Estimates of initiation, cessation, and relapse rates of tobacco cigarette smoking and e-cigarette use can facilitate projections of longer-term impact of their use. We aimed to derive transition rates and apply them to validate a microsimulation model of tobacco that newly incorporated e-cigarettes.We fit a Markov multi-state model (MMSM) for participants in Waves 1–4.5 of the Population Assessment of Tobacco and Health (PATH) longitudinal study. The MMSM had nine cigarette smoking and e-cigarette use states (current/former/never use of each), 27 transitions, two sex categories, and four age categories . We estimated transition hazard rates, including initiation, cessation, and relapse. We then validated the Simulation of Tobacco and Nicotine Outcomes and Policy (STOP) microsimulation model, by: (a) using transition hazard rates derived from PATH Waves 1–4.5 as inputs, and (b) comparing STOP-projected prevalence of smoking and e-cigarette use at 12 and 24 months to empirical data from PATH Waves 3 and 4. We compared the goodness-of-fit of validations with “static relapse” and “time-variant relapse,” wherein relapse rates did not or did depend on abstinence duration.Per the MMSM, youth smoking and e-cigarette use was generally more volatile (lower probability of maintaining the same e-cigarette use status over time) than that of adults. Root-mean-squared error (RMSE) for STOP-projected versus empirical prevalence of smoking and e-cigarette use was <0.7% for both static and time-variant relapse simulations, with similar goodness-of-fit . PATH empirical estimates of prevalence of smoking and e-cigarette use were mostly within the margins of error estimated by both simulations.A microsimulation model incorporating smoking and e-cigarette use transition rates from a MMSM accurately projected downstream prevalence of product use. The microsimulation model structure and parameters provide a foundation for estimating the behavioral and clinical impact of tobacco and e-cigarette policies. Electronic cigarettes (e-cigs) are battery-operated nicotine delivery devices that produce an aerosol for inhalation (“vaping”) but do not burn tobacco. There has been a dramatic increase in the use of e-cigs in the last decade in the US, sparking debates about their potential harms and benefits. E-cig use among US high school students rose from 1.5% in 2011 to 27.5% in 2019, raising concern about a youth vaping epidemic and the potential for nicotine addiction or transition to cigarette smoking among those who otherwise might not use tobacco or nicotine products –6. MeanwTo inform simulation models, estimates of transition rates between various states of cigarette smoking, e-cig use, and dual use–including initiation, cessation, and relapse–are needed. These estimates, themselves, may also illuminate associations between e-cig use and smoking initiation, cessation, and relapse. Transition modeling is increasingly being used to evaluate secular trends and policy impacts. There are some published statistical models of tobacco and nicotine product transition rates –16. HoweWe sought to estimate individual-level behavioral transitions, among both youth and adults, between each combination of current, former, and never smoking and e-cig use states. We also aimed to validate a microsimulation model of tobacco smoking and e-cig use using these transition estimates as input parameters, determining how accurately the transition estimates would project the subsequent prevalence of smoking and e-cig use. If validated, individual-level behavioral transitions can be used in simulation models to project longer-term clinical and economic outcomes associated with smoking, e-cig use, and associated regulatory policies, which is an eventual goal of this methodologic development.We analyzed transitions in cigarette smoking and e-cig  use in the Population Assessment of Tobacco and Health (PATH) Study, a nationally representative longitudinal survey of the US noninstitutionalized population ages 12 years and older –19. WaveThe PATH data include weights to adjust for bias introduced by complex survey design and non-response. We weighted responses with Wave 4 and 4.5 longitudinal weights, as recommended by the PATH investigators . We accoWe merged data across youth and adult questionnaire responses from Waves 1–4.5 and classified participants as current, former, or never cigarette smokers, and as current, former, or never e-cig users . We consContinuous time Markov multi-state models are suitable for estimating, from longitudinal data, the hazard rates at which individuals transition between pre-determined states. These models do not require exact transition times to be observed and allow multiple transitions to occur consecutively between observations, or consecutively over several observations . In thisThe multi-state model directly estimates transition hazard rates, which measure the instantaneous likelihood of each allowable transition. Annual cumulative transition probabilities, also derived from hazard rates, estimate the proportion of people in a prior state expected to be in a subsequent state one year later, where some individuals will undergo multiple transitions within the year . We presThe Simulation of Tobacco and Nicotine Outcomes and Policy (STOP) microsimulation model includes age- and sex-stratified probabilities of tobacco and nicotine product initiation, cessation, and relapse. We previously validated the STOP model with tobacco smoking data and mortality data from the National Health Interview Survey and the linked National Death Index . SubsequWe populated STOP’s initial age, sex, and cigarette smoking and e-cig use distributions with the corresponding estimates at PATH Wave 2 . However, smoking relapse probabilities are known to depend on duration of abstinence , 27. It The protocol for this study was approved by the Mass General Brigham Institutional Review Board, which waived the need for informed consent as all data were fully anonymized and publicly available.The weighted PATH survey response rate among adults in Waves 1–4 was 73.5% and among youth in Waves 1–4.5 was 74.6% . Among tWe applied the newly derived transition probabilities (converted from hazard rates) as input parameters in the STOP model and compared STOP model output for cigarette smoking prevalence and e-cig use prevalence to that reported in subsequent waves of PATH. Root-mean-squared error for model-projected versus empirical prevalence of smoking and e-cig use was less than 0.7% for both the static relapse and time-variant relapse STOP simulations, with no significant difference in goodness-of-fit between the two . The largest contributors of error were, in the static relapse simulation, the proportion of former smoker/never e-cig users at Wave 4 , and, in the time-variant relapse simulation, the proportion of former smoker/never e-cig users at Wave 3 . Empirical estimates of prevalence of smoking and e-cig use from PATH were mostly within the margin of error estimated by both static relapse and time-variant relapse simulations  that aims to curb e-cig use may be beneficial for youth public health but harmful for adult public health if it deters adult cigarette smokers from switching to a potentially less harmful product.This analysis includes several aspects chosen to maximize its utility in parametrizing a tobacco and nicotine policy microsimulation model. First, as per PATH definitions, we focused on established rather than experimental cigarette smoking and e-cig use, which better aligns with longer-term health risks , 13, 28.Youth e-cig uptake itself is noteworthy. We found that youth never smoker/never e-cig users were less likely than their adult counterparts to start smoking but more likely to start using e-cigs. Youth current e-cig users were very likely to quit, but equally likely to subsequently restart e-cig use even if they did quit, a dynamic that can only be captured by longitudinal data such as those in the PATH Study. Further study is warranted to investigate if this instability is indicative of casual use that is dependent upon social context  or is indicative of nicotine addiction and difficulty maintaining abstinence after cessation attempts.This study cannot determine causal relationships. However, it adds nuance to the evidence of the associations between e-cig use and smoking transitions. In contrast to prior studies, we separated never smoker/current and former e-cig users from former smoker/current and former e-cig users, and could therefore limit the confounding associated with smoking history –13.As the PATH Study collects additional waves of data, future models may incorporate the number of previous quit attempts and other sociodemographic variables as covariates to adjust for confounding. We accounted for age and sex. The magnitude and frequency of meaningful differences between transition rates was greater when comparing youth and adults than when comparing women and men.This work has several limitations. The majority of observations in PATH Waves 1–4.5 predate the rapid rise of pod-based e-cigs such as JUUL in late 2017 and 2018, the EVALI reports in 2019–2020, and the COVID-19 pandemic that emerged in the US in 2020. Considering the higher nicotine content and more rapid nicotine absorption of JUUL products relative to other e-cigs, and the collateral consequences of the EVALI outbreak and the COVID-19 pandemic, it is plausible that the dynamics of smoking and e-cig use may have changed since 2018. Further, it is plausible that transition rates, such as the rate of e-cig initiation among e-cig naïve youth, may have changed over the course of Waves 1–4.5 in the context of time-varying regulatory policies and market changes. However, results of the internal validation suggest that such a change is not significant enough to reduce the predictive power of our model. Our validation exercise was limited by dependence on a subset of PATH data as the validation set. Questions about tobacco smoking and e-cig use differ across cohort studies, complicating comparisons especially of dual use states and limiting the ability to perform meaningful independent external validation.Our estimates account for age and sex but not for race, socio-economic status, level of nicotine dependence, household member and peer use of cigarettes and e-cigs, exposure to product advertising, and local tobacco regulatory policies, which may be associated with differences in cigarette smoking and e-cig use behaviors. Our inclusion of many states and transitions made the computational intensity of including such variables infeasible. Nonetheless, our results provide background age- and sex-based transitions, from nationally representative PATH data, that can be used to evaluate the overall impact of policies. Additionally, our estimates can be integrated with race/ethnicity, education, and income hazard ratios estimated by others, in models with fewer states and transitions, to project smoking and e-cig use stratified by these characteristics . Our defIn summary, we used longitudinal data around tobacco cigarette smoking and e-cig use among youth and adults from PATH Waves 1–4.5 to develop a continuous-time Markov multi-state model with nine cigarette smoking and e-cig use states, 27 allowable instantaneous transitions, two sex categories, and four age categories. We subsequently validated a tobacco and e-cig outcomes and policy microsimulation model parametrized with these newly derived transition estimates. The microsimulation model predicted prevalence of cigarette smoking and e-cig use over a 2-year time horizon with RMSE <0.7% compared with empirical data. In future work, simulation models can be populated with these estimates along with additional data on smoking and e-cig use-stratified mortality, noncommunicable disease incidence, quality of life, and health care costs to predict the outcomes and cost-effectiveness of proposed policy interventions targeting smoking and e-cig use .S1 File(DOCX)Click here for additional data file.


