Epidemiology of Tuberculosis Among People Living With HIV in the African Cohort Study From 2013 to 2021

INTRODUCTION

Tuberculosis (TB) is a leading cause of death by an infectious disease worldwide and among people living with HIV (PLWH).1–3 PLWH are 15–21 times more likely to develop active TB and more likely to die from it when they do, compared with people without HIV.1 In 2020, a total of 1.5 million people died of TB, including 214,000 PLWH.1,4 As of 2020, the estimated TB incidence in sub-Saharan Africa was 220 per 100,000 population per year, and the estimated TB incidence among PLWH was significantly higher at 2279 per 100,000 population per year.1

Given that TB disproportionately affects PLWH, proper diagnosis, treatment, and prevention of TB is critical. While 85% of those newly initiated on TB treatment in 2018 achieved treatment success, a large gap remains between the number of diagnosed TB cases and the number of estimated cases.1 Despite advances in TB diagnostics, TB among PLWH is still underdiagnosed because of atypical signs, symptoms, and the paucibacillary nature of the disease in this population.1,5 Diagnosing TB in PLWH is particularly challenging because one of the main diagnostic methods for TB until recently, GeneXpert MTB/RIF, was only 82%–88% sensitive in PLWH.5–7 Although the WHO has recommended Xpert MTB/RIF Ultra replace Xpert MTB/RIF in 2017, utilization of this more sensitive assay has been slow in resource-limited settings.8 As such, a comprehensive approach to TB diagnosis using bacteriological testing in conjunction with clinical assessments are paramount to the identification and treatment of TB, particularly in lower resource settings.

Continual programmatic assessments have highlighted gaps in HIV/TB service delivery in a variety of settings that have informed resource allocation and prioritization of activites.9–11 This has allowed HIV/TB programs to evolve from increasing TB case detection among PLWH, to scale-up of TB preventive treatment.9–12 The African Cohort Study (AFRICOS) is uniquely positioned to characterize progress in TB diagnosis and prevention in PLWH across 4 high HIV-TB burden African countries (Uganda, Kenya, Nigeria, and Tanzania), given participants are enrolled from the President's Emergency Plan for AIDS Relief (PEPFAR)–supported programs and can serve as a proxy to assess strengths and gaps in current PEPFAR HIV-TB programmatic strategies. We assessed the prevalence and incidence of HIV-TB co-occurring in AFRICOS and identified factors associated with prevalent and incident TB to better understand the current drivers of HIV-TB co-occurrence and gaps in TB screening and diagnosis. In addition, we characterized the current methods of diagnosis in prevalent cases to inform strategies to optimize active case finding to treat and subsequently prevent TB transmission.

METHODS Study Design and Setting

AFRICOS is a prospective, observational cohort study, enrolling PLWH and HIV-uninfected participants at 12 PEFPAR supported sites across 5 programs in Uganda, Kenya (South Rift Valley and Kisumu), Tanzania, and Nigeria, as previously described.13 Individuals were eligible for enrollment if they were aged 15 years or older and consented to data and specimen collection (see Figure S1, Supplemental Digital Content, https://links.lww.com/QAI/C12).

Laboratory Methods

Laboratory assessments included quantification of CD4 T-lymphocyte count and viral load (VL) (copies/mL). Sputum samples were collected from all participants annually, regardless of symptoms, or at any visit that a participant presented with any of the cardinal TB symptoms of cough, fever, night sweats, or weight loss. Samples from study initiation in 2013 to December 2021 were evaluated for active TB and for rifampicin resistance using the Cepheid Gene Xpert MTB/RIF platform. Additional clinical diagnostics included mycobacterial culture and molecular or culture-based drug resistance testing.

