scholarly journals Adoption of Electronic Dental Records: Examining the Influence of Practice Characteristics on Adoption in One State

2018 ◽  
Vol 09 (03) ◽  
pp. 635-645 ◽  
Author(s):  
Zain Chauhan ◽  
Mohammad Samarah ◽  
Kim Unertl ◽  
Martha Jones
Keyword(s):  
Logistic Regression ◽  
Organizational Factors ◽  
Predictor Variables ◽  
Binomial Logistic Regression ◽  
Dental Clinics ◽  
Practice Characteristics ◽  
Practice Size ◽  
Ehr Adoption ◽  
Dental Records ◽  
Electronic Dental Records

Objective Compared with medicine, less research has focused on adoption rates and factors contributing to the adoption of electronic dental records (EDRs) and certified electronic health records (EHRs) in the field of dentistry. We ran two multivariate models on EDR adoption and certification-capable EHR adoption to determine environmental and organizational factors associated with adoption. Methods We conducted telephone survey of a 10-item questionnaire using disproportionate stratified sampling procedure of 149 dental clinics in Tennessee in 2017 measuring adoption of dental information technology (IT) (EDRs and certification-capable EHRs) and practice characteristics, including region, rurality, specialty, and practice size. We used binomial logistic regression models to determine associations of adoption with predictor variables. Results A total of 77% of surveyed dental clinics in Tennessee had adopted some type of EDR system. When the definitions of certification capable were applied, the adoption rates in dental clinics dropped to 58%. A binomial logistic regression model for the effects of rurality, specialization, and practice size on the likelihood that a clinic would adopt an EHR product was statistically significant (chi-square (3) = 12.41, p = 0.0061). Of the three predictor variables, specialization and practice size were significant: Odds of adopting an EHR is 67% lower for specialists than for general dentists; and clinics with two or more practicing dentists were associated with a much greater likelihood of adopting an EHR-capable system (adjusted odds ratio = 3.09, p = 0.009). Conclusion Findings from this study indicate moderate to high levels of overall dental IT adoption. However, adoption rates in dental clinics do remain lower than those observed in office-based physician practices in medicine. Specialization and practice size were significant predictors of EHR-capable system adoption. Efforts to increase EHR adoption in dentistry should be mindful of potential disparities in smaller practices and between dental specialties and generalists.

scholarly journals COVID-19-Related Changes in Perceived Household Food Waste in the United States: A Cross-Sectional Descriptive Study

2021 ◽  
Vol 18 (3) ◽  
pp. 1104
Author(s):  
Kelly Cosgrove ◽  
Maricarmen Vizcaino ◽  
Christopher Wharton
Keyword(s):  
Logistic Regression ◽  
Food Waste ◽  
The United States ◽  
Food Groups ◽  
Related Factors ◽  
Cross Sectional ◽  
Household Level ◽  
Binomial Logistic Regression ◽  
Household Food ◽  
Fresh Vegetables

Food waste contributes to adverse environmental and economic outcomes, and substantial food waste occurs at the household level in the US. This study explored perceived household food waste changes during the COVID-19 pandemic and related factors. A total of 946 survey responses from primary household food purchasers were analyzed. Demographic, COVID-19-related household change, and household food waste data were collected in October 2020. Wilcoxon signed-rank was used to assess differences in perceived food waste. A hierarchical binomial logistic regression analysis was conducted to examine whether COVID-19-related lifestyle disruptions and food-related behavior changes increased the likelihood of household food waste. A binomial logistic regression was conducted to explore the contribution of different food groups to the likelihood of increased food waste. Perceived food waste, assessed as the estimated percent of food wasted, decreased significantly during the pandemic (z = −7.47, p < 0.001). Food stockpiling was identified as a predictor of increased overall food waste during the pandemic, and wasting fresh vegetables and frozen foods increased the odds of increased food waste. The results indicate the need to provide education and resources related to food stockpiling and the management of specific food groups during periods of disruption to reduce food waste.