Psidium guajava L. (guava) leaves have demonstrated their in vitro and in vivo effect against diabetes mellitus (DM). However, there is a lack of literature concerning the effect of the individual phenolic compounds present in the leaves in DM disease. The aim of the present work was to identify the individual compounds in Spanish guava leaves and their potential contribution to the observed anti-diabetic effect. Seventy-three phenolic compounds were identified from an 80% ethanol extract of guava leaves by high performance liquid chromatography coupled to electrospray ionization and quadrupole time-of-flight mass spectrometry. The potential anti-diabetic activity of each compound was evaluated with the DIA-DB web server that uses a docking and molecular shape similarity approach. The DIA-DB web server revealed that aldose reductase was the target protein with heterogeneous affinity for compounds naringenin, avicularin, guaijaverin, quercetin, ellagic acid, morin, catechin and guavinoside C. Naringenin exhibited the highest number of interactions with target proteins dipeptidyl peptidase-4, hydroxysteroid 11-beta dehydrogenase 1, aldose reductase and peroxisome proliferator-activated receptor. Compounds catechin, quercetin and naringenin displayed similarities with the known antidiabetic drug tolrestat. In conclusion, the computational workflow showed that guava leaves contain several compounds acting in the DM mechanism by interacting with specific DM protein targets. Diabetes mellitus (DM) is one of the most serious and increasing health disorders in the world. Today, this disease affects 382 million people, and it is expected that the number of affected could rise to 592 million people by 2035 . The cauPsidium guajava L. have been widely employed as hypoglycaemic agents . The. (2013) . m/z, fragments, molecular formula, score and error (ppm).The phenolic compounds have been identified according to the data previously published  and conshttp://bio-hpc.ucam.edu/dia-db (accessed on 2-December-2021)) to predict the antidiabetic activity of compounds. DIA-DB uses two different approaches, namely LBVS and SBVS.We used the DIA-DB  web servLBVS methods exploit all existing available information  about known active and inactive compounds. DIA-DB exploits shape information for checking existence in the database of compounds similar to the ones used in the input query. For that purpose, DIA-DB uses internally the shape complementary tool weighted Gaussin algorithm (WEGA) .SBVS identifies compounds which can bind to a target protein with high affinity. This is achieved by determining the optimal binding position by docking each query molecule to a database of protein targets involved in diabetes and available in DIA-DB and then ranking the compound–targets interactions according to their estimated binding affinity values, namely docking scores. The SBVS protocol implemented in DIA-DB employs the Autodock Vina docking program . Vina fiBased on the obtained SBVS and LBVS DIA-DB predictions, we checked a posteriori the existence of bibliographical references confirming our predictions.


Immunogenic cell death (ICD) is a unique phenomenon that can trigger comprehensive, adaptive immune responses through damage-associated molecular patterns, offering a promising avenue for tumor immunotherapy. However, the role of ICD-related genes and their correlation with endometrial carcinoma (EC), the most prevalent gynecologic malignancy, remains unclear. This study examined genetic, transcriptional, and clinical data of EC obtained from the Cancer Genome Atlas database. Unsupervised clustering analysis was utilized to identify distinct ICD clusters based on the expression of ICD-related genes. Regarding the different clusters, their survival analysis, assessment of the immune microenvironment, immune cell infiltration, immune checkpoint analysis, and tumor mutation burden analysis were performed. Furthermore, an ICD risk signature was established using univariate Cox regression and least absolute shrinkage and selection operator analysis. The Chi-square test was employed to investigate the relationship between the ICD score and clinical features. Multiple computational analytical tools were used to assess immune annotation, somatic mutations, tumor mutation burden, and response to immunotherapy and chemotherapy drugs in different ICD score groups. Two ICD clusters were identified, indicating that the ICD-high cluster was associated with improved prognosis, abundant immune cell infiltration, and enrichment of pathways related to immunologic activation. Moreover, the ICD risk signature showed predictive value for the immune microenvironment, immunotherapy response, chemotherapy susceptibility, and prognosis in EC. Our findings offer novel insights into personalized treatment strategies for EC patients. Endometrial hyperplasia serves as a precursor to EC. Recent reports suggest that the activity of the TRPM7 ion channel may indicate the progression from endometrial hyperplasia to early-stage EC. While early-stage EC cases can often be successfully treated through a hysterectomy, advanced EC poses significant challenges with a bleak prognosis and a lack of reliable markers and effective treatment options. However, advancements in targeted chemotherapeutic approaches, such as immunotherapy targeting immune checkpoints and novel chemotherapy strategies, have emerged based on the molecular understanding of EC. Therefore, further exploration of molecular mechanisms through ongoing studies will enhance treatment strategies for advanced EC and improve the prognosis prediction for EC patients.An estimated 417,000 new cases of endometrial cancer (EC) were reported worldwide in 2020, which is the sixth most common cancer in women.,4 ICD can activate immunogenic signals called damage-associated molecular patterns (DAMPs) in dead tumor cells. DAMPs include cytokines , nucleic acids pathway, high mobility group box1, and extracellular release of immunostimulatory factors, such as adenosine triphosphate.,6–8 ICD and its association with DSMPs may reportedly improve the outcomes of various cancers. For example, during ICD, rapid production of type I IFN is stimulated by malignant cells via activation of the endosomal pattern recognition toll-like receptor 3 (TLR3). On the other hand, secreted IFN can bind to IFN-α and IFN-β receptors expressed on the surface of cancer cells to trigger the release of chemokine (C-X-C motif) ligand 10, which mediates chemotaxis and immune stimulation to cancer cells. As mentioned above, the ICD process of DAMPs induces the secretion of immunogenic signals in tumor cells, which can also activate dendritic cells to attenuate immunosuppression in tumors. Previous studies showed that some chemotherapeutic agents, such as cyclophosphamide, bleomycin, doxorubicin, epirubicin, mitoxantrone, idarubicin, and bortezomib could induce ICD and stimulate the anti-tumor immune response. However, the role of ICD-related genes and their correlations with EC remains unclear.Immunogenic cell death (ICD) is a type of regulated cell death triggered by chemotherapeutic agents, radiotherapy, oncolytic viruses, physicochemical therapies, and photodynamic therapy.This study analyzed the genetic, transcriptional, and clinical data of EC collected from the Cancer Genome Atlas (TCGA) database. This study systematically examined the correlation between genes related to immune cell dysfunction (ICD) and the clinicopathological features of patients with EC. As a result, 2 ICD clusters were identified, along with an ICD risk signature. Utilizing these ICD clusters and the ICD-related risk signature, we identified biomarkers associated with ICD, enabling the prediction of the immune microenvironment status, chemotherapy susceptibility, and prognosis of EC patients. The results of this study provide new insights into individualized treatment strategies for EC patients.https://portal.gdc.cancer.gov/). Those data were randomly separated into the training or test group at a ratio of 5:5. A total of 34 immune cell dysfunction-related genes (ICDRGs) were identified by the previous studies . In this study, all information and clinical matrix involved were downloaded from the public database. Approval from the ethics committee and written informed consent from patients were not required.The RNA sequence data and the interrelated clinical information of 541 EC samples, such as therapeutic information, somatic mutation data, copy number variation data, and information about 23 normal human endometrial samples, were collected from the TCGA  in the training set. Further analysis using the least absolute shrinkage and selection operator Cox regression model and the R package of “clusterSur” narrowed the range of candidate genes, followed by the multivariate Cox analysis to establish a signature. The TCGA-listed samples (n = 541) were divided into a training group (n = 272) and a test group (n = 269), with the propensity score matching model. According to the median risk scores, all samples were also divided into low-risk (ICD score < median) and high-risk (ICD score > median) groups. The Kaplan–Meier survival curve was performed to compare the difference in survival outcomes between the 2 groups. The R packages of “risk plot” and “ROC” were used for risk factor analysis and receiver operating characteristic curve analysis, respectively. Finally, the survival rates of both high-risk and low-risk groups were also evaluated. In addition, time-dependent ROC curves for 1-, 3- and 5-year survival were used to assess the ability of the signature to predict the prognosis of the EC patients in both high-risk and low-risk groups.A unitive signature was developed to predict overall survival based on clinically significant characteristics and the ICDRGs in EC. First, the univariate Cox regression analysis of all possible ICDRGs was performed to identify the prognostic genes with a significant prognostic value . The univariate and multivariate analyses were performed to determine if the risk score could be used as an independent prognostic factor, that is, independent of other clinical and pathological factors. A stratum analysis was also performed to determine whether the ICD score retained its predictive power in the different subgroups.https://cistrome.shinyapps.io/timer/), CIBERSORT (https://cibersortx.stanford.edu/. stanford.edu/), CIBERSORT-ABS, QUANTISEQ, MCPcounter, EPIC, and XCELL (https://xcell.ucsf.edu/) were used to estimate the proportion of tumor-infiltrating immune cells in the high- and low-risk groups. Single-sample gene set enrichment analysis (ssGSEA), a variation of the gene set enrichment analysis (GSEA) algorithm, was used to evaluate the association of 28 infiltrating immune cell types in the high- and low-risk groups. Spearman’s correlation analysis was used to evaluate the differential expression of the immune-checkpoint molecules between the high- and low-risk groups. The MHC genes that belong to the immunoinhibitory and immunostimulatory genes are involved in the tumor evasion mechanisms. The analysis data of MHC genes were obtained from the previous study. Calculation of immunophenoscore (IPS) was performed using the Cancer Immunome Database (https://tcia.at/home). Based on the status of cytotoxic T lymphocyte antigen 4 (CTLA-4) and programmed death-1 (PD-1) expression, the difference in IPS between the high- and low-risk groups was compared.To reflect the immune status of the analyzed samples, the difference in several scores between the high- and low-risk groups was graphically displayed as a violin chart by the ESTIMATE algorithm, including the immune status score, microsatellite instability score, dysfunction score, exclusion score, tumor immune dysfunction score, and exclusion total score. The R package of “immunocor” was used to quantify the infiltration of immune cells in both high- and