Identifying predictors of progression to AIDS and mortality post-HIV infection using parametric multistate model

OBJECTIVES: The human immunodeficiency virus (HIV) has already remained as a major public health problem all over the world. This study aimed to identify the prognostic factors influencing the disease progression in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) in Iran, using parametric multi-state models to take into account the intermediate event in the analysis.   METHOD(S): The data of the present retrospective cohort study was extracted in Tehran from April 2004 to March 2014. The number of 2473 HIV-infected patients in Behavioral Diseases Counseling Centers was enrolled. The outcomes of interest were the transition times from HIV diagnosis to AIDS and AIDS to death. The effect of several prognostic factors on both transitions was investigated. RESULTS: Parametric models indicated that AIDS progression was significantly associated with an increase in age (P = 0.017), low education (P = 0.026), and a decreased CD4 cell count (P = 0.001). Furthermore, the AIDS-related death was significantly associated with male sex (P = 0.010), tuberculosis coinfection (P = 0.001), antiretroviral therapy (P = 0.001) and a decreased CD4 cell count (P = 0.035). CONCLUSION: The results of this study indicated that CD4 cell count was one of the most important prognostic factors that affected and accelerated both HIV→AIDS and AIDS→DEATH transitions and antiretroviral treatment was found to be an effective measure in decelerating survival of patients with AIDS to death state. The usual Cox Model is not able to identify some of these prognostic factors.&nbsp

Prediction of Multiple sclerosis disease using machine learning classifiers: a comparative study

INTRODUCTION: Hamedan Province is one of Iran's high-risk regions for Multiple Sclerosis (MS). Early diagnosis of MS based on an accurate system can control the disease. The aim of this study was to compare the performance of four machine learning techniques with traditional methods for predicting MS patients. METHODS: The study used information regarding 200 patients through a case-control study conducted in Hamadan, Western Iran, from 2013 to 2015. The performance of six classifiers was used to compare their performance in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), negative likelihood ratio (LR-) and total accuracy. RESULTS: Random Forest (RF) model illustrated better performance among other models in both scenarios. It had greater specificity (0.67), PPV (0.68) and total accuracy (0.68). The most influential diagnostic factors for MS were age, birth season and gender. CONCLUSIONS: Our findings showed that despite all the six methods performed almost similarly, the RF model performed slightly better in terms of different criteria in prediction accuracy. Accordingly, this approach is an effective classifier for predicting MS in the early stage and control the disease

Quality of Cohort Studies Reporting Post the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement

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

Socioeconomic status and health literacy as the important predictors of general health in Iran: a structural equation modeling approach

Background: We aimed to determine the level of health literacy (HL), and its association with general health. In addition, we investigated the direct and indirect association of socioeconomic status (SES) and general health among the adult population in Iran. Methods: This cross-sectional study involved 750 literate adults' people. The SES was assessed based on the owning of assets. HL was evaluated using a validated questionnaire in Iran. General health was assessed using the WHO general health questionnaire. The simple and adjusted linear regression models, and structural equation modeling (SEM) were used for data analysis. Results: In adjusted model, female gender, higher level of education, use of books, pamphlets, or brochures as a source of health information, the higher level of SES were positively associated with higher HL. In addition, the HL was significantly associated with higher scores of general health. Results of SEM showed that the direct effect of SES on general health was not significant, but the indirect effect via HL was significant (path coefficient: 0.24; p<0.001). Conclusion: Results of our study indicated HL is strongly associated with general health among the adult population. SES had a significant indirect association with general health via the effect on health literacy

Exploring the spatial patterns of three prevalent cancer latent risk factors in Iran; Using a shared component model

