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Search Results: 1 - 10 of 6353 matches for " Gary King "
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EI: A Program for Ecological Inference
Gary King
Journal of Statistical Software , 2004,
The Effects of International Monetary Fund Loans on Health Outcomes
Megan Murray ,Gary King
PLOS Medicine , 2008, DOI: 10.1371/journal.pmed.0050162
Measuring total health inequality: adding individual variation to group-level differences
Emmanuela Gakidou, Gary King
International Journal for Equity in Health , 2002, DOI: 10.1186/1475-9276-1-3
Abstract: We develop a measure of total health inequality – encompassing all inequalities among people in a society, including variation between and within groups – by adapting a beta-binomial regression model. We apply it to children under age two in 50 low- and middle-income countries. Our method has been adopted by the World Health Organization and is being implemented in surveys around the world; preliminary estimates have appeared in the World Health Report (2000).Countries with similar average child mortality differ considerably in total health inequality. Liberia and Mozambique have the largest inequalities in child survival, while Colombia, the Philippines and Kazakhstan have the lowest levels among the countries measured.Total health inequality estimates should be routinely reported alongside average levels of health in populations and groups, as they reveal important policy-related information not otherwise knowable. This approach enables meaningful comparisons of inequality across countries and future analyses of the determinants of inequality.The distribution of health, or health inequality, has become prominent on global policy agendas as researchers have come to regard average health status as an inadequate summary of a country's health performance [1,2]. Almost all health inequality studies have in fact documented differences in average health status across groups of people. Those with an economic focus have measured differences in average health status across income groups [3,4]. Researchers with a sociological focus have examined inequalities in average health status among social classes [5,6]. and those with a political focus have looked at how political structure is related to differences in the average level of health [7]. Other scholars have focused on differences in average health status among racial or ethnic groups or by educational attainment or occupation [8-10]. And most researchers consider differences across political entities such as countries or
The future of death in America
Samir Soneji,Gary King
Demographic Research , 2011,
Abstract: Population mortality forecasts are widely used for allocating public health expenditures, setting research priorities, and evaluating the viability of public pensions, private pensions, and health care financing systems. Although we know a great deal about patterns in and causes of mortality, most forecasts are still based on simple linear extrapolations that ignore covariates and other prior information. We adapt a Bayesian hierarchical forecasting model capable of including more known health and demographic information than has previously been possible. This leads to the first age- and sex-specific forecasts of American mortality that simultaneously incorporate, in a formal statistical model, the effects of the recent rapid increase in obesity, the steady decline in tobacco consumption, and the well known patterns of smooth mortality age profiles and time trends. Formally including new information in forecasts can matter a great deal. For example, we estimate an increase in male life expectancy at birth from 76.2 years in 2010 to 79.9 years in 2030, which is 1.8 years greater than the U.S. Social Security Administration projection and 1.5 years more than U.S. Census projection. For females, we estimate more modest gains in life expectancy at birth over the next twenty years from 80.5 years to 81.9 years, which is virtually identical to the Social Security Administration projection and 2.0 years less than U.S. Census projections. We show that these patterns are also likely to greatly affect the aging American population structure. We offer an easy-to-use approach so that researchers can include other sources of information and potentially improve on our forecasts too.
Verbal Autopsy Methods with Multiple Causes of Death
Gary King,Ying Lu
Statistics , 2008, DOI: 10.1214/07-STS247
Abstract: Verbal autopsy procedures are widely used for estimating cause-specific mortality in areas without medical death certification. Data on symptoms reported by caregivers along with the cause of death are collected from a medical facility, and the cause-of-death distribution is estimated in the population where only symptom data are available. Current approaches analyze only one cause at a time, involve assumptions judged difficult or impossible to satisfy, and require expensive, time-consuming, or unreliable physician reviews, expert algorithms, or parametric statistical models. By generalizing current approaches to analyze multiple causes, we show how most of the difficult assumptions underlying existing methods can be dropped. These generalizations also make physician review, expert algorithms and parametric statistical assumptions unnecessary. With theoretical results, and empirical analyses in data from China and Tanzania, we illustrate the accuracy of this approach. While no method of analyzing verbal autopsy data, including the more computationally intensive approach offered here, can give accurate estimates in all circumstances, the procedure offered is conceptually simpler, less expensive, more general, as or more replicable, and easier to use in practice than existing approaches. We also show how our focus on estimating aggregate proportions, which are the quantities of primary interest in verbal autopsy studies, may also greatly reduce the assumptions necessary for, and thus improve the performance of, many individual classifiers in this and other areas. As a companion to this paper, we also offer easy-to-use software that implements the methods discussed herein.
