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Perubahan iklim berpotensi meningkatkan risiko penyakit berbasis lingkungan, termasuk diare. Di Indonesia, prevalensi diare balita masih tergolong tinggi, meskipun menurun dari 12,3% (Riskesdas 2018) menjadi 9,8% (SSGI 2020). Kondisi ini menunjukkan adanya faktor lain yang memengaruhi, termasuk parameter iklim yang belum banyak diteliti secara spesifik dalam konteks Indonesia.
Penelitian ini bertujuan untuk mengembangkan model prediksi risiko diare secara komparatif pada dua zona iklim berbeda: monsunal (Nusa Tenggara Barat) dan ekuatorial (Sumatera Barat). Desain penelitian adalah studi ekologi, dengan data sekunder tahun 2017-2021 yang diperoleh dari Kementerian Kesehatan (kasus diare), BPS (akses air minum tidak aman, sanitasi terbatas, higiene terbatas, status ekonomi dan kepadatan penduduk), dan BMKG (suhu udara, kelembapan, curah hujan). Analisis dilakukan menggunakan regresi binomial negatif.
Hasil menunjukkan bahwa curah hujan berhubungan signifikan terhadap kejadian diare di Sumbar (IRR=0,998) dan NTB (IRR=1,002). Suhu udara hanya signifikan di Sumbar (IRR= 0,955), sedangkan kelembapan hanya signifikan di NTB (IRR=0,954). Akses air minum tidak aman dan sanitasi terbatas berhubungan signifikan di kedua provinsi, sedangkan higiene terbatas tidak menunjukkan hubungan signifikan. Tingkat kemiskinan berpengaruh signifikan hanya di NTB (IRR=1,025). Model prediksi menunjukkan performa yang baik, meskipun akurasinya berada pada kategori rendah hingga sedang.
Kesimpulannya, variabilitas iklim berkontribusi terhadap risiko diare dengan pola yang berbeda antarwilayah. Faktor lokal seperti letak geografis, infrastruktur, dan ketersediaan layanan dasar—khususnya akses terhadap air minum aman dan sanitasi layak—memegang peran penting. Diperlukan penguatan kolaborasi lintas sektor dan keterlibatan masyarakat untuk pengendalian diare yang adaptif terhadap perubahan iklim.
Climate change can exacerbate environment-related disease, including diarrhea. In Indonesia, diarrhea prevalence among children under five remains high, although it declined from 12,3% (Basic Health Research, 2018) to 9,8% (National Health Survey, 2020). This indicates the influence of additional factors, including climatic parameters that have not been thoroughly examined in the Indonesian context.
This study developed a comparative diarrhea risk prediction model across two climate zones: monsunal (West Nusa Tenggara) and equatorial (West Sumatera). An ecological design was employed using 2017-2021 secondary data from the Ministry of Health (diarrhea cases), the Central Bureau of Statistics (BPS) (unsafe drinking water access, sanitation, hygiene, economic status, population density), and the Meteorology, Climatology, and Geophysics Agency (BMKG) (temperature, humidity, rainfall). Data were analyzed using negative binomial regression.
Rainfall was significantly associated with diarrhea incidence in both provinces (West Sumatera IRR = 0,998; West Nusa Tenggara IRR = 1,002). Air temperature was significant only in West Sumatera (IRR = 0,955), while humidity was significant only in West Nusa Tenggara (IRR = 0,954). Unsafe water access and poor sanitation were significant in both provinces, whereas hygiene showed no association. Poverty was significant only in West Nusa Tenggara (IRR = 1,025). The model performed well, with accuracy in the low-to-moderate range.
In conclusion, climate variability contributes to diarrhea risk, with distinct patterns across regions. Local factors such as geography, infrastructure, and the availability of basic services— particularly access to safe drinking water and adequate sanitation—play a crucial role. Strengthening cross-sectoral collaboration and community engangement is essential for developing climate-adaptive diarrhea control strategies.
