Assessing trends and predictors of tuberculosis in Taiwan
© Liao et al; licensee BioMed Central Ltd. 2012
Received: 20 September 2011
Accepted: 12 January 2012
Published: 12 January 2012
Variety of environmental and individual factors can cause tuberculosis (TB) incidence change. The purpose of this study was to assess the characteristics of TB trends in the period 2004 - 2008 in Taiwan by month, year, gender, age, temperature, seasonality, and aborigines.
The generalized regression models were used to examine the potential predictors for the monthly TB incidence in regional and national scales.
We found that (i) in Taiwan the average TB incidence was 68 per 100,000 population with mortality rate of 0.036 person-1 yr-1, (ii) the highest TB incidence rate was found in eastern Taiwan (116 per 100,000 population) with the largest proportion of TB relapse cases (8.17%), (iii) seasonality, aborigines, gender, and age had a consistent and dominant role in constructing TB incidence patterns in Taiwan, and (iv) gender, time trend, and 2-month lag maximum temperature showed strong association with TB trends in aboriginal subpopulations.
The proposed Poisson regression model is capable of forecasting patterns of TB incidence at regional and national scales. This study suggested that assessment of TB trends in eastern Taiwan presents an important opportunity for understanding the time-series dynamics and control of TB infections, given that this is the typical host demography in regions where these infections remain major public health problems.
KeywordsTuberculosis Seasonality Weather Aboriginal health Poisson regression model Taiwan
Tuberculosis (TB) is caused by infection with the pathogen Mycobacterium tuberculosis . A recent World Health Organization (WHO) report documented the diagnosis of nearly 10 million new cases of TB in 2007 with an estimated 1.3 million deaths from TB in the same year . Therefore, TB remains a leading cause of death resulting in high morbidity and mortality worldwide, with an estimate of one-third of world's population is infected with TB bacilli . On the basis of these statistics, TB is among the top ten causes of death worldwide. Despite predictions of a decline in global incidence, the number of new cases is continuous to grow.
Over 50% of global TB cases are found in Southeastern Asia and the Western Pacific . The TB incidence and mortality rates in Taiwan varied geographically, with higher rates in southern and eastern regions than in the northern region . The epidemiological study in Taiwan found that incidence and mortality rate of TB infection were 62.0-74.6 (per 100,000 population) and 3.3-5.7 (per 100,000 per population) in the period 2002-2008, respectively . The notification rate and mortality rate were higher in eastern Taiwan than in the national scale . Hsueh et al.  found that aborigines and people living in mountainous regions in eastern Taiwan had higher incidence rates.
Hsueh et al.  and Lee et al.  implicated that high disease burden of TB and inadequate current control infrastructure and training for TB implementation, e.g., directly observed treatment short-course (DOTS) strategy, are posing a great impact on public health in Taiwan, leading to current challenges to TB control such as the increasing burden of patients with multidrug-resistant TB infection, the persistent high rate of mortality, and unsatisfactory compliance of treatment.
Previous studies have reported a strong association between seasonality and TB in temperate, tropical, and subtropical regions [6–13]. They found that seasonal peaks of TB cases generally occurred at the end of winter [9, 11] and at the beginning of the summer season [7, 8, 10]. They implicated that increased indoor exposure in winter, diagnostic delays, and an association with vitamin D levels explained this phenomenon [6, 14]. There are strong interactions between the effects of seasonality and the effects of weather because seasonality might influence the season of emergence of active TB, making both sensitive to additional stresses such as climate change. Little is known about the time-series dynamics of TB trends in Taiwan taking into account seasonal patterns and weather effects.
Dye and Williams  have characterized the impact of key demographic and epidemiological factors on TB trends including (i) epidemiological differences among populations because some infected people are at high risk of developing active TB and some kinds of people and patients transmit more infections than others, (ii) demographic transition because populations are aging and a relatively high risk of TB among the elderly is a real possibility, and (iii) rejuvenation of the TB epidemic among young adults.
It is believed that by understanding the factors that affect TB trends will help to make control programs more successful. Despite this far-reaching importance, empirical estimates of long-term trends in TB epidemics in Taiwan remain limited. On the other hand, despite differences in scale and approach, it is clear that long-term estimates of TB trends are a necessary. The purpose of this study was to assess the characteristics of TB trends from 2004-2008 in Taiwan by month, year, gender, age, temperature, seasonality, aborigines/nonaborigines and to provide pronounced generalized regression models to examine the potential predictors for the monthly TB incidence in regional and national scales.