Analysis on application level based on ordinal logistic regression and best of advanced manufacturing technologies (AMT) selection based on fuzzy-TOPSIS integration approach

2021 ◽  
pp. 1-11
Author(s):  
Guilian Wang ◽  
Liyan Zhang ◽  
Jing Guo
Keyword(s):  
Logistic Regression ◽  
Organizational Factors ◽  
Fuzzy Topsis ◽  
Ordinal Logistic Regression ◽  
Industrial Robots ◽  
Policy Implications ◽  
Market Pricing ◽  
Factors Affecting ◽  
Pricing Power ◽  
The Core Competence

This paper try to fully reveal the key factors affecting the the level of AMT application in micro- and small enterprises (MSEs) from its organizational factors by ordinal logistic regression. The results show that MSEs have a relatively high level of AMT application as a whole due to the maturity and cost reduction of basic technologies such as artificial intelligence, digital manufacturing and industrial robots. In this paper we propose manufacturing world analysis at Application using Logistic Regression and best AMT selection using Fuzzy-TOPSIS Integration approach.Considering the influence mechanism of each factor, the important factors that affect the application level of AMT are the enterprise’s market pricing power, the main production types, technical, market and management capabilities, organization development incentives and the interaction with external stakeholders. Based on the results above, the following policy implications are proposed: further expanding the customized production in MSEs to gradually improve the market pricing power, expanding the core competence of enterprises, enhancing the employee autonomy, and strengthening the interaction with industry organizations.

Characteristics of Adenocarcinoma of the Gallbladder Following Cholecystectomies in the Hispanic Population

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S64-S65
Author(s):  
David Gustafson ◽  
Osvaldo Padilla
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Incidental Finding ◽  
Conditional Logistic Regression ◽  
Gallbladder Disease ◽  
Predictor Variables ◽  
Wall Thickening ◽  
Conditional Logistic Regression Analysis

Abstract Introduction Gallbladder adenocarcinoma (GBC) is a rare malignancy. Frequency of incidental adenocarcinoma of the gallbladder in the literature is approximately 0.2% to 3%. Typically, GBC is the most common type and is discovered late, not until significant symptoms develop. Common symptoms include right upper quadrant pain, nausea, anorexia, and jaundice. A number of risk factors in the literature are noted for GBC. These risk factors are also more prevalent in Hispanic populations. This study sought to compare patients with incidental gallbladder adenocarcinomas (IGBC) to those with high preoperative suspicion for GBC. Predictor variables included age, sex, ethnicity, radiologic wall thickening, gross pathology characteristics (wall thickness, stone size, stone number, and tumor size), histologic grade, and staging. Methods Cases of GBC were retrospectively analyzed from 2009 through 2017, yielding 21 cases. Data were collected via Cerner EMR of predictor variables noted above. Statistical analysis utilized conditional logistic regression analysis. Results The majority of patients were female (n = 20) and Hispanic (n = 19). There were 14 IGBCs and 7 nonincidental GBCs. In contrast with previous research, exact conditional logistic regression analysis revealed no statistically significant findings. For every one-unit increase in AJCC TNM staging, there was a nonsignificant 73% reduction in odds (OR = 0.27) of an incidental finding of gallbladder carcinoma. Conclusion This study is important in that it attempts to expand existing literature regarding a rare type of cancer in a unique population, one particularly affected by gallbladder disease. Further studies are needed to increase predictive knowledge of this cancer. Longer studies are needed to examine how predictive power affects patient outcomes. This study reinforces the need for routine pathologic examination of cholecystectomy specimens for cholelithiasis.

scholarly journals Evaluating the role for renal biopsy in T1 and T2 renal masses: A single-centre study

2018 ◽  
Vol 12 (5) ◽  
pp. E226-30 ◽  
Author(s):  
Dylan Hoare ◽  
Howard Evans ◽  
Heidi Richards ◽  
Rahim Samji
Keyword(s):  
Logistic Regression ◽  
Adverse Events ◽  
Diagnostic Accuracy ◽  
Renal Biopsy ◽  
Predictive Value ◽  
Lesion Size ◽  
Renal Masses ◽  
Ct Guided ◽  
Binomial Logistic Regression ◽  
T1 And T2

