Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models.

Digital point-occurrence records from the Global Biodiversity Information Facility (GBIF) and other data providers enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time-consuming and often unfeasible, given that the databases may contain thousands or millions of records. Automated data cleaning pipelines are therefore of high importance. Taking North American Ephedra as a model, we examined how different data cleaning pipelines (using, e.g., the GBIF web application, and four different R packages) affect downstream species distribution models (SDMs). We also assessed how data differed from expert data. From 13,889 North American Ephedra observations in GBIF, the pipelines removed 31.7% to 62.7% false positives, invalid coordinates, and duplicates, leading to datasets between 9484 (GBIF application) and 5196 records (manual-guided filtering). The expert data consisted of 704 records, comparable to data from field studies. Although differences in the absolute numbers of records were relatively large, species richness models based on stacked SDMs (S-SDM) from pipeline and expert data were strongly correlated (mean Pearson's r across the pipelines: .9986, vs. the expert data: .9173). Our results suggest that all R package-based pipelines reliably identified invalid coordinates. In contrast, the GBIF-filtered data still contained both spatial and taxonomic errors. Major drawbacks emerge from the fact that no pipeline fully discovered misidentified specimens without the assistance of taxonomic expert knowledge. We conclude that application-filtered GBIF data will still need additional review to achieve higher spatial data quality. Achieving high-quality taxonomic data will require extra effort, probably by thoroughly analyzing the data for misidentified taxa, supported by experts.

Assessing intrastate shipments from interstate data and expert opinion.

Live animal shipments are a potential route for transmitting animal diseases between holdings and are crucial when modelling spread of infectious diseases. Yet, complete contact networks are not available in all countries, including the USA. Here, we considered a 10% sample of Interstate Certificate of Veterinary Inspections from 1 year (2009). We focused on distance dependence in contacts and investigated how different functional forms affect estimates of unobserved intrastate shipments. To further enhance our predictions, we included responses from an expert elicitation survey about the proportion of shipments moving intrastate. We used hierarchical Bayesian modelling to estimate parameters describing the kernel and effects of expert data. We considered three functional forms of spatial kernels and the inclusion or exclusion of expert data. The resulting six models were ranked by widely applicable information criterion (WAIC) and deviance information criterion (DIC) and evaluated through within- and out-of-sample validation. We showed that predictions of intrastate shipments were mildly influenced by the functional form of the spatial kernel but kernel shapes that permitted a fat tail at large distances while maintaining a plateau-shaped behaviour at short distances better were preferred. Furthermore, our study showed that expert data may not guarantee enhanced predictions when expert estimates are disparate.

USING EXPERT OPINION IN HEALTH TECHNOLOGY ASSESSMENT: A GUIDELINE REVIEW.

OBJECTIVES: External experts can be consulted at different stages of an HTA. When using vague information sources, it is particularly important to plan, analyze, and report the information processing in a standardized and transparent way. Our objective was to search and analyze recommendations regarding where and how to include expert data in HTA. METHODS: We performed a systematic database search and screened the Internet pages of seventy-seven HTA organizations for guidelines, recommendations, and methods papers that address the inclusion of experts in HTA. Relevant documents were downloaded, and information was extracted in a standard form. Results were merged in tables and narrative evidence synthesis. RESULTS: From twenty-two HTA organizations, we included forty-two documents that consider the use of expert opinion in HTA. Nearly all documents mention experts in the step of preparation of the evidence report. Six documents address their role for priority setting of topics, fifteen for scoping, twelve for the appraisal of evidence and results, another twelve documents mention experts when considering the dissemination of HTA results. During the assessment step, experts are most often asked to amend the literature search or to provide expertise for special data analyses. Another issue for external experts is to appraise the HTA results and refer them back to a clinical and social context. Little is reported on methods of expert elicitation when their input substitutes study data. CONCLUSIONS: Despite existing recommendations on the use of expert opinion in HTA, common standards for elicitation are scarce in HTA guidelines.