A Cluster-wise Linear Regression Model to Investigate the Effect of Demographical and Clinical Variables on the Average Depression Score.

Background: Depression is a prevalent illness in the world. Given the importance of mental disorders, many researchers have investigated the effects of different variables on average depression scores. In this study, we decided to investigate the effect of some explanatory variables on the average depression score. Methods: The data were provided from the second phase of the Kerman Coronary Artery Diseases Risk Factors study (KERCADRS), which took place between 2014 and 2018. To obtain more precise connections between depression ratings and predictor variables, we employed a cluster-wise linear regression model. Results: The total number of the participants in this study was 9811, out of whom 2144 were allocated to cluster 1, 4540 to cluster 2, and 3127 to cluster 3. The average depression score was 13.76 ± 7.6 in cluster 1, 4.39 ± 4.7 in cluster 2, and 10.83 ± 6.7 in cluster 3. However, the average depression score for all the data was 8.5 ± 7.2. In all the clusters, the average depression score of females was significantly greater than that of men (P < 0.001). In cluster 1, the age category of 35-54 years, in cluster 2, the age category of 55-80 years, and in cluster 3, the age category of 15-34 years had a maximum average depression score. Conclusion: We may classify the 3 clusters as having a low (cluster 2), moderate (cluster 3), or high (cluster 1) depression score, according to the age group with the highest artery diseases risk. The patients were 55-80 years, 15-34 years, and 35-54 years in cluster 2 (low), cluster 3 (moderate), and cluster 1 (high), respectively.

An Evaluation of The Diagnostic Value of Sputum Smears Microscopy and Pcr Relative to Sputum Culture in The Diagnosis of Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis in Iran.

Background: Tuberculosis is one of the oldest known diseases in humans, and early detection of tuberculosis is one of the main measures to decrease the spread of tuberculosis. In many parts of the world, including Iran, the diagnosis of tuberculosis is based on the detection of acid-fast bacillus in sputum smear microscopy and PCR. this study aimed to synthesize evidence on the diagnostic accuracy of sputum smear and PCR compared to sputum culture for the diagnosis of PT in Iranian patients. Methods: This systematic review and meta-analysis was conducted based on PRISMA guideline for systematic review and meta-analysis. Eligible studies were cross-sectional original diagnostic studies published in English and Persian in Iran which examined the sensitivity or specificity(study outcome) of sputum smear microscopy or PCR( as the test) relative to sputum culture (as the gold standard/comparator) among Iranian patients suspected of having tuberculosis( study population). Studies whose data were not complete or extractable were excluded. Results: A total of 3518 subjects were evaluated from 15 eligible studies. The pooled sensitivity of sputum smear and PCR was 75.12 (95% CI: 66.68-83.56) and 88.02 (95% CI: 82.87-93.27), respectively. The specificity for sputum smear and PCR was 93.94 (95% CI: 91.26-96.63) and 91.82 (95% CI: 87.29-96.35) respectively. The sensitivity of both sputum smears was higher in studies published after 2010, and had higher quality. The specificity of sputum smear was a bit lower in studies published after2010 but higher in studies with higher quality. The specificity of PCR was higher in studies published after 2010 but higher in studies with higher quality. Conclusion:The increased sensitivity of sputum smear and PCR during recent years suggests the improvement of preparation and laboratory methods in recent years. However, the imperfect sensitivity of these tests highlights the need for a more accurate diagnostic method for the detection of pulmonary tuberculosis in Iran.

Development of the Quality of Life Questionnaire (SF-36) for Patients with ß-Thalassemia Major and ß-Thalassemia Intermedia Based on Extended Rasch Analysis.

