Health Insurance Coverage and Hypertension Control in China: Results from the China Health and Nutrition Survey

Background

China has rapidly expanded health insurance coverage over the past decade but its impact on hypertension control is not well known. We analyzed factors associated with hypertension and the impact of health insurance on the management of hypertension in China from 1991 to 2009.

Methods and Findings

We used individual-level data from the China Health and Nutrition Survey (CHNS) for blood pressure, BMI, and other socio-economic variables. We employed multi-level logistic regression models to estimate the factors associated with prevalence and management of hypertension. We also estimated the effects of health insurance on management of hypertension using propensity score matching. We found that prevalence of hypertension increased from 23.8% (95% CI: 22.5–25.1%) in 1991 to 31.5% (28.5–34.7%) in 2009. The proportion of hypertensive patients aware of their condition increased from 31.7% (28.7–34.9%) to 51.1% (45.1–57.0%). The proportion of diagnosed hypertensive patients in treatment increased by 35.5% in the 19 years, while the proportion of those in treatment with controlled blood pressure remained low. Among diagnosed hypertensives, health insurance increased the probability of receiving treatment by 28.7% (95% CI: 10.6–46.7%) compared to propensity-matched individuals not covered by health insurance.

Conclusions

Hypertension continues to be a major health threat in China and effective control has not improved over time despite large improvements in awareness and treatment access. This suggests problems in treatment quality, medication adherence and patient understanding of the condition. Improvements in hypertension management, quality of medical care for those at high risk, and better health insurance packages are needed.     

Kinetic Curve Type Assessment for Classification of Breast Lesions Using Dynamic Contrast-Enhanced MR Imaging

ObjectiveThe aim of this study was to employ a kinetic model with dynamic contrast enhancement-magnetic resonance imaging to develop an approach that can efficiently distinguish malignant from benign lesions.ResultsAn average sensitivity of 82%, a specificity of 65%, an area under the receiver operating characteristic curve of 0.76, and a positive predictive value of 82% and negative predictive value of 63% was shown with the kinetic model (p = 0.017, 0.052, 0.068), as compared to an average sensitivity of 80%, a specificity of 55%, an area under the receiver operating characteristic of 0.69, and a positive predictive value of 79% and negative predictive value of 57% with the time-signal intensity curve method (p = 0.003, 0.004, 0.008). The diagnostic consistency of the three radiologists was shown by the κ-value, 0.857 (p<0.001) with the method based on the time-signal intensity curve and 0.826 (p<0.001) with the method of the kinetic model.ConclusionsAccording to the statistic results based on the 46 lesions, the kinetic modeling curve method showed higher sensitivity, specificity, positive and negative predictive values as compared with the time-signal intensity curve method in lesion classification.     

Schizandrin A Inhibits Microglia-Mediated Neuroninflammation through Inhibiting TRAF6-NF-κB and Jak2-Stat3 Signaling Pathways

Microglial-mediated neuroinflammation has been established as playing a vital role in pathogenesis of neurodegenerative disorders. Thus, rational regulation of microglia functions to inhibit inflammation injury may be a logical and promising approach to neurodegenerative disease therapy. The purposes of the present study were to explore the neuroprotective effects and potential molecular mechanism of Schizandrin A (Sch A), a lignin compound isolated from Schisandra chinesnesis. Our observations showed that Sch A could significantly down-regulate the increased production of nitric oxide (NO), tumor necrosis factor (TNF)-α and interleukin (IL)-6 induced by lipopolysaccharide (LPS) both in BV-2 cells and primary microglia cells. Moreover, Sch A exerted obvious neuroprotective effects against inflammatory injury in neurons when exposed to microglia-conditioned medium. Investigations of the mechanism showed the anti-inflammatory effect of Sch A involved the inhibition of inducible nitric oxide synthase (iNOS) and cyclooxygenase 2 (COX-2) expression levels and inhibition of the LPS-induced TRAF6-IKKβ-NF-κB pathway. Furthermore, inhibition of Jak2-Stat3 pathway activation and Stat3 nuclear translocation also was observed. In conclusion, SchA can exert anti-inflammatory and neuroprotective effects by alleviating microglia-mediated neuroinflammation injury through inhibiting the TRAF6-IKKβ-NF-κB and Jak2-Stat3 signaling pathways.     

Remediation of Nitrogen-Contaminated Sediment Using Bioreactive,Thin-layer Capping with Biozeolite

In situ amendment of nitrogen-contaminated sediment using bioreactive, thin-layer capping (BTC) with biozeolite (i.e., zeolite with heterotrophic nitrifiers as well as aerobic denitrifiers attached) was studied herein. BTC with biozeolite for nitrogen-contaminated sediment management was evaluated through long-term (170 days) sediment incubation laboratory experiments. The results showed that BTC with relatively small dose rates (<10 kg m^?2) of biozeolite reduced the total nitrogen (TN) concentration in overlying water by over 90%, so it was effective in reducing the amount of N released from sediment. Higher-dose rates of biozeolite capping achieved an even higher removal efficiency. With the DO concentration of 1.5 ～ 6.5 mg L^?1 in overlying water, the reduction efficiency of TN in overlying water using BTC was higher than that less than 1 mg L^?1. In BTC systems, biological regeneration (i.e., heterotrophic nitrifiers attached to zeolite can regenerate the zeolite ion exchange capacity for ammonium) occurred in biozeolite which was saturated with ammonium during the nitrification period. In addition, TN contents in surface sediment in BTC systems were reduced at different levels after the experiment. These findings indicate that the BTC can be a feasible remedial approach to reduce N in overlying water and sediment in eutrophic water bodies. In the BTC, N load was reduced by the added biozeolite through adsorbing ammonium (NH₄⁺-N), converting NH₄⁺-N into nitrate nitrogen (NO₃^?-N) and nitrogen gas (N₂), and assimilating inorganic nitrogen.