low-risk groups. Several programs, such as TIMER  of each patient was calculated, and the comparison of the difference of TMB between the high- and low-risk groups was implemented. Survival analysis was also completed according to the TMB score of the EC patients.The chemotherapy response of the EC patients to chemotherapeutic agents was estimated using the Genomics of Drug Sensitivity in Cancer database. The half maximal inhibitory concentration (IC50) of anticancer drugs in the high- and low-risk groups was determined by the R package of “oncoPredict.”P < .05.R survival package was used for survival analysis, and the survival rate of each group was tested by the Log-Rank test hypothesis. The Kruskal–Wallis test was used to compare the difference in data of two or more groups. The Wilcoxon test was also used to compare the difference in data between the 2 groups. The Kaplan–Meier method was used to generate the survival curve of each subgroup in the data set. The chi-square test was used to compare the difference in the mutation frequency between the ICD score subgroup and somatic cells. Spearman correlation analysis was used to calculate the correlation coefficient. All statistical analyses were performed using R versions 4.1.0 and 4.0.0. The statistical significance was set to A total of 11 downregulated ICDRGs and 10 upregulated ICDRGs were found in the samples of EC  and cluster B , based on the expression levels of ICDRGs  and cluster ICD low (97.23%). However, the mutational probability of PTEN in the cluster ICD-high group (81%) was higher than in the cluster ICD-low group (45%) . Moreover, the survival of the EC patients in the high-TMB group was better than that in the low-TMB group -related genes play a crucial role in regulating immune responses,,24 our study also included comparing the expression level of HLA-related genes between different groups. Results showed that the expression of most HLA-related genes was upregulated in the cluster-high ICD group. In addition, previous studies revealed that the expression of immune-checkpoint genes is closely associated with the benefits of immunotherapy.–27 The expression levels of common immune-checkpoint genes in the 2 cluster subgroups were also compared, which showed a higher expression of the checkpoint genes in the cluster-high ICD group. Notably, several well-known immunotherapy targets, such as PDCD1 (PD-1), CD274 (PD-L1), and CTLA4, were included in the cluster-high ICD group. All the results indicated that the high ICD cluster is associated with immunotherapy responses, which might be a potential biomarker for predicting the response to an ICI.On the other hand, TMB is closely associated with the benefit of immunotherapy in cancer patients. Similar results were also included in our study. Interestingly, we found that ICD scores still affected the survival of EC patients when their TMB score was the same. Besides, most tumor patients with MSI-H had an improved prognosis and a better anti-tumor immune response than those with microSatellite Instability- low (MSI-L) or MSS. Our study observed a higher rate of MSI-H in the low-risk ICD group, which had a better OS and immunotherapy sensitivity than those in the high-risk ICD group, consistent with other reports. Furthermore, there was a negative correlation with the IC50 of tamoxifen, cisplatin, 5-fluorouracil, axitinib, olaparib, niraparib, oxaliplatin, and leflunomide in the low-risk ICD group. We also found higher IPS scores in the low-ICD score group. Consequently, cancer patients in the low-ICD risk group might be the appropriate candidates for receiving the combination of immunotherapy and chemotherapy.Next, our study developed an ICD risk signature, which was confirmed to be an independent predictor for the prognosis of EC patients. Compared with the low-risk ICD group, there were significant variations in overall survival, immunization landscape, mutations, TMB, chemotherapy efficacy, and chemotherapeutic drug resistance in the high-risk ICD group. While a low-ICD risk score was significantly associated with a better OS and related to high levels of immune infiltration, lower immune, stromal, and TME scores, suggesting there is more opportunity to trigger an immune response by the tumor in the low-ICD risk group. It has been reported that low TMB is associated with poorer survival.In conclusion, 2 ICD clusters and an ICD risk signature were identified in this study. The association of ICD subtypes with multiple factors was also investigated, such as variations in the immune microenvironment, immunotherapy response, chemotherapy susceptibility, and prognosis for EC. Our findings provide new insights into the individualized treatment strategies for EC patients.We thank Medjaden Inc. for the scientific editing of this manuscript.Data curation: Yongjin Luo.Project administration: Xiaoxia Hu.Supervision: Xiaoxia Hu.Writing – original draft: Zhen Liu, Linhong Su.


Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are adverse reactions that affect the mucocutaneous surfaces by causing necrosis and detachment of the epidermis. The difference between SJS and TEN is in the percentage of the body surface area (BSA) affected. TEN is known to affect greater BSA than SJS. The pathogenesis of SJS/TEN is attributed to drug-specific cell-mediated cytotoxic reactions that directly and indirectly lead to keratinocyte apoptosis through mediators. Clinical presentation begins with influenza-like symptoms, with the disease affecting the skin, oral, ocular, and urogenital regions most frequently. Although SJS/TEN is mainly due to various drugs, infection and vaccination can also induce SJS/TEN. This review outlines a compilation of all drugs implicated in SJS/TEN cases based on studies, mainly in case reports and other study types. Drug classes implicated in SJS/TEN cases include antibiotics, anticonvulsants, antineoplastics, analgesics, and diuretics, among others. There is no fully established diagnostic modality for SJS/TEN; treatment is done mainly by withdrawing the offending agent. In addition to withdrawing the offending agent, a multidisciplinary care team is essential in managing these patients. Several pharmacologic modalities have also been proposed in treatment, but there is still insufficient evidence for the efficacy of one against the other. Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are adverse reactions that affect the mucocutaneous surfaces, causing necrosis and detachment of the epidermis. SJS and TEN are known to be on a similar disease spectrum, with the difference being the amount of body surface area (BSA) affected. SJS affects less than 10% of the BSA, while TEN affects greater than 30% of the BSA [The incidence of SJS/TEN varies, but it is extremely low. In the UK, the incidence of SJS/TEN was 5.76 cases per million per year . In FranIn addition to HIV and cancer, genetic predispositions also put one at risk for having SJS/TEN. Patients with human leukocyte antigen (HLA)-B*15:02 and HLA-B*15:11 have an increased risk of SJS/TEN with carbamazepine use, especially in Asian populations -9. HLA-BPathogenesisThe pathogenesis of SJS/TEN has yet to be understood entirely. Research has shown that SJS/TEN is due to a cell-mediated cytotoxic reaction through a CD8+ cell that leads to keratinocyte apoptosis. These cell-mediated T-cell reactions are drug-specific and directed against the native form of the drug. It is proposed that the different drugs that cause SJS/TEN bind with the major histocompatibility complex (MHC) class I and T-cell receptors (TCR) and lead to the expansion of drug-specific cytotoxic T cells that cause keratinocyte apoptosis both directly and indirectly through the release of cell mediators .As noted, keratinocyte apoptosis is caused by cytotoxic mediated reactions through drug-specific CD8+ cytotoxic cells and natural killer (NK) cells directly and indirectly through mediators. Some of the mediators that have been proposed for these reactions include Fas-ligand, tumor necrosis factor (TNF)-alpha, perforin/granzyme, TNF-related apoptosis-inducing ligand (TRAIL), and granulysin. Granulysin, primarily known to be produced by cytotoxic T-cells and NK cells, has been majorly implicated in SJS/TEN. In a study, granulysin was the most highly expressed cytotoxic molecule through gene expression in SJS/TEN patients. The level of granulysin in blister fluid has also been correlated with the severity of SJS/TEN. When granulysin was decreased, the cytotoxicity was also decreased . AnotherOther studies have also pointed out necroptosis contributes to keratinocyte death in SJS/TEN. The necroptosis process occurred because of the annexin A1 produced by monocytes binding to formyl peptide receptor 1 (FRP-1), leading to SJS/TEN .Clinical presentationSJS/TEN often begins with a prodromal nonspecific symptom that looks like influenza. Symptoms include fever often greater than 38°C, malaise, odynophagia, photophobia, and conjunctival injection. Sometimes, skin involvement is the first manifestation of the disease, occurring with blistering or skin tenderness. Early stages of SJS/TEN cutaneous lesions begin with poorly defined erythematous macules with purpuric centers or diffuse erythema, which could be tender to touch. The lesions are often symmetric and spare the scalp, palm, and soles of the body. It often begins at the face and then spreads to the thorax. It often exhibits a positive Nikolsky sign (slippage of the top layer of the skin from the bottom layer when rubbed). As the disease progresses, vesicles and bullae form on the skin, which later slough off. The degree of skin involvement based on total body surface area (TBSA) is a marker of the disease's severity, so it should be assessed regularly. Mucosal involvement, generally oral, ocular, and urogenital, often occurs in about 90% of patients . Ocular Chronic progression of the disease leads to multiple organ damage. For the skin, it can result in hypo/hyperpigmentation, scarring, and chronic pruritus. Ocular involvement includes dry eyes, symblepharon, corneal scarring, and rarely blindness. Long-term urogenital complications include adhesion formation, urethral stenosis, scarring, and strictures. Bronchiectasis, obstructive disorders, and chronic bronchitis/bronchiolitis are common presentations for the pulmonary system. In addition, long-term psychiatric consequences such as anxiety, depression, and post-traumatic stress disorder have been reported .Due to extensive skin detachment, acute complications include massive fluid loss, infections, electrolyte imbalance, hypovolemic shock with renal failure, pulmonary complications , gastrointestinal complications, and disseminated intravascular complications (DIC).Drug etiologies associated with SJS/TENMycoplasma pneumoniae and herpes simplex virus have been associated with SJS/TEN. Although rare, SJS/TEN has also been reported after vaccinations, especially in childhood [The cause of SJS/TEN is most often due to drugs. It takes approximately four weeks of continuous medication use for SJS/TEN to develop. Additionally, infections like hildhood . In one-AntibioticsAntibiotics are one of the classes of drugs that have been implicated in SJS/TEN. Among the antibiotics, sulfonamides are the ones that have been most implicated. The most common sulfonamide that causes SJS/TEN is trimethoprim-sulfamethoxazole (cotrimoxazole). Attached in Table Anticonvulsants/Antiepileptics DrugsAnticonvulsants have also been implicated majorly in causing SJS/TEN. Various alleles have been suspected to be associated with some antiepileptic drugs (AEDs) in causing SJS/TEN. Carbamazepine-induced SJS/TEN is associated with the HLA-B*1502 allele . OxcarbaBenzodiazepines/Non-benzodiazepinesBenzodiazepines and non-benzodiazepines, which are used to treat many conditions, have also been implicated in SJS/TEN. Chlormezanone was discontinued worldwide