Background and aims: The aim of this study was the modeling of the incidence rates of Colorectal, breast and prostate cancers using a shared component model in order to explore the spatial pattern of their shared risk factors (i.e., obesity and low physical activity) affecting on cancer incidence, and also to estimate the relative weight of these shared components. Methods: In this study, the new cases of colorectal, breast and prostate cancers information provided by the Management Center of Ministry of Health and Medical Education in 2009 were analyzed. The Bayesian shared component model was used. In addition, BYM (Besag, York and Mollie) model was applied to investigate the geographical pattern of disease incidence rates, individually. Results: The larger effect of obesity on the incidence of the relevant cancers was found in Ardabil, West Azarbaijan, Gilan, Zanjan, Kurdistan, Qazvin, Tehran, Mazandaran, Hamadan, Kermanshah, Semnan, Golestan, Yazd and Kerman, and this component was more important for prostate cancer compared to colorectal and breast cancers. In addition, low physical activity shared component had more effect on the incidence of colorectal and breast cancers in Ardabil, Zanjan, Qazvin, Tehran, Mazandaran, Markazi, Lorestan, Kermanshah, Ilam, Khuzestan, South Khorasan, Yazd, Kerman and Fars, and also, this component was more important for Breast cancer compared to Colorectal cancer. Conclusion: Based on deviance Information criterion, combined modeling of three understudy cancers using a shared component model was better than modeling them individually using BYM model

Bayesian modeling of clustered competing risks survival times with spatial random effects

n some studies, survival data are arranged spatially such as geographical regions. Incorporating spatial associationin these data not only can increase the accuracy and efficiency of the parameter estimation, but it also investigatesthe spatial patterns of survivorship. In this paper, we considered a Bayesian hierarchical survival model in thesetting of competing risks for the spatially clustered HIV/AIDS data. In this model, a Weibull Parametric distributionwith the spatial random effects in the form of county-failure type-level was used. A multivariate intrinsic conditionalautoregressive (MCAR) distribution was employed to model the areal spatial random effects. Comparison amongcompeting models was performed by the deviance information criterion and log pseudo-marginal likelihood. Weillustrated the gains of our model through the simulation studies and application to the HIV/AIDS data

Comparison of Salivary pH, Buffering Capacity and Alkaline Phosphatase in Smokers and Healthy Non-Smokers : Retrospective cohort study

Objectives: Saliva contains alkaline phosphatase (ALP)—a key intracellular enzyme related to destructive processes and cellular damage—and has buffering capacity (BC) against acids due to the presence of bicarbonate and phosphate ions. Smoking may have deleterious effects on the oral environment due to pH changes which can affect ALP activity. This study aimed to evaluate the salivary pH, BC and ALP activity of male smokers and healthy non-smokers. Methods: This retrospective cohort study took place between August 2012 and December 2013. A total of 251 healthy male non-smokers and 259 male smokers from Hamadan, Iran, were selected. Unstimulated whole saliva was collected from each participant and pH and BC were determined using a pH meter. Salivary enzymes were measured by spectrophotometric assay. Results: Mean salivary pH (7.42 ± 0.48 and 7.52 ± 0.43, respectively; P = 0.018) and BC (3.41 ± 0.54 and 4.17 ± 0.71; P = 0.001) was significantly lower in smokers compared to non-smokers. Mean ALP levels were 49.58 ± 23.33 IU/L among smokers and 55.11 ± 27.85 IU/L among non-smokers (P = 0.015). Conclusion: Significantly lower pH, BC and ALP levels were observed among smokers in comparison to a healthy control group. These salivary alterations could potentially be utilised as biochemical markers for the evaluation of oral tissue function and side-effects among smokers. Further longitudinal studies are recommended to evaluate the effects of smoking on salivary components

Application of random survival forest for competing risks in prediction of cumulative incidence function for progression to AIDS

