Open-Source Intelligence for Detection of Radiological Events and Syndromes Following the Invasion of Ukraine in 2022: Observational Study

BACKGROUND

On February 25, 2022, Russian forces took control of the Chernobyl power plant after continuous fighting within the Chernobyl exclusion zone. Continual events occurred in the month of March, which raised the risk of potential contamination of previously uncontaminated areas and the potential for impacts on human and environmental health. The disruption of war has caused interruptions to normal preventive activities, and radiation monitoring sensors have been nonfunctional. Open-source intelligence can be informative when formal reporting and data are unavailable.

OBJECTIVE

This paper aimed to demonstrate the value of open-source intelligence in Ukraine to identify signals of potential radiological events of health significance during the Ukrainian conflict.

METHODS

Data were collected from search terminology for radiobiological events and acute radiation syndrome detection between February 1 and March 20, 2022, using 2 open-source intelligence (OSINT) systems, EPIWATCH and Epitweetr.

RESULTS

Both EPIWATCH and Epitweetr identified signals of potential radiobiological events throughout Ukraine, particularly on March 4 in Kyiv, Bucha, and Chernobyl.

CONCLUSIONS

Open-source data can provide valuable intelligence and early warning about potential radiation hazards in conditions of war, where formal reporting and mitigation may be lacking, to enable timely emergency and public health responses.

Development of a framework to assess the quality of data sources in healthcare settings

OBJECTIVE

The purpose of this study was to develop a framework to assess the quality of healthcare data sources.

MATERIALS AND METHODS

First, a systematic review was performed and a thematic analysis of included literature conducted to identify items relating to the quality of healthcare data sources. Second, expert advisory group meetings were held to explore experts' perception of the results of the review and identify gaps in the findings. Third, a framework was developed based on the findings.

RESULTS

Synthesis of the review results and expert advisory group meetings resulted in 8 parent themes and 22 subthemes. The parent themes were: Governance, leadership, and management; Data; Trust; Context; Monitoring; Use of information; Standardization; Learning and training. The 22 subthemes were: governance, finance, organization, characteristics, time, data management, data quality, ethics, access, security, quality improvement, monitoring and feedback, dissemination, analysis, research, standards, linkage, infrastructure, documentation, definitions and classification, learning, and training.

DISCUSSION

The herein presented framework was developed using a robust methodology which included reviewing literature and extracting data source quality items, filtering, and matching items, developing a list of themes, and revising them based on expert opinion. To the best of our knowledge, this study is the first to apply a systematic approach to identify aspects related to the quality of healthcare data sources.

CONCLUSIONS

The framework, can assist those using healthcare data sources to identify and assess the quality of a data source and inform whether the data sources used are fit for their intended use.

Antimicrobial Consumption in Latin American Countries: First Steps of a Long Road Ahead

Background:

Irrational antimicrobial consumption (AMC) became one of the main global health problems in recent decades.

Objective:

In order to understand AMC in Latin-American Region, we performed the present research in 6 countries.

Methods:

Antimicrobial consumption (J01, A07A, P01AB groups) was registered in Argentina, Chile, Colombia, Costa Rica, Paraguay, and Peru. Source of information, AMC type, DDD (Defined Daily Doses), DID (DDD/1000 inhabitants/day), population were variables explored. Data was analyzed using the Global Antimicrobial Resistance and Use Surveillance System (GLASS) tool.

Results:

Source of information included data from global, public, and private sectors. Total AMC was highly variable (range 1.91-36.26 DID). Penicillin was the most consumed group in all countries except in Paraguay, while macrolides and lincosamides were ranked second. In terms of type of AMC according to the WHO-AWaRe classification, it was found that for certain groups like “Reserve,” there are similarities among all countries.

Conclusion and Relevance:

This paper shows the progress that 6 Latin-American countries made toward AMC surveillance. The study provides a standardized approach for building a national surveillance system for AMC data analysis. These steps will contribute to the inclusion of Latin-America among the regions of the world that have periodic, regular, and quality data of AMC.

NextGen Public Health Surveillance and the Internet of Things (IoT)

Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.