in 1996 due to severe and rare side effects of TEN. Table SulfonylureasSome case reports have implicated glipizide and glimepiride in SJS/TEN ,62. HoweDiureticsTable AnalgesicsSome analgesics have been associated with SJS/TEN. This is often common with nonsteroidal anti-inflammatory drugs (NSAIDs). A study that examined a total of 1,868 reports of SJS adverse events associated with five NSAIDs showed that ibuprofen had the highest association with SJS. Ibuprofen also had the highest rate of hospitalization but the lowest fatality rate . Table 6AntidepressantsAntidepressants, which are commonly used to treat depression, have also been associated with inducing SJS/TEN. Table Xanthine Oxidase InhibitorsAllopurinol, a drug used in the first-line treatment of gout, is a common cause of SJS/TEN. A particular case of a 33-year-old male diagnosed with gout and subsequently started on allopurinol began to demonstrate symptoms of TEN after 10 days of use. The patient was also found to test positive for HLA-B*5801 . It is iAndrogenic HormonesAndrogenic anabolic steroids: SJS was reported in the case of a 41-year-old patient abusing anabolic steroids. The patient had been participating in competitive bodybuilding for roughly 20 years while taking varying doses of anabolic androgenic steroids (AAS). Further, the case confirms that the patient was not on other medications besides AAS .Danazol: Other androgenic drugs include danazol, which is like testosterone and can be used to treat endometriosis. Additionally, danazol has been approved as a second-line agent for treating hematologic disorders related to SLE, including thrombocytopenia. Danazol has been implicated in triggering SJS .Antineoplastics DrugsMany antineoplastic drugs have been associated with SJS/TEN cases, as shown in Table Immunosuppressants/ImmunomodulatorsMizoribine: This immunosuppressant is used to treat renal transplant patients, systemic lupus erythematosus (SLE), lupus nephritis, and several childhood nephrotic syndromes such as IgA nephropathy. It has been implicated in one case of SJS, which occurred in a 32-year-old Japanese woman after receiving mizoribine for treating SLE with lupus nephritis for six months .CorticosteroidsCorticosteroids are a mainstay in treating patients with early-stage SJS/TEN. However, it is crucial to know that administering high-dose corticosteroids in certain patients may paradoxically induce SJS/TEN. Some of these patient conditions may act as predisposing factors, including collagen disease, autoimmune disorders, or brain tumors .Antiparasitic DrugsAlbendazole: Albendazole is an antiparasitic drug that can treat hydatid disease and other infections caused by tapeworms. A case report details albendazole causing TEN in a 16-year-old female .Ivermectin: An antiparasitic drug that is FDA-approved to treat a variety of internal nematode infections has been documented in several reports of SJS/TEN . TherefoAntiviral DrugsAntivirals have also been associated with SJS/TEN cases, as noted in Table AntifungalTerbinafine: In one case, terbinafine was known to induce SJS/TEN overlap. The likelihood of this association is weak but possible .AggrenoxAggrenox is a combination drug of acetylsalicylic acid (aspirin) and dipyridamole, which works to inhibit platelet activation and is thus used as an anticoagulant often to reduce the risk of stroke in high-risk patients. At least one case of Aggrenox-induced SJS has been reported. One occurred in a 74-year-old Chinese woman who was switched from aspirin to Aggrenox after she had experienced a transient ischemic attack (TIA) despite treatment with aspirin. She developed acute-onset progressive SJS within 24 hours from the first dose .AntihistaminesA case of SJS/TEN overlap induced by fexofenadine-pseudoephedrine (Telfast-D) was reported in a middle-aged woman on treatment for persistent allergic rhinitis and sinusitis. Fexofenadine, an antihistamine sold under the brand name Allegra, is an over-the-counter drug used to treat allergy symptoms. After two doses, she initially presented with a swollen lip, which progressed to diffuse blistering with a positive Nikolsky sign over the week. She was ultimately diagnosed with SJS/TEN .Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor BlockersThese drugs are rarely reported to be associated with causing SJS/TEN. However, one report mentions the onset of SJS in a patient after being recently prescribed ramipril for hypertension . AnotherAminosalicylatesMesalamine and sulfasalazine, which are known aminosalicylates, have been found to have a causal association with causing SJS/TEN. The estimated absolute risk of four cases per 100,000 users was found to develop SJS/TEN .Other causes that have been implicated in SJS/TEN casesAcetylcysteineAcetylcysteine is an antioxidant and glutathione inducer that is utilized for the treatment of acetaminophen overdose. It has been implicated in a reported case of SJS .Ursodeoxycholic AcidA drug used in the treatment of primary biliary cholangitis, as well as various other cholestatic disorders, has also been rarely implicated in cases of SJS/TEN. One ursodeoxycholic acid (UDCA)-induced SJS/TEN case occurred in a 24-year-old female patient being treated for deranged liver function tests. However, it should be noted that she was also prescribed concomitant omeprazole and domperidone, which also could have contributed to the induction of the severe cutaneous reaction .Strontium RanelateThe anti-osteoporotic agent boasts an excellent safety profile, aside from several cases of inducing SJS/TEN. In one case, strontium ranelate triggered what was diagnosed as mild SJS with a good prognosis in a 70-year-old woman after one month of beginning drug use. Although classified as mild, she exhibited multiple itching erythematous macules and plaques, which progressed to involve the entire body with severe oral and ocular mucosa involvement. Overall, early diagnosis and treatment led to a good prognosis in this patient. Thus, this adverse effect should be considered with this drug use .Other non-therapeutic agents/chemicals in the etiology of SJS include iodine contrast, iopentol, commercial cannabinoid oil, and carbamate -121.Diagnosis and diagnostic markersThere are currently no established diagnostic criteria for SJS/TEN. Diagnosis begins with suspicion in those with fever, flu-like symptoms, or SJS/TEN skin-like changes recently exposed to any drug or offending agent that could have triggered it. Usually, it takes one to four weeks of drug use before developing SJS/TEN symptoms . Skin biAssessment of the likely drug causality is vital in diagnosis, as early withdrawal of the offending agent is a good prognostic factor. Detailed history and clinical insight are required to pinpoint the offending agent that could have triggered the process. An algorithm of drug causality for epidermal necrolysis (ALDEN) has been created to assess drug causality, especially in those taking multiple drugs. The ALDEN score is based on six factors /rechallenge; dechallenge; type of drug (notoriety); other possible etiologic alternatives) . The finRecent research is looking for diagnostic markers to aid in diagnosing SJS/TEN. Markers that have been considered in research include the rapid granulysin test, CCR7, RIP3, galectin-7, granzyme B, and interferon gamma (IFN-y) ,126. HowManagementThe first step in the management of SJS/TEN is dependent on the etiology of the disease. In infectious cases, treatment of the infection is essential. If drugs are the cause, withdrawal of the offending agent or drug is vital as prompt withdrawal decreases mortality . As noteAfter dealing with the primary source of the disease, the main treatment modality is supportive. A multidisciplinary care team is essential as SJS/TEN affects various organs. Regarding the skin, wound care of patients is vital in preventing further infection or complications. Wound care could be achieved through conservative means  or surgical means . No evidence exists for surgical management of wounds above conservative means . Other sSeveral pharmacologic modalities have been proposed to treat SJS/TEN. Some are systemic corticosteroids, intravenous immunoglobulin (IVIG), cyclosporine, plasmapheresis, and anti-TNF agents. Some evidence has shown both positive and negative plausibility to their use . HoweverIn conclusion, SJS/TEN are often adverse reactions that affect the skin and mucous membranes of the mouth, eyes, and urogenital region. The difference between SJS and TEN is in the percentage of BSA affected. The pathogenesis of SJS/TEN is due to a cell-mediated cytotoxic reaction that leads to keratinocyte apoptosis through direct and indirect mediators. A major mediator that has been implicated is granulysin. Drugs often cause SJS/TEN, but other etiologies like infections have been suggested. One-third of cases of SJS/TEN are also known to be from idiopathic causes. There is no established diagnostic modality regarding diagnosis, but apoptotic keratinocytes with inflammatory infiltrate are the key findings. Management of SJS/TEN is generally the removal of the precipitating agent and supportive treatment.


Burkitt’s lymphoma (BL), an aggressive kind of non-Hodgkin’s B-cell lymphoma. On the other hand, appendiceal carcinoid tumors are uncommon neuroendocrine neoplasms.The authors report a case of a 15-year-old Syrian adolescent who was admitted to our hospital due to a persistent, severe generalized abdomen pain accompanied by nausea, vomiting, loss of appetite, and inability to pass stool or gas. An abdominal radiograph revealed dilated intestinal loops with air-fluid levels. The patient underwent emergency surgery through which a retroperitoneal mass was removed as well as part of the ileum and the appendix. The final diagnosis was consistent with intestinal BL associated with an appendiceal carcinoid tumor.The correlation between gastrointestinal carcinoids and other types of tumors was frequently reported. However, there have been few reports of carcinoid tumors being associated with lymphoreticular system cancers. BLs were classified into three variants: endemic, sporadic, and acquired immunodeficiency-associated BL while appendiceal neuroendocrine tumors were classified as the following: well-differentiated neuroendocrine tumors with benign or uncertain malignant potential; well-differentiated neuroendocrine carcinoma with low malignant potential; and mixed exocrine-neuroendocrine carcinoma.Our article demonstrates an unusual association between BL and an appendiceal carcinoid tumor that highlights the significant role of histological and immunohistochemical staining in confirming the diagnosis, as well as the role of surgery in treating the complications of intestinal BLs. HighlightsBurkitt’s lymphoma is an aggressive kind of non-Hodgkin’s B-cell lymphoma.Appendiceal carcinoid tumors are uncommon benign neuroendocrine neoplasms.There have been few reports of carcinoid tumors being associated with lymphoreticular system cancers.Chemotherapy is the mainstay of Burkitt’s lymphoma.Surgery is the main treatment of appendiceal carcinoid tumor.Burkitt’s lymphoma (BL), an aggressive kind of non-Hodgkin’s B-cell lymphoma, typically identified in children and young adults. It has three clinical-epidemiological variants: endemic, sporadic, and acquired immunodeficiency-associated BL9 /l) and elevated levels of C-reactive protein , otherwise normal.We report a case of a 15-year-old Syrian adolescent who was admitted to our hospital due to a persistent, severe generalized abdomen pain accompanied by nausea, vomiting, loss of appetite, and inability to pass stool or gas. Family history was unremarkable as well as surgical, drug, and allergic histories. Physical examination revealed a soft, tender abdomen without guarding. Lab findings were significant for leukocytosis : Not applicable.Not commissioned, externally peer-reviewed.


