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Force of tuberculosis infection among adolescents in a high HIV and TB prevalence community: a cross-sectional observation study

Keren Middelkoop12*, Linda-Gail Bekker12, Hua Liang3, Lisa DH Aquino1, Elaine Sebastian1, Landon Myer45 and Robin Wood12

Author Affiliations

1 Desmond Tutu HIV Centre, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa

2 Department of Medicine, University of Cape Town, Cape Town, South Africa

3 Department of Biostatics and Computational Biology, University of Rochester Medical Center, Rochester, USA

4 Centre for Infectious Diseases Epidemiology & Research, School of Public Health & Family Medicine, University of Cape Town, Cape Town, South Africa

5 International Centre for AIDS Care and Treatment Programs and Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA

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BMC Infectious Diseases 2011, 11:156  doi:10.1186/1471-2334-11-156

The electronic version of this article is the complete one and can be found online at:

Received:15 October 2010
Accepted:1 June 2011
Published:1 June 2011

© 2011 Middelkoop et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.



Understanding of the transmission dynamics of tuberculosis (TB) in high TB and HIV prevalent settings is required in order to develop effective intervention strategies for TB control. However, there are little data assessing incidence of TB infection in adolescents in these settings.


We performed a tuberculin skin test (TST) and HIV survey among secondary school learners in a high HIV and TB prevalence community. TST responses to purified protein derivative RT23 were read after 3 days. HIV-infection was assessed using Orasure® collection device and ELISA testing. The results of the HIV-uninfected participants were combined with those from previous surveys among primary school learners in the same community, and force of TB infection was calculated by age.


The age of 820 secondary school participants ranged from 13 to 22 years. 159 participants had participated in the primary school surveys. At a 10 mm cut-off, prevalence of TB infection among HIV-uninfected and first time participants, was 54% (n = 334/620). HIV prevalence was 5% (n = 40/816). HIV infection was not significantly associated with TST positivity (p = 0.07). In the combined survey dataset, TB prevalence was 45% (n = 645/1451), and was associated with increasing age and male gender. Force of infection increased with age, from 3% to 7.3% in adolescents ≥20 years of age.


We show a high force of infection among adolescents, positively associated with increasing age. We postulate this is due to increased social contact with infectious TB cases. Control of the TB epidemic in this setting will require reducing the force of infection.


Tuberculosis (TB) remains a major cause of morbidity and mortality in the world[1]. In order to develop effective intervention strategies for TB control, it is important to understand TB transmission in high burden settings. While there have been recent studies assessing TB infection in young children[2-5], there are few data assessing TB infection in older children and adolescents in communities with high TB and HIV burdens[6].

Incidence of TB infection is a measure of current transmission in a community. While repeated testing of uninfected individuals over time is a conventional method for determining incidence of a disease, this methodology is both labour and time intensive, and is further complicated by the boosting of the immune response in immunology-based tests such as the tuberculin skin test (TST)[7,8]. Therefore alternative approaches for calculating incidence from prevalence data have been developed. The annual risk of TB infection (ARTI), calculated from TB infection prevalence data, is an averaged measure of risk of TB infection over the lifetime of the study participants[7,9]. The limitation of this measure is that ARTI only provides an estimate of current transmission or incidence if calculated in very young participants. In comparison, force of infection, defined as the proportion of susceptible individuals that have become infected with Mycobacterium tuberculosis in a specified period, can be calculated using changes in age-specific prevalence rates[10-12], and provides an estimate of recent infection incidence across a wider age range.

This approach is a relatively new concept that has been utilized to estimate incidence in diseases in which true incidence is difficult or costly to measure, such as glaucoma[13] and more recently, HIV[12,14]. The mathematical techniques utilized are based on the principle that prevalence is a function of incidence and duration of illness or infectivity[15]. This principle holds true under the assumptions of disease stability. Data from this community suggest that TB transmission has remained relatively constant over the past decade, as evidenced by the stable TB notification rates among HIV-uninfected adults from 1997 to 2008[16], as well as the stable childhood TB over the same time period[2].

We performed a TST survey among adolescents in a high HIV and TB prevalence community to assess prevalence of TB infection and force of infection by age.


This study was the third in a series of cross-sectional tuberculin skin testing surveys performed among school-attending children in the study community. The first two surveys were performed in the local primary school in 2006 and 2007[2]. The survey reported in this manuscript was performed in children attending secondary school in the study community in 2009. The same methodology was used in all three surveys, with the addition of HIV testing in the secondary school survey. Children were eligible if they were resident in the community and registered at the local secondary school. Parental consent, and assent from participants <18 years of age, were obtained prior to enrolment. Adolescents ≥18 years of age provided written consent. Basic demographic information was collected and participants were examined for the presence of a BCG scar. Participants received the TST regardless of BCG scar status.

