Did multimodal in-office/telemedicine prenatal care implemented during the pandemic perform similarly to in-office prenatal care?

In a recent study published in JAMA Network Open, researchers compared the performance of a multimodal prenatal health care model combining in-office and telemedicine visits to an in-office-only prenatal health care model between the pre-pandemic and pandemic period.

Prenatal Health Care Outcomes Before and During the COVID-19 Pandemic Among Pregnant Individuals and Their Newborns in an Integrated US Health System
Study: Prenatal Health Care Outcomes Before and During the COVID-19 Pandemic Among Pregnant Individuals and Their Newborns in an Integrated US Health System. Image Credit: Studio Romantic/Shutterstock.com

They retrieved electronic health record (EHR) data of the cohort population of 151,464 pregnant females from the Kaiser Permanente Northern California (KPNC) healthcare system. They included all females who delivered a live or stillbirth between July 1, 2018, and October 21, 2021, and analyzed data between January 2022 and May 2023.

Background

Pregnant females need frequent contact with the health care system throughout their pregnancy. Since it was difficult during the coronavirus disease 2019 (COVID-19) pandemic, KPMC implemented telemedicine in prenatal health care. 

It prevented these females from contracting COVID-19, but, more importantly, it helped them reach out to patients in rural areas who have other barriers to health care, e.g., lack of transportation.

The rapid adoption and integration of telemedicine in prenatal healthcare delivery systems during the COVID-19 pandemic offered a unique opportunity to reorganize prenatal healthcare models and investigate whether its permanent integration into the system could improve maternal and newborn health outcomes.

About the study

In the present study, researchers evaluated whether a multimodal model of telemedicine and in-office visits used during the COVID-19 pandemic was equally effective as the standard in-office healthcare system used before the pandemic. 

In addition, they explored whether the outcomes of this model were influenced by factors such as race or ethnicity, socioeconomic background, whether individuals spoke a language other than English, or residency in a rural area.

The study had several primary and secondary outcomes. Preeclampsia and eclampsia, fatal maternal morbidity, preterm birth, cesarean delivery, and neonatal intensive care unit (NICU) admission were the primary outcomes and its secondary outcomes were gestational diabetes (GD), hypertension, depression, low birth weight, venous thromboembolism (VTE), newborn Apgar score <7, and transient tachypnea.

The study has three time intervals of interest, for which the researchers assessed distributions of demographic and clinical characteristics, care processes, and health outcomes for birth deliveries as standardized mean differences (SMDs). They also examined changes in rates of perinatal outcomes for each model using an interrupted time series (ITS) design.

For binary outcomes, the team used log-binomial regression concerning the week of delivery, providing point and interval estimates of relative percentage change in delivery outcomes scaled to a four-week change. The study analyses did not account for age, race or ethnicity, prepregnancy body mass index (BMI), or neighborhood deprivation index (NDI).

Results and conclusion

Across three study intervals, 75,836, 34,799, and 40,829 of 151,464 individuals were unexposed, partially exposed, and fully exposed to the multimodal prenatal health care model at T1, T2, and T3, respectively. All three intervals fell under the COVID-19 pandemic period. 

The average number of prenatal visits via telemedicine was higher than visits during the pre-pandemic era per individual (21.3% vs. 11.1%). Overall, across the three time intervals, no clinically notable differences were found in gestational depression (GD) rates, depression screenings or blood pressure measurements.

Importantly, the results showed no clinically significant changes for the majority of primary health outcomes, except NICU admission rates. Per ITS analysis, these rates were stable from the pre-pandemic to the pandemic period. 

First, these rates fell and then rose during the first and second pandemic periods, likely due to full exposure to the multimodal prenatal healthcare model. However, the causal mechanisms governing the observed increase in NICU admission rates require further assessment. 

ITS analysis also showed that the rates of gestational depression and hypertension were already high during the pre-pandemic period and increased further through the pandemic era. Furthermore, the authors noted no differences in the weekly GD incidence rates and preterm birth, preeclampsia, or NICU admission rates across the two study periods.

The study findings pointing out an increase in the risk of fatal maternal morbidity among Black females and an Apgar score of <7 among Asians and people from the Pacific Islands require further investigation. However, it is possible that these were chance findings as the team performed many regression analyses, and there was no direct association of these observations with the multimodal prenatal care model.

In previous qualitative assessments of telemedicine programs, pregnant females and their healthcare clinicians in rural or urban regions of the United States of America perceived telemedicine as a positive experience. 

Language barriers or health disparities did not affect the uptake of telemedicine. Thus, integrating telemedicine into prenatal health care appears to be a suitable substitute for in-office care, especially for populations living in rural areas. 

Given the multimodal healthcare model, including in-office and telemedicine and traditional in-office prenatal healthcare model, showed comparable performances, the authors advocated their continued use beyond the pandemic.

Journal reference:
Neha Mathur

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.

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