IDEAS home Printed from https://ideas.repec.org/a/eee/ehbiol/v37y2020ics1570677x19300930.html
   My bibliography  Save this article

Birth weight, neonatal care, and infant mortality: Evidence from macrosomic babies

Author

Listed:
  • Brilli, Ylenia
  • Restrepo, Brandon J.

Abstract

This study demonstrates that rule-of-thumb health treatment decision-making exists when assigning medical care to macrosomic newborns with an extremely high birth weight and estimates the short-run health return to neonatal care for infants at the high end of the birth weight distribution. Using a regression discontinuity design, we find that infants born with a birth weight above 5000 grams have a 2 percentage-point higher probability of admission to a neonatal intensive care unit and a 1 percentage-point higher probability of antibiotics receipt, compared to infants with a birth weight below 5000 grams. We also find that being born above the 5000-gram cutoff has a mortality-reducing effect: infants with a birth weight larger than 5000 grams face a 0.15 percentage-point lower risk of mortality in the first week and a 0.20 percentage-point lower risk of mortality in the first month, compared to their counterparts with a birth weight below 5000 grams. We do not find any evidence of changes in health treatments and mortality at macrosomic cutoffs lower than 5000 grams, which is consistent with the idea that such treatment decisions are guided by the higher expected morbidity and mortality risk associated with infants weighing more than 5000 grams.

