BACKGROUND: Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection.
METHODS: This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China.
RESULTS: When the spatio-temporal variation was small (time delay coefficient: 0.1-0.2, spatial sparsity:0.1-0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2-0.3, spatial sparsity: 0.6-0.9).
CONCLUSIONS: This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators-the time delay coefficient and spatial sparsity-into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

BACKGROUND: Pediatric liver transplantation is a very resource-intensive therapy. This study aimed to identify the changes made between two epochs of management and analyze their influence on length of stay (LOS).
METHODS: Data from a single center were obtained from the liver transplant and Pediatric Intensive Care Unit (PICU) databases for 336 transplants (282 children) performed between 2000 and 2021. Transplants were analyzed in two epochs, before and after July 2012, representing a change in postoperative anticoagulation management. Differences in graft recipient demographics and perioperative management factors were compared between epochs. Multivariate regression was performed to identify the complications that correlated most strongly with hospital LOS.
RESULTS: There was a difference in hospital LOS between Epoch 1 (Median = 31.7 days) and Epoch 2 (Median = 26.3 days) (p < 0.001), but not in PICU LOS (E1 Median = 7.3 days, E2 Median = 7.4 days; p = 0.792). Epoch 2 saw increased use of split grafts (60.6% of total), decreased pediatric end-stage liver disease (PELD) score at transplant (Average = 16.7; p < 0.001), decreased invasive ventilation time (Average = 4.48 days; p < 0.001), and decreased hepatic artery thrombosis (HAT) rates (E1 = 14.4%, E2 = 4.3%; p < 0.001) without an associated increase in bleeding rates.
CONCLUSIONS: Hospital LOS has reduced in Epoch 2 due to refinements in intraoperative and postoperative management. There is increased emphasis on early extubation and increased use of noninvasive ventilatory techniques in Epoch 2. Split grafts have effectively expanded our graft donor pool and reduced transplant waitlist times.

UNASSIGNED: In sub-Saharan Africa, achieving universal health coverage (UHC) and protecting populations from health-related financial hardship remain challenging goals. Subsequently, community-based health insurance (CBHI) has gained interest in low and middle-income countries, such as Ethiopia. However, the rural-urban disparity in CBHI enrollment has not been properly investigated using multivariate decomposition analysis. Therefore, this study aimed to assess the rural-urban disparity of CBHI enrollment in Ethiopia using the Ethiopian Mini Demographic Health Survey 2019 (EMDHS 2019).
UNASSIGNED: This study used the latest EMDHS 2019 dataset. STATA version 17.0 software was used for analyses. The chi-square test was used to assess the association between CBHI enrollment and the explanatory variables. The rural-urban disparity of CBHI enrollment was assessed using the logit-based multivariate decomposition analysis. A p-value of <0.05 with a 95% confidence interval was used to determine the statistical significance.
UNASSIGNED: The study found that there was a significant disparity in CBHI enrollment between urban and rural households (p < 0.001). Approximately 36.98% of CBHI enrollment disparities were attributed to the compositional (endowment) differences of household characteristics between urban and rural households, and 63.02% of the disparities were due to the effect of these characteristics (coefficients). The study identified that the age and education of the household head, family size, number of under-five children, administrative regions, and wealth status were significant contributing factors for the disparities due to compositional differences between urban and rural households. The region was the significant factor that contributed to the rural-urban disparity of CBHI enrollment due to the effect of household characteristics.
UNASSIGNED: There were significant urban-rural disparities in CBHI enrollment in Ethiopia. Factors such as age and education of the household head, family size, number of under-five children, region of the household, and wealth status of the household contributed to the disparities attributed to the endowment, and region of the household was the contributing factor for the disparities due to the effect of household characteristics. Therefore, the concerned body should design strategies to enhance equitable CBHI enrollment in urban and rural households.

