The insufficient understanding and misdiagnosis of clinically diagnosed pulmonary tuberculosis (PTB) without an aetiological evidence is a major problem in the diagnosis of tuberculosis (TB). This study aims to confirm the value of Long non-coding RNA (lncRNA) n344917 in the diagnosis of PTB and construct a rapid, accurate, and universal prediction model.A total of 536 patients were prospectively and consecutively recruited, including clinically diagnosed PTB, PTB with an aetiological evidence and non-TB disease controls, who were admitted to West China hospital from Dec 2014 to Dec 2017. The expression levels of lncRNA n344917 of all patients were analyzed using reverse transcriptase quantitative real-time PCR. Then, the laboratory findings, electronic health record (EHR) information and expression levels of n344917 were used to construct a prediction model through the Least Absolute Shrinkage and Selection Operator algorithm and multivariate logistic regression.https://ziruinptb.shinyapps.io/shiny/.The factors of n344917, age, CT calcification, cough, TBIGRA, low-grade fever and weight loss were included in the prediction model. It had good discrimination , consistency and clinical availability. It also showed a good replicability in the validation cohort. Finally, it was encapsulated as an open-source and free web-based application for clinical use and is available online at Combining the novel potential molecular biomarker n344917, laboratory and EHR variables, this web-based prediction model could serve as a user-friendly, accurate platform to improve the clinical diagnosis of PTB. Mycobacterium tuberculosis (MTB) itself or specific biomarkers of the host immune response (Tuberculosis (TB) remains the leading cause of mortality and morbidity worldwide . The mosresponse . For theresponse . Althougresponse .GSE119143) was significantly down-regulated in the peripheral blood mononuclear cells (PBMC) of patients with clinically diagnosed PTB . According to the NONCODE database, n344917 was highly expressed in both lymphocytes and leukocytes, which suggests that it may be associated with the immune response to M. tuberculosis. Specifically, it could be valuable to further investigate the application of n344917 in the identification of clinically diagnosed PTB.The long cultivation time, high equipment requirements and unqualified sputum sample quality constitute important limitations of the diagnostic ability and use of pathogen−based detection, causing insufficient understanding and misdiagnosis of clinically diagnosed PTB . The resc-indices ± SEM = 0.947 ± 0.028). However, the value of this model in the clinically diagnosed PTB was not investigated. In addition, due to the limitations of the sample size, different populations and lack of external validation, it is difficult for many prediction models to precisely identify TB  variables in a combined prediction model. ntify TB . Many stIn this study, we aimed to address the following research questions: (1) verifying the diagnostic value of the novel molecular biomarker of n344917; (2) developing and validating a rapid and universal prediction model in laboratory combining n344917, laboratory and EHR variables to identify clinically diagnosed PTB.This research was designed to be performed in three steps. First, the expression level of n344917 selected by microarray was detected using reverse transcriptase quantitative real-time PCR in all participants for biomarker verification step. Next, in the modeling step, we used n344917, laboratory and EHR variables to extract the features and construct a prediction model in the derivation cohort. The internal 10-fold cross validation were used to test the model. Finally, during the external validation step, the optimal model was encapsulated as an open-source and free predictive web-based application. We evaluated it to further confirm that can produce good performance in an independent validation cohort. For the derivation cohort, we prospectively and consecutively recruited patients with a clinical suspicion of PTB without an aetiological evidence  who were admitted from Dec 2014 to Jan 2017 to the Respiratory and Infection Department of the West China Hospital, Sichuan University. The inclusion criteria included the following: (a) age ≥ 18 years; (b) clinical manifestations > 2 weeks and a disease history with high suspect of PTB. (c) at least two successive AFB sputum smears, one MTB-DNA PCR and one mycobacterium culture were all negative. (d) anti-tuberculosis therapy < 7 days on admission. Clinically diagnosed PTB and non-TB control patients were diagnosed following the Chinese diagnostic criteria for PTB by two experienced pulmonologists separately .The independent validation cohort consisted of patients collected prospectively from Jan 2017 to Dec 2017 using a similar collection strategy. The difference was that clinically suspected PTB patients who eventually got an aetiological evidence were also enrolled to validate the generalization ability of the model.Informed consents were obtained from all the participants. This study was approved by the Clinical Trials and Biomedical Ethics Committee of West China [no. 2014 (198)] and was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.TMRT reagent Kit with gDNA Eraser . The expression level of n344917 was detected using qRT-PCR according to the SYBR method. The reaction system was as follows: 5 μL of Mix , 0.2 μL of specific forward primers, 0.2 μL of specific reverse primers, and 1 μL of reverse transcripts.The experimental scheme referred to the preceding exploration of our research group . PeripheCorresponding EHR  and laboratory variables were collected on admission. The specific clinical manifestation included cough, expectoration, chest discomfort, hemoptysis, low-grade fever (37.4–38°C), weight loss , night sweats, poor appetite and fatigue. Medical imaging examination was performed by radiologists and the imaging characteristics associated with PTB  were specifically evaluated. Laboratory variables, including complete blood count, coagulation function and biochemical examination, were collected through the laboratory management system of West China Hospital of Sichuan University and all the laboratory inspections were performed by qualified laboratory personnel in accordance with the standard operating procedure.t] to compress the coefficients of the variables. The variables with a coefficient of 0 are eliminated, and a panel of optimal and representative variables are finally obtained. This can effectively avoid the influence of factors like the number of variables, different orders of magnitude, various units and possible co-linearity between the indicators on the classical analysis methods  regression analysis was used for variable selection, which is an important method in data fitting . LASSO r methods . In thisThe optimal model was encapsulated as an open-source and free predictive web-based application through the “shiny R” program package . Both clU test and chi-square test were used to analyze the continuous variables and categorical variables, respectively. LASSO was used for feature selection. Multivariate logistic regression was used to construct the identification model. The LASSO algorithm was implemented using the “glmmet” package, and the logistic regression model was established using the “glm” package. The web-based application was built using the “shiny” package of R. All statistical analyses were done using R version 3.5.0.The continuous variables were represented by the median (upper and lower quartiles), and the categorical variables were represented by the frequency (percentage). The Mann–Whitney A total of 536 PTB patients were finally included in this research. The mean of age groups in PTB and non-PTB groups was 38.87 (age range 18–81 years) and 57.29 (age range 18–89 years), respectively. The derivation cohort consisted of 269 cases , while the validation cohort consisted of 267 cases . The laboratory findings and EHR data of these patients are shown in p < 0.001; Compared with the non-TB control subjects, n344917 was significantly down-regulated in the clinically diagnosed PTB patients , and low-grade fever and weight loss, as listed in The risk of clinically diagnosed PTB was calculated as follows:https://ziruinptb.shinyapps.io/shiny/; We used the “R shiny” package to provide a visual and operational GUI for this model, where the user can directly obtain the prediction probability by entering or selecting a variable in the web-based application (P < 0.05), indicating that the predictive performance was significantly improved after adding n344917. The comparison between the prediction results and the follow-up results showed that the model has a sensitivity of 88.98%, a specificity of 86.43%, a positive predictive value of 85.61%, and a negative predictive value of 89.63%; plus, the AUC value was 0.88  in an independent validation cohort for evaluating the performance of the web-based application . The NRIwas 0.88 . The calwas 0.88 . In the was 0.88 , the verEtiological detection is the most important basis for the PTB diagnosis . NeverthTo some extent, our prediction model avoids these deficiencies and has the advantages of prospective, relatively sufficient sample size, satisfactory performance and independent validation cohort validation. In this study, we establish a new approach to assist the identification of PTB patients via three main steps. First, the candidate biomarker, lncRNA n344917, is confirmed to be down-regulated in clinically diagnosed PTB. Second, a novel rapid universal and comprehensive prediction model combining the molecular biomarker, EHR and laboratory variables, is constructed. This will make up for the deficiency of the detection method and enhance the identification of clinically diagnosed PTB from more aspects. Third, the prediction model is encapsulated as a user-friendly web-based application and externally validated.P < 0.001). Next, we use the LASSO algorithm to comprehensively analyze 35 variables including the biomarker, EHR and laboratory variables. In the past, many researchers believed that clinical indicators with significant statistical differences may be closely related to the disease and can be used to construct diagnostic models. However, studies have increasingly found that it is not objective and may result in wasting data resources and even misleading decisions (P < 0.05)].The results confirm the significant differential expression of n344917 in the clinically diagnosed PTB tuberculosis  can be found in the article/The studies involving human participants were reviewed and approved by Clinical Trials and Biomedical Ethics Committee of West China. The patients/participants provided their written informed consent to participate in this study.BY and CT had full access to all the data in the study, were responsible for the integrity of the data, and supervised the work. ZM, MW, SG, and YZ conceptualized and designed the experiment, and analyzed or interpreted the data. ZM and MW drafted the manuscript. BY and XH critically revised the manuscript for important intellectual content. ML, XH, HB, and QW collected the data. BY obtained funding. All authors contributed to the article and approved the submitted version.https://www.editsprings.com/) for the expert linguistic services provided and also to all individuals who participated in or helped with this research project.The authors would like to express their gratitude to EditSprings (The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This study aimed to create a prediction model of postoperative pulmonary complications for the patients with emergency cerebral hemorrhage surgery.Patients with hemorrhage surgery who underwent cerebral hemorrhage surgery were included and divided into two groups: patients with or without pulmonary complications. Patient characteristics, previous history, laboratory tests, and interventions were collected. Univariate and multivariate logistic regressions were used to predict postoperative pulmonary infection. Multiple machine learning approaches have been used to compare their importance in predicting factors, namely K-nearest neighbor (KNN), stochastic gradient descent (SGD), support vector classification (SVC), random forest (RF), and logistics regression (LR), as they are the most successful and widely used models for clinical data.Three hundred and fifty four patients with emergency cerebral hemorrhage surgery between January 1, 2017 and December 31, 2020 were included in the study. 