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Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group.
Methods
T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9).
Results
PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges’ g = 0.22, pcorrected = .001), and smaller cerebellar WM volume (peak Hedges’ g = 0.14, pcorrected = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (pcorrected = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (pcorrected = .001).
Conclusions
PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.
The impact of the self-sealing band on interior ballistics is investigated during the gun launching, and a high-precision interior ballistics coupling algorithm that takes leakage into account is proposed. This study focuses on a 65 mm short-barrel, equal-caliber balanced cannon, integrating Abaqus finite element software with an interior ballistics calculation programme. It uses a User-defined AMPlication Load (VUAMP) subroutine to achieve real-time coupling calculations of the chamber pressure and self-sealing band deformation, correcting variations in the chamber pressure. Experimental results show that the coupling algorithm offers the higher precision compared to traditional interior ballistics models and can effectively capture the impact of leakage on the interior ballistics performance. Further research reveals that changes in the charge amount and assembly gap significantly affect the sealing performance of the self-sealing band and the leakage of propellant gases, which in turn influence the chamber pressure and projectile velocity. The high-precision coupling algorithm proposed in this paper provides the effective theoretical support for the design of the self-sealing band and the analysis of cannon performance.
We present the first results from a new backend on the Australian Square Kilometre Array Pathfinder, the Commensal Realtime ASKAP Fast Transient COherent (CRACO) upgrade. CRACO records millisecond time resolution visibility data, and searches for dispersed fast transient signals including fast radio bursts (FRB), pulsars, and ultra-long period objects (ULPO). With the visibility data, CRACO can localise the transient events to arcsecond-level precision after the detection. Here, we describe the CRACO system and report the result from a sky survey carried out by CRACO at 110-ms resolution during its commissioning phase. During the survey, CRACO detected two FRBs (including one discovered solely with CRACO, FRB 20231027A), reported more precise localisations for four pulsars, discovered two new RRATs, and detected one known ULPO, GPM J1839 $-$10, through its sub-pulse structure. We present a sensitivity calibration of CRACO, finding that it achieves the expected sensitivity of 11.6 Jy ms to bursts of 110 ms duration or less. CRACO is currently running at a 13.8 ms time resolution and aims at a 1.7 ms time resolution before the end of 2024. The planned CRACO has an expected sensitivity of 1.5 Jy ms to bursts of 1.7 ms duration or less and can detect $10\times$ more FRBs than the current CRAFT incoherent sum system (i.e. 0.5 $-$2 localised FRBs per day), enabling us to better constrain the models for FRBs and use them as cosmological probes.
Three new species of Gyrodactylus were identified from the body surface of the Triplophysa species from the Qinghai-Tibet Plateau, Gyrodactylus triplorienchili n. sp. on Triplophysa orientalis in northern Tibet, G. yellochili n. sp. on T. sellaefer and T. scleroptera and G. triplsellachili n. sp. on T. sellaefer and T. robusta in Lanzhou Reach of the Yellow River. The three newly identified species share the nemachili group species’ characteristic of having inturning hamulus roots. Gyrodactylus triplorienchili n. sp. shared a quadrate sickle heel and a thin marginal hook sickle, two morphological traits that set them apart from G. yellochili n. sp. However, they may be identified by the distinct shapes of the sickle base and marginal hook sickle point. Gyrodactylus triplsellachili n. sp. had much larger opisthaptoral hard part size than the other two species. The three new species show relatively low interspecific differences of 2.9–5.3% p-distance for ITS1-5.85-ITS2 rDNA sequences. Phylogenetic analysis indicated that the three new species formed a well-supported monophyletic group (bp = 99) with the other nemachili group species.
