Rapid identification of Burkholderia mallei and Burkholderia pseudomallei by intact cell Matrix-assisted Laser Desorption/Ionisation mass spectrometric typing

Axel Karger
Rüdiger Stock
Mario Ziller
Mandy C Elschner
Barbara Bettin
Falk Melzer
Thomas Maier
Markus Kostrzewa
Holger C Scholz
Heinrich Neubauer
Herbert Tomaso
Rapid identification of Burkholderia mallei and Burkholderia pseudomallei by intact cell Matrix-assisted Laser Desorption/Ionisation mass spectrometric typing
R E S E A R C H A R T I C L E Open Access
Background: Burkholderia (B.) pseudomallei and B. mallei are genetically closely related species. B. pseudomallei causes melioidosis in humans and animals, whereas B. mallei is the causative agent of glanders in equines and rarely also in humans. Both agents have been classified by the CDC as priority category B biological agents. Rapid identification is crucial, because both agents are intrinsically resistant to many antibiotics. Matrix-assisted laser desorption/ionisation mass spectrometry (MALDI-TOF MS) has the potential of rapid and reliable identification of pathogens, but is limited by the availability of a database containing validated reference spectra. The aim of this study was to evaluate the use of MALDI-TOF MS for the rapid and reliable identification and differentiation of B. pseudomallei and B. mallei and to build up a reliable reference database for both organisms.Results: A collection of ten B. pseudomallei and seventeen B. mallei strains was used to generate a library of reference spectra. Samples of both species could be identified by MALDI-TOF MS, if a dedicated subset of the reference spectra library was used. In comparison with samples representing B. mallei, higher genetic diversity among B. pseudomallei was reflected in the higher average Eucledian distances between the mass spectra and a broader range of identification score values obtained with commercial software for the identification of microorganisms. The type strain of B. pseudomallei (ATCC 23343) was isolated decades ago and is outstanding in the spectrum-based dendrograms probably due to massive methylations as indicated by two intensive series of mass increments of 14 Da specifically and reproducibly found in the spectra of this strain. Conclusions: Handling of pathogens under BSL 3 conditions is dangerous and cumbersome but can be minimized by inactivation of bacteria with ethanol, subsequent protein extraction under BSL 1 conditions and MALDI-TOF MS analysis being faster than nucleic amplification methods. Our spectra demonstrated a higher homogeneity in B. mallei than in B. pseudomallei isolates. As expected for closely related species, the identification process with MALDI Biotyper software (Bruker Daltonik GmbH, Bremen, Germany) requires the careful selection of spectra from reference strains. When a dedicated reference set is used and spectra of high quality are acquired, it is possible to distinguish both species unambiguously. The need for a careful curation of reference spectra databases is stressed.

Background Burkholderia (B.) pseudomallei and B. mallei are genetically closely related bacterial species that can cause fatal disease in humans and animals. B. pseudomallei is a facultative intracellular soil bacterium and the cause of melioidosis, which has the highest prevalence in the hot and humid regions of Southeast Asia, and Northern Australia. The infection can be acquired by contact with contaminated soil or water by inhalation or percutaneously. Human infections can be asymptomatic, can present with focal lesions that may affect almost any organ, or may be disseminate resulting in pneumonia and septicaemia. In certain regions of Asia melioidosis is a major cause of human morbidity and acute systemic melioidosis has a case fatality rate of up to 50% even if treated [1,2]. Melioidosis has been described in wild animals, but also in farm and pet animals and can be spread by animal trade and transport [3]. Both species are pathovars of a single genomospecies which was divided historically in two separate species due to their clinical impact and host tropism. B. thailandensis is the third closely related species of the so-called "Pseudomallei complex" which has been out-grouped from the species B. pseudomallei based on arabinose fermentation and its markedly lower pathogenicity. B. thailandensis and B. pseudomallei are soil bacteria that share the same geographical distribution.

B. mallei is a gram-negative, non-motile obligate pathogen and the causative agent of glanders and farcy in equines (horses, donkeys, mules). In horses, glanders primarily presents with purulent nasal discharge, inflammation of the mucous membranes of the upper respiratory tract, and poor general condition, whereas farcy is a chronic cutaneous disease with formation of nodules which may develop into ulcers. Equines are the only known reservoir. Contact with infected animals, ingestion of glanderous meat and exposure to aerosols can cause B. mallei infections in humans. Human glanders is highly lethal and resembles melioidosis. Chronic and latent infections can exacerbate into the acute form even after 15 years in both diseases. Both bacterial species are intrinsically resistant to many antibiotics including ampicillin and broad-and expanded-spectrum cephalosporines due to the production of a beta-lactamase [4]. B. mallei and B. pseudomallei have been classified by the CDC as priority category B biological agents.

Isolation and microbiological identification of B. pseudomallei and B. mallei from clinical samples can take up to one week. Commercial biochemical test systems for B. mallei are not available and B. pseudomallei may be misidentified as Chromobacterium violaceum or other bacteria [5][6][7]. Latex agglutination using a monoclonal antibody was shown to be a valuable technique for the rapid identification of B. pseudomallei in positive blood cultures, but no commercial tests are available [8,9]. Real-time PCR systems have been developed for diagnosing and differentiating as rapid alternatives to biochemical tests, but few have been validated on clinical samples [10][11][12][13].

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) for identification of bacteria has become a useful tool for the rapid identification of bacteria (see [14] for a recent review). In some studies intact cell mass spectrometry (ICMS) showed better correlation to genetic markers than conventional morphological classification [15]. The technique and modified procedures including a digestion step ('shotgun mass mapping') have been successfully used for subspecies-level classification in some species [16]. Characteristic features of ICMS are simple sample preparation procedures of whole cells, spectrum acquisition in the mass range between approximately 2,000 and 15,000 Da and analysis based upon comparison of sample spectra with reference spectra. By statistical approaches, similarity between mass spectra can be exploited for the identification of microorganisms. MALDI-TOF MS was also established for identification of non-fermenting gram-negative bacteria isolated from cystic fibrosis patients in Brazil [17]. Patients with cystic fibrosis suffer primarily under infections with Pseudomonads, but Burkholderiae play also an important role. In the Brazilian study a comprehensive number of Burkholderia species was included and could be identified correctly in most cases. However, neither B. pseudomallei nor B. mallei were among the clinical isolates tested. Sporadic cases of melioidosis in cystic fibrosis patients have been described in the literature and seem to be an emerging problem [18][19][20][21][22]. Due to increased travel activity, international trade, climate change, and the potential threat of bioterrorist attacks infections caused by B. pseudomallei and B. mallei can become a serious problem.

The aim of this study was to evaluate the potential benefit of MALDI-TOF MS for the rapid and reliable identification and differentiation of B. pseudomallei and B. mallei.

