An investigation of geographical clusters of leptospirosis during the outbreak in Pangandaran, West Java, Indonesia.

Leptospirosis is neglected in many tropical developing countries, including Indonesia. Our research on this zoonotic disease aimed to investigate epidemiological features and spatial clustering of recent leptospirosis outbreaks in Pangandaran, West Java. The study analysed data on leptospirosis notifications between September 2022 and May 2023. Global Moran I and local indicator for spatial association (LISA) were applied. Comparative analysis was performed to characterise the identified hotspots of leptospirosis relative to its neighbourhoods. A total of 172 reported leptospirosis in 40 villages from 9 sub-districts in Pangandaran District were analysed. Of these, 132 cases (76.7%) were male. The median age was 49 years (interquartile range [IQR]: 34-59 years). Severe outcomes including renal failure, lung failure, and hepatic necrosis were reported in up to 5% of the cases. A total of 30 patients died, resulting in the case fatality rate (CFR) of 17.4%. Moran's I analysis showed significant spatial autocorrelation (I=0.293; p=0.002) and LISA results identified 7 High-High clusters (hotspots) in the Southwest, with the total population at risk at 26,184 people. The hotspots had more cases among older individuals (median age: 51, IQR: 36-61 years; p<0.001), more farmers (79%, p=0.001) and more evidence of the presence of rats (p=0.02). A comprehensive One Health intervention should be targeted towards these high-risk areas to control the transmission of leptospirosis. More empirical evidence is needed to understand the role of climate, animals and sociodemographic characteristics on the transmission of leptospirosis in the area studied.

Climate and vector-borne diseases in Indonesia: a systematic literature review and critical appraisal of evidence.

Climate is widely known as an important driver to transmit vector-borne diseases (VBD). However, evidence of the role of climate variability on VBD risk in Indonesia has not been adequately understood. We conducted a systematic literature review to collate and critically review studies on the relationship between climate variability and VBD in Indonesia. We searched articles on PubMed, Scopus, and Google Scholar databases that are published until December 2021. Studies that reported the relationship of climate and VBD, such as dengue, chikungunya, Zika, and malaria, were included. For the reporting, we followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A total of 66 out of 284 studies were reviewed. Fifty-two (78.8%) papers investigated dengue, 13 (19.7%) papers studied malaria, one (1.5%) paper discussed chikungunya, and no (0%) paper reported on Zika. The studies were predominantly conducted in western Indonesian cities. Most studies have examined the short-term effect of climate variability on the incidence of VBD at national, sub-national, and local levels. Rainfall (n = 60/66; 90.9%), mean temperature (Tmean) (n = 50/66; 75.8%), and relative humidity (RH) (n = 50/66; 75.8%) were the common climatic factors employed in the studies. The effect of climate on the incidence of VBD was heterogenous across locations. Only a few studies have investigated the long-term effects of climate on the distribution and incidence of VBD. The paucity of high-quality epidemiological data and variation in methodology are two major issues that limit the generalizability of evidence. A unified framework is required for future research to assess the impacts of climate on VBD in Indonesia to provide reliable evidence for better policymaking.

Anopheline Diversity in Indonesia: An Evaluation of Animal-Baited Sampling Techniques.

Indonesia has rich Anopheline (Diptera: Culicidae) mosquito species living in various types of ecosystems. The study was conducted to profile and compare Anopheles diversity, equitability, and dominance in various ecosystems using different animal-based sampling techniques. The present study analyzed a subset of data collected from a nation-wide vector and animal reservoirs survey in 2016. Analyses were restricted to three ecosystem types (forest, nonforest, and coastal areas) in Java and Sumatera Islands. A total of 5,477 Anopheles were collected by using animal-baited (n = 1,909) and animal-baited trap nets (n = 1,978), consisting of 23 Anopheline species. Overall, Anopheles vagus was the most abundant species, followed by An. subpictus and An. barbirostris. Among the three ecosystems, the forest had a higher diversity index (H' = 1.98), but each ecosystem has its specific predominant species. Compared with the animal-baited method, the Anopheles abundance collected by animal-baited trap nets was two-fold higher. Ecosystem, elevation, and sampling methods were associated with the abundance of female Anopheles (P-value < 0.001). Our findings revealed that Anopheles were found in a different ecosystem, indicating the potential of malaria transmission. This suggests that improved malaria vector surveillance is essential in all types of ecosystem. Furthermore, the study suggested that animal-baited trap nets could be used as the standard method of outdoor resting sampling in Indonesia in addition to the traditional human landing collection approach.

Sub-national disparities in accessing anti-malarial drug treatment in eastern Indonesia.

BACKGROUND: Poor access to health care providers was among the contributing factors to less prompt and ineffective malaria treatment. This limitation could cause severe diseases in remote areas. This study examined the sub-national disparities and predictors in accessing anti-malarial drug treatment among adults in Eastern Indonesia. METHODS: The study analyzed a subset of the 2018 National Basic Health Survey conducted in all 34 provinces in Indonesia. We extracted socio-demographic data of 4655 adult respondents diagnosed with malaria in the past 12 months in five provinces in Eastern Indonesia. The association between socio-demographic factors and the access to anti-malarial drug treatment was assessed using logistic regression. RESULTS: Over 20% of respondents diagnosed with malaria within last 12 months admitted that they did not receive anti-malarial drug treatment (range 12-29.9%). The proportion of untreated cases was 12.0% in East Nusa Tenggara, 29.9% in Maluku, 23.1% in North Maluku, 12.7% in West Papua, and 15.6% in Papua. The likelihood of receiving anti-malarial drug treatment was statistically lower in Maluku (adjusted OR = 0.258; 95% CI 0.161-0.143) and North Maluku (adjusted OR = 0.473; 95% CI 0.266-0.840) than those in Eastern Nusa Tenggara (reference). Urban respondents were less likely to receive malaria treatment than rural (adjusted OR = 0.545; 95% CI 0.431-0.689). CONCLUSIONS: This study found that there were sub-national disparities in accessing anti-malarial drug treatment in Eastern Indonesia, with a high proportion of untreated malaria cases across the areas. Findings from this study could be used as baseline information to improve access to anti-malarial drug treatment and better target malaria intervention in Eastern Indonesia.

