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The Effectiveness of Health Management-Assisted Technology on Glycated Hemoglobin Levels in Patients with Type 2 Diabetes Mellitus: Meta-Analysis Novianto, Fajar; Amalin, Atika Mima; Handayani, Anggun Fitri; Ambarsari, Anggraini; Ode, Diana; Azizah, Alfi Makrifatul; Pamilih, Ayu Trisni; Damalita, Annisa Fitriana; Firda, Fathiyyatu Assa'diy; Mubarok, Ahmad Syauqi
Journal of Health Policy and Management Vol. 6 No. 2 (2021)
Publisher : Masters Program in Public Health, Universitas Sebelas Maret, Indonesia

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Background: Given the number of patients failing to achieve control of Diabetes Mellitus (DM), it causes an increase in the incidence of DM complications. Along with the rapid development of technology in this era, this study aimed to prove the effectiveness of technology-based health management compared to usual treatment for levels glycated hemoglobin (HbA1c) in type 2 diabetes mellitus patients.Subjects and Method: This was a meta-analysis using a randomized controlled trial. Articles were obtained from PubMed, Google Scholar, and ResearchGate databases. The articles used in this study were those published from 2012-2021. The search article was carried out by considering the eligibility of the criteria determined using the PICO model. Population: type 2 DM patients (HbA1c>7%), Intervention: health management-assisted technology, Comparison: usual care Outcome: HbA1c levels. There were 10 articles used with a sample size of 1693 people who were divided into two groups (845 people in the health management-assisted technology group and 848 people in the group usual care). Articles were analyzed using Review Manager 5.3 Application to determine the Standard Mean Difference (SMD) and heterogeneity of the study sample.Results: From 10 articles that were processed using RevMan 5.3, significant results were obtained, this is indicated by the overall effect (diamond) which does not touch the vertical line H0 (d= 0) and can also be seen from the 95% CI range of -0.62 to -0.13 which shows significant because it does not pass the number 0 (SMD= -0.37; 95% CI= -0.62 to -0.13; p= 0.003). The heterogeneity of the research data shows I2 = 82% so that the distribution of the data is very heterogeneous (random effects model).Conclusion: Using technology to help health management of patients with type 2 diabetes mellitus can reduce HbA1c levels compared to usual care.Keywords: Health management, technology, diabetes mellitus, HbA1cCorrespondence: Fajar Novianto. Center for Research and Development of Medicinal Plants and Traditional Medicine, National Institute of Health of Health, Jl. Raya Lawu No. 11 Karanganyar, Central Java. Email: dr.fajarnovianto@gmail.com.Journal of Health Policy and Management (2021), 06(02): 81-93https://doi.org/10.26911/thejhpm.2021.06.02.01

The Effectiveness of Ivermectin on the Risk of Mortality in COVID-19 Patients: A Meta Analysis Pamilih, Ayu Trisni; Tamtomo, Didik Gunawan; Murti, Bhisma
Journal of Epidemiology and Public Health Vol. 6 No. 4 (2021)
Publisher : Masters Program in Public Health, Universitas Sebelas Maret, Indonesia

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Background: The Coronavirus Disease 2019 (COVID-19) has become the highest priority of global pandemic. New and repurposed drugs are being tested on mild to moderate levels of COVID-19 to help suppress transmission of the virus. Ivermectin is one of the repurposed drugs with known safety records with more than 2.5 billion doses dispensed in the past. This study aims to estimate the effectiveness of ivermectin in reducing the risk of mortality in COVID-19 patients based on the results of a number of previous similar studies.Subjects and Method: This study is a systematic review and meta-analysis. This study used secondary data in the form of data from previous study results. A systematic and comprehensive database search was carried out through several databases including: PubMed, Science Direct, Google Scholar, and Springer Link. Analysis of this study was using RevMan 5.3 software. This study used the eligibility criteria with the PICO model, populations: covid-19 patients, intervention: ivermectin administration, comparison: patients without ivermectin, outcome: mortality in COVID-19 patients. The inclusion criteria used were full paper in English and Indonesian with a randomized controlled trial, including the number of deaths, and the primary study was conducted in a hospital. The keywords used to search the database were

The Effectiveness of Ivermectin on the Risk of Mortality in COVID-19 Patients: A Meta Analysis Pamilih, Ayu Trisni; Tamtomo, Didik Gunawan; Murti, Bhisma
Journal of Epidemiology and Public Health Vol. 6 No. 4 (2021)
Publisher : Masters Program in Public Health, Universitas Sebelas Maret, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (31.605 KB)

