Explore the vast range of titles available.

Background People in Taiwan enjoy comprehensive National Health Insurance coverage. However, under the global budget constraint, hospitals encounter enormous challenges. This study was designed to examine Taiwan medical centers' efficiency and factors that influence it. Methods We obtained data from open sources of government routine publications and hospitals disclosed by law to the National Health Insurance Administration, Ministry of Health and Welfare, Taiwan. The dynamic data envelopment analysis ( DDEA) model was adopted to estimate all medical centers' efficiencies during 2015–2018. Beta regression models were used to model the efficiency level obtained from the DDEA model. We applied an input-oriented approach under both the constant returns-to-scale (CRS) and variable returns-to-scale (VRS) assumptions to estimate efficiency. Results The findings indicated that 68.4% (13 of 19) of medical centers were inefficient according to scale efficiency. The mean efficiency scores of all medical centers during 2015–2018 under the CRS, VRS, and Scale were 0.85, 0.930, and 0.95,respectively. Regression results showed that an increase in the population less than 14 years of age, assets, nurse-patient ratio and bed occupancy rate could increase medical centers' efficiency. The rate of emergency return within 3-day and patient self-pay revenues were associated significantly with reduced hospital efficiency ( p  < 0.05). The result also showed that the foundation owns medical center has the highest efficiency than other ownership hospitals. Conclusions The study results provide information for hospital managers to consider ways they could adjust available resources to achieve high efficiency.

This study estimates the efficiency of 19 tertiary hospitals in Taiwan using a two-stage analysis of Data Envelopment Analysis (DEA) and TOBIT regression. It is a retrospective panel-data study and includes all the tertiary hospitals in Taiwan. The data were sourced from open information hospitals legally required to disclose to the National Health Insurance (NHI) Administration, Ministry of Health and Welfare. The variables, including five inputs (total hospital beds, total physicians, gross equipment, fixed assets net value, the rate of emergency transfer in-patient stay over 48 h) and six outputs (surplus or deficit of appropriation, length of stay, the total relative value units [RVUs] for outpatient services, total RVUs for inpatient services, self-pay income, modified EBITDA) were adopted into the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) model. In the CCR model, the technical efficiency (TE) from 2015–2018 increases annually, and the average efficiency of all tertiary hospitals is 96.0%. In the BCC model, the highest pure technical efficiency (PTE) was in 2018 and the average efficiency of all medical centers is 99.1%. The average scale efficiency of all medical centers was 96.8% in the BBC model, meaning investment can be reduced by 3.2% and the current production level can be maintained with a fixed return to scale. Correlation coefficient analysis shows that all variables are correlated positively; the highest was the number of beds and the number of days in hospital (r = 0.988). The results show that TE in the CCR model was similar to PTE in the BCC model in four years. The difference analysis shows that more hospitals must improve regarding surplus or deficit of appropriation, modified EBITDA, and self-pay income. TOBIT regression reveals that the higher the bed-occupancy rate and turnover rate of fixed assets, the higher the TE; and the higher number of hospital beds per 100,000 people and turnover rate of fixed assets, the higher the PTE. DEA and TOBIT regression are used to analyze the other factors that affect medical center efficiency, and different categories of hospitals are chosen to assess whether different years or different types of medical centers affect operational performance. This study provides reference values for the improvable directions of relevant large hospitals’ inefficiency decision-making units through reference group analysis and slack variable analysis.

