Effect of COVID-19 pandemic on mental health hospital admissions: comparative population-based study

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.

Urban–Rural Differences in Clinical Characteristics of Prostate Cancer at Initial Diagnosis: A Single-Center Observational Study in Anhui Province, China

BackgroundPeople residing in rural areas have higher prostate cancer (PCa) mortality to incidence ratio (M/I) and worse prognosis than those in urban areas of China. Clinical characteristics at initial diagnosis are significantly associated with biochemical recurrence, overall survival, and PCa disease-free survival.ObjectiveThis study aimed at investigating the clinical characteristics at initial diagnosis of urban and rural PCa patients and to establish a logistic regression model for identifying independent predictors for high-grade PCa.Materials and MethodsClinical characteristics for PCa patients were collected from the largest prostate biopsy center in Anhui province, China, from December 2015 to March 2019. First, urban–rural disparities in clinical characteristics were evaluated at initial diagnosis. Second, based on pathological findings, we classified all participants into the benign+ low/intermediate-grade PCa or high-grade PCa groups. Univariate and multivariate logistic regression analyses were performed to identify independent factors for predicting high-grade PCa, while a nomogram for predicting high-grade PCa was generated based on all independent factors. The model was evaluated using area under receiver-operating characteristic (ROC) curve as well as calibration curve analyses and compared to a model without the place of residence factor of individuals.ResultsStatistically significant differences were observed between urban and rural PCa patients with regard to tPSA, PSA density (PSAD), and Gleason score (GS) (p &lt; 0.05). Logistic regression analysis revealed that tPSA [OR = 1.060, 95% confidence interval (CI): 1.024, 1.098], PSAD (OR = 14.678, 95%CI: 4.137, 52.071), place of residence of individuals (OR = 5.900, 95%CI: 1.068, 32.601), and prostate imaging reporting and data system version 2 (PI-RADS v2) (OR = 4.360, 95%CI: 1.953, 9.733) were independent predictive factors for high-grade PCa. The area under the curve (AUC) of the nomogram was greater than that of the model without the place of residence of individuals. The calibration curve of the nomogram indicated that the prediction curve was basically fitted to the standard curve, suggesting that the prediction model had a better calibration ability.ConclusionsCompared to urban PCa patients, rural PCa patients presented elevated tPSA, PSAD levels, and higher pathological grades. The place of residence of the individuals was an independent predictor for high-grade PCa in Anhui Province, China. Therefore, appropriate strategies, such as narrowing urban-rural gaps in access to health care and increasing awareness on the importance of early detection should be implemented to reduce PCa mortality rates.

The Legacy of Conflict: Reconstruction and Migration in the Aftermath of Civil War in Tajikistan

How do civil war and subsequent reconstruction efforts affect international migration? Although a wealth of evidence points to violent conflict’s effects on contemporaneous migration and although a rich body of literature examines development’s effects on migration, we know less about the intersection of conflict, development, and migration. This article examines the intersection of these factors nearly a decade after the 1992–1997 civil war in Tajikistan, combining data from the 2007 Tajikistan Living Standards Survey, the Uppsala Conflict Data Program, and original interviews. In a series of logistic regression models, I show that conflict fatalities do not have a direct effect on subsequent migration, while the number of years a district has had a development resource center directly increases the likelihood of migrating. However, the interaction between development and conflict is negative and significant. These findings suggest that conflict’s legacy does not directly impact the likelihood of respondents migrating but instead changes the nature of the relationship between development and migration. This finding illuminates conflict’s potential long-term consequences for migration and extends the migration-development nexus by addressing the role of conflict in the relationship between development and migration. In particular, it suggests that migration research in conflict-affected countries should incorporate measures of both conflict and development, even after a given conflict has ended.

