This paper develops a conceptual model and an indicator system for measuring economic resilience of resource-based cities based on the theory of evolutionary resilience and the related concepts of persistence, adaptation, and transformation. Nineteen resource-based cities in Northeast China were analyzed using the indicator system. The results showed that Liaoning and Jilin provinces had higher economic resilience than Heilongjiang Province. Panjin, Benxi, and Anshan in Liaoning Province were the top three cities, while Shuangyashan and other coal-based cities in Heilongjiang Province ranked last. Metals-and petroleum-based cities had significantly higher resilience than coal-based cities. The differences in persistence, adaptability, transformation, and resilience among resource-based cities decreased since the introduction of the Northeast Revitalization Strategy in 2003. Forestry-based cities improved the most in terms of resilience, followed by metals-based and multiple-resource cities; however, resilience dropped for coal-based cities, and petroleum-based cities falling the most. The findings illustrate the importance and the way to develop a differentiated approach to improve resilience among resource-based cities.

Sustainable urbanization is not only an important research topic in the field of urbanization, but also the development direction of new-type urbanization. In this paper, we construct an index system to evaluate sustainable urbanization potential with the entropy method. Results show that potential values of sustainable urbanization in most cities are not high. Cities with higher sustainable urbanization potential values are mainly located in the central part of Northeast China. Environmental potential of sustainable urbanization is the main contributor to sustainable urbanization potential in Northeast China. There is no absolute relationship between city size and potential value, large city does not always mean greater potential. Correlation analysis shows that urbanization rate cannot reflect the sustainable urbanization potential of a region. Population urbanization is not the ultimate goal of sustainable urbanization. Unilateral pursue urbanization rate cannot improve the potential of sustainable urbanization. Towards sustainable urbanization, governments in Northeast China should revitalize local economy, pay more attention to the rural areas and develop low-carbon economy or ecological economy. Finally, this paper highlights the importance of choosing more integrated methodology or new models for measuring sustainable urbanization potential in view of the shortcomings of one method.

Objective The study aims to investigate public awareness of coronavirus disease 2019 (COVID-19) and measure levels of anxiety during the outbreak. Method A total of 2115 subjects from 34 provinces in China were evaluated. A questionnaire was designed, which covers demographic characteristics, knowledge of COVID-19, and factors that influenced anxiety during the outbreak to test public awareness and determine the impact of the outbreak on people's lives. In addition, a generalized anxiety disorder (GAD) scale was utilized to assess anxiety levels during the outbreak. Lastly, the chi-square test and multiple logistic regression analysis were used to identify factors associated with levels of public anxiety. Results A majority of respondents reported high levels of awareness of COVID-19. A total of 1107 (52.3%), 707 (33.4%), 154 (7.3%), and 147 (7%) respondents exhibited no, mild, moderate, and severe levels of anxiety, respectively. Results of the chi-square test and multiple logistic regression analysis demonstrated that respondents (a) with no college education, (b) are unaware of neighbors who may have been infected, (c) who spent considerable time collecting information and browsing negative information related to the virus, (d) are unhealthy, and (e) displayed low levels of awareness of the transmission routes were highly likely to be anxious. Conclusion During the outbreak, the majority of people exhibited high levels of awareness and knowledge regarding preventive measures from COVID-19. The absence of psychological anxiety was observed in more than half of the respondents. Adaptive responses to anxiety and high levels of awareness about COVID-19 may have protected the public during the outbreak.

