Analysis of latent tuberculosis and mycobacterium avium infection data using mixture models
- José I Villate^{1, 2}Email author,
- Berta Ibáñez^{3},
- Valentín Cabriada^{4},
- José I Pijoán^{5},
- Jorge Taboada^{1} and
- Arantza Urkaregi^{6}
DOI: 10.1186/1471-2458-6-240
© Villate et al; licensee BioMed Central Ltd. 2006
Received: 11 April 2006
Accepted: 28 September 2006
Published: 28 September 2006
Abstract
Background
Estimation of the frequency of latent tuberculosis infection (LTBI) is difficult in areas with low tuberculosis infection rates and high exposure to non-tuberculous mycobacteria (NTM), including BCG vaccination. The objective was to assess LTBI and M avium infection and to estimate their probability based on skin tests responses in an infant population from a region with the aforementioned characteristics.
Methods
A population-based tuberculin skin test (TST) and sensitin (M avium) survey was conducted on seven years old infants in Biscay, a province from The Basque Country (Spain). 2268 schoolchildren received sensitin and 5277 TST. Participation rate was 89%. Commonly used estimation methods were compared with a method based on the fit of mixture models using the Expectation Maximization algorithm. Functions estimating the probabilities of LTBI and M avium infection given the observed skin tests responses were developed for vaccinated and unvaccinated children.
Results
LTBI prevalences varied widely according to the estimation method. The mixture model provided prevalences higher than expected although intermediates between those obtained by currently recommended approaches. Exposure to previous BCG vaccine produces an upward shift of an average of about 3 mm on the induration size to attain the same probability of infection.
Conclusion
Our results confirm the commonplace exposure to NTM which effect should be taken into account when performing and assessing tuberculin surveys. The use of mixture analysis under the empirical Bayes framework allows to better estimate the probability of LTBI in settings with presence of other NTM and high BCG-vaccination coverage. An estimation of the average effect of BCG vaccination on TST induration is also provided. These models maximise information coming from classical tuberculin surveys and could be used together with the newly developed blood tests to improve survey's specificity and cost-effectiveness.
Background
Tuberculosis is a well-recognised worldwide health problem [1]. However, the picture differs dramatically depending on the socioeconomic development of the area in question. While in developing countries tuberculosis is still a first order challenge, it is being kept increasingly under control in developed countries. In the latter, the diagnosis and treatment of Latent Tuberculosis Infection (LTBI) is now necessary in order to eliminate tuberculosis [2]. New blood tests [3, 4] are already changing the approach to this problem, and are to be implemented in the near future in western industrialised countries. However, tuberculin skin tests (TST) remain today the most used tools for LTBI diagnostic [5] and treatment [2] decisions at a global scale.
Unfortunately, the interpretation of TST results suffers from several drawbacks when performed on populations where the BCG vaccination rate is high [6]. This situation becomes even more awkward when cross-reactivity due to infection with nontuberculous mycobacteria (NTM) is present [7, 8] (a subject which has not been addressed much so far) and the survey is carried out in a region with low tuberculosis prevalence [9].
In the region under study, a province in the Basque Country (Spain), all the aforementioned characteristics that make the interpretation of TST results difficult are present. The Basque Country is the only community that has had a programme of systematic BCG vaccination in use for more than two decades [10], in spite of being one of the communities with the highest socioeconomic level. Despite the current debate regarding whether it is appropriate to maintain this policy in our region due to a substantial decline in tuberculosis prevalence rates, methods that allow a continuous and accurate monitoring of LTBI in settings of this kind are still required.
Several statistical approaches have been attempted to cope with the confounding effect of NTM infection (BCG vaccination included) in the accurate estimation of LTBI. Traditionally, methods that propose fixed general cut-off points have been in use [11–14]. However, problems in determining the prevalence of LTBI using these methods have long been recognised, making the exploration of new statistical methods [7] necessary. Along these lines, Neuenschwander et al. [15–17] have applied the so-called mixture model analysis [18] to this problem, first focusing on unvaccinated individuals and then extending this approach to BCG-vaccinated subjects. They have shown mixture models to be a flexible tool for analysing data of this kind.
