Can major public health emergencies increase the participation of commercial insurance? Evidence from China.

Background: Public health emergencies have a lasting impact on a country's economic and social development. However, commercial insurance can disperse these negative consequences and reduce risk losses. Method: Based on the Chinese Household Tracking Survey and Peking University Digital Inclusive Finance Index, this study employed a difference-in-differences model to test the impact of the COVID-19 outbreak on commercial insurance participation and the impact mechanism. Results: The analysis showed that the outbreak of COVID-19 improved residents' risk perception, risk preference and digital finance and promoted their participation in commercial insurance, commercial endowment insurance, and commercial medical insurance. Conclusion: Major public health emergencies can increase commercial insurance participation, but the promotional effect of commercial insurance on rural and low-income individuals is relatively limited. To tap into potential customers, financial institutions should focus on vulnerable societal groups. This study supplements the relevant literature on the impact of major public health emergencies on commercial insurance participation.

The relationship between urologic cancer outcomes and national Human Development Index: trend in recent years.

OBJECTIVES: To describe the influence of the socioeconomic development on worldwide age-standardized incidence and mortality rates, as well as mortality-to-incidence ratio (MIR) and 5-year net survival of urologic cancer patients in recent years. METHODS: The Human Development Index (HDI) values were obtained from the United Nations Development Programme, data on age-standardized incidence/mortality rates of prostate, bladder and kidney cancer were retrieved from the GLOBOCAN database, 5-year net survival was provided by the CONCORD-3 program. We then evaluated the association between incidence/MIR/survival and HDI, with a focus on geographic variability as well as temporal patterns during the last 6 years. RESULTS: Urologic cancer incidence rates were positively correlated with HDIs, and MIRs were negatively correlated with HDIs. Prostate cancer survival also correlated positively with HDIs, solidly confirming the interrelation among cancer indicators and socioeconomic factors. Most countries experienced incidence decline over the most recent 6 years, and a substantial reduction in MIR was observed. Survival rates of prostate cancer have simultaneously improved. CONCLUSION: Development has a prominent influence on urologic cancer outcomes. HDI values are significantly correlated with cancer incidence, MIR and survival rates. HDI values have risen along with increased incidence and improved outcomes of urologic caner in recent years.

Prediction of lymph node metastatic status in superficial esophageal squamous cell carcinoma using an assessment model combining clinical characteristics and pathologic results: A retrospective cohort study.

BACKGROUND: It is important to identify the risk of lymph node metastasis (LNM) in patients with superficial esophageal squamous carcinoma (SESC) who have received endoscopic resection (ER). We aimed to develop a risk-predicting model for metastasis of SESC to lymph nodes using clinicopathological features and pathological results. METHODS: Clinical data on 539 consecutive patients who underwent esophagectomy for SESC in our hospital were collected. Their post-surgical pathological results were assessed and analyzed. Multivariate logistic regression was used to identify all independent risk factors associated with LNM that then were incorporated into the prediction model. RESULTS: LNM was identified in 53 of 366 patients and 30 of 173 patients by positive histopathological results in the training and validation cohorts. The risk factors associated with LNM were large tumor size, poor tumor grade, deep invasion, and presence of angiolymphatic invasion. The model achieved good discriminatory ability of 0.80 (95%CI, 0.74-0.86) and 0.81 (95%CI, 0.75-0.86) in predicting LNM in the training and validation cohorts respectively. A LNM-predicting nomogram was formed with an area under curve of 0.80 (95% CI, 0.74-0.86), which had well-fitted calibration curves. CONCLUSIONS: A prediction model was constructed to generates 3 categories for estimated LNM risk in SESC patients. It provides a practical way of estimation of LNM risk in SESC patients who had received ER.