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We analyzed spatial and temporal patterns in temperature extremes from 31 stations located throughout Iran for the period 1961 to 2010. As with many other parts of the globe, we found that the number of days (a) with high maximum temperatures was rising, (b) with high minimum temperatures was rising, and (c) with low minimum temperatures was declining; all of these trends were statistically significant at the 0.05 level of confidence. Population records from 1956 to 2011 at the station locations allowed us to reveal that the rate of human population growth was positively related to the increase in the number of days with high maximum temperatures and negatively related to days with low maximum temperatures. Our research shows a number of identifiable anthropogenic signals in the temperature records from Iran, but unlike most other studies, the signals are stronger with indices related to maximum, not minimum, temperatures. [ABSTRACT FROM AUTHOR]

Background Income inequality has been associated with worse health outcomes in several but not all studies. The heterogeneity across studies may be explained by the variations in the size of area or population over which income inequality was evaluated. Moreover, the studies above a certain inequality threshold, conducted more recently, and incorporating a time lag may have stronger associations between income inequality and health. The authors investigated if the strength of the association between income inequality and health was altered by these factors. Methods The authors conducted a multivariate meta-regression analysis using nine multilevel cohort studies on income inequality and mortality and 14 multilevel cross-sectional studies on income inequality and self-rated health. Results Among cross-sectional studies, studies evaluating country-level inequality (average population>24 million) were more likely to show a stronger association between income inequality and poor health compared with those evaluating income inequality within small average populations (<820 000). There were no significant differences in the effect size of inequality-health association relating to the differences in the population size within a country across which income inequality was evaluated in both cross-sectional and cohort studies. The authors found that the threshold effects, period effects and lag effects were independent of the population size. Conclusions Income inequality at the country level may have stronger adverse contextual effects on health than inequality in smaller areas, perhaps by best reflecting social stratification in a society. Furthermore, we found that threshold, period and lag effects were independent of area unit for evaluating inequality, which may have important policy implications. [ABSTRACT FROM AUTHOR]

Family-based designs protect analyses of genetic effects from bias that is due to population stratification. Investigators have assumed that this robustness extends to assessments of gene-environment interaction. Unfortunately, this assumption fails for the common scenario in which the genotyped variant is related to risk through linkage with a causative allele. Bias also plagues other methods of assessment of gene-environment interaction. When testing against multiplicative joint effects, the case-only design offers excellent power, but it is invalid if genotype and exposure are correlated in the population. The authors describe 4 mechanisms that produce genotype-exposure dependence: exposure-related genetic population stratification, effects of family history on behavior, genotype effects on exposure, and selective attrition. They propose a sibling-augmented case-only (SACO) design that protects against the former 2 mechanisms and is therefore valid for studying young-onset disease in which genotype does not influence exposure. A SACO design allows the ascertainment of genotype and exposure for cases and exposure for 1 or more unaffected siblings selected randomly. Conditional logistic regression permits assessment of exposure effects and gene-environment interactions. Via simulations, the authors compare the likelihood-based inference on interactions using the SACO design with that based on other designs. They also show that robust analyses of interactions using tetrads or disease-discordant sibling pairs are equivalent to analyses using the SACO design. [ABSTRACT FROM PUBLISHER]

Traditionally, people have inhabited places with ready access to fresh water. Today, over 50% of the global population lives in urban areas, and water can be directed via tens of kilometres of pipelines. Still, however, a large part of the world's population is directly dependent on access to natural freshwater sources. So how are inhabited places related to the location of freshwater bodies today? We present a high-resolution global analysis of how close present-day populations live to surface freshwater. We aim to increase the understanding of the relationship between inhabited places, distance to surface freshwater bodies, and climatic characteristics in different climate zones and administrative regions. Our results show that over 50% of the world's population lives closer than 3 km to a surface freshwater body, and only 10% of the population lives further than 10 km away. There are, however, remarkable differences between administrative regions and climatic zones. Populations in Australia, Asia, and Europe live closest to water. Although populations in arid zones live furthest away from freshwater bodies in absolute terms, relatively speaking they live closest to water considering the limited number of freshwater bodies in those areas. Population distributions in arid zones show statistically significant relationships with a combination of climatic factors and distance to water, whilst in other zones there is no statistically significant relationship with distance to water. Global studies on development and climate adaptation can benefit from an improved understanding of these relationships between human populations and the distance to fresh water. [ABSTRACT FROM AUTHOR]