The quality of life (QoL) questionnaire (SF-36) contains 36 questions in eight subscales. It requires much time to fill in by the respondent. The objective of this study was to use Rasch models to develop a questionnaire that brings the desired outcome of the QoL of people. Therefore, a new questionnaire was provided that is more motivating and time-saving for respondents than SF-36. The QoL of life assessment data of 325 ß-thalassemia major (ß-TM) and ß-thalassemia intermedia (ß-TI) patients in Kerman, Iran, was used as research data. In this study, the appropriate questions were classified in a limited dimensional framework using the exploratory factor analysis (EFA). The correctness of the factor structure was assessed by confirmatory factor analysis (CFA). The quality of the questions was evaluated by Rasch modeling [partial credit model (PCM)] and item analysis to ensure the validity and reliability of the questionnaire. Finally, the Pearson correlation coefficient was used to compare the new questionnaire with the previous one. This process resulted in the development of a new questionnaire with five subscales and 20 questions. The construct validity of the new questionnaire was good. The reliability index of the questionnaire was 0.75, and the Pearson correlation coefficient between the QoL scores gained from the previous and the new questionnaires was 0.93 that indicates the strength of the correlation. The use of Rasch analysis in this study resulted in the development of a new reliable and valid questionnaire.

Comparison of methods to Estimate Basic Reproduction Number (R 0) of influenza, Using Canada 2009 and 2017-18 A (H1N1) Data.

BACKGROUND: The basic reproduction number (R 0) has a key role in epidemics and can be utilized for preventing epidemics. In this study, different methods are used for estimating R 0's and their vaccination coverage to find the formula with the best performance. MATERIALS AND METHODS: We estimated R 0 for cumulative cases count data from April 18 to July 6, 2009 and 35-2017 to 34-2018 weeks in Canada: maximum likelihood (ML), exponential growth rate (EG), time-dependent reproduction numbers (TD), attack rate (AR), gamma-distributed generation time (GT), and the final size of the epidemic. Gamma distribution with mean and standard deviation 3.6 ± 1.4 is used as GT. RESULTS: The AR method obtained a R 0 (95% confidence interval [CI]) value of 1.116 (1.1163, 1.1165) and an EG (95%CI) value of 1.46 (1.41, 1.52). The R 0 (95%CI) estimate was 1.42 (1.27, 1.57) for the obtained ML, 1.71 (1.12, 2.03) for the obtained TD, 1.49 (1.0, 1.97) for the gamma-distributed GT, and 1.00 (0.91, 1.09) for the final size of the epidemic. The minimum and maximum vaccination coverage were related to AR and TD methods, respectively, where the TD method has minimum mean squared error (MSE). Finally, the R 0 (95%CI) for 2018 data was 1.52 (1.11, 1.94) by TD method, and vaccination coverage was estimated as 34.2%. CONCLUSION: For the purposes of our study, the estimation of TD was the most useful tool for computing the R 0, because it has the minimum MSE. The estimation R 0 > 1 indicating that the epidemic has occurred. Thus, it is required to vaccinate at least 41.5% to prevent and control the next epidemic.

The relation between air pollution and respiratory deaths in Tehran, Iran- using generalized additive models.

BACKGROUND: Some epidemiological evidence has shown a relation between ambient air pollution and adverse health outcomes. The aim of this study was to investigate the effect of air pollution on mortality from respiratory diseases in Tehran, Iran. METHODS: In this ecological study, air pollution data was inquired from the Tehran Province Environmental Protection Agency and the Tehran Air Quality Control Company. Meteorological data was collected from the Tehran Meteorology Organization and mortality data from the Tehran Cemetery Mortality Registration. Generalized Additive Models (GAM) was used for data analysis with different lags, up to 15 days. A 10-unit increase in all pollutants except CO (1-unit) was used to compute the Relative Risk of deaths. RESULTS: During 2005 until 2014, 37,967 respiratory deaths occurred in Tehran in which 21,913 (57.7%) were male. The strongest relationship between NO2 and PM10and respiratory death was seen on the same day (lag 0), and was respectively (RR = 1.04, 95% CI: 1.02-1.07) and (RR = 1.03, 95% CI: 1.02-1.04). O3 and PM2.5 had the strongest relationship with respiratory deaths on lag 2 and 1 respectively, and the RR was equal to 1.03, 95% CI: 1.01-1.05 and 1.06, 95% CI: 1.02-1.10 respectively. NO2, O3, PM10 and PM2.5 also showed significant relations with respiratory deaths in the older age groups. CONCLUSIONS: The findings of this study showed that O3, NO2, PM10 and PM2.5 air pollutants were related to respiratory deaths in Tehran. Reducing ambient air pollution can save lives in Tehran.

Hospital service quality - patient preferences - a discrete choice experiment.