In this paper, a brand-new adaptive fault-tolerant non-affine integrated guidance and control method based on reinforcement learning is proposed for a class of skid-to-turn (STT) missile. Firstly, considering the non-affine characteristics of the missile, a new non-affine integrated guidance and control (NAIGC) design model is constructed. For the NAIGC system, an adaptive expansion integral system is introduced to address the issue of challenging control brought on by the non-affine form of the control signal. Subsequently, the hyperbolic tangent function and adaptive boundary estimation are utilised to lessen the jitter due to disturbances in the control system and the deviation caused by actuator failures while taking into account the uncertainty in the NAIGC system. Importantly, actor-critic is introduced into the control framework, where the actor network aims to deal with the multiple uncertainties of the subsystem and generate the control input based on the critic results. Eventually, not only is the stability of the NAIGC closed-loop system demonstrated using Lyapunov theory, but also the validity and superiority of the method are verified by numerical simulations.
The specific and multifaceted service needs of young people have driven the development of youth-specific integrated primary mental healthcare models, such as the internationally pioneering headspace services in Australia. Although these services were designed for early intervention, they often need to cater for young people with severe conditions and complex needs, creating challenges in service planning and resource allocation. There is, however, a lack of understanding and consensus on the definition of complexity in such clinical settings.
Methods
This retrospective study involved analysis of headspace’s clinical minimum data set from young people accessing services in Australia between 1 July 2018 and 30 June 2019. Based on consultations with experts, complexity factors were mapped from a range of demographic information, symptom severity, diagnoses, illness stage, primary presenting issues and service engagement patterns. Consensus clustering was used to identify complexity subgroups based on identified factors. Multinomial logistic regression was then used to evaluate whether these complexity subgroups were associated with other risk factors.
Results
A total of 81,622 episodes of care from 76,021 young people across 113 services were analysed. Around 20% of young people clustered into a ‘high complexity’ group, presenting with a variety of complexity factors, including severe disorders, a trauma history and psychosocial impairments. Two moderate complexity groups were identified representing ‘distress complexity’ and ‘psychosocial complexity’ (about 20% each). Compared with the ‘distress complexity’ group, young people in the ‘psychosocial complexity’ group presented with a higher proportion of education, employment and housing issues in addition to psychological distress, and had lower levels of service engagement. The distribution of complexity profiles also varied across different headspace services.
Conclusions
The proposed data-driven complexity model offers valuable insights for clinical planning and resource allocation. The identified groups highlight the importance of adopting a holistic and multidisciplinary approach to address the diverse factors contributing to clinical complexity. The large number of young people presenting with moderate-to-high complexity to headspace early intervention services emphasises the need for systemic change in youth mental healthcare to ensure the availability of appropriate and timely support for all young people.
Adolescence is a period marked by highest vulnerability to the onset of depression, with profound implications for adult health. Neuroimaging studies have revealed considerable atrophy in brain structure in these patients with depression. Of particular importance are regions responsible for cognitive control, reward, and self-referential processing. However, the causal structural networks underpinning brain region atrophies in adolescents with depression remain unclear.
Objectives
This study aimed to investigate the temporal course and causal relationships of gray matter atrophy within the brains of adolescents with depression.
Methods
We analyzed T1-weighted structural images using voxel-based morphometry in first-episode adolescent patients with depression (n=80, 22 males; age = 15.57±1.78) and age, gender matched healthy controls (n=82, 25 males; age = 16.11±2.76) to identify the disease stage-specific gray matter abnormalities. Then, with granger causality analysis, we arranged the patients’ illness duration chronologically to construct the causal structural covariance networks that investigated the causal relationships of those atypical structures.
Results
Compared to controls, smaller volumes in ventral medial prefrontal cortex (vmPFC), dorsal anterior cingulate cortex (dACC), middle cingulate cortex (MCC) and insula areas were identified in patients with less than 1 year illness duration, and further progressed to the subgenual ACC, regions of default, frontoparietal networks in longer duration. Causal network results revealed that dACC, vmPFC, MCC and insula were prominent nodes projecting exerted positive causal effects to regions of the default mode and frontoparietal networks. The dACC, vmPFC and insula also had positive projections to the reward network, which included mainly the thalamus, caudate and putamen, while MCC also exerted a positive causal effect on the insula and thalamus.
Conclusions
These findings revealed the progression of structural atrophy in adolescent patients with depression and demonstrated the causal relationships between regions involving cognitive control, reward and self-referential processes.