Background: The therapeutic efficacy of cytokine-induced killer (CIK) cells versus dendritic cells (DC) co-cultured with CIK cells (DC-CIK) in treating esophageal cancer (EC) remains unclear due to the absence of a direct comparison of these two regimens. This study evaluated the comparative efficacy and safety of CIK cells versus DC-CIK using network meta-analysis in treating EC.Material and methods: We identified eligible studies from previous meta-analyses, then conducted an updated search to retrieve additional trials between February 2020 and July 2021. The primary outcomes included overall survival (OS), objective response rate (ORR), and disease control rate (DCR), and the secondary outcomes included quality of life improved rate (QLIR) and adverse events (AEs). A network meta-analysis of 12 studies was conducted using ADDIS software.Results: Twelve studies were identified, including six comparing CIK or DC-CIK plus chemotherapy (CT) with CT alone. Immunotherapy plus CT significantly improved overall survival (OS) , objective response rate (ORR) , disease control rate (DCR) , and quality of life improvement rate (QLIR) . DC-CIK+CT decreased the risk of leukopenia compared with CT alone. However, no statistical difference was detected between CIK-CT and DC-CIK+CT.Conclusion: Based on the available evidence, we concluded that CIK cell treatment is superior to CT alone, but CIK-CT and DC-CIK+CT may be comparable in treating EC. However, comparing CIK-CT and DC-CIK+CT is only based on indirect evidence, so it is undoubtedly necessary to conduct studies to compare CIK-CT with DC-CIK+CT in EC patients directly. Esophageal cancer (EC) is one of the most common digestive malignant tumors. It has been reported that approximately 604,100 new EC cases and 544,076 deaths resulted from EC in 2020 ; howeverin vitro and has a stronger antitumor activity and a broader spectrum [Studies suggest that immunodeficiency plays a crucial role in the relapse and metastasis of EC ; thus, ispectrum .CIK cells have been extensively applied in at-cancer treatment owing to the following two reasons, including a) the cytotoxicity of CIK cells could not be affected by immune inhibitors , and b) It is noted that immunotherapy based on CIK or DC-CIK has been demonstrated to be superior to CT alone for treating EC patients. However, the comparative therapeutic efficacy and safety of CIK versus DC-CIK remain unclear because the study directly comparing these two regimes is absent, significantly limiting the appropriate selection of therapeutic strategies for treating EC patients. It is exciting that network meta-analysis, as an expansion of conventional pairwise meta-analysis, provides a possible strategy for evaluating the difference between the two interventions that were never directly compared. Therefore, we performed this network meta-analysis to assess whether immunotherapy based on CIK+CT significantly differed from immunotherapy based on DC-CIK+CT in therapeutic efficacy and safety for the treatment of EC.This network meta-analysis was conducted according to the recommendations proposed by the Cochrane Collaboration (CC) , and allWe designed a two-step search strategy to identify eligible studies. Firstly, we identified previously published meta-analyses from PubMed and China National Infrastructure Knowledgement (CNKI) and retrieved eligible studies. In the second phase, two reviewers independently searched PubMed and CNKI to retrieve additional relevant studies published between February 2020 and July 2021. Subject heading terms and complimentary words were used to construct the search strategy. We summarized the search strategy of PubMed in We selected studies using EndNote software according to the following three steps: a) we first removed repeat records by matching the title, author, and journal of each record, b) we initially excluded ineligible records by screening titles and abstracts of remaining unique records, and c) we excluded ineligible studies by checking eligibility based on full texts. We recorded the number of excluded studies and the reasons for excluding each study.According to the previous meta-analyses, we developed the following inclusion criteria: a) patients were confirmed as EC based on histopathology and cytological diagnostic criteria; b) randomized controlled trials (RCTs) contained at least a CT arm and either a CIK+CT arm or a DC-CIK+CT arm; c) studies reported at least one of the six outcome measures of interest, including overall survival (OS), objective response rate (ORR), disease control rate (DCR), quality of life improvement rate (QLIR), adverse events (AEs), including gastrointestinal adverse reaction (GIAR) or leukopenia. Studies were excluded if they met the following criteria: a) abstract without sufficient data; b) Did not report sufficient data; c) repeat studies with insufficient information and poor quality; and d) other treatments such as radiotherapy, target therapy, and Chinese herbal medicine were incorporated into regimes.The primary outcomes included overall survival (OS) and treatment efficacy involving objective response rate (ORR) and disease control rate (DCR). The secondary outcomes included quality of life improved rate and adverse events, including gastrointestinal adverse reactions and leukopenia. OS was described as the time from initiating treatment to death from any cause . ORR andTwo independent reviewers used a standard information extraction table designed by our team based on Microsoft Word to extract the essential information: name of the first author, publication year, details of regimes, including culture conditions, cell dose (once), cycles of CIK treatments, timing relative to CT, sample size, age, outcomes of interest, and details of methodological quality. We invited a third reviewer to assist us in resolving any disagreement.Studies were reviewed for risk of bias by using the Cochrane Collaboration’s risk of bias tool  from the2 statistic [We first conducted a pairwise meta-analysis built on the random-effects model using RevMan 5.3  to establish the role of immunotherapy plus CT for EC. All outcomes in this network meta-analysis were dichotomous variables, and therefore, we used the odds ratio (OR) with a 95% confidence interval (CI) to calculate the pooled results. Heterogeneity was examined using the Chi-square test  and I2 statistic . Moreovewww.drugis.org) to conduct a network meta-analysis built on Markov Chain Monte Carlo (MCMC) simulation. We set up the following parameters to calculate network meta-analysis: 4 chains, 20,000 tuning iterations, 50,000 simulation iterations, the thinning interval of 10, 10,000 inference samples, and a variance scaling factor of 2.5 [Following pairwise meta-analysis, we utilized the aggregate data drug information system (ADDIS) software  and lack of outcome (n = 2). Eventually, 12 eligible studies [We captured three eligible meta-analyses during the initial search phase. Then, a total of 18 potentially eligible studies were identified from them. An updated search in PubMed and CNKI did not identify any additional study. According to our selection criteria, we excluded six for the following reasons: ineligible regimes  or DC-CIK+CT  was also better than CT alone for OS ; however, no statistical difference was detected between DC-CIK+CT and CIK+CT  or CT alone  for OS . Rankingp < 0.001) was better than CT alone for ORR. Subgroup analysis revealed that CIK+CT  or DC-CIK+CT  was also better than CT alone for OS  or DC-CIK+CT  was superior to CT alone; however, no statistical difference was detected between DC-CIK+CT and CIK+CT  . Rankingp < 0.001) was better than CT alone for DCR (p < 0.001) or DC-CIK+CT  was also better than CT alone  or DC-CIK+CT  was superior to CT alone for DCR; however, no statistical difference was detected between DC-CIK+CT and CIK+CT  . Rankingp < 0.001) compared with CT alone (p = 0.03) or DC-CIK+CT  was also better than CT alone  was superior to CT alone for QLIR; however, no statistical difference was detected between CIK+CT and DC-CIK+CT  or CT alone  . RankingAmong 12 included studies, only 3 studies , 37, 45 EC remains one of the most common digestive malignant tumors worldwide . AlthougCurrently, three meta-analyses –20 have Additionally, we investigated the potential treatment value of CIK or DC-CIK for treating patients with EC based on subgroup analysis, which does not occur in previous meta-analyses. In the pairwise meta-analysis, the treatment efficacy of CIK or DC-CIK in treating EC is suggested. However, a significant difference between DC-CIK+CT and CT alone for OS and a significant difference between CIK+CT and CT alone for ORR is not retained in network meta-analysis, which may result from the limited number of included studies for individual comparison. More importantly, to date, no study has been conducted to directly compare CIK with DC-CIK for the treatment of EC patients, and thus it’s unclear whether there is a difference between these two regimes. This network meta-analysis first investigates the comparative efficacy and safety between CIK+CT and DC-CIK+CT in EC patients. Interestingly, our network meta-analysis does not detect the difference between CIK+CT and DC-CIK+CT for all outcomes among EC patients.Although this network meta-analysis generates more reliable and robust findings for decision-making, several limitations must be further interpreted. First, 12 eligible studies with a limited sample size were included in the final analysis, which greatly impaired the reliability of pooled results. Second, we did not conduct a publication bias test and small study effects due to a limited number of eligible studies for individual comparison. And thus, we must consider the possible impact of publication bias on the pooled results when interpreting pooled results. Third, a closed-loop cannot be available for any outcomes, and thus we could not evaluate inconsistency between direct and indirect comparisons with star-shaped network geometry. Forth, we cannot design subgroup analysis to investigate treatment efficacy and safety due to the limited number of eligible studies, although variations are detected in cell dose, KPS, and tumor stage. Fifth, all eligible studies are conducted in China; thus, these findings should be cautiously implemented in other cultural settings. Sixth, we also must acknowledge that variations in the sources of CIK and DC cells were not further investigated due to insufficient eligible studies, which might negatively affect the reliability of the pooled results.Our network meta-analysis further demonstrated that immunotherapy plus CT is better than CT alone for treating EC patients. Meanwhile, CIK+CT or DC-CIK+CT is superior to CT alone for OS and treatment efficacy. However, CIK+CT and DC-CIK+CT may be statistically comparable for EC patients. Therefore, we concluded that CIK cell treatment is superior to CT alone in treating patients with EC. However, our findings of comparing CIK-CT and DC-CIK+CT are only obtained from indirect evidence, and therefore, it is undoubtedly necessary to conduct studies to directly compare CIK-CT with DC-CIK+CT in EC patients in the future.


