Secondary Logo

Journal Logo

Clinical Science

Monocyte-to-Lymphocyte Ratio Is Associated With Tuberculosis Disease and Declines With Anti-TB Treatment in HIV-Infected Children

Choudhary, Rewa K MD, MPH*; Wall, Kristin M. PhD, MS; Njuguna, Irene MBChB, MSc, MPH‡,§; Pavlinac, Patricia B. PhD, MS; LaCourse, Sylvia M. MD, MPH; Otieno, Vincent MBChB, MPH#; Gatimu, John MPH#; Stern, Joshua MS; Maleche-Obimbo, Elizabeth MBChB, MPH#; Wamalwa, Dalton MBChB, MPH#; John-Stewart, Grace MD, PhD§,║,¶,**; Cranmer, Lisa M. MD, MPH*

Author Information
JAIDS Journal of Acquired Immune Deficiency Syndromes: February 1, 2019 - Volume 80 - Issue 2 - p 174-181
doi: 10.1097/QAI.0000000000001893



Mycobacterium tuberculosis disease [tuberculosis (TB)] is a leading cause of mortality in HIV-infected children.1 In 2016, there were over 1 million incident cases of TB and 253,000 TB-related deaths in children younger than 15 years.2 Microbiologic diagnosis of TB in children is difficult given paucibacillary disease and the operational challenges of obtaining respiratory specimens in young children who are unable to produce sputum.3 Host biomarkers for TB disease may provide an alternative to pathogen-based biomarkers for TB diagnosis.4 T-cell activation markers and transcriptional profiling5–9 have shown utility for diagnosis in children, but are costly and require specialized equipment and training.

The blood monocyte-to-lymphocyte ratio (MLR), derived from blood counts that are routinely collected in resource-limited settings for the management of acute illness, has been shown to predict progression to TB disease in children and adults.10–12 In a cohort of HIV-infected and HIV-exposed uninfected South African and Botswanan children, elevated MLRs at 3–4 months of age predicted onset of TB disease by the age of 2 years.10 Among HIV-infected African women, the elevated MLR during pregnancy was associated with increased risk of incident TB disease over 18 months of postpartum follow-up, even when controlling for CD4+ count, antiretroviral treatment (ART), and World Health Organization HIV stage.11 Less is known about the diagnostic performance of the MLR, but an Italian study of adults without HIV found that an MLR cutoff >0.285 had high sensitivity and specificity (91% and 94%, respectively) to identify patients with culture-confirmed TB.13 Furthermore, the MLR may be useful as an indicator of treatment response, as demonstrated in a cohort of Chinese adults with TB in which MLRs normalized to ranges similar to those of healthy controls after a 6-month treatment course.14

To the best of our knowledge, the performance of the MLR as a diagnostic biomarker for TB has not been evaluated in children with or without HIV disease. In addition, previous studies performed have not accounted for important cofactors that may alter the MLR, such as immunosuppression,12 nutritional status,15 and malaria coinfection.12,15–17 In a cohort of hospitalized Kenyan HIV-infected children starting antiretroviral therapy with well-classified TB disease status, we investigated the association between MLR and active TB disease determined at enrollment, and evaluated MLR changes over a 6-month period as a potential indicator of treatment response. We evaluated CD4+ count, nutritional status, and malaria infection as potential confounders of the association between MLR and TB.


Study Population

We conducted a longitudinal cohort study nested within the Pediatric Urgent Start of HAART (PUSH) randomized clinical trial (NCT02063880).18 Study subjects in the parent trial were HIV-infected, antiretroviral therapy-naive children aged 12 years or younger, hospitalized in Kenya. Participants were randomized in the parent trial to urgent (less than 48 hours) or early (7–14 days) antiretroviral therapy and were excluded if they had a central nervous system (CNS) infection at enrollment. Children were excluded from this subanalysis if they initiated treatment for TB more than 14 days before or after enrollment, or if they did not complete at least one test for microbiologic confirmation of TB diagnosis.