Typically, in subtropical Taiwan (22°-25°N and 120°-122°E), the average (± standard deviation) temperatures during spring (March to May), summer (June to August), fall (September to November), and winter (December to February) from 1999 to 2009 were 22.27 ± 2.97, 29.07 ± 1.00, 24.55 ± 2.96, and 17.37 ± 1.17°C, respectively, with a relative humidity ranging from 75-78% and a maximum rainfall occurring in June (7800 mm) and August (8527 mm) (Taiwan Central Weather Bureau, http://www.cwb.gov.tw/V7/index.htm). Typically, most tropical and subtropical countries experience these weather conditions.
Monthly-based disease burden of TB data by gender, age groups, and subpopulation groups in Taiwan were obtained from reports of Centers for Disease Control of Taiwan (Taiwan CDC) for the period 2004-2008 . According to Taiwan CDC (http://www.cdc.gov.tw) , we geographically divided Taiwan into six regions of northern, central, southern, southern Kao-Ping, eastern Hwa-Tung, and island regions to analyze the trends of TB incidence. We calculated the TB incidence rate (per 100,000 population) by annual regional confirmed TB case over total regional population number. We also pooled all regional data to represent national scale of TB cases. Here we used mean age and ratio of male and female TB cases as the input data. Note that we calculated the prevalence of TB from the product of incidence and duration of illness. The average monthly mean, maximum, and minimum temperature data were obtained from Taiwan Central Weather Bureau in the period 2004 to 2008. We also compiled the subpopulation of aborigine data in local region of Taiwan. There were nearly 79,000 and 89,000 aborigines in Taitung and Hwalien County, respectively, and were approximately 34% of total aborigine population in Taiwan (MOI, http://www.moi.gov.tw/stat/index.aspx) . In addition, Hwalien County is the major region having the largest aborigines' population. All data used in this research are openly available.
Spearman rank correlation tests were performed to investigate the correlation between TB incidence rates and minimum, mean, and maximum temperatures and further to examine the lagged effects with a lag of 0 to 4 months of temperature on TB incidence. TB trends were estimated for two selected regions where the highest TB incidence was evident among six study areas in the period 2004-2008.
where Y(t) is the expected TB incidence at time t, β 0 is the intercept, t and t 2 are the linear and quadratic time trends, respectively, β 1 through β 8 represent the fitted coefficients, and represent the seasonality with five cycles , T t-n represents the monthly temperatures with n-month lag (°C), respectively, age t, male t , and female t represent the monthly TB incidence data based on age, male, and female at time t, respectively.
Eq. (1) is one simple expression of generalized linear models that do not require prior knowledge of the shape of the response function. To assess the characteristics of TB trends of aborigine subpopulations, the Poisson regression model in Eq. (1) was also fitted with the available TB data for the selected study regions in question. To ensure robustness, the model was tested by forecasting dynamic TB time-series in 2008 based on TB data in the period 2004-2007.
The Statistica® software (Version 6.0, StatSoft, Tulsa, OK, USA) was used to perform Spearman's rank correlation tests and other related statistical analyses. Poisson regression model was conducted in the open-source language R (Version 2.11.1, The R Foundation for Statistical Computing, 2010). To compare modeled and observed results, the best fit was evaluated using root-mean-squared-error (RMSE), computed from where N denotes the number of measurements, I m,n is the TB incidence data, and I s,n is the simulation result corresponding to data point n. Akaike information criterion (AIC) was also used to assess model fit and can be expressed as AIC = 2 k-2ln(L) where k is the number of predictors in the Poisson regression model, and L is the maximized value of the likelihood function for the estimated model. The minimum of RMSE and AIC can be used to judge the model fitness in data description and prediction.
Quantitative TB epidemiological data
Incidence, prevalence, relapse and mortality rates of TB in Taiwan region (2004-2008)
Overall, in Taiwan the average TB incidence, mortality, and relapse proportion were estimated to be 68 per 100,000 population, 0.036 person-1 yr-1, and 4.38%, respectively, for the period 2004-2008 (Table 1). The highest TB incidence rate was found in eastern Taiwan (116 per 100,000 population) with the estimated prevalence, mortality rate, and relapse proportion of 151 per 100,000 population, 0.040 person-1 yr-1, and 8.17%, respectively, for the period 2004-2008 (Table 1). The eastern Taiwan had the largest proportion of TB relapse cases (8.17%) in which Hwalien County gave 8.68% relapse proportion (Table 1).