Background: Endometrial cancer is the most common gynaecological cancer in the western world, the incidence increasing in the United Kingdom by over 40% since 1993. Two types of endometrial cancer exist - oestrogen-dependent type 1 with good prognosis and non-oestrogen-dependent type 2 with poor prognosis. The histopathological distribution of the increase in endometrial cancer is unknown. This study investigates the observed incidence trends of the two types, the age, stage, and socioeconomic distribution of this increase and survival outcome.Methods: Data were analysed from 6867 women with endometrial cancer registered between 1994 and 2006, at a UK population-based cancer registry.Results: Increased endometrial cancer incidence is confined to type 1 cancers with a significant increase in age standardised incidence rate (ASR) from 12.0 per 100,000 (confidence interval (CI) 10.7-13.2) in 1994 to 16.3 per 100,000 (CI 14.9-17.7), P<0.001 in 2006, while ASR of type 2 cancer changed from 2.5 per 100,000 (CI 2.0-3.1) in 1994 to 2.2 per 100,000 (CI 1.7-2.7) in 2006, which was not statistically significant P>0.05. Increase in type 1 cancer is most marked in age groups 60-69 years (P<0.001) and 70-79 years (P<0.001) and distributed equally among socioeconomic quintiles. While outcome for type 1 cancer has improved, 1-year survival in type 2 cancer is unchanged from 73.1% in 1994 to 74.3%, P=0.089 and 5-year survival decreased from 55.1% to 40.9%, P=0.001.Conclusion: Increased incidence in endometrial cancer is confined to type 1 cancers, seen most in the 60-79 age groups and across all socioeconomic quintiles. Survival in type 2 cancer has decreased significantly. Urgent research is needed to investigate prevention strategies in type 1 and improve therapy in type 2 cancers. [ABSTRACT FROM AUTHOR]

Background: Greater epidemiologic understanding of the relationships among fetal-infant mortality and its prognostic factors, including birthweight, could have vast public health implications. A key step toward that understanding is a realistic and tractable framework for analyzing birthweight distributions and fetal-infant mortality. The present paper is the second of a two-part series that introduces such a framework. Methods: We propose estimating birthweight-specific mortality within each component of a normal mixture model representing a birthweight distribution, the number of components having been determined from the data rather than fixed a priori. Results: We address a number of methodological issues related to our proposal, including the construction of confidence intervals for mortality risk at any given birthweight within a component, for odds ratios comparing mortality within two different components from the same population, and for odds ratios comparing mortality within analogous components from two different populations. As an illustration we find that, for a population of white singleton infants, the odds of mortality at 3000 g are an estimated 4.15 times as large in component 2 of a 4-component normal mixture model as in component 4 (95% confidence interval, 2.04 to 8.43). We also outline an extension of our framework through which covariates could be probabilistically related to mixture components. This extension might allow the assertion of approximate correspondences between mixture components and identifiable subpopulations. Conclusions: The framework developed in this paper does not require infants from compromised pregnancies to share a common birthweight-specific mortality curve, much less assume the existence of an interval of birthweights over which all infants have the same curve. Hence, the present framework can reveal heterogeneity in mortality that is undetectable via a contaminated normal model or a 2-component normal mixture model. [ABSTRACT FROM AUTHOR]

Frequentist confidence intervals for population ranks and their statistical justifications are not well established, even though there is a great need for such procedures in practice. How do we assign confidence bounds for the ranks of health care facilities, schools, and financial institutions based on data that do not clearly separate the performance of different entities apart? The commonly used bootstrap-based frequentist confidence intervals and Bayesian intervals for population ranks may not achieve the intended coverage probability in the frequentist sense, especially in the presence of unknown ties or "near ties" among the populations to be ranked. Given random samples from k populations, we propose confidence bounds for population ranking parameters and develop rigorous frequentist theory and nonstandard bootstrap inference for population ranks, which allow ties and near ties. In the process, a notion of modified population rank is introduced that appears quite suitable for dealing with the population ranking problem. The proposed methodology and theoretical results are illustrated through simulations and a real dataset from a health research study involving 79 Veterans Health Administration (VHA) facilities. The results are extended to general risk adjustment models. [ABSTRACT FROM AUTHOR]

