Alcohol Use Disorder Incidence, Mortality, and Disability-Adjusted Life Years in Estonia, 1990 to 2019: A Joinpoint Regression Analysis Using Global Burden of Disease Study

Background: Alcohol consumption is widely recognized as a leading cause of premature mortality and a significant global health concern. This study aimed to investigate the trends in alcohol use disorders, including incidence, mortality, and disability-adjusted life years (DALYs), in Estonia from 1990 to 2019 using data from the Global Burden of Disease (GBD) dataset. Methods: The GBD study is a comprehensive epidemiological research effort that analyzes various causes of death, diseases, injuries, and risk factors across multiple countries and territories. In this study, we utilized the GBD dataset to estimate annual incidence, prevalence, mortality, years of life lost, years living with disabilities, and DALY rates by gender and age from 1990 to 2019. To assess the trends in these indices, including DALY, incidence, mortality, and 1-mortality-to-incidence ratio (1-MIR), joinpoint regression analysis was employed. This allowed for the identification of significant changes in trends at specific time points and the calculation of annual percent change between these points. Results: Our findings revealed an overall decreasing trend in the incidence rate of alcohol use disorder over the study period. However, the average DALY, mortality, and 1-MIR trends did not exhibit significant variation during this time. Moreover, we observed a more substantial decline in alcohol use disorders among men compared to women from 1990 to 2019. Specifically, the incidence of alcohol use disorders demonstrated a significant increase from 1990 to 2000, followed by a decline from 2010 to 2018, and continued to decrease from 2017 to 2019. Conclusion: This study provides important insights into the changing trends of alcohol use disorders, including incidence and mortality, in Estonia from 1990 to 2019. Our findings indicate a decreasing pattern over time, suggesting a positive shift in alcohol consumption behavior. Additionally, we observed that men had higher rates of MIR, DALY, mortality, and incidence of alcohol use disorders compared to women. These results emphasize the need for targeted health prevention programs to sustain and further promote the downward trend in alcohol-related disorders

Factors affecting long-survival of patients with esophageal cancer using non-mixture cure fraction model

Objective: Esophageal cancer (EC) is one of the gastrointestinal malignancies with a very high morbidity and mortality rate due to poor prognosis. This study aims to assess the effects of risk factors on survival and cure fraction of patients with EC in a population of Iranian patients using a non-mixture cure fraction model. Methods: This retrospective cohort study was conducted on 127 patients with EC who were diagnosed during 2009-2010 and were followed up for 5 years in East-Azarbaijan, Iran. Stepwise selection and non-mixture cure fraction model were used to find the risk factors of EC survival patients. Results: The mean (±standard deviation) diagnosis age of the EC was 66.92(±11.95). One, three and five-year survival probabilities were 0.44 (95% confidence interval (CI): 0.36-0.54), 0.2 (95% CI: 0.14-0.28) and 0.13 (95% CI: 0.08-0.2) respectively. Female sex (Estimate=-0.99; 95% confidence interval (CI): -1.41,-0.58; p-value<0.001), low level socioeconomic status (Estimate=0.39; 95%CI: 0.12,0.66; p-value=0.043), the group who did not do esophagectomy surgery (Estimate=0.58; 95%CI: 0.17,0.99; p-value=0.005) and unmarried group (Estimate=0.58; 95%CI: 0.11-1.05; p-value=0.015) were found as the significant predictor of survival and cure fraction of the EC patients. Population cure rate was 0.11 (95%CI: 0.07-0.19) and Cure fraction was estimated 5.11 percent. Conclusion: This study found gender, socioeconomic status, Esophagectomy surgery and marital status as the potential risk factors for survival and cure fraction of Iranian EC patients. Moreover, non- mixture cure fraction provides more accurate and more reliable insight into long-term advantages of EC therapy compared to standard classic survival analysis alternatives

Assessing the Diagnostic Power of Cystatin C and Creatinine in Detection of Chronic Kidney Disease

