Abstract
Background
Although households of tuberculosis (TB) cases represent a setting for intense transmission of Mycobacterium tuberculosis, household exposure accounts for <20% of transmission within a community. The aim of this study was to estimate excess risk of M. tuberculosis infection among household and extra-household contacts of index cases.
Methods
We performed a cross-sectional study in Kampala, Uganda, to delineate social networks of TB cases and matched controls without TB. We estimated the age-stratified prevalence difference of TB infection between case and control networks, partitioned as household and extra-household contacts.
Results
We enrolled 123 index cases, 124 index controls, and 2415 first-degree network contacts. The prevalence of infection was highest among household contacts of cases (61.5%), lowest among household contacts of controls (25.2%), and intermediary among extra-household TB contacts (44.9%) and extra-household control contacts (41.2%). The age-adjusted prevalence difference between extra-household contacts of cases and their controls was 5.4%. The prevalence of infection was similar among the majority of extra-household case contacts and corresponding controls (47%).
Conclusions
Most first-degree social network members of TB cases do not have adequate contact with the index case to experience additional risk for infection, but appear instead to acquire infection through unrecognized exposures with infectious cases in the community.
Keywords: tuberculosis, latent tuberculosis infection, household contact investigation, transmission, tuberculin skin test
Most first-degree social network members of tuberculosis cases do not have adequate contact with the index case to experience additional risk for infection, but appear to acquire infection through exposures with infectious cases in unrecognized contact networks in the community.
The global burden of tuberculosis (TB) peaked in 2000 and has since declined by 1.5% per year. Although encouraging, there are unambiguous signs that global control of TB is not yet achieved, especially in sub-Saharan Africa. Tuberculosis is the ninth leading cause of death worldwide and the leading cause of death from an infectious disease, as >1.3 million people die per year [1].
The cornerstone of TB control today depends on the detection and treatment of infectious cases. The household of an infectious index case an ideal setting for active case finding [2, 3] because it is an environment of intense transmission of Mycobacterium tuberculosis [4]. The effectiveness of household investigation on TB control, however, is unclear [5–7]. One reason for its limited effectiveness is that <20% of TB infection in a community is attributable to household exposures [8–12], meaning that most new infections occur through exposures outside of the household. What remains unknown are the patterns of adequate contact between infectious cases and their contacts in the community. Anecdotal reports of well-documented outbreaks show that TB is transmitted in community settings, such as homeless shelters, bars, prisons, and hospitals [13–16], but these studies do not quantify the rate of transmission nor do they delineate the full scope of interactions between index cases and their extra-household contacts.
The current study was designed to estimate community transmission of M. tuberculosis by evaluating both household and extra-household contacts of infectious index cases of TB. Our hypothesis was that infectious TB cases place some of their first-degree social network members at high risk for infection during the weeks and months before diagnosis whether they live in the household or not. To address this hypothesis, we used self-reported social networks of TB cases to create a formal framework for estimating both household and extra-household transmission.
METHODS
Between June 2013 and January 2017, we conducted a cross-sectional study of index TB cases and their first-degree contacts within egocentric social networks in Lubaga Division, Kampala, Uganda. We enrolled index TB cases 15 years or older through Lubaga Hospital and the Kitebi and Kawaala clinics of the Kampala Capital City Authority. All TB cases were registered in and treated by the Uganda National Tuberculosis Control Program. A case of infectious TB was defined as a patient with symptoms consistent with pulmonary TB who had at least 1 positive sputum smear for acid-fast bacilli. Index controls were selected from residents of Lubaga Division who did not have TB as determined by symptom screening and were frequency-matched to the index cases based on age, sex, and parish.
To identify first-degree contacts from the networks of TB cases and index controls, we performed structured interviews of index participants to list individuals with whom the participant had a personal relationship. To limit recall bias, we used standard prompts and recent time-frames [17]. Contacts were classified as household contacts if they resided in the household of an index participant for the previous 3 months and had eaten meals in the household at least weekly; otherwise, contacts were classified as extra-household.
Index participants and first-degree contacts in the study were evaluated for active TB, latent TB infection (LTBI), and human immunodeficiency virus (HIV) infection using routine interviews and diagnostic testing. All TB cases were confirmed by growth of M. tuberculosis in culture. The tuberculin skin test was performed using the Mantoux method (Tubersol, Sanofi Pasteur). LTBI was defined as an induration of ≥10 mm among HIV-seronegative contacts and 5 mm among children and HIV-seropositive contacts [18]. Tuberculous infection was defined as infection with M. tuberculosis, either as active TB or as LTBI. Contacts who did not meet definitions for TB infection were classified as uninfected.