Designing verbal autopsy studies
Gary King, Ying Lu, Kenji Shibuya
Population Health Metrics , 2010, DOI: 10.1186/1478-7954-8-19
Abstract: We introduce methods, simulations, and interpretations that can improve the design of automated, data-derived estimates of CSMRs, building on a new approach by King and Lu (2008). Our results generate advice for choosing symptom questions and sample sizes that is easier to satisfy than existing practices. For example, most prior effort has been devoted to searching for symptoms with high sensitivity and specificity, which has rarely if ever succeeded with multiple causes of death. In contrast, our approach makes this search irrelevant because it can produce unbiased estimates even with symptoms that have very low sensitivity and specificity. In addition, the new method is optimized for survey questions caretakers can easily answer rather than questions physicians would ask themselves. We also offer an automated method of weeding out biased symptom questions and advice on how to choose the number of causes of death, symptom questions to ask, and observations to collect, among others.With the advice offered here, researchers should be able to design verbal autopsy surveys and conduct analyses with greatly reduced statistical biases and research costs.Estimates of cause-specific morality rates (CSMRs) are urgently needed for many research and public policy goals, but high quality death registration data exists in only 23 of 192 countries [1]. Indeed, more than two-thirds of deaths worldwide occur without any medical death certification [2]. In response, researchers are increasingly turning to verbal autopsy analyses, a technique "growing in importance" [3]. Verbal autopsy studies are now widely used in the developing world to estimate CSMRs, disease surveillance, and sample registration [4-6], as well as risk factors, infectious disease outbreaks, and the effects of public health interventions [7-9].The idea of verbal autopsy analyses is to ask (usually around 10-100) questions about symptoms (including some signs and other indicators) of the caretakers of randomly sel
Deaths from heart failure: using coarsened exact matching to correct cause-of-death statistics
Gretchen A Stevens, Gary King, Kenji Shibuya
Population Health Metrics , 2010, DOI: 10.1186/1478-7954-8-6
Abstract: We propose that coarsened exact matching be used to infer the underlying causes of death where only the mode of death is known. We focus on the case of heart failure in US, Mexican, and Brazilian death records.Redistribution algorithms derived using this method assign the largest proportion of heart failure deaths to ischemic heart disease in all three countries (53%, 26%, and 22% respectively), with larger proportions assigned to hypertensive heart disease and diabetes in Mexico and Brazil (16% and 23% vs. 7% for hypertensive heart disease, and 13% and 9% vs. 6% for diabetes). Reassigning these heart failure deaths increases the US ischemic heart disease mortality rate by 6%.The frequency with which physicians list heart failure in the causal chain for various underlying causes of death allows for inference about how physicians use heart failure on the death certificate in different settings. This easy-to-use method has the potential to reduce bias and increase comparability in cause-of-death data, thereby improving the public health utility of death records.Effective national and international public health planning and policymaking requires accurate information on population health, especially about deaths and their causes. Death statistics can provide evidence to evaluate health reforms and to identify poorly served populations or diseases. In countries with complete or nearly complete vital registration, including most high-income and some middle-income countries, death statistics are compiled from death certificates. However, inaccurately or incompletely completed death certificates may compromise cause-of-death data in these countries. Physician practice in filling death certificates may vary over place and time [1]. This may result in death rates calculated from death certificate data that are biased or are not comparable across regions, countries, or over time. Inconsistent cause-of-death assignment among cardiovascular causes of death is particularly impor
anchors: Software for Anchoring Vignette Data
Jonathan Wand,Gary King,Olivia Lau
Journal of Statistical Software , 2011,
Abstract: When respondents use the ordinal response categories of standard survey questions in different ways, the validity of analyses based on the resulting data can be biased. Anchoring vignettes is a survey design technique intended to correct for some of these problems. The anchors package in R includes methods for evaluating and choosing anchoring vignettes, and for analyzing the resulting data.
cem: Software for Coarsened Exact Matching
Stefano M. Iacus,Gary King,Giuseppe Porro
Journal of Statistical Software , 2009,
Abstract: This program is designed to improve causal inference via a method of matching that iswidely applicable in observational data and easy to understand and use (if you understandhow to draw a histogram, you will understand this method). The program implements thecoarsened exact matching (CEM) algorithm, described below. CEM may be used aloneor in combination with any existing matching method. This algorithm, and its statisticalproperties, are described in Iacus, King, and Porro (2008).
Amelia II: A Program for Missing Data
James Honaker,Gary King,Matthew Blackwell
Journal of Statistical Software , 2011,
Abstract: Amelia II is a complete R package for multiple imputation of missing data. The package implements a new expectation-maximization with bootstrapping algorithm that works faster, with larger numbers of variables, and is far easier to use, than various Markov chainMonte Carlo approaches, but gives essentially the same answers. The program also improves imputation models by allowing researchers to put Bayesian priors on individual cell values, thereby including a great deal of potentially valuable and extensive information. Italso includes features to accurately impute cross-sectional datasets, individual time series, or sets of time series for different cross-sections. A full set of graphical diagnostics are also available. The program is easy to use, and the simplicity of the algorithm makes itfar more robust; both a simple command line and extensive graphical user interface are included.
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