Data Collection and Definitions

On enrollment and at subsequent visits every 6 months, PLWH completed a physical examination, medical history, sociodemographic questionnaire, TB symptom screening, and phlebotomy. Participants were classified as having a history of TB if they had a TB diagnostic WHO code abstracted from their medical records before enrollment. Demographic variables collected include sex, age, marital status, education, employment status, number of residents in household, year of enrollment (dichotomized into before vs after 2017 to reflect the time of PEPFAR program wide scale-up of isoniazid preventive therapy), and clinical site. HIV-specific variables included antiretroviral therapy (ART) use (yes, no) and regimen abstracted from medical records, self-reported ART adherence in the past month (no missed ART doses, missed ≥1 doses), duration on ART, length of time in HIV clinical care, length of time since HIV diagnosis, CD4 count (<200 cells/mm3, ≥200 cells/mm3), VL (on ART for less than 6 months, on ART for 6 or more months and VL <1000 copies/mL and on ART for 6 or more months and VL ≥1000 copies/mL), TB diagnosis method (bacteriological, clinical), hyperglycemia, and body mass index (BMI). Additional variables included in the analysis were substance use and incarceration status. Definitions and categorizations of analytic variables not specified here have been previously described and summarized in Table S1, Supplemental Digital Content, https://links.lww.com/QAI/C13.13

Active TB was defined as meeting one of the following criteria: (1) bacteriologically confirmed through smear microscopy, culture, or WHO-approved rapid diagnostics (including GeneXpert MTB/RIF), (2) clinically indicated and having initiated combination therapy for active TB in the absence of bacteriological confirmation, or (3) identified by medical record abstraction within 3 months of enrollment. Participants were considered to be on combination therapy for active TB at enrollment if they were receiving (1) rifampicin (RIF), isoniazid (INH), ethambutol, and pyrazinamide or (2) INH and RIF for the final 4 months of treatment for active TB. Participants solely prescribed INH-based TB regimens were considered to be on preventative therapy.

We determined TB prevalence at entry or within 3 months of enrollment into AFRICOS, counting (1) previous diagnoses (those receiving continued combination TB therapy); (2) diagnoses made because of initial testing on entry into the cohort and within 3 months of enrollment; and (3) diagnosis based on WHO or ICD-10 codes in medical records at entry or within 3 months of enrollment.

Statistical Methods

Descriptive statistics using Pearson χ2 tests were used to determine significant differences in clinical and sociodemographic variables among participants with prevalent TB, compared with those without TB, at or within 3 months of enrollment. Logistic regression was used to estimate unadjusted and adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for associations between clinical and sociobehavioral factors and prevalent TB disease.

Incidence rates (IRs) were calculated for participants living with HIV without TB at or within 3 months of enrollment, as the number of new TB diagnoses divided by person-years (PY) of follow-up. CIs were calculated using the quadratic approximation to the Poisson log likelihood for the log–rate parameter. Cox proportional hazard models were used to assess unadjusted and adjusted hazard ratios and 95% CIs for associations between time to incident TB and clinical and sociobehavioral predictor variables. Time-varying covariates were accounted for in the model. Participants were censored at competing events (either loss to follow-up or death). Time at risk of TB started at entry into the cohort, and failure or censor date was observed over the duration of the study at follow-up visits.

All variables that were associated with prevalent or incident TB (P < 0.2) in the bivariate analysis were included in the respective multivariable analysis in addition to the following variables identified a priori: age, sex, and clinical site. Additionally, we tested for multicollinearity; variables with variance inflation factors greater than 10 were removed from the multivariable model. Missing data were folded into the reference category for modeling. All Analyses were performed in SAS software, version 9.3 (SAS Institute, Cary, NC) and Stata software, version 16.0 (StataCorp, College Station, TX)

Ethical Clearances

The study was approved by Institutional Review Boards of the Walter Reed Army Institute of Research, Makerere University School of Public Health, Kenya Medical Research Institute, Tanzania National Institute of Medical Research, and Nigerian Ministry of Defense.

