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Programs written in R to do the tests on nucleotides are available from http://www.maths.usyd.edu.au/u/johnr/testsym/

BackgroundBirth control is the conscious control of the birth rate by methods which temporarily prevent conception by interfering with the normal process of ovulation, fertilization, and implantation. High contraceptive prevalence rate is always expected for controlling births for those countries that are experiencing high population growth rate. The factors that influence contraceptive prevalence are also important to know for policy implication purposes in Bangladesh. This study aims to explore the socio-economic, demographic and others key factors that influence the use of contraception in Bangladesh.MethodsThe contraception data are extracted from the 2014 Bangladesh Demographic and Health Survey (BDHS) data which were collected by using a two stage stratified random sampling technique that is a source of nested variability. The nested sources of variability must be incorporated in the model using random effects in order to model the actual parameter effects on contraceptive prevalence. A mixed effect logistic regression model has been implemented for the binary contraceptive data, where parameters are estimated through generalized estimating equation by assuming exchangeable correlation structure to explore and identify the factors that truly affect the use of contraception in Bangladesh.ResultsThe prevalence of contraception use by currently married 15–49 years aged women or their husbands is 62.4%. Our study finds that administrative division, place of residence, religion, number of household members, woman’s age, occupation, body mass index, breastfeeding practice, husband’s education, wish for children, living status with wife, sexual activity in past year, women amenorrheic status, abstaining status, number of children born in last five years and total children ever died were significantly associated with contraception use in Bangladesh.ConclusionsThe odds of women experiencing the outcome of interest are not independent due to the nested structure of the data. As a result, a mixed effect model is implemented for the binary variable ‘contraceptive use’ to produce true estimates for the significant determinants of contraceptive use in Bangladesh. Knowing such true estimates is important for attaining future goals including increasing contraception use from 62 to 75% by 2020 by the Bangladesh government’s Health, Population & Nutrition Sector Development Program (HPNSDP).Electronic supplementary materialThe online version of this article (10.1186/s12889-018-5098-1) contains supplementary material, which is available to authorized users.

Most phylogenetic methods are model-based and depend on Markov models designed to approximate the evolutionary rates between nucleotides or amino acids. When Markov models are selected for analysis of alignments of these characters, it is assumed that they are close approximations of the evolutionary processes that gave rise to the data. A variety of methods have been developed for estimating the fit of Markov models, and some of these methods are now frequently used for the selection of Markov models. In a growing number of cases, however, it appears that the investigators have used the model-selection methods without acknowledging their inherent shortcomings. This chapter reviews the issue of model selection and model evaluation.

Background: Low birth weight (< 2.5 kg) is an important indicator of health and development of infants throughout their life. Aims: This study aimed to determine the prevalence and risk factors for low birth weight in Jordan and its association with under-5 mortality. Methods: In this secondary analysis, data were extracted from the 2012 Jordan Population and Family Health Survey for 9734 live births born in the 5 years preceding the survey and for which birth weight was reported. Data were collected on child and maternal characteristics. Multivariable regression analysis was used to determine the significant risk factors for low birth weight and mortality. Results: Of the 9734 births analysed, 13.8% were low birth weight and 1.3% were very low birth weight. Mother's age (< 30 and ≥ 35 years), education (less than higher education), birth interval (< 24 months and first birth), unplanned pregnancy, household wealth status (poorest and richest), consanguinity, residence (central and south regions of Jordan), female sex, birth order (1 and ≥ 6), twin birth and maternal smoking during pregnancy were significant risk factors for low birth weight. The risk of death under 5 years of age was 4.8 times higher in children with low birth weight than children with normal birth weight. Conclusions: The high rate of low birth weight and its increasing rate in Jordan is a challenge for public health. Preventing low birth weight neonates and increasing their survival need to be prioritized in the national health strategy. Special care needs to be taken for the high-risk groups identified in this study.

SummaryThis study examined the recent level, trends and determinants of consanguineous marriage in Jordan using time-series data from the Jordan Population and Family Health Surveys (JPFHSs). According to the 2012 JPFHS, 35% of all marriages were consanguineous in Jordan in 2012. There has been a declining trend in consanguinity in the country, with the rate decreasing from a level of 57% in 1990. Most consanguineous marriage in 2012 were first cousin marriages, constituting 23% of all marriages and 66% of all consanguineous marriages. The data show that women with a lower age at marriage, older marriage cohort, larger family size, less than secondary level of education, rural place of residence, no employment, no exposure to mass media, a monogamous marriage, a husband with less than higher level of education and lower economic status, and those from the Badia region, were more likely to have a consanguineous marriage. Increasing age at marriage, level of education, urbanization and knowledge about the health consequences of consanguinity, and the ongoing socioeconomic and demographic transition in the country, will be the driving forces for further decline in consanguinity in Jordan.

The selection of an optimal model for data analysis is an important component of model-based molecular phylogenetic studies. Owing to the large number of Markov models that can be used for data analysis, model selection is a combinatorial problem that cannot be solved by performing an exhaustive search of all possible models. Currently, model selection is based on a small subset of the available Markov models, namely those that assume the evolutionary process to be globally stationary, reversible, and homogeneous. This forces the optimal model to be time reversible even though the actual data may not satisfy these assumptions. This problem can be alleviated by including more complex models during the model selection. We present a novel heuristic that evaluates a small fraction of these complex models and identifies the optimal model.

Most phylogenetic methods are model-based and depend on models of evolution designed to approximate the evolutionary processes. Several methods have been developed to identify suitable models of evolution for phylogenetic analysis of alignments of nucleotide or amino acid sequences and some of these methods are now firmly embedded in the phylogenetic protocol. However, in a disturbingly large number of cases, it appears that these models were used without acknowledgement of their inherent shortcomings. In this chapter, we discuss the problem of model selection and show how some of the inherent shortcomings may be identified and overcome.