PURPOSE: High quality healthcare is important to all patients. If healthcare is felt to be high quality, then patients will be satisfied, and the relationship between patients and healthcare providers will improve. Patient satisfaction is among the most commonly used service quality indicators; however, it is not fully known which factors influence satisfaction. Therefore, it is necessary to pay attention to the elements that affect both healthcare quality and patient satisfaction. Nowadays, several methods are used in health economics to assess patient preferences, prioritize them and help health policy makers improve services. Discrete choice experiment (DCE) is one method that is useful to elicit patient preferences regarding healthcare services. The purpose of this paper is to apply DCE and elicit patient preferences in medical centers to rank certain healthcare quality factors. DESIGN/METHODOLOGY/APPROACH: The descriptive, analytical study used a cross-sectional questionnaire that the authors developed. In total, 12 scenarios were chosen after applying fractional factorials. The questionnaire was completed by patients who were admitted to Kerman General Teaching Hospitals, South-East Iran in 2015. Patient preferences were identified by calculating the characteristics' marginal effects and prioritizing them. The generalized estimation equation (GEE) model was used to determine attribute effects on patient preferences. FINDINGS: In total, 167 patients completed the questionnaire. Prioritizing the attributes showed that "physical examination" was the most important attribute. Other key features included "cleanliness," "training after discharging," "medical staff attention," "waiting for admission" and "staff attitude." All attributes were statistically significant ( p<0.05) except staff behavior. No demographic characteristic was significant. PRACTICAL IMPLICATIONS: To increase hospital patient satisfaction, health policy makers should develop programs to enhance healthcare quality and hospital safety by increasing physical examination quality and other services. ORIGINALITY/VALUE: To estimate DCE independent variables, logistic regression models are usually used. The authors used the GEE model to estimate discrete choice experiment owing the explanatory variables' dependency.

Short-term effects of air pollution on respiratory mortality in Ahvaz, Iran.

Background: Urban air pollutants may affect respiratory mortality. This study was conducted to investigate this effect in Ahvaz, one of the most polluted cities in the world. Methods: The impact of 7 major air pollutants including O3, PM10, NO2, CO, and SO2 were evaluated on respiratory mortality in different gender and age groups using a quasi-Poisson, second degree polynomial constrained, distributed lag model, with single and cumulative lag structures adjusted by trend, seasonality, temperature, relative humidity, weekdays, and holiday. Data were analyzed using the dlnm package in R x64 3.2.5 software. Significance level was set at less than 0.05. Results: In adjusted models, for each IQR increase of O3 in the total population, the risk ratio (RR) for respiratory deaths in 0 to 14- day lags was, respectively, 1.009 (95% CI:1.001-1.016) and 1.009 (95% CI:1.002-1.017), and it was 1.021 (95% CI: 1.002-1.040) in cumulative 0 to 14- day lags. For PM10, in the total population and in adjusted models after 0 to 14- day lags and in cumulative lags of 0 to 14 for an IQR increase in the mean concentration of PM10, the RR for respiratory deaths increased significantly and was, respectively, 1.027 (95% CI:1.002-1.051), 1.029 (95% CI:1.006-1.052), and 1.065 (95% CI:1.005-1.128). NO2 showed a significant association with respiratory deaths only in the 18 to 60 year- old age group and in 9- day lags (RR= 1.318, 95% CI:1.002-1.733). Finally, the results showed that for an IQR increase in the mean concentration of CO and SO2, the adjusted RR for respiratory deaths in 9- day lags in the total population was, respectively, RR= 1.058 (95% CI:1.008-1.111) and 1.126 (95% CI:1.034-1.220). Conclusion: Air pollution in Ahvaz is probably causing increased respiratory mortality.

Death from respiratory diseases and temperature in Shiraz, Iran (2006-2011).

