Gina Samaan

Factors associated with case fatality of human H5N1 virus infections in Indonesia: a case series.

BACKGROUND Indonesia has had the most human cases of highly pathogenic avian influenza A (H5N1) and one of the highest case-fatality rates worldwide. We described the factors associated with H5N1 case-fatality in Indonesia. METHODS Between June, 2005, and February, 2008, there were 127 confirmed H5N1 infections. Investigation teams were deployed to investigate and manage each confirmed case; they obtained epidemiological and clinical data from case-investigation reports when possible and through interviews with patients, family members, and key individuals. FINDINGS Of the 127 patients with confirmed H5N1 infections, 103 (81%) died. Median time to hospitalisation was 6 days (range 1-16). Of the 122 hospitalised patients for whom data were available, 121 (99%) had fever, 107 (88%) cough, and 103 (84%) dyspnoea on reaching hospital. However, for the first 2 days after onset, most had non-specific symptoms; only 31 had both fever and cough, and nine had fever and dyspnoea. Median time from onset to oseltamivir treatment was 7 days (range 0-21 days); treatment started within 2 days for one patient who survived, four (36.4%) of 11 receiving treatment within 2-4 days survived, six (37.5%) of 16 receiving treatment within 5-6 days survived, and ten (18.5%) of 44 receiving treatment at 7 days or later survived (p=0.03). Initiation of treatment within 2 days was associated with significantly lower mortality than was initiation at 5-6 days or later than 7 days (p<0.0001). Mortality was lower in clustered than unclustered cases (odds ratio 33.3, 95% CI 3.13-273). Treatment started at a median of 5 days (range 0-13 days) from onset in secondary cases in clusters compared with 8 days (range 4-16) for primary cases (p=0.04). INTERPRETATION Development of better diagnostic methods and improved case management might improve identification of patients with H5N1 influenza, which could decrease mortality by allowing for earlier treatment with oseltamivir.

Environmental sampling for avian influenza virus A (H5N1) in live-bird markets, Indonesia.

TOC Summary: This method is time and labor efficient and minimizes potential risk for virus aerosolization.

Influenza seasonality and vaccination timing in tropical and subtropical areas of southern and south-eastern Asia

OBJECTIVE To characterize influenza seasonality and identify the best time of the year for vaccination against influenza in tropical and subtropical countries of southern and south-eastern Asia that lie north of the equator. METHODS Weekly influenza surveillance data for 2006 to 2011 were obtained from Bangladesh, Cambodia, India, Indonesia, the Lao Peoples Democratic Republic, Malaysia, the Philippines, Singapore, Thailand and Viet Nam. Weekly rates of influenza activity were based on the percentage of all nasopharyngeal samples collected during the year that tested positive for influenza virus or viral nucleic acid on any given week. Monthly positivity rates were then calculated to define annual peaks of influenza activity in each country and across countries. FINDINGS Influenza activity peaked between June/July and October in seven countries, three of which showed a second peak in December to February. Countries closer to the equator had year-round circulation without discrete peaks. Viral types and subtypes varied from year to year but not across countries in a given year. The cumulative proportion of specimens that tested positive from June to November was > 60% in Bangladesh, Cambodia, India, the Lao Peoples Democratic Republic, the Philippines, Thailand and Viet Nam. Thus, these tropical and subtropical countries exhibited earlier influenza activity peaks than temperate climate countries north of the equator. CONCLUSION Most southern and south-eastern Asian countries lying north of the equator should consider vaccinating against influenza from April to June; countries near the equator without a distinct peak in influenza activity can base vaccination timing on local factors.

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.

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.

Critical control points for avian influenza A H5N1 in live bird markets in low resource settings

Live bird markets can become contaminated with and become a source of transmission for avian influenza viruses including the highly pathogenic H5N1 strain. Many countries affected by the H5N1-virus have limited resources for programs in environmental health, sanitation and disease control in live bird markets. This study proposes five critical control points (CCPs) to reduce the risk of H5N1-virus contamination in markets in low resource settings. The CCPs were developed based on three surveys conducted in Indonesia: a cross-sectional survey in 119 markets, a knowledge, attitudes and practice survey in 3 markets and a microbiological survey in 83 markets. These surveys assessed poultry workflow, market infrastructure, hygiene and regulatory practices and microbiological contamination with the H5N1-virus. The five CCPs identified were (1) reducing risk of receiving infected birds into the market, (2) reducing the risk of virus spread between different bird flocks in holding cages, (3) reducing surface contamination by isolating slaughter processes from other poultry-related processes, (4) minimizing the potential for contamination during evisceration of carcasses and (5) reducing the risk of surface contamination in the sale zone of the market. To be relevant for low resource settings, the CCPs do not necessitate large infrastructure changes. The CCPs are suited for markets that slaughter poultry and have capacity for daily disposal and removal of solid waste from the market. However, it is envisaged that the CCPs can be adapted for the development of risk-based programs in various settings.

Genetic characterization of clade 2.3.2.1 avian influenza A(H5N1) viruses, Indonesia, 2012.

