Factors associated with a history of critical wandering among Medic-Alert subscribers.

BACKGROUND: Critical wandering occurs when an individual living with dementia leaves a location and is unaware of place or time. Critical wandering incidents are expected to increase with the growing prevalence of persons living with dementia worldwide. We investigated the association between demographic, psychopathological, and environmental factors and a history of critical wandering among Medic-Alert subscribers, both with and without dementia. METHODS: Our retrospective study included data of 25,785 Canadian Medic-Alert subscribers who were aged 40 years or older. We used multivariable logistic regression analysis to examine the associations between a history of critical wandering and dementia status as psychopathological independent variable, controlled by demographic (age, ethnic background, sex at birth, Canadian languages spoken) and environmental (living arrangement, population density) factors. RESULTS: The overall study sample comprised of mainly older adults (77.4%). Medic-Alert subscribers who were older, male sex at birth, living with dementia, of a minority ethnic group and who did not have proficiency in an official Canadian language had a higher likelihood of a history of critical wandering. Residing in an urban environment, in an institution or with a family member, were environmental factors associated with a higher likelihood of a history of critical wandering. CONCLUSIONS: People living with dementia experience a higher likelihood of a history of critical wandering compared to those without dementia. Medic-Alert and similar organizations can develop algorithms based on the associated factors that can be used to flag risks of critical wandering. This can inform preventative strategies at the individual and community levels.

Pediatric and Young Adult Household Transmission of the Initial Waves of SARS-CoV-2 in the United States: Administrative Claims Study.

BACKGROUND: The correlates responsible for the temporal changes of intrahousehold SARS-CoV-2 transmission in the United States have been understudied mainly due to a lack of available surveillance data. Specifically, early analyses of SARS-CoV-2 household secondary attack rates (SARs) were small in sample size and conducted cross-sectionally at single time points. From these limited data, it has been difficult to assess the role that different risk factors have had on intrahousehold disease transmission in different stages of the ongoing COVID-19 pandemic, particularly in children and youth. OBJECTIVE: This study aimed to estimate the transmission dynamic and infectivity of SARS-CoV-2 among pediatric and young adult index cases (age 0 to 25 years) in the United States through the initial waves of the pandemic. METHODS: Using administrative claims, we analyzed 19 million SARS-CoV-2 test records between January 2020 and February 2021. We identified 36,241 households with pediatric index cases and calculated household SARs utilizing complete case information. Using a retrospective cohort design, we estimated the household SARS-CoV-2 transmission between 4 index age groups (0 to 4 years, 5 to 11 years, 12 to 17 years, and 18 to 25 years) while adjusting for sex, family size, quarter of first SARS-CoV-2 positive record, and residential regions of the index cases. RESULTS: After filtering all household records for greater than one member in a household and missing information, only 36,241 (0.85%) of 4,270,130 households with a pediatric case remained in the analysis. Index cases aged between 0 and 17 years were a minority of the total index cases (n=11,484, 11%). The overall SAR of SARS-CoV-2 was 23.04% (95% CI 21.88-24.19). As a comparison, the SAR for all ages (0 to 65+ years) was 32.4% (95% CI 32.1-32.8), higher than the SAR for the population between 0 and 25 years of age. The highest SAR of 38.3% was observed in April 2020 (95% CI 31.6-45), while the lowest SAR of 15.6% was observed in September 2020 (95% CI 13.9-17.3). It consistently decreased from 32% to 21.1% as the age of index groups increased. In a multiple logistic regression analysis, we found that the youngest pediatric age group (0 to 4 years) had 1.69 times (95% CI 1.42-2.00) the odds of SARS-CoV-2 transmission to any family members when compared with the oldest group (18 to 25 years). Family size was significantly associated with household viral transmission (odds ratio 2.66, 95% CI 2.58-2.74). CONCLUSIONS: Using retrospective claims data, the pediatric index transmission of SARS-CoV-2 during the initial waves of the COVID-19 pandemic in the United States was associated with location and family characteristics. Pediatric SAR (0 to 25 years) was less than the SAR for all age other groups. Less than 1% (n=36,241) of all household data were retained in the retrospective study for complete case analysis, perhaps biasing our findings. We have provided measures of baseline household pediatric transmission for tracking and comparing the infectivity of later SARS-CoV-2 variants.

Analysis of the factors affecting the loss of correction effect in patients with congenital scoliosis after one stage posterior hemivertebrae resection and orthosis fusion.

