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.

Geo-clusters and socio-demographic profiles at village-level associated with COVID-19 incidence in the metropolitan city of Jakarta: An ecological study.

The Special Capital Region of Jakarta is the epicentre of the transmission of COVID-19 in Indonesia. However, much remains unknown about the spatial and temporal patterns of COVID-19 incidence and related socio-demographic factors explaining the variations of COVID-19 incidence at local level. COVID-19 cases at the village level of Jakarta from March 2020 to June 2021 were analyzed from the local public COVID-19 dashboard. Global and local spatial clustering of COVID-19 incidence was examined using the Moran's I and local Moran analysis. Socio-demographic profiles of identified hotspots were elaborated. The association between village characteristics and COVID-19 incidence was evaluated. The COVID-19 incidence was significantly clustered based on the geographical village level (Moran's I = 0.174; p = .002). Seventeen COVID-19 high-risk clusters were found and dynamically shifted over the study period. The proportion of people aged 20-49 (incidence rate ratio [IRR] = 1.016; 95% confidence interval [CI]: 1.012-1.019), proportion of elderly (≥50 years) (IRR = 1.045; 95% CI = 1.041-1.050), number of households (IRR = 1.196; 95% CI = 1.193-1.200), access to metered water for washing, and the main occupation of the residents were village level socio-demographic factors associated with the risk of COVID-19. Targeted public health responses such as restriction, improved testing and contact tracing, and improved access to health services for those vulnerable populations are essential in areas with high-risk COVID-19.

Spatial and temporal variation of dengue incidence in the island of Bali, Indonesia: An ecological study.

BACKGROUND: Dengue fever control in the tropical island of Bali in Indonesia carries important significance both nationally and globally, as it is one of the most endemic islands in Indonesia and a worldwide popular travel destination. Despite its importance, the spatial and temporal heterogeneity in dengue risk and factors associated with its variation in risk across the island has not been not well explored. This study was aimed to analyze for the first time the geographical and temporal patterns of the incidence of dengue and to quantify the role of environmental and social factors on the spatial heterogeneity of dengue incidence in Bali. METHODS: We analyzed retrospective dengue notification data at the sub-district level (Kecamatan) from January 2012 to December 2017 which obtained from the Indonesian Ministry of Health. Seasonality in notified dengue incidence was assessed by seasonal trend decomposition analysis with Loess (STL) smoothing. Crude standardized morbidity rates (SMRs) of dengue were calculated. Moran's I and local indicators of spatial autocorrelation (LISA) analysis were employed to assess spatial clustering and high-risk areas over the period studied. Bayesian spatial and temporal conditional autoregressive (CAR) modeling was performed to quantify the effects of rainfall, temperature, elevation, and population density on the spatial distribution of risk of dengue in Bali. RESULTS: Strong seasonality of dengue incidence was observed with most cases notified during January to May. Dengue incidence was spatially clustered during the period studied with high-risk kecamatans concentrated in the south of the island, but since 2014, the high-risk areas expanded toward the eastern part of the island. The best-fitted CAR model showed increased dengue risk in kecamatans with high total annual rainfall (relative risk (RR): 1.16 for each 1-mm increase in rainfall; 95% Credible interval (CrI): 1.03-1.31) and high population density (RR: 7.90 per 1000 people/sq.km increase; 95% CrI: 3.01-20.40). The RR of dengue was decreased in kecamatans with higher elevation (RR: 0.73 for each 1-m increase in elevation; 95% CrI: 0.55-0.98). No significant association was observed between dengue RR and year except in 2014, where the dengue RR was significantly lower (RR: 0.53; 95% CrI: 0.30-0.92) relative to 2012. CONCLUSIONS: Dengue incidence was strongly seasonal and spatially clustered in Bali. High-risk areas were spread from kecamatans in Badung and Denpasar toward Karangasem and Klungkung. The spatial heterogeneity of dengue risk across Bali was influenced by rainfall, elevation, and population density. Surveillance and targeted intervention strategies should be prioritized in the high-risk kecamatans identified in this study to better control dengue transmission in this most touristic island in Indonesia. Local health authorities should recommend travelers to use personal protective measures, especially during the peak epidemic period, before visiting Bali.