The parent study was reviewed and approved by the Institutional Review Board (IRB) at the University of Washington, the University of Nairobi/Kenyatta National Hospital Ethical Review Committee (UoN/KNH ERC), and the Pharmacy and Poisons Board in Kenya, and is in accordance with the Helsinki Declaration of 1975. Written informed consent was obtained from all participants' caregivers in their preferred language (English, Kiswahili, or Dholuo).

Study Procedures

All children were evaluated at enrollment for pulmonary TB by symptom screening, physical examination, tuberculin skin test (TST), chest radiograph, 2 sputum or gastric aspirate samples for direct Ziehl–Neelsen smear microscopy and liquid culture using BACTEC Mycobacteria Growth Indicator Tube (MGIT) 960 system (Becton Dickinson, Sparks, MD), one sputum or gastric aspirate specimen for PCR using Xpert MTB/RIF (Cepheid, Sunnyvale, CA), and one stool specimen for PCR using Xpert MTB/RIF. For TST, 5 units (0.1 mL) of purified protein derivative (RT23 solution; Sanofi Pasteur, Lyon, France) were injected intradermally and a study nurse measured induration 48–72 hours later. Children were also evaluated at enrollment for symptoms and signs of extrapulmonary TB.

Blood specimens were obtained from each participant for full blood count and differential count (including monocytes and lymphocytes) at enrollment and 4, 12, and 24-week follow-up visits. CD4+ percentage was determined at enrollment, and 4- and 24-week follow-up visits. Full blood counts were performed on an automated MS4 Haematology analyzer (Melet Schloesing Laboratoires, Osny, France) and A·T 5 diff Coulter counter (Beckman Coulter, Inc., Brea, CA). The MLR was calculated after conclusion of the parent trial and was not considered in the diagnostic evaluation of children. A study nurse evaluated growth parameters (height, weight, middle upper arm, and head circumference) at every encounter.

Participants were treated with combination antiretroviral therapy (abacavir and lamivudine with nevirapine, efavirenz, or lopinavir/ritonavir) according to Kenyan Ministry of Health guidelines.19 Children with suspected TB as assessed clinically by hospital medical officers were treated with a 6-month regimen of rifampin, isoniazid, pyrazinamide, and ethambutol per Kenyan National TB Program guidelines.20 ART regimens were adjusted as needed for children receiving concurrent TB treatment to avoid medication interactions.20


The MLR was determined by dividing absolute monocyte counts by absolute lymphocyte counts at each study time point. Weight-for-age Z-scores (WAZ) and weight-for-height Z-scores (WHZ) were calculated based on WHO growth curves using WHO ANTHRO software (version 3.2.2 World Health Organization, Geneva, Switzerland).21

Children were classified as having microbiologically confirmed TB, unconfirmed TB (clinical presentation suggestive of TB with at least 2 of the following: TB symptoms, abnormal CXR, positive TST or TB exposure history, or response to anti-TB treatment), or unlikely TB at enrollment based on international consensus clinical case definitions for pulmonary TB.22 For the purposes of this analysis, failure to thrive was defined by underweight (WAZ ≤ −2) or wasting (WHZ ≤ −2 if under 5 years of age or MUAC <12.5 for ages 5–12 years). Response to anti-TB treatment was defined as increase in weight and resolution of enrollment TB symptoms over 24 weeks of follow-up.

Statistical Analysis

Children were stratified by confirmed, unconfirmed, or unlikely pulmonary TB classification. Descriptive measures of frequency (counts and percentages for categorical variables, medians and interquartile ranges [IQRs] for continuous variables) were calculated for all covariates. Fisher exact tests were used to compare the distributions of categorical variables between confirmed versus unlikely TB groups and unconfirmed versus unlikely TB groups. Wilcoxon 2-sample tests were used to compare the distributions of continuous variables between confirmed versus unlikely TB groups and unconfirmed versus unlikely TB groups. Study power was calculated using OpenEpi (Version 3.01, updated June 4, 2013).23

