Dataset Information

Data Sources for Trait Databases: Comparing the Phenomic Content of Monographs and Evolutionary Matrices.

ABSTRACT: Databases of organismal traits that aggregate information from one or multiple sources can be leveraged for large-scale analyses in biology. Yet the differences among these data streams and how well they capture trait diversity have never been explored. We present the first analysis of the differences between phenotypes captured in free text of descriptive publications ('monographs') and those used in phylogenetic analyses ('matrices'). We focus our analysis on osteological phenotypes of the limbs of four extinct vertebrate taxa critical to our understanding of the fin-to-limb transition. We find that there is low overlap between the anatomical entities used in these two sources of phenotype data, indicating that phenotypes represented in matrices are not simply a subset of those found in monographic descriptions. Perhaps as expected, compared to characters found in matrices, phenotypes in monographs tend to emphasize descriptive and positional morphology, be somewhat more complex, and relate to fewer additional taxa. While based on a small set of focal taxa, these qualitative and quantitative data suggest that either source of phenotypes alone will result in incomplete knowledge of variation for a given taxon. As a broader community develops to use and expand databases characterizing organismal trait diversity, it is important to recognize the limitations of the data sources and develop strategies to more fully characterize variation both within species and across the tree of life.

SUBMITTER: Dececchi TA

PROVIDER: S-EPMC4871461 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Data Sources for Trait Databases: Comparing the Phenomic Content of Monographs and Evolutionary Matrices.

Dececchi T Alex TA Mabee Paula M PM Blackburn David C DC

PloS one 20160518 5

Databases of organismal traits that aggregate information from one or multiple sources can be leveraged for large-scale analyses in biology. Yet the differences among these data streams and how well they capture trait diversity have never been explored. We present the first analysis of the differences between phenotypes captured in free text of descriptive publications ('monographs') and those used in phylogenetic analyses ('matrices'). We focus our analysis on osteological phenotypes of the lim ...[more]

PMID: 27191170

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Project description:BACKGROUND:Multimorbidity is a global health issue, particularly for older adults in the primary care setting. An adequate portrayal of its epidemiology is essential to properly identify and understand the health care needs of this population. This study aimed to compare the differences in the prevalence of selected chronic conditions and multimorbidity, including its associated characteristics, using health survey/self-reported (SR) information only, administrative (Adm) data only and the combined (either) sources. METHODS:This was a secondary analysis of survey data from the first cycle of the Longitudinal Survey on Senior's Health and Health Services Use linked to health-Adm data. The analytical sample consisted of 1625 community-dwelling older adults (≥65 years) recruited in the waiting rooms of primary health clinics in a selected administrative region of the province of Quebec. Seventeen chronic conditions were assessed according to two different data sources. We examined the differences in the observed prevalence of chronic conditions and multimorbidity and the agreement between data sources. RESULTS:The prevalence of each of the 17 chronic conditions ranged from 1.2 to 68.7% depending on the data source. The agreement between different data sources was highly variable, with kappa coefficients (κ) ranging from 0.05 to 0.73. Multimorbidity was very high in this population, with an estimated prevalence of up to 95.9%. In addition, we found that the association between sociodemographic and behavioural factors and the presence of multimorbidity varied according to the different data sources and thresholds. CONCLUSIONS:This is the first study to simultaneously investigate chronic conditions and multimorbidity prevalence among primary care older adults using combined SR and health-Adm data. Our results call attention to (1) the possibility of underestimating cases when using a single data source and (2) the potential benefits of integrating information from different data sources to increase case identification. This is an important aspect of characterizing the health care needs of this fast-growing population.

Project description:BackgroundEstimating multimorbidity (presence of two or more chronic conditions) using administrative data is becoming increasingly common. We investigated (1) the concordance of identification of chronic conditions and multimorbidity using self-report survey and administrative datasets; (2) characteristics of people with multimorbidity ascertained using different data sources; and (3) whether the same individuals are classified as multimorbid using different data sources.MethodsBaseline survey data for 90,352 participants of the 45 and Up Study-a cohort study of residents of New South Wales, Australia, aged 45 years and over-were linked to prior two-year pharmaceutical claims and hospital admission records. Concordance of eight self-report chronic conditions (reference) with claims and hospital data were examined using sensitivity (Sn), positive predictive value (PPV), and kappa (?).The characteristics of people classified as multimorbid were compared using logistic regression modelling.ResultsAgreement was found to be highest for diabetes in both hospital and claims data (? = 0.79, 0.78; Sn = 79%, 72%; PPV = 86%, 90%). The prevalence of multimorbidity was highest using self-report data (37.4%), followed by claims data (36.1%) and hospital data (19.3%). Combining all three datasets identified a total of 46 683 (52%) people with multimorbidity, with half of these identified using a single dataset only, and up to 20% identified on all three datasets. Characteristics of persons with and without multimorbidity were generally similar. However, the age gradient was more pronounced and people speaking a language other than English at home were more likely to be identified as multimorbid by administrative data.ConclusionsDifferent individuals, with different combinations of conditions, are identified as multimorbid when different data sources are used. As such, caution should be applied when ascertaining morbidity from a single data source as the agreement between self-report and administrative data is generally poor. Future multimorbidity research exploring specific disease combinations and clusters of diseases that commonly co-occur, rather than a simple disease count, is likely to provide more useful insights into the complex care needs of individuals with multiple chronic conditions.

Dataset Information

Data Sources for Trait Databases: Comparing the Phenomic Content of Monographs and Evolutionary Matrices.

Publications

Data Sources for Trait Databases: Comparing the Phenomic Content of Monographs and Evolutionary Matrices.

Similar Datasets

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