The purpose of the present study is threefold: a) to estimate the prevalence of LTBI and Mycobacterium avium (M avium) infection in the infant population of The Basque Country (Spain), b) to find a method that could help us to accurately discriminate LTBI from infection with other mycobacteria, including BCG vaccination and c) to help determine the probability of LTBI and M avium infection according to tuberculin or sensitin induration sizes respectively.
Methods
Population and sample size
The region under study is called Biscay, a province of The Basque Country with a population of 1.2 million inhabitants. In 1997, 13 smear-positive sputum cases among the total 38 incident tuberculosis cases per 100,000 population were notified in the region [19]. Our study population consisted of all children living in Biscay and turning seven years of age during 1998. We decided to study school-age children based on two grounds: to allow for an effect of the BCG vaccination to still be present and accessibility criteria. This accounts for 8430 children.
To estimate the required sample size, we used results from a previous survey in the same region [20]. A vaccination coverage rate of 89%, a participation rate of 80%, a maximum prevalence of 20% among BCG-vaccinated and 2% among unvaccinated children, respectively, with TST induration size ≥ 10 mm were anticipated. Standard errors of 0.4 and 0.35% were respectively accepted for the prevalence estimations. A required sample size of 6250 children was obtained, a quantity close to the total population size assuming a recruitment rate of 80%, so eventually we decided to recruit as many schoolchildren as possible, since the study could provide the potential benefit of detecting additional children with very large indurations, requiring further examination in order to assess the need for prophylactic treatment.
Survey design
This study focuses on some aspects of a more general research project devoted to investigate the distribution of tuberculin and sensitin induration sizes as well as the relationship among them and the so-called booster effect. In order to accomplish this endeavour, recruited children were randomly distributed in three groups. Two thirds received tuberculin as the first test and one third received the M avium sensitin, which was the most prevalent MNT in our region at the study time (data provided by the Basque Country Public Health Department). Out of the children receiving tuberculin as the first test, half were given a repeat dose of tuberculin one week later, and half were given the sensitin. Children who had received the sensitin as the first test were given a tuberculin one week later. In this paper, only results regarding administration of the first skin test are shown.
The field work was planned to be conducted in 5 months so as to prevent substantial changes in the children's characteristics and personnel involved in the process that could influence results.
Firstly, an informative campaign among parents and teachers was conducted, presenting the aim and methods of the study, and encouraging them to participate. In addition, an informative leaflet was handed out. Parents had to give written informed consent prior to inclusion of their children in the study. The vaccine information recorded in the Personal Paediatric Record was requested. The protocol was approved by the hospital's Research Ethics Committee.
There were two criteria that led to children not receiving the skin test: absence of written informed permission and the existence of documented evidence of previous positive TST.
Demographic data, past medical history and vaccination status were recorded at study entry. As the latter was deemed a key point in this research, we used three methods to improve its classification: a) certificate of BGC vaccination in the Personal Paediatric Record; b) confirmation of BGC vaccination in the province register; c) BGC scar. To classify a child in the non-BCG group, all three methods had to have negative results and to classify a child as BCG-vaccinated, at least two of three criteria had to be positive. Those who did not fulfil these requirements were considered to have an unknown vaccination status.
Skin tests
Intradermal injections with 0.1 ml of 2 TU PPD-RT23 tuberculin (bioequivalent to 5 TU PPD-S) and 0.1 ml of RS-10 (M. avium) sensitin were used. The PPD RS-10 was chosen because it is the most frequently isolated NTM among the patients in our area. All tuberculin doses belonged to the same lot (L-03) and sensitin doses came from two lots (L-66 and L-68) [21]. Both tuberculin and sensitin were prepared in and obtained from Statens Serum Institut in Copenhagen, Denmark.
The tuberculin skin test was administered intradermally [22] and the induration diameter was measured transversely to the long axis of the arm 72 hours after application.
Test application and reading were performed by a specifically trained team led by a specialist in respiratory medicine and carried out by eight highly experienced nurses. Four two-nurse groups were assembled, and each group performed double and independent readings.
