A Remote Monitoring System for Rodent Infestation Based on LoRaWAN.

Rodent infestations are a common problem that can result in several issues, including diseases, damage to property, and crop loss. Conventional methods of controlling rodent infestations often involve using mousetraps and applying rodenticides manually, leading to high manpower expenses and environmental pollution. To address this issue, we introduce a system for remotely monitoring rodent infestations using Internet of Things (IoT) nodes equipped with Long Range (LoRa) modules. The sensing nodes wirelessly transmit data related to rodent activity to a cloud server, enabling the server to provide real-time information. Additionally, this approach involves using images to auxiliary detect rodent activity in various buildings. By capturing images of rodents and analyzing their behavior, we can gain insight into their movement patterns and activity levels. By visualizing the recorded information from multiple nodes, rodent control personnel can analyze and address infestations more efficiently. Through the digital and quantitative sensing technology proposed at this stage, it can serve as a new objective indicator before and after the implementation of medication or other prevention and control methods. The hardware cost for the proposed system is approximately USD 43 for one sensor module and USD 17 for one data collection gateway (DCG). We also evaluated the power consumption of the sensor module and found that the 3.7 V 18,650 Li-ion batteries in series can provide a battery life of two weeks. The proposed system can be combined with rodent control strategies and applied in real-world scenarios such as restaurants and factories to evaluate its performance.

A systematic overview of rare disease patient registries: challenges in design, quality management, and maintenance.

Patient registries serve to overcome the research limitations inherent in the study of rare diseases, where patient numbers are typically small. Despite the value of real-world data collected through registries, adequate design and maintenance are integral to data quality. We aimed to describe an overview of the challenges in design, quality management, and maintenance of rare disease registries.A systematic search of English articles was conducted in PubMed, Ovid Medline/Embase, and Cochrane Library. Search terms included "rare diseases, patient registries, common data elements, quality, hospital information systems, and datasets". Inclusion criteria were any manuscript type focused upon rare disease patient registries describing design, quality monitoring or maintenance. Biobanks and drug surveillances were excluded.A total of 37 articles, published between 2001 and 2021, met the inclusion criteria. Patient registries covered a wide range of disease areas and covered multiple geographical locations, with a predisposition for Europe. Most articles were methodological reports and described the design and setup of a registry. Most registries recruited clinical patients (92%) with informed consent (81%) and protected the collected data (76%). Whilst the majority (57%) collected patient-reported outcome measures, only few (38%) consulted PAGs during the registry design process. Few reports described details regarding quality management (51%) and maintenance (46%).Rare disease patient registries are valuable for research and evaluation of clinical care, and an increasing number have emerged. However, registries need to be continuously evaluated for data quality and long-term sustainability to remain relevant for future use.

Cognitive evaluation in unaccompanied refugee children: a systematic review.

OBJECTIVE: This study aims to identify what existing literature has shown about possible cognitive alterations in unaccompanied refugee children. DATA SOURCES: The search was performed in the Web of Science, PsycInfo, Scopus, and PubMed databases, including articles published in any year and in any language. The research was submitted to the Prospero protocol (ID: CRD42021257858), and the quality of the included articles was evaluated using the Mixed Methods Appraisal Tool. DATA SYNTHESIS: Memory and attention are the main topics identified, largely because they are related to symptoms of post-traumatic stress disorder. However, low specificity was observed in the conduction of cognitive assessments, leading to relevant inconsistencies in the collected data. CONCLUSIONS: The use of psychological assessment instruments that are either poorly adapted or not adapted at all to the populations studied casts doubt on the validity of the data produced so far.

Definition, Composition, and Harmonization of Core Datasets Within the German Center for Lung Research.

Core datasets are the composition of essential data items for a certain research scope. As they state commonalities between heterogeneous data collections, they serve as a basis for cross-site and cross-disease research. Therefore, researchers at the national and international levels have addressed the problem of missing core datasets. The German Center for Lung Research (DZL) comprises five sites and eight disease areas and aims to gain further scientific knowledge by continuously promoting collaborations. In this study, we elaborated a methodology for defining core datasets in the field of lung health science. Additionally, through support of domain experts, we have utilized our method and compiled core datasets for each DZL disease area and a general core dataset for lung research. All included data items were annotated with metadata and where possible they were assigned references to international classification systems. Our findings will support future scientific collaborations and meaningful data collections.

