H5N1 Indonesia

What is the case fatality rate of H5N1 in Indonesia compared to other regions?

The high mortality rate appears to be specifically associated with clade 2.1 of highly pathogenic avian influenza A/H5N1 virus that circulates in Indonesia . Detailed virological analyses from 180 Indonesian patients suggest that differences in virulence and other viral characteristics may contribute to this exceptionally high mortality .

What demographic characteristics are associated with H5N1 infections in Indonesia?

Based on comprehensive analysis of 180 laboratory-confirmed H5N1 cases:

The demographic profile indicates that H5N1 infection in Indonesia affects a wide age range with a slight predominance in females .

What are the primary clinical manifestations of H5N1 infection in Indonesian patients?

In the early stages of infection, symptoms are predominantly nonspecific. Analysis of 108 clinical histories revealed that during the first two days after symptom onset:

• 30% (32/108) of patients presented with fever and cough

• 8% (9/108) presented with fever and dyspnea

• Most patients progressed to pneumonia, with 104 of 125 hospitalized patients (83.2%) being diagnosed with pneumonia either immediately or shortly after admission

The progression of disease appears to be rapid, with respiratory symptoms quickly developing into severe pneumonia in the majority of cases .

How do viral factors contribute to the high mortality associated with H5N1 in Indonesia?

Several viral factors have been identified as potentially contributing to the high mortality rate:

• Viral Load: Higher nasopharyngeal viral loads are strongly associated with poor clinical outcomes .

• Extended Viremia: Indonesian patients show higher detection rates of viral RNA in blood (85%) compared to Vietnamese H5N1-infected patients (56%) .

• Gastrointestinal Involvement: Detection rates of viral RNA in gastrointestinal specimens are higher in Indonesian patients (80%) compared to Vietnamese patients (71%) .

• Reduced Antiviral Response: Early antiviral responses to oseltamivir treatment appear less pronounced in Indonesian patients compared to Vietnamese patients infected with H5N1 clade 1 .

• Genetic Mutations: The emergence of mutations in the matrix protein conferring adamantane resistance has been associated with increasing case fatality rates over time .

What surveillance systems have been implemented for H5N1 detection in Indonesia?

Indonesia has implemented both outpatient and inpatient surveillance systems to monitor for seasonal influenza and avian influenza A(H5N1):

• Outpatient Surveillance: Screening for influenza-like illness (ILI) among outpatients.

  • A study conducted between October 2011 and September 2014 in East Jakarta found that 31% (1,875/6,008) of outpatients with influenza-like illness tested positive for influenza virus .

• Inpatient Surveillance: Monitoring of severe acute respiratory infection (SARI) among hospitalized patients.

  • In the same study period, 15% (571/3,811) of SARI case-patients tested positive for influenza virus .

• Laboratory Confirmation Protocol: The national procedure for avian influenza case investigation requires sending clinical specimens to the National Institute of Health Research and Development (NIHRD) for diagnostic and confirmatory testing using H5-specific reverse-transcriptase polymerase chain reaction (RT-PCR) .

What is the efficiency of current H5N1 surveillance strategies in Indonesia?

While comprehensive surveillance systems are in place, the efficiency of broad-based screening may be limited:

• Despite 28% (2,810/10,135) of surveilled case-patients reporting exposure to poultry, only 1 SARI case-patient with an H5N1 virus infection was detected during a three-year surveillance period in East Jakarta .

• This suggests that targeted screening among case-patients with high-risk poultry exposures (e.g., recent visits to live bird markets or close proximity to sick or dead poultry) may be a more efficient routine surveillance strategy for H5N1 virus in these settings .

What patterns of antiviral treatment and response have been observed in Indonesian H5N1 cases?

The analysis of Indonesian H5N1 cases revealed several important patterns regarding antiviral treatment:

• Treatment Coverage: Of the 127 confirmed cases in the early period (June 2005 to February 2008), 88 patients (69%) received oseltamivir treatment .

• Timing of Treatment: The median time between symptom onset and antiviral treatment was 7 days, with a range of 0 to 21 days .

• Treatment Outcomes: Delayed antiviral treatment was associated with increased mortality. Patients who began treatment within 2 days of symptom onset showed significantly better survival rates compared to those who started treatment later .

• Antiviral Response: Indonesian patients infected with clade 2.1 H5N1 viruses showed less pronounced early responses to oseltamivir treatment compared to Vietnamese patients infected with clade 1 viruses .

What antiviral resistance patterns have been documented in Indonesian H5N1 isolates?

Detailed virological analyses have documented the emergence of antiviral resistance in Indonesian H5N1 isolates:

• Adamantane Resistance: Mutations in the matrix (M2) gene conferring resistance to adamantane-class antiviral drugs have been observed, and their emergence has been associated with increasing case fatality rates over time .

• Oseltamivir Resistance: Mutations in the neuraminidase gene that confer resistance to oseltamivir have been assessed. While the specific prevalence of oseltamivir resistance mutations wasn't detailed in the search results, the methodology for their detection has been established through RT-PCR analysis of clinical specimens .

The emergence of antiviral resistance presents significant challenges for treatment and may contribute to the high mortality observed in Indonesian cases.

How has Indonesia's policy on H5N1 virus sample sharing evolved?

Indonesia's position on sharing H5N1 virus samples has undergone significant evolution:

• Initial Cooperation: Prior to 2007, Indonesia routinely shared H5N1 virus samples with the World Health Organization (WHO) .