Influencing Factors on Health Information to Improve Public Health Literacy in the Official WeChat Account of Guangzhou CDC

Background: Social media is used as a new channel for health information. In China, the official WeChat account is becoming the most popular platform for health information dissemination, which has created a good opportunity for the Centers for Disease Control and Prevention to facilitate health information online to improve emergency public health literacy.Methods: Data were collected from the Guangzhou CDC i-Health official WeChat account between April 1, 2018 and April 30, 2019. Descriptive analysis was performed for basic information about the followers and posts of the official WeChat account. Multiple logistic regression analysis was used to analyze the association among various factors of posts on engagement of followers of the official WeChat account.Results: Among 187,033 followers, the total numbers of post views, shares, likes, add to favorites, and comments for 213 posts were 1,147,308, 8,4671, and 5,535, respectively. Engagement of followers peaked on the dissemination date and gradually declined. The main post topics were health education posts and original posts. In the multiple logistic regression model, the number of post views was found to be significantly associated with infectious disease posts (AOR: 3.20, 95% CI: 1.16–8.81), original posts (AOR: 10.20, 95% CI: 1.17–89.28), and posts with title-reflected content (AOR: 2.93, 95% CI: 1.16–8.81).Conclusion: Our findings facilitate the government to formulate better strategies and improve the effectiveness of public information dissemination.

Using Base-ml to Learn Classification of Common Vestibular Disorders on DizzyReg Registry Data

Background: Multivariable analyses (MVA) and machine learning (ML) applied on large datasets may have a high potential to provide clinical decision support in neuro-otology and reveal further avenues for vestibular research. To this end, we build base-ml, a comprehensive MVA/ML software tool, and applied it to three increasingly difficult clinical objectives in differentiation of common vestibular disorders, using data from a large prospective clinical patient registry (DizzyReg).Methods: Base-ml features a full MVA/ML pipeline for classification of multimodal patient data, comprising tools for data loading and pre-processing; a stringent scheme for nested and stratified cross-validation including hyper-parameter optimization; a set of 11 classifiers, ranging from commonly used algorithms like logistic regression and random forests, to artificial neural network models, including a graph-based deep learning model which we recently proposed; a multi-faceted evaluation of classification metrics; tools from the domain of “Explainable AI” that illustrate the input distribution and a statistical analysis of the most important features identified by multiple classifiers.Results: In the first clinical task, classification of the bilateral vestibular failure (N = 66) vs. functional dizziness (N = 346) was possible with a classification accuracy ranging up to 92.5% (Random Forest). In the second task, primary functional dizziness (N = 151) vs. secondary functional dizziness (following an organic vestibular syndrome) (N = 204), was classifiable with an accuracy ranging from 56.5 to 64.2% (k-nearest neighbors/logistic regression). The third task compared four episodic disorders, benign paroxysmal positional vertigo (N = 134), vestibular paroxysmia (N = 49), Menière disease (N = 142) and vestibular migraine (N = 215). Classification accuracy ranged between 25.9 and 50.4% (Naïve Bayes/Support Vector Machine). Recent (graph-) deep learning models classified well in all three tasks, but not significantly better than more traditional ML methods. Classifiers reliably identified clinically relevant features as most important toward classification.Conclusion: The three clinical tasks yielded classification results that correlate with the clinical intuition regarding the difficulty of diagnosis. It is favorable to apply an array of MVA/ML algorithms rather than a single one, to avoid under-estimation of classification accuracy. Base-ml provides a systematic benchmarking of classifiers, with a standardized output of MVA/ML performance on clinical tasks. To alleviate re-implementation efforts, we provide base-ml as an open-source tool for the community.

