Journal of Biostatistics and Epidemiology
Volume:3 Issue: 2, Spring 2017

Low birth weight (LBW) is a strong predictor of an individual baby's survival. LBW is defined by the World Health Organization (WHO) as weight at birth of less than 2500 g.he current study aimed to study the pattern of the reporting system of birth weight in India and examine the heaping at certain digits, assess the agreement between actual birth weight and birth weight reported based on recall, and identify key determinants of birth weight reporting in India.
Methods & Materials: The National Family Health Survey (NFHS-3) data was used for the present study. The sensitivity, specificity, positive predictive value and negative predictive value were calculated to assess the magnitude of the misclassification bias. In addition, univariate and multivariable analyses were also analyzed. Chi-square test was used to detect the associations and Cohen's kappa statistic was used for agreement between categories of birth weights and birth sizes.
Mothers’ recalled assessment of baby’s size as small or normal were in agreement with the categories of birth weight as LBW or normal weight (K = 0.46, P Due to this poor reporting system prevalen in the country, the actual prevalence of LBW can get affected. Method of reporting can also affect the actual scenario of the LBW due to hypothetical or memory recall base birth size.

Statistical modeling is one of the most suitable methods for analyzing the relationship between health and medical issues. In the situation of analysis of zero-inflated data, there are different methods. In this study, the models Poisson, Poisson gamma, and Poisson lognormal regression were compared.
Methods & Materials: This cross-sectional study was conducted to determine the influential factors on decay-missing-filled (DMF) index by the three mentioned models using the data of 808 first-grade children of the primary school in Kerman, Iran. The command PROC NLMIXED in SAS software was applied for fitting the models on data. For comparing the models, we applied the Akaike’s criterion (AIC), mean square error (MSE) criterion and confidence interval (CI).
The AIC and CI showed that the Poisson lognormal model was better than the others due to a level of significance. The variables of the students’ place of living, mothers’ jobs, fathers’ jobs, the region, sex, optic problems, and behavioral problems had a significant effect on DMF index.
Poisson lognormal was better than the other models in dental health data.

Previous studies about hypertension and risk factors have shown the linear relationship between them. However, we can improve the fit of models with some changes and have a better form for estimation of coefficients and interpret the effects of variables.
Methods & Materials: This survey was a cross-sectional study from 2010 to 2011 in Yazd, Iran. The participants were among the subjects aged from 40 to 80. Body mass index (BMI), sex, age, renal failure, history of diabetes (years of disease), type of diabetes (type 1 or type 2), the number of cigarettes per day and years of smoking were predictors and the binary response returned to hypertension (yes or no). The traditional logistic model was used for determining the relationship between covariates and the outcome. Then, the models were modified with multivariable fractional polynomials.
Our findings displayed fitting the multivariable fractional polynomials (MFP) model in the parametric model which was the best fit for the modeling. The difference deviance in MFP was 21.952 (P MFP model approach is an alternative procedure that can solve previous problems about the categorical approach, step function, and cut- off points.

The term hepatitis applies to a wide group of clinical and pathological condition that is often caused by damage to the liver by various factors including viral infections. The present study aimed to assess the awareness of non-medical students about the viral hepatitis disease.
Methods & Materials: In this cross-sectional descriptive study, 298 students of the two universities in Tehran were selected. The data collecting tool was a two-part questionnaire. The first part examined the student demographic information, and the second part consisted of 10 questions about of viral hepatitis. Questionnaires were distributed among the students and then the data were analyzed by SPSS software.
Among the 298 respondents, there were 224 women with a mean age of 26 years. 155 people (52%) were students of medicine and biosciences and 143 (48%) were students of the humanities. There was a significant difference between the awareness of the correct answer and the fields of study. Therefore, the awareness level of paramedical and biological sciences students was higher than other disciplines.
The mean level of awareness of respondents was 32.95%. The average level of awareness in students of paramedical and biological sciences (31.99%) was higher than the average level of awareness in students of humanities (16.06%). Albeit the entire study population was composed of the young and educated people, their awareness of viral hepatitis was low. It strongly reflects poor knowledge of society and especially our young people as a group at risk.

Adding parameters to an existing distribution to expand the family of distributions is a very common approach for developing more flexible models. Several ways for generating new distributions from classic ones have been developed.
Methods & Materials: A generalization of Topp-Leone generator of distributions was introduced. Several of its fundamental properties were obtained such as quantiles, moments, moment generating function (MGF), order statistics and maximum likelihood estimator (MLE).
We provided four sub-models of the new family which extended some of the basic lifetime models such as exponential, Weibull, gamma and generalized exponential distributions. These distributions exhibited a wide range of shapes varying skewness and different forms of hazard rate function (HRF).
We have provided four new distributions. The flexibility of the proposed distributions and increased range of skewness were able to fit and capture features in one real dataset much better than some competitor distributions.