Statistical analysis
Descriptive analysis
The distribution of induration diameters was described graphically using histograms and categorised using 5 mm-intervals. The prevalence of LTBI was estimated using three methods proposed in the literature [14, 23, 24]. The first criterion, probably the most widely used, considers all reactions ≥ 10 mm to be a marker of LTBI [14]. The second one multiplies the proportion of reactions of size ≥ 14 mm by 1.22 [24], and the last one, also known as the mirror method, considers all indurations of 17 mm counted once and indurations of ≥ 18 mm counted twice to obtain the estimated number of infections [23, 24].
Prevalence estimation using mixture models
This statistical approach has been extensively used in situations where a population is composed of several subgroups and the main issue is to find out which subgroup one individual belongs to [25]. Neuenschwander et al [15] applied this method to the analysis of LTBI prevalence, initially restricted to an unvaccinated population and later on was extended to BCG-vaccinated populations [16, 17].
In our setting there are four possible subgroups according to tuberculin induration response: individuals with null induration after intradermal injection, individuals with reaction due to NTM, individuals with reaction due to previous BCG vaccination and individuals with reaction due to LTBI. Therefore, for BCG-vaccinated children the mixture model will be comprised of four components in the following sequential order [16]:
y _{ j }≈ p _{0} ${I}_{\{{y}_{j}=0\}}$ + p _{1} f _{ EM }(y _{ j }, θ_{1}) + p _{2} f _{ BCG }(y _{ j }, θ_{2}) + p _{3} f _{ TB }(y _{ j }, θ_{3}) (1).
where y _{ j }represents the induration (in millimetres) for the j ^{ th }individual, ${I}_{\{{y}_{j}=0\}}$ is an indicator function (takes value 1 if y _{ j }= 0 and 0 otherwise), f _{ EM }, f _{ BCG }and f _{ TB }represent the distribution for each subgroup respectively: environmental mycobacteria reaction, BCG vaccination reaction and LTBI reaction, being, p _{1}, p _{2} and p _{3} the corresponding probabilities and p _{0} = 1-(p _{1}+ p _{2}+p _{3}) the probability associated with null induration. For unvaccinated children, the f _{ BCG }component is excluded resulting in a three-component mixture model. In this model, the order of the components reflects the assumption of increasing component influences on the induration sizes. A similar model was derived for the sensitin analysis, changing the component order (f _{ BCG }, f _{ TB }and f _{ EM }).
To analyse the tuberculin and sensitin responses, we tested different parametric distributions for each component, namely, normal, lognormal and Weibull, as suggested by Neuenschwander et al. [16]. Their Bayesian approach to parameter estimation led to convergence problems in our data, and therefore we used an alternative procedure within the empirical-Bayes framework to tackle this problem: the EM estimation algorithm [26] in the presence of incomplete data. This method need not incorporate prior information from subject-matter knowledge. Parameter confidence intervals have been derived using parametric bootstrap techniques [27], which have already been used in mixture models applications [28]. These methods were programmed in the freely available R software [29] [see Additional file 2].
Results
I-Participation and BCG coverage
Children's distribution according to BCG vaccination status. % are row percentages.
Unvaccinated | BCG-vaccinated | Total Analysed | |
---|---|---|---|
Sensitin | 207 9.1% | 2061 90.9% | 2268 100% |
Tuberculin | 560 10.6% | 4717 89.4% | 5277 100% |
TOTAL | 767 10.2% | 6778 89.8% | 7545 100% |
II-Tuberculin and sensitin induration distributions
Distribution of tuberculin and sensitin induration according to vaccination status.
Unvaccinated | BCG-vaccinated | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Induration (mm) | 0 | 1–4 | 5–9 | 10–14 | 15–19 | ≥ 20 | 0 | 1–4 | 5–9 | 10–14 | 15–19 | ≥ 20 |
Tuberculin | 527 94.1% | 10 1.8% | 15 2.7% | 4 0.7% | 3 0.5% | 1 0.2% | 3491 74% | 218 4.6% | 648 13.7% | 266 5.6% | 76 1.6% | 18 0.4% |
Sensitin | 172 83.1% | 8 3.9% | 15 7.2% | 8 3.9% | 3 1.4% | 1 0.5% | 1334 64.7% | 99 4.8% | 406 19.7% | 183 8.8% | 35 1.7% | 4 0.2% |
III-Prevalence estimation using cut-off points
Different criteria for estimating prevalence of infection.