The WHO-recommended standard Mantoux test of 2TU of PPD (Purified Protein Derivative) RT23 with Tween 80 (Statens Seruminstitut, Copenhagen) was administered intradermally to the volar surface of the left forearm by a trained nurse. The tuberculin reaction size was read by a trained assessor three days following the inoculation. The presence or absence of a reaction was noted, and, where present, the size of the induration was measured along perpendicular axes using standard calipers. Participants also provided an oral transudate specimen for HIV testing, using the Orasure® collection device and Vironostika Uni-Form II HIV-1 and HIV-2 plus 0 ELISA test (bioMérieux SA, Marcy l'Etoile, France). HIV results were anonymous but linked to TST results and all adolescents were encouraged to have separate voluntary counseling and testing at local facilities. HIV testing was not performed in the primary school surveys[2].

This study was approved by the University of Cape Town's Research Ethics Committee. All children with a TST reaction ≥10 mm were recalled for investigation for active tuberculosis and referred to the local clinic for further management if appropriate.

Data were analyzed using STATA 9.0 (StataCorp, College Station, Texas). Analysis was performed in two parts: firstly on the secondary school dataset, excluding HIV-infected individuals, and secondly on the secondary school dataset combined with the primary school dataset, excluding the HIV-infected individuals from the secondary school survey. Children who declined HIV testing were considered to be HIV-uninfected for the purposes of the TB analysis.

TST results were calculated as the mean of the two diameters of the TST reaction: a positive reaction was defined at 10 mm cut-off, based on clinical guidelines[17,18]. ARTI was calculated as 1-(1-prevalence)1/(mean age+0.5)[19]. As the age in full years at participants' last birthday was used, 0.5 was added to the mean age for the calculation of ARTI[19]. The secondary school sample was divided into age quartiles and TB prevalence and ARTI were calculated overall and for each age group. Bivariate analyses employed Student's t-, Fisher's exact and Wilcoxon sum rank tests, as appropriate. Multiple logistic regression models were developed to examine factors associated with positive TST results.

HIV data secondary school participants

Univariate and multivariate logistic regression models were developed to determine the demographic characteristics associated with HIV, and to assess for an association between HIV and TB infection. In these models, a TST reaction size of ≥5 mm was used as a positive cut-off for TB infection in HIV-infected adolescents, in keeping with clinical guidelines[17,18].

Subset of repeat TST participants

Overall, 159 students who had participated in the 2007 survey also took part in the secondary school survey, resulting in repeat tuberculin testing of these participants. Repeated TST tests may be associated with boosting of the immune response, complicating the interpretation of the second TST reaction size[7,8]. We, therefore, analyzed this subset of participants separately from the rest of the cohort. In keeping with existing literature, we defined a true conversion to TST positivity between the two surveys as a change from a negative result (<10 mm) on original TST to a positive result (≥10 mm) on the second test, with an absolute reaction size increase of at least 6 mm[20-22]. Bivariate analyses used Wilcoxon sum rank and Student's t tests, as appropriate for comparison of participants with repeated TST compared to first time participants in 2009. A multivariate regression model was developed to compare reaction sizes between the two groups. The McNemar test was used for matched comparison of 2007 and 2009 TST results.

Combined surveys database

In order to assess the effect of age on TB prevalence and force of infection, we combined the primary and secondary survey datasets, excluding HIV-infected participants from 2009, as well as the second test in those participants who had repeat tests in 2009. The participants were divided into age quartiles and TB prevalence and ARTI were calculated overall and for each age group.

Smoothed prevalence of TB infection by age was calculated from predictive logistic regression models on the combined primary and secondary school dataset, overall and stratified by gender, excluding those who tested HIV-infected in the secondary school. Force of infection was calculated at specific ages for the pool of individuals who remained uninfected [annual change in prevalence/(1-prevalence)]. Trends in ARTI and force of infection were assessed using Cox-Stuart test for trend[23,24].

For all analysis, 95% confidence intervals (CI) were based on the Poisson distribution and all statistical tests were 2-sided at alpha = 0.05.


