Although several causal models relevant to epidemiology have been proposed, a key question that has remained unanswered is why some people at high-risk for a particular disease do not develop the disease while some people at low-risk do develop it. The equivalence model, proposed herein, addresses this dilemma. The equivalence model provides a graphical description of the overall effect of risk and protective factors at the individual level. Risk factors facilitate the occurrence of the outcome (the development of disease), whereas protective factors inhibit that occurrence. The equivalence model explains how the overall effect relates to the occurrence of the outcome. When a balance exists between risk and protective factors, neither can overcome the other; therefore, the outcome will not occur. Similarly, the outcome will not occur when the units of the risk factor(s) are less than or equal to the units of the protective factor(s). In contrast, the outcome will occur when the units of the risk factor(s) are greater than the units of the protective factor(s). This model can be used to describe, in simple terms, causal inferences in complex situations with multiple known and unknown risk and protective factors. It can also justify how people with a low level of exposure to one or more risk factor(s) may be affected by a certain disease while others with a higher level of exposure to the same risk factor(s) may remain unaffected.

Although several causal models relevant to epidemiology have been proposed, a key question that has remained unanswered is why some people at high-risk for a particular disease do not develop the disease while some people at low-risk do develop it. The equivalence model, proposed herein, addresses this dilemma. The equivalence model provides a graphical description of the overall effect of risk and protective factors at the individual level. Risk factors facilitate the occurrence of the outcome (the development of disease), whereas protective factors inhibit that occurrence. The equivalence model explains how the overall effect relates to the occurrence of the outcome. When a balance exists between risk and protective factors, neither can overcome the other; therefore, the outcome will not occur. Similarly, the outcome will not occur when the units of the risk factor(s) are less than or equal to the units of the protective factor(s). In contrast, the outcome will occur when the units of the risk factor(s) are greater than the units of the protective factor(s). This model can be used to describe, in simple terms, causal inferences in complex situations with multiple known and unknown risk and protective factors. It can also justify how people with a low level of exposure to one or more risk factor(s) may be affected by a certain disease while others with a higher level of exposure to the same risk factor(s) may remain unaffected.

Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in high-dimension and small sample size cases with a single survival endpoint. However, little effort has been directed toward addressing competing risks where there is more than one failure risks. This study compared three typical variable selection techniques including Lasso, elastic net, and likelihood-based boosting for high-dimensional time-to-event data with competing risks. The per-formance of these methods was evaluated via a simulation study by analyzing a real dataset related to bladder cancer patients using time-dependent receiver operator characteristic (ROC) curve and bootstrap .632+prediction error curves. The elastic net penalization method was shown to outper-form Lasso and boosting. Based on the elastic net, 33 genes out of 1381 genes related to bladder cancer were selected. By fitting to the Fine and Gray model, eight genes were highly significant (P< 0.001). Among them, expression of RTN4, SON, IGF1R, SNRPE, PTGR1, PLEK, and ETFDH was associated with a decrease in survival time, whereas SMARCAD1 expression was associated with an increase in survival time. This study indicates that the elastic net has a higher capacity than the Lasso and boosting for the prediction of survival time in bladder cancer patients. Moreover, genes selected by all methods improved the predictive power of the model based on only clinical variables, indicating the value of information contained in the microarray features.

OBJECTIVES: Examining the premature death rate represents the first step in estimating the overall burden of disease, reflecting a full picture of how different causes affect population health and providing a way of monitoring and evaluating population health. The present study was conducted to assess the burden of premature mortality in Hamadan Province, Iran in 2006 and 2010. METHODS: To calculate years of potential life lost (YPLL), the dataset was categorized into 5-year age groups based on each person's age at death. Then the age groups were subtracted from the relevant age-based life table produced by the World Health Organization in 2009. The YPLL for each individual were then added together to yield the total YPLL for all individuals in the population who died in a particular year. Finally, we calculated the YPLL for all sex-, age-, and cause-specific mortality rates and reported them as percentages. RESULTS: We analyzed 18,786 deaths, 9,127 of which occurred in 2006 and 9,659 in 2010. Mortality rates were higher in men than women for all age groups both in 2006 and 2010. In addition, age-specific mortality rates in both genders for all age groups were higher in 2010 than in 2006. The percentage of YPLL from ischemic heart diseases, cerebrovascular diseases, transport accidents, and intentional self-harm were among the greatest sources of premature death. CONCLUSION: The results of the present survey indicate that the eight major causes of premature death in both 2006 and 2010 were non-communicable diseases, especially ischemic heart diseases, cerebrovascular diseases, transport accidents, and intentional self-harm. Furthermore, our findings indicate a change in the role of non-communicable diseases in premature mortality in recent years.
Female ; Humans ; Iran ; Life Expectancy ; Life Tables ; Male ; Mortality, Premature ; Myocardial Ischemia ; Resin Cements ; World Health Organization

