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Objective: To investigate the environmental and social aspects of poverty contributing to malaria incidence in Indonesia from 2016 to 2020. Methods: Random forest regression was used to analyse the independent variables contributing to malaria incidence. Environmental conditions were extracted from remotely sensed data, including vegetation, land temperature, soil moisture, precipitation, and elevation. In contrast, the social aspects of poverty were obtained from government statistical reports. Results: From 2016 to 2020, the contribution of each environmental and social aspect of poverty to malaria incidence fluctuated annually. Generally, the top three essential variables were people aged 15 years and above, experiencing poverty (variable importance/VI=32.0%), people experiencing poverty who work in the agricultural sector (VI=14.4%), and precipitation (VI=9.8%). It was followed by people experiencing poverty who are unemployed (VI=9.2%), land temperature (VI=5.2%), people experiencing poverty who have low education (VI=8.0%), soil moisture (VI=7.4%), elevation (VI=6.0%), and vegetation (VI=3.8%). Conclusions: Poverty and variables related to climate have become the crucial determinants of malaria in Indonesia. The government must strengthen malaria surveillance through climate change mitigation and adaptation programs and accelerate poverty alleviation programs to support malaria elimination.

Background: The Public Health Center (PHC) is a gatekeeper to formal healthcare in Indonesia. PHCs in Central Java have met the basic health facility readiness standard, but there are still numerous challenges to increasing the utilization of PHCs. Aims: This study aimed to analyze the factors related to the utilization of PHC in Central Java. Methods: This cross-sectional study used secondary data from the 2018 Indonesia Basic Health Research, with 63,118 total samples. The independent variables were residence, age, gender, marital status, education, insurance, and socioeconomic, while the dependent variable was the utilization of PHC. The data were analyzed using a binary logistic regression. Results: The average rate of utilization of PHC in Central Java in 2018 was 5.7%. Those aged 46-65 and >65 years old, women, married and widowers, and those with health insurance had a higher possibility of utilizing PHC. Meanwhile, those who graduated from secondary and tertiary school, the employed, and the wealthiest group had a lower possibility of utilizing PHC. Conclusion: The factors related to the utilization of PHC in Central Java are age group, gender, marital status, education level, working status, health insurance ownership, and socioeconomic status. Keywords: basic health service, health service access, public health, public health center

Introduction: At the end of December 2020, there were 93,035 Covid19 cases reported in Central Java. The spatial analysis is useful for assessing the association of environmental and health services factors with new Covid19 cases. Methods: This study was conducted to identify a spatial autocorrelation between environmental conditions and health services on new Covid19 cases in Central Java Province in 2020. The data were obtained from Central Java Profile Published in 2021 with a cross-sectional design. This autocorrelation regression technique was used to determine the relationship between districts/cities for new Covid19 cases. The independent variables in this study were environmental factors such as access to quality drinking water, access to quality sanitation, percentage of Open Defecation Free (ODF) villages, and percentage of healthy food management places. In addition, the independent variables also covered health service factors such as the number of public health centers, hospitals, medical personnel, and population density. Results and Discussion: The findings found that in Central Java province, the factors that influenced new Covid19 cases included population density (p-value 0.0001; Morran I -0.032) and the number of medical personnel (p-value 0.0001; Morrans I 0.021). Conclusion: The new cases of Covid19 in Central Java Province formed a clustered pattern. Factors significantly influencing the regression test are population density and the number of medical personnel. Besides that, spatial autocorrelation was also found in other variables in this study but was not significant.

Objective: To assess the prevalence of Mycobacterium (M.) tuberculosis infection and its associated risks in children under fifteen years of age. Methods: Based on secondary data from the 2018 Indonesian Basic Health Survey (RISKESDAS 2018), this cross-sectional study employed M. tuberculosis infection in children as a dependent variable and age level, sex, region, location, family case positive, and smoking as independent variables. Geospatial analysis was applied to show the prevalence of M. tuberculosis infection and multivariate regression was performed to analyze the risk factors of getting the infection among children under 15 years of age. Results: Prevalence ranges of M. tuberculosis infection among children aged 0-14 years, babies (age

Measurement(s) land cover Technology Type(s) visual interpretation of satellite imagery Sample Characteristic - Location Indonesia