BackgroundHeart failure (HF) is an end-stage manifestation of and cause of death in coronary heart disease (CHD). The objective of this study was to establish and validate a non-invasive diagnostic nomogram to identify HF in patients with CHD.MethodsWe retrospectively analyzed the clinical data of 44,772 CHD patients from five tertiary hospitals. Univariate logistic regression analyses and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify independent factors. A nomogram based on the multivariate logistic regression model was constructed using these independent factors. The concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to evaluate the predictive accuracy and clinical value of this nomogram.ResultsThe predictive factors in the multivariate model included hypertension, age, and the total bilirubin, uric acid, urea nitrogen, triglyceride, and total cholesterol levels. The area under the curve (AUC) values of the nomogram in the training set, internal validation set, external validation set1, and external validation set2 were 0.720 (95% CI: 0.712–0.727), 0.723 (95% CI: 0.712–0.735), 0.692 (95% CI: 0.674–0.710), and 0.655 (95% CI: 0.634–0.677), respectively. The calibration curves indicated that the nomogram had strong calibration. DCA and CIC indicated that the nomogram can be used as an effective tool in clinical practice.ConclusionThe developed predictive model combines the clinical and laboratory factors of patients with CHD and is useful in individualized prediction of HF probability for clinical decision-making during treatment and management.

BackgroundLiver cirrhosis is a major global health and economic challenge, placing a heavy economic burden on patients, families, and society. This study aimed to investigate medical expenditure trends in patients with liver cirrhosis and assess the drivers for such medical expenditure among patients with liver cirrhosis.MethodsMedical expenditure data concerning patients with liver cirrhosis was collected in six tertiary hospitals in Chongqing, China, from 2012 to 2020. Trends in medical expenses over time and trends according to subgroups were described, and medical expenditure compositions were analyzed. A multiple linear regression model was constructed to evaluate the factors influencing medical expenditure. All expenditure data were reported in Chinese Yuan (CNY), based on the 2020 value, and adjusted using the year-specific health care consumer price index for Chongqing.ResultsMedical expenditure for 7,095 patients was assessed. The average medical expenditure per patient was 16,177 CNY. An upward trend in medical expenditure was observed in almost all patient subgroups. Drug expenses were the largest contributor to medical expenditure in 2020. A multiple linear regression model showed that insurance type, sex, age at diagnosis, marital status, length of stay, smoking status, drinking status, number of complications, autoimmune liver disease, and the age-adjusted Charlson comorbidity index score were significantly related to medical expenditure.ConclusionConservative estimates suggest that the medical expenditure of patients with liver cirrhosis increased significantly from 2012 to 2020. Therefore, it is necessary to formulate targeted measures to reduce the personal burden on patients with liver cirrhosis.

BackgroundComprehensive eye examinations for diabetic retinopathy is poorly implemented in medically underserved areas. There is a critical need for a widely available and economical tool to aid patient selection for priority retinal screening. We investigated the possibility of a predictive model for retinopathy identification using simple parameters.MethodsClinical data were retrospectively collected from 4, 159 patients with diabetes admitted to five tertiary hospitals. Independent predictors were identified by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, and a nomogram was developed based on a multivariate logistic regression model. The validity and clinical practicality of this nomogram were assessed using concordance index (C-index), area under the receiver operating characteristic curve (AUROC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).ResultsThe predictive factors in the multivariate model included the duration of diabetes, history of hypertension, and cardiovascular disease. The three-variable model displayed medium prediction ability with an AUROC of 0.722 (95%CI 0.696-0.748) in the training set, 0.715 (95%CI 0.670-0.754) in the internal set, and 0.703 (95%CI 0.552-0.853) in the external dataset. DCA showed that the threshold probability of DR in diabetic patients was 17-55% according to the nomogram, and CIC also showed that the nomogram could be applied clinically if the risk threshold exceeded 30%. An operation interface on a webpage (https://cqmuxss.shinyapps.io/dr_tjj/) was built to improve the clinical utility of the nomogram.ConclusionsThe predictive model developed based on a minimal amount of clinical data available to diabetic patients with restricted medical resources could help primary healthcare practitioners promptly identify potential retinopathy.