Results

Construction of a reference database

A custom made set of 34 reference spectra, which are called main spectra (MSP) in the MALDI Biotyper terminology (Bruker Daltonik GmbH, Bremen, Germany), was generated and used as the basis for all further calculations. This reference spectra set included all strains listed in Table 1  . This set of 34 samples will be referred to as the 'custom reference set'. The full set of MALDI Biotyper reference database entries will be referred to as 'MALDI Biotyper reference set'. In a first analysis, spectra of the custom reference set were queried against a combined database composed of the custom reference set of 34 Burkholderia samples and the MALDI Biotyper reference set. For every queried spectrum, MALDI Biotyper software generates a score-based ranked list of organisms. The organism with the highest score is ranked first ('top hit') and its species is taken as the result of the query. MSP scores are calculated by an algorithm that compares the masses of a tested sample with those present in the MSP of the spectra collection by agglomeration of scores for every mass and performing a normalisation that will result in a final value between 0 (unrelated) and 1000 (identical). For convenience, the result is given as logarithmic value smaller than or equal to 3.0. Masses of the tested spectrum will be scored in a weighted fashion depending on their location within a narrower or a wider mass tolerance window centred on the masses of the MSP. Additionally, the score for every coinciding mass of the tested spectrum will be weighted according to the frequency with which the corresponding mass of the MSP has been found in the single spectra that were used for the construction of the MSP. Thus, scores carry information on the number of coinciding masses found in the tested spectrum and the MSP, the mass aberration that is observed between the corresponding masses of the tested spectrum and the MSP and the reproducibility of the respective masses of the MSP. Cut-off values for reliable species determination cannot be theoretically calculated and have to be determined empirically. According to the manufacturer, experience has shown that scores exceeding 2.0 will allow reliable genus identification and species identification in the majority of cases. Scores calculated for all spectra of the custom reference set among them are summarized in Figure 1. In the hit lists of all tested specimen, the highest-ranking entry represented the same species as the tested specimen, indicating that, within the given database, the standard MALDI Biotyper identification procedure reliably allows the determination of Burkholderia species including the differentiation between B. mallei and B. pseudomallei. Even though species identification was correct in all cases, the distribution of scores in Figure 1 gave rise to concern about the reliability of the discrimination of the three members of the Pseudomallei group: B. thailandensis produced relatively high scores with some of the B. mallei and B. pseudomallei samples, and B. pseudomallei generally produced relatively high scores with B. mallei. Therefore, a set of B. mallei and B. pseudomallei samples was additionally cultivated and processed in two different laboratories and queried using the custom reference set as database in order to challenge the identification procedure.  Figure 2) with MALDI Biotyper software resulted in the expected clustering of the two species. Interestingly, the B. pseudomallei type strain ATCC 23343 separated notably from other B. pseudomallei representatives. This was at least in part caused by the appearance of two series of masses between 5,000 and 5,084 Da and 8,500 and 8,565 Da which were not detected in any of the other samples ( Figure 3). The observation of multiple mass differences of 14 Da in these series suggests that they were caused by multiple methylations being specific for this strain. The mass series reproducibly appeared in all single spectra used to calculate the MSP of the B. pseudomallei strain ATCC 23343 and were also observed in independent replicates of the spectra with a freshly cultivated specimen. The identity of the modified molecule is unknown. A dendrogram was constructed from the MSP of the B. mallei and B. pseudomallei strains listed in Table 1 and the Burkholderia, Chromobacterium, and Rhodococcus species from Table 2 which were added from the MALDI Biotyper database ( Figure 4). As expected, score-based distances between B. mallei and B. pseudomallei were smaller than between the other Burkholderia species and B. mallei/B. pseudomallei and B. thailandensis formed a distinct group which was separated from the other species of the Burkholderia genus.

The distance relations of B. mallei and B. pseudomallei were further analysed after transfer of the mass lists into statistical programming language R. Based on the mass alignment, a cluster analysis was performed, a distance matrix was calculated, and the distances within and between the B. mallei and B. pseudomallei strains were calculated. To test the influence of the peak intensities on the clustering behavior, cluster analysis was performed with the quantitative and qualitative data. For the latter purpose the quantitative alignment containing the intensities of every mass peak was transformed into a qualitative binary table by marking the absence or presence of a mass with 0 and 1, respectively. From both tables, distance matrices were calculated and visualized as Sammon-plots ( Figure 5). For qualitative and quantitative data the average normalized distances between B. mallei strains were smaller than between B. pseudomallei strains (0.57 vs. 0.73 for the binary data and 0.46 vs. 0.71 when peak intensities of the spectra were included) confirming the score-based clustering in Figure 2 that suggests a higher variation among B. pseudomallei than among B. mallei strains. As a measure for the separation of the two species, the weighted ratio between the distances of B. mallei and B. pseudomallei strains within the species and the distances between the species was calculated. Transition from qualitative to quantitative data showed slight improvement (0.82 vs. 0.74) in the species separation indicating that peak intensities are relevant for the discrimination of the two species and should not be neglected. Cluster analysis with the  quantitative data using the k-means algorithm indicated the presence of two clusters which were congruent with the two Burkholderia species whereas cluster analysis based on the qualitative data failed to do so. On basis of the qualitative data, which weights every mass equally for the calculation of the distance, B. pseudomallei ATCC 23343 was notably separated from all other spectra, most probably because of the multiple modifications shown in Figure 3. As some B. mallei and B. pseudomallei specimen from the reference spectrum set produced quite high scores with the respective other species, it was essential to test the practicability of the custom reference set in a routine laboratory setting with samples prepared in a different  laboratory. The panel of samples used for this test (Table 3, the 'test set') only partially overlapped with the custom reference set (Table 1) so that not only inter-laboratory variation was tested but also the ability of the custom reference set to discriminate newly appearing isolates like those from a glanders outbreak in the United Arabic Emirates in 2004.  Table 1.

Not surprisingly, these isolates located close to the centers of their respective species in the Sammon plot visualization of the distance matrix ( Figure 5). Finally, multivariate statistics on basis of the four different statistical approaches (Genetic Algorithm, Support Vector Machine, Supervised Neural Network, Quick Classifier) available in ClinProTools 3.0 showed that B. mallei and B. pseudomallei could be well separated with cross validation results ranging between 98.95% and 100.00% (data not shown). Principal Component Analysis (PCA) carried out with ClinProTools 3.0 ( Figure 6) further confirmed the separation of both species and also the broader distribution of B. pseudomallei in comparison with B. mallei.

