Browsing by Author "Lin, Sheng-Hsuan"

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Cardiac Autonomic Modulation during on-Call Duty under Working Hours Restriction
(2020-02-01)
Chien, Jien-Wen
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Chen, Chung-Yen
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Lin, Sheng-Hsuan
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Lin, Shih-Wen
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Lin, Yu-Hsuan
Background: Medical residency is a time of high stress and long working hours, which increase the risk of cardiovascular disease. This study aimed to investigate the autonomic modulation of resident physicians throughout the on-call duty cycle. Methods: Spectral analysis of heart rate variability (HRV) was used to compute cardiac parasympathetic modulation (high-frequency power, HF) and cardiac sympathetic modulation (normalized low-frequency power, LF%, and the ratio of LF and HF, LF/HF) of 18 residents for a consecutive 4-day cycle. Results: Male residents show reduced cardiac sympathetic modulation (i.e., higher LF/HF and LF%) than the female interns. Medical residents' cardiac parasympathetic modulation (i.e., HF) significantly increased on the first and the second post-call day compared with the pre-call day. In contrast, LF% was significantly decreased on the first and the second post-call day compared with the pre-call day. Similarly, LF/HF was significantly decreased on the second post-call day compared with the pre-call day. LF/HF significantly decreased on the first post-call day and on the second post-call day from on-call duty. Conclusion: The guideline that limits workweeks to 80 h and shifts to 28 h resulted in reduced sympathetic modulation and increased parasympathetic modulation during the two days following on-call duty.
Development of a mobile application (App) to delineate "digital chronotype" and the effects of delayed chronotype by bedtime smartphone use
(2019-03-01)
Lin, Yu-Hsuan
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Wong, Bo-Yu
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Lin, Sheng-Hsuan
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Chiu, Yu-Chuan
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Pan, Yuan-Chien
The widespread use and deep reach of smartphones motivate the use of mobile applications to continuously monitor the relationship between circadian system, individual sleep patterns, and environmental effects. We selected 61 adults with 14-day data from the "Know Addiction" database. We developed an algorithm to identify the "sleep time" based on the smartphone behaviors. The total daily smartphone use duration and smartphone use duration prior to sleep onset were identified respectively. We applied mediation analysis to investigate the effects of total daily smartphone use on sleep through pre-sleep use (PS). The results showed participants' averaged pre-sleep episodes within 1 h prior to sleep are 2.58. The duration of three pre-sleep uses (PS1-3) maybe a more representative index for smartphone use before sleep. Both total daily duration and the duration of the last three uses prior to sleep of smartphone use significantly delayed sleep onset, midpoint of sleep and reduced total sleep time. One hour of increased smartphone use daily, delays the circadian rhythm by 3.5 min, and reduced 5.5 min of total sleep time (TST). One hour of increased pre-sleep smartphone use delayed circadian rhythm by 1.7 min, and reduced 39 s of TST. The mediation effects of PSI 3 significantly impacted on these three sleep indicators. PS1-3 accounted for 14.3% of total daily duration, but the proportion mediated of delayed circadian rhythm was 44.0%. We presented "digital chronotype" with an automatic system that can collect high temporal resolution data from naturalistic settings with high ecological validity. Smartphone screen time, mainly mediated by pre-sleep use, delayed the circadian rhythm and reduced the total sleep time.
Impact of Cumulative Unhealthy Sleep Practices in Adolescence on Substance Use in Young Adulthood Estimated Using Marginal Structural Modeling
(2020-04-09)
Ho, Chia-Yi
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Lin, Sheng-Hsuan
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Tsai, Meng-Che
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Yu, Tsung
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Strong, Carol
Objectives The purpose of this study was to identify the impact of chronic, unhealthy sleep practices in adolescence on substance use in young adulthood. Unhealthy sleep practices in adolescent samples exhibit a bidirectional relationship with substance use. The relationship is further complicated if we consider that confounders such as depression vary over time and are often in response to adolescents' prior poor sleep practice, which can be addressed by a counterfactual approach using a marginal structural model. Methods Data in this study are from the Taiwan Youth Project, a longitudinal study that started in 2000 and surveyed 2,690 7th grade students at age 13. Outcomes include frequency of cigarette smoking and alcohol drinking at age 21. Three unhealthy sleep practices were included in this study: short sleep, social jetlag, and sleep disturbance. We used a marginal structural model with stabilized inverse probability-of-treatment weights to address time-varying confounders in each wave and a total sample of 1,678 adolescents with complete information for this study. Results Accumulated waves of sleep disturbance and social jetlag in adolescence were significantly associated with cigarette use in young adulthood. Accumulated social jetlag but not sleep disturbance was also associated with alcohol use in adulthood. Accumulated waves of short sleep were not associated with later alcohol use, but were negatively correlated with cigarette use. Conclusion Interventions that aim to reduce the likelihood of substance use in young adulthood should consider confronting unhealthy sleep practices, in particular the discrepancy between bedtimes on school days and weekends and sleep disturbance.
Mediation analysis for new recognition criteria, working hours and overwork-related disease: a nationwide ecological study using 11-year follow-up data in Taiwan
(2019-08-01)
Lin, Sheng-Hsuan
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Chou, Meng-Ying
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Lin, Ro-Ting
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統計學研究所
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Institute of Statistics
Objectives Taiwan revised its criteria for overwork-related cerebrovascular and cardiovascular disease (CCVD) in 2010. A new definition of overwork increased the number of recognised cases. Meanwhile, actual average working hours decreased. We estimated the effects of the revised criteria on the number of overwork-related CCVD cases and the mediation effect through reduced working hours. Methods From the Labor Insurance of Taiwan, we collected data on the total number of overwork-related CCVD cases from 2006 to 2016 and average monthly working hours for 13 industry groups. We conducted causal mediation analysis to investigate the mechanism of the effect of new criteria on CCVD mediated by working hours. Results From 2006 to 2016, 594 overwork-related cases of CCVD were recognised across 13 industry groups. After introducing the new criteria, overwork-related CCVD increased by 8.40 cases (per one million person-years) (95% CI 4.53 to 15.05), which resulted from a decrease of 1.54 (95% CI 0.22 to 3.82) cases due to reduced working hours (mediation effect) and an increase of 9.93 (95% CI 5.24 to 18.17) cases related to the effect of the criteria change and other covariates excluding working hours (alternative effect). Conclusions Working hours are an important mediator of the effect of policy on the rate of overwork-related CCVD. Introducing new criteria for recognising overwork-related disease might raise awareness and prompt reductions in working hours, which also help to reduce CCVD. Our findings suggest that understanding mediation effects is important to evaluating national health policies.
On identification of agonistic interaction: Hepatitis B and C interaction on hepatocellular carcinoma
(2019-06-15)
Lin, Sheng-Hsuan
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Huang, Yen-Tsung
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Yang, Hwai-I
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統計學研究所
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Institute of Statistics
Agonistic interaction is one of the most important types of mechanistic interaction, which is difficult to be distinguished from synergistic interaction by empirical data. In this study, we propose four approaches that suffice to identify and estimate the agonistic interaction: (1) to make a strong assumption that synergism does not exist; (2) to exploit information from a third factor by assuming that this factor is a necessary component for the background condition of synergistic interaction but is not involved in other mechanisms; (3) to consider a third factor necessary for the background condition of agonistic interaction but not involved in other mechanisms; and (4) similar to (3) but to allow flexibility that the third factor may have a main effect on the outcome and/or a synergistic effect with the two risk factors of interest. We applied the proposed methods to quantify the agonism of Hepatitis B and C viruses (HBV and HCV) infections on liver cancer using a Taiwanese cohort study (n = 23 820; HBV carrier n = 4149 (17.44%), HCV carrier n = 1313 (5.52%)). The result demonstrated that agonistic interaction is more dominant compared with synergistic interaction, which explains the findings that the dual infected patients do not have a significantly higher risk of liver cancer than those with single infection. By exploiting an additional risk factor that satisfies certain assumptions, these approaches potentially fill the gap between mechanistic and causal interactions, contributing the comprehensive understanding of causal mechanisms.
On the relationship of machine learning with causal inference
(2020-02-01)
Lin, Sheng-Hsuan
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Ikram, Mohammad Arfan
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統計學研究所
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Institute of Statistics
PROPHYLACTIC ANTIBIOTIC REDUCES THE RISK OF PERITONITIS AFTER INVASIVE GYNECOLOGIC PROCEDURES
(2019-07-01)
Fan, Pei-Yi
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Chan, Ming-Jen
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Lin, Sheng-Hsuan
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Wu, Hsin-Hsu
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Chang, Ming-Yang
Background: Peritonitis is a serious complication after invasive procedures in patients undergoing peritoneal dialysis (PD). Most studies that have investigated peritonitis following invasive gynecologic procedures enrolled small patient populations. This study focuses on the clinical presentation, outcomes, and effects of prophylactic antibiotic use before invasive techniques. Methods: A retrospective study was conducted on patients who underwent invasive gynecologic procedures between 2005 and 2015 in a tertiary medical center. Eligible patients were identified and enrolled and their demographic data were collected. The use of prophylactic antibiotics and the outcomes of peritonitis were recorded. Results: Twenty-six gynecologic procedures were performed on 18 PD patients. Seven episodes of peritonitis occurred in 6 patients after invasive gynecologic procedures. Eleven procedures were preceded by prophylactic antibiotic treatment (6 oral cefadroxil, 1 oral cefuroxime, 1 oral clindamycin, 1 intravenous [IV] ceftriaxone, 1 IV ceftazidime, and 1 IV cefazolin). The pathogens were diverse (group B Streptococcus, group D Streptococcus, E. coli, and Enterococcus). All episodes of peritonitis were successfully treated using intraperitoneal antibiotics without recurrence, technique failure, or mortality. The odds ratio of peritonitis in the non-prophylaxis group was 20.29 (95% confidence interval 1.01 - 406.35, p = 0.0103). Conclusion: The use of prophylactic antibiotic treatment considerably reduced the risk of peritonitis after invasive gynecologic procedures.
