Risk factors for multidrug-resistant tuberculosis among tuberculosis patients: a case-control study.

BACKGROUND: Multidrug-resistant tuberculosis (MDR-TB) did not receive major attention until recently in sub-Saharan Africa where the tuberculosis incidence and risk factors are highest. Factors leading to development of drug resistance need to be understood to develop appropriate control strategies for national programs. The objective of this study was to identify the risk factors for MDR-TB among tuberculosis patients. METHODS: A case-control study was conducted to assess sociodemographic, behavioral and clinical risk factors using a structured questionnaire and clinical record reviewing. The data were entered and analyzed using SPSS windows version 16. Descriptive analysis was done to generate summary values for the variables and those significant variables in the bivariate analysis at p-value less than 0.25 were entered to multivariable logistic regression to identify independent determinants. Statistical significance was declared at p-value less than or equal to 0.05. RESULTS: A total of 90 cases and 90 controls were included in the study. Age of respondents (adjusted odds ratio [AOR] =7; 95% confidence interval [CI]: 2.6-24.5), living in a household with only one room (AOR=5; 95%CI: 1.68-15.38), history of previous treatment (AOR=21; 95% CI: 17.8-28) and being HIV infected (AOR=3.1; 95%CI: 1.02-9.4) were found to be independent predictors of MDR-TB. CONCLUSION: In light of these findings, the strategies in controlling MDR-TB should emphasize on patients with HIV coinfection, young patients, those who have a history of previous treatment, and those living in crowded places.

Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros.

Count data are collected repeatedly over time in many applications, such as biology, epidemiology, and public health. Such data are often characterized by the following three features. First, correlation due to the repeated measures is usually accounted for using subject-specific random effects, which are assumed to be normally distributed. Second, the sample variance may exceed the mean, and hence, the theoretical mean-variance relationship is violated, leading to overdispersion. This is usually allowed for based on a hierarchical approach, combining a Poisson model with gamma distributed random effects. Third, an excess of zeros beyond what standard count distributions can predict is often handled by either the hurdle or the zero-inflated model. A zero-inflated model assumes two processes as sources of zeros and combines a count distribution with a discrete point mass as a mixture, while the hurdle model separately handles zero observations and positive counts, where then a truncated-at-zero count distribution is used for the non-zero state. In practice, however, all these three features can appear simultaneously. Hence, a modeling framework that incorporates all three is necessary, and this presents challenges for the data analysis. Such models, when conditionally specified, will naturally have a subject-specific interpretation. However, adopting their purposefully modified marginalized versions leads to a direct marginal or population-averaged interpretation for parameter estimates of covariate effects, which is the primary interest in many applications. In this paper, we present a marginalized hurdle model and a marginalized zero-inflated model for correlated and overdispersed count data with excess zero observations and then illustrate these further with two case studies. The first dataset focuses on the Anopheles mosquito density around a hydroelectric dam, while adolescents' involvement in work, to earn money and support their families or themselves, is studied in the second example. Sub-models, which result from omitting zero-inflation and/or overdispersion features, are also considered for comparison's purpose. Analysis of the two datasets showed that accounting for the correlation, overdispersion, and excess zeros simultaneously resulted in a better fit to the data and, more importantly, that omission of any of them leads to incorrect marginal inference and erroneous conclusions about covariate effects.

Risk factors of diarrhoeal disease in under-five children among health extension model and non-model families in Sheko district rural community, Southwest Ethiopia: comparative cross-sectional study.

BACKGROUND: Worldwide diarrheal disease is the second leading cause of death in under-five year's children. In Ethiopia diarrhoea kills half million under-five children every year second to pneumonia. Poor sanitation, unsafe water supply and inadequate personal hygiene are responsible for 90% of diarrhoea occurrence; these can be easily improved by health promotion and education. The Ethiopian government introduced a new initiative health extension programme in 2002/03 as a means of providing a comprehensive, universal, equitable and affordable health service. As a strategy of the programme; households have been graduated as model families after training and implementing the intervention packages. Therefore the aim of the study was to assess risk factor of diarrheal disease in under-five children among health extension model and non-model families. METHOD: A community based comparative cross-sectional study design was employed in 2012 at Sheko district. Multi-stage sampling technique was employed to select 275 model and 550 non-model households that had at least one under-five children. Data was collected using structured questioner and/or checklist by trained data collectors. A summery descriptive, binary and multivariate logistic regression was computed to describe the functional independent predictors of childhood diarrhoea. RESULT: The two weeks diarrhoea prevalence in under-five children among health extension model and non-model households were 6.4% and 25.5%, respectively. The independent predictors of childhood diarrhoea revealed in the study were being mothers can't read and write [OR: 1.74, 95% CI: (1.03, 2.91)], monthly family income earn less than 650 Birr [OR: 1.75, 95% CI: (1.06, 2.88)], mothers hand washing not practice at critical time [OR: 2.21, 95% CI: (1.41, 3.46)], not soap use for hand washing [OR: 7.40, 95% CI: (2.61, 20.96)], improper refuse disposal [OR: 3.19, 95% CI: (1.89, 5.38)] and being non-model families for the health extension programme [OR: 4.50, 95% CI: (2.52, 8.03]. CONCLUSION: The level of diarrheal disease variation was well explained by maternal education, income, personal hygiene, waste disposal system and the effect of health extension programme. Thus encouraging families to being model families for the programme and enhancing community based behavioural change communication that emphasize on personal hygiene and sanitation should be strengthening to reduce childhood diarrhoea.

