scholarly journals Hospitalized older adult: predictors of functional decline

2021 ◽  
Vol 29 ◽  
Author(s):  
João Paulo de Almeida Tavares ◽  
Lisa Alexandra Nogueira Veiga Nunes ◽  
Joana Catarina Gonçalves Grácio
Keyword(s):  
Social Support ◽  
Cognitive Deficit ◽  
Functional Decline ◽  
Binary Logistic Regression ◽  
Demographic Variables ◽  
Binary Logistic Regression Model ◽  
Mechanical Restraint ◽  
Previous Hospitalization ◽  
The Impact

Objective: to identify the predictors of functional decline in hospitalized individuals aged 70 or over, between: baseline and discharge; discharge and follow-up, and baseline and three-month follow-up. Method: a prospective cohort study conducted in internal medicine services. A questionnaire was applied (clinical and demographic variables, and predictors of functional decline) at three moments. The predictors were determined using the binary logistic regression model. Results: the sample included 101 patients, 53.3% female, mean age of 82.47 ± 6.57 years old. The predictors that most contributed to decline in hospitalization were the following: previous hospitalization (OR=1.8), access to social support (OR=4.86), cognitive deficit (OR=6.35), mechanical restraint (OR=7.82), and not having a partner (OR=4.34). Age (OR=1.18) and medical diagnosis (OR=0.10) were the predictors between discharge and follow-up. Being older, delirium during hospitalization (OR=5.92), and presenting risk of functional decline (OR=5.53) were predictors of decline between the baseline and follow-up. Conclusion: the most relevant predictors were age, previous hospitalization, cognitive deficit, restraint, social support, not having a partner, and delirium. Carrying out interventions aimed at minimizing the impact of these predictors can be an important contribution in the prevention of functional decline.

scholarly journals The Impact of International Remittances on Poverty Alleviation in Bangladesh

2019 ◽  
Vol 4 (1) ◽  
pp. 67-86 ◽  
Author(s):  
Bezon Kumar
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Poverty Alleviation ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Primary Data ◽  
Binary Logistic Regression Model ◽  
Household Poverty ◽  
International Remittances ◽  
The Impact

This article mainly explores to what extent international remittances alleviate household poverty in Bangladesh. This study uses primary data collected from 216 households and employs multi-methods. Firstly, I measure the level of household poverty through Foster-Greer-Thorbecke index. The article secondly focuses on the impact of remittances on household poverty using a binary logistic regression model. I found that the level of poverty among remittance recipient households is notably lower than households that are not receiving remittances. Similarly, the probability of a household being poor is alleviated by 28.07 per cent if the household receives remittance. It can be suggested that nursing international remittances can be useful for poverty alleviation in Bangladesh. 

scholarly journals Impact of Socio-Demographic factors on survival of under-five children in Nigeria

2019 ◽  
Vol 4 (8) ◽  
pp. 44-48
Author(s):  
Abdulmumeen Adekunle Issa ◽  
Waheed Babatunde Yahya ◽  
Eyitayo Tejumola Jolayemi
Keyword(s):  
Secondary Education ◽  
Descriptive Analysis ◽  
Binary Logistic Regression ◽  
Demographic Variables ◽  
Demographic And Health Surveys ◽  
Binary Logistic Regression Model ◽  
Data Set ◽  
Under Five ◽  
Under Five Children ◽  
Survival Status

A number of discussions on mortality or survival patterns of under-five children in Nigeria have been presented in the literature over years, most of which were characterized by descriptive analysis, in which facts were reported by percentages, ratio and measures of association to mention a few.  In this study, binary logistic regression model was employed to model the survival status (dead or alive) of under-five children in Nigeria as a function of some socio-demographic variables. Results from this study revealed that ten socio-demographic variables among several others were significantly associated with the survivals of under-five children in Nigeria. Specifically, the results showed that children that were born in urban area, that were exclusively breastfed, that were among the first four children in the family, whose mothers have secondary education and post-secondary education have significant increased odds, to about 27%, 580%, 20%, 22% and 102% respectively, of surviving beyond age five than their counterparts in the reference categories of the above identified risk factors (odds ratio is 1.271(p < 0.0001 for urban), 6.810 (p<0.0001 for breastfeeding), 1.197 (p < 0.0001 for birth order), 1.225 (p = 0.001 for secondary education) and 2.023 (p < 0.0001 for higher education)). Results from this work indicated that more enlightenment program is required to stem the alarming increase in under-five mortality rate in Nigeria which currently stood at 112%. Data set from Nigerian Demographic and Health Surveys (NDHS) report for 2008 was employed in this study.

scholarly journals Construction, internal validation and implementation in a mobile application of a scoring system to predict nonadherence to proton pump inhibitors

