scholarly journals The impact of methodological decisions on climate reconstructions using WA-PLS

2020 ◽  
pp. 1-16 ◽  
Author(s):  
Mark G. Turner ◽  
Dongyang Wei ◽  
Iain Colin Prentice ◽  
Sandy P. Harrison
Keyword(s):  
Reference Data ◽  
Training Data ◽  
Weighted Averaging ◽  
Data Set ◽  
Last Glacial Period ◽  
Methodological Choices ◽  
Climate Reconstructions ◽  
Significant Difference ◽  
Climate Space ◽  
The Impact

Abstract Most techniques for pollen-based quantitative climate reconstruction use modern assemblages as a reference data set. We examine the implication of methodological choices in the selection and treatment of the reference data set for climate reconstructions using Weighted Averaging Partial Least Squares (WA-PLS) regression and records of the last glacial period from Europe. We show that the training data set used is important because it determines the climate space sampled. The range and continuity of sampling along the climate gradient is more important than sampling density. Reconstruction uncertainties are generally reduced when more taxa are included, but combining related taxa that are poorly sampled in the data set to a higher taxonomic level provides more stable reconstructions. Excluding taxa that are climatically insensitive, or systematically overrepresented in fossil pollen assemblages because of known biases in pollen production or transport, makes no significant difference to the reconstructions. However, the exclusion of taxa overrepresented because of preservation issues does produce an improvement. These findings are relevant not only for WA-PLS reconstructions but also for similar approaches using modern assemblage reference data. There is no universal solution to these issues, but we propose a number of checks to evaluate the robustness of pollen-based reconstructions.

Association of consensus molecular subtypes (CMS) with time to progression (TTP), progression free survival (PFS), and overall survival (OS) with second-line FOLFIRI ± regorafenib in metastatic colorectal cancer (mCRC).

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 597-597
Author(s):  
Michael Sangmin Lee ◽  
Sara R. Selitsky ◽  
Joel S. Parker ◽  
James Todd Auman ◽  
Yunhan Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Training Data ◽  
Prognostic Impact ◽  
Line Treatment ◽  
Second Line ◽  
Data Set ◽  
Significant Difference ◽  
The Impact

597 Background: LCCC1029 was a 2:1 randomized phase II trial of second-line FOLFIRI plus either regorafenib or placebo in mCRC that showed no statistically significant difference in PFS or OS. CMS, defined using gene expression, is prognostic for PFS and OS in previously untreated mCRC, but the impact of CMS in second-line treatment is unclear, as well as its impact on regorafenib efficacy. Methods: RNAseq on archival tumor tissue was successfully performed in 68 LCCC1029 patients (49 on regorafenib, 19 on placebo). A multinomial elastic net CMS classifier was trained using 6 CRC gene expression data sets with known CMS classification. We built our model with only CMS1-4 classified samples and then applied it to normalized and median adjusted RNASeq from LCCC1029 to classify all samples into CMS1-4. TTP, PFS, and OS were compared using Kaplan-Meier method and log-rank tests, and hazard ratios were estimated using Cox proportional hazards method. Results: Our model had > 93% sensitivity and specificity for CMS1-4 in the training data set; the 17% of non-consensus samples in the training data were predominantly labeled CMS2. We classified the LCCC1029 samples as CMS1 (12%), CMS2 (63%), CMS3 (4%), and CMS4 (21%). CMS was prognostic for TTP (log-rank p=0.03), with median for CMS1 of 2.0 months (95% CI 0.0-4.8) versus 5.6 months (5.3-5.9) for CMS2 and 7.8 months (5.5-10.1) for CMS4. There was a trend toward association between CMS and either PFS (log-rank p = 0.11) or OS (log-rank p = 0.085). CMS2 had superior OS compared to CMS1 (HR 0.39, 95% CI 0.17-0.87, p = 0.02). With our limited sample size, we found no significant interaction between CMS and treatment arm for TTP, PFS, or OS. Conclusions: CMS is associated with significant differences in TTP in second-line treatment of mCRC in LCCC1029, and specific CMS types also have differences in OS. Thus, the prognostic impact of CMS extends to second-line treatment in mCRC, meriting further study of CMS classification in additional non-first-line studies.

