Hertel J.,University of Greifswald |
Hertel J.,German Center for Neurodegenerative Diseases |
Van der Auwera S.,University of Greifswald |
Van der Auwera S.,German Center for Neurodegenerative Diseases |
And 13 more authors.
Metabolomics | Year: 2017
Introduction: Different normalization methods are available for urinary data. However, it is unclear which method performs best in minimizing error variance on a certain data-set as no generally applicable empirical criteria have been established so far. Objectives: The main aim of this study was to develop an applicable and formally correct algorithm to decide on the normalization method without using phenotypic information. Methods: We proved mathematically for two classical measurement error models that the optimal normalization method generates the highest correlation between the normalized urinary metabolite concentrations and its blood concentrations or, respectively, its raw urinary concentrations. We then applied the two criteria to the urinary 1H-NMR measured metabolomic data from the Study of Health in Pomerania (SHIP-0; n = 4068) under different normalization approaches and compared the results with in silico experiments to explore the effects of inflated error variance in the dilution estimation. Results: In SHIP-0, we demonstrated consistently that probabilistic quotient normalization based on aligned spectra outperforms all other tested normalization methods. Creatinine normalization performed worst, while for unaligned data integral normalization seemed to most reasonable. The simulated and the actual data were in line with the theoretical modeling, underlining the general validity of the proposed criteria. Conclusions: The problem of choosing the best normalization procedure for a certain data-set can be solved empirically. Thus, we recommend applying different normalization procedures to the data and comparing their performances via the statistical methodology explicated in this work. On the basis of classical measurement error models, the proposed algorithm will find the optimal normalization method. © 2017, Springer Science+Business Media New York.
Schmidt C.O.,Institute of Community Medicine |
Fahland R.A.,Institute of Community Medicine |
Franze M.,Institute of Community Medicine |
Splieth C.,University of Greifswald |
And 4 more authors.
Health Education Research | Year: 2010
Enhancing health literacy is a keystone in health promotion. Yet, most studies on health literacy are limited to functional literacy levels. Furthermore, little evidence is available from children. Based on Nutbeam's outcome model for health promotion, this study aims (i) to elaborate a set of short scales to measure important health literacy domains in children and (ii) to analyse their associations among each other, with health behaviour as an intermediate health outcome, subjective health, social status and gender. The sample comprised 852 school children in fifth grade, aged 9-13 years, in Western Pomerania, Germany. Items were taken from the child's questionnaire to form short scales for health-related knowledge, attitudes, communication and behaviour. The internal consistencies of the communication and attitude scales were 0.73 and 0.57, respectively. Unidimensional scalability of the knowledge and behaviour scales was supported by item response models. Associations between health scales were modest. In regression analyses, social status and gender predicted only health knowledge and communication but not health behaviours, attitudes and self-efficacy. Health knowledge was not associated with any other scale. Our results suggest that targeting one specific component of health literacy in children is likely to exert only small effects on health status and health behaviour. © The Author 2009. Published by Oxford University Press. All rights reserved.
Pillai R.K.,Medical College |
Mehendale S.,NIE |
Awasthi S.,King George Univesrsity |
Ravi Varman G.,Institute of Community Medicine
Clinical Epidemiology and Global Health | Year: 2015
The concept of research for the postgraduates, its scope and the debating points in the Indian context are described and discussed in this article. The scope of research as part of postgraduate activity is discussed along with the importance of methodology, followed by the barriers faced in doing good research. After this introductory part, the points for debate are identified and listed as initiation points for discussion. The alternate viewpoints are also described for each discussion point. The suggestions put forth are listed as points for initiating further discussion and debate. © 2015 INDIACLEN.
Kubota Y.,Public Health |
Moriyama Y.,The Public Health Institute of Kochi Prefecture |
Yamagishi K.,Institute of Community Medicine |
Tanigawa T.,Ehime University |
And 9 more authors.
Atherosclerosis | Year: 2010
Objective: To examine the association between concentrations of serum vitamin C, a contributive factor to prevention of cardiovascular disease and levels of hs-CRP, a risk factor for cardiovascular disease, in population-based samples of middle-aged men and women. Design: A cross-sectional study. Methods and results: The subjects were 778 men and 1404 women, aged 40-69 years, who participated in a cardiovascular risk survey in Kyowa, Ibaraki prefecture in 2002 as part of the Circulatory Risk in Communities Study (CIRCS). Inverse associations between serum vitamin C concentrations and hs-CRP levels were established for both men and women. Multivariable-adjusted mean values of hs-CRP for the lowest to highest quintiles of vitamin C levels were 0.75, 0.65, 0.61, 0.61 and 0.47 mg/L (P for trend <0.001) for men, and 0.56, 0.51, 0.49, 0.41 and 0.41 mg/L (P for trend <0.001) for women. The inverse association between vitamin C and hs-CRP was stronger for non-smoking men and women, non-overweight women and postmenopausal women. Conclusions: Serum vitamin C concentrations were found to be inversely associated with hs-CRP levels in both men and women, primarily among non-smokers, non-overweight women and postmenopausal women. The respective roles of serum vitamin C and hs-CRP levels in the development of cardiovascular disease thus warrant further investigation. © 2009 Elsevier Ireland Ltd. All rights reserved.