Of the 959 children enrolled in the secondary school, 839 were eligible for study participation (87%). Ineligibility was due to residence outside of the community (n = 80) or having dropped out of school (n = 40). Consent/assent was obtained for 820 children (98% of those eligible). Refusal by parent or learner accounted for 18 non-consenters and 1 child was not consented due to absenteeism over the study period. All 820 consented students were enrolled. Four of the children enrolled declined tuberculin skin testing, and four children declined HIV testing. Of the 816 children who underwent skin testing, 813 (99.6%) had the TST reaction read within 72-96 hours (Figure 1). No study-related adverse events were noted in the three children assessed outside the window period and these participants were excluded from the analysis.

thumbnailFigure 1. Consort Diagram for Secondary School Cohort. * including the 3 participants who declined HIV testing

Table 1 shows the demographic characteristics of the secondary school study cohort. Of the 813 participants who completed TST testing, 159 had received tuberculin skin testing in the 2007 survey in the community. These participants were excluded from the main analysis and their results are presented in a sub-analysis. Of the remaining 654 children, all but three of the participants underwent HIV testing. In total 34 participants of the remaining participants tested HIV-infected, and were excluded from the TB analysis (n = 620; Figure 1).

Table 1. Demographic characteristics of the secondary school sample.

Among the 620 participants, ages ranged from 13 to 22 years, with a mean age of 17.5 years, and 59% of participants were female. The majority of the children did not have a BCG scar (87%); one child's BCG scar status was not recorded, and this participant was excluded from analysis involving BCG scar status.

TST reaction sizes ranged from 0 to 30 mm (median = 11.5 mm; IQR: 0-16.5 mm), and 222 children had a TST result of 0 mm (36%). The frequency distribution of reaction sizes >0 mm are presented in Figure 2. There was no significant difference between the median size of reactions in participants with BCG scars compared to those without scars (11 vs 11.5 mm respectively, p = 0.24).

thumbnailFigure 2. Frequency distribution of TST reactions (>0 mm) in HIV-uninfected participants in secondary school survey.

At the 10 mm cut-off 334 participants (54%) had a positive TST result. There was no significant difference in the defined TB positivity by BCG scar status (p = 0.99). In a multivariate logistic regression model predicting the relative odds of a positive TST result, age was positively associated with a positive TST result (adjusted odds ratio (OR) for a 1-year increase in age: 1.10, 95% CI: 1.01 - 1.21; p = 0.03), as was male gender (adjusted OR for female compared to male: 0.65, 95% CI: 0.47-0.90; p = 0.01).

The overall ARTI for this sample was 4.2% (95% CI: 3.8-4.7%). Table 2 reports TB prevalence and ARTI overall, and by age quartiles. The ARTI did not differ significantly across the age quartiles (p = 0.75) or across age by years from 14 to 21 years of age (p = 0.63).

Table 2. Secondary school TB prevalence and ARTI by age quartile for the 10 mm cut-off point (excluding HIV-infected and repeat TST participants):

Sub-analysis: HIV and TB infection

Of the 820 participants enrolled in the secondary school survey, 816 (99.5%) consented to HIV testing. In total 40 (4.9%) participants tested HIV-positive. In a logistic regression model, HIV infection was associated with increasing age (OR: 1.3; 95% CI: 1.1-1.6; p = 0.001) and female gender (OR: 2.3; 95% CI: 1.1-4.7; p = 0.03).

Among the 809 participants who underwent both TST and HIV testing, 38 were HIV-infected, four of whom had previous TST testing in 2007. The median TST reaction size among HIV-infected participants (amongst those who did not have repeat TST assessment; n = 34) was smaller than that of HIV-uninfected participants (0 vs 11.5 mm; p = 0.08), and this difference was significant when adjusted for age and gender (p = 0.04). Using the revised cut-off of ≥5 mm, 15 (40%) HIV-infected participants were TST positive. In a multivariate logistic regression model adjusted for age and gender, HIV infection was not significantly associated with TST positivity (OR = 0.53; 95% CI: 0.27-1.05; p = 0.07).

Sub-analysis: Repeated TST testing

In 2009, 159 students who had participated in the 2007 survey took part in the secondary school survey, four of whom were HIV-infected. The mean reaction size in 2009 among participants with repeat tests was larger compared to participants who tested for the first time (15 vs 11.5 mm; p < 0.001), and this finding persisted when adjusted for age and gender (p < 0.001). Overall, TB prevalence did not differ between those that had a repeat TST test and those participants who were testing for the first time (58 vs 54%, p = 0.29).