The tuberculosis treatment success rate in Indonesia in 2023 did not reach the 90% target. Treatment success impacts the reduction of infection spread and drug resistance cases, making early prediction of treatment success crucial. This study aims to develop a machine-learning model to predict treatment success. Data from Indonesia's Tuberculosis Information System (SITB) cohort was used. The study included productive-age patients (15-64 years) diagnosed with drug-sensitive tuberculosis who received treatment from January 1, 2020, to December 31, 2023. Data was randomly split into training (80%) and testing (20%) sets for model validation, with cross-validation performed. The algorithms used include decision tree, random forest, multilayer perception, extreme gradient boosting, and logistic regression. A consensus was reached for decision-making variables required in performing machine learning-based modeling of SITB data to predict treatment success using modeling of SITB data to predict treatment success using the Delphi method. The results of the study show that the random forest machine learning algorithm had the best performance and highest accuracy in predicting treatment success. This machine learning–based prediction tool can provide early predictions with SHAP (SHapley Additive ExPlanations) interpretation, helping healthcare workers make informed decisions more easily.
Latar belakang: Keberhasilan ASI eksklusif di Indonesia masih rendah. Self-efficacy ayah dan ibu berperan penting dalam praktik menyusui. Penelitian ini bertujuan mendapatkan model prediksi penghentian ASI eksklusif pada 0-12 postpartum menggunakan Paternal dan Maternal BSE framework.
Metode: Penelitian menggunakan metode kualitatif dan kuantitatif. Studi kualitatif menggunakan FGD terhadap 5 informan ayah secara purposive dengan tujuan menggali infomasi mengenai paternal BSE. Desain studi kuantitatif longitudinal bertujuan mengevaluasi model penelitian. Sebanyak 201 pasangan yang bersalin di rumah sakit ibu dan anak di Kota Tangerang Selatan berhasil diikuti sampai 12 minggu postpartum. Data kuantitatif dikumpulkan melalui kuesioner dan dianalisis menggunakan SEM-PLS untuk menguji pengaruh antar variabel dan mendapatkan nilai prediktif model.
Hasil: Instrumen Paternal BSE dan sumber self-efficacy ayah valid dan reliabel. Faktor signifikan yang berpengaruh terhadap penghentian ASI eksklusif meliputi paternal BSE, maternal BSE, pengalaman ayah, pengalaman ibu, persuasi verbal bagi ibu, kondisi fisik emosi ibu, usia ibu, frekuensi ANC, dan tipe keluarga. Secara tidak langsung, ada pengaruh pengalaman ayah terhadap penghentian ASI eksklusif melalui paternal dan maternal BSE.
Simpulan: Model paternal-maternal BSE dapat memprediksi penghentian ASI eksklusif dengan baik.
Saran: Penghentian ASI eksklusif dapat dicegah dengan melibatkan ayah secara aktif melalui edukasi, pendampingan, dan penguatan paternal BSE sejak masa kehamilan.
Background: The rate of exclusive breastfeeding in Indonesia remains low. Both paternal and maternal self-efficacy play a crucial role in supporting breastfeeding practices. This study aims to develop a predictive model for exclusive breastfeeding cessation during the first 0–12 months postpartum using the Paternal and Maternal Breastfeeding Self-Efficacy (BSE) framework.
Methods: This study employed both qualitative and quantitative methods. The qualitative phase involved focus group discussions (FGDs) with five purposively selected fathers to explore aspects of paternal BSE. The quantitative phase used a longitudinal design to evaluate the proposed model. A total of 201 couples who delivered at a maternal and child hospital in South Tangerang City were followed up to 12 weeks postpartum. Quantitative data were collected using questionnaires and analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine relationships between variables and assess model predictive.
Results: The Paternal BSE instrument and sources of paternal self-efficacy were found to be valid and reliable. Significant factors influencing exclusive breastfeeding cessation included paternal BSE, maternal BSE, fathers’ experience, mothers’ experience, verbal persuasion for mothers, mothers’ physical and emotional condition, maternal age, antenatal care (ANC) frequency, and family type. Indirectly, there was a relationship between fathers’ experience and exclusive breastfeeding cessation through paternal and maternal BSE.
Conclusion: The paternal-maternal BSE model effectively predicts exclusive breastfeeding cessation.
Recommendation: Exclusive breastfeeding cessation can be prevented by actively involving fathers through education, support, and strengthening paternal BSE starting from pregnancy.