53.7% (190/354) of the patients developed postoperative pulmonary complications (PPC). Stepwise logistic regression analysis revealed four independent predictive factors associated with pulmonary complications, including current smoker, lymphocyte count, clotting time, and ASA score. In addition, the RF model had an ideal predictive performance.According to our result, current smoker, lymphocyte count, clotting time, and ASA score were independent risks of pulmonary complications. Machine learning approaches can also provide more evidence in the prediction of pulmonary complications. Complications after major surgery occur frequently and are an important cause of mortality and morbidity, especially when they affect the lungs . Indeed,However, the current predictive indicators are very limited in severe craniocerebral surgery, especially cerebral hemorrhage , 4. PatiThe study was approved by our local institutional review board. The clinical data of patients who underwent emergency ICH surgery at a single institution during a 4-year period between January 1, 2017 and December 31, 2020 were reviewed and analyzed in a retrospective fashion. The characteristics of the patients included in this study were sex, age, education, medical history , respiratory history, whether a current smoker, Glasgow coma scale (GCS), glucose, Albumin (Alb), WBC, lymphocyte count, leukocyte, RBC, platelet, clotting time, early enteral nutrition, preventive tracheotomy respirator use, operative time, anesthesia time, the blood loss, ASA classification, and craniotomy.In accordance with past studies –7, theset-test and one-way ANOVA was performed to determine characteristics that were statistically significantly different between the two groups. Pearson correlation analysis was performed for the risk factors and variables with P < 0.05 were deemed to have statistically significant associations. Variables with P < 0.05, as determined by univariate analysis, were included for multivariate analysis. Multivariate logistic regression analysis was employed to identify independent predictors of unfavorable outcomes.Statistical analysis using Student's A method that combines automatic algorithms and artificial selection aimed at dimension reduction was used for feature extraction from thousands of variables in this analysis. All features were selected by clinicians based on their experience in diagnosis before automatic analysis. The random forest algorithm was used for final extraction. According to the descending order of importance, the feature score higher than 0.0005 was selected for final analysis. Multiple algorithms were chosen to improve the probability of good discrimination performance. This study used the following classifiers: K-nearest neighbor (KNN), stochastic gradient descent (SGD), support vector classification (SVC), random forest (RF), and logistics regression (LR).The whole data samples were randomly split into training and test sets according to a division of 7:3. Optimal features and hyperparameters combinations for the model were determined on the training set. Furthermore, 5-fold cross-validation (23) was used in the process of feature selection and hyperparameters .The important indicators of the machine learning model include precision and recall. Precision refers to the actual positive samples among all predicted positive samples. The formula is as follows: Precision = TP/(FP + TP). Recall refers to the probability of being predicted to be a positive sample in all samples. Its formula is as follows: Recall rate = TP/(TP+FN). To consider the two factors, F1 score were calculated as F1 = 2 precision-recall rate/.To assess the discriminative performance of this risk score in both the development and validation subsamples, we used the c-statistic, which was also displayed graphically as the area under the receiver operating characteristic (ROC) curve. An area under the ROC curve (AUC) of 0.5 indicates no discrimination, whereas an AUC of 1 indicates perfect discrimination.The model was subsequently tested on the independent test set, which had not been seen by the model during the training process so as to avoid overfitting. To avoid bias due to the random split of the training and test sets, the above procedures were repeated 10 times, and the performance of different models was compared. The comparison of different models' performance in the 10 repeats was examined by Wilcoxon signed ranks test as suggested by a previous study , 10. AllP > 0.05) (P < 0.05), as shown in The study included 354 patients with emergency cerebral hemorrhage surgery between January 1, 2017, and December 31, 2020. Furthermore, 53.7% (190/354) of the patients developed PPC during hospitalization. The mean age was 55.79 ± 14.31 years and the sex ratio was 71.1% in the PPC group; while the mean age was 54.77 ± 54.77 ± 18.49 years and the sex ratio was 79.9% in the non-PPC group ( > 0.05) . UnivariP < 0.05). Current smoker, lymphocyte count, clotting time, and ASA classification were all independent influencing factors for the occurrence of pulmonary complications . The results showed that the chi-square test of likelihood ratio suggested that the regression model had statistical significance , operative time (0.257506), leukocyte (0.264244), anesthesia time (0.291870), preventive tracheotomy (0.342191), ASA (0.345156) was closely correlated with PPC . In the Postoperative pulmonary complications (PPC) are a well-described cause of post-surgical detrimental outcomes, including intensive care unit admission, prolonged admissions, perioperative mortality, and increased hospital expenditures in patients who underwent surgery. Moreover, the complication rate of neurosurgery is naturally high. Previous studies have shown that pulmonary complications occur between 1.3 and 22%, depending on the different types of neurosurgery , 11. NowIn this study, the incidence of PPC reached 53.7%. Since all our patients were in emergent and severe conditions comparative, the incidence of PPC tended to be higher. Some studies have shown that the mortality rate of patients undergoing decompressive craniectomy is as high as 40.9%. In this study, traditional logistic reviews identified some independent risk factors by univariate and multivariate regression analyses. In particular, current smoker, lymphocyte count, clotting time, and ASA classification were independent risk factors for PPC. It was basically consistent with the results of previous studies –20. HoweIn terms of preventing PPC, some of these risk factors are controllable and some are not. Careful timing of surgery, smoking reduction, regulation of blood glucose, and preventive use of antibiotics could minimize complications. However, most of the thrombo-embolic events are often unpredictable and unpreventable, and low molecular weight heparin in the context of protocols for thromboprophylaxis could also be a beneficial attempt .p-value of blood glucose is on the margin of 0.05, so that it could be missing in the analysis. Comprehensively, the LR model performed better overall, including in the training set and test set. In particular, the test set performance remained stable, far outperforming other machine learning methods. As a most popular machine learning algorithm, RF provides accurate results without exhaustive hyper-parameter tuning and can be applied to both regression and classification problems, when the number of potential explanatory variables is far more than the observed values. In addition, all the other five models showed moderate classification ability (AUC ranging from 0.6 to 0.71). The current study could be considered as a novel exploration of the modified machine learning approach for PPC. In particular, the machine learning model can find some potential risk factors, such as the blood glucose index in this paper that could not be found in previous studies due to different learning models, especially in the case of limited sample size.In recent years, machine learning has been used to predict the prognosis of various neurological diseases with remarkable results –27. RF iAlthough machine learning models are powerful, they are often more complex, which makes them difficult to understand like a: “black box” . TherefoThis study has several limitations. First, the diagnosis of PPC relied on the attending physicians' evaluation in this retrospective study; therefore, the potential of either underestimation or overestimation of the actual incidence of PPC could not be avoided. Our inclusion indicators were relatively loose, and although some patients were diagnosed with pulmonary edema, they did not need special intervention. Second, the definition of PPC was based on radiology evidence rather than etiological results. Another limitation of the study is that the diagnosis of PPC was occasionally a clinical one and that there was no clear source of infection. At the same time, our study was a retrospective analysis, and the number of specimens was relatively low considering the large amount required in machine learning analysis. Fortunately, we have adopted a variety of machine learning models to analyze and process the data to minimize the omission of important indicators.Finally, in this study, the prediction models of pulmonary complications in patients with severe emergency ICH were established. Compared with traditional statistical methods, the machine learning model was more comprehensive and flexible, providing new ideas for the prediction model of pulmonary complications in the future.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.The studies involving human participants were reviewed and approved by the Ethics Committee of the First Affiliated Hospital of University of Science and Technology of China . Written informed consent from the patients/participants or patients/participants legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.The present study was supported by the Fundamental Research Funds for the Central Universities (Grant no. WK9110000126).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


There was a slight improvement in physical and mental HRQoL over 4 years in this HIV cohort, but the burden of unmet basic needs took its toll on those over 50 years of age. Regularly assessing unmet basic needs is recommended given the impact these can have on HRQOL for people living with HIV. Recognition of unmet needs is vital, as is the development of timely interventions.Despite significant advances in antiretroviral therapy, unmet basic needs can negatively impact health-related quality of life (HRQoL) in people living with HIV, especially as they age. We aimed to examine the effect of unmet basic needs across age groups on changes in HRQoL over a 4-year period in persons with HIV. Physical and mental HRQoL scores from the Positive Spaces, Healthy Spaces cohort interviewed in 2006 (n = 538), 2007 (n = 506), and 2009 (n = 406) were examined across three age groups according to their unmet needs for food, clothing, and housing. Individual growth curve model analyses were used to investigate changes over time, adjusting for demographics, employment, living conditions, social supports, HIV status, and health behavior risks. Low scores on physical and mental HRQoL were positively associated with higher number of unmet basic needs  = 0.87,  Currently, an estimated 37.7 million people are living with HIV globally, 27.5 million of whom, at the end of 2020, were on antiretroviral therapy . In 2016People living with HIV face numerous health challenges. HIV infection can lead to persistent inflammation , as wellIn addition, a variety of the social determinants of health impinge on older persons living with HIV and can compromise their health-related quality of life (HRQoL). Important factors are poverty , unemploThe concept and definition of unmet basic needs vary. Its exact scope depends on the circumstances of the target population and, with reference to the HIV population, can include: gaps in social services or inaccessibility of drug treatment, or lack of medical care for women or for those with concurrent addiction . It can In a previous study by our group , we defiHRQoL is becoming increasingly relevant because, despite lifespan improvement of persons with HIV since the advent of antiretroviral therapy , adults The focus of the present study is to investigate the changes of physical and mental HRQoL over time, and to determine whether these changes vary with the number of unmet basic needs at progressive ages. We hypothesized that both physical and mental HRQoL would change significantly over time and that the burden of unmet basic needs