Using a 10 mm cut-off for positivity, in 2007, 76 (48%) of the 159 participants had a positive TST result; in 2009 91 (57%) tested TST positive. Overall, 19 of the 83 participants who were TST negative in 2007 tested positive in 2009 (p = 0.003), and four (5%) reverted to a TST negative result. Using the definition outlined above, 18 of the 19 convertors had an increase in reaction size of ≥6 mm and were therefore considered true new infections with TB. These 18 participants, from the pool of 83 susceptible children in 2007, equate to an incidence of 22% over two years (95 CI: 13-32%), or an annual incidence of infection of 11% ([18/83]/2).

Combined Primary and Secondary school database

In order to assess the effect of age on TB prevalence and force of infection, we investigated combining the primary and secondary school surveys. We compared the prevalence of TST positive results for the three cohorts in the overlapping age ranges of 10 to 12 years and 14 to 16 years. The chi-squared test for comparison was not significant (survey 1 and 2: p = 0.66; survey 2 and 3: p = 0.46), nor was survey year a significant risk factor in multivariate regression model for TST positivity (p = 0.23). Therefore we combined these two datasets. In the primary school survey, TST readings were available for 831 of the 832 children enrolled. Therefore the combined database, excluding the participants who tested HIV-infected, was comprised of 1,451 participants.

Ages of the combined cohort ranged from 5 to 22 years, with a mean age of 13.6 years (standard deviation = 4.1), and 52% of participants were female. The majority of the children did not have a BCG scar (80%); BCG scar status was not available for two participants and they were therefore excluded from analysis involving BCG scar status.

TST reaction sizes ranged from 0 to 30 mm (mean = 8.0; median = 0 mm). Overall 728 (50%) of the participants had no reaction to the TST. At the 10 mm cut-off 645 participants (45%) had a positive TST result (Table 3). TB prevalence by age is presented in Figure 3. In a multivariate logistic regression model a positive TST result was significantly associated with age (adjusted OR for a 1-year increase in age: 1.17, 95% CI: 1.10 - 1.25; p < 0.001) and male gender (adjusted OR for female compared to male: 0.74, 95% CI: 0.60-0.92; p = 0.01). TST positivity was not associated with BCG scar status (p = 0.40).

Table 3. TB prevalence and ARTI by age quartile for the 10 mm cut-off point for the three surveys combined (excluding HIV-infected and second tests in repeat TST participants):

thumbnailFigure 3. TB prevalence in combined dataset, by age (in years). Note: This figure is based on the unadjusted data presented in Table 3.

Table 4 reports the smoothed prevalence of TB infection by age, as predicted by our logistic regression model. Also reported in Table 4 are the ARTI and force of infection by age, calculated from the smoothed prevalence. The overall ARTI for this sample was 4.1% (95% CI: 3.8-4.4%). The ARTI did not differ significantly across age by years from 6 to 22 years of age (p = 0.15). The force of infection increased with increasing age, and this trend was significant (p = 0.01).

Table 4. Predicted prevalence, ARTI and force of infection by age in study cohort


This is one of the first studies to report TB infection prevalence, ARTI and force of infection in adolescents in a high TB and HIV prevalent setting.

In this study we confirmed the high ARTI rate reported in the primary school children[2], and show a high force of infection. ARTI provides a measure of the averaged risk of infection over the participants' lifetime. Therefore this measure pertains to the annual risk over a period of up to 20 years, and consequently the ARTI provides little information on the current transmission within the study population.

Force of infection is a measure of recent transmission and the high rates reported here are in keeping with the substantial burden of TB prevalence[25] and notifications[26] in this community and in South Africa[1]. We demonstrated that force of infection was positively associated with increasing age. These findings are consistent with those reported prior to the HIV epidemic[27], as well as by more recent mathematical modeling[6]. The advantage that this study had over the modeling paper is that of greater numbers of older adolescent participants all recruited from the same community, and the ability to show that the force of infection continues to increase up to approximately 20 years of age. In adulthood force of infection becomes harder to measure due to a reduced proportion of susceptible individuals, and the inability to identify secondary infections.

Force of infection is a function of the probability of an effective encounter with an infectious TB case, and as such is a product of TB disease prevalence and mixing patterns. We postulate that the association between force of infection and age may be due to increasing social mixing patterns, resulting from changing social interactions associated with age. We have previously shown that TB infection in primary school children is associated with an adult TB case on their residential plot[28], while TB transmission between adults is due to social interactions off the residential plots[6]. Data from Europe shows that number of social contacts peak in adolescents[29], suggesting that the likelihood of contact with infectious persons may also peak at this time. The social interactions, and therefore risk of TB infection, of mid-teens may more closely resemble that of adults rather than younger children in the community.

