Climatic factors associated with epidemic dengue in Palembang, Indonesia: implications of short-term meteorological events on virus transmission.

An extensive outbreak of dengue fever and dengue hemorhagic fever occurred in the city of Palembang, South Sumatra, Indonesia from late 1997 through March/April 1998. All surveyed administrative areas (kelurahan) in Palembang were found to be 'permissive' for dengue virus transmission; and all areas that had Aedes (subgenus Stegomyia) larval mosquitoes in abundance experienced increased cases of DHF during the epidemic. The Aedes House Index (HI) for combined Aedes aegypti and Aedes albopictus was recorded every 3 months before, during, and after the epidemic. Ten surveyed sentinel sites (October-December 1997) immediately preceding the epidemic peak had a combined HI of 25% (range 10-50.8%). Entomological surveys during the peak epidemic period (January-April) showed a combined HI of 23.7% (range: 7.6-43.8%). Kelurahans with the highest numbers of reported dengue cases had an HI exceeding 25%; however, there was no discernable relationship between elevated HI and increased risk of DHF incidence. Despite the unusual climatic conditions during late 1997 created throughout the region by the El Niño Southern Oscillation (ENSO), the house indices during both wet and dry months remained above 23% for the 4 quarterly (3-month) periods surveyed in the second half of 1997 and first half of 1998. Rainfall returned to near normal monthly levels shortly before the reported increase in human cases. However, mean ambient air temperatures continued above normal (+0.6 to 1.2 degrees C) and were sustained over the months leading up to and during the epidemic. Evidence suggests that an ENSO-driven increase in ambient temperature had a marked influence on increased virus transmission by the vector population. We explore the apparent associations of entomological and climatic effects that precipitated the epidemic before the influx of reported human cases.

Tracking the re-emergence of epidemic chikungunya virus in Indonesia.

Twenty-four distinct outbreaks of probable chikungunya (CHIK) etiology were identified throughout Indonesia from September 2001 to March 2003, after a near 20-year hiatus of epidemic CHIK activity in the country. Thirteen outbreak reports were based on clinical observations alone, and 11 confirmed by serological/virological methods. Detailed epidemiological profiles of two investigated outbreaks in Bogor and Bekasi are presented. Human sera were screened using an ELISA for IgM and IgG anti-CHIK antibodies. Additionally, reverse transcriptase PCR and virus isolation were attempted for virus identification. The mean age of cases was 37 +/- 18 years in Bogor and 33 +/- 20 years in Bekasi. There was no outstanding case-clustering, although outbreak-affected households were observed to be geographically grouped within villages. The attack rates in Bogor and Bekasi were 2.8/1000 and 6.7/1000 inhabitants respectively. Both outbreaks started in the rainy season following increased Aedes aegypti and A. albopictus densities.

Brugia Rapid™ antibody responses in communities of Indonesia in relation to the results of 'transmission assessment surveys' (TAS) for the lymphatic filariasis elimination program.

BACKGROUND: The Global Programme to Eliminate Lymphatic Filariasis recommends the transmission assessment survey (TAS) as the preferred methodology for determining whether mass drug administration can be stopped in an endemic area. Because of the limited experience available globally with the use of Brugia Rapid™ tests in conducting TAS in Brugia spp. areas, we explored the relationship between the antibody test results and Brugia spp. infection as detected by microfilaremia in different epidemiological settings. METHODS: The study analyzes the Brugia Rapid™ antibody responses and microfilaremia in all ages at three study sites in: i) a district which was classified as non-endemic, ii) a district which passed TAS, and iii) a district which failed TAS. Convenience sampling was done in each site, in one to three purposefully selected villages with a goal of 500 samples in each district. RESULTS: A total of 1543 samples were collected from residents in all three study sites. In the site which was classified as non-endemic and where MDA had not been conducted, 5 % of study participants were antibody positive, none was positive for microfilaremia, and age-specific antibody prevalence peaked at almost 8 % in the 25-34 year-old age range, with no antibody-positive results found in children under eight years of age. In the site that had passed TAS, 1 % of participants were antibody positive and none was positive for microfilaremia. In the site which failed TAS, 15 % of participants were antibody positive, 0.2 % were microfilaremic, and age-specific antibody prevalence was highest in 6-7 year olds (30 %), but above 8 % in all age levels above 8 years old. CONCLUSIONS: These results from districts which followed the current WHO guidance for mapping, MDA, and implementing TAS, while providing antibody profiles of treated and untreated populations under programmatic settings, support the choice of antibody prevalence in the 6- and 7-year-old age group in TAS for making stopping MDA decisions. Since only one study participant was microfilaremic, no conclusions could be drawn about the relationship between antibodies and microfilaremia and further longitudinal studies are required to understand this relationship.

