Fertility dynamics and life history tactics vary by socioeconomic position in a transitioning cohort of postreproductive Chilean women

Globally, mortality and fertility rates generally fall as resource abundance increases. This pattern represents an evolutionary paradox insofar as resource-rich ecological contexts can support higher numbers of offspring, a component of biological fitness. This paradox has not been resolved, in part because the relationships between fertility, life history strategies, reproductive behavior, and socioeconomic conditions are complex and cultural-historically contingent. We aim to understand how we might make sense of this paradox in the specific context of late-twentieth-century, mid–demographic transition Chile. We use distribution-specific generalized linear models to analyze associations between fertility-related life-history traits—number of offspring, ages at first and last reproduction, average interbirth interval, and average number of live births per reproductive span year—and socioeconomic position (SEP) using data from a cohort of 6,802 Chilean women born between 1961 and 1970. We show that Chilean women of higher SEP have shorter average interbirth intervals, more births per reproductive span year, later age at first reproduction, earlier ages at last reproduction, and, ultimately, fewer children than women of lower SEP. Chilean women of higher SEP consolidate childbearing over a relatively short time span in the middle of their reproductive careers, whereas women of lower SEP tend to reproduce over the entirety of their reproductive lifespans. These patterns may indicate that different SEP groups follow different pathways toward declining fertility during the demographic transition, reflecting different life-history trade-offs in the process

Individual stochasticity in the life history strategies of animals and plants

The life histories of organisms are expressed as rates of development, reproduction, andsurvival. However, individuals may experience differential outcomes for the same set ofrates. Such individual stochasticity generates variance around familiar mean measures oflife history traits, such as life expectancy and the reproductive number R0. By writing lifecycles as Markov chains, we calculate variance and other indices of variability for longevity,lifetime reproductive output (LRO), age at offspring production, and age at maturity for 83animal and 332 plant populations from the COMADRE and COMPADRE matrix databases. Wefind that the magnitude within and variability between populations in variance indices inLRO, especially, are surprisingly high. We furthermore use principal components analysis toassess how the inclusion of variance indices of different demographic outcomes affects lifehistory constraints. We find that these indices, to a similar or greater degree than the mean,explain the variation in life history strategies among plants and animals

Prospective Evaluation of a Model of Prediction of Invasive Bacterial Infection Risk among Children with Cancer, Fever, and Neutropenia

A risk prediction model for invasive bacterial infection (IBI) was prospectively evaluated among children presenting with cancer, fever, and neutropenia. The model incorporated assessment of 5 previously identified risk factors: serum level of C-reactive protein (CRP) 90 mg/L, hypotension, identification of relapse of leukemia as the cancer type, platelet count of 50,000 platelets/mm3, and recent receipt of chemotherapy [16]. Children were uniformly evaluated at enrollment and were classified as having high or low risk for IBI according to a model that considers the number and type of variables present. Of the 263 febrile episodes evaluated during a 17-month period, 140 (53%) were in IBI-positive children. The sensitivity, specificity, and positive and negative predictive values of the model were 92%, 76%, 82%, and 90%, respectively. Identification of these 5 risk factors during the first 24 h of hospitalization was helpful in discriminating between children with a high or low risk for IBI.Financial support: Proyecto FONDECYT grant 1000680