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Keywords:

  • gestation;
  • ultrasound estimate;
  • LMP estimate;
  • perterm rate;
  • post-term rate

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Although early ultrasound (<20 weeks' gestation) systematically underestimates the gestational age of smaller fetuses by approximately 1–2 days, this bias is relatively small compared with the large error introduced by last menstrual period (LMP) estimates of gestation, as evidenced by the number of implausible birthweight-for-gestational age. To characterise this misclassification, we compared gestational age estimates based on LMP from California birth certificates with those based on early ultrasound from a California linked Statewide Expanded Alpha-fetoprotein Screening Program (XAFP). The final sample comprised 165 908 women. Birthweight distributions were plotted by gestational age; sensitivity and positive predictive value for preterm rates according to LMP were calculated using ultrasound as the ‘gold standard’.

For gestational ages 20–27 and 28–31 weeks, the LMP-based birthweight distributions were bimodal, whereas the ultrasound-based distributions were unimodal, but had long right tails. At 32–36 weeks, the LMP distribution was wider, flatter, and shifted to the right, compared with the ultrasound distribution. LMP vs. ultrasound estimates were, respectively, 8.7% vs. 7.9% preterm (<37 weeks), 81.2% vs. 91.0% term (37–41 weeks), and 10.1% vs. 1.1% post-term (≥42 weeks). The sensitivity of the LMP-based preterm birth estimate was 64.3%, and the positive predictive value was 58.7%. Overall, 17.2% of the records had estimates with an absolute difference of >14 days. The groups most likely to have inconsistent gestational age estimates included African American and Hispanic women, younger and less-educated women, and those who entered prenatal care after the second month of pregnancy. In conclusion, we found substantial misclassification of LMP-based gestational age.

The 2003 revised US Standard Certificate of Live Birth includes a new gestational age item, the obstetric estimate. It will be important to assess whether this estimate addresses the problems presented by LMP-based gestational age.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Problems with the accuracy of gestational age computed by last menstrual period (LMP) on birth certificates have been documented.1–5 Evidence of this inaccuracy is illustrated by birthweight distributions that are bimodal at gestational ages <32 weeks, with the modal birthweight of the second peak consistent with that of term infants.1,4 Inaccuracy of LMP-based gestational age can be caused by biologically associated errors in menstrual cycles and by human error in recall or data entry.6,7 Inherent in estimating gestational age with LMP is the assumption that all women have a regular 28-day menstrual cycle and ovulate 14 days after the first day of their LMP. However, because timing of ovulation varies, even with accurate recall and data entry of the LMP, estimates of gestational age based on LMP can be inaccurate. For example, one study found that 10% of women had cycles <25 days long, 12% were between 31 and 35 days, and 3% were 36 days or longer, while 5% were too irregular to say.8

Time from LMP to ovulation is more likely to be longer, as opposed to shorter, than 14 days, resulting in an overestimation of gestational age when using LMP.9,10 Biologically associated error can also occur if early bleeding in pregnancy is thought to be menstruation or if LMP is missing because of amenorrhoea. Clinicians are well aware of the shortcomings of LMP, and standard practice is to base gestational age estimates on early ultrasound (<20 weeks) or other factors when LMP is uncertain. In addition, clinicians frequently substitute ultrasound-based gestational age estimates for LMP-based estimates when the two disagree.11

