Multiple mortality optima due to heterogeneity in the birth cohort: A continuous model of birth weight by gestational age-specific infant mortality

Heterogeneity identified at birth and blood pressure in adulthood

OBJECTIVE

In the developmental programming literature, the association of birth weight and blood pressure later in life is modest at best. This article reexamines this issue using Covariate Density Defined mixture of regressions (CDDmr) to determine if a latent variable, based on birth weight, and known to influence infant mortality, provides a stronger indicator of developmental programming.

METHODS

CDDmr identifies two latent components in the birth weight distribution, generally interpreted in the infant mortality literature as "compromised" and "normal" fetal development. The data are taken from the 1958 British National Child Development Study.

RESULTS

In the 1958 cohort, the "compromised" component consists of about 10% of the birth weight distribution. Compared to "normal" births, the mean systolic blood pressure at age 45 of "compromised" births is 20.3 (females) and 26.4 (males) mm Hg higher than "normal" births. The relative risks of stage-I-II systolic hypertension (>140 mm Hg) of "compromised" births compared to "normal" births are 10.7 (females) and 4.2 (males). The relative risks of stage-II systolic hypertension (>160 mm Hg) are considerably higher, 241.6 (females) and 74.3 (males). In general, the highest blood pressures are observed in "compromised" births with birth weights within the normal (about 3.0-3.5 kg) birth weight range. Results for diastolic blood pressure are similar.

CONCLUSIONS

These associations are much stronger than those identified using conventional methods. CDDmr is likely to be useful for studying the dynamics of developmental programming particularly in large cohort studies where birth weight is often the only indicator of programming available. Am. J. Hum. Biol. 28:545-554, 2016. © 2016 Wiley Periodicals, Inc.

Joint Bayesian analysis of birthweight and censored gestational age using finite mixture models

Birthweight and gestational age are closely related and represent important indicators of a healthy pregnancy. Customary modeling for birthweight is conditional on gestational age. However, joint modeling directly addresses the relationship between gestational age and birthweight, and provides increased flexibility and interpretation as well as a strategy to avoid using gestational age as an intermediate variable. Previous proposals have utilized finite mixtures of bivariate regression models to incorporate well-established risk factors into analysis (e.g. sex and birth order of the baby, maternal age, race, and tobacco use) while examining the non-Gaussian shape of the joint birthweight and gestational age distribution. We build on this approach by demonstrating the inferential (prognostic) benefits of joint modeling (e.g. investigation of 'age inappropriate' outcomes like small for gestational age) and hence re-emphasize the importance of capturing the non-Gaussian distributional shapes. We additionally extend current models through a latent specification which admits interval-censored gestational age. We work within a Bayesian framework which enables inference beyond customary parameter estimation and prediction as well as exact uncertainty assessment. The model is applied to a portion of the 2003-2006 North Carolina Detailed Birth Record data (n=336129) available through the Children's Environmental Health Initiative and is fitted using the Bayesian methodology and Markov chain Monte Carlo approaches.

Thinking outside the curve, part I: modeling birthweight distribution

BACKGROUND

Greater epidemiologic understanding of the relationships among fetal-infant mortality and its prognostic factors, including birthweight, could have vast public health implications. A key step toward that understanding is a realistic and tractable framework for analyzing birthweight distributions and fetal-infant mortality. The present paper is the first of a two-part series that introduces such a framework.

METHODS

We propose describing a birthweight distribution via a normal mixture model in which the number of components is determined from the data using a model selection criterion rather than fixed a priori.

RESULTS

We address a number of methodological issues, including how the number of components selected depends on the sample size, how the choice of model selection criterion influences the results, and how estimates of mixture model parameters based on multiple samples from the same population can be combined to produce confidence intervals. As an illustration, we find that a 4-component normal mixture model reasonably describes the birthweight distribution for a population of white singleton infants born to heavily smoking mothers. We also compare this 4-component normal mixture model to two competitors from the existing literature: a contaminated normal model and a 2-component normal mixture model. In a second illustration, we discover that a 6-component normal mixture model may be more appropriate than a 4-component normal mixture model for a general population of black singletons.

CONCLUSIONS

The framework developed in this paper avoids assuming the existence of an interval of birthweights over which there are no compromised pregnancies and does not constrain birthweights within compromised pregnancies to be normally distributed. Thus, the present framework can reveal heterogeneity in birthweight that is undetectable via a contaminated normal model or a 2-component normal mixture model.

Maternal age and infant mortality: a test of the Wilcox-Russell hypothesis

It has been argued (e.g., the Wilcox-Russell hypothesis) that (low) birth weight is a correlate of adverse birth outcomes but is not on the “causal” pathway to infant mortality. However, the US national policy for reducing infant mortality is to reduce low birth weight. If these theoretical views are correct, lowering the rate of low birth weight may have little effect on infant mortality. In this paper, the authors use the “covariate density defined mixture of logistic regressions” method to formally test the Wilcox-Russell hypothesis that a covariate which influences birth weight, in this case maternal age, can influence infant mortality directly but not indirectly through birth weight. The authors analyze data from 8 populations in New York State (1985–1988). The results indicate that among the populations examined, 1) maternal age significantly influences the birth weight distribution and 2) maternal age also affects infant mortality directly, but 3) the influence of maternal age on the birth weight distribution has little or no effect on infant mortality, because the birth-weight-specific mortality curve shifts accordingly to compensate for changes in the birth weight distribution. These results tend to support the Wilcox-Russell hypothesis for maternal age.

Modeling the pediatric paradox: birth weight by gestational age

The "pediatric paradox" of African versus European American infant mortality is often observed with respect to birth weight, but rarely to gestational age, even though the two measures are biologically related. This paper models the pediatric paradox by birth weight and gestational age simultaneously, using Covariate Density Defined mixture of logistic regressions (CDDmlr) fitted to 1985-1988 New York State births. The model controls for unobserved heterogeneity and isolates the pediatric paradox in the "compromised" subpopulation. The paradox is not limited to low birth weights and/or short gestational ages, but surrounds the normal birth range. Nevertheless, the pediatric paradox is only observed in the marginal distribution of birth weight and not the marginal distribution of gestational age. These results are consistent with the hypothesis that higher fetal losses in the "compromised" subpopulation may be responsible for the pediatric paradox and that African versus European American infant mortality differentials are underestimated.