The cytoarchitecture of the striatum is remarkably homogeneous, in contrast to the regional variation in striatal functions. Whether differences in the intrinsic membrane properties of striatal neurons contribute to regional heterogeneity has not been addressed systematically. We made recordings throughout the young adult mouse striatum under identical conditions, with synaptic input blocked, from four major striatal neuron types, namely, the two subtypes of spiny projection neurons (SPNs), cholinergic interneurons (ChIs), and fast-spiking GABAergic interneurons (FSIs), sampling at least 100 cells per cell type. Regional variation manifested across all cell types. All cell types in the nucleus accumbens (NAc) shell had higher input impedance and increased excitability. Cells in the NAc core were differentiated from the caudate-putamen (CPu) for both SPN subtypes by smaller action potentials and increased excitability. Similarity between the two SPN subtypes showed regional variation, differing more in the NAc than in the CPu. So, in the Str, both the intrinsic properties of interneurons and projection neurons are regionally heterogeneous, with the greatest difference between the NAc and CPu; greater excitability of NAc shell neurons may make the region more susceptible to activity-dependent plasticity.

BACKGROUND: Postoperative liver metastasis significantly impacts the prognosis of pancreatic neuroendocrine tumor (panNET) patients after R0 resection. Combining computational pathology and deep learning radiomics can enhance the detection of postoperative liver metastasis in panNET patients.
METHODS: Clinical data, pathology slides, and radiographic images were collected from 163 panNET patients post-R0 resection at Fudan University Shanghai Cancer Center (FUSCC) and FUSCC Pathology Consultation Center. Digital image analysis and deep learning identified liver metastasis-related features in Ki67-stained whole slide images (WSIs) and enhanced CT scans to create a nomogram. The model\'s performance was validated in both internal and external test cohorts.
RESULTS: Multivariate logistic regression identified nerve infiltration as an independent risk factor for liver metastasis (p < 0.05). The Pathomics score, which was based on a hotspot and the heterogeneous distribution of Ki67 staining, showed improved predictive accuracy for liver metastasis (AUC = 0.799). The deep learning-radiomics (DLR) score achieved an AUC of 0.875. The integrated nomogram, which combines clinical, pathological, and imaging features, demonstrated outstanding performance, with an AUC of 0.985 in the training cohort and 0.961 in the validation cohort. High-risk group had a median recurrence-free survival of 28.5 months compared to 34.7 months for the low-risk group, showing significant correlation with prognosis (p < 0.05).
CONCLUSIONS: A new predictive model that integrates computational pathologic scores and deep learning-radiomics can better predict postoperative liver metastasis in panNET patients, aiding clinicians in developing personalized treatments.

BACKGROUND: From March 7 to April 7, 2020, the Community of Madrid (CoM), Spain, issued interventions in response to the COVID-19 epidemic, including hospital referral triage protocols for long-term care facility (LTCF) residents (March 18-25). Those with moderate to severe physical disability and cognitive impairment were excluded from hospital referral. This research assesses changes in the association between daily hospital referrals and the deaths of LTCF residents attributable to the triage protocols.
METHODS: Daily hospital referrals and all-cause mortality from January to June 2020 among LTCF residents and the CoM population aged 65 + were obtained. Significant changes in LTCF resident daily hospital referrals time series, and in-LTCF and in-hospital daily deaths, were examined with tests for breaks and regimes in time series. Multivariate time series analyses were conducted to test changes in the associations between LTCF resident hospital referrals with daily deaths in-hospital and in-LTCF, and in the CoM population aged 65 + when the triage protocols were implemented.
RESULTS: Among LTCF residents, hospital referrals declined sharply from March 6 to March 23, 2020. Increases in LTCF residents\' daily deaths occurred from March 7 to April 1, followed by a decrease reaching pre-epidemic levels after April 28. The daily ratio of in-hospital deaths to in-LTCF deaths reached its lowest values from March 9 to April 19, 2020. The four versions of the triage protocol, published from March 18 to March 25 had no impact on further changes in the association of hospital referrals with daily deaths of LTCF residents in-hospital or in-LTCF.
CONCLUSIONS: While LTCF residents\' deaths increased, hospital referrals of LTCF residents decreased with the introduction of the CoM governmental interventions on March 7. They were implemented before the enactment of the triage protocols, protecting hospitals from collapse while overlooking the need for standards of care within LTCFs. The CoM triage protocols sanctioned the existing restrictions on hospital referrals of LTCF residents.