The occurrence of depression in adolescence, a critical period of brain development, linked with neuroanatomical and cognitive abnormalities. Neuroimaging studies have identified hippocampal abnormalities in those of adolescent patients. However, few studies have investigated the atypically developmental trends in hippocampal subfields in adolescents with depression and their relationships with cognitive dysfunctions.
Objectives
To explore the structural abnormalities of hippocampal subfields in patients with youth depression and examine how these abnormalities associated with cognitive deficits.
Methods
We included a sample of 79 first-episode depressive patients (17 males, age = 15.54±1.83) and 71 healthy controls (23 males, age = 16.18±2.85). The severity of these adolescent patients was assessed by depression scale, suicidal risk and self-harm behavior. Nine cognitive tasks were used to evaluate memory, cognitive control and attention abilities for all participants. Bilateral hippocampus were segmented into 12 subfields with T1 and T2 weighted images using Freesurfer v6.0. A mixed analysis of variance was performed to assess the differences in subfields volumes between all patients and controls, and between patients with mild and severe depression. Finally, LASSO regression was conducted to explore the associations between hippocampal subfields and cognitive abnormalities in patients.
Results
We found significant subfields atrophy in the CA1, CA2/3, CA4, dentate gyrus, hippocampal fissure, hippocampal tail and molecular layer subfields in patients. For those patients with severe depression, hippocampal subfields showed greater extensive atrophy than those in mild, particularly in CA1-4 subfields extending towards the subiculum. These results were similar across various severity assessments. Regression indicated that hippocampal subfields abnormalities had the strongest associations with memory dysfunction, and relatively week associations with cognitive control and attention. Notably, CA4 and dentate gyrus had the highest weights in the regression model.
Conclusions
As depressive severity increases, hippocampal subfield atrophy tends to spread from CA regions to surrounding areas, and primarily affects memory function in patients with youth depression. These results suggest hippocampus might be markers in progression of adolescent depression, offering new directions for early clinical intervention.
Syphilis remains a serious public health problem in mainland China that requires attention, modelling to describe and predict its prevalence patterns can help the government to develop more scientific interventions. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, hybrid SARIMA-LSTM model, and hybrid SARIMA-nonlinear auto-regressive models with exogenous inputs (SARIMA-NARX) model were used to simulate the time series data of the syphilis incidence from January 2004 to November 2023 respectively. Compared to the SARIMA, LSTM, and SARIMA-LSTM models, the median absolute deviation (MAD) value of the SARIMA-NARX model decreases by 352.69%, 4.98%, and 3.73%, respectively. The mean absolute percentage error (MAPE) value decreases by 73.7%, 23.46%, and 13.06%, respectively. The root mean square error (RMSE) value decreases by 68.02%, 26.68%, and 23.78%, respectively. The mean absolute error (MAE) value decreases by 70.90%, 23.00%, and 21.80%, respectively. The hybrid SARIMA-NARX and SARIMA-LSTM methods predict syphilis cases more accurately than the basic SARIMA and LSTM methods, so that can be used for governments to develop long-term syphilis prevention and control programs. In addition, the predicted cases still maintain a fairly high level of incidence, so there is an urgent need to develop more comprehensive prevention strategies.
We present source detection and catalogue construction pipelines to build the first catalogue of radio galaxies from the 270 $\rm deg^2$ pilot survey of the Evolutionary Map of the Universe (EMU-PS) conducted with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The detection pipeline uses Gal-DINO computer vision networks (Gupta et al. 2024, PASA, 41, e001) to predict the categories of radio morphology and bounding boxes for radio sources, as well as their potential infrared host positions. The Gal-DINO network is trained and evaluated on approximately 5 000 visually inspected radio galaxies and their infrared hosts, encompassing both compact and extended radio morphologies. We find that the Intersection over Union (IoU) for the predicted and ground-truth bounding boxes is larger than 0.5 for 99% of the radio sources, and 98% of predicted host positions are within $3^{\prime \prime}$ of the ground-truth infrared host in the evaluation set. The catalogue construction pipeline uses the predictions of the trained network on the radio and infrared image cutouts based on the catalogue of radio components identified using the Selavy source finder algorithm. Confidence scores of the predictions are then used to prioritise Selavy components with higher scores and incorporate them first into the catalogue. This results in identifications for a total of 211 625 radio sources, with 201 211 classified as compact and unresolved. The remaining 10 414 are categorised as extended radio morphologies, including 582 FR-I, 5 602 FR-II, 1 494 FR-x (uncertain whether FR-I or FR-II), 2 375 R (single-peak resolved) radio galaxies, and 361 with peculiar and other rare morphologies. Each source in the catalogue includes a confidence score. We cross-match the radio sources in the catalogue with the infrared and optical catalogues, finding infrared cross-matches for 73% and photometric redshifts for 36% of the radio galaxies. The EMU-PS catalogue and the detection pipelines presented here will be used towards constructing catalogues for the main EMU survey covering the full southern sky.