Tuberculin (0.1 ml 2TU-RT23) | Sensitin (0.1 ml RS-10 M avium) | |||
---|---|---|---|---|
{ø ≥ 10 mm} * | {ø ≥ 14 mm} × 1.22^{†} | {ø = 17 mm}+2 × {ø ≥ 18 mm} (Mirror method)^{‡} | {ø ≥ 12 mm}^{§} | |
Unvaccinated | 1.43% | 0.87% | 0.71% | 4.35% |
BCG-vaccinated | 7.63% | 3.05% | 1.55% | 5.29% |
Overall | 6.97% | 2.82% | 1.46% | 5.20% |
IV-Mixture models analysis
Among the theoretical distributions tested, the lognormal and Weibull distributions seemed too flexible to provide an adequate fit to our data, which are characterised by showing a high proportion of zero indurations and not having clearly identifiable modes for the components. In contrast, the normal mixture models provided a proper fit as reflected in comparatively good BIC results and good adjustment to the observed distribution. In Table 4 we provide the parameters' estimates of the component of interest for each mixture model.
Estimation of the parameters of the mixture models for the tuberculin and sensitin test.
Component | Distribution | Mean | Variance | p (%) | 95% CI (p) | ||
---|---|---|---|---|---|---|---|
Tuberculin Test | Unvaccinated | LTBI | Normal | 13.38 | 5.86 | 1.51% | (0.36, 3.10) |
BCG-vaccinated | LTBI | Normal | 12.95 | 4.84 | 4.88% | (1.7, 5.8) | |
Sensitin Test | Unvaccinated | Environmental Mycobacteria | Normal | 12.53 | 4.33 | 7.01% | (1.9,10.9) |
BCG-vaccinated | Environmental Mycobacteria | Normal | 11.84 | 3.84 | 7.77% | (0.99,12.96) |
Tuberculin
The LTBI estimated prevalence in the BCG-vaccinated group, although showing wide confidence intervals, is higher than expected, especially as compared with its counterpart prevalence figure in the unvaccinated group.
Sensitin
M avium estimated prevalence (Table 4) is very similar regardless of the vaccination status, around 7.5%. Similarly to figures 1a and 1b, figures 2a and 2b show a better fit of the mixture model in the BCG-vaccinated group. The relative influence of the last component seems to be bigger for the sensitin test than for the tuberculin test.
Discussions and conclusions
We present the results of a tuberculin survey conducted in a western European population characterised by a low-intermediate TB incidence rate and high vaccination coverage. The prevalences of LTBI and M avium infection are estimated using different mixture models based on empirical-Bayes estimation methods and compared to traditional estimation approaches. Additionally, a decision-aid tool that capitalises on our own data and provides estimates of the LTBI probability as a function of induration instead of externally drawn cut-off proposals is presented. This approach and the results obtained shed some light on a complex issue with remarkable importance both from clinical and public health perspectives.
In a setting with low-intermediate tuberculosis incidence rates, high BCG coverage and a suspected presence of MNT, data of good quality is essential. For this reason, we designed this study taking special care in controlling the quality of all its procedures. Skilled personnel were enrolled, and a rigorous training process was undertaken in order to ensure homogeneous administration and reading of the derivatives [30]. Double and independent readings are not, as far as we know, a routine procedure in tuberculin and sensitin surveys.
Two features of this procedure deserve to be highlighted: the rapid execution of the fieldwork which ensures stability of conditions and the high participation rate (about 90%). Furthermore, the classification of vaccination statuses was very stringent, requiring at least two out of the three criteria to be positive: certificate of BCG vaccination, register confirmation and BCG scar [31].