Receiver operating characteristic curves of MLR cutoff values were used to distinguish children with confirmed TB, unconfirmed TB, and unlikely TB. The optimal MLR diagnostic cutoff was determined based on the maximum value of Youden's index, J, where J = sensitivity + specificity − 1.24

General estimating equations (GEEs) were used to estimate the association between TB status and changes in repeated MLR measures over the study period. We also estimated this association for absolute monocyte count and absolute lymphocyte counts individually. We evaluated baseline and time-varying CD4+ percentage, WAZ and WHZ, and malaria infection as potential confounders of the association between MLR and TB. Time-varying CD4+ percentage was considered a priori as a confounder of interest because CD4+ percentage is associated with active TB and the MLR, as was time since enrollment because it is a proxy for TB treatment over time. We assessed for multicollinearity and selected the most parsimonious model. Because of the known association of age with TB disease,25,26 a sensitivity analysis was performed with age included as an additional cofactor in our final adjusted model. Sensitivity analyses restricting participants to children who had completed the study and children who were treated for TB were also conducted.

Two-sided P values <0.05 were considered statistically significant. All analyses were conducted using SAS software (version 9.4; SAS Institute Inc., Cary, NC).


Cohort Characteristics

Of 183 randomized children from April 2013 to May 2015 in the parent trial, we included 160 children in this secondary analysis. Twenty-three children were excluded for the following reasons: 1 for HIV-negative status, 1 for concurrent CNS infection, 13 for TB treatment 14 days before or after enrollment, and 8 for failing to receive microbiologic confirmation testing. Three patients had signs of extrapulmonary TB infection in conjunction with pulmonary TB (one child with lymphadenitis, a second with miliary infiltrate on chest radiography, and a third with miliary infiltrate and vertebral spondylitis on chest radiography). There were 2 patients who had extrapulmonary TB (miliary infiltrate on chest radiography) without evidence of pulmonary TB. Thirteen children met criteria for confirmed TB (8.1%), 67 (41.9%) had unconfirmed TB, and 80 (50.0%) were unlikely to have TB (see Figure, Supplemental Digital Content 1,, patient inclusion flowchart). Overall, the median age at enrollment was 22.8 months (IQR 10.0–62.7), 87 children (54.4%) were males, 30 (18.8%) children died during the study period, and 13 (8.1%) were lost to follow-up (Table 1)

Baseline and Time-Varying Characteristics of Study Participants in TB-Confirmed Versus TB Unlikely, and TB-Unconfirmed Versus TB Unlikely Diagnostic Classification Groups

The median enrollment CD4+ percentages for children in the confirmed and unconfirmed groups were similar to the CD4+ percentage in the unlikely TB group (P = 0.10 and P = 0.21, respectively); however, over all study intervals, the median CD4+ percentage was significantly lower for participants in the confirmed and unconfirmed groups as compared to the unlikely TB group (P = 0.01 and P = 0.01, respectively). Over all study intervals, the median WAZ and WHZ were lower for the confirmed and unconfirmed TB children as compared to those in the unlikely TB group (WAZ: P < 0.01 and P < 0.01, respectively; WHZ: P < 0.01 and P < 0.01, respectively). Overall, 25 children were diagnosed with malaria during the study period, with 15 confirmed by positive blood smears and the remainder diagnosed clinically. Of 25 children with malaria, none had confirmed TB (P = 0.23 compared with that of unlikely TB), 5 had unconfirmed TB (P = 0.03 compared with that of unlikely TB), and 20 were unlikely to have TB.

MLR Diagnostic Utility

At enrollment, the median MLR for children with confirmed TB [0.407 (IQR 0.378–0.675)] was higher compared with that in children with unconfirmed [0.207 (IQR 0.148–0.348), P < 0.01] or unlikely [0.212 (IQR 0.138–0.391), P = 0.01] TB. Children with unconfirmed TB had similar MLRs compared to children with unlikely TB (P = 0.87) (Table 2). The study power calculated to detect the difference in the MLR between the confirmed TB (n = 13, median MLR = 0.407) and unlikely TB (n = 79, median MLR = 0.212) groups was 70.0% (assuming α = 0.05 with the 2-tailed t test).