Introduction: Once used primarily in the identification of renal metastasis and lymphomas, various urological bodies are now adopting an expanded role for the renal biopsy. We sought to evaluate the role of the renal biopsy in a Canadian context, focusing on associated adverse events, radiographic burden, and diagnostic accuracy.Methods: This retrospective review incorporated all patients undergoing ultrasound (US)/computed tomography (CT)-guided biopsies for T1 and T2 renal masses. There were no age or lesion size limitations. The primary outcome of interest was the correlation between initial biopsy and final surgical pathology. A binomial logistic regression analysis was conducted to determine any confounding factors. Secondary outcomes included the accuracy of tumour cell typing, grading, the safety profile, and radiographic burden associated with these patients.Results: A total of 148 patients satisfied inclusion criteria for this study. Mean age and lesions size at detection were 60.9 years (±12.4) and 3.6 cm (±2.0), respectively. Most renal masses were identified with US (52.7%) or CT (44.6%). Three patients (2.0%) experienced adverse events of note. Eighty-six patients (58.1%) proceeded to radical/partial nephrectomy. Our biopsies held a diagnostic accuracy of 90.7% (sensitivity 96.2%, specificity 87.5%, positive predictive value 98.7%, negative predictive value 70.0%, kappa 0.752, p<0.0005). Binomial logistic regression revealed that age, lesion size, number of radiographic tests, time to biopsy, and modality of biopsy (US/CT) had no influence on the diagnostic accuracy of biopsies.Conclusions: Renal biopsies are safe, feasible, and diagnostic. Their role should be expanded in the routine evaluation of T1 and T2 renal masses.

scholarly journals Predicting Hospitalization following Psychiatric Crisis Care using Machine Learning

2019 ◽  
Author(s):  
Matthijs Blankers ◽  
Louk F. M. van der Post ◽  
Jack J. M. Dekker
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithms ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Predictor Variables ◽  
Gradient Boosting ◽  
K Nearest Neighbors ◽  
Psychiatric Crisis ◽  
Crisis Care

Abstract Background: It is difficult to accurately predict whether a patient on the verge of a potential psychiatric crisis will need to be hospitalized. Machine learning may be helpful to improve the accuracy of psychiatric hospitalization prediction models. In this paper we evaluate and compare the accuracy of ten machine learning algorithms including the commonly used generalized linear model (GLM/logistic regression) to predict psychiatric hospitalization in the first 12 months after a psychiatric crisis care contact, and explore the most important predictor variables of hospitalization. Methods: Data from 2,084 patients with at least one reported psychiatric crisis care contact included in the longitudinal Amsterdam Study of Acute Psychiatry were used. The accuracy and area under the receiver operating characteristic curve (AUC) of the machine learning algorithms were compared. We also estimated the relative importance of each predictor variable. The best and least performing algorithms were compared with GLM/logistic regression using net reclassification improvement analysis. Target variable for the prediction models was whether or not the patient was hospitalized in the 12 months following inclusion in the study. The 39 predictor variables were related to patients’ socio-demographics, clinical characteristics and previous mental health care contacts. Results: We found Gradient Boosting to perform the best (AUC=0.774) and K-Nearest Neighbors performing the least (AUC=0.702). The performance of GLM/logistic regression (AUC=0.76) was above average among the tested algorithms. Gradient Boosting outperformed GLM/logistic regression and K-Nearest Neighbors, and GLM outperformed K-Nearest Neighbors in a Net Reclassification Improvement analysis, although the differences between Gradient Boosting and GLM/logistic regression were small. Nine of the top-10 most important predictor variables were related to previous mental health care use. Conclusions: Gradient Boosting led to the highest predictive accuracy and AUC while GLM/logistic regression performed average among the tested algorithms. Although statistically significant, the magnitude of the differences between the machine learning algorithms was modest. Future studies may consider to combine multiple algorithms in an ensemble model for optimal performance and to mitigate the risk of choosing suboptimal performing algorithms.

A National Cancer Database (NCDB) analysis of the impact of nonmetastatic colon cancer sidedness on survival.

2021 ◽  
Vol 39 (3_suppl) ◽  
pp. 139-139
Author(s):  
Deven Patel ◽  
Timothy DiPeri ◽  
Brian Cox ◽  
Andrew Eugene Hendifar ◽  
Arsen Osipov ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Logistic Regression ◽  
Adjuvant Chemotherapy ◽  
Large Population ◽  
Stage I ◽  
Prognostic Impact ◽  
Metastatic Colon Cancer ◽  
Prognostic Features ◽  
Binomial Logistic Regression ◽  
The Impact