Regarding the analysis of our TST results, it is remarkable how the method used influences the estimated LTBI prevalence (Table 3). Almost 7% out of the 5277 children presented an induration size greater than or equal to 10 mm., and therefore this cut-off point does not seem to be specific enough in the detection of LTBI due to the presence of NTM, BCG included [32]. When the "ø ≥ 14 mm × 1.22" method is utilised, almost 3% of the children are classified as infected by M tuberculosis. Using the mirror method [11, 14, 24], the LTBI prevalence would be 1.46%, which is more in agreement with tuberculosis rates in our region. Similar conclusions are reached by Salaniponi et al. [24] who also faced similar problems when applying these criteria in a different socio-demographic context.
Concerning environmental mycobacteria, there has not been any study so far in our country with sensitin using a sample as large as ours. Using the Palmer and Edwards criteria [13] referred to by Bosman [14], at least 5.2% out of the 2268 children who received sensitin as the first skin test would be infected with M. avium. These children were given TST one week later, and 3% of those who have had an induration size equal to or larger than 5 mm to the sensitin had null reaction to the tuberculin [33].
When comparing sensitin reactions with tuberculin ones using 5 mm-interval induration categories, the proportion of children with positive responses is clearly higher for the sensitin (Table 2). These findings provide additional support to the idea that exposure to environmental mycobacteria is common in areas with low tuberculosis infection rates and its effect should be accounted for when studying LTBI prevalence [34, 35].
Because of the difficulty of disentangling the origin of the induration, we chose mixture models since they provide an appropriate framework in situations where a measurable variable (diameter of induration) is observed but the subpopulation (representing the main cause of skin test reaction) to which each individual belongs is unknown. From this analysis, we can obtain not only prevalence estimations but also information about the characteristics of each subpopulation and the individual classification probabilities. The estimation procedure of the parameters of a mixture model in general can be difficult when the number of components is large and/or when the component distributions overlap to a great extent. For this reason, it is more difficult to analyse BCG-vaccinated children than unvaccinated children, where convergence problems may arise in the estimation procedure. To minimise this problem, we analysed the data assuming several distributions for the components. Eventually, the normal mixture model provided both a reasonably good fit to the data and clinically interpretable estimates. We fitted the models using an empirical Bayesian analysis, which is a time-saving alternative to the full Bayesian approach used in this context by other authors [15], with the additional advantage that there is no need to include prior knowledge. To test this alternative estimation method for robustness, it has also been applied to data obtained from a rather different setting (summary data from the 1975 tuberculin survey in Korea analysed in [16] using full Bayesian techniques and kindly provided by HL Rieder) with very similar results and interpretation provided by both methods.
The interpretation of the mixture model results show that the prevalence of LTBI among unvaccinated children is about 1.5%. On the other hand, the prevalence of LTBI among BCG-vaccinated children is about 4.9%. These figures, although lower than those obtained with the 10 mm cut-off approach, still remain higher than we expected. We think that this fact could be due to the low expected prevalence in this region, which makes it difficult for the mixture model to estimate the relevant last component (attributed to LTBI) with sufficient precision. In spite of the fact that our study enrolled almost 90% of the target population, it may be possible that among non-participants LTBI would be more frequent, contributing to the imprecision of the last component. However, for populations with a higher presence of Mycobacterium tuberculosis, the model should perform better, even in BCG-vaccinated populations. This is reinforced by our findings regarding M. avium infection, with a higher prevalence in our study.
This mixture model approach, hence, in our view provides important information concerning the effect of NTM exposure on skin reactions and also about LTBI and M avium infection probabilities according to induration size (figures 1 and 2).
Focusing on the probability of LTBI (figures 1c and 1d), it seems that on average, exposure to previous BCG vaccine shifts around 3 mm towards greater induration size to attain the same probability of infection in our study population. In particular, to attain a LTBI probability of around 97%, the observed induration is 14 mm. for unvaccinated and 17 mm. for BCG-vaccinated children, results that were similar regardless of the distributional assumptions adopted. This finding agrees with current guidelines for LTBI treatment [2] and can be considered as a useful, induration size calibrated aid to be used in addition to other epidemiological and clinical information to make individual decisions.
