Journal of Biometrics & Biostatistics

Malnutrition is a general term for a medical condition caused by an improper or insufficient diet. Under nutrition is prevalent around the world. The objective of this study was to assess the prevalence of under nutrition and factors affecting nutritional status among children aged 6-59 months in Haramaya, eastern Ethiopia. The data were collected by using a well-structured pretested questionnaire. Anthropometric measurements of the children were taken to assess the nutritional status of the children. The prevalence of stunting, wasting and underweight were 36.07% [95% CI (0.314, 0.408)], 14.43% [95% CI (0.110, 0.179)] and 23.63% [95% CI (0.195, 0.278)], respectively. The current study showed that the prevalence of child under-nutrition is highly prevalent in Haramaya district. Community based nutrition program should be established; continuous nutrition supervision based on each nutritional status indicators and special attention to severely malnourished children is necessary to attempt the problem of malnutrition.

This paper investigates the deterministic and stochastic fluctuations of a predator-prey model. The predator is experienced in hunting two different prey simultaneously. Each prey has logistic growth in the absence of the predator. The rate of experience of the predator in hunting each prey is varied using a simulated dataset. The deterministic and stochastic nature of the dynamics of the system are investigated. Stability analysis is performed, using slight perturbation around the non-zero, interior equilibrium point, to determine where the system loses stability. The variation of the predatory experience parameter causes the system to experience Hopf bifurcations. These stability changes and the addition of stochastic noise are explored using time series graphs. The co-existence and extinction of the populations are affected over time .

Objectives: The goal of this study is to investigate time durations in the disease-free state and the preclinical state of lung cancer for male and female smokers, using lung cancer data from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Methods: We applied a modified likelihood function to the lung cancer data, to obtain maximum likelihood estimate and make Bayesian inference of the transition probability from the disease-free to the preclinical state, and the sojourn time distribution. The data was stratified by age and gender for smokers in the periodic screening program. A scaled Beta distribution was used for the transition probability density function, and a Weibull distribution was used to model the sojourn time in the preclinical state. Results: The epidemiological estimate of screening sensitivity is 0.649 for males and 0.68 for females. The transition probabilities are not the same for males and females: it is increasing monotonically to 80 years old for males; while it has a single maximum at age 72.5 for females. For male, the maximum likelihood estimate of mean sojourn time is 1.82 years, the Bayesian posterior mean and median sojourn time is 1.50 and 1.48 years, respectively. For female, the corresponding maximum likelihood estimate, posterior mean and median sojourn time are 1.84, 1.74 and 1.79 years respectively. The Bayesian mean lifetime risks for male and female smokers developing lung cancer are 12.0%, and 6.8%, respectively. Conclusion: Our estimation showed that male smokers are more susceptible to lung cancer, because they have a higher lifetime risk and higher transition probability density than the same aged female smokers. Once they enter into the preclinical state, the male smokers have a shorter mean sojourn time than the female, meaning that they are quicker to develop clinical symptom of lung cancer.

Autoradiography is a specific biological tool used to detect radioactive materials by using X-ray photographic films. A technically simple technique to be used for characterizing receptors and localizing their positions in the tissues. Moreover its detection sensitivity could be enhanced using fluorography by transforming radioactive emissions into light.

Background: Tobacco use is one of the foremost causes of preventable morbidity and mortality. The objective of the study was to identify the pattern of smokeless tobacco use and to estimate the prevalence and to identify Sociodemographic correlates of smokeless tobacco consumption. Data and methods: We used the data from the 2010 Global Adult Tobacco Survey (GATS) in Bangladesh. The data were representative for men and women aged 15 years and above. The survey was based on a three-stage stratified cluster sample of household. Information of a total of 9629 adults, aged 15 years and over, was analyzed by stratified them into urban (4857) and rural (4772) groups. Stata Version 11.0 and Excel were used to analyze the dataset. Predictors of prevalence for smokeless tobacco use were analyzed using selected socioeconomic and demographic characteristics that include residence, age, gender, education, occupation and wealth index. Associations between smokeless tobacco consumption and the explanatory variables were estimated using simple and multiple logistic regression model. Results: Current smokeless tobacco users daily were significantly higher (p=0.001) in urban females (21.92%) in comparison to urban males (18.13%) and also significantly higher (p<0.001) in rural females (30.60%) in comparison to rural males (25.92%). In the multivariate analysis the adjusted OR were significantly higher in rural area (OR=1.15, 95% CI=1.02-1.29) in comparison to urban area. Similarly adjusted OR were significantly higher in females (OR=1.68, 95% CI=1.38-2.30) in comparison to males. The adjusted OR increased with age from 3.09 (95% CI=2.45-3.93) in the age group 25-34 to 12.70 (95% CI=10.02-16.11) among individuals with 46 years and above age group, in comparison to individuals in the age group less than or equal to 24 years. Education and smokeless tobacco use showed significant inverse relationship with significantly elevated OR in the low education group (no formal schooling, OR=4.28, 95% CI=2.48-7.38), less than primary school completed (OR=3.28, 95% CI=1.89-5.68) and primary school completed (OR=3.61, 95% CI=2.08-6.29) in comparison to high education group (post graduate degree). Conclusion: The prevalence of current smokeless tobacco consumption is high among rural women and among all smokeless tobacco products, the prevalence of the use of betel quid with tobacco was the highest in Bangladesh. Awareness should be given priority to decrease the growing smokeless tobacco consumption. Also tobacco control campaigns should target rural poor older women and monitor all forms of smokeless tobacco products used by the population.

