Abstract

Abstract Background Ramadan during pregnancy has been found to be associated with various adverse health effects in the offspring, which are largely attributed to maternal intermittent fasting. At the same time, many pregnant Muslims decide to fast during Ramadan. With a growing Muslim population in Europe, Ramadan fasting during pregnancy may become an even more prominent issue in the future. Since pregnant women regularly exchange with healthcare professionals, it is pivotal to evaluate the role of prenatal consultation on Ramadan for the maternal fasting decision. Methods This study uses survey data on pregnant Muslims from Mainz, Germany (N = 326). Data on fasting during pregnancy, consultation with healthcare professionals on fasting during pregnancy, as well as a rich set of socio-economic background characteristics were collected. We estimate the effect of prenatal consultation on the number of days fasted using a log-normal hurdle model. To test whether our controlled estimates might be biased by unobservable confounders, we use the Oster (2019) method. Results The share of women who fasted at least one day was 36.5% (mean: 17 fasted days). Among these women, receiving prenatal consultation on fasting during pregnancy reduced the number of fasted days by 10.8 days (95% CI: -19.03, -2.56). Results from the Oster method show that this result is not biased by unobservable confounders. Conclusions This study shows that prenatal consultation on Ramadan fasting reduces the number of fasted days among pregnant Muslims who intend to fast during pregnancy. These findings imply that prenatal caregivers should be encouraged to discuss Ramadan fasting with their Muslim patients. Further research is needed to develop guidelines for prenatal advice on Ramadan during pregnancy in a framework of shared decision-making. Key messages • Controlled effect estimates show that potentially harmful maternal Ramadan fasting behavior during pregnancy can be mitigated by prenatal caregivers. • This study demonstrates a novel way to exploit real-world data in the absence of randomization by quantitatively assessing the impact of unobservable confounders.

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