Hostname: page-component-7c8c6479df-r7xzm Total loading time: 0 Render date: 2024-03-19T03:38:03.579Z Has data issue: false hasContentIssue false

Sex and age differences in Mycobacterium tuberculosis infection in Brazil

Published online by Cambridge University Press:  08 June 2018

P. Fernandes*
Affiliation:
Division of Infectious Diseases, Boston Children's Hospital, Boston, Massachusetts, USA Preventive Medicine Program, Department of Medicine, University of California, Los Angeles, Los Angeles, California, USA
Y. Ma
Affiliation:
Boston University School of Public Health, Boston, MA, USA
M. Gaeddert
Affiliation:
Section of Infectious Diseases, Department of Medicine, Boston Medical Center and Boston University School of Medicine, Boston, Massachusetts, USA
T. Tsacogianis
Affiliation:
Boston University School of Public Health, Boston, MA, USA
P. Marques-Rodrigues
Affiliation:
Núcleo de Doenças Infecciosas (NDI), Universidade Federal do Espírito Santo (UFES), Vitória, Brazil
G. Fregona
Affiliation:
Núcleo de Doenças Infecciosas (NDI), Universidade Federal do Espírito Santo (UFES), Vitória, Brazil
A. Loomans
Affiliation:
Boston University School of Public Health, Boston, MA, USA
E. C. Jones-López
Affiliation:
Section of Infectious Diseases, Department of Medicine, Boston Medical Center and Boston University School of Medicine, Boston, Massachusetts, USA
R. Dietze
Affiliation:
Núcleo de Doenças Infecciosas (NDI), Universidade Federal do Espírito Santo (UFES), Vitória, Brazil
J. J. Ellner
Affiliation:
Section of Infectious Diseases, Department of Medicine, Boston Medical Center and Boston University School of Medicine, Boston, Massachusetts, USA
L. F. White
Affiliation:
Boston University School of Public Health, Boston, MA, USA
N. S. Hochberg
Affiliation:
Boston University School of Public Health, Boston, MA, USA Section of Infectious Diseases, Department of Medicine, Boston Medical Center and Boston University School of Medicine, Boston, Massachusetts, USA
*
Author for correspondence: P. Fernandes, E-mail: pfernandes@mednet.ucla.edu
Rights & Permissions [Opens in a new window]

Abstract

Globally, the prevalence of tuberculosis (TB) disease is higher in males. This study examined the effect of sex and age on Mycobacterium tuberculosis (Mtb) infection. Demographic and exposure data were collected on household contacts of sputum smear-positive pulmonary TB patients in Brazil. Contacts with tuberculin skin test induration ⩾10 mm at baseline or 12 weeks were considered Mtb infected. The study enrolled 917 household contacts from 160 households; 508 (55.4%) were female, median age was 21.0 years (range 0.30–87.0) and 609 (66.4%) had Mtb infection. The proportion infected increased with age from 63.3% in girls <5 years to 75.4% in women ⩾40 years and from 44.9% in boys <5 years to 73.6% in men ⩾40 years. Multivariable modelling showed the odds of infection increased between age 5 and 14 years among female contacts (OR 1.5 per 5-year age increase; 95% CI 1.1–2.2; P = 0.02) and between ages 0–4 and 15–39 years among male contacts (OR 2.7, 95% CI 0.83–8.9 and 1.1, 95% CI 0.99–1.3 per 5-year age increase; P = 0.10, 0.07, respectively). The study suggests that the age at which Mtb infection increases most is different in females compared with males. Studies are needed to explore whether these findings are due to differences in host susceptibility, exposure outside the household or other factors.

Type
Original Paper
Copyright
Copyright © Cambridge University Press 2018 

Introduction

There were 10.4 million cases of tuberculosis (TB) globally in 2015 and 1.8 million people died from the disease [1]. A state of clinically absent disease with a persistent immune response to stimulation by Mycobacterium tuberculosis (Mtb) antigens is defined as latent TB infection [2], hereby referred to as ‘Mtb infection’ [Reference Salgame3]. It is estimated that 5–10% of those with Mtb infection will progress to clinically active disease over their lifetime [2].