Intervalles de confiance à rétrécisseur pour l'espérance d'une loi normale: utilisation d'une valeur initiale au profit d'une meilleure efficacité Si l'espérance inconnue d'une population univariée est assez proche d'une valeur initiale, l'erreur quadratique moyenne d'un bon estimateur à rétrécisseur sera plus faible que celle de la moyenne échantillonnale. Ce principe est connu depuis longtemps, mais il ne semble pas avoir été appliqué à l'estimation par intervalle. L'auteur présente des intervalles de confiance 'à rétrécisseur' bilatéraux à 95% et 99% pour l'espérance d'une loi normale. Ces intervalles sont plus courts que l'intervalle habituel fondé sur la loi de Student dès que l'espérance de la population se situe dans un 'intervalle effectif.' On peut observer une réduction de 20% de la longueur moyenne de l'intervalle quand l'espérance de la population est assez proche de la valeur initiale. L'auteur montre aussi comment certains estimateurs ponctuels à rétrécisseur d'une espérance univariée quelconque peuvent être modifiés de façon à élargir l'intervalle effectif. [ABSTRACT FROM AUTHOR]

Objective To investigate the effect of immigration on life expectancy in Australia for the period from 1981 to 2003, and to compare life expectancy of the Australian-born population with that of other countries in the Organisation for Economic Cooperation and Development (OECD). Methods Standard life-table methods using age-specific all-cause mortality and population data from 1981 to 2003 were used to calculate life expectancy at birth ( e0) for the total Australian population (including migrants) and for people born in Australia (excluding migrants). Mean differences in life expectancy for each sex were compared using paired t-tests. Rankings of life expectancy among OECD countries were reassessed, and rank changes measured using the Wilcoxon signed rank test. Findings Life expectancy of males and females was significantly lower in the Australian-born group than in the total Australian population. During 1981 to 2003, there was a mean difference in life expectancy of 0.41 years (95% confidence interval, CI: 0.37-0.44; t17 = 27.0; P < 0.0001) in males and 0.29 years (95% CI: 0.26-0.31; t17 = 27.6; P < 0.0001) in females between the Australian-born and the total population. After excluding migrant groups, Australia no longer ranked among the top five OECD countries with the highest life expectancy in the two most recent years examined. Conclusion While Australia has one of the highest life expectancies in the industrialized world, this is partly attributable to immigration of populations with low rates of mortality. This effect needs to be considered in international comparative assessments of mortality levels. [ABSTRACT FROM AUTHOR]

The annual percent change (APC) is often used to measure trends in disease and mortality rates, and a common estimator of this parameter uses a linear model on the log of the age-standardized rates. Under the assumption of linearity on the log scale, which is equivalent to a constant change assumption, APC can be equivalently defined in three ways as transformations of either (1) the slope of the line that runs through the log of each rate, (2) the ratio of the last rate to the first rate in the series, or (3) the geometric mean of the proportional changes in the rates over the series. When the constant change assumption fails then the first definition cannot be applied as is, while the second and third definitions unambiguously define the same parameter regardless of whether the assumption holds. We call this parameter the percent change annualized (PCA) and propose two new estimators of it. The first, the two-point estimator, uses only the first and last rates, assuming nothing about the rates in between. This estimator requires fewer assumptions and is asymptotically unbiased as the size of the population gets large, but has more variability since it uses no information from the middle rates. The second estimator is an adaptive one and equals the linear model estimator with a high probability when the rates are not significantly different from linear on the log scale, but includes fewer points if there are significant departures from that linearity. For the two-point estimator we can use confidence intervals previously developed for ratios of directly standardized rates. For the adaptive estimator, we show through simulation that the bootstrap confidence intervals give appropriate coverage. [ABSTRACT FROM AUTHOR]