OpenDataPsy: An Open-Data Repository with Standardized Storage and Description for Research in Psychiatry.

Sharing health data could avoid duplication of effort in data collection, reduce unnecessary costs in future studies, and encourage collaboration and data flow within the scientific community. Several repositories from national institutions or research teams have making their datasets available. These data are mainly aggregated at spatial or temporal level, or dedicated to a specific field. The objective of this work is to propose a standardized storage and description of open datasets for research purposes. For this, we selected 8 publicly accessible datasets, covering the fields of demographics, employment, education and psychiatry. Then, we studied the format, nomenclature (i.e., files and variables names, modalities of recurrent qualitative variables) and descriptions of these datasets and we proposed on common and standardized format and description. We made available these datasets in an open gitlab repository. For each dataset, we proposed the raw data file in its original format, the cleaned data file in csv format, the variables description, the data management script and the descriptive statistics. Statistics are generated according to the type of variables previously documented. After one year of use, we will evaluate with the users if the standardization of the data sets is relevant and how they use the dataset in real life.

Data Collection and Analysis Methods for Smart Nudging to Promote Physical Activity: Protocol for a Mixed Methods Study.

New digital technologies like activity trackers, nudge concepts, and approaches can inspire and improve personal health. There is increasing interest in employing such devices to monitor people's health and well-being. These devices can continually gather and examine health-related information from people and groups in their familiar surroundings. Context-aware nudges can assist people in self-managing and enhancing their health. In this protocol paper, we describe how we plan to investigate what motivates people to engage in physical activity (PA), what influences them to accept nudges, and how participant motivation for PA may be impacted by technology use.

Health Research Requires Efficient Platforms for Data Collection from Personal Devices.

Data from consumer-based devices for collecting personal health-related data could be useful in diagnostics and treatment. This requires a flexible and scalable software and system architecture to handle the data. This study examines the existing mSpider platform, addresses shortcomings in security and development, and suggests a full risk analysis, a more loosely coupled component- based system for long term stability, better scalability, and maintainability. The goal is to create a human digital twin platform for an operational production environment.

Model-Driven Dementia Prevention and Intervention Platform.

Most types of dementia, including Alzheimer's disease, are not curable. However, there are risk factors, such as obesity or hypertension, that can promote the development of dementia. Holistic treatment of these risk factors can prevent the onset of dementia or delay it in its early stages. To support individualized treatment of risk factors in dementia, this paper presents a model-driven digital platform. It enables monitoring of biomarkers using smart devices from the internet of medical things (IoMT) for the target group. The collected data from such devices can be used to optimize and adjust treatment in a patient in the loop manner. To this end, providers such as Google Fit and Withings have been connected to the platform as example data sources. To achieve treatment and monitoring data interoperability with existing medical systems, internationally accepted standards such as FHIR are used. The configuration and control of the personalized treatment processes are achieved using a self-developed domain-specific language. For this language, an associated diagram editor was implemented, which allows the management of the treatment processes through graphical models. This graphical representation should help treatment providers to understand and manage these processes more easily. To investigate this hypothesis, a usability study was conducted with twelve participants. We were able to show that such graphical representations provide advantages in clarity in reviewing the system, but lack in easy set-up (compared to wizard-style systems).

Cross-Registry Benchmarking of Data Quality: Lessons Learned.

Feedback of data quality measures to study sites is an established procedure in the management of registries. Comparisons of data quality between registries as a whole are missing. We implemented a cross-registry benchmarking of data quality within the field of health services research for six projects. Five (2020) and six (2021) quality indicators were selected from a national recommendation. The calculation of the indicators was adjusted to the registries' specific settings. Nineteen (2020) and 29 results (2021) could be included in the yearly quality report. Seventy-four per cent (2020) and 79% (2021) of the results did not include the threshold in their 95%-confidence-limits. The benchmarking revealed several starting points for a weak-point analysis through a comparison of results with a predefined threshold as well as through comparisons among each other. In the future, a cross-registry benchmarking might be part of services provided through a health services research infrastructure.

The Smart Data Extractor, a Clinician Friendly Solution to Accelerate and Improve the Data Collection During Clinical Trials.

In medical research, the traditional way to collect data, i.e. browsing patient files, has been proven to induce bias, errors, human labor and costs. We propose a semi-automated system able to extract every type of data, including notes. The Smart Data Extractor pre-populates clinic research forms by following rules. We performed a cross-testing experiment to compare semi-automated to manual data collection. 20 target items had to be collected for 79 patients. The average time to complete one form was 6'81" for manual data collection and 3'22" with the Smart Data Extractor. There were also more mistakes during manual data collection (163 for the whole cohort) than with the Smart Data Extractor (46 for the whole cohort). We present an easy to use, understandable and agile solution to fill out clinical research forms. It reduces human effort and provides higher quality data, avoiding data re-entry and fatigue induced errors.

DeepTSE: A Time-Sensitive Deep Embedding of ICU Data for Patient Modeling and Missing Data Imputation.

Missing data is a common problem in the intensive care unit as a variety of factors contribute to incomplete data collection in this clinical setting. This missing data has a significant impact on the accuracy and validity of statistical analyses and prognostic models. Several imputation methods can be used to estimate the missing values based on the available data. Although simple imputations with mean or median generate reasonable results in terms of mean absolute error, they do not account for the currentness of the data. Furthermore, heterogeneous time span of data records adds to this complexity, especially in high-frequency intensive care unit datasets. Therefore, we present DeepTSE, a deep model that is able to cope with both, missing data and heterogeneous time spans. We achieved promising results on the MIMIC-IV dataset that can compete with and even outperform established imputation methods.

The Inverse Data Law: Market Imperatives, Data, and Quality in AI Supported Care.

Over the last decade, the explosion of "Big Data" and its fusion with AI has led many to believe that the development and integration of AI systems in healthcare will usher in a transformative revolution that democratises access to high quality healthcare and collectively improve patient outcomes. However, the nature of market forces in the evolving data economy, has started to show evidence that the opposite is more likely to be true. This paper argues that there is a poorly understood "Inverse Data Law" that will exacerbate the widening health divide between affluent and marginalised communities because: (1) data used to train AI systems favour individuals that are already engaged with healthcare, who have the lowest burden of disease, but the highest purchasing power; and (2) data used to drive market decisions around investment in AI health technology favours tools that increase the commodification of healthcare through over-testing, over-diagnosis, and the acute and episodic management of disease, over tools that support the patient to prevent disease. This dangerous combination is more likely to cripple efforts towards preventative medicine, as data collection and utilisation tends to be inversely proportional to the needs of the patients served - the inverse data law. The paper concludes by introducing important methodological considerations in the design and evaluation of AI systems to promote systems improvement for marginalised users.

Monkeypox resurgence and its implications for Dentistry - A scoping review.

INTRODUCTION: Monkeypox caused by the Monkeypox virus, a member of the Orthopoxvirus genus, is currently considered a major concern among healthcare authorities due to its high transmissibility rate. Currently, no specific treatment is available for this disease, due to which healthcare professionals, specifically Dentists, are required to look out for symptoms at early stages to prevent its spread. OBJECTIVE: To analyze the role of dentists in identifying Monkeypox cases and limiting its spread. METHODS: We conducted a scoping review on monkeypox and its oral manifestation. PRISMA protocols were observed in data collection. The relevant literature search was conducted in relevant databases like PubMed, Scopus, Web of Science, Embase, CINAHL, and Google Scholar. Relevant articles related to Monkeypox, and Dentistry were included in the final review. Articles published from March 2022- September 2022 were included in the review. Keywords and Mesh words related to monkeypox, and dentistry were used as part of the search strategy. RESULTS: A total of 1881 articles were reviewed, among which 7 articles were included. Dentists were strongly advised to be on high alert for Monkeypox symptoms due to their close contact with patients. Around 70% of Monkeypox cases reported oral lesions at early stages, which requires a differential diagnosis from other oral lesions. Considering this, dentists should be well-versed in this new and emerging threat. CONCLUSION: Although dentists have been shown to play an important role in the treatment of monkeypox, there is insufficient data available. More research on dentistry and monkeypox will be needed in the near future.