Identification of taxon-specific biomarker ions

Mass spectra of the reference spectrum set were analysed for species-specific masses which may be used for species identification independent of the score values considered so far. For that purpose the mass lists of the MSP generated with MALDI Biotyper software were evaluated in detail. An alignment of all masses occurring in the spectra was constructed as a table in which every column represented the mass spectrum of a sample and every row the intensity of a mass occurring in a certain mass range. The alignment contained a total of 350 masses. One of them, 4,410 Da, was found in the spectra of all 34 samples and also in every one of the single spectra underlying the MSPs so that this mass can be considered a reliable indicator of the set of nine Burkholderia species that was

Discussion

In recent years MALDI-TOF MS has been introduced in microbiological laboratories as a time saving diagnostic approach supplementing morphological, biochemical, and molecular techniques for identification of microbes [23]. In several studies the comparability with conventional identification procedures was assessed with generally good correlation, but discordances were seen on the species and even on the genus level [24,25]. This proteomic profiling approach was successfully applied in routine identification of bacterial isolates from blood culture with the exception of polymicrobial samples and streptococci [26]. The identification of Burkholderia spp. and other non-fermenting bacteria using MALDI-TOF MS was investigated in cystic fibrosis (CF) patients as Burkholderia spp. (mainly of the cepacia-complex) cause a relevant number of life-threatening infections in these patients [27][28][29]. It was demonstrated that MALDI-TOF MS is a useful tool for rapid identification in the routine laboratory. B. pseudomallei can be the cause of melioidosis in CF patients and travelers to tropical regions, but this bacterium and the closely related species B. mallei  A B Figure 5 Sammon representation of the spectrum-based distance relations of B. mallei and B. pseudomallei. Diagrams A and B were calculated from qualitative or quantitative distance matrices derived from the mass alignment of the spectra, respectively. Members of the dedicated reference spectrum set for the discrimination of B. mallei and B. pseudomallei are underlined. Sammon representations allow visualising distance matrices in a two-dimensional plot with minimized distortion.

was not included in previous MALDI-TOF MS studies [18][19][20][21][22]30,31]. Natural catastrophes like the tsunami in Indonesia (2004) and occasional flooding in other tropical regions resulted in elevated incidence of melioidosis and several cases among travelers and tourists [32][33][34][35][36]. B. mallei and B. pseudomallei are biological agents which further underlines the need for rapid detection tools. Identification of Burkholderia ssp. and distinction of B. mallei and B. pseudomallei from other species was feasible. This was also true for the closely related bacterium B. thailandensis. All strains grew well within 48 hours and could then be readily prepared for MALDI-TOF MS. Due to the close relationship of B. mallei and B. pseudomallei, it was not surprising that the search for species-identifying biomarker ions discriminating these species was not successful. Obviously, more complicated mass signatures are required for this purpose and, as we could show after separate statistical evaluation of qualitative and quantitative data, peak intensities also play a crucial role for the discrimination of B. mallei and B. pseudomallei. However, group-specific masses like 9,713 Da, standing for the Pseudomallei complex (B. mallei/B. pseudomallei/B. thailandensis) or 6,551, exclusively found in B. mallei and B. pseudomallei may be of use for the discrimination of these three species.

For the identification of B. mallei and B. pseudomallei samples under routine laboratory conditions, it was necessary to reduce the reference spectrum set to avoid misclassifications. Interestingly, the reference spectrum set optimized for spectrum-based discrimination neither contained the type strain ATCC 23344 T (B. mallei) nor ATCC 23343 T (B. pseudomallei). One reason for the exclusion of ATCC 23343 could be the occurrence of two peak series with repeating mass increments of 14 Da most probably representing polymethylated proteins. This strain has been shown to have unique immunological features. In an immunization experiment with a panel of 14 B. pseudomallei strains, ATCC 23343 induced monoclonal antibodies in mice which did not cross-react with any of the other B. pseudomallei strains [37]. These peculiarities may indicate that this type strain has been genetically modified by frequent subcultivation or misuse of media. To our knowledge, similar modifications which may have an impact on List of strains used to evaluate the reliability of ICMS-based discrimination of B. mallei and B. pseudomallei using a dedicated set of reference strains. Column 'Score' designates the score value of the top-ranking hit in the dedicated database, which in all cases represented the same species as the tested sample. ( T , typestrain). The unsupervised statistical analysis separates both species based on the three major principle components. While B. mallei form a relatively uniform cluster, significant diversity can be observed for B. pseudomallei. Analysis of the spectra from the specimens in Table 1 yielded very similar results (data not shown).

B.mallei

B.pseudomallei

classification of bacteria have not been reported to-date. These series were specific for the isolate and also for two molecules within the observed mass range.

Conclusions

In this study we have demonstrated that isolates of the closely related species B. mallei and B. pseudomallei can be identified using MALDI-TOF MS. Dangerous and cumbersome handling under BSL 3 conditions can be minimized by inactivation of the isolates with ethanol and subsequent MALDI-TOF MS analysis that requires much less time than nucleic acid amplification methods [38]. The reference spectra exhibited a higher homogeneity among B. mallei than among B. pseudomallei. The type strain of B. pseudomallei ATCC 23343 was isolated decades ago and separated from the other B. pseudomallei specimens in the dendrograms which is probably due to polymethylation as indicated by two intensive series of mass increments of 14 Da. To our knowledge, this is the first report of such a modification in whole cell MALDI-TOF MS spectra of microorganisms. As expected for closely related species, especially when one of them, B. pseudomallei, displays the broad distribution of MALDI-phenotypes that was observed, the identification process requires the selection of spectra from representatives of the centers of their respective distance distributions. Uncritical inclusion of all available samples as references in a library was counterproductive for the identification process. Only by selecting an appropriate set of reference spectra (Table 3) it was possible to identify all strains. This underlines the need for careful curation of reference spectra databases used for the identification of microorganisms.

Methods

Bacterial strains

A comprehensive collection of B. mallei and B. pseudomallei strains, referred to as the 'reference set' , were tested (Table 1) and compared with spectra of closely related and other clinically relevant bacteria ( Table 2) included in the MALDI Biotyper Reference Library (version 3.0, Bruker Daltonics, Bremen, Germany). Strain identity was confirmed using Gram staining, motility testing, and real-time PCR assays targeting fliC and fliP as described previously [11,12], and a species-specific DNA-microarray [39]. Strains were obtained from the Friedrich-Loeffler-Institut, Jena, Germany and the Bundeswehr Institute of Microbiology in Munich, Germany. Strain Dubai 7 was kindly provided by the Central Veterinary Reference Laboratory, Dubai, UAE. Some strains originated from the Robert Koch Institute in Berlin, Germany, that coordinated a project of the European Union for the "Establishment of Quality Assurances for Detection of Highly Pathogenic Bacteria of Potential Bioterrorism Risk". Spectra from the set of strains enlisted in Table 3, referred to as 'test set' , were recorded with an Autoflex mass spectrometer (Bruker) in a second laboratory and queried against the reference set to test for robustness and inter-laboratory variation.

