scholarly journals Hospital-Nursing Home Transfer Patterns and Influence on Nursing Home Clostridium difficile Infection Rates

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S402-S403
Author(s):  
Lauren Campbell ◽  
Kristen Bush ◽  
Ghinwa Dumyati
Keyword(s):  
Nursing Home ◽  
Network Analysis ◽  
Online Survey ◽  
Negative Binomial ◽  
Network Clustering ◽  
Emerging Infections ◽  
Clustering Methods ◽  
Data Set ◽  
Patient Transfers ◽  
Network Analyses

Abstract Background Little is known as to how hospital C. difficile infection (CDI) may impact nursing home (NH) CDI, or how patient transfers may modify this relationship. This study aims to examine a possible association between hospital and NH CDI rates, and whether NH CDI rates are influenced by patient transfers from hospital to NH. Methods Patient transfers among the 5 hospitals and 34 NHs in Monroe County, NY were identified from the Minimum Data Set (MDS) 3.0 and Medicare Provider Analysis and Review files for 2011–13, and aggregated to the NH level. NH and hospital CDI rates were obtained from Emerging Infections Program CDI population surveillance and National Healthcare Safety Network data, respectively. Multivariate negative binomial regression modeled the association between hospital CDI rate (weighted by hospital-to-NH transfers/overall transfers among hospitals and NHs) and NH CDI rate, controlling for NH covariates from NH Compare and the Online Survey, Certification, and Reporting files. Patient transfer networks between hospitals and NHs were constructed, and basic network analysis of transfer patterns was conducted to confirm contributing factors to NH CDI rates from the multivariate model. Results When weighted hospital CDI rate increased by 1%, NH CDI rate increased by 18% (P = 0.016). Antibiotic and feeding tube prevalence were associated with a 4% and 8% increase in NH CDI rate, respectively (P≤0.014). Network analysis confirmed multivariate results and detected hospital-NH pairs with high edge weights (number of transfers) where NHs receiving patients from hospitals with high CDI rates had higher CDI rates. Network clustering methods were used to identify 2 sub-networks within overall annual networks and clusters of hospital-NH pairs for targeted intervention. Conclusion Hospital CDI rate, adjusting for patient transfers, is associated with higher NH CDI rates in multivariate and network analyses, suggesting that NHs with a large inflow of patients from hospitals may need to implement stricter infection prevention practices to reduce transmission among residents. By identifying regional sub-networks, network analysis can also be used to actively manage facility CDI and prevent spread to other healthcare facilities. Disclosures All authors: No reported disclosures.

Race, Education, and the Treatment of Depression in Nursing Homes

2012 ◽  
Vol 24 (5) ◽  
pp. 752-778 ◽  
Author(s):  
Michele J. Siegel ◽  
Judith A. Lucas ◽  
Ayse Akincigil ◽  
Dorothy Gaboda ◽  
Donald R. Hoover ◽  
...  
Keyword(s):  
Nursing Home ◽  
Nursing Homes ◽  
Online Survey ◽  
High School Graduates ◽  
Nursing Home Residents ◽  
Depression Treatment ◽  
Data Set ◽  
Treatment Of Depression ◽  
Logistic Regressions ◽  
Facility Level

Objectives: We investigate, among older adult nursing home residents diagnosed with depression, whether depression treatment differs by race and schooling, and whether differences by schooling differ by race. We examine whether Blacks and less educated residents are placed in facilities providing less treatment, and whether differences reflect disparities in care. Method: Data from the 2006 Nursing Home Minimum Data Set for 8 states ( n = 124,431), are merged with facility information from the Online Survey Certification and Reporting system. Logistic regressions examine whether resident and/or facility characteristics explain treatment differences; treatment includes antidepressants and/or psychotherapy. Results: Blacks receive less treatment (adj. OR = .79); differences by education are small. Facilities with more Medicaid enrollees, fewer high school graduates, or more Blacks provide less treatment. Discussion: We found disparities at the resident and facility level. Facilities serving a low-SES (socioeconomic status), minority clientele tend to provide less depression care, but Blacks also receive less depression treatment than Whites within nursing homes (NHs).

