Factors Affecting Gastric Cancer Using Conditional Logistic Regression Using Bayesian Method: Case-Control Study
Introduction: Gastric cancer is one of the most common and deadly cancers in Iran. Gastric cancer is highly dependent on nutritional factors and geographical location. Therefore, the aim of this study was to evaluate the effect of nutritional factors on gastric cancer in Hamadan-Iran.
Method: This study was performed as a matched case-control study that each case had two controls that matched with cases in age (±5 years) and gender at Diagnostic and Treatment Center of Mahdieh in Hamedan, Iran. First and second control groups contain persons with and without family history of cancer, respectively. Information of nutritional, epidemiological and confounding variables were collected for 100 cases and 200 controls. Controls from hospital samples, friends and acquaintances of the case group were selected. Data were collected using a researcher-made questionnaire. Data were analyzed using conditional logistic regression by Bayesian method.
Results: Findings showed that, compared with individuals in the case group with the family history group with factors hot food (OR=2.35, 0.95%CrI=(1.82,5.19)), black tea (OR=1.60, 0.95%CrI (1.44,1.72)) cigarettes (OR=2.13, 0.95%CrI=(1.68,2.96)), red meat (OR=4.28, 0.95%CrI=(3.11,8.37)), residence (OR=3.15, 0.95%CrI= (1.62,5.65)), fruit (OR=0.75, 0.95% CrI=(0.63,0.83)) and vegetables (OR=0.76, 0.95%CrI=(0.59,0.85)) there was a strong statistical correlation. The results were also valid for the second control group.
Conclusion: The study showed that many controllable nutritional factors in Hamadan affect the incidence of gastric cancer. It is recommended that policymakers and managers inform the public about the risk factors and prevention of gastric cancer through the publication of brochures, television and newspapers.
Investigating Effective Factors In Out-Of-Pocket Health Payment And Its Catastrophic Expenditure Among Households With Elderly People In Iran: An Application Of Heckman Model To Control Sample Selection
Introduction: Universal health coverage is a critical goal for low- and middle-income countries, with
equitable access to healthcare services being essential to achieving this objective. With the elderly
population requiring greater healthcare services, it is crucial to plan for their healthcare needs. This study
aims to evaluate the determinants of out-of-pocket payment (OOP) and catastrophic healthcare expenditure
among households with elderly individuals in Iran.
Methods: This study analyzed the 2018 Household Income-Expenditure Survey in Iran to examine the
socio-economic factors affecting OOP (per purchasing power parity International Doller – PPP. Int $) and
catastrophic healthcare expenditure in households with elderly members. Using survey probit regression
model with Heckman selection, the study identified determinants of OOP and catastrophic healthcare
expenditures. A survey probit regression model with Heckman selection has been applied to identify the
determinants of out-of-pocket (OOP) and catastrophic healthcare expenditures. The approach allowed for
the examination of variables that may have impacted the likelihood of incurring OOP and catastrophic
healthcare expenditures, while accounting for potential selection bias.
Results: Rural households (with difference 60.78 PPP. Int$) and non-owning homes (with difference 98.83
PPP.Int$) had higher OOP than their urban and owning counterparts, respectively. Larger households also
had higher OOP, with those with five or more members having the highest. High-income households also
had higher OOP. Additionally, smaller households had a lower chance of facing catastrophic healthcare
expenses. Lastly, the Mills ratio was negative.
Conclusion: Our study reveals insufficient observed out-of-pocket (OOP) payments for healthcare in
Iran to cover the "needed" OOP, indicating a possible financial burden on households. This highlights the
need to address inequalities in healthcare access and expenditure for households with elderly individuals,
particularly in rural areas and larger households. Policymakers should implement targeted interventions to
reduce OOP for these vulnerable groups. Future research should include socio-economic factors that affect
access to healthcare services
The Coagulopathy-Predicting Factors In Acute Trauma Patients Using The Generalized Estimation Equations Model
Introduction: Coagulation disorder is one of the major phenomena following the trauma which can
deteriorate the condition of the patients. The aim of this study is to determine some factors predicting the
incidence of coagulation disorder among acute trauma patients.
Methods: The generalized estimation equations were used to determine the predictors of blood
coagulation disorders in a sample of 736 people over 16 years of age with acute trauma in Shahid Rajaei
Hospital in Shiraz. The response variable was converted based on PT, PTT, INR, and fibrinogen level
criteria as a two-state variable (with/without coagulation disorder). In the data analysis, the correlation of
the coagulation disorder was considered in the first and second stages.
Results:The prevalence of coagulation disorders (mild, moderate and severe) was 19% in two stages and
coagulation disorders (moderate and severe) was 7.5%. Motor vehicle accident was the most common
cause of injury.The variables of blood sugar, diastolic blood pressure, pH, and sodium had a significant
effect on coagulation disorders (mild, moderate, and severe). Moreover, blood phosphorus, age, and
pupillary reflex had a significant effect on coagulation disorders (moderate and severe).
