Summary Two main sources of data for species distribution models (SDMs) are site‐occupancy (SO) data from planned surveys, and presence‐background (PB) data from opportunistic surveys and other sources. SO surveys give high quality data about presences and absences of the species in a particular area. However, due to their high cost, they often cover a smaller area relative to PB data, and are usually not representative of the geographic range of a species. In contrast, PB data is plentiful, covers a larger area, but is less reliable due to the lack of information on species absences, and is usually characterised by biased sampling. Here we present a new approach for species distribution modelling that integrates these two data types. We have used an inhomogeneous Poisson point process as the basis for constructing an integrated SDM that fits both PB and SO data simultaneously. It is the first implementation of an Integrated SO–PB Model which uses repeated survey occupancy data and also incorporates detection probability. The Integrated Model's performance was evaluated, using simulated data and compared to approaches using PB or SO data alone. It was found to be superior, improving the predictions of species spatial distributions, even when SO data is sparse and collected in a limited area. The Integrated Model was also found effective when environmental covariates were significantly correlated. Our method was demonstrated with real SO and PB data for the Yellow‐bellied glider (Petaurus australis) in south‐eastern Australia, with the predictive performance of the Integrated Model again found to be superior. PB models are known to produce biased estimates of species occupancy or abundance. The small sample size of SO datasets often results in poor out‐of‐sample predictions. Integrated models combine data from these two sources, providing superior predictions of species abundance compared to using either data source alone. Unlike conventional SDMs which have restrictive scale‐dependence in their predictions, our Integrated Model is based on a point process model and has no such scale‐dependency. It may be used for predictions of abundance at any spatial‐scale while still maintaining the underlying relationship between abundance and area.

A ccurate and current estimates of cancer incidence and prevalence are required to quantify the health burden of cancer and to direct current and future allocation of health care funds. 1 In recent decades, cancer registries have become the most accurate source of cancer data.2 However, the acquisition of cancer registry data is not always feasible for use in epidemiological studies, as data linkage to the registry can be costly and time consuming. In particular, there may be ethical restrictions on the use of cancer data or data may only be provided in an aggregated fashion. As an alternative, collection of cancer data by self-report may be a reasonable surrogate to cancer registry data. However, before self-reported cancer data can be used in research, it is important to know the accuracy of such data compared to that recorded on registries, as well as the patient characteristics that predict accurate self-reports.The sensitivity of self-reported cancer data compared to cancer data obtained from a registry has been assessed in several studies in other developed countries, and ranges from 61% to 90%, varying considerably by cancer site. High sensitivities have been observed for self-reported breast (85.4-90%), prostate (78.9%), and lung cancers (74.1%), while low sensitivity has been observed for self-reported bowel cancer (16%-60.4%) and melanoma of the skin (53%). 1,3-5 However, only a few large population-based studies have evaluated the characteristics of people who self-report wrongly. 6,7 Furthermore, the findings from these studies may not be generalisable to the Australian context, as they only include women and were conducted in other countries. Given the differences in health care systems between countries, the validity of self-reported cancer may be different in Australia.In Australia, cancer is a notifiable disease by law and detailed cancer data are routinely collected by the state and territory cancer registries. The cancer data are then collated by the Australian Cancer Database (ACD), a national registry established in 1983. The objective of this study is to determine the validity of self-reported all-cause and site-specific cancer in a national, population- AbstractObjective: The aim of this study is to determine the validity of self-reported cancer data by comparing it to the Australian Cancer Database (ACD).Methods: Self-reported data were obtained from the Australian Diabetes, Obesity and Lifestyle (AusDiab) study, which were then linked to the ACD up until 31 December 2010. Positive predictive value, negative predictive value, sensitivity and specificity were calculated. Cohen's kappa coefficient (ĸ) was also calculated to assess the agreement between self-reported cancer and the ACD. Logistic regression was used to examine the determinants associated with false negative and false positive reporting. Results:The overall sensitivity of self-report cancer was 71.1%, and sensitivities showed great variation by cancer site. Higher sensitivities were observed for breast (90.7%), bowel (77.8%) a...

Extinctions are difficult to observe. Estimating the probability that a taxon has gone extinct using data from the field aids prioritisation of conservation interventions and environmental monitoring. There have been recent advances in approaches to estimating this probability from records. However, complete assessment requires consideration of the type, timing and certainty of records, the timing, scope and severity of threats, and the timing, extent and reliability of surveys. Until recently, no single method could integrate these different sources and qualities of data into a single measure. Here we describe a new, accessible method for estimating the probability that a taxon is extinct based on different kinds of both record and survey data, and accounting for data quality. The model takes into account uncertainties in input parameter estimates and provides bounds on estimates of the extinction probability. We illustrate application of the model using information for the Alaotra Grebe Tachybaptus rufolavatus. Application of this approach should facilitate more efficient allocation of conservation resources by enabling scenario analyses that inform investments in searches and management interventions for potentially extinct taxa. It should also provide more reliable estimates of recent extinction rates

Despite different socioeconomic settings in Singapore and Mauritius, we observed rising diabetes prevalence among South Asians but stable prevalence in Chinese in both countries. This provides further evidence that ethnicity contributes to the development of diabetes, and that there should be an increased emphasis on future prevention strategies targeting South Asian populations in these countries.

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