RESULTS TB Prevalence and Diagnosis Methods

From 21 January 2013 to 1 December 2021, 3171 PLWH were enrolled, and 93 (2.9%) had TB at or within 3 months of the enrollment visit. The median age of our analytic sample was 37.4 (Interquartile range: 29.3–45.5) years, and 1858 individuals (58.6%) were female (Table 1). A higher proportion of PLWH with prevalent TB was aged 25–39 years, male, and had primary or some secondary education. A greater proportion of PLWH with TB was significantly underweight, had CD4 counts <200 cells/mm3, was on ART for <6 months, and was incarcerated compared with their TB-free counterparts. Of the 93 cases of prevalent HIV-TB co-occurrence identified, most of them (65.6%) were bacteriologically confirmed (Table 1). Of the 132 participants who underwent RIF resistance testing, 3% (n = 4) had resistance, of which 2 were cases with prevalent TB.

TABLE 1. - Enrollment Characteristics of PLWH by TB Co-occurrence Status Total n = 3171 No TB n = 3078 TB Case n = 93 P * Age (years)† <0.001  15–24 539 (17.0) 528 (17.2%) 11 (11.8%)  25–39 1346 (42.4%) 1288 (41.8%) 58 (62.4%)  40–49 808 (25.5%) 790 (25.7%) 18 (19.4%)  50+ 477 (15.0%) 471 (15.3%) 6 (6.5%) Sex 0.014  Male 1313 (41.4%) 1263 (41.0%) 50 (53.8%)  Female 1858 (58.6%) 1815 (59.0%) 43 (46.2%) Clinical site 0.32  Kayunga, Uganda 553 (17.4%) 539 (17.5%) 14 (15.1%)  South Rift Valley, Kenya 1095 (34.5%) 1059 (34.4%) 36 (38.7%)  Kisumu West, Kenya 563 (17.8%) 547 (17.8%) 16 (17.2%)  Mbeya, Tanzania 607 (19.1%) 585 (19.0%) 22 (23.7%)  Abuja & Lagos, Nigeria 353 (11.1%) 348 (11.3%) 5 (5.4%) Marital status† 0.33  Not married 1503 (47.4%) 1464 (47.6%) 39 (41.9%)  Married 1666 (52.5%) 1613 (52.4%) 53 (57.0%) Education† 0.010  None or some primary 1014 (32.0%) 996 (32.4%) 18 (19.4%)  Primary or some secondary 1281 (40.4%) 1231 (40.0%) 50 (53.8%)  Secondary and above 874 (27.6%) 850 (27.6%) 24 (25.8%) Currently employed† 0.59  No 1981 (62.5%) 1921 (62.4%) 60 (64.5%)  Yes 1188 (37.5%) 1156 (37.6%) 32 (34.4%) Year enrolled 0.24  2013–2017 2773 (87.4%) 2688 (87.3%) 85 (91.4%)  2018–2020 398 (12.6%) 390 (12.7%) 8 (8.6%) Total # people in household† 0.99