Underwater Acoustic Target Recognition Based on Generative Adversarial Network Data Augmentation

2021 ◽  
Vol 263 (2) ◽  
pp. 4558-4564
Author(s):  
Minghong Zhang ◽  
Xinwei Luo
Keyword(s):  
Data Augmentation ◽  
Target Recognition ◽  
Training Data ◽  
Small Samples ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Underwater Acoustic ◽  
Adversarial Network ◽  
Acoustic Target ◽  
The Impact

Underwater acoustic target recognition is an important aspect of underwater acoustic research. In recent years, machine learning has been developed continuously, which is widely and effectively applied in underwater acoustic target recognition. In order to acquire good recognition results and reduce the problem of overfitting, Adequate data sets are essential. However, underwater acoustic samples are relatively rare, which has a certain impact on recognition accuracy. In this paper, in addition of the traditional audio data augmentation method, a new method of data augmentation using generative adversarial network is proposed, which uses generator and discriminator to learn the characteristics of underwater acoustic samples, so as to generate reliable underwater acoustic signals to expand the training data set. The expanded data set is input into the deep neural network, and the transfer learning method is applied to further reduce the impact caused by small samples by fixing part of the pre-trained parameters. The experimental results show that the recognition result of this method is better than the general underwater acoustic recognition method, and the effectiveness of this method is verified.

scholarly journals Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaowen Liang ◽  
Jinsui Yu ◽  
Jianyi Liao ◽  
Zhiyi Chen
Keyword(s):  
Predictive Value ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Diagnostic Efficiency ◽  
Clinical Value ◽  
Data Set ◽  
Multiple Organs ◽  
Significant Difference ◽  
Segmented Images ◽  
The Impact

Objective. The incidence of superficial organ diseases has increased rapidly in recent years. New methods such as computer-aided diagnosis (CAD) are widely used to improve diagnostic efficiency. Convolutional neural networks (CNNs) are one of the most popular methods, and further improvements of CNNs should be considered. This paper aims to develop a multiorgan CAD system based on CNNs for classifying both thyroid and breast nodules and investigate the impact of this system on the diagnostic efficiency of different preprocessing approaches. Methods. The training and validation sets comprised randomly selected thyroid and breast nodule images. The data were subgrouped into 4 models according to the different preprocessing methods (depending on segmentation and the classification method). A prospective data set was selected to verify the clinical value of the CNN model by comparison with ultrasound guidelines. Diagnostic efficiency was assessed based on receiver operating characteristic (ROC) curves. Results. Among the 4 models, the CNN model using segmented images for classification achieved the best result. For the validation set, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of our CNN model were 84.9%, 69.0%, 62.5%, 88.2%, 75.0%, and 0.769, respectively. There was no statistically significant difference between the CNN model and the ultrasound guidelines. The combination of the two methods achieved superior diagnostic efficiency compared with their use individually. Conclusions. The study demonstrates the probability, feasibility, and clinical value of CAD in the ultrasound diagnosis of multiple organs. The use of segmented images and classification by the nature of the disease are the main factors responsible for the improvement of the CNN model. Moreover, the combination of the CNN model and ultrasound guidelines results in better diagnostic performance, which will contribute to the improved diagnostic efficiency of CAD systems.

Drying out conifers classification employing TripleSat satellite data

2021 ◽  
Author(s):  
Hleb Litvinovich ◽  
Sviatlana Guliaeva ◽  
Ilya Bruchkouski ◽  
Volha Siliuk ◽  
Leanid Katkouski
Keyword(s):  
Reference Data ◽  
Vegetation Indices ◽  
Classification Algorithm ◽  
Training Data ◽  
Underlying Surface ◽  
Coefficient Function ◽  
Data Set ◽  
Coniferous Trees ◽  
Reflectance Coefficient ◽  
Airborne Experiment

