A systematic review of the literature on survey questionnaires to assess self-medication practices (2017)

Dnyanesh Limaye Vaidehi Limaye Gerard Krause Gerhard Fortwengel

Self-medication is of great public health importance as it often bypasses regulatory mechanisms to assure quality of health care. Nevertheless there are no established standards on how to assess self-medication. We therefore intended to systematically retrieve questionnaires and survey tools used to capture self-medication, with the aim to identify the scope of information investigated in this context and commonalities between the tools. We conducted a systematic review of the literature on questionnaires used for self-medication assessment by searching PubMed and Web of Science databases using the combinations of following keywords; self-medication, self-prescription, non-prescription, questionnaire. Truncation was used to ensure retrieval of all possible variations of search terms. The search was limited to articles published between 1st January 2000 and 31st December 2015, human studies and English language. Duplicate and irrelevant studies were excluded from the final review. A total of 158 studies were included in the review. Studies were from diverse geographical locations, most of the studies were from Nigeria 16 (10.1%) followed by India 10 (6.3%) and Iran 8 (5%). Forty-three studies (27.2%) focused on antibiotic self-medication. Majority of the studies (106; 67%) were done with adult populations. The components addressed by the questionnaires covered: reasons for self-medications in 147 (93%) studies, purchasing source in 136 (86%) studies, medical conditions to be treated in 153 (96.8%) studies, adverse events in 67 (42.4%) studies, use of prescribing information in 24 (15.1%) studies and antibiotic resistance awareness in 20 (46.5%) antibiotic studies. For 74 (46.8%) studies, survey questionnaires were self-administered and most studies (57; 36%) were done at homes of respondents. Thirty-seven (23.4%) studies did not report any recall period for self-medication practices. Study response rates varied from 17.9% to 100%, and while validity of the study questionnaire was reported for 100 (63.3%) studies, 15 (9.5%) studies reported reliability test of the study questionnaire. There is a large variety of questionnaires being used for investigating self-medication practices making comparability and meta-analyses very difficult. It is desirable to have a basic set of standardized survey questions on this topic to make available for future research groups in this field.

IT Security in Production Facilities (2017)

Karl-Heinz Niemann

This document concerns IT security in production facilities. It is intended for small and medium-sized enterprises that are looking for a simple procedural model for ensuring IT security in production areas. In order to raise readers’ awareness of IT security in production facilities, security incidents are presented in section 2. It is clear that cyber attacks on production facilities in this day and age are not random, but are instead based on a targeted process. An overview of the most important standards and recommendations on the topic of “IT security in production” then follows in section 3. Section 4 develops a concept for setting up an IT security system for small and medium-sized enterprises (SMEs) on the basis of a ten-point plan. The focus is not only on technical measures, but also in particular on the most frequently neglected organizational measures. Section 5 then describes the outlook for future requirements and solutions in the context of Industry 4.0.

IT-Sicherheit in Produktionsanlagen (2017)

Karl-Heinz Niemann

Dieses Dokument befasst sich mit der IT-Sicherheit von Produktionsanlagen. Es richtet sich an kleine und mittlere Unternehmen, welche auf der Suche nach einem einfachen Vorgehensmodell für die Sicherstellung der IT-Sicherheit im Produktionsbereich sind. Um Leser für die Notwendigkeit der IT-Sicherheit von Produktionsanlagen zu sensibilisieren, werden in Kapitel 2 Sicherheitsvorfälle dargestellt. Es zeigt sich, dass Cyber-Angriffe auf Produktionsanlagen heute kein Zufall mehr sind, sondern auf einem gezielten Vorgehen beruhen. Danach folgt in Kapitel 3 ein Überblick über die wichtigsten Normen und Empfehlungen zum Themenfeld „IT-Sicherheit in der Produktion“. Kapitel 4 entwickelt an Hand eines Zehn-Punkte-Plans ein Konzept für den Aufbau eines IT-Sicherheitssystems für kleine und mittlere Unternehmen (KMU). Dabei liegt der Schwerpunkt nicht nur auf den technischen, sondern insbesondere auch auf den häufig vernachlässigten organisatorischen Maßnahmen. Abschließend gibt Kapitel 5 einen Ausblick auf künftige Anforderungen und Lösungen im Kontext von Industrie 4.0

Antibiotic resistances in livestock: a comparative approach to identify an appropriate regression model for count data (2017)

Anke Hüls Cornelia Frömke Katja Ickstadt Katja Hille Johanna Hering Christiane von Münchhausen Maria Hartmann Lothar Kreienbrock

Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial resistance in livestock holdings. In general, regression models are used to describe these relationships between environmental factors and resistance outcome. Besides the study design, the correlation structures of the different outcomes of antibiotic resistance and structural zero measurements on the resistance outcome as well as on the exposure side are challenges for the epidemiological model building process. The use of appropriate regression models that acknowledge these complexities is essential to assure valid epidemiological interpretations. The aims of this paper are (i) to explain the model building process comparing several competing models for count data (negative binomial model, quasi-Poisson model, zero-inflated model, and hurdle model) and (ii) to compare these models using data from a cross-sectional study on antibiotic resistance in animal husbandry. These goals are essential to evaluate which model is most suitable to identify potential prevention measures. The dataset used as an example in our analyses was generated initially to study the prevalence and associated factors for the appearance of cefotaxime-resistant Escherichia coli in 48 German fattening pig farms. For each farm, the outcome was the count of samples with resistant bacteria. There was almost no overdispersion and only moderate evidence of excess zeros in the data. Our analyses show that it is essential to evaluate regression models in studies analyzing the relationship between environmental factors and antibiotic resistances in livestock. After model comparison based on evaluation of model predictions, Akaike information criterion, and Pearson residuals, here the hurdle model was judged to be the most appropriate model.