Five tips for conducting remote qualitative data collection in COVID times: theoretical and pragmatic considerations.

OBJECTIVE: To provide five methodological and pragmatic tips for conducting remote qualitative data collection during the context of the COVID-19 pandemic. METHOD: The tips presented in this article are drawn from insights of our own experiences as researchers conducting remote qualitative research and from the evidence from the literature on qualitative methods. The relevant literature was identified through searches using relevant keywords in the following databases: CINAHL, PubMed, SCOPUS, and Web of Science. Searches were limited to articles in English and Portuguese, published from 2010 to 2021, to ensure a current understanding of the phenomenon. RESULTS: Five tips are provided: 1) Pay attention to ethical issues; 2) Identify and select potential participants; 3) Choose the type of remote interview; 4) Be prepared to conduct the remote interview; and 5) Build rapport with the participant. CONCLUSION: Despite the challenges in conducting remote data collection, strengths are also acknowledged and our experience has shown that it is feasible to recruit and interview participants remotely. The discussions presented in this article will benefit, now and in the future, other research teams who may consider collecting data for their qualitative studies remotely.

What data collection methods work best for COVID19 outbreak surveillance for people with end stage kidney disease? An observational cohort study using the UK Renal Registry.

BACKGROUND: Patients on kidney replacement therapy (KRT) are vulnerable to severe illness from COVID-19. Timely, accurate surveillance is essential for planning and implementing infection control at local, regional and national levels. Our aim was to compare two methods of data collection for COVID-19 infections amongst KRT patients in England. METHODS: Adults receiving KRT in England were linked to two sources of data on positive COVID-19 tests recorded March-August 2020: (1) submissions from renal centres to the UK Renal Registry (UKRR) and (2) Public Health England (PHE) laboratory data. Patient characteristics, cumulative incidence by modality (in-centre haemodialysis (ICHD), home HD, peritoneal dialysis (PD) and transplant), and 28-day survival were compared between the two sources. RESULTS: 2,783/54,795 patients (5.1%) had a positive test in the combined UKRR-PHE dataset. Of these 2,783, 87% had positive tests in both datasets. Capture was consistently high for PHE (> 95% across modalities) but varied for UKRR (ranging from ICHD 95% to transplant 78%, p < 0.0001). Patients captured only by PHE were more likely to be on transplant or home therapies (OR 3.5 95% CI [2.3-5.2] vs. ICHD) and to be infected in later months (OR 3.3 95%CI [2.4-4.6] for May-June, OR 6.5 95%CI [3.8-11.3] for July-August, vs. March-April), compared to patients in both datasets. Stratified by modality, patient characteristics and 28-day survival were similar between datasets. CONCLUSIONS: For patients undergoing ICHD treatment the collection of data submitted directly by renal centres allows constant monitoring in real time. For other KRT modalities, using a national swab test dataset through frequent linkage may be the most effective method. Optimising central surveillance can improve patient care by informing interventions and assisting planning at local, regional and national levels.

[Comparative study of medical common data models for FAIR data sharing].

The common data model (CDM) is an important tool to facilitate the standardized integration of multi-source heterogeneous healthcare big data, enhance the consistency of data semantic understanding, and promote multi-party collaborative analysis. The data collections standardized by CDM can provide powerful support for observational studies, such as large-scale population cohort study. This paper provides an in-depth comparative analysis of the data storage structure, term mapping pattern, and auxiliary tools development of the three international typical CDMs, then analyzes the advantages and limitations of each CDM and summarizes the challenges and opportunities faced in the CDM application in China. It is expected that exploring the advanced technical concepts and practical patterns of foreign countries in data management and sharing will provide references for promoting FAIR (findable, accessible, interoperable, reusable) construction of healthcare big data in China and solving the current practical problems, such as the poor quality of data resources, the low degree of semantization, and the inabilities of data sharing and reuse.

