Search

BackgroundA mobile app generates passive data, such as GPS data traces, without any direct involvement from the user. These passive data have transformed the manner of traditional assessments that require active participation from the user. Passive data collection is one of the most important core techniques for mobile health development because it may promote user retention, which is a unique characteristic of a software medical device. ObjectiveThe primary aim of this study was to quantify user retention for the “Staff Hours” app using survival analysis. The secondary aim was to compare user retention between passive data and active data, as well as factors associated with the survival rates of user retention. MethodsWe developed an app called “Staff Hours” to automatically calculate users’ work hours through GPS data (passive data). “Staff Hours” not only continuously collects these passive data but also sends an 11-item mental health survey to users monthly (active data). We applied survival analysis to compare user retention in the collection of passive and active data among 342 office workers from the “Staff Hours” database. We also compared user retention on Android and iOS platforms and examined the moderators of user retention. ResultsA total of 342 volunteers (224 men; mean age 33.8 years, SD 7.0 years) were included in this study. Passive data had higher user retention than active data (P=.011). In addition, user retention for passive data collected via Android devices was higher than that for iOS devices (P=.015). Trainee physicians had higher user retention for the collection of active data than trainees from other occupations, whereas no significant differences between these two groups were observed for the collection of passive data (P=.700). ConclusionsOur findings demonstrated that passive data collected via Android devices had the best user retention for this app that records GPS-based work hours.

Purpose: Periprosthetic joint infection (PJI) is a leading cause of joint arthroplasty failure, potentially leading to critical complications like vertebral osteomyelitis (VO). The factors contributing to VO after PJI and the outcomes for these patients are not well understood. Our study aims to (1) identify risk factors for VO following PJI and (2) assess the clinical outcomes in these cases. Methods: We included PJI patients treated surgically at our centre from January 2006 to December 2020, excluding those with simultaneous VO post‐PJI. Our focus was on patients with VO occurring after PJI, monitored for at least 5 years. Analysis included patient comorbidities, PJI treatment approaches, pathogen identification and clinical outcomes. Results: Of 1701 PJI cases, 21 (1.23%) developed VO. Key risk factors for VO post‐PJI were identified: systemic inflammatory response syndrome, substance misuse, polymicrobial infection and undergoing at least three stages of resection arthroplasty (odds ratios: 1.86, 54.28, 52.33 and 31.88, respectively). Adverse outcomes were noted in VO patients, with recurrent VO in 6/21 and repeated PJIs in 18/21 cases. Conclusions: Patients with PJI, especially those with certain risk factors, have an increased likelihood of developing VO and encountering negative outcomes. The potential role of bacteremia in the development of VO after PJI needs further exploration. Level of Evidence: Level III. [ABSTRACT FROM AUTHOR]

Multiple mediation analysis is a powerful methodology to assess causal effects in the presence of multiple mediators. Several methodologies, such as G‐computation and inverse‐probability‐weighting, have been widely used to draw inferences about natural indirect effects (NIEs). However, a limitation of these methods is their potential for model misspecification. Although powerful semiparametric methods with high robustness and consistency have been developed for inferring average causal effects and for analyzing the effects of a single mediator, a comparably robust method for multiple mediation analysis is still lacking. Therefore, this theoretical study proposes a method of using multiply robust estimators of NIEs in the presence of multiple ordered mediators. We show that the proposed estimators not only enjoy the multiply robustness to model misspecification, they are also consistent and asymptotically normal under regular conditions. We also performed simulations for empirical comparisons of the finite‐sample properties between our multiply robust estimators and existing methods. In an illustrative example, a dataset for liver disease patients in Taiwan is used to examine the mediating roles of liver damage and liver cancer in the pathway from hepatitis B/C virus infection to mortality. The model is implemented in the open‐source R package "MedMR." [ABSTRACT FROM AUTHOR]

Causal mediation analysis is advantageous for mechanism investigation. In settings with multiple causally ordered mediators, path-specific effects have been introduced to specify the effects of certain combinations of mediators. However, most path-specific effects are unidentifiable. An interventional analog of path-specific effects is adapted to address the non-identifiability problem. Moreover, previous studies only focused on cases with two or three mediators due to the complexity of the mediation formula in a large number of mediators. In this study, we provide a generalized definition of traditional path-specific effects and interventional path-specific effects with a recursive formula, along with the required assumptions for nonparametric identification. Subsequently, a general approach is developed with an arbitrary number of multiple ordered mediators and with time-varying confounders. All methods and software proposed in this study contribute to comprehensively decomposing a causal effect confirmed by data science and help disentangling causal mechanisms in the presence of complicated causal structures among multiple mediators. [ABSTRACT FROM AUTHOR]