 Introduction: In patients with renal disorders, a sudden decrease in glomerular filtration rate (GFR) would not result in rapid rise concentrations of Creatinine. The present study aimed to assess diagnostic accuracy of serum Cystatin C as an appropriate alternative to serum Creatinine for early detection of Chronic Kidney Disease (CKD).Materials and Methods: In this study, 72 patients, 48 female and 24 male were selected. Serum Cystatin C and serum Creatinine were assayed, using enzyme-linked immunosorbent assay (ELISA) and routine methods, respectively. Glomerular filtration rate (eGFR) was estimated by Cockcroft and Gault formula. Receiver operating characteristics (ROC) analysis was adopted to evaluate diagnostic accuracy of serum Cystatin C and serum Creatinine.Results: Using Pearson's Correlation Coefficient analysis among Creatinine, Cystatin C and eGFR showed Serum Cystatin C was better than Creatinine. The sensitivity, specificity and AUC for Serum Cystatin C were 0.88, 0.70 and 0.85, and for Serum Creatinine, they  were 0.60, 0.80 and 0.68 respectively.Conclusion: Our results showed that in early stages of CKD, Cystatin C is a more accurate biomarker for kidney function than Creatinine  

Epidemiology of small intestine cancer in Iran

Background Little is known about the epidemiology of small intestine (SI) cancer in Iran, a rare cancer entity worldwide. Aims The aim of the present study was to investigate the incidence patterns and survival rates of SI cancer in Iran through a population-based study. Methods and Results Data on all reported cases of SI cancer were extracted from the Iran National Cancer Registry based on ICD-O-3 codes. Age-standardized incidence rates (ASIR), age-specific incidence rates, standardized rate ratios (SRR), time trends, and absolute survival rates were calculated. During 2005-2015, a total of 4928 SI cancers (ASIR: 0.87/100 000) were diagnosed, including 2835 carcinomas (ASIR: 0.51), 214 neuroendocrine malignancies (ASIR: 0.04), 228 sarcomas (ASIR: 0.04), and 704 lymphomas (ASIR: 0.11). Carcinomas and lymphomas occurred more frequently in men than in women (SRR: 1.37/100 000 and 1.85/100 000, respectively), while the other two histological subtypes were almost equally distributed. 78% of carcinomas and 53% of neuroendocrine tumors were located in the duodenum. Sarcomas occurred most frequently in the jejunum (41%), while lymphomas were most frequently in the ileum (44%). From 2005 to 2015, the number of reported cases of SI cancer increased by 9.6% per year. The median age of diagnosis for women and men was 61. The absolute 5-year survival rate was 35.3%, varying by sex, age, and subtype. Carcinomas had the lowest survival rate (24.1%) while neuroendocrine carcinomas had the highest survival rate (69.7%). Conclusion Epidemiological patterns of SI cancer in Iran differed slightly from patterns in the United States and the United Kingdom. In contrast to other countries, the neuroendocrine form is presented as the rarest subtype in Iran. The overall incidence of SI cancer was lower in Iran than in high-income countries. In contrast, the average prognosis of SI cancer was worse in Iran, indicating the need to improve early detection, diagnosis, and treatment

Application of a non-parametric non-mixture cure rate model for analyzing the survival of patients with colorectal cancer in Iran

OBJECTIVES Colorectal cancer (CRC) patients are considered to have been cured when the mortality rate of individuals with the disease returns to the same level as expected in the general population. This study aimed to assess the impact of various risk factors on the cure fraction of CRC patients using a real dataset of Iranian CRC patients with a non-mixture non-parametric cure model. METHODS This study was conducted on the medical records of 512 patients who were definitively diagnosed with CRC at Taleghani Hospital, Tehran, Iran from 2001 to 2007. A non-mixture non-parametric cure rate model was applied to the data after using stepwise selection to identify the risk factors of CRC. RESULTS For non-cured cases, the mean survival time was 1,243.83 days (95% confidence interval [CI], 1,174.65 to 1,313.00) and the median survival time was 1,493.00 days (95% CI, 1,398.67 to 1,587.33). The 1- and 3-year survival rates were 92.9% (95% CI, 91.0 to 95.0) and 73.4% (95% CI, 68.0 to 79.0), respectively. Pathologic stage T1 of the primary tumor (estimate=0.58; p=0.013), a poorly differentiated tumor (estimate=1.17; p<0.001), a body mass index (BMI) between 18.6 and 24.9 kg/m2 (estimate=−0.60; p=0.04), and a BMI between 25.0 and 29.9 kg/m2 (estimate=−1.43; p<0.001) had significant impacts on the cure fraction of CRC in the multivariate analysis. The proportion of cured patients was 64.1% (95% CI, 56.7 to 72.4). CONCLUSIONS This study found that the pathologic stage of the primary tumor, tumor grade, and BMI were potential risk factors that had an impact on the cure fraction. A non-mixture non-parametric cure rate model provides a flexible framework for accurately determining the impact of risk factors on the long-term survival of patients with CRC

