Measurement error in a multi-level analysis of air pollution and health: a simulation study.

BACKGROUND: Spatio-temporal models are increasingly being used to predict exposure to ambient outdoor air pollution at high spatial resolution for inclusion in epidemiological analyses of air pollution and health. Measurement error in these predictions can nevertheless have impacts on health effect estimation. Using statistical simulation we aim to investigate the effects of such error within a multi-level model analysis of long and short-term pollutant exposure and health. METHODS: Our study was based on a theoretical sample of 1000 geographical sites within Greater London. Simulations of "true" site-specific daily mean and 5-year mean NO2 and PM10 concentrations, incorporating both temporal variation and spatial covariance, were informed by an analysis of daily measurements over the period 2009-2013 from fixed location urban background monitors in the London area. In the context of a multi-level single-pollutant Poisson regression analysis of mortality, we investigated scenarios in which we specified: the Pearson correlation between modelled and "true" data and the ratio of their variances (model versus "true") and assumed these parameters were the same spatially and temporally. RESULTS: In general, health effect estimates associated with both long and short-term exposure were biased towards the null with the level of bias increasing to over 60% as the correlation coefficient decreased from 0.9 to 0.5 and the variance ratio increased from 0.5 to 2. However, for a combination of high correlation (0.9) and small variance ratio (0.5) non-trivial bias (> 25%) away from the null was observed. Standard errors of health effect estimates, though unaffected by changes in the correlation coefficient, appeared to be attenuated for variance ratios > 1 but inflated for variance ratios < 1. CONCLUSION: While our findings suggest that in most cases modelling errors result in attenuation of the effect estimate towards the null, in some situations a non-trivial bias away from the null may occur. The magnitude and direction of bias appears to depend on the relationship between modelled and "true" data in terms of their correlation and the ratio of their variances. These factors should be taken into account when assessing the validity of modelled air pollution predictions for use in complex epidemiological models

Extrapolation for Time-Series and Cross-Sectional Data

Extrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts’ commonly held opinions) and on empirical studies. Some of the more important principles are:• In selecting and preparing data, use all relevant data and adjust the data for important events that occurred in the past.• Make seasonal adjustments only when seasonal effects are expected and only if there is good evidence by which to measure them.• In extrapolating, use simple functional forms. Weight the most recent data heavily if there are small measurement errors, stable series, and short forecast horizons. Domain knowledge and forecasting expertise can help to select effective extrapolation procedures. When there is uncertainty, be conservative in forecasting trends. Update extrapolation models as new data are received.• To assess uncertainty, make empirical estimates to establish prediction intervals.• Use pure extrapolation when many forecasts are required, little is known about the situation, the situation is stable, and expert forecasts might be biased

How do MNC R&amp;D laboratory roles affect employee international assignments?

Research and development (R&#38;D) employees are important human resources for multinational corporations (MNCs) as they are the driving force behind the advancement of innovative ideas and products. International assignments of these employees can be a unique way to upgrade their expertise; allowing them to effectively recombine their unique human resources to progress existing knowledge and advance new ones. This study aims to investigate the effect of the roles of R&#38;D laboratories in which these employees work on the international assignments they undertake. We categorise R&#38;D laboratory roles into those of the support laboratory, the locally integrated laboratory and the internationally interdependent laboratory. Based on the theory of resource recombinations, we hypothesise that R&#38;D employees in support laboratories are not likely to assume international assignments, whereas those in locally integrated and internationally interdependent laboratories are likely to assume international assignments. The empirical evidence, which draws from research conducted on 559 professionals in 66 MNC subsidiaries based in Greece, provides support to our hypotheses. The resource recombinations theory that extends the resource based view can effectively illuminate the international assignment field. Also, research may provide more emphasis on the close work context of R&#38;D scientists rather than analyse their demographic characteristics, the latter being the focus of scholarly practice hitherto

Applying refinement to the use of mice and rats in rheumatoid arthritis research

Rheumatoid arthritis (RA) is a painful, chronic disorder and there is currently an unmet need for effective therapies that will benefit a wide range of patients. The research and development process for therapies and treatments currently involves in vivo studies, which have the potential to cause discomfort, pain or distress. This Working Group report focuses on identifying causes of suffering within commonly used mouse and rat ‘models’ of RA, describing practical refinements to help reduce suffering and improve welfare without compromising the scientific objectives. The report also discusses other, relevant topics including identifying and minimising sources of variation within in vivo RA studies, the potential to provide pain relief including analgesia, welfare assessment, humane endpoints, reporting standards and the potential to replace animals in RA research

Factors associated with HIV testing among male injecting drug users: findings from a cross-sectional behavioural and biological survey in Manipur and Nagaland, India

BACKGROUND: Although targeted interventions in India require all high-risk groups, including injecting drug users (IDUs), to test for HIV every 6 months, testing uptake among IDUs remains far from universal. Our study estimates the proportion of IDUs who have taken an HIV test and identifies the factors associated with HIV testing uptake in Nagaland and Manipur, two high HIV prevalence states in India where the epidemic is driven by injecting drug use. METHODS: Data are drawn from the cross-sectional Integrated Behavioural and Biological Assessment (2009) of 1650 male IDUs from two districts each of Manipur and Nagaland. Participants were recruited using respondent-driven sampling (RDS). Descriptive data were analysed using RDSAT 7.1. Multivariate logistic regression analysis was undertaken using STATA 11 to examine the association between HIV testing and socio-demographic, behavioural and programme exposure variables. RESULTS: One third of IDUs reported prior HIV testing, of whom 8 % had tested HIV-positive. Among those without prior testing, 6.2 % tested HIV-positive in the current survey. IDUs aged 25–34 years (adjusted odds ratio (OR) = 1.41; 95 % confidence interval (CI) = 1.03–1.93), married (Adjusted OR = 1.56; 95 % CI = 1.15–2.12), had a paid sexual partner (Adjusted OR = 1.64; 95 % CI = 1.24–2.18), injected drugs for more than 36 months (Adjusted OR = 1.38; 95 % CI = 1.06–1.81), injected frequently (Adjusted OR = 1.49; 95 % CI = 1.12–1.98) and had high-risk perception (Adjusted OR = 1.68; 95 % CI = 1.32–2.14) were more likely than others to test for HIV. Compared to those with no programme exposure, IDUs who received counselling, or counselling and needle/syringe services, were more likely to test for HIV. CONCLUSIONS: HIV testing uptake among IDUs is low in Manipur and Nagaland, and a critical group of HIV-positive IDUs who have never tested for HIV are being missed by current programmes. This study identifies key sub-groups—including early initiators, short duration and less frequent injectors, perceived to be at low risk—for promoting HIV testing. Providing needles/syringes alone is not adequate to increase HIV testing; additionally, interventions must provide counselling services to inform all IDUs about HIV testing benefits, facilitate visits to testing centres and link those testing positive to timely treatment and care

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies

<p>Abstract</p> <p>Background</p> <p>Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta.</p> <p>Methods</p> <p>Daily measures of twelve ambient air pollutants were analyzed: NO₂, NO_x, O₃, SO₂, CO, PM₁₀mass, PM_2.5mass, and PM_2.5components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits.</p> <p>Results</p> <p>Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed.</p> <p>Conclusions</p> <p>For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.</p

Geographic and Racial Variation in Premature Mortality in the U.S.: Analyzing the Disparities

Life expectancy at birth, estimated from United States period life tables, has been shown to vary systematically and widely by region and race. We use the same tables to estimate the probability of survival from birth to age 70 (S70), a measure of mortality more sensitive to disparities and more reliably calculated for small populations, to describe the variation and identify its sources in greater detail to assess the patterns of this variation. Examination of the unadjusted probability of S70 for each US county with a sufficient population of whites and blacks reveals large geographic differences for each race-sex group. For example, white males born in the ten percent healthiest counties have a 77 percent probability of survival to age 70, but only a 61 percent chance if born in the ten percent least healthy counties. Similar geographical disparities face white women and blacks of each sex. Moreover, within each county, large differences in S70 prevail between blacks and whites, on average 17 percentage points for men and 12 percentage points for women. In linear regressions for each race-sex group, nearly all of the geographic variation is accounted for by a common set of 22 socio-economic and environmental variables, selected for previously suspected impact on mortality; R2 ranges from 0.86 for white males to 0.72 for black females. Analysis of black-white survival chances within each county reveals that the same variables account for most of the race gap in S70 as well. When actual white male values for each explanatory variable are substituted for black in the black male prediction equation to assess the role explanatory variables play in the black-white survival difference, residual black-white differences at the county level shrink markedly to a mean of −2.4% (+/−2.4); for women the mean difference is −3.7% (+/−2.3)