Controlling TB requires understanding which epidemiological and medical factors place individuals at higher risk of TB and which are protective. It is surprising that little is known about the potential impact of one's sex given the fact that the male:female ratio of TB cases is 1.7:1 worldwide and as high as 3.1:1 in certain settings such as Vietnam; the excess in male cases becomes most pronounced in early adulthood [1, Reference Roelsgaard, Iversen and Bløcher4, Reference Horton5]. Postulated reasons include greater exposure of males to infectious TB cases outside of the home, higher prevalence of risk factors for progression to disease in males (e.g. smoking, alcohol use) and under-detection of females due to differences in the volume or quality of expectorated sputum or access to care, although the latter likely varies greatly by setting [Reference Holmes, Hausler and Nunn6Reference Ramsay10].

Data from community-based surveys suggest that some of the sex differences observed in active TB may reflect differences in Mtb infection prevalence between males and females [Reference Roelsgaard, Iversen and Bløcher4, Reference Holmes, Hausler and Nunn6, Reference Escobar11]. Surveys conducted between 1948 and 1951 in 15 countries concluded that the prevalence of infection was equal in males and females at younger ages, but that around puberty, male prevalence began to exceed that of females [Reference Nyboe12]. Similar patterns have been found more recently in India, Korea and several African countries [Reference Roelsgaard, Iversen and Bløcher4, Reference Fine13Reference Lee16]. As with the active TB sex differences, this increased likelihood of infection in males has been attributed to greater exposure in the community after adolescence, but this hypothesis is difficult to confirm [Reference Holmes, Hausler and Nunn6].

In the present study, we assess how the prevalence of Mtb infection varies with sex and age. We sought to determine whether there are biological sex differences that could affect the risk of Mtb infection. By using data from a study of pulmonary TB cases and their household contacts in Brazil, we overcame one major limitation of community-based studies in that comparable household exposure to infectious cases was known.

Materials and methods

Study design

This was a secondary data analysis using previously collected data from a household contact study conducted in Vitória, Brazil between 2008 and 2013. Study methods have been reported previously [Reference Jones-López17]. Briefly, inclusion criteria for pulmonary TB patients were (1) age ⩾18 years; (2) cough ⩾3 weeks; (3) new TB episode with sputum smear ⩾2+ acid-fast bacilli (AFB) and subsequent Mtb growth in culture; and (4) having ⩾3 household contacts. Index TB patients who were HIV-infected (or refused testing) or had a history of TB treatment were excluded from the primary study of the effect of Mtb strain on disease transmission. The rationale behind this was that HIV infection could alter host-susceptibility to and/or transmission of TB disease [Reference Carvalho18, Reference Martinez19] and prior treatment could affect the current strain of Mtb and the immune response [Reference Borrell, Trauner and Gagneux20]. In addition, the incidence rate of HIV and TB coinfection was relatively low in Brazil (<10/100 000 population) [21], preventing robust analysis in this coinfected sub-population without a marked increase in study sample size. All household contacts of the index case were enrolled within 2 weeks of index case enrolment if they met ⩾1 of the following culturally-adapted criteria for close contact with the index case for ⩾3 months: (1) sleeping under the same roof or sharing meals ⩾5 days/week; (2) watching television nights/weekends, or; (3) other significant contact (85% visited the household ⩾18 days/month). For the purposes of this analysis, household contacts were excluded if they had a history of TB disease.

Study measurements

Demographic and clinical data were collected to assess infectiousness of index cases and household contact exposure. Index case cough severity was measured using a self-reported visual analogue cough scale (1–10 for increasing severity) [Reference Raj and Birring22], which was categorised into three groups (0–3, 4–6 and 7–10). Functional status was recorded using the Karnofsky performance scale (0–100; ⩽70 represents a significant restriction of daily activities) [Reference Karnofsky and Burchenal23]. Up to three sputum specimens were collected for AFB smear microscopy and culture at enrolment. Chest X-rays (CXR) were performed and scored based on a validated score (percentage of lung infected plus 40 points for cavitation) [Reference Ralph24].