Kindler S.,University of Greifswald |
Samietz S.,University of Greifswald |
Houshmand M.,University of Greifswald |
Grabe H.J.,University of Greifswald |
And 8 more authors.
Journal of Pain | Year: 2012
Previous studies have associated depression and temporomandibular joint disorders (TMDs). The temporality, however, remains to be clarified. Most patient studies have selected subjects from treatment facilities, whereas in epidemiological studies a clinical examination has not been performed. In this study the 5-year follow-up data of the population-based Study of Health in Pomerania (SHIP) were analyzed. To estimate the effect of symptoms of depression and those of anxiety on the risk of TMD pain, the Composite International Diagnostic-Screener (CID-S) and a clinical functional examination with palpation of the temporomandibular joint and the masticatory muscles were used. After exclusion of subjects having joint pain at baseline, a sample of 3,006 Caucasian participants with a mean age of 49 years resulted. Of those, 122 participants had signs of TMD joint pain upon palpation. Subjects with symptoms of depression had an increased risk of TMD joint pain upon palpation (rate ratio: 2.1; 95% confidence interval: 1.5-3.0; P < .001). Anxiety symptoms were associated with joint and with muscle pain. The diagnosis, prevention, and therapy of TMD pain should also consider symptoms of depression and those of anxiety, and appropriate therapies if necessary. Perspective: Depressive and anxiety symptoms should be considered as risk factors for TMD pain. Depressive symptoms are specific for joint pain whereas anxiety symptoms are specific for muscle pain, findings that deserve detailed examination. These findings may support decision-making in treating TMD. © 2012 by the American Pain Society.
Meyer J.,Institute of Community Medicine |
Fredrich D.,Institute of Community Medicine |
Piegsa J.,Institute of Community Medicine |
Habes M.,Institute of Community Medicine |
And 2 more authors.
Computer Methods and Programs in Biomedicine | Year: 2013
A Central Data Management (CDM) system based on electronic data capture (EDC) software and study specific databases is an essential component for assessment and management of large data volumes in epidemiologic studies. Conventional CDM systems using web applications for data capture depend on permanent access to a network. However, in many study settings permanent network access cannot be guaranteed, e.g. when participants/patients are visited in their homes. In such cases a different concept for data capture is needed. The utilized EDC software must be able to ensure data capture as stand-alone instance and to synchronize captured data with the server at a later point in time. This article describes the design of the mobile information capture (MInCa) system an EDC software meeting these requirements. In particular, we focus on client and server design, data synchronization, and data privacy as well as data security measures. The MInCa software has already proven its efficiency in epidemiologic studies revealing strengths and weaknesses concerning both concept and practical application which will be addressed in this article. © 2012 Elsevier Ireland Ltd.
Meyer J.,Institute of Community Medicine |
Ostrzinski S.,Institute of Community Medicine |
Fredrich D.,Institute of Community Medicine |
Havemann C.,Institute of Community Medicine |
And 2 more authors.
Computer Methods and Programs in Biomedicine | Year: 2012
This article describes the concept of a "Central Data Management" (CDM) and its implementation within the large-scale population-based medical research project "Personalized Medicine" The CDM can be summarized as a conjunction of data capturing, data integration, data storage, data refinement, and data transfer. A wide spectrum of reliable "Extract Transform Load" (ETL) software for automatic integration of data as well as "electronic Case Report Forms" (eCRFs) was developed, in order to integrate decentralized and heterogeneously captured data. Due to the high sensitivity of the captured data, high system resource availability, data privacy, data security and quality assurance are of utmost importance. A complex data model was developed and implemented using an Oracle database in high availability cluster mode in order to integrate different types of participant-related data. Intelligent data capturing and storage mechanisms are improving the quality of data. Data privacy is ensured by a multi-layered role/right system for access control and de-identification of identifying data. A well defined backup process prevents data loss. Over the period of one and a half year, the CDM has captured a wide variety of data in the magnitude of approximately 5. terabytes without experiencing any critical incidents of system breakdown or loss of data. The aim of this article is to demonstrate one possible way of establishing a Central Data Management in large-scale medical and epidemiological studies. © 2011 Elsevier Ireland Ltd.
PubMed | Institute of Community Medicine
Type: Journal Article | Journal: Computer methods and programs in biomedicine | Year: 2012
This article describes the concept of a Central Data Management (CDM) and its implementation within the large-scale population-based medical research project Personalized Medicine. The CDM can be summarized as a conjunction of data capturing, data integration, data storage, data refinement, and data transfer. A wide spectrum of reliable Extract Transform Load (ETL) software for automatic integration of data as well as electronic Case Report Forms (eCRFs) was developed, in order to integrate decentralized and heterogeneously captured data. Due to the high sensitivity of the captured data, high system resource availability, data privacy, data security and quality assurance are of utmost importance. A complex data model was developed and implemented using an Oracle database in high availability cluster mode in order to integrate different types of participant-related data. Intelligent data capturing and storage mechanisms are improving the quality of data. Data privacy is ensured by a multi-layered role/right system for access control and de-identification of identifying data. A well defined backup process prevents data loss. Over the period of one and a half year, the CDM has captured a wide variety of data in the magnitude of approximately 5terabytes without experiencing any critical incidents of system breakdown or loss of data. The aim of this article is to demonstrate one possible way of establishing a Central Data Management in large-scale medical and epidemiological studies.