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3455 ◽  
Author(s):  
Emma Mares-García ◽  
Antonio Palazón-Bru ◽  
David Manuel Folgado-de la Rosa ◽  
Avelino Pereira-Expósito ◽  
Álvaro Martínez-Martín ◽  
...  
Keyword(s):  
Proton Pump Inhibitors ◽  
Proton Pump ◽  
Mobile Application ◽  
External Validation ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Internal Validation ◽  
Primary Variable ◽  
Bootstrap Validation

Background Other studies have assessed nonadherence to proton pump inhibitors (PPIs), but none has developed a screening test for its detection. Objectives To construct and internally validate a predictive model for nonadherence to PPIs. Methods This prospective observational study with a one-month follow-up was carried out in 2013 in Spain, and included 302 patients with a prescription for PPIs. The primary variable was nonadherence to PPIs (pill count). Secondary variables were gender, age, antidepressants, type of PPI, non-guideline-recommended prescription (NGRP) of PPIs, and total number of drugs. With the secondary variables, a binary logistic regression model to predict nonadherence was constructed and adapted to a points system. The ROC curve, with its area (AUC), was calculated and the optimal cut-off point was established. The points system was internally validated through 1,000 bootstrap samples and implemented in a mobile application (Android). Results The points system had three prognostic variables: total number of drugs, NGRP of PPIs, and antidepressants. The AUC was 0.87 (95% CI [0.83–0.91], p < 0.001). The test yielded a sensitivity of 0.80 (95% CI [0.70–0.87]) and a specificity of 0.82 (95% CI [0.76–0.87]). The three parameters were very similar in the bootstrap validation. Conclusions A points system to predict nonadherence to PPIs has been constructed, internally validated and implemented in a mobile application. Provided similar results are obtained in external validation studies, we will have a screening tool to detect nonadherence to PPIs.

scholarly journals Differential Influences of Social Support on App Use for Diabetes Self-Management – a Mixed Methods Approach

2020 ◽  
Author(s):  
Nicola Brew-Sam ◽  
Arul Chib ◽  
Constanze Rossmann
Keyword(s):  
Social Support ◽  
Decision Making ◽  
Logistic Regression ◽  
Shared Decision Making ◽  
Healthcare Professionals ◽  
Binary Logistic Regression ◽  
Patient Communication ◽  
Self Management ◽  
Shared Decision ◽  
The Impact

Abstract Background The impact of social support on diabetes management and health outcomes has been investigated comprehensively, with recent studies examining social support delivered via digital technologies. This paper argues that social support has an impact on the use of diabetes technologies. Specifically, we postulate differences between the impact of healthcare professional versus non-professional (family/friends) support on mobile app use for diabetes self-management. Methods This research employed a triangulation of methods including exploratory semi-structured face-to-face interviews (N= 21, Study 1) and an online survey (N= 65, Study 2) with adult type 1 and type 2 diabetes patients. Thematic analysis (Study 1) was used to explore the relevance of social support (by professionals versus non-professionals) for diabetes app use. Binary logistic regression (Study 2) was applied to compare healthcare decision-making, healthcare-patient communication, and the support by the personal patient network as predictors of diabetes app use, complemented by other predictors from self-management and technology adoption theory. Results The interviews (Study 1) demonstrated that (technology-supported) shared decision-making and supportive communication by healthcare professionals depended on the medical specialty of attending physicians. The personal patient network was perceived as either facilitating or hindering the use of mHealth for self-management. Binary logistic regression (Study 2) showed that the specialty of the physician significantly predicted the use of diabetes apps, with supervision by diabetes specialists increasing the likelihood of app use (as opposed to general practitioners). In addition, specialist care positively related to a higher chance of shared decision-making and better physician-patient communication. The support by the personal patient network predicted diabetes app use in the opposite direction, with less family/friend support increasing the likelihood of app use. Conclusion The results emphasize the relevance of support by healthcare professionals and by the patient network for diabetes app use and disclose differences from the existing literature. In particular, we found that the use of diabetes apps may increase in the absence of social support by family or friends (e.g., compensation for lack of support), and that use of diabetes apps may decrease when such support is high (e.g., no perceived need to use technology). Implications for practice are discussed.

scholarly journals Persistence of Fatigue among COVID-19 Survivors in Bangladesh: A Two-month after Follow-up Study

2021 ◽  
Vol 33 (1) ◽  
pp. 57-63
Author(s):  
Md Khairul Islam ◽  
Mohammad Murad Hossain ◽  
Md Monowar Hossain ◽  
Md Mohiuddin Sharif ◽  
Fahima Sharmin Hossain ◽  
...  
Keyword(s):  
Logistic Regression Model ◽  
Telephone Interview ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Follow Up Study ◽  
Laboratory Markers ◽  
Study Participants ◽  
Prevalent Symptom ◽  
D Dimer