Median Blood MLR by Visit Week and TB Classification

The optimal MLR cutoff value of 0.378 identified 10 of 13 confirmed TB patients as having TB disease with sensitivity 77% (95% CI: 50%–92%), specificity 78% (95% CI: 71%–84%), positive predictive value 24% (95% CI: 14%–39%), negative predictive value 97% (95% CI: 93%–99%), positive likelihood ratio 3.5 (95% CI: 3.1–3.9), and negative likelihood ratio 0.3 (95% CI: 0.2–0.6). The corresponding area under the receiver operating characteristic curve was 0.74 (95% CI: 0.58–0.89) (see Fig. 1 and Table, Supplemental Digital Content 2,, MLR cutoff values). In sensitivity analyses, comparing the confirmed TB group with the unlikely TB group yielded similar diagnostic testing results.

Receiver operating characteristic curve (A) and sensitivity and specificity curve (B) for MLR cutoffs identifying TB-confirmed patients. The optimal MLR cutoff above 0.378 had sensitivity 77%, specificity 78%, positive predictive value 24%, and negative predictive value 97%.

Longitudinal Changes in the MLR

The association between TB status and MLR over all study intervals was significant for the confirmed TB group compared with the unlikely TB group [β = 0.32, standard error (SE) 0.13, P = 0.01] on univariate analysis. There was no significant difference between the MLR among children with unconfirmed TB compared to children unlikely to have TB on univariate analysis (Table 3). In our multivariable GEE model adjusting for time-varying CD4+ percentage and visit week since enrollment, the association between confirmed TB diagnosis and MLR remained significant (β = 0.27, SE 0.12, P = 0.02; reference unlikely TB group), and the association between unconfirmed TB and MLR remained nonsignificant (β = 0, SE 0.03, P = 0.99; reference unlikely TB group). No other potential confounders were added to this model because of lack of association with MLR (malaria status), TB status (age), or collinearity with time-varying CD4 percentage (time-varying WAZ and WHZ). Sensitivity analyses with age included as a covariate did not significantly affect the model. Excluding the 2 patients with only extrapulmonary TB did not significantly change our findings. In addition, MLRs were not significantly different between the 2 treatment arms of the parent trial (urgent or early antiretroviral therapy).

Association of TB-Confirmed and TB-Unconfirmed Groups With Absolute Monocyte Count, Absolute Lymphocyte Count, and the MLR Over All Study Intervals

The 2 components of the MLR, absolute blood monocyte count and absolute blood lymphocyte count, were analyzed individually in unadjusted analyses for their association with TB status using GEE modeling (see Table, Supplemental Digital Content 3,, median monocyte and lymphocyte counts by visit week and TB group). Over all study intervals, there were no statistically significant associations between the absolute monocyte or lymphocyte count and the TB groups (Table 3).

Over 24 weeks of anti-TB treatment, the median MLR declined by 0.298 among children with confirmed TB (P = 0.01) and was similar to MLR levels of children unlikely to have TB by week 12 of TB treatment (P = 0.21) (Fig. 2 and Table 2). Among unconfirmed patients who received TB treatment, however, there was no difference in the median MLR when compared with the unlikely TB group at all study intervals.

Median blood MLR over visit weeks from enrollment by TB classification (confirmed, unconfirmed, or unlikely TB). The median MLR in the TB-confirmed group declined to levels similar to the TB-unconfirmed and TB unlikely groups by 12 weeks of anti-TB treatment.

In sensitivity analyses restricted to children with a complete 6-month follow-up (excluding those who died or who were lost to follow-up), children with confirmed TB had a trend of higher median MLR over all study intervals as compared to children with unconfirmed or unlikely TB, but this did not reach statistical significance (P = 0.09 and P = 0.08, respectively). The trend of the MLR among the 3 TB groups over the study period was similar to the longitudinal findings with all participants included (see Table and Figure, Supplemental Digital Content 4,, median MLR by visit week and TB group among those patients who completed the study).

