The population of the USSR

Official population data for the USSR for 1986 and 1987 are presented. Tables are included on age and sex distribution; rural and urban population; birth, death, and natural increase rates, 1970-1986; fertility and mortality by sex, 1970-1986; birth order; age-specific birth rates by rural and urban area and Union republic; age-specific death rates, 1970-1986; infant mortality, 1970-1986; life expectancy, 1926-1986; life tables; marriage and age at marriage; and divorce.     

The Impact of Ovarian Cancer on Life Expectancy in Japan

The purpose of this study was to determine how life expectancy is modified by ovarian cancer from 1950-2000. The contributions of ovarian cancer to life expectancy were estimated. The age characteristics of ovarian cancer were detected using the Gompertz relational mortality model. The patterns between years of potential life lost (YPLL) and mortality were obtained by fitting a linear regression equation to the natural logarithm of their ratios. YPLLs are substantially higher in Ireland than in Japan. However, the rates of change were much higher in Japan than in Ireland. YPLLs changed from 0.02 year in 1950 to 0.12 year in 2000. In Japan, there was a sixfold increase in the proportion of YPLLs for death from ovarian cancer relative to those for death from gynaecological cancers during the last half century. The impact of ovarian cancer on life expectancy clearly increased and the age-specific mortality tend to ageing.     

A comparison of models for dynamic life tables. Application to mortality data from the Valencia Region (Spain)

The graduation of mortality data aims to estimate the probabilities of death at age x, q ( x ), by means of an age-dependent function, whose parameters are adjusted from the crude probabilities that are directly obtainable from the data. However, current life tables have a problem, the need for periodic updates due to changes in mortality over short periods of time. The table containing mortality rates for different ages in different years, q ( xt ), is called a dynamic life table, which captures mortality variation over time. This paper proposes a review of the most commonly used dynamic models and compares the results obtained by each of them when applied to mortality data from the Valencia Region (Spain). The result of the comparison leads us to the conclusion that the Lee-Carter method offers the best results for both sexes, while that based on Heligman and Pollard functions provides the best fit for men alone. Our working method is of additional interest as it may be applied to mortality data for a wide range of ages in any geographical location, allowing the most appropriate dynamic life table to be selected for the case at hand.     

Alternative models for the heterogeneity of mortality risks among the aged

The authors examine how sensitive the estimates of heterogeneity in the mortality risks in a population are to the choices of two types of function, "one describing the age-specific rate of increase of mortality risks for individuals and the other describing the distribution of mortality risks across individuals." U.S. data from published Medicare mortality rates for the period 1968-1978 are used to analyze total mortality among the aged. "In addition, national vital statistics data for the period 1950-1977 were used to analyze adult lung cancer mortality. For these data, the estimates of structural parameters were less sensitive to reasonable choices of the heterogeneity distribution (gamma vs. inverse Gaussian) than to reasonable choices of the hazard rate function (Gompertz vs. Weibull)."     

Statistical materials: natural increase of population in the USSR

Data on the population of the USSR by sex and Union republic as of January 1, 1982, are presented. Data are included on population by sex; birth, death, and natural growth rates, 1980 and 1981; distribution of births, deaths, and marriages by month, 1981; distribution of births by birth order, 1981; age-specific birth rates for rural and urban areas, 1980 and 1981; mortality from cardiovascular disease and cancer, 1980 and 1981; marriages by age of bride and groom, 1981; and divorces by duration of marriage and age of husband and wife, 1981.     

The estimation of adopted mortality and morbidity rates using model and the phase type law: the Turkish case

Abstract

This paper aims to estimate mortality rate, morbidity-mortality rates of a chronic disease utilizing phase type law in the frame of two and three state processes. The application on commonly used mortality tables in Turkey are adopted to process to estimate the future mortalities with respect to phase type distribution for the purpose of justifying. Using one absorbing state, two and three state Models calculate the time until absorbing of the death and death by phase type distribution for each gender. Consequently, the 3-state probabilities in estimating the mortality-morbidity rates of IHD for Turkish population yield a significant information on the health management and pricing health insurance products.     

Multilevel modelling of the geographical distributions of diseases

Multilevel modelling is used on problems arising from the analysis of spatially distributed health data. We use three applications to demonstrate the use of multilevel modelling in this area. The first concerns small area all-cause mortality rates from Glasgow where spatial autocorrelation between residuals is examined. The second analysis is of prostate cancer cases in Scottish counties where we use a range of models to examine whether the incidence is higher in more rural areas. The third develops a multiple-cause model in which deaths from cancer and cardiovascular disease in Glasgow are examined simultaneously in a spatial model. We discuss some of the issues surrounding the use of complex spatial models and the potential for future developments.     