Abstract: Dispersal has important implications on an individual, population and metapopulation level. Dispersal rates, however, are difficult to measure. In this paper we introduce a method that uses census data, i.e. repeated counts of number of individuals per location and point in time, to estimate dispersal rates. The rationale underlying this method is that local stochastic disturbances which dissipate in subdivided populations create a covariance structure, the details of which depend on how strongly the local populations are coupled. This covariance structure can be used to estimate dispersal rates. We describe this process using a stochastic model for growth and dispersal which explicitly accounts for the geometry of the patchy population. A regression of the covariance structure of this model is then used to infer the growth rate near equilibrium and the dispersal rate. We study the distribution of the estimated parameters and obtain confidence intervals using a bootstrap analysis and a Monte Carlo technique. We study how the confidence intervals depend on the model’s parameters, the robustness of the estimating scheme, and discuss the applicability of our method. [Copyright &y& Elsevier]

Background: To examine why people lack a regular source of ambulatory care (RSAC) and explore whether this commonly used access measure accurately identifies population subgroups at risk for barriers to continuity care. Methods: Using data from a 1986 national telephone survey, we performed a content analysis of subjects' verbatim reports as to why they lacked an RSAC (n = 5,748). Results: The 16.4 percent of respondents who lacked an RSAC gave the following reasons: 1) financial problems, 8 percent; 2) local resource inaccessibility, 5 percent; 3) not wanting a regular source of ambulatory care, 61 percent; and 4) transitory loss of their regular source of ambulatory care, 18 percent. However, some sociodemographic subgroups reported substantially more problems with access barriers, and these disparities were often not detected by the global measure. RSAC. The poor were not more likely than the non-poor to lack an RSAC (odds ratio [OR] = 0.8; 95% confidence interval, [0.6, 1.1]), but were much more likely to lack, an RSAC for financial reasons (OR = 5.2 [2.6, 10.6]). Similarly, rural respondents were not more likely than urban dwellers to lack an RSAC, but were more likely to lack an RSAC because of local resource inaccessibility (OR = 5.8 [2.8, 11.9]). Conclusions: We conclude that the global measure, RSAC, is not an accurate indicator of whether population subgroups have access barriers to obtaining a source of continuity care. [ABSTRACT FROM AUTHOR]

In this article we propose some new notions of limiting P values for hypothesis testing. The limiting P value (LP) here not only provides the usual attractive interpretation of a P value as the strength in support of the null hypothesis coming from the observed data, but also has several advantages. First, it allows us to resample directly from the empirical distribution (in the bootstrap implementations), rather than from the estimated population distribution satisfying the null constraints. Second, it serves as a test statistic and as a P value simultaneously, and thus enables us to obtain test results directly without having to construct an explicit test statistic and then establish or approximate its sampling distribution. These are the two steps generally required in a standard testing procedure. Using bootstrap and the concept of data depth, we have provided LP's for a broad class of testing problems where the parameters of interest can be either finite or infinite dimensional. Some computer simulation results show the generality and the computational feasibility of our approach. [ABSTRACT FROM AUTHOR]

Let pi[sub N] be a finite population of size N whose elements have distinct X values X[sub (1)] < ... < X[sub (N)]. Let chi ([sub 1]) < ... < x(sub (n)] denote the order statistics of a simple random sample of size n taken from pi [sub N] without replacement. An outer confidence interval is formed for the quantile interval [X ([sub t]), X [sub (u)] (1

A sample is taken from a population consisting of g + 1 groups or classes with unknown probability p[sub j] that an individual chosen is from class j, j = 0 ..... g. The null hypothesis Ho: p[sub j] is greater than or equal to P[sub j](j = 1 ,..., g), implying that none of the classes 1,..., g is underrepresented, must be tested; P[sub j] is the known fraction of the population belonging to class j. A modification of the chi-squared goodness-of-fit test that takes into account the one-sided nature of the hypothesis is derived and illustrated. The test for overrepresentation is also given. The test for under-representation is important for equal opportunity cases, and the test for overrepresentation is important in food and drug testing. [ABSTRACT FROM AUTHOR]

