Fuzzy‑set qualitative comparative analysis and fuzzy cognitive maps: Exploring pregnancy outcomes and maternal depression

The maternal antenatal attachment scale (MAAS), the pregnancy outcome questionnaire (POQ) and the Centre for Epidemiologic Studies Depression Scale (CESD), among other approaches, have been developed to address pregnancy-related psychological issues. However, the need to develop and validate effective scales to screen the complex experiences of pregnant women continues to be extensively discussed in the literature. The aim of the present study was to build and validate fuzzy models that represent the necessary and sufficient causal combinations that lead to higher levels of anxiety regarding pregnancy outcomes, maternal prenatal attachment to the unborn child and depressive symptoms, respectively. For this purpose, measurements from the MAAS, POQ and CESD scales, along with demographic data, were collected from 135 pregnant women, including cases of natural conception (NC) and assisted reproduction (ART) births. Fuzzy-set qualitative comparative analysis (FSQCA) was employed to produce sets of causal combinations, which were validated against their consistency and coverage. These combinations were then used to develop and validate fuzzy cognitive maps (FCMs) to model the fluctuations in the status of pregnant women. To the best of our knowledge, the present study is the first to utilize FSQCA or FCM to address this issue. The results indicated that the POQ was the distinguishing factor between NC and ART that led to higher MAAS levels. Marital status (MS) and state anxiety were found to lead to higher POQ levels for pregnancies derived from NC. For pregnancies following ART, the factors to consider include income, week of pregnancy, MS, MAAS intensity and trait anxiety. POQ was found to lead to higher levels of CESD for ART pregnancies, while NC, MS and state anxiety are also prerequisites. On the whole, the present study demonstrates that the proposed FSQCA- and FCM-based approach enables obstetricians and midwives to incorporate their expertise in evaluating cases on an individual basis, while also providing a framework for creating intelligent systems to support healthcare policy decisions.