Early detection of pandemic (H1N1) 2009, Bangladesh

Lessons learned from identifying clusters of severe acute respiratory infections with influenza sentinel surveillance, Bangladesh, 2009-2020

Background

We explored whether hospital-based surveillance is useful in detecting severe acute respiratory infection (SARI) clusters and how often these events result in outbreak investigation and community mitigation.

Methods

During May 2009-December 2020, physicians at 14 sentinel hospitals prospectively identified SARI clusters (i.e., ≥2 SARI cases who developed symptoms ≤10 days of each other and lived <30 min walk or <3 km from each other). Oropharyngeal and nasopharyngeal swabs were tested for influenza and other respiratory viruses by real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). We describe the demographic of persons within clusters, laboratory results, and outbreak investigations.

Results

Field staff identified 464 clusters comprising 1427 SARI cases (range 0-13 clusters per month). Sixty percent of clusters had three, 23% had two, and 17% had ≥4 cases. Their median age was 2 years (inter-quartile range [IQR] 0.4-25) and 63% were male. Laboratory results were available for the 464 clusters with a median of 9 days (IQR = 6-13 days) after cluster identification. Less than one in five clusters had cases that tested positive for the same virus: respiratory syncytial virus (RSV) in 58 (13%), influenza viruses in 24 (5%), human metapneumovirus (HMPV) in five (1%), human parainfluenza virus (HPIV) in three (0.6%), adenovirus in two (0.4%). While 102/464 (22%) had poultry exposure, none tested positive for influenza A (H5N1) or A (H7N9). None of the 464 clusters led to field deployments for outbreak response.

Conclusions

For 11 years, none of the hundreds of identified clusters led to an emergency response. The value of this event-based surveillance might be improved by seeking larger clusters, with stronger epidemiologic ties or decedents.

Major zoonotic diseases of public health importance in Bangladesh

Zoonotic diseases cause repeated outbreaks in humans globally. The majority of emerging infections in humans are zoonotic. COVID‐19 is an ideal example of a recently identified emerging zoonotic disease, causing a global pandemic. Anthropogenic factors such as modernisation of agriculture and livestock farming, wildlife hunting, the destruction of wild animal habitats, mixing wild and domestic animals, wildlife trading, changing food habits and urbanisation could drive the emergence of zoonotic diseases in humans. Since 2001, Bangladesh has been reporting many emerging zoonotic disease outbreaks such as nipah, highly pathogenic avian influenza, pandemic H1N1, and COVID‐19. There are many other potential zoonotic pathogens such as Ebola, Middle East respiratory syndrome coronavirus, Kyasanur forest disease virus and Crimean–Congo haemorrhagic fever that may emerge in the future. However, we have a limited understanding of zoonotic diseases’ overall risk in humans and associated factors that drive the emergence of zoonotic pathogens. This narrative review summarised the major emerging, re‐emerging, neglected and other potential zoonotic diseases in Bangladesh and their associated risk factors. Nipah virus and Bacillus anthracis caused repeated outbreaks in humans. More than 300 human cases with Nipah virus infection were reported since the first outbreak in 2001. The highly pathogenic avian influenza virus (H5N1) caused more than 550 outbreaks in poultry, and eight human cases were reported so far since 2007. People of Bangladesh are frequently exposed to zoonotic pathogens due to close interaction with domestic and peri‐domestic animals. The rapidly changing intensified animal–human–ecosystem interfaces and risky practices increase the risk of zoonotic disease transmission. The narrative review's findings are useful to draw attention to the risk and emergence of zoonotic diseases to public health policymakers in Bangladesh and the application of one‐health approach to address this public health threat.

Characteristics of COVID-19 in Homeless Shelters : A Community-Based Surveillance Study

BACKGROUND

Homeless shelters are a high-risk setting for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission because of crowding and shared hygiene facilities.

OBJECTIVE

To investigate SARS-CoV-2 case counts across several adult and family homeless shelters in a major metropolitan area.

DESIGN

Cross-sectional, community-based surveillance study. (ClinicalTrials.gov: NCT04141917).

SETTING

14 homeless shelters in King County, Washington.

PARTICIPANTS

A total of 1434 study encounters were done in shelter residents and staff, regardless of symptoms.

INTERVENTION

2 strategies were used for SARS-CoV-2 testing: routine surveillance and contact tracing ("surge testing") events.

MEASUREMENTS

The primary outcome measure was test positivity rate of SARS-CoV-2 infection at shelters, determined by dividing the number of positive cases by the total number of participant encounters, regardless of symptoms. Sociodemographic, clinical, and virologic variables were assessed as correlates of viral positivity.