<p>Drying out of coniferous trees (Picea abies) due to bark beetle infestation and other diseases leads to a high rate of conifers mortality. The coniferous forests in Belarus are largely exposed to damage by the bark beetle, the early symptoms of which are the changes in the color and loss of shine of the needles.  </p><p>Purpose of the work is to identify drying out stages combining the TripleSat multispectral satellite data (spatial resolution 3.2 m MS, 0.8 m PAN, bands R, G, B, NIR) for the test coniferous forest area in Belarus (53.65419º N, 27.640213º E) with quasi-synchronous airborne photo-spectral measurements which have been used as a reference data. Airborne measurements of reflectance coefficient function of underlying coniferous trees have been carried out by employing two spectrometers (wavelength range 400-900 nm, spectral resolution 4.3 nm) and photo-camera (visible range, FOV 50º) mounted on board of Diamond DA40NG aircraft in nadir geometry.  </p><p>Airborne RGB-images have been used for visual identification of the type of underlying surface and for subsequent training data set formation. Training data consist of several sets (10 – 20) of vegetation indexes for each type of underlying surface. The linear discriminant analysis (LDA) classification algorithm has been applied in this study for distinguishing the conifers drying out stages. A set of vegetation indices evaluated for each reflectance coefficient function has been applied as input data for LDA classification algorithm.</p><p>LDA classification algorithm has been employed to the TripleSat image for identification drying out stages of coniferous trees. The reference data for LDA classification algorithm of the TripleSat image included the combination of coordinates and corresponding types of underlying surface obtained from the results of the airborne experiment classification. A set of vegetation indices has been derived for each pixel of the image and used as input data for LDA algorithm; also vegetation indices calculated for the reference pixels have been applied for training data set formation.</p><p>The classification accuracy of three conifers drying out stages based on the airborne experiment is estimated to be in a range of 27 - 74%. The verification of TripleSat classification results has been performed by visual comparison with high resolution aerial images.</p>

The impact of internet financial reporting on Egyptian company’s performance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amani Hussein ◽  
Ghadir Nounou
Keyword(s):  
Financial Reporting ◽  
Stock Price ◽  
Stock Return ◽  
Regulatory Body ◽  
Regression Analyses ◽  
Data Set ◽  
Content Type ◽  
Company Value ◽  
Significant Difference ◽  
The Impact

Purpose This study aims to examine the impact of internet financial reporting (IFR) on companies’ performances as measured by three performance indicators, namely, stock price, stock returns and company value. Design/methodology/approach A sample of 139 non-financial companies listed in the Egyptian stock exchange is used and classified as 108 IFR companies and 31 non-IFR companies. To test the research hypotheses, an independent t-test and multiple linear regression analyses are used. Findings The results indicate that there are no significant differences between IFR companies and non-IFR companies for both stock price and stock return variables. Conversely, there is a significant difference between IFR companies and non-IFR companies in the company value variable. These results imply rejecting hypotheses H1 and H4 and accepting the hypothesis of H7 that the presence of IFR has an impact on company value. The multiple regression analyses results indicate a significant relation between the scope of IFR and stock price. Likewise, between the degree of IFR and company value. Both degree and scope of IFR have an insignificant impact on stock return, which infer that applying different performance measures can reveal different conclusions. Research limitations/implications This research is a snapshot of IFR limited to a cross-sectional study and could not study the longitudinal data of internet reporting. Second, Marston and Polei (2004) contend that “weights contain an element of subjectivity, which cannot be completely avoided in the composition of such a score” (p. 297) and a variation in the disclosure index can lead to a modification in the results (Kaur and Kaur, 2020). This research applied a weighted index to measure the degree of IFR, which may affect the results and may change it if other indexes are applied. Moreover, the scores of the degree and scope of information disclosure are assumed to be similar every year due to the lack of information regarding the variations in content and presentation in the companies’ websites. Finally, the absence of a complete data set and stock prices for some companies in the sample. Practical implications To enhance the quantity and quality of IFR could be implemented through setting regulations and standards to govern IFR practices companies in Egypt. Moreover, the trade-off of the requirement of the Egyptian Financial Supervisory Authority for Egyptian companies make information available online and the secrecy culture profound in the Egyptian society (Ahmed et al., 2015) involve assigning a regulatory body for monitoring the IFR practices to ensure disseminating timely and accurate information that helps investors make rational decisions. Social implications The researchers recommend the suggestion to have an external assurance conducted by external auditors to enhance the accuracy and credibility of the IFR information. Originality/value Based on prior literature, no studies in Egypt compare between IFR companies and non-IFR companies concerning stock price and company value as measured by Tobin’s Q. Moreover, few research studies in Egypt covered the degree of IFR disclosure whilst not addressing the impact on the stock price. In addition, no prior study examined the scope of IFR disclosure in Egypt. Therefore, the research findings attribute to literature.

