Auswahl der wissenschaftlichen Literatur zum Thema „Radiological co-Exposures“

Background. High tibial osteotomy (HTO) is an effective surgery in treating medial compartment knee osteoarthritis (KOA) combined with varus deformity. An accurate orthopaedy is the key and challenge to the success of HTO. Therefore, we designed a calibratable patient-specific instrumentation (PSI) to assist surgery and evaluated its accuracy and clinical outcomes by comparing with conventional operation (CO). Materials and Methods. 37 patients (39 knees) with medial compartment KOA were randomly divided into the PSI and CO groups and underwent medial open-wedge high tibial osteotomy (MOWHTO) from September 2020 to May 2021. The postoperative radiological outcomes were compared with the preoperative measurements or target values to evaluate the accuracy of correction in the two groups. The American Knee Society Score (AKSS), complication rate, number of intraoperative radiation exposures, blood loss volume, and operative duration were analysed to evaluate the clinical outcomes in the two groups. Results. The designed target values were better achieved in the PSI group than in the CO group. The mean absolute difference between the postoperative measurements and preoperative targets was significantly lower in the PSI group than in the CO group (weight-bearing line (WBL) ratio, 1.97 ± 1.83 % vs .5.42 ± 4.41 % , P = 0.002 ; hip-knee-ankle (HKA) angle, 1.12 ± 0.86 ° vs. 2.27 ± 1.97 °, P = 0.018 ). The operative duration was significantly shorter ( P = 0.014 ), and the number of radiation exposures ( P < 0.001 ) and volume of intraoperative blood loss ( P = 0.003 ) were significantly lower in the PSI group than in the CO group. The clinical AKSS score at 3 and 6 months postoperatively and the functional AKSS score at 3 months postoperatively were significantly higher in the PSI group than in the CO group ( P = 0.042 , 0.040, and 0.034, respectively). Conclusion. For patients with medial compartment KOA, calibratable PSI can assist the surgeon in MOWHTO with superior accuracy and clinical efficacy. This study was conducted under Randomized Controlled Trial Details (RCT) with Registry Number ChiCTR2000038619.

Abstract A summary of updated biological effects data resulting from chronic radon inhalation exposures of animals is presented. Emphasis is placed on the carcinogenic effects of radon and radon decay products, including the influences of radon progeny exposure rate, unattached fraction and disequilibrium, and co-exposures to other pollutants. Plausible values are provided for the radon (radon progeny) lifetime lung cancer risk coefficients.

This study details the distribution and trends of doses from occupational radiation exposure among radiation workers from participating medical institutions in Kenya, where monthly dose measurements were collected for a period of one year (January to December 2007) using thermoluminescent dosimeters. A total of 367 medical radiation workers were monitored, comprising 27% radiologists, 2% oncologists, 4% dentists, 5% physicists, 45% technologists, 4% nurses, 3% film processor technicians, 4% auxiliary staff, and 5% radiology office staff. The average annual effective dose for all subjects ranged from 1.19 to 2.52 mSv. Among these workers, technologists received the largest annual effective dose. The study forms the initiation stage of wider, comprehensive and more frequent monitoring of occupational radiation exposures and long-term investigations into its accumulation patterns, which could form the basis of future records on the detrimental effects of radiation, characteristic of workers in the medical sector, and other co-factors in a developing country such as Kenya.

The study of occupational hazards from BIR in selected crude oil production pipes storage locations in Niger Delta Region of Nigeria has been carried out using two well calibrated radiation monitoring meters (Digilert Tm 100 and Radalert Tm 200). A global positioning system (GPS 76 CSX) was also used to geographically co-ordinate the sampling locations. Measurements were carried out in forty two (42) selected locations in oil producing area of Niger Delta. The following parameters were estimated to determine the level of occupational exposures by crude oil production pipes dealers and customers. The result of the highest exposure rate was observed in Warri Steel Village, Delta State and the lowest value was in Ogunu, Warri, Delta State with respective values of 61.4 and 12.2 µRh-1. The mean exposure rate value for all the test study locations was 19.18 ± 10.25 µRh-1. The absorbed dose values ranged from 106.1 to 533.7nGyhr-1 with mean value of 166.73 ± 89.08 nGyh-1 while the calculated annual effective dose range from 162.71 to 818.23 µSvy-1 with an average value of 255.60 ± 136.57 µSvy-1 and the excess lifetime cancer risk ranges from 0.45 to 2.25 × 10-3 with mean value of 0.70 ± 0.38 × 10-3. All the radiation hazard parameters determined exceeded their respective world safe values. This research work indicated that the crude oil production pipes radioactive scales may have impacted the storage locations radiologically. The elevated radiation hazard parameters observed in this study may pose ill health effects to those working and leaving in the studied locations especially long term ionizing radiation exposure.

