Coupling Effects of Depression and Functional Disability: Using Bivariate Latent Change Score Model

The purpose of the study was to examine the coupling effect of depression and functional disability over four time points using the data from the Health and Retirement Study (HRS). The sample included participants who survived to 98 years or older (N = 458). Four alternative latent change score models were computed to examine the univariate and bivariate effects among depressive symptoms (CES-D) and functional disabilities (ADL): No-coupling, univariate model of ADL to change in CES-D, univariate model of CES-D to change in ADL, and bivariate model. As hypothesized, the no-coupling model did not fit the data well, χ2 (26) = 164.86, CFI = 0.85, RMSEA = 0.11. Model 2, ADL predicting change in CES-D, did not fit the data well, χ2 (25) = 164.18, CFI = 0.85, RMSEA = 0.11. Model 3, CES-D predicting change in ADL, also did not fit the data, χ2 (25) = 148.06, CFI = 0.87, RMSEA = 0.10. The bivariate model fit the data well, χ2 (21) = 66.94, CFI = 0.95, RMSEA = 0.07, and was the best fitting model. All level to change effects were significant in model 4. One’s CES-D at prior waves was positively associated with change in ADL at subsequent waves, and ADL at prior waves was positively associated with change in CES-D at subsequent waves. In conclusion, there is a significant coupling effect between depressive symptoms and ADL over time. Future health policies should monitor older adults’ mental and functional health simultaneously for their possible spillover effects.

Electricity Load and Internet Traffic Forecasting Using Vector Autoregressive Models

This study was conducted to investigate the applicability of measuring internet traffic as an input of short-term electricity demand forecasts. We believe our study makes a significant contribution to the literature, especially in short-term load prediction techniques, as we found that Internet traffic can be a useful variable in certain models and can increase prediction accuracy when compared to models in which it is not a variable. In addition, we found that the prediction error could be further reduced by applying a new multivariate model called VARX, which added exogenous variables to the univariate model called VAR. The VAR model showed excellent forecasting performance in the univariate model, rather than using the artificial neural network model, which had high prediction accuracy in the previous study.

Investigating microcrystalline cellulose crystallinity using Raman spectroscopy

Microcrystalline cellulose (MCC) is a semi-crystalline material with inherent variable crystallinity due to raw material source and variable manufacturing conditions. MCC crystallinity variability can result in downstream process variability. The aim of this study was to develop models to determine MCC crystallinity index (%CI) from Raman spectra of 30 commercial batches using Raman probes with spot sizes of 100 µm (MR probe) and 6 mm (PhAT probe). A principal component analysis model separated Raman spectra of the same samples captured using the different probes. The %CI was determined using a previously reported univariate model based on the ratio of the peaks at 380 and 1096 cm−1. The univariate model was adjusted for each probe. The %CI was also predicted from spectral data from each probe using partial least squares regression models (where Raman spectra and univariate %CI were the dependent and independent variables, respectively). Both models showed adequate predictive power. For these models a general reference amorphous spectrum was proposed for each instrument. The development of the PLS model substantially reduced the analysis time as it eliminates the need for spectral deconvolution. A web application containing all the models was developed. Graphic abstract

COVID-19 outbreak and beyond: the information content of registered short-time workers for GDP now- and forecasting

The number of short-time workers from January to April 2020 is used to now- and forecast quarterly GDP growth. We purge the monthly log level series from the systematic component to extract unexpected changes or shocks to log short-time workers. These monthly shocks are included in a univariate model for quarterly GDP growth to capture timely, current-quarter unexpected changes in growth dynamics. Included shocks additionally explain 24% in GDP growth variation. The model is able to forecast quite precisely the decrease in GDP during the financial crisis. It predicts a mean decline in GDP of 5.7% over the next two quarters. Without additional growth stimulus, the GDP level forecast remains persistently 4% lower in the long run. The uncertainty is large, as the 95% highest forecast density interval includes a decrease in GDP as large as 9%. A recovery to pre-crisis GDP level in 2021 lies only in the upper tail of the 95% highest forecast density interval.