Regional and national TB trends
Spearman's rank correlation coefficient (ρ) for time-lag effect with temperature (°C) in Taiwan, Hwalien and Taitung Counties, respectively
Summary of Poisson regression models used in this study
Poisson regression modela
Fitted coefficients in Poisson regression model for Hwalien and Taitung Counties during 2004-2008
Fitted coefficients in Poisson regression model for Taiwan during 2004-2008
Aborigine and gender effects on TB incidence
TB trends in regional and national scales
This study was to assess the characteristics of TB trends for the period 2004-2008 by month, year, gender, age, temperature, seasonality, aborigines/nonaborigines and to provide a best-fit generalized regression model to examine the potential predictors for TB incidences in Taiwan with regional and national scales. Since TB is caused by infection of the pathogen M. tuberculosis, the activity of pathogen can be influenced by weather and environmental conditions. The host behavior and healthy status can also cause the susceptibility of TB infection. Thus we examined different factors which may affect TB events.
We found that Hwalien (124 per 100,000 population) and Taitung (104 per 100,000 population) Counties situated at eastern Taiwan had the highest TB incidences among Taiwan regions in the period 2004-2008. Generally, more than 90% of TB cases found in adults aged 25-64 and older, with the largest fraction among > 64-year olds. The eastern Taiwan had the largest proportion of TB relapse cases (8.17%) in which Hwalien County gave 8.68% relapse proportion in the period 2004-2008. Overall, the average TB incidence, mortality, and relapse proportion were estimated to be 68 per 100,000 population, 0.036 person-1 yr-1, and 4.38%, respectively, in Taiwan for the period 2004-2008. Our result indicates that there is a trend of an increasing proportion of TB among the elderly. The plausible reason may be that with the falling of birth rate and increasing of longevity, the aging of populations is rising in Taiwan.
Fares et al.,  indicated that lower temperature during winter seasons may induce the susceptibility to respiratory epithelium infection. Furthermore, temperature is also an important climatic factor that influences TB seasonal trends. Specifically, the mean temperature with 1-month lag had highly significant effect on TB trends in Taiwan, whereas mean temperature with a 3-month lag showed highly significant in Hwalien County at eastern Taiwan. Therefore, in accordance with the idea of the impact of weather variations on TB infections, temperature emerged as the primary environmental correlate of TB incidence patterns at regional and national scales tested here. Based on this finding, changes in temperature may have strong consequences for the patterns of TB incidence.
There were various reasons that cause temperature lagged effects on TB trends. Naranbat et al.,  hypothesized that temperature may change the indoor/outdoor (I/O) ratio for TB susceptible and infected population, and further influence the transmission probability for M. tuberculosis. Therefore, the longer incubation period may subsequently delay the TB detection and notification. The interval between observable immunological response and infection may delay over 7 weeks . The I/O ratio can also affect Vitamin D intake by sunshine levels which can decrease the risk for TB illness [14, 20]. Our results found that there were different lagged effects with temperature on TB incidence such as 1 month-lag in national scale in Taiwan and 3 month-lag in regional scale in Hwalien County. We speculated that climatic properties were the important factor influenced by geographic location, for example, the relative lower temperature and higher rainfall in eastern Taiwan than other regions. The climate factor may further influence the sunshine proportion to residents on Vitamin D intake.
Our findings revealed that there had weak relationship between (lagged) temperature and TB incidence. We thus further considered the seasonality impact on TB incidence separately with temperature effect. The seasonality may exist in many infectious diseases due to variety of human behaviors, environmental conditions, and other uncertain factors. Greenman et al.,  indicated that external forcing can cause some cycles, oscillations, and even chaotic phenomenon in disease dynamics of populations. Therefore, we considered the seasonality as a major factor which can be seemed as predictor to better understand Taiwan TB trend. The five cycles of seasonality has been applied to investigate periodic trend of monthly new TB cases and possible causes of seasonal trend in previous China TB study . In this study, we also found that the Taiwan TB seasonality had similar pattern with TB epidemic in China. Thus we used five-cycle pattern as seasonality factor to improve the predictability of TB incidence rate. Although the seasonality showed insignificant in regional scales, the properties were detected significantly in national scale.