The main objective of this analysis was to determine the excess risk of TB by comparing the prevalence of TB infection among the first-degree contacts of the index cases and controls in both household and extra-household settings. The sample size was chosen to detect a 12.5% difference between household contacts of index cases and their controls and a 6.5% difference between extra-household contacts of index cases and controls, using a 5% error and 80% power. After determining the characteristics of index TB cases and frequency-matched controls, we stratified the analysis by household or extra-household status within the index groups of case or control to compare the distributions of the prevalence of infection among contacts. Because of imbalances in age, we stratified further by age category (<15 years and ≥15 years) to evaluate for confounding and effect modification. To estimate the excess risk of TB infection [19] among the household and extra-household contacts of index cases, we calculated the age-adjusted prevalence difference between contacts of index cases and controls. We also identified a subset of close extra-household contacts to determine whether social proximity was associated with infection. We categorized close contact using kin relationship to the index and the index’s perception of social closeness. We performed a hierarchical, generalized linear model [20] to account for nested exposure within the social networks of index cases and potential residual confounding. We estimated the prevalence difference according to contact setting, controlling sex, and HIV serostatus, and stratified by age category. Data were analyzed using SAS version 9.3 and R version 3.5.1 software packages.
The study was approved by the University of Georgia Institutional Review Board, the Higher Degrees Research and Ethics Committee at Makerere University School of Public Health, and the Uganda National Council for Science and Technology.
RESULTS
We enrolled 247 index participants: 123 TB cases and 124 frequency-matched controls (Figure 1). Index participants were similar as regards to matched characteristics, education, and religious affiliation (Table 1), though differed by marital status. The mean age was 30.6 years (standard deviation [SD], 10.1 years) for index cases and 32.0 years (SD, 9.5 years) for controls. HIV prevalence was higher among TB cases than among index controls. The majority of cases had a smear grade of 3+; by design, index controls were smear negative. Index participants reported 2415 first-degree contacts, of whom 2050 (85%) were traced and agreed to participate in the study (Figure 1). Of the 2050 contacts, 1006 (49%) were contacts of TB cases and 1044 (51%) were contacts of index controls. Of the 1006 TB case contacts, 382 (38%) were household contacts and 624 (62%) were extra-household contacts; of the 1044 control contacts, 214 (21%) were household contacts and 830 (79%) extra-household contacts. Child contacts of index cases outnumbered those of index controls (267 and 172, respectively); they were similar in all characteristics except for religious affiliation (Table 2). Adult contacts of index cases were more often HIV seropositive, female, and lived in the household of the index case (Table 2).
Figure 1.
Enrollment of index participants and social network members. Abbreviation: TB, tuberculosis.
Table 1.
Comparison of Demographic and Clinical Characteristics of Tuberculosis Index Cases and Matched Index Controls
| Characteristics | TB Cases (n = 123, %) | Matched Controls (n = 124, %) | All (N = 247) | P Value |
|---|---|---|---|---|
| Sex, No. | ||||