BACKGROUND: To analyze the factors affecting the loss of correction effect in patients with congenital scoliosis after one stage posterior hemivertebra resection, orthosis, fusion and internal fixation. METHODS: Thirty-nine patients with congenital scoliosis (CS) who underwent one-stage posterior hemivertebra resection, orthosis, fusion and internal fixation were retrospectively included in Hebei Children's Hospital General demographic information of patients was collected. Preoperative and postoperative imaging indicators were compared, Including cobb Angle of the main curvature of the spine, segmental Cobb Angle, compensatory cephalic curve, compensatory curve on the caudal side, segmental kyphosis, coronal balance, sagittal balance, thoracic kyphosis, lumbar lordosis, and apical vertebra translation. Correlation analysis is used to evaluate the factors affecting the loss of judgment and correction effect, and the correlation indicators are included in the multi-factor Logistics regression. RESULTS: In terms of radiographic indicators in the coronal plane, compared to preoperative values, significant improvements were observed in postoperative Cobb Angle of main curve (8.00°±4.62° vs. 33.30°±9.86°), Segmental Cobb angle (11.87°±6.55° vs. 31.29°±10.03°), Compensatory cephalic curve (6.22°±6.33° vs. 14.75°±12.50°), Compensatory curve on the caudal side (5.58°±3.43° vs. 12.61°±8.72°), coronal balance (10.95 mm ± 8.65 mm vs. 13.52 mm ± 11.03 mm), and apical vertebra translation (5.96 mm ± 5.07 mm vs. 16.55 mm ± 8.39 mm) (all P < 0.05). In the sagittal plane, significant improvements were observed in Segmental kyposis Angle (7.60°±9.36° vs. 21.89°±14.62°, P < 0.05) as compared to preoperative values. At the last follow-up, Segmental kyphosis Angle (6.09°±9.75° vs. 21.89°±14.62°, P < 0.05), Thoracic kyphosis (26.57°±7.68° vs. 24.06°±10.49°, P < 0.05) and Lumbar lordosis (32.12°±13.15° vs. 27.84°±16.68°, P < 0.05) had statistical significance compared with the preoperative department. The correlation analysis showed that the correction effect of the main curve Cobb angle was correlated with fixed segment length (rs=-0.318, P = 0.048), postoperative segment Cobb angle (rs=-0.600, P < 0.001), preoperative apical vertebra translation (rs = 0.440, P = 0.005), and spinal cord malformation (rs=-0.437, P = 0.005). The correction effect of segmental kyphosis was correlated with age (rs = 0.388, P = 0.037). The results of the multivariate logistic regression analysis revealed that postoperative segmental Cobb angle > 10° (OR = 0.011, 95%CI:0.001-0.234, P = 0.004), associated spinal cord anomalies (OR = 24.369, 95%CI:1.057-561.793, P = 0.046), and preoperative apical translation > 10 mm (OR = 0.012, 95%CI:0.000-0.438, P = 0.016) were influential factors in the progression of the main curve Cobb angle. CONCLUSION: The one-stage posterior hemivertebra resection and short-segment corrective fusion with internal fixation are effective means to treat congenital scoliosis. However, attention should be paid to the loss of correction and curve progression during follow-up. Patients with spinal cord malformation and a large preoperative apical vertebra translation have a greater risk of losing the correction after surgery.

Construction of a Diagnostic Model for Distinguishing Benign or Malignant Bone Cancer by Mining miRNA Expression Data.

Bone tumor is a kind of rare cancer, the location of which is mainly in bone tissue as well as cartilage tissue. Bone tumor is mainly classified into benign and malignant types. The survival rate of patients with bone tumors can be considerably improved by early detection, and the danger of amputation caused by bone tumors can be greatly reduced. In this study, we first screened the top 25% serum miRNAs with the greatest variance in patients with malignant and benign bone tumor and healthy individuals. The expression of serum miRNAs in patients with bone tumor was then examined using unsupervised clustering and PCA, and the results revealed that the overall expression of serum miRNAs was ineffective in distinguishing patients with benign/malignant bone tumors. Subsequently, we screened 19 miRNA biomarkers that could be used to determine the benign/malignant bone tumor of patients by LASSO logistic regression. These genes were validated using ROC curves. Results showed that there were 11 miRNAs that could accurately distinguish benign/malignant bone tumor alone. These 11 miRNAs were, namely, hsa-miR-192-5p, hsa-miR-137, hsa-miR-142-3p, hsa-miR-155-3p, hsa-miR-1205, hsa-miR-1273a, hsa-miR-3187-3p, hsa-miR-1255b-2-3p, hsa-miR-1288-5p, hsa-miR-6836-5p, and hsa-miR-6862-5p. Next, we established a diagnostic model using logistic regression and validated the diagnostic model using ROC curves; the result of which showed that the model had good diagnostic efficacy. Then, we also verified that the diagnostic model established by these 11 miRNAs could distinguish patients with benign/malignant bone tumor using unsupervised clustering as well as PCA. Finally, by using qPCR, we validated the expression of 11 miRNAs in the serum of patients with malignant and benign bone tumors, as well as healthy volunteers. The results were consistent with the trend of miRNAs expression in public databases. In summary, we examined the differential expression of serum miRNAs in individuals with benign and malignant bone tumors and discovered 11 miRNA biomarkers that could be utilized to discriminate between the two.

Prevalence and determinants of wife-beating in Bangladesh: evidence from a nationwide survey.

