Determining the likely place of HIV acquisition for migrants in Europe combining subject-specific information and biomarkers data

In most HIV-positive individuals, infection time is only known to lie between the time an individual started being at risk for HIV and diagnosis time. However, a more accurate estimate of infection time is very important in certain cases. For example, one of the objectives of the Advancing Migrant Access to Health Services in Europe (aMASE) study was to determine if HIV-positive migrants, diagnosed in Europe, were infected pre- or post-migration. We propose a method to derive subject-specific estimates of unknown infection times using information from HIV biomarkers' measurements, demographic, clinical, and behavioral data. We assume that CD4 cell count (CD4) and HIV-RNA viral load trends after HIV infection follow a bivariate linear mixed model. Using post-diagnosis CD4 and viral load measurements and applying the Bayes' rule, we derived the posterior distribution of the HIV infection time, whereas the prior distribution was informed by AIDS status at diagnosis and behavioral data. Parameters of the CD4-viral load and time-to-AIDS models were estimated using data from a large study of individuals with known HIV infection times (CASCADE). Simulations showed substantial predictive ability (e.g. 84% of the infections were correctly classified as pre- or post-migration). Application to the aMASE study ( n = 2009) showed that 47% of African migrants and 67% to 72% of migrants from other regions were most likely infected post-migration. Applying a Bayesian method based on bivariate modeling of CD4 and viral load, and subject-specific information, we found that the majority of HIV-positive migrants in aMASE were most likely infected after their migration to Europe

Impact of covariate omission and categorization from the Fine–Gray model in randomized-controlled trials

In this paper, we study the statistical issues related to the omission and categorization of important covariates in the context of the Fine–Gray model in randomized-controlled trials with competing risks. We show that the omission of an important covariate from the Fine–Gray model leads to attenuated estimates for treatment effect and loss of proportionality in general. Our simulation studies reveal substantial attenuation in the estimate for treatment effect and the loss of statistical power, while dichotomizing a continuous covariate leads to similar but less pronounced impact. Our results are illustrated using data from a randomized clinical trial of HIV-infected individuals. The relative merits of conducting an adjusted versus an unadjusted analysis of treatment effect in light of both statistical and practical considerations are discussed

Investigating regional differences in short-term effects of air pollution on daily mortality in the APHEA project: a sensitivity analysis for controlling long-term trends and seasonality.

Short-term effects of air pollution on daily mortality in eight western and five central-eastern European countries have been reported previously, as part of the APHEA project. One intriguing finding was that the effects were lower in central-eastern European cities. The analysis used sinusoidal terms for seasonal control and polynomial terms for meteorologic variables, but this is a more rigid approach than the currently accepted method, which uses generalized additive models (GAM). We therefore reanalyzed the original data to examine the sensitivity of the results to the statistical model. The data were identical to those used in the earlier analyses. The outcome was the daily total number of deaths, and the pollutants analyzed were black smoke (BS) and sulfur dioxide (SO(2)). The analyses were restricted to days with pollutant concentration < 200 microg/m(3) and < 150 microg/m(3) alternately. We used Poisson regression in a GAM model, and combined individual city regression coefficients using fixed and random-effect models. An increase in BS by 50 microg/m(3) was associated with a 2.2% and 3.1% increase in mortality when analysis was restricted to days < 200 microg/m(3) and < 150 microg/m(3), respectively. The corresponding figures were 5.0% and 5.6% for a similar increase in SO(2). These estimates are larger than the ones published previously: by 69% for BS and 55% for SO(2). The increase occurred only in central-eastern European cities. The ratio of western to central-eastern cities for estimates was reduced to 1.3 for BS (previously 4.8) and 2.6 for SO(2) (previously 4.4). We conclude that part of the heterogeneity in the estimates of air pollution effects between western and central-eastern cities reported in previous publications was caused by the statistical approach used and the inclusion of days with pollutant levels above 150 microg/m(3). However, these results must be investigated further

Short term effects of ambient sulphur dioxide and particulate matter on mortality in 12 European cities : results from time series data from the APHEA project