• Sample Sharing Suspension: In early 2007, Indonesia stopped sharing H5N1 virus samples with WHO to protest what it perceived as inequitable access to costly pandemic vaccines that companies in developed countries produce from the shared samples . During this period, the government shared only a few samples with WHO labs.

• Partial Resumption: In May 2008, Health Minister Siti Fadilah Supari announced that Indonesia would begin sharing H5N1 genetic sequences with the Global Initiative on Sharing Avian Influenza Data (GISAID) .

• Motivating Factors: Indonesia's position was summarized by Minister Supari: "We have always promoted the sharing of influenza data, all we ask for is that it be done in a fair, transparent, and equitable manner" .

What are the limitations of sharing only genetic sequence data versus virus isolates?

The distinction between sharing genetic sequence data and actual virus isolates has important research implications:

• Benefits of Sequence Data:

  • Allows tracking of genetic evolution of the virus

  • Permits identification of mutations associated with virulence and antiviral resistance

  • Facilitates development of diagnostic tests

• Limitations of Sequence-Only Approach:

  • Cannot assess phenotypic characteristics of the virus

  • Unable to determine vaccine efficacy against circulating strains

  • Cannot perform neutralization tests or other assays requiring live virus

• Expert Perspective: Researchers working with H5N1 viruses have expressed that while genetic sequences are valuable, having actual virus isolates would provide more comprehensive data for pandemic preparedness .

The sharing of both sequence data and virus isolates is considered optimal for complete virological assessment and pandemic preparedness.

What methodologies have been employed to assess viral load and its relationship to clinical outcomes?

Several sophisticated methodologies have been employed to quantify H5N1 viral load and analyze its relationship to clinical outcomes:

• Specimen Collection Protocol: Comprehensive collection of multiple specimen types including:

  • Upper respiratory tract specimens (nasal, pharyngeal)

  • Lower respiratory tract specimens (pleural, tracheal, bronchial)

  • Gastrointestinal specimens (rectal, fecal)

  • Blood samples

• Quantification Method: Quantitative RT-PCR to measure H5N1 RNA levels in various clinical specimens .

• Temporal Analysis: Sequential specimen collection during hospitalization to monitor viral load dynamics over time and in response to treatment .

• Comparative Analysis: Statistical comparison of viral loads between fatal and non-fatal cases to establish correlations with clinical outcomes .

These methodologies have confirmed that higher viral loads in both throat and nasal specimens are associated with fatal outcomes in Indonesian H5N1 cases .

How can researchers effectively design studies to assess the impact of clade-specific differences in H5N1 virulence?

Based on the research findings, effective study design to assess clade-specific differences in H5N1 virulence should include:

• Multi-site Comparative Cohorts: Establish parallel cohorts in regions with different predominant H5N1 clades (e.g., clade 2.1 in Indonesia vs. clade 1 in Vietnam).

• Standardized Clinical Assessment: Implement uniform clinical assessment protocols to enable valid cross-clade comparisons.

• Comprehensive Virological Analysis:

  • Quantify viral load in multiple specimen types

  • Sequence analysis to identify clade-specific genetic markers

  • Assessment of antiviral resistance mutations

  • In vitro phenotypic characterization of isolates

• Treatment Response Monitoring: Systematically document response to antivirals with standardized protocols across cohorts.

• Host Factor Analysis: Include assessment of host genetic factors and immune responses that may interact with viral factors.

This comprehensive approach would allow researchers to disentangle the contributions of viral factors, host factors, and healthcare system variables to observed differences in mortality rates between different H5N1 clades.

How do seasonal patterns of H5N1 in Indonesia compare with patterns of seasonal influenza?

Surveillance data from East Jakarta (2011-2014) provides insights into the temporal patterns of both seasonal influenza and H5N1:

• Seasonal Influenza Pattern: Influenza A(H1N1)pdm09, influenza A(H3N2), and influenza B virus infections were detected throughout the surveillance period, with the epidemic season extending from November through May .

• Regional Variation: Among the more densely populated western and central islands of Indonesia, influenza activity typically peaks in December and January, correlating with the rainy season .

• Extended Season in Urban Areas: Data from Jakarta suggests a longer peak in influenza activity occurring from December through May .

• H5N1 Cases: Unlike seasonal influenza, H5N1 cases did not demonstrate clear seasonality in the limited data available, suggesting that risk factors for H5N1 transmission may be less dependent on seasonal variables .

Understanding these patterns is essential for designing appropriate surveillance strategies and allocating healthcare resources efficiently.

What factors distinguish sporadic cases from cluster cases of H5N1 in Indonesia?

Analysis of Indonesian H5N1 cases has revealed important distinctions between sporadic cases and cluster cases:

• Mortality Difference: Sporadic cases (those not part of a family or case cluster) demonstrated higher mortality rates than cluster cases .

• Geographic Variation: Urban cases showed higher mortality than rural cases, potentially related to differences in healthcare access, time to hospitalization, or viral factors .

• Exposure Patterns: Among Indonesian cases with documented exposure history, close proximity to sick or dead poultry was the most common risk factor, though evidence suggests that human-to-human transmission may have occurred in some cluster scenarios .

This differentiation between sporadic and cluster cases has important implications for understanding transmission dynamics and developing effective control strategies.