Journal of Biostatistics and Epidemiology
Volume:4 Issue: 4, Autumn 2018

The diagnostic accuracy of a test is the ability to discriminate accuratelybetween patients who have and do not have the target disease. A common problem in assessing thediagnostic accuracy of doctors is the unknown true disease status which in the literature is referredas “absence of a gold standard”.  In this article, a Naïve Bayesian network with hidden class node and a clusteringbased algorithm for categorical data named K-modes are proposed for estimating the diagnosticaccuracy of 5 physicians in diagnosing Diabetic Retinopathy. Also to assess and compare the efficiencies of these models, a simulation study with two different scenarios is conducted. Simulation study indicates that for Naïve Bayesian network and the non-rare disease, say forprevalence 0.1 and 0.2, as the sample size increases so the coverage probability. But for high prevalencevalues, say 0.5, coverage probabilities are not as good as those of non-rare disease. K-modes algorithm's efficiency decreases by the increase in the number of records, but it achieves betterresults when there are a small number of records, prevalence is approximately 0.3 and sensitivitiesare high. Results of the real data set reveal that sensitivities for all physicians except one, were higher than 85% and all specificities were higher than 90%. Also the estimated prevalence happensto be 0.32. Through simulations and data analysis we show that this new approach based on Naïve Bayesian networks provides a useful alternative to traditional latent class modeling approaches usedin this setting.

Today, quality of life has attracted more attention and there is a relationship between disease and quality of life. This study was conducted to evaluate the quality of life in diabetics and non-diabetics and other demographic variables related to the quality of life of individuals.  This cross-sectional study was carried out on a random sample of 7533 subjects, using the Health Survey data in Yazd. For assessing the quality of life, SF8 questionnaire was used. Data were analyzed by using, t-test and linear regression model in SPSS19. There were, significantly different between quality of life in diabetics and non-diabetics individual, and this relationship can be generalized to the subscales of quality of life(P<0.001). It has shown that becoming old, being female and being single were associated with decreasing quality of life, and increasing the level of education and having assurance had a significant role in improving the quality of life(p<0.05). Since diabetic patient had lower quality of life than non-diabetics. It is suggested, to promote the quality of life of patients, health authorities should plan the solutions for supporting and promoting patients with diabetes` health.

Knowledge Management (KM) is widely known as a critical issue in offices, factories and organizations. The present study intends to predict the success or failure of KM implementation in the automotive industry.  we have tried to analyze and predict the degree to which how successfully we can implement KM in the automotive industry using fuzzy inference method (FIS). In this regard, after data collection, and employed FIS software to analyze our results. As our results show, the projected level for the implementation of KM in Iran Khodro was about 58%. Given that this study was conducted in five different, but related parts in Iran Khodro as well as these five sectors were less similar in terms of structure, individual and usage of technology, we should not expect similar results about the rate of implementing knowledge management. Our results would be important and interesting, because they will provide the basis for how successfully establish KM in the automotive industry and similar organizations in order to improve the efficiency and productivity in organizations.

Gestational diabetes mellitus (GDM) is the most common metabolic disorder in pregnancy which is caused due to insulin resistance and carries several risks for the mother and neonate. So, an updated data from its prevalence in different geographical areas is important.  This cross-sectional analytic study was performed on pregnant women referring health centers in Qom city from June 2013 to September 2014 for prenatal care. In week 10 of pregnancy, fasting blood sugar (FBS) was checked for all participants and those who had FBS >126mg/dL for 2 times were excluded from the study and all participants who remained in the study underwent oral glucose tolerance test with 75 gr glucose at 24-28weeks of gestation. Gestational diabetes was diagnosed on the basis of at least one abnormal responses of glucose≥92mg/dL, 1-hour glucose ≥180 mg/dL or 2-hour glucose ≥153mg/dL. A total of 4988 pregnant women enrolled the study. Based on 75g oral glucose tolerance (OGT) test results at 24-28 weeks of gestation, 1036 women (20.76%) had gestational diabetes. Gestational diabetes was significantly associated with maternal age, body mass index, history of gestational diabetes, a family history of type II diabetes in first-degree relatives, history of preterm labor, known hypothyroidism before pregnancy and history of macrosomia. Gestational diabetes has a high prevalence in Qom city, and it seems that new studies are needed to determine its prevalence in other regions.

Knowledge, attitude and practices regarding dengue are latent variables which are substantiated through manifest variables. The manifest variables that form the indicative construct of knowledge, attitude and practice can be factored into sub-constructs such that the impact of each indicative variable can be verified.