139 Background: Differences in embryological origin and tumor biology distinguish right-sided colon cancer (RCC) from left-sided colon cancer (LCC). Previous studies characterizing the prognostic impact of colon cancer laterality on clinical outcomes in non-metastatic colon cancer have been conflicting, thus closer examination is needed. Methods: Using the NCDB, patients with stage I-III colon cancer between 2004-2014 were stratified according to tumor location; RCC vs. LCC. Patient (pt) and tumor characteristics were compared in univariate analysis, survival (OS) was estimated by Kaplan-Meier (KM) curves and Cox proportional hazards modeling. Binomial logistic regression analysis was utilized to identify variables associated with colon cancer laterality. Results: Of the 342,735 pts who met inclusion criteria, 210,343 (61.4%) were diagnosed with RCC, and 132,392 (38.6%) with LCC. Pts with RCC were older (mean 71.6 vs. 66.4 years, p< 0.001) and predominantly female (65% vs. 35%, p< 0.001) compared to those with LCC. A trend towards poorer OS was seen in pts with RCC (mean 91.0 mos [95% CI: 90.2-91.8]) compared to LCC (112.2 mos [95% CI: 110.9-113.6]) in unadjusted analysis. On Cox multivariable adjusted analyses there was a significant but minimal impact on OS and laterality (hazard ratio or HR [LCC as ref] 0.978, 95% CI 0.967-0.989 p< 0.0001). Multiple unadjusted KM survival analyses showed RCC with T4 disease, high-grade, LVI/PNI, positive margins, N0-N2 disease, tumor deposits, and receipt of adjuvant chemotherapy had poorer OS than those features in LCC (all p < 0.0001). Binomial logistic regression showed RCCs were significantly more likely to be higher grade (odds ratio or OR 2.024) and MSI-H (OR 2.010) with trends (nonsignificant) towards more likely having N1-2 positive disease, LVI, less receipt of adjuvant chemotherapy, and fewer tumor deposits. Conclusions: The impact of sidedness on prognosis in stage I-III colon cancer is complex. In this large, population-based study, RCC tends to be associated with more adverse prognostic features than LCC. More investigation into the biologic differences between RCC and LCC is warranted and how they impact phenotype and survival.

Occurrence of Toxoplasma gondii antibodies and associated risk factors in women in selected districts of Punjab province, Pakistan

Parasitology ◽  
2020 ◽  
Vol 147 (10) ◽  
pp. 1133-1139
Author(s):  
Shahzad Ali ◽  
Zona Amjad ◽  
Tahir Mahmood Khan ◽  
Abdul Maalik ◽  
Anam Iftikhar ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Toxoplasma Gondii ◽  
Pregnant Women ◽  
Congenital Toxoplasmosis ◽  
Sampling Location ◽  
Serum Samples ◽  
Cross Sectional ◽  
Binomial Logistic Regression ◽  
Punjab Province

AbstractToxoplasmosis is a parasitic zoonotic disease caused by Toxoplasma (T.) gondii. Limited data are available on the occurrence of T. gondii in women especially pregnant women in Pakistan. The present study aimed to determine the occurrence and risk factors associated with T. gondii in pregnant and non-pregnant women in Punjab Province, Pakistan. A cross-sectional study was conducted and 593 samples were collected from pregnant (n = 293) and non-pregnant (n = 300) women of District Headquarter Hospitals of Chiniot, Faisalabad, Jhang and Okara, Pakistan. Data related to demographic parameters and risk factors were collected using a pretested questionnaire on blood sampling day. Serum samples were screened for antibodies (IgG) against T. gondii using ELISA. A univariant and binomial logistic regression was applied to estimate the association between seropositive and explanatory variables considering the 95% confidence interval. P value ⩽0.05 was considered statistically significant for all analysis. Out of 593, 44 (7.42%) women were seropositive for T. gondii IgG antibodies. Occupation, age, sampling location, socioeconomic status, contact with cat, pregnancy status and trimester of pregnancy were significantly associated with seropositivity for T. gondii antibodies. Location and trimester of pregnancy were identified as potential risk factors for T. gondii seropositivity based on binomial logistic regression. Toxoplasma gondii is prevalent in pregnant and non-pregnant women. Therefore, now a necessitated awareness is required to instruct the individuals about these infectious diseases (toxoplasmosis) and their control strategies to maintain the health of human population. Moreover, health awareness among public can help the minimization of T. gondii infection during pregnancy and subsequent risk of congenital toxoplasmosis.

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