The quality of reporting of cohort studies published in the most prestigious scientific medical journals was investigated to indicate to what extent the items in the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist are addressed. Six top scientific medical journals with high impact factor were selected including New England Journal of Medicine, Journal of the American Medical Association, Lancet, British Medical Journal, Archive of Internal Medicine, and Canadian Medical Association Journal. Ten cohort studies published in 2010 were selected randomly from each journal. The percentage of items in the STROBE checklist that were addressed in each study was investigated. The total percentage of items addressed by these studies was 69.3 (95% confidence interval: 59.6 to 79.0). We concluded that reporting of cohort studies published in the most prestigious scientific medical journals is not clear enough yet. The reporting of other types of observational studies such as case-control and cross-sectional studies particularly those being published in less prestigious journals expected to be much more imprecise.
American Medical Association ; Archives ; Case-Control Studies ; Checklist ; Cohort Studies ; Internal Medicine ; New England

OBJECTIVES: To achieve a polio-free certification in Iran, a nationwide active surveillance program for acute flaccid paralysis (AFP) was set up following World Health Organization guidelines. This article describes the results of an eight-year surveillance of AFP in Hamadan, in the west of Iran. METHODS: A standard set of minimum core variables were collected. All cases of non-polio AFP in children aged <15 years old were reported. Two stool specimens were collected within 14 days of the onset of paralysis. RESULTS: During the eight-year survey, 88 AFP cases aged <15 years old were reported. About 40% (35/88) of cases were aged < or =5 years, 56% (49/88) were boys, 19 (21.6%) had fever at the onset of paralysis, 74 (84.0%) had complete paralysis within four days of onset, and 22 (24.7%) had asymmetric paralysis. More than one AFP case was detected per 100,000 children aged <15 years old in all years. The risk of AFP in patients aged <5 years old was almost double that of older patients. Guillain-Barre Syndrome was the major leading cause of AFP (66/88). Adequate stool specimens were collected from 85% of AFP patients. All stool specimens were tested virologically, but no wild polioviruses were detected. CONCLUSION: The active surveillance of non-polio AFP was efficient over the last eight years and exceeded 1.0 case per 100,000 children aged <15 years old. Nonetheless, there was a decreasing trend in the detection of AFP cases during the last two years and should be the focus of the policymakers' special attention, although AFP cases were still above the target level.
Aged ; Certification ; Child ; Fever ; Guillain-Barre Syndrome ; Humans ; Iran ; Paralysis ; Poliomyelitis ; Poliovirus ; Population Surveillance ; World Health Organization

PURPOSE: Rheumatoid arthritis (RA) is a chronic multi-systemic disease that causes damage to the bone and connective tissues. This study was conducted in order to accurately measure the correlation between RA and periodontitis, and to obtain an unbiased estimate of the effect of RA on periodontal indices. METHODS: In this historical cohort study, which was conducted from February to May 2011 in Hamadan city, Iran, 53 exposed people (with RA) were compared with 53 unexposed people (without RA) in terms of clinical periodontal indices (the outcomes of interest) including 1) plaque index (PI), 2) bleeding on probing (BOP), and 3) clinical attachment loss (CAL). RESULTS: A sample of 106 volunteers were evaluated, 53 rheumatoid versus 53 non-rheumatoid subjects. There was a statistically significant correlation between RA and BOP (P<0.001) and between RA and CAL (P<0.001). However, there was no statistically significant correlation between RA and any of the periodontal indices. No correlation was seen between gender and any of the indices either. There was a strong positive correlation between age and all three periodontal indices (P<0.001). CONCLUSIONS: The present study indicated a potential effect of RA on periodontal indices. However, much more evidence based on a prospective cohort study is needed to support the cause and effect relationship between RA and periodontal indices.
Arthritis, Rheumatoid ; Cohort Studies ; Connective Tissue ; Hemorrhage ; Iran ; Periodontal Index ; Periodontitis