When restricting participants to only those treated for TB, children who died during the study period or had treatment failure had a trend of higher enrollment median MLRs [0.346 (IQR 0.271–0.632)] compared with those who had response to TB treatment [0.209 (IQR 0.147–0.375), P = 0.06] (see Table, Supplemental Digital Content 5.1,, comparing the median MLR among those who had TB treatment response versus failure). Among children with TB treatment response, the pattern of longitudinal changes in the MLR for the confirmed and unconfirmed TB groups was similar to the curves when all participants were included (see Table and Figure, Supplemental Digital Content 5.2, 5.3,, median MLR by visit week and TB group among those patients who had TB treatment response). Longitudinally, children with confirmed TB who did not respond to TB treatment or who died before completing the study showed a trend toward higher MLRs compared to children with unconfirmed or unlikely TB, although sample size was limited for this sensitivity analysis (see Table and Figure, Supplemental Digital Content 5.4–5.6,


Among HIV-infected children, the blood MLR distinguished children with microbiologically confirmed pulmonary TB disease from those with unconfirmed or unlikely TB. An MLR value above 0.378 was associated with moderate sensitivity (77%) and specificity (78%) to identify confirmed TB cases at enrollment. By 12 weeks of anti-TB treatment, the median MLR of the confirmed TB group declined to levels similar to the unconfirmed and unlikely TB groups. Furthermore, children across all diagnostic groups who were treated for TB and died or did not respond to treatment had a trend of higher median enrollment MLRs compared with those who had treatment success.

The MLR cutoff value above 0.378 demonstrated a good overall TB diagnostic performance in our cohort of hospitalized HIV-infected Kenyan children. Our cutoff was higher and had lower sensitivity and specificity compared with a study of Italian adults in which an MLR cutoff of 0.285 had sensitivity 91% and specificity 94% for identifying active TB in HIV-negative adults compared with healthy controls.13 We hypothesize that the optimal cutoff value from our analysis was higher because we compared confirmed TB patients with pooled unconfirmed and unlikely TB patients who were all HIV-infected and hospitalized rather than healthy controls. When La Manna et al13 compared participants with TB to those with latent TB infection, the optimal MLR cutoff increased to 0.305 with decreased sensitivity and specificity.

The sensitivity of the MLR to detect confirmed TB is comparable with other rapid diagnostic methods for microbiologic confirmation that use non–sputum-based sample collection in children. Studies evaluating stool Xpert MTB/RIF reported test sensitivities between 32% and 81% and specificities between 99% and 100% as compared to the gold standard of culture-positive respiratory specimens.27–31 Sensitivity of stool Xpert MTB/RIF improved when data were restricted to HIV-infected children (63%–80%).27,29,31 Nasopharyngeal aspirate Xpert MTB/RIF assays had similar diagnostic performance to stool samples in children (sensitivity: 39%–65% and specificity: 98%–99%),32,33 whereas urinary lipoarabinomannan assays performed less well (sensitivity from 0% to 70% and specificity from 60% to 97% depending on HIV infection status and type of assay).31,34–36 A meta-analysis of HIV-negative and positive adults found the sensitivity of serum C-reactive protein >1.0 mg/dL for TB diagnosis ranged between 56% to 96% and specificity between 0% to 67%; elevated C-reactive protein levels have been observed in children with active TB but diagnostic performance in children is not known.37,38 Although the sensitivities of these rapid diagnostic tests in children are similar to the sensitivity of the MLR in our cohort, the specificity of the MLR was lower, which may be an important limitation for clinicians to consider.

In settings with limited capacity for microbiologic testing, the MLR may be an inexpensive and rapid tool to inform clinical TB diagnosis. Despite the scale-up of Xpert MTB/RIF, it is underused in many populations and children may have lower odds of getting Xpert MTB/RIF testing.39 In a 2012 study of 47 sites, the test was used for only 4% of patients, possibly because of limitations in electrical power, transportation, and cartridge availability.40 In addition, lack of clinical staff training and program guideline knowledge may hinder Xpert MTB/RIF implementation.41 The MLR, therefore, may be a more accessible test in some settings.