Regarding M avium infection, some 7–8% of the children could be infected according to the mixture model results, without significant differences among BCG-vaccinated and unvaccinated, with both prevalences being higher than those obtained from the TST. BCG vaccine seems to have a similar effect on sensitin and tuberculin induration responses, requiring an upward shift of an average of about 3 mm to attain the same probability of infection.
Comparing the probability of infection with M tuberculosis and M avium as a function of induration, it is clear that the diameters needed to consider infection are lower for sensitin than for tuberculin. According to the results for the sensitin test, 8 mm of induration for unvaccinated and 10 mm for BCG-vaccinated children show a probability of M avium infection of about 25%, attaining a probability of infection above 98% with indurations of 12 and 15 mm for unvaccinated and BCG-vaccinated respectively.
In summary, we can conclude that the survey carried out using both tuberculin and sensitin skin tests supports the fact that the presence of Environmental Mycobacteria in our region cannot be neglected. The approach proposed here to analyse the results of the TST provides an alternative method for LTBI prevalence estimation based on the information obtained from our own local setting data instead of using external criteria and endorses current LTBI treatment decisions in populations similar to ours [2]. Recently developed and currently available blood tests represent the biggest change since introduction of the TST test for the diagnostic and therapeutic management of LTBI in developed countries [4]. CDC guidelines recommend utilising whole blood IFN-γ test QFT-G instead of TST although a combined use of both tests may increase the diagnostic yield insomuch as specificity is improved [36]. This combined strategy may also maximise cost-effectiveness [37, 38], at least in adults as recent research in children suggests in them tests interpretation can be more complex [39]. If this is the case our method may add valuable information and we recommend its use to analyse TBC surveys, especially in areas with intermediate-high TBC prevalences in order to better deal with this still challenging problem.
Declarations
Acknowledgements
We strongly acknowledge the invaluable help provided by Dr. HL Rieder (International Union Against Tuberculosis and Lung Disease, Paris, France) by kindly providing us with requested datafiles and making useful comments and remarks to draft versions of this paper.
We also wish to express our gratitude to other people for their contribution for the development of this work and we want to emphasize this acknowledgment to J.R. Palenzuela, who was the designer of the database, to M.I. Urcelay who coordinated the skin survey and to M.J. Olazaguirre, who entered the data in the database.
This research project was supported by the Health Department of the Basque Government and Osakidetza-Servicio vasco de Salud and by a grant from the "Fondo de Investigaciones Sanitarias" from the Spanish Ministry of Health, code 96/1171.
Authors’ Affiliations
References
- Maher D, Raviglione M: Global epidemiology of tuberculosis. Clin Chest Med. 2005, 26: 167-182. 10.1016/j.ccm.2005.02.009.View ArticlePubMedGoogle Scholar
- American Thoracic Society, Centers for Disease Control and Prevention: Targeted tuberculin testing and treatment of latent tuberculosis infection. Am J Respir Crit Care Med. 2000, 161: S221-47.View ArticleGoogle Scholar