| Male | 84 (68) | 85 (69) | 169 (68) | 1.000 |
| Female | 39 (32) | 39 (31) | 78 (32) | |
| Age category, y | ||||
| 15–19 | 5 (4.1) | 3 (2.4) | 8 (3.2) | .392 |
| 20–29 | 59 (48.0) | 49 (39.5) | 108 (43.7) | |
| 30–39 | 37 (30.1) | 42 (33.9) | 79 (32.0) | |
| ≥40 | 22 (17.9) | 30 (24.2) | 52 (21.1) | |
| Marital status | <.001 | |||
| Married | 57 (46.3) | 91 (73.4) | 148 (59.9) | |
| Not married | 66 (53.7) | 33 (26.6) | 99 (40.1) | |
| Religion | .785 | |||
| Christian | 86 (69.9) | 89 (71.8) | 175 (70.9) | |
| Muslim | 27 (22.0) | 28 (22.6) | 55 (22.3) | |
| Other | 10 (8.1) | 7 (5.6) | 17 (6.9) | |
| Education | .170 | |||
| None | 7 (5.7) | 2 (1.6) | 9 (3.6) | |
| Primary level | 41 (33.3) | 49 (39.5) | 90 (36.4) | |
| Post primary level | 75 (61.0) | 73 (58.9) | 148 (59.9) | |
| Income, USD/mo | .089 | |||
| <80 | 88 (71.5) | 75 (60.5) | 163 (66.0) | |
| ≥80 | 35 (28.5) | 49 (39.5) | 84 (34.0) | |
| Egocentric network size, mean (IQR) | 8 (7–9) | 8 (7–9) | 8 (7–9) | .437 |
| Household, median (IQR) | 3 (1–5) | 1 (0–3) | 2 (0–4) | <.001 |
| Extra-household, median (IQR) | 5 (3–7) | 7 (5–8) | 6 (4–8) | <.001 |
| HIV status | .002 | |||
| Positive | 20 (16.3) | 6 (4.8) | 26 (10.5) | |
| Negative | 98 (79.7) | 118 (95.2) | 216 (87.4) | |
| Other | 5 (4.1) | 0 (0.0) | 5 (2.0) | |
| Microscopy | <.001 | |||
| 0 to 1+ | 15 (12.1) | 124 (100)a | 139 (56.2) | |
| 2+ | 20 (16.3) | … | 20 (8.1) | |
| 3+ | 88 (71.5) | … | 88 (35.6) |
Data are presented as no. (%) unless otherwise indicated.
Abbreviations: HIV, human immunodeficiency virus; IQR, interquartile range; TB, tuberculosis; USD, United States dollars.
aControls were smear negative by definition.
Table 2.
Comparison of Demographic and Social Characteristics of First-degree Social Network Contacts of Tuberculosis Index Cases and Index Controls, Stratified by Age Category
| Age <15 y | Age ≥15 y | |||||
|---|---|---|---|---|---|---|
| Variables | Contacts of Cases | Contacts of Controls | P Value | Contacts of Cases | Contacts of Controls | P Value |
| Sex | ||||||
| Male | 142 (53.2) | 82 (47.7) | .303 | 341 (46.1) | 496 (56.9) | <.001 |
| Female | 125 (46.8) | 90 (52.3) | 398 (53.9) | 376 (43.1) | ||
| HIV status | ||||||
| Positivea | 5 (1.9) | 3 (1.7) | 1.000 | 77 (10.4) | 63 (7.2) | .029 |
| Negative | 260 (97.4) | 169 (98.3) | 660 (89.3) | 808 (92.7) | ||
| Refused/not done | 2 (0.7) | 0 (0.0) | 2 (0.3) | 1 (0.1) | ||
| Marital status | ||||||
| Married | 3 (1.1) | 4 (2.3) | .554 | 425 (57.5) | 522 (59.9) | .403 |
| Not married | 264 (98.9) | 168 (97.7) | 311 (42.1) | 349 (40.0) | ||
| Unknown | 0 (0.0) | 0 (0.0) | 3 (0.4) | 1 (0.1) | ||
| Religion | ||||||
| Christian | 153 (57.3) | 117 (68.0) | .003 | 469 (63.5) | 612 (70.2) | .010 |
| Muslim | 81 (30.3) | 49 (28.5) | 180 (24.4) | 186 (21.3) | ||
| Other | 33 (12.4) | 6 (3.5) | 88 (11.9) | 73 (8.4) | ||
| Unknown | 0 (0.0) | 0 (0.0) | 2 (0.3) | 1 (0.1) | ||
| Education | ||||||
| None | 144 (53.9) | 84 (48.8) | .413 | 24 (3.2) | 39 (4.5) | .093 |
| Primary | 107 (40.1) | 73 (42.4) | 254 (34.4) | 333 (38.2) | ||
| Postprimary | 16 (6.0) | 15 (8.7) | 459 (62.1) | 499 (57.2) | ||
| Unknown | 0 (0.0) | 0 (0.0) | 2 (0.3) | 1 (0.1) | ||
| Income, USD/mo | ||||||
| <80 | 261 (97.8) | 169 (98.3) | 1.000 | 504 (68.2) | 555 (63.6) | .054 |
| ≥80 | 1 (0.4) | 0 (0.0) | 231 (31.3) | 314 (36.0) | ||
| Unknown | 5 (1.9) | 3 (1.7) | 4 (0.5) | 3 (0.3) | ||
| Exposure status | ||||||
| Household | 180 (67.4) | 115 (66.9) | .987 | 202 (27.3) | 99 (11.4) | <.001 |
| Extra-household | 87 (32.6) | 57 (33.1) | 537 (72.7) | 773 (88.6) | ||
Data are presented as no. (%) unless otherwise indicated.