Clinical information was collected on household contacts and all TB suspects were referred for further evaluation to the Municipal TB clinic. Exposure of the household contact to the index case was assessed by sleeping proximity, contact hours, whether the contact was the caregiver, a number of meals shared and relationship (categorised as spousal, parental/child or other). Bacille Calmette-Guérin (BCG) vaccination status was based on visualisation of a scar; uncertain scar status was considered vaccinated. A tuberculin skin test (TST; Tubersol®, Sanofi Pasteur) was performed at baseline; those with TST <10 mm had a repeat TST after 8–12 weeks to identify TST converters. To ensure consistency in test performance, staff were trained in TST placement and reading and completed inter- and intra-reader evaluations (κ  > 90%) [Reference Jones-López17]. For this analysis, Mtb infection was defined as TST ⩾10 mm at baseline or follow-up.

Statistical methods, ethics

We assessed univariate associations between index case and contact characteristics and Mtb infection status (our outcome of interest) by considering these one at a time in a model with contact age, sex and their interaction (our covariates of principal interest). Attributes with P < 0.10 in univariate analyses and/or biologically plausible attributes were included in the multivariable logistic regression models predicting the same outcome of interest. Both univariate and multivariable analyses were performed by logistic regression models fit using generalised estimating equations with a compound symmetry structure to account for household clustering. Using a penalised spline transformation and a piecewise-linear model of contact age, we established age categories of 0–4, 5–14, 15–39 and ⩾40 to determine the relationship between age and odds of Mtb infection in contacts within each age group. The oldest age category was chosen as such because once individuals were 40 years old, the risk of TST infection did not change with age. We report odds ratios for a 1- and 5-year age increase. We report the adjusted probability of Mtb infection by age and sex by transforming the odds to the probability scale and calculate corresponding Wald-type confidence intervals. Analyses were conducted in SAS 9.3 (SAS Institute, Cary NC) and R version 3.2.2 (r-project.org).

The study was approved by the Comitê de Ética em Pesquisa do Centro de Ciências da Saúde – Universidade Federal do Espírito Santo, the Comissão Nacional de Ética em Pesquisa (CONEP) and the Institutional Review Boards of Boston University Medical Campus and New Jersey Medical School–Rutgers University (formerly University of Medicine and Dentistry of New Jersey). Written informed consent and assent were obtained in accordance with age-specific ethical guidelines from participating institutions.

Results

Index case and contact characteristics

Details of study non-participation have been reported previously [Reference Jones-López17]. Among the 160 participating index cases, the median age was 35.7 years (range: 18.0–81.8 years) and 107 (66.9%) of them were male (Table 1). The median duration of symptoms prior to enrolment was 13.0 weeks (range: 2.0–52.2 weeks). The median CXR score was 73.3 (range: 0.0–140.0); sputum AFB grade was 3+ in 128 (80.0%) of cases.

Table 1. Characteristics of index pulmonary tuberculosis case-patients in Vitória, Brazil (N = 160)

AFB, acid-fast bacilli; N/A, not applicable; CXR, chest X-ray; HHCs, household contacts.

a Karnofsky score = functional ability performance scale.

Among 934 household contacts, 17 (2.0%) had a history of TB disease and were excluded. For the remaining 917 household contacts, they tended to be of the opposite gender of the index case (P = 0.03), their median age was 21.0 years (range: 0.30–87.0 years), 508 (55.4%) of them were female and 688 (75.0%) were BCG-vaccinated (Tables 2 and 3). The largest proportion of contacts (404; 44.1%) slept in a different room from the index case but in the same house. Contacts commonly spent ⩽6 h (281; 30.6%) or 7–12 h (277; 30.2%) per day with the index case; most (62.4%) shared at least one meal with the index case. Index cases were parents or children of 319 (34.8%) contacts, brothers or sisters of 142 (16.0%), spouses of 94 (10.3%), other relatives of 247 (27.0%) and unrelated to 115 (13.0%). Male household contacts were younger than female household contacts (median 19.0 vs. 23.0 years; P < 0.01), less likely to provide care to the IC (15.2% vs. 36.0%; P < 0.01) and more likely to have a detectable BCG scar (76.8% vs. 73.6%; P = 0.03).

