“Our study opens the possibility to start exploring the development and validation of easy-to-use tools to predict health-related behaviors in settings with scarcity of resources,” they wrote. Nonetheless, the researchers deemed the models a step in the right direction, not just for reproductive and maternal health, but for all areas of health in low-resource settings. According to the researchers, more data-such as an increased sample size, or information on how much women used health services before getting pregnant or during previous pregnancies-would improve the models. They were most successful at identifying women at high risk of failing to attend prenatal care appointments, but not specific enough to identify women at moderate risk. Additional prenatal visits will be conducted. All labs will be conducted during these visits, rather than in a separate appointment as is sometimes done. The models showed modest performance, the study found. Specifically, because of COVID-19, in-person prenatal care at Michigan Medicine has now been reduced to an initial prenatal visit, an anatomy ultrasound, and the 28-, 36-, and 39-week visits. Based on this information-and taking into account socioeconomic, demographic, nutritional, obstetric, and medical-history related factors-the researchers then developed six models to predict prenatal care attendance among women in the rural region. COVID-19 vaccination is recommended during pregnancy in any. The ESPI Electronic Cohort study is collecting information from the medical records of women who received prenatal care at three participating sites and reached the end of their pregnancies between March 2020 and February 2021. To fill in this gap, they analyzed health data from 2,195 women in Amhara Region, Ethiopia, who were pregnant between December 2018 and March 2020, finding that 582 of them failed to attend at least one prenatal care appointment during their pregnancy. In addition, severe infection with COVID-19 carries risks to maternal, fetal and neonatal health. Epidemiology of SARS-CoV-2 in Pregnancy and Infancy (ESPI) Network. No prior studies have aimed to build models that identify women in poor countries at risk of skipping prenatal care visits during their pregnancies, according to the co-authors. Pediatr. Sebastien Haneuse, professor in the Department of Biostatistics, was also a co-author. Harvard Chan School co-authors included several members of the Department of Epidemiology, including Grace Chan, associate professor Bryan Wilder and Frederick Goddard, visiting scientists Clara Pons-Duran, postdoctoral research fellow and Delayehu Bekele, department associate. The study was published on May 31 in JAMA Open Network. June 12, 2023-Through predictive models, it may be possible to identify pregnant women in low-resource settings who are at high risk of failing to attend prenatal care, in order to develop interventions to encourage their attendance, according to a new study led by Harvard T.H.
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