Introducing Depth Information Into Generative Target Tracking

Common visual features used in target tracking, including colour and grayscale, are prone to failure in a confusingly similar-looking background. As the technology of three-dimensional visual information acquisition has gradually gained ground in recent years, the conditions for the wide use of depth information in target tracking has been made available. This study focuses on discussing the possible ways to introduce depth information into the generative target tracking methods based on a kernel density estimation as well as the performance of different methods of introduction, thereby providing a reference for the use of depth information in actual target tracking systems. First, an analysis of the mean-shift technical framework, a typical algorithm used for generative target tracking, is described, and four methods of introducing the depth information are proposed, i.e., the thresholding of the data source, thresholding of the density distribution of the dataset applied, weighting of the data source, and weighting of the density distribution of the dataset. Details of an experimental study conducted to evaluate the validity, characteristics, and advantages of each method are then described. The experimental results showed that the four methods can improve the validity of the basic method to a certain extent and meet the requirements of real-time target tracking in a confusingly similar background. The method of weighting the density distribution of the dataset, into which depth information is introduced, is the prime choice in engineering practise because it delivers an excellent comprehensive performance and the highest level of accuracy, whereas methods such as the thresholding of both the data sources and the density distribution of the dataset are less time-consuming. The performance in comparison with that of a state-of-the-art tracker further verifies the practicality of the proposed approach. Finally, the research results also provide a reference for improvements in other target tracking methods in which depth information can be introduced.

Knowledge about, attitude toward, and practices in skin care among Thai adolescents

BACKGROUND

Skin care for maintaining skin integrity includes cleansing, skin product use, and photoprotection. Inappropriate skin care can lead to skin problems.

AIMS

To evaluate the knowledge, attitude, and practices in skin care among Thai adolescents.

PATIENTS/METHODS

Questionnaire-based, descriptive, cross-sectional study.

RESULTS

A total of 588 Thai adolescent students (mean age: 15.6 ± 1.8 years, 50.5% female) were included. Of those who responded, 99.5% knew the benefits of cleansing, and 95.9% knew the benefits of skin care products. Skin products, moisturizer, and sunscreen were used by 87.8%, 80.8%, and 71.5% of students, respectively. Female teenagers used moisturizers, cosmetics, and sunscreen significantly more than males (p = 0.001, p = 0.001, and p < 0.001, respectively). High school teenagers applied cosmetics more than junior high school teenagers (p = 0.004). Ninety-three percent of adolescents knew the effects of sunlight, but only 27.4% regularly applied sunscreen. The sources of knowledge were from person, online social media, print media, and television/radio in 88.5%, 77.5%, 30.7%, and 26.1%, respectively. Data from physicians and parents were trusted by 65.3% and 64.2%, respectively. Most (74.1%) adolescents searched for data from more than 1 source. Adolescent females and high school adolescents demonstrated significantly more accurate knowledge and practice in cleansing and photoprotection (p < 0.001) compared with adolescent males and junior high school adolescents. Knowledge and practices did not significantly correlate with underlying skin diseases or monthly allowance.

CONCLUSION

Gender and education level were found to significantly influence knowledge and practice in skin care among adolescents in Thailand.

The Danish Multiple Sclerosis Registry

OBJECTIVES

The Danish Multiple Sclerosis Registry is the oldest operative and nationwide MS registry. We present The Danish Multiple Sclerosis Registry with its history, data collection, scientific contribution, and national and international research collaboration.

MATERIALS AND METHODS

Detailed description of data collection, completeness, quality optimizing procedures, funding, and legal, ethical and data protection issues are provided.

RESULTS

The total number of registered cases with clinical isolated syndrome and multiple sclerosis since 1956 was by start of May 2020 30,023 of whom 16,515 cases were alive and residing in Denmark, giving a prevalence rate of about 284 per 100,000 population. The mean annual number of new cases receiving an MS diagnosis was 649 per year in the period 2010 to 2019. In total, 7,945 patients (48.1%) are receiving disease modifying therapy at the start of May 2020.

CONCLUSIONS

Multiple Sclerosis registers are becoming increasingly important, not only for epidemiological research but also by quantifying the burden of the disease for the patients and society and helping health care providers and regulators in their decisions. The Danish Multiple Sclerosis Registry has served as data source for a number of scientific publications including epidemiological studies on changes in incidence and mortality, cohort studies investigating risk factors for developing MS, comorbidities and socioeconomic outcomes in the MS population, and observational studies on effectiveness of disease modifying treatments outside the narrow realms of randomized clinical trials.