Background: The Coronavirus Disease 2019 (COVID-19) has become the highest priority of global pandemic. New and repurposed drugs are being tested on mild to moderate levels of COVID-19 to help suppress transmission of the virus. Ivermectin is one of the repurposed drugs with known safety records with more than 2.5 billion doses dispensed in the past. This study aims to estimate the effectiveness of ivermectin in reducing the risk of mortality in COVID-19 patients based on the results of a number of previous similar studies.Subjects and Method: This study is a systematic review and meta-analysis. This study used secondary data in the form of data from previous study results. A systematic and comprehensive database search was carried out through several databases including: PubMed, Science Direct, Google Scholar, and Springer Link. Analysis of this study was using RevMan 5.3 software. This study used the eligibility criteria with the PICO model, populations: covid-19 patients, intervention: ivermectin administration, comparison: patients without ivermectin, outcome: mortality in COVID-19 patients. The inclusion criteria used were full paper in English and Indonesian with a randomized controlled trial, including the number of deaths, and the primary study was conducted in a hospital. The keywords used to search the database were

Faktor Yang Mempengaruhi Kepatuhan Perempuan Usia Subur Dalam Deteksi Dini Ca Serviks Lubis, Angela Ditauli; Pamilih, Ayu Trisni
Jurnal Borneo Cendekia Vol 9 No 2 (2025): Jurnal Borneo Cendekia
Publisher : STIKES Borneo Cendekia Medika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54411/jbc.v9i2.682

Cervical cancer is the fourth most common cancer in women globally, with 64,000 new cases in 2020. In Indonesia, cervical cancer ranks second after breast cancer, with 36,633 cases accounting for 17.2% of all cancers in women. This case has a high mortality rate of 21,003 deaths accounting for 19.1% of all cancer deaths. The purpose of this study was to determine the factors that influence women's compliance with early detection of cervical cancer, including individual, social, and health service factors. This research is a quantitative research with analytical survey with cross sectional approach. Sampling using simple random sampling technique with a sample size of 112 respondents with inclusion criteria of women aged 25-50 years who have been married, willing to be respondents and can read and write well. Data analysis using descriptive frequency test, chi square test, and multiple logistic regression test. The results of this study obtained a relationship between age p value (0.000), education p value (0.104), occupation p value (0.002), parity p value (0.000), general health condition p value (0.018), distance to health facilities p value (0.006), knowledge p value (0.021), family support p value (0.011), and the role of health workers p value (0.000) with women's compliance with early detection of cervical cancer. The results of multiple logistic regression test obtained the most influential variable, namely age p value (0.045) with women's compliance with early detection of cervical cancer Based on statistical tests obtained the results of the most influential factor, namely age factor. The conclusion of this study is that it is hoped that this study can increase women's knowledge regarding cervical cancer screening.

Multimethodology Analysis of Determinants of Breast Cancer Diagnosis Machine Learning Lubis, Dita Anggriani; Irnawati, Yuli; Pamilih, Ayu Trisni; Gultom, Ria Fazelita Br
Jurnal Penelitian Pendidikan IPA Vol 12 No 1 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i1.12497

Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, highlighting the urgent need for accurate and interpretable diagnostic models. While machine learning has shown promise in classification tasks, many existing models lack transparency and overlook the individual contribution of cellular features essential for clinical decision-making.This study proposes an integrative and explainable framework to identify the most influential cellular-level features in distinguishing between benign and malignant breast tumors. Using a publicly available dataset comprising 569 observations and 32 numerical features, we conducted a multi-step analysis. Feature relevance was first evaluated using Pearson correlation. Random Forest and Recursive Feature Elimination (RFE) were employed to rank and refine the feature subset, followed by Principal Component Analysis (PCA) for dimensionality reduction and pattern visualization. SHapley Additive exPlanations (SHAP) were utilized to interpret individual predictions. Complementary statistical tests, including t-tests and chi-square analyses, assessed associations between tumor characteristics and diagnosis. A logistic regression model was developed to evaluate predictive performance.Key cellular features—such as mean radius, texture, and concavity—were consistently identified as highly predictive of diagnosis. RFE demonstrated that fewer than 10 features were sufficient for optimal classification. The logistic regression model achieved high accuracy, offering a simpler yet effective alternative for prediction.By combining statistical methods with interpretable machine learning, this study presents a transparent and clinically relevant approach to breast cancer diagnosis. The integration of SHAP values bridges the gap between model performance and interpretability, supporting more informed and personalized clinical decisions. Future work should consider external validation, image-based features, and patient demographic variables to enhance generalizability.

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