Population Datasets Research Articles

On the basis of sex: outcomes in myelodysplastic syndromes

Sex has long been relevant in patients with myelodysplastic syndromes given there is a higher prevalence1 and poorer prognosis2 among men compared with women. However, this relationship has been traditionally viewed as one that is confounded by disease genetics or caused by differential responses to therapy,3 and not necessarily as an independent prognostic factor. In The Lancet Haematology, the GenoMed4All consortium4 used large European population datasets to challenge this dogma. The authors' data show that male sex is associated with a poorer prognosis in myelodysplastic syndromes than female sex, particularly in patients with low-risk prognostic scores according to the Revised International Prognostic Scoring System (IPSS-R).

An Inverse Correlation between the Atherogenic Index of Plasma and Heart Failure: An Analysis of the National Health and Nutrition Examination Survey 2017-March 2020 Pre-Pandemic Data.

The atherogenic index of plasma (AIP) is associated with cardiovascular diseases. Nevertheless, limited studies have investigated the association between AIP and the risk of heart failure (HF) in the general population. This study aimed to explore the association between AIP and HF risk using a large-scale population dataset from the National Health and Nutrition Examination Survey (NHANES) 2017-March 2020 Pre-pandemic data. A total of 5598 individuals were included in the analysis of the association between AIP and HF from the NHANES database. The relationship between AIP and HF was examined using multivariate logistic regression and smooth curve fitting. An association between the two was detected based on the odds ratios (ORs) and 95% confidence intervals (CIs). The overall prevalence of HF among the 5598 participants analyzed was 3.21%. Compared with individuals in the lowest quartile of AIP, participants in the higher quartiles showed a significantly reduced probability of HF. Smooth curve fitting analysis revealed a linear association between AIP and HF. Compared with individuals in Q1 of the AIP, participants in Q2 (OR = 0.38, 95% CI = 0.2-0.72, p = 0.0033), Q3 (OR = 0.24, 95% CI = 0.12-0.48, p < 0.0001), and Q4 (OR = 0.32, 95% CI = 0.14-0.74, p = 0.0075) had a significantly decreased risk of HF after adjusting for other risk factors. Analysis of subgroup strata revealed that AIP may interact with age and statin use (p for interaction = 0.012 and 0.0022, respectively). Our results suggest that a high AIP value is negatively correlated with HF prevalence. The AIP may be an effective method for identifying individuals at a high risk of HF.

Anthropometric dataset for the German working-age population using 3D body scans from a regional epidemiological health study and a weighting algorithm

For the ergonomic design of workplaces and products, a representative anthropometric dataset of the working-age population is needed. As body proportions are constantly changing and the latest publicly available dataset for Germany was published in 2004 (data collection period 1999–2002), the aim of this study was to create and publish an updated anthropometric dataset of the German working-age population. Within a regional epidemiological health study, 3D body scan data from 2313 subjects were collected and used to create an anthropometric dataset with a total of 39 ISO 7250-1 measures. To approximate the goal of generating representative values for Germany, the collected regional dataset was weighted with an algorithm, using values from a known nationally representative survey. Based on the weighted dataset, a gender stratified percentile table with values for the 5th, 50th, and 95th percentile was calculated. Practitioner summary: Body proportions are constantly changing and the latest publicly available anthropometric dataset for Germany was published in 2004. A new dataset was created, using 3D body scans from an epidemiological health study and a weighting algorithm. Ultimately, percentile tables with values for the 5th, 50th, and 95th percentile are published.

TREML2 Gene Expression and Its Missense Variant rs3747742 Associate with White Matter Hyperintensity Volume and Alzheimer's Disease-Related Brain Atrophy in the General Population.