would be negatively associated with HRQoL over the period of the study. We also hypothesized that these associations would be most pronounced in the oldest study participants. The PSHP cohort was a five-year observational study that evaluated the effects of housing and other supports on the health and well-being of a community-based sample of adults living with HIV who came from key populations in the province of Ontario, Canada. A detailed description of the PSHP cohort is available elsewhere . In summPeer research assistants interviewed 602 participants in 2006, re-interviewed them in 2007 (n = 509), and again in 2009 (n = 442). Eligibility for the current study was participation in at least two of these interviews. This resulted in a total of 1486 observations from 538 participants in 2006 (baseline), 506 participants in 2007, and 442 participants in 2009. Those who are known to have died between 2007 and 2009 (n = 18) or those who died in 2009 (n = 2) were included in the analyses, but those who died between 2006 and 2007 (n = 15) were excluded. The physical and mental HRQoL scores for those who died between 2007 and 2009 were, as would be expected, significantly lower relative to those who survived. We note that initial physical and mental HRQoL scores of those who were not available or lost for follow-up interviews in 2007 or 2009 were not statistically different from those who remained in the cohort over all the years of the study. Compared to those who attended all three interviews, there were no significant differences at baseline in the number of unmet basic needs in those who missed a subsequent follow-up interview. However, those who missed their follow-up appointment in 2007 were significantly more likely than others to report significant alcohol or drug use, history of incarceration or homelessness, and depression at their baseline interview. Those who were missing the 2009 interview were significantly more likely than others to be male, gay, bisexual, or be men who have sex with men, middle-aged, living alone, and financially disadvantaged. Conceptualizing basic needs. For this study, we defined ‘unmet basic needs’ as the inability to pay for food (Q. 60a), clothing (Q. 61a), housing-related costs , and, also, those who endorsed being at risk for homelessness (Q. 57) . The intOutcome variables. The concept of HRQoL is here defined as a person’s measurable rating of his or her physical and mental health, and social functioning. These are represented by physical and mental HRQoL scores, derived from the 35-item Medical Outcomes Study-HIV Health Survey (MOS-HIV), and used previously to evaluate HRQoL . ResultsPredictor. A total number of unmet basic needs was generated based on a questionnaire endorsement of items about the ability to pay for food, clothing, housing-related costs, or feeling subjectively at risk of homelessness. Unmet basic needs were calculated at each of the three survey times  and for the four-year study period.time was coded as ‘0’ (2006), ‘1’ (2007), and ‘3’ (2009) in our analyses. Because the PSHP cohort was surveyed three times between March 2006 and December 2009, Covariates. Participant age was categorized as ‘young’ (20–34 years old), ‘middle-aged’ (35–49 years of age), and ‘old’ (50 years and older). Sixteen covariates, some time invariant and some time variant, and all known from previous studies to negatively affect the health and well-being of people living with HIV, were coded 0 and 1. Time-invariant variables were: baseline demographics such as gender , ethnicity (Caucasian vs. others), sexual orientation , and residence in the Greater Toronto area (yes vs. no). Time-variant variables were: educational level (above vs. below high school degree), employment status (employed vs. unemployed (including retired or disabled)), personal gross income (above vs. below the sample median cut-off of CAD 1150 dollars per month), living arrangements , social support  , drug ab2), Cochran–Armitage Trend, Student t, and Kruskal–Wallis tests, as appropriate. The four-year trajectories of physical and mental HRQoL in persons living with HIV in the PSHP cohort were evaluated. Descriptive analyses were performed with Chi-square (χtime) were utilized to evaluate changes in physical and mental HRQoL scores over time and to determine the influence of unmet basic needs on these changes across the age groups. The mixed-effects regression method can handle missing data and irregular time measures for both time-invariant and time-varying variables [A series of individual growth curve models  with mixariables . For thitime with random slopes was added to examine the rates of change in physical and mental HRQoL scores. In Model 3, we added unmet basic needs to assess the relationships between unmet basic needs and the two outcomes, using met needs for the reference group. In Model 4, age groups were added to evaluate the association of age on the two summary scores, using the youngest age group as the reference group. In Model 5, covariates were added while maintaining the significance level of intercepts and linear slopes [time. Finally, in Model 6, we included the interactions between unmet basic needs and age groups. We report on six separate models of physical and mental HRQoL outcomes. In Model 1, the random intercepts and no variables were included. It serves as the baseline and estimates the initial mean of summary HRQoL scores . In Moder slopes , as wellp < 0.05 was used to define statistical significance throughout. All data analyses were performed using the SAS® OnDemand for Academics, SAS Institute Inc., Cary, NC, USA.To evaluate the fitness of each model, a -2 Res Log-Likelihood (-2 LL), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) ,52 were At baseline (2006), the median age of participants was 43.0 , and median education was 6.1 . The sample consisted of 134 (24.9%) and 404 (75.1%) female and male participants, respectively. Overall, there were 54.7% men who have sex with men and 8.5% bisexual or lesbian individuals. Approximately, 11.9% of people identified as being from Africa, the Caribbean, or Black, 12.5% identified as being Indigenous or First Nations, and 2.4% identified as having Asian background. In this cohort, 19.2% were people who used drugs. Roughly 40.0% were from the Greater Toronto area. The proportion of unemployed was 79.0% for the sample. The duration of HIV infection (self-reported by participants) was 11.0 years , and the duration of receiving combination antiretroviral therapy was 8.0 years . A majority of participants were in the middle-aged group (60.0%), with 14.8% in the ‘younger’ and 25.2% in the ‘older’ age groups . The proportions and number of unmet needs for each period and over time are presented in At baseline (2006), the mean physical HRQoL of participants was 42.8 (standard error (SE), 0.47) and mental HRQoL was 43.9 (0.51). Both physical and mental HRQoL scores for participants were significantly decreased in parallel with the rising number of unmet basic needs .time was added to Model 2, a linear increase in physical and mental HRQoL scores was observed, β = 0.51 (0.15), β = 0.78 (0.18), respectively. After adding the quadric time, the association was no longer statistically significant and eliminated the influence of time. For this reason, the quadric time was not kept in this model nor in the following models. When unmet basic needs were separately added to Model 3, we observed that increasing low scores of physical and mental HRQoL were significantly associated with numbers of unmet basic needs. The interactions between unmet basic needs and time were not significant, suggesting that unmet basic needs remained relatively the same over time. After adding age groups into Model 4, unmet basic needs remained significant (p < 0.05), while mental HRQoL scores fell by eight points when there were 4-unmet needs (p = 0.009).nificant , while oThe four-year trajectories of physical and mental HRQoL in persons living with HIV in the PSHP cohort were evaluated. Trajectories of physical and mental HRQoL scores illustrated the trend over time. We observed that mean scores were reduced by the number of unmet basic needs in physical HRQoL a,b. SimiThis is the first longitudinal Canadian study in persons living with HIV using individual growth curve modeling to investigate the rates of change in physical and mental HRQoL in relation to the varying number of unmet basic needs across age groups. met’ needs and, gradually, fewer unmet needs, while the two younger participant groups (age 20–34 years old and 35–49 years old) reported fewer ‘met’ needs over time. Overall, we found a high number of unmet basic needs in access to food and clothing, inability to cover housing-related costs, and feeling at risk of homelessness in our PSHP sample, with approximately 86% of participants endorsing at least one of these items. The evaluation of changes in unmet basic needs over time was not significant, suggesting that these challenges were relatively stable over the four years. The oldest participants reported more ‘There is evidence that older HIV adults endorse good health and enjoy life . This miIn our evaluation of HRQoL, we observed that both the physical and mental health scores of our participants fell below those of the Canadian general population , but thaThe presence and severity of social determinants of health such as poverty  to incomWhen we examined the effect of unmet basic needs on HRQOL, we found that the number of unmet basic needs were significantly and negatively associated with lower scores in both physical and mental HRQoL―and these relationships were more pronounced (controlling for selected covariates) in participants aged 50 and older. These findings have important clinical significance and implications for overall HIV care, particularly for older adults. Antiretroviral therapies alone cannot markedly improve HRQoL of people living with HIV ,58. Regutime and the number of unmet basic needs, suggesting that the relationship between the risk of unmet basic needs and physical or mental HRQoL scores in this cohort was relatively stable. However, we did find that aging had a significant impact, as shown by the fact that HRQoL trajectories, both physical and mental, were significantly influenced by the number of unmet basic needs in the oldest aged group. These findings suggest that, as one ages, unmet basic needs become important determinants and predictors of health status in persons living with HIV. We found no interactions between There are several potential limitations to this study. Our cohort was not initially designed to study unmet basic needs, and no validated instruments exist for their measurement. In addition, our data are based on self-reports for which there is no objective verification. Because of the nature of retro-observational prospective data, it is also self-evident that our results show only associations; they cannot prove causality. As well, about one of every six baseline participants was dropped from the analysis. We could not specify comorbidities because they were not listed in the PSHP files. However, we did control for many of the most common confounders. Our data were derived from a cohort recruited to address housing needs so that recruitment may have been skewed to a relatively disadvantaged population with more housing instability and increased unmet basic needs than would be expected. While some health information was incomplete, we were able to use a sophisticated statistical method, the individual growth curve, to manage missing data and to analyze the rates of change in our longitudinal data ,60,61. Our main findings are that basic needs  can exert a significant and negative effect on both physical and mental HRQoL particularly as people living with HIV get older. Given these associations, a patient’s ability to meet basic needs stands out as an important indicator of overall health. Primary health care, specialty health care, case management, and care coordination are currently available for patients with HIV in most developed countries, but they have focused on care engagement, treatment adherence, viral suppression, attention to treatment side-effects, and longevity more than on well-being and quality of life. National governments have a major role to play to ensure their citizens’ well-being by passing legislation and providing funding and appropriate resources for interventions that benefit HIV populations. These include accessibility and timeliness of medical and psychosocial, patient-centered, evidence-based care and continuity.