A capture-recapture model that depends on the "catchability" of the individuals in a closed population is considered. A test is derived to decide if the capture efficiencies are the same for two capture attempts. The test given assumes no a priori information about the population and can be used even if one is dealing with relatively small populations. Statistical inferences are drawn about the size of the population. In particular, a family of point estimators for the population size, N, is investigated and compared with the maximum likelihood estimator. Further, confidence intervals for the capture efficiency, is an element of, are combined with joint confidence regions for N and is an element of to obtain confidence intervals for N. It is found that this procedure yields confidence intervals for N whose actual confidence levels are not strongly dependent on the nuisance parameter is an element of. For N less than or equal to 200 the procedure given for finding confidence intervals for N is found to be more satisfactory than the standard approach of simply replacing is an element of with its maximum-likelihood estimate. [ABSTRACT FROM AUTHOR]

We investigate in this article various techniques for determining a tolerance limit such that the probability is gamma that at least a proportion P of a population, produced in batches, exceeds that limit. If the variance ratio is known, one may obtain exact tolerance limits. For settings where the variance ratio is not necessarily known, we describe a procedure, based on the Satterthwaite approximation, for obtaining conservative tolerance limits. The factors based on the Satterthwaite approximation are smaller than those obtained using the procedure of Lemon (1977). [ABSTRACT FROM AUTHOR]

This article deals with multiple comparison methods for a set of populations which can be described in terms of testing hypotheses of homogeneity of subsets. Criteria for evaluating procedures are defined in terms of the set of outcomes of all tests, termed the pattern of decisions. In particular complexity is introduced as a new criterion based on interpretability of that pattern. The criteria are defined and discussed in a general framework and then applied to the comparison of normal means. For the cases considered, results favor an F over a range procedure, and are mixed in the comparison of the F and the range procedure with a new gap procedure. [ABSTRACT FROM AUTHOR]

Criteria are considered for testing the hypothesis (in multiresponse data) that the observations are a random sample from one multinormal population versus the alternative that, for some partition of the data, the observations arise from two multinormal populations with different means. This hypothesis is analogous to the univariate formulation of Engelman and Hartigan (1969), in which they studied a likelihood ratio test for clusters. A union intersection (UI) criterion is developed that is more manageable computationally than the multivariate likelihood ratio (LR) criterion. The UI and LR criteria and a "linear discrimination" statistic are shown, however, to be equivalent. Some properties of the tests are provided. [ABSTRACT FROM AUTHOR]

Explicit expressions for the level of significance and power of Stein's two-stage hypothesis testing procedure are derived when the normal population is mixed with another normal population differing in the mean, Approximate expansions for these expressions have been obtained, and numerical studies of the size have been carried out using the expansions. Some numerical indication of the accuracy of the approximations has also been obtained, Stein's two-stage procedure is quite robust against mixtures of normal populations differing in location parameters. Similar conclusions can be drawn regarding the coverage probabilities of confidence intervals generated by this procedure. [ABSTRACT FROM AUTHOR]

Suppose mu[sub i], sigma[sup 2, sub i] are the mean and variance of the ith normal population, i = 1, 2, 3 (we assume the parameters to be all unknown). For given nonzero constants lambda[sub 1], lambda[sub 2], lambda[sub 3], suppose mu= SIGMA[sup 3, sub i]=1 lambda[sub i] mu[sub i]. Sequential procedures have been derived (i) to arrive at a fixed width confidence interval for mu, and (ii) to estimate mu pointwise with minimum risk (loss being squared error). Some desirable asymptotic properties are proved for the stopping times in both cases. Monte Carlo studies were made for the problem in (i) with moderate optimal sample sizes. [ABSTRACT FROM AUTHOR]

Within the Neyman-Pearson framework of hypothesis testing with fixed-error-level specifications, two-stage designs are obtained such that sample size is minimized when the alternative hypothesis is true. Normally distributed variates with known variance and binomially distributed variates are considered. It is shown that when the alternative hypothesis is true, these optimal two-stage designs generally achieve between one-half and two-thirds of the ASN differential between the two extremes of analogous fixed-sample designs (maximum ASN) and item-by-item Wald SPRT design (minimum ASN when alternative hypothesis is true). [ABSTRACT FROM AUTHOR]