Although the common pathology of Alzheimer's disease (AD) and white matter hyperintensities (WMH) is disputed, the gene TREML2 has been implicated in both conditions: its whole-blood gene expression was associated with WMH volume and its missense variant rs3747742 with AD risk. We re-examined those associations within one comprehensive dataset of the general population, additionally searched for cross-relations and illuminated the role of the apolipoprotein E (APOE) ε4 status in the associations. For our linear regression and linear mixed effect models, we used 1949 participants from the Study of Health in Pomerania (Germany). AD was assessed using a continuous pre-symptomatic MRI-based score evaluating a participant's AD-related brain atrophy. In our study, increased whole-blood TREML2 gene expression was significantly associated with reduced WMH volume but not with the AD score. Conversely, rs3747742-C was significantly associated with a reduced AD score but not with WMH volume. The APOE status did not influence the associations. In sum, TREML2 robustly associated with WMH volume and AD-related brain atrophy on different molecular levels. Our results thus underpin TREML2's role in neurodegeneration, might point to its involvement in AD and WMH via different biological mechanisms, and highlight TREML2 as a worthwhile target for disentangling the two pathologies.

A new urban change detection method based on the local G and local spatial heteroscedasticity statistics

AbstractAccurate detection of urban changes is critical for guiding city planning that leads to smart and sustainable development. Few existing methods are able to detect urban changes in a timely manner while preserving great details of spatial heterogeneity. In this article, we propose a new method that combines two spatial statistics, namely the local G and local spatial heteroscedasticity. By jointly analyzing the results of both statistics, we design the Urban Development Index (UDI) to assess the types of urban changes that each spatial unit has been experiencing. Based on the sequences of UDI, we can understand the regional patterns of urban changes by linking to urban morphology. We conduct experiments with both a synthetic dataset and a Rwanda population dataset. The results demonstrate that our method can not only identify completed and ongoing phenomena of urban transition, but also unveil the heterogeneous nature of growth and/or shrinkage inside a city.

Patient-Specific Auto-Segmentation of Target and OARs via Deep Learning on Daily Fan-Beam CT for Adaptive Prostate Radiotherapy

<h3>Purpose/Objective(s)</h3> Contouring the target and organs at risk (OARs) of the treated patient in each fraction is a key step of daily adaptive radiotherapy. We designed a patient-specific deep learning study to improve the auto-segmentation performance for adaptive prostate radiotherapy, by leveraging the daily image-guidance fan-beam CT images of the specific patient available in the current IGRT workflow. <h3>Materials/Methods</h3> Our institution has set up a newly developed biology-guided radiotherapy (BgRT) system enabling high-quality fan-beam CT imaging for daily treatments. The data of the first prostate patient treated with this system including 26 daily fan-beam CT scans were utilized in this study. A population network was first trained for the auto-segmentation of the prostate and seven pelvic OARs, based on a population dataset containing 56 different patient cases treated on the regular Linac. The pre-trained population network was intentionally fine-tuned to adapt to this specific patient with a transfer learning method. The selected OARs of this patient were contoured by two radiation oncologists on the initial treatment planning and all sets of daily fan-beam CT images as the ground truth for the patient-specific learning. Three different fine-tuning methods, i.e., fine-tuning the last convolution layer, the last convolution block and the whole network were analyzed. A longitudinal study was conducted by exploring the relationship between the auto-segmentation performance and the number of the sequential prior data used for learning. The performance of the patient-specific network was compared with the population network as well as the clinical rigid registration method to highlight its efficiency in radiotherapy practice. <h3>Results</h3> The Dice similarity coefficient (DSC) results of the proposed patient-specific network were 0.882, 0.934, 0.864, 0.827, 0.952, 0.954, 0.921 and 0.904 for the prostate, bladder, rectum, seminal vesicle, left and right femoral head, bowel and lymph nodes, respectively, outperforming the population network (mean DSC of 0.627) and the clinical registration method (mean DSC of 0.716). The whole network fine-tuning method outperformed the other two fine-tuning methods, showing it is more effective in this study. The contouring accuracy of the patient-specific network gradually increased with the increment of longitudinal training cases and approached saturation with more than four training cases. <h3>Conclusion</h3> This study proposes an accurate auto-segmentation method based on the patient-specific deep learning for adaptive prostate radiotherapy. By utilizing the image and contour similarity between the treatment fractions, the patient-specific auto-segmentation could outperform the common population network and the clinical registration method by a large margin, and thus is promising to facilitate more precise radiotherapy based on the new BgRT system.