Non-removal strategy for outliers in predictive models: The PAELLA algorithm case

2019 ◽  
Vol 28 (4) ◽  
pp. 418-429
Author(s):  
Manuel Castejón-limas ◽  
Hector Alaiz-Moreton ◽  
Laura Fernández-Robles ◽  
Javier Alfonso-Cendón ◽  
Camino Fernández-Llamas ◽  
...  
Keyword(s):  
Predictive Model ◽  
Robust Regression ◽  
Training Data ◽  
Data Set ◽  
Outlier Identification ◽  
Sampling Process ◽  
Series Of Experiments ◽  
Fourth Experiment ◽  
Fitting In ◽  
The Impact

Abstract This paper reports the experience of using the PAELLA algorithm as a helper tool in robust regression instead of as originally intended for outlier identification and removal. This novel usage of the algorithm takes advantage of the occurrence vector calculated by the algorithm in order to strengthen the effect of the more reliable samples and lessen the impact of those that otherwise would be considered outliers. Following that aim, a series of experiments is conducted in order to learn how to better use the information contained in the occurrence vector. Using a contrively difficult artificial data set, a reference predictive model is fit using the whole raw dataset. The second experiment reports the results of fitting a similar predictive model but discarding the samples marked as outliers by PAELLA. The third experiment uses the occurrence vector provided by PAELLA in order to classify the observations in multiple bins and fit every possible model changing which bins are considered for fitting and which are discarded in that particular model. The fourth experiment introduces a sampling process before fitting in which the occurrence vector represents the likelihood of being considered in the training data set. The fifth experiment considers the sampling process as an internal step to be performed interleaved between the training epochs. The last experiment compares our approach using weighted neural networks to a state of the art method.

Clinical outcomes in 1731 patients undergoing mitral valve surgery for rheumatic valve disease

Heart ◽  
2017 ◽  
Vol 104 (10) ◽  
pp. 841-848 ◽  
Author(s):  
Wan Kee Kim ◽  
Ho Jin Kim ◽  
Joon Bum Kim ◽  
Sung-Ho Jung ◽  
Suk Jung Choo ◽  
...  
Keyword(s):  
Mitral Valve ◽  
Propensity Score ◽  
Large Data ◽  
Matched Cohort ◽  
Valve Repair ◽  
Data Set ◽  
Term Survival ◽  
Significant Difference ◽  
The Impact

ObjectiveUnlike degenerative mitral valve (MV) disease, the advantages of valve repair procedure over replacement have been debated in rheumatic MV disease. This study aims to evaluate the impact of procedural types on long-term outcomes through analyses on a large data set from an endemic area of rheumatic disease.MethodsWe evaluated 1731 consecutive patients (52.3±12.5 years; 1190 women) undergoing MV surgery for rheumatic MV disease between 1997 and 2015. Long-term survival and valve-related outcomes were compared between repair and replacement procedures. To adjust for selection bias, propensity score analyses were performed.ResultsPatients undergoing repair were younger and had more predominant mitral regurgitation than mechanical and bioprosthetic replacement groups (61.6% vs 15.6% vs 24.4%; P<0.001). During follow-up (130.9±27.7 months), 283 patients (16.3%) died and 256 patients (14.8%) experienced valve-related complications. Propensity score matching yielded 188 pairs of repair and replacement patients that were well balanced for baseline covariates. In the matched cohort, there was no significant difference in the mortality risk between the repair and replacement groups (HR, 1.24; 95% CI 0.62 to 2.48). The risk of composite valve-related complications, however, was significantly lower in repair group (HR, 0.57; 95% CI 0.33 to 0.99) principally derived by a lower risk of haemorrhagic events (HR, 0.23; 95% CI 0.07 to 0.70). The incidence of reoperation was not significantly different between groups in the matched cohort (HR, 1.62; 95% CI 0.49 to 5.28).ConclusionValve repair in well-selected patients with severe rheumatic MV disease led to comparable survival, but superior valve-related outcomes compared with valve replacement surgery.

scholarly journals Influence of Treated Wastewater Irrigation on Soil Nutritional-Chemical Attributes Using Soil Quality Index

2021 ◽  
Vol 13 (4) ◽  
pp. 1952
Author(s):  
Salar Rezapour ◽  
Amin Nouri ◽  
Hawzhin M. Jalil ◽  
Shawn A. Hawkins ◽  
Scott B. Lukas
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Treated Wastewater ◽  
Corn Yield ◽  
Regression Equations ◽  
Soil Quality Index ◽  
Data Set ◽  
Significant Difference ◽  
Chemical Attributes ◽  
The Impact

