Delivering universal health coverage for an aging population : an analysis of the Chinese rural health insurance program

There is now high level international commitment to the goal of universal health coverage. But how can countries make this a reality in the face of a limited budget and an aging population? Since 2008, China has been rolling out an ambitious reform program, which aims to achieve affordable health insurance coverage for all Chinese citizens. Under this reform program, Chinese living in rural areas are eligible to enroll in a subsidized scheme called the New Cooperative Medical System (NCMS). Using a three stage game model involving a government, a private fund manager and population, we explore the impact of population aging on NCMS. Our model highlights the role of government regulation and subsidy in ensuring operation efficiency of the system. We show that at optimality the government sets the operating framework for the fund manager to constrain the potential for monopoly profits. The Government subsidizes the scheme to prevent an adverse selection death spiral. However, the effectiveness of the subsidy in achieving this goal is moderated by the age structure of the population. Our model gives insights into the strengths of the NCMS framework and also can be used to support decisions about resource allocation and understand how scheme dynamics may unfold as the Chinese population ages further

Coronary artery disease as an independent predictor of short-term and long-term outcomes in patients with type-B aortic dissection undergoing thoracic endovascular repair

Background and aimsPrevious studies reported a high prevalence of concomitant coronary artery disease (CAD) in patients with Type B aortic dissection (TBAD). However, there is too limited data on the impact of CAD on prognosis in patients with TBAD. The present study aimed to assess the short-term and long-term impact of CAD on patients with acute or subacute TBAD undergoing thoracic endovascular aortic repair (TEVAR).MethodsWe retrospectively evaluated 463 patients with acute or subacute TBAD undergoing TEVAR from a prospectively maintained database from 2010 to 2017. CAD was defined before TEVAR by coronary angiography. Multivariable logistic and cox regression analyses were performed to evaluate the relationship between CAD and the short-term as well as long-term outcomes.ResultsAccording to the results of coronary angiography, the 463 patients were divided into the following two groups: CAD group (N = 148), non-CAD group (N = 315). In total, 12 (2.6%) in-hospital deaths and 54 (12%) all-cause deaths following a median follow-up of 48.1 months were recorded. Multivariable analysis revealed that CAD was an independent predictor of in-hospital major adverse clinical events (MACE) (odd ratio [OR], 2.33; 95% confidence interval [CI], 1.07–5.08; p = 0.033), long-term mortality [hazard ratio (HR), 2.11, 95% CI, 1.19–3.74, P = 0.011] and long-term MACE (HR, 1.95, 95% CI, 1.26–3.02, P = 0.003). To further clarify the relationship between the severity of CAD and long-term outcomes, we categorized patients into three groups: zero-vessel disease, single-vessel disease and multi-vessel disease. The long-term mortality (9.7 vs. 14.4 vs. 21.2%, P = 0.045), and long-term MACE (16.8 vs. 22.2 vs. 40.4%, P = 0.001) increased with the number of identified stenosed coronary vessels. Multivariable analysis indicated that, multi-vessel disease was independently associated with long-term mortality (HR, 2.38, 95% CI, 1.16–4.89, P = 0.018) and long-term MACE (HR, 2.79, 95% CI, 1.65–4.73, P = 0.001), compared with zero-vessel disease.ConclusionsCAD was associated with short-term and long-term worse outcomes in patients with acute or subacute TBAD undergoing TEVAR. Furthermore, the severity of CAD was also associated with worse long-term prognosis. Therefore, CAD could be considered as a useful independent predictor for pre-TEVAR risk stratification in patients with TBAD

Succession of Composition and Function of Soil Bacterial Communities During Key Rice Growth Stages

Elucidating the succession of soil microbial communities and microbial functions at key plant growth stages is a major goal of microbial ecology research. In this study, we investigated the succession of soil bacteria during four fertilizer treatments (control, NPK, NPK + pig manure, and NPK + straw) and at three crucial rice growth stages (tillering, heading, and ripening) in paddy soil from a rice-wheat cropping system over a 10-year period. The results showed that the bacterial community and function composition of the control treatment was significantly different from that of the other treatments with NPK fertilizers, and S1 from others stages (ANOSIM, p < 0.05). The application of pig manure could reduce the effects of applying NPK fertilizers on bacterial communities in heading and ripening stages, but the effects of straw returning is not obvious. Variance partitioning analyses (VPA) suggested that pH, OM, and AK appeared to be key factors responsible for the microbial community changes observed in all the treatments or stages. The correlation results showed the bacterial families different between S1 and other stages such as Micromonosporaceae, Nocardioidaceae, Gaiellaceae, and Anaerolineaceae etc., were correlated with bacterial KEGG metabolic pathways. In addition, the topological of the soil bacterial community network with more nodes, links and higher Maximal degree at the heading stage and maintained relatively similar topological structures at the heading and ripening stages. However, the topological of the functional networks at the ripening stage were a small yet complicated co-occurring network with 209 nodes, 789 links, higher Average connectivity (avgK), and Maximal degree. These results suggest an obvious succession of soil bacteria and bacterial function at the key rice growth stages, but the topological of functional network structure of bacteria changes a little in the early and middle stages of rice, while its changes significantly in the ripening stage of rice growth