Social unrest affects people's health and well-being. People's health-related needs during social unrest are concerns in both research and clinical practice. This study aimed to build and test a framework to describe and understand the health status and needs of people with post-traumatic stress disorder (PTSD) during social unrest.This study was a cross-sectional survey. A total of 460 people who had experienced post-traumatic distress as a result of the social unrest in 2019 and 2020 were included. A conceptual model comprised four essential areas, namely posttraumatic distress symptoms, participation restrictions, perceived stigma and functional disability, was built from literature. Part 1 validated four instruments that evaluate and define the factor structure of these four areas, In Part II, structural equation modeling was used to test and validate a combined model.Factors underlying the four areas were defined. Analysis using structural equation modeling confirmed a best fit of the model. PTSD symptoms, perceived stigma and participation restriction during social unrest contributed significantly to functional disability; PTSD symptoms exerted a direct effect on participation restriction and perceived stigma; and the effect of PTSD symptoms on functional disability was mediated through its influence on perceived stigma during social unrest.A community-based inclusive approach is essential to understand the holistic needs of people with PTSD during social unrest. To improve health and well-being in addition to evaluating mental health impacts, considering interactions with the rapid change and stressful social environment is essential. Social unrest affects people's health and well-being. Health encompasses physical, mental, and social well-being  and is nThe health-related problems of people during social unrest are recent concerns of both research and practice. In June 2019, extensive protests began in Hong Kong, bringing with them prolonged social unrest that has extended into 2020. A research study on this unrest reported weighted prevalence rates of depressive symptoms and suicidal ideation of 37.4 and 4.3%, respectively , 6. PTSDHealth of people during social unrest has long been studied under a medical model. Health issues is understood as an individual and a medical phenomenon that results in limited psychological and mental functioning . Post-trThis study aimed to build a framework to understand the health of people with PTSD during social unrest based on a community-based inclusive development approach. This approach encourages the building of inclusive, resilient, and equitable communities in which disadvantaged people are empowered and given the opportunity to lead inclusive and healthy lives . The delThe most commonly reported psychological impairment among people during social unrest is post-traumatic distress . Trauma Impairment of body structures and functions represents a deviation from certain generally accepted population standards. Such impairments can be temporary or permanent; progressive, regressive, or static; and intermittent or continuous . ActivitParticipation is involvement in life situation . ParticiContextual factors represent the complete background of an individual's life and living. Discrimination and social stigma are critical environmental factors that limit participation and contribute to disability . Stigma As concluded from the above review, a preliminary model with four major areas were formed to understand the health of people during social unrest: (1) posttraumatic stress and psychological impairments, (2) body functions and activity limitations (disability), (3) participation restrictions, and (4) perceived stigma.The aim of this study was to formulate and test a proposed model to understand and predict the health and disability of people with PTSD during social unrest. The research questions are as follows:What are the valid measurements of PTSD symptoms, participation restrictions, perceived stigma and functional disability for people affected by post-traumatic distress during social unrest?What are the factor structure of the four essential constructs as confirmed with evidence?What is the model of best fit to measure and predict the disability of people with PTSD during social unrest?This study is important in understanding the factors for health and disability among the people with PTSD in social unrest. There is currently no universal framework to understand and describe the health and disability of people with PTSD based on a community-based inclusive approach. Health and inclusion cannot be fully understood by merely measuring the ability of an individual in relation to the medical condition. The World Health Organization  states tIt was a cross-sectional study. The study was conducted following three steps.Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (PCL-5), (2) Explanatory Model Interview Catalog (EMIC) Stigma Scale, (3) Participation Scale , and (4) WHO Disability Assessment Schedule 2.0 (WHODAS 2.0). Reliability analysis and Rasch model analysis were done to confirm the reliability and dimensionality of these scales for use with people with PTSD.Based on the aforementioned conceptual base to understand the impact of social unrest on the health on people, the four essential areas to understand the health and disability of people with PTSD were regarded as (1) PTSD symptoms, (2) social stigma, (3) participation, and (4) functional disability. A valid and reliable measurement tool was required to measure each area. Four instruments were then selected based on their psychometric properties and relevance as reviewed by literature. The four selected instruments were: the (1) PTSD Checklist for Constructs of each domains must be defined by measureable variables that can be collated to form a factor . Each faStructural equation modeling (SEM) was used to test the relationship among PTSD symptoms, participation restrictions, stigma and functional disability. Three hypotheses were tested through this model. The path diagram for testing is showed in H1: Perceived stigma is predicted ty PTSD symptoms among people affected by posttraumatic distress (p2).H2: Participation restriction is predicted by PTSD symptoms (p1) and perceived stigma (p3) among people with PTSD.H3: Functional disability is predicted by PTSD symptoms (p4), perceived stigma (p5) and participation restriction (p6) among people with PTSD.n = 460) while Step 3 (SEM) involved only those who matched with the diagnostic criteria as stated in the DSM 5 as revealed by the PCL 5. Participants got a total score higher than 31 were eligible for this part of the study  being aged 18 years and over, (2) reported experience of post-traumatic distress in social unrest, (3) having mental clarity, and (4) having sufficient cognitive ability to comply with the instructions to complete the tests. Participants were recruited openly through different sources. Different organizations working with people with PTSD in local community were contacted and liaised by the research team, with the research team liaison reaching out to the target groups and participants. A total of 460 participants were enrolled into this study. Step 2  involved all enrolled participants  to 5 (extremely). The PCL-5 corresponds to DSM-5 criteria for PTSD. In its original language, it has strong internal consistency, test–retest reliability, and convergent and discriminant validity (The Posttraumatic Stress Disorder Checklist for the DSM-5 (PCL-5) was used in this study to evaluate the level of posttraumatic distress symptoms. It is a 20-item self-report measure that assesses PTSD symptoms. Respondents indicated the extent to which they had been bothered by each PTSD symptom over the past month on a 5-point scale, with responses ranging from 1 , community life , and social life  , 24. ResThe WHO Disability Assessment Schedule 2.0 (WHODAS 2.0) measures the level of functioning and disability in six domains of life: cognition, mobility, self-care, getting along, life activities, and participation. WHODAS 2.0 is a practical, generic assessment instrument that can measure disability at the population and clinical levels . It contThe data were cleaned to exclude missing data. The internal consistency and construct validity, of these scales as applied to these specific participants were examined. Reliability analysis of the four instruments was performed using IBM SPSS Statistics for Windows, version 25 . Cronbach's alpha was calculated to assess the internal consistency of the instruments. Convergent validity and reliability analysis of the four scales was conducted using SPSS. Rasch model analysis of the four scales was performed using Winstep to examine the internal validity and dimensionality of the overall score of the four scales. Data were first cleaned based on diagnosis of misfit person. Participants were excluded if the point measure correlation had a negative value, the outfit mean-square (MNSQ) value was >2, and the Z-standard value was >2 . DimensiConfirmatory factor analysis (CFA) was conducted for each instrument to determine their factor structure using SPSS Amos Version 26 (IBM Corp.). The factor structure of each instrument for testing was mostly based on literature data. The estimated correlation among factors and model fit indices were used to assess the model fit. Factor loadings of constructs >0.70, 0.63, 0.55, 0.45, and 0.32, were considered excellent, very good, good, fair, and poor, respectively . To ensuPost-hoc power analysis was conducted using semPower to determine the actually achieved power to detect model misspecification and target effect  were computed.The internal consistency of the four instruments was high. Cronbach's alphas for the PCL 5, WHODAS 2.0, P-scale, and EMIC stigma scale were 0.94, 0.97, 0.92, and 0.80, respectively. The Cronbach's alpha values of all items in each scale were >0.75, indicating that the overall reliability of the scale was not greatly affected by any one item .Correlations among the total scores of the four scales were evaluated to test the convergent validity and assess the relationships between the four scales. Because the total scores of all four scales were not normally distributed, Spearman's rank-order correlation was used. As presented in The internal validity and dimensionality of the four scales for use with people experienced post-traumatic distress in Hong Kong was confirmed using Rasch model analysis. After data cleaning, 455, 442, 432, and 434 valid cases were