RESULTS

Among 1434 encounters, 29 (2% [95% CI, 1.4% to 2.9%]) cases of SARS-CoV-2 infection were detected across 5 shelters. Most (n = 21 [72.4%]) were detected during surge testing events rather than routine surveillance, and most (n = 21 [72.4% {CI, 52.8% to 87.3%}]) were asymptomatic at the time of sample collection. Persons who were positive for SARS-CoV-2 were more frequently aged 60 years or older than those without SARS-CoV-2 (44.8% vs. 15.9%). Eighty-six percent of persons with positive test results slept in a communal space rather than in a private or shared room.

LIMITATION

Selection bias due to voluntary participation and a relatively small case count.

CONCLUSION

Active surveillance and surge testing were used to detect multiple cases of asymptomatic and symptomatic SARS-CoV-2 infection in homeless shelters. The findings suggest an unmet need for routine viral testing outside of clinical settings for homeless populations.

PRIMARY FUNDING SOURCE

Gates Ventures.

Improved Global Capacity for Influenza Surveillance

CDC’s international capacity-building program shows evidence of progress.

During 2004–2009, the Centers for Disease Control and Prevention (CDC) partnered with 39 national governments to strengthen global influenza surveillance. Using World Health Organization data and program evaluation indicators collected by CDC in 2013, we retrospectively evaluated progress made 4–9 years after the start of influenza surveillance capacity strengthening in the countries. Our results showed substantial increases in laboratory and sentinel surveillance capacities, which are essential for knowing which influenza strains circulate globally, detecting emergence of novel influenza, identifying viruses for vaccine selection, and determining the epidemiology of respiratory illness. Twenty-eight of 35 countries responding to a 2013 questionnaire indicated that they have leveraged routine influenza surveillance platforms to detect other pathogens. This additional surveillance illustrates increased health-system strengthening. Furthermore, 34 countries reported an increased ability to use data in decision making; data-driven decisions are critical for improving local prevention and control of influenza around the world.

Improved Global Capacity for Influenza Surveillance

During 2004-2009, the Centers for Disease Control and Prevention (CDC) partnered with 39 national governments to strengthen global influenza surveillance. Using World Health Organization data and program evaluation indicators collected by CDC in 2013, we retrospectively evaluated progress made 4-9 years after the start of influenza surveillance capacity strengthening in the countries. Our results showed substantial increases in laboratory and sentinel surveillance capacities, which are essential for knowing which influenza strains circulate globally, detecting emergence of novel influenza, identifying viruses for vaccine selection, and determining the epidemiology of respiratory illness. Twenty-eight of 35 countries responding to a 2013 questionnaire indicated that they have leveraged routine influenza surveillance platforms to detect other pathogens. This additional surveillance illustrates increased health-system strengthening. Furthermore, 34 countries reported an increased ability to use data in decision making; data-driven decisions are critical for improving local prevention and control of influenza around the world.

Mild Respiratory Illness Among Young Children Caused by Highly Pathogenic Avian Influenza A (H5N1) Virus Infection in Dhaka, Bangladesh, 2011

Background

In March 2011, a multidisciplinary team investigated 2 human cases of highly pathogenic avian influenza A(H5N1) virus infection, detected through population-based active surveillance for influenza in Bangladesh, to assess transmission and contain further spread.

Methods

We collected clinical and exposure history of the case patients and monitored persons coming within 1 m of a case patient during their infectious period. Nasopharyngeal wash specimens from case patients and contacts were tested with real-time reverse-transcription polymerase chain reaction, and virus culture and isolates were characterized. Serum samples were tested with microneutralization and hemagglutination inhibition assays. We tested poultry, wild bird, and environmental samples from case patient households and surrounding areas for influenza viruses.

Results

Two previously healthy case patients, aged 13 and 31 months, had influenzalike illness and fully recovered. They had contact with poultry 7 and 10 days before illness onset, respectively. None of their 57 contacts were subsequently ill. Clade 2.2.2.1 highly pathogenic avian influenza H5N1 viruses were isolated from the case patients and from chicken fecal samples collected at the live bird markets near the patients' dwellings.

Conclusion

Identification of H5N1 cases through population-based surveillance suggests possible additional undetected cases throughout Bangladesh and highlights the importance of surveillance for mild respiratory illness among populations frequently exposed to infected poultry.

Infection with influenza A(H1N1)pdm09 during the first wave of the 2009 pandemic: Evidence from a longitudinal seroepidemiologic study in Dhaka, Bangladesh

Background

We determined influenza A(H1N1)pdm09 antibody levels before and after the first wave of the pandemic in an urban community in Dhaka, Bangladesh.