Table 2. Index case characteristics for 917 household contacts in Vitória, Brazil, stratified by the gender of the contactsa

HHCs, household contacts; AFB, acid-fast bacilli; NA, not applicable; CXR, chest X-ray.

a Data for each index case are reported as many times as the number of household contacts they have (e.g. data are reported three times for the same index case if they have three household contacts).

b Karnofsky score = functional ability performance scale.

Table 3. Characteristics of household contacts of pulmonary tuberculosis case-patients in Vitória, Brazil (N = 917)

BMI, body mass index; TST, tuberculin skin test; BCG, Bacillus Calmette-Guérin vaccine; IC, index case; HHC, household contact.

a TST positive if induration ⩾10 mm.

Mtb infection by household contact age and sex

Overall, 609 (66.4%) household contacts had Mtb infection (Table 3). In a univariate analysis to identify potential predictors of Mtb infection, age and sex emerged as significant predictor variables (P-values of <0.01 and 0.05, respectively, Supplemental Table S1). The unadjusted distribution of Mtb infection with age showed that the proportion of Mtb-infected household contacts increased with age from 63.3% in girls age <5 years and 44.9% in boys <5 years to relatively equal proportions in ages >5 years (Fig. 1). By age ⩾40, the proportion of TST-positives was 73.6% in males and 75.4% in females (Fig. 1). The differences in Mtb infection between sexes were not significant within any age category in the unadjusted analysis.

Fig. 1. Distribution of Tuberculin Skin Test positivity (⩾10 mm) of all household contacts by age categories and sex. Numbers within each bar represents the number of study subjects in that category. Differences between males and females within each age group were not significant (P-values <0.05).

Covariates in the multivariable models were biologically plausible and/or significant on univariate analysis (P < 0.10 in Supplementary Table S1) and included measures of index case disease severity (visual analogue cough scale category, AFB grade, CXR score), contact susceptibility (BCG status) and exposure to index case (sleeping proximity, hours of contact, relation). The final multivariable models included 827 contacts (Table 4); because of missing data, 90 were excluded (34 missing TST results, 35 missing BCG and 36 missing relevant exposures as seen in Table 3). The odds of Mtb infection increased with age until 40 years in both sexes but was not significantly different between females and males in any age group (Table 4). Among females, the odds increased most between 5 and 14 years of age (OR 1.5 per 5-year increase; 95% CI 1.1–2.2; P = 0.02). In contrast, males had the greatest increase of Mtb infection between 0 and 4 years of age (OR 2.7 per 5-year increase; 95% CI 0.83, 8.9; P = 0.10) but the only borderline statistically significant increase in infection occurred between 15 and 39 years of age (OR 1.1 per 5-year increase; 95% CI 0.99–1.3; P = 0.07). Within each sex, the rate of change of Mtb infection was not significantly different between age categories. By age 14, females had a somewhat higher adjusted probability of infection compared with males (0.60, 95% CI 0.58–0.62 in females vs. 0.53, 95% CI 0.51–0.55 in males). Among older contacts, ages 15–39 years, the probability of infection was similar between sexes (range: 0.62–0.74 for females; range: 0.54–0.75 for males).

Table 4. Adjusted 1- and 5-year odds of Mycobacterium tuberculosis infection stratified by age and sex of household contact (N = 827)a

HHC, Household contact; N/A, Not applicable.

a Variables included in the multivariable model: household contact age categories, sex, interaction term of age category and sex, sleeping proximity to the index case, hours of contact with the index case, relationship to the index case and presence of BCG scar; and index case sputum smear grade, visual analogue cough scale category and chest X-ray score.

b Odds ratio and 95% confidence interval of Mtb infection with increasing age. Within each sex, the rate of change of Mtb infection was not significantly different between age categories.

c Odds ratio for this age group not reported since the odds are flat for this group and hence there is no change in odds ratio as age increases.

d Within each age group, the rate of change of Mtb infection was not significantly different between sexes.