included in the analysis of the PCL 5, WHODAS 2.0, P-scale, and EMIC stigma scale, respectively. Determined from the summary statistics of the Rasch model analysis on the four scales , all scap < 0.05). PTSD was a higher-order factor, and the factor loadings of the six factors to PTSD were also high (0.70–0.96). The model demonstrated a good fit to the data . As a result, the underlying factors for PTSD symptoms as measured by the PCL 5 were defined as intrusion, avoidance, negative affect, anhedonia, dysphoric arousal, and anxious arousal.After data cleaning, 460 participants remained for analysis. All items were normally distributed, with univariate skewness and kurtosis values lower than 2 and 7, respectively . Maximumn = 500, with a 95% bias-corrected interval). The tested model structure was based upon our previous work on the validation of the P-Scale (2(132) = 475.51, p < 0.01, CMIN/df = 3.60, CFI = 0.91, GFI = 0.90, NNFI = 0.87, RMSEA = 0.07, SRMR = 0.09, TLI = 0.89]. Factor loadings of all items to their respective factors were high (0.61–0.76) and the three factors loaded significantly to the higher-order factors . The three factors underlying participation were confirmed as work (or school), community life, and social engagement.In total, 452 participants were included for analysis. The value of Cronbach's alpha representing the internal consistency of the scale was 0.92. The data did not have a multivariate normal distribution; the critical ratio of multivariate kurtosis was 74.01 . Model t P-Scale  which for = 0.29–0.80) indicated no problems with multicollinearity. The model demonstrated adequate fit to the data . However, the factor loadings of two items to the factors were below 0.3. The theoretical base of the EMIC Stigma Scale was reviewed, and the factor Internal Perception comprised disclosure and feelings. A second-order factor model was then proposed and tested. Fit indices indicated a good fit . Factor loadings of the items to the factors were high (0.40–0.96), and all factors loaded significantly to the higher-order factors . As a result, the confirmed factors underlying stigma were confirmed as internal perception, external perceptions, rejections, marriage, and courtesy stigma.A total of 451 participants were included for analysis. The value of Cronbach's alpha was 0.79. The data did not have a multivariate normal distribution; it had a critical ratio of multivariate kurtosis of 16.87. The model comprised five factors: (1) internal perceptions, (2) external perceptions, (3) rejection, (4) marriage, and (5) courtesy stigma . Correla2(579) = 3337.79, p < 0.01, CMIN/df = 5.77, CFI = 0.78, GFI = 0.65, NNFI = 0.75, RMSEA = 0.10, SRMR = 0.11, TLI = 0.77]. The 7-factor model demonstrated a moderate fit . Factor loadings of all items to their respective factors were high (0.53–0.93). All seven factors loaded significantly to limitations in body functions . As a result, the seven confirmed factors underlying body functions and activities were cognition, mobility, self-care, getting along, work/school, involvement in society, and household activities.A total of 458 participants were included for analysis. Reliability analysis showed the value of Cronbach's alpha of the WHODAS 2.0 for use with people affected by post-traumatic distress in Hong Kong was 0.96.The data did not have a multivariate normal distribution; the critical ratio of multivariate kurtosis was > 5 . Model tpost hoc power analysis using semPower (2(183) = 615.07, p < 0.01, CMIN/df = 3.36, CFI = 0.92, GFI = 0.88, RMSEA = 0.07, SRMR = 0.05, TLI = 0.91]. Modification indices were checked to improve the model fit. When the two errors were covaried, the model was reanalysed. The revised model fit the data well .Two hundred sixty participants, who scored 31 or above in the PCL 5, were included in this part. The results of semPower  showed, p < 0.05). Perceived stigma had direct effect on participation restriction and functional disability (p < 0.05). Participation restriction directly affected functional disability (p < 0.05). PTSD symptoms, as the sole predictor of perceived stigma, explained 91% of the variance in perceived stigma. The relationship between PTSD symptoms and participation restriction is partially mediated by perceived stigma. Altogether, PTSD symptoms and perceived stigma explained 83% of the variance in participation restriction. Moreover, the interaction of the PTSD symptoms, perceived stigma and participation restriction explained a total of 44% of the variance in functional disability.In this study, four valid measurement tools were validated to measure the health of people with PTSD as affected by social unrest. Based on a community-based inclusive development approach, these four basic areas were regarded as essential: PTSD symptoms, participation restrictions, perceived stigma and functional disability. The PCL-5, WHODAS 2.0, P- Scale, and EMIC Stigma Scale, for which construct validity and internal consistency were confirmed in this study, can be used as screening tools to facilitate the identification of health-related needs of people affected by posttraumatic distress during social unrest. The internal consistency of these four scales was found to be high (Cronbach's α > 0.8) and it indicated a good reliability. This result was similar to the previous studies on other target groups of people with chronic disabilities , 23, 42.The factor structures of the four instruments as confirmed with the CFA were similar to past studies when these instruments were used with other target groups. A six-factor model in PCL5 was confirmed in this study and the factor structure as revealed by the CFA was similar to Liu et al.'s study . The sixEssential factors underlying the four areas laid a solid theoretical base for the framework. Factor loadings for the factors under each domain were high. Intrusion, avoidance, negative affect, anhedonia, dysphoric arousal, and anxious arousal were the essential factors contributing to PTSD in response to traumatic experience. People commonly report sleep disturbance, flashbacks, emotional responses, and difficulty concentrating after witnessing traumatic events, whether in person or through media sources. Cognition, mobility, self-care, getting along, household activities, work or school activities, and participation in school are limitations in body functions and activities that may lead to functional disability. Those affected may be preoccupied with thoughts related to the trauma, encounter difficulties in interpersonal relationships, or reduce their involvement in different social spheres. Participation restrictions reflect problems in the community, social life and work, or school life. Inability to fulfill life roles due to the issues may negatively affect health and quality of life. In the perceived stigma dimension, factors affecting health during social unrest were fear of disclosure, negative feelings toward oneself, rejection, courtesy stigma, problems in marriage, and negative external perceptions. All these can be disabling to one's self-image and incite negative feelings toward oneself.The theoretical model of this study was confirmed using structural equation modeling. Functional disability of people with PTSD in social unrest was revealed to be predicted by PTSD symptoms, participation restriction and perceived stigma. Following the ICF framework , health The confirmed measurement model has the potential to be used to describe and evaluate the health impact of social unrest on individuals to facilitate interventions. Past studies have focused primarily on the psychological impact of posttraumatic distress on mental health. Posttraumatic distress is a signature feature associated with traumatic experience, violence and human rights abuse . Other tTo our knowledge, this is the first study to build a conceptual model to understand and describe the health impacts of PTSD during social unrest through a community-based inclusive development approach. Other than focusing on the problems of the individuals, this model brings also insight into the rights to participation in society. Traditional intervention approach center on improving psychological and physical functioning of the person. Inclusion refers to the respect of rights to participation and self-determination in society. Other than focusing on the problems of the individuals, this model brings also insight into the change required in the social systems and community. Our model emphasized also participation restriction and perceived stigma, which may indicate the necessity of change toward greater inclusion in social systems or even a change in the society itself.The sample size was adequate for CFA and SEM. Validation of the framework was achieved through the validation of the individual tools and the conceptual model. This framework has the potential to be used in population-based samples as well as in other cultures and contexts. However, this study used mainly an online mode to collect data. Those who did not familiar with the online platform were therefore not be able to participate in this study. Further testing of this model in community health studies on PTSD that involve other cultures, groups, or populations to generate strong evidence are warranted. Besides, we tested mainly the reliability, convergent validity and dimensionality of the four instruments. Further testing on discriminative validity of these instruments is deemed necessary.In the present study, a conceptual model describing and evaluating people's health during social unrest was built. This model involved employing a community-based development approach, which is essential to understanding the holistic needs of people during social unrest, to consider individuals' interactions with their social environment. This model can inform practitioners, researchers, and policy makers on the holistic health needs of people in times of social unrest. In particular, it can facilitate the design of programmes for monitoring and improving health and well-being.chunge@eduhk.hk.The data contributing to these analyses are held on a secure database and all data generated or analyzed during this study are not publicly available as this may be linked to specific program and staff. Sharing of such data may breach confidentiality. Requests to access the datasets should be directed to The studies involving human participants were reviewed and approved by the Education University of Hong Kong. The patients/participants provided their written informed consent to participate in this study.EC wrote the main manuscript text and prepared all figures and tables.Data collection of this study was supported by the Additional Research Fund of the Department of Special Education and Counseling at the Education University of Hong Kong.