Frailty in Older Adults and Internal and Forced Migration in Urban Neighborhood Contexts in Colombia.

Objective: We investigated the association between the density of internal human migration, in the urban neighborhood, on frailty in the older adult population in Colombia. Methods: The data used in this study are from four Colombian population surveys. We analyzed 633 census tracts with a sample of 2,194 adults 60 years and over for frailty (measured using the Fried criteria). We considered the proportion of inhabitants in a census tract with a history of internal migration as the exposure variable considering three temporalities. For contextual forced migration, we identified two types: 5-year, and 1-year. Poisson multivariable regression models with two hierarchical levels (individual and census tracts) were estimated. Results: The prevalence of pre-fragile/frailty was 80.63% [CI 95%: 77.67, 83.28]. The prevalence ratio were significantly higher for the older adults who live in neighborhoods where a higher proportion of internal migrants reside. Conclusion: We conclude that older adults who lived in neighborhoods with a high proportion of internal migrants experience more frailty. Potential explanations are that neighborhoods with high internal migration could experience social (l increase in cultural heterogeneity, in the perception of insecurity, violence and physical conditions (pressure on local economies and services, leading elderly residents to compete for neighborhood resources), translated into social stress.

Challenges of Integrating APOL1 Genetic Test Results into the Electronic Health Record.

OBJECTIVES: Integrating genetic test results into the electronic health record (EHR) is essential for integrating genetic testing into clinical practice. This article describes the organizational challenges of integrating discrete apolipoprotein L1 (APOL1) genetic test results into the EHR for a research study on culturally sensitive genetic counseling for living kidney donors. METHODS: We convened a multidisciplinary team across three institutions (Northwestern University, Northwestern Memorial HealthCare [NMHC], and OHSU Knight Diagnostic Laboratories [KDL]), including researchers, physicians, clinical information technology, and project management. Through a series of meetings over a year between the team and the genetic testing laboratory, we explored and adjusted our EHR integration plan based on regulatory and budgetary constraints. RESULTS: Our original proposal was to transmit results from KDL to NMHC as structured data sent via Health Level Seven (HL7) v2 message. This was ultimately deemed infeasible given the time and resources required to establish the interface, and the low number of samples to be processed for the study (n = 316). We next explored the use of Epic's Care Everywhere interoperability platform, but learned it was not possible as a laboratory test ordered for a research study; even though our intent was to study the APOL1 genetic test result's clinical use and impact, test results were still considered "research results." Faced with two remaining options-downloading a PDF from the KDL laboratory portal or scanning a faxed result from KDL-only a PDF of the APOL1 test result could be integrated into the EHR, reinforcing the status quo. CONCLUSION: Even with early and ongoing stakeholder engagement, dedicated project management, and funding, unanticipated implementation challenges-especially for research projects-can result in drastic design tradeoffs.

REDbox: a comprehensive semantic framework for data collection and management in tuberculosis research.

Clinical research outcomes depend on the correct definition of the research protocol, the data collection strategy, and the data management plan. Furthermore, researchers often need to work within challenging contexts, as is the case in tuberculosis services, where human and technological resources for research may be scarce. Electronic Data Capture Systems mitigate such risks and enable a reliable environment to conduct health research and promote result dissemination and data reusability. The proposed solution is based on needs pinpointed by researchers, considering the need for an accommodating solution to conduct research in low-resource environments. The REDbox framework was developed to facilitate data collection, management, sharing, and availability in tuberculosis research and improve the user experience through user-friendly, web-based tools. REDbox combines elements of the REDCap and KoBoToolbox electronic data capture systems and semantics to deliver new valuable tools that meet the needs of tuberculosis researchers in Brazil. The framework was implemented in five cross-institutional, nationwide projects to evaluate the users' perceptions of the system's usefulness and the information and user experience. Seventeen responses (representing 40% of active users) to an anonymous survey distributed to active users indicated that REDbox was perceived to be helpful for the particular audience of researchers and health professionals. The relevance of this article lies in the innovative approach to supporting tuberculosis research by combining existing technologies and tailoring supporting features.