An analysis of trends and determinants of health insurance and healthcare utilisation in the Russian population between 2000 and 2004: the 'inverse care law' in action

BACKGROUND: The break-up of the USSR brought considerable disruption to health services in Russia. The uptake of compulsory health insurance rose rapidly after its introduction in 1993. However, by 2000 coverage was still incomplete, especially amongst the disadvantaged. By this time, however, the state health service had become more stable, and the private sector was growing. This paper describes subsequent trends and determinants of healthcare insurance coverage in Russia, and its relationship with health service utilisation, as well as the role of the private sector. METHODS: Data were from the Russia Longitudinal Monitoring Survey, an annual household panel survey (2000-4) from 38 centres across the Russian Federation. Annual trends in insurance coverage were measured (2000-4). Cross-sectional multivariate analyses of the determinants of health insurance and its relationship with health care utilisation were performed in working-age people (18-59 years) using 2004 data. RESULTS: Between 2000 and 2004, coverage by the compulsory insurance scheme increased from 88% to 94% of adults; however 10% of working-age men remained uninsured. Compulsory health insurance coverage was lower amongst the poor, unemployed, unhealthy and people outside the main cities. The uninsured were less likely to seek medical help for new health problems. 3% of respondents had supplementary (private) insurance, and rising utilisation of private healthcare was greatest amongst the more educated and wealthy. CONCLUSION: Despite high population insurance coverage, a multiply disadvantaged uninsured minority remains, with low utilisation of health services. Universal insurance could therefore increase access, and potentially contribute to reducing avoidable healthcare-related mortality. Meanwhile, the socioeconomically advantaged are turning increasingly to a growing private sector

Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies

Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method with a special binary ‘dummy’ y-variable and it is commonly used for classification purposes and biomarker selection in metabolomics studies. Several statistical approaches are currently in use to validate outcomes of PLS-DA analyses e.g. double cross validation procedures or permutation testing. However, there is a great inconsistency in the optimization and the assessment of performance of PLS-DA models due to many different diagnostic statistics currently employed in metabolomics data analyses. In this paper, properties of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q2 and Discriminant Q2 (DQ2) are discussed. All four diagnostic statistics are used in the optimization and the performance assessment of PLS-DA models of three different-size metabolomics data sets obtained with two different types of analytical platforms and with different levels of known differences between two groups: control and case groups. Statistical significance of obtained PLS-DA models was evaluated with permutation testing. PLS-DA models obtained with NMC and AUROC are more powerful in detecting very small differences between groups than models obtained with Q2 and Discriminant Q2 (DQ2). Reproducibility of obtained PLS-DA models outcomes, models complexity and permutation test distributions are also investigated to explain this phenomenon. DQ2 and Q2 (in contrary to NMC and AUROC) prefer PLS-DA models with lower complexity and require higher number of permutation tests and submodels to accurately estimate statistical significance of the model performance. NMC and AUROC seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies

Population-based nutrikinetic modeling of polyphenol exposure

The beneficial health effects of fruits and vegetables have been attributed to their polyphenol content. These compounds undergo many bioconversions in the body. Modeling polyphenol exposure of humans upon intake is a prerequisite for understanding the modulating effect of the food matrix and the colonic microbiome. This modeling is not a trivial task and requires a careful integration of measuring techniques, modeling methods and experimental design. Moreover, both at the population level as well as the individual level polyphenol exposure has to be quantified and assessed. We developed a strategy to quantify polyphenol exposure based on the concept of nutrikinetics in combination with population-based modeling. The key idea of the strategy is to derive nutrikinetic model parameters that summarize all information of the polyphenol exposure at both individual and population level. This is illustrated by a placebo-controlled crossover study in which an extract of wine/grapes and black tea solids was administered to twenty subjects. We show that urinary and plasma nutrikinetic time-response curves can be used for phenotyping the gut microbial bioconversion capacity of individuals. Each individual harbours an intrinsic microbiota composition converting similar polyphenols from both test products in the same manner and stable over time. We demonstrate that this is a novel approach for associating the production of two gut-mediated γ-valerolactones to specific gut phylotypes. The large inter-individual variation in nutrikinetics and γ-valerolactones production indicated that gut microbial metabolism is an essential factor in polyphenol exposure and related potential health benefits

Conservation status of Asian elephants: the influence of habitat and governance

Understanding the drivers of Asian elephant (Elephas maximus) abundance and distribution is critical for effective elephant conservation, yet no such analysis exists despite decades of assessments and planning. We explored the influence of habitat- and governance-related drivers on elephant abundance across the 13 Asian elephant range countries. We tested competing statistical models by integrating a binary index of elephant abundance (IEA) derived from expert knowledge with different predictor variables including habitat, human population, socioeconomics, and governance data. We employed logistic regression and model-averaging techniques based on Akaike’s Information Criterion to identify the best-performing subset among our 12 candidate models and used the model-averaged results to predict IEA in other areas in Asia where elephant population status is currently unknown. Forest area was our strongest single predictor variable. The best performing model, however, featured a combination of habitat and governance variables including forest area, level of corruption, proportional mix of forest and agriculture, and total agricultural area. Our predictive model identified five areas with medium–high to high probability to have populations with >150 elephants, which we believe should be surveyed to assess their status. Asian elephants persist in areas that are dominated by forest but also seem to benefit from a mix of agricultural activities. A relatively low level of corruption is also important and we conclude that effective governance is essential for maintaining Asian elephant populations. Asian elephant populations cannot be maintained solely in protected areas but need well-managed, mixed-use landscapes where people and elephants coexist