Novel epigenetic link between gestational diabetes mellitus and macrosomia

Background & objectives: Examine maternal gestational diabetes mellitus (GDM), macrosomia and DNA methylation in candidate genes IGF1, IGF2, H19, ARHGRF11, MEST, NR3C1, ADIPOQ and RETN. Materials & methods: 1145 Children (572 GDM cases and 573 controls) from The Tianjin GDM study, including 177 with macrosomia, had blood DNA collection at median age 5.9 (range: 3.1–10.0). We used logistic regression to screen for associations with GDM and model macrosomia on 37 CpGs, and performed mediation analysis. Results: One CpG was associated with macrosomia at false discovery rate (FDR) <0.05 (cg14428359 in MEST); two (cg19466922 in MEST and cg26263166 in IGF2) were associated at p < 0.05 but mediated 26 and 13%, respectively. Conclusion: MEST and IGF2 were previously identified for potential involvement in fetal growth and development ( Trial Registration number: NCT01554358 [ClinicalTrials.gov] ).

Assessing the Road Traffic Crashes among Novice Female Drivers in Saudi Arabia

Background: Recently (in 2018), females were legally allowed to drive and use automobiles in Saudi Arabia (SA) for the first time. This study investigated and analyzed the general fear of driving (GFDS), perceived self-confidence (PSCR), socio-economic variables, demographic distribution, and self-reported RTCs in novice female drivers from SA. Methods: The work was based on survey responses from 9608 participants from the first generation of female drivers from SA. Factor analysis was used to extract GFDS and PSCR scales. Results: Cronbach’s α values of 0.781 and 0.800 were observed for GFDS and PSCR, respectively. Logistic regression was employed to model road traffic collisions (RTCs) as a function of all significant variables. The results showed that of the 17.4% of geographically distributed respondents who reported RTCs, only 4% reported severe or minor injuries, and the rest (96.0%) of the accidents involved property damage. The GFDS and PSCR values showed a positive association with the RTCs of novice female drivers. Furthermore, age was not a significant influencing factor in the RTCs of novice female drivers. However, exposure factors were positively associated with the risk of RTC involvement. Conclusions: Female novice drivers who were single, divorced/widowed, employed, and had higher individual incomes were at higher risk of getting into RTCs. The female drivers who hired personal trainers, compared to those who did not, exhibited similar chances of getting involved in RTCs. An extra on-road in-traffic driving lesson is suggested to be included in the new-driver license training program for drivers with higher GFDS in SA.

COMPLEX ASSOCIATION ANALYSIS OF ANTIOXIDANT GENES POLYMORPHIC DNA-MARKERS WITH AGE IN THE ETHNIC GROUP OF ABKHAZIANS

Впервые в этнической группе абхазов выполнен анализ ассоциаций полиморфных ДНК-маркеров генов антиоксидантной системы CAT (rs1001179), MSRA (rs10098474), GPX1 (rs1050450), GSR (rs1002149), GSTP1 (rs1695), SOD1 (rs2070424), SOD2 (rs4880), PON1 (rs662), PON2 (rs7493) с возрастом. С использованием ROC-анализа и логистической регрессии установлено, что спектр частот аллелей и генотипов полиморфных маркеров генов PON1 и GSTP1 меняется на протяжении всего исследуемого возрастного периода (21 107 лет); распределение частот аллелей и генотипов по полиморфным маркерам генов CAT и SOD2 изменяется на рубеже 60 лет. Методом Монте-Карло марковскими цепями определены мультилокусные генетические маркеры долголетия. У лиц 60-107 лет статистически значимо повышена частота встречаемости паттернов GSTP 1* G/G + PON 1* G ( OR =6,59, PFDR =0,018) и GSTP 1* G/G + SOD 1 *A ( OR =3,4, PFDR =0,041); аллель GSTP 1* A в разных комбинациях с аллелями PON 1*A, PON2 * C и CAT * C встре чается реже (OR=0,3, PFDR<0,05). For the first time in the ethnic group of Abkhazians, the association analysis of polymorphic DNA-markers of the antioxidant genes CAT ( rs 1001179), MSRA ( rs 10098474), GPX 1 ( rs 1050450), GSR ( rs 1002149), GSTP 1 ( rs 1695), SOD 1 ( rs 2070424), SOD 2 ( rs 4880) , PON 1 ( rs 662), PON 2 ( rs 7493) with age was performed. Using ROC-analysis and logistic regression, it was found that the spectrum of alleles and genotypes frequencies of PON 1 and GSTP 1 genes polymorphic markers change throughout the studied age period (21-107 years old); the distribution of allele and genotype frequencies of CAT and SOD 2 genes polymorphic markers changes within the age of 60 years. Multilocus genetic markers of longevity were determined by the Monte Carlo Markov chain method. Among persons in the age range 60-107 years, the frequency of observation of the patterns GSTP 1* G/G + PON 1* G ( OR =6,59, P FDR=0,018) and GSTP 1* G/G + SOD 1 *A ( OR =3,4, P FDR=0,041) is statistically significantly increased; the GSTP 1* A allele in various combinations with the PON 1* A , PON 2* C and CAT * C alleles are less common ( OR =0,3, P FDR<0,05).