However, while early ultrasound has been established clinically as the gold standard, questions have been raised as to its validity for use in research. One common concern is that ultrasound may introduce biases because it is based on fetal growth, and thus could systematically result in the assignment of incorrect lower gestational age estimates for smaller infants.12,13 Recent studies have found that early (<20 weeks) ultrasound-based gestational age formulas are fairly accurate, with random errors of ±10 days [95% confidence interval (CI)].14 In addition, fetuses with characteristics associated with small fetal size, such as first births and female sex, were found to be systematically dated 1–2 days younger.15,16 Another large study of singleton pregnancies with ultrasound examinations between 12 and 22 weeks found no evidence that growth-restricted fetuses were systematically classified incorrectly at lower gestational ages, and that the discrepancy between the LMP-based gestational age and the ultrasound-based gestational age was primarily related to ovulation later than the assumed 14 days.17 Thus, while early ultrasound may systematically underestimate gestational age for smaller fetuses by 1–2 days on average, this bias is relatively small compared with the large magnitude of error indicated by records with implausible birthweight-for-gestational age based on LMP.4,9,18 Previous studies comparing LMP-based and ultrasound-based gestational age have found high rates of gestational age misclassification by LMP. However, these studies have been limited to clinic- or hospital-based samples,9,12,18 to women with reliable LMP dates,12 or to studies outside the US.12,18 Therefore, whether the findings of these studies can be generalised to other populations is unknown.

We sought to better understand and characterise the misclassification found with gestational age estimated by using LMP from birth certificates. Because US birth certificates do not include information on early ultrasound, we compared gestational age estimates based on LMP from California birth certificates with gestational age estimates based on early ultrasound (≤20 weeks' gestation) from a population-based prenatal screening programme in which approximately 70% of the State's pregnant women participate. Unlike previous studies, this inquiry benefited from a large sample derived from the cohort of women who delivered in California in 2002.

Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The study population was defined as pregnant women enrolled in the Statewide California Expanded Alpha-fetoprotein Screening Program (XAFP) who gave birth to a live singleton infant during 2002, and who had an estimated gestational age based on ultrasound recorded on their XAFP screening form. The XAFP is a triple marker screening programme offered to all women entering prenatal care by 20 weeks' gestation. When maternal blood is drawn for this screen, the medical provider fills out a form dating the pregnancy based on LMP, physical examination, and/or ultrasound, when available. Using SuperMatch 2001 software (SuperMATCH Concepts and Reference Version 3.10, Vality Technology Incorporated, March 2001), a probabilistic method was employed to link records from the XAFP and Statewide Newborn Screening programmes and birth certificates using mother's name, date of birth, social security number, delivery date, XAFP accession date, telephone number, street address, city and zip code. A conservative certainty cut-off was used to minimise false matches.

In 2002, there were 530 926 livebirths in California. Of these, 327 218 livebirth records (62%) linked to an XAFP record from the same pregnancy, with approximately 86% of XAFP records successfully linking to a livebirth record. Failure to link records may have resulted from data entry errors, pregnancies that did not end in a livebirth, or women who moved out of State before delivery. Among the linked records, 195 616 (59.8%) women had ultrasound reported on the XAFP records. After excluding 3238 women with multiple births, 192 378 women were eligible for the study. Of these, we excluded records missing LMP on the birth certificate (n = 26 249) or with gestational age at birth of <20 weeks by either LMP (n = 206) or ultrasound (n = 30). The final sample comprised 165 908 women (50.7% of livebirth records linked to an XAFP record, 32.2% of livebirths in California in 2002, Table 1).

Table 1.  Maternal demographic and pregnancy characteristics by study eligibility and inclusion status, California livebirths, 2002
CharacteristicEligible and included (n = 165 908) %Ineligible or excluded (n = 349 481) %
  1. LMP, last menstrual period; NA, not applicable.

  2. Due to rounding or missing values totals may not add up to 100%.

Race/ethnicity
 White31.830.2
 African American5.46.1
 Asian7.86.8
 Hispanic48.351.0
 Other6.75.8
Age (years)
 <207.410.9
 20–2421.024.6
 25–3458.346.9
 ≥3513.217.6
Education (years)
 <1224.931.1
 1228.129.0
 >1247.039.9
Payment source (delivery)
 Medi-Cal36.146.7
 Private62.247.8
 Uninsured1.12.9
 Other0.62.5
Month prenatal care began
 1–276.165.7
 3–422.124.3
 5–6NA6.4
 ≥7NA3.6
Parity
 040.638.6
 132.831.3
 ≥226.530.1
Infant birthweight (g)
 <25004.95.0
 ≥250095.195.0
LMP-based gestational age (weeks)
 <378.79.0
 37–4181.284.2
 42–448.05.6
 ≥452.11.2