scholarly journals “Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Qin Wu ◽  
Xingqin Qi ◽  
Eddie Fuller ◽  
Cun-Quan Zhang
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Clustering Algorithm ◽  
Data Sets ◽  
Network Clustering ◽  
Clustering Methods ◽  
Social Network Data ◽  
Centrality Score ◽  
Vertex Centrality ◽  
Guided Clustering

Within graph theory and network analysis, centrality of a vertex measures the relative importance of a vertex within a graph. The centrality plays key role in network analysis and has been widely studied using different methods. Inspired by the idea of vertex centrality, a novel centrality guided clustering (CGC) is proposed in this paper. Different from traditional clustering methods which usually choose the initial center of a cluster randomly, the CGC clustering algorithm starts from a “LEADER”—a vertex with the highest centrality score—and a new “member” is added into the same cluster as the “LEADER” when some criterion is satisfied. The CGC algorithm also supports overlapping membership. Experiments on three benchmark social network data sets are presented and the results indicate that the proposed CGC algorithm works well in social network clustering.

scholarly journals Predicting Public Uptake of Digital Contact Tracing During the COVID-19 Pandemic: Results From a Nationwide Survey in Singapore

10.2196/24730 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e24730
Author(s):  
Young Ern Saw ◽  
Edina Yi-Qin Tan ◽  
Jessica Shijia Liu ◽  
Jean CJ Liu
Keyword(s):  
Online Survey ◽  
Mobile Apps ◽  
National Level ◽  
Nationwide Survey ◽  
Primary Outcome Measure ◽  
Mobile App ◽  
Contact Tracing ◽  
Data Set ◽  
Network Analyses ◽  
Pandemic Severity

Background During the COVID-19 pandemic, new digital solutions have been developed for infection control. In particular, contact tracing mobile apps provide a means for governments to manage both health and economic concerns. However, public reception of these apps is paramount to their success, and global uptake rates have been low. Objective In this study, we sought to identify the characteristics of individuals or factors potentially associated with voluntary downloads of a contact tracing mobile app in Singapore. Methods A cohort of 505 adults from the general community completed an online survey. As the primary outcome measure, participants were asked to indicate whether they had downloaded the contact tracing app TraceTogether introduced at the national level. The following were assessed as predictor variables: (1) participant demographics, (2) behavioral modifications on account of the pandemic, and (3) pandemic severity (the number of cases and lockdown status). Results Within our data set, the strongest predictor of the uptake of TraceTogether was the extent to which individuals had already adjusted their lifestyles because of the pandemic (z=13.56; P<.001). Network analyses revealed that uptake was most related to the following: using hand sanitizers, avoiding public transport, and preferring outdoor over indoor venues during the pandemic. However, demographic and situational characteristics were not significantly associated with app downloads. Conclusions Efforts to introduce contact tracing apps could capitalize on pandemic-related behavioral adjustments among individuals. Given that a large number of individuals is required to download contact tracing apps for contact tracing to be effective, further studies are required to understand how citizens respond to contact tracing apps. Trial Registration ClinicalTrials.gov NCT04468581, https://clinicaltrials.gov/ct2/show/NCT04468581

Depression in the first year of stay for elderly long-term nursing home residents in the U.S.A.

2010 ◽  
Vol 22 (7) ◽  
pp. 1161-1171 ◽  
Author(s):  
D. R. Hoover ◽  
M. Siegel ◽  
J. Lucas ◽  
E. Kalay ◽  
D. Gaboda ◽  
...  
Keyword(s):  
Nursing Home ◽  
Online Survey ◽  
Nursing Home Residents ◽  
First Year ◽  
Data Set ◽  
Never Married ◽  
Time Period ◽  
Large Numbers ◽  
Antidepressant Use

