The authors present evaluation of possible untimely death rate due to ambient air pollution in Russian cities. For evaluation, the authors used data of everyday monitoring of air pollution in 1993 and 1998, carried out by Russian Hydrometeorology Service. Findings are that 219,000-233,000 untimely deaths (or 15-17% of total annual death rate) in Russia could be caused by air pollution. The authors discuss possible factors influencing exactness of the presented evaluation.
A data set concerning cardiac arrests treated by the Emergency Medical Service in Trondheim during a nine year period is analysed. The relationship between the occurrence of cardiac arrest and covariates related to weather and season is examined. The covariate order method is used in the analysis of the data. It is explained how this method can be extended to recurrent event data, and the practical usefulness and flexibility of the method is demonstrated in these analyses. In the analyses a significant relationship between outdoor air temperature, or factors closely related to outdoor air temperature, and the occurrence of cardiac arrest is found. The incidence of cardiac arrest decreases with increasing temperature. Further a significant effect of snowfall is also found, with increased intensity of cardiac arrest on days with snowfall. A more borderline significant effect of precipitation is also identified.
A case-cohort sample of adoptees was collected to investigate genetic and environmental influences on premature death, which motivated us to supplement existing simulation results to explore the performance of various estimators proposed for case-cohort samples of survival data. We studied six regression coefficients estimators, which differ with regard to the weighting scheme used in a pseudo-likelihood function, and two different estimators of their variances. Compared to earlier simulation studies, we changed the following conditions: type of explanatory variable, the distribution of lifetimes, and the percentage of deaths in the full cohort. The latter condition affected the performance of the estimated variances of the regression coefficients, where we found a systematic bias of the estimator, proposed by Self and Prentice, dependent on the percentages of deaths. This dependence of percentages of death was different for different sizes of case-cohort studies. A robust variance estimator showed a better overall performance. The estimators of regression coefficients compared did not differ much, the estimators proposed by Kalbfleisch and Lawless and by Prentice performing very well. Results of the case-cohort data of adoptees were not in conflict with earlier findings of a moderate genetic influence on premature death in adulthood.
Immunisation information systems (IIS) are valuable tools for monitoring vaccination coverage and for estimating vaccine effectiveness and safety. Since 2009, an advanced IIS has been developed in Denmark and will be implemented during 2012–14. This IIS is based on a database existing since 2000. The reporting of all administered vaccinations including vaccinations outside the national programme will become mandatory. Citizens will get access to data about their own vaccinations and healthcare personnel will get access to information on the vaccinations of their patients. A national concept of identification, a national solution combining a personal code and a card with codes, ensures easy and secure access to the register. From the outset, the IIS will include data on childhood vaccinations administered from 1996 and onwards. All Danish citizens have a unique identifier, a so called civil registration number, which allows the linking of information on vaccinations coming from different electronic data sources. The main challenge will be to integrate the IIS with the different electronic patient record systems currently existing at general practitioner, vaccination clinic and hospital level thereby avoiding double-entry. A need has been identified for an updated international classification of vaccine products on the market. Such a classification would also be useful for the future exchange of data on immunisations from IIS between countries.
The purpose of this study was to examine the association between the frequency of alcohol consumption and stroke mortality among eastern Finnish men.
This study is a population-based sample of men with an average follow-up of 20.2 years. A total of 2609 men with no history of stroke at baseline participated in the study. During the follow-up, 66 deaths from stroke occurred.
After adjustment for systolic blood pressure, smoking, BMI, diabetes, and socioeconomic status, the relative risk (RR) among men who consumed alcohol 2.5 times per week after adjustment for risk factors. When the total amount of alcohol consumption (g/week) was taken into account with other covariates, RR was 0.71 (95% CI, 0.30-1.68; P = 0.437) for men with alcohol consumption 2.5 times per week compared with nondrinkers, RR was 3.03 (95% CI, 1.19-7.72; P = 0.020).
This study shows a strong association between the frequency of alcohol consumption and stroke mortality, independent of total amount of alcohol consumption. The risk of stroke death was the highest among men who consumed alcohol >2.5 times per week.
It has been well known that ignoring measurement error may result in substantially biased estimates in many contexts including linear and nonlinear regressions. For survival data with measurement error in covariates there has been extensive discussion in the literature with the focus being on the Cox proportional hazards models. However, the impact of measurement error on accelerated failure time (AFT) models has received little attention, though AFT models are very useful in survival data analysis. In this paper, we discuss AFT models with error-prone covariates and study the bias induced by the naive approach of ignoring measurement error in covariates. To adjust for such a bias, we describe a simulation and extrapolation method. This method is appealing because it is simple to implement and it does not require modelling the true but error-prone covariate process that is often not observable. Asymptotic normality for the resulting estimators is established. Simulation studies are carried out to evaluate the performance of the proposed method as well as the impact of ignoring measurement error in covariates. The proposed method is applied to analyse a data set arising from the Busselton Health study (Australian J. Public Health 1994; 18:129-135).
When competing risks are present, two types of analysis can be performed: modelling the cause specific hazard and modelling the hazard of the subdistribution. This paper contrasts these two methods and presents the benefits of each. The interpretation is specific to the analysis performed. When modelling the cause specific hazard, one performs the analysis under the assumption that the competing risks do not exist. This could be beneficial when, for example, the main interest is whether the treatment works in general. In modelling the hazard of the subdistribution, one incorporates the competing risks in the analysis. This analysis compares the observed incidence of the event of interest between groups. The latter analysis is specific to the structure of the observed data and it can be generalized only to another population with similar competing risks.
Comment In: Stat Med. 2007 Aug 15;26(18):3521-3; author reply 352317476646
Comment In: Stat Med. 2007 Aug 30;26(19):3676-9; author reply 3679-8017299738
The Li Ka Shing Knowledge Institute, Keenan Research Centre, St. Michael's Hospital, 30 Bond Street, Toronto, Ontario, M5B 1W8, Canada; Department of Nephrology, Humber River Hospital, 200 Church Street, Toronto, Ontario M9N 1N8, Canada. Electronic address: firstname.lastname@example.org.
Period analysis has been shown to provide more up-to-date estimates of cancer survival than traditional methods of survival analysis. There is, however, a tradeoff between up-to-dateness and precision of period survival estimates: increasing up-to-dateness by restricting the analysis to a relatively short period, such as the most recent calendar year, goes along with loss of precision. Recently, a model-based approach was proposed, in which more precise period survival estimates for the most recent year can be obtained through modeling of survival trends within a recent 5-year period. We assess possibilities to extend the time window used for modeling to come up with even more precise, but equally accurate and up-to-date estimates of prognosis. Empirical evaluation using data from the Finnish Cancer Registry shows that extension of the time window to about 10 years provides, in most cases, as accurate results as using a 5-year time window (whereas further extension may lead to considerably less accurate results in some cases). Using 10-year time windows for modeling, SEs of survival estimates can be approximately halved compared with conventional period survival estimates for the most recent calendar year. Furthermore, we present a modification of the modeling approach, which allows extension to 10-year time windows to be achieved without the need to include additional cohorts of patients diagnosed longer time ago and which provides similarly accurate survival estimates at comparable levels of precision in most cases. Our analyses indicate opportunities to further maximize benefits of model-based period analysis of cancer survival.