BACKGROUND: People with bipolar disorder (BD) tend to show widespread cognitive impairment compared to healthy controls. Impairments in processing speed (PS), attention and executive function (EF) may represent \'core\' impairments that have a role in wider cognitive dysfunction. Cognitive impairments appear to relate to structural brain abnormalities in BD, but whether core deficits are related to particular brain regions is unclear and much of the research on brain-cognition associations is limited by univariate analysis and small samples.
METHODS: Euthymic BD patients (n = 56) and matched healthy controls (n = 26) underwent T1-weighted MRI scans and completed neuropsychological tests of PS, attention and EF. We utilised public datasets to develop normative models of cortical thickness (n = 5977) to generate robust estimations of cortical abnormalities in patients. Canonical correlation analysis was used to assess multivariate brain-cognition associations in BD, controlling for age, sex and premorbid IQ.
RESULTS: BD showed impairments on tests of PS, attention and EF, and abnormal cortical thickness in several brain regions compared to healthy controls. Impairments in tests of PS and EF were most strongly associated with cortical thickness in the left inferior temporal, right entorhinal and right temporal pole areas.
CONCLUSIONS: Impairments in PS, attention and EF can be observed in euthymic BD and may be related to abnormal cortical thickness in temporal regions. Future research should continue to leverage normative modelling and multivariate methods to examine complex brain-cognition associations in BD. Future research may benefit from exploring covariance between traditional brain structural morphological metrics such as cortical thickness, cortical volume and surface area.

BACKGROUND: Perinatal mortality is a global health problem, especially in Ethiopia, which has the highest perinatal mortality rate. Studies about perinatal mortality were conducted in Ethiopia, but which factors specifically contribute to the change in perinatal mortality across time is unknown.
OBJECTIVE: To assess the trend and multivariate decomposition of perinatal mortality in Ethiopia using EDHS 2005-2016.
METHODS: A community-based, cross-sectional study design was used. EDHS 2005-2016 data was used, and weighting has been applied to adjust the difference in the probability of selection. Logit-based multivariate decomposition analysis was used using STATA version 14.1. The best model was selected using the lowest AIC value, and variables were selected with a p-value less than 0.05 at 95% CI.
RESULTS: The trend of perinatal mortality in Ethiopia decreased from 37 per 1000 births in 2005 to 33 per 1000 births in 2016. About 83.3% of the decrease in perinatal mortality in the survey was attributed to the difference in the endowment (composition) of the women. Among the differences in the endowment, the difference in the composition of ANC visits, taking the TT vaccine, urban residence, occupation, secondary education, and birth attendant significantly decreased perinatal mortality in the last 10 years. Among the differences in coefficients, skilled birth attendants significantly decreased perinatal mortality.
CONCLUSIONS: The perinatal mortality rate in Ethiopia has declined over time. Variables like ANC visits, taking the TT vaccine, urban residence, occupation, secondary education, and skilled birth attendants reduce perinatal mortality. To reduce perinatal mortality more, scaling up maternal and newborn health services has a critical role.

Time-to-event data are often recorded on a discrete scale with multiple, competing risks as potential causes for the event. In this context, application of continuous survival analysis methods with a single risk suffers from biased estimation. Therefore, we propose the multivariate Bernoulli detector for competing risks with discrete times involving a multivariate change point model on the cause-specific baseline hazards. Through the prior on the number of change points and their location, we impose dependence between change points across risks, as well as allowing for data-driven learning of their number. Then, conditionally on these change points, a multivariate Bernoulli prior is used to infer which risks are involved. Focus of posterior inference is cause-specific hazard rates and dependence across risks. Such dependence is often present due to subject-specific changes across time that affect all risks. Full posterior inference is performed through a tailored local-global Markov chain Monte Carlo (MCMC) algorithm, which exploits a data augmentation trick and MCMC updates from nonconjugate Bayesian nonparametric methods. We illustrate our model in simulations and on ICU data, comparing its performance with existing approaches.

A neural signature of serial dependence has been found, which mirrors the attractive bias of visual information seen in behavioral experiments.