OBJECTIVES: Diabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-mean, and random forests) to classify persons with and without diabetes. METHODS: The data set used in this study included 6,500 subjects from the Iranian national non-communicable diseases risk factors surveillance obtained through a cross-sectional survey. The obtained sample was based on cluster sampling of the Iran population which was conducted in 2005-2009 to assess the prevalence of major non-communicable disease risk factors. Ten risk factors that are commonly associated with diabetes were selected to compare the performance of six classifiers in terms of sensitivity, specificity, total accuracy, and area under the receiver operating characteristic (ROC) curve criteria. RESULTS: Support vector machines showed the highest total accuracy (0.986) as well as area under the ROC (0.979). Also, this method showed high specificity (1.000) and sensitivity (0.820). All other methods produced total accuracy of more than 85%, but for all methods, the sensitivity values were very low (less than 0.350). CONCLUSIONS: The results of this study indicate that, in terms of sensitivity, specificity, and overall classification accuracy, the support vector machine model ranks first among all the classifiers tested in the prediction of diabetes. Therefore, this approach is a promising classifier for predicting diabetes, and it should be further investigated for the prediction of other diseases.
Cross-Sectional Studies ; Data Mining ; Developing Countries ; Humans ; Iran ; Logistic Models ; Mass Screening ; Prevalence ; Risk Factors ; ROC Curve ; Sensitivity and Specificity ; Support Vector Machine

OBJECTIVES: Understanding the geographic variation of HIV/AIDS mortality risk and human immunodeficiency virus (HIV) infection could help identify high-burden areas. The aim of our study was to evaluate the effects of predictors of the time interval between HIV diagnosis to death, while accounting for spatial correlations across counties, and to assess patterns of spatial inequalities in the risk of HIV/AIDS mortality in Hamadan Province, Iran. METHODS: This retrospective study was conducted on 585 patients. The outcome in this study was the time period between the date of HIV/AIDS diagnosis and the date of death. A Weibull regression model with spatial random effects was used. RESULTS: According to multivariate analysis, there were significant associations between age, tuberculosis co-infection, and marital status and the risk of death. In terms of spatial inequalities, a cluster of counties was identified with a somewhat higher death hazard in the north, northwest, northeast, and central regions. Additionally, a cluster with a somewhat lower hazard was identified in the south, southwest, southeast, and west regions. CONCLUSIONS: The spatial pattern of HIV/AIDS death risk could reflect inequalities in access to antiretroviral therapy and public health services. Our results underscore the importance of attention to vulnerable groups in urban areas.
Cohort Studies* ; Coinfection ; Diagnosis ; HIV ; Humans ; Iran* ; Marital Status ; Mortality* ; Multivariate Analysis ; Public Health ; Retrospective Studies* ; Socioeconomic Factors* ; Survival Analysis ; Tuberculosis

OBJECTIVES: Chronic kidney disease (CKD) is a major public health problem worldwide. Predicting CKD incidence rates and case numbers at the national and global levels is vital for planning CKD prevention programs. METHODS: Data on CKD incidence rates and case numbers in Iran from 1990 to 2019 were extracted from the Global Burden of Disease online database. The average annual percentage change was computed to determine the temporal trends in CKD age-standardized incidence rates from 1990 to 2019. A Bayesian age-period-cohort model was used to predict the CKD incidence rate and case numbers through 2030. RESULTS: Nationally, CKD cases increased from 97,300 in 1990 to 315,500 in 2019. The age-specific CKD incidence rate increased from 168.52 per 100,000 to 382.98 per 100,000 during the same period. Between 2020 and 2030, the number of CKD cases is projected to rise to 423,300. The age-specific CKD incidence rate is projected to increase to 469.04 in 2030 (95% credible interval, 399.20 to 538.87). In all age groups and etiological categories, the CKD incidence rate is forecasted to increase by 2030. CONCLUSIONS CKD case numbers and incidence rates are anticipated to increase in Iran through 2030. The high level of CKD incidence in people with diabetes mellitus, hypertension, and glomerulonephritis, as well as in older people, suggests a deficiency of attention to these populations in current prevention plans and highlights their importance in future programs for the national control of CKD.