Anorexia, pain and peripheral neuropathy are associated with a decrease in quality of life in patients with advanced pancreatic cancer receiving outpatient chemotherapy — a retrospective observational study

Background Cancer chemotherapy usually improves clinical outcomes in patients with advanced pancreatic cancer (APC), but can also cause moderate-to-severe adverse events (AEs). We investigated the relationship between moderate-to-severe AEs and quality of life (QOL) in patients with APC who received outpatient chemotherapy. Methods We recruited APC patients who received outpatient chemotherapy in Gifu University Hospital between September 2017 and December 2018. Adverse events related to chemotherapy were assessed by a pharmacist collaborating with a physician using common terminology criteria for AEs (CTCAE) ver 4.0, and QOL of patients was self-assessed by patients using the five-level EuroQol five-dimensional questionnaire (EQ-5D-5L Japanese edition 2). Associations between the EQ-5D-5L utility value and serious AEs were assessed using proportional odds logistic regression. Results A total of 59 patients who received 475 chemotherapy cycles were included. The proportional odds logistic regression indicated that grade ≥ 2 anorexia, pain and peripheral neuropathy were significantly correlated with a decreased EQ-5D-5L utility value. Pharmaceutical intervention for these AEs significantly improved the patients’ EQ-5D-5L utility value. Conclusions Anorexia, pain and peripheral neuropathy were significantly associated with a decrease in QOL. It is assumed that appropriate pharmaceutical intervention with particular emphasis on these AEs can improve the QOL of pancreatic cancer patients receiving outpatient chemotherapy.

In-hospital Mortality in SARS-CoV-2 stratified by Gender: A Retrospective Study

Background: The aim of this study was to determine in-hospital mortality in patients presenting with acute respiratory syndrome corona virus 2 (SARS-CoV-2) and to evaluate for any differences in outcome according to gender. Methods: Patients with SRS-CoV-2 infection were recruited into this retrospective cohort study between February 26 and September 8, 2020 and strаtified ассоrding tо the gender. Results: In tоtаl оf 3360 раtients (meаn аge 44 ± 17 years) were included, of whom 2221 (66%) were mаle. The average length of hospitalization was 13 days (range: 2–31 days). During hospitalization and follow-up 176 patients (5.24%) died. Mortality rates were significantly different according to gender (p=<0.001). Specifically, male gender was associated with significantly greater mortality when compared to female gender with results significant at an alpha of 0.05, LL = 28.67, df = 1, p = 0.001, suggesting that gender could reliably determine mortality rates. The coefficient for the males was significant, B = 1.02, SE = 0.21, HR = 2.78, p< .001, indicating that an observation in the male category will have a hazard 2.78 times greater than that in the female category. Multivariate logistic regression confirmed male patients admitted with SARS-CoV-2had higher сumulаtive аll-саuse in-hоsрitаl mоrtаlity (6.8% vs. 2.3%; аdjusted оdds rаtiо (аОR), 2.80; 95% (СI): [1.61 - 5.03]; р < 0.001). Conclusions: Male gender was an independent predictor of in-hospital death in this study. The mortality rate among male SARS-CoV-2 patients was 2.8 times higher when compared with females.