LMP-based gestational age at delivery was calculated using LMP and date of birth from the birth certificate. Ultrasound-based gestational age at delivery was calculated using the ultrasound-based estimate of gestational age on the date the ultrasound was performed, and the date of delivery on the birth certificate. We categorised the two gestational age variables into five groups based on completed weeks: 20–27, 28–31, 32–36, 37–41 and ≥42 weeks.

To explore predictors of inconsistent gestational age, we obtained infant birthweight, race/ethnicity, mother's age, education, source of payment for delivery, and month of entry into prenatal care from the birth certificate.

We first compared the birthweight distributions for each gestational age group using LMP-based and ultrasound-based gestational age estimates. We also calculated the sensitivity and positive predictive value of the LMP-based gestational age, using ultrasound as the gold standard. We compared the mean birthweight and whether the infant was placed in a neonatal intensive care unit (NICU) for estimates that were concordant and discordant for gestational age. For this analysis only we divided the group of 20–27 weeks into two gestational age categories (20–23 and 24–27 weeks) to more closely examine differences. The NICU variable was obtained from the Statewide Newborn Screening programme database, and indicates whether the infant was in a NICU at the time of specimen collection (median time between delivery and specimen collection, 29 h). We compared the demographic characteristics of women with inconsistent ultrasound- and LMP-based gestational age estimates. We defined inconsistent as an absolute difference >14 days and used this cut-off to identify gross errors in gestational age. All demographic characteristics were entered into a logistic regression model to assess the independent effects of each risk factor on inconsistent estimates, holding the other characteristics constant. Finally, we calculated preterm delivery rates for LMP- and ultrasound-based estimates overall, and by race/ethnicity, age, parity, education, month of entry into prenatal care, and infant's sex.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Women included in the sample differed from those not included in that they were disproportionately aged 25–34 years, more educated and less likely to have Medi-Cal (California's Medicaid programme) (Table 1). The women included were also more likely to have begun prenatal care in the first 2 months of pregnancy and were more likely to have delivered post-term (≥42 weeks' gestation) based on LMP. The two groups were similar in racial/ethnic and low birthweight rates.

Figures 1–3 present the birthweight distribution by LMP- and ultrasound-based gestational age. For LMP-based gestational ages 20–27 weeks (Fig. 1) and 28–31 weeks (Fig. 2), the birthweight distribution is bimodal, whereas the distribution based on ultrasound gestational age is not, but it has a long right tail. For LMP-based gestational age 32–36 weeks (Fig. 3), the birthweight distribution is wider, flatter, and shifted to the right compared with the ultrasound-based distribution. For both LMP- and ultrasound-based gestational ages 37–41 weeks (figure not shown), birthweight distributions overlap and appear normally distributed. For LMP-based gestational age ≥42 weeks (figure not shown), the birthweight distribution is wider, flatter, and shifted to the left, compared with the ultrasound-based distribution.

image

Figure 1. Birthweight distribution of singleton births delivered at 20–27 weeks' gestation according to ultrasound (n = 733) and last menstrual period (LMP) (n = 745).

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image

Figure 2. Birthweight distribution of singleton births delivered at 28–31 weeks' gestation according to ultrasound (n = 1091) and last menstrual period (LMP) (n = 1235).

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image

Figure 3. Birthweight distribution of singleton births delivered at 32–36 weeks' gestation according to ultrasound (n = 11 410) and last menstrual period (LMP) (n = 12 499).