AB0916 FACTORS THAT DEMONSTRATED SIGNIFICANT CORRELATION WITH BONE MINERAL DENSITY GAIN WITH DENOSUMAB ADMINISTRATION FOR PATIENT WITH RHEUMATOID ARTHRITIS

Background:Denosumab, a monoclonal antibody of receptor activator of NF-κB ligand promotes a strong action for bone mineral density (BMD) gain. This agent is often used for patient with rheumatoid arthritis (RA) because of its strong anti-osteoclastogenesis action, with that joint structural damage is induced. However, factors what affects BMD gain for patient with RA is still unclear.Objectives:Factors that may affect BMD gain for patient with RA is evaluated statistically.Methods:Patients with RA to whom denosumab is administrated consecutively three shots or more were picked up. BMD in lumbar spine (LS) and femoral neck (FN) measured with dual-energy X-ray absorptiometry was routinely measured at the initial administration (BL). BMDs were measured every six months when another denosumab is administrated. Change of BMD for each bone was calculated. Patient’s age at BL, at onset of RA, disease duration, sex, anti-cyclic citrullinated polypeptide antibodies (ACPA), whether denosumab is naïve, body mass index (BMI) at BL were harvested. BMD in each bone, serum tartrate resistant acid phosphatase 5b (TRACP5b), total type one procollagen-N-peptide (P1NP), calcium (Ca), creatinine (Cr), cystatin C (CysC), estimated glomerular filtration ratio based on CysC (eGFR), serum Cr-to-CysC ratio (Cr/CysC), and Barthel Index, were measured at BL and every six months thereafter. Relationship between BMD gain from BL to second administration and such like factors at BL were evaluated with linear regression analysis at first with univariate model and then multivariate model with factors that demonstrated statistical significance within 5%. Binary logistic regression analysis for these factors was also performed according to BMD gain. These procedures were performed as a same manner regarding with BMD gain from BL to third administration.Results:A total of 397 patients with 43 males (10.4%) and 354 females (89.6%) were recruited. Average age was 81.3 and average disease duration of RA was 6.9years. 227 patients (57.4%) was denosumab naïve, and prior to BL, 170 patients were already administered with alendronate in 26, risedronate in 26, minodronate in 23, ibandronate in 12, raloxifene in 39, bazedoxifene in 7, teriparatide in 36.BMD gain in LS from BL to the second administration demonstrated significant correlation with age and TRACP5b at BL with univariate model, and only aging correlated significant negative correlation with BMD gain with multivariate model. In binary logistic regression analysis, aging demonstrated no significant regression with BMD gain. From BL to third administration, BMD gain also demonstrated significant correlation with aging, but no correlation with TRACP5b, but Cr/CysC at BL. These two factors also demonstrated significant correlation with BMD gain in LS, in these aging demonstrated negative and Cr/CysC demonstrated positive correlation. Cr/CysC demonstrated significant regression with BMD gain in LS from BL to the third with binary logistic regression analysis.BMD gain in FN from BL to the second demonstrated significant correlation with age, BMD in FN at BL, and ACPA with univariate model, and all of the three demonstrated significant correlation with BMD gain with multivariate model. However, no factors demonstrated significant regression with BMD gain with binary logistic regression analysis. From BL to third administration, BMD gain in FN demonstrated significant correlation with aging and BMD in FN at BL. However, BMD in FN at BL demonstrated the only factor to correlate with BMD gain in FN. BMD in FN at BL demonstrated significant regression with BMD gain in FN from BL to the third with binary logistic regression analysis.Conclusion:These results suggested that BMD gain in LS and FN was affected by different factors. These results may be helpful reference in choosing denosumab against osteoporosis in RA patient.Disclosure of Interests:None declared

A seven-year surveillance of epidemiology of malaria reveals travel and gender are the key drivers of dispersion of drug resistant genotypes in Kenya