Abbreviations: HIV, human immunodeficiency virus; USD, United States dollars.
aAmong the 148 HIV-seropositive contacts in the study, 74 were already in care and 74 were newly diagnosed in the study and referred for care.
Of the 1006 TB case contacts, 515 had TB infection for a prevalence of 51.2% (95% confidence interval [CI], 48.1%–54.3%) and 19 (1.9%) had TB disease (1 with HIV on antiretroviral therapy). Of the 1044 control contacts, 396 had TB infection for a prevalence of 37.9% (95% CI, 35.0%–40.9%) and 4 (0.4%) had TB disease without HIV coinfection. The prevalence of TB infection was highest among household contacts of TB cases (61.5%; Table 3), lowest among household contacts of controls (25.2%), and intermediary among extra-household contacts of TB cases (44.9%) and extra-household contacts of controls (41.2%).
Table 3.
Prevalence of Tuberculosis Infection Among Contacts of 123 Tuberculosis Index Cases and 124 Index Controls According to Household or Extra-household Contact
| Case Contacts (n = 1006) | Control Contacts (n = 1044) | |||
|---|---|---|---|---|
| Outcome | Household (n = 382) | Extra-household (n = 624) | Household (n = 214) | Extra-household (n = 830) |
| No. (%) [95% CI] | ||||
| Tuberculosis infection | 235 (61.5) | 280 (44.9) | 54 (25.2) | 342 (41.2) |
| [56.6–66.4] | [41.0–48.8] | [19.4–31.1] | [37.9–44.6] | |
| No tuberculosis infection | 145 (38.0) | 329 (52.7) | 154 (72.0) | 476 (57.3) |
| [33.2–42.8] | [48.8–56.6] | [65.9–78.0] | [54.0–60.7] | |
| Refused test | 2 (0.5) | 15 (2.4) | 6 (2.8) | 12 (1.4) |
| [.1–1.2] | [1.2–3.6] | [.6–5.0] | [.6–2.2] | |
Abbreviation: CI, confidence interval.
When we stratified by age category among household contacts of TB cases, the risk of infection was 50.6% for children aged <5 years, 62.6% for children aged 5–14 years, and 65.3% for contacts 15 years or older (Table 4; Figure 2). The adjusted overall prevalence difference between household contacts of index cases and index controls was 36.8% (95% CI, 26.8%–46.9%), but was lower among older contacts (31.7%; Table 4) compared with younger ones (47.2%). The prevalence of TB infection among extra-household contacts of index cases was 5 times greater among older contacts compared with children (50.5% vs 10.3%; P = .000). The adjusted prevalence difference between extra-household contacts of index cases and controls was 5.4% (95% CI, .6%–10.1%). Despite the difference in prevalence by age, the prevalence difference was similar for younger (6.8%; Table 4) and older (7.6%) contacts.
Table 4.
Stratified Analysis of Crude and Adjusteda Risk Difference by Age Category
| Contacts of Cases | Contacts of Controls | Crude Risk Difference (95% CI) | Adjusted Risk Difference (95% CI) | ||||
|---|---|---|---|---|---|---|---|
| Characteristics | Age Category, y | No. | TB Infection, No. (%) | No. | TB Infection, No. (%) | ||
| Contact status | |||||||
| Household | <15 y | 180 | 103 (57.2) | 115 | 12 (10.4) | 46.8 (37.6–56.0) | 47.2 (35.3–59.0) |
| <5 y | 81 | 41 (50.6) | 40 | 1 (2.5) | 48.1 (36.0–60.2) | NEb | |
| 5–14 y | 99 | 62 (62.6) | 75 | 11 (14.7) | 48.0 (35.4–60.5) | 48.8 (32.6–65.0) | |
| ≥15 y | 202 | 132 (65.3) | 99 | 42 (42.4) | 22.9 (11.1–34.7) | 31.7 (18.1–45.3) | |
| Extra-household | <15 y | 87 | 9 (10.3) | 57 | 2 (3.5) | 6.8 (−1.2 to 14.9) | NEb |
| <5 y | 39 | 8 (20.5) | 18 | 0 (0) | 20.5 (7.4–33.6) | NEb | |
| 5–14 y | 48 | 1 (2.1) | 39 | 2 (5.1) | −3.0 (−11.2 to 5.1) | −5.6 (−36.8 to 25.5) | |
| ≥15 y | 537 | 271 (50.5) | 773 | 340 (44.0) | 6.5 (.1–12.0) | 7.6 (.7–14.5) | |
Abbreviations: CI, confidence interval; NE, no estimate; TB, tuberculosis.