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According to LMP-based gestational age estimates, 8.7% of the infants were preterm (<37 weeks), 81.2% were term (37–41 weeks) and 10.1% were post-term (≥42 weeks). In comparison, according to ultrasound-based estimates, 7.9% of the infants were preterm, 91.0% were term and 1.1% were post-term. Using ultrasound as the gold standard, the overall sensitivity (the percentage of true preterm deliveries correctly identified by LMP) was 64.3%, and the positive predictive value (the percentage of those found to be preterm by LMP that were ‘true’ preterm) was 58.7% (Table 2). The sensitivity and positive predictive value were higher for the gestation group of 20–27 weeks than the other preterm groups. They were lowest for the post-term group, with a sensitivity of 33.6% and positive predictive value of 3.7%. When stratified by race/ethnicity, Hispanics had the lowest sensitivity and positive predictive value for less than 37 weeks. Whereas whites had similar sensitivity and positive predictive value, Hispanics and African Americans had lower positive predictive value than sensitivity, meaning that the number of infants falsely identified as preterm using LMP estimates exceeded the number of true preterm infants missed by these estimates.

Table 2.  Sensitivity and positive predictive value of last menstrual period estimate of gestational age using ultrasound estimates as the gold standard, total study population and by racial/ethnic groups
Gestational age (weeks)Sensitivity % [95% CI]Positive predictive value % [95% CI]
All women
 <3764.3 [63.5, 65.1]58.7 [57.9, 59.5]
 20–2776.9 [73.9, 80.0]75.7 [72.6, 78.8]
 28–3160.4 [57.4, 63.5]49.9 [47.1, 52.7]
 32–3657.6 [56.7, 58.5]52.8 [51.9, 53.7]
 37–4185.6 [85.5, 85.8]95.9 [95.8, 96.0]
 ≥4233.6 [31.5, 35.8]3.6 [3.3, 3.9]
White
 <3766.8 [65.3, 68.3]68.8 [67.3, 70.3]
 20–2774.0 [67.1, 80.9]76.0 [69.2, 82.8]
 28–3164.9 [59.2, 70.6]62.9 [57.2, 68.6]
 32–3662.3 [60.7, 63.9]64.4 [62.8, 66.0]
 37–4188.0 [87.7, 88.3]96.2 [96.0, 96.4]
 ≥4238.8 [35.3, 42.3]5.8 [5.2, 6.4]
African American
 <3771.8 [69.0, 74.6]63.7 [60.9, 66.5]
 20–2776.9 [69.0, 84.8]83.0 [75.6, 90.4]
 28–3158.7 [49.9, 67.5]52.6 [44.2, 61.0]
 32–3661.1 [57.6, 64.6]52.8 [49.5, 56.1]
 37–4183.7 [82.9, 84.5]95.2 [94.7, 95.7]
 ≥4228.5 [20.7, 36.3]3.9 [2.7, 5.1]
Hispanic
 <3760.9 [59.7, 62.1]52.2 [51.1, 53.3]
 20–2777.2 [73.0, 81.4]72.5 [68.2, 76.8]
 28–3156.6 [52.3, 60.9]41.7 [38.0, 45.4]
 32–3653.0 [51.7, 54.3]45.8 [44.6, 47.0]
 37–4183.4 [83.1, 83.7]95.6 [95.4, 95.8]
 ≥4229.5 [26.3, 32.7]2.5 [2.2, 2.8]
Asian
 <3768.9 [65.8, 72.0]62.6 [59.5, 65.7]
 20–2786.4 [76.3, 95.5]80.8 [69.5, 92.1]
 28–3162.9 [50.9, 74.9]68.4 [56.3, 80.5]
 32–3664.2 [60.8, 67.6]57.4 [54.0, 60.8]
 37–4189.1 [88.5, 89.7]97.1 [96.8, 97.4]
 ≥4227.5 [18.8, 36.2]2.7 [1.7, 3.7]