Background: A limited number of studies have exclusively assessed fatigue among post-COVID patients. Our study aimed to assess the persistence and associations of fatigue among COVID-19 survivors after two months of recovery from their primary illness. Methods: During hospital admission from August to September, 2020, a total of 400 patients were diagnosed to be suffering from fatigue using Chalder fatigue scale. After obtaining informed written consent, patients were followed up two months later over telephone. A total of 332 participants participated in the interview (63 patients could not be traced and another 5 patient died within two months). Patients were asked to categorize their present fatigue condition based on a simplified questionnaire developed for telephone interview. Results: Among study participants, 62.9% (n=207) were found to be still suffering from fatigue two months after their hospital discharge. A significant association of fatigue was found with age (p=0.000), hypertension (RR: 1.51; CI: 1.15-1.99; p=0.002), diabetes mellitus (RR: 1.45; CI: 1.08-1.95; p=0.010), ischemic heart disease (RR: 2.04; CI: 1.15-3.64; p=0.011), on admission SpO2 (p=0.000), on admission serum ferritin (p=0.000), d-dimer (p=0.000), CRP (p=0.000), and Hb% (p=0.019). Binary logistic regression model revealed significant association of age and onadmission SpO2 with persistence of fatigue. Conclusions: Fatigue is a highly prevalent symptom among the COVID-19 survivors with significant association between fatigue and patients clinical and laboratory markers. Bangladesh J Medicine July 2022; 33(1) : 57-63

The impact of employee participation in online innovation communities on idea quality

Kybernetes ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jing Yu ◽  
Chen Liu
Keyword(s):  
New Product Development ◽  
Idea Generation ◽  
Binary Logistic Regression ◽  
Positive Influence ◽  
User Innovation ◽  
Binary Logistic Regression Model ◽  
Content Type ◽  
Idea Quality ◽  
The Impact ◽  
Innovation Community

Purpose Online user innovation community (OUIC) has become a vital source for enterprises to obtain user innovation ideas and interact with users in new product development. However, most studies only focus on the relationship between users and ideas, often ignoring the influence of employees in the innovation platform. The purpose of this study is to explore the impact of employee behaviors on idea quality in OUIC. Design/methodology/approach In this paper, the authors collected sample data of open user innovation community – Idea Exchange – and then, the authors examined the direct roles of employee’s idea generation behaviors and idea promotion behaviors on idea quality and the moderating roles of social networks position and enthusiasm by using binary logistic regression model. Findings Results indicated that employee’s idea generation behaviors and idea promotion behaviors have a positive influence on users’ idea quality. Also, the social network position and characteristics show the moderation effect of employee behavior and idea quality. Originality/value This study is different from prior studies because it emphasizes the role of employees in the open source platform. The findings suggest that enterprises and platform managers pay more attention to the impact of employees and improve the quality of ideas and promote the development of OUIC.

scholarly journals Neurocritical care complications and interventions influence the outcome in aneurysmal subarachnoid hemorrhage

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alexander Hammer ◽  
Frank Erbguth ◽  
Matthias Hohenhaus ◽  
Christian M. Hammer ◽  
Hannes Lücking ◽  
...  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Decompressive Craniectomy ◽  
Odds Ratio ◽  
Neurocritical Care ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Binary Logistic Regression ◽  
World Federation ◽  
Binary Logistic Regression Model ◽  
One Year ◽  
The Impact

Abstract Background This observational study was performed to show the impact of complications and interventions during neurocritical care on the outcome after aneurysmal subarachnoid hemorrhage (SAH). Methods We analyzed 203 cases treated for ruptured intracranial aneurysms, which were classified regarding clinical outcome after one year according to the modified Rankin Scale (mRS). We reviewed the data with reference to the occurrence of typical complications and interventions in neurocritical care units. Results Decompressive craniectomy (odds ratio 21.77 / 6.17 ; p < 0.0001 / p = 0.013), sepsis (odds ratio 14.67 / 6.08 ; p = 0.037 / 0.033) and hydrocephalus (odds ratio 3.71 / 6.46 ; p = 0.010 / 0.00095) were significant predictors for poor outcome and death after one year beside “World Federation of Neurosurgical Societies” (WFNS) grade (odds ratio 3.86 / 4.67 ; p < 0.0001 / p < 0.0001) and age (odds ratio 1.06 / 1.10 ; p = 0.0030 / p < 0.0001) in our multivariate analysis (binary logistic regression model). Conclusions In summary, decompressive craniectomy, sepsis and hydrocephalus significantly influence the outcome and occurrence of death after aneurysmal SAH.

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