Hospitalized patients with pandemic (H1N1) 2009, Kenya

Characterizing the Countrywide Epidemic Spread of Influenza A(H1N1)pdm09 Virus in Kenya between 2009 and 2018

The spatiotemporal patterns of spread of influenza A(H1N1)pdm09 viruses on a countrywide scale are unclear in many tropical/subtropical regions mainly because spatiotemporally representative sequence data are lacking. We isolated, sequenced, and analyzed 383 A(H1N1)pdm09 viral genomes from hospitalized patients between 2009 and 2018 from seven locations across Kenya. Using these genomes and contemporaneously sampled global sequences, we characterized the spread of the virus in Kenya over several seasons using phylodynamic methods. The transmission dynamics of A(H1N1)pdm09 virus in Kenya were characterized by (i) multiple virus introductions into Kenya over the study period, although only a few of those introductions instigated local seasonal epidemics that then established local transmission clusters, (ii) persistence of transmission clusters over several epidemic seasons across the country, (iii) seasonal fluctuations in effective reproduction number (Re) associated with lower number of infections and seasonal fluctuations in relative genetic diversity after an initial rapid increase during the early pandemic phase, which broadly corresponded to epidemic peaks in the northern and southern hemispheres, (iv) high virus genetic diversity with greater frequency of seasonal fluctuations in 2009-2011 and 2018 and low virus genetic diversity with relatively weaker seasonal fluctuations in 2012-2017, and (v) virus spread across Kenya. Considerable influenza virus diversity circulated within Kenya, including persistent viral lineages that were unique to the country, which may have been capable of dissemination to other continents through a globally migrating virus population. Further knowledge of the viral lineages that circulate within understudied low-to-middle-income tropical and subtropical regions is required to understand the full diversity and global ecology of influenza viruses in humans and to inform vaccination strategies within these regions.

Spatial clustering of livestock Anthrax events associated with agro-ecological zones in Kenya, 1957-2017

Background

Developing disease risk maps for priority endemic and episodic diseases is becoming increasingly important for more effective disease management, particularly in resource limited countries. For endemic and easily diagnosed diseases such as anthrax, using historical data to identify hotspots and start to define ecological risk factors of its occurrence is a plausible approach. Using 666 livestock anthrax events reported in Kenya over 60 years (1957–2017), we determined the temporal and spatial patterns of the disease as a step towards identifying and characterizing anthrax hotspots in the region.

Methods

Data were initially aggregated by administrative unit and later analyzed by agro-ecological zones (AEZ) to reveal anthrax spatio-temporal trends and patterns. Variations in the occurrence of anthrax events were estimated by fitting Poisson generalized linear mixed-effects models to the data with AEZs and calendar months as fixed effects and sub-counties as random effects.

Results

The country reported approximately 10 anthrax events annually, with the number increasing to as many as 50 annually by the year 2005. Spatial classification of the events in eight counties that reported the highest numbers revealed spatial clustering in certain administrative sub-counties, with 12% of the sub-counties responsible for over 30% of anthrax events, whereas 36% did not report any anthrax disease over the 60-year period. When segregated by AEZs, there was significantly greater risk of anthrax disease occurring in agro-alpine, high, and medium potential AEZs when compared to the agriculturally low potential arid and semi-arid AEZs of the country (p < 0.05). Interestingly, cattle were > 10 times more likely to be infected by B. anthracis than sheep, goats, or camels. There was lower risk of anthrax events in August (P = 0.034) and December (P = 0.061), months that follow long and short rain periods, respectively.

Conclusion

Taken together, these findings suggest existence of certain geographic, ecological, and demographic risk factors that promote B. anthracis persistence and trasmission in the disease hotspots.

Influenza A(H1N1)pdm09 and postpandemic influenza in Lithuania

The objective of this study is to describe the clinical and epidemiological characteristics of patients hospitalized in Lithuania who are infected with influenza A(H1N1)pdm09 and to compare pandemic A(H1N1) pdm09 infection with postpandemic.

In total, 146 subjects hospitalized with influenza A(H1N1) pdm09 were identified from 2009–2011. There were 53 during the initial pandemic wave in the summer of 2009, 69 during the peak pandemic period, and 24 during the “postpandemic” period that we included in this study. There were 22 subjects who died after laboratory confirmation of influenza A(H1N1)pdm09.

No deaths were documented during the first wave. Subjects presenting during the peak of pandemic influenza had a greater incidence of fever (100% vs 77.4%; p<0.001), dry cough (95.7% vs 82.7%; p=0.01), and vomiting (26.1% vs 1.9%, p<0.001) as compared with patients infected during the first wave. The rate of bacterial pneumonia was 18.8% (13/69) during the peak pandemic period and 12.5% (3/24, p=0.754) during the postpandemic period. None of the postpandemic influenza subjects’ intensive care unit stays were due to pneumonia.

The hospitalized early 2009 H1N1 pandemic cases and postpandemic cases were milder compared with those at the peak of pandemic activity.

Global burden of influenza as a cause of cardiopulmonary morbidity and mortality

Severe acute respiratory infections, including influenza, are a leading cause of cardiopulmonary morbidity and mortality worldwide. Until recently, the epidemiology of influenza was limited to resource-rich countries. Emerging epidemiological reports characterizing the 2009 H1N1 pandemic, however, suggest that influenza exerts an even greater toll in low-income, resource-constrained environments where it is the cause of 5% to 27% of all severe acute respiratory infections. The increased burden of disease in this setting is multifactorial and likely is the result of higher rates of comorbidities such as human immunodeficiency virus, decreased access to health care, including vaccinations and antiviral medications, and limited healthcare infrastructure, including oxygen therapy or critical care support. Improved global epidemiology of influenza is desperately needed to guide allocation of life-saving resources, including vaccines, antiviral medications, and direct the improvement of basic health care to mitigate the impact of influenza infection on the most vulnerable populations.