In order to evaluate ultrasound as a measure of gestational age versus LMP, we compared the mean birthweights of infants with gestational age estimates that were concordant and discordant, using LMP and ultrasound (Table 3). Among discordant gestational age groups, mean birthweights categorised by ultrasound were closer to the mean birthweights of infants with concordant estimates than those categorised by LMP. In Table 3, mean birthweights for gestational age categories as determined by ultrasound (columns) were more similar to one another than were mean birthweights for gestational age categories determined from the LMP (rows). However, some misclassification among infants <37 weeks' gestation based on ultrasound was apparent (potentially due to clerical error), as mean birthweights increased with increasing LMP-based gestational age among infants with discordant estimates. Examination of percentage of infants in the NICU showed that ultrasound estimates of gestational age were more consistent with what would be expected. Preterm gestational age groups determined by ultrasound had a higher percentage of infants in the NICU than did those determined by LMP.

Table 3.  Mean birthweight and NICU admissions by cross-tabulation of LMP-baseda and ultrasound-basedb gestational age estimates
LMP gestational age (weeks)Ultrasound
20–2324–2728–3132–3637–4142–44≥45Total
  • a

    LMP from birth certificate.

  • b

    Ultrasound from XAFP screening form.

  • c

    Missing data.

  • d

    Birthweight means were not calculated for n < 10.

  • LMP, last menstrual period; NICU, neonatal intensive care unit.

20–23
 n13432692500206
 Mean birthweight (g)481810dd3 437dd996
 (SD)(109)(526)  (485)  (1035)
 % NICUc88836780066
24–27
 n5534359235810539
 Mean birthweight (g)59085712912 1643 409dd1 213
 (SD)(255)(281)(430)(531)(516)  (900)
 % NICU9698975620084
28–31
 n8107616218286101 235
 Mean birthweight (g)d91513952 2113 347dd1 946
 (SD) (257)(364)(524)(435)  (942)
 % NICU10098976330069
32–36
 n01727265685 56030212 449
 Mean birthweight (g)d953159525843 2863677d2 876
 (SD) (212)(391)(522)(482)(391) (643)
 % NICU0100934050025
37–41
 n812404245129 218117312134 708
 Mean birthweight (g)d148217602 8733 453381135453 437
 (SD) (1220)(828)(495)(456)(458)(467)(471)
 % NICU388085183704
42–44
 n141519512 539538113 293
 Mean birthweight (g)dd19442 8653 5223828d3 522
 (SD)  (923)(554)(463)(485) (482)
 % NICUc7593213604
≥45
 n57111523 2257623 478
 Mean birthweight (g)dd13092 7923 5123831d3 470
 (SD)  (273)(550)(468)(571) (538)
 % NICU10010091243705
Total
 n211522101911 410150 911181817165 908
 Mean birthweight (g)62088714632 6913 45438153441 
 (SD)(593)(367)(433)(536)(459)(470)(547) 
 % NICU89979532360 

Overall, 17.2% of gestational age estimates had an absolute difference of >14 days between the two sources (Table 4); for 4.0% of the records, the ultrasound-based estimate was greater than the LMP-based estimate and for 13.2% the LMP-based estimate was greater than the ultrasound-based estimate. African American and Hispanic women compared with white women had a greater percentage of records with inconsistent LMP and ultrasound gestational age, as did women aged <35 years compared with their older counterparts, women with fewer years of education compared with women with ≥13 years, and multiparae compared with primiparae. Women who, according to birth certificate records, entered into prenatal care in the third or fourth month of pregnancy had infants with higher rates of inconsistent estimates compared with women who entered in the first or second month. We found the same groups of women with higher inconsistent estimates when we stratified by LMP estimate > ultrasound estimate and LMP estimate < ultrasound estimate (data not shown).

Table 4.  Proportion of women with inconsistenta estimates of gestational age and adjusted odds ratios for inconsistent estimates by selected maternal and pregnancy characteristics
 Inconsistent (%)Adjusted ORd[95% CI]
  • a

    Inconsistent is >14 days absolute difference between LMP estimate and ultrasound estimate.

