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All Journal Health Notions Journal of Information Systems and Informatics PREPOTIF : Jurnal Kesehatan Masyarakat Jurnal Indonesia : Manajemen Informatika dan Komunikasi Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Kesehatan Masyarakat Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal) Preventif : Jurnal Kesehatan Masyarakat Jurnal Epidemiologi Kesehatan Indonesia
Popy Yuniar
Department Of Population Studies And Biostatistics, Faculty Of Public Health, Universitas Indonesia, Depok, Indonesia
Computer Science & IT Dentistry Education Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Nursing Public Health

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Prevalence and Determinant Factors of Depression among Pregnant Women in PONED Health Center in Tangerang City Area Hayati, Dian Geumala; Kamso, Sudijanto; Yuniar, Popy; Devi, Yuli Puspita
Jurnal Epidemiologi Kesehatan Indonesia Vol. 10, No. 1
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

According to World Health Organization (WHO), approximately 10% of pregnant women experience mental disorders, with depression being particularly prevalent. In developing countries, this rate is was even higher, reaching 19.8% after childbirth and 15.6% during pregnancy. Various studies conducted in Indonesia show that the prevalence of depression among pregnant women ranges widely, from 21.4% to 26.2%.This study aimed to determine the prevalence and risk factors associated with depression during pregnancy in pregnant women at the PONED Community Health Center in Tangerang City Area. A facility-based cross-sectional study was conducted with 105 pregnant women attending antenatal check-ups at the 17 PONED Community Health Center in Tangerang City in October 2024. The sample was obtained with a consecutive sampling technique. Information regarding sociodemographic, obstetric, and psychosocial factors was collected through questionnaires. Depression was assessed using the Edinburgh Postnatal Depression Scale (EPDS), a valid and reliable tool for screening depression in pregnant women. Univariate analysis was employed to determine the prevalence of depression and the characteristics of the pregnant women, bivariate analysis using the chi-square test, and multivariate analysis with a logistic regression model. The research findings revealed that the prevalence of depression among pregnant women was 22%(95% CI: 14%-30%). The most significant factor associated with depression was a history of stress due to life events (p-value = 0.004). Respondents with a history of stress were 8.6 times more likely to develop depression compared to those without such a history, even after adjusting for variables such as gravida, age, occupation, and income  (Adjusted Odds Ratio = 8.62; 95% CI 2.03 – 36.69). This highlights the importance of screening for depression during antenatal care to identify and prevent further morbidityand mortality wrelated to antenatal depression. Keywords: Antenatal depression, mental health, pregnant women, risk factors, history of stress, EPDS
Spatial Autocorrelation of Diarrhea Cases in West Java Province in 2023 Tri Wahyudi; Popy Yuniar; Martya Rahmaniati
Media Publikasi Promosi Kesehatan Indonesia Vol. 7 No. 9 (2024)
Publisher : Fakultas Kesehatan Masyarakat, Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/mppki.v7i9.5973

Abstract

Introduction: Diarrhea have been being a significant public health threat for the community due to its impact on morbidity and even mortality especially among infants and toddlers. Understanding the pattern of diarrhea, how the key factors influence the prevalence of the disease and which areas are at the highest risk could help in controlling diarhhea. Objective: Through spatial autocorrelation analysis of diarrhea prevalence with several risk factors, such as open defecation behavior, population density, access to proper sanitation, availability of drinking water facilities, and the number of health centers in West Java Province in 2023, this study aims to identify which districts/cities in the province are at high risk of diarrhea. Method: This ecological study analyzed 27 districts/cities in West Java Province in 2023 using secondary data from Open Data Jabar. The dependent variable was the number of diarrhea cases, while independent variables included key factors influencing the prevalence of diarrhea. The Moran index was used for autocorrelation tests, The study used Geoda software version 1.22. Result: The study found positive autocorrelation pattern between diarrhea prevalence and population density as well as access to proper sanitation. Negative autocorrelatios found for the other factors i.e. the number of drinking water facilities, the number of villages that stopped open defecation and the number of health centers. The risk analysis reveals four districts categorized as high risk of diarrhea: Depok City, Bekasi City, Bogor City, and Cianjur City. Depok City has the highest score of 12, due to high population density and lack of access to proper sanitation. Bekasi, Bogor City, and Cianjur have the next highest scores, with proper sanitation being the largest contributing factor. Conclusion: Autocorrelation analysis can help understand diarrhea patterns and factors influencing its prevalence, provide guidance for program implementation and prioritization to address the most high risk areas.
Enhancing Digital Health Services in ASEAN Countries: Comparison of Indonesia, Singapore, and Thailand Milla Herdayati; Popy Yuniar
Jurnal Kesehatan Masyarakat Vol. 21 No. 3 (2026)
Publisher : Universitas Negeri Semarang in collaboration with Ikatan Ahli Kesehatan Masyarakat Indonesia (IAKMI Tingkat Pusat) and Jejaring Nasional Pendidikan Kesehatan (JNPK)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/kemas.v21i3.23162