aAdjusted by continuous age (within category), sex, human immunodeficiency virus serostatus of contact, and clustering according to index case.
bModel did not converge.
Figure 2.
Prevalence of latent tuberculosis infection (LTBI) among household and extra-household contacts of 123 index tuberculosis cases and 124 index controls, stratified by age category.
When stratified by sputum smear grade, the prevalence difference increased among household contacts, from 23.4% (95% CI, 6.2%–40.7%) to 32.5% (95% CI, 17.7%–47.2%) to 38.6 (95% CI, 30.6%–46.6%) for exposure to cases with low, 2+, and 3+ sputum smear grade, respectively. The crude prevalence difference among extra-household contacts did not vary according to smear grade.
Among 597 extra-household contacts of index cases with complete social network information, 521 contacts (87%) were older than 15 years. In this group, a subset of contacts had a prevalence of TB infection comparable to that of household contacts (64.7% [95% CI, 55.3%–74.1%]). This subset comprised 70 close family members and 32 close work associates (Figure 3). This subset accounted for 19.6% of older extra-household contacts. The remaining extra-household contacts (n = 419) of index cases had a prevalence that was similar to the prevalence in adult extra-household contacts of index controls (47.3% vs 45.8%, respectively; P = .65). Of 76 child extra-household contacts, 21 were close family members, and only 2 of these family members (9.5%) had TB infection.
Figure 3.
Prevalence of latent tuberculosis infection (LTBI) among extra-household contacts 15 years and older according to nature of their relationship to index case.
DISCUSSION
In this study, we used first-degree contacts in egocentric social networks of infectious index TB cases to create a framework for understanding transmission of M. tuberculosis in an African urban setting. When we partitioned the social network members into household and extra-household components, we found, as expected, that the household of index cases was an environment for intense transmission of M. tuberculosis [6], but outside of the household, we found that risk of infection attributable to the index case was modest and substantially lower than household exposure. This modest excess risk of infection due to extra-household exposure to infectious index cases may not fully account for the high prevalence of infection observed among adults in the community.
A fundamental challenge in understanding M. tuberculosis transmission in the community is to characterize social mixing between cases and contacts, especially over long and variable infectious periods of cases. To add structure to these mixing patterns, we expanded our previous household contact study design [21, 22] by enrolling first-degree members from the social networks of index cases, regardless of whether the contact lived in the household or not. In this way, we created a formal framework for estimating the infection risk conferred by the index case not only in the household but also outside the household in the community.
A unique aspect of the study was the sampling strategy of contacts. Most contact investigation studies sample contacts within geographically located family units, such as households [22–25] or family compounds [26], and do not include concurrent community samples. Our sampling frame was based on social relationships that included household and community contacts. We thereby reduced the selection bias inherent in contact investigation studies. Other epidemiologic studies have used social networks to delineate chains of transmission among patients with culture-confirmed disease [27–29], but this approach greatly underestimates transmission of M. tuberculosis because only a fraction of newly infected contacts ever develop disease during observation. Our study uses TB infection as the outcome of interest, thereby avoiding the effect of variable latency.
We further refined the design to include a contemporaneous, local set of control networks to estimate TB in the community. This feature was critical because it allowed us to estimate prevalence differences, which accounted for background prevalence of infection in the population, and thereby estimated the additional, or excess, risk associated with exposure to the index case. Although we believe our estimates to be valid, it is possible that the selected control group was not representative of TB infection in the population. We believe this issue to be minimal because the age- and sex-specific prevalence in our study aligns with previous studies from Kampala [4, 30]. It is also possible that there is residual confounding, despite frequency matching on age category, sex, and parish. To address this issue, we adjusted for potential confounders and compared crude and adjusted estimates of risk.
