496 research outputs found
Quantifying statistical uncertainty in the attribution of human influence on severe weather
Event attribution in the context of climate change seeks to understand the
role of anthropogenic greenhouse gas emissions on extreme weather events,
either specific events or classes of events. A common approach to event
attribution uses climate model output under factual (real-world) and
counterfactual (world that might have been without anthropogenic greenhouse gas
emissions) scenarios to estimate the probabilities of the event of interest
under the two scenarios. Event attribution is then quantified by the ratio of
the two probabilities. While this approach has been applied many times in the
last 15 years, the statistical techniques used to estimate the risk ratio based
on climate model ensembles have not drawn on the full set of methods available
in the statistical literature and have in some cases used and interpreted the
bootstrap method in non-standard ways. We present a precise frequentist
statistical framework for quantifying the effect of sampling uncertainty on
estimation of the risk ratio, propose the use of statistical methods that are
new to event attribution, and evaluate a variety of methods using statistical
simulations. We conclude that existing statistical methods not yet in use for
event attribution have several advantages over the widely-used bootstrap,
including better statistical performance in repeated samples and robustness to
small estimated probabilities. Software for using the methods is available
through the climextRemes package available for R or Python. While we focus on
frequentist statistical methods, Bayesian methods are likely to be particularly
useful when considering sources of uncertainty beyond sampling uncertainty.Comment: 41 pages, 11 figures, 1 tabl
Semiparametric Bayesian Density Estimation with Disparate Data Sources: A Meta-Analysis of Global Childhood Undernutrition
Undernutrition, resulting in restricted growth, and quantified here using
height-for-age z-scores, is an important contributor to childhood morbidity and
mortality. Since all levels of mild, moderate and severe undernutrition are of
clinical and public health importance, it is of interest to estimate the shape
of the z-scores' distributions.
We present a finite normal mixture model that uses data on 4.3 million
children to make annual country-specific estimates of these distributions for
under-5-year-old children in the world's 141 low- and middle-income countries
between 1985 and 2011. We incorporate both individual-level data when
available, as well as aggregated summary statistics from studies whose
individual-level data could not be obtained. We place a hierarchical Bayesian
probit stick-breaking model on the mixture weights. The model allows for
nonlinear changes in time, and it borrows strength in time, in covariates, and
within and across regional country clusters to make estimates where data are
uncertain, sparse, or missing.
This work addresses three important problems that often arise in the fields
of public health surveillance and global health monitoring. First, data are
always incomplete. Second, different data sources commonly use different
reporting metrics. Last, distributions, and especially their tails, are often
of substantive interest.Comment: 41 total pages, 6 figures, 1 tabl
Quantifying the effect of interannual ocean variability on the attribution of extreme climate events to human influence
In recent years, the climate change research community has become highly
interested in describing the anthropogenic influence on extreme weather events,
commonly termed "event attribution." Limitations in the observational record
and in computational resources motivate the use of uncoupled,
atmosphere/land-only climate models with prescribed ocean conditions run over a
short period, leading up to and including an event of interest. In this
approach, large ensembles of high-resolution simulations can be generated under
factual observed conditions and counterfactual conditions that might have been
observed in the absence of human interference; these can be used to estimate
the change in probability of the given event due to anthropogenic influence.
However, using a prescribed ocean state ignores the possibility that estimates
of attributable risk might be a function of the ocean state. Thus, the
uncertainty in attributable risk is likely underestimated, implying an
over-confidence in anthropogenic influence.
In this work, we estimate the year-to-year variability in calculations of the
anthropogenic contribution to extreme weather based on large ensembles of
atmospheric model simulations. Our results both quantify the magnitude of
year-to-year variability and categorize the degree to which conclusions of
attributable risk are qualitatively affected. The methodology is illustrated by
exploring extreme temperature and precipitation events for the northwest coast
of South America and northern-central Siberia; we also provides results for
regions around the globe. While it remains preferable to perform a full
multi-year analysis, the results presented here can serve as an indication of
where and when attribution researchers should be concerned about the use of
atmosphere-only simulations
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)
Childrenās height and weight in rural and urban populations in low-income and middle-income countries: a systematic analysis of population-representative data
Background Urban living aff ects childrenās nutrition and growth, which are determinants of their survival, cognitive
development, and lifelong health. Little is known about urbanārural diff erences in childrenās height and weight, and
how these diff erences have changed over time. We aimed to investigate trends in childrenās height and weight in rural
and urban settings in low-income and middle-income countries, and to assess changes in the urbanārural diff erentials
in height and weight over time.
Methods We used comprehensive population-based data and a Bayesian hierarchical mixture model to estimate
trends in childrenās height-for-age and weight-for-age Z scores by rural and urban place of residence, and changes in
urbanārural diff erentials in height and weight Z scores, for 141 low-income and middle-income countries between
1985 and 2011. We also estimated the contribution of changes in rural and urban height and weight, and that of
urbanisation, to the regional trends in these outcomes.
Findings Urban children are taller and heavier than their rural counterparts in almost all low-income and middleincome
countries. The urbanārural diff erential is largest in Andean and central Latin America (eg, Peru, Honduras,
Bolivia, and Guatemala); in some African countries such as Niger, Burundi, and Burkina Faso; and in Vietnam and
China. It is smallest in southern and tropical Latin America (eg, Chile and Brazil). Urban children in China, Chile,
and Jamaica are the tallest in low-income and middle-income countries, and children in rural areas of Burundi,
Guatemala, and Niger the shortest, with the tallest and shortest more than 10 cm apart at age 5 years. The heaviest
children live in cities in Georgia, Chile, and China, and the most underweight in rural areas of Timor-Leste, India,
Niger, and Bangladesh. Between 1985 and 2011, the urban advantage in height fell in southern and tropical Latin
America and south Asia, but changed little or not at all in most other regions. The urbanārural weight diff erential also decreased in southern and tropical Latin America, but increased in east and southeast Asia and worldwide, because weight gain of urban children outpaced that of rural children.Interpretation Further improvement of child nutrition will require improved access to a stable and aff ordable food supply and health care for both rural and urban children, and closing of the the urbanārural gap in nutritional status
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