United States vital statistics and the measurement of gestational age

Joyce A Martin1

  • 1National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD 20782, USA. jamartin@cdc.gov

Estimates of the gestational age of the newborn based on US Birth Certificate data are extensively used to monitor trends in infant and maternal health and to improve our understanding of adverse pregnancy outcome. Two measures of gestational age, the 'date of the last normal menses' (LMP) and the 'clinical estimate of gestation' (CE), have been available from birth certificate data since 1989. Reporting irregularities with the LMP-based measure are well-documented, and important questions remain regarding the derivation of the CE. Changes in perinatal medicine and in vital statistics reporting in recent years may have importantly altered gestational age data based on vital statistics. This study describes how gestational age measures are collected and edited in US national vital statistics, and examines changes in the reporting of these measures by race and Hispanic origin between 1990 and 2002. Data are drawn from the National Center for Health Statistics' restricted use US birth files for 1990-2002. Bivariable statistics are used. The percentage of records with missing LMP dates declined markedly over the study period, overall, and for each racial/Hispanic origin group studied. A marked shift in the distribution of the CE of gestational age was also observed, suggesting changes both in the true distribution of age at birth, and in the derivation of this measure. Agreement between the LMP-based and CE estimates increased over the study period, especially among preterm births. However, a high proportion of LMP dates continue to be missing or invalid and the derivation of the CE is still uncertain. In sum, although the reporting of gestational age measures in vital statistics appears to have improved between 1990 and 2002, substantial concerns with both the LMP-based and the CE persist. Efforts to identify approaches to further improve upon the quality of these data are needed.

Related Concept Videos

Guidelines For Measuring Vital Signs01:19

Guidelines For Measuring Vital Signs

Following these guidelines can help nurses accurately measure vital signs, assess changes in patient conditions, and provide timely treatment when necessary. Adhering closely to the guidelines ensures the accuracy and reliability of the results.
Before taking a patient's vital signs, a nurse would consider and assess the patient's comfort level and ensure appropriate equipment is available.
Applications of Life Tables01:22

Applications of Life Tables

Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
Measurement: Derived Units03:02

Measurement: Derived Units

The International System of Units or SI system, by international agreement, has fixed measurement units for seven fundamental properties: length, mass, time, temperature, electric current, amount of substance, and luminosity. These are called the SI base units.
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...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Measurement: Standard Units03:38

Measurement: Standard Units

Every measurement provides three kinds of information: the size or magnitude of the measurement (a number), a standard of comparison for the measurement (a unit), and an indication of the uncertainty of the measurement. While the number and unit are explicitly represented when a quantity is written, the uncertainty is an aspect of the errors in the measurement results.