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Birth Outcome Measures and Infant Mortality

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Abstract

Recent studies have proposed alternative birth outcome measures as means of assessing infant mortality risk; nevertheless, there hasn’t yet been an integrated analysis of these approaches. We review 14 strategies, including various combinations of birth weight, gestational age, fetal growth rate, and Apgar scores—as predictors of early neonatal, late neonatal, and postneonatal mortality, and infant mortality. Using the NCHS linked birth/infant death file for 2001, we construct multivariate logit models and assess the associations between each of the 14 key birth outcome measures and four mortality outcomes. We find that all evaluated birth outcome measures are strong predictors, but Apgar scores are the strongest among all models for all outcomes, independent of birth weight and gestational age. Apgar scores’ predictive power is stronger for Mexican-, white-, and female-infants than for black- and male-infants. Second, all birth outcome measures remain significantly associated with mortality, but their predictive power reduces drastically over time. These findings suggest a rule of thumb for predicting infant mortality odds: when available, Apgar scores should always be included along with birth weight (or LBW status) and gestational age. Additionally, these findings argue for the continued study of low birthweight, gestational age, and Apgar scores as independently salient health outcomes.

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Notes

  1. However, in 2002, the infant mortality rate increased for the first time in more than 40 years (NCHS 2006).

  2. In 2003, the neonatal mortality rate was 9.3 per 1,000 for non-Hispanic blacks, 3.8 per 1,000 for non-Hispanic whites; the postneonatal mortality rate was 4.3 per 1,000 for blacks and 1.9 per 1,000 for whites (NCHS, Health 2006, Table 19).

  3. Though it doesn’t mean that heavier is unequivocally better. Very heavy newborns (i.e. more than 9.5 lb), usually resulting from gestational diabetes, also face an increased mortality rate. The “optimal” birth weight, in terms of the lowest mortality rate, is about 9.5 lb. The benefit of an increase in birth weight is relatively large when below LBW (5.5 lb), and diminishes as birth weight increases, until it is no longer beneficial.

  4. This increase is in part a result of the rising rate of multiple births. The rapid rise in multiple births accounted for 2.3 and 3.1% of all live births in 1990 and 2000, respectively; triplets and higher-order multiples accounted for an increasing proportion of multiple births (3.1 and 5.8%, respectively), an estimated 53% of which were the result of assisted reproductive technologies (Luke and Brown 2006).

  5. WHO defines preterm birth as delivery before 37 completed weeks of gestation and SGA as a birth weight below the 10th percentile for gestational age (above the 10th percentile are called adequate for gestational age or AGA).

  6. Umbach (1996) found that the residual distribution in the upper tail has little impact on infant mortality (Wilcox 2001).

  7. This study used 2001 birth cohort data, which consists of all the births in 2001 and linked deaths to infants born in 2001 regardless the death occurred in 2001 or 2002. In 2001, 98.8% of all infant death records were successfully matched to their corresponding birth records. Please note this birth cohort data is different from the “period” data, which contains all the infant deaths occurred in a give year, regardless of their birth cohort.

  8. Previous research suggests that the greatest degree of error in reporting gestational age or birth weight is at the lower extreme of the distribution (Hummer et al. 1999). Therefore, it is common for researchers to exclude some extreme cases. For example, Hummer et al. (1999)’s study restricted their cases to 500+ g and 22+ weeks, and Solis et al. (2000) restricted their cases to births of 28+ weeks and 500+ g. Our criteria is more inclusive.

  9. Maternal race/ethnicity is used to identify subgroups.

  10. We applied Solis et al.’s approach to each racial- and gender-specific group. An essential step of their approach is identifying the optimal combination of birth weight and gestational age that produces the lowest infant mortality rate, and then calculating the distance of any particular birth weight to the “optimal” combination. However, as Figs. 1 and 2 show, where the infant mortality rate is mapped on dimensions of birth weight and gestational age for black female and black male infants, respectively, the optimal combination of birth weight and gestational age can be very scattered, which make it impossible to identify one point or a concentrated area that produces the lowest mortality rate. Further, standardizing birth weight to a gestational-age-specific z-score scale doesn’t produce a smooth reverse J-shape infant mortality rate curve that enables identification of the minimum, as showed in Solis et al.’s paper. To smooth the data, many cases, especially those “outliers” that are of research interest, will need to be removed from the data set. Thus, while this “optimal combination” approach is compelling in theory, the key step where the optimal combination of birth weight and gestational age is identified is found to be rather subjective, and can hardly be universal across subgroups.

  11. That is, mothers with problematic pregnancies may receive more prenatal care than is considered “best practice” for mothers with normal pregnancies.

  12. California doesn’t report maternal smoking or drinking on its birth certificates; therefore missing indicators for these two risk factors are included for California, which measure both the effect of missing smoking/drinking and of California. A dummy variable with ‘1’ for missing and ‘0’ for not missing is entered into a regression analysis alongside the new explanatory variable (i.e., the one with missing values assigned to the series mean), which provides information on whether or not the cases assigned missing values differ from those without missing values on the outcome variable. This procedure also adjusts the estimate of the explanatory variable so that it is not biased by the missing values assigned. This is a standard method of dealing with missing data. Unfortunately, it may produce biased estimates of the coefficients (Allison 2001). On the other hand, this is a better alternative than excluding births (listwise deletion) in CA since they represent such a large proportion of all births in the U.S.

  13. Since the focus of this paper is not on risk factors or indicators other than birth outcome measures, all covariates are omitted from Tables 4 and 5 due to space limitation.

  14. Results of cross-validation do not vary between Model 10 and 12 in each gender- and race-specific group; therefore, we omit the MSE in Table 5.

  15. Except for early neonatal mortality of Mexican female, where Model 12 is slightly better than model 10 and 11. This is probably due to the small sample size.

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Acknowledgements

This study is partially funded by the Pardee RAND Graduate School Dissertation Award. The authors appreciate the thoughtful comments from Robert A. Hummer, RAND researchers Julie DaVanzo and Becky Kilburn, and the two anonymous reviewers.

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  1. Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Room E4153, Baltimore, MD, 21205, USA

    Sai Ma

  2. Department of Sociology, San Diego State University, 5500 Campanile Drive, San Diego, CA, 92182, USA

    Brian Karl Finch

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Correspondence to Sai Ma.

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Ma, S., Finch, B.K. Birth Outcome Measures and Infant Mortality. Popul Res Policy Rev 29, 865–891 (2010). https://doi.org/10.1007/s11113-009-9172-3

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