We hypothesize that elevation in the MLR among children with confirmed TB disease could reflect higher mycobacterial burden. Monocytes proliferate in the presence of mycobacterial growth before migrating and differentiating into macrophages, whereas CD4+ T lymphocytes are the primary effectors of adaptive immune response to M. tuberculosis infection.42,43 Naranbhai et al44 have shown in vitro that higher MLR was associated with mycobacterial growth and that an elevated ratio of gene expression transcripts of monocytes to lymphocytes was associated with TB disease in vivo. In our cohort, higher MLR among children with treatment failure or death and decline in the MLR with anti-TB treatment (consistent with previous studies in adults)13,14 support our hypothesis that the MLR may be a useful biomarker for mycobacterial burden and increased risk of mortality. Future studies to evaluate MLR change over time in the first few weeks after TB treatment initiation will be useful to assess its role as a marker of early treatment response and mortality risk in this population. In our study, the MLR did not distinguish children with microbiologically unconfirmed TB from children who were unlikely to have TB, possibly because of the lower mycobacterial burden in those with unconfirmed TB. Additional studies to explore the utility of the MLR among children with unconfirmed TB may help inform clinical TB treatment decisions if microbiological confirmation cannot be obtained.

This study contributes to a growing body of research on the MLR as a biomarker for TB diagnosis in children, which is vital because it is difficult to obtain respiratory samples for microbiologic diagnosis in this population. All children in our study were comprehensively evaluated for pulmonary TB with 2 samples for smear microscopy and culture, Xpert MTB/RIF, and chest X-ray. Classifying children who had negative microbiologic testing but clinical signs of TB allowed us to analyze the utility of the MLR for patients with unconfirmed TB. We also evaluated the MLR longitudinally, allowing us to explore MLR changes over the TB treatment period. Moreover, we assessed for potential confounding by nutritional status and immunosuppression that may affect the MLR. Our results may not be generalizable to all pediatric populations because our cohort was limited to hospitalized HIV-infected children with TB, excluding those with CNS infections. The study was also limited by a relatively small sample size and had a low number of confirmed TB cases among younger children. However, our power to detect differences in the MLR between the confirmed and unlikely TB groups was robust.


In summary, the blood MLR distinguished Kenyan hospitalized HIV-infected children with microbiologically confirmed pulmonary TB disease from children with unlikely TB. The blood MLR could be a useful diagnostic tool for TB disease in settings where bacteriological confirmation is difficult to obtain. The MLR could also be evaluated as a component of future clinical diagnostic algorithms and/or biomarker for TB treatment response.


The authors thank children and caregivers who participated in the study. The authors also thank the Pediatric Urgent Start of HAART (PUSH) trial staff for their administrative, clinical, and data support.