Some studies have suggested that the number of deaths increases as temperatures drops or rises above human thermal comfort zone. The present study was conducted to evaluate the relation between respiratory-related mortality and temperature in Shiraz, Iran. In this ecological study, data about the number of respiratory-related deaths sorted according to age and gender as well as average, minimum, and maximum ambient air temperatures during 2007-2011 were examined. The relationship between air temperature and respiratory-related deaths was calculated by crude and adjusted negative binomial regression analysis. It was adjusted for humidity, rainfall, wind speed and direction, and air pollutants including CO, NOx, PM10, SO2, O3, and THC. Spearman and Pearson correlations were also calculated between air temperature and respiratory-related deaths. The analysis was done using MINITAB16 and STATA 11. During this period, 2598 respiratory-related deaths occurred in Shiraz. The minimum number of respiratory-related deaths among all subjects happened in an average temperature of 25 °C. There was a significant inverse relationship between average temperature- and respiratory-related deaths among all subjects and women. There was also a significant inverse relationship between average temperature and respiratory-related deaths among all subjects, men and women in the next month. The results suggest that cold temperatures can increase the number of respiratory-related deaths and therefore policies to reduce mortality in cold weather, especially in patients with respiratory diseases should be implemented.

Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

BACKGROUND: Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. MATERIALS AND METHODS: We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). RESULTS: The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). CONCLUSION: Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

Assessment of the impact of the malaria elimination programme on the burden of disease morbidity in endemic areas of Iran.

BACKGROUND: Controlling and preventive measures considerably reduced malaria incidence in Iran over the past few years, which confined the endemic areas to some regions in the southeastern Iran. The National Malaria Elimination Programme commenced in 2010. With regard to the presumption that the elimination programme interventions have accelerated the declining trend of malaria incidence across the endemic areas of Iran, the present study attempted to assess the effectiveness of the elimination programme by reviewing malaria incidence status, over a 14-year period, and comparing the trend of malaria incidence across malaria-endemic areas between the control and pre-elimination phase, and the elimination phase. METHODS: A retrospective analysis of malaria surveillance data was conducted in a 14-year period (2001-2014), using multilevel Poisson regression. The epidemiological malaria maps and indicators also were developed and compared between the control and pre-elimination phase, and the elimination phase. RESULTS: The mean of malaria incidence was 2.2 (1.7-2.7) for the entire study period. This rate was 3.4 (2.6-4.1) in the control and pre-elimination phase, and 0.41 (0.25-0.57) for the elimination phase. During the malaria elimination phase, the decline of annual malaria incidence had significantly accelerated and autochthonous cases had the greatest difference in malaria incidence decline (compared to the control and pre-elimination phase), whereas, falciparum cases had the lowest difference in malaria incidence decline, followed by non-Iranian and imported cases. Furthermore, there was a decline in Iranians to non-Iranians ratio and an increase in the ratios of over 15 to under 15, as well as male to female, in the elimination phase in comparison to the control and pre-elimination phase. CONCLUSIONS: It seems that the decline of malaria transmission, which has been initiated over the past few years, has accelerated as a result of the elimination programme, and Iran is approaching the goals set regarding the elimination of this disease.

The topical treatment of anthroponotic cutaneous leishmaniasis with the tincture of thioxolone plus benzoxonium chloride (Thio-Ben) along with cryotherapy: a single-blind randomized clinical trial.

A simple efficacious topical treatment for cutaneous leishmaniasis (CL) is still an unresolved challenge. This study aimed to evaluate the efficacy of the topical use of thioxolone plus benzoxonium chloride (Thio-Ben) tincture in combination with cryotherapy in comparison with intralesional meglumine antimoniate (Glucantime) along with cryotherapy in treating anthroponotic CL (ACL). The study was conducted in Leishmaniasis Center in Dadbin Health Care Clinic, Kerman, Iran. Sixty-four CL lesions were randomly assigned to receive Thio-Ben plus cryotherapy (TC) (n = 32) or Glucantime plus cryotherapy (GC) (n = 32). Thio-Ben was used topically every other day and Glucantime was used intralesionally once a week for a maximum of 3 months. In both study groups, cryotherapy was administered using liquid nitrogen once every 2 weeks. Of 64 recruited lesions, 47 lesions completed the study protocol. Twenty lesions (91%) in TC group and 23 lesions (92%) in GC group showed complete cure. TC group showed faster clinical response. Pain, hypersensitivity reaction, dizziness, and nausea were only seen in GC group. This study showed that the topical use of Thio-Ben combined with cryotherapy has a good efficacy in treating ACL with the benefit that Thio-Ben has more patient compliance and less side effects than intralesional Glucantime.

