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Batam City is the largest contributor to Dengue Hemorrhagic Fever (DHF) cases in the Riau Islands. One of the biggest challenges in the transmission of dengue fever in Batam City is the existence of shophouses and slum areas that are not intended for use (squatters). The aim of this research was to develop a model for controlling dengue fever in shophouses and squatter environments in Batam City. This research was quantitative analytical research with an ecological study approach. The research period started from August 2022 - May 2023. The population and samples for spatial analysis were 44 sub-districts and for statistical tests were 767 dengue fever with 88 samples. The results of the analysis showed that variables which were risk factors include vector density (shophouses: OR=6,2, squatters: OR=11,2), population mobility (shophouses: OR=6,2, squatters: OR=6,5), temperature (shophouses: OR=6,0, squatters: OR=7,3), rainfall (shophouses: OR=6,5, squatters: OR=8,4), humidity (shophouses: OR=7,1, squatters: OR=5,7), and house construction (shophouses: OR=5,0). The output of this research was the GWR model which showed that the variables Squatters Proportion, Temperature, Vector Density and Population Density had a significant effect on the number of dengue fever cases in Batam City (R2=77.13%). The model for controlling dengue fever that can be implemented are dengue management based on niche, including regional regulations requiring arranging used goods around squatters and empowering school children in eradicating larvae.
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.
Background: Dengue Hemorrhagic Fever (DHF) is a viral infection transmitted to humans through the bite of an infected mosquito. The main vectors that transmit the dengue virus are Aedes aegypti and Aedes albopictus. The city with the highest number of dengue cases in Indonesia in 2021 is Depok City with 3,155 cases with an Incidence Rate (IR) of 151.2 cases per 100,000 population. During the last 10 years from 2012-2020, the trend of dengue cases in Depok City tends to increase. Objective: To determine the relationship between climatic factors and population density with the incidence of DHF in Depok City in 2012-2021. Methods: This study uses an ecological study with correlation analysis to see the relationship between climatic factors (temperature, humidity, and rainfall) in the same month (non-time lag), climatic factors with a 1-month lag (time lag 1), and density population with DHF Incidence Rate. Results: The correlation analysis results showed a significant relationship between non-time lag humidity and time lag 1 humidity with DHF Incidence Rate (p = 0.000 and p = 0.000) with the strength of the relationship being positive (r = 0.332 and r-0.451). The results of the multiple linear regression test produce a predictive model with the equation IR DBD = -47.353 + 0.784 (Temperature) + 0.394 (Relative Humidity) + 0.023 (Rainfall). Based on the results of the regression equation, if it is simulated with a combination of the temperature of 26,1oC, humidity of 82.9%, and rainfall of 14.9 mm, there will be an increase in IR of DHF by 10 cases per 100,000 population.