Early introduction and delayed dissemination of pandemic influenza, Gabon

Active surveillance in health care centers in Gabon during 2009–2011 detected 72 clinical cases of pandemic (H1N1) 2009 (pH1N1). We found that pH1N1 virus was introduced in mid-2009 but spread throughout the country in 2010. Thus, Gabon was also affected by pH1N1.

Assessing parents' knowledge and attitudes towards seasonal influenza vaccination of children before and after a seasonal influenza vaccination effectiveness study in low-income urban and rural Kenya, 2010-2011

Background

Influenza vaccine is rarely used in Kenya, and little is known about attitudes towards the vaccine. From June-September 2010, free seasonal influenza vaccine was offered to children between 6 months and 10 years old in two Population-Based Infectious Disease Surveillance (PBIDS) sites. This survey assessed attitudes about influenza, uptake of the vaccine and experiences with childhood influenza vaccination.

Methods

We administered a questionnaire and held focus group discussions with parents of children of enrollment age in the two sites before and after first year of the vaccine campaign. For pre-vaccination focus group discussions, we randomly selected mothers and fathers who had an eligible child from the PBIDS database to participate. For the post-vaccination focus group discussions we stratified parents whose children were eligible for vaccination into fully vaccinated, partially vaccinated and non-vaccinated groups.

Results

Overall, 5284 and 5755 people completed pre and post-vaccination questionnaires, respectively, in Kibera and Lwak. From pre-vaccination questionnaire results, among parents who were planning on vaccinating their children, 2219 (77.6%) in Kibera and 1780 (89.6%) in Lwak said the main reason was to protect the children from seasonal influenza. In the pre-vaccination discussions, no parent had heard of the seasonal influenza vaccine. At the end of the vaccine campaign, of 18,652 eligible children, 5,817 (31.2%) were fully vaccinated, 2,073 (11.1%) were partially vaccinated and, 10,762 (57.7%) were not vaccinated. In focus group discussions, parents who declined vaccine were concerned about vaccine safety or believed seasonal influenza illness was not severe enough to warrant vaccination. Parents who declined the vaccine were mainly too busy [251(25%) in Kibera and 95 (10.5%) in Lwak], or their child was away during the vaccination period [199(19.8%) in Kibera; 94(10.4%) in Lwak].

Conclusion

If influenza vaccine were to be introduced more broadly in Kenya, effective health messaging will be needed on vaccine side effects and frequency and potential severity of influenza infection.

Overview of influenza virus infections in Kenya: past, present and future

The World Health Organization (WHO) estimates that acute lower respiratory infections account for 4 million deaths per year. The rates are even higher in developing countries. Influenza, a virus causing respiratory infections, has widely been studied in developed countries. However, there is paucity of data on its epidemiology, seasonality and burden in most developing countries. In the contrary, Kenya (a developing country) has an elaborate national epidemio-surveillance network for influenza, where a lot of data is generated on the epidemiology and seasonality of influenza in Kenya and the East African region. Several steps have been taken to control influenza in Kenya, including vaccination and surveillance programs. However, some challenges still exist. This article explores the pattern of influenza and existing interventions in Kenya, and highlights suggestions on what can be done to adequately control this virus in future.

2009 pandemic influenza A (H1N1) virus outbreak and response--Rwanda, October, 2009-May, 2010

Background

In October 2009, the first case of pandemic influenza A(H1N1)pdm09 (pH1N1) was confirmed in Kigali, Rwanda and countrywide dissemination occurred within several weeks. We describe clinical and epidemiological characteristics of this epidemic.

Methods

From October 2009 through May 2010, we undertook epidemiologic investigations and response to pH1N1. Respiratory specimens were collected from all patients meeting the WHO case definition for pH1N1, which were tested using CDC’s real time RT-PCR protocol at the Rwandan National Reference Laboratory (NRL). Following documented viral transmission in the community, testing focused on clinically severe and high-risk group suspect cases.

Results

From October 9, 2009 through May 31, 2010, NRL tested 2,045 specimens. In total, 26% (n = 532) of specimens tested influenza positive; of these 96% (n = 510) were influenza A and 4% (n = 22) were influenza B. Of cases testing influenza A positive, 96.8% (n = 494), 3% (n = 15), and 0.2% (n = 1) were A(H1N1)pdm09, Seasonal A(H3) and Seasonal A(non-subtyped), respectively. Among laboratory-confirmed cases, 263 (53.2%) were children <15 years and 275 (52%) were female. In total, 58 (12%) cases were hospitalized with mean duration of hospitalization of 5 days (Range: 2–15 days). All cases recovered and there were no deaths. Overall, 339 (68%) confirmed cases received oseltamivir in any setting. Among all positive cases, 26.9% (143/532) were among groups known to be at high risk of influenza-associated complications, including age <5 years 23% (122/532), asthma 0.8% (4/532), cardiac disease 1.5% (8/532), pregnancy 0.6% (3/532), diabetes mellitus 0.4% (2/532), and chronic malnutrition 0.8% (4/532).

Conclusions

Rwanda experienced a PH1N1 outbreak which was epidemiologically similar to PH1N1 outbreaks in the region. Unlike seasonal influenza, children <15 years were the most affected by pH1N1. Lessons learned from the outbreak response included the need to strengthen integrated disease surveillance, develop laboratory contingency plans, and evaluate the influenza sentinel surveillance system.