Application of WHO's guideline for the selection of sentinel sites for hospital-based influenza surveillance in Indonesia.

BACKGROUND: A sentinel hospital-based severe acute respiratory infection (SARI) surveillance system was established in Indonesia in 2013. Deciding on the number, geographic location and hospitals to be selected as sentinel sites was a challenge. Based on the recently published WHO guideline for influenza surveillance (2012), this study presents the process for hospital sentinel site selection. METHODS: From the 2,165 hospitals in Indonesia, the first step was to shortlist to hospitals that had previously participated in respiratory disease surveillance systems and had acceptable surveillance performance history. The second step involved categorizing the shortlist according to five regions in Indonesia to maximize geographic representativeness. A checklist was developed based on the WHO recommended attributes for sentinel site selection including stability, feasibility, representativeness and the availability of data to enable disease burden estimation. Eight hospitals, a maximum of two per geographic region, were visited for checklist administration. Checklist findings from the eight hospitals were analyzed and sentinel sites selected in the third step. RESULTS: Six hospitals could be selected based on resources available to ensure system stability over a three-year period. For feasibility, all eight hospitals visited had mechanisms for specimen shipment and the capacity to report surveillance data, but two had limited motivation for system participation. For representativeness, the eight hospitals were geographically dispersed around Indonesia, and all could capture cases in all age and socio-economic groups. All eight hospitals had prerequisite population data to enable disease burden estimation. The two hospitals with low motivation were excluded and the remaining six were selected as sentinel sites. CONCLUSIONS: The multi-step process enabled sentinel site selection based on the WHO recommended attributes that emphasize right-sizing the surveillance system to ensure its stability and maximizing its geographic representativeness. This experience may guide other countries interested in adopting WHO's influenza surveillance standards for sentinel site selection.

Managing seasonal influenza: oseltamivir treatment policy in indonesia?

To manage cases of avian influenza A/H5N1 virus infection and in anticipation of a pandemic triggered by this virus, Indonesia purchased and distributed oseltamivir to the government health facilities. Oseltamivir is an antiviral drug that was developed for the treatment of influenza infections. Disease surveillance and research suggests that seasonal influenza (A/H1N1, A/H3N2 or B) results in considerable morbidity and mortality in Indonesia, where over 15% of influenza-like illness and severe acute respiratory illness patients test positive for the influenza virus. Indonesia currently limits oseltamivir for the management of avian influenza A/H5N1cases and in anticipation of a pandemic triggered by the A/H5N1 virus. We present the evidence for the use of oseltamivir in the treatment of seasonal influenza infections so that doctors have the option to prescribe the drug. We propose that the benefits of this approach will largely outweigh the risk of antiviral resistance. We recommend that oseltamivir be available for administration to patients with seasonal influenza infections, especially for those hospitalized and for groups with high risk of complications and adverse outcomes. Overall, this will reduce morbidity and mortality of seasonal influenza.

Risk factors for cluster outbreaks of avian influenza A H5N1 infection, Indonesia.

BACKGROUND: By 30 July 2009, Indonesia had reported 139 outbreaks of avian influenza (AI) H5N1 infection in humans. Risk factors for case clustering remain largely unknown. This study assesses risk factors for cluster outbreaks and for secondary case infection. METHODS: The 113 sporadic and 26 cluster outbreaks were compared on household and individual level variables. Variables assessed include those never reported previously, including household size and genealogical relationships between cases and their contacts. RESULTS: Cluster outbreaks had larger households and more blood-related contacts, especially first-degree relatives, compared with sporadic case outbreaks. Risk factors for cluster outbreaks were the number of first-degree blood-relatives to the index case (adjusted odds ratio [aOR], 1.50; 95% confidence interval [CI]: 1.20-1.86) and index cases having direct exposure to sources of AI H5N1 virus (aOR, 3.20; 95% CI: 1.15-8.90). Risk factors for secondary case infection were being aged between 5 and 17 years (aOR, 8.32; 95% CI: 1.72-40.25), or 18 and 30 years (aOR, 6.04; 95% CI: 1.21-30.08), having direct exposure to sources of AI H5N1 virus (aOR, 3.48; 95% CI: 1.28-9.46), and being a first-degree relative to an index case (aOR, 11.0; 95% CI: 1.43-84.66). Siblings to index cases were 5 times more likely to become secondary cases (OR, 4.72; 95% CI: 1.67-13.35). CONCLUSIONS: The type of exposure and the genealogical relationship between index cases and their contacts impacts the risk of clustering. The study adds evidence that AI H5N1 infection is influenced by, and may even depend on, host genetic susceptibility.

Avian influenza H5N1 transmission in households, Indonesia.

BACKGROUND: Disease transmission patterns are needed to inform public health interventions, but remain largely unknown for avian influenza H5N1 virus infections. A recent study on the 139 outbreaks detected in Indonesia between 2005 and 2009 found that the type of exposure to sources of H5N1 virus for both the index case and their household members impacted the risk of additional cases in the household. This study describes the disease transmission patterns in those outbreak households. METHODOLOGY/PRINCIPAL FINDINGS: We compared cases (n = 177) and contacts (n = 496) in the 113 sporadic and 26 cluster outbreaks detected between July 2005 and July 2009 to estimate attack rates and disease intervals. We used final size household models to fit transmission parameters to data on household size, cases and blood-related household contacts to assess the relative contribution of zoonotic and human-to-human transmission of the virus, as well as the reproduction number for human virus transmission. The overall household attack rate was 18.3% and secondary attack rate was 5.5%. Secondary attack rate remained stable as household size increased. The mean interval between onset of subsequent cases in outbreaks was 5.6 days. The transmission model found that human transmission was very rare, with a reproduction number between 0.1 and 0.25, and the upper confidence bounds below 0.4. Transmission model fit was best when the denominator population was restricted to blood-related household contacts of index cases. CONCLUSIONS/SIGNIFICANCE: The study only found strong support for human transmission of the virus when a single large cluster was included in the transmission model. The reproduction number was well below the threshold for sustained transmission. This study provides baseline information on the transmission dynamics for the current zoonotic virus and can be used to detect and define signatures of a virus with increasing capacity for human-to-human transmission.