The global shortage of skilled anaesthesiologists, surgeons and obstetricians is a leading cause of high unmet surgical need. Although anaesthetic and surgical task-shifting are widely practised to mitigate this barrier, little is known about their safety and efficacy. This systematic review seeks to highlight the existing evidence on the clinical outcomes of patients operated on by non-physicians or non-specialist physicians globally. Relevant articles were identified by searching four databases  in all languages between January 2008 and February 2022. Retrieved documents were screened against pre-specified inclusion and exclusion criteria, and their qualities were appraised critically. Data were extracted by two independent reviewers and findings were synthesized narratively. In total, 40 studies have been included. Thirty-five focus on task-shifting for surgical and obstetric procedures, whereas four studies address anaesthetic task-shifting; one study covers both interventions. The majority are located in sub-Saharan Africa and the USA. Seventy-five per cent present perioperative mortality outcomes and 85% analyse morbidity measures. Evidence from low- and middle-income countries, which primarily concentrates on caesarean sections, hernia repairs and surgical male circumcisions, points to the overall safety of non-surgeons. On the other hand, the literature on surgical task-shifting in high-income countries (HICs) is limited to nine studies analysing tube thoracostomies, neurosurgical procedures, caesarean sections, male circumcisions and basal cell carcinoma excisions. Finally, only five studies pertaining to anaesthetic task-shifting across all country settings answer the research question with conflicting results, making it difficult to draw conclusions on the quality of non-physician anaesthetic care. Overall, it appears that non-specialists can safely perform high-volume, low-complexity operations. Further research is needed to understand the implications of surgical task-shifting in HICs and to better assess the performance of non-specialist anaesthesia providers. Future studies must adopt randomized study designs and include long-term outcome measures to generate high-quality evidence. Key messagesAlthough non-specialists are the main providers of surgical care in many resource-limited settings, the impact of anaesthetic and surgical task-shifting on patients’ clinical outcomes remains poorly understood.In low- and middle-income countries, non-specialists perform comparably to specialist providers of surgical care in low-complexity, high-volume procedures .Although surgical task-shifting is predominantly practised in low- and middle-income settings, non-physician clinicians and non-specialist physicians also operate in high-income countries to reduce the lack of specialist surgeons.Future research must further investigate the safety of anaesthetic task-shifting globally.Evidence shows that there is a significant unmet ‘surgical need’ globally, with up to 5 billion people facing barriers to accessing surgical care and 2 billion people without any access  guidelines , task-shifting and synonyms (i.e. task-sharing and task-delegation) and non-specialist healthcare professionals . Although we generally selected broad search terms pertaining to anaesthesia and surgery, without focusing on a particular surgical discipline, we chose to include keywords for caesarean section, laparotomy and open fracture management. This decision was made as these Bellwether procedures are indicative of functioning and comprehensive district-level surgical care delivery systems  in 2008. The searches were not restricted by language or by country setting.Study participants were patients who received anaesthesia or sedation as part of a surgical procedure or who underwent surgery. A surgical procedure was defined as ‘the incision, excision or manipulation of tissue that requires regional or general anaesthesia or profound sedation to control pain’ . A non-specialist physician was as a medical doctor who had completed medical school but who did not have formal training in surgery or anaesthesia [i.e. general practitioner (GP)]. Studies without a relevant comparator were excluded.Studies that reported on anaesthetic or surgical outcome data were included. The primary outcomes of this systematic review were short-term and long-term mortality measures. Secondary outcomes comprised all other patient clinical outcomes because of anaesthetic and surgical task-shifting. We anticipated that common secondary outcome measures would include morbidity measures, length of hospital stay, unplanned hospital readmission, reoperation and patient-reported outcomes.We included all randomized controlled trials (RCTs), quasi-experimental studies and prospective and retrospective observational studies and cross-sectional studies. Qualitative studies were excluded as the main clinical outcomes of interest centred around clinical safety, as opposed to patient perspectives and experiences of the anaesthetic and surgical care received. Existing literature reviews, case studies, conference abstracts and commentaries were not eligible.After removing duplicates in EndNote, two authors independently screened all results against the pre-specified inclusion criteria to identify eligible studies: first, they reviewed ‘titles and abstracts’ and, subsequently, the full texts of the selected articles were reviewed. Discrepancies were resolved through discussions and by involving a third reviewer until a consensus was reached. The screening process was managed through Rayyan.The following data items were recorded for each study to establish combinability: the country setting and the healthcare facilities where patients were treated; the occurrence of either surgical or anaesthetic task-shifting and the characteristics of the procedures performed and the characteristics of surgical or anaesthetic providers, including the training received and the level of supervision of non-specialists. We also noted study designs and relevant clinical outcome variables.In the event of missing data, we contacted study authors for clarification. All data points were independently extracted by one author into a pre-tested Microsoft Excel form; subsequently, a second author checked and confirmed the accuracy and completeness of the recorded information.The risk of bias of randomized studies was appraised through Version 2 of the ‘Cochrane risk-of-bias tool for randomized trials’ (RoB 2) . DomainsGiven the heterogeneous nature of the interventions and outcomes assessed, we could not pool study data in a meta-analysis. Instead, we performed a narrative synthesis of results. To describe the findings, studies were grouped by task-shifting category (surgery or anaesthesia), location (LMICs or HICs) and surgical procedure type.Our initial search strategy identified 15 002 publications. Following duplicate removal, 9543 articles were screened at ‘title and abstract’ stage, and then, 131 full-text papers were reviewed. Two additional articles were identified through references, leading to a total of 40 studies being included in this systematic review. Further details are outlined in the PRISMA study flow diagram .Thirty-five studies exclusively addressed surgical task-shifting  and fourAll studies were published between 2008 and 2021, with 24 papers (60%) published since 2015. Based on the World Bank income group classification, 17 studies were in low-income economies, 8 studies in lower-middle-income settings, 2 in upper-middle-income settings and 11 in HICs. Thirty studies (75%) reported on perioperative mortality and 34 studies (85%) analysed morbidity measures, including perioperative complications, hospital length of stay and duration of the operative procedure. Fourteen articles (35%) adopted prospective study designs, including one RCT . Similarly, P < 0.001). In a retrospective, cross-sectional study in Burkina Faso, P < 0.001). Similarly, there was an equivalent distribution of secondary outcomes at 2 weeks (postoperative complications) and 1 year post-surgery . In a retrospective study across seven healthcare facilities in the Pwani Region of Tanzania, In total, nine studies in sub-Saharan African countries addressed task-shifting for general surgery procedures. Most studies focused on elective or emergent hernia repairs: four studies compared the performance of non-physicians with medical doctors, whereas two studies compared non-specialist physicians and qualified surgeons. P < 0.01) as well as secondary outcomes at 2 weeks and 1 year post-surgery. Similarly, in a prospective study in Kenya, According to P = 0.03). Analogously, postoperative hospital stay was longer for the intervention group  in the case of prostatectomy; no other outcome parameter differed between the two groups of operators .Beyond hernia repairs, P < 0.001).Four studies assessed outcomes of surgical male circumcisions undertaken by NPCs or physicians in sub-Saharan African settings (P < 0.001) and by SACHOs  proved the safety of task-shifting through this programme.P < 0.001). In a study by P < 0.001) and higher reoperation rates  in patients operated on by clinical officers although these differences ceased after controlling for burn victims.P < 0.001).In an 8-year retrospective study in Melaka Hospital , P = 0.02). No deaths occurred as a direct result of the tube thoracostomies. In a study by P = 0.006) and experience significantly longer ICU length of stay , there were no differences in overall hospital length of stay, ventilator days and hospital disposition. Overall, in both papers, advanced practice providers were proven to be safe providers of tube thoracostomies.Two studies focused on tube thoracostomies in the USA. P = 0.04). P = 0.0128); instead, there was a clinically significant difference in minor complication rates .Three publications discussed neurosurgical procedures. Two studies from the USA focused on surgical circumcisions by non-physicians in the paediatric population. P < 0.01) and maternal hospital length of stay , there was no evidence of increased risk for mothers and newborns. During the study period, no maternal deaths occurred in either healthcare facility and no statistically significant differences were found for intraoperative and infectious complications and newborn outcomes. Overall, mothers operated on by family physicians experienced fewer maternal postoperative complications .P < 0.001), with the risk of incomplete excision being six times higher for procedures completed by a GP .Finally, Five papers implemented a comparative approach to study patients’ clinical outcomes following the administration of anaesthesia or sedation for surgical procedures. Three studies were set in LMICs and two in the USA. All studies compared the performance of non-physicians with that of qualified anaesthesiologists.P = 0.05) and complications .Two publications were set in the USA. Our review identified 40 studies assessing patient outcomes because of anaesthetic and surgical task-shifting published between 2008 and 2021. All but four of the studies focused on surgical task-shifting, and the majority were conducted in sub-Saharan Africa. Only one study was an RCT, highlighting the limited high-quality evidence on the clinical implications of task-shifting.In LMICs, the body of evidence mostly pertains to caesarean sections, general surgery, including hernia repairs, and surgical male circumcisions. Overall, evidence indicates that these common and low-complexity procedures have been performed safely by trained non-physicians and non-specialist physicians, with comparable outcomes between intervention and control groups. This aligns with existing literature that compared caesarean sections, hernia repairs and male circumcisions between non-physician and physician operators, without reporting significant differences in key outcomes (To the best of our knowledge, this is the most comprehensive literature review with a broad scope to synthesize the evidence of intraoperative anaesthetic and surgical task-shifting across all World Bank income groups. We employed a broad eligibility criterion with a comprehensive search strategy across four databases to capture all the existing literature examining the clinical impact of task-shifting to non-physicians or non-specialist physicians.Our search strategy excluded any articles published prior to 2008, possibly omitting some research studies on the clinical implications of task-shifting. However, while task-shifting may have been studied in certain settings before, we focused on a period with sustained policy focus following the WHO’s first conference on task-shifting . A methoDue to the overall poor quality of the included studies, and their heterogeneous nature, we were precluded from performing a meta-analysis of the extracted data. Nonetheless, our narrative synthesis offers broad insights into the clinical areas and the settings where task-shifting has been adopted, with important lessons for policy and practice. Since many studies employed non-randomized study designs, often with poor comparability between intervention and control arms, most articles in this review were deemed to be at moderate or serious risk of bias. Many studies failed to account for patient, procedure and healthcare setting characteristics, leading to imbalances between patient groups. For instance, specialists may have managed patients at higher risk of experiencing poorer outcomes and performed the most complex operations. It is also possible that non-specialists may have been more likely to be stationed in rural and public hospitals, which are often underresourced and lack accessibility to life-saving equipment or appropriate referral systems.It was not uncommon for retrospective studies to report high rates of missing outcome data caused by the difficulty in retrieving patient files and their incompleteness. In studies where non-specialists operated under supervision, the direct involvement of physicians in the procedures often remained unclear, possibly leading to biased results. Finally, many studies included a short follow-up period, often measuring perioperative or in-hospital mortality and morbidity rates. However, anaesthetic and surgical complications are likely to manifest over time. Our review has also highlighted that there is a substantial gap in the literature responding to the research question beyond sub-Saharan Africa and the USA. This is not surprising, as surgical task-shifting is illegal in many parts of the world, but it is common practice in many countries across Africa.Further research is needed to understand the implications of surgical task-shifting in middle-income countries and HICs and to better assess the performance of non-specialist anaesthesia providers, given the scarcity of available literature. Studies on surgical task-shifting should adopt a broader lens, performing a robust analysis of all aspects of perioperative care, beyond non-specialists’ technical performance in the operating theatre. It remains essential to evaluate the care provided by non-specialists across the perioperative pathway, including preoperative and postoperative management. Focus must also be placed on non-technical skills, such as clinical decision-making, communication and situational awareness. To strengthen the quality of the available evidence, future studies should adopt randomized study designs and include long-term outcome measures to generate high-quality evidence.Our results suggest that task-shifting in low-complexity procedures such as caesarean sections and hernia repairs may be implemented safely. This is an important finding, given the high unmet need in obstetrics and general surgery in many low-resource settings (Training programmes should be recognized as an indispensable component of successful task-shifting initiatives. These enable the extended surgical team to develop technical skills as well as non-technical skills, including decision-making, effective communication and situational awareness. Importantly, training ought to be tailored to local contexts, preparing trainees for the challenging working conditions, which they would face in poorly resourced settings (Although task-shifting has been extensively advocated as a solution for the human resources crisis in global surgery, some studies have suggested that trained non-specialists may also be prone to leave the areas in greatest need to seek personal opportunities elsewhere (Research predominately from sub-Saharan Africa suggests that non-specialists have performed common and low-complexity procedures, including caesarean sections and general surgery, with comparable outcomes to specialist physicians, thereby increasing access to safe and timely essential surgery. However, there remains a lack of high-quality research on the safety of surgical task-shifting for more complex procedures in LMICs and to further study the adoption of this strategy in HICs. More focus must be placed on anaesthesia care to better assess the performance of non-specialist providers in this field. In all contexts, for task-shifting to be sustainable and effective, policy efforts must centre around its formal recognition in regulatory and governance systems, around strengthening adequate training and supervision programmes and investigating strategies to favour the retention of healthcare workers in resource-poor contexts.czad059_SuppClick here for additional data file.