Systematic Study of Patient-Specific Organs at Risk Auto-Segmentation on Daily kVCT Images for Adaptive Head and Neck Radiotherapy

<h3>Purpose/Objective(s)</h3> A newly developed biology-guided radiotherapy (BgRT) system has been set up in our institution, which enables high-quality kVCT imaging for each treatment fraction. This study explored deep learning-based patient-specific auto-segmentation to facilitate adaptive radiotherapy, based on data of the first head and neck patient case treated with this new system. <h3>Materials/Methods</h3> The esophagus, larynx, and pharynx were selected as the organs at risk (OAR) for auto-segmentation analysis. A population network was first learned on a population dataset, which contains 67 different patient cases from conventional intensity-modulated radiotherapy (IMRT). Then the pre-trained population network was adapted to this specific patient using a transfer learning method based on the longitudinal data from the initial treatment planning and 17 sets of daily image guidance kVCT images. The selected OARs were contoured by a radiation oncologist on 18 sets of images as the ground truth. The performance of the patient-specific network was compared with the population network as well as the clinical rigid registration method. The corresponding dosimetric impacts resulting from different segmentation and registration methods were also investigated. <h3>Results</h3> The initial Dice similarity coefficient (DSC) results of the population network on the patient-specific evaluation set were 0.821, 0.537, 0.739 for esophagus, larynx, and pharynx respectively, and they were markedly improved to 0.887, 0.889, 0.850 with the proposed patient-specific network. These results also outperformed the results of 0.670, 0.792, 0.709 with the registration-based method in current clinical practice. The contouring accuracy of the patient-specific network gradually increased with the increase of longitudinal training cases and approached saturation with more than six training cases. The qualification result also shows the population network was adapted to the specific manual contouring style after the patient-specific learning. Discrepancies of the mean doses by using the manual contour and the patient-specific auto-segmentation result were -1.29%, -1.05%, -0.13% for the three OARs respectively, better than the differences of 4.80%, -6.56%, -1.84% using the registration contour. The OAR dose underestimation in some treatment fractions based on the registration contour was markedly improved when using the patient-specific auto-segmentation result. <h3>Conclusion</h3> Auto-segmentation of daily kVCT images was studied based on patient-specific learning to facilitate future adaptive radiotherapy. Compared with the common population network and the clinical registration method, the patient-specific network could achieve a much better contouring and dosimetric accuracy and thus is promising to become a powerful tool in adaptive radiotherapy applications.

A data set of distributed global population and water withdrawal from 1960 to 2020

Population and water withdrawal data sets are currently faced with difficulties in collecting, processing and verifying multi-source time series, and the spatial distribution characteristics of long series are also relatively lacking. Time series is the basic guarantee for the accuracy of data sets, and the production of long series spatial distribution is a realistic requirement to expand the application scope of data sets. Through the time-consuming and laborious basic processing work, this research focuses on the population and water intake time series, and interpolates and extends them to specific land uses to ensure the accuracy of the time series and the demand of spatially distributed data sets. This research provides a set of population density and water intensity products from 1960 to 2020 distributed to the administrative units or the corresponding regions. The data set fills the gaps in the multi-year data set for the accuracy of population density and the intensity of water withdrawal.

Diseasomics: Actionable machine interpretable disease knowledge at the point-of-care.