Nonlinear simulations of Alfvén modes (AMs) driven by energetic particles (EPs) in the presence of turbulence are performed with the gyrokinetic particle-in-cell code ORB5. The AMs carry a heat flux, and consequently they nonlinearly modify the plasma temperature profiles. The isolated effect of this modification on the dynamics of turbulence is studied by means of electrostatic simulations. We find that turbulence is reduced when the profiles relaxed by the AM are used, with respect to the simulation where the unperturbed profiles are used. This is an example of indirect interaction of EPs and turbulence. First, an analytic magnetic equilibrium with circular concentric flux surfaces is considered as a simplified example for this study. Then, an application to an experimentally relevant case of ASDEX Upgrade is discussed.
TDuring COVID-19 pandemic, it was noticed that it was students who were mostly affected by the changes that aroused because of the pandemic. The interesting part is whether students’ well-being could be associated with their fields of study as well as coping strategies.
Objectives
In this study, we aimed to assess 1) the mental health of students from nine countries with a particular focus on depression, anxiety, and stress levels and their fields of study, 2) the major coping strategies of students after one year of the COVID-19 pandemic.
Methods
We conducted an anonymous online cross-sectional survey on 12th April – 1st June 2021 that was distributed among the students from Poland, Mexico, Egypt, India, Pakistan, China, Vietnam, Philippines, and Bangladesh. To measure the emotional distress, we used the Depression, Anxiety, and Stress Scale-21 (DASS-21), and to identify the major coping strategies of students - the Brief-COPE.
Results
We gathered 7219 responses from students studying five major studies: medical studies (N=2821), social sciences (N=1471), technical sciences (N=891), artistic/humanistic studies (N=1094), sciences (N=942). The greatest intensity of depression (M=18.29±13.83; moderate intensity), anxiety (M=13.13±11.37; moderate intensity ), and stress (M=17.86±12.94; mild intensity) was observed among sciences students. Medical students presented the lowest intensity of all three components - depression (M=13.31±12.45; mild intensity), anxiety (M=10.37±10.57; moderate intensity), and stress (M=13.65±11.94; mild intensity). Students of all fields primarily used acceptance and self-distraction as their coping mechanisms, while the least commonly used were self-blame, denial, and substance use. The group of coping mechanisms the most frequently used was ‘emotional focus’. Medical students statistically less often used avoidant coping strategies compared to other fields of study. Substance use was only one coping mechanism that did not statistically differ between students of different fields of study. Behavioral disengagement presented the highest correlation with depression (r=0.54), anxiety (r=0.48), and stress (r=0.47) while religion presented the lowest positive correlation with depression (r=0.07), anxiety (r=0.14), and stress (r=0.11).
Conclusions
1) The greatest intensity of depression, anxiety, and stress was observed among sciences students, while the lowest intensity of those components was found among students studying medicine.
2) Not using avoidant coping strategies might be associated with lower intensity of all DASS components among students.
3) Behavioral disengagement might be strongly associated with greater intensity of depression, anxiety, and stress among students.
4) There was no coping mechanism that provided the alleviation of emotional distress in all the fields of studies of students.