*P-values for changes in odds ratios within each age category.

Discussion

The objective of this study was to determine whether there are potential sex differences in Mtb infection by analyzing results from a household contact study in Brazil. We assessed differences in Mtb infection by sex and age that occurred independent of exposure to the TB case. This study found 66.4% of household contacts had evidence of Mtb infection compared with the community prevalence of 33.0% [2], reflecting greater exposure in a closed setting. The prevalence of infection increased with age, with the unadjusted analysis showing a 12.0% and 29.0% difference between the youngest and the oldest age groups in females and males, respectively. In multivariable models, the increase in infection was significant at a younger age in females (ages 5–14 years) in contrast to males, in whom the increase in infection was borderline significant at an older age (ages 15–39 years). Young female contacts (age 5–14) also had an overall higher probability of infection within that age group that was not seen for males the same age nor for females of other age groups. Sex differences in Mtb prevalence disappeared by age 40.

Household contact studies have shown increased Mtb infection risk with older age [Reference Lienhardt8, Reference Lee16, Reference Espinal25Reference Akhtar and Rathi27] and among male contacts for some [Reference Lienhardt8, Reference Lee16]. To our knowledge, however, only one other household contact study directly examined the interaction of age and sex [Reference Lienhardt8]. In a study in The Gambia, the odds of infection increased with age similarly in both sexes up to age 15, after which males were at a greater risk; the model did not examine how the effects varied within age groups after age 15. Our study, by contrast, evaluated the effect of sex in older age groups as well and we found young girls and older men were more likely to be infected. It is possible that our findings were different because we controlled for other factors that may have influenced Mtb infection (including index case sputum grade and contact hours between case and contact, among others). Another possibility is that we observed sex differences at a younger age because social interactions and exposure within households and the community differ between countries.

There are several potential explanations why young girls (age 5–14) are at increased odds of Mtb infection. It is plausible that young girls are more exposed to Mtb in the household than young boys, perhaps because they perform more activities within the home; however, we controlled for the amount of time spent with the index case and whether the household contact was their caregiver. Another explanation could be biological differences in susceptibility to Mtb infection. Studies in mice have shown that sex hormones may alter susceptibility to mycobacterial infections [Reference Yamamoto28, Reference Tsuyuguchi29]. In experimental conditions, estrogen is protective against Mtb infection, increasing the Th1 immune response, cytokine production (i.e. TNFα, IFNγ) and macrophage activity that facilitate control of Mtb [Reference Fish30Reference Molloy32]. Since girls 5–14 years are mostly pre-pubertal (average age of menarche in Brazil is 11.7 years) [Reference Feibelmann33] and the suggested protective effect of estrogen has not yet become dominant, they could be more susceptible to infection.

Our finding of increasing odds of Mtb infection in older males has been seen in contact studies [Reference Lienhardt8, Reference Lee16]. It has been suggested that older males have increased exposure through greater social contact, particularly to other adult males in the community [Reference Dodd34]. An increased prevalence of risk factors among males like smoking [Reference Watkins and Plant9] and alcoholism [Reference Narasimhan35], which increase susceptibility to Mtb infection, could also explain the higher rate of infection in older males. Biological differences between sexes in immune responses to Mtb infection may play a role as well [Reference Nhamoyebonde and Leslie36].

The increased odds of infection for males age 15–39 years is notable given the higher prevalence of TB disease in adult males compared with adult females [1]. The increased prevalence of TB disease in adult males may reflect the fact that those recently infected are more likely to develop the active disease; hence more Mtb infection in adult males suggests greater TB disease prevalence. Although our study was not designed to assess progression to disease, it is also possible that due to biological reasons or comorbidities (e.g. alcohol use), males may be more likely to progress from infection to TB disease [Reference Holmes, Hausler and Nunn6Reference Ramsay10]. Gender differences in rates of progression to disease have been reported previously [Reference Roelsgaard, Iversen and Bløcher4].

