Wondwosen Kassahun

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

BackgroundWorldwide 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.MethodA 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.ResultThe 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].ConclusionThe 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.

A zero-inflated overdispersed hierarchical Poisson model:

Count data are most commonly modeled using the Poisson model, or by one of its many extensions. Such extensions are needed for a variety of reasons: (1) a hierarchical structure in the data, e.g., due to clustering, the collection of repeated measurements of the outcome, etc.; (2) the occurrence of overdispersion (or underdispersion), meaning that the variability encountered in the data is not equal to the mean, as prescribed by the Poisson distribution; and (3) the occurrence of extra zeros beyond what a Poisson model allows. The first issue is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. Overdispersion is often dealt with through a model developed for this purpose, such as, for example, the negative-binomial model for count data. This can be conceived through a random Poisson parameter. Excess zeros are regularly accounted for using so-called zero-inflated models, which combine either a Poisson or negative-binomial model with an atom at zero. The novelty of this article is that it combines all these features. The work builds upon the modelling framework defined by Molenberghs et al. (2010) in which clustering and overdispersion are accommodated for through two separate sets of random effects in a generalized linear model.

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

BackgroundThough 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.MethodsA 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.ResultsThe 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.ConclusionsConcerted efforts to encourage modern contraceptive use, women education, and breastfeeding should be made.

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

BackgroundIn 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.MethodsTwo 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.ResultsThe 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.ConclusionWe 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.

A joint model for hierarchical continuous and zero-inflated overdispersed count data

Many applications in public health, medical and biomedical or other studies demand modelling of two or more longitudinal outcomes jointly to get better insight into their joint evolution. In this regard, a joint model for a longitudinal continuous and a count sequence, the latter possibly overdispersed and zero-inflated (ZI), will be specified that assembles aspects coming from each one of them into one single model. Further, a subject-specific random effect is included to account for the correlation in the continuous outcome. For the count outcome, clustering and overdispersion are accommodated through two distinct sets of random effects in a generalized linear model as proposed by Molenberghs et al. [A family of generalized linear models for repeated measures with normal and conjugate random effects. Stat Sci. 2010;25:325–347]; one is normally distributed, the other conjugate to the outcome distribution. The association among the two sequences is captured by correlating the normal random effects describing the continuous and count outcome sequences, respectively. An excessive number of zero counts is often accounted for by using a so-called ZI or hurdle model. ZI models combine either a Poisson or negative-binomial model with an atom at zero as a mixture, while the hurdle model separately handles the zero observations and the positive counts. This paper proposes a general joint modelling framework in which all these features can appear together. We illustrate the proposed method with a case study and examine it further with simulations.