Race, intervening variables, and two components of low birth weight

J E Kallan1

  • 1Studies and Surveys Unit, National Research Council, Washington, DC 20418.

Demography
|August 1, 1993
PubMed

Insights

The racial gap in low birth weight (LBW) is partly explained by factors influencing preterm birth and intrauterine growth retardation. Sociodemographic, attitudinal, and behavioral variables better explain racial differences in intrauterine growth retardation than in preterm birth.

Area of Science:

  • Sociology
  • Public Health
  • Epidemiology

Background:

  • The disparity in low birth weight (LBW) between Black and White populations remains a significant public health concern.
  • Previous research has often treated LBW as a singular outcome, limiting causal understanding.
  • A causal framework is needed to dissect the components of LBW and their determinants.

Purpose of the Study:

  • To investigate the determinants of racial differences in the two primary components of LBW: preterm birth (PRETERM) and intrauterine growth retardation (IUGR).
  • To examine the intervening variables through which race and sociodemographic factors influence these adverse pregnancy outcomes.
  • To identify the net determinants of PRETERM and IUGR when considering a comprehensive set of explanatory variables.

Main Methods:

  • Utilized data from the 1988 National Survey of Family Growth.
  • Employed a partially causal framework to analyze the relationships between race, sociodemographic variables, and LBW components.
  • Developed statistical models to assess the influence of intervening variables on PRETERM and IUGR.

Main Results:

  • Race and other exogenous variables influence PRETERM and IUGR through distinct pathways and downstream variables.
  • Racial disparities in IUGR were more statistically explained by intervening sociodemographic, attitudinal, and behavioral variables.
  • Racial differences in PRETERM were partly explained by intervening health-related variables, indicating a more complex etiology.

Conclusions:

  • Understanding the distinct determinants of PRETERM and IUGR is crucial for addressing racial disparities in LBW.
  • Sociodemographic, attitudinal, and behavioral factors play a significant role in explaining racial differences in IUGR.
  • Targeted interventions addressing health-related factors may be necessary to mitigate racial disparities in PRETERM.

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...
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...