Intact cell mass spectrometry (ICMS)

Samples were prepared as described previously [16,40]. Briefly, the bacteria were cultivated under BSL 3 conditions on a nutrient blood agar containing 3% glycerol at 37°C for 48 hours. Specimens from single colonies were thoroughly suspended in 300 μl water and precipitated by addition of 900 μL ethanol (98% v/v). This treatment inactivated the bacteria as was demonstrated by growth controls and the specimens could be further tested under BSL 1 conditions. After sedimentation for five minutes at 10,000 g min -1 the supernatant was carefully removed and the sediment suspended in 50 μL of 70% (v/v) formic acid. After mixing with 50 μL acetonitrile, the suspension was centrifuged as described above and the supernatant transferred to a fresh tube. 1.5 μL of the extract was spotted onto a steel MALDI target plate and allowed to dry at ambient temperature. Finally, the dried extract was overlaid with 2 μL of a saturated solution of α-Cyano-4-hydroxycinnamic acid in 50% acetonitrile/ 2.5% trifluoroacetic acid as matrix and was again allowed to dry. A custom-made database of reference spectra was generated using the MALDI Biotyper software (version 2.0, Bruker Daltonik GmbH, Bremen, Germany) following the guidelines of the manufacturer. Each sample was spotted onto six target spots of a steel MALDI target plate. Spectra were acquired with an Ultraflex TM I instrument (Bruker Daltonik GmbH) in the linear positive mode in the range of 2,000 to 20,000 Da. Acceleration Voltage was 25 kV and the instrument was calibrated in the range between 3,637.8 and 16,952.3 Da with Bacterial Test Standard calibrant (BTS, Bruker Daltonik GmbH). Four single mass spectra with 500 shots each were acquired from each spot and a reference spectrum calculated from the 24 single spectra. Reference spectra contained the parameters peak mass and intensity and additional information on the reproducibility of the mass peaks, i.e. the frequency of occurrence of every peak in the underlying 24 single spectra. Reference spectra were generated within the mass range of 2,000 to 20,000 Da with the default parameter settings in the MALDI Biotyper software. The number of peaks was limited to 100 per reference spectrum and all peaks of a reference spectrum were normalized to the most intense peak with an intensity of 1.0. The minimum frequency of occurrence within the 24 single spectra was set to 50% for every mass. Peaklists of reference spectra were exported for further evaluation in the statistical programming language R.

To test the inter-laboratory variation and the robustness of the classification by using MALDI Biotyper software, a set of B. mallei and B. pseudomallei test samples from a second laboratory (Table 3) was queried against the reference spectra set described above. These spectra were recorded at the Bundeswehr Institute of Microbiology with an Autoflex mass spectrometer (Bruker Daltonik GmbH, Bremens). Spectra were generated for the test set in the same way as for the reference set. A query of all test samples was performed and the resulting scores were transferred into a data matrix, the 'score matrix' , in which every column represented a member of the reference set and every line a test sample. The power of certain combinations of representatives of the two classes within the reference set to discriminate samples of the test set was tested as follows: the columns representing the combination of reference spectra to be evaluated were selected from the score matrix and every member of the test set was classified by assigning it to the class of the member of the reduced reference set with the highest score. The number of correct and incorrect assignments was then calculated for the test set. This procedure simulates a MALDI Biotyper query with a reduced number of spectra in the reference database.

Transfer of data into statistical programming language R Peak lists of the reference spectra generated by the MALDI Biotyper software were converted into a format compatible with the R-package caMassClass which offers a number of functions to evaluate mass spectra [41] for further statistical analysis in R version 2.10.1. available at the R-project homepage [42]. Peak lists were aligned by the msc.peaks.align command of caMassClass and transformed into a binary mass table where rows represented all unique masses of the aligned spectra set and every column represented the spectrum of one sample. The size of the mass ranges defining a unique peak in the alignment, designated as bin size, was restricted to a maximum of 2,000 ppm. Among other features, the algorithm of the msc.peaks.align command minimizes the bin size in the given range, maximizes the space between bins and ensures that no two peaks of the same spectrum are in the same bin. For the calculation of qualitative data, the presence of the respective mass in the spectrum of a sample was marked with 1, absence with 0, i.e. all mass intensities were removed. These tables were the basis for the calculation of distances (R-routine 'dist' , parameter 'binary' for the distance measure) which were used for the construction of cladograms, Sammon plots [43], and k-means cluster analysis using the R-routines 'hclust' (parameter 'ward' for the agglomeration method) [44], 'sammon' (used with default settings) and 'kmeans' (three initial cluster centers, maximum of 100 iterations, Hartigan-Wong algorithm [45]).

Statistical analysis with ClinProTools software

Raw spectra from the specimens in Table 3 were imported into ClinProTools 3.0 software for statistical analysis. Each species was represented by 20 to 24 spectra to cover measurement variability. The multiple spectra of multiple species were imported as a "class" for the respective species. ClinProTools preformed a normalization and recalibration of mass spectra before further analysis, thereby reducing measurement variability effects significantly. Peak picking was performed based on the overall average spectrum over the whole mass range (signal to noise threshold of 5). Further spectra processing parameters were: baseline correction (convex hull), resolution (300 ppm), smoothing (Savitzky Golay, 5 cycles with 2 m/z width), Multivariate statistical analyses were performed using the four supervised algorithms and PCA which are implemented in ClinProTools. For the Genetic Algorithm, models with maximum 5 peaks and 50 generations were calculated and k-nearest neighbor (kNN) classification was performed with 5 neighbors. Also for Support Vector Machine the maximum number of peaks was set to 5 and kNN classification was performed with 5 neighbors. Supervised Neural Network was calculated with automated optimization of peak number, maximum 25. For the Quick Classifier, a maximum number of differentiating peaks of 25 was allowed; selection of peaks was based on ranking in t-test. For PCA, "level" scaling was selected.

Authors' contributions AK performed MALDI-TOF MS experiments, data analysis and participated in drafting the manuscript. RS worked in the BSL3 laboratory, performed MALDI-TOF MS experiments and data analysis. MZ developed R-scripts and participated in the mathematical analysis of mass spectra and in solving classification problems. MCE coordinated the work in the BSL3 laboratory, performed cultivation and PCR assays. BB performed MALDI-TOF MS experiments and data analysis. FM worked in the BSL3 laboratory, performed cultivation and PCR assays. TM performed MALDI-TOF MS experiments and data analysis. MK performed data analysis and statistical examination. HCS worked in the BSL3 laboratory, performed cultivation and PCR assays, and critically reviewed the manuscript. HN critically reviewed the manuscript. HT participated in the design of the study, coordinated the experiments, and participated in drafting the manuscript. MK and TM are employees of Bruker Daltonik GmbH, the manufacturer of the MALDI Biotyper system used in this study. All authors read and approved the final manuscript.

(B. mallei and B. pseudomallei) and additionally samples from B. ambifaria (DSM 16087), B. cenocepacia (ATCC BAA-245), B. dolosa (DSM 16088), B. glathei (ATCC 29195), B. multivorans (DSM 13243), B. stabilis (DSM 16586), and B. thailandensis (ATCC 700388)

Figure 1
1Summary of the MALDI Biotyper queries with the reference spectrum set. The three panels summarize the score-oriented hit lists that the thirty-four strains of the custom reference set produced when queried against the reference spectrum set plus all representatives of the Burkholderia genus present in the MALDI Biotyper reference database. The three panels represent queries of B. mallei (A), B. pseudomallei (B) and other members of the B. genus (C). Filled circles, squares and open circles indicate scores produced by database entries representing B. mallei, B. pseudomallei or any of the other species in the reference database. Note that for all samples the highest ranking hit represents a member of the respective Burkholderia species.

Figure 2
2Dendrogram obtained for Burkholderia mallei and Burkholderia pseudomallei strains. Spectrum-based distances between members of the B. mallei species are usually smaller than between representatives of B. pseudomallei.

Figure 3
3Using the complete custom reference set as database a number of misclassifications of the test set isolates occurred. Considering the distribution of scores (Figure 1) and the distance relations between B. mallei and B. pseudomallei(Figure 5), this was not unexpected and obviously a consequence of the indiscriminate inclusion of all available B. mallei and B. pseudomallei samples Unique modification patterns found for two proteins of B. pseudomallei ATCC23343 T . Two regions of representative spectra of the three strains Burkholderia (B.) mallei Bogor (panel A), B. pseudomallei NCTC 1688 (panel B) and B. pseudomallei ATCC 23343 (panel C) are shown. Two striking series of multiple peaks with m/z distances of 14 Da were observed in B. pseudomallei ATCC 23343 but in no other of the tested isolates.into the custom reference set. Classification could be substantially improved by selecting combinations of isolates of B. mallei and B. pseudomallei to form a dedicated reference set which is optimized for the discrimination of the two species. To screen the complete custom reference set of B. mallei and B. pseudomallei for appropriate combinations of isolates, the outcome of a database query was simulated with all permutations of up to four members of each species. The smallest reference group yielding error-free results was composed of two B. mallei (M1, NCTC10247) and three B. pseudomallei (EF15660, PITT 225A, NCTC01688) isolates which are highlighted by an asterisk in

Figure 4
4Spectrum-based dendrogram representing Burkholderia mallei, Burkholderia pseudomallei, and other relevant bacteria. The dendrogram was constructed based on the MALDI Biotyper scores. Note that distances between B. mallei and B. pseudomallei isolates are small compared to the distances of other B. species. B. mallei/B. pseudomallei and B. thailandensis separate as distinct group from the other species of the B. genus. analysed (B. mallei, B pseudomallei, B, thailandensis, B. ambifaria, B. cenocepacia, B. dolosa, B. glathe, B. multivorans, B. stabilis). Seven more masses (3,655 [doubly charged 7,309], 5,195, 6,551, 7,169, 7,309, 8,628 and 9,713 Da) were present in all B. mallei and B. pseudomallei samples but also in one or more of the other Burkholderia species. Considering the close relation of B. thailandensis with B. mallei and B. pseudomallei, mass 9,713 Da is of interest, which was specific for all B. mallei, B. pseudomallei, and B. thailandensis samples, i.e. the Pseudomallei group. Finally, 6,551 Da was present in all B. mallei and B. pseudomallei samples but in none of the other species, making it an effective discriminator between the B. mallei/pseudomallei group and the other representatives of the genus Burkholderia. Concerning the distinction of B. mallei and B. pseudomallei, statistical analysis with ClinProTools 3.0 software revealed a number of masses with significant class separation between the two species based on peak intensity. Most significant separation could be obtained based on the masses 7,553 and 5,794 which differ significantly in intensity between the two species.

Figure 6
6Principal component analysis of spectra derived from B. mallei and B. pseudomallei. Principle Component Analysis of ten strains of B. mallei and ten strains of B. pseudomallei, respectively.

Table 1
1Burkholderia (B.) mallei and B. pseudomallei strains Constituents of the reduced reference set dedicated for the discrimination of B. mallei and B. pseudomallei. Characteristics of Burkholderia (B.) mallei and B. pseudomallei strains used to establish the database for the identification and differentiation with MALDI-TOF mass spectrometry. Species identity was confirmed by real-time PCR assays targeting a sequence of the fliC gene that is specific for both species but does not discriminate B. mallei from B. pseudomallei. The real-time PCR assay targeting fliP is specific for B. mallei. Motility was also assessed as a phenotypic marker because B. pseudomallei is motile while B. mallei is not.Bacteria
Origin
Country
Year
fliC
fliP
Motility

B. mallei

ATCC 23344 T
Human
China
1942
+
+
-

NCTC 120
unknown
United Kingdom
1920
+
+
-

NCTC 10230
Horse
Hungary
1961
+
+
-

NCTC 10247*
Human
Turkey
1960
+
+
-

NCTC 10260
Human
Turkey
1949
+
+
-

M1*
unknown
unknown
unknown
+
+
-

M2
unknown
unknown
unknown
+
+
-

Rotz7 (SVP)
unknown
unknown
unknown
+
+
-

32
unknown
unknown
unknown
+
+
-

34
unknown
unknown
unknown
+
+
-

235
unknown
unknown
unknown
+
+
-

237
unknown
unknown
unknown
+
+
-

242
unknown
unknown
unknown
+
+
-

Bogor
Horse
Indonesia
unknown
+
+
-

Mukteswar
Horse
India
unknown
+
+
-

Zagreb
Horse
former-Yugoslavia
unknown
+
+
-

Dubai 7
Horse
United Arab Emirates
2004
+
+
-

B. pseudomallei

ATCC 23343 T
Human
unknown
<1957
+
-
+

EF 15660*
unknown
unknown
unknown
+
-
+

NCTC 1688*
Rat
Malaysia
1923
+
-
+

PITT 225A*
Human
Thailand
1986
+
-
+

PITT 521
Human
Pakistan
1988
+
-
+

PITT 5691
unknown
unknown
unknown
+
-
+

120107RR0019
Human
Italy
2007
+
-
+

H05410-0490
Human
Asia
unknown
+
-
+

03-04448
Human
unknown
unknown
+
-
+

03-04450
unknown
unknown
unknown
+
-
+

T type strain.
*

Biotyper was repeated with single spectra of the B. mallei and B. pseudomallei samples fromTable 1. The results were very similar to those obtained with MSP. For B. mallei samples, scores between 2.60 and 2.93 were observed, whereas B. pseudomallei were recognized with scores in the range from 2.57 to 2.92. The topranking hit of the hit-list correctly indicated the species of all queried samples. Scores of all top-ranking hits exceeded 2.8. Construction of a score-based dendrogram of B. mallei and B. pseudomallei samples (It is known that cultivation condi-
tions can influence the outcome of ICMS experiments.
In an interlaboratory comparison that was performed in
three laboratories with B. thailandensis we had observed
that cultivation on different growth media (Columbia 5%
Sheep Blood agar (CSB), chocolate agar, and McConkey
agar) and different cultivation periods (24, 48 and 72 h)
had a notable influence on the scores in the identifica-
tion procedure (data not shown). To avoid any variance
caused by differing growth conditions, all B. mallei and
B.pseudomallei were grown on CSB and the cultivation
period of 48 h was strictly observed.

Discrimination of B. mallei and B. pseudomallei

Scores between B. mallei samples listed in Table 1 ran-
ged between 2.56 and 2.94, whereas those between B.
pseudomallei samples ranged between 2.25 and 2.89. For
B. mallei samples, the score range over 2.72 was com-
pletely reserved for correct species assignments and the
top scores of all isolates reached this threshold. Due to
the stronger variation of B. pseudomallei, such a well-
defined threshold for correct species assignments could
not be defined for this species.
As MSP will usually not be constructed for routine
samples, the identification procedure with MALDI

B.mallei NCTC 10230 B.mallei M1 B.mallei M2 B.mallei ATCC 23344 B.mallei Bogor B.mallei Rotz 7(SVP) B.mallei Zagreb B.mallei Dubai 7 B.mallei 237 B.mallei NCTC 10247 B.mallei 242 B.mallei 34 B.mallei NCTC 10260 B.mallei NCTC 120 Lister B.mallei Mukteswar B.pseudomallei 03 04448 B.pseudomallei 120107RR0019 B.pseudomallei 03 04450 B.pseudomallei PITT 225 B.pseudomallei EF 15660 B.pseudomallei H05410-0490 B.pseudomallei PITT 521 B.pseudomallei ATCC 23343 B.pseudomallei NCTC 1688 B.pseudomallei PITT 56910
200
400
600
800
1000

B.mallei 235

B.mallei 32

Distance Level

Table 2
2Bacteria investigated for specificity testingSpecies
Strain

Burkholderia (B.) ambifaria
LMG 11351

B. ambifaria
DSM 16087 T

B. anthina
DSM 16086 T

B. anthina
LMG 16670

B. caledonica
LMG 19076 T

B. caribensis*
DSM 13236 T

B. cenocepacia
LMG 12614

B. cenocepathia*
ATCC BAA-245

B. cepacia
MB_7544_05

B. cepacia
DSM 11737

B. cepacia
18875_1 CHB

B. cepacia
DSM 9241

B. cepacia
DSM 50181

B. cepacia
LMG 2161

B. cepacia*
DSM 7288 T

B. cepacia
ATCC 25416 T

B. dolosa
DSM 16088

B. fungorum
LMG 20227 T

B. gladioli
Wv22575 CHB

B. gladioli
DSM 4285 T

B. glathei
DSM 50014 T

B. glumae
DSM 9512 T

B. multivorans
LMG 14293

B. multivorans
DSM 13243 T

B. phenazinium
DSM 10684 T

B. phymatum
LMG 21445 T

B. plantarii
DSM 9509 T

B. pyrrocinia
DSM 10685 T

B. pyrrocinia
LMG 14191 T

B. sacchari
LMG 19450 T

B. stabilis
LMG 14294 T

B. stabilis
DSM 16586 T

B. terricola
LMG 20594 T

B. thailandensis
DSM 13276 T

B. thailandensis*
ATCC 700388

B. tropica
DSM 15359 T

B. tuberum
LMG 21444 T

B. vietnamiensis
LMG 10929 T

B. xenovorans
LMG 21463 T

Chromobacterium (C.) subtsugae
DSM 17043 T

C. violaceum
C49 MVO

C. violaceum
DSM 30191 T

Rhodococcus (R.) equi
DSM 1990

R. equi
DSM 20295

R. equi
DSM 20307 T

R. equi
DSM 43950

R. equi*
DSM 44426

Table 2
2B.cepacia DSM 7288T B.cepacia DSM 9241 B.stabilis DSM 16586T B.cenocepacia LMG 12614 B.cepacia LMG 2161 B.pyrrocinia LMG 14191T B.stabilis LMG 14294T B.ambifaria LMG 11351 B.anthina DSM 16086T B.anthina LMG 16670Bacteria investigated for specificity testing
(Continued)

R. equi
DSM 46064

R. equi
559 LAL

T type strain.
List of bacteria to be differentiated from Burkholderia mallei and Burkholderia
pseudomallei using MALDI-TOF mass spectrometry. These bacteria include
closely related bacteria, possible sample contaminants, bacteria with very
similar clinical presentation and other relevant bacteria. MSP reference spectra
were constructed for the species indicated with an asterisk (*); all other
samples indicate isolates of the MALDI Biotyper database.

Table 3
3Bacteria used to test the reliability of ICMS-based discrimination of Burkholderia mallei and Burkholderia pseudomalleiSpecies
Strain designation
Score

B. mallei
32
2.470

34
2.475

237
2.189

242
2.550

ATCC 23344 T
2.382

Bogor
2.522

Mukteswar
2.554

Zagreb
2.472

NCTC 120
2.478

NCTC 10260
2.092

NCTC 10247
2.325

NCTC 10230
1.960

05-767
2.329

05-762
2.515

05-2316
2.496

Dubai3-10, 14-17*
2.437 -2.630

B. pseudomallei
EF 15660
2.692

NCTC 1688
2.489

06-2372
2.588

06-2377
2.621

06-2379
2.427

06-2388
2.603

06-2393
2.328

06-2395
2.633

06-772
2.379

*B. mallei isolates from several horses isolated during the glanders outbreak in
UAE 2004.

http://www.biomedcentral.com/1471-2180/12/229
Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Institute of Bacterial Infections and Zoonoses, Naumburger Str. 96 a, Jena
AcknowledgementsWe are grateful to Gabi Echle, Katja Fischer, Michaela Ganss, and Robert Schneider for their excellent technical assistance.
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Age-specific mortality patterns in Central Mozambique during and after the end of the Civil War

Bruce H Noden
John C Pearson
Aurelio Gomes
Age-specific mortality patterns in Central Mozambique during and after the end of the Civil War
R E S E A R C H Open Access
Background: In recent years, vigorous debate has developed concerning how conflicts contribute to the spread of infectious diseases, and in particular, the role of post-conflict situations in the epidemiology of HIV/AIDS. This study details the age-specific mortality patterns among the population in the central provincial capital of Beira, Mozambique, during and after the Mozambican civil war which ended in 1992. Methods: Data was collected from the death register at Beira's Central Hospital between 1985 and 2003 and descriptively analyzed.Results: The data show two distinct periods: before and after the peace agreements in 1992. Before 1992 (during the civil war), the main impact of mortality was on children below 5 years of age, including still births, accounting for 58% of all deaths. After the war ended in 1992, the pattern shifted dramatically and rapidly to the 15-49 year old age group which accounted for 49% of all deaths by 2003. Conclusions: As under-5 mortality rates were decreasing at the end of the conflict, rates for 24-49 year old adults began to dramatically increase due to AIDS. This study demonstrates that strategies can be implemented during conflicts to decrease mortality rates in one vulnerable population but post-conflict dynamics can bring together other factors which contribute to the rapid spread of other infectious diseases in other vulnerable populations.

Background

In recent years, vigorous debate has developed concerning how conflicts contribute to the spread of infectious diseases, and in particular, the role of post-conflict situations in the epidemiology of HIV/AIDS [1][2][3]. It has been widely assumed that the disarray accompanying conflicts contributes significantly to the spread of HIV in local population [4]. However, others have shown that the spread of HIV occurs primarily in the post-conflict period when the isolation of the population is removed and the freedom to travel improves [1,4,5]. Central Mozambique is a unique place in which to evaluate the relationship between conflict and mortality related to infectious diseases as an extended civil war ended in 1992. The aim of this retrospective study was to detail the age-specific mortality patterns among the population in the central provincial capital of Beira, Mozambique, during and after the Mozambican civil war.

Methods

Setting

Beira, Mozambique, the provincial capital of the central province of Sofala, is situated on the Indian Ocean. Since the 1950s, Beira has served as the major port city for the landlocked neighbouring countries of Zimbabwe, Zambia and Malawi. During the civil war which began in 1976, Beira was relatively isolated, except for the influx of truckers and military personnel from neighbouring countries of Zimbabwe, Zambia and Malawi assisting to keep the 'Beira Corridor' open to trade. When the peace accords were signed in 1992, Mozambique was one of the poorest countries in the world [6].

The city now has a population of 432,000 [7] and is now considered the third largest city in Mozambique.

The Central Hospital of Beira is one of only 3 central hospitals in Mozambique [8]. While serving as a primary (local) and secondary (provincial) medical centre for one of the largest urban populations in Mozambique, it also acts as a tertiary referral centre for Central Mozambique [9]. It is the main training centre [8] with an associated medical school (Catholic University of Mozambique Faculty of Health Sciences) since 2001. Therefore, it is a major catchment hospital for critical cases from which mortality studies are appropriate.

Data collection

Data for this study was collected from the death register at Beira's Central Hospital. All deaths are required by law to be registered before burial. Therefore, the data from the Central Hospital ensures that most deaths occurring between 1985 and 2003 were recorded.

The population of Beira was not precisely known until the first census in 1997. At that time, general estimates were made for both the province of Sofala (of which Beira is the capital) and the city of Beira [10]. The percentage distribution of mortality was accordingly compiled using published age distribution categories [11].

This study was part of an outreach program of the Faculty of Health Sciences of the Catholic University of Mozambique. The medical students involved in the data collection were closely supervised by experienced nurses and the complete anonymity of the names in the register was ensured at all times. The overall supervision was done by an experienced physician.

For this study, data from the death registers was transferred to worksheets providing information of register number, sex, age, place of birth, place of residence, date of death and diagnosis. All data was then coded using ICD-10, three digits, entered into Excel worksheets and converted to an Access database, closely supervised by a physician. For the current study, only age was analyzed. Where the age at death was not known, the diagnosis was checked, and if indicative of the age category (e.g. born dead or premature), the death was recorded. If not, 'unknown' age was recorded. Analysis was performed using EPI Info version 3.2 and graphs created using Excel 2007.

Results

Between 1985 and 2003, there was a dramatic shift in the age-specific mortality patterns in populations in Beira, Mozambique ( Figure 1). This shift was most obviously observed in the increase in the median age of mortality ( Figure 2). As infant mortality rates were decreasing, there was a substantial increase in mortality among 25-49 year olds ( Figure 3). In terms of actual population, total numbers of deaths in Beira increased 2. In 1985, main causes of death among 15-49 year olds were tuberculosis (15%), anaemia (8%), and gunshots wounds (7%) (Figure 4). Patterns changed slightly in 1992 with tuberculosis (14%), anaemia (8%), malaria (8%), and gastroenteritis (not cholera) (6%). Only two deaths were ascribed to HIV/AIDS. By 2003, AIDS/ Immunodeficiency accounted for 29% of deaths followed by tuberculosis (18%) and malaria (18%).

The infant mortality rate in Beira was 65.8 (per 1000) in 1997, but fell to 38.

Discussion

The data suggest two major trends occurred in the Mozambique between 1985 and 2003. These trends demonstrate the issues impacting conflict and post-conflict situations [1,4,5]. First, during the civil war (before 1992), mortality was highest among children below 5 years of age (including still births), accounting for two thirds of all deaths. This reduction is confirmed by Cutts et al. [12] in an epidemiological study done in the communities of Beira during that period. The low mortality rate among 15-49 year olds during the same period is notable, particularly during a time of conflict. Because of the poor state of the health services in Central Mozambique due to the destruction of most rural health posts, the primary focus of humanitarian organizations (i.e. International Committee of the Red Cross (ICRC)) was to reduce maternal and child-related mortality [13]. The overall effect was a dramatic decrease in mortality in the under-5 children in Central Mozambique [13] and Southern Mozambique [14] which is also observable in this Beira Central Hospital data set.

The post-conflict trend occurred 5 years after the civil war ended (post-1992) when the mortality pattern dramatically shifted to those older than 15 years old, with the greatest impact occurring among 25-49 year olds. During this period, the main cause of death was attributed to AIDS. Until the late 1996, the epidemiology of HIV infections in Central Mozambique was unknown. Routine testing for HIV among pregnant females was implemented in Maputo (the capital of Mozambique) in 1986. However, it did not begin in Beira until 1996 [15]. During these 10 years of non-monitoring, HIV expanded dramatically in Central Mozambique. By 1996, 17% of the pregnant women in Beira were already infected with HIV. This was also found in other major urban centers in Central Mozambique, Chimoio and Tete, with HIV prevalences of 19.2% and 22.5%, respectively among pregnant women. As of 2006, the HIV prevalence rate in Mozambique was 16.2% [15] with the highest HIV prevalence registered in the central province of Sofala with an official rate of 26.5%, with Beira registering 38% [15]. From this data, it is apparent that during the conflict stage, the government and humanitarian groups focused to ensure appropriate health care to the under-5 cohort in the early 1990s (a component more easily addressed during the conflict stage). During the same conflict period, unmonitored HIV spread rapidly among vulnerable populations in Central Mozambique. While truck drivers and military personnel from neighbouring countries with high HIV prevalence [16][17][18][19] may have brought the HIV to relatively isolated urban populations [1], local populations were primed for an HIV epidemic because of high prevalence of untreated STIs [20,21]. Additionally, after the peace accords in 1992, large numbers of refugees returned from neighbouring countries [16][17][18] with unknown HIV prevalence. We believe that the mixing of HIV-infected refugees into the these relatively isolated communities with high prevalence of untreated STIs [4,22] contributed to the explosion of HIV which culminated in increased AIDS mortality in Central Mozambique in mid-to-late 1990s. Finally, the influx of UN peacekeepers in 1993 with their perceived wealth and the creation of a large commercial sex industry in Beira, may have also contributed to the spread of HIV in the community [4]. The Mozambique national program for HIV prevention only began to respond in Central Mozambique in 1999 while it had been actively working in Maputo since 1986.

While possible limitations occurred, every effort was made to reduce their effects. One limitation is the possible bias introduced into the analysis by misreporting of the ages at death, particularly that of rounding of ages at death. While common for mortality-focused studies [23][24][25]   because of the large sample size used and the completeness of the data set, we believe the trends were not altered significantly by this possible limitation.

Another limitation involves the completeness of the death registry from only one institution, even one as regionally significant as Beira Central Hospital. A law requires the registration of all deaths occurring in the Beira area at the mortuary of the Central Hospital in order to receive a certificate for a burial plot assigned by an area municipal office [9]. It is not possible, however, to control for those who die at home and are buried in 'informal' cemeteries. In their 1996-1997 study in Beira, Songane & Bergstrom [9] reported a high rate of underreporting of maternal deaths in the official register. Apart from the four public cemeteries (one which is already full), there are 10 + private cemeteries in which mortality information is not documented. The fact that there were 2.3 × more recorded deaths in 2003 compared with 1985 leads one to consider that misreporting could have affected some of the patterns observed in our study, particularly early during the conflict phase.

Another factor involves the contribution of reduced under-5 mortality on the increased proportion of mortality observed among 24-49 year olds between 1992 and 2003. From the data, we do not believe that this reduction had much of an effect. Over the period of study, our data records that under-5 mortality numbers stayed relatively constant (while the proportion in the mortality population decreased) whereas the total mortality among 15-49 year olds increased 5.4×. Where the effect of a decrease under-5 mortality (observed locally [13] and nationally [26]) may be seen is in the slight increase of life expectancy rates in Mozambique from 42. 7 (1985) to 47.6 (2003) [27].

It is notable that during the war period in the Beira Central Hospital records, 58% (1985) to 50% (1992) of the mortalities registered were children under 5 years old. While gender was not analyzed for this study, maternal mortality in Beira was estimated at 10.3% by Cutts et al. [12] but the authors felt their informal methods underestimated the true number. Even with their conservative estimate, the combined child and maternal mortality in Beira during the civil war years was over 60% of the total mortality.

During the late 1980s, Central Mozambique was involved in civil war which systematically focused on rural health services [28]. Rural clinics were systematically destroyed and health workers were kidnapped or killed. Ambulances and health vehicles were routinely attacked and supply support systems to rural clinics were seriously affected. Between 1983 and 1987, the mortality rates of displaced persons were 2.7-6.3 times higher than the national average. By 1987, trauma was the leading cause of death among adults in Tete province. This is observable in our data set with the third highest cause of mortality in Beira in 1985 being gunshot wounds. During this conflict period, 1.2 million persons were displaced from their homes [28]. Because of this massive disruption, Beira filled with persons escaping from the unstable situation with many being women and children. In 1993, Cutts et al. [12] determined that two major risk factors for child mortality in Beira were recent migration (child born outside Beira) and absence of a father. This indicates that women may have been searching for sanctuary in Beira while men were either fighting or working in neighboring countries. So, agreeing with conclusions raised by O'Hare and Southall [29], this data re-emphasizes the impact of conflict on maternal and child health and leads to a special appeal to wealthy countries who sell the weapons to the warring groups in developing countries to consider afresh what it means to 'do no harm' at the expense of getting income from weapon sales. With the end of the war, there may have been a dramatic shift in population with men returning which could have helped explain some of the patterns observed in the late 1990s-early 2000s.

Even including the other causes among the 24-49 year olds in Beira, HIV/AIDS was the main cause of death in the post-conflict period. The epidemic began 'silently' in the population in the mid-1980s. By 2000, the HIV seroprevalence among pregnant women in Beira was just under 30% [30]. Until the mid-2000s, it was considered shameful to put AIDS as the cause of death on the register. It is interesting to note in our data that until the early 2000s, tuberculosis was the main cause of death among 15-49 year olds. It begs the question of how many of those deaths from tuberculosis were actually caused by undiagnosed HIV/AIDS. However, while both tuberculosis and malaria were significant among  year olds in 2003, HIV/AIDS quickly became the leading cause. These mortality patterns among 15-49 year olds were also corroborated by others inside Mozambique [26,31] and in Southern Africa [14].

Conclusions

In conclusion, while much effort went into reducing the mortality of children under 5 at the close of the Mozambique civil war, a silent HIV epidemic had already entered into relatively isolated communities. Without an HIV prevention strategy in place, the influx of refugees from neighbouring countries with high HIV rates together with local populations with high STI rates worked together to significantly impact adult mortality in the post-conflict state. This demonstrates that during conflicts, resources can be focused to reduce mortality rates in one population cohort. However, the dynamics in the immediate post-conflict period can bring together other factors which contribute to the rapid spread of different infectious diseases in vulnerable populations, thus nullifying the previous gains.

3 × between 1985 (2450) and 2003 (5605).

2 in 2001 and 38.0 in 2003. Also, the child mortality rate (1-4 year olds) fell from 13.0 (2001) to 9.4 (2003). While the mortality rates for 5-14 year olds remained stable (1.7/1000) between 2001 and 2003, there was a small rise in the mortality rates among 15-24 year olds from 3.6 to 5.0 and even larger increases for the 25-49 year old group (9.0 to 13.1), the 50-64 age group (11.2 to 23.6) and in those older than 65 (40.3 to 57.8). Interestingly, while the Beira infant mortality rate (per 1000) fell from 65.8 in 1997 to 38.2 in 2001, the overall mortality rate for Sofala province dropped from 144 (1997) to 128 (2003). When Beira was excluded, the mortality rate for the rest of province of Sofala hardly changed from 167 (1997) to 164 (2003).

Figure 1 Figure 2
12Age-specific mortality in the city of Beira, Mozambique, 1985-2003. Median age of mortality in the city of Beira, Mozambique, 1985-2003.

Figure 3
3Mortality rates (%) of prenatal (ages 0-1) and adults (ages 25-49) in Beira, Mozambique, 1985-2003. 1 -No AIDS recorded 2 -only 2 cases of AIDS recorded

Figure 4
4Top 3 causes of mortality among 15-49 year olds in Beira, Mozambique, 1985, 1992 and 2003.

, this can underestimate the mortality of some age groups and overestimate for others. However,38%

22%

18%

13%

8%

19%
22%

30%

37%
39%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

1985
1992
1997
2000
2003

0 1 yr olds

25 49yr olds

Noden et al. Conflict and Health 2011, 5:8 http://www.conflictandhealth.com/content/5/1/8 Page 5 of 6
Acknowledgements and FundingOur grateful thanks to Rev. Dr. Fransisco Ponsi and Rev. Dr. Elias Arroyo for their encouragement and assistance. Special thanks for the assistance to Mr. Joaquim Chitejo Gotine, Conservador, 1 st Conservatoria do Registo Civil in the city of Beira as well as the first graduating class of medical students of the Catholic University of Mozambique. BHN would like to specifically thank his colleagues in both AIM International (Mozambique) as well as UCM for the many helpful discussions in putting these ideas together. Salary support for AG during part of this study was funded by CIPRA (National Institutes of Health, USA #1 R03 AI056325-01).Authors' contributionsThe three authors participated in designing the study. AG organized the data collection. RJCP carried out the data analysis. BHN drafted the first version of the paper. All authors extensively reviewed all drafts, made comprehensive changes, and approved the final draft.Competing interestsThe authors declare that they have no competing interests.
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