We have also shown a changing risk of TB infection by gender: in the primary school surveys, gender was not associated with TB infection[2], but in the secondary school survey, male gender was associated with increased TB infection. As males get older, their risk of TB infection out-strips that of females of similar age, and this is consistent with other reports in the literature[9,21]. This increased risk may be due to a combination of increased biological susceptibility, differing immunological responses or differences in socialization patterns of male compared to female adolescents.

The lack of association between HIV and TB infection is a key finding: adolescents infected with HIV did not appear to be at higher risk for acquiring primary TB infection. This is in keeping with findings reported from lower HIV and TB prevalent settings[30,31], and consistent with studies that suggest the establishment of TB infection is mechanistic, as opposed to immune-based[32]. However, adolescents are at increased risk of progression to TB disease in the first two years following TB infection[33], and the high infection rates in this community place adolescents at substantial risk of TB disease.

The high incidence rate of TB infection in the subset of participants tested 2 years apart, confirms the high force of infection. However, due to the boosting of the immune response with enhanced allergy noted with repeated TSTs[8,34-38], the incident rate in this subset is higher than the force of infection. This is highlighted by the larger median TST reaction sizes in participants with a repeat test compared to first time participants. Similarly, reduced and anergic responses to TST have been noted in HIV-infected patients[39-42], as evidenced by the smaller median reaction sizes in HIV-infected participants compared to HIV-uninfected participants. These two scenarios highlight a limitation of using an immune-based test for determining TB infection.

Only 20% of the children in this study had an observable BCG scar, despite the South African policy to vaccinate all infants with the BCG vaccine[43]. However BCG scarring may be variable[38,44] and we found no difference in the distribution of TST results between those children with or without BCG scars.

In this study, only 1% of TST results were weakly positive (1 to 5 mm), suggesting minimal cross reaction with environmental mycobacteria[19]. Given the high TB prevalence in the sample, the positive predictive value of the TST is likely to be high.

HIV testing was not performed in the primary school surveys. However, the Actuarial Society of South Africa (ASSA) 2003 AIDS and Demographic model for the African population[45,46] reports an HIV prevalence of <4% in this age group. Therefore HIV is unlikely to have substantially impacted TST readings in this group and, given the anergic reactions to TST associated with HIV[39-42], any impact of HIV is likely to result in an underestimate of TB infection. We were able to exclude HIV-infected children from the secondary school TB analysis, thereby reducing the potential bias resulting from dual infections.

This study had a very low refusal rate (<4% across all three surveys), and as such volunteerism is unlikely to have biased our results. However, it should be noted that the school-attending children may not be representative of all the children in the community, in particular with regards to risk of HIV infection[47,48]. However, should either HIV or TB infection be higher among non-school attending children, this would result in an underestimation of the prevalence and force of TB infection in this study.

The high force of infection in this community would result in significant rates of primary and secondary TB infection and, given the high HIV prevalence among adults[6], and the considerable increased risk of progression to TB disease in HIV-infected individuals[30,49], these findings could explain the substantial incidence of TB disease in this setting. Control of the TB epidemic requires an increasing proportion of non-infected individuals in a population, in other words, a decreased force of infection[50]. In order to reduce the force of infection, National Tuberculosis Programmes need to decrease the prevalence of infectious cases in the community. We have previously shown that a high coverage antiretroviral treatment (ART) program will reduce TB prevalence among HIV-infected participants, due to both improved immune function and the active TB case-finding among patients initiating ART[25]. Extending active case finding to HIV-uninfected residents may substantially reduce the burden of infectious cases. Social programs, such as improved housing, that impact the environment in which individuals interact may also lead to a reduction in the incidence of TB infection.


In conclusion, these data suggest a substantial force of infection among adolescents, which is associated with increasing age. This is most likely due to changing social mixing patterns among adolescents, resulting in increased contact with infectious TB cases. Studies into social interaction patterns at different ages in this setting may help to better understand this increasing risk of TB infection. HIV infection was not associated with increased risk of TB infection. However, the extremely high force of infection, together with the high community HIV prevalence, explains the overwhelming burden of TB disease in this township. Control of the TB epidemic will require reducing the force of infection, and further studies assessing intervention strategies such as those suggested here are required.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

KM, LGB, RW, LM and LDHA designed the study. KM, LDHA and ES collected the data. KM did the analyses with input from RW, LM and HL. KM wrote the paper with input from all the authors who each approved the final version.


