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BJPsych Open ◽  
2021 ◽  
Vol 7 (5) ◽  
Author(s):  
Fabian Bonello ◽  
Daniela Zammit ◽  
Anton Grech ◽  
Victoria Camilleri ◽  
Rachel Cremona

Background The coronavirus disease 2019 (COVID-19) global pandemic caused mental health services to be downscaled to abide by the public health restrictions issued. Aims The aim of this study was to investigate whether the pandemic and resultant restrictions had an impact on Malta's admissions to hospital for mental health issues by assessing the number and nature of psychiatric admissions to our only national mental health hospital. Method Data collection was carried out retrospectively for the 13-week period between 7 March 2020 and 4 June 2020, compared with the equivalent in 2019. Demographic data was obtained and descriptive statistical analysis through the use of the χ²-test, z-test and logistic regression model were used to compare both data-sets, using a P-value of 0.05. Results An overall reduction in admissions to hospital was noted in 2020 when compared with 2019, recorded to be lowest in March 2020 with a steady acceleration of admissions up until May 2020 (χ2(3) = 22.573, P < 0.001). This coincided with a decelerated rate of positive COVID-19 cases locally. In 2020, there were significantly higher female admissions (χ2(1) = 10.197, P < 0.001), increased presentations of self-harm/suicidal ideation (P < 0.001) and higher involuntary admissions using the Mental Health Act (χ2(1) = 4.904, P = 0.027). The logistic regression model identified total length of stay in hospital, primary mental health diagnosis, gender and month of admission as variables significantly associated with an admission. Conclusions Our first population-wide study confirms that the COVID-19 pandemic and subsequent public health restrictions had an impact on the population's hospital admissions for mental health issues.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shiji Chen ◽  
Yanhui Song ◽  
Junping Qiu ◽  
Vincent Larivière

PurposeThis study explores whether interdisciplinary components' citation intensity (ICCI) affects papers' scientific impact. In this study, the term “interdisciplinary components” refers to the disciplines that are different from the discipline to which the target research belongs. The citation intensity is the degree of density or sparseness of the paper citation network for a discipline. Previous studies have shown that the scientific impact of interdisciplinary research is influenced by interdisciplinarity and its properties, namely, variety, balance and disparity. However, the effect of ICCI on scientific impact has not been comprehensively explored.Design/methodology/approachThis study is based on the entire publication database of the Web of Science for the year 2000, where the authors provide an indicator to measure the ICCI of each publication. A tobit regression model is used to examine the effect of ICCI on scientific impact, controlling for a range of variables associated with the characteristics of the publications studied.FindingsThe results show that ICCI has a positive effect on scientific impact. The authors’ results further point out that ICCI displays a curvilinear inverted U-shape relationship with scientific impact. It means that including more citation-intensive interdisciplinary components can increase the scientific impact of interdisciplinary research. However, excessive use of citation-intensive interdisciplinary components may reduce the scientific impact of interdisciplinary research.Originality/valueThis study shows that, in addition to interdisciplinarity, the scientific impact of interdisciplinary research is also affected by the citation characteristics of interdisciplinary components, namely ICCI.


Author(s):  
Gaylan Dana ◽  
Sanjit Roy

Abstract Antenatal Care (ANC) is one of the four pillars initiatives of the Safe Motherhood. Since MMR is high in rural areas of Bangladesh so to reduce MMR the uptake of ANC visit from trained provider is important. The objective of this study was to see the trends of 4+ ANC visit and identify the factors associated with the number of antenatal visits in rural areas.This study used the data generated from Bangladesh Demographic and Health Survey (BDHS) 2004-2014 to observe the trends and factors associated of 4+ ANC visit. The results of bivariate and multivariate analyses confirm that divisions, wealth, education and media exposure had strong influence on rural women’s 4 + ANC visit. Result of logistic regression model shows that poor and less educated women of rural areas were less likely to seek 4+ ANC visit than urban areas. This outcome of the paper suggests that rural women economic status and education has significant effect on 4+ ANC visit. The findings will help to design appropriate strategies, programs and policies for the improvement of rural women’s maternal healthcare seeking behaviour.


