Community-Level Urban Green Space Equity Evaluation Based on Spatial Design Network Analysis (sDNA): A Case Study of Central Wuhan, China.

Urban green spaces (UGSs) provide numerous irreplaceable environmental and social benefits to humankind, but the lack of baseline information makes it difficult to propose a reasonable greening strategy so as to achieve an equitable allocation of community green spaces. This paper divides UGSs into three classes using the spatial design network analysis (sDNA) and quantifies the UGS accessibility of communities in central Wuhan. Based on these results and the Gini coefficient, we analyze the UGS equity of the spatial distribution at the community level, then propose future greening strategies both at the city and community levels. The results show that the railway station and old Wuhan city are the core areas of traffic network strength (TNS). UGSs are evenly distributed in the core areas of TNS, but the number of UGSs in non-core areas is small, and their distribution is relatively uneven, and the number of communities with medium UGS accessibility is the largest, carrying the densest residential population. Most communities perform well in terms of UGS equity, but the UGS equity of 163 communities, covering a population of more than one million, remains to be improved. The method and conclusions of this study will contribute to the future greening policy making of 965 communities in central Wuhan, thus promoting the orderly planning and high-quality construction of community living circles.

A nomogram incorporated lifestyle indicators for predicting nonalcoholic fatty liver disease.

ABSTRACT: Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, and its pathogenesis is complicated and triggered by unbalanced diet, sedentary lifestyle, and genetic background. The aim of this study was to construct and validate a nomogram incorporated lifestyle habits for predicting NAFLD incidence.The overall cohort was divided into training set and test set as using computer-generated random numbers. We constructed the nomogram by multivariate logistic regression analysis in the training set. Thereafter, we validated this model by concordance index, the area under the receiver operating characteristic curve (ROC), net reclassification index, and a calibration curve in the test set. Additionally, we also evaluated the clinical usefulness of the nomogram by decision curve analysis.There were no statistically significant differences about characteristics between training cohort (n = 748) and test cohort (n = 320). Eleven features (age, sex, body mass index, drinking tea, physical exercise, energy, monounsaturated fatty acids, polyunsaturated fatty acids, hypertension, hyperlipidemia, diabetes) were incorporated to construct the nomogram, concordance index, the area under the ROC curve, net reclassification index were 0.801, 0.801, and 0.084, respectively, indicating the nomogram have good discrimination of predicting NAFLD incidence. Also, the calibration curve showed good consistency between nomogram prediction and actual probability. Moreover, the decision curve showed that when the threshold probability of an individual is within a range from approximately 0.5 to 0.8, this model provided more net benefit to predict NAFLD incidence risk than the current strategies.This nomogram can be regarded as a user-friendly tool for assessing the risk of NAFLD incidence, and thus help to facilitate management of NAFLD including lifestyle and medical interventions.

Development and validation of a nomogram for predicting nonalcoholic fatty liver disease in the non-obese Chinese population.

The purpose of this study was to construct and validate a model for predicting nonalcoholic fatty liver disease (NAFLD) in the non-obese Chinese population. A total of 13240 NAFLD-free individuals at baseline from a 4-y longitudinal study were allocated to a training cohort (n=8872) and a validation cohort (n=4368). The overall incidence of NAFLD was 13%. Nine significant predictors including age, gender, body mass index, fasting blood glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol, uric acid and alanine aminotransferase were identified and constructed for the nomogram using cox proportional hazards regression analyses. The concordance index was 0.804 and 0.802 in the training and validation cohorts, respectively. In the training cohort, the area under the ROC curve (AUC) for 1-y, 2-y, 3-y and 4-y risk was 0.835, 0.825, 0.816 and 0.782, respectively. Likewise, in the validation cohort, the AUC for 1-y, 2-y, 3-y and 4-y risk was 0.817, 0.820, 0.814 and 0.813. The calibration curves for NAFLD risk showed excellent accuracy in the predictive modeling of the nomogram, internally and externally. The nomogram categorized individuals into high- and low-risk groups, and the DCA displayed the clinical usefulness of the nomogram for predicting NAFLD incidence. Our nomogram can predict a personalized risk of NAFLD in the non-obese Chinese population. This nomogram can serve as a simple and affordable tool for stratifying individuals at a high risk of NAFLD, and thus serve to expedite treatment of NAFLD.