Objectives: To carry out a prospective combined quantitative analysis of the associations between all cause mortality and ambient particulate matter and sulphur dioxide. Design: Analysis of time series data on daily number of deaths from all causes and concentrations of sulphur dioxide and particulate matter (measured as black smoke or particles smaller than 10 ìm in diameter (PM10)) and potential confounders. Setting: 12 European cities in the APHEA project (Air Pollution and Health: a European Approach). Main outcome measure: Relative risk of death. Results:In western European cities it was found that an increase of 50 ìg/m3 in sulphur dioxide or black smoke was associated with a 3% (95% confidence interval 2% to 4%) increase in daily mortality and the corresponding figure for PM10 was 2% (1% to 3%). In central eastern European cities the increase in mortality associated with a 50 ìg/m3 change in sulphur dioxide was 0.8% (−0.1% to 2.4%) and in black smoke 0.6% (0.1% to 1.1%). Cumulative effects of prolonged (two to four days) exposure to air pollutants resulted in estimates comparable with the one day effects. The effects of both pollutants were stronger during the summer and were mutually independent. Conclusions:The internal consistency of the results in western European cities with wide differences in climate and environmental conditions suggest that these associations may be causal. The long term health impact of these effects is uncertain, but today's relatively low levels of sulphur dioxide and particles still have detectable short term effects on health and further reductions in air pollution are advisable

Model Kebijakan Penanggulangan Korupsi di Universitas Negeri YOGYAKARTA

Penelitian ini bertujuan untuk mengetahui kebijakan Universitas Negeri Yogyakarta dalam menanggulangi korupsi dan menemukan model kebijakan yang diinginkan Universitas Negeri Yogyakarta dalam menanggulangi korupsi. Penelitian ini adalah penelitian survei dengan pendekatan kuantitatif dan kualitatif. Sampel penelitian ditentukan secara multy stage sampling dengan teknik pengumpulan data dengan angket, dokumen dan diperkuat dengan pengumpulan data melalui Focus Group Discussion (FGD), dan validasi instrumen melalui validitas isi (content validity). Data dianalisis secara deskriptif. Hasil penelitian menunjukkan bahwa kebijakan penanggulangan korupsi di UNY tidak ada secara khusus dikeluarkan. Kebijakan yang ada mengikuti dan mempertahankan kebijakan yang lebih tinggi, yaitu dari Pemerintah. Model kebijakan penangggulangan korupsi di UNY yang digunakan adalah Model Rasional, yaitu kebijakan penanggulangan korupsi yang dikeluarkan merupakan aspirasi semua staf yang ada di unit kerja dan harus menekankan pada aspek efisiensi atas beban kerja pada unit kerja yang bersangkutan. Adapun kebijakan yang sudah ada yang berasal dari Pemerintah pusat dijadikan pedoman

Human immunotypes impose selection on viral genotypes through viral epitope specificity

BACKGROUND: Understanding the genetic interplay between human hosts and infectious pathogens is crucial for how we interpret virulence factors. Here, we tested for associations between HIV and host genetics, and interactive genetic effects on viral load (VL) in HIV+ ART-naive clinical trial participants. METHODS: HIV genomes were sequenced and the encoded amino acid (AA) variants were associated with VL, human single nucleotide polymorphisms (SNPs) and imputed HLA alleles, using generalized linear models with Bonferroni correction. RESULTS: Human (388,501 SNPs) and HIV (3,010 variants) genetic data was available for 2,122 persons. Four HIV variants were associated with VL (p-values<1.66×10 -5). Twelve HIV variants were associated with a range of 1-512 human SNPs (p-value<4.28×10 -11). We found 46 associations between HLA alleles and HIV variants (p-values<1.29×10 -7). We found HIV variants and immunotypes when analyzed separately, were associated with lower VL, whereas the opposite was true when analyzed in concert. Epitope binding prediction showed HLA alleles to be weaker binders of associated HIV AA variants relative to alternative variants on the same position. CONCLUSIONS: Our results show the importance of immunotype specificity on viral antigenic determinants, and the identified genetic interplay puts emphasis that viral and human genetics should be studied in the context of each other