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

Timothy B Gage1, Fu Fang, Erin O'Neill

  • 1Department of Anthropology, College of Arts and Sciences, University at Albany-State University of New York, Albany, NY 12222, USA. tbg97@albany.edu

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.

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Two-Way ANOVA01:17

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...