Background Lead-exposed workers may suffer adverse health effects under the currently regulated blood lead (BPb) levels. However, a probabilistic assessment about lead exposure-associated anemia risk is lacking. The goal of this study was to examine the association between lead exposure and anemia risk among factory workers in Taiwan. Methods We first collated BPb and indicators of hematopoietic function data via health examination records that included 533 male and 218 female lead-exposed workers between 2012 and 2014. We used benchmark dose (BMD) modeling to estimate the critical effect doses for detection of abnormal indicators. A risk-based probabilistic model was used to characterize the potential hazard of lead poisoning for job-specific workers by hazard index (HI). We applied Bayesian decision analysis to determine whether BMD could be implicated as a suitable BPb standard. Results Our results indicated that HI for total lead-exposed workers was 0.78 (95% confidence interval: 0.50–1.26) with risk occurrence probability of 11.1%. The abnormal risk of anemia indicators for male and female workers could be reduced, respectively, by 67–77% and 86–95% by adopting the suggested BPb standards of 25 and 15 μg/dL. Conclusions We conclude that cumulative exposure to lead in the workplace was significantly associated with anemia risk. This study suggests that current BPb standard needs to be better understood for the application of lead-exposed population protection in different scenarios to provide a novel standard for health management. Low-level lead exposure risk is an occupational and public health problem that should be paid more attention.

Mass gatherings, particularly in the context of sporting events, pose distinct public health challenges, particularly regarding the medical needs of athletes. Despite the critical role of on-site medical care in reducing hospital transfers, studies that directly compare injury patterns across various sporting events are scarce. This study analyzed injury rates and severity among athletes participating in various sports during the National Games in Taiwan. By doing so, the study aimed to enhance medical preparedness for future large-scale sporting events. This retrospective study involved a chart review of athletes who sought medical support during the National Games in Taiwan in 2017, 2019, and 2021. Data on patient demographics, event type, injury characteristics, and the need for hospital transport were collected. Sports were categorized in accordance with the American Heart Association and American College of Cardiology classifications ( based on their static and dynamic components). Statistical analyses were conducted to determine injury rates and identify factors associated with hospital transfers. Over the three years, 20,585 athletes participated in the games, with 527 (2.56%) seeking medical support. Injury rates significantly varied among the groups of sports with differing static and dynamic demands. Sports classified as having both high static and dynamic demands had the highest injury rates. Handball, martial arts, and rowing resulted in the highest rates of medical support-seeking behavior. The need for hospital transport was more frequently associated with sports that involved both high static and dynamic demands. The study highlights the varying injury patterns across different sports in mass gatherings, with sports involving higher physical demands posing greater risks of injury. These findings underscore the importance of developing tailored medical preparedness strategies for different types of sports to ensure timely and effective provision of medical care during large-scale sporting events.

Background Cardiovascular magnetic resonance (CMR)–derived extracellular volume (ECV) requires a hematocrit (Hct) to correct contrast volume distributions in blood. However, the timely assessment of Hct can be challenging and has limited the routine clinical application of ECV. The goal of the present study was to evaluate whether ECV measurements lead to significant error if a venous Hct was unavailable on the day of CMR. Methods 109 patients with CMR T1 mapping and two venous Hcts (Hct 0 : a Hct from the day of CMR, and Hct 1 : a Hct from a different day) were retrospectively identified. A synthetic Hct (Hct syn ) derived from native blood T1 was also assessed. The study used two different ECV methods, (1) a conventional method in which ECV was estimated from native and postcontrast T1 maps using a region-based method, and (2) an inline method in which ECV was directly measured from inline ECV mapping. ECVs measured with Hct 0 , Hct 1 , and Hct syn were compared for each method, and the reference ECV (ECV 0 ) was defined using the Hct 0 . The error between synthetic (ECV syn ) and ECV 0 was analyzed for the two ECV methods. Results ECV measured using Hct 1 and Hct syn were significantly correlated with ECV 0 for each method. No significant differences were observed between ECV 0 and ECV measured with Hct 1 (ECV 1 ; 28.4 ± 6.6% vs. 28.3 ± 6.1%, p = 0.789) and between ECV 0 and ECV calculated with Hct syn (ECV syn ; 28.4 ± 6.6% vs. 28.2 ± 6.2%, p = 0.45) using the conventional method. Similarly, ECV 0 was not significantly different from ECV 1 (28.5 ± 6.7% vs. 28.5 ± 6.2, p = 0.801) and ECV syn (28.5 ± 6.7% vs. 28.4 ± 6.0, p = 0.974) using inline method. ECV syn values revealed relatively large discrepancies in patients with lower Hcts compared with those with higher Hcts. Conclusions Venous Hcts measured on a different day from that of the CMR examination can still be used to measure ECV. ECV syn can provide an alternative method to quantify ECV without needing a blood sample, but significant ECV errors occur in patients with severe anemia.