Physicians establish diagnosis by assessing a patient's signs, symptoms, age, sex, laboratory test findings and the disease history. All this must be done in limited time and against the backdrop of an increasing overall workload. In the era of evidence-based medicine it is utmost important for a clinician to be abreast of the latest guidelines and treatment protocols which are changing rapidly. In resource limited settings, the updated knowledge often does not reach the point-of-care. This paper presents an artificial intelligence (AI)-based approach for integrating comprehensive disease knowledge, to support physicians and healthcare workers in arriving at accurate diagnoses at the point-of-care. We integrated different disease-related knowledge bodies to construct a comprehensive, machine interpretable diseasomics knowledge-graph that includes the Disease Ontology, disease symptoms, SNOMED CT, DisGeNET, and PharmGKB data. The resulting disease-symptom network comprises knowledge from the Symptom Ontology, electronic health records (EHR), human symptom disease network, Disease Ontology, Wikipedia, PubMed, textbooks, and symptomology knowledge sources with 84.56% accuracy. We also integrated spatial and temporal comorbidity knowledge obtained from EHR for two population data sets from Spain and Sweden respectively. The knowledge graph is stored in a graph database as a digital twin of the disease knowledge. We use node2vec (node embedding) as digital triplet for link prediction in disease-symptom networks to identify missing associations. This diseasomics knowledge graph is expected to democratize the medical knowledge and empower non-specialist health workers to make evidence based informed decisions and help achieve the goal of universal health coverage (UHC). The machine interpretable knowledge graphs presented in this paper are associations between various entities and do not imply causation. Our differential diagnostic tool focusses on signs and symptoms and does not include a complete assessment of patient's lifestyle and health history which would typically be necessary to rule out conditions and to arrive at a final diagnosis. The predicted diseases are ordered according to the specific disease burden in South Asia. The knowledge graphs and the tools presented here can be used as a guide.

Survival for patients with metastatic colon cancer underwent cytoreductive colectomy in the era of rapid development of anticancer drugs: A real-world analysis based on updated population dataset of 2004-2018.

Objective: Metastatic colon cancer (mCC) poses a great threat to the survival of patients suffering from it. In the past decade, many clinical trials have been carried out to improve the prognosis of patients with mCC. Numerous treatments have emerged, and satisfactory efficacy has been demonstrated in randomized phase III trials in highly selective patients with mCC. Our present study aims to investigate whether these therapeutic advances can be reflected to the broader mCC patients who performed cytoreductive colectomy. Method: General and prognostic data for patients diagnosed with mCC who underwent cytoreductive colectomy between 2004-2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Survival was analyzed using the Kaplan-Meier method and Cox proportional hazards model. The hazard ratio (HR) and its 95% confidence interval (CI) were used to evaluate the influence of risk factors on prognosis. Results: A total of 26,301 patients diagnosed with mCC treated with cytoreductive colectomy were included in this study. The median overall survival was 19months (range, 17-23). The good prognosis was associated with patients diagnosed at the most recent year, younger age, non-black race, female, married, without previous history of malignancy, no second malignancy onset, descending/sigmoid/splenic flexure colon tumor, normal CEA levels at diagnosis, low primary tumor burden, T1/T2 stage, N0 stage, single organ metastasis, underwent surgical resection of synchronous distant metastatic lymph nodes or organs, a high number of lymph-node examinations, low positive lymph-node ratio and received adjuvant chemotherapy. The proportion of patients surviving for ≥24months increased from 37% in 2004 to 44.2% in 2016 (p < 0.001), especially in ≤49years patients [46.8% in 2004 to 57.8% in 2016 (p < 0.001)]. The percentage of patients who died within 3months decreased between 2004 and 2018 (from 19.6% to 15.7%; p < 0.001). Conclusion: Over a span of 15years, the long-term survival has improved in real-world mCC patients who were treated with cytoreductive colectomy, especially among younger patients. However, the median overall survival remains not substantial.

The impact of low input DNA on the reliability of DNA methylation as measured by the Illumina Infinium MethylationEPIC BeadChip

ABSTRACT DNA methylation (DNAm) is commonly assayed using the Illumina Infinium MethylationEPIC BeadChip, but there is currently little published evidence to define the lower limits of the amount of DNA that can be used whilst preserving data quality. Such evidence is valuable for analyses utilizing precious or limited DNA sources. We used a single pooled sample of DNA in quadruplicate at three dilutions to define replicability and noise, and an independent population dataset of 328 individuals (from a community-based study including US-born non-Hispanic Black and white persons) to assess the impact of total DNA input on the quality of data generated using the Illumina Infinium MethylationEPIC BeadChip. We found that data are less reliable and more noisy as DNA input decreases to 40ng, with clear reductions in data quality; and that low DNA input is associated with a reduction in power to detect EWAS associations, requiring larger sample sizes. We conclude that DNA input as low as 40ng can be used with the Illumina Infinium MethylationEPIC BeadChip, provided quality checks and sensitivity analyses are undertaken.