The Prevalence of Metabolic Syndrome According to Different Criteria and its Associated Factors in Type 2 Diabetic Patients in Kerman, Iran.

Metabolic syndrome is highly prevalent in type 2 diabetics and is a strong risk factor for cardiovascular diseases in such patients. The aim of this study was to determine the prevalence of metabolic syndrome according to the three criteria of ATPIII, IDF and the new criteria for metabolic syndrome diagnosis in Kerman, Iran. This cross-sectional study was performed on 950 diabetic type 2 patients. Data was analyzed by independent t-test, chi-square and logistic regression using the SPSS (revision 20) software. The prevalence of metabolic syndrome in Kerman was 73.4, 64.9, and 70.4%, according to the above criteria. Fasting blood sugar, gender, triglyceride, HDL, waist circumference, and systolic blood pressure were related to the prevalence of metabolic syndrome according to the above-mentioned criteria.The prevalence of metabolic syndrome is high in type 2 diabetic patients and the above-mentioned factors exacerbate the situation.

Factors affecting Hemoglobin A1c in the longitudinal study of the Iranian population using mixed quantile regression.

Diabetes, a major non-communicable disease, presents challenges for healthcare systems worldwide. Traditional regression models focus on mean effects, but factors can impact the entire distribution of responses over time. Linear mixed quantile regression models (LQMMs) address this issue. A study involving 2791 diabetic patients in Iran explored the relationship between Hemoglobin A1c (HbA1c) levels and factors such as age, sex, body mass index (BMI), disease duration, cholesterol, triglycerides, ischemic heart disease, and treatments (insulin, oral anti-diabetic drugs, and combination). LQMM analysis examined the association between HbA1c and the explanatory variables. Associations between cholesterol, triglycerides, ischemic heart disease (IHD), insulin, oral anti-diabetic drugs (OADs), a combination of OADs and insulin, and HbA1c levels exhibited varying degrees of correlation across all quantiles (p < 0.05), demonstrating a positive effect. While BMI did not display significant effects in the lower quantiles (p > 0.05), it was found to be significant in the higher quantiles (p < 0.05). The impact of disease duration differed between the low and high quantiles (specifically at the quantiles of 5, 50, and 75; p < 0.05). Age was discovered to have an association with HbA1c in the higher quantiles (specifically at the quantiles of 50, 75, and 95; p < 0.05). The findings reveal important associations and shed light on how these relationships may vary across different quantiles and over time. These insights can serve as guidance for devising effective strategies to manage and monitor HbA1c levels.

A Cross-Sectional Comparative Study of Morbidity and Mortality of COVID-19 Disease in four Cities of Northern Khorasan Province using Ratio of Coefficient of Variation.

Background: In December 2019, the spread of a new infectious disease was reported in Wuhan, caused by a new coronavirus named COVID-19 by the World Health Organization. This study aims to compare the dispersion of COVID-19 disease among four Iranian cities in North Khorasan named Bojnord, Farooj, Jajarm, and Shirvan. Materials and Methods: This cross-sectional study includes information about the daily morbidity and mortality of COVID-19 in 1124 patients from March to May 2021. The analysis of variance method, Scheffe post hoc technique and Leven's test are used to compare the means and the variances of daily morbidity and mortality of these cities. Finally, the coefficients of variation (CVs) of the morbidity and mortality are compared. Results: The means of daily morbidity in Bojnord, Farooj, Jajarm, and Shirvan cities are 6.387, 0.946, 1.150, and 2.193, respectively. Furthermore, the means of daily mortality in Bajnourd, Farooj, Jajarm, and Shirvan are 0.763, 0.193, 0.161, and 0.290, respectively. The means and the variances of both daily mortality and morbidity are significantly different in all four cities (P < 0.05). Furthermore, CVs of daily morbidity in the cities of Bojnord, Farooj, Jajarm, and Shirvan are 0.665, 1.026, 1.032, and 0.787, respectively. The CVs of daily mortality in these cities are 1.196, 2.052, 2.468, and 1.728, respectively. The CVs of both daily mortality and morbidity are significantly different in all four cities (P < 0.05). Conclusion: The ratio of CVs is a good option for comparing the spread of COVID-19 in different regions with different means and variances.