BackgroundDepression is associated with an increased risk of death in patients with coronary heart disease (CHD). This study aimed to explore the factors influencing depression in elderly patients with CHD and to construct a prediction model for early identification of depression in this patient population.Materials and methodsWe used propensity-score matching to identify 1,065 CHD patients aged ≥65 years from four hospitals in Chongqing between January 2015 and December 2021. The patients were divided into a training set (n = 880) and an external validation set (n = 185). Univariate logistic regression, multivariate logistic regression, and least absolute shrinkage and selection operator regression were used to determine the factors influencing depression. A nomogram based on the multivariate logistic regression model was constructed using the selected influencing factors. The discrimination, calibration, and clinical utility of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) and clinical impact curve (CIC), respectively.ResultsThe predictive factors in the multivariate model included the lymphocyte percentage and the blood urea nitrogen and low-density lipoprotein cholesterol levels. The AUC values of the nomogram in the training and external validation sets were 0.762 (95% CI = 0.722–0.803) and 0.679 (95% CI = 0.572–0.786), respectively. The calibration curves indicated that the nomogram had strong calibration. DCA and CIC indicated that the nomogram can be used as an effective tool in clinical practice. For the convenience of clinicians, we used the nomogram to develop a web-based calculator tool (https://cytjt007.shinyapps.io/dynnomapp_depression/).ConclusionReductions in the lymphocyte percentage and blood urea nitrogen and low-density lipoprotein cholesterol levels were reliable predictors of depression in elderly patients with CHD. The nomogram that we developed can help clinicians assess the risk of depression in elderly patients with CHD.

Background Although the elderly constitute more than a third of hepatocellular carcinoma (HCC) patients, they have not been adequately represented in treatment and prognosis studies. Thus, there is not enough evidence to guide the treatment of such patients. The objective of this study is to identify the prognostic factors of older patients with HCC and to construct a new prognostic model for predicting their overall survival (OS). Methods 2,721 HCC patients aged ≥ 65 were extracted from the public database-Surveillance, Epidemiology, and End Results (SEER) and randomly divided into a training set and an internal validation set with a ratio of 7:3. 101 patients diagnosed from 2008 to 2017 in the First Affiliated Hospital of Zhejiang University School of Medicine were identified as the external validation set. Univariate cox regression analyses and multivariate cox regression analyses were adopted to identify these independent prognostic factors. A predictive nomogram-based risk stratification model was proposed and evaluated using area under the receiver operating characteristic curve (AUC), calibration curves, and a decision curve analysis (DCA). Results These attributes including age, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, alpha-fetoprotein level, fibrosis score, bone metastasis, lung metastasis, and grade were the independent prognostic factors for older patients with HCC while predicting survival duration. We found that the nomogram provided a good assessment of OS at 1, 3, and 5 years in older patients with HCC (1-year OS: (training set: AUC = 0.823 (95%CI 0.803–0.845); internal validation set: AUC = 0.847 (95%CI 0.818–0.876); external validation set: AUC = 0.732 (95%CI 0.521–0.943)); 3-year OS: (training set: AUC = 0.813 (95%CI 0.790–0.837); internal validation set: AUC = 0.844 (95%CI 0.812–0.876); external validation set: AUC = 0.780 (95%CI 0.674–0.887)); 5-year OS: (training set: AUC = 0.839 (95%CI 0.806–0.872); internal validation set: AUC = 0.800 (95%CI 0.751–0.849); external validation set: AUC = 0.821 (95%CI 0.727–0.914)). The calibration curves showed that the nomogram was with strong calibration. The DCA indicated that the nomogram can be used as an effective tool in clinical practice. The risk stratification of all subgroups was statistically significant (p < 0.05). In the stratification analysis of surgery, larger resection (LR) achieved a better survival curve than local destruction (LD), but a worse one than segmental resection (SR) and liver transplantation (LT) (p < 0.0001). With the consideration of the friendship to clinicians, we further developed an online interface (OHCCPredictor) for such a predictive function (https://juntaotan.shinyapps.io/dynnomapp_hcc/). With such an easily obtained online tool, clinicians will be provided helpful assistance in formulating personalized therapy to assess the prognosis of older patients with HCC. Conclusions Age, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, AFP level, fibrosis score, bone metastasis, lung metastasis, and grade were independent prognostic factors for elderly patients with HCC. The constructed nomogram model based on the above factors could accurately predict the prognosis of such patients. Besides, the developed online web interface of the predictive model provide easily obtained access for clinicians.