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Record W3173242359

Prevalence and the Risk Factors Associated with HIV-TB Co-Infection Among Clinic Attendees in Dots and Art Centres in Ibadan, Nigeria

2021· article· en· W3173242359 on OpenAlexaff
Adeloye Adewale Idowu, Ayinde Abayomi Oluwasegun, Ohue Michael, Tope Gloria Olatunde-Aiyedun, Ogunode Niyi Jacob

Bibliographic