BACKGROUND: Intimate partner violence (IPV) is a global public health concern, with women in low- and middle-income countries (LMICs) bearing a disproportionately high burden. This study investigates the prevalence and factors correlated with attitudes regarding wife-beating among Bangladeshi women in urban-rural contexts. METHODS: A sample of 13,033 urban women and 51,344 rural women data from the Bangladesh Multiple Indicator Cluster Survey (MICS) 2019 were analyzed using the Chi-square test and ordinal logistic regression model. RESULTS: The findings reveal that arguing with her husband is the widespread reason for wife-beating in Bangladesh (urban: 17.3%, rural: 21.9%), followed by neglecting the children (urban: 12.7%, rural: 15.8%). About 8% of urban women and 10% of rural women favoured the opinion that refusing to involve sexual intercourse is a legitimate justification for wife-beating. In comparison, around 5% feel that a husband has a right to beat his wife due to burning food. The respondents' age, education, marital status, number of children, socioeconomic level, any health or physical difficulty, having problems becoming pregnant, and the husband's age are all significant factors in justifying wife-beating. CONCLUSIONS: Bangladesh has a massive challenge in eliminating IPV. Women from lower socioeconomic classes, low levels of education, other challenges, and residents of rural areas are particularly more vulnerable than their urban counterparts. Therefore, it is vital to develop a proper action plan that considers women's education and occupation to raise awareness of the various implications of wife-beating in women, particularly in Bangladesh's rural areas.

Pregnancy complicated with hepatitis B virus infection and preterm birth: a retrospective cohort study.

BACKGROUND: We aimed to investigate whether maternal chronic hepatitis B virus (HBV) infection affects preterm birth (PTB) in pregnant women. METHODS: We retrospectively analyzed HBV-infected and non-infected pregnant women attending antenatal care at Fujian Maternity and Child Health Hospital, Fuzhou, China between January 1, 2016 to December 31, 2018. Participants were divided into HBV infection (n = 1302) and control (n = 12,813) groups. We compared baseline data, pregnancy and perinatal complications, and preterm delivery outcomes between groups. Performed multiple logistics regression analysis to adjust for confounding factors. Finally, we compared early PTB outcome between different HBV DNA level groups. RESULTS: The incidence of preterm birth (gestation less than 37 weeks) was similar between the groups, early preterm birth (gestation less than 34 weeks) were significantly more among the HBV infection group than among the controls (1.6% VS. 0.8%; P = 0.003). After adjusting for confounding factors through logistics regression, HBV infection was found to be an independent early PTB risk factor gestation (adjusted odds ratio 1.770; 95% confidence interval [1.046-2.997]). The incidence of early PTB in < 500 group, 500 ~ 2.0 × 10e5 group and > 2.0 × 10e5 group was not statistically significant (P = 0.417). CONCLUSION: HBV infection is an independent risk factor for early PTB, and the risk did not seem to be influenced by the levels of HBV DNA. Comprehensive programs focusing on pregnant women with HBV infection would reduce the incidence of adverse outcomes.

Subscribers' perception of quality of services provided by Ghana's National Health Insurance Scheme - what are the correlates?

BACKGROUND: Ghana's National Health Insurance Scheme (NHIS) has witnessed an upsurge in enrollment since its inception in 2003, with over 40% of the Ghanaian population actively enrolled in the scheme. While the scheme strives to achieve universal health coverage, this quest is derailed by negative perceptions of the quality of services rendered to NHIS subscribers. This paper presents an analysis on perceptions of service quality provided to subscribers of Ghana's NHIS with emphasis on rural and urban scheme policy holders, using a nationally representative data. METHODS: The study used data from the 2014 Ghana Demographic and Health Survey. Ordered logistic regressions were estimated to identify the correlates of perceived quality of care of services rendered by the NHIS. Also, chi-square statistics were performed to test for significant differences in the proportions of subscribers in the two subsamples (rural and urban). RESULTS: Rural subscribers of the NHIS were found to identify more with better perception of quality of services provided by the NHIS than urban subscribers. Results from the chi-square statistics further indicated that rural subscribers are significantly different from urban subscribers in terms of the selected socioeconomic and demographic characteristics. In the full sample; age, out-of-pocket payment for healthcare and region of residence proved significant in explaining perceived quality of services rendered by the NHIS. Age, out-of-pocket payment for healthcare, region of residence, wealth status, and access to media were found to be significant predictors of perceived quality of services provided to both rural and urban subscribers of the NHIS. The significance of these variables varied among men and women in rural and urban areas. CONCLUSION: Different factors affect the perception of quality of services provided to rural and urban subscribers of Ghana's NHIS. Health financing policies geared toward improving the NHIS-related services in rural and urban areas should be varied.

Combined effect of urinary monohydroxylated polycyclic aromatic hydrocarbons and impaired lung function on diabetes.

