Background & Aim: Multi-state models can help better understand the process of chronic diseases such as cancers.  These models  are influenced  by assumptions  like individual  homogeneity.  This study aimed to investigate the effect of lack of individual homogeneity  assumption  in multi-state models.
Methods & Materials: To investigate the effect of lack of individual homogeneity assumption in multi-state  models,  tracking  model  as well as frailty  factor  with gamma  distribution  were used. Accordingly,  without  any  simulation  and  only  based  on  asymptotic  theory,  the  bias  of  mean transition rate which is among the basic parameters of the multi-state models was studied.
Results: Analysis of the effect of individual homogeneity assumption misspecification revealed that for  different  number  of  follow-ups  as  well  as  censoring  time,  the  mean  transition  rate  and  its variance  were underestimated.  In addition,  if there is a lot of heterogeneity  in reality and if the individual  homogeneous  multi-state  model  is fitted, a significant  bias will exist in the estimated mean transition rate and its variance. The results of this study also showed that the intensity of bias increases with an increase in the degree of heterogeneity.  But with an increase in the number of follow-ups, the intensity of bias decreases, to some extent.
Conclusion: Disregarding individual homogeneity assumption in a heterogeneous population causes bias in the estimation of multi-state model parameters and with an increase in the degree of heterogeneity, the intensity of bias will increase too.

Background  & Aim: This study aimed  to estimate  and project  the current  and future disability burden of typhoid fever in Iran associated with climate and population to provide best policies for climate change adaptation.
Methods & Materials: Years lost due to disabilities (YLDs) were measured as burden estimation in this study. The temperature was selected as climate variable. Future temperature rising (projected for 2030 and 2050) used according to Intergovernmental  Panel on Climate  Change reports. Typhoid fever incidence in 2010 applied as the baseline data for YLDs calculation. The previous published regression   models   were   considered   for  YLDs’   future   projections. Furthermore,   the   future demographic change was included for YLDs calculation.
Results: Compared with the YLDs in 2010, increasing temperature and demographic change may lead to a 5.5-9% increase in the YLDs by 2030 and a 13.7-22% increase by 2050 if other factors remain  constant.  The  highest YLDs  was  projected  for  >  45  years  old  (56.3%)  in  2050  under temperature rising and population change scenario.
Conclusion:  Climate  change  and  aging  may  impact  on  burden  of  typhoid fever  in  the  future. Adaptive strategies should be considered to prevent and reduce the health burden of climate change.

Background & Aim: One of the common used models in time series is auto regressive integrated moving  average (ARIMA)  model.  ARIMA  will  do modeling  only  linearly.  Artificial  neural networks (ANN) are modern methods that be used for time series forecasting.  These models can identify non-linear relationships  among data. The breast cancer has the most mortality of cancers among women. The aim of this study was fitting the both ARIMA and ANNs models on the breast cancer mortality and comparing the accuracy of those in parameter estimating and forecasting.
Methods & Materials: We used the mortality of breast cancer data for comparing two models. The data are the number of deaths caused by breast cancer in 105 months in Kerman province. Each of ARIMA and ANNs models is fitted and chose the best one of each method separately, with some diagnostic criteria. Then, the performance of them is compared a minimum of mean squared error and mean absolute error.
Results: This comparison shows that the performance of ANNs models in parameter estimating and forecasting is better than ARIMA model.
Conclusion:  It  seems  that  the  breast  cancer  mortality  has  a  non-linear pattern,  and  the  ANNs approach can be more useful and more accurate than ARIMA method.