Bayesian Parametric Accelerated Failure Time Spatial Model and its Application to Prostate Cancer

Prostate cancer is the most common cancer diagnosed in American men and the second leading cause of death from malignancies. There are large geographical variation and racial disparities existing in the survival rate of prostate cancer. Much work on the spatial survival model is based on the proportional hazards model, but few focused on the accelerated failure time model. In this paper, we investigate the prostate cancer data of Louisiana from the SEER program and the violation of the proportional hazards assumption suggests the spatial survival model based on the accelerated failure time model is more appropriate for this data set. To account for the possible extra-variation, we consider spatially-referenced independent or dependent spatial structures. The deviance information criterion (DIC) is used to select a best fitting model within the Bayesian frame work. The results from our study indicate that age, race, stage and geographical distribution are significant in evaluating prostate cancer survival.     

Bayesian analysis of spatial data using different variance and neighbourhood structures

In disease mapping, the overall goal is to study the incidence or mortality risk caused by a specific disease in a number of geographical regions. It is common to assume that the response variable follows a Poisson distribution, whose average rate can be explained by a group of covariates and a random effect. For this random effect, it is considered conditional autoregressive (CAR) models, which carry information about the neighbourhood relationship between the regions. The focus of this paper was to explore and compare some CAR models proposed in the literature. An application with epidemiological data was conducted to model the risk of death due to Crohn's Disease and Ulcerative Colitis in the State of São Paulo – Brazil. Finally, a simulation study was done to strengthen the results and assess the performance of the models in the presence of various levels of spatial dependence.     

Statistical materials. Part 3: life expectancy,infant mortality,and age-specific death rates

Official data are presented in this one-page item on average life expectancy by sex in the USSR from 1926-1927 to 1985; infant mortality by sex, 1970-1985; and age-specific death rates, 1969-1970 to 1984-1985.     

A shared component model for detecting joint and selective clustering of two diseases

The study of spatial variations in disease rates is a common epidemiological approach used to describe the geographical clustering of diseases and to generate hypotheses about the possible 'causes' which could explain apparent differences in risk. Recent statistical and computational developments have led to the use of realistically complex models to account for overdispersion and spatial correlation. However, these developments have focused almost exclusively on spatial modelling of a single disease. Many diseases share common risk factors (smoking being an obvious example) and, if similar patterns of geographical variation of related diseases can be identified, this may provide more convincing evidence of real clustering in the underlying risk surface. We propose a shared component model for the joint spatial analysis of two diseases. The key idea is to separate the underlying risk surface for each disease into a shared and a disease-specific component. The various components of this formulation are modelled simultaneously by using spatial cluster models implemented via reversible jump Markov chain Monte Carlo methods. We illustrate the methodology through an analysis of oral and oesophageal cancer mortality in the 544 districts of Germany, 1986–1990.     

A Bayesian approach to mixture cure models with spatial frailties for population‐based cancer relative survival data

As the treatments of cancer progress, a certain number of cancers are curable if diagnosed early. In population‐based cancer survival studies, cure is said to occur when mortality rate of the cancer patients returns to the same level as that expected for the general cancer‐free population. The estimates of cure fraction are of interest to both cancer patients and health policy makers. Mixture cure models have been widely used because the model is easy to interpret by separating the patients into two distinct groups. Usually parametric models are assumed for the latent distribution for the uncured patients. The estimation of cure fraction from the mixture cure model may be sensitive to misspecification of latent distribution. We propose a Bayesian approach to mixture cure model for population‐based cancer survival data, which can be extended to county‐level cancer survival data. Instead of modeling the latent distribution by a fixed parametric distribution, we use a finite mixture of the union of the lognormal, loglogistic, and Weibull distributions. The parameters are estimated using the Markov chain Monte Carlo method. Simulation study shows that the Bayesian method using a finite mixture latent distribution provides robust inference of parameter estimates. The proposed Bayesian method is applied to relative survival data for colon cancer patients from the Surveillance, Epidemiology, and End Results (SEER) Program to estimate the cure fractions. The Canadian Journal of Statistics 40: 40–54; 2012 © 2012 Statistical Society of Canada     

Statistical materials,Part 1: natural increase of population in the USSR

Data are presented on the population of the USSR by sex and Union Republic as of January 1, 1983. Data are included on birth rate, death rate, and natural growth rate, 1981 and 1982; distribution of births, deaths, and marriages by month, 1982; distribution of births by birth order, 1982; age-specific birth rates by rural or urban area and Union Republic, 1981 and 1982; mortality from cardiovascular disease and cancer, 1981 and 1982; marriages by age of bride and groom, 1982; and number of divorces by duration of marriage and age of husband and wife, 1982.     