With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to $z = 3$ before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify $\approx$76% of the highest redshift sources—sources at redshift $z > 2.51$—as being in either the highest bin ($z > 2.51$) or second highest ($z = 2.25$).
We present the Widefield ASKAP L-band Legacy All-sky Blind surveY (WALLABY) Pilot Phase I Hi kinematic models. This first data release consists of Hi observations of three fields in the direction of the Hydra and Norma clusters, and the NGC 4636 galaxy group. In this paper, we describe how we generate and publicly release flat-disk tilted-ring kinematic models for 109/592 unique Hi detections in these fields. The modelling method adopted here—which we call the WALLABY Kinematic Analysis Proto-Pipeline (WKAPP) and for which the corresponding scripts are also publicly available—consists of combining results from the homogeneous application of the FAT and 3DBarolo algorithms to the subset of 209 detections with sufficient resolution and $S/N$ in order to generate optimised model parameters and uncertainties. The 109 models presented here tend to be gas rich detections resolved by at least 3–4 synthesised beams across their major axes, but there is no obvious environmental bias in the modelling. The data release described here is the first step towards the derivation of similar products for thousands of spatially resolved WALLABY detections via a dedicated kinematic pipeline. Such a large publicly available and homogeneously analysed dataset will be a powerful legacy product that that will enable a wide range of scientific studies.
We present WALLABY pilot data release 1, the first public release of H i pilot survey data from the Wide-field ASKAP L-band Legacy All-sky Blind Survey (WALLABY) on the Australian Square Kilometre Array Pathfinder. Phase 1 of the WALLABY pilot survey targeted three $60\,\mathrm{deg}^{2}$ regions on the sky in the direction of the Hydra and Norma galaxy clusters and the NGC 4636 galaxy group, covering the redshift range of $z \lesssim 0.08$. The source catalogue, images and spectra of nearly 600 extragalactic H i detections and kinematic models for 109 spatially resolved galaxies are available. As the pilot survey targeted regions containing nearby group and cluster environments, the median redshift of the sample of $z \approx 0.014$ is relatively low compared to the full WALLABY survey. The median galaxy H i mass is $2.3 \times 10^{9}\,{\rm M}_{{\odot}}$. The target noise level of $1.6\,\mathrm{mJy}$ per 30′′ beam and $18.5\,\mathrm{kHz}$ channel translates into a $5 \sigma$ H i mass sensitivity for point sources of about $5.2 \times 10^{8} \, (D_{\rm L} / \mathrm{100\,Mpc})^{2} \, {\rm M}_{{\odot}}$ across 50 spectral channels (${\approx} 200\,\mathrm{km \, s}^{-1}$) and a $5 \sigma$ H i column density sensitivity of about $8.6 \times 10^{19} \, (1 + z)^{4}\,\mathrm{cm}^{-2}$ across 5 channels (${\approx} 20\,\mathrm{km \, s}^{-1}$) for emission filling the 30′′ beam. As expected for a pilot survey, several technical issues and artefacts are still affecting the data quality. Most notably, there are systematic flux errors of up to several 10% caused by uncertainties about the exact size and shape of each of the primary beams as well as the presence of sidelobes due to the finite deconvolution threshold. In addition, artefacts such as residual continuum emission and bandpass ripples have affected some of the data. The pilot survey has been highly successful in uncovering such technical problems, most of which are expected to be addressed and rectified before the start of the full WALLABY survey.
Mental health regional differences during pregnancy through the COVID-19 pandemic is understudied.
Objectives
We aimed to quantify the impact of the COVID-19 pandemic on maternal mental health during pregnancy.
Methods
A cohort study with a web-based recruitment strategy and electronic data collection was initiated in 06/2020. Although Canadian women, >18 years were primarily targeted, pregnant women worldwide were eligible. The current analysis includes data on women enrolled 06/2020-11/2020. Self-reported data included mental health measures (Edinburgh Perinatal Depression Scale (EPDS), Generalized Anxiety Disorders (GAD-7)), stress. We compared maternal mental health stratifying on country/continents of residence, and identified determinants of mental health using multivariable regression models.