Methods

We identified a cohort of households by stratified random sampling. We collected baseline serum specimens during July‐August 2009, just prior to the initial wave of the 2009 pandemic in this community and a second specimen during November 2009, after the pandemic peak. Paired sera were tested for antibodies against A(H1N1)pdm09 virus using microneutralization assay and hemagglutinin inhibition (HI) assay. A fourfold increase in antibody titer by either assay with a titer of ≥40 in the convalescent sera was considered a seroconversion. At baseline, an HI titer of ≥40 was considered seropositive. We collected information on clinical illness from weekly home visits.

Results

We tested 779 paired sera from the participants. At baseline, before the pandemic wave, 1% overall and 3% of persons >60 years old were seropositive. After the first wave of the pandemic, 211 (27%) individuals seroconverted against A(H1N1)pdm09. Children aged 5‐17 years had the highest proportion (37%) of seroconversion. Among 264 (34%) persons with information on clinical illness, 191 (72%) had illness >3 weeks prior to collection of the follow‐up sera and 73 (38%) seroconverted. Sixteen (22%) of these 73 seroconverted participants reported no clinical illness.

Conclusion

After the first pandemic wave in Dhaka, one in four persons were infected by A(H1N1)pdm09 virus and the highest burden of infection was among the school‐aged children. Seroprevalence studies supplement traditional surveillance systems to estimate infection burden.

Evaluating Hospital-Based Surveillance for Outbreak Detection in Bangladesh: Analysis of Healthcare Utilization Data

BACKGROUND

The International Health Regulations outline core requirements to ensure the detection of public health threats of international concern. Assessing the capacity of surveillance systems to detect these threats is crucial for evaluating a country's ability to meet these requirements.

METHODS AND FINDINGS

We propose a framework to evaluate the sensitivity and representativeness of hospital-based surveillance and apply it to severe neurological infectious diseases and fatal respiratory infectious diseases in Bangladesh. We identified cases in selected communities within surveillance hospital catchment areas using key informant and house-to-house surveys and ascertained where cases had sought care. We estimated the probability of surveillance detecting different sized outbreaks by distance from the surveillance hospital and compared characteristics of cases identified in the community and cases attending surveillance hospitals. We estimated that surveillance detected 26% (95% CI 18%-33%) of severe neurological disease cases and 18% (95% CI 16%-21%) of fatal respiratory disease cases residing at 10 km distance from a surveillance hospital. Detection probabilities decreased markedly with distance. The probability of detecting small outbreaks (three cases) dropped below 50% at distances greater than 26 km for severe neurological disease and at distances greater than 7 km for fatal respiratory disease. Characteristics of cases attending surveillance hospitals were largely representative of all cases; however, neurological disease cases aged <5 y or from the lowest socioeconomic group and fatal respiratory disease cases aged ≥60 y were underrepresented. Our estimates of outbreak detection rely on suspected cases that attend a surveillance hospital receiving laboratory confirmation of disease and being reported to the surveillance system. The extent to which this occurs will depend on disease characteristics (e.g., severity and symptom specificity) and surveillance resources.

CONCLUSION

We present a new approach to evaluating the sensitivity and representativeness of hospital-based surveillance, making it possible to predict its ability to detect emerging threats.

Antiviral strategies for emerging influenza viruses in remote communities

BACKGROUND

Due to the lack of timely access to resources for critical care, strategic use of antiviral drugs is crucial for mitigating the impact of novel influenza viruses with pandemic potential in remote and isolated communities. We sought to evaluate the effect of antiviral treatment and prophylaxis of close contacts in a Canadian remote northern community.

METHODS

We used an agent-based, discrete-time simulation model for disease spread in a remote community, which was developed as an in-silico population using population census data. Relative and cumulative age-specific attack rates, and the total number of infections in simulated model scenarios were obtained.

RESULTS

We found that early initiation of antiviral treatment is more critical for lowering attack rates in a remote setting with a low population-average age compared to an urban population. Our results show that a significant reduction in the relative, age-specific attack rates due to increasing treatment coverage does not necessarily translate to a significant reduction in the overall arrack rate. When treatment coverage varies from low to moderate, targeted prophylaxis has a very limited impact in reducing attack rates and should be offered at a low level (below 10%) to avoid excessive waste of drugs.

CONCLUSIONS

In contrast to previous work, for conservative treatment coverages, our results do not provide any convincing evidence for the implementation of targeted prophylaxis. The findings suggest that public health strategies in remote communities should focus on the wider availability (higher coverage) and timely distribution of antiviral drugs for treatment of clinically ill individuals.