Associations of type 2 diabetes with exposure to polycyclic aromatic hydrocarbons and reduced lung function have been reported. The aim of the present study was to investigate effect of reduced lung function and exposure to background PAHs on diabetes. A total of 2730 individuals were drawn from the Wuhan-Zhuhai (WHZH) Cohort Study (n=3053). Participants completed physical examination, measurement of lung function and urinary monohydroxylated polycyclic aromatic hydrocarbons (OH-PAHs). Risk factors for type 2 diabetes were identified by multiple logistic regression analysis, and the presence of additive interaction between levels of urinary OH-PAHs and lower lung function was evaluated by calculation of the relative excess risk due to interaction (RERI) and attributable proportion due to interaction (AP). Urinary OH-PAHs levels was positively associated with type 2 diabetes among individuals with impaired lung function (p<0.05). Forced expiratory volume in one second (FEV1, odd ratio (OR): 0.664, 95% confidence interval (CI): 0.491-0.900) and forced vital capacity (FVC, OR: 0.693, 95% CI: 0.537-0.893) were negatively associated with diabetes among individuals. Additive interaction of higher urinary levels of OH-PAHs and lower FVC (RERI: 0.679, 95% CI: 0.120-1.238); AP: 0.427, 95% CI: 0.072-0.782) was associated with diabetes. Exposure to background PAHs was related to diabetes among individuals with lower lung function. Urinary levels of OH-PAHs and reduced lung function had an additive effect on diabetes.

Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology.

Landslides are a natural threat that poses a severe risk to human life and the environment. In the Kumaon mountains region in Uttarakhand (India), Nainital is among the most vulnerable areas prone to landslides inflicting harm to livelihood and civilization due to frequent landslides. Developing a landslide susceptibility map (LSM) in this Nainital area will help alleviate the probability of landslide occurrence. GIS and statistical-based approaches like the certainty factor (CF), information value (IV), frequency ratio (FR) and logistic regression (LR) are used for the assessment of LSM. The landslide inventories were prepared using topography, satellite imagery, lithology, slope, aspect, curvature, soil, land use and land cover, geomorphology, drainage density and lineament density to construct the geodatabase of the elements affecting landslides. Furthermore, the receiver operating characteristic (ROC) curve was used to check the accuracy of the predicting model. The results for the area under the curves (AUCs) were 87.8% for logistic regression, 87.6% for certainty factor, 87.4% for information value and 84.8% for frequency ratio, which indicates satisfactory accuracy in landslide susceptibility mapping. The present study perfectly combines GIS and statistical approaches for mapping landslide susceptibility zonation. Regional land use planners and natural disaster management will benefit from the proposed framework for landslide susceptibility maps.

Establishment and verification of a prediction model based on clinical characteristics and computed tomography radiomics parameters for distinguishing benign and malignant pulmonary nodules.

Background: The radiographic classification of pulmonary nodules into benign versus malignant categories is a pivotal component of early lung cancer diagnosis. The present study aimed to investigate clinical and computed tomography (CT) clinical-radiomics nomogram for preoperative differentiation of benign and malignant pulmonary nodules. Methods: This retrospective study included 342 patients with pulmonary nodules who underwent high-resolution CT (HRCT) examination. We assigned them to a training dataset (n=239) and a validation dataset (n=103). There are 1781 tumor characteristics quantified by extracted features from the lesion segmented from patients' CT images. The features with poor reproducibility and high redundancy were removed. Then a least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation was used to further select features and build radiomics signatures. The independent predictive factors were identified by multivariate logistic regression. A radiomics nomogram was developed to predict the malignant probability. The performance and clinical utility of the clinical-radiomics nomogram was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: After dimension reduction by the LASSO algorithm and multivariate logistic regression, four radiomic features were selected, including original_shape_Sphericity, exponential_glcm_Maximum Probability, log_sigma_2_0_mm_3D_glcm_Maximum Probability, and ogarithm_firstorder_90Percentile. Multivariate logistic regression showed that carcinoembryonic antigen (CEA) [odds ratio (OR) 95% confidence interval (CI): 1.40 (1.09-1.88)], CT rad score [OR (95% CI): 2.74 (2.03-3.85)], and cytokeratin-19-fragment (CYFRA21-1) [OR (95% CI): 1.80 (1.14-2.94)] were independent influencing factors of malignant pulmonary nodule (all P<0.05). The clinical-radiomics nomogram combining CEA, CYFRA21-1 and radiomics features achieved an area of curve (AUC) of 0.85 and 0.76 in the training group and verification group for the prediction of malignant pulmonary nodules. The clinical-radiomics nomogram demonstrated excellent agreement and practicality, as evidenced by the calibration curve and DCA. Conclusions: The clinical-radiomics nomogram combined of CT-based radiomics signature, along with CYFRA21-1 and CEA, demonstrated strong predictive ability, calibration, and clinical usefulness in distinguishing between benign and malignant pulmonary nodules. The use of CT-based radiomics has the potential to assist clinicians in making informed decisions prior to biopsy or surgery while avoiding unnecessary treatment for non-cancerous lesions.

Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery.

Background: Acute kidney injury (AKI) is a relevant complication after cardiac surgery and is associated with significant morbidity and mortality. Existing risk prediction tools have certain limitations and perform poorly in the Chinese population. We aimed to develop prediction models for AKI after valvular cardiac surgery in the Chinese population. Methods: Models were developed from a retrospective cohort of patients undergoing valve surgery from December 2013 to November 2018. Three models were developed to predict all-stage, or moderate to severe AKI, as diagnosed according to Kidney Disease: Improving Global Outcomes (KDIGO) based on patient characteristics and perioperative variables. Models were developed based on lasso logistics regression (LLR), random forest (RF), and extreme gradient boosting (XGboost). The accuracy was compared among three models and against the previously published reference AKICS score. Results: A total of 3,392 patients (mean [SD] age, 50.1 [11.3] years; 1787 [52.7%] male) were identified during the study period. The development of AKI was recorded in 50.5% of patients undergoing valve surgery. In the internal validation testing set, the LLR model marginally improved discrimination (C statistic, 0.7; 95% CI, 0.66-0.73) compared with two machine learning models, RF (C statistic, 0.69; 95% CI, 0.65-0.72) and XGBoost (C statistic, 0.66; 95% CI, 0.63-0.70). A better calibration was also found in the LLR, with a greater net benefit, especially for the higher probabilities as indicated in the decision curve analysis. All three newly developed models outperformed the reference AKICS score. Conclusion: Among the Chinese population undergoing CPB-assisted valvular cardiac surgery, prediction models based on perioperative variables were developed. The LLR model demonstrated the best predictive performance was selected for predicting all-stage AKI after surgery. Clinical trial registration: Trial registration: Clinicaltrials.gov, NCT04237636.

Relationship between phthalates exposures and hyperuricemia in U.S. general population, a multi-cycle study of NHANES 2007-2016.

BACKGROUND: Phthalates exposure might cause kidney damage and a potential risk for hyperuricemia. However, direct evidence on phthalates and hyperuricemia is somewhat limited. OBJECTIVE: To examine associations between 10 phthalates metabolites and hyperuricemia in a large-scale representative of the U.S. METHODS: A cross-sectional study of 6865 participants aged over 20 from NHANES 2007-2016 was performed. All participants had complete data on ten phthalate metabolites (MECPP, MnBP, MEHHP, MEOHP, MiBP, cx-MiNP, MCOP, MCPP, MEP, MBzP), hyperuricemia, and covariates. We used multivariable logistics regression, restricted cubic splines (RCS) model, and Bayesian kernel machine regression (BKMR) models to assess single, nonlinear, and mixed relationships between phthalate metabolites and hyperuricemia. As a complement, we also assessed the relationship between phthalate metabolites and serum uric acid (SUA) levels. RESULTS: The multivariable logistics regression showed that MECPP, MEOHP, MEHHP, MBzP, and MiBP were generally positively associated with hyperuricemia (PFDR < 0.05), especially in MiBP (Q3 (OR (95 %): 1.31 (1.02, 1.68)) and Q4 (OR (95 %): 1.68 (1.27, 2.24)), compared to Q1). All ten phthalate metabolites had a linear dose-response relationship with hyperuricemia in the RCS model (P for non-linear >0.05). BKMR showed that mixed phthalate metabolites were associated with a higher risk of hyperuricemia, with MBzP contributing the most (groupPIP = 0.999, condPIP = 1.000). We observed the consistent results between phthalate metabolites and SUA levels in three statistical models. The relationship between phthalate metabolites and hyperuricemia remained in the sensitivity analysis. CONCLUSIONS: The present study suggests that exposure to phthalates, individually or jointly, might increase the risk of hyperuricemia. Since hyperuricemia influences on the quality of life, more explorations are needed to confirm these findings.

Association between urinary phthalate metabolites and hyperuricemia in US adults.

Phthalate metabolites have been detected from urine in most of the US population and have become a public health problem. However, the association between phthalate metabolites and hyperuricemia has been scarcely studied so far. We aimed to evaluate if phthalate metabolites were associated with hyperuricemia in US adults. A total of 8816 participants of the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 were included in our study. We used multivariable logistic regression models and restricted cubic spline (RCS) models to explore the association between urinary phthalate metabolites and hyperuricemia. Then, stratified analyses were conducted by sex and age. The prevalence of hyperuricemia in the study sample was 20.35%. Compared to the lowest quantile, the odds ratios (ORs) and 95% confidence intervals (CIs) for hyperuricemia were all statistically significant in following phthalate metabolites: 1.34 (1.13-1.58) for the second quartile in Mono-isobutyl phthalate (MiBP), 1.21 (1.01-1.46) for the highest quartile in Mono-(carboxyoctyl) phthalate (MCOP), 0.66 (0.56-0.76) for the second quartile in Mono-(2-ethyl)-hexyl phthalate (MEHP), 1.22 (1.05-1.43) for quartile 2 in Benzyl butyl phthalate (ΣBBP), and 1.43 (1.22-1.66) for the third quartile in high molecular-weight phthalate (ΣHigh MWP), respectively. Our results indicate that several urinary phthalate metabolites are positively associated with the odds of hyperuricemia.

