Factors Associated With Pulmonary Tuberculosis-HIV Co-Infection in Treatment-Naive Adults in Jos, North Central Nigeria

Agbaji, OO; Ebonyi, AO; St, Meloni; Ja, Aznar; Akanbi, Maxwell O; Oguche, Stephen; Agaba, PA; SagayAS,; Okonkwo, Prosper; Kanki, Phyllis J.

doi:10.4172/2155-6113.1000222

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…The results showed that, patients with CD4 T-lymphocytes count less than 200 cells/μl were about 2 times more likely to develop TB as compared to CD4 T-lymphocytes count more than 200 cells/μl. A study conducted in Nigeria revealed similar finding where lower CD4+ lymphocyte count was observed in co-infected patients than mono infected patients 21 . Substance use such as cigarette smoking and alcohol consumption were the predictors of TB/HIV co-infection in HIV positive in the current study.…”

Section: Discussionmentioning

confidence: 62%

Access, attitudes and training in information technologies and evidence-based medicine among medical students at University of Zimbabwe College of Health Sciences

et al. 2016

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Background: Tuberculosis is one of the world's most common causes of death in the era of Human immunodeficiency virus. The purpose of this study was to determine the prevalence and associated factors of TB/HIV co-infection. Methods: Hospital based retrospective studies were conducted among adult HIV-positive patients. Logistic regression method and Chi square test were applied. Results: A total of 571 HIV positive study participants were enrolled. Of these, 158 (27.7%) were found to have pulmonary tuberculosis. Lower baseline CD4 count<200cell/μl, patients who drunk alcohol, patients who were ambulatory at the initiation of ART, patients whose marital status was single were significant predictors for increased risk of tuberculosis in PLWHIV (P <0.05). Non smoker patients, patients in WHO clinical stage I, patients in WHO clinical stage II and ownership of the house had significant protective benefit against risk of TB (P <0.05). Conclusion:The prevalence of TB/HIV co-infection in adults on ART in our study was moderately high. Having advanced clinical status and presence of risk factors were found to be the predicting factors for co-infection. The health office should open TB/HIV co-infection units in the hospitals and health workers should be cautious when a patient has an advanced disease.

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Section: Discussionmentioning

confidence: 62%

Access, attitudes and training in information technologies and evidence-based medicine among medical students at University of Zimbabwe College of Health Sciences

et al. 2016

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HIV–TB co-infection and associated risk factors among HIV positive patients at Olabisi Onabanjo University Teaching Hospital, Ogun State, South-West Nigeria

et al. 2016

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Modelling of viral load dynamics and CD4 cell count progression in an antiretroviral naive cohort: using a joint linear mixed and multistate Markov model

et al. 2020

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Background: Patients infected with HIV may experience a succession of clinical stages before the disease diagnosis and their health status may be followed-up by tracking disease biomarkers. In this study, we present a joint multistate model for predicting the clinical progression of HIV infection which takes into account the viral load and CD4 count biomarkers. Methods: The data is from an ongoing prospective cohort study conducted among antiretroviral treatment (ART) naïve HIV-infected women in the province of KwaZulu-Natal, South Africa. We presented a joint model that consists of two related submodels: a Markov multistate model for CD4 cell count transitions and a linear mixed effect model for longitudinal viral load dynamics. Results: Viral load dynamics significantly affect the transition intensities of HIV/AIDS disease progression. The analysis also showed that patients with relatively high educational levels (β = − 0.004; 95% confidence interval [CI]:-0.207, − 0.064), high RBC indices scores (β = − 0.01; 95%CI:-0.017, − 0.002) and high physical health scores (β = − 0.001; 95%CI:-0.026, − 0.003) were significantly were associated with a lower rate of viral load increase over time. Patients with TB co-infection (β = 0.002; 95%CI:0.001, 0.004), having many sex partners (β = 0.007; 95%CI:0.003, 0.011), being younger age (β = 0.008; 95%CI: 0.003, 0.012) and high liver abnormality scores (β = 0.004; 95%CI:0.001, 0.01) were associated with a higher rate of viral load increase over time. Moreover, patients with many sex partners (β = − 0.61; 95%CI:-0.94, − 0.28) and with a high liver abnormality score (β = − 0.17; 95%CI:-0.30, − 0.05) showed significantly reduced intensities of immunological recovery transitions. Furthermore, a high weight, high education levels, high QoL scores, high RBC parameters and being of middle age significantly increased the intensities of immunological recovery transitions. Conclusion: Overall, from a clinical perspective, QoL measurement items, being of a younger age, clinical attributes, marital status, and educational status are associated with the current state of the patient, and are an important contributing factor to extend survival of the patients and guide clinical interventions. From a methodological perspective, it can be concluded that a joint multistate model approach provides wide-ranging information about the progression and assists to provide specific dynamic predictions and increasingly precise knowledge of diseases.

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Factors Associated With Pulmonary Tuberculosis-HIV Co-Infection in Treatment-Naive Adults in Jos, North Central Nigeria

Cited by 6 publications

References 14 publications

Access, attitudes and training in information technologies and evidence-based medicine among medical students at University of Zimbabwe College of Health Sciences

Access, attitudes and training in information technologies and evidence-based medicine among medical students at University of Zimbabwe College of Health Sciences

HIV–TB co-infection and associated risk factors among HIV positive patients at Olabisi Onabanjo University Teaching Hospital, Ogun State, South-West Nigeria

Modelling of viral load dynamics and CD4 cell count progression in an antiretroviral naive cohort: using a joint linear mixed and multistate Markov model

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