Transmission of epidemic dengue hemorrhagic fever in easternmost Indonesia.

In April 2001, a second suspected outbreak of dengue hemorrhagic fever in the easternmost region of Indonesia was investigated in Merauke, a town located in the southeastern corner of Papua, by the Indonesian Ministry of Health and the U.S. Naval Medical Research Unit No. 2. Principal case criteria of hemorrhagic disease provided for a study enrollment of 15 clinically acute and 37 convalescing subjects. Additionally, 32 comparable age/sex controls were selected from neighboring households. Laboratory diagnosis involved three testing methodologies: virus isolation by cell culture, a reverse transcriptase-polymerase chain reaction (RT-PCR) assay, and serologic assays. Antibody (IgM) to dengue virus was detected in 27% of the acute clinical cases, 30% of the convalescing cases, and only 3% of the matched controls. Dengue 3 was the only viral serotype detected from acute serum samples by the RT-PCR. The mean +/- SD age of the acute and convalescing cases was 7.8 +/- 5.4 years. Overall hospital records accounted for 172 suspected outbreak cases, all urban residents of Merauke with no recent travel history outside the area. The estimated outbreak-associated case fatality rate among all suspected dengue cases was 1.2%. A seven-year retrospective review of hospital records in Merauke showed negligible disease reporting involving hemorrhagic disease prior to the outbreak.

EWORS: using a syndromic-based surveillance tool for disease outbreak detection in Indonesia.

BACKGROUND: Electronic syndromic surveillance for early outbreak detection may be a simple, effective tool to rapidly bring reliable and actionable outbreak data to the attention of public health authorities in the developing world. METHODS: Twenty-nine signs and symptoms from patients with conditions compatible with infectious diseases are collected from selected Provincial hospitals and analyzed daily. Data is e-mailed on a daily basis to a central data management and analysis center. Automated data analysis may be viewed at the hospital or the Early Warning Outbreak Response System (EWORS) hub at the central level (National Institute of Health Research and Development/NIHRD). CONCLUSION: The Indonesian Ministry of Health (MoH) has adopted EWORS since 2006 and will use it as a complementary surveillance tool in wider catchment areas throughout the country. Socialization to more users is still being conducted under collaboration of three Directorate Generals (DGs) of the MoH; DG of NIHRD, DG of Medical Services and DG of Communicable Disease Control and Prevention. Currently, EWORS is being adapted to facilitate detecting a potential outbreak of pandemic influenza in the region, and automated procedures for outbreak detection have been added.

Statistical analyses in disease surveillance systems.

The performance of disease surveillance systems is evaluated and monitored using a diverse set of statistical analyses throughout each stage of surveillance implementation. An overview of their main elements is presented, with a specific emphasis on syndromic surveillance directed to outbreak detection in resource-limited settings. Statistical analyses are proposed for three implementation stages: planning, early implementation, and consolidation. Data sources and collection procedures are described for each analysis.During the planning and pilot stages, we propose to estimate the average data collection, data entry and data distribution time. This information can be collected by surveillance systems themselves or through specially designed surveys. During the initial implementation stage, epidemiologists should study the completeness and timeliness of the reporting, and describe thoroughly the population surveyed and the epidemiology of the health events recorded. Additional data collection processes or external data streams are often necessary to assess reporting completeness and other indicators. Once data collection processes are operating in a timely and stable manner, analyses of surveillance data should expand to establish baseline rates and detect aberrations. External investigations can be used to evaluate whether abnormally increased case frequency corresponds to a true outbreak, and thereby establish the sensitivity and specificity of aberration detection algorithms.Statistical methods for disease surveillance have focused mainly on the performance of outbreak detection algorithms without sufficient attention to the data quality and representativeness, two factors that are especially important in developing countries. It is important to assess data quality at each state of implementation using a diverse mix of data sources and analytical methods. Careful, close monitoring of selected indicators is needed to evaluate whether systems are reaching their proposed goals at each stage.