Dwindling water resources have drawn global attention to the reuse of treated wastewater (TWW) for irrigation. However, the impact of continuous TWW applications on soil quality and the proper quantification and monitoring frameworks have not been well-understood. This study aims to provides an insight into the impact of flood irrigation of urban TWW on soil nutritional-chemical attributes and the potential application of multiple soil quality indices for a corn cropping system. To achieve that goal, we pursued the Total Data Set (TDS) and Minimum Data Set (MDS) approaches, as well as the Integrated Quality Index (IQI) and Nemoro Quality Index (NQI) models. A total of 17 soil nutritional-chemical indicators (0–50 cm depths) were determined for the soils irrigated with TWW (five sites) and well water (one site as control) in West Azerbaijan province in northwestern Iran. Results revealed a significant difference in the majority of soil nutritional-chemical attributes, IQI-TDS, NQI-TDS, IQI-MDS, NQI-MDS, and corn yield between the TWW-irrigated and well-irrigated soils. Irrigation with TWW resulted in a significant increase in the amount of organic matter and cation exchange capacity by 9–17% and 17–26%, respectively, macronutrients (N, P, K, Ca, and Mg) by 22–164%, and the majority of trace metals (Fe, Mn, Zn, and Cu) by 17–175%, suggesting an improvement in soil nutrients and an increase in productivity. Comparing to the soil in control sites, the TWW irrigation caused a notable increase in the values of IQI-TDS, NQI-TDS, IQI-MDS, and NQI-MDS models ranging 14.6–29.5%, 19.1–25.5%, 21.7–33.3%, and 18.4–23.7%, respectively. This implies that soil quality was ameliorated to a significant extent with TWW irrigation. These improvements resulted in a remarkable increase in corn yield ranging from 12.5% to 28.1%. The regression equations revealed that up to 78%, 47%, 72%, and 36% of the variance in the IQI-TDS, NQI-TDS, IQI-MDS, and NQI-MDS models, respectively, could be captured by corn yield. The results of the regression and correlation analyses showed that the IQI-MDS model was more accurate than the other models in assessing soil quality and predicting crop yield. These findings may be an effective and practical tool for policy making, implementation, and management of soil irrigated with TWW.

scholarly journals Association of Obesity on Laboratory Profiles of Individuals with Type 1 Von Willebrand Disease and Low VWF in the Athn Dataset

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2415-2415
Author(s):  
Beverly Schaefer ◽  
Dunlei Cheng ◽  
Peter A. Kouides
Keyword(s):  
Research Funding ◽  
Management Practices ◽  
Laboratory Data ◽  
Von Willebrand Disease ◽  
Data Set ◽  
Significant Difference ◽  
Prevalence Of Obesity ◽  
Von Willebrand ◽  
The Impact