Our study had a few limitations. Although we attempted to measure interaction between the household contact and index case as quantitatively as possible (e.g. number of hours spent together, number of meals shared, bed sharing, etc), one cannot easily measure all interactions between individuals; hence, it is possible that the observed differences in infection reflected unmeasured differences in household exposure. Future studies could evaluate more objective measures of exposure to Mtb (such as aerosol production) [Reference Jones-López37] as well as sociological measurements of family interactions to better define transmission dynamics. Similarly, we were unable to assess exposure to all potentially infectious individuals outside the household, so it is plausible (and likely) that some of the risk observed in older males reflected community exposure. Even so, the observed sex differences in the younger ages still underscore that biologic differences may play a role in the timing of Mtb infection. Index cases with HIV infection and household contacts with a prior history of TB disease were excluded from the analysis and therefore extrapolation of our results to these populations may be inappropriate. Furthermore, we used a visible scar as evidence of BCG vaccination, but scar formation can be variable [Reference Santiago38, Reference Dhanawade39]. We defined infection as TST ⩾10 mm to standardise interpretation of results irrespective of BCG vaccination and non-tuberculous mycobacteria (NTM) exposure, which could result in false-positive reactions. It has been shown, however, that false-positive TST reactions due to BCG vaccination at infancy (as in Brazil) and NTM sensitivity are relatively low (~8.5% and ~2.3%, respectively) [Reference Farhat40]. Notably, our results were similar when defining infection as TST ⩾5 mm and ⩾10 mm (data not shown). Lastly, it is possible that our P-values could have been underestimated since we determined the parameters of a penalised spline used to determine our model from the data we ultimately used in the analysis.

Conclusions

The odds and timing of Mtb infection in close contacts of TB cases varies by their sex. We found the odds of Mtb infection increased significantly with age in young girls (age 5–14) while in young adult males (age 15–39), the increase in odds was borderline significant. Studies are needed to determine whether the observed differences are due to sex-based, hormonally mediated changes in biological susceptibility, particularly the development of a protective effect for girls after menarche.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0950268818001450.

Acknowledgements

We are grateful to the study staff and to the participants and their families. We also thank Dr C Robert Horsburgh, Jr for an insightful review of the manuscript.

This study was supported by NIH-NIAID award [U01 AI065663] (International Collaboration in Infectious Diseases Research) and funds from the Section of Infectious Diseases at Boston Medical Center and Núcleo de Doenças Infecciosas, Universidade Federal do Espírito Santo. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest

None.

Ethical standards

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

Disclaimers

None.