Objective: There has remained a need to better understanding of prognostic factors that affect the survival or risk in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), particularly in developing countries. The aim of the present study aimed to identify the prognostic factors influencing AIDS progression in HIV positive patients in Hamadan province of Iran, using random survival forest in the presence of competing risks (death from causes not related to AIDS). This method considers all interactions between variables and their nonlinear effects. Method(s): A data set of 585 HIV-infected patients extracted from 1997 to 2011 was utilized. The effect of several prognostic factors on cumulative incidence function (probability) of AIDS progression and death were investigated. Result: The used model indicated that using antiretroviral therapy tuberculosis co-infection are two top most important variables in predicting cumulative incidence function for AIDS progression in the presence of competing risks, respectively. The patients with tuberculosis had much higher predicted cumulative incidence probability. Predicted cumulative incidence probability of AIDS progression was also higher for mother to child mode of HIV transmission. Moreover, transmission type and gender were two top most important variables for the competing event. Men and those patients with IDUS transmission mode had higher predicted risk compared to others. Conclusion: Considering nonlinear effects and interaction between variables, confection with tuberculosis was the most important variable in prediction of cumulative incidence probability of AIDS progression

Analyzing socioeconomic related health inequality in mothers and children using the concentration index

Background: The effect of socioeconomic inequity on major public health indices such as maternal and child mortality rates in low- and middle-income countries are less understood and needs to be evaluated through the concentration index. Method: This cross-sectional study was conducted in 2012 in Hamadan City, the west of Iran, and 1400 households were enrolled through a stratified cluster random sampling method. The effect of inequity on health outcomes was investigated via a three-stage procedure including: (a) definition of health outcomes; (b) measuring socioeconomic status using an asset index; and (c) measuring inequality of health outcome using concentration index (CI). Results: There was inequality for all outcomes of interest. The CI was negative for low birth weight, underweight, stunting, wasting, minor injuries, moderate injuries, consanguineous marriage, child with disability, short birth spacing, and adolescent pregnancy indicating the disproportionate concentration of the health outcomes among the poor. On the other hand, CI was positive for preterm birth, Nonexclusive breastfeeding, severe injuries, incomplete health care, cesarean section, and advanced maternal age indicating opposite conclusion. Conclusion: According to our results, there is a health inequality between the poor and the rich subgroups which may increase the risk of mothers and infant mortality and morbidity rates among the poor while the majority of the conditions related to the health outcomes are preventable

Modeling the trajectory of CD4 cell count and its effect on the risk of AIDS progression and TB infection among HIV-infected patients using a joint model of competing risks and longitudinal ordinal data

Background: This study was conducted to better understand the influence of prognostic factors and the trend of CD4 cell count on the risk of progression to acquired immunodeficiency syndrome (AIDS) and tuberculosis (TB) infection among patients with human immunodeficiency virus (HIV) in a developing country.  Methods: The information of 1530 HIV-infected patients admitted in Behavioral Diseases Counseling Centers, Tehran, Iran, (2004-2014) was analyzed in this study. A joint model of ordinal longitudinal outcome and competing events is used to model longitudinal measurements of CD4 cell count and the risk of TB-infection and AIDS-progression among HIV patients, simultaneously.  Results: The results revealed that the trend of CD4 cell count had a significant association with the risk of TB-infection and AIDS-progression (p<0.001). Higher ages (p<0.001), the history of being in prison (p=0.013), receiving antiretroviral therapy (ART) (p<0.001) and isoniazid preventive therapy (IPT) (p<0.001) were associated with the positive trend of CD4 cell count. Higher ages were also associated with higher risks of TB (p<0.001) and AIDS-progression (p<0.001). Furthermore, ART (p=.0009) and IPT (p<0.001) were associated with a lower risk of TB-infection. In addition, ART (p<0.001) was associated with a lower risk of AIDS-progression. Moreover, individuals being imprisoned (p=0.001) and abusing alcohol (p=0.012) were more likely to have TB-co-infection.  Conclusions: The used joint model provided a flexible framework for simultaneous studying of the effects of covariates on the level of CD4 cell count and the risk of progression to TB and AIDS. This model also assessed the effect of CD4 trajectory on the hazards of competing events.&nbsp