Barriers and gaps in tuberculosis care and treatment in Iran: A multi-center qualitative study.

Introduction: Patients with Tuberculosis (TB) still have barriers in accessing high quality care and treatment services. In this qualitative study, we investigated barriers in accessing TB health services including confirmatory diagnosis, treatment adherence and recurrence of pulmonary TB using patients, physicians, and policy makers point of view. Materials and methods: In this qualitative research from November to March 2021, 3 policy makers from the Ministry of Health, 12 provincial TB experts and physicians from the TB control program and 33 patients diagnosed with TB from 4 provinces were enrolled for a semi-structured in-depth interview. All interviews were audio recorded and then transcribed. Framework analysis was done by MAXQDA 2018 software to identify key themes. Results: Several barriers reported for TB care and treatment: Poor knowledge of patents about TB symptoms, failure to screen for TB among at-risk patients by physicians, similar symptoms between TB and other lung diseases, low sensitivity of TB diagnostic tests, incomplete case finding and contact-tracing, stigma related to TB, and patients poor adherence due to long TB treatment. In addition, COVID-19 pandemic disrupted TB services and decreased detection, care and treatment services for TB patients. Conclusion: Our findings highlight the need for interventions to increase public and healthcare providers awareness about TB symptoms, using more sensitive diagnostic tests, and interventions to reduce stigma, and improve case finding and contact tracing effort. Improving patients' adherence required better monitoring and shorter effective treatment regimes.

Comparison of the Effective Reproduction Number (Rt) Estimation Methods of COVID-19 Using Simulation Data Based on Available Data from Iran, USA, UK, India, and Brazil.

BACKGROUND: Accurate determination of the effective reproduction number (Rt) is a very important strategy in the epidemiology of contagious diseases, including coronavirus disease 2019 (COVID-19). This study compares different methods of estimating the Rt of susceptible population to identify the most accurate method for estimating Rt. STUDY DESIGN: A secondary study. METHODS: The value of Rt was estimated using attack rate (AR), exponential growth (EG), maximum likelihood (ML), time-dependent (TD), and sequential Bayesian (SB) methods, for Iran, the United States, the United Kingdom, India, and Brazil from June to October 2021. In order to accurately compare these methods, a simulation study was designed using forty scenarios. RESULTS: The lowest mean square error (MSE) was observed for TD and ML methods, with 15 and 12 cases, respectively. Therefore, considering the estimated values of Rt based on the TD method, it was found that Rt values in the United Kingdom (1.33; 95% CI: 1.14-1.52) and the United States (1.25; 95% CI: 1.12-1.38) substantially have been more than those in other countries, such as Iran (1.07; 95% CI: 0.95-1.19), India (0.99; 95% CI: 0.89-1.08), and Brazil (0.98; 95% CI: 0.84-1.14) from June to October 2021. CONCLUSION: The important result of this study is that TD and ML methods lead to a more accurate estimation of Rt of population than other methods. Therefore, in order to monitor and determine the epidemic situation and have a more accurate prediction of the incidence rate, as well as control COVID-19 and similar diseases, the use of these two methods is suggested to more accurately estimate Rt.

Forecasts of cardiac and respiratory mortality in Tehran, Iran, using ARIMAX and CNN-LSTM models.

Cardiovascular diseases belong to the leading causes of disability and premature death worldwide, including in Iran. It is predicted that the burden of the disease in Iran in 2025 will be more than doubled compared to 2005. Therefore, many forecasting models have been used to predict disease progression, estimate mortality rates, and assess risk factors. Our study focused on two time series prediction on models: autoregressive integrated moving average with exogenous variable (ARIMAX) and Convolutional neural network-long short-term memory network (CNN-LSTM). ARIMAX (6,1,6) had the best MSE of 0.655 among time series regression models. The prediction of this model shows a significant association in lag 4 and lag 6. Nitrogen dioxide (NO2) was also significant in lag 6, while CNN-LSTM had a much better MSE of 0.21. For the time series analysis and forecasts studied in this paper, deep learning models provided more accurate results than classical methods such as ARIMAX.

Determination of the trend of incidence of cutaneous leishmaniasis in Kerman province 2014-2020 and forecasting until 2023. A time series study.

