Prehospital Response Time Delays for Emergency Patients in Events of Concurrent Mass Casualty Incidents

2017 ◽  
Vol 12 (1) ◽  
pp. 94-100
Author(s):  
Jungeun Kim ◽  
Chu Hyun Kim ◽  
Sang Do Shin ◽  
Ju Ok Park
Keyword(s):  
Linear Regression ◽  
Response Time ◽  
Multiple Linear Regression ◽  
Response Times ◽  
Linear Regression Analysis ◽  
Spillover Effect ◽  
Multiple Linear Regression Analysis ◽  
Mass Casualty ◽  
Emergency Patients ◽  
Before And After

AbstractObjectiveWe investigated the extent of delays in the response time of emergency medical services (EMS) as an impact of mass casualty incidences (MCIs) in the same area.MethodsWe defined an MCI case as an event that resulted in 6 or more patients being transported by EMS, and prehospital response time as the time from the call to arrival at the scene. We matched patients before and after MCIs by dividing them into categories of 3 hours before, 0-1 hour after, 1-2 hours after, and 2-3 hours after the MCIs. We compared prehospital response times using multiple linear regression.ResultsA total of 33,276 EMS-treated patients were matched. The prehospital response time for the category of 3 hours before the MCIs was 8.8 minutes (SD: 8.2), treated as the reference, whereas that for the category of 0-1 hour after the MCI was 11.3 minutes (P<0.01). The multiple linear regression analysis revealed that prehospital response time increased by 2.5 minutes (95% CI: 2.3-2.8) during the first hour and by 0.3 minutes (95% CI: 0.1-0.6) during the second hour after MCIs.ConclusionThere were significant delays in the prehospital response time for emergency patients after MCIs, and it lasted for 2 hours as the spillover effect. (Disaster Med Public Health Preparedness. 2018;12:94–100)

Factors influencing the prescribed dose of opioid analgesics in cancer patients

2020 ◽  
Vol 16 (4) ◽  
pp. 247-252
Author(s):  
Momoyo Hashimoto, MPharm ◽  
Kazuki Aogaki, BPharm ◽  
Chikako Numata, PhD ◽  
Kensuke Moriwaki, PhD ◽  
Yoshinobu Matsuda, MD ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Rating Scale ◽  
Cytochromes P450 ◽  
Linear Regression Analysis ◽  
Oral Morphine ◽  
Opioid Analgesics ◽  
Multiple Linear Regression Analysis ◽  
Pain Severity ◽  
Before And After

The dose of opioids prescribed for cancer pain does not always correlate with the actual pain severity. However, there is little evidence to explain this observation. In the present study, we sought to determine factors that influence the dose of opioid analgesics. A total of 227 patients who were administered opioids between August 2012 and May 2016 and later expired within the Department of Palliative Care at Ashiya Municipal Hospital were included, and the following variables were examined: age, sex, type of cancer, Verbal Rating Scale before and after the administration of the maximum prescribed dose of opioids, type of opioids and route of administration, blood test results, pain severity, and use of adjuvants. Data were analyzed using step-wise multiple linear regression. Median of the maximum prescribed dose of opioids, expressed in oral morphine equivalent, was 68.6, 60.0, and 39.2 mg for patients aged 65, 65-74, and ≥75 years, respectively. Step-wise multiple linear regression analysis further demonstrated that an increase in age by 1 year decreased the maximum prescribed dose of opioids by 0.98-fold (p = 0.006). Other factors that influenced the maximum prescribed dose of opioids included the use of analgesic adjuvants (1.91-fold, p = 0.001), oral administration (0.54-fold, p = 0.016), and elevated level of bilirubin (0.95-fold by 0.1 mg/dL increase, p = 0.013). Opioids examined in the study are metabolized in the liver by cytochromes P450 or by glucuronidation. Thus, if reduced drug metabolism causes the reduction in the maximum prescribed dose of opioids, liver function may contribute to this effect. Based on our findings that old age is associated with a lower prescribed dose of opioids, future studies should examine additional variables included in laboratory tests in more detail and measure hepatic blood flow to determine the cause of this association.

PENGARUH FAKTOR-FAKTOR INTERNAL PERBANKAN TERHADAP NON PERFORMING FINANCING PADA BANK UMUM SYARIAH

2019 ◽  
Vol 6 (2) ◽  
pp. 115-130
Author(s):  
Eka Ambara Harci Putranta ◽  
Lilik Ambarwati
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Capital Adequacy ◽  
Annual Reports ◽  
Regression Methods ◽  
Capital Adequacy Ratio ◽  
Negative Effect ◽  
Independent Variable

The study aims to analyze the influence of internal banking factors in the form of: Capital Adequency Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing at Sharia Banks. This research method used multiple linear regression analysis with the help of SPSS 16.00 software which is used to see the influence between the independent variables in the form of Capital Adequacy Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing. The sample of this study was 3 Islamic Commercial Banks, so there were 36 annual reports obtained through purposive sampling, then analyzed using multiple linear regression methods. The results showed that based on the F Test, the independent variable had an effect on the NPF, indicated by the F value of 17,016 and significance of 0,000, overall the independent variable was able to explain the effect of 69.60%. While based on the partial t test, showed that CAR has a significant negative effect, Total assets have a significant positive effect with a significance value below 0.05 (5%). Meanwhile FDR does not affect NPF.