Multi-morbidity is a public health priority as it is associated with an increased risk of mortality and a substantial healthcare burden. Smoking is considered a predisposing factor for multi-morbidity, but evidence for an association between multi-morbidity and nicotine dependence is insufficient. This study aimed to explore the association between smoking status, nicotine dependence, and multi-morbidity in China.We recruited 11031 Chinese citizens from 31 provinces in 2021 using a multistage stratified cluster sampling strategy to ensure the study population represented national population characteristics. The association between smoking status and multi-morbidity was analyzed using binary logistic regression and multinomial logit regression models. We then analyzed the associations between four kinds of smoking status , nicotine dependence, and multi-morbidity among participants who were current smokers.Compared with non-smokers, the odds of multi-morbidity were higher among ex-smokers . The risk of multi-morbidity was greater in participants who were underweight/overweight/obese  compared with those who were normal weight. and also greater for drinkers  than non-drinkers. Compared with children who began smoking at the age of <15 years, participants aged >18 years had a lower likelihood of multi-morbidity . People who consumed ≥31 cigarettes per day  and those who smoked when ill in bed  were more likely to have multi-morbidity.Our findings show that smoking behavior, including initiation age, frequency of daily smoking, and still smoking during illness or in public, is a critical risk factor for multi-morbidity, especially when combined with alcohol consumption, physical inactivity, and abnormal weight . This highlights the crucial effect of smoking cessation in the prevention and control of multi-morbidity, especially in patients with three or more diseases. Implementing smoking and lifestyle interventions to promote health would both benefit adults and prevent the next generation from initiating habits that increase the risk of multi-morbidity. As the world population ages, the consequences of multi-morbidity are becoming more prevalent and severe. The number of individuals affected by multi-morbidity is increasing worldwide. A study in Scotland reported that 65% of patients aged ≥65 years and 80% of those ≥85 years had at least two chronic health conditions2. A study in southern China showed that >10% of 162464 community household residents had multi-morbidity3. Another study of 0.5 million middle-aged and elderly Chinese adults reported that 16% of the participants were multi-morbid4. It is widely recognized that multi-morbidity is associated with an increased risk of mortality2, decreased quality of life5, and substantial healthcare burden6. Multimorbidity further increases the financial burden and causes patients to be unable to pay out-of-pocket expenditures7. Thus, multi-morbidity has become a global public health priority requiring effective intervention. To design better prevention and management strategies, researchers need more data on the factors that influence multi-morbidity8.Multi-morbidity is defined as the simultaneous presence of two or more chronic health disorders10 and modifiable behaviors such as physical activity, obesity11, and smoking12. Smoking, in particular, has been identified as a predisposing factor for multi-morbidity in disease-free populations12. A similar conclusion was reached in a cross-sectional study of 109695 Canadians aged >12 years; people who smoked had a much higher prevalence of multi-morbidity than those who had never smoked13. In China, a large-scale prospective study among 0.5 million adults reported that people who smoke are more likely to develop cardiometabolic multi-morbidity14. However, most studies have been conducted in a limited geographical area or among an elderly population. Moreover, most research in China has focused on the relationship between smoking and the risk of specific disease comorbidity among hospital-based individuals.A variety of factors are related to multi-morbidity, including sociodemographic factors15, making tobacco-related illness the primary cause of preventable and premature mortality16. A review concluded that smoking has an apparent relationship with five comorbid domains: psychiatric, cancer, cardiovascular, pulmonary, and HIV infection17. Moreover, the number of patients with multiple chronic diseases is expected to increase. However, few studies have focused on smoking as a risk factor for multi-morbidity. Existing studies on multi-morbidity often do not investigate smoking behaviour in sufficient detail. Most researchers simply divide the population into smokers and non-smokers without further research on the association of multi-morbidity with tobacco use characteristics and dependence. A systematic review showed that most studies that diagnosed tobacco use status and dependence were conducted in developed countries and focused on a relationship with a singular disease domain17. Therefore, there is a need for detailed measures of smoking status among people with multi-morbidity. Surveys to explore the association of detailed smoking status with multi-morbidity are especially needed in developing countries, where people have a higher proportion of unhealthy smoking habits and a higher prevalence of multi-morbidity. Furthermore, most studies were conducted on people aged >30 years, and evidence is sparse regarding multi-morbidity and smoking among the Chinese general population.Tobacco use represents a significant public health challenge worldwide. The Global Burden of Disease Study 2019 indicates that tobacco smoking causes 8 million deaths every year18.This large, nationally representative, cross-sectional study investigated the relationship between smoking and multi-morbidity to estimate the different risks of multi-morbidity among different states of tobacco-smoking behavior. We hope to ascertain the specific relationship between smoking and multiple diseases and provide evidence to improve future interventions in multi-morbidity19. Multistage, stratified cluster sampling was used to recruit participants from 120 cities across China’s 31 provinces from 10 July to 15 September 2021. In the first stage, 120 cities were selected: one capital city and one non-capital city in every province, autonomous region, and municipality. Based on sex, age, and region strata, 100 samples were selected from 120 cluster cities with a probability proportional to the population size. A unique web link for the electronic questionnaire was distributed face-to-face by trained investigators to voluntary participants in each city where the survey was conducted. Participation in the study was voluntary and all respondents provided informed consent. The interviewers were invited to a community healthcare center or community neighborhood committee to complete the questionnaire. After questionnaire collection, the quality check was conducted by two researchers independently. The screening criteria to determine high quality or valid questionnaire were as follows: 1) passed the logic check questions (Supplementary file); 2) speeder check to eliminate those with response time of fewer than 240 seconds; 3) deleted incomplete or repeated questionnaires; and 4) deleted questionnaires with all selected options being the same or having regularity. In total, 11031 valid questionnaires of high quality with signed informed consent were obtained. The China Family Health Index (CFHI) survey questionnaire includes the following modules: demographics, a short form of the Family Health Scale, and a 10-item Big Five inventory.This is a nationwide cross-sectional study on the Chinese population aged ≥18 years20. The FTND is a standard 6-item instrument that measures the degree of physical addiction to nicotine, including the quantity of cigarette consumption, the compulsion to use, and the degree of physical dependence. Supplementary file 21. In this study, FTND demonstrated good internal consistency with Cronbach’s coefficient α = 0.661.The participants were asked about their current smoking status, which was classified as non-smoker, current smoker, or ex-smoker. For those who were current smokers, we asked them further: ‘How many cigarettes do you smoke per day?’, ‘What age did you first smoke?’, and ‘Do you have difficulty controlling your need to smoke in many non-smoking places?’. The data collected were classified as follows: age at first smoking , cigarettes per day , smoking when ill in bed , and smoking in public places . Nicotine dependence was then assessed using the Fagerström test for nicotine dependence (FTND)1. In accordance with previous studies, multi-morbidity was defined as the concurrence of two or more chronic conditions in the same individual. The diseases were selected based on insights from the 25 leading causes of DALYs in China22, the morbidities recommended as the core for any multi-morbidity measure by a UK-based study2, and the data available in CFHI datasets. The variable used for the self-reported diagnosis was the question: ‘Are you currently diagnosed with any of the following diseases?’ followed by a list of 20 diseases: hypertension, coronary heart disease, stroke, thrombus, diabetes, Alzheimer’s disease, Parkinson’s disease, viral hepatitis, neoplasms, chronic enteritis, chronic gastritis, emotional disorders, asthma, dyslipidemia, chronic obstructive pulmonary disease, chronic kidney disease, fatty liver disease, thyroid disease, osteoporosis, and lumbar disc herniation. Participants who answered ‘yes’ to the question were classified as having a self-reported chronic condition.The primary outcome measure of the study was multi-morbidity. A systematic review of definitions and measurements of multi-morbidity found that multi-morbidity was commonly defined as the presence of multiple diseases or conditions, with a cutoff of two or more2, or underweight/overweight/obese). In detail, monthly average income in the household was classified into quartiles  with approximately the same number of people in each group. Alcohol consumption was measured by asking the participants: ‘Have you had any alcohol in the last 12 months?’