Sensors vs. experts - a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients (2011)

Michael Marschollek Anja Rehwald Klaus-Hendrik Wolf Matthias Gietzelt Gerhard Nemitz Hubertus Meyer zu Schwabedissen Mareike Schulze

Background: Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data. Methods: In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients’ fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched. Results: Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores. Conclusions: Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model’s performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach.

Nonparametric relevance-shifted multiple testing procedures for the analysis of high-dimensional multivariate data with small sample sizes (2008)

Cornelia Frömke Ludwig A. Hothorn Siegfried Kropf

Background: In many research areas it is necessary to find differences between treatment groups with several variables. For example, studies of microarray data seek to find a significant difference in location parameters from zero or one for ratios thereof for each variable. However, in some studies a significant deviation of the difference in locations from zero (or 1 in terms of the ratio) is biologically meaningless. A relevant difference or ratio is sought in such cases. Results: This article addresses the use of relevance-shifted tests on ratios for a multivariate parallel two-sample group design. Two empirical procedures are proposed which embed the relevanceshifted test on ratios. As both procedures test a hypothesis for each variable, the resulting multiple testing problem has to be considered. Hence, the procedures include a multiplicity correction. Both procedures are extensions of available procedures for point null hypotheses achieving exact control of the familywise error rate. Whereas the shift of the null hypothesis alone would give straight-forward solutions, the problems that are the reason for the empirical considerations discussed here arise by the fact that the shift is considered in both directions and the whole parameter space in between these two limits has to be accepted as null hypothesis. Conclusion: The first algorithm to be discussed uses a permutation algorithm, and is appropriate for designs with a moderately large number of observations. However, many experiments have limited sample sizes. Then the second procedure might be more appropriate, where multiplicity is corrected according to a concept of data-driven order of hypotheses.

In-vitro antioxidant activity of ethanolic extract of Centaurea behen (2012)

Pushplata Chougule Reshma Pawar Dnyanesh Limaye Y. M. Joshi Vilasrao Kadam

The present investigation was conducted to investigate the in-vitro activity of ethanolic extract of roots of Centaurea behens by using DPPH radical scavenging activity, nitric oxide radical scavenging activity, hydrogen peroxide radical scavenging activity, hydroxyl radical. Result suggests that the extract possess significant antioxidant activity as compared to the standard ascorbic acid and thus further in vivo investigation is required to evaluate the medicinal significance of the extract which can be used for assessing the possible therapeutic importance of the drug.

Wearable sensors in healthcare and sensor-enhanced health information systems: all our tomorrows? (2012)

Michael Marschollek Matthias Gietzelt Mareike Schulze Martin Kohlmann Bianying Song Klaus-Hendrik Wolf

Wearable sensor systems which allow for remote or self-monitoring of health-related parameters are regarded as one means to alleviate the consequences of demographic change. This paper aims to summarize current research in wearable sensors as well as in sensor-enhanced health information systems. Wearable sensor technologies are already advanced in terms of their technical capabilities and are frequently used for cardio-vascular monitoring. Epidemiologic predictions suggest that neuro-psychiatric diseases will have a growing impact on our health systems and thus should be addressed more intensively. Two current project examples demonstrate the benefit of wearable sensor technologies: long-term, objective measurement under daily-life, unsupervised conditions. Finally, up-to-date approaches for the implementation of sensor-enhanced health information systems are outlined. Wearable sensors are an integral part of future pervasive, ubiquitous and person-centered health care delivery. Future challenges include their integration into sensor-enhanced health information systems and sound evaluation studies involving measures of workload reduction and costs.

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups (2012)

Michael Marschollek Mehmet Gövercin Stefan Rust Matthias Gietzelt Mareike Schulze Klaus-Hendrik Wolf Elisabeth Steinhagen-Thiessen

Background: Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients’ assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). Methods: A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital’s data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. Results: The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Conclusions: Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.

Sensor-based fall risk assessment - dagger of the mind? (2013)

Michael Marschollek Mareike Schulze Matthias Gietzelt Nigel H. Lovell Stephen J. Redmond

Fall events and their severe consequences represent not only a threatening problem for the affected individual, but also cause a significant burden for health care systems. Our research work aims to elucidate some of the prospects and problems of current sensor-based fall risk assessment approaches. Selected results of a questionnaire-based survey given to experts during topical workshops at international conferences are presented. The majority of domain experts confirmed that fall risk assessment could potentially be valuable for the community and that prediction is deemed possible, though limited. We conclude with a discussion of practical issues concerning adequate outcome parameters for clinical studies and data sharing within the research community. All participants agreed that sensor-based fall risk assessment is a promising and valuable approach, but that more prospective clinical studies with clearly defined outcome measures are necessary.