In regional scale of Hwalien County, age was the only predictor of TB trends identified as statistically significant among considered parameters in the Poisson regression model. Our analysis of TB trends at the regional scale reveals that the appearance of irregular annual TB incidences invariably coincides with the seasonality. None of the considered parameters generally explained variation in TB incidence in Taitung County. Yet, seasonality, aboriginal subpopulation, male, and age groups all showed significant relationships with TB trends in Taiwan. It is interesting to note that gender, time trend, and 2-3-month lag maximum temperature showed stronger association with TB trends in aboriginal subpopulations.
Our study showed that seasonal peaks of TB incidences generally occurred at late spring to early summer seasons in Taiwan. Thus dynamic consequences of seasonal variation in TB incidence appeared in Taiwan. This result was consistent with the study in China . The relatively high significant association of seasonality, gender, age group with national scale TB trends leads to more regular TB time-series dynamics in Taiwan than those in regional Hawlien and Taitung Counties. This emphasizes the potential disadvantages of extrapolating TB trends for these sorts of highly non-linear systems without a detailed understanding of regional parameters. Therefore, better data related to regional scale are required to account for these outcomes. Furthermore, the quality of the regional data allows us a rare opportunity to generate data-driven statistical models to assess TB trends in Taiwan.
TB infection appears to persist throughout several years even in relatively small populations, possibly through recurring infections in adults. If individuals experiencing their first infection are the primary drivers of endemics, then demographic changes will have a strong influence on endemic time-series. Therefore, differences in population demographics and epidemiology of TB diseases, and, potentially, vaccine effectiveness, would need to be carefully considered when estimating the TB trends in local regions. Hsueh et al.,  found that TB incidence in male patients was 2.2-fold higher than that in female patients. In this study, we found the similar results for male as higher prevalence group that can influence the TB trends in Taiwan (p < 0.001). Thus we thought that gender differences could be a factor accounting for the TB incidence.
We used AIC and RMSE to assess the model fitness in this study. The RMSE is a measure of the differences between the original datasets and predictive values by optimal model. The RMSE can also measure the average magnitude of the error by quadratic scoring function. The differences were being aggregated into a single measure to test the model predictive power. The AIC is a measure of the relative goodness of fit for statistical model. It is often used to describe the trade-off between bias and variance in model construction, or loosely speaking between accuracy and complexity of the model. AIC is an assessment for model selection not only reflects goodness of fit, but also includes a criterion that is an increasing function of the number of estimated parameters. Based on AIC method, our result found that Hwalien County had more optimal fitted models than Taitung County.
Our Poisson regression analysis indicated that the performance of RMSE did not obviously improve the predictability of TB trends in Taitung County by adding other predictors as age and gender. This revealed that these factors had only weak effects on the TB incidence in regional scale. Therefore, the age and gender show significant influence on TB trends in Hwalien County than in Taitung County.
Limitations and implications
There are some limitations in our analyses. First, our database is limited to regional scale for which sufficient records were accessible to determine more significant predictors. A large gap is the age- and gender-specific TB cases in local scale, where data remain scarce. Likewise, we did not consider subpopulation effects, and had limited TB burden and trends data before 2004.
Second, our results are based on relatively coarse TB data provided by limited sources. The assessment of regional trends in incidence requires judgments about the reliability of case notifications reported by individual regional counties . With these limitations, the results and forecasts present herein should be interpreted with caution.
Last, we analyzed only a subset of mechanisms that may shape TB trends regionally and nationally. We did not consider the factors such as numbers of alcoholics, diabetics, HIV, nature resistance-associated macrophage protein 1 (NRAMP 1), smokers, and military and inmate subpopulations, that have proven critical to TB trends variability [3, 15, 23–28]. Thus, those factors may be incorporated into the TB trends models to improve the predictability but they could have important dynamic consequences, which are worth exploring in future research. Generalized regression models rooted in regional data are important for providing clear recommendations for control strategies.
Although it was parameterized on the basis of observations from 2004-2007, the proposed Poisson regression model predicts the qualitative patterns of TB incidence rate at the regional and national scale in 2008. Assessing the impact of potential predictors on TB trends in Taiwan cannot neglect the self-intrinsic errors of missing variables for the selected Poisson regression model. This study suggests that more detailed surveillance data could well explain the peak values rather than missing information. Therefore, seeking novel mechanisms and providing both biological plausibility and epidemiological evidence for existing theories are still needed in future studies [22, 29]. Assessment of TB trends in eastern Taiwan presents an important opportunity to understand the time-series dynamics and control of TB infections, given that this is the typical host demography in regions where these infections remain major public health problems.