Evaluation of Neutrophil Gelatinase-Associated Lipocalin and Cystatin C in Early Diagnosis of Chronic Kidney Disease in the Absence of the Gold Standard

Background: Glomerular filtration rate (GFR) is considered as a gold standard of kidney function. However, using GFR as the gold standard is not common in clinical practice, because its direct measurement is usually expensive, cumbersome, and invasive. In the present study, we assessed the predictive power of two other biomarkers, Cystatin-C (Cys-C) and Neutrophil Gelatinase-Associated Lipocalin (NGAL) for early detection of chronic kidney diseases (CKD) in the absence of a gold standard. Materials and Methods: In this study, 72 patients who referred to the Shohadaye Tajrish Hospital of Tehran, Iran, for measuring their kidney function were studied. The ELISA method was utilized for measuring plasma NGAL (PNGAL) and serum Cys-C (SCys-C). The Bayesian latent class modeling approach was applied to asses the predictive power of these biomarkers. Results: While both the biomarkers had rather high sensitivities (PNGAL=91%, SCys-C=89%), the specificity of SCys-C biomarker was very lower than the one of PNGAL (SCys-C=56%, PNGAL=94%). The estimated area under the receiver operating characteristic (ROC) curve for SCys-C as the single biomarker for the diagnosis of CKD was about 0.76, while a similar estimate for PNGAL was 0.93. The added value of PNGAL to SCys-C for the diagnosis of CKD in terms of the ROC curve was about 0.19, while the added value of SCys-C to PNGAL was less than 0.02. Conclusion: In general, our findings suggest that PNGAL can be utilized as a single reliable biomarker for early detection of CKD. In addition, results showed that when a perfect gold standard is not available, Bayesian approaches to latent class models could lead to more precise sensitivity and specificity estimates of imperfect tests. Keywords:Chronic Kidney Diseases; Neutrophil Gelatinase-Associated Lipocalin; Cystatin C; Bayesian Approach; Latent Class Model; Sensitivity; Specificit

Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models

BackgroundBreast cancer (BC) survival prediction can be a helpful tool for identifying important factors selecting the effective treatment reducing mortality rates. This study aims to predict the time-related survival probability of BC patients in different molecular subtypes over 30 years of follow-up.Materials and methodsThis study retrospectively analyzed 3580 patients diagnosed with invasive breast cancer (BC) from 1991 to 2021 in the Cancer Research Center of Shahid Beheshti University of Medical Science. The dataset contained 18 predictor variables and two dependent variables, which referred to the survival status of patients and the time patients survived from diagnosis. Feature importance was performed using the random forest algorithm to identify significant prognostic factors. Time-to-event deep-learning-based models, including Nnet-survival, DeepHit, DeepSurve, NMLTR and Cox-time, were developed using a grid search approach with all variables initially and then with only the most important variables selected from feature importance. The performance metrics used to determine the best-performing model were C-index and IBS. Additionally, the dataset was clustered based on molecular receptor status (i.e., luminal A, luminal B, HER2-enriched, and triple-negative), and the best-performing prediction model was used to estimate survival probability for each molecular subtype.ResultsThe random forest method identified tumor state, age at diagnosis, and lymph node status as the best subset of variables for predicting breast cancer (BC) survival probabilities. All models yielded very close performance, with Nnet-survival (C-index=0.77, IBS=0.13) slightly higher using all 18 variables or the three most important variables. The results showed that the Luminal A had the highest predicted BC survival probabilities, while triple-negative and HER2-enriched had the lowest predicted survival probabilities over time. Additionally, the luminal B subtype followed a similar trend as luminal A for the first five years, after which the predicted survival probability decreased steadily in 10- and 15-year intervals.ConclusionThis study provides valuable insight into the survival probability of patients based on their molecular receptor status, particularly for HER2-positive patients. This information can be used by healthcare providers to make informed decisions regarding the appropriateness of medical interventions for high-risk patients. Future clinical trials should further explore the response of different molecular subtypes to treatment in order to optimize the efficacy of breast cancer treatments