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: Patients in the postoperative period following bariatric surgery are at risk of developing eating disorders. This study aims to analyze the relation between bariatric surgery and the development and recurrence of eating disorders. Material and methods: A literature review was carried out on 15 November 2020. Fourteen studies that met the eligibility criteria were included for qualitative synthesis, and 7 studies for meta-analysis. Results: The prevalence of eating disorders in the postoperative period was 7.83%, based on the 7 studies in the meta-analysis. Binge eating disorder alone was 3.81%, which was the most significant factor, and addressed in 6 of these studies. Conclusion: The investigated studies have significant methodological limitations in assessing the relation between bariatric surgery and eating disorders, since they mostly present data on prevalence. PROSPERO CRD42019135614. Bariatric surgery has been one of the main and most effective treatments for obesity. In 2011, approximately 340,000 cases of bariatric surgery were registered worldwide, with the United States (USA) and Brazil being the two largest performers of this procedure  [This shows a rapid increase over the last decade, highlighting the need to better understand the outcomes in the post-operative period, such as non-preexisting eating disorders. Data regarding its incidence in the postoperative period are scarce due to the lack of follow-up, screening, or standardization in the evaluation of these comorbidities .The current literature provides no significant number of publications that address the role of bariatric surgery as a factor that can lead to the development of new cases of eating disorders, especially in patients with no previous history of them. Current studies show the need for better understanding, because they do not consider surgery in depth as a potential risk factor: Opozda M et al., Williams-Kerver GA et al. and Brode CS et al. verify the significance of recurrence and new cases of eating disorders ,5,6. MeaThe added value of this study is the fact that, to the extent of our knowledge, this is the first systematic review with a meta-analysis that considers several eating disorders, with data screened from 1985 to 2020. Our aim is to analyze the relation between bariatric surgery and the development or recurrence of eating disorders in patients with or without pre-existing history. Thus, this review highlights with qualitative and quantitative data an underrated topic in the current literature, identifying its limitations in such a way as to guide and suggest ideas for new research.This systematic review was carried out in accordance with the items of Preferred Reports for Systematic Reviews and Protocol Meta-Analysis (PRISMA-P) . This stDrafting of the research question was based on the PICO strategy , consideThe articles were selected from their titles and abstracts according to their data relevance and regardless of their publication status.The following study designs were considered: randomized and non-randomized controlled clinical trials, prospective and retrospective cohorts, cross-sectionals, and case controls. Reports and case series, reviews, letters to editors, research protocols, and conference proceedings were not considered.Study participants were adult patients in the postoperative period of bariatric surgery, evaluated or treated in any type of institution.Data related to the authors, date and location (country) of the publication, type of study, types of bariatric surgery, and psychiatric evaluations performed were collected and arranged in tables. Data were also collected regarding the number of patients analyzed in the study, sex, age, pre and postoperative body mass index (BMI), type of disorder and pre and postoperative symptoms, reported limitations, objectives, and conclusions of all studies.Studies were excluded if: (1) they did not present data related to the number of patients diagnosed with eating disorders or who are restricted to symptoms or scores; (2) were incomplete unpublished articles or with full text inaccessible to the authors; (3) were in languages other than English and Portuguese.www.pubmed.com, accessed date: 15 November 2020; EMBASE—www.embase.com, accessed date: 15 November 2020; Cochrane Library—www.thecochranelibrary.com, accessed date: 15 November 2020; Database of the National Institute of Health. In addition, a manual search of theses, meetings, references, study records, and contact with specialists in the field was carried out.The survey was conducted on 15 November 2020, without language or date restrictions, in the following databases: Medline (via PubMed)—The keywords were used equally in all databases, respecting their heterogeneities .The keywords were: “bariatric surgery”, “feeding and eating disorders”, “anorexia nervosa”, “bulimia nervosa”, “binge-eating disorder”, “pica”, “food addiction”, “night eating syndrome”.The search strategy was: (Bariatric Surgery) AND ((Feeding and Eating Disorders) OR (Anorexia Nervosa) OR (Bulimia Nervosa) OR (Binge-Eating Disorder) OR (Pica) OR (Food Addiction) OR (Night Eating Syndrome)).The data for each study were extracted independently by three authors . Disagreements were resolved by consensus. If no consensus was reached, a fourth author (AM) was consulted.All studies were analyzed according to their titles and abstracts, according to inclusion and exclusion criteria. If the eligibility criteria were met, the full text was extracted. All studies with full text evaluated are described in the “Results” section.Missing data were clarified by contacting the authors directly.Three authors  carried out the data validation through the discussion of the selected studies. If no consensus was reached, a fourth author (AM) was consulted.The bias risks for the studies were assessed using the criteria of the Study Quality Assessment Tools | National Heart, Lung, and Blood Institute (NHLBI) . InterveAll selected studies were considered.The characteristics of study participants were presented as means, minimum and maximum values for quantitative variables, and as frequencies and percentages for qualitative variables. The prevalence values and 95% confidence intervals were calculated using the Wilson method due to small frequency values .The meta-analysis was developed to evaluate the results of the systematic review. To assess the global heterogeneity between the studies, Cochran’s Q test was calculated, as well as the I2 (percentage of variation). A forest-plot was used to present the results of the studies’ association measures and their respective 95% confidence intervals.Statistical analyses were performed using the Stata/MP 14.0 software for Windows.For the statistical analysis, we evaluated the total number of the following data for both before and after the interventions: patients, patients with any sort of eating disorder and patients with each type of eating disorder separately. Also, we considered the type of study and the type of surgery.The electronic search found 1825 results for the keywords used. After removing 498 duplicates, 155 potentially eligible studies were identified. Of these, 60 studies did not fulfil the inclusion criteria and 81 did not fulfil the exclusion criteria. Only 14 studies were included in qualitative synthesis and 7 in meta-analysis .After reading the articles included in the systematic review, the following factors were analyzed to determine the level of evidence: study design and selection, detection, loss, and reporting and information bias. The summary of the risk of bias analysis for each of the included articles is shown in All the articles analyzed presented uncertain detection bias, since they did not explicitly inform the blinding method of the study in the methodology.Mack I et al.  was the In the case-control of Rand CSW et al. , it was All 4 cross-sectional studies showed high reporting bias due to the use of self-reported questionnaires without following the sample to re-evaluate results. However, all showed low information bias. In addition, the loss bias was classified as low due to the lack of follow-up in cross-sectional studies. Larsen JK et al.  showed uAll 8 cohorts had low reporting bias and 6 of them had low selection bias. In addition, one study stood out for its low loss and information bias: Scholtz S et al.  maintainAll included studies were complete and had been published. Doubts about the available data were supplemented by contacting the respective authors. The demographic characteristics collected are shown in 2 (range 44.30–54.10) and 35.6 kg/m2 (range 26.80–45.40).All studies elected a total of 5774 participants. Considering that Colles SJ et al.  partiallOf the 14 selected articles, 5 (35.71%) were recently published—later than 2015, 4 (28.57%) studied more than one type of bariatric surgery, and 7 (50%) applied more than one method of assessing eating disorders.Only three articles documented detailed data on the postoperative development of new types of eating disorders for previously healthy patients or those with another eating disorder. Hsu LKG et al. presented the general psychiatric history of the sample and classified it according to the types of eating disorders-or their absence-before and after surgery: 2 patients with a history of NES converted their disorder to BED (Binge-Eating Disorder) or BN (Bulimia Nervosa) after surgery . Luiz LBTwo articles presented only symptomatologic data or referring to questionnaire scores instead of data referring to the incidence of eating disorders diagnosed at some point in the study, either in the pre or postoperative period. In Powers PS et al., the frequency of vomiting episodes was reported by patients in the postoperative period—46 occasional (79%) and 19 weekly (33%) . De ZwaaOnly two studies used a differentiated methodology from the others to analyze the impact of postoperative eating disorders. Larsen JK et al. used 3 different groups, preoperative period/pre-surgery, short-, and long-term after surgery, establishing the incidence of BED in each  . Howeverp < 0.001). For all studies, the overall prevalence of eating disorders was 7.83% (95% CI = 4.30–11.37%). Three studies showed values lower than 