1. Cox JA, Lukande RL, Lucas S, et al. Autopsy causes of death in HIV-positive individuals in sub-Saharan Africa and correlation with clinical diagnoses. AIDS Rev. 2010;12:183–194.
2. World Health Organization. Global Tuberculosis Report 2017. 2017. Available at: Accessed August 1, 2017.
3. Venturini E, Turkova A, Chiappini E, et al. Tuberculosis and HIV co-infection in children. BMC Infect Dis. 2014;14(suppl 1):S5.
4. Perez-Velez CM, Roya-Pabon CL, Marais BJ. A systematic approach to diagnosing intra-thoracic tuberculosis in children. J Infect. 2017;74(suppl 1):S74–S83.
5. Rozot V, Patrizia A, Vigano S, et al. Combined use of Mycobacterium tuberculosis-specific CD4 and CD8 T-cell responses is a powerful diagnostic tool of active tuberculosis. Clin Infect Dis. 2015;60:432–437.
6. Portevin D, Moukambi F, Clowes P, et al. Assessment of the novel T-cell activation marker-tuberculosis assay for diagnosis of active tuberculosis in children: a prospective proof-of-concept study. Lancet Infect Dis. 2014;14:931–938.
7. Anderson ST, Kaforou M, Brent AJ, et al. Diagnosis of childhood tuberculosis and host RNA expression in Africa. N Engl J Med. 2014;370:1712–1723.
8. Kaforou M, Wright VJ, Oni T, et al. Detection of tuberculosis in HIV-infected and -uninfected African adults using whole blood RNA expression signatures: a case-control study. PLoS Med. 2013;10:e1001538.
9. Berry MP, Graham CM, McNab FW, et al. An interferon-inducible neutrophil- driven blood transcriptional signature in human tuberculosis. Nature. 2010;466:973–977.
10. Naranbhai V, Kim S, Fletcher H, et al. The association between the ratio of monocytes:lymphocytes at age 3 months and risk of tuberculosis (TB) in the first two years of life. BMC Med. 2014;12:120.
11. Naranbhai V, Moodley D, Chipato T, et al. The association between the ratio of monocytes: lymphocytes and risk of tuberculosis among HIV-infected postpartum women. J Acquir Immune Defic Syndr. 2014;67:573–575.
12. Naranbhai V, Hill AV, Abdool Karim SS, et al. Ratio of monocytes to lymphocytes in peripheral blood identifies adults at risk of incident tuberculosis among HIV-infected adults initiating antiretroviral therapy. J Infect Dis. 2014;209:500–509.
13. La Manna MP, Orlando V, Dieli F, et al. Quantitative and qualitative profiles of circulating monocytes may help identifying tuberculosis infection and disease stages. PLoS One. 2017;12:e0171358.
14. Wang J, Yin Y, Wang X, et al. Ratio of monocytes to lymphocytes in peripheral blood in patients diagnosed with active tuberculosis. Braz J Infect Dis. 2015;19:125–131.
15. Cegielski JP, McMurray DN. The relationship between malnutrition and tuberculosis: evidence from studies in humans and experimental animals. Int J Tuberc Lung Dis. 2004;8:286–298.
16. Berens-Riha N, Kroidl I, Schunk M, et al. Evidence for significant influence of host immunity on changes in differential blood count during malaria. Malar J. 2014;13:155.
17. Jelliffe DB, Chandra RK. Immunocompetence in undernutrition. J Pediatr. 1972;81:1194–1200.
18. Njuguna IN, Cranmer LM, Otieno VO, et al. Urgent versus post-stabilisation antiretroviral treatment in hospitalised HIV-infected children in Kenya (PUSH): a randomised controlled trial. Lancet HIV. 2018;5:e12–e22.
19. National AIDS/STI Control Program (NASCOP). Guidelines for Antiretroviral Therapy in Kenya. 4th ed. Republic of Kenya: Ministry of Medical Services; 2011.
20. Kenya Ministry of Health. Guidelines for Management of Tuberculosis and Leprosy in Kenya. Republic of Kenya: Division of Leprosy Tuberculosis and Lung Disease; 2013.
21. WHO Anthro and Macros [Computer Program]. Version 3.2.2. World Health Organization, 2011. Available at: Accessed August 1, 2017.
22. Graham SM, Cuevas LE, Jean-Philippe P, et al. Clinical case definitions for classification of intrathoracic tuberculosis in children: an update. Clin Infect Dis. 2015;61(suppl 3):S179–S187.