ANALISIS PENGARUH INFLASI, KURS DAN IHSG TERHADAP RETURN REKSA DANA SAHAM PT NIKKO SECURITIES INDONESIA PERIODE 2014-2017

2019 ◽  
Vol 2 (2) ◽  
pp. 103
Author(s):  
Muhammad Rois Rois ◽  
Manarotul Fatati Fatati ◽  
Winda Ihda Magfiroh
Keyword(s):  
Regression Analysis ◽  
Exchange Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Stock Price ◽  
Price Index ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Equity Fund ◽  
Stock Price Index

This study aims to determine the effect of Inflation, Exchange Rate and Composite Stock Price Index (IHSG) to Return of PT Nikko Securities Indonesia Stock Fund period 2014-2017. The study used secondary data obtained through documentation in the form of PT Nikko Securities Indonesia Monthly Net Asset (NAB) report. Data analysis is used with quantitative analysis, multiple linear regression analysis using eviews 9. Population and sample in this research are PT Nikko Securities Indonesia. The result of multiple linear regression analysis was the coefficient of determination (R2) showed the result of 0.123819 or 12%. This means that the Inflation, Exchange Rate and Composite Stock Price Index (IHSG) variables can influence the return of PT Nikko Securities Indonesia's equity fund of 12% and 88% is influenced by other variables. Based on the result of the research, the variables of inflation and exchange rate have a negative and significant effect toward the return of PT Nikko Securities Indonesia's equity fund. While the variable of Composite Stock Price Index (IHSG) has a negative but not significant effect toward Return of Equity Fund of PT Nikko Securities Indonesia

scholarly journals Can chlorophyll-a in meso-oligotrophic shallow waters be estimated using statistical approaches and empirical models from MODIS imagery?

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Andres Mauricio Munar ◽  
José Rafael Cavalcanti ◽  
Juan Martin Bravo ◽  
David Manuel Lelinho Da Motta Marques ◽  
Carlos Ruberto Fragoso Júnior
Keyword(s):  
Linear Regression ◽  
Spatial Heterogeneity ◽  
Multiple Linear Regression ◽  
Chlorophyll A ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Accurate Estimation ◽  
Shallow Waters ◽  
Chl A ◽  
Input Variables

ABSTRACT Accurate estimation of chlorophyll-a (Chl-a) concentration in inland waters through remote-sensing techniques is complicated by local differences in the optical properties of water. In this study, we applied multiple linear regression (MLR), artificial neural network (ANN), nonparametric multiplicative regression (NPMR) and four models (Appel, Kahru, FAI and O14a) to estimate the Chl -a concentration from combinations of spectral bands from the MODIS sensor. The MLR, NPMR and ANN models were calibrated and validated using in-situ Chl -a measurements. The results showed that a simple and efficient model, developed and validated through multiple linear regression analysis, offered advantages (i.e., better performance and fewer input variables) in comparison with ANN, NPMR and four models (Appel, Kahru, FAI and O14a). In addition, we observed that in a large shallow subtropical lake, where the wind and hydrodynamics are essential factors in the spatial heterogeneity (Chl-a distribution), the MLR model adjusted using the specific point dataset, performed better than using the total dataset, which suggest that would not be appropriate to generalize a single model to estimate Chl-a in these large shallow lakes from total datasets. Our approach is a useful tool to estimate Chl -a concentration in meso-oligotrophic shallow waters and corroborates the spatial heterogeneity in these ecosystems.

scholarly journals The Association of Circulating MiR-29b and Interleukin-6 with Subclinical Atherosclerosis

2017 ◽  
Vol 44 (4) ◽  
pp. 1537-1544 ◽  
Author(s):  
Yu-qing Huang ◽  
Jie Li ◽  
Ji-yan Chen ◽  
Ying-ling Zhou ◽  
An-ping Cai ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Interleukin 6 ◽  
Subclinical Atherosclerosis ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Control Group ◽  
Study Group ◽  
Circulating Levels

Background/Aims: Although it is widely acknowledged that atherosclerosis is mainly a chronic inflammatory process, in which both miR-29b and interleukin-6 (IL-6) play multifaceted roles, the association between miR-29b and IL-6 remains unknown. The aim of the present study was to explore the relationship between miR-29b and IL-6 and to test whether circulating levels of miR-29b and IL-6 could predict atherosclerosis. Methods: A total of 170 participants were divided into two groups according to carotid intima-media thickness (CIMT): study group (CIMT ≥ 0.9mm) and control group (CIMT < 0.9mm). Levels of circulating miR-29b and IL-6 were measured by quantitative real-time polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA), respectively. The association of miR-29b and IL-6 levels with CIMT was assessed using Spearman correlation analysis and multiple linear regression analysis. Results: The study group showed higher miR-29b levels (31.61 ± 3.05 vs. 27.91 ± 1.71 Ct, p < 0.001) and IL-6 levels (3.40 ± 0.67 vs. 2.99 ± 0.37 pg/ml, p < 0.001), compared with the control group. CIMT was positively correlated with miR-29b (r = 0.587, p < 0.001) and IL-6 (r = 0.410, p < 0.001), and miR-29b levels were also correlated with IL-6 (r = 0.242, p = 0.001). Multiple linear regression analysis also showed that CIMT was positively correlated with miR-29b and IL-6. After adjustment for age, body mass index, systolic blood pressure, total cholesterol and C-reactive protein, CIMT was still closely correlated with miR-29b and IL-6. The combination of miR-29b and IL-6 (AUC = 0.901, p < 0.001) offered a better predictive index for atherosclerosis than either miR-29b (AUC = 0.867, p < 0.001) or IL-6 (AUC = 0.747, p < 0.001) alone. Conclusion: Circulating levels of miR-29b and IL-6 may be independently correlated with subclinical atherosclerosis, and may serve as novel biomarkers for the identification of atherosclerosis.

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