5% , Smith KE et al. presented a lower confidence interval than the others (95% CI = 2.30–4.79%) [The results of the meta-analysis  demonstr0–4.79%) ,24,25.p = 0.081) and prevalence values between 3.33–7.58%. In addition, the studies of the AGB group (p = 0.048) were homogeneous, with prevalence values between 3.39% and 17.24%. Finally, Rand CSW et al. did not specify the type of surgery performed, being classified as “other”, with a prevalence of 27.03% [In the stratified analysis by surgery type, only Mack I et al.  presentef 27.03% .A funnel-plot was constructed considering all studies of the meta-analysis to assess publication bias . Three sThe types of evaluations found in this study were the following psychiatric questionnaires to assess the development of eating disorders and their variations applied to the mentioned participants: (1) Eating Disorders Examination (EDE), (2) Three Factor Eating Questionnaire (TFEQ), (3) Medical Outcomes Study Short Form-36 Health Survey (SF-36), (4) Binge-Eating Scale (BES), (5) Eating Disorder Inventory (EDI), (6) other methods.The “other methods” group includes tools that were referred less than 3 times throughout the studies: Structured Interview for Eating Disorders, Patient Health Questionnaire, Dutch Eating Behavior Questionnaire, Dutch Fat Consumption Questionnaire, Satiety-Questionnaire, Obesity Psychosocial State Questionnaire, Questionnaire on Eating and Weight Patterns, Cancer Council Victoria Food Frequency Questionnaire, Beck Depression Inventory, Multidimensional Body Self-Relations Questionnaire, Self-evaluation of LOCE and BED, Alcohol Use Disorders Identification Test, Binge Scale Questionnaire, Eating Attitudes Test, Bulimia Cognitive Distortion Scale, Body Parts Satisfaction Questionnaire, Structured Clinical Interview, Structured Interview for Anorexia and Bulimia nervosa.EDE is a tool to help diagnose eating disorders in general. It addresses various food and self-image issues  through self-reported questionnaires and criteria . Berg K.The TFEQ is a scale that measures three types of eating behavior: cognitive restraint, uncontrolled eating and emotional eating. A psychometric analysis carried out in 2009 found reliability in the questionnaire  and, a dSF-36 addresses issues of quality of life and physical and mental health factors. Higher scores indicate healthier results. Although these factors are not specific for eating disorders, the tool has a significant consistency, reliability, and validation ,32.BES is a specific scale to measure binge eating behavior that can be used before and after bariatric surgery. Its isolated use is not sufficient for the diagnosis of BED, being only a clinical aid. In addition, a significant number of false-positive results should be considered for screening the disease in candidates for surgery .EDI is an extensive tool composed, in its most recent version (EDI-3), by 91 questions that quantify eating behaviors and assist in the diagnosis of eating disorders. Its most recent version was evaluated with excellent sensitivity and specificity, good discriminatory validation, and satisfactory consistency .All variations and translations of these tools were considered during our analysis, although they did not receive their own ratings.It is important to note that there is a lack of standardization of several elements that makes it difficult to collect highly reliable data: (1) there is more than one guideline for the diagnosis of eating disorders, DSM   and ICD Bariatric surgery incidence is increasing considerably, making its consequences significant, such as the incidence and recurrence of eating disorders. This systematic review analyzed 14 articles, 7 of which were eligible for meta-analysis, which included data on bariatric surgery and eating disorders in the postoperative period, with different proposals for evaluation methods.The methods analyzed are heterogeneous. The most part of instruments approach the individual in a more generalized way, but there are also significant differences between some of them: the TFEQ identifies three types of pathological eating behaviors only related to restriction and disinhibition ,30; and Parker K. et al. compared the effectiveness of EDE, TFEQ, SF-36 and other evaluation methods in the context of bariatric surgery, both in candidates and in patients . The resDespite this, the studies analyzed used several questionnaires, 5 of which were more significant and prevalent. This heterogeneity implies the absence of a gold-standard method for assessing eating disorders, leaving the examiner to choose the questionnaires. Consequently, during the screening process, many of the excluded studies presented only symptom or score data, without presenting definitive diagnoses, due to the multiplicity of guidelines (ICD and DSM). Thus, it is difficult to identify the disorders and, as a result, to characterize the onset or recurrence of the disorder after surgery.From the studies that presented with definitive diagnoses it was possible to make the qualitative analysis, considering the time of follow-up of the studies and relating them with the results presented. We observed that, regardless of the type of surgery performed, the operation considerably reduces the rates of eating disorders and symptoms. However, in relation to the prolonged postoperative period, some studies have presented conflicting data.Smith KE et al. presented, after 7 years, an increase in the prevalence of disorders and symptoms: from 2.1% to 4% of BED and from 24.6% to 26.4% of LOCE . A hypotIn addition, among the 12 studies that reported any symptomatic data, only Scholtz S et al. did not show symptoms rates higher than the rates of disorders. This may have occurred due to the small sample size (24 patients), which may generate bias due to the select sample of patients who already had symptoms .The articles used for the analysis have a greater amount of data related to symptoms compared to the disorders themselves. This can be attributed to the large number of non-standard questionnaires that were applied. De Zwaan M et al. used two types of questionnaires to assess the presence of BED in its sample, obtaining two different results: 14 patients diagnosed with BED by QEWP (Questionnaire on Eating and Weight Patterns) and 15 by EDE-BSV . Thus, wTherefore, depending on non-standard methods, it was difficult to determine the relation between bariatric surgery and the development of eating disorders. This possibility, however, should not be ruled out, since other types of surgery have already been shown to be associated with the development of mental disorders, such as cardiac surgery, gastrointestinal cancer surgery, or liver transplantation ,43,44.In addition, bariatric surgery, specifically, also has its participation, acting for the development of impulsive disorders, such as alcohol and substance abuse . The patUnlike the relationship shown between bariatric surgery and substance abuse disorders, establishing the same for eating disorders is a difficulty. This was the purpose of this systematic review, for the high prevalence of these disorders in bariatric surgery patients . The SymThe most common limitations reported in the studies analyzed involve the use of a cross-sectional study design; the total duration of the study; the sample size and the use of self-reported questionnaires .Despite the limitations, some studies have presented different methodologies. Hsu LKG et al., Luiz LB et al. and Kalarchian MA 2016) et al. provided data on the incidence of eating disorders in the postoperative period, which is necessary for an adequate assessment of the relationship between bariatric surgery and the development of these disorders ,20,21. F16 et al.In order to better assess the relation between bariatric surgery and the development of eating disorders, we propose that future studies expose data relating to the pre and postoperative periods, reporting the number of new cases, remissions and conversions. We also suggest the use of randomized, single-arm trials with patients undergoing bariatric surgery. The analysis would be accompanied by a historical control group, with participants eligible for surgery who chose not to perform it. Thus, it would be possible to establish an association or risk factor between surgery and the development of eating disorders.Among the limitations of our systematic review is that we did not consider the possible interference of comorbidities both before and after surgery. In addition, there may be differences in results due to the use of different versions of diagnostic manuals between studies, since the articles analyzed date from 1996 to 2019. There was a lack of standardized instruments validated for bariatric patients, possibly limiting the generalization of our results to the bariatric population.For a greater quality of evaluation, we emphasize the need for further studies to find alternatives to self-reported questionnaires and standardized instruments, in addition to more objective diagnostic methods. Functional magnetic resonance imaging, for example, has proved to identify neural networks involved in eating disorders, although its study is still incipient. More research is needed in this area, but they can be a way to standardize more objective diagnostic methods .The current literature has a greater focus on bariatric surgery as a treatment for obesity, but it has important methodological limitations to evaluate its relationship with the development of eating disorders. An example of this are the studies analyzed, which mostly present only prevalence data. In this review, the total prevalence of eating disorders was 7.83%, based on the 7 studies in the meta-analysis. Considering only BED, which constitutes 6 of these studies, it was 3.81%, being the most significant disorder. This relevance can be attributed to the greater number of studies that approach it, constituting 13 studies out of a total of 14 from our review.Even with postoperative prevalence below 10%, such disorders can significantly influence prognosis and weight loss. However, the role of surgery in the development of eating disorders or in the evolution of pre-existing ones is not well established. Thus, a rigorous and standardized psychiatric assessment is necessary, actively seeking to identify these disorders, which may be against surgical indication. Furthermore, to establish an association and risk assessment, more research is needed in this area, using more appropriate models, as suggested in this review.


