Low-birthweight prevention programs: the enigma of failure

C Stevens-Simon1, M Orleans

  • 1Colorado Adolescent Maternity Program, University of Colorado Health Sciences Center, Denver, USA.

Birth (Berkeley, Calif.)
|February 3, 2000
PubMed

Insights

Comprehensive prenatal care programs show mixed results in preventing low birthweight. More rigorous research is needed to understand which factors contribute to success or failure in these interventions.

Area of Science:

  • Public Health
  • Obstetrics
  • Neonatal Medicine

Background:

  • Low birthweight is a leading cause of newborn illness and death in the U.S.
  • Prenatal care programs aim to prevent low birthweight and its associated complications.

Purpose of the Study:

  • To identify factors influencing the effectiveness of comprehensive, multicomponent prenatal care programs.
  • To understand why some prenatal care interventions succeed and others fail in preventing low birthweight.

Main Methods:

  • A review of obstetric, pediatric, and public health program evaluations, research reports, and commentaries.
  • Analysis of English language literature from the last four decades on prenatal care efficacy for low birthweight prevention.

Main Results:

  • Inconsistent service delivery and variable definitions hindered quantitative analysis.
  • Research designs often focused on risk factor clusters, obscuring causal links to low birthweight.
  • Failure to examine process variables led to overstating negative intervention outcomes.

Conclusions:

  • Few rigorous evaluations of well-designed prenatal care programs exist.
  • Improved intervention designs and evaluation studies are necessary.
  • Evidence on the costs and benefits of low birthweight prevention strategies is urgently needed.
Abstract

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...
Pathophysiology of Diabetes01:20

Pathophysiology of Diabetes

Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
Type 1 diabetes is characterized by autoimmune-mediated destruction of pancreatic β cells, with environmental factors potentially triggering this process in genetically susceptible individuals. Despite many not having a family history, certain genes increase susceptibility, suggesting a...
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...
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: