Eurosurvellance: Avian influenza A(H5N1) in humans: new insights from a line list of World Health Organization confirmed cases

Eurosurveillance, Volume 16, Issue 32, 11 August 2011
Research articles
Avian influenza A(H5N1) in humans: new insights from a line list of World Health Organization confirmed cases, September 2006 to August 2010
L Fiebig ()1,2, J Soyka1,2, S Buda1, U Buchholz1, M Dehnert1, W Haas1

Robert Koch Institute, Department for Infectious Disease Epidemiology, Respiratory Infections Unit, Berlin, Germany
These authors contributed equally to this article

Excerpt:

Case fatality
Fifty-six percent (132/235) of confirmed cases died. The CFR differed across countries ranging from 28% (27/98) in Egypt to 87% (71/82) in Indonesia. The cCFR and the 19-month rCFR indicated a decline in case fatality over the study period (Figure 1). Whereas the cCFR was little affected by the outcome of new cases and had only slightly decreased, the rCFR had steeply declined in the period from April 2008 to April 2009. Until mid 2008, a large proportion of cases occurred in Indonesia (country with highest CFR) and shifted thereafter to Egypt (country with lowest CFR). Accordingly, country-specific rCFRs for Indonesia and Egypt were less steep than the overall rCFR. The 19-months rCFR was privileged as it was less affected by case-free periods than rCFRs calculated over shorter periods (not shown).

In Egypt, fatal cases had a median age of 25 years, which was, at significant level, higher than the age of cases who survived (four years, p<0.001; Table 2). The CFR in Egypt was significantly higher in women than in men, (39% (22/56) vs 12% (5/42) respectively, p=0.003), which was not observed elsewhere (China: 63% (5/8) in women vs 70% (7/10) in men, p=1.0; Indonesia: 84% (43/51) vs 90% (28/31), p=0.521; Vietnam: 58% (7/12) vs 69% (9/13), p=0.688; remaining countries: 80% (4/5) vs 40% (2/5), p=0.524, respectively).

A significant difference in time from symptom onset to hospitalisation between survivors and fatal cases was only found in Egypt (one day vs four and a half days respectively, p=0.001, Table 3). All 19 cases worldwide hospitalised eight days after symptom onset or later had died.

Figure 3 shows the CFR in function of the time from symptom onset to hospitalisation, stratified by Egypt and Asian countries (grouped).

Figure 3. Time from confirmed avian influenza A(H5N1) human cases’ symptom onset to hospitalisation and case fatality rate stratified for Egypt and Asian countries, September 2006–August 2010 (n=197)



The median time from symptom onset to death was nine days (N=118), irrespective of the patients’ sex (p=0.605), and without significant difference across age groups (p=0.564, data not shown) or reporting countries (p=0.213).

The multivariable logistic regression revealed that odds of fatal outcome increased by 33% with each day that passed from symptom onset until hospitalisation (OR: 1.33, 95% CI: 1.11–1.60, p=0.002). In relation to children of 0–9 years, odds of fatal outcome were more than six times higher in 10–19 year-olds and 20–29 year-olds (OR: 6.06, 95% CI: 1.89–19.48, p=0.002 and OR: 6.16, 95% CI: 2.05–18.53, p=0.001, respectively), and nearly five times higher in patients of 30 years and older (OR: 4.71, 95% CI: 1.56–14.27, p=0.006).

Using Indonesia as a reference, odds of dying were lower elsewhere, namely by 92% in Egypt (OR: 0.08, 95% CI: 0.03–0.22, p<0.001), by 81% in China (OR: 0.19, 95% CI: 0.04–0.90, p=0.036), and by 79% in Vietnam (OR: 0.21, 95% CI: 0.06–0.75, p=0.016), but not in the grouped remaining countries (OR: 0.23, 95% CI: 0.04–1.27, p=0.091). Exposure to poultry was not significant and none of the interaction terms significantly improved the model fit. They were thus not retained in the final model.

Discussion and conclusions

With this study, we summarised the current global AI situation in humans. It is, to our knowledge, the first study that not only analysed human AI cases worldwide on the basis of a line list collected over several years but in addition made these case-based data available online. We found that a longer delay from symptom onset to hospital admission and belonging to older age groups were associated with higher mortality in AI patients, and that the situation in Egypt differed markedly from other countries with highest AI incidences in children and lowest CFR.

With our line list, cumulative case numbers published by WHO [4] could be largely reproduced: 235 of 256 WHO confirmed cases (92%) and additional 59 unconfirmed cases were captured between September 2006 and August 2010. The identified median reporting delay of 11 days after symptom onset may partly be explained by a deferred presentation to healthcare facilities as well as by the time needed for pathogen confirmation. About 52% of confirmed cases had been reported elsewhere in a median of three days prior to the WHO report. Because delays in availability of information could hamper investigations of the source of infection and of clusters of human cases [30], it could be beneficial to report and document probable cases in parallel with confirmed ones [31].

Confirmed cases had a median age of 18 years, which is consistent with earlier findings, although investigation periods and affected countries varied [2,19,21]. The identified predominance of female cases in Indonesia and Egypt and the low age median among Egyptian cases support findings from previous studies [2,23-25]. Schroedl [32] examined the mean age of cases in Egypt over four seasons between August 2006 and July 2009 and found a declining age-based pattern over time, but did not address sex-specific differences. We found, in line with other studies, a significantly older age of female cases than male cases, whose proportion had increased since 2008 in Egypt [24,25]. Chen et al., analysing AI cases worldwide before June 2006, also identified sex-specific differences in the age-groups of 4 to 6 years (95% male) and 25 to 30 years (83% female) [33]. They assumed particularly high levels of exposure in pre-school boys playing outdoors and housewives taking care of fowl and frequenting live markets. Fasina et al. suggested a similar explanation for the situation in Egypt [25].

Ninety-six percent of the cases had reportedly direct or indirect contact to potentially infected poultry, recognised as the most important risk factor for humans AI [8,34]. The WHO Clinical Case summary Form [35], where e.g. “poultry” can be checked as “most likely source of infection” has enhanced the systematic collection of information since 2007. However, currently reported information yields little insights into the actual source of infection and the intensity and quality of exposure needed to infect humans [36-38].

The median time from symptom onset to hospitalisation was four days, which is remarkably stable when compared to earlier studies [19,21]. If time to hospital admission is regarded as an indicator for monitoring case management and patients’ awareness [31], no progress would be evident from a global perspective so far.

The cases’ average CFR was 56%, which is widely consistent with findings from earlier investigation periods [2,19,23]. Using a 19-month rolling CFR, we found a clear decrease in case fatality, which persisted when stratifying for Egypt and Indonesia. It could thus not simply be explained by a predominance of Egyptian cases since 2009. Regarding the decreasing CFR in Egypt, Schroedl [32] suggested that the circulating AI virus strain may have become less virulent and more apt to spreading among children.

Analytical results revealed lowest odds of dying for Egyptian cases, even when adjusted for age, sex and time to hospitalisation. Thus, the high proportion of survivors in Egypt cannot be entirely explained – as often assumed – by sex-specific differences in CFR [21,24] and the high proportion of children among AI patients in Egypt [5], as well as short delays from symptom onset to hospitalisation [25].

It cannot be ruled out, that different virus clades circulating in Egypt (clade 2.2) and Asia (clades 2.1 and 2.3) shape the country-specific epidemiological features [2,23]. Differences in CFR across countries and changes over time might also partly be explained by differences in intensity and quality of exposure, health-seeking behaviour, reporting attitudes, overall performance of the surveillance system, and access to diagnostics and medical care [23,27,39,40], such as the time to start of oseltamivir treatment, the antiviral recommend by WHO for human infections with AI virus [2]. However, country-specific details on its administration are widely unknown and it remains controversial up to how many days after symptom onset the application of the antiviral reduces mortality [30,41]. In our study all patients hospitalised eight or more days after symptom onset died. This suggests a rather narrow time window for antiviral drug administration.

Our study was solely based on data from publicly available case reports and is subject to several limitations. Our monitoring instrument was only entirely implemented in August 2006 and thus trend analyses were not exploited to its full extent. Within the used reports, negative values, e.g. “case not hospitalised”, were not systematically mentioned, which may lead to biases. Time specifications, e.g. on dates of exposure or hospitalisation, needed for time-to-event analyses, were often incomplete. Case reports did not systematically contain details on medical care and specific antiviral treatment. Therefore, analyses were restricted to “hospitalisation” as general indicator for access to medical care. Given the sparse information on possible contact with infected individuals and clusters of human AI cases available from the serial reports within the investigated period, clusters could not be evaluated as initially planned. Other studies reporting on clustered cases had mostly accessed additional case-investigation reports and patient interviews [23,30]. We based our analyses on WHO confirmed cases, although unconfirmed cases had been recorded in our line list, due to lacking information for probable and suspected cases. Including probable cases in our analyses did, however, not change the cases’ sex ratio or CFR substantially when compared to confirmed cases only.

Our study points out that data extracted from the public domain already yields pertinent epidemiological information for assessing the current situation and developments of AI in humans. A line list format as provided would enhance the analysability of key data, their updating, and the evaluation of variables needed. Several countries monitor the global AI situation, whether they currently face human AI cases, e.g. Egypt [25], or not, e.g. France [27]. This indicates a common interest in data and if they were directly provided in such format, this would help to save time and resources for public health authorities and researchers.

A line list needs to be flexible in view of potential new information to be entered. New variables and parameter values might come up, when the minimum dataset suggested Bird and Farrar [31] on direct and indirect exposures to avian influenza A(H5N1) confirmed and non-confirmed poultry and human exposures would be implemented or when results from prospective studies involving exposed and unexposed individuals as designed by Kayali et al. [34] are available. Unconfirmed cases would ideally be recorded as systematically as confirmed cases, either in a common or separate database as suggested by Bird and Farrar [31].

Presenting cases in the format of a line list is not a goal in itself, but a prerequisite for targeting surveillance and identifying risk factors, as well as a starting point for prospective studies, e.g. investigating potential human-to-human transmission, the transmissibility of avian influenza viruses, and host-related factors including age-dependent immunity in humans [33,42].

We would like to encourage that an anonymised case-based database for AI in humans is directly placed publicly and continuously updated, e.g. by an internationally renowned organisation such as WHO. Open access to analysable data might accelerate the identification and implementation of research questions and surveillance priorities and thus enhance our understanding of – still mostly fatal – AI in humans and permit the rapid detection of epidemiological changes with implications for human health.

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