Spatial distribution of caesarean deliveries and their determinants in Bangladesh: evidence from linked data of population and health facility survey.

Background: Health facility-level factors play a crucial role in women's access to and use of caesarean section (CS) services, but lacks relevant evidence. The study aimed to understand the effects of health facility-level factors on CS delivery in Bangladesh. Methods: The 2017-18 Bangladesh Demographic and Health Survey (2017-18 BDHS) and the 2017 Bangladesh Health Facility Survey (2017 BHFS) were linked and analysed in this study. The sample comprised of 4954 women gave at least one live birth within three years preceding the survey. The outcome variable was delivery through CS (yes, no) and the explanatory variables were health facility-level, individual-level, household-level, and community-level factors. Moran's I and Getis-Ord General G statistic were used to identify the hotspots of delivery through CS. Mixed-effect multilevel logistic regression was used to examine the association of the outcome variable with explanatory variables. Findings: Around 33% of women in Bangladesh underwent CS in their most recent pregnancies. The hotspots of delivery through CS are located primarily in Rajshahi, Dhaka, and Khulna divisions. The likelihood of delivered through CS increased with the rising scores of the management (Adjusted Odds Ratio (AOR), 1.83; 95% CI 1.04-2.07) and infrastructure (AOR, 3.14; 95% CI 1.40-5.12) of the nearest health facility. The readiness of health facilities to provide comprehensive obstetric care was significantly associated with an increased likelihood of delivery through CS (AOR, 2.18; 95% CI 1.15-3.28). These relationships were strong for private than non-government and government health facilities. Interpretation: The proximity of comprehensive obstetric care facilities to women's residences and their readiness to provide services play critical roles in the access to and use of CS in Bangladesh. The findings highlight the importance of necessary healthcare personnel, including midwives, availability of government hospitals where undue CS are avoided, and awareness-building programmes about the adverse effects of CS delivery. Funding: None.

Association between blood selenium with parkinson's disease in the US (NHANES 2011-2020).

Selenium is an essential trace element for human health, playing a key role in regulating cellular oxidative stress, immune response, and inflammation. In recent years, the association between selenium and Parkinson's disease (PD) has aroused people's attention. The objective of this study is to investigate the relationship between blood selenium concentrations and PD risk in a sample of U.S. adults. A cross-sectional study was conducted using the National Health and Nutrition Examination Survey (NHANES) data from 2011-2020 and included 15,660 adults aged over 40 years old. Univariate logistic regression and multivariate logistic regression models were utilized to analyze the association between blood selenium concentrations and PD prevalence. Additionally, the restricted cubic spline (RCS) model was applied to investigate the dose-response relationships between blood selenium and PD. The findings indicated a link between elevated blood selenium levels and a reduced occurrence of Parkinson's disease (PD). Notably, this association between blood selenium and PD exhibited a non-linear pattern, wherein the decline in PD risk was more pronounced at higher selenium concentrations than at lower levels. An inflection point emerged at approximately 2.4 µmol/L, beyond which the rate of decline in risk significantly diminished with increasing selenium levels. A potential association between blood selenium concentrations and PD has been observed, with PD patients having lower blood selenium levels compared to non-PD patients. Higher levels of blood selenium may have a protective effect against PD. However, further prospective studies are needed to investigate the effect of blood selenium in PD patients and to determine causality.

Predicting Anxiety and Depression Among Patients With COVID-19 in Concentrated Isolation at Medical Camps in Vietnam: A Descriptive Cross-Sectional Study.

Introduction: This study aims to assess the requirement for anxiety and depression treatment for patients with coronavirus disease 2019 (COVID-19) in medical camps in Bac Giang province, Vietnam. This information can help improve the government policy to reduce anxiety and depression in patients with COVID-19. Methods: A total of patients with 427 COVID-19 participated in the survey conducted from 5 to 15 June 2021 in Bac Giang province. The survey included 17 questions about the general characteristics of the patients, 15 questions to assess common COVID-19 symptoms, the Patient Health Questionnaire-9 (PHQ-9), and General Anxiety Disorder-7 (GAD-7) scores, and four questions to assess hospital reviews, including facilities, food, medical staff, and living conditions. Logistics regression analyses were conducted to assess the association between COVID-19 symptoms and high anxiety and depression (HAD) status. Results: A logistic regression analysis evaluated the risk factors in need of intervention. Our study showed that lower hospital review scores (odd ratio = 0.98; 95% confident interval = 0.97-0.99) were found to be a risk needing intervention. It was also identified that older patients (odd ratio = 1.1; 95% confident interval = 1.03-1.18), women (odd ratio = 1.31; 95% confident interval = 1.09-1.31), patients who were primary income earners in the family (odd ratio = 1.15; 95% confident interval = 1.03-1.28), patients who had headaches (odd ratio = 1.16; 95% confident interval = 1.06-1.21), and patients who had joint pain (odd ratio = 1.17; 95% confident interval = 1.06- 1.3) were risk factors for HAD status. Conclusion: Our research shows that every 10-year age increase was associated with a 10% increase in the likelihood of HAD status. Study subjects being primary income earners were also associated with a 15% increased risk of having HAD status. This study showed that a decrease in family income due to COVID-19 caused an increase in high-level anxiety/depression status.