Using dengue epidemics and local weather in Bali, Indonesia to predict imported dengue in Australia.

BACKGROUND: Although the association between dengue in Bali, Indonesia, and imported dengue in Australia has been widely asserted, no study has quantified this association so far. METHODS: Monthly data on dengue and climatic factors over the past decade for Bali and Jakarta as well as monthly data on imported dengue in Australia underwent a three-stage analysis. Stage I: a quasi-Poisson regression with distributed lag non-linear model was used to assess the associations of climatic factors with dengue in Bali. Stage II: a generalized additive model was used to quantify the association of dengue in Bali with imported dengue in Australia with and without including the number of travelers in log scale as an offset. Stage III: the associations of mean temperature and rainfall (two climatic factors identified in stage I) in Bali with imported dengue in Australia were examined using stage I approach. RESULTS: The number of dengue cases in Bali increased with increasing mean temperature, and, up to a certain level, it also increased with increasing rainfall but dropped off for high levels of rainfall. Above a monthly incidence of 1.05 cases per 100,000, dengue in Bali was almost linearly associated with imported dengue in Australia at a lag of one month. Mean temperature (relative risk (RR) per 0.5 °C increase: 2.95, 95% confidence interval (CI): 1.87, 4.66) and rainfall (RR per 7.5 mm increase: 3.42, 95% CI: 1.07, 10.92) in Bali were significantly associated with imported dengue in Australia at a lag of four months. CONCLUSIONS: This study suggests that climatic factors (i.e., mean temperature and rainfall) known to be conducive of dengue transmission in Bali can provide an early warning with 4-month lead time for Australia in order to mitigate future outbreaks of local dengue in Australia. This study also provides a template and framework for future surveillance of travel-related infectious diseases globally.

Accuracy of Siemens hearcheck™ navigator as a screening tool for hearing loss

Objective@#To calculate the accuracy, sensitivity, specificity and positive predictive values of the Siemens HearCheck™ Navigator in detecting hearing loss and to compare values of these parameters when the examination is done in a soundproof booth and in a quiet room.@*Methods@#Design: Analytical, cross-sectional study Setting: Tertiary Public University Hospital Patients: Patients seen at the Ear Unit of a tertiary public university hospital from June 2009 to August 2010 were tested using the Siemens HearCheck™ Navigator and pure tone audiometry, inside a soundproof audiometry booth and in a quiet room with an ambient noise of 50dB, with a different investigator for each examination. Each ear was treated as a separate subject. Results obtained from the HearCheck™ Navigator were designated as observed values and were classified as “no hearing loss” for green light, and “with hearing loss” for yellow or red lights. Results were compared with pure tone air conduction averages designated as gold standard values. Normal hearing acuity (0-25 dB) was classified as no hearing loss. Pure tone air conduction averages of 26dB and above were classified as “with hearing loss” and were further stratified as mild hearing loss (26-40dB) and moderate or worse hearing loss (>41 dB). Observed and gold standard values were compared and tabulated in a 2x2 table for all levels of hearing loss, mild hearing loss, and moderate or worse hearing loss. Accuracy, sensitivity, specificity, positive and negative predictive values of the Siemens HearCheck™ Navigator inside a soundproof audiometry booth and in a quiet room were determined using pure tone audiometry as the gold standard.@*Results@#100 patients (200 ears) were tested, with a median age of 43 years old (range 15-75), and an almost equal number of male and female participants (52 males, 48 females). Accuracy rate of the Siemens HearCheck™ Navigator inside the soundproof audiometry booth and in a quiet room were 82.5% and 84% respectively for all levels of hearing loss. Sensitivity, specificity, positive and negative predictive values were similar whether the examination was done inside the soundproof audiometry booth or in a quiet room. These values were notably higher in patients with moderate or worse hearing loss compared to patients with mild hearing loss.@*Conclusion@#The Siemens HearCheck™ Navigator shows potential as an accurate, portable, easy-to-use tool to screen for hearing loss, especially for cases of moderate or worse hearing loss, without the need for soundproof audiometry booths or special training. It is recommended that further studies be done to differentiate degrees of hearing loss, and to evaluate its usefulness in other target populations, including school children and the elderly.