 ≤

3 1005 (31.7%) 976 (31.7%) 29 (31.2%)  >3-6 1489 (47.0%) 1445 (46.9%) 44 (47.3%)  >6 669 (21.1%) 650 (21.1%) 19 (20.4%) Consume alcohol† 0.78  No 2582 (81.4%) 2506 (81.4%) 76 (81.7%)  Yes 587 (18.5%) 571 (18.6%) 16 (17.2%) Smoker† 0.95  No 3026 (95.4%) 2938 (95.5%) 88 (94.6%)  Yes 142 (4.5%) 138 (4.5%) 4 (4.3%) Ever been incarcerated† 0.007  No 2846 (89.8%) 2771 (90.0%) 75 (80.6%)  Yes 322 (10.2%) 305 (9.9%) 17 (18.3%) Hyperglycemia†,‡ 0.89  No 2826 (89.1%) 2744 (89.1%) 82 (88.2%)  Yes 296 (9.3%) 287 (9.3%) 9 (9.7%) Time since HIV diagnosis† <0.001  <1 yr 1177 (37.1%) 1118 (36.3%) 59 (63.4%)  1-5 yrs 794 (25.0%) 771 (25.0%) 23 (24.7%)  >5 yrs 1157 (36.5%) 1146 (37.2%) 11 (11.8%) Duration in HIV care† <0.001  <6 mo 1056 (33.3%) 997 (32.4%) 59 (63.4%)  6 months to <2 yrs 424 (13.4%) 413 (13.4%) 11 (11.8%)  ≥2 yrs 1665 (52.5%) 1642 (53.3%) 23 (24.7%) Duration on ART† <0.001  ART-naïve 900 (28.4%) 862 (28.0%) 38 (40.9%)  <6 mo 437 (13.8%) 411 (13.4%) 26 (28.0%)  6 months to <2 yrs 433 (13.7%) 423 (13.7%) 10 (10.8%)  2 years to <4 yrs 354 (11.2%) 347 (11.3%) 7 (7.5%)  ≥4 yrs 1042 (32.9%) 1030 (33.5%) 12 (12.9%) ART regimen 0.002  AZT/NVP/3 TC 509 (16.1%) 504 (16.4%) 5 (5.4%)  AZT/EFV/3 TC 168 (5.3%) 165 (5.4%) 3 (3.2%)  TDF/NVP/3 TC 176 (5.6%) 174 (5.7%) 2 (2.2%)  PI-based 191 (6.0%) 187 (6.1%) 4 (4.3%)  TLE 974 (30.7%) 936 (30.4%) 38 (40.9%)  TLD 204 (6.4%) 203 (6.6%) 1 (1.1%)  Other 49 (1.5%) 47 (1.5%) 2 (2.2%)  ART-naïve 900 (28.4%) 862 (28.0%) 38 (40.9%) Missed doses ART (past month)† 0.002  Not on ART 900 (28.4%) 862 (28.0%) 38 (40.9%)  No missed doses ART 1937 (61.1%) 1885 (61.2%) 52 (55.9%)  Missed 1+ doses ART 333 (10.5%) 331 (10.8%) 2 (2.2%) Body mass index (BMI)† 0.002  Underweight 372 (11.7%) 344 (11.2%) 28 (30.1%)  Normal 2022 (63.8%) 1967 (63.9%) 55 (59.1%)  Overweight/obese 770 (24.3%) 761 (24.7%) 9 (9.7%) CD4 count† <0.001  <200 cells/mm3 586 (18.5%) 547 (17.8%) 39 (41.9%)  ≥200 cells/mm3 2546 (80.3%) 2492 (81.0%) 54 (58.1%) Viral load†  <6 months on ART 1333 (42.0%) 1269 (41.2%) 64 (68.8%) <0.001  <1000 copies/mL 1581 (49.9%) 1559 (50.6%) 22 (23.7%)  ≥1000 copies/mL 216 (6.8%) 210 (6.8%) 6 (6.5%) History of pulmonary TB§ <0.001  No 2957 (93.3%) 2893 (94.0%) 64 (68.8%)  Yes 214 (6.7%) 185 (6.0%) 29 (31.2%) TB diagnosis method¦ —  Bacteriological 61 (65.6%)  Clinical 32 (34.4%)

Data are presented as n (column %).

Abbreviations: PLWH, people living with HIV; TB, tuberculosis; ABC, abacavir; 3 TC, lamivudine; AZT, azidothymidine (zidovudine); TDF, tenofovir; TLE, tenofovir/lamivudine/efavirenz; TLD, tenofovir/lamivudine/dolutegravir.

*P values were calculated using Pearson χ2 tests; Bold indicates significance at P < 0.05.

†Percentages do not add up to 100 due to missing values.

‡Hyperglycemia was defined as a fasting glucose >99 mg/dL, nonfasting glucose >199 mg/dL, or receipt of hypoglycemic medications.

§History of pulmonary TB was defined as any of the following WHO codes abstracted from the participant's medical record: W38.1 (Positive Gene Expert), W36.1 (Smear +), or W50.0.