INTRODUCTION: Cutaneous leishmaniasis (CL) is currently a health problem in several parts of Iran, particularly Kerman. This study was conducted to determine the incidence and trend of CL in Kerman during 2014-2020 and its forecast up to 2023. The effects of meteorological variables on incidence was also evaluated. MATERIALS AND METHODS: 4993 definite cases of CL recorded from January 2014 to December 2020 by the Vice-Chancellor for Health at Kerman University of Medical Sciences were entered. Meteorological variables were obtained from the national meteorological site. The time series SARIMA methods were used to evaluate the effects of meteorological variables on CL. RESULTS: Monthly rainfall at the lag 0 (ß = -0.507, 95% confidence interval:-0.955,-0.058) and monthly sunny hours at the lag 0 (ß = -0.214, 95% confidence interval:-0.308,-0.119) negatively associated with the incidence of CL. Based on the Akaike information criterion (AIC) the multivariable model (AIC = 613) was more suitable than univariable model (AIC = 690.66) to estimate the trend and forecast the incidence up to 36 months. CONCLUSION: The decreasing pattern of CL in Kerman province highlights the success of preventive, diagnostic and therapeutic interventions during the recent years. However, due to endemicity of disease, extension and continuation of such interventions especially before and during the time periods with higher incidence is essential.

Short-term and long-term survival of patients with gastric cancer.

AIM: The aim of this study was to apply the Bayesian mixture cure rate frailty model to determine the factors that influence short-term and long-term survival of patients with gastric cancer. BACKGROUND: Determining the risk factors of gastric cancer is currently considered very important, because the disease has become one of the most dangerous types of mortal cancers. Therefore, it is possible to determine the effective risk factors of short-term and long-term survival in patients through utilizing this model. METHODS: The present retrospective study was conducted on 339 gastric cancer patients whose data was recorded in hospitals of Kerman province, Iran, during 2001-2015. In the study, the Bayesian mixture cure rate frailty model was used to determine the effective factors of short-term and long-term survival in patients. RESULTS: In the present study, the event of interest occurred for 57.5% of patients. Over time, the survival rate of cancer patients reached its lowest point, approximately 0.3 at the end of study. According to the results of the present study, variables of chemotherapy (ß=-0.35 (-0.75, -0.03) and OR=1.59 (1.08, 2.19)), morphology (ß =-0.98(-1.45, -0.48) and OR=2.99 (1.78, 4.17)), and metastasis (ß =0.42(0.10, 0.93) and OR=0.39(0.01, 0.84)) were identified as effective factors in short-term and long-term survival of patients. CONCLUSION: The effective factors of long-term and short-term survival can be identified by utilizing the Bayesian mixture cure rate frailty model, while it is impossible through conventional models of survival analysis. Chemotherapy, morphology, and metastasis are the most important effective factors of short-term and long-term survival in patients with gastric cancer.

Bayesian mixture cure rate frailty models with an application to gastric cancer data.

Mixture cure rate models are commonly used to analyze lifetime data with long-term survivors. On the other hand, frailty models also lead to accurate estimation of coefficients by controlling the heterogeneity in survival data. Gamma frailty models are the most common models of frailty. Usually, the gamma distribution is used in the frailty random variable models. However, for survival data which are suitable for populations with a cure rate, it may be better to use a discrete distribution for the frailty random variable than a continuous distribution. Therefore, we proposed two models in this study. In the first model, continuous gamma as the distribution is used, and in the second model, discrete hyper-Poisson distribution is applied for the frailty random variable. Also, Bayesian inference with Weibull distribution and generalized modified Weibull distribution as the baseline distribution were used in the two proposed models, respectively. In this study, we used data of patients with gastric cancer to show the application of these models in real data analysis. The parameters and regression coefficients were estimated using the Metropolis with Gibbs sampling algorithm, so that this algorithm is one of the crucial techniques in Markov chain Monte Carlo simulation. A simulation study was also used to evaluate the performance of the Bayesian estimates to confirm the proposed models. Based on the results of the Bayesian inference, it was found that the model with generalized modified Weibull and hyper-Poisson distributions is a suitable model in practical study and also this model fits better than the model with Weibull and Gamma distributions.