Statistical materials. Part 2: natural population increase in the USSR

Selected vital statistics for the USSR for 1984 are presented. Data are included on birth, death, and natural increase rates, 1983-1984; the distribution of births, deaths, and marriages by month; birth order; age-specific birth rates by rural and urban areas; age-specific birth rates by union republics; distribution of marriages by age and sex; distribution of married couples by age of husband and wife; and divorces by length of marriage and age of husband and wife.     

Statistical materials: natural increase of population in the USSR

Data are included on the population of the USSR by sex and Union Republic as of January 1, 1981; birth, death, and natural growth rates for 1979 and 1980; distribution of births, deaths, and marriages by month, 1980; distribution of births by birth order; age distribution of birth rates in urban and rural areas and by Union Republic; death rates from cardiovascular disease and cancer, 1979 and 1980; age at first marriage and marriages by age of bride and groom, 1980; and divorces by duration of marriage and age of spouses, 1980.     

A Proportional Hazards Regression Model for the Subdistribution with Covariates‐adjusted Censoring Weight for Competing Risks Data

With competing risks data, one often needs to assess the treatment and covariate effects on the cumulative incidence function. Fine and Gray proposed a proportional hazards regression model for the subdistribution of a competing risk with the assumption that the censoring distribution and the covariates are independent. Covariate‐dependent censoring sometimes occurs in medical studies. In this paper, we study the proportional hazards regression model for the subdistribution of a competing risk with proper adjustments for covariate‐dependent censoring. We consider a covariate‐adjusted weight function by fitting the Cox model for the censoring distribution and using the predictive probability for each individual. Our simulation study shows that the covariate‐adjusted weight estimator is basically unbiased when the censoring time depends on the covariates, and the covariate‐adjusted weight approach works well for the variance estimator as well. We illustrate our methods with bone marrow transplant data from the Center for International Blood and Marrow Transplant Research. Here, cancer relapse and death in complete remission are two competing risks.     

Hierarchical Bayes estimation of mortality rates for disease mapping

Mapping of incidence rates or mortality rates (relative risks) from diseases like cancer and leukemia is of primary importance in an epidemiological study. The usual procedure is to map the standardized mortality ratio (SMR) across different geographical regions. Direct use of SMR may not be worthwhile, particularly for small places, as it does not take into account the high variability for different population sizes over different regions and the spatial patterns of the regions under study. In this paper a hierarchical Bayes approach is presented in smoothing the relative risks and providing the measures of uncertainty associated with these estimates of relative risks.     

Biplot models applied to cancer mortality rates

"A graphical method developed by Gabriel to display the rows and columns of a matrix is applied to tables of age- and period-specific cancer mortality rates. It is particularly useful when the pattern of age-specific rates changes with time. Trends in age-specific rates and changes in the age distribution are identified as projections. Three examples [from England and Wales] are given."     

Childhood mortality spatial distribution in Ethiopia

The risk of a child dying before completing five years of age is highest in Sub-Saharan African countries. But Child mortality rates have shown substantial decline in Ethiopia. For this study, the 2000, 2005 and 2011 Ethiopian Demographic Survey (EDHS) was used. Generalized linear mixed model with spatial covariance structure was adapted. The model allowed for spatial correlation, and leads to the more realistic estimate for under-five mortality risk factors. The analysis showed that the risk of under-five mortality shows decline in years. But, some regions showed increase in years. The study highlights the need to implement better education for family planning and child care to improve the under-five mortality situation in some administrative areas.     

Spatial smoothing of low birth weight rate in Bangladesh using Bayesian hierarchical model

The term low birth weight refers an event where a newborn baby has a weight that is less than 2500?g. This is an essential indicator while the interest is in public health issues such as infant mortality, maternal complications, and antenatal care, etc. of a country, particularly, for a developing country like Bangladesh. The regional development programs are in the current priority list of Bangladesh government and other policy makers. Many of such regional development programs may need the spatial distribution of relative risk for low birth weight that can be obtained by mapping the risks over small area domains like the districts of Bangladesh. This study aims to find whether is there any spatial dependence among the relative risks of low birth weight for the districts of Bangladesh. This has been investigated using Moran's I statistic and a significant spatial dependence in the relative risks was found. Then, attempt has been made to rediscover the spatial distribution based on the idea of spatial smoothing. A Bayesian hierarchical model is used considering percent received antenatal care and female labor force participation as covariates to smooth the observed relative risks of low birth weight in 64 districts of Bangladesh. Revised spatial distribution taking the spatial dependence under consideration through intrinsic conditional autoregressive model is derived and showed in choropleth map along with its different behaviors.