Measurement error caused by spatial misalignment in environmental epidemiology

Copyright @ 2009 Gryparis et al - Published by Oxford University Press.In many environmental epidemiology studies, the locations and/or times of exposure measurements and health assessments do not match. In such settings, health effects analyses often use the predictions from an exposure model as a covariate in a regression model. Such exposure predictions contain some measurement error as the predicted values do not equal the true exposures. We provide a framework for spatial measurement error modeling, showing that smoothing induces a Berkson-type measurement error with nondiagonal error structure. From this viewpoint, we review the existing approaches to estimation in a linear regression health model, including direct use of the spatial predictions and exposure simulation, and explore some modified approaches, including Bayesian models and out-of-sample regression calibration, motivated by measurement error principles. We then extend this work to the generalized linear model framework for health outcomes. Based on analytical considerations and simulation results, we compare the performance of all these approaches under several spatial models for exposure. Our comparisons underscore several important points. First, exposure simulation can perform very poorly under certain realistic scenarios. Second, the relative performance of the different methods depends on the nature of the underlying exposure surface. Third, traditional measurement error concepts can help to explain the relative practical performance of the different methods. We apply the methods to data on the association between levels of particulate matter and birth weight in the greater Boston area.This research was supported by NIEHS grants ES012044 (AG, BAC), ES009825 (JS, BAC), ES007142 (CJP), and ES000002 (CJP), and EPA grant R-832416 (JS, BAC)

Synthesis of perfluoroalkylene dianilines

The objective of this contrast was to optimize and scale-up the synthesis of 2,2-bis(4-aminophenyl)-hexafluoropropane and 1,3-bis(4-aminophenyl)hexafluoropropane, as well as to explore avenues to other perfluoroalkyl-bridged dianilines. Routes other than Friedel-Crafts reaction leading to 2,2-bis(4-aminophenyl)hexafluoropropane were investigated. The processes utilizing bisphenol-AF were all unsuccessful; reactions aimed at the production of 4-(hexafluoro-2-halo-isopropyl)aniline from the hydroxyl intermediate failed to yield the desired products. Tailoring the conditions of the Friedel-Crafts reaction of 4-(hexafluoro-2-hydroxyisopropyl)aniline, aniline, and aluminum chloride by using hydrochloride salts and selecting optimum reagent ratios, reaction times, and temperature resulted in approx. 20% yield of pure crystallized 2,2-bis(4-aminophenyl)hexafluoropropane in 0.2 mole reaction batches. Yields up to approx. 40% were realized in small, approx. 0.01 mole, batches. The synthesis of 1,3-bis(4-aminophenyl)hexafluoropropane starting with perfluoroglutarimidine was reinvestigated. The yield of the 4-step reaction sequence giving 1,3-bis(4-acetamidophenyl)hexafluoropropane was raised to 44%. The yield of the subsequent hydrolysis process was improved by a factor of approx. 2. Approaches to prepare other perfluoroalkyl-bridged dianilines were unsuccessful. Reactions reported to proceed readily with trifluoromethyl substituents failed when longer chain perfluoroalkyl groups were employed

Rising rural body-mass index is the main driver of the global obesity epidemic in adults [Letter]

Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities1,2. This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity3,4,5,6. Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017—and more than 80% in some low- and middle-income regions—was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing—and in some countries reversal—of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories

Practical large-scale spatio-temporal modeling of particulate matter concentrations

The last two decades have seen intense scientific and regulatory interest in the health effects of particulate matter (PM). Influential epidemiological studies that characterize chronic exposure of individuals rely on monitoring data that are sparse in space and time, so they often assign the same exposure to participants in large geographic areas and across time. We estimate monthly PM during 1988--2002 in a large spatial domain for use in studying health effects in the Nurses' Health Study. We develop a conceptually simple spatio-temporal model that uses a rich set of covariates. The model is used to estimate concentrations of $PM_{10}$ for the full time period and $PM_{2.5}$ for a subset of the period. For the earlier part of the period, 1988--1998, few $PM_{2.5}$ monitors were operating, so we develop a simple extension to the model that represents $PM_{2.5}$ conditionally on $PM_{10}$ model predictions. In the epidemiological analysis, model predictions of $PM_{10}$ are more strongly associated with health effects than when using simpler approaches to estimate exposure. Our modeling approach supports the application in estimating both fine-scale and large-scale spatial heterogeneity and capturing space--time interaction through the use of monthly-varying spatial surfaces. At the same time, the model is computationally feasible, implementable with standard software, and readily understandable to the scientific audience. Despite simplifying assumptions, the model has good predictive performance and uncertainty characterization.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS204 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Parallelizing Gaussian Process Calculations in R