Age was an effect modifier of the excess risk of infection among household contacts but not among extra-household contacts. The effect modification was evident within the household contacts as the excess risk was larger among younger contacts compared with older ones. We interpret this effect modification to indicate age assortment whereby children mix with other children and infrequently mix with adults, except parents [31]. As a result, young children were more likely to acquire infection in the household than by encounters outside the household.
In extra-household first-degree network members, we found that the excess risk of infection from exposure to an index case was modest (5.4%), and substantially lower than in the household setting. In contrast to household exposure, we did not observe effect modification by age in the extra-household setting. As expected, the prevalence of latent infection among older contacts exceeded the prevalence among younger contacts 5-fold, but the age-specific excess risk estimates were similar by age category.
The extra-household adult contacts were not a homogeneous group as regards risk of infection. A subset of case network members had a prevalence of infection that was similar to the prevalence in the household contacts, indicating that they may have experienced adequate contact with the index case to transmit M. tuberculosis [32]. This subset of contacts was defined by social closeness both with kin and work associates. The remaining first-degree network members lacked the social closeness and had a prevalence of infection resembling the community prevalence, suggesting previous exposure to 1 or more undetected, infectious cases.
Since the extra-household risk of infection was modest, our findings raise the question whether community exposure within the social network of infectious index cases can account for the high prevalence of LTBI found among adults in the community. It is plausible that community prevalence of infection is maintained through transmission to contacts not identified as part of reported social networks but instead as part of unspecified contact networks. It may also be the product of slow transmission dynamics of TB [33] or of casual transmission [34] in settings outside of the household, such as schools [8, 35], hospitals and clinics [36, 37], and transportation hubs [8, 38].
Other explanations, however, are plausible. Recent research has shown that cases of TB can produce aerosols containing M. tuberculosis through normal tidal respiration [39, 40], implying that cough may not be necessary for transmission. We are also beginning to understand better the prehistory of TB when cough is absent, or a minor symptom, and how cases may be infectious before overt clinical disease is detected [41, 42]. Based on this emerging line of research, we postulate that transmission from occult infectious cases may be the driving force behind epidemics of TB in African cities.
The ascertainment of our social networks was incomplete. We believe this limitation is minor because our estimates of infection are in line with previous household contact studies [6] and community infection surveys [30]. Because the study was cross-sectional, we did not directly measure the incidence of TB infection. Instead, we inferred incidence using the prevalence difference among contacts of cases and controls, or the excess risk attributable to exposure to an index case. This approach is used to estimate the annual risk of TB infection [43] and the secondary attack rates of TB infection in households [4]. In this analysis, we made steady-state assumptions about the incidence of infection and migration of infected persons to and from the study population [44]. Finally, we confined our epidemiologic analysis to first-degree contacts in the social networks of index participants, though a more expansive study of community networks is underway in Lubaga Division.
Given these caveats, we conclude that most social network members of TB cases do not experience additional risk of infection from exposure to that case but instead acquire infection through unrecognized exposures with infectious cases in the community. These undetected and infectious cases, possibly without cough, may be the hidden drivers of transmission in ongoing, local epidemics. Since community transmission of M. tuberculosis was not fully captured using the social network framework, further research is needed to map “hotspots” of ongoing transmission [45, 46] to geographic locations so that public health interventions, such as active case finding and community-based preventive therapy, may be targeted to areas where it is most needed [47, 48]. Only when transmission of M. tuberculosis is mitigated will there be fewer new infections and lower rates of TB disease.
Notes
Acknowledgments. The authors thank the Uganda National Tuberculosis and Leprosy Control Programme, Lubaga Hospital, the municipal leaders of Lubaga Division of Kampala, and the Kampala Capital City Authority for their cooperation and support of the field work needed to complete this study. The authors also acknowledge the home health visitors for their invaluable contributions to the science of this study and the Epidemiology in Action research group of the Global Health Institute, University of Georgia, for their critical insights.
Disclaimer. The sponsors of this study played no role in the design, conduct, analysis, or interpretation of the study.
Financial support. This work was supported by the National Institute of Allergy and Infectious Diseases and the Fogarty International Center of the National Institutes of Health (grant numbers R01 AI093856, NO1-AI95383, D43-TW01004); and the Schlumberger Foundation Faculty for the Future Fellowship.
Potential conflicts of interest. The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest.
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