We found that in Taiwan the average TB incidence was 68 per 100,000 population with a mortality rate of 0.036 person-1 yr-1 in the period 2004-2008. The highest TB incidence rate was found in eastern Taiwan (116 per 100,000 population) with the largest proportion of TB relapse cases. We found that seasonality, aborigines, gender, and age had a consistent and dominant role in constructing TB incidence patterns in Taiwan, whereas these mechanisms were inconsistently supported for TB trends in regional scale. We also found that gender, time trend, and 2-month lag maximum temperature showed stronger association with TB trends in aboriginal subpopulations. The proposed Poisson regression model is capable of forecasting the patterns of TB incidence at the regional and national scales based on the previous data information. We anticipated that our Taiwan-based analysis can be extended to the context of developing countries, where M. tuberculosis remains a substantial cause of expected morbidity and mortality.
No external sources of funding were received for this research
- Kaufmann SHE: Tuberculosis: deadly combination. Nat. 2008, 453: 295-296. 10.1038/453295a.View ArticleGoogle Scholar
- World Health Organization: Global tuberculosis control-epidemiology, strategy, financing, WHO report 2009 (WHO/HTM/TB/2009.411). 2010, [http://www.who.int/tb/publications/global_report/2009/en/index.html]Google Scholar
- Hsueh PR, Liu YC, So J, Liu CY, Yang PC, Luh KT: Mycobacterium tuberculosis in Taiwan. J Infect. 2006, 52: 77-85. 10.1016/j.jinf.2005.08.008.View ArticlePubMedGoogle Scholar
- Centers for Disease Control: Department of Health, Executive Yuan Taiwan. Taiwan tuberculosis incidence and mortality rate, 2002-2008 (in Chinese). 2009, [http://www.cdc.gov.tw/public/Data/9123117163971.pdf]Google Scholar
- Lee JJ, Wu RL, Lee YS, Wu YC, Chiang CY: Treatment outcome of pulmonary tuberculosis in eastern Taiwan-experience at a medical center. J Formos Med Assoc. 2007, 106: 25-30. 10.1016/S0929-6646(09)60212-6.View ArticlePubMedGoogle Scholar
- Douglas AS, Strachan DP, Maxwell JD: Seasonality of tuberculosis: The reverse of other respiratory diseases in the UK. Thorax. 1996, 51: 944-946. 10.1136/thx.51.9.944.View ArticlePubMedPubMed CentralGoogle Scholar
- Rios M, Garcia JM, Sanchez JA, Perez D: A statistical analysis of the seasonality in pulmonary tuberculosis. Eur J Epidemiol. 2000, 16: 483-488. 10.1023/A:1007653329972.View ArticlePubMedGoogle Scholar
- Thorpe LE, Frieden TR, Laserson KF, Wells C, Khatri GR: Seasonality of tuberculosis in India: is it real and what does it tell us?. Lancet. 2004, 364: 1613-1614. 10.1016/S0140-6736(04)17316-9.View ArticlePubMedGoogle Scholar
- Atun RA, Samyshkin YA, Drobniewski F, Kuznetsov SI, Fedorin IM, Coker RJ: Seasonal variation and hospital utilization for tuberculosis in Russia: hospitals as social care institutions. Eur J Public Health. 2005, 15: 350-354. 10.1093/eurpub/cki018.View ArticlePubMedGoogle Scholar
- Leung CC, Yew WW, Chan TYK, Tam CM, Chan CY, Chan CK, Tang N, Chang KC, Law Ws: Seasonal pattern of tuberculosis in Hong Kong. Int J Epidemiol. 2005, 34: 924-930. 10.1093/ije/dyi080.View ArticlePubMedGoogle Scholar
- Nagayama N, Ohmori M: Seasonality in various forms of tuberculosis. Int J Tuberc Lung Dis. 2006, 10: 1117-1122.PubMedGoogle Scholar
- Luqureo FJ, Sanchez-Padilla E, Simon-Soria F, Eiros JM, Golub JE: Trend and seasonality of tuberculosis in Spain, 1996-2004. Int J Tuberc Lung Dis. 2008, 12: 221-224.Google Scholar
