Skip to main content

Prematurity and low birth weight: geospatial analysis and recent trends

Abstract

Prematurity and low birth weight are of concern in neonatal health. In this work, geospatial analysis was performed to identify the existence of statistically significant clusters of prematurity and low birth weight using Moran’s I. Data was obtained from March of Dimes and the National Center for Health Statistics for the years 2015 to 2019. Analysis demonstrated the presence of hotspot (High-High) and coldspot (Low-Low) geographic clusters of these variables in regions across the United States. Additionally, factorial ANOVA was performed, and revealed the significance of demographic variables of interest. Given the strong relationship between these two variables, regions that are hotspots for one variable, but not the other, are of particular interest for further study.

Letter to the editor

Dear Editor,

It has been previously established that prematurity (PM) and low birth weight (LBW) are of concern when assessing neonatal health: Prior works have demonstrated the role of these variables in predicting neonatal morbidity and mortality [1]. Additionally, previous research has shown the role of both the health of the mother and her socioeconomic environment in the prevalence of these two conditions [2]. We aimed to use geospatial analysis techniques to identify whether statistically significant clusters of PM (< 37 weeks) and LBW (< 5.5 lbs) exist on a nationwide level and to further explore the socioeconomic determinants associated with those clusters.

We used birth and C-section data from the March of Dimes and the National Center for Health Statistics during the years 2015–2019 across 3105 US counties [3]. Moran’s I statistic was calculated to categorize individual counties as either Not Significant or as one of 4 statistically significant (p < 0.05) cluster classifications: High-High (H-H), High-Low (H-L), Low-High (L-H), Low-Low (L-L) [4]. In this attribution system, the first term designates the relative value of a given county compared to the national average; the second attribute reflects the relative value of neighboring counties compared to the national average. Demographic data was obtained from the American Community Survey (US Census Bureau). Factorial ANOVA was performed to evaluate the significance of contributory socioeconomic variables of interest at a significance level of 0.001.

Visualization of the cluster designations at the county level demonstrated clear geographic trends (Fig. 1). For both the PM and LBW analyses, there was an expansive H-H cluster that was persistent across the Southern states. There were 3 distinct expansive L-L clusters encompassing the New England states, the Midwest, and the Pacific Northwest. A LBW H-H cluster encompassed Colorado and northern New Mexico, yet this was not seen in the PM analysis. Similarly, multiple significant PM H-H clusters were identified in Texas, but not LBW clusters. Factorial ANOVA across clusters revealed significant contributions of various socioeconomic factors at a significance level of 0.001 for both the PM and LBW analyses (Tables 1 and 2).

Fig. 1
figure 1

Geospatial mappings and analysis for (A) prematurity, (B) low birth weight, and (C) joint prematurity and low birth weight. Color designations reflect Moran’s I spatial categorizations. For joint mappings, High and Low designations represent agreement and Other represents areas of disagreement between the two variables

Table 1 Factorial ANOVA across preterm birth clusters. Asterisks reflect significance at a significance level of 0.001
Table 2 Factorial ANOVA across low birth weight clusters. Asterisks reflect significance at a significance level of 0.001

PM and LBW have previously been demonstrated to have a strong relationship, so it is unsurprising that the identified spatial clusters of these variables have substantial overlap, However, what is of particular interest are the regions that are clusters for one variable but not the other. For instance, there are regions of Texas where several counties are significantly higher in PM but not LBW. Conversely, there is a large region of Colorado where there is a substantial incidence of LBW despite that region not having high prematurity. Given that factors traditionally associated with prematurity would not explain this increase, it is important to look for other explanations. The Colorado Department of Public Health has previously proposed the contribution of high altitude to pregnancy-induced hypertension as a possible explanatory factor [5]. The inverse relationship in Texas is harder to attribute to an isolated cause; though, the prevalence of large, medically underserved immigrant communities in the identified regions is likely a contributing factor. The ANOVA findings in this study underscore the importance of many socioeconomic factors that differentiate the clusters, including race and various economic markers (e.g., SNAP, insurance type, educational status). Interestingly, the rural/urban character between clusters did not significantly differ in this analysis.

Since PM and LBW demonstrate similar geospatial patterns across the United States, and a strong relationship exists between these two factors, regions that are high in one variable and not the other are of particular interest for further study.

Availability of data and materials

This study uses publicly available data that is available through March of Dimes and the National Center for Health Statistics at https://www.marchofdimes.org/peristats/Peristats.aspx. Demographic data was obtained through the US Census Bureau American Community Survey (ACS), which is available at https://www.census.gov/programs-surveys/acs/data.html. Definitions of race and ethnicity used in this work are derived from those used in the ACS.

Abbreviations

LBW:

Low birth weight

PM:

Prematurity

H-H:

High-High

H-L:

High-Low

L-L:

Low-Low

L-H:

Low-High

ANOVA:

Analysis of Variance

References

  1. Basel KCA, Singh PL. S. Low birth weight and its associated risk factors: Health facility-based case-control study. PLoS ONE. 2020;15(6):e0234907.

  2. Hidalgo-Lopezosa P, Jiménez-Ruz A, Carmona-Torres JM, Hidalgo-Maestre M, Rodríguez-Borrego MA, López-Soto PJ. Sociodemographic factors associated with preterm birth and low birth weight: A cross-sectional study. Women Birth. 2019 Dec;32(6):e538–43.

  3. PeriStats. White Plains (NY): March of Dimes Perinatal Data Center. 2021. [cited 2021 Nov 24]. Available from: https://www.marchofdimes.org/peristats/.

  4. Vanderlaan J, Edwards JA, Dunlop A. Geospatial variation in caesarean delivery. Nurs Open. 2020;7(2):627–33.

    Article  Google Scholar 

  5. Bailey BA, Donnelly M, Bol K, Moore LG, Julian CG. High Altitude Continues to Reduce Birth Weights in Colorado. Matern Child Health J. 2019 Nov;23(11):1573–80.

Download references

Acknowledgements

Not applicable.

Funding

The authors have no external funding sources to disclose.

Author information

Authors and Affiliations

Authors

Contributions

NP conceived the study and performed data cleaning and analysis. BK and ML interpreted the data and drafted the manuscript. LA reviewed the manuscript and provided additional insights. KC provided oversight of the project and contributed to its direction. All authors have made a significant intellectual contribution to this work and consent to its publication. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Bradley Kaptur.

Ethics declarations

Ethics approval and consent to participate

This study solely uses publicly available data. As such, informed consent and IRB review were waived per federal guideline 45 CFR 46 and institutional policy at the authors’ institutions.

Consent for publication

All authors of the manuscript have read and agreed to its content and approve of the final version for publication.

Competing interests

The authors declare no conflicts of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Peterman, N., Kaptur, B., Lewis, M. et al. Prematurity and low birth weight: geospatial analysis and recent trends. matern health, neonatol and perinatol 8, 2 (2022). https://doi.org/10.1186/s40748-022-00137-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40748-022-00137-x

Keywords

  • Prematurity
  • Low birth weight
  • Geospatial
  • Cluster
  • Neonatal