Prediction of single pulmonary nodule growth by CT radiomics and clinical features - a one-year follow-up study.

Background: With the development of imaging technology, an increasing number of pulmonary nodules have been found. Some pulmonary nodules may gradually grow and develop into lung cancer, while others may remain stable for many years. Accurately predicting the growth of pulmonary nodules in advance is of great clinical significance for early treatment. The purpose of this study was to establish a predictive model using radiomics and to study its value in predicting the growth of pulmonary nodules. Materials and methods: According to the inclusion and exclusion criteria, 228 pulmonary nodules in 228 subjects were included in the study. During the one-year follow-up, 69 nodules grew larger, and 159 nodules remained stable. All the nodules were randomly divided into the training group and validation group in a proportion of 7:3. For the training data set, the t test, Chi-square test and Fisher exact test were used to analyze the sex, age and nodule location of the growth group and stable group. Two radiologists independently delineated the ROIs of the nodules to extract the radiomics characteristics using Pyradiomics. After dimension reduction by the LASSO algorithm, logistic regression analysis was performed on age and ten selected radiological features, and a prediction model was established and tested in the validation group. SVM, RF, MLP and AdaBoost models were also established, and the prediction effect was evaluated by ROC analysis. Results: There was a significant difference in age between the growth group and the stable group (P < 0.05), but there was no significant difference in sex or nodule location (P > 0.05). The interclass correlation coefficients between the two observers were > 0.75. After dimension reduction by the LASSO algorithm, ten radiomic features were selected, including two shape-based features, one gray-level-cooccurence-matrix (GLCM), one first-order feature, one gray-level-run-length-matrix (GLRLM), three gray-level-dependence-matrix (GLDM) and two gray-level-size-zone-matrix (GLSZM). The logistic regression model combining age and radiomics features achieved an AUC of 0.87 and an accuracy of 0.82 in the training group and an AUC of 0.82 and an accuracy of 0.84 in the verification group for the prediction of nodule growth. For nonlinear models, in the training group, the AUCs of the SVM, RF, MLP and boost models were 0.95, 1.0, 1.0 and 1.0, respectively. In the validation group, the AUCs of the SVM, RF, MLP and boost models were 0.81, 0.77, 0.81, and 0.71, respectively. Conclusions: In this study, we established several machine learning models that can successfully predict the growth of pulmonary nodules within one year. The logistic regression model combining age and imaging parameters has the best accuracy and generalization. This model is very helpful for the early treatment of pulmonary nodules and has important clinical significance.

Score for the Risk and Overall Survival of Lung Metastasis in Patients First Diagnosed With Soft Tissue Sarcoma: A Novel Nomogram-Based Risk Assessment System.

Background: Metastatic soft tissue sarcoma (STS) patients have a poor prognosis with a 3-year survival rate of 25%. About 30% of them present lung metastases (LM). This study aimed to construct 2 nomograms to predict the risk of LM and overall survival of STS patients with LM. Materials and Methods: The data of patients were derived from the Surveillance, Epidemiology, and End Results database during the period of 2010 to 2015. Logistic and Cox analysis was performed to determine the independent risk factors and prognostic factors of STS patients with LM, respectively. Afterward, 2 nomograms were, respectively, established based on these factors. The performance of the developed nomogram was evaluated with receiver operating characteristic curves, area under the curve (AUC) calibration curves, and decision curve analysis (DCA). Results: A total of 7643 patients with STS were included in this study. The independent predictors of LM in first-diagnosed STS patients were N stage, grade, histologic type, and tumor size. The independent prognostic factors for STS patients with LM were age, N stage, surgery, and chemotherapy. The AUCs of the diagnostic nomogram were 0.806 in the training set and 0.799 in the testing set. For the prognostic nomogram, the time-dependent AUC values of the training and testing set suggested a favorable performance and discrimination of the nomogram. The 1-, 2-, and 3-year AUC values were 0.698, 0.718, and 0.715 in the training set, and 0.669, 0.612, and 0717 in the testing set, respectively. Furthermore, for the 2 nomograms, calibration curves indicated satisfactory agreement between prediction and actual survival, and DCA indicated its clinical usefulness. Conclusion: In this study, grade, histology, N stage, and tumor size were identified as independent risk factors of LM in STS patients, age, chemotherapy surgery, and N stage were identified as independent prognostic factors of STS patients with LM, these developed nomograms may be an effective tool for accurately predicting the risk and prognosis of newly diagnosed patients with LM.