23. Dean AG, Sullivan K, Soe MM. OpenEpi: Open Source Epidemiologic Statistics for Public Health, Version 3.01. 2017. Available at: Accessed August 1, 2017.
24. Perkins NJ, Schisterman EF. The inconsistency of “optimal” cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol. 2006;163:670–675.
25. Marais BJ, Donald PR, Gie RP, et al. Diversity of disease in childhood pulmonary tuberculosis. Ann Trop Paediatr. 2005;25:79–86.
26. Marais BJ, Hesseling AC, Gie RP, et al. The bacteriologic yield in children with intrathoracic tuberculosis. Clin Infect Dis. 2006;42:e69–71.
27. Nicol MP, Spiers K, Workman L, et al. Xpert MTB/RIF testing of stool samples for the diagnosis of pulmonary tuberculosis in children. Clin Infect Dis. 2013;57:e18–e21.
28. Walters E, van der Zalm MM, Palmer M, et al. Xpert MTB/RIF on stool is useful for the rapid diagnosis of tuberculosis in young children with severe pulmonary disease. Pediatr Infect Dis J. 2017;36:837–843.
29. Chipinduro M, Mateveke K, Makamure B, et al. Stool Xpert(R) MTB/RIF test for the diagnosis of childhood pulmonary tuberculosis at primary clinics in Zimbabwe. Int J Tuberc Lung Dis. 2017;21:161–166.
30. Moussa H, Bayoumi FS, Mohamed AM. Gene Xpert for direct detection of Mycobacterium tuberculosis in stool specimens from children with presumptive pulmonary tuberculosis. Ann Clin Lab Sci. 2016;46:198–203.
31. LaCourse SM, Pavlinac PB, Cranmer LM, et al. Stool Xpert MTB/RIF and urine lipoarabinomannan (LAM) for diagnosing tuberculosis in hospitalized HIV-infected children. AIDS. 2018;32:69–78.
32. Zar HJ, Workman L, Isaacs W, et al. Rapid diagnosis of pulmonary tuberculosis in African children in a primary care setting by use of Xpert MTB/RIF on respiratory specimens: a prospective study. Lancet Glob Health. 2013;1:e97–e104.
33. Zar HJ, Workman L, Isaacs W, et al. Rapid molecular diagnosis of pulmonary tuberculosis in children using nasopharyngeal specimens. Clin Infect Dis. 2012;55:1088–1095.
34. Kroidl I, Clowes P, Reither K, et al. Performance of urine lipoarabinomannan assays for paediatric tuberculosis in Tanzania. Eur Respir J. 2015;46:761–770.
35. Nicol MP, Allen V, Workman L, et al. Urine lipoarabinomannan testing for diagnosis of pulmonary tuberculosis in children: a prospective study. Lancet Glob Health. 2014;2:e278–e284.
36. Iskandar A, Nursiloningrum E, Arthamin MZ, et al. The diagnostic value of urine lipoarabinomannan (LAM) antigen in childhood tuberculosis. J Clin Diagn Res. 2017;11:EC32–EC35.
37. Yoon C, Chaisson LH, Patel SM, et al. Diagnostic accuracy of C-reactive protein for active pulmonary tuberculosis: a meta-analysis. Int J Tuberc Lung Dis. 2017;21:1013–1019.
38. Pavan Kumar N, Anuradha R, Andrade BB, et al. Circulating biomarkers of pulmonary and extrapulmonary tuberculosis in children. Clin Vaccin Immunol. 2013;20:704–711.
39. Oliwa JN, Maina J, Ayieko P, et al. Variability in distribution and use of tuberculosis diagnostic tests in Kenya: a cross-sectional survey. BMC Infect Dis. 2018;18:328.
40. Clouse K, Blevins M, Lindegren ML, et al. Low implementation of Xpert MTB/RIF among HIV/TB co-infected adults in the International Epidemiologic Databases to Evaluate AIDS (IeDEA) program. PLoS One. 2017;12:e0171384.
41. Rendell NL, Bekhbat S, Ganbaatar G, et al. Implementation of the Xpert MTB/RIF assay for tuberculosis in Mongolia: a qualitative exploration of barriers and enablers. PeerJ. 2017;5:e3567.
42. Schluger NW, Rom WN. The host immune response to tuberculosis. Am J Respir Crit Care Med. 1998;157:679–691.
43. Scriba TJ, Coussens AK, Fletcher HA. Human immunology of tuberculosis. Microbiol Spectr. 2017;5:1–24.
44. Naranbhai V, Fletcher HA, Tanner R, et al. Distinct transcriptional and anti-mycobacterial profiles of peripheral blood monocytes dependent on the ratio of monocytes: lymphocytes. EBioMedicine. 2015;2:1619–1626.

tuberculosis; HIV; children; monocytes; lymphocytes; biomarkers

Supplemental Digital Content

Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.