Secondary analysis was based on the interRAI Mental Health assessment. The WSIB group differed from all other admissions on almost all characteristics considered. Most notably, depression (65.08 vs. 57.02%), traumatic life events (25.48 vs. 15.58%), substance use (58.02 vs. 46.92%), daily pain (38.31 vs. 12.15%) and sleep disturbance (48.95 vs. 37.12%) were much higher in the WSIB group. Females with WSIB coverage had more depression (74.36 vs. 59.91%) and traumatic life events (30.00 vs. 22.97%), whereas males had more substance issues (63.62 vs. 47.95%). In addition, persons under the age of 55 had more substance issues  and traumatic life events , while those 25–54 years had more daily pain (41.67% vs. <25 = 3.77% and 55 ± 34.23%) and sleep disturbance (50.74% vs. <25 = 33.96% and 55 ± 45.94%). All variables differed significantly by sex and age within the comparison group, though not always following the patterns observed in the WSIB group. Future research examining mental health needs and outcomes among injured workers receiving inpatient psychiatric services is needed, and should take into account sex and age.The rise of mental health issues in the workplace is widely known. Though mental health issues were not covered by the Workplace Safety Insurance Board (WSIB) in Ontario (Canada) until 2018, it was listed as responsible for payment of inpatient psychiatric hospital stays between 2006 and 2016. This population-level observational analytic study compares the clinical and service needs of 1,091 individuals admitted to inpatient psychiatry with WSIB coverage to all other admissions ( That mental health conditions are on the rise is widely known. These affect hundreds of millions of people internationally, and are now recognized among the leading causes of disability . By 2030The rise of mental health issues in the workplace is equally widely known. A systematic review and meta-analysis found that ~18% of workers met the criteria for common mental health conditions , with a lifetime prevalence of just under 30%  collected between January 1, 2006 and December 31, 2016 on all persons admitted to an adult inpatient psychiatric bed or unit. Anonymized data are held on a secure server at the University of Waterloo as part of a data-sharing agreement between the Ontario Ministry of Health and interRAI, a not-for-profit research consortium. Exemption from review for secondary analysis of anonymized data was granted by the Lakehead University Research Ethics Board, as per the Tri-Council Policy Statement .Data are based on the RAI Mental Health assessment –12, whicAdmission assessments were completed on 449,128 unique individuals in the study period; if individuals had more than one admission, the most recent assessment was used. The group of interest are workers who experience mental health issues, defined as those for whom WCB/WSIB was listed as a source responsible for payment. The comparison group represents all other admissions.Items related to personal information  and stay  were used, as were DSM-V diagnostic categories . All embedded scales available in the assessment were used to describe clinical characteristics: Activities of Daily Living Hierarchy (ADLH) , AggressJust as scales represent the combination of items to provide information on overall status in different areas, items are also combined to flag issues. These are called clinical assessment protocols (CAPs), and identify needs related to safety , social life , economic issues , autonomy , and health promotion  , 13, 20.t-test, ANOVA). Given the very large number of admission assessments, a strict Type I error rate was used ; only findings that meet this criteria are described. The same reporting format is used for all results: (a) description of between group differences, (b) description of within-WSIB group differences by age and sex, and (c) description of within-Other group differences by age and sex that differ from what was found in the WSIB group. As such, within-Other group differences by sex and age that are not described are similar to those within the WSIB group.The prevalence of persons admitted with WSIB coverage is shown annually and overall. Descriptive statistics  inform on all variables, and relevant tests of significance were used to report on differences within and between groups , equal proportions lived alone and the majority lived in a private home. However, more people in the WSIB group were married, lived with a spouse and other(s), whereas more in the other group had never been married, lived with non-relatives or other relatives, and had been admitted from an acute or other setting. The WSIB group was less often admitted for all reasons listed, with one exception: this group had more admissions due to problems with addiction or dependency. The mean length of stay was about 4 days, and did not significantly differ between groups .The WSIB mean age was higher .p = 0.02), mania (p = 0.17), social withdrawal (p = 0.10), and ADL impairment (p = 0.09). A higher proportion of those in the WSIB group had substance-related, mood, and anxiety disorders, whereas schizophrenia/psychotic disorders were more prevalent in the other group. More persons in the WSIB group exceeded the cut-off scores for depression, possible substance problem, and pain; moderate or worse cognitive impairment, severe aggression, and positive symptoms were more common in the other group.Differences existed between the two groups for all but personality disorder diagnosis  and scales . More persons in the youngest group had schizophrenia/psychotic disorder, severe aggression, possible substance problem, and positive symptoms, while more in the 25-54 years group had diagnoses related to substances, mood, and anxiety disorders and exhibited signs of daily pain. The oldest most often had moderate or worse cognitive and ADL impairment. Age differences did not exist for personality disorder diagnosis (p = 0.21), depression (p = 0.06), social withdrawal (p = 0.47), and mania (p = 0.49).In the WSIB group, more females had a mood disorder diagnosis and exceeded the cut-offs for depression and social withdrawal, whereas more males had a substance-related diagnosis and exceeded the cut-off for possible substance problem. There were no sex differences for the other diagnostic categories , social relationships (p = 0.52), interpersonal conflict (p = 0.07), exercise (p = 0.71), and falls (p = 0.09).CAPs for traumatic life events, substance use, weight management, sleep disturbance, and pain were more often triggered in the WSIB group; harm to others, self-care, supports for discharge, criminal activity, personal finances, education and unemployment, control interventions, medication management and adherence, rehospitalization, and smoking were more common in the comparison group. There were no differences for suicidality and purposeful self-harm . The youngest group more frequently triggered almost all CAPs, including harm to others, suicidality and purposeful self-harm, self-care, criminal activity, personal finances, education and employment, medication management and adherence, rehospitalization, and substance use. Traumatic life events, smoking, sleep disturbance, and pain were most common among those aged 25 to 54 years. There were no age differences for social relationships (p = 0.20), support for discharge (p = 0.09), interpersonal conflict (p = 0.06), control interventions (p = 0.06), weight management (p = 0.30), exercise (p = 0.26), and falls (p = 0.20).Females in the WSIB group more often had needs related to suicidality and purposeful self-harm, social relationships, interpersonal conflict, and traumatic life events. Males more often triggered CAPs for criminal activity, smoking, and substance use. There were no differences by sex for all other CAPs . More specifically, they had more needs related to safety , social life , economic issues , and autonomy , and displayed more severe aggression, positive symptoms, and issues with substances. While in and of itself, it is not surprising that the youngest group would more often experience some of these issues , it is somewhat surprising that they are experiencing higher rates of  workers , to our The interRAI instrument contains over 300 items on the strengths, preferences, needs, and service use in inpatient psychiatry, but it does not cover work-related disability specifically. As such, it does not provide information on the timing or nature of the work-related injury. Similarly, the instrument assesses for presence of traumatic events, depression, self-harm, substance us, and pain, for example, but does not have specific information on the timing of onset. Intersectoral collaboration is needed to develop mechanisms to link WSIB and inpatient psychiatry data to better understand the mental health needs of injured workers, and to explore the causal pathways involved.A major strength of this study is the scope of variables included. The instrument's items, scales, and CAPs have demonstrated reliability and validity, and allow researchers to analyze a range of characteristics, thus providing a comprehensive description of the study population. A second strength is the population-based nature of the data. All persons admitted into a psychiatric facility or psychiatric unit in a hospital are required to be assessed with this instrument. Therefore, the entire population of interest is included in this study. A limitation, however, is that there is no way to know why the person was covered by the WSIB, nor when this occurred. They could have had a physical injury or a severe traumatic experience at work that resulted in work disability, which could have happened 5 years ago or 1 year ago. As stated previously, future studies linking WSIB claims and interRAI inpatient psychiatry population data are recommended. This will become increasingly important given the 2018 policy change to include coverage for chronic mental stress. Another drawback includes the inability to assess the timeline of the mental illness that led to the admission of the individual at a psychiatric facility and the injury that resulted in WSIB coverage. It would be interesting to see the effects of time off work on mental health illness, as well as the order of events between work injury and mental health illness.This paper provides a description of individuals who had their inpatient psychiatric stay paid for by WSIB  over an 11-year period. Future studies should examine impacts of the policy introduced in January 2018 to determine whether changes are observed in the characteristics and needs of individuals admitted to inpatient psychiatry for (or related to) work-related mental health injuries. Sex- and age-based analyses are also needed to further elucidate the relationships observed. Data linkage with WSIB claims would allow further understanding of the circumstances of the workplace injury, as well as potentially enable understanding of causal pathways between workplace injury and mental health.https://www.cihi.ca/en/access-data-and-reports/make-a-data-request.The data analyzed in this study is subject to the following licenses/restrictions: The data are made available to interRAI Fellows for research use under an existing license agreement interRAI has with the Canadian Institute for Health Information; note that the agreement is for research only, not commercial use. Students working under the supervision of an interRAI Fellow can apply for free access to the data, but are subject to terms of use. As part of interRAI's agreement with the Canadian Institute for Health Information, the data may not be transmitted to third parties; therefore, the data used in this study cannot be made available to others. Those interested in using the data can apply directly to the Canadian Institute for Health Information for access. Requests to access these datasets should be directed to Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The ethics committee waived the requirement of written informed consent for participation.MH conducted the analyses of the data, with input from the LM and VK. MH drafted the initial version of the manuscript, with major writing contributions by the LM. VL finalized the manuscript. All authors contributed to the development of the study topic and design, and provided valuable and important feedback on the manuscript to its completion.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.