- Akhtar S, Mohammad HG: Seasonality in pulmonary tuberculosis among migrant workers entering Kuwait. BMC Infect Dis. 2008, 8: 3-10.1186/1471-2334-8-3.View ArticlePubMedPubMed CentralGoogle Scholar
- Wilkinson RJ, Llewelyn M, Toossi Z, Patel P, Pasvol G, Lalvani A, Wright D, Latif M, Davidson RN: Influence of vitamin D deficiency and vitamin D receptor polymorphisms on tuberculosis among Gujarati Asians in west London: a case-control study. Lancet. 2000, 355: 618-621. 10.1016/S0140-6736(99)02301-6.View ArticlePubMedGoogle Scholar
- Dye C, Williams BG: The population dynamics and control of tuberculosis. Science. 2010, 328: 856-861. 10.1126/science.1185449.View ArticlePubMedGoogle Scholar
- Centers for Disease Control, Republic of China (Taiwan), [http://www.cdc.gov.tw]
- Ministry of the Interior, Department of statistics, Republic of China (Taiwan), [http://www.moi.gov.tw/stat/index.aspx]
- Liu L, Zhao XQ, Zhou Y: A tuberculosis model with seasonality. Bull Math Biol. 2010, 72: 931-952. 10.1007/s11538-009-9477-8.View ArticlePubMedGoogle Scholar
- Centers for Disease Control, Department of Health, Executive Yuan Taiwan: Taiwan tuberculosis control report 2007-2009. 2010, [http://www.cdc.gov.tw]Google Scholar
- Fares A: Seasonality of tuberculosis. J Glob Infect Dis. 2011, 3: 46-55. 10.4103/0974-777X.77296.View ArticlePubMedPubMed CentralGoogle Scholar
- Naranbat N, Nymadawa P, Schopfer K, Rieder HL: Seasonality of tuberculosis in an Eastern-Asian country with an extreme continental climate. Eur Respir J. 2009, 34: 921-925. 10.1183/09031936.00035309.View ArticlePubMedGoogle Scholar
- Greenman J, Kamo M, Boots M: External forcing of ecological and epidemiological systems: a resonance approach. Physica D. 2004, 190: 136-151. 10.1016/j.physd.2003.08.008.View ArticleGoogle Scholar
- Dye C, Watt CJ, Bleed DM, Hosseini SM, Raviglione MC: Evolution of tuberculosis control and prospects for reducing tuberculosis incidence, prevalence, and deaths globally. JAMA-J Am Med Assoc. 2005, 293: 2767-2775. 10.1001/jama.293.22.2767.View ArticleGoogle Scholar
- Chiang CY, Hsu CJ, Hsu PK, Suo J, Lin TP: Pulmonary tuberculosis in the Taiwanese prison population. J Formos Med Assoc. 2002, 101: 537-541.PubMedGoogle Scholar
- Chiang CY, Suo J, Yu MC, Yang SL, Lin TP: Screening for pulmonary tuberculosis among military conscripts in Taiwan. J Formos Med Assoc. 2002, 101: 841-845.PubMedGoogle Scholar
- Pai M, Mohan A, Dheda K, Leung CC, Yew WW, Christopher DJ, Sharma SK: Lethal interaction: the colliding epidemics of tobacco and tuberculosis. Expert Rev Anti-Infect Ther. 2007, 5: 385-391. 10.1586/1478722.214.171.1245.View ArticlePubMedGoogle Scholar
- Dooley KE, Tang T, Golub JE, Dorman SE, Cronin W: Impact of diabetes mellitus on treatment outcomes of patients with active tuberculosis. AmJTrop Med Hyg. 2009, 80: 634-639.Google Scholar
- Lin HH, Ezzati M, Chang HY, Murray M: Association between tobacco smoking and active tuberculosis in Taiwan: prospective cohort study. Am J Respir Crit Care Med. 2009, 180: 475-480. 10.1164/rccm.200904-0549OC.View ArticlePubMedGoogle Scholar
- Baussano I, Williams BG, Nunn P, Beggiato M, Fedeli U, Scano F: Tuberculosis incidence in prison: a systematic review. PLoS Med. 2010, 7: e1000381-10.1371/journal.pmed.1000381.View ArticlePubMedPubMed CentralGoogle Scholar
- Russell DG, Barry CE, Flynn JL: Tuberculosis: what we don't know can, and does, hurt us. Science. 2010, 328: 852-856. 10.1126/science.1184784.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/12/29/prepub
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