Background: Myocardial Infarction (MI) is a coronary heart disease that is one of the main causes of the mortality over the globe. There are various clinical and non-clinical risk factors that can be further classified as modifiable and non-modifiable. This study has explored the role of the some Non Clinical factors like; Gender, Education and Family History with MI using Meta-analysis approach. Methods: The published literature from 1990 to 2015 on MI was collected by using several databases and search engines. A review of the collected literature (28 studies) showed that the studies under analysis were of different origins and had different objectives. For each study, Odds Ratio and 95% confidence intervals was extracted and pooled with a random effect model, weighting for the inverse of the variance. Meta-analysis software version 2.0 was used to analyze heterogeneity analysis and estimate pooled estimates through random effect model. Results: The study has showed that gender (OR=1.391 and 95% C.I.: 1.140, 1.697), family history of heart diseases (OR=3.206 95% C.I.:2.064, 4.981) and low education level or illiteracy (OR=1.552 and 95%C.I.: 1.132, 2.128) are the significant risk factors in developing Myocardial Infarction. Conclusion: This study has concluded that included factors in this study are significantly related to the Myocardial Infarction. Gender difference, family history of heart disease and low education are the important risk factors in causing this fatal disease.

The associations of nutrition factors and physical activities with adult diabetes are inconsistent; while most of these factors are inter correlated. The aims of this study are to overcome the disturbance of the multicollinearity of the risk factors and examine the associations of these factors with diabetes using the principal component analysis (PCA) and regression analysis with principal component scores (PCS). Totally, 659 adults with diabetes and 2827 non-diabetic were selected from the 2012 Health Information National Trends Survey (HINTS 4, Cycle 2). PCA was utilized to deal with multicollinearity of the risk factors. Weighted univariate and multiple logistic regression analyses were used to estimate the associations of potential factors and PCS with diabetes. The odds ratios (ORs) with 95% confidence intervals (CIs) were estimated. The first 3 PCs for nutrition factors and physical activities could explain 70% variances. The first principal component (PC1) is a measure of nutrition factors (fruit and vegetables consumption), PC2 is a measure for physical activities (moderate exercise and strength training), and PC3 is about calorie information use and soda use. Weighted multiple logistic regression showed that African Americans, middle aged adults (45-64 years), elderly (65+), never married, and with lower education were associated with increased odds of diabetes. After adjusting for others factors, the PC1 showed marginal association with diabetes (OR=0.84, 95% CI=0.70-1.01); while PC2 and PC3 revealed significant associations with diabetes (OR=0.73, 95% CI=0.61-0.86 and OR=0.85, 95% CI=0.74-0.99, respectively). In conclusion, PCA can be used to reduce the indicators in complex survey data. The first 3 PCs of nutrition factors and physical activities were associated with diabetes. Promotion of health food and physical activities should be encouraged to help decrease the prevalence of diabetes.