  • b

    Adjusted for all characteristics simultaneously.

Race/ethnicity
 White13.2Reference
 African American19.01.3 [1.2, 1.4]
 Asian13.81.1 [1.0, 1.2]
 Hispanic20.51.3 [1.2, 1.4]
 Other14.41.1 [1.0, 1.2]
Age (years)
 <2022.51.7 [1.6, 1.8]
 20–2420.91.6 [1.5, 1.7]
 25–3416.01.3 [1.2, 1.3]
 ≥3513.5Reference
Education (years)
 <1222.41.4 [1.3, 1.4]
 1219.01.2 [1.2, 1.3]
 >1213.3Reference
Month prenatal care began
 1–215.3Reference
 3–422.01.5 [1.4, 1.5]
Parity
 015.5Reference
 117.11.2 [1.1, 1.2]
 ≥219.81.3 [1.3, 1.4]

Preterm delivery rates differed according to maternal characteristics when using LMP and ultrasound (Table 5). For example, the odds ratio (OR) for preterm delivery for African American infants compared with white infants was higher for LMP-based at 1.8 [95% CI 1.7, 1.9] than for ultrasound-based gestational age estimates at 1.5 [95% CI 1.4, 1.6]. A similar pattern was found for education. The OR of 1.2 for preterm delivery for male infants compared with female infants was the same for LMP-based and ultrasound-based gestational age estimates, and thus there was no evidence that gestational age based on ultrasound resulted in higher preterm rates among fetuses known to be smaller, such as females.

Table 5.  Preterm ratesa and UORs using LMP- and ultrasound-based gestational age estimates for selected characteristics
CharacteristicLMPUltrasound
Preterm rateUOR [95% CI]Preterm rateUOR [95% CI]
  • a

    Rates are limited to gestational ages between 20 and 44 weeks.

  • LMP, last menstrual period; UOR, unadjusted odds ratio.

Race/ethnicity
 White7.3Reference7.3Reference
 African American12.51.8 [1.7, 1.9]10.81.5 [1.4, 1.6]
 Asian7.31.0 [0.9, 1.1]6.50.9 [0.8, 1.0]
 Hispanic9.81.4 [1.3, 1.4]8.11.1 [1.1, 1.2]
 Other9.21.3 [1.2, 1.4]8.41.2 [1.1, 1.2]
Age (years)
 <2010.81.0 [0.9, 1.1]9.01.0 [0.9, 1.0]
 20–248.70.8 [0.8, 0.9]7.60.8 [0.8, 0.9]
 25–348.30.8 [0.7, 0.8]7.60.8 [0.8, 0.8]
 ≥3510.6Reference9.3Reference
Education (years)
 <1210.41.4 [1.3, 1.4]8.31.1 [1.1, 1.2]
 129.41.2 [1.2, 1.3]8.31.1 [1.1, 1.2]
 >127.8Reference7.5Reference
Month prenatal care began
 1–28.9Reference8.1Reference
 3–48.61.0 [0.9, 1.0]7.30.9 [0.9, 0.9]
Parity
 08.81.1 [1.0, 1.1]8.31.2 [1.2, 1.3]
 18.0Reference7.0Reference
 ≥210.01.3 [1.2, 1.3]8.41.2 [1.2, 1.3]
Infant gender
 Female8.2Reference7.2Reference
 Male9.61.2 [1.1, 1.2]8.61.2 [1.2, 1.3]