Asteroseismology and Interferometry

Asteroseismology provides us with a unique opportunity to improve our understanding of stellar structure and evolution. Recent developments, including the first systematic studies of solar-like pulsators, have boosted the impact of this field of research within Astrophysics and have led to a significant increase in the size of the research community. In the present paper we start by reviewing the basic observational and theoretical properties of classical and solar-like pulsators and present results from some of the most recent and outstanding studies of these stars. We centre our review on those classes of pulsators for which interferometric studies are expected to provide a significant input. We discuss current limitations to asteroseismic studies, including difficulties in mode identification and in the accurate determination of global parameters of pulsating stars, and, after a brief review of those aspects of interferometry that are most relevant in this context, anticipate how interferometric observations may contribute to overcome these limitations. Moreover, we present results of recent pilot studies of pulsating stars involving both asteroseismic and interferometric constraints and look into the future, summarizing ongoing efforts concerning the development of future instruments and satellite missions which are expected to have an impact in this field of research.Comment: Version as published in The Astronomy and Astrophysics Review, Volume 14, Issue 3-4, pp. 217-36

Fluid intake and incidence of renal cell carcinoma in UK women

Background:It has been suggested that the apparent protective effect of alcohol intake on renal cell carcinoma may be due to the diluting effect of carcinogens by a high total fluid intake. We assessed the association between intakes of total fluids and of specific beverages on the risk of renal cell carcinoma in a large prospective cohort of UK women.Methods:Information on beverage consumption was obtained from a questionnaire sent 3 years after recruitment into the Million Women Study. Cox proportional hazards models were used to estimate relative risks (RRs) and 95% confidence intervals (CIs) for renal cell carcinoma associated with beverage consumption adjusted for age, region of residence, socioeconomic status, smoking, and body mass index.Results:After an average of 5.2 years of follow-up, 588 cases of renal cell carcinoma were identified among 779 369 women. While alcohol intake was associated with a reduced risk of renal cell carcinoma (RR for 2 vs 1 drink per day: 0.76; 95% CI: 0.61-0.96; P for trend0.02), there was no association with total fluid intake (RR for 12 vs 7 drinks per day: 1.15; 95% CI: 0.91-1.45; P for trend0.3) or with intakes of specific beverages.Conclusions:The apparent protective effect of alcohol on the risk of renal cell carcinoma is unlikely to be related to a high fluid intake. © 2011 Cancer Research UK All rights reserved

Socio-economic class, rurality and risk of cutaneous melanoma by site and gender in Sweden

<p>Abstract</p> <p>Background</p> <p>Cutaneous melanoma (CM) is a cancer usually associated with high socio-economic level in the literature. Few studies have, however, assessed this relationship by gender and site or the association between CM and rurality.</p> <p>Methods</p> <p>A major-sized historical occupational Swedish cohort comprising 2,992,166 workers was used to estimate relative risk of cutaneous melanoma, broken down by gender and anatomical site, for occupational sectors (as a proxy of socio-economic class) and rurality. To this end, Poisson models were fitted for each site in men and women, including occupational sector and town size, with adjustment for age, period of diagnosis and geographical area as possible confounding factors.</p> <p>Results</p> <p>White collar workers presented a marked increased of risk in men in all melanoma cases, as well as in trunk, upper and lower limbs. This pattern was less clear for women, in which some heterogeneity appeared, as low risks in lower socioeconomic sectors in trunk, or risk excesses in white collar workers in lower limbs did not achieve statistical significance. Males also showed significant differences in risk by rural/urban distribution, but in women this association was limited to CM of lower limb. Risk of CM of head/neck did not vary by occupational sector or town size, thus depicting a specific epidemiological profile, which proved common to both sexes.</p> <p>Conclusion</p> <p>While differences in risk between men and women could suggest greater homogeneity in UV-exposure behaviour among women, the uniform risk pattern in head and neck melanoma, present in both sexes, might support the coexistence of different aetiological pathways, related to anatomical site.</p