Introduction: Obesity is associated with endothelial dysfunction, hemostatic and fibrinolytic disturbances. The relationship between obesity and elevated Von Willebrand Factor (VWF) is complex and not fully elucidated. There is a significant knowledge gap regarding the impact of BMI on VWF levels. Given the proinflammatory effect associated with abdominal obesity, we hypothesized that there would be an increased prevalence of obesity among individuals with Low VWF (LVWF) compared to Type 1 Von Willebrand Disease (T1VWD) in the ATHN (American Thrombosis and Hemostasis Network) dataset. Methods: A retrospective review of de-identified patients included in the ATHN dataset as of March 2018 was performed. The dataset was queried for all patients with a diagnosis of "T1VWD," who were over 18 years of age when labs were drawn and when BMI was recorded, who had VWF Ristocetin cofactor (RCO) levels <50 IU/dL and who had BMI entered within 24 months of the date of lab entry. Subjects were categorized with VWF RCO ≤30% as T1VWD and 30-50% as LVWF. We used the NIH definitions for BMI (BMI<18.5, underweight; 18.5-24.9, normal; 25-29.9, overweight; 30-39.9, obese; BMI >40, extremely obese). Results: Of the 6939 patients with T1VWD in the ATHN dataset, 4754 patients had VWF RCO <50%, 1019 were above the age of 18, resulting in 548 evaluable subjects with BMI and laboratory metrics. There were 186 patients in the T1VWD cohort, and 362 patients in the LVWF cohort, with a Female:Male ratio >3:1 (Table 1). BMI was treated as a continuous measurement and on bivariate analysis there was not a statistically significant difference (p=0.593), with mean BMI 28.2 (17.2-52) in T1VWD and 28.6 (15.3-55.4) in LVWF. Prevalence of obesity (BMI ≥30) was not significantly different between cohorts (T1VWF 32% vs. LVWF 36%, p=0.345, Table 1). The prevalence of obesity by age (18-39, 40-59, >60 years) was similar among both cohorts with the exception of a larger proportion of obese individuals over the age of 60 in the LVWF cohort (63% vs 25%). Mean FVIII level for LVWF cohort was significantly higher compared to that for T1VWD cohort (80% vs. 53%; p<0.001). In addition, extremely obese patients had an elevated mean FVIII level compared to overweight patients (81% vs. 60%; p=0.041, Table 2). Among individuals with BMI≥30, there were increased rates for Black race (p=0.013), and Medicaid and Medicare rates (p=0.028) when compared to non-obese individuals (Table 3). While rates of obesity are known to vary regionally, no conclusions could be drawn as there was disproportionate geographic clustering in states with well-established hemophilia treatment centers. Conclusions: Our analysis identified that 34.8% of adults categorized as VWD in the ATHN data set are obese, with similar prevalence among T1VWD and LVWF. This finding, coupled with associated race, ethnic and socioeconomic risk factors can help prioritize prevention and weight management as a critical component of the comprehensive care model. Increasing degree of obesity may be associated with elevated FVIII, and should be studied prospectively in larger cohorts and the potential impact on cardiovascular risk. Limitations of this analysis include incomplete laboratory data, lack of longitudinal laboratory data, unknown potential confounders including pregnancy or medication effect, and non-uniform geographic distribution of patients. Further research is needed to evaluate the impact of obesity on bleeding phenotype, bleeding related complications, and management practices, as well as the effects of weight change on VWF and FVIII levels. Disclosures Schaefer: Siemens: Research Funding; Stago: Research Funding.

scholarly journals Impact of the Physician Orders for Life-Sustaining Treatment (POLST) Program Maturity Status on the Nursing Home Resident’s Place of Death

2019 ◽  
Author(s):  
Aluem Tark ◽  
Mansi Agarwal ◽  
Andrew Dick ◽  
Jiyoun Song ◽  
Patricia Stone
Keyword(s):  
Nursing Home ◽  
Nursing Homes ◽  
Treatment Program ◽  
Nursing Home Residents ◽  
Vital Statistics ◽  
Short Stay ◽  
Data Set ◽  
Significant Difference ◽  
Life Sustaining Treatment ◽  
The Impact

Abstract Background: The Physician Orders for Life-Sustaining Treatment program was developed to enhance quality of care delivered at end-of-life. Although positive impacts of the Physician Orders for Life-Sustaining Treatment Program use have been identified, the association between a state’s program maturity status and nursing home resident’s likelihood of dying in their current care settings, nursing homes, remain unanswered. Objective: Examine the impact of the Physician Orders for Life-Sustaining Program maturity status on nursing home residents’ odds of dying in nursing homes.Methods: Program maturity status data were linked with multiple datasets: Minimum Data Set, Vital Statistics Data, Master Beneficiary Summary File, Certification and Survey Provider Enhanced Reports, and Area Health Resource File. Stratifying residents on long-stay and short-stay, we used descriptive statistics and multivariable logistic regression models for total 595,152 individuals.Results: Controlling for individual and contextual variables, long-stay nursing home residents living in states where the program was mature status had 20% increased odds of dying in nursing homes (OR: 1.20; CI 1.02-1.43) compared to those who resided in states with non-conforming status. Individuals residing in states with developing program status showed 11% increase in odds of dying in nursing homes (OR: 1.12; CI 1.02-1.24) compared to non-conforming status. No significant difference was noted for short-stay nursing home residents.Conclusion: Mature and developing maturity status were associated with greater likelihood of dying in nursing homes among long-stay residents. Our findings inform that a well-structured advance care planning program such as Physician Orders for Life-Sustaining Program enhances care outcomes among older adults living in nursing homes.

Sign in / Sign up

Export Citation Format

Share Document