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This study aims to explore the link between prevalent risk factors for early childhood diarrhea, including hygiene, feeding, weaning practices and maternal education and the occurrence and severity of early childhood diarrhea in Saudi Arabia.

A case-control study was conducted, involving 98 mothers from the Eastern Region of Saudi Arabia (51 cases and 47 controls). Data were collected from both hospital and community sources. The collected data were statistically analyzed and depicted using descriptive statistics and frequency tables.

Demographic data revealed that 60% of mothers were housewives, 75% had normal deliveries and all babies were full term. In the study cohort, 44% of children aged one to two years. Four domains were compared: diarrheal management, hygiene, weaning and feeding practices. Diarrheal management was suboptimal in some cases: 29% increased fluid intake, 10% maintained adequate food intake, 50% sought medical advice, 58% were familiar with oral rehydration solutions and only 37% used them. Hygiene practices were deficient, with 35% using wipes or sanitizers, 64% handwashing before feeding and 52% adhering to the recommended 10-s duration. Controls exhibited better hygiene practices. Weaning practices were generally similar, with no significant differences between the two groups.

To the best of the authors’ knowledge, this is the first study to collectively report on the risk factors linked to early childhood diarrhea in Saudi Arabia. This study yields significant insights, highlighting the crucial role of managing diarrhea, educating mothers and implementing proper household practices in impacting the occurrence and severity of this perilous ailment.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.