Improving the reporting of cancer-specific mortality and survival in research using cancer registry data

Population-based registries are increasingly used in cancer research. In such studies, cancer-specific mortality or survival is frequently used as the primary outcome. To determine whether a putative cancer was part of the causal chain of events leading to death, cancer registries primarily rely on death certificates. Hence, they depend on the subjective interpretation of information available to medical examiners at the time of death. Misclassification may occur: studies report misclassification of cancer as a cause of death in 15%–35% of death certificates based on evaluation by expert panels and/or autopsy reports. Further misclassification may occur when coding death causes in the cancer registry. Researchers should be aware of potential misclassification bias when using cancer registry data. Differential misclassification may bias the results towards or away from the null hypothesis, depending on whether there is relative over- or under-reporting of cancer-related deaths in one group. Strategies to improve reporting of cancer-specific survival/mortality include (1) describing the procedure used to identify cancer-specific deaths; (2) considering the use of multiple definitions of cancer-related deaths (strict/liberal definitions of cancer-specific deaths, and/or addition of relative survival as an outcome); and (3) reporting cancer-specific survival/mortality together with the objectively measured parameters overall survival or all-cause mortality.     

Meta‐analysis shows that environmental DNA outperforms traditional surveys,but warrants better reporting standards

1. Decades of environmental DNA (eDNA) method application, spanning a wide variety of taxa and habitats, has advanced our understanding of eDNA and underlined its value as a tool for conservation practitioners. The general consensus is that eDNA methods are more accurate and cost‐effective than traditional survey methods. However, they are formally approved for just a few species globally (e.g., Bighead Carp, Silver Carp, Great Crested Newt). We conducted a meta‐analysis of studies that directly compare eDNA with traditional surveys to evaluate the assertion that eDNA methods are consistently “better.”
2. Environmental DNA publications for multiple species or single macro‐organism detection were identified using the Web of Science, by searching “eDNA” and “environmental DNA” across papers published between 1970 and 2020. The methods used, focal taxa, habitats surveyed, and quantitative and categorical results were collated and analyzed to determine whether and under what circumstances eDNA outperforms traditional surveys.
3. Results show that eDNA methods are cheaper, more sensitive, and detect more species than traditional methods. This is, however, taxa‐dependent, with amphibians having the highest potential for detection by eDNA survey. Perhaps most strikingly, of the 535 papers reviewed just 49 quantified the probability of detection for both eDNA and traditional survey methods and studies were three times more likely to give qualitative statements of performance.
4. Synthesis and applications: The results of this meta‐analysis demonstrate that where there is a direct comparison, eDNA surveys of macro‐organisms are more accurate and efficient than traditional surveys. This conclusion, however, is based on just a fraction of available eDNA papers as most do not offer this granularity. We recommend that conclusions are substantiated with comparable and quantitative data. Where a direct comparison has not been made, we caution against the use of qualitative statements about relative performance. This consistency and rigor will simplify how the eDNA research community tracks methods‐based advances and will also provide greater clarity for conservation practitioners. To this end suggest reporting standards for eDNA studies.

     

Improving inference for aerial surveys of bears: The importance of assumptions and the cost of unnecessary complexity

Obtaining useful estimates of wildlife abundance or density requires thoughtful attention to potential sources of bias and precision, and it is widely understood that addressing incomplete detection is critical to appropriate inference. When the underlying assumptions of sampling approaches are violated, both increased bias and reduced precision of the population estimator may result. Bear (Ursus spp.) populations can be difficult to sample and are often monitored using mark‐recapture distance sampling (MRDS) methods, although obtaining adequate sample sizes can be cost prohibitive. With the goal of improving inference, we examined the underlying methodological assumptions and estimator efficiency of three datasets collected under an MRDS protocol designed specifically for bears. We analyzed these data using MRDS, conventional distance sampling (CDS), and open‐distance sampling approaches to evaluate the apparent bias‐precision tradeoff relative to the assumptions inherent under each approach. We also evaluated the incorporation of informative priors on detection parameters within a Bayesian context. We found that the CDS estimator had low apparent bias and was more efficient than the more complex MRDS estimator. When combined with informative priors on the detection process, precision was increased by >50% compared to the MRDS approach with little apparent bias. In addition, open‐distance sampling models revealed a serious violation of the assumption that all bears were available to be sampled. Inference is directly related to the underlying assumptions of the survey design and the analytical tools employed. We show that for aerial surveys of bears, avoidance of unnecessary model complexity, use of prior information, and the application of open population models can be used to greatly improve estimator performance and simplify field protocols. Although we focused on distance sampling‐based aerial surveys for bears, the general concepts we addressed apply to a variety of wildlife survey contexts.     

Improving biodiversity surrogates for conservation assessment: A test of methods and the value of targeted biological surveys

Aim

Conservation assessment and planning across extensive regions rely on the use of mapped or modelled surrogates because direct field‐based inventories of biodiversity rarely provide complete spatial coverage. Surrogates are assumed to represent spatial patterns in the distribution of biodiversity, yet the validity of this assumption is rarely evaluated. Here, we use data from new biological surveys targeting poorly known taxonomic groups across sparsely surveyed landscapes to test: (1) the performance of established and novel surrogates; and (2) the value of targeted survey data in further improving surrogate effectiveness.

Location

Continental Australia.

Methods

Surrogates were derived from either mapped land classifications (bioregions, vegetation types), or models of spatial turnover in biodiversity composition. Models were derived by linking best‐available biological observations to high‐resolution mapped climate, terrain and soil attributes using generalized dissimilarity modelling (GDM). The performance of surrogates was evaluated using survey data for eight biological groups collected as part of the Bush Blitz programme ( http://bushblitz.org ). For the GDM‐based surrogates, within‐ and cross‐taxon performance was first evaluated for models fitted to biological data available prior to Bush Blitz, and then for models enhanced through the addition of the Bush Blitz data.

Results

All of the tested surrogates performed significantly better than random across all eight biological groups. GDM‐based surrogates performed over 10% better on average than the best performing combination of mapped land classifications. The addition of Bush Blitz targeted data in GDM‐based surrogates led to further improvements in surrogate performance.

Main conclusions

Our results support continued investment in targeted biological survey programmes to enhance the performance of surrogates and ensure that surrogates represent a wider breadth of biodiversity. The strong performance of compositional turnover modelling, relative to mapped land classifications, suggests that this surrogate strategy deserves greater consideration in future conservation assessments and has potential for use in continental‐scale monitoring of biodiversity.