Determinants of inter birth interval among married women living in rural pastoral communities of Southern Ethiopia: a case control study.

BACKGROUND: Though birth interval has beneficial effects on health status of the mother and their children, it is affected by range of factors some of which are rooted in social and cultural norms and the reproductive behaviors of individual women. However, there was limited data showed the determinants of birth intervals in rural pastoral communities of South Ethiopia. Therefore, the study was aimed to assess the determinants of inter birth interval among women's of child bearing age in Yaballo Woreda, Borena zone, Oromia Regional State, Ethiopia. METHODS: A community based unmatched case-control study with multi stage sampling technique was conducted from January to March 2012. Cases were women with two subsequent birth intervals of less than three years and controls were women with two subsequent birth intervals between three and above years. Simple random sampling technique was employed to select six hundred fifty two (326 cases and 326 controls) study subjects. All explanatory variables that were associated with the outcome variable (birth interval) during bivariate analysis were included in the final logistic model. Multivariable backward logistic regression when P values less than or equal to 0.05 and 95% CI were used to determine independent determinants for the outcome of interest. RESULTS: The median duration of birth interval was 31 & 40 months among cases and controls respectively. Variables such as number of children (AOR 3.73 95% CI: (1.50, 9.25), use of modern contraceptives (AOR 5.91 95% CI: (4.02, 8.69), mothers' educational status (AOR 1.89 95% CI: (1.15, 3.37), and sex of the child (AOR 1.72 95% CI: (1.17, 2.52) were significantly associated with birth intervals. CONCLUSIONS: Concerted efforts to encourage modern contraceptive use, women education, and breastfeeding should be made.

Family planning knowledge, attitude and practice among married couples in Jimma Zone, Ethiopia.

BACKGROUND: Understanding why people do not use family planning is critical to address unmet needs and to increase contraceptive use. According to the Ethiopian Demographic and Health Survey 2011, most women and men had knowledge on some family planning methods but only about 29% of married women were using contraceptives. 20% women had an unmet need for family planning. We examined knowledge, attitudes and contraceptive practice as well as factors related to contraceptive use in Jimma zone, Ethiopia. METHODS: Data were collected from March to May 2010 among 854 married couples using a multi-stage sampling design. Quantitative data based on semi-structured questionnaires was triangulated with qualitative data collected during focus group discussions. We compared proportions and performed logistic regression analysis. RESULT: The concept of family planning was well known in the studied population. Sex-stratified analysis showed pills and injectables were commonly known by both sexes, while long-term contraceptive methods were better known by women, and traditional methods as well as emergency contraception by men. Formal education was the most important factor associated with better knowledge about contraceptive methods (aOR = 2.07, p<0.001), in particular among women (aOR(women )= 2.77 vs. aOR(men) = 1.49; p<0.001). In general only 4 out of 811 men ever used contraception, while 64% and 43% females ever used and were currently using contraception respectively. CONCLUSION: The high knowledge on contraceptives did not match with the high contraceptive practice in the study area. The study demonstrates that mere physical access (proximity to clinics for family planning) and awareness of contraceptives are not sufficient to ensure that contraceptive needs are met. Thus, projects aiming at increasing contraceptive use should contemplate and establish better counseling about contraceptive side effects and method switch. Furthermore in all family planning activities both wives' and husbands' participation should be considered.

Modeling overdispersed longitudinal binary data using a combined beta and normal random-effects model.

BACKGROUND: In medical and biomedical areas, binary and binomial outcomes are very common. Such data are often collected longitudinally from a given subject repeatedly overtime, which result in clustering of the observations within subjects, leading to correlation, on the one hand. The repeated binary outcomes from a given subject, on the other hand, constitute a binomial outcome, where the prescribed mean-variance relationship is often violated, leading to the so-called overdispersion. METHODS: Two longitudinal binary data sets, collected in south western Ethiopia: the Jimma infant growth study, where the child's early growth is studied, and the Jimma longitudinal family survey of youth where the adolescent's school attendance is studied over time, are considered. A new model which combines both overdispersion, and correlation simultaneously, also known as the combined model is applied. In addition, the commonly used methods for binary and binomial data, such as the simple logistic, which accounts neither for the overdispersion nor the correlation, the beta-binomial model, and the logistic-normal model, which accommodate only for the overdispersion, and correlation, respectively, are also considered for comparison purpose. As an alternative estimation technique, a Bayesian implementation of the combined model is also presented. RESULTS: The combined model results in model improvement in fit, and hence the preferred one, based on likelihood comparison, and DIC criterion. Further, the two estimation approaches result in fairly similar parameter estimates and inferences in both of our case studies. Early initiation of breastfeeding has a protective effect against the risk of overweight in late infancy (p = 0.001), while proportion of overweight seems to be invariant among males and females overtime (p = 0.66). Gender is significantly associated with school attendance, where girls have a lower rate of attendance (p = 0.001) as compared to boys. CONCLUSION: We applied a flexible modeling framework to analyze binary and binomial longitudinal data. Instead of accounting for overdispersion, and correlation separately, both can be accommodated simultaneously, by allowing two separate sets of the beta, and the normal random effects at once.