Effect of BMI on the value of serum progesterone to predict clinical pregnancy outcome in IVF/ICSI cycles: a retrospective cohort study

BackgroundNumerous research have investigated the predictor role of progesterone (P) level on the human Chorionic Gonadotropin (hCG) trigger day of assisted reproductive technology (ART) outcomes. However, the relationship of progesterone levels on hCG day to clinical pregnancy outcomes in IVF/ICSI cycles for patients with different BMI groups is still elusive. This study aimed to investigate the effects of progesterone elevation on triggering day on clinical pregnancy rate (CPR) of IVF/ICSI cycles in patients with different female BMI.MethodsWe conducted a retrospective cohort study included 6982 normal-weight parents (18.5Kg/m2≤BMI<25Kg/m2) and 2628 overweight/obese patients (BMI≥25Kg/m2) who underwent fresh day 3 cleavage embryo transfer (ET) in IVF/ICSI cycles utilizing GnRH agonist to control ovarian stimulation.ResultsThe interaction between BMI and P level on triggering day on CPRs was significant (p<0.001). The average level of serum P was reduced with the increase in maternal BMI. Serum P adversely affected CPR in distinct BMI groups. In the normal weight group, CPRs were decreasedas serum P concentrations gradually increased (p<0.001 for overall trend). The CPRs (lower than 65.8%) of progesterone level > 1.00 ng/ml on triggering day were significantly lower than that (72.4%) of progesterone level <0.5 ng/ml. In the overweight/obese group, CPRs showed a decrease statistically with progesterone levels of ≥2.00 ng/ml compared to progesterone levels of <0.5 ng/ml (51.0% VS. 64.9%, p=0.016). After adjusting for confounders, progesterone elevation (PE) negatively correlated with CPRs only in the normal weight group (OR: 0.755 [0.677–0.841], p<0.001), not in the overweight/obese group (p=0.063).ConclusionWomen with higher BMI exhibited a lower progesterone level on triggering day. Additionally, PE on hCG day is related to decreased CPRs in GnRH agonist IVF/ICSI cycles with cleavage embryo transfers regardless of women’s BMI level (normal weight VS. overweight/obesity)

Enrofloxacin Induces Intestinal Microbiota-Mediated Immunosuppression in Zebrafish

The immunosuppressive effects of antibiotics and the potential associations with the intestinal microbiota of the host have been increasingly recognized in recent years. However, the detailed underlying mechanisms of immune interference of antibiotics in environmental organisms remain unclear, particularly at the early life stage of high sensitivity. To better understand the gut microbiome and immune function interactions, the vertebrate model, zebrafish, was treated with environmentally relevant concentrations of a frequently detected antibiotic, enrofloxacin (ENR), ranging from 0.01 to 100 μg/L. 16S ribosomal RNA sequencing indicated diminished diversity, richness, and evenness of intestinal flora following ENR treatment. Twenty-two taxa of gut bacteria including Rickettsiales, Pseudomonadales, and Flavobacteriales were significantly correlated with immunosuppressive biomarkers, including a significant decrease in the abundance of macrophages and neutrophils. To validate the immunomodulatory effects due to altered intestinal microbial populations, zebrafish reared under sterile and non-sterile husbandry conditions were compared after ENR treatment. A significant inhibitory effect was induced by ENR treatment under non-sterile conditions, while the number of macrophages and neutrophils, as well as biomarkers of immunosuppressive effects, were significantly salved in zebrafish under sterile conditions, confirming for the first time that immunosuppression by ENR was closely mediated through alterations of the intestinal microbiome in fish.publishedVersio

The state-of-the-art of urban climate change modeling and observations

As an effect of climate change, cities need detailed information on urban climates at decision scale that cannot be easily delivered using current observation networks, nor global and even regional climate models. A review is presented of the recent literature and recommendations are formulated for future work. In most cities, historical observational records are too short, discontinuous, or of too poor quality to support trend analysis and climate change attribution. For climate modeling, on the other hand, specific dynamical and thermal parameterization dedicated to the exchange of water and energy between the atmosphere and the urban surfaces have to be implemented. Therefore, to fully understand how cities are impacted by climate change, it is important to have (1) simulations of the urban climate at fine spatial scales (including coastal hazards for coastal cities) integrating global climate scenarios with urban expansion and population growth scenarios and their associated uncertainty estimates, (2) urban climate observations, especially in Global South cities, and (3) spatial data of high resolution on urban structure and form, human behavior, and energy consumption