Exploring the tradeoff between data privacy and utility with a clinical data analysis use case.

BACKGROUND: Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset's utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset's utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility. METHODS: Predictive modeling of emergency department length of stay was used as a data analysis use case. A logistic regression model was developed with 1155 patient cases extracted from a clinical data warehouse of an academic medical center located in Seoul, South Korea. Nineteen de-identified datasets were generated based on various de-identification configurations using ARX, an open-source software for anonymizing sensitive personal data. The variable distributions and prediction results were compared between the de-identified datasets and the original dataset. We examined the association between data privacy and utility to determine whether it is feasible to identify a viable tradeoff between the two. RESULTS: All 19 de-identification scenarios significantly decreased re-identification risk. Nevertheless, the de-identification processes resulted in record suppression and complete masking of variables used as predictors, thereby compromising dataset utility. A significant correlation was observed only between the re-identification reduction rates and the ARX utility scores. CONCLUSIONS: As the importance of health data analysis increases, so does the need for effective privacy protection methods. While existing guidelines provide a basis for de-identifying datasets, achieving a balance between high privacy and utility is a complex task that requires understanding the data's intended use and involving input from data users. This approach could help find a suitable compromise between data privacy and utility.

Educational Needs Assessment for Family Caregivers of Dementia Patients.

People living with dementia are highly dependent on caregivers. We conducted an online survey with regard to caregivers' educational experiences, needs, and expectations. We found that most of the participants lacked educational experiences and expected updated methods through metaverse in virtual reality. Therefore, future studies should verify the effectiveness of education.

Quality of Person-Generated Healthy Walking Data: An Explorative Analysis.

Despite the potential benefits of Person Generated Health Data (PGHD), data quality issues impede its use. This study examined the effect of different methods for filtering armband data on determining the amount of healthy walking and the consistency between healthy walking captured using armbands and health diaries. Four weeks of armband and health diary data were acquired from 103 college students. Armband data filtering was performed using heart rate measures and minimum daily step counts as a proxy for adequate daily wear time. No substantial differences in the filtered armband datasets were observed by filtering methods. Significant gaps were observed between healthy walking amounts determined from armband data and through the health diary. Future studies need to explore more diverse data filtering methods and their impact on health outcome assessments.

From intention to action: The factors affecting health data sharing intention and action.

INTRODUCTION: Effective prevention and treatment of diseases requires utilization of health-related lifestyle data, which has thus become increasingly important. According to some studies, participants were willing to share their health data for use in medical care and research. Although intention does not always accurately reflect action, few studies have examined the question of whether data-sharing intention leads to data-sharing action. OBJECTIVE: The aim of this study was to examine the extent of actualizing data-sharing intention to data-sharing action and to identify the factors that influence data-sharing intention and action. METHODS: A web-based survey of members of a university examined the data-sharing intention and issues of concern when making decisions on data sharing. The participants were asked to deposit their armband data for use in research at the end of the survey. A comparison of data-sharing intention and action in relation to the participants' characteristics was performed. Factors having a significant effect on data-sharing intention and action were identified using logistic regressions. RESULTS: Of 386 participants, 294 expressed willingness to share health data. However, only 73 participants deposited their armband data. The primary reason for refusal to deposit armband data was the inconvenience of the data transfer process (56.3%). Appropriate compensation had a significant effect on data-sharing intention (OR: 3.3, CI: 1.86-5.75) and action (OR: 2.8, CI: 1.14-8.21). The compensation for data sharing (OR:2.8, CI:1.14-8.21) and familiarity with data (OR:3.1, CI:1.36-8.21) were significant predictors of data sharing action, however, data-sharing intention was not (OR: 1.5, CI:0.65-3.72). CONCLUSION: Despite expressing willingness to share their health data, the participants' intention was not actualized to data-sharing behavior for depositing armband data. Implementation of a streamlined data transfer process and providing appropriate compensation might facilitate data-sharing. These findings could be useful in development of strategies to facilitate sharing and reuse of health data.

Paenibacillus swuensis sp. nov., a bacterium isolated from soil.

Strain DY6(T), a Gram-positive endospore-forming motile rod-shaped bacterium, was isolated from soil in South Korea and characterized to determine its taxonomic position. Phylogenetic analyses based on the 16S rRNA gene sequence of strain DY6(T) revealed that strain DY6(T) belongs to the genus Paenibacillus in the family Paenibacillaceae in the class Bacilli. The highest degree of sequence similarities of strain DY6(T) were found with Paenibacillus gansuensis B518(T) (97.9%), P. chitinolyticus IFO 15660(T) (95.3%), P. chinjuensis WN9T (94.7%), and P. rigui WPCB173(T) (94.7%). Chemotaxonomic data revealed that the predominant fatty acids were anteiso-C(15:0) (38.7%) and C(16:0) (18.0%). A complex polar lipid profile consisted of major amounts of diphosphatidylglycerol, phosphatidylethanolamine, and phosphatidylglycerol. The predominant respiratory quinone was MK-7. Based on these phylogenetic, chemotaxonomic, and phenotypic data, strain DY6(T) (=KCTC 33026(T) =JCM 18491(T)) should be classified as a type strain of a novel species, for which the name Paenibacillus swuensis sp. nov. is proposed.

Small subunit ribosomal RNA and mitochondrial cytochrome c oxidase subunit 1 gene sequences of 21 strains of the parasitic scuticociliate Miamiensis avidus (Ciliophora, Scuticociliatia).

The scuticociliate Miamiensis avidus is a histophagous parasite that causes high mortality in cultured marine fishes. Small subunit ribosomal RNA (SSU rRNA) and mitochondrial cytochrome c oxidase subunit 1 (cox1) genes were analyzed for 21 strains of M. avidus isolated from diseased olive flounder (Paralichthys olivaceus), ridged-eye flounder (Pleuronichthys cornutus), and spotted knifejaw (Oplegnathus fasciatus) in Korea and Japan (collected in 2003-2007). Analysis of SSU rRNA gene sequences (1,759 bp) indicates they are very conserved with less than 0.17% (3 nucleotides) differences suggesting that SSU rRNA are useful to identify M. avidus; however, the cox1 gene (900 bp) has higher variations with intraspecific divergences up to 5.67% (51 nucleotides). A distance tree of cox1 gene sequences based on a neighbor-joining analysis can separate 21 strains into five cox1 types (two heterogeneous clusters and three individual branches). The cox1-type matches with serotype of strains but do not reflect geographical origins, host species, or pathogenicity.