Malaria drug resistance is a global public health concern. Though parasite mutations have been associated with resistance, other factors could influence the resistance. A robust surveillance system is required to monitor and help contain the resistance. This study established the role of travel and gender in dispersion of chloroquine resistant genotypes in malaria epidemic zones in Kenya. A total of 1,776 individuals presenting with uncomplicated malaria at hospitals selected from four malaria transmission zones in Kenya between 2008 and 2014 were enrolled in a prospective surveillance study assessing the epidemiology of malaria drug resistance patterns. Demographic and clinical information per individual was obtained using a structured questionnaire. Further, 2 mL of blood was collected for malaria diagnosis, parasitemia quantification and molecular analysis. DNA extracted from dried blood spots collected from each of the individuals was genotyped for polymorphisms in Plasmodium falciparum chloroquine transporter gene (Pfcrt 76), Plasmodium falciparum multidrug resistant gene 1 (Pfmdr1 86 and Pfmdr1 184) regions that are putative drug resistance genes using both conventional polymerase chain reaction (PCR) and real-time PCR. The molecular and demographic data was analyzed using Stata version 13 (College Station, TX: StataCorp LP) while mapping of cases at the selected geographic zones was done in QGIS version 2.18. Chloroquine resistant (CQR) genotypes across gender revealed an association with chloroquine resistance by both univariate model (p = 0.027) and by multivariate model (p = 0.025), female as reference group in both models. Prior treatment with antimalarial drugs within the last 6 weeks before enrollment was associated with carriage of CQR genotype by multivariate model (p = 0.034). Further, a significant relationship was observed between travel and CQR carriage both by univariate model (p = 0.001) and multivariate model (p = 0.002). These findings suggest that gender and travel are significantly associated with chloroquine resistance. From a gender perspective, males are more likely to harbor resistant strains than females hence involved in strain dispersion. On the other hand, travel underscores the role of transport network in introducing spread of resistant genotypes, bringing in to focus the need to monitor gene flow and establish strategies to minimize the introduction of resistance strains by controlling malaria among frequent transporters.

A machine learning approach to univariate time series forecasting of quarterly earnings

We propose our quarterly earnings prediction (QEPSVR) model, which is based on epsilon support vector regression (ε-SVR), as a new univariate model for quarterly earnings forecasting. This follows the recommendations of Lorek (Adv Account 30:315–321, 2014. 10.1016/j.adiac.2014.09.008), who notes that although the model developed by Brown and Rozeff (J Account Res 17:179–189, 1979) (BR ARIMA) is advocated as still being the premier univariate model, it may no longer be suitable for describing recent quarterly earnings series. We conduct empirical studies on recent data to compare the predictive accuracy of the QEPSVR model to that of the BR ARIMA model under a multitude of conditions. Our results show that the predictive accuracy of the QEPSVR model significantly exceeds that of the BR ARIMA model under 24 out of the 28 tested experiment conditions. Furthermore, significance is achieved under all conditions considering short forecast horizons or limited availability of historic data. We therefore advocate the use of the QEPSVR model for firms performing short-term operational planning, for recently founded companies and for firms that have restructured their business model.

Implementasi Model Finansial Distres Pada Perusahaan Manufaktur Yang Terdaftar di IDX Tahun 2015-2017

Manufacturing Company is a sample of this study, the dependent variable used in this study is a binary variable, namely whether the company is in financial distress or non-financial distress. Hypothesis testing uses binary logistic regression (Binary Logistic Regression) because the dependent variable is a combination of metric and non-metric (nominal). The model used is the Altman Z-Score model, Springate S-Score, Grover G-Score, Zmijewski X-Score, and univariate models. Of the five models, the best model is the Springate S-Score with a Nagelkerke R2 value of 0.582. the second is, Zmijewski X-Score with a value of 0.227 and the third best is the Univariate Model with a value of 0.042. Of the three best models, namely the Springate S-Score, Zmijewski X-Score and the Univariate Model. The implementation is that the ratios in these models are very important to be considered by companies as a sensitivity tool so that companies do not experience financial distress. ratios that are often used are ratios related to the company's ability to manage and produce net working capital, sales, debt and ability to generate profits from sales and profits from assets.