. Participants who answered ‘Yes, within the last 30 days’ or ‘Yes, before the last 30 days’ were classified as ‘drinkers’, and those who answered ‘No’ were classified as ‘non-drinkers’. Physical activity was estimated by summing the metabolic equivalent task (MET) hours per week (h/week) spent based on the activities from the International Physical Activity Questionnaire (IPAQ). Moderate-to-vigorous activities included bodybuilding, walking, swimming, cycling, exercise with sports equipment, and other aerobic exercises (Supplementary file Table S2). The details of the measurements have been described elsewhere23. We grouped physical activity into active and inactive categories based on the median of total physical activity in all participants (<20.3 MET-h/week). BMI (kg/m2) was classified into normal from 18.5 to 24.0 kg/m2, otherwise, it was defined as abnormal .This study collected information on the following covariates: demographic factors including age , sex , and residence ; socioeconomic factors including monthly average income , education level , employment status , and insurance ; health behaviors including alcohol consumption , and physical activity ; and anthropometric indicators including body mass index  in each population were calculated using percentages and 95% confidence intervals (CIs) against the total population. Differences between different variables and smoking status and multi-morbidity were compared using the chi-squared test.A two-stage statistical analysis was undertaken. First, we conducted multivariable logistic regression analysis to examine whether smoking status  is associated with the presence of multi-morbidity . Two nested logistic models were fitted: 1) unadjusted; and 2) adjusted for various demographic  and socioeconomic factors , lifestyle factors . Second, the outcomes were divided into four categories: no disease, 1, 2, and ≥3 diseases. Multinomial or ordinal logit regression model was utilized to examine the association between smoking status and different number of chronic diseases with ‘no disease’ as the reference group. We conducted an overall test of proportional odds assumption. If p<0.05, multinomial logistic regression was appropriate to be adopted; otherwise, ordinal logistic regression would be adopted. Results are reported as adjusted odds ratios (AORs) and 95% CI.We then used a multivariable logistic regression model of participants who were current smokers to analyze the association of multi-morbidity  with four kinds of tobacco-smoking status: age at first smoking, cigarette consumption per day, smoking when ill in bed, and inability to stop smoking in public places. The associations were tested in 2-tailed tests and considered statistically significant if p<0.05. All analyses were performed using Stata 16 MP  and R software.A significant association was observed between smoking status and multi-morbidity. Compared with non-smokers, the odds of having multi-morbidity were higher among ex-smokers . The association with multi-morbidity was greater in participants aged >30 years than in those ≤30 years and slightly greater for insured versus uninsured participants. Urban dwellers  and unemployed participants  were more likely to be multi-morbid. An income of >7500 RMB  was a protective factor for multi-morbidity. Being underweight/overweight/obese , and a drinker  were risk factors for multi-morbidity .Proportional odds assumption was tested with p=0.30. Ordinal logistic regression analysis was performed to analyze the relationship between smoking status and the number of diseases. Using disease-free status as a reference, the effect of 1, 2, and ≥3 diseases were analyzed. The results showed that smoking status, smoking cessation, increasing age, and alcohol consumption were risk factors for disease. Compared to non-smokers, current smokers had a 1.25 times greater risk of being comorbid with two or more diseases  and ex-smokers had nearly two times the likelihood of having ≥3 diseases  .In this large-scale nationally representative study in China, 20% of the participants, aged mainly 30–60 years, had a smoking history. Compared with nonsmokers, ex-smokers had the highest risk of multimorbidity, followed by current smokers. Current smokers and those who quit smoking were more likely to have a higher number of chronic diseases. Participants who started smoking during childhood (aged <15 years), had greater cigarette consumption per day, smoked when ill in bed, and had high nicotine dependence were independently associated with a greater risk of multi-morbidity.24. Data from the China Kadoorie Biobank study revealed adverse effects from high-risk lifestyle factors (e.g. current smoking and quitting because of illness) on the risk of cardiometabolic multi-morbidity and death among 0.5 million adults aged 30–79 years14. In an longitudinal study of ageing conducted in the UK, physical inactivity combined with smoking increased the risk of multi-morbidity twice as much as the former alone, and combinations of unhealthy lifestyle factors significantly increased the multi-morbidity hazard25. Women aged 45–50 years who were current smokers were found to have increased odds of diabetes, heart disease, and stroke multi-morbidities in the Australian Longitudinal Study on Women's Health26. Our findings are largely consistent with those of prior studies and found a further positive association between smoking history and a higher number of multi-morbidities. This highlights the crucial effect of smoking cessation in the prevention and control of multi-morbidity, especially in patients with ≥3 diseases.The relationship between current smokers and multi-morbidity was positive and significant but attenuated when adjusted for socioeconomic status and lifestyle factors. This may be explained by the confounding effect of age and a potential association between smoking cessation and multi-morbidity, since an adverse effect of tobacco use on health was identified. Some evidence has been found for an association between smoking status and the risk of multi-morbidity. In a multicenter cohort study across 10 European countries, pre-diagnostic unhealthy lifestyle behaviors, including smoking, were strongly associated with the risks of developing chronic non-communicable diseases, cancer multimorbidity, and cardiometabolic diseases27. Studies in developing countries have shown that smoking is the leading risk factor for death; 87% of deaths occur among current smokers15. Thus, reducing the prevalence of smoking is a global health concern. However, the relatively high smoking prevalence and low rate of cessation in our Chinese population highlight the need for effective policies and management strategies for smoking prevention and control of tobacco use. Moreover, measures should be taken at the policy, primary-health promotion, and education levels to reduce the number of underage smokers.Previously the relationship between smoking use and multi-morbidity has not been explored fully. This study goes beyond, as we explored nicotine dependence-related smoking factors as predictors of multi-morbidity among current smokers. Most current smokers in China began smoking younger than an age of 18 years, and those who began smoking earlier showed a greater risk of multi-morbidity than smokers who started at a later age. Moreover, about 25% of smokers could not stop smoking in public, and about 25% still smoked when ill in bed. People highly dependent on tobacco were more likely to have multi-morbidity, suggesting that treatment for nicotine dependence has the potential for preventing multi-morbidity. This finding is consistent with previous studies conducted in middle-income countries. For example, a large-scale study in Cuba showed that people who had smoked since childhood had difficulty quitting smoking and a higher risk of premature death26, suggest the association of unhealthy lifestyle with multi-morbidity, emphasizing the need to integrate comprehensive lifestyle interventions into multi-morbidity management and to improve clinical outcomes. Further research should be undertaken to investigate the dynamic trends in lifestyle-behavior changes to the first occurrence, types, and progress of multi-morbidity and mortality. Moreover, etiological pathways shared among multiple unhealthy-lifestyle factors leading to multi-morbidity need to be investigated further29. Our research indicates that sociodemographic characteristics are influencing factors on multi-morbidity and the number of diseases. People aged >30 years, living in urban areas, unemployed, with low income, and with insurance have a greater risk of multi-morbidity and more types of disease. The OR of multi-morbidity and the number of diseases increased gradually with age and was highest among people aged >60 years. These results are consistent with those of previous studies with regard to age32, residence34, occupational status36, income38, and insurance40. Further work is required to investigate the causes of the unequal distribution of multi-morbidity across the sociodemographic spectrum, especially among different socioeconomic groups. Eliminating health disparities remains a crucial issue in promoting better health.Our results, consistent with previous researchThis large-scale national representative study examined the epidemiology of multi-morbidity and tobacco use across the whole population to explore their relationship in the Chinese population. Multi-morbidity was defined using an international definition and included 20 types of chronic diseases that are common in China. We also explored multiple types of tobacco-smoking behavior, including age at smoking initiation, daily cigarette consumption, smoking when ill in bed, and inability to control smoking in public places. Given the size and diversity of the study design, our findings can be generalized to the greater Chinese population.This study has several limitations. First, this study suffers from the typical limitations of a cross-sectional design; thus, we were unable to establish a causal relationship between multi-morbidity and smoking. Although the study asked participants about their starting age of smoking, it did not ask the age of onset of different diseases. As a result, we could not determine the temporal relationship between smoking initiation and multi-morbidity in this cross-sectional study. Also, smoking cessation due to multi-morbidity may cause reverse causality; therefore, the risk of smoking might have been underestimated. Further longitudinal studies are needed to examine the unbiased effects of smoking on multi-morbidity. Second, self-reported questionnaires can be influenced by recall bias. The age at smoking initiation, cigarette consumption per day, chronic disease types, and other self-reported information may have been misreported. Because the social acceptability of nicotine dependence and multi-morbidity is low, smoking status and multimorbidity may be under-reported, suggesting that our findings might have underestimated the actual prevalence of smoking and multi-morbidity. Third, this study consisted mainly of young people aged ≤30 years; therefore, only a few participants had developed complex multi-morbidities. Counting multi-morbidity as the number of chronic diseases is sufficient to show a risk from smoking. Further research should explore the effect of different sources and severities of smoking exposure on the first occurrence, progress, and outcomes of various multi-morbidity patterns.This nationally representative large-scale study showed that smoking status and nicotine dependence, combined with unhealthy lifestyle behaviors , are crucial risk factors for multi-morbidity and an increased number of chronic diseases. Most current smokers in China began smoking before the age of 18 years, about 25% of smokers could not stop smoking in public areas, and about 20% still smoked when ill in bed. People highly dependent on tobacco were more likely to have multi-morbidity, suggesting that treatment for nicotine dependence can be a strategy to improve health. Our study demonstrates the potential of evidence-based tobacco control policy implementation and enforcement in helping manage multi-morbidity of people in China. This policy would both benefit adults and prevent the next generation from initiating smoking habits.Click here for additional data file.