Background  &  Aim:  Assessment  could  be  assumed  as  a  valuable  mean of  highlighting  the organization strengths and spotting its weaknesses. Academies are not exceptions  in this regard. Knowing the items, which entail more concentrated  attention, the leadership  of the academy will shift the resources to compensate the extenuations. This study aimed to provide the Iran Academy of Medical Sciences (AMS) a model of assessment and development of its credibility.
Methods & Materials: Reviewing the scientific literatures about the components of credibility of an organization, three components were elected, 1. Structure, 2. Performance, and 3. Acceptability. Assessing  this academy,  a framework  for summarizing  the information  of other  academies  was developed. For the next steps, to improve the quality of the framework and to study more AMS, we decided to search the internet for more countries and academies.
Results: We find that 16 indices and their 77 measures could be used to assess the AMS.
Conclusion:  Establishing  a well-defined  system with a trained  staff devoted to assess  the AMS activities, would be in the favor of evaluating the AMS annually; and by publication of strategic reports, AMS strengths would be reinforced and its weaknesses would be reformed.

Longitudinal  study  plays  an  important  role  in  the  epidemiological,  clinical, and  social  science studies. In these kinds of studies, every individual is observed frequently during a period of time. The statistical analysis of longitudinal presents special opportunities  and challenges. The repeated outcomes for one individual tend to be correlated among themselves also one of the problems that we face in longitudinal studies is the missing data. These two issues are taken into account in this article. By using the logit link function, designed for longitudinal data, we introduce a mixed model, and then present the evaluation of variance components by Bayesian methods. The applied method exploits the non-conjugate priors. The conjugate priors, however, are easier to deal with. Finally, an application of the model in a clinical experiment is presented.

Background & Aim: Visceral leishmaniasis (VL) or kala-azar is a parasitic disease caused by the species of Leishmania donovani complex. Mediterranean type of disease is endemic in some parts of Iran and more than 95% of seropositivity cases were reported in children up to 12 years of age. A cross-sectional study was conducted to determine the seroprevalence of VL in nomadic tribe’s population of the Kerman Province.
Methods & Materials: Totally, 862 blood samples were collected from children up to 12 years old from nomadic tribes of the studied area. Before sampling, a questionnaire  was filled out for each case.  All  the  collected  blood  samples were  examined  after  the  plasma  separating  by  direct agglutination test for detection of anti-Leishmania infantum antibodies. The cut-off titer of ≥ 1:3200 with specific clinical features was considered as VL.
Results: Altogether, 25 (2.6%) of the collected plasma samples showed anti-Leishmania antibodies at titers ≥ 1:800 and 6 of them (0.6%) showed titers ≥ 1:3200 with mild clinical manifestations. None of the seropositive  cases had a history of kala-azar.  Children  of 5-8 years old showed the highest seroprevalence rate (4.1%). Also, there were not any significant differences between the rate of seropositivity in males (0.58%) and females (0.67%), (P = 0.225).
Conclusion: Although the seroprevalence of VL is relatively low in children up to 12 years old from nomadic tribes of the studied area, due to the importance  of the disease, the surveillance  system should be monitored by health authorities.

The  main  assumptions  in  liner  mixed  model  are normality  and  independency  of  random  effect component.   Unfortunately,   these   two  assumptions   might   be  unrealistic   in  some   situations. Therefore, in this paper, we will discuss about the analysis of Bayesian analysis of non-normal and non-independent mixed model using skew-normal/independent distributions, and finally, this methodology is illustrated through an application  to a triglyceride data from Isfahan’s Mobarakeh Steel Company Cohort Study.

Conditional  methods  of adjustment  are often used to quantify  the effect  of the exposure on the outcome.  As  a  result,  the  stratums-specific  risk  ratio  estimates  are  reported  in  the  presence  of interaction   between   exposure  and  confounder(s)   in  the  literature,  even  if  the  target  of  the intervention on the exposure is the total population and the interaction itself is not of interest. The reason is that researchers and practitioners  are less familiar with marginal methods of adjustment such as inverse-probability-weighting  (IPW) and standardization and marginal causal effects which have causal interpretations for the total population even in the presence of interaction. We illustrate the relation  between  marginal  causal  effects  estimated  by IPW and standardization  methods  and conditional  causal effects estimated  by traditional  methods in four simple scenarios based on the presence  of  confounding   and/or  effect  modification.   The  data  analysts   should  consider   the intervention level of the exposure for causal effect estimation, especially in the presence of variables which are both confounders and effect modifiers.