¦Bacteriological confirmation of TB was diagnosed by GeneXpert MTB/RIF; clinical diagnosis of TB was indicated if the participant was receiving combination therapy for active TB in the absence of bacteriological confirmation.


Factors Associated With Prevalent TB

After adjustment, the odds of prevalent TB were significantly higher among those who had completed primary school/had some secondary-level education and secondary-level education and above, compared with those with no/some primary education (Table 2). The adjusted odds of TB were 2.59 times greater among PLWH who had been diagnosed with HIV for 1–5 years compared with PLWH diagnosed with HIV for >5 years (95% CI: 1.17–5.71). Compared with PLWH with a normal BMI, there were greater adjusted odds of TB among those who were underweight (aOR 2.51, 95% CI: 1.52–4.15). Those with a CD4 count <200 cells/mm3 had higher adjusted odds of TB compared with those with higher CD4 counts (aOR 1.89, 95% CI: 1.18–3.03). Associations between prevalent TB and age, sex, incarceration status, ART regimen, and VL did not persist after adjustment (Table 2).

TABLE 2. - Factors Associated With Prevalent TB at Enrollment Among PLWH OR (95% CI) P aOR (95% CI)† P Age (years)  15–24* Ref —  25–39 2.17 (1.23–4.16) 0.02 1.50 (0.73–3.08) 0.27  40–49 1.10 (0.51–2.34) 0.81 0.86 (0.37–1.97) 0.71  50+ 0.61 (0.23–1.67) 0.34 0.46 (0.16–1.35) 0.15 Sex  Female Ref —  Male 1.67 (1.11–2.53) 0.02 1.35 (0.85–2.15) 0.20 Clinical site  Kayunga, Uganda Ref —  South Rift Valley, Kenya 1.31 (0.70–2.45) 0.40 1.52 (0.74–3.11) 0.25  Kisumu West, Kenya 1.13 (0.54–2.33) 0.74 1.49 (0.66–3.36) 0.34  Mbeya, Tanzania 1.45 (0.73–2.86) 0.28 1.52 (0.71–3.28) 0.28  Abuja & Lagos, Nigeria 0.55 (0.20–1.55) 0.26 0.62 (0.19–1.97) 0.41 Education  None or some primary* Ref —  Primary or some secondary 2.13 (1.25–3.64) 0.006 2.08 (1.17–3.69) 0.01  Secondary and above 1.48 (0.81–2.72) 0.20 2.03 (1.02–4.05) 0.04 Ever been incarcerated  No* Ref —  Yes 2.03 (1.19–3.49) 0.01 1.78 (0.95–3.34) 0.07 Time since HIV diagnosis  >5 yrs* Ref —  1–5 yrs 3.22 (1.56–6.65) 0.002 2.59 (1.17–5.71) 0.01  <1 yr 5.70 (2.98–10.91) <0.001 2.48 (0.99–6.21) 0.05 Duration in HIV care  ≥2 yrs* Ref —  6 months to <2 yrs 1.09 (0.44–2.70) <0.07  <6 mo 4.29 (2.63–6.99) <0.001 ART regimen  TLE Ref —  TLD 0.12 (0.02–0.89) 0.03 0.23 (0.03–1.87) 0.17  AZT/NVP/3 TC 0.24 (0.10–0.63) 0.003 0.66 (0.23–1.89) 0.43  AZT/EFV/3 TC 0.45 (0.14–1.47) 0.18 0.91 (0.25–3.27) 0.88  TDF/NVP/3 TC 0.28 (0.07–1.18) 0.08 0.57 (0.12–2.62) 0.46  PI-Based 0.53 (0.19–1.49) 0.22 1.08 (0.33–3.57) 0.89  Other 1.05 (0.25–4.48) 0.94 1.34 (0.26–6.89) 0.72  ART naïve 1.09 (0.69–1.72) 0.72 0.70 (0.40–1.22) 0.20 Body mass index (BMI)  Normal

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