Suggested Citation

  • Brilli, Ylenia & Restrepo, Brandon J., 2020. "Birth weight, neonatal care, and infant mortality: Evidence from macrosomic babies," Economics & Human Biology, Elsevier, vol. 37(C).
    • Handle: RePEc:eee:ehbiol:v:37:y:2020:i:c:s1570677x19300930
    DOI: 10.1016/j.ehb.2019.100825
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1570677X19300930
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ehb.2019.100825?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Alan I. Barreca & Jason M. Lindo & Glen R. Waddell, 2016. "Heaping-Induced Bias In Regression-Discontinuity Designs," Economic Inquiry, Western Economic Association International, vol. 54(1), pages 268-293, January.
    2. Douglas Almond & Joseph J. Doyle & Amanda E. Kowalski & Heidi Williams, 2010. "Estimating Marginal Returns to Medical Care: Evidence from At-risk Newborns," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(2), pages 591-634.
    3. Sebastian Calonico & Matias D. Cattaneo & Rocio Titiunik, 2014. "Robust Nonparametric Confidence Intervals for Regression‐Discontinuity Designs," Econometrica, Econometric Society, vol. 82, pages 2295-2326, November.
    4. Reif, Simon & Wichert, Sebastian & Wuppermann, Amelie, 2018. "Is it good to be too light? Birth weight thresholds in hospital reimbursement systems," Journal of Health Economics, Elsevier, vol. 59(C), pages 1-25.
    5. N. Meltem Daysal & Marianne Simonsen & Mircea Trandafir & Sanni Breining, 2022. "Spillover Effects of Early-Life Medical Interventions," The Review of Economics and Statistics, MIT Press, vol. 104(1), pages 1-16, March.
    6. David M. Cutler & Ellen Meara, 2000. "The Technology of Birth: Is It Worth It?," NBER Chapters, in: Frontiers in Health Policy Research, Volume 3, pages 33-68, National Bureau of Economic Research, Inc.
    7. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
    8. Andrew Gelman & Guido Imbens, 2019. "Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 447-456, July.
    9. Lee, David S. & Card, David, 2008. "Regression discontinuity inference with specification error," Journal of Econometrics, Elsevier, vol. 142(2), pages 655-674, February.
    10. Michal Kolesár & Christoph Rothe, 2018. "Inference in Regression Discontinuity Designs with a Discrete Running Variable," American Economic Review, American Economic Association, vol. 108(8), pages 2277-2304, August.
    11. Choi, Jin-young & Lee, Myoung-jae, 2019. "Twins are more different than commonly believed, but made less different by compensating behaviors," Economics & Human Biology, Elsevier, vol. 35(C), pages 18-31.
    12. Jürges, Hendrik & Köberlein, Juliane, 2015. "What explains DRG upcoding in neonatology? The roles of financial incentives and infant health," Journal of Health Economics, Elsevier, vol. 43(C), pages 13-26.
    13. David S. Lee & Thomas Lemieux, 2010. "Regression Discontinuity Designs in Economics," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.
    14. Brigham R. Frandsen, 2017. "Party Bias in Union Representation Elections: Testing for Manipulation in the Regression Discontinuity Design when the Running Variable is Discrete," Advances in Econometrics, in: Regression Discontinuity Designs, volume 38, pages 281-315, Emerald Group Publishing Limited.
    15. Daysal, N. Meltem & Trandafir, Mircea & van Ewijk, Reyn, 2019. "Low-risk isn’t no-risk: Perinatal treatments and the health of low-income newborns," Journal of Health Economics, Elsevier, vol. 64(C), pages 55-67.
    16. Cutler David M. & Meara Ellen, 2000. "The Technology of Birth: Is It Worth It?," Forum for Health Economics & Policy, De Gruyter, vol. 3(1), pages 1-37, January.
    17. Prashant Bharadwaj & Katrine Vellesen L?ken & Christopher Neilson, 2013. "Early Life Health Interventions and Academic Achievement," American Economic Review, American Economic Association, vol. 103(5), pages 1862-1891, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daysal, N. Meltem & Trandafir, Mircea & van Ewijk, Reyn, 2019. "Low-risk isn’t no-risk: Perinatal treatments and the health of low-income newborns," Journal of Health Economics, Elsevier, vol. 64(C), pages 55-67.
    2. Mauricio Villamizar‐Villegas & Freddy A. Pinzon‐Puerto & Maria Alejandra Ruiz‐Sanchez, 2022. "A comprehensive history of regression discontinuity designs: An empirical survey of the last 60 years," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1130-1178, September.
    3. Matias D. Cattaneo & Rocío Titiunik, 2022. "Regression Discontinuity Designs," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 821-851, August.
    4. Adam C. Sales & Ben B. Hansen, 2020. "Limitless Regression Discontinuity," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 143-174, April.
    5. Canaan, Serena, 2022. "Parental leave, household specialization and children’s well-being," Labour Economics, Elsevier, vol. 75(C).
    6. Lee, Ajin, 2020. "How do hospitals respond to managed care? Evidence from at-risk newborns," Journal of Public Economics, Elsevier, vol. 184(C).
    7. Yang He & Otávio Bartalotti, 2020. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 211-231.
    8. Jin-young Choi & Myoung-jae Lee, 2017. "Regression discontinuity: review with extensions," Statistical Papers, Springer, vol. 58(4), pages 1217-1246, December.
    9. Di Giacomo, Marina & Piacenza, Massimiliano & Siciliani, Luigi & Turati, Gilberto, 2022. "The effect of co-payments on the take-up of prenatal tests," Journal of Health Economics, Elsevier, vol. 81(C).
    10. Onda, Masayuki & Seyler, Edward, 2020. "English learners reclassification and academic achievement: Evidence from Minnesota," Economics of Education Review, Elsevier, vol. 79(C).
    11. Xu, Ke-Li, 2017. "Regression discontinuity with categorical outcomes," Journal of Econometrics, Elsevier, vol. 201(1), pages 1-18.
    12. Guido Imbens & Stefan Wager, 2019. "Optimized Regression Discontinuity Designs," The Review of Economics and Statistics, MIT Press, vol. 101(2), pages 264-278, May.
    13. Christelis, Dimitris & Georgarakos, Dimitris & Sanz-de-Galdeano, Anna, 2020. "The impact of health insurance on stockholding: A regression discontinuity approach," Journal of Health Economics, Elsevier, vol. 69(C).
    14. Machin, Stephen & McNally, Sandra & Ruiz-Valenzuela, Jenifer, 2020. "Entry through the narrow door: The costs of just failing high stakes exams," Journal of Public Economics, Elsevier, vol. 190(C).
    15. Bhalotra, Sonia & Chakravarty, Abhishek & Gulesci, Selim, 2020. "The price of gold: Dowry and death in India," Journal of Development Economics, Elsevier, vol. 143(C).
    16. Berlinski, Samuel & Ramos, Alejandra, 2020. "Peer effects in the decision to apply for a professional excellence award," Labour Economics, Elsevier, vol. 67(C).
    17. Crespo Cristian, 2020. "Beyond Manipulation: Administrative Sorting in Regression Discontinuity Designs," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 164-181, January.
    18. Einiö, Elias & Overman, Henry G., 2020. "The effects of supporting local business: Evidence from the UK," Regional Science and Urban Economics, Elsevier, vol. 83(C).
    19. Feng, Andy & Graetz, Georg, 2017. "A question of degree: The effects of degree class on labor market outcomes," Economics of Education Review, Elsevier, vol. 61(C), pages 140-161.
    20. Andrew McEachin & Thurston Domina & Andrew Penner, 2020. "Heterogeneous Effects of Early Algebra across California Middle Schools," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(3), pages 772-800, June.

    More about this item

    Keywords

    Birth weight; Macrosomia; Health care; Medical inputs; Infants; Mortality;
    All these keywords.

    JEL classification:

    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ehbiol:v:37:y:2020:i:c:s1570677x19300930. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/622964 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.