Conclusion: Predictors of coagulation disorders (mild-moderate-severe) include blood sugar, diastolic
blood pressure, pH, and sodium. Moreover, blood phosphorus, age, and pupil reflex are predictors of
moderate and severe coagulopathy. this model that taking into account the exchangeable correlation of
first- and second-stage coagulopathy had a better fit than the model ignoring this correlation.
Introduction: There are different mathematical models describing the propagation of an epidemic. These
models can be divided into phenomenological, compartmental, deep learning, and individual-based methods.
From other viewpoints, we can classify them into macroscopic or microscopic, stochastic or deterministic,
homogeneous or heterogeneous, univariate or multivariate, parsimonious or complex, or forecasting or
This paper defines a novel univariate bi-partite time series model able to describe spreading a communicable
infection in a population in terms of the relative increment of the cumulative number of confirmed cases. The
introduced model can describe different stages of the first wave of the outbreak of a communicable disease
from the start to the end.
Methods: The outcome of the model is relative increment, and it has five positive parameters: the length of
the first days of spreading and the relative increment in these days, the potent of the mildly decreasing trend
(after the significant decrease), and the adjusting coefficient to adapt this trend to the initial pattern, and the
fixed ratio of the mean to the variance.
Results: We use it to describe the propagation of various disease outbreaks, including the SARS (2003),
the MERS (2018), the Ebola (2014-2016), the HIV/AIDS (1990-2018), the Cholera (2008-2009), and the
COVID-19 epidemic in Iran, Italy, the UK, the USA, China and four of its provinces; Beijing, Guangdong,
Shanghai, and Hubei (2020). In all mentioned cases, the model has an acceptable performance. In addition,
we compare the goodness of this model with the ARIMA models by fitting the propagation of COVID-19 in
Iran, Italy, the UK, and the USA.
Conclusion: The introduced model is flexible enough to describe a broad range of epidemics. In comparison
with ARIMA time series models, our model is more initiative and less complicated, it has fewer parameters,
the estimation of its parameters is more straightforward, and its forecasts are narrower and more accurate. Due
to its simplicity and accuracy, this model is a good tool for epidemiologists and biostatisticians to model the
first wave of an epidemic.
Evaluating The Agreement Between K-Median And Latent Class Analysis For Clustering Of Psychological Distress Prevalence Evaluating the agreement between k-median and LCR
Introduction: Psychological distress (PD) is one of the most common mental disorders in the general population. Psychological distress is considered a public health priority due to its adverse effects on quality of life, health, performance, and productivity. It can also predict several serious mental illnesses, such as depressive disorder and anxiety. In this study, we intend to identify the behavioral pattern of PD in the population of 18 to 65 years old in Mashhad using two methods, K-median and Latent Class Analysis (LCA), and evaluate the agreement between the two methods.
Methods: This cross-sectional study was performed on 38058 individuals referred to community health care centers at Mashhad of Iran in 2019. The information used in this study was extracted from Sina Electronic Health Record System (SinaEHR) database. A demographic information checklist and 6-item Kessler psychological distress scale (K-6) were used for data collection. K-median and LCA were used for data analysis.
Results: Out of 38058 participants, 49.3% were women, 86.1% were married, and 63.6% had a diploma and under diploma education. The LCA identified three patterns of PD in answering the items of the K-6 questionnaire, including severe PD (19.7%), low PD (36.7%), and no PD (43.5%). Three clusters were identified by the K-Median method: 1) severe PD (22.0%), 2) low PD (31.1%), and 3) and no PD (46.9%). The agreement between K-Median and LCA was kappa = 0.862.
Conclusion: About 20% of people were classified as having severe PD. Both LCA and k-median methods can reasonably identify the latent pattern of PD with significant entropy, and there was almost complete agreement between the two methods in data clustering. Considering the advantages of the LCA, this method is recommended to identify the latent pattern of PD based on the k-6 questionnaire.
Association Of Statin Therapy On Clinical Outcomes In Covid-19 Patients: An Updated Systematic Review And Meta-Analysis On All Related Evidences statin therapy in COVID-19 patients
Background: Statins is a class of lipid-lowering drugs and our previous investigations showed that statins have antiviral effects and have a wound healing effect in the lung. This systematic review and meta-analysis aimed to evaluate the effects of statin therapy on mortality and clinical outcomes in COVID-19 patients.
Methods: A comprehensive search was conducted in international databases, including MEDLINE, Scopus, Web of Science, and Embase from December 1, 2019 until January 26, 2022 without any restriction in language. The random-effects model was used to estimate the pooled odds ratio (OR).
Results: The statin therapy overally was associated with decrease in odds of ventilation [pooled OR (95% CI): 0.85 (0.70 to 0.99)] and mortality [pooled OR (95% CI): 0.73 (0.66 to 0.81)] but had no effects on the ICU admission [pooled OR (95% CI): 0.93 (0.77 to 1.12)], oxygen therapy [pooled OR (95% CI): 0.85 (0.70 to 0.99)], recovery [pooled OR (95% CI): 1.85 (0.35 to 9.92)], kidney failure [pooled OR (95% CI): 1.01 (0.73 to 1.40)], hospitalization [pooled OR (95% CI): 1.45 (0.88 to 2.36)], asymptomatic disease [pooled OR (95% CI): 1.33 (0.24 to 7.44)], and ARDS [pooled OR (95% CI): 1.15 (0.88 to 1.49)].