Assessing sex differential in COVID-19 mortality rate by age and polymerase chain reaction test results: an Iranian multi-center study

Background The aim of this study is to evaluate the sex differential effect in the COVID-19 mortality by different age groups and polymerase chain reaction (PCR) test results. Research design In a multicenter cross-sectional study from 55 hospitals in Tehran, Iran, patients were categorized as positive, negative, and suspected cases. Results A total of 25,481 cases (14,791 males) were included in the study with a mortality rate of 12.0%. The mortality rates in positive, negative, and suspected cases were 20.55%, 9.97%, and 7.31%, respectively. Using a Cox regression model, sex had a significant effect on the hazard of death due to COVID-19 in adult and senior male patients having positive and suspected PCR test results. However, sex was not found as significant factor for mortality in patients with a negative PCR test in different age groups. Conclusions Regardless of other risk factors, we found that the effect of sex on COVID-19 mortality varied significantly in different age groups. Therefore, appropriate strategies should be designed to protect adult and senior males from this deadly infectious disease. Furthermore, owing to the considerable death rate of COVID-19 patients with negative test results, new policies should be launched to increase the accuracy of diagnosis tests

Contrastive analysis of diagnostic tests evaluation without gold stand-ard: review article

Considering the advancement of medical sciences, diagnostic tests have been developed to distinguish patients from healthy population. Therefore, Determining and evaluation of the diagnostic accuracy tests is of great importance. The accuracy of a test under evaluation is determined through the amount of agreement between its results with the results of the gold standard, and this test accuracy can be defined based on sensitivity, specificity, positive predictive value, negative predictive value and the area under the receiver operative characteristic curve (AUC). Gold standard is an accurate and error- free method to determine the presence or absence of disease of interest and classify patients, which is not available in some diseases and situations as this method is costly or invasive. In these cases, reference standard is a best available replacement method to be used by physicians to diagnostic disease. However, in some situation, the acceptable reference standard is invasive or costly and does not exist or unreliable. It can be imperfect and results of the reference standard method are not necessarily error- free and cannot be applied to everyone in the study; all these cases point to the conditions in which the gold standard is not available. The use of reference standard including error causes to incorrect separation of patients from healthy population and thus, it cannot be a comparing measure for other diagnostic tests and its results are inaccurate. Therefore, other alternatives methods are needed for evaluation and determine the diagnostic accuracy tests when the gold standard does not exist. Imputation method, correct imperfect reference standard method, the construct reference standard method, latent class models, differential verification, composite reference standard and discrepant analysis are of these alternative methods. Each of these methods, considering its features, advantages, and limitations can be used to evaluate the accuracy of diagnostic test in the absence of gold standard. The present study gave an overview of methods to evaluation of diagnostic accuracy tests when there is no gold standard and the focus of this study was on explain the concept of these solutions, review and compare them and their strengths and weaknesses

Predictors of postoperative pain six months after breast surgery

Abstract Breast cancer, with a high prevalence and survival rate, leads to long-term complications. A major sequel is acute or chronic postoperative pain, and we investigated the possible relationship with clinical and psychological variables. Patients undergoing breast surgery filled out the loneliness (ULS-8) and depression (HADS) questionnaires. Patients rated their pain intensity with the Numerical Rating Scale (0–10, NRS) two days, seven days, and six months after surgery. Of 124 patients, the mean age was 45.86 years old, and the pain scores on the second and seventh postoperative days were 5.33 and 3.57, respectively. Sixth-month pain was significantly correlated with the acute scores with a mean of 3.27; and in the multivariate analysis, it was significantly associated with preoperative pain (p-value = 0.007), self-reported loneliness (p-value = 0.010), and adjuvant radiotherapy (p-value = 0.004). In conclusion, loneliness may be a risk factor for postoperative pain in breast surgery