Geographical heterogeneity and socio-ecological risk profiles of dengue in Jakarta, Indonesia.

The aim of this study was to assess the role of climate variability on the incidence of dengue fever (DF), an endemic arboviral infection existing in Jakarta, Indonesia. The work carried out included analysis of the spatial distribution of confirmed DF cases from January 2007 to December 2018 characterising the sociodemographical and ecological factors in DF high-risk areas. Spearman's rank correlation was used to examine the relationship between DF incidence and climatic factors. Spatial clustering and hotspots of DF were examined using global Moran's I statistic and the local indicator for spatial association analysis. Classification and regression tree (CART) analysis was performed to compare and identify demographical and socio-ecological characteristics of the identified hotspots and low-risk clusters. The seasonality of DF incidence was correlated with precipitation (r=0.254, P<0.01), humidity (r=0.340, P<0.01), dipole mode index (r= -0.459, P<0.01) and Tmin (r= -0.181, P<0.05). DF incidence was spatially clustered at the village level (I=0.294, P<0.001) and 22 hotspots were identified with a concentration in the central and eastern parts of Jakarta. CART analysis showed that age and occupation were the most important factors explaining DF clustering. Areaspecific and population-targeted interventions are needed to improve the situation among those living in the identified DF high-risk areas in Jakarta.

Paediatric dengue infection in Cirebon, Indonesia: a temporal and spatial analysis of notified dengue incidence to inform surveillance.

BACKGROUND: The recent situation of dengue infection in Cirebon district is concerning due to an upsurge trend since the year 2010. The largest dengue outbreak was reported in 2016 which has affected more than 1600 children. A study was conducted to explore the temporal variability of dengue outbreak in Cirebon's child population in during 2011-2017, and to assess the short-term effects of climatic and environmental factor on dengue incidence. In addition, the spatial pattern of dengue incidence in children and high-risk villages were investigated. METHODS: A total of 4597 confirmed dengue cases in children notified from January 2011 to December 2017 were analysed. Seasonal decomposition analysis was carried out to examine the annual seasonality. A generalized linear model (GLM) was applied to assess the short-term effect of climate and normalized difference vegetation index (NDVI) on dengue incidence. The incidence rate ratio (IRR) of the final model was reported. Spatial analyses were conducted by using Moran's I and local indicator of spatial association (LISA) analyses to explore geographical clustering in incidence and to identify high-risk villages for dengue, respectively. RESULTS: An annual dengue epidemic period was observed with peaks occurring every January/February. Based on the GLM, temperature at a lag 4 months (IRR = 1.27; 95% confidence interval, 95% CI: 1.22-1.31, P < 0.001), rainfall at a lag 2 months (IRR = 0.99, 95% CI: 0.99-0.99, P < 0.001), humidity at lag 0 month (IRR = 1.05, 95% CI: 1.04-1.06, P < 0.001) and NDVI at a lag 1 month (IRR = 3.07, 95% CI: 1.94-4.86, P < 0.001) were associated with dengue incidence in children. The dengue incidence in children was spatially varied and clustered at the village level across Cirebon. During 2011-2017, a total of 38 high-risk villages for dengue were identified, which were mainly located in the northern part of Cirebon. CONCLUSIONS: Seasonal patterns of dengue incidence in children in Cirebon were strongly associated with rainfall, temperature, humidity and NDVI variability, suggesting that climatic and environmental data could be used to help predict dengue outbreaks. Our spatial analysis revealed a clustered pattern in dengue incidence and high-risk villages for dengue across Cirebon, suggesting that effective interventions such as vector surveillance and school-based campaigns should be prioritized around the identified high-risk villages. Temporal and spatial analytical tools could be utilized to support local health authorities to apply timely and targeted public health interventions and help better planning and decision-making in order to minimize the impact of dengue outbreaks.

Space-time scan statistics of 2007-2013 dengue incidence in Cimahi City, Indonesia.

Four dengue serotypes threatened more than 200 million people and has spread to over 400 districts in Indonesia. Furthermore, 26 districts in most densely populated province, West Java, have been declared as hyperendemic areas. Cimahi is an endemic city with the highest population (14,969 people per square kilometer). Evidence on distribution pattern of dengue cases is required to discover the spread of dengue cases in Cimahi. A study has been conducted to detect clusters of dengue incidence during 2007-2013. A temporal spatial analysis was performed using SaTScan™ software incorporated confirmed dengue monthly data from the Municipality Health Office and population data from a local Bureau of Statistics. A retrospective space-time analysis with a Poisson distribution model and monthly precision was performed. Our results revealed a significant most likely cluster (p<0.001) throughout period of study. The most likely cluster was detected in the centre of the city and moved to the northern region of Cimahi. Cimahi, Karangmekar, and Cibabat village were most likely cluster in 2007-2010 (p <0.001; RR = 2.16-2.98; pop at risk 12% total population); Citeureup were detected as the most likely cluster in 2011-2013 (p <0.001; RR 5.77), respectively. Temporaly, clusters were detected in the first quarter of each year each. In conclusion, a dynamic spread of dengue initiated from the centre to its surrounding areas during the period 2007-2013. Our study suggests the use of GIS to strengthen case detection and surveillance. An in-depth investigation to relevant risk factors in high-risk areas in Cimahi city is encouraged.