The objective of this study was to assess the determinants of the decision to purchase private health insurance (PHI) in China. Nationally representative data from the fourth wave of the China Household Finance Survey from 2017 were used, and the dataset comprised 105,691 individuals aged 18 years or older. The Andersen health services utilization model was used to inform the research. Chi-square tests and logistic regression analyses were used to estimate the decision to purchase PHI. The proportion of the sample that had PHI was small, at 5.06%, but coverage for social basic medical insurance (SBMI) was 90.64%. Among PHI holders, the overwhelming majority (87.40%) also had SBMI. Logistic regression analysis demonstrated that predisposing factors (age, education, marital status, household size), enabling factors (household income, SBMI status, geographical factors, household medical expense, and medical debt), and needs-based factors (health status) were statistically significant determinants of the decision to purchase PHI. This study suggests that the socio-economic circumstances of households play a crucial role in the decision to acquire PHI. The findings may be used by the insurance industry to inform actions to enhance PHI coverage and by policy decision-makers that seek to improve equality in access to PHI.

Uncertainty is inherent in spatial data and spatio-temporal phenomena. Spatial data uncertainty generally refers to error, inexactness, fuzziness and ambiguity. The goals of research on spatial data uncertainty are to investigate how uncertainties arise, or are created and propagated in the spatial data process. Based on information theory, considering the characteristics of randomicity of positional data and fuzziness of attribute data and taking entropy as a measure, this paper proposes the stochastic entropy model of spatial positional data uncertainty and fuzzy entropy model of spatial attribute data uncertainty. Usually, both randomicity and fuzziness exist in spatial data simultaneously, so their co-uncertainty is also investigated and quantified in this paper. A novel spatial data uncertainty measure, total entropy, is presented. Total entropy can be used as a uniform measure to quantify the total spatial data uncertainty caused by stochastic uncertainty and fuzzy uncertainty.

Background: To investigate the prevalence and indoor environmental influencing factors of wheezing and asthma among preschool children in Urumqi, Xinjiang, China to provide a strong basis for prevention and control. Methods: In August 2019, a cross-sectional epidemiological study involving 8153 preschool children was conducted in 60 kindergartens in Urumqi. The mean age of the children who participated in the survey was 5.27 ± 1.10 years. Additionally, 51.9% were boys, 86.9% were Han Chinese, and an 81.53% survey response rate was observed. The childhood wheeze and asthma survey used was the ALLHOME-2 questionnaire, and the childhood home dwelling and living environment survey used was the DBH questionnaire. Partial adjustments were made according to the geographical environment of Urumqi and the living habits of the residents.Results: The prevalence of wheezing and asthma in children was 4.7% and 2.0%, respectively. Multivariate unconditional logistic regression results suggested that ethnicity (odds ratio (OR)=1.39, 95% confidence interval (95%CI)=1.05–1.84), birth pattern (OR=1.24, 95%CI=1.00–1.53), family history of asthma (OR=5.00, 95%CI=3.36–7.44), carpet or floor bedding at home (OR=1.40, 95%CI=1.05–1.87), purchasing new furniture in the mother’s residence during pregnancy (OR=1.58,95%CI=1.06–2.36), pet keeping in the residence at age 0–1 (OR=1.55, 95%CI=1.13–2.13), passive smoking in the child's residence (OR=1.35, 95%CI=1.01–1.80), and having mould or hygroma in the child's residence at age 0–1 (OR=1.72, 95%CI=1.12–2.64) were risk factors for wheezing. In addition, sex (OR=0.73, 95%CI=0.59-0.90) was a protective factor for wheezing. Birth pattern (OR=1.46, 95%CI=1.06–2.00), family history of asthma (OR=7.06,95%CI=4.33–11.53), carpet or floor bedding at home (OR=2.20, 95%CI=1.50–3.23), and pet keeping in the residence at age 0–1 (OR=1.64, 95%CI=1.04–1.83) were risk factors for asthma, whereas gender (OR=0.58, 95% CI=0.42–0.80) was a protective factor for asthma. Conclusion: This survey indicates that preschool children in Urumqi have a higher risk of wheezing and asthma. Risk factors that may cause an elevated risk of wheezing or asthma have also been identified.

ABSTRACT:Accurate and timely change detection of Earth's surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Many traditional methods of change detection only use a part of polarization information and the supervised threshold selection. Those methods are insufficiency and time-costing. In this paper, we present a novel unsupervised change-detection method based on quad-polarimetric SAR data and automatic threshold selection to solve the problem of change detection. First, speckle noise is removed for the two registered SAR images. Second, the similarity measure is calculated by the test statistic, and automatic threshold selection of KI is introduced to obtain the change map. The efficiency of the proposed method is demonstrated by the quad-pol SAR images acquired by Radarsat-2 over Wuhan of China

No abstract