Cattle Number Estimation on Smart Pasture Based on Multi-Scale Information Fusion

In order to solve the problem of intelligent management of cattle numbers in the pasture, a dataset of cattle density estimation was established, and a multi-scale residual cattle density estimation network was proposed to solve the problems of uneven distribution of cattle and large scale variations caused by perspective changes in the same image. Multi-scale features are extracted by multiple parallel dilated convolutions with different dilation rates. Meanwhile, aiming at the “grid effect” caused by the use of dilated convolution, the residual structure is combined with a small dilation rate convolution to eliminate the influence of the “grid effect”. Experiments were carried out on the cattle dataset and dense population dataset, respectively. The experimental results show that the proposed multi-scale residual cattle density estimation network achieves the lowest mean absolute error (MAE) and means square error (RMSE) on the cattle dataset compared with other density estimation methods. In ShanghaiTech, a dense population dataset, the density estimation results of the multi-scale residual network are also optimal or suboptimal in MAE and RMSE.

Validating a membership disclosure metric for synthetic health data.

BackgroundOne of the increasingly accepted methods to evaluate the privacy of synthetic data is by measuring the risk of membership disclosure. This is a measure of the F1 accuracy that an adversary would correctly ascertain that a target individual from the same population as the real data is in the dataset used to train the generative model, and is commonly estimated using a data partitioning methodology with a 0.5 partitioning parameter.ObjectiveValidate the membership disclosure F1 score, evaluate and improve the parametrization of the partitioning method, and provide a benchmark for its interpretation.Materials and methodsWe performed a simulated membership disclosure attack on 4 population datasets: an Ontario COVID-19 dataset, a state hospital discharge dataset, a national health survey, and an international COVID-19 behavioral survey. Two generative methods were evaluated: sequential synthesis and a generative adversarial network. A theoretical analysis and a simulation were used to determine the correct partitioning parameter that would give the same F1 score as a ground truth simulated membership disclosure attack.ResultsThe default 0.5 parameter can give quite inaccurate membership disclosure values. The proportion of records from the training dataset in the attack dataset must be equal to the sampling fraction of the real dataset from the population. The approach is demonstrated on 7 clinical trial datasets.ConclusionsOur proposed parameterization, as well as interpretation and generative model training guidance provide a theoretically and empirically grounded basis for evaluating and managing membership disclosure risk for synthetic data.

Challenges of Large-Scale Data Processing in the 1990s: The IPUMS Experience.

When it was launched in 1991, the Integrated Public Use Microdata Series (IPUMS) project faced a challenging environment and limited resources. Few datasets were interoperable and much data collected at great public expense was inaccessible to most researchers. Documentation of datasets was nonstandardized, incomplete, and inadequate for automated processing. With insufficient attention to preservation, valuable scientific data were disappearing (see Bogue et al., 1976). IPUMS was established to address these critical issues. At the outset, IPUMS faced daunting barriers of inadequate data processing, storage, and network capacity. This anecdote describes the improvised computational infrastructure developed in the decade from 1989 to 1999 to process, manage, and disseminate the world's largest population datasets. We use a combination of archival sources, interviews, and our own memories to trace the development of the IPUMS computing environment during a period of explosive technical innovation. The development of IPUMS is part of a larger story of the development of social science infrastructure in the late 20th century and its contribution to democratizing data access.

Genome-wide association study of traits in sacred lotus uncovers MITE-associated variants underlying stamen petaloid and petal number variations.