Six-way decomposition of causal effects: Unifying mediation and mechanistic interaction
(1970-01-01)
Huang, Yen-Tsung
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Tai, An-Shun
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Chou, Meng-Ying
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Lin, Geng-Xian
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Lin, Sheng-Hsuan
The sufficient component cause (SCC) model and counterfactual model are two common methods for causal inference, each with their own advantages: the SCC model allows the mechanistic interaction to be detailed, whereas the counterfactual model features a systemic framework for quantifying causal effects. Hence, integrating the SCC and counterfactual models may facilitate the conceptualization of causation. Based on the marginal SCC (mSCC) model, we propose a novel counterfactual mSCC framework that includes the steps of definition, identification, and estimation. We further propose a six-way effect decomposition for assessing mediation and the mechanistic interaction. The results demonstrate that when all variables are binary, the six-way decomposition is an extension of four-way decomposition and that without agonism, the six-way decomposition is reduced to four-way decomposition. To illustrate the utility of the proposed decomposition, we apply it to a Taiwanese cohort to examine the mechanism of hepatitis C virus (HCV)-induced hepatocellular carcinoma (HCC) with liver inflammation measured by alanine aminotransferase (ALT) as a mediator. Among the HCV-induced HCC cases, 62.27% are not explained by either mediation or interaction in relation to ALT; 9.32% are purely mediated by ALT; 16.53% are caused by the synergistic effect of HCV and ALT; and 9.31% are due to the mediated synergistic effect of HCV and ALT. In summary, we introduce an SCC model framework based on counterfactual theory and detail the required identification assumptions and estimation procedures; we also propose a six-way effect decomposition to unify mediation and mechanistic interaction analyses.
Stochastic approach for mechanistic interaction under longitudinal studies with noninformative right censoring
(1970-01-01)
Lin, Jui-Hsiang
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Lin, Kuan-I
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Lee, Wen-Chung
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Lin, Sheng-Hsuan
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統計學研究所
Characterizing the mechanistic interactions between exposures and diseases is one of the most critical issues in epidemiologic studies. Previous studies have proposed a stochastic sufficient component cause framework, under which each sufficient cause is treated as a stochastic process instead of a time-invariant random variable. However, different types of mechanistic interactions such as synergism and agonism cannot be further identified. In this study, we proposed a stochastic marginal sufficient component cause model to conceptualize and identify agonism and synergism by exploiting the additional information. We further provided six approaches to identify and estimate agonism and synergism based on an additive hazard model and a complementary log model. Researchers can easily adjust confounding factors by including appropriate covariates into a regression model. Simulations have proven that approaches under three models are all valid tests. The power of an additive hazard model increases as the total follow-up time increases and is higher than that of the other two models. We applied this method to a Taiwanese cohort data set to investigate the mechanistic interaction among hepatitis B and C viruses on the incidence of hepatocellular carcinoma. The hazard of people with agonistic interaction is 1.28x10(-5) (95% CI: 6.97x10(-6), 1.87x10(-5)), and the cumulative hazard of those people is 7.41x10(-2) (95% CI: 4.09x10(-2), 1.07x10(-1)), which is approximately 3.5 times stronger than that of synergistic interaction. The proposed method makes it possible to quantify different types of mechanistic interactions in longitudinal studies with censored data.
Temporal Stability of Smartphone Use Data: Determining Fundamental Time Unit and Independent Cycle
(2019-03-26)
Pan, Yuan-Chien
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Lin, Hsiao-Han
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Chiu, Yu-Chuan
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Lin, Sheng-Hsuan
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Lin, Yu-Hsuan
Background: Assessing human behaviors via smartphone for monitoring the pattern of daily behaviors has become a crucial issue in this century. Thus, a more accurate and structured methodology is needed for smartphone use research. Objective: The study aimed to investigate the duration of data collection needed to establish a reliable pattern of use, how long a smartphone use cycle could perpetuate by assessing maximum time intervals between 2 smartphone periods, and to validate smartphone use and use/nonuse reciprocity parameters. Methods: Using the Know Addiction database, we selected 33 participants and passively recorded their smartphone usage patterns for at least 8 weeks. We generated 4 parameters on the basis of smartphone use episodes, including total use frequency, total use duration, proactive use frequency, and proactive use duration. A total of 3 additional parameters (root mean square of successive differences, Control Index, and Similarity Index) were calculated to reflect impaired control and compulsive use. Results: Our findings included (1) proactive use duration correlated with subjective smartphone addiction scores, (2) a 2-week period of data collection is required to infer a 2-month period of smartphone use, and (3) smartphone use cycles with a time gap of 4 weeks between them are highly likely independent cycles. Conclusions: This study validated temporal stability for smartphone use patterns recorded by a mobile app. The results may provide researchers an opportunity to investigate human behaviors with more structured methods.