ABSTRACT Schistosomiasis in Indonesia is caused by the trematode worm, Schistosoma japonicum, with the snail Oncomelania hupensis lindoensis as the intermediate host. The presence of these snails is an indicator of determining the habitat area for the intermediate host of schistosomiasis. The aim of the study was to map the distribution of snail habitat in the Lindu endemic area. An observational study with a cross-sectional design was conducted in August 2021. The study was conducted in two villages in the Lindu Highlands, Sigi Regency, Central Sulawesi Province. The number of snail habitats in the Lindu Plateau found 25 habitats, covering an area of ​​27,088 m2. Snail density and infection rate (IR) were found to be 27.06 snails/minute (IR 0.68%). Based on the research, it can be concluded that the distribution pattern of the snail habitat in Lindu was clustered in certain areas. Habitat in Anca Village was most widely found in neglected coffee plantation areas, and a small part in primary forest areas. Snail habitat in Tomado Village was mostly found in watersheds, parapa grass in swamp areas, and uncultivated rice fields. Environmental management that can be carried out by multi sectors are the creating of rice fields, the creation of fish ponds, and the diversion of community paths in the Lore Lindu National Park area that passes through the habitat of schistosomiasis intermediate snails. ABSTRAK Schistosomiasis di Indonesia disebabkan oleh cacing trematoda jenis Schistosoma japonicum dengan hospes perantara keong Oncomelania hupensis lindoensis. Keberadaan keong tersebut sebagai indikator penetapan daerah habitat hospes perantara schistosomiasis. Tujuan penelitian adalah untuk untuk memetakan distribusi habitat keong di wilayah endemis Lindu. Jenis penelitian adalah observasional dengan desain potong lintang yang dilakukan pada bulan Agustus tahun 2021. Penelitian dilakukan di Desa Anca dan Tomado Kecamatan Lindu Kabupaten Sigi, Provinsi Sulawesi Tengah. Jumlah habitat keong di Dataran Tinggi Lindu ditemukan 25 habitat, seluas 27.088 m2. Kepadatan keong dan tingkat infection rate (IR) ditemukan sebesar 27,06 keong/menit (IR 0,68%). Berdasarkan penelitian dapat disimpulkan bahwa pola persebaran habitat keong di Lindu adalah mengelompok/clustered pada daerah tertentu. Habitat di Desa Anca ditemukan paling banyak di daerah kebun kopi yang terabaikan, dan sebagian kecil di daerah hutan primer dengan kanopi tertutup. Habitat keong di Desa Tomado paling banyak ditemukan di daerah aliran air, parapa, rawa, dan sawah tidak diolah. Manajemen lingkungan yang dapat dilakukan oleh lintas sektor yaitu pencetakan sawah, pembuatan kolam ikan, dan pengalihan jalur masyarakat dalam Kawasan Taman Nasional Lore Lindu yang melewati habitat keong perantara schistosomiasis.

Objective: To investigate the environmental and social aspects of poverty contributing to malaria incidence in Indonesia from 2016 to 2020. Methods: Random forest regression was used to analyse the independent variables contributing to malaria incidence. Environmental conditions were extracted from remotely sensed data, including vegetation, land temperature, soil moisture, precipitation, and elevation. In contrast, the social aspects of poverty were obtained from government statistical reports. Results: From 2016 to 2020, the contribution of each environmental and social aspect of poverty to malaria incidence fluctuated annually. Generally, the top three essential variables were people aged 15 years and above, experiencing poverty (variable importance/VI=32.0%), people experiencing poverty who work in the agricultural sector (VI=14.4%), and precipitation (VI=9.8%). It was followed by people experiencing poverty who are unemployed (VI=9.2%), land temperature (VI=5.2%), people experiencing poverty who have low education (VI=8.0%), soil moisture (VI=7.4%), elevation (VI=6.0%), and vegetation (VI=3.8%). Conclusions: Poverty and variables related to climate have become the crucial determinants of malaria in Indonesia. The government must strengthen malaria surveillance through climate change mitigation and adaptation programs and accelerate poverty alleviation programs to support malaria elimination. [ABSTRACT FROM AUTHOR]

Abstract Background Malaria, a parasitic infection, is a life-threatening disease in South Sumatra Province, Indonesia. This study aimed to investigate the spatial association between malaria occurrence and environmental risk factors. Methods The number of confirmed malaria cases was analysed for the year 2013 from the routine reporting of the Provincial Health Office of South Sumatra. The cases were spread over 436 out of 1613 villages. Six potential ecological predictors of malaria cases were analysed in the different regions using ordinary least square (OLS) and geographically weighted regression (GWR). The global pattern and spatial variability of associations between malaria cases and the selected potential ecological predictors was explored. Results The importance of different environmental and geographic parameters for malaria was shown at global and village-level in South Sumatra, Indonesia. The independent variables altitude, distance from forest, and rainfall in global OLS were significantly associated with malaria cases. However, as shown by GWR model and in line with recent reviews, the relationship between malaria and environmental factors in South Sumatra strongly varied spatially in different regions. Conclusions A more in-depth understanding of local ecological factors influencing malaria disease as shown in present study may not only be useful for developing sustainable regional malaria control programmes, but can also benefit malaria elimination efforts at village level.

Introduction: At the end of December 2020, there were 93,035 Covid19 cases reported in Central Java. The spatial analysis is useful for assessing the association of environmental and health services factors with new Covid19 cases. Methods: This study was conducted to identify a spatial autocorrelation between environmental conditions and health services on new Covid19 cases in Central Java Province in 2020. The data were obtained from Central Java Profile Published in 2021 with a cross-sectional design. This autocorrelation regression technique was used to determine the relationship between districts/cities for new Covid19 cases. The independent variables in this study were environmental factors such as access to quality drinking water, access to quality sanitation, percentage of Open Defecation Free (ODF) villages, and percentage of healthy food management places. In addition, the independent variables also covered health service factors such as the number of public health centers, hospitals, medical personnel, and population density. Results and Discussion: The findings found that in Central Java province, the factors that influenced new Covid19 cases included population density (p-value 0.0001; Morran I -0.032) and the number of medical personnel (p-value 0.0001; Morrans I 0.021). Conclusion: The new cases of Covid19 in Central Java Province formed a clustered pattern. Factors significantly influencing the regression test are population density and the number of medical personnel. Besides that, spatial autocorrelation was also found in other variables in this study but was not significant. [ABSTRACT FROM AUTHOR]