2021 ◽  
pp. 073346482110356
Author(s):  
Katherine A. Kennedy ◽  
Katherine M. Abbott ◽  
John R. Bowblis

Objectives: The objective of this study was to examine the relationship between high wages and empowerment practices on certified nursing assistant (CNA) retention, necessary for providing high-quality care for nursing home (NH) residents. Methods: Measures of provider-level CNA empowerment and wages from the 2015 Ohio Biennial Survey were used to estimate two regression models on retention ( n = 719), one without and one with an interaction term of high wages and high empowerment. Results: Only in the context of the interacted model were NHs that provided both high wages and high empowerment associated with a 7.09 percentage-point improvement in the CNA retention rate ( p = .0003). Individually, high wages and a high empowerment score were not statistically significant in either regression model. Discussion: Retaining CNAs in NH communities requires a combination of empowerment practices (e.g., involving CNAs in decision-making about hiring other staff) and high hourly wages.


2021 ◽  
pp. 089011712110261
Author(s):  
Wenxue Lin ◽  
Joshua E. Muscat

Purpose: Determine whether dual tobacco users have different levels of knowledge about nicotine addiction, perceived harm beliefs of low nicotine cigarettes (LNCs) and beliefs about electronic cigarettes (e-cigarettes) Design: Quantitative, Cross-sectional Setting: Health Information National Trends Survey 5 (Cycle 3, 2019) Participants: Nationally representative adult non-smokers (n=3113), exclusive cigarette smokers (n=302), and dual (cigarette and e-cigarette) users (n=77). Measures: The survey included single item measures on whether nicotine causes addiction and whether nicotine causes cancer. A five-point Likert scale assessed comparative harm of e-cigarettes and LNCs relative to conventional combustible cigarettes (1=much more harmful, 3=equally harmful…5 = much less harmful, or don’t know). Analysis: We used weighted multiple linear regression model to estimate means and 95% confidence intervals (CI) of e-cigarettes and LNCs beliefs by current tobacco user status. Results: Over 97% of dual users, 83% of non-smokers and 86% of exclusive cigarette smokers correctly identified that nicotine is addictive. The majority of subjects incorrectly identified nicotine as a cause of cancer, with dual users having the lowest proportion of incorrect responses (60%). Dual users rated e-cigarette harmfulness as less harmful than combustibles (mean=2.20; 95% CI=1.73, 2.66) while exclusive cigarette smokers and non-smokers rated them as similarly harmful. LNCs were considered equally harmful and addictive as conventional cigarettes. Conclusion: Dual users had a higher knowledge base of tobacco-related health effects. The effectiveness of policies or medical recommendations to encourage smokers to switch from cigarettes to LNCs or e-cigarettes will need to consider accurate and inaccurate misperceptions about the harm and addictiveness of nicotine. Improved public health messages about different tobacco products are needed.


2021 ◽  
Author(s):  
Kindu Kebede Gebre ◽  
Million Wesenu Demissie

Abstract Background: The recent outbreak of Novel Coronavirus (SARS-CoV-2) Disease (COVID-19) has put the world on alert and impacting societies around the world in an unprecedented manner. The main aims of this study was to investigate the association among the socio-demographic factors with traveling history of COVID-19 Patients in Ethiopia during stay at home state of emergency. Methods: A total of 162 respondents with COVID-19 during March 13, 2020 to May 6, 2020 in Ethiopia were used. Two sided chi-square test was used to test the association between the socio demographic factors among COVID-19 Patients. A log-complement logistic regression model was used to compute the health ratios (HR) and 95% confidence interval (CI) to measure the effect of those factors. Results: The data was analyzed using 162 patients of severe acute respiratory syndrome corona virus-2. An association was found between traveling history of COVID-19 infected patients and Gender (male vs female) [B =5.410, p<0.020] and Age group [a=13.082, p<0.004]. Log-complement logistic regression model showed that Gender and Age were significant factors associated to traveling history of COVID-19 Patients. Health ratio showed that increasing risk of traveling history for COVID-19 patients associated with higher number of males [ HR=0.5895, 95%CI: 0.4007-0.8672, P<0.0073] and Age group 18-39 years [HR=0.4139, 95%CI: 0.2385-0.7184, P<0.0017] on patients of COVID-19. Akaike information criteria with minimum value [AIC=1.2158] indicated that Log complement logistic regression model was fitted the data well for the similar dataset of patients’ with novel corona virus. Conclusions: Male Gender and Age group 18-39 years are significant socio-demographic factors associated to traveling history of patients with corona virus disease. Further socio-demographic investigations are required to better understand the extent of association with Gender and Age for effective intervention and fight this pandemic to preserve lives.