References

1.World Health Organization (2016) Global Tuberculosis Report 2016. Switzerland: World Health Organization.Google Scholar
2.World Health Organization (2017) WHO | Latent Tuberculosis Infection (LTBI). WHO. Available at http://www.who.int/tb/challenges/ltbi/en/ (Accessed 8 August 2017).Google Scholar
3.Salgame, P et al. (2015) Latent tuberculosis infection – Revisiting and revising concepts. Tuberculosis 95, 373384.Google Scholar
4.Roelsgaard, E, Iversen, E and Bløcher, C (1964) Tuberculosis in tropical Africa: an epidemiological study*. Bulletin of the World Health Organization 30, 459.Google Scholar
5.Horton, KC et al. (2016) Sex differences in tuberculosis burden and notifications in low- and middle-income countries: a systematic review and meta-analysis. PLOS Medicine 13, e1002119.Google Scholar
6.Holmes, CB, Hausler, H and Nunn, P (1998) A review of sex differences in the epidemiology of tuberculosis. The International Journal of Tuberculosis and Lung Disease 2, 96104.Google Scholar
7.Borgdorff, MW et al. (2000) Gender and tuberculosis: a comparison of prevalence surveys with notification data to explore sex differences in case detection. The International Journal of Tuberculosis and Lung Disease 4, 123132.Google Scholar
8.Lienhardt, C et al. (2003) Risk factors for tuberculosis infection in Sub-Saharan Africa. American Journal of Respiratory and Critical Care Medicine 168, 448455.Google Scholar
9.Watkins, RE and Plant, AJ (2005) Does smoking explain sex differences in the global tuberculosis epidemic? Epidemiology and Infection 134, 333.Google Scholar
10.Ramsay, A et al. (2009) Sputum, sex and scanty smears: new case definition may reduce sex disparities in smear-positive tuberculosis. The International Journal of Tuberculosis and Lung Disease 13, 613619.Google Scholar
11.Escobar, AL et al. (2004) Tuberculin reactivity and tuberculosis epidemiology in the Pakaanóva (Wari’) Indians of Rondônia, south-western Brazilian Amazon. The International Journal of Tuberculosis and Lung Disease 8, 4551.Google Scholar
12.Nyboe, J (1957) Interpretation of tuberculosis infection age curves. Bulletin of the World Health Organization 17, 319339.Google Scholar
13.Fine, PEM et al. (1999) Tuberculin sensitivity: conversions and reversions in a rural African population. The International Journal of Tuberculosis and Lung Disease 3, 962975.Google Scholar
14.Black, GF et al. (2001) Relationship between IFN-gamma and skin test responsiveness to Mycobacterium tuberculosis PPD in healthy, non-BCG-vaccinated young adults in Northern Malawi. The International Journal of Tuberculosis and Lung Disease 5, 664672.Google Scholar
15.Balasubramanian, R et al. (2004) Gender disparities in tuberculosis: report from a rural DOTS programme in south India. The International Journal of Tuberculosis and Lung Disease 8, 323332.Google Scholar
16.Lee, SJ et al. (2014) Risk factors for latent tuberculosis infection in close contacts of active tuberculosis patients in South Korea: a prospective cohort study. BMC Infectious Diseases 14, 566.Google Scholar
17.Jones-López, EC et al. (2014) Importance of cough and M. tuberculosis strain type as risks for increased transmission within households. PLOS ONE 9, e100984.Google Scholar
18.Carvalho, ACC et al. (2001) Transmission of Mycobacterium tuberculosis to contacts of HIV-infected tuberculosis patients. American Journal of Respiratory and Critical Care Medicine 164, 21662171.Google Scholar
19.Martinez, L et al. (2016) Infectiousness of HIV-seropositive patients with tuberculosis in a high-burden African setting. American Journal of Respiratory and Critical Care Medicine 194, 11521163.Google Scholar
20.Borrell, S and Trauner, A (2017) Strain diversity and the evolution of antibiotic resistance. In Gagneux, S (ed.), Strain Variation in the Mycobacterium tuberculosis Complex: Its Role in Biology, Epidemiology and Control. Cham: Springer International Publishing, pp. 263279.Google Scholar
21.World Health Organization. (2017) WHO | Tuberculosis country profiles. Available at http://www.who.int/tb/country/data/profiles/en/ (Accessed 18 October 2017).Google Scholar
22.Raj, AA and Birring, SS (2007) Clinical assessment of chronic cough severity. Pulmonary Pharmacology & Therapeutics 20, 334337.Google Scholar