Abstract

Digital health is transforming healthcare globally, with ASEAN nations like Singapore, Thailand, and Indonesia showing diverse progress. This study analyzes and compares their digital health services to pinpoint best practices and improvement areas. Employing a mixed-methods approach involving literature reviews, policy analysis, and data gathering on infrastructure and service adoption from government and academic sources, the research focuses on governance, investment, infrastructure, and service uptake. Findings reveal Singapore as a digital health frontrunner due to its strong governance, substantial investment, advanced infrastructure, and high adoption. Thailand shows progress but grapples with decentralized governance and urban-rural divides. Indonesia's developing sector faces fragmented governance, limited investment, and infrastructure deficits. The study underscores the importance of centralized governance and robust investment for successful digital health implementation, highlighting Singapore's cohesive strategy as a beneficial model. Thailand and Indonesia need to enhance policy consistency and infrastructure development to improve the effectiveness of digital health services in ASEAN. Ultimately, advancing digital health in ASEAN necessitates a comprehensive strategy encompassing strengthened governance, increased investment, better infrastructure, and greater public awareness, drawing lessons from successful models to improve regional healthcare access and quality.
A Temporal Patient Risk Timeline Framework for Longitudinal Clinical Deterioration Surveillance Using Hospital Data Warehouse and Machine Learning Herwanto; Dian Nugraha; Popy Yuniar
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1680

Abstract

Clinical deterioration among hospitalized patients remains a major challenge in inpatient care management. Conventional deterioration monitoring approaches frequently rely on isolated physiological observations and static threshold-based scoring systems, which may inadequately represent the longitudinal nature of patient instability. This study aimed to develop and evaluate a retrospective temporal patient risk timeline framework for clinical deterioration surveillance using hospital data warehouse and machine learning. A retrospective longitudinal deterioration surveillance framework was developed using hourly inpatient observations derived from hospital operational and clinical data sources. A temporal XGBoost model was developed to predict ICU transfer within the subsequent 24-hour observation window under severe class imbalance conditions. To evaluate the additional value of temporal feature engineering, baseline non-temporal models including logistic regression, random forest, and non-temporal XGBoost were also developed using static observation-level variables. Operational feasibility was retrospectively demonstrated through a dashboard prototype integrating deterioration prioritization, patient timeline visualization, and longitudinal surveillance. The final dataset consisted of 20,181 inpatient admission episodes represented by 2,233,143 hourly observations. ICU transfer occurred in 712 admissions. Because the prediction task was formulated as hourly deterioration surveillance, ICU transfer labels represented only 0.65% of all hourly observations, indicating severe class imbalance. The temporal XGBoost model achieved an AUROC of 0.606 and an AUPRC of 0.009. While predictive discrimination was modest, the framework enabled continuous longitudinal representation of patient deterioration and retrospective surveillance of clinical instability. The findings suggest that temporal patient representation may provide operational and interpretative value beyond static snapshot-based deterioration monitoring approaches.
Co-Authors Artha Prabawa Besral Birjaman, Fathiyya Aliyah Dian Nugraha Diana Diana Diana, Tyra Septi Dion Zein Nurizin Diyanto, Aris Dodi Angga Kusuma Dwisaputro, Sigit Enny Yuliana Hayati, Dian Geumala Herwanto Herwanto Indang Trihandini Intan, Yosephine Roma Kemal N. Siregar Khoiriyah, Latifatul Kusuma, Dodi Angga Maimunah, Rana Martya Rahmaniati Milla Herdayati, Milla Minawati Minawati, Minawati Muhammad Azhar Murti, Fadhaa Aditya Kautsar Muyassar, Ibnu Nurizin, Dion Zein Putri, Hertina Raisa R Sutiawan Ramadhianisa, Nadazaira Alifia Riko Setiawan, Riko Ririn Arminsih Wulandari Serafina, Annabel Setiadi, Rizky Sudijanto Kamso Sufiyana Jannatin, Illa Sutiawan, R Tri Wahyudi Umi Kalsum Utami, Asti Annisa Wiyanti, Zulvi Yuli Puspita Devi Yuliana, Enny