Understanding the genetic variants responsible for floral trait diversity is important for the molecular breeding of ornamental flowers. Widely used in water gardening for thousands of years, the sacred lotus exhibits a wide range of diversity in floral organs. Nevertheless, the genetic variations underlying various morphological characteristics in lotus remain largely unclear. Here, we performed a genome-wide association study of sacred lotus for 12 well-recorded ornamental traits. Given a moderate linkage disequilibrium level of 32.9 kb, we successfully identified 149 candidate genes responsible for seven flower traits and plant size variations, including many pleiotropic genes affecting multiple floral-organ-related traits, such as NnKUP2. Notably, we found a 2.75-kb presence-and-absence genomic fragment significantly associated with stamen petaloid and petal number variations, which was further confirmed by re-examining another independent population dataset with petal number records. Intriguingly, this fragment carries MITE transposons bound by siRNAs and is related to the expression differentiation of a nearby candidate gene between few-petalled and double-petalled lotuses. Overall, these genetic variations and candidate genes responsible for diverse lotus traits revealed by our GWAS highlight the role of transposon variations, particularly MITEs, in shaping floral trait diversity.

Spatiotemporal association of carbon dioxide emissions in China's urban agglomerations

The reduction of carbon dioxide (CO2) emissions and sustainable development in low-carbon ways are of great significance to urban agglomerations. However, few studies are exploring the relationship between CO2 emissions and socioeconomic development at city levels from the perspective of clusters of regions. Based on the open data of inventory for anthropogenic CO2 emissions, nighttime light data, and population dataset as a proxy for the socioeconomic development levels of urban agglomerations, we used Mann–Kendall trend test, Tapio decoupling analysis, and spatial autocorrelation analysis to explore the spatiotemporal association of CO2 emissions and the impact of socioeconomic development on emissions in the nineteen urban agglomerations in China. Findings showed that the growth of CO2 emissions in China was primarily concentrated in urban agglomerations. The CO2 emissions in eastern coastal and northern urban agglomerations were much higher than those in other areas, while the emissions in western urban agglomerations were the lowest. The periodic characteristics of CO2 emissions were consistent with China's five-year development plan. Urban agglomerations in the early stage from 2000 to 2002 or with developed and stable industrial structures tended to achieve decoupling. High-high (HH) clusters of socioeconomic development with CO2 emissions were mainly distributed in urban agglomerations of the Beijing-Tianjin-Hebei region (BTH), the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), Yangtze River Delta (YRD), Huhhot-Baotou-Ordos-Yulin (HBOY), Shandong Peninsula (SP), and Central Shanxi (CS). Most of the clusters except those in HBOY shrunk from 2000 to 2010 and remained relatively stable from 2010 to 2019. These urban agglomerations should promote synergistic emission reduction. High-low (HL) clusters mostly appeared in central cities with a high socioeconomic level and surrounding cities with low CO2 emissions s, i.e., in urban agglomerations of Chengdu-Chongqing region (CC), the Beibu Gulf (BG), and Lanzhou-Xining (LX). These urban agglomerations with prominent polarization phenomena should adhere to regional overall coordination and thus minimize total regional costs of CO2 emission reduction. The results could provide references for the synergistic reduction of CO2 emissions and the coordinated development in urban agglomerations.

City spatial structure and smart city innovation: the case of China

PurposeThis study aims to analyze the relevance of the city spatial structure for smart city innovation from the perspective of agglomeration externalities, and discusses whether there is heterogeneity in innovation across different geographical areas and population scales of cities.Design/methodology/approachThe authors construct the centralization and concentration indexes to conceptualize the city spatial structure of 286 cities (prefecture-level) in China based on the LandScan Global Population Dataset from 2001 to 2016. A fixed-effects panel data model is employed to analyze the relationship between the spatial structure and the innovation ability of smart cities; the results were validated through robustness tests and heterogeneity analyses.FindingsThe study found that the more concentrated and more evenly the distribution of urban population, namely the more city spatial structure tends to be weak-monocentricity, the higher the level of innovation in smart cities. The relevance of the weak-monocentricity structure and smart city innovation varies significantly depending on their geographical location and the size of the city. This result is more applicable to cities in the eastern and central regions, as well as to cities with smaller populations.Originality/valueThe adjustment and optimization of the city spatial structure is important for enhancing smart city construction. Unlike previous studies, which mostly use a single dimension of “the proportion of population in sub-centres to the population of all central areas” to measure city spatial structure, the authors employed the spatial centralization and spatial concentration. It is hoped that this study can guide smart city construction from the perspective of the development model of city spatial structure.

Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa

BackgroundAccess to healthcare is imperative to health equity and well-being. Geographic access to healthcare can be modeled using spatial datasets on local context, together with the distribution of existing health facilities and populations. Several population datasets are currently available, but their impact on accessibility analyses is unknown. In this study, we model the geographic accessibility of public health facilities at 100-meter resolution in sub-Saharan Africa and evaluate six of the most popular gridded population datasets for their impact on coverage statistics at different administrative levels.MethodsTravel time to nearest health facilities was calculated by overlaying health facility coordinates on top of a friction raster accounting for roads, landcover, and physical barriers. We then intersected six different gridded population datasets with our travel time estimates to determine accessibility coverages within various travel time thresholds (i.e., 30, 60, 90, 120, 150, and 180-min).ResultsHere we show that differences in accessibility coverage can exceed 70% at the sub-national level, based on a one-hour travel time threshold. The differences are most notable in large and sparsely populated administrative units and dramatically shape patterns of healthcare accessibility at national and sub-national levels.ConclusionsThe results of this study show how valuable and critical a comparative analysis between population datasets is for the derivation of coverage statistics that inform local policies and monitor global targets. Large differences exist between the datasets and the results underscore an essential source of uncertainty in accessibility analyses that should be systematically assessed.

Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways

Spatially explicit population grid can play an important role in climate change, resource management, sustainable development and other fields. Several gridded datasets already exist, but global data, especially high-resolution data on future populations are largely lacking. Based on the WorldPop dataset, we present a global gridded population dataset covering 248 countries or areas at 30 arc-seconds (approximately 1 km) spatial resolution with 5-year intervals for the period 2020–2100 by implementing Random Forest (RF) algorithm. Our dataset is quantitatively consistent with the Shared Socioeconomic Pathways’ (SSPs) national population. The spatially explicit population dataset we predicted in this research is validated by comparing it with the WorldPop dataset both at the sub-national and grid level. 3569 provinces (almost all provinces on the globe) and more than 480 thousand grids are taken into verification, and the results show that our dataset can serve as an input for predictive research in various fields.

Design and Evaluation of a Computational Phenotype to Identify Patients With Metastatic Breast Cancer Within the Electronic Health Record.

Outcomes for patients with metastatic breast cancer (MBC) are continually improving as more effective treatments become available. Granular data sets of this unique population are lacking, and the standard method for data collection relies largely on chart review. Therefore, using electronic health records (EHR) collected at a tertiary hospital system, we developed and evaluated a computational phenotype designed to identify all patients with MBC, and we compared the effectiveness of this algorithm against the gold standard, clinical chart review. A cohort of patients with breast cancer were identified according to International Classification of Diseases codes, the institutional tumor registry, and SNOMED codes. Chart review was performed to determine whether distant metastases had occurred. We developed a computational phenotype, on the basis of SNOMED concept IDs, which was applied to the EHR to identify patients with MBC. Contingency tables were used to aggregate and compare results. A total of 1,741 patients with breast cancer were identified using data from International Classification of Diseases codes, the tumor registry, and/or SNOMED concept identifiers. Chart review of all patients classified each patient as having MBC (n = 416; 23.9%) versus not (n = 1,325; 75.9%). The final computational phenotype successfully classified 1,646 patients (95% accuracy; 82% sensitivity; 99% specificity). Hospital systems with robust EHRs and reliable mapping to SNOMED have the ability to use standard codes to derive computational phenotypes. These algorithms perform reasonably well and have the added ability to be run at disparate health care facilities. Better tooling to navigate the polyhierarchical structure of SNOMED ontology could yield better-performing computational phenotypes.