Bone loss is a major health problem that many aging individuals will face and thus research focusing on enhancing bone formation is of great importance. Cell biology or in vitro studies are particularly useful in exploring the exact effects a vitamin, supplement or drug has on particular processes within a certain cell type. Although there have been many cell biology articles focusing on the effects of vitamin D, K1 or K2 addition on bone formation in vitro, there has yet to be a consensus amongst the literature. The purpose of this article is to determine the effects of vitamin D, K1 and K2 supplementation on osteoblast maturation parameters through meta-analysis of past cell biology literature. A Hedges d effect size was calculated for each experiment extracted from past literature and the experiments were grouped by experiment and cell type. Homogeneity was assessed by the Cochran’s Q test, while the effect sizes’ departure from zero was assessed by a 95% confidence interval and a non-directional test. Supplementation with vitamin D, K1 and K2, along with the combination of vitamin K2+ 1,25-dihydroxyvitamin D, increased bone mineralization, while not consistently affecting all of the other parameters associated with bone formation. Vitamin K2 and D addition had variable effects on bone formation using different cell types, which calls into question the suitability of particular cell lines as models for clinical trials. Therefore, the conditions and parameters in which bone formation is studied in vitro must be considered carefully before running a vitamin supplementation or drug-testing experiment.

Background: There are different approaches to the assessment of the severity of trauma in a victim and to the provision of specialized health care. Some of them are based on the development of scales and logistic models, using expert systems or statistical methods, to assess the severity of injury and the probability of a particular outcome. This article presents the results of a study on the feasibility of developing and applying various statistical models in order to predict the outcome in the case of different types of trauma, based on data on the status of victims with severe trauma. Patients and methods: We present selected information about 373 victims, admitted and treated at the Department of Traumatic Shock of the GI «V.T. Zaycev Kharkiv Research Institute of General and Emergency Surgery» of NAMS of Ukraine; the records, which relate to patients with severe and combined trauma, were made between 1985 and 2015. The initial database contained 263 victims who had positive outcomes (survived), while 110 had fatal outcomes. Most of the patients presented with an open trauma (285 cases), then there were 80 cases with a closed injury and only 8 cases with a combined injury. Results: To estimate the probability of the outcome for various types of trauma we have developed a predictive model, based on a logistic relationship. Categorical variables, indicating the presence or absence of various types of trauma, were used in the model. Information about the eventual outcome for a given victim with the indicated type of trauma was used as the dependent variable. The logit model which we obtained has a high predictive accuracy in predicting positive outcomes. Thus, based on the a posteriori analysis, 92% of cases in which victims survived were correctly recognized by the model. In view of the fact that abdominal trauma is the commonest of all trauma mechanisms, we constructed a predictive model to estimate the probability of various outcomes in the case of abdominal trauma or injury to certain organs of the abdominal cavity.       Linear discriminant functions were developed by us and used for the classification of possible outcomes depending on the condition of the victim and the resuscitation measures carried out. The model presented has a high predictive accuracy: on the basis of a posteriori analysis using data of discriminant functions, correct conclusions were drawn in 90% of cases when there was a positive outcome, and in 75% of cases when the outcome was fatal. Conclusion: We conclude that it is reasonable to use the statistical model developed, along with other qualitative and quantitative methods of prognostic determination of outcomes for victims with severe injuries. As different models have different predictive accuracy and require the provision of different information, it is necessary to use a sufficiently large number of techniques to derive accurate predictions and to choose the right tactics for diagnosis and treatment.

In order to maintain life, living organism’s product and transform small molecules called metabolites. Metabolomics aims at studying the development of biological reactions resulting from a contact with a physio-pathological stimulus, through these metabolites. The 1H-NMR spectroscopy is widely used to graphically describe a metabolite composition via spectra. Biologists can then confirm or invalidate the development of a biological reaction if specific NMR spectral regions are altered from a given physiological situation to another. However, this pro-cess supposes a preliminary identification step which traditionally consists in the study of the two first components of a Principal Component Analysis (PCA). This paper presents a new methodology in two main steps providing knowledge on specific 1H-NMR spectral areas via the identification of biomarkers and via the visualization of the effects caused by some external changes. The first step implies Independent Component Analysis (ICA) in order to decompose the spectral data into statistically independent components or sources of information. The in-dependent (pure or composite) metabolites contained in bio fluids are discovered through the sources, and their quantities through mixing weights. Specific questions related to ICA like the choice of the number of components and their ordering are discussed. The second step consists in a statistical modelling of the ICA mixing weights and introduces statistical hypothesis tests on the parameters of the estimated models, with the objective of selecting sources which present biomarkers (or significantly fluctuating spectral regions). Statistical models are considered here for their adaptability to different possible kinds of data or contexts. A computation of contrasts which can lead to the visualization of changes on spectra caused by changes of the factor of interest is also proposed. This methodology is innovative because multi-factors studies (via the use of mixed models) and statistical confirmations of the factors effects are allowed together.