We consider parallel computation for Gaussian process calculations to overcome computational and memory constraints on the size of datasets that can be analyzed. Using a hybrid parallelization approach that uses both threading (shared memory) and message-passing (distributed memory), we implement the core linear algebra operations used in spatial statistics and Gaussian process regression in an R package called bigGP that relies on C and MPI. The approach divides the covariance matrix into blocks such that the computational load is balanced across processes while communication between processes is limited. The package provides an API enabling R programmers to implement Gaussian process-based methods by using the distributed linear algebra operations without any C or MPI coding. We illustrate the approach and software by analyzing an astrophysics dataset with n = 67, 275 observations

Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors

Background: Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations. Methods: We developed generalized additive mixed models that describe regional and small-scale spatial and temporal gradients (and corresponding uncertainties) in monthly mass concentrations of fine (PM2.5), inhalable (PM10), and coarse mode particle mass (PM2.5–10) for the conterminous United States (U.S.). These models expand our previously developed models for the Northeastern and Midwestern U.S. by virtue of their larger spatial domain, their inclusion of an additional 5 years of PM data to develop predictions through 2007, and their use of refined geographic covariates for population density and point-source PM emissions. Covariate selection and model validation were performed using 10-fold cross-validation (CV). Results: The PM2.5 models had high predictive accuracy (CV R2=0.77 for both 1988–1998 and 1999–2007). While model performance remained strong, the predictive ability of models for PM10 (CV R2=0.58 for both 1988–1998 and 1999–2007) and PM2.5–10 (CV R2=0.46 and 0.52 for 1988–1998 and 1999–2007, respectively) was somewhat lower. Regional variation was found in the effects of geographic and meteorological covariates. Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region (CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999–2007). Conclusions: Our models provide estimates of monthly-average outdoor concentrations of PM2.5, PM10, and PM2.5–10 with high spatial resolution and low bias. Thus, these models are suitable for estimating chronic exposures of populations living in the conterminous U.S. from 1988 to 2007

Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries: pooled analysis of 2,086 population-based studies with 65 million participants

Background Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents. Methods For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence. Findings We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls. Interpretation The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks

National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants.

Excess bodyweight is a major public health concern. However, few worldwide comparative analyses of long-term trends of body-mass index (BMI) have been done, and none have used recent national health examination surveys. We estimated worldwide trends in population mean BMI. We estimated trends and their uncertainties of mean BMI for adults 20 years and older in 199 countries and territories. We obtained data from published and unpublished health examination surveys and epidemiological studies (960 country-years and 9·1 million participants). For each sex, we used a Bayesian hierarchical model to estimate mean BMI by age, country, and year, accounting for whether a study was nationally representative. Between 1980 and 2008, mean BMI worldwide increased by 0·4 kg/m(2) per decade (95% uncertainty interval 0·2-0·6, posterior probability of being a true increase >0·999) for men and 0·5 kg/m(2) per decade (0·3-0·7, posterior probability >0·999) for women. National BMI change for women ranged from non-significant decreases in 19 countries to increases of more than 2·0 kg/m(2) per decade (posterior probabilities >0·99) in nine countries in Oceania. Male BMI increased in all but eight countries, by more than 2 kg/m(2) per decade in Nauru and Cook Islands (posterior probabilities >0·999). Male and female BMIs in 2008 were highest in some Oceania countries, reaching 33·9 kg/m(2) (32·8-35·0) for men and 35·0 kg/m(2) (33·6-36·3) for women in Nauru. Female BMI was lowest in Bangladesh (20·5 kg/m(2), 19·8-21·3) and male BMI in Democratic Republic of the Congo 19·9 kg/m(2) (18·2-21·5), with BMI less than 21·5 kg/m(2) for both sexes in a few countries in sub-Saharan Africa, and east, south, and southeast Asia. The USA had the highest BMI of high-income countries. In 2008, an estimated 1·46 billion adults (1·41-1·51 billion) worldwide had BMI of 25 kg/m(2) or greater, of these 205 million men (193-217 million) and 297 million women (280-315 million) were obese. Globally, mean BMI has increased since 1980. The trends since 1980, and mean population BMI in 2008, varied substantially between nations. Interventions and policies that can curb or reverse the increase, and mitigate the health effects of high BMI by targeting its metabolic mediators, are needed in most countries. Bill & Melinda Gates Foundation and WHO