La population des mineurs d'uranium est une population de référence pour étudier l'effet sanitaire d'une exposition chronique à différentes sources de rayonnements ionisants (RI) : le radon, les rayonnements gamma et les poussières d'uranium. Cependant lors de ces études deux problèmes statistiques se posent : 1) les expositions des mineurs sont mesurées à une erreur près ; 2) les expositions aux trois sources de RIs sont fortement corrélées entre elles. En épidémiologie des RIs, les erreurs de mesure sur les expositions sont souvent ignorées et les risques sanitaires sont estimés source par source, ignorant les effets synergiques ou antagonistes des expositions simultanées. Ce travail, se découpant en deux parties, vise à promouvoir l'utilisation de modèles hiérarchiques bayésiens, permettant de répondre aux deux problèmes posés précédemment. L'ensemble des méthodes statistiques proposées est appliqué lors de l'estimation d'un risque sanitaire à partir de données de survie dans la cohorte française des mineurs d'uranium.Un modèle est proposé permettant d'estimer un risque sanitaire associé à une exposition chronique au radon en considérant des erreurs de mesure complexes sur ces expositions au radon. En effet, les erreurs de mesure considérées dépendent du lieu de travail du mineur ainsi que de ses habitudes de travail qui changent peu au cours du temps. Ces erreurs sont donc corrélées spatialement et temporellement. De plus, elles sont hétéroscédastiques : leurs variances décroissent au cours du temps en même temps que les méthodes d'évaluation des expositions au radon se perfectionnent. Les modèles proposés sont utilisés afin de considérer les erreurs de mesure sur les expositions au radon lors de l'estimation du risque de décès par cancer du poumon, cancer du rein, cancer du cerveau et du système nerveux central et leucémie. La correction des erreurs de mesure et l'estimation du risque sanitaire sont faites simultanément afin que l'estimation du coefficient de risque tienne compte de l'incertitude sur les expositions. Un algorithme MCMC est implémenté en Python 3.8 afin de mener l'inférence du modèle dans un cadre bayésien. Une étude par simulations est ensuite menée afin d'estimer l'impact d'une mauvaise spécification du modèle sur les estimations de risques.La prise en compte simultanée des trois expositions aux RIs est réalisée à l'aide des modèles de mélange de régressions sur profil d'expositions (PRM). Ces modèles permettent de créer des groupes de mineurs ayant un profil d'expositions similaire et un risque sanitaire similaire. Comme précédemment, l'inférence des groupes et l'estimation du risque sanitaire sont effectuées simultanément afin que l'incertitude sur le regroupement soit prise en compte dans l'estimation du risque. Le nombre de groupes dans le modèle est infini mais seulement un nombre fini de groupes sont non vides. Cette particularité, impliquant que le nombre de paramètre du modèle soit infini, introduit une difficulté dans l'inférence du modèle. De plus, les sorties de l'algorithme d'inférence ne sont pas interprétables directement : un travail de post-traitement doit être effectué afin de former les différents groupes d'individus. Tandis que le choix du post-traitement utilisé a un impact sur le regroupement des individus, les recommandations quant à celui-ci sont quasi inexistantes dans la littérature. Ce travail propose une implémentation en Python d'un algorithme MCMC peu coûteux en temps permettant d'inférer les modèles PRM. Cet algorithme est utilisé afin d'estimer le risque de décès par cancer du poumon dans la cohorte française des mineurs d'uranium associé à des expositions simultanées au radon, aux rayonnements gamma et aux poussières d'uranium. Enfin une étude par simulations est menée afin de comparer les différentes procédures de post-traitement et émettre des recommandations quant à l'utilisation de ces dernières
The population of uranium miners is a reference population for studying the health effects of chronic exposure to various sources of ionising radiation (IR): radon, gamma rays and uranium dust. However, two statistical problems arise in these studies: 1) the miners' exposures measures are error-prone; 2) the exposures to the three sources of IR are highly correlated. In radiation epidemiology, measurement errors in exposures are often ignored and health risks are estimated source by source, ignoring the synergistic or antagonistic effects of simultaneous exposures. The aim of this work, which is divided into two parts, is to promote the use of hierarchical Bayesian models to address the two problems raised above. All the statistical methods proposed in this work are applied to estimate a health risk from survival data in the French cohort of uranium miners.A model is proposed for estimating a health risk while considering complex measurement errors on radon exposures. These measurement errors depend on the miner's workplace and its work habits which change little over time. These errors are therefore spatially and temporally correlated. They are also heteroscedastic: their variances decrease over time as methods for assessing radon exposure improve. The proposed models are used account for measurement errors in radon exposure when estimating the risk of death by lung cancer, kidney cancer, brain and central nervous system cancer and leukaemia. The correction of the measurement errors and the estimation of the health risk are carried out simultaneously so that the estimation of the risk coefficient account for the uncertainty in the exposures. An MCMC algorithm was implemented in Python 3.8 to infer the model within a Bayesian framework. A simulations study is then carried out to estimate the impact of model misspecification on risk estimates.The three exposures to IRs are considered simultaneously when assessing a health risk by using profil regressions mixture (PRM) models. These models are used to create groups of miners with similar exposure profiles and similar health risks. As before, the inference of groups and the estimation of health risk are carried out simultaneously so that the uncertainty in the grouping is accounted in the estimation of risk. The number of groups in the model is infinite, but only a finite number of groups are non-empty. This assumption, which implies that the number of model parameters is infinite, introduces a difficulty in inferring the model. In addition, the output of the inference algorithm cannot be interpreted directly: post-processing must be carried out in order to form the different groups of individuals. While the choice of post-processing used has an impact on the grouping of individuals, there are only few guidelines on this in the scientific literature. This work proposes a Python implementation of a time-efficient MCMC algorithm for inferring PRM models. This algorithm is used to estimate the risk of death from lung cancer in the French cohort of uranium miners associated with simultaneous exposure to radon, gamma rays and uranium dust. Finally, a simulation study is carried out to compare different post-treatment procedures and provide guidelines on their use