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Perbedaan Kenaikan Berat Badan dan Kadar Hemoglobin pada Ibu Hamil Primigravida dengan Multigravida Dinda Aziza Bryllianna; Hendarto, Hendy; Henderi, Hendera
SENTRI: Jurnal Riset Ilmiah Vol. 5 No. 1 (2026): SENTRI : Jurnal Riset Ilmiah, Januari 2026
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v5i1.5309

Abstract

Maternal Mortality Rate (MMR) remains a major challenge in public health, making the monitoring of pregnant women’s onditions, including hemoglobin (Hb) levels and weight gain highly important. This study aims to analyze the differences in hemoglobin levels and weight gain between primigravida and multigravida pregnant women at Made Public Health Center. This research employed a comparative quantitative approach with a cross-sectional design and utilized secondary data from medical records. The sample consisted of 78 respondents selected through stratified random sampling based on gravida status. The variables examined included gravida status as the independent variable and hemoglobin levels as well as weight gain as the dependent variables. Data were analyzed using the Chi-Square test with a significance level of p < 0.05. The results showed a statistically significant difference between gravida status and hemoglobin levels (p = 0.003), with multigravida mothers having a 4.5-times higher risk of experiencing anemia compared to primigravida mothers. Additionally, there was a statistically significant difference between gravida status and weight gain during pregnancy (p = 0.000), where multigravida mothers had a 7.3-times higher risk of experiencing abnormal weight gain. These findings highlight that gravida status is closely related to the nutritional status of pregnant women and can serve as a basis for evaluating antenatal care services, particularly for the multigravida group who are at higher risk.
A Critical Review of the Role of Molecular Epidemiology in Identifying Risk Factors for Chronic Diseases Tanayah, Muhammad Edo; Dinda Aziza Bryllianna
Science Publication: Journal of Public Health and Nutrition Vol. 1 No. 2 (2025): September 2025
Publisher : PT Yapindo Jaya Abadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64965/spjphn.v1i2.35

Abstract

ABSTRACT This study aims to provide a critical review of the role of molecular epidemiology in identifying chronic disease risk factors, emphasizing how molecular integration enhances the precision and validity of epidemiological analyses. Employing a qualitative descriptive approach through a systematic literature review, this research collected and analyzed data from peer-reviewed scientific articles, books, and official reports published between 2015 and 2025. Data were examined through document analysis, thematic coding, and inductive synthesis, allowing the identification of core themes and conceptual relationships within the existing body of knowledge. The findings reveal that molecular epidemiology bridges the gap between traditional population-based approaches and molecular biology by integrating genomic, metabolomic, and environmental data to uncover the biological mechanisms underlying chronic diseases such as cancer, diabetes, and cardiovascular disorders. Furthermore, multiomic profiling and machine learning models have improved risk prediction accuracy, clarified gene–environment interactions, and enabled the classification of molecular disease subtypes. However, challenges remain in biomarker validation, data standardization, and clinical translation. This study concludes that molecular epidemiology contributes significantly to the advancement of precision medicine by promoting more personalized prevention and intervention strategies. Its theoretical and practical implications extend to public health, data science, and biomedical research, underscoring the need for interdisciplinary collaboration and equitable access to molecular technologies in global health contexts. Keywords: molecular epidemiology, chronic diseases, gene–environment interaction, multiomics, precision medicine.