Results
Of 2,109 pregnant women recruited, 1,932 were from Canada, 48 the United States (US), 73 Europe, 35 Africa, and 21 Asia/Oceania. Mean depressive symptom scores were lower in Canada (EPDS 8.2, SD 5.2) compared to the US (EPDS 10.5, SD 4.8) and Europe (EPDS 10.4, SD 6.5) (p<0.05), regardless of being infected or not. Maternal anxiety, stress, decreased income and access to health care due to the pandemic were increasing maternal depression. The prevalence of severe anxiety was similar across country/continents. Maternal depression, stress, and earlier recruitment during the pandemic (June/July) were associated with increased maternal anxiety.
Conclusions
In this first international study on the impact of the COVID-19 pandemic, CONCEPTION has shown significant country/continent-specific variations in depressive symptoms during pregnancy, whereas severe anxiety was similar regardless of place of residence. Strategies are needed to reduce COVID-19’s mental health burden in pregnancy.
Surgical management is the mainstay of treatment for tumours in the parapharyngeal space. This study aimed to evaluate the indications, limits and technical nuances of the endoscopic transoral approach.
Method
Thirteen patients with parapharyngeal space tumours that were treated between May 2017 and November 2020 were included in this retrospective study.
Results
All patients underwent surgery for complete oncological resection except one patient who received treatment for diagnostic purposes. No major complications were reported, with excellent control of the vital structures of the parapharyngeal space.
Conclusion
The endoscopic transoral approach to the parapharyngeal space is a promising alternative approach for selected parapharyngeal space tumours with satisfactory outcomes.
Background: Despite a higher prevalence of traumatic spinal cord injury (TSCI) amongst Canadian Indigenous peoples, there is a paucity of studies focused on Indigenous TSCI. We present the first Canada-wide study comparing TSCI amongst Canadian Indigenous and non-Indigenous peoples. Methods: This study is a retrospective analysis of prospectively-collected TSCI data from the Rick Hansen Spinal Cord Injury Registry (RHSCIR) from 2004-2019. We divided participants into Indigenous and non-Indigenous cohorts and compared them with respect to demographics, injury mechanism, level, severity, and outcomes. Results: Compared with non-Indigenous patients, Indigenous patients were younger, more female, less likely to have higher education, and less likely to be employed. The mechanism of injury was more likely due to assault or transportation-related trauma in the Indigenous group. The length of stay for Indigenous patients was longer. Indigenous patients were more likely to be discharged to a rural setting, less likely to be discharged home, and more likely to be unemployed following injury. Conclusions: Our results suggest that more resources need to be dedicated for transitioning Indigenous patients sustaining a TSCI to community living and for supporting these patients in their home communities. A focus on resources and infrastructure for Indigenous patients by engagement with Indigenous communities is needed.
The incidence of scarlet fever has increased dramatically in recent years in Chongqing, China, but there has no effective method to forecast it. This study aimed to develop a forecasting model of the incidence of scarlet fever using a seasonal autoregressive integrated moving average (SARIMA) model. Monthly scarlet fever data between 2011 and 2019 in Chongqing, China were retrieved from the Notifiable Infectious Disease Surveillance System. From 2011 to 2019, a total of 5073 scarlet fever cases were reported in Chongqing, the male-to-female ratio was 1.44:1, children aged 3–9 years old accounted for 81.86% of the cases, while 42.70 and 42.58% of the reported cases were students and kindergarten children, respectively. The data from 2011 to 2018 were used to fit a SARIMA model and data in 2019 were used to validate the model. The normalised Bayesian information criterion (BIC), the coefficient of determination (R2) and the root mean squared error (RMSE) were used to evaluate the goodness-of-fit of the fitted model. The optimal SARIMA model was identified as (3, 1, 3) (3, 1, 0)12. The RMSE and mean absolute per cent error (MAPE) were used to assess the accuracy of the model. The RMSE and MAPE of the predicted values were 19.40 and 0.25 respectively, indicating that the predicted values matched the observed values reasonably well. Taken together, the SARIMA model could be employed to forecast scarlet fever incidence trend, providing support for scarlet fever control and prevention.