Challenges With Identifying Indication for Examination in Breast Imaging as a Key Clinical Attribute in Practice, Research, and Policy

PURPOSE

To assess indication for examination for four breast imaging modalities and describe the complexity and heterogeneity of data sources and ascertainment methods.

METHODS

Indication was evaluated among the Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) breast cancer research centers (PRCs). Indication data were reported overall and separately for four breast imaging modalities: digital mammography (DM), digital breast tomosynthesis (DBT), ultrasound (US), and magnetic resonance imaging (MRI).

RESULTS

The breast PRCs contributed 236,262 women with 607,735 breast imaging records from 31 radiology facilities. We found a high degree of heterogeneity for indication within and across six data sources. Structured codes within a data source were used most often to identify indication for mammography (59% DM, 85% DBT) and text analytics for US (45%) and MRI (44%). Indication could not be identified for 17% of US and 26% of MRI compared with 2% of mammography examinations (1% DM, 3% DBT).

CONCLUSIONS

Multiple and diverse data sources, heterogeneity of ascertainment methods, and nonstandardization of codes within and across data systems for determining indication were found. Consideration of data sources and standardized methodology for determining indication is needed to assure accurate measurement of cancer screening rates and performance in clinical practice and research.

Medication records in the emergency department: agreement between paper-based charts and automated dispensing device

BACKGROUND

Research exploring the agreement between traditional medication records and electronic records generated by an automated dispensing device has been limited.

OBJECTIVE

To evaluate the extent of agreement between medication administration records written in paper-based emergency department charts and records generated by an automated dispensing device with regard to the presence or absence of a single, prespecified medication.

METHODS

Medication administration records in paper-based emergency department charts and medication dispensation records generated by an automated dispensing device were evaluated for concordance. The primary outcome measure was agreement between the 2 sources with regard to the presence or absence of a record for salbutamol by metered-dose inhaler (MDI) for randomly selected patients who presented to a pediatric emergency department with wheeze-related illness from January 1, 2008, to December 31, 2009.

RESULTS

In total, 1172 patient visits met the inclusion criteria. Of these, records for 1013 visits showed agreement between the paper-based emergency department chart and the dispensation record of the automated dispensing device (kappa = 0.71, 95% confidence interval 0.67-0.75). This value did not meet the target kappa of 0.80. Stratification by time of day, day of week, month, season, or year of presentation at triage or by triage level or disposition (whether or not the patient was admitted to the hospital ward) did not significantly affect the level of agreement between the 2 sources.

CONCLUSIONS

Agreement between records of salbutamol MDI administration in paper-based charts and dispensation records from an automated dispensing device was substantial, but discrepancies were present. There are significant quality management, legal, clinical, and research reasons to strive for concordance between multiple records with respect to medication use in the emergency department. Data generated by automated dispensing devices have potential value for research, but their strengths and limitations need to be understood.

Practical issues in handling data input and uncertainty in a budget impact analysis

The objective of this paper was to address the importance of dealing systematically and comprehensively with uncertainty in a budget impact analysis (BIA) in more detail. The handling of uncertainty in health economics was used as a point of reference for addressing the uncertainty in a BIA. This overview shows that standard methods of sensitivity analysis, which are used for standard data set in a health economic model (clinical probabilities, treatment patterns, resource utilisation and prices/tariffs), cannot always be used for the input data for the BIA model beyond the health economic data set for various reasons. Whereas in a health economic model, only limited data may come from a Delphi panel, a BIA model often relies on a majority of data taken from a Delphi panel. In addition, the dataset in a BIA model also includes forecasts (e.g. annual growth, uptakes curves, substitution effects, changes in prescription restrictions and guidelines, future distribution of the available treatment modalities, off-label use). As a consequence, the use of standard sensitivity analyses for BIA data set might be limited because of the lack of appropriate distributions as data sources are limited, or because of the need for forecasting. Therefore, scenario analyses might be more appropriate to capture the uncertainty in the BIA data set in the overall BIA model.