Conclusions: The present meta-analysis showed that statin therapy was associated with a reduced risk of mortality and ventilation in patients with COVID-19 but had no effects on other clinical outcomes.
The Prediction Of Alexithymia Using Depression, Anxiety, Stress, And Demographics In Undergraduate Students
Aims: Alexithymia is a psychiatric disorder in which people become emotionally frustrated. This study aims to model the role of depression, anxiety, and stress in alexithymia prediction.
Materials and Methods: In this cross-sectional study, 260 undergraduate students were selected via multi-stage cluster sampling. The Toronto Alexithymia Scale (TAS-20) and depression, anxiety and stress scale have been used to collect data. The association between qualitative variables was examined using Chi-square test and LASSO logistic regression was fitted for alexithymia prediction.
Results: The mean± SD of participants’ age was 20.7± 3.2 years. Of all, 197 (75.8%) students were female and 236 (90.8%) were single. According to the cutoff point for TAS-20, 30.8% of the students displayed signs of alexithymia. The rate of alexithymia was significantly higher among males (42.9% versus 26.9%, P=0.02) and among nursing (45.9%) and anesthesia (44.8%) students than other undergraduate students. The proportion of students with anxiety, depression, and stress were 45.0%, 15.8%, and 9.2%, respectively. 51.2% of the depressed students had alexithymia, while only 26.9% of non-depressed students were alexithymic (P=0.002). LASSO logistic regression showed that odds of alexithymia was significantly higher among male students (OR=1.40, 95% CI=1.03, 1.90), students with depression (OR=1.73, 95% CI=1.18, 2.54), students who had anxiety (OR=1.42, 95% CI=1.07, 1.89), and nursing students (OR=1.62, 95% CI=1.07, 2.45).
Conclusion: The results of this study indicate the importance role of anxiety and depression in predicting alexithymia. Due to the high prevalence of alexithymia among college students, we suggest the routine evaluation of college students for alexithymia.
An Integrative Bayesian Model Analysis Of Patient Characteristics And Treatment Variables To Understand Lung Cancer Survival Rates In Kerman Province, Iran
Introduction: Lung cancer (LC) is the most common type of cancer and causes of death among males.
This study aims to estimate the survival rate of lung cancer patients by employing the benefits of Bayesian
modeling in determining factors affecting the survival of lung cancer in Kerman province, Iran.
Methods: We conducted a historical cohort study of 195 patients with lung cancer from 2016 to 2018. In
this study, we used linear dependent Dirichlet process (LDDP), and employed some results of the previous
study as informative prior for better estimation.
Results: Of the 195 patients, 160 died. The mean age of patients at the time of diagnosis was 62.43±12.55.
The median survival time of patients was 10.4 months. Men accounted for 75.9% of the total patients. One,
two, and three-year survival rate was 44.5%, 22.9%, and 16.4%, respectively. The multivariable model
results showed that treatments were significant. Other variables had no significant effect.
Conclusion: Our study highlights the importance of prompt diagnosis and appropriate treatment in
improving the survival rate of lung cancer patients. We found that patients who received at least one
usual lung cancer treatment, such as chemotherapy, radiation therapy, or surgery, had higher survival rates
compared to those who did not receive any treatment. While our study has some limitations, such as its
retrospective design, our use of Bayesian modeling techniques allowed us to effectively incorporate prior
information from previous studies to improve estimation accuracy
The Association between Dietary Antioxidant Indices and Cardiac Disease: Baseline Data of Kharameh Cohort Study
Oxidative stress contributes to the development of cardiovascular disease. Tools for evaluating the anti-inflammatory and antioxidative characteristics of an individual’s diet as a whole may be valuable for assessing the combined effects of dietary antioxidants on health. This population-based study aimed to investigate the association between dietary antioxidants and cardiac disease.
In this population-based cross-sectional study, 10439 individuals aged 40-70 years were recruited during 2014-2017 in Kherameh cohort study which is a part of the Prospective Epidemiological Research Studies in Iran (PERSIAN). The food frequency questionnaire (FFQ) with 130 food items was used to assess the dietary intakes. Vitamin A, E, C, selenium, zinc and Manganese intakes were used to compute dietary antioxidant index (DAI) and dietary antioxidant quality score (DAQs).
The participants’ mean age was 52.1± 8.3 years. Among all, 4356 (41.7%) were overweight and 1892 (18.1%) were obese. According to the results, odds of cardiac diseases decreased by increasing DAI score (OR=0.80, Pvalue <0.001). , Odds of cardiac diseases increased by lower DAQS after adjusting for demographic variables including age, sex, BMI, Marital status and hypertension (OR=0.799, P value=0.002)
The role of anti-oxidants in reducing the odds of cardiovascular disease is very important. Our results highlighted that DAQS and DAI had protective effect on the odds of cardiovascular disease. Therefore, it is suggested that anti-oxidants as zinc, manganese, selenium, and vitamins A, E and C should be taken through food to reduce the risk of the disease.