23.Karnofsky, DA and Burchenal, JH. (1949) The Clinical Evaluation of Chemotherapeutic Agents in Cancer. Evaluation of Chemotherapeutic Agents. New York: Columbia University Press, pp. 191205.Google Scholar
24.Ralph, AP et al. (2010) A simple, valid, numerical score for grading chest x-ray severity in adult smear-positive pulmonary tuberculosis. Thorax 65, 863869.Google Scholar
25.Espinal, MA et al. (2000) Infectiousness of Mycobacterium tuberculosis in HIV-1-infected patients with tuberculosis: a prospective study. The Lancet 355, 275280.Google Scholar
26.Lewinsohn, DA et al. (2008) Whole blood interferon-gamma responses to Mycobacterium tuberculosis antigens in young household contacts of persons with tuberculosis in Uganda. PLOS ONE 3, e3407.Google Scholar
27.Akhtar, S and Rathi, SK (2009) Multilevel modeling of household contextual determinants of tuberculin skin test positivity among contacts of infectious tuberculosis patients, Umerkot, Pakistan. The American Journal of Tropical Medicine and Hygiene 80, 351358.Google Scholar
28.Yamamoto, Y et al. (1991) Sex differences in host resistance to Mycobacterium marinum infection in mice. Infection and Immunity 59, 40894096.Google Scholar
29.Tsuyuguchi, K et al. (2001) Effect of oestrogen on Mycobacterium avium complex pulmonary infection in mice. Clinical and Experimental Immunology 123, 428434.Google Scholar
30.Fish, EN (2008) The X-files in immunity: sex-based differences predispose immune responses. Nature Reviews Immunology 8, 737744.Google Scholar
31.O'Garra, A et al. (2013) The immune response in tuberculosis. Annual Review of Immunology 31, 475527.Google Scholar
32.Molloy, EJ et al. (2003) Sex-specific alterations in neutrophil apoptosis: the role of estradiol and progesterone. Blood 102, 26532659.Google Scholar
33.Feibelmann, TCM et al. (2015) Puberty in a sample of Brazilian schoolgirls: timing and anthropometric characteristics. Archives of Endocrinology and Metabolism 59, 105111.Google Scholar
34.Dodd, PJ et al. (2016) Age- and sex-specific social contact patterns and incidence of Mycobacterium tuberculosis infection. American Journal of Epidemiology 183, 156166.Google Scholar
35.Narasimhan, P et al. (2017) High rates of latent TB infection in contacts and the wider community in South India. Transactions of The Royal Society of Tropical Medicine and Hygiene 111, 5561.Google Scholar
36.Nhamoyebonde, S and Leslie, A (2014) Biological differences between the sexes and susceptibility to tuberculosis. Journal of Infectious Diseases 209, S100S106.Google Scholar
37.Jones-López, EC et al. (2013) Cough aerosols of Mycobacterium tuberculosis predict new infection. A household contact study. American Journal of Respiratory and Critical Care Medicine 187, 10071015.Google Scholar
38.Santiago, EM et al. (2003) A prospective study of Bacillus Calmette-Guérin scar formation and tuberculin skin test reactivity in infants in Lima, Peru. Pediatrics 112, e298e298.Google Scholar
39.Dhanawade, SS et al. (2015) Scar formation and tuberculin conversion following BCG vaccination in infants: a prospective cohort study. Journal of Family Medicine and Primary Care 4, 384387.Google Scholar
40.Farhat, M et al. (2006) False-positive tuberculin skin tests: what is the absolute effect of BCG and non-tuberculous mycobacteria? [Review Article]. The International Journal of Tuberculosis and Lung Disease 10, 11921204.Google Scholar
Figure 0

Table 1. Characteristics of index pulmonary tuberculosis case-patients in Vitória, Brazil (N = 160)

Figure 1

Table 2. Index case characteristics for 917 household contacts in Vitória, Brazil, stratified by the gender of the contactsa

Figure 2

Table 3. Characteristics of household contacts of pulmonary tuberculosis case-patients in Vitória, Brazil (N = 917)

Figure 3

Fig. 1. Distribution of Tuberculin Skin Test positivity (⩾10 mm) of all household contacts by age categories and sex. Numbers within each bar represents the number of study subjects in that category. Differences between males and females within each age group were not significant (P-values <0.05).

Figure 4

Table 4. Adjusted 1- and 5-year odds of Mycobacterium tuberculosis infection stratified by age and sex of household contact (N = 827)a

Supplementary material: File

Fernandes et al. supplementary material

Table S1

Download Fernandes et al. supplementary material(File)
File 26.9 KB