Jinhang Jiang, Karthik Srinivasan
Walmart International Technology, Bentonville AR, USA
School of Business, University of Kansas, Lawrence KS, USA
Cite: Jiang J., Srinivasan K. Comparing Pregnancy and Childbirth-related Hospital Visits in Arizona Before and During COVID-19 Using Network Analysis. J. Digit. Sci. 3(2), 37 – 52 (2021). https://doi.org/10.33847/2686-8296.3.2_2
Abstract. The COVID-19 pandemic has had a severe effect on all facets of human society, including healthcare. One of the primary concerns in healthcare is understanding and mitigating the impact of the pandemic on pregnancy and childbirth. While several studies have looked at challenges such as contract tracing of positive cases, predicting confirmed cases and deaths in individuals and communities, few studies have examined differences in hospitalization and treatment of pregnant mothers and infant care in large populations. In this study, the prevalence and co-occurrence of pregnancy and childbirth-related diagnoses reported in Arizona State hospitals for three sixth-month periods – before COVID-19 (second half of 2019), COVID-19 onset (first half of 2020), and COVID-19 (second half of 2020) are analyzed using network analysis. The results show that there are considerable differences in ego networks of few diagnoses during these time periods warranting further investigation into the causality of such population changes.
Keywords: Health analytics, Network analysis, Ego networks, Exploratory data analysis (EDA), COVID-19 data analysis, Pre-term and post-term conditions, Pregnancy and childbirth, Newborn diagnoses.
References
1. Agusto, F. B. et al. Impact of public sentiments on the transmission of COVID-19 across a geographical gradient. medRxiv 2021.01.29.21250655 (2021) doi:10.1101/2021.01.29.21250655.
2. Iwendi, C. et al. COVID-19 patient health prediction using boosted random forest algorithm. Frontiers in Public Health 8, (2020).
3. Yadaw, A. S. et al. Clinical features of COVID-19 mortality: development and validation of a clinical prediction model. The Lancet Digital Health 2, (2020).
4. Jamieson, D. J. & Rasmussen, S. A. Protecting Pregnant Women and Their Infants From COVID-19: Clues From Maternal Viral Loads, Antibody Responses, and Placentas. JAMA network open vol. 3 (2020).
5. Gharoie Ahangar, R., Pavur, R., Fathi, M. & Shaik, A. Estimation and demographic analysis of COVID-19 infections with respect to weather factors in Europe. Journal of Business Analytics 3, (2020).
6. Herbrich, R., Rastogi, R. & Vollgraf, R. CRISP: A probabilistic model for individual-level COVID-19 infection risk estimation based on contact data. arXiv (2020).
7. Gephi. Gephi Tutorial Quick Start. Gephi (2010).
8. Barabasi, A. & Frangos, J. Linked: The New Science Of Networks Science. (Perseus Books Group, 2014).
9. Harenberg, S. et al. Community detection in large-scale networks: a survey and empirical evaluation. Wiley Interdisciplinary Reviews: Computational Statistics 6, 426–439 (2014).
10. Albert, R. & Barabási, A. L. Statistical mechanics of complex networks. Reviews of Modern Physics 74, (2002).
11. Nordhausen, K. Statistical Analysis of Network Data with R. International Statistical Review 83, (2015).
12. Dai, Y., Wang, S., Xiong, N. N. & Guo, W. A survey on knowledge graph embedding: Approaches, applications and benchmarks. Electronics (Switzerland) 9, (2020).
13. Srinivasan, K., Currim, F. & Ram, S. Predicting High Cost Patients at Point of Admission using Network Science. IEEE Journal of Biomedical and Health Informatics 22, 1970–1977 (2018).
14. Hidalgo, C. A., Blumm, N., Barabási, A. L. & Christakis, N. A. A Dynamic Network Approach for the Study of Human Phenotypes. PLoS Computational Biology 5, (2009).
15. Steinhaeuser, K. & Chawla, N. v. A Network-Based Approach to Understanding and Predicting Diseases. in Social Computing and Behavioral Modeling 1–8 (Springer US, 2009).
16. Healy, C. M. COVID-19 in Pregnant Women and Their Newborn Infants. JAMA Pediatrics (2021) doi:10.1001/jamapediatrics.2021.1046.
17. Schwartz, D. A. An analysis of 38 pregnant women with COVID-19, their newborn infants, and maternal-fetal transmission of SARS-CoV-2: Maternal coronavirus infections and pregnancy outcomes. Archives of Pathology and Laboratory Medicine 144, (2020).
18. CDC. Breastfeeding and Caring for Newborns. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/pregnancy-breastfeeding.html (2021).
19. Pollard, T. J. et al. The eICU collaborative research database, a freely available multi-center database for critical care research. Scientific Data (2018) doi:10.1038/sdata.2018.178.
20. Reyna, M. A. et al. Pathway and network analysis of more than 2500 whole cancer genomes. Nature Communications 11, (2020).
21. Davenport, L. COVID-19 May Have Worsened GDM Pregnancy Outcomes. Medscape https://www.medscape.com/viewarticle/950009 (2021).
Published online 28.12.2021
