Some socioeconomic and demographic factors contributing to the use of condoms among bar maids were studied in selected urban areas in Tanzania. Bar maids were classified according to whether or not they use condoms, and logistic regression was used in the analysis. Of the demographic variables studied, age, marital status, education level, use of alcohol and wage rate showed a significant relationship at the 1% level between individual factors and use of condoms. The odds ratios show that girls aged 10-14 are over 18,000 times more likely not to use condoms compared with women aged 30 and above. Those who drink are 6.6 times (1/0.165) less likely to use condoms compared with those who do not drink. In other words, alcohol consumption can be a stimulus for an individual not to use condoms. It is clear that young girls who drink are at the highest risk of contracting HIV/AIDS in comparison with older females. A multiple logistic regression model shows all the aforementioned factors to be significant at the 1% level. A policy recommendation is made that the government should impose restrictions as far as employment of bar maids is concerned.

: This paper identifies and analyses factors associated with the success or failure of the emerging Savings and Credit Cooperative Societies (SACCOS) in Tanzania Mainland. Multi-stage sampling technique was employed to come up with four regions among ten purposively selected for the study. Four research questions were formulated to guide the study. 156 respondents participated in the study and data were analyzed using IBM SPSS Statistics version 20.0 software. Principal component analysis and logistic regression analysis were used in the analysis.The study revealed that,lack of access to loans for individuals, a need of soft loans for business and non business; and pledges for loans from government, prominent politicians and other stakeholders were the main factors that led to the SACCOS inception. It was shown that accepting 'non members businesses, having and adhering to conflict management strategy, number of members at initial stage and employing fulltime professional management made a significant contribution to success performance of SACCOS.Moreover, rural SACCOS have shown poor performance as compared to SACCOS operating in urban areas while education level has shown a positive relationship with SACCOS' performance. The findings also showed that lack of commitment to members, lack of patience, shifting to other areas and loan default were the major reasons for members' withdrawal from SACCOS.This study recommended encouraging female members for SACCOS' leadership since they have shown success performance in most of the SACCOS they lead. Also, this study finding recommended giving priority to female members in borrowing.

The objective of this paper is to apply principal components and factor analysis techniques in assessing factors associated with fertility differentials in Tanzania. The study utilized secondary data from 2010 Tanzania Demographic and Health Survey (2010 TDHS) dataset. Three factors were identified as the main factors associated to fertility differentials in Tanzania. The first factor was woman' education and awareness, the second factor was woman' demographic characteristics and the third factor was woman' economic status. Among those factors, woman' education and awareness was found to contribute more than all other factors in explaining fertility differentials in Tanzania. The study concluded that, in order to attain a desirable fertility level in the country, woman' education especially on the issue of family planning needs to be improved.

This study examined some factors associated with the utilization of maternal health care servicesby adolescent mothers (15-19 years) in Tanzania in order to provide advice accordingly. The studyused cross-sectional study of adolescent mothers aged 15-19 years using Demographic HealthSurvey and Malaria indicator Survey 2015/16 data. The dependent variables were number ofantenatal care visits, the place where an adolescent mother delivered and post-natal checkup(adolescent mother’s health checking after being discharged or after a home delivery). Theindependent variables were birth order, education level of a mother, marital status of a mother,media exposure, wealth index, distance to health facility. Multiple binary logistic regression wasused to examine an association between each dependent variable and their respective independentvariables. Data was analyzed using IBM SPSS statistics and STATA. This study used 550adolescent mothers in the analysis. Majority of the adolescent mothers had less than four AntenatalCare (ANC) visits (53.5%), while 68.5% of adolescent mothers delivered at a health facility.Adolescent mothers with two or more children had less odds of having at least four ANCscompared to those with one child, whereas adolescent mothers with at least secondary educationhad greater odds of delivering at a health facility compared to those who had no education.Adolescent mothers who had at least four antenatal care visits and those who are married hadgreater odds of checking their health after being discharged compared to adolescent mothers whohad less than 4 ANCs and single adolescent mothers. It was advised that provision of maternaleducation to young girls on the importance of safe delivery and health checking after delivery isvery important to reduce adolescent maternal morbidity and mortality in the country. Keywords: Adolescent; Maternal Health; Logistic regression; Chi-square

Background Clinical data are at risk of having missing or incomplete values for several reasons including patients’ failure to attend clinical measurements, wrong interpretations of measurements, and measurement recorder’s defects. Missing data can significantly affect the analysis and results might be doubtful due to bias caused by omission of missed observation during statistical analysis especially if a dataset is considerably small. The objective of this study is to compare several imputation methods in terms of efficiency in filling-in the missing data so as to increase the prediction and classification accuracy in breast cancer dataset. Methods Five imputation methods namely series mean, k-nearest neighbour, hot deck, predictive mean matching, and multiple imputations were applied to replace the missing values to the real breast cancer dataset. The efficiency of imputation methods was compared by using the Root Mean Square Errors and Mean Absolute Errors to obtain a suitable complete dataset. Binary logistic regression and linear discrimination classifiers were applied to the imputed dataset to compare their efficacy on classification and discrimination. Results The evaluation of imputation methods revealed that the predictive mean matching method was better off compared to other imputation methods. In addition, the binary logistic regression and linear discriminant analyses yield almost similar values on overall classification rates, sensitivity and specificity. Conclusion The predictive mean matching imputation showed higher accuracy in estimating and replacing missing/incomplete data values in a real breast cancer dataset under the study. It is a more effective and good method to handle missing data in this scenario. We recommend to replace missing data by using predictive mean matching since it is a plausible approach toward multiple imputations for numerical variables, as it improves estimation and prediction accuracy over the use complete-case analysis especially when percentage of missing data is not very small.