Carbon monoxide (CO) poisoning may cause toxicity to the cardiovascular system. However, the association between CO poisoning and the risk of major adverse cardiovascular events (MACE) remains unestablished. We investigated the incidence of MACE after CO poisoning in Taiwan and evaluated whether CO-poisoned individuals had a higher risk of MACE than did the general population. Using Taiwan's National Health Insurance Research Database (NHIRD) during 2005-2013, a nationwide population-based cohort study was conducted among patients who experienced CO poisoning between 2005 and 2013. CO poisoning was defined according to the International Classification of Diseases, Ninth Revision, Clinical Modification codes. The study cohort comprised patients with CO poisoning between 2005 and 2010 (N = 13,939). Each patient was matched according to age, sex and index date with four randomly selected controls from the comparison cohort (N = 55,756). All patients were followed from the study date until MACE development, death, or the end of 2013. The hazard ratios for MACE were compared between the two cohorts by using Cox proportional hazards regressions analyses. Incident cases of MACE were identified from the NHIRD. After adjustment for potential confounders, the study cohort was independently associated with a higher MACE risk (adjusted hazard ratio, 2.00; 95% confidence interval, 1.83-2.18). This population-based cohort study indicated that patients with CO poisoning have a higher risk of MACE than do individuals without CO poisoning.

Dyslipidemia is associated with glomerular injury. However, the effect of statins on chronic kidney disease (CKD) progression remains controversial. We aimed to investigate the efficacy of statins for renal protection in patients with CKD. The retrospective cohort study comprised 3441 patients diagnosed with CKD in multiple medical centers. We divided the patients into two cohorts based on statin prescription, and compared proportions and risks of CKD progression events between the two groups. CKD progression event was defined as an average annual decline of eGFR >5 mL/min/1.73 m 2 or advancement to the dialysis stage. The result revealed that among all incident patients with CKD, 28.7% and 30.3% of the users and nonusers demonstrated CKD progression, respectively. The crude odds ratio (OR) of CKD progression was 0.93 [95% confidence interval (CI) 0.78–1.10]. After adjustment for baseline characteristics, the adjusted OR was 0.80 (95% CI 0.63–1.01). The sensitivity analysis results showed consistent OR for CKD progression, stratification by age, sex, Charlson score, and statins use within 1 year before index date. The effect of statins was significant in patients with CKD stage 3B-5 (OR 0.68, 95% CI 0.48–0.95), but not statistically significant in those with CKD stage 1–3A (OR 0.97, 95% CI 0.68–1.38). The effect of statins was significant in patients with proteinuria ≥1000 mg/day (OR 0.63, 95% CI 0.43–0.92), but not statistically significant in those with proteinuria <1000 mg/day (OR 1.02, 95% CI 0.74–1.41).

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

[This corrects the article DOI: 10.1371/journal.pone.0176465.].

This study was designed to identify whether the position and size of the region of interest (ROI) influence extracellular volume fraction (ECV) measurements. Patients with localized (n = 203) or infiltrative (n = 215) cardiomyopathies and 36 normal controls were enrolled in this study. ECV measurements at 4 different regions, including the anterior, septal, posterior and lateral wall regions, were measured, and all groups were compared. Regional ECV was correlated with the corresponding regional wall thickness. The diagnostic power to differentiate the myocardial abnormalities was evaluated for each myocardial region. ECVs measured using five different ROI sizes within each myocardial region were compared. Our results showed that ECVs varied among the myocardial regions, and this variation was significantly associated with regional wall thicknesses. For the detection of myocardial abnormalities, regional ECV revealed similar results as ECV derived from the whole region except for the anterior region. No significant difference was found in the ECVs measured using the five different ROI sizes. In conclusion, CMR-derived ECVs vary with myocardial region, and this variation is significantly associated with the regional wall thickness. In contrast, the measured size of the ROI has less of an effect on the ECV.