ABSTRACTBackground: Understanding the prevalence, incidence and cofactors of depression among long-term elderly nursing home (LTNH) residents domiciled for eight months or more may help optimize depression treatment in this vulnerable group. We quantified first year depression in American LTNH residents and the associations between depression and resident/facility characteristics.Methods: Data were obtained from the Minimum Data Set and Online Survey Certification and Reporting for 634,060 LTNH residents admitted from 1999 to 2005 in 4,216 facilities. Depression first diagnosed at admission and at subsequent quarterly intervals through the first year of stay was examined. Logistic regressions modeled correlates of newly identified depression in each time-period.Results: Recorded depression at admission and during the first year increased from 1999 to 2005. By 2005, 54.4% of LTNH residents had depression diagnosed over the first year; 32.8% at admission and a further 21.6% later during the first year. Antidepressant use was reported prior to depression diagnosis for 48% of those first identified depressed after admission. Men, non-Hispanic blacks, never married, and severely-cognitively impaired LTNH residents were less often identified with depression, particularly at admission. Pain and physical comorbidity were positively associated with depression identified throughout the first year. Prior institutionalization was associated with depression at admission, but not new depression after admission. Facility characteristics had weaker associations with depression.Conclusions: High depression rates at admission and during the first year indicate a need to monitor and treat large numbers of American LTNH residents for depression. Reduced associations between demographics and depression as stays progress suggest other factors have increased roles in depression etiology.

scholarly journals Predicting Public Uptake of Digital Contact Tracing During the COVID-19 Pandemic: Results From a Nationwide Survey in Singapore (Preprint)

2020 ◽  
Author(s):  
Young Ern Saw ◽  
Edina Yi-Qin Tan ◽  
Jessica Shijia Liu ◽  
Jean CJ Liu
Keyword(s):  
Online Survey ◽  
Mobile Apps ◽  
National Level ◽  
Nationwide Survey ◽  
Primary Outcome Measure ◽  
Mobile App ◽  
Contact Tracing ◽  
Data Set ◽  
Network Analyses ◽  
Pandemic Severity

BACKGROUND During the COVID-19 pandemic, new digital solutions have been developed for infection control. In particular, contact tracing mobile apps provide a means for governments to manage both health and economic concerns. However, public reception of these apps is paramount to their success, and global uptake rates have been low. OBJECTIVE In this study, we sought to identify the characteristics of individuals or factors potentially associated with voluntary downloads of a contact tracing mobile app in Singapore. METHODS A cohort of 505 adults from the general community completed an online survey. As the primary outcome measure, participants were asked to indicate whether they had downloaded the contact tracing app TraceTogether introduced at the national level. The following were assessed as predictor variables: (1) participant demographics, (2) behavioral modifications on account of the pandemic, and (3) pandemic severity (the number of cases and lockdown status). RESULTS Within our data set, the strongest predictor of the uptake of TraceTogether was the extent to which individuals had already adjusted their lifestyles because of the pandemic (z=13.56; <i>P</i>&lt;.001). Network analyses revealed that uptake was most related to the following: using hand sanitizers, avoiding public transport, and preferring outdoor over indoor venues during the pandemic. However, demographic and situational characteristics were not significantly associated with app downloads. CONCLUSIONS Efforts to introduce contact tracing apps could capitalize on pandemic-related behavioral adjustments among individuals. Given that a large number of individuals is required to download contact tracing apps for contact tracing to be effective, further studies are required to understand how citizens respond to contact tracing apps. CLINICALTRIAL ClinicalTrials.gov NCT04468581, https://clinicaltrials.gov/ct2/show/NCT04468581

Minimum Data Set for Incontinence-Associated Dermatitis (MDS-IAD) in adults: Design and pilot study in nursing home residents

2018 ◽  
Vol 27 (4) ◽  
pp. 191-198
Author(s):  
Karen Van den Bussche ◽  
Sofie Verhaeghe ◽  
Ann Van Hecke ◽  
Dimitri Beeckman
Keyword(s):  
Nursing Home ◽  
Pilot Study ◽  
Nursing Home Residents ◽  
Minimum Data Set ◽  
Data Set ◽  
Minimum Data

scholarly journals Fall incidents in nursing home residents: development of a predictive clinical rule (FINDER)