The genus Laticauda (Reptilia: Elapidae), commonly known as sea kraits, is venomous marine amphibious snakes distributed throughout the south and southeast Asian islands and mostly found in coastal waters. To facilitate genetic studies, we have developed microsatellite loci for L. semifasciata using the 454 GS-FLX pyrosequencing technique. A total of 65,680 sequences containing a minimum of five repeat motifs were identified from 451,659 reads. Among 80 loci containing more than nine repeat units, 34 primer sets (42.5%) produced strong PCR products, of which 21 were polymorphic among 36 samples of L. semifasciata. All loci exhibited high genetic variability, with an average of 7.38 alleles per locus, and the mean observed and expected heterozygosities were 0.73 and 0.76, respectively. The cross-species amplification of these loci in two laticaudine species, L. colubrina and L. laticaudata, revealed a high transferability (78.6%) and polymorphism (59.5%) of the loci. Our work demonstrated the utility of next-generation 454 sequencing as the rapid and cost-effective method for development of microsatellite markers. The high level of polymorphism in these microsatellite loci will be useful for the detection of population subdivision and the study of migration, gene flow, relatedness and philopatry of L. semifasciata and other laticaudine species. [ABSTRACT FROM AUTHOR]

A main goal of epidemiology is to provide an impact of an exposure on health outcomes. The attributable fraction (AF) is a widely used measure for quantifying its contribution. Various methods have been developed to estimate AF, including standardization, inverse probability of treatment weighting, and doubly robust methods. However, the validity of these methods is established based on the conditional exchangeability assumption, which cannot be tested using only observed data. To assess how vulnerable the research findings are to departures from this assumption, researchers need to conduct a sensitivity analysis. In this study, we propose novel sensitivity analysis methods for AF. Sensitivity analysis problems are formulated as optimization problems, and analytic solutions for the problem are derived. We illustrate our proposed sensitivity analysis methods with a publicly available dataset and examine how the AF of the mother's smoking status during pregnancy for low birth weight changes to the degree of unmeasured confounding., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: The Mendelian randomization approach uses genetic variants as instrumental variables to study the causal association between the risk factors and health outcomes of interest. We aimed to examine the relation between alcohol consumption and cardiovascular risk factors using two genetic variants as instrumental variables: alcohol dehydrogenase 1B ( ADH1B ) rs1229984 and aldehyde dehydrogenase 2 ( ALDH2 ) rs671., Methods: Using data collected in the Taiwan Biobank-an ongoing, prospective, population-based cohort study-our analysis included 129,032 individuals (46,547 men and 82,485 women) with complete data on ADH1B rs1229984 and ALDH2 rs671 genotypes and alcohol drinking status. We conducted instrumental variables regression analysis to examine the relationship between alcohol drinking and body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting glucose, glycated hemoglobin (HbA1c), triglycerides, high-density lipoprotein cholesterol (HDLc), and low-density lipoprotein cholesterol (LDLc)., Results: In the rs1229984-instrumented analysis, alcohol drinking was only associated with higher levels of SBP in men and lower levels of DBP in women. In the rs671-instrumented analysis, alcohol drinking was associated with higher levels of BMI, SBP, DBP, fasting glucose, triglycerides, HDLc and lower levels of LDLc in men; alcohol drinking was associated with higher levels of HDLc and lower levels of SBP, HbA1c, and triglycerides in women., Conclusion: Using Mendelian randomization analysis, some of our study results among men echoed findings from the previous systematic review, suggesting that alcohol drinking may be causally associated with higher levels of BMI, SBP, DBP, fasting glucose, triglycerides, HDLc, and lower levels of LDLc. Although alcohol drinking is beneficial to a few cardiovascular risk factors, it is detrimental to many others. The assumptions that underlie the Mendelian randomization approach should also be carefully examined when interpreting findings from such studies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Chien, Wong, Tai, Shr and Yu.)