The study was funded by the National Institutes of Health (Comprehensive Integrated Programme of Research on AIDS [CIPRA]) grant 1U19AI053217. National Institutes of Health (CIPRA) grant 1U19AI053217 to K.M., L.G.B. and R.W); NIH CIPRA grant 1U19AI05321 and NIH RO1 grant AI058736-02 to R.W. The content of this publication does not necessarily reflect the views or policies of NIAID, nor does mention of trade names, commercial projects, or organizations imply endorsement by the US Government.

HL's research was a collaborative project of the Developmental Centre for AIDS Research (D-CFAR) University of Rochester, NY, and NSF grants DMS0806097 and DMS 1007167


  1. World Health Organization: Global tuberculosis control: surveillance, planning, financing. [] webcite


    Accessed 02 October 2010

  2. Middelkoop K, Bekker LG, Myer L, Dawson R, Wood R: Rates of tuberculosis transmission to children and adolescents in a community with a high prevalence of HIV infection among adults.

    Clin Infect Dis 2008, 47:349-355. PubMed Abstract | Publisher Full Text OpenURL

  3. Shanaube K, Sismanidis C, Ayles H, Beyers N, Schaap A, Lawrence KA, Barker A, Godfrey-Faussett P: Annual risk of tuberculous infection using different methods in communities with a high prevalence of TB and HIV in Zambia and South Africa.

    PLoS One 2009, 4:e7749. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  4. Odhiambo JA, Borgdorff MW, Kiambih FM, Kibuga DK, Kwamanga DO, Ng'ang'a L, Agwanda R, Kalisvaart NA, Misljenovic O, Nagelkerke NJ, Bosman M: Tuberculosis and the HIV epidemic: increasing annual risk of tuberculous infection in Kenya, 1986-1996.

    Am J Public Health 1999, 89:1078-1082. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  5. Egwaga SM, Cobelens FG, Muwinge H, Verhage C, Kalisvaart N, Borgdorff MW: The impact of the HIV epidemic on tuberculosis transmission in Tanzania.

    AIDS 2006, 20:915-921. PubMed Abstract | Publisher Full Text OpenURL

  6. Wood R, Liang H, Wu H, Middelkoop K, Oni T, Rangaka MX, Wilkinson RJ, Bekker LG, Lawn SD: Changing prevalence of tuberculosis infection with increasing age in high-burden townships in South Africa.

    Int J Tuberc Lung Dis 2010, 14:406-412. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  7. Cauthen GM, Pio A, ten Dam HG: Annual risk of tuberculous infection. 1988.

    Bull World Health Organ 2002, 80:503-511. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  8. Thompson NJ, Glassroth JL, Snider DE, Farer LS: The booster phenomenon in serial tuberculin testing.

    Am Rev Respir Dis 1979, 119:587-597. PubMed Abstract OpenURL

  9. Sutherland I: Recent studies in the epidemiology of tuberculosis, based on the risk of being infected with tubercle bacilli.

    Adv Tuberc Res 1976, 19:1-63. PubMed Abstract OpenURL

  10. Podgor MJ, Leske MC: Estimating incidence from age-specific prevalence for irreversible diseases with differential mortality.

    Stat Med 1986, 5:573-578. PubMed Abstract | Publisher Full Text OpenURL

  11. Nagelkerke N, Heisterkamp S, Borgdorff M, Broekmans J, Van Houwelingen H: Semi-parametric estimation of age-time specific infection incidence from serial prevalence data.

    Stat Med 1999, 18:307-320. PubMed Abstract | Publisher Full Text OpenURL

  12. Hallett TB, Zaba B, Todd J, Lopman B, Mwita W, Biraro S, Gregson S, Boerma JT: Estimating incidence from prevalence in generalised HIV epidemics: methods and validation.

    PLoS Med 2008, 5:e80. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  13. Leske MC, Ederer F, Podgor M: Estimating incidence from age-specific prevalence in glaucoma.

    Am J Epidemiol 1981, 113:606-613. PubMed Abstract OpenURL

  14. Williams B, Gouws E, Wilkinson D, Karim SA: Estimating HIV incidence rates from age prevalence data in epidemic situations.

    Stat Med 2001, 20:2003-2016. PubMed Abstract | Publisher Full Text OpenURL

  15. Freeman J, Hutchison GB: Prevalence, incidence and duration.

    Am J Epidemiol 1980, 112:707-723. PubMed Abstract OpenURL

  16. Middelkoop K, Bekker LG, Myer L, Johnson LF, Kloos M, Morrow C, Wood R: Antiretroviral therapy and TB notification rates in a high HIV prevalence South African community.