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e042941
Author(s):  
Vanja Milosevic ◽  
Aimee Linkens ◽  
Bjorn Winkens ◽  
Kim P G M Hurkens ◽  
Dennis Wong ◽  
...  
Keyword(s):  
Nursing Home ◽  
Fall Risk ◽  
Nursing Home Residents ◽  
High Sensitivity ◽  
Secondary Outcome ◽  
Operating Characteristics ◽  
Retrospective Case ◽  
Data Set ◽  
Fall Incident ◽  
Clinical Rule

ObjectivesTo develop (part I) and validate (part II) an electronic fall risk clinical rule (CR) to identify nursing home residents (NH-residents) at risk for a fall incident.DesignObservational, retrospective case–control study.SettingNursing homes.ParticipantsA total of 1668 (824 in part I, 844 in part II) NH-residents from the Netherlands were included. Data of participants from part I were excluded in part II.Primary and secondary outcome measuresDevelopment and validation of a fall risk CR in NH-residents. Logistic regression analysis was conducted to identify the fall risk-variables in part I. With these, three CRs were developed (ie, at the day of the fall incident and 3 days and 5 days prior to the fall incident). The overall prediction quality of the CRs were assessed using the area under the receiver operating characteristics (AUROC), and a cut-off value was determined for the predicted risk ensuring a sensitivity ≥0.85. Finally, one CR was chosen and validated in part II using a new retrospective data set.ResultsEleven fall risk-variables were identified in part I. The AUROCs of the three CRs form part I were similar: the AUROC for models I, II and III were 0.714 (95% CI: 0.679 to 0.748), 0.715 (95% CI: 0.680 to 0.750) and 0.709 (95% CI: 0.674 to 0.744), respectively. Model III (ie, 5 days prior to the fall incident) was chosen for validation in part II. The validated AUROC of the CR, obtained in part II, was 0.603 (95% CI: 0.565 to 0.641) with a sensitivity of 83.41% (95% CI: 79.44% to 86.76%) and a specificity of 27.25% (95% CI 23.11% to 31.81%).ConclusionMedication data and resident characteristics alone are not sufficient enough to develop a successful CR with a high sensitivity and specificity to predict fall risk in NH-residents.Trial registration numberNot available.

Exploring fake news identification using word and sentence embeddings

2021 ◽  
pp. 1-8
Author(s):  
V.T Priyanga ◽  
J.P Sanjanasri ◽  
Vijay Krishna Menon ◽  
E.A Gopalakrishnan ◽  
K.P Soman
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Breeding Ground ◽  
Fake News ◽  
Data Set ◽  
Highly Correlated ◽  
Use Of Social Media ◽  
The Liar

The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis.

scholarly journals Ownership Variation in Violated Regulations and National Care Standards: Evidence From Social Care Providers

2021 ◽  
pp. 089976402110014
Author(s):  
Anders M. Bach-Mortensen ◽  
Ani Movsisyan
Keyword(s):  
Social Care ◽  
Negative Binomial ◽  
Third Sector ◽  
Care Providers ◽  
Data Set ◽  
Logistic Regression Models ◽  
For Profit ◽  
Care Services ◽  
Significant Difference ◽  
Research Gap

Social care services are increasingly provisioned in quasi-markets in which for-profit, public, and third sector providers compete for contracts. Existing research has investigated the implications of this development by analyzing ownership variation in latent outcomes such as quality, but little is known about whether ownership predicts variation in more concrete outcomes, such as violation types. To address this research gap, we coded publicly available inspection reports of social care providers regulated by the Care Inspectorate in Scotland and created a novel data set enabling analysis of ownership variation in violations of (a) regulations, and (b) national care standards over an entire inspection year ( n = 4,178). Using negative binomial and logistic regression models, we find that for-profit providers are more likely to violate non-enforceable outcomes (national care standards) relative to other ownership types. We did not identify a statistically significant difference between for-profit and third sector providers with regard to enforceable outcomes (regulations).

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