Meta-analysis is a quantitative method for summarizing single studies. In a meta-analysis, one tries to quantify the treatment effect, assigning more weight to large studies than to small studies. A much used method for weighting is the inverse variance method. If all studies have measured the results in the same way, the results can be used directly in the meta-analysis, but if the same outcome is measured in different ways across different studies, one has to use a standardized effect size where results are converted to a common scale. If it is believed that the effect is consistent across various populations and settings, one can employ a fixed-effect model. If systematic differences in effect can be expected, a random-effects model is used. Meta-analyses are often depicted as forest plots. Each line represents one study where the effect estimate is marked as a point on a line, with each end of the line representing the confidence interval around it. The meta-analysis is shown as a diamond where the width illustrates the uncertainty around the estimate. If all study results point in the same direction, the meta-analysis is considered "homogeneous". But if the studies vary in their effect size and direction, the findings are "heterogeneous". The strength of meta-analysis is that it can be used to summarize a large body of information in one number. This is also its limitation. One number cannot describe the variation that exists across different studies. [ABSTRACT FROM AUTHOR]

The article responds to an article in the current 2007 issue which is critical of the authors' earlier publication in the journal. The authors agree that populations of most states are growing, that cities are sprawling, and that species around growing cities may be imperiled by urban population growth and expansion. To do an actual experiment using cities with a control would be unrealistic. The statistical findings of the authors was that there was no evidence that spatial-distribution aspects of sprawl are ecologically relevant, but that human population growth was the primary cause of species imperilment in the U.S.

This article presents information on statistical calculations carried out to estimate the largest mean. A single-sample procedure is proposed for obtaining asymptotically optimum confidence intervals for the largest mean of normal populations with common unknown variance. In this article a single-sample procedure is considered for this situation of common unknown variance. The procedure is analogous to the well known procedure based on the distribution for the one sample problem and utilizes a pooled estimate of the common population variance.

It has been argued (e.g., the Wilcox-Russell hypothesis) that (low) birth weight is a correlate of adverse birth outcomes but is not on the “causal” pathway to infant mortality. However, the US national policy for reducing infant mortality is to reduce low birth weight. If these theoretical views are correct, lowering the rate of low birth weight may have little effect on infant mortality. In this paper, the authors use the “covariate density defined mixture of logistic regressions” method to formally test the Wilcox-Russell hypothesis that a covariate which influences birth weight, in this case maternal age, can influence infant mortality directly but not indirectly through birth weight. The authors analyze data from 8 populations in New York State (1985–1988). The results indicate that among the populations examined, 1) maternal age significantly influences the birth weight distribution and 2) maternal age also affects infant mortality directly, but 3) the influence of maternal age on the birth weight distribution has little or no effect on infant mortality, because the birth-weight-specific mortality curve shifts accordingly to compensate for changes in the birth weight distribution. These results tend to support the Wilcox-Russell hypothesis for maternal age. [ABSTRACT FROM AUTHOR]

Gene-environment-wide interaction studies of disease occurrence in human populations may be able to exploit the same agnostic approach to interrogating the human genome used by genome-wide association studies. The authors discuss 2 methods for taking advantage of possible independence between a single nucleotide polymorphism they call G (a genetic factor) and an environmental factor they call E while maintaining nominal type I error in studying G-E interaction when information on many genes is available. The first method is a simple 2-step procedure for testing the null hypothesis of no multiplicative interaction against the alternative hypothesis of a multiplicative interaction between an E and at least one of the markers genotyped in a genome-wide association study. The added power for the method derives from a clever work-around of a multiple testing procedure. The second is an empirical-Bayes–style shrinkage estimation framework for G-E interaction and the associated tests that can gain efficiency and power when the G-E independence assumption is met for most Gs in the underlying population and yet, unlike the case-only method, is resistant to increased type I error when the underlying assumption of independence is violated. The development of new approaches to testing for interaction is an example of methodological progress leading to practical advantages. [ABSTRACT FROM AUTHOR]