Triangle plots of three-level explanatory variables

Thalassaemias are genetic blood disorders which cause varying degrees of anaemia. Their geographical distribution suggests a compensating protection against malaria, which kills between 0.5 and 2.5 million people per year in developing countries. Neal Alexander describes a study in Papua New Guinea which estimated this association more directly, and a triangle plot which clarified, for himself and his non-statistician colleagues, the relative risks of malaria for those with none, one or two copies of the relevant haemoglobin mutation.     

Community,Family, and Race/Ethnic Differences in Health Status in Rural Areas1

Abstract Many race-specific differences in health outcomes that have been observed in previous research have been attributed to class and racebased group differences which either facilitate or constrain health opportunities and behaviors. These include such variables as different rates of poverty, health insurance coverage, and access to medical care. However, these relationships have been inadequately examined in rural communities where minority status may be even more detrimental to health than in urban areas, due to various constraints on access to health care. We present an analysis that assesses the effects of community, family structure, sociodemographic, and medical care variables on self-reported health status among Hispanics, Mrican Americans, and non-Hispanic whites in six rural communities in Florida. Community structural characteristics had a significant effect on self-reported health, as did some of the measures of how respondents “experience” community. These relationships held even when other sets of variables were added to the models. Family/household characteristics and sociodemographic and medical care variables were less important in explaining self-reported health status. These findings suggest that community continues to be important in explaining differences in health status in rural areas.     

Split Hamiltonian Monte Carlo

We show how the Hamiltonian Monte Carlo algorithm can sometimes be speeded up by “splitting” the Hamiltonian in a way that allows much of the movement around the state space to be done at low computational cost. One context where this is possible is when the log density of the distribution of interest (the potential energy function) can be written as the log of a Gaussian density, which is a quadratic function, plus a slowly-varying function. Hamiltonian dynamics for quadratic energy functions can be analytically solved. With the splitting technique, only the slowly-varying part of the energy needs to be handled numerically, and this can be done with a larger stepsize (and hence fewer steps) than would be necessary with a direct simulation of the dynamics. Another context where splitting helps is when the most important terms of the potential energy function and its gradient can be evaluated quickly, with only a slowly-varying part requiring costly computations. With splitting, the quick portion can be handled with a small stepsize, while the costly portion uses a larger stepsize. We show that both of these splitting approaches can reduce the computational cost of sampling from the posterior distribution for a logistic regression model, using either a Gaussian approximation centered on the posterior mode, or a Hamiltonian split into a term that depends on only a small number of critical cases, and another term that involves the larger number of cases whose influence on the posterior distribution is small.     

Statement of Withdrawal: Statistical analysis of an endemic disease from a capture–recapture experiment

There are a number of statistical techniques for analysing epidemic outbreaks. However, many diseases are endemic within populations and the analysis of such diseases are complicated by changing population demography. Motivated by the spread of cowpox among rodent populations, a combined mathematical model for population and disease dynamics is introduced. An MCMC algorithm is then constructed to make statistical inference for the model based on data being obtained from a capture–recapture experiment. The statistical analysis is used to identify the key elements in the spread of the cowpox virus.     

Consensus in the Marital Dyad: Couples' Perceptions of Contraception,Communication, and Family Life

Abstract

The present study was based on data collected (separately for husbands and wives) from 365 couples to determine levels of consensus on a series of variables related to the family forming process. Following Scheff (1967), consensus was operationalized as both agreement and coorientation in the marital dyad. The data generally indicated low levels of consensus on such variables as contraceptive efficacy, desired family size, child spacing, unwanted pregnancies, communication with spouse, and levels of alienation. The results are discussed within the framework of family planning and effective fertility control.     

Notes and Communications “A DECISION THEORY EXAMPLE IN FOOTBALL”: A COMMENT*

Janssen and Daniel analyzed the choice between a one- or a two-point conversion for a particular game situation in college football. Their decision criteria was maximum expected utility based on a von Neumann-Morgenstern utility function defined over the games outcomes. An alternative approach based on a stochastic dominance criterion is presented that does not rely on knowledge of the relative importance of tying vs. winning; rather, it relies on a notion of consistency in the sequential problem.     

The Untold Story of 50 Years of Adolescent Fertility in West Africa: A Cohort Perspective on the Quantum,Timing, and Spacing of Adolescent Childbearing

Although recent studies examine overall fertility trends in West Africa, few using advanced demographic techniques focus on adolescents. This study explores long‐term patterns of adolescent childbearing in 12 West African countries using 51 Demographic and Health Surveys covering birth cohorts that span 54 years (1940–1994). We employ classic demographic measures as well as disaggregation by early‐ (10–14 years old), middle‐ (15–17), and late adolescence (18–19). Cohort‐based estimates of total adolescent births, parity progression ratios, and rapid repeat birth probabilities reveal little change over time. Most women begin childbearing in adolescence, the progression to additional adolescent births remains common, and the incidence of rapid repeat births is high. In recent cohorts, women exit adolescence with an average of between 0.4 (Ghana) to 1.3 (Niger) births. Contrary to common assumptions, it is women commencing motherhood in early‐ and middle‐, not later adolescence, who account for most West African adolescent fertility.