the atmosphere and is not predictable beyond ~10 days. Asymptotic predictability of global precipitation was found to be 14.7% of total precipitation variance using 1900–2007 data, with only minor increases in predictability using shorter and presumably less error-prone records. This estimate was derived based on concurrent SSTA–precipitation relationships and therefore constitutes the maximum skill achievable assuming perfect forecasts of the evolution of the SSTA field. Imparting lags on the SSTA–precipitation relationship, the 3-, 6-, 9-, and 12-month predictability of global precipitation was estimated to be 7.3%, 5.4%, 4.2%, and 3.7%, respectively, demonstrating the comparative gains that can be achieved by developing improved SSTA forecasts compared to developing improved SSTA–precipitation relationships. Finally, the actual average cross-validated predictive skill was found to be 2.1% of the total precipitation variance using the full 1900–2007 dataset and was dominated by the El Niño–Southern Oscillation (ENSO) phenomenon. This indicates that there is still significant potential for increases in predictive skill through improved parameter estimates, the use of longer and/or more reliable datasets, and the use of larger spatial fields to substitute for limited temporal records.Seth Westra and Ashish Sharm

Multivariate streamflow forecasting using independent component analysis

Seasonal forecasting of streamflow provides many benefits to society, by improving our ability to plan and adapt to changing water supplies. A common approach to developing these forecasts is to use statistical methods that link a set of predictors representing climate state as it relates to historical streamflow, and then using this model to project streamflow one or more seasons in advance based on current or a projected climate state. We present an approach for forecasting multivariate time series using independent component analysis (ICA) to transform the multivariate data to a set of univariate time series that are mutually independent, thereby allowing for the much broader class of univariate models to provide seasonal forecasts for each transformed series. Uncertainty is incorporated by bootstrapping the error component of each univariate model so that the probability distribution of the errors is maintained. Although all analyses are performed on univariate time series, the spatial dependence of the streamflow is captured by applying the inverse ICA transform to the predicted univariate series. We demonstrate the technique on a multivariate streamflow data set in Colombia, South America, by comparing the results to a range of other commonly used forecasting methods. The results show that the ICA-based technique is significantly better at representing spatial dependence, while not resulting in any loss of ability in capturing temporal dependence. As such, the ICA-based technique would be expected to yield considerable advantages when used in a probabilistic setting to manage large reservoir systems with multiple inflows or data collection points.Seth Westra, Ashish Sharma, Casey Brown and Upmanu Lal