- Mori T, Sakatani M, Yamagishi F, Takashima T, Kawabe Y, Nagao K, Shigeto E, Harada N, Mitarai S, Okada M, Suzuki , et al: Specific detection of tuberculosis infection: an interferon-γ-based assay using new antigens. Am J Respir Crit Care Med. 2004, 170: 59-64. 10.1164/rccm.200402-179OC.View ArticlePubMedGoogle Scholar
- Davies PDO, Drobniewski F: The use of interferon-γ-based blood tests for the detection of latent tuberculosis iinfection. Eur Respir J. 2006, 28: 1-3. 10.1183/09031936.06.00047606.View ArticlePubMedGoogle Scholar
- Brodie D, Schluger NW: The diagnosis of tuberculosis. Clin Chest Med. 2005, 26: 247-271. 10.1016/j.ccm.2005.02.012.View ArticlePubMedGoogle Scholar
- Menzies R, Vissandjee B: Effect of Calmette Guérin vaccination on tuberculin reactivity. Am Rev Respir Dis. 1992, 145: 621-625.View ArticlePubMedGoogle Scholar
- Rieder H: Epidemiologic basis of tuberculosis control. 1999, Paris: International Union Against Tuberculosis and lung DiseaseGoogle Scholar
- Palmer CE, Long MW: Effects of infection with atypical mycobacteria on BCG vaccination and tuberculosis. Am Rev Respir Dis. 1996, 94: 553-568.Google Scholar
- Rieder HL: Methodological issues in the estimation of the tuberculosis problem from tuberculin surveys. Tubercle Lung Dis. 1995, 76: 114-121. 10.1016/0962-8479(95)90552-9.View ArticleGoogle Scholar
- Departamento de Sanidad del Gobierno Vasco. Manual de vacunaciones. (Spanish). 2001, Vitoria-Gasteiz: Servicio Central de Publicaciones del Gobierno Vasco, 1
- Bleiker MA, Sutherland I, Styblo K, ten Dam HG, Misljenovic O: Guidelines for estimating the risks of tuberculous infection from tuberculin test results in a representative sample of children. Bulletin of the International Union Against Tuberculosis and Lung Disease. 1989, 64: 7-12.PubMedGoogle Scholar
- Rust P, Thomas J: A method for estimating the prevalence of tuberculous Infection. Am J Epidemiol. 1975, 101: 311-322.PubMedGoogle Scholar
- Palmer CE, Edwards LB: Epidemiological studies of tuberculin sensitivity. Am J Hyg. 1958, 68: 213-PubMedGoogle Scholar
- Bosman MCJ, Swai OB, Kwamanga DO, Agwanda R, Idukitta G, Mislijenovic O: National tuberculin survey of Kenya. Int J Tuber Lung Dis. 1998, 2 (4): 272-280.Google Scholar
- Neuenschwander BE, Zwahlen M, Kim SJ, Engel RR, Rieder HL: Trends in the prevalence of infection with Mycobacterium tuberculosis in Korea from 1965 to 1995: an analysis of seven surveys by mixture models. Int J Tuber Lung Dis. 2000, 4: 719-729.Google Scholar
- Neuenschwander BE, Zwahlen M, Kim SJ, Lee EG, Rieder HL: Determination of the prevalence of Infection with Mycobacterium tuberculosis among persons vaccinated with Bacillus Calmette-Guérin in South Korea. Am J Epidemiol. 2002, 155: 654-663. 10.1093/aje/155.7.654.View ArticlePubMedGoogle Scholar
- Dubuis M, Fiekert K, Johnston M, Neuenschwander BE, Rieder HL: A tuberculin skin test survey among Afghan children in Kabul. Int J Tuber Lung Dis. 2004, 8 (9): 1065-1072.Google Scholar
- Titterington DM, Smith AFM, Makov UE: Statistical Analysis of Finite Mixture Distributions. 1985, New York: John Wiley & SonsGoogle Scholar
- MPTR Working Group: Tuberculosis in Spain: Results of the Multicentre Project for Tuberculosis Research. 2000, Madrid: The Carlos III Institute of Public HealthGoogle Scholar
- Sanz MA, Errézola M: La infección tuberculosa en Euskadi. Resultados de la encuesta tuberculínica, Vizcaya 1983–1985. (Spanish). 1986, Vitoria: Departamento de trabajo, Sanidad y Seguridad Social. Gobierno VascoGoogle Scholar
- Won Reyn CF, Magnusson M: Use and standardisation of the mycobacterium avium sensitin skin-test reagent. CID. 1995, 21: 1527-1528.View ArticleGoogle Scholar
- American Thoracic Society, Centers for Disease Control: The tuberculin skin test. Am Rev Respir Dis. 1981, 124: 356-363.Google Scholar