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Using ultrasound-based gestational age as the gold standard, this study found evidence of misclassification of gestational age based on LMP. We found a greater percentage of false preterm infants, resulting in inflation of the preterm delivery rate. In addition, African Americans and Hispanics had a greater percentage of records with misclassified gestational age than white women, resulting in inflated racial/ethnic disparities in preterm rates. The same pattern was found for women with less education. The birthweight distributions for gestational ages <32 weeks were bimodal when based on LMP but unimodal when based on ultrasound. While concerns have been raised that ultrasound-based gestational age results in misclassification of fetuses smaller than expected, we found no evidence of this bias in our study, as the ORs of preterm delivery for male infants compared with female infants were the same for LMP- and ultrasound-based gestational ages. The majority of inconsistent estimates for LMP-based post-term infants were of gestational ages greater than those arrived at by ultrasound. This is consistent with our knowledge that ovulation is more likely to occur later than the assumed 14 days after the first day of the LMP rather than earlier.

Our finding that the ultrasound-based gestational age distribution had fewer post-term deliveries is consistent with those of other studies.9,18 However, findings regarding preterm rates are not consistent: one study found higher preterm rates using ultrasound estimates,18 another found preterm rates to be similar between ultrasound and LMP estimates,9 while we found higher preterm rates with LMP-based estimates. These inconsistent findings suggest that the amount and type of error in LMP-based gestational age can vary depending upon characteristics of the sample and data collection methods. Some types of error, such as delayed ovulation, result in overestimation of gestational age, whereas poor recall could cause error in either direction. The predominant direction of the error will determine an overall under- or overestimation of gestational age compared with the ‘true’ estimate. With LMP, it is likely that more than one type of error is affecting the estimate of gestational age and contributing to bidirectional misclassification.

Our study benefited from a large sample size that included a subpopulation of women from the cohort who gave birth in California in 2002. While this study population may be more representative and have more statistical power than those based on hospital or clinic samples,9,12,13,18 characteristics of women included in our sample differed from those not included in several important ways. Our sample included more women with post-term gestational age based on LMP, which is a marker for poor dating. Women screened in the XAFP programme who received ultrasound were more likely to have had post-term LMP dates than those who did not receive ultrasound, suggesting that uncertain dates might have been an indication for the ultrasound. Therefore, the LMP-based dates for women included in our sample may be less reliable than the LMP-based dates for the general population. If so, the misclassification rate of gestational age from LMP could be lower in the general population than found in this study.

On the other hand, women in our sample were more educated, less likely to have Medicaid coverage, older (with the exception of women aged ≥35 years, who are eligible for amniocentesis without XAFP screening), and entered prenatal care earlier on average than excluded women, attributes associated with more reliable LMP dates. It is reassuring that LMP-based preterm rates among included and excluded women were similar, suggesting that the misclassification of LMP-based gestational age among preterm infants may indeed be representative of the general population of California. Finally, while we assumed ultrasound to be the gold standard when estimating sensitivity and positive predictive value, we found some evidence of error in ultrasound-based gestational age estimates. Therefore, the sensitivity and positive predictive value of LMP-based gestational age may be higher for the entire cohort of infants in California than described in our sample.

In conclusion, our study provides evidence that a substantial amount of misclassification results when using LMP-based gestational age estimates, and this misclassification can lead to inflated preterm delivery rates. In addition, differences in preterm delivery rates between whites and African Americans, and between whites and Hispanics, can also be inflated. Including ultrasound-based estimates of gestational age on the birth certificate would help to improve the accuracy of preterm delivery rates, yet not all women receive an ultrasound before 20 weeks' gestation. Those who receive ultrasound may have uncertain LMP dates (an indication for ultrasound), and are more likely to be privately insured. The 2003 revised US standard birth certificate includes a new gestational age item, the obstetric estimate, which is the clinician's best estimate of gestational age at delivery given available dating information, including ultrasound but excluding neonatal assessments. Validation of this item will be important to assess whether it helps address the problems presented with LMP-based gestational age.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The California Department of Health Services, Genetic Disease Branch collected the XAFP and Newborn Screening programme records and the California Center for Health Statistics provided the birth cohort files. Allen Hom and Steve Graham of the Sequoia Foundation conducted the record linkage.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • 1
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