Multivariate logistic regression analysis of the correlation between five biomarkers and ovarian cancer in patients with intermediate-risk: A prospective cross-sectional study.

Objective: To find potential diagnostic biomarkers for ovarian cancer (OC), a prospective analysis of the expression of five biomarkers in patients with intermediate-risk and their correlation with the occurrence of OC was conducted. Method: A prospective observational study was carried out, patients who underwent surgical treatment with benign or malignant ovarian tumors in our hospital from January 2020 to February 2021 were included in this study, and a total of 263 patients were enrolled. Based on the postoperative pathological results, enrolled patients were divided into ovarian cancer group and benign tumor group (n = 135). The ovarian cancer group was further divided into a mid-stage group (n = 46) and an advanced-stage group (n = 82). The basic information of the three groups of patients was collected, the preoperative imaging data of the patients were collected to assess the lymph node metastasis, the preoperative blood samples were collected to examine cancer antigen 125 (CA125), carbohydrate antigen 19-9 (CA19-9), Neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and the postoperative pathological data were sorted and summarized. Result: The average during of disease in the advanced ovarian cancer group was 0.55 ± 0.18 years higher than the benign tumor group (0.43 ± 0.14 years), p < 0.001. In the advanced ovarian cancer group, the ratio of patients with the tumor, node, metastasis (TNM) stage IV (64.63%), with tumor Grade stage II and III (93.90%), and without lymph node metastasis (64.63%) was respectively more than that in the mid-stage group (accordingly 0.00, 36.96, 23.91%) (p < 0.001); The ratio of patients with TNM grade III in the mid-stage group (73.91%) was more than that in the advanced group (35.37%) (p < 0.001). The levels of the five biomarkers: CA19-9, CA125, NLR, PLR, and BDNF were different among the three groups (p < 0.001). Conclusion: CA19-9, CA125, NLR, PLR, BDNF are five biomarkers related to the occurrence of ovarian cancer and are risk factors for it. These five biomarkers and their Combined-Value may be suitable to apply in the diagnosis and the identification of ovarian cancer in patients with intermediate-risk.

Study to Identify and Evaluate Predictor Factors for Primary Open-Angle Glaucoma in Tertiary Prophylactic Actions.

The aim of this study is to develop a predictive model with several explanatory variables that can guide ophthalmologists to make a more objective assessment of the evolution of open-angle glaucoma (OAG) during tertiary prevention. Objectives: The evaluation of risk factors and different predictors of symptom progression between patients with POAG and non-glaucoma patients (NG), as well as between primary open-angle glaucoma with high intraocular pressure (POAG) and primary open-angle glaucoma with normal intraocular pressure (NTG), in tertiary prophylactic activities. Methods: This research is an analytical epidemiological study of a prospective cohort. For the study, we took into account personal medical history, physical ophthalmological examination, intraocular pressure (IOP) values, and visual field (VF) parameters, examined with the Opto AP-300 Automated Perimeter using the "fast threshold" strategy. The results of gonioscopy were inconsistently recorded; they were not considered in the study due to missing values, the processing of which would have seriously distorted the statistical analysis. Ophthalmological examination was completed with a dichotomous questionnaire entitled "Symptom Inventory", made according to the accusations of patients resulting from a "focus group" study. The study was carried out in the ophthalmology office within the Integrated Outpatient Clinic of the Emergency Clinical Hospital of Oradea, Bihor County (IOCECHO) between January−December 2021. The threshold of statistical significance was defined for p value < 0.05. The obtained results were statistically processed with specialized software SPSS 22. Results: The study included 110 people, of which 71 (64.54%) had POAG (IOP > 21 mmHg) and 39 people (35.46%) had NTG (IOP < 21 mmHg), the two groups being statistically significantly different (χ2 = 9.309, df = 1, p = 0.002). For the POAG group, glaucomatous loss was early, AD < −6 dB, according to the staging of glaucomatous disease, HODAPP classification. In addition, the groups of POAG and NTG patients was compared with a group of 110 NG patients, these three groups being statistically significantly different (χ2 = 34.482, df = 2, p = 0.000). Analysis of confounding factors (age, sex, residence, marital status) shows a statistically significant relationship only for age (F = 2.381, df = 40, p = 0.000). Sex ratio for the study groups = 5.11 for OAG and =5.87 for NG. After treatment (prostaglandin analogues and neuroprotective drugs) IOP decreased statistically significantly for both POAG and NTG. Conclusions: this study identified possible predictors of OAG, at the 5% level (risk factors and symptoms as independent variables) using a dichotomous questionnaire tool with a complementary role in tertiary prophylactic activities. The implementation of the focus group interview results as a socio-human research technique will be supportive to clinicians.