After reports of unusually high mortality rates among ducks on farms in Java Island, Indonesia, in September 2012, influenza A(H5N1) viruses were detected and characterized. Sequence analyses revealed all genes clustered with contemporary clade 2.3.2.1 viruses, rather than enzootic clade 2.1.3 viruses, indicating the introduction of an exotic H5N1 clade into Indonesia.

Causes of Mortality for Indonesian Hajj Pilgrims: Comparison between Routine Death Certificate and Verbal Autopsy Findings

Background Indonesia provides the largest single source of pilgrims for the Hajj (10%). In the last two decades, mortality rates for Indonesian pilgrims ranged between 200–380 deaths per 100,000 pilgrims over the 10-week Hajj period. Reasons for high mortality are not well understood. In 2008, verbal autopsy was introduced to complement routine death certificates to explore cause of death diagnoses. This study presents the patterns and causes of death for Indonesian pilgrims, and compares routine death certificates to verbal autopsy findings. Methods Public health surveillance was conducted by Indonesian public health authorities accompanying pilgrims to Saudi Arabia, with daily reporting of hospitalizations and deaths. Surveillance data from 2008 were analyzed for timing, geographic location and site of death. Percentages for each cause of death category from death certificates were compared to that from verbal autopsy. Results In 2008, 206,831 Indonesian undertook the Hajj. There were 446 deaths, equivalent to 1,968 deaths per 100,000 pilgrim years. Most pilgrims died in Mecca (68%) and Medinah (24%). There was no statistically discernible difference in the total mortality risk for the two pilgrimage routes (Mecca or Medinah first), but the number of deaths peaked earlier for those traveling to Mecca first (p=0.002). Most deaths were due to cardiovascular (66%) and respiratory (28%) diseases. A greater proportion of deaths were attributed to cardiovascular disease by death certificate compared to the verbal autopsy method (p<0.001). Significantly more deaths had ill-defined cause based on verbal autopsy method (p<0.001). Conclusions Despite pre-departure health screening and other medical services, Indonesian pilgrim mortality rates were very high. Correct classification of cause of death is critical for the development of risk mitigation strategies. Since verbal autopsy classified causes of death differently to death certificates, further studies are needed to assess the method’s utility in this setting.

The East Jakarta Project: surveillance for highly pathogenic avian influenza A(H5N1) and seasonal influenza viruses in patients seeking care for respiratory disease, Jakarta, Indonesia, October 2011–September 2012

Indonesia has reported the most human infections with highly pathogenic avian influenza (HPAI) A(H5N1) virus worldwide. We implemented enhanced surveillance in four outpatient clinics and six hospitals for HPAI H5N1 and seasonal influenza viruses in East Jakarta district to assess the public health impact of influenza in Indonesia. Epidemiological and clinical data were collected from outpatients with influenza-like illness (ILI) and hospitalized patients with severe acute respiratory infection (SARI); respiratory specimens were obtained for influenza testing by real-time reverse transcription-polymerase chain reaction. During October 2011-September 2012, 1131/3278 specimens from ILI cases (34·5%) and 276/1787 specimens from SARI cases (15·4%) tested positive for seasonal influenza viruses. The prevalence of influenza virus infections was highest during December-May and the proportion testing positive was 76% for ILI and 36% for SARI during their respective weeks of peak activity. No HPAI H5N1 virus infections were identified, including hundreds of ILI and SARI patients with recent poultry exposures, whereas seasonal influenza was an important contributor to acute respiratory disease in East Jakarta. Overall, 668 (47%) of influenza viruses were influenza B, 384 (27%) were A(H1N1)pdm09, and 359 (25%) were H3. While additional data over multiple years are needed, our findings suggest that seasonal influenza prevention efforts, including influenza vaccination, should target the months preceding the rainy season.

Factors that influence adherence to antiretroviral treatment in an urban population, Jakarta, Indonesia

Introduction Although the number of people receiving antiretroviral therapy (ART) in Indonesia has increased in recent years, little is known about the specific characteristics affecting adherence in this population. Indonesia is different from most of its neighbors given that it is a geographically and culturally diverse country, with a large Muslim population. We aimed to identify the current rate of adherence and explore factors that influence ART adherence. Methods Data were collected from ART-prescribed outpatients on an HIV registry at a North Jakarta hospital in 2012. Socio-demographic and behavioral characteristics were explored as factors associated with adherence using logistics regression analyses. Chi squared test was used to compare the difference between proportions. Reasons for missing medication were analyzed descriptively. Results Two hundred and sixty-one patients participated, of whom 77% reported ART adherence in the last 3 months. The level of social support experienced was independently associated with adherence where some social support (p = 0.018) and good social support (p = 0.039) improved adherence compared to poor social support. Frequently cited reasons for not taking ART medication included forgetting to take medication (67%), busy with something else (63%) and asleep at medication time (60%). Discussion This study identified that an increase in the level of social support experienced by ART-prescribed patients was positively associated with adherence. Social support may minimize the impact of stigma among ART prescribed patients. Based on these findings, if social support is not available, alternative support through community-based organizations is recommended to maximize treatment success.