- Styblo K: The first round of the National Tuberculin Survey in Tanzania, 1983–1987. TSRU Progress Report. 1992, 1: 127-164.Google Scholar
- Salaniponi FML, Kwanjana J, Veen J, Misjelnovic O, Borgdoff MW: Risk of infection with Mycobacterium tuberculosis in Malawi: national tuberculin survey 1994. Int J Tuber Lung Dis. 2004, 8 (6): 718-723.Google Scholar
- Böhning D, Schalattman P, Lindsay B: Computer – assisted analysis of mixtures (C.A.MAN):statistical algorithms. Biometrics. 1992, 48: 283-303. 10.2307/2532756.View ArticlePubMedGoogle Scholar
- Dempster AP, Laird NM, Rubin DB: Maximum likelihood from incomplete data via the EM algorithms. J Roy Statist Soc. 1977, 39: 1-38.Google Scholar
- Davison AC, Hinkley DV: Bootstrap Methods and Their Application. 1997, London: Cambridge University PressView ArticleGoogle Scholar
- Ugarte MD, Militino AF, Ibáñez B: Confidence intervals for relative risks in disease mapping. Biom J. 2003, 45: 410-425. 10.1002/bimj.200390022.View ArticleGoogle Scholar
- R Development Core Team: The Comprehensive R Archive Network. October 2005 (cited 2 November 2005). 2005, [http://lib.stat.cmu.edu/R/CRAN]October (cited 2 November 2005)
- Arnadottir T, Rieder HL, Trébucq A, Waaler HT: Guidelines for conducting tuberculin skin test surveys in high prevalence countries. Tuber Lung Dis. 1996, 77 (Suppl): 1-20. 10.1016/S0962-8479(96)90127-6.View ArticlePubMedGoogle Scholar
- Young TK, Mirdad S: Determinants of tuberculin sensitivity in a child population covered by mass BCG vaccination. Tuber Lung Dis. 1992, 73: 94-100. 10.1016/0962-8479(92)90062-O.View ArticlePubMedGoogle Scholar
- Wang L, Turner MO, Elwood RK, Schulzer M, FitzGerald JM: A meta-analysis of the effect of Bacille Calmette Guérin vaccination on tuberculin skin test. Thorax. 2002, 57: 804-809. 10.1136/thorax.57.9.804.View ArticlePubMedPubMed CentralGoogle Scholar
- Villate JI, Cabriada V, Sanz A, Urcelay MI, Galarza A, Díez I: Infección por Mycobacterium avium en la población infantil de Bizkaia. Influencia de la BCG. (Spanish). Archc Bronconeumol. 1999, 35 (S2): 52-Google Scholar
- Larson LO, Magnusson M, Skoogh BE, Lind A: Sensitivity to sensitins and tuberculin in Swedish children. IV. The influence of BCG-vaccination. Eur Respir J. 1992, 5: 584-586.Google Scholar
- Kröger L, Katila ML, Korppi M, Pietikäinen M: Skin reactivity to atypical mycobacteria among healthy Finnish preschool children vaccinated with BCG vaccine at birth. Acta Paedriatr. 1992, 81–5: 891-895.View ArticleGoogle Scholar
- Diel R, Ernst M, Döscher G, Visuri-Karbe L, Greinert U, Niemann S, Nienhaus A, Lange C: Avoiding the effect of BCG vaccination in detecting Mycobacterium tuberculosis infection with a blood test. Eur Respir J. 2006, 28: 16-23. 10.1183/09031936.06.00107005.View ArticlePubMedGoogle Scholar
- Diel R, Nienhaus A, Lange C, Schaberg T: Cost optimization of screening for latent tuberculosis in close contacts. Eur Respir J. 2006, 28 (1): 35-44. 10.1183/09031936.06.00011806.View ArticlePubMedGoogle Scholar
- Wrighton-Smith P, Zellweger J-P: Direct cost of three models for the screenin of latent tuberculosis infection. Eur Respir J. 2006, 28: 45-50. 10.1183/09031936.06.00005906.View ArticlePubMedGoogle Scholar
- Connell TG, Curtis N, Ranganathan SC, Buttery JP: Performance of a whole blood interferon gamma assay for detecting latent infection with Mycobacterium tuberculosis in children. Thorax. 2006, 61: 616-620. 10.1136/thx.2005.048033.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/6/240/prepub
Pre-publication history
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.