    J Acquir Immune Defic Syndr 2011, 56:263-269. PubMed Abstract | Publisher Full Text OpenURL

  17. The South African Tuberculosis Control Programme. Practical Guidelines [] webcite

    Pretoria, South Africa: South African Department of Health; 2004.

    Accessed 02 October 2010

  18. Guidance for National Tuberculosis Programmes on the management of tuberculosis in children. Chapter 1: introduction and diagnosis of tuberculosis in children

    Int J Tuberc Lung Dis 2006, 10:1091-1097. PubMed Abstract | Publisher Full Text OpenURL

  19. Arnadottir T, Rieder HL, Trebucq A, Waaler HT: Guidelines for conducting tuberculin skin test surveys in high prevalence countries.

    Tuber Lung Dis 1996, 1(Suppl 1):77-19. OpenURL

  20. Bass JA Jr, Serio RA: The use of repeat skin tests to eliminate the booster phenomenon in serial tuberculin testing.

    Am Rev Respir Dis 1981, 123:394-396. PubMed Abstract OpenURL

  21. Fine PE, Bruce J, Ponnighaus JM, Nkhosa P, Harawa A, Vynnycky E: Tuberculin sensitivity: conversions and reversions in a rural African population.

    Int J Tuberc Lung Dis 1999, 3:962-975. PubMed Abstract | Publisher Full Text OpenURL

  22. Menzies D: Interpretation of repeated tuberculin tests. Boosting, conversion, and reversion.

    Am J Respir Crit Care Med 1999, 159:15-21. PubMed Abstract | Publisher Full Text OpenURL

  23. Cox DR, Stuart A: Some quick sign tests for trend in location and dispersion.

    Biometrika 1955, 42:80-95. OpenURL

  24. Conover WJ: Practical Nonparametric Statistics. John Wiley & Sons, Inc; 1999:169-176.

  25. Middelkoop K, Bekker LG, Myer L, Whitelaw A, Grant AD, Kaplan G, McIntyre J, Wood R: Antiretroviral program associated with reduction in untreated prevalent tuberculosis in a South African township.

    Am J Respir Crit Care Med 2010, 182(8):1080-5. PubMed Abstract | Publisher Full Text OpenURL

  26. Middelkoop K, Wood R, Myer L, Sebastian E, Bekker LG: Can antiretroviral therapy contain a previously escalating TB epidemic in a high HIV prevalence community?

    5th International AIDS Society Conference on HIV Pathogenesis, Treatment and Prevention



  27. Nyboe J: Interpretation of tuberculosis infection age curves.

    Bull World Health Organ 1957, 17:319-339. PubMed Abstract | PubMed Central Full Text OpenURL

  28. Middelkoop K, Bekker LG, Morrow C, Zwane E, Wood R: Childhood tuberculosis infection and disease: a spatial and temporal transmission analysis in a South African township.

    S Afr Med J 2009, 99:738-743. PubMed Abstract | PubMed Central Full Text OpenURL

  29. Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, Massari M, Salmaso S, Tomba GS, Wallinga J, Heijne J, Sadkowska-Todys M, Rosinska M, Edmunds WJ: Social contacts and mixing patterns relevant to the spread of infectious diseases.

    PLoS Med 2008, 5:e74. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  30. Selwyn PA, Hartel D, Lewis VA, Schoenbaum EE, Vermund SH, Klein RS, Walker AT, Friedland GH: A prospective study of the risk of tuberculosis among intravenous drug users with human immunodeficiency virus infection.

    N Engl J Med 1989, 320:545-550. PubMed Abstract | Publisher Full Text OpenURL

  31. Sheriff FG, Manji KP, Manji MP, Chagani MM, Mpembeni RM, Jusabani AM, Alwani ZR, Karimjee TS: Latent tuberculosis among pregnant mothers in a resource poor setting in Northern Tanzania: a cross-sectional study.

    BMC Infect Dis 2010, 10:52. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  32. Riley RL: Transmission and Environmental Control of Tuberculosis. In Tuberculosis. A Comprehensive International Approach. Volume 66. Edited by Reichman LB, Hershfield ES. New York: Marcel Dekker, Inc; 1993::123-136. OpenURL

  33. Sutherland I: The Evolution of Clinical Tuberculosis in Adolescents.

    Tubercle 1966, 47:308. OpenURL

  34. Snider DE Jr, Cauthen GM: Tuberculin skin testing of hospital employees: infection, "boosting," and two-step testing.

