Pendekatan Precision Public Health untuk Pencegahan Anemia pada Ibu Hamil Berbasis Profil Risiko Individual
DOI:
https://doi.org/10.59680/anestesi.v2i2.2440Keywords:
Digital Health, Gestational Anemia, Maternal Health, Precision Public Health, Risk StratificationAbstract
Anemia among pregnant women remains a serious global public health problem, with an estimated prevalence of approximately 40% worldwide. In Indonesia, the prevalence reaches 48.9% nationally and 51.2% in South Sulawesi, exceeding the World Health Organization (WHO) threshold for a severe public health concern. This high prevalence highlights the limitations of conventional mass supplementation programs, which often fail to address the multifactorial and heterogeneous causes of gestational anemia. This article aims to examine the concept of Precision Public Health (PPH) in maternal health, identify multidimensional risk factors associated with gestational anemia, propose an individual risk stratification model applicable to Indonesia’s primary healthcare system, and discuss implementation opportunities and policy recommendations. A systematic literature review was conducted following the PRISMA 2020 guidelines using PubMed/MEDLINE, Scopus, Web of Science, Cochrane Library, and Google Scholar databases, covering studies published between 2018 and 2025. Of the 1,024 records identified, 25 studies met the inclusion criteria and were analyzed narratively. The findings indicate that the PPH approach can identify seven major risk factor categories, including nutritional deficiencies, obstetric factors, chronic diseases and infections, socioeconomic determinants, behavioral factors, environmental determinants, and genetic profiles. Data-driven interventions and risk stratification were found to be more effective than conventional approaches. Supported by digital technologies, artificial intelligence, and genomic biomarkers, PPH has the potential to enhance the effectiveness, efficiency, and equity of anemia prevention programs for pregnant women in Indonesia.
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Copyright (c) 2024 Asyima Asyima, Ruqaiyah Ruqaiyah, Masriadi Masriadi, Hadriani Irwan, Noviyani Hartuti, Nurasia Natsir, Suharti Bukhari
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