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Background: In recent years, a range of novel smartphone-derived data streams about human mobility have become available on a near-real-time basis. These data have been used, for example, to perform traffic forecasting and epidemic modeling. During the COVID-19 pandemic in particular, human travel behavior has been considered a key component of epidemiological modeling to provide more reliable estimates about the volumes of the pandemic's importation and transmission routes, or to identify hot spots. However, nearly universally in the literature, the representativeness of these data, how they relate to the underlying real-world human mobility, has been overlooked. This disconnect between data and reality is especially relevant in the case of socially disadvantaged minorities., Objective: The objective of this study is to illustrate the nonrepresentativeness of data on human mobility and the impact of this nonrepresentativeness on modeling dynamics of the epidemic. This study systematically evaluates how real-world travel flows differ from census-based estimations, especially in the case of socially disadvantaged minorities, such as older adults and women, and further measures biases introduced by this difference in epidemiological studies., Methods: To understand the demographic composition of population movements, a nationwide mobility data set from 318 million mobile phone users in China from January 1 to February 29, 2020, was curated. Specifically, we quantified the disparity in the population composition between actual migrations and resident composition according to census data, and shows how this nonrepresentativeness impacts epidemiological modeling by constructing an age-structured SEIR (Susceptible-Exposed-Infected- Recovered) model of COVID-19 transmission., Results: We found a significant difference in the demographic composition between those who travel and the overall population. In the population flows, 59% (n=20,067,526) of travelers are young and 36% (n=12,210,565) of them are middle-aged (P<.001), which is completely different from the overall adult population composition of China (where 36% of individuals are young and 40% of them are middle-aged). This difference would introduce a striking bias in epidemiological studies: the estimation of maximum daily infections differs nearly 3 times, and the peak time has a large gap of 46 days., Conclusions: The difference between actual migrations and resident composition strongly impacts outcomes of epidemiological forecasts, which typically assume that flows represent underlying demographics. Our findings imply that it is necessary to measure and quantify the inherent biases related to nonrepresentativeness for accurate epidemiological surveillance and forecasting., (©Chuchu Liu, Petter Holme, Sune Lehmann, Wenchuan Yang, Xin Lu. Originally published in JMIR Formative Research (https://formative.jmir.org), 28.06.2024.)

Introduction: Health visiting is a community service provided to families with children under five in England and is a key focus of early years policy. Individual-level data on health visiting is captured in the Community Services Data Set (CSDS), an administrative dataset of publicly funded community services across England. Analyses of CSDS are considered experimental as the dataset matures., Objectives: In this study, we aimed to identify health visiting contacts in the CSDS and assess the completeness of these data from 2016/17 to 2019/20 compared to external reference data., Methods: We identified the number of the four mandated postnatal health visiting contacts delivered, excluding those scheduled but not attended, between April 2016 and March 2020. We compared counts by local authority (LA) and financial quarter against the Office for Health Improvement and Disparities' Health Visitor Service Delivery Metrics (HVSDM) to identify a subnational subset of complete CSDS data. We explored the representativeness of this subset., Results: During the study period, 10.2 million health visiting contacts were delivered to 2.4 million children in England. Of these, we identified 3.9 million mandated contacts based on CSDS codes and age at time of contact, which represented 44.7% of all mandated contacts reported in the HVSDM for the same period. There were 63 LAs with complete CSDS data in at least one quarter, which were broadly representative of English LAs overall. Variables related to staff characteristics were highly missing and only 13 LAs had four or more successive quarters of complete data needed for longitudinal, child-level analyses., Conclusions: We identified a subnational subset of complete CSDS data, compared to external reference data, which can be used for health visiting research. Until improvements are made to its completeness, analyses (particularly those requiring longitudinal data) may not be generalisable to the whole child population., Competing Interests: Conflicts of interest: None

Introduction: Recent years have seen an increase in linkages between survey and administrative data. It is important to evaluate the quality of such data linkages to discern the likely reliability of ensuing research. Evaluation of linkage quality and bias can be conducted using different approaches, but many of these are not possible when there is a separation of processes for linkage and analysis to help preserve privacy, as is typically the case in the UK (and elsewhere)., Objectives: We aimed to describe a suite of generalisable methods to evaluate linkage quality and population representativeness of linked survey and administrative data which remain tractable when users of the linked data are not party to the linkage process itself. We emphasise issues particular to longitudinal survey data throughout., Methods: Our proposed approaches cover several areas: i) Linkage rates, ii) Selection into response, linkage consent and successful linkage, iii) Linkage quality, and iv) Linked data population representativeness. We illustrate these methods using a recent linkage between the 1958 National Child Development Study (NCDS; a cohort following an initial 17,415 people born in Great Britain in a single week of 1958) and Hospital Episode Statistics (HES) databases (containing important information regarding admissions, accident and emergency attendances and outpatient appointments at NHS hospitals in England)., Results: Our illustrative analyses suggest that the linkage quality of the NCDS-HES data is high and that the linked sample maintains an excellent level of population representativeness with respect to the single dimension we assessed., Conclusions: Through this work we hope to encourage providers and users of linked data resources to undertake and publish thorough evaluations. We further hope that providing illustrative analyses using linked NCDS-HES data will improve the quality and transparency of research using this particular linked data resource., Competing Interests: Statement on conflicts of interest: The authors have no conflicts of interest to declare.

In recent years, considerable efforts have been devoted to minimizing the environmental consequences from building industry globally, as the industry is notorious for its significant resource consumption and environmental emissions. However, due to a lack of data representativeness in many parts of the world, considerable variations were observed among studies, and thus makes life cycle assessment (LCA) results difficult for the decision-making purpose. In addition, the selection of low impact materials and the management of end-of-life building waste are the most important concern. By considering several strategies including data representativeness, low carbon material, and end-of-life building waste management, this study aimed to enhance the accuracy of such assessment and sustainability performance of building. An integrated LCA framework is proposed for implementing those strategies through a case specific high-rise residential building in Hong Kong. Based on case-specific/regional data, carbon emission of building was evaluated with a cradle-to-construction system boundary with the functional unit of 1 m 2 of gross floor area using the IMPACT 2002+ method, and then compared to that of selecting generic databases under different scenarios. The results demonstrate that the application of case-specific and generic data would significantly influence the outcomes of the case study, as the deviations of certain magnitudes were mostly derived from different building materials that it can lead to an underestimation of carbon emissions of up to 28%. Along with using alternative materials, the adoption of the proposed materials cycling and resource recovery approach at the end-of-life building could lead to a reduction of 14% of the total emissions (i.e. excluding the use and renovation of building). The results would support data selection for accuracy of evaluation which can be used as benchmark where recognized database is not available and promote sustainability performance of buildings locally, while the proposed framework could be adopted for comprehensive evaluation globally., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives., (© 2019 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)