Breastfeeding and post-perinatal infant deaths in the United States, A national prospective cohort analysis

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Summary

Background

Reducing infant mortality is a major public health goal. The potential impact of breastfeeding on infant deaths is not well studied in the United States (US).

Methods

We analyzed linked birth–death certificates for 3,230,500 US births that occurred in 2017, including 6,969 post-perinatal deaths from 7–364 days of age as the primary outcome, further specified as late-neonatal (7–27 days) or post-neonatal (28–364 days) deaths. The primary exposure was ‘ever breastfed’ obtained from birth certificates. Multiple logistic regression examined associations of ever breastfeeding with post-perinatal deaths and specific causes of deaths, controlling for maternal and infant factors.

Findings

We observed an adjusted reduced odds ratio (AOR)= 0·74 with 95% confidence intervals (CI)=0·70–0·79 for the association of breastfeeding initiation with overall infant deaths (7–364 days), AOR=0·60 (0·54–0·67) for late-neonatal deaths, and AOR=0·81 (0·76–0·87) for post-neonatal deaths. In race/ethnicity-stratified analysis, significant associations of breastfeeding initiation with reduced odds of overall infant deaths were observed for Hispanics [AOR=0·64 (0·55–0·74)], non-Hispanic Whites [AOR=0·75 (0·69–0·81)], non-Hispanic Blacks [AOR=0·83 (0·75–0·91)], and non-Hispanic Asians [AOR=0·51 (0·36–0·72)]. Across racial/ethnic groups, effect sizes for late-neonatal deaths were consistently larger than those for post-neonatal deaths. Significant effects of breastfeeding initiation were observed for deaths due to infection [AOR=0·81(0·69–0·94)], Sudden Unexpected Infant Death [AOR=0·85 (0·78–0·92)], and necrotizing enterocolitis [AOR=0·67 (0·49–0·90)].

Interpretation

Breastfeeding initiation is significantly associated with reduced odds of post-perinatal infant deaths in multiple racial and ethnic groups within the US population. These findings support efforts to improve breastfeeding in infant mortality reduction initiatives.

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Most cited references 30

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Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect.

Cesar Victora, Rajiv Bahl, Aluísio Barros … (2016)

The importance of breastfeeding in low-income and middle-income countries is well recognised, but less consensus exists about its importance in high-income countries. In low-income and middle-income countries, only 37% of children younger than 6 months of age are exclusively breastfed. With few exceptions, breastfeeding duration is shorter in high-income countries than in those that are resource-poor. Our meta-analyses indicate protection against child infections and malocclusion, increases in intelligence, and probable reductions in overweight and diabetes. We did not find associations with allergic disorders such as asthma or with blood pressure or cholesterol, and we noted an increase in tooth decay with longer periods of breastfeeding. For nursing women, breastfeeding gave protection against breast cancer and it improved birth spacing, and it might also protect against ovarian cancer and type 2 diabetes. The scaling up of breastfeeding to a near universal level could prevent 823,000 annual deaths in children younger than 5 years and 20,000 annual deaths from breast cancer. Recent epidemiological and biological findings from during the past decade expand on the known benefits of breastfeeding for women and children, whether they are rich or poor.

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Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.

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Author and article information

Journal

Journal ID (nlm-journal-id): 9918232503006676

Journal ID (pubmed-jr-id): 50861

Journal ID (nlm-ta): Lancet Reg Health Am

Journal ID (iso-abbrev): Lancet Reg Health Am

Title: Lancet Regional Health. Americas

ISSN (Electronic): 2667-193X

Publication date Nihms-submitted: 13 March 2022

Publication date (Print): January 2022

Publication date PMC-release: 29 July 2022

Volume: 5

Electronic Location Identifier: 10.1016/j.lana.2021.100094

Affiliations

[a ]Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA

[b ]Division of General and Community Pediatrics, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH

[c ]Department of Biostatistics, Epidemiology and Informatics, Center for Clinical Epidemiology and Biostatistics, Center of Excellence in Environmental Toxicology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA

[d ]Bureau of Epidemiology, Informatics, and Emergency Preparedness, Shelby County Health Department, Memphis, Tennessee

[e ]Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA

[f ]Department of Environmental and Public Health Sciences, Division of Epidemiology, University of Cincinnati College of Medicine, Cincinnati, OH

[g ]Department of Pediatrics, Division of Infectious Diseases, University of Cincinnati College of Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH

[h ]U.S. Public Health Service, Rockville, MD

Author notes

Contributors

RL and JW developed the study protocol and designed the study with input from all authors. RL, JW, AC, JMK, ALM, and CGP developed the analysis strategy. RL, JMN, JC, and CGP obtained the data. RL and JC analyzed the data and created the tables and figure. RL and JW wrote the first draft. All authors reviewed, made inputs to data interpretation, and approved the final paper.

[* ]Corresponding Author: Ruowei Li, MD, PhD, 4770 Buford Hwy NE, Mail-Stop S107-5, Atlanta, GA 30341-3717, (404) 498-5798, ril6@ 123456cdc.gov (R. Li).

Article

Manuscript ID: HHSPA1788419

DOI: 10.1016/j.lana.2021.100094

PMC ID: 9335131

PubMed ID: 35911656

SO-VID: 80b0df04-7d76-4131-a3ff-59c2dc0657ab

License:

This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/)

Breastfeeding and post-perinatal infant deaths in the United States, A national prospective cohort analysis

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Summary

Background

Methods

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Core Readings in Statistical Mediation Analysis

Most cited references 30

Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect.

Sensitivity Analysis in Observational Research: Introducing the E-Value.

Structural racism and health inequities in the USA: evidence and interventions

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