Author(s):  
Ebrahim Jaafaripooyan ◽  
Ali Akbarisari ◽  
Abbas Rahimiforoushani ◽  
Zahra Abedini

Background: As fast and accurate techniques, advanced medical imaging technologies (AMIT) allow healthcare professionals to better diagnose and treat various health conditions, which translates into higher use of non-invasive operational procedures. Objectives: The current study intended to investigate the effect of inpatient use of MRI and CT scan on the inpatient mortality and length of stay (LOS) in Tehran general university hospitals. Methods: Data were collected from all general university hospitals in Tehran in 2017. A multiple linear regression model was constructed for each combination of technology and outcomes (i.e., mortality and LOS), and all models were controlled for patients’ demographic and clinical characteristics and structural profile of hospitals. In calculating hospital standardized mortality ratio (HSMR) for each of 72 diagnosis groups related to death, a binary logistic regression model was fitted with predictors including LOS, admission type, comorbidity level, sex, and age. Results: The use of CT varied from 0.39 to 149.35, and MRI from 0.24 to 80.23 exams per 100 discharges. The HSMR ranged from 76.8% to 146%, and the average length of stay (ALOS) was 3 - 8.46 days. MRI and CT had no significant effect on the HSMR and ALOS. Conclusions: Further use of AMIT was not linked with improved efficiency and quality but was associated with better resource management in healthcare organizations. Effective management of the AMIT use requires clear rules and regulations with assertive commitment, in addition to establishing clinical guidelines with the support of insurance companies.


2021 ◽  
Vol 103-B (8) ◽  
pp. 1358-1366
Author(s):  
Chapman Wei ◽  
Theodore Quan ◽  
Kevin Y. Wang ◽  
Alex Gu ◽  
Safa C. Fassihi ◽  
...  

Aims This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). Methods Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay. Results The predictability of the ANN model, area under the curve (AUC) = 0.801, was similar to the logistic regression model (AUC = 0.796) and identified certain variables as important factors to predict same-day discharge. The ten most important factors favouring same-day discharge in the ANN model include preoperative sodium, preoperative international normalized ratio, BMI, age, anaesthesia type, operating time, dyspnoea status, functional status, race, anaemia status, and chronic obstructive pulmonary disease (COPD). Six of these variables were also found to be significant on logistic regression analysis. Conclusion Both ANN modelling and logistic regression analysis revealed clinically important factors in predicting patients who can undergo safely undergo same-day discharge from an outpatient TKA. The ANN model provides a beneficial approach to help determine which perioperative factors can predict same-day discharge as of 2018 perioperative recovery protocols. Cite this article: Bone Joint J 2021;103-B(8):1358–1366.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lee M. Dunham ◽  
John Garcia

PurposeThis study examines the effect of firm-level investor sentiment on a firm's level of financial distress.Design/methodology/approachThe authors use Bloomberg's firm-level, daily investor sentiment scores derived from firm-level news and Twitter content in a beta-regression model to explain the variability in a firm's financial distress.FindingsThe results indicate that improvements (deterioration) in investor sentiment derived from both news articles and Twitter content lead to a decrease (increase) in the average firm's financial distress level. We also find that the effect of sentiment derived from Twitter on a firm's financial distress is significantly stronger than the sentiment derived from news articles.Research limitations/implicationsOur proxy for financial distress is Bloomberg's financial distress measures, which may be an imperfect measure of financial distress. Our results have important implications for market participants in assessing the determinants of financial distress.Practical implicationsOur sample period covers four years (2015–2019), which is determined by Bloomberg sentiment data availability.Social implicationsMarket participants are increasingly using social media to express views on firms and seek information that might be used to determine a firm's level of financial distress. Our study links investor sentiment derived from social media (Twitter) and traditional news articles to financial distress.Originality/valueBy examining the relationship between a firm's sentiment and its financial distress, this paper advances our understanding of the factors that drive a firm's financial distress. To our knowledge, this is the first study to link US firms' investor sentiment derived from firm-level news and Twitter content to a firm's financial distress.


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