Evaluation of the sub-constructs of knowledge, attitude and practices regarding dengue using a Partial least square path models with R programming language.

The measurement model revealed the sub-constructs that are negatively affecting the latent variables and the ones that are having low impact.

This analysis gives the possibility of observing the exact knowledge, attitude and practices regarding dengue that are inadequate among respondents. The result from this methodological approach can be used as an aid for the community health programs and campaigns on how to enlighten the populace of interest on the required awareness about dengue, attitude towards dengue and the preventive practices that are deficient among them.

The study was conducted in the aim to figure out the demographic factors that fueling prevalence of H.I.V/AIDS in 2011/12-Mbeya region. The demographic data of the cross-sectional years 2011/12 was used. The study encompassed male and female individuals aged 15-49.  The binary logistic regression model was used employed and demographic factors that were considered to have an impact on the prevalence of the epidemic were included in our analysis. The result shows that demographic variables had significant effect on the prevalence of H.I.V/AIDS in the year 2011/12- Mbeya region for all respondents. However, except for male circumcision for male individuals, polygamous, travelers and pregnancy status for female individuals, the demographic parameters such as respondent age, respondent sex, paid sex (commercial sex) and unsafe sex had significant effect on the prevalence of H.I.V/AIDS in the year 2011/12 in Mbeya region and female were the most affected individuals compared to male individuals. H.I.V infection was highly prevalent in urban areas than rural areas. As compared to other previous studies, a shift of the epidemic from rural areas to urban areas, and from young age (15-24) to middle age group (30-39). To prevent further prevalent of H.I.V/AIDS a substantial number of those infected present with AIDS related symptoms and may be in need of ART. As a comprehensive response, prevention efforts should be intensified to target and addresses identified transmission parameters and include individuals of all ages. In urban areas, commercial sex workers should be targeted to capture those at risk of H.I.V infection. Concurrent attempts to expand access to treatments and care for those infected in urban and rural areas are needed as this will increase opportunity for prevention. Safe sex should be insisted by providing people with protective measures and then insisting them to use it regularly.

Diabetes mellitus is a common, chronic, metabolic syndrome characterized by hyperglycemia as a cardinal biochemical feature. Type-1 diabetes is a continuing hormonal deficiency disorder that has significant short-term impacts on health and lifestyle and is associated with major long-term complications like heart failure, kidney, hypertension, eye damage, etc. which reduced life expectancy. The main objective of this study was to assess the risk factor that increase prevalence of type-1 diabetes mellitus and to determine their relationship with outcome of type-1 diabetes mellitus over time.  To address this objective linear mixed effect model was applied using the random blood sugar of 970 diabetic patient children during treatment period of 3 years at Hiwot Fana hospital which have been implemented in statistical packages STATA, SAS and R. This study found that the mean progression of random blood sugar level of diabetic children was decreased over time after they starts their follow up and medications. The linear distribution also accounts 92 % variability of the data was explained by the covariates which were included in the study. The variable age, residence, family history, nutrition status, early diet, body mass index, electrolytes and renal function test had significant effect on the change of sugar level (p < 0.05). The cumulative incidence of type-1 diabetes mellitus disease was increased due to presence of co-infections and decreased with pharmacological diabetes treatment. The linear mixed effects model fitted was appropriate for the estimation of sugar levels based on the risk factor variables for type-1 diabetes mellitus patient children. RBS = Random blood sugar; FH = Family history; UM = under malnutrition; OM = Over malnutrition; RFT= Renal function test; NS=Nutritional status

depression is one of the main problems and disrupting daily life activities in women. Due to the important role of women in society and the effect of depression on this group activity the aims of the current study was to investigate of the identification effective factors on women's depression in Khuzestan province. In this cross-sectional study, 899 women who referred to health centers in Ahwaz were selected by cluster sampling method. For analysis purpose, multivariate and univariate linear regression was used. All analysis performed by SPSS version 19 with regarding α: 0.05 for the significant level. in case of effective factors on depression score, number of Education Years, Competence score, Relatedness score, Autonomy score, Presence of Meaning in Life score, Search for Meaning in Life score had the significant effect on depression score (p<0.001). Education level, marital status, education level, Social Competence, relatedness, Autonomy; Presence of meaning in life, Search for meaning in life and Depression are effective factors on depression so focusing on this factors can have the very important role in prevention programs.