    Am J Infect Control 1984, 12:305-311. PubMed Abstract | Publisher Full Text OpenURL

  35. Al Mazrou AM: Booster effect of two-step tuberculin skin testing among hospital employees from areas with a high prevalence of tuberculosis.

    Infect Control Hosp Epidemiol 2004, 25:1117-1120. PubMed Abstract | Publisher Full Text OpenURL

  36. Menzies R, Vissandjee B, Amyot D: Factors associated with tuberculin reactivity among the foreign-born in Montreal.

    Am Rev Respir Dis 1992, 146:752-756. PubMed Abstract OpenURL

  37. Menzies R, Vissandjee B, Rocher I, St Germain Y: The booster effect in two-step tuberculin testing among young adults in Montreal.

    Ann Intern Med 1994, 120:190-198. PubMed Abstract | Publisher Full Text OpenURL

  38. Friedland IR: The booster effect with repeat tuberculin testing in children and its relationship to BCG vaccination.

    S Afr Med J 1990, 77:387-389. PubMed Abstract OpenURL

  39. Suwanagool S, Chuenarom V, Pechthanon L, Sonjai A, Leelarasamee A, Pathrakom C: A comparative study of tuberculin skin test reactivity between asymptomatic HIV-1 seropositive subjects and healthy volunteers.

    Asian Pac J Allergy Immunol 1995, 13:139-144. PubMed Abstract OpenURL

  40. Cobelens FG, Egwaga SM, van Ginkel T, Muwinge H, Matee MI, Borgdorff MW: Tuberculin skin testing in patients with HIV infection: limited benefit of reduced cutoff values.

    Clin Infect Dis 2006, 43:634-639. PubMed Abstract | Publisher Full Text OpenURL

  41. Markowitz N, Hansen NI, Wilcosky TC, Hopewell PC, Glassroth J, Kvale PA, Mangura BT, Osmond D, Wallace JM, Rosen MJ, Reichman LB: Tuberculin and anergy testing in HIV-seropositive and HIV-seronegative persons. Pulmonary Complications of HIV Infection Study Group.

    Ann Intern Med 1993, 119:185-193. PubMed Abstract | Publisher Full Text OpenURL

  42. Huebner RE, Schein MF, Hall CA, Barnes SA: Delayed-type hypersensitivity anergy in human immunodeficiency virus-infected persons screened for infection with Mycobacterium tuberculosis.

    Clin Infect Dis 1994, 19:26-32. PubMed Abstract | Publisher Full Text OpenURL

  43. Buchanan M, Fransman D: BCG Vaccination change over in South Africa. [] webcite

    Child Health Unit

    Accessed 10 October 2010


  44. Grindulis H, Baynham MI, Scott PH, Thompson RA, Wharton BA: Tuberculin response two years after BCG vaccination at birth.

    Arch Dis Child 1984, 59:614-619. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  45. Johnson LF, Dorrington RE: Modelling the demographic impact of HIV/AIDS in South Africa and the likely impact of interventions.

    Demographic Research 2006, 14:541-574. OpenURL

  46. Actuarial Society of South Africa: ASSA2003 AIDS and Demographic model. [] webcite


    Accessed 10 October 2010

  47. Pettifor AE, Levandowski BA, MacPhail C, Padian NS, Cohen MS, Rees HV: Keep them in school: the importance of education as a protective factor against HIV infection among young South African women.

    Int J Epidemiol 2008, 37:1266-1273. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  48. Pettifor AE, Rees HV, Kleinschmidt I, Steffenson AE, MacPhail C, Hlongwa-Madikizela L, Vermaak K, Padian NS: Young people's sexual health in South Africa: HIV prevalence and sexual behaviors from a nationally representative household survey.

    AIDS 2005, 19:1525-1534. PubMed Abstract | Publisher Full Text OpenURL

  49. Daley CL, Small PM, Schecter GF, Schoolnik GK, McAdam RA, Jacobs WR Jr, Hopewell PC: An outbreak of tuberculosis with accelerated progression among persons infected with the human immunodeficiency virus. An analysis using restriction-fragment-length polymorphisms.

    N Engl J Med 1992, 326:231-235. PubMed Abstract | Publisher Full Text OpenURL

  50. Krivinka R, Drapela J, Kubik A, Dankova D, Krivanek J, Ruzha J, Mikova Z, Hejdova E: Epidemiological and Clinical Study of Tuberculosis in the District of Kolin, Czechoslovakia: second report (1965-72).

    Bulletin of World Health Organisation 1974, 51:59-69. OpenURL

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