Pi-Ching Yu

Dr. Pi-Ching Yu


Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
5 September 2024 in JMIR Public Health and Surveillance
Verified
Journal Article
Open access
FLFang Chen Liu
Fang Chen Liu
BCBao-Chung Chen
Bao-Chung Chen
YHYao-Ching Huang
Yao-Ching Huang
SHShi-Hao Huang
Shi-Hao Huang
RCRen-Jei Chung
Ren-Jei Chung
CYChia-Peng Yu
Chia-Peng Yu
Background: Young children are susceptible to enterovirus (EV) infections, which cause significant morbidity in this age group. Objective: This study investigated the characteristics of virus strains and the epidemiology of EVs circulating among young children in Taiwan from 2011 to 2020. Methods: Children diagnosed with EV infections from 2011 to 2020 were identified from the routine national health insurance data monitoring disease system, real-time outbreak and disease surveillance system, national laboratory surveillance system, and Statistics of Communicable Diseases and Surveillance Report, a data set (secondary data) of the Taiwan Centers for Disease and Control. Four primary outcomes were identified: epidemic features, characteristics of sporadic and cluster cases of EV infections, and main cluster institutions. Results: From 2011 to 2020, between 10 and 7600 person-times visited the hospitals for EV infections on an outpatient basis daily. Based on 2011 to 2020 emergency department EV infection surveillance data, the permillage of EV visits throughout the year ranged from 0.07‰ and 25.45‰. After typing by immunofluorescence assays, the dominant type was coxsackie A virus (CVA; 8844/12,829, 68.9%), with most constituting types CVA10 (n=2972), CVA2 (n=1404), CVA6 (n=1308), CVA4 (n=1243), CVA16 (n=875), and CVA5 (n=680); coxsackie B virus CVB (n=819); echovirus (n=508); EV-A71 (n=1694); and EV-D68 (n=10). There were statistically significant differences (P<.001) in case numbers of EV infections among EV strains from 2011 to 2020. Cases in 2012 had 15.088 times the odds of being EV-A71, cases in 2014 had 2.103 times the odds of being CVA, cases in 2015 had 1.569 times the odds of being echovirus, and cases in 2018 had 2.274 times the odds of being CVB as cases in other years. From 2011 to 2020, in an epidemic analysis of EV clusters, 57 EV clusters were reported. Clusters that tested positive included 53 (53/57, 93%) CVA cases (the major causes were CVA6, n=32, and CVA10, n=8). Populous institutions had the highest proportion (7 of 10) of EV clusters. Conclusion: This study is the first report of sporadic and cluster cases of EV infections from surveillance data (Taiwan Centers for Disease and Control, 2011‐2020). This information will be useful for policy makers and clinical experts to direct prevention and control activities to EV infections that cause the most severe illness and greatest burden to the Taiwanese.

14 October 2022 in Medicine
Verified
Journal Article
Open access
SHShih-Chun Hsing
Shih-Chun Hsing
CCChu-Chieh Chen
Chu-Chieh Chen
SHShi-Hao Huang
Shi-Hao Huang
YHYao-Ching Huang
Yao-Ching Huang
RCRen-Jei Chung
Ren-Jei Chung
STShih-En Tang
Shih-En Tang
To investigate whether previous exposure to obstructive sleep apnea (OSA) increases the risk of obesity in obese and nonobese patients. We identified 24,363 obese patients diagnosed between January 1, 2000, and December 31, 2015, in the Taiwan Longitudinal Health Insurance Database (LHID) 2005 National Health Insurance Research Database; 97,452 sex-, age- and index date-matched nonobese patients were identified from the same database. This study is based on the ninth edition of the International Classification of Sleep Disorders. Multiple logistic regression was used to analyze the previous exposure of obese patients to OSA. P < .05 was considered significant. The average age of 121,815 patients was 44.30 ± 15.64 years old; 42.77% were males, and 57.23% were females. Obese patients were more likely to be exposed to OSA than nonobese patients (adjusted odds ratio [AOR] = 2.927, 95% CI = 1.878–4.194, P < .001), and the more recent the exposure period was, the more severely obese the patient, with a dose-response effect (OSA exposure < 1 year, AOR = 3.895; OSA exposure 1 year, <5 years, AOR = 2.933; OSA exposure 5 years, AOR = 2.486). The probability of OSA exposure in obese patients was 2.927 times that in nonobese patients, and the longer the exposure duration was, the more severe the obesity situation, with a dose-response effect (OSA exposure < 1 year, AOR = 2.251; OSA exposure 1 year, <5 years, AOR = 2.986; OSA exposure 5 years, AOR = 3.452). The risk of obesity in subjects with OSA was found to be significantly higher in this nested case–control study; in particular, a longer exposure to OSA was associated with a higher likelihood of obesity, with a dose-response effect.

YHYao-Ching Huang
Yao-Ching Huang
SHShi-Hao Huang
Shi-Hao Huang
RCRen-Jei Chung
Ren-Jei Chung
BWBING-LONG WANG
BING-LONG WANG
CCChi-Hsiang Chung
Chi-Hsiang Chung
CLChieh-Hua Lu
Chieh-Hua Lu
Objective: To investigate whether central sleep apnea (CSA) is associated with an increased risk of obesity. Materials and methods: From 1 January 2000 to 31 December 2015, we screened 24,363 obese patients from the 2005 longitudinal health insurance database, which is part of the Taiwan National Health Insurance Research Database. From the same database, 97,452 non-obese patients were also screened out. Age, gender, and index dates were matched. Multiple logistic regression was used to analyze the previous exposure risk of obese and CSA patients. A p-value of <0.05 was considered significant. Results: Obese patients were more likely to be exposed to CSA than non-obese patients would (AOR = 2.234, 95% CI = 1.483–4.380, p < 0.001). In addition, the closeness of the exposure time to the index time is positively correlated with the severity of obesity and has a dose–response effect (CSA exposure < 1 year, AOR = 2.386; CSA exposure ≥ 1 year and <5 years, AOR = 1.725; CSA exposure time ≥ 5 years, AOR = 1.422). The CSA exposure time of obese patients was 1.693 times that of non-obese patients. Longer exposure time is associated with more severe obesity and has a dose-response effect (CSA exposure < 1 year, AOR = 1.420; CSA exposure ≥ 1 year and <5 years, AOR = 2.240; CSA ≥ 5 years, AOR = 2.863). Conclusions: In this case-control study, patients with CSA had a significantly increased risk of obesity. Long-term exposure to CSA and obesity is more likely and has a dose-response effect.

YHYao-Ching Huang
Yao-Ching Huang
CYChia-Peng Yu
Chia-Peng Yu
BWBING-LONG WANG
BING-LONG WANG
RCRen-Jei Chung
Ren-Jei Chung
ILIau-Jin Lin
Iau-Jin Lin
SWSheng-Tang Wu
Sheng-Tang Wu
This study aims to understand the trend distribution of violent injuries in Taiwan from 2000 to 2015. It used the data of outpatient, emergency, and hospitalization of 2 million people in the National Health Insurance sample from 2000 to 2015. We analyzed children and adolescents (hereinafter referred to as children, 0–17 years old), adults (18–64 years old), and The Elderly (over 65 years old) who suffered for the first time. The standardized rate of medical treatment for violent injuries was compared annually using the Poisson regression method. A total of 11,077 victims (7163 men, 3914 women) suffered violence during the 15 years, and the standardized rate of medical treatment for violence in adults dropped from 6.01 (1/104) in 2001 to 2.58 (1/104) in 2015. The standardized rate of medical treatment in adults over the years was higher than that in children (2.962001, 1.232015) and The Elderly (3.522001, 1.622015). The medical treatment rate of the adult generation is higher than that of the children and the elderly. The relative hazard ratio (RR) decreased from 2.38 in 2001 to 1.13 in 2014 (but the RR in 2014 was not significant). Furthermore, the rate of adult violence treatment has been decreasing every year, which shows that the government has achieved remarkable results in general violence prevention. With the accelerated aging of Taiwan’s population, it is expected that older adults exposed to the risk of violence will also increase and become more serious. Therefore, the government should continue to pay attention to this issue.

SHShi-Hao Huang
Shi-Hao Huang
ILIau-Jin Lin
Iau-Jin Lin
PYPi-Ching Yu
Pi-Ching Yu
BWBING-LONG WANG
BING-LONG WANG
CCChi-Hsiang Chung
Chi-Hsiang Chung
GWGwo-Jang Wu
Gwo-Jang Wu
Objective: To investigate whether children with maltreatment exposure were associated with the risk of psychiatric disorders, suicide, and death. Methods: A retrospective cohort study was conducted, with 1592 child maltreatment cases and 6368 comparison cohort (1:4) matched for gender, age, and index year, from the Longitudinal Generation Tracking Database (LGTD2000) sampled from Taiwan National Health Insurance Research Database (NHIRD) in 2000, backtracking between 2000–2015 in Taiwan. The stratified Cox regression model was used to compare the risk of developing a mental illness and poor prognosis during the 15 years of follow-up. Results: There were 473 in the cohort with child maltreatment (675.10 cases per 100,000 person years) and 1289 in the comparison cohort (453.82 cases per 100,000 person years) that developed mental illness and poor prognosis. The stratified Cox regression model revealed that the adjusted hazard ratio (HR) was 1.91 to 11.76 (p < 0.05) after adjusting for monthly income level, occupation, and CCI after violence. Conclusion: Exposure to child maltreatment is associated with the risk of psychiatric disorders, but not suicide or death. This finding could be a reminder for clinicians about the mental health problems in patients with child maltreatment.

PYPi-Ching Yu
Pi-Ching Yu
RCRen-Jei Chung
Ren-Jei Chung
YHYao-Ching Huang
Yao-Ching Huang
SHShi-Hao Huang
Shi-Hao Huang
BWBING-LONG WANG
BING-LONG WANG
GWGwo-Jang Wu
Gwo-Jang Wu
Objectives: To investigate whether chronic obstructive pulmonary disease (COPD) affects nonapnea sleep disorder (NASD) on the risk of obesity. Materials and Methods: From 1 January 2000 to 31 December 2015, a total of 24,363 patients with obesity from the 2005 Longitudinal Health Insurance Database were identified; 97,452 patients without obesity were also identified from the same database. Multiple logistic regression was used to analyze the previous exposure risk of patients with obesity and NASD. A p value of <0.05 was considered significant. Results: The risk of developing obesity in patients with COPD is 3.05 times higher than that in patients without COPD. Patients with COPD with NASD had a 1.606-fold higher risk of developing obesity than those without NASD. Patients with obesity were more likely to be exposed to NASD than did those without obesity (adjusted odds ratio, 1.693; 95% confidence interval, 1.575–1.821, p < 0.001). Furthermore, the closeness of the exposure period to the index time was positively associated with the severity of obesity, with a dose–response effect. The exposure duration of NASD in patients with obesity was 1.693 times than that in those without obesity. Longer exposure durations were associated with more severe obesity, also with a dose–response effect. Conclusions: The COPD effect of NASD increases the subsequent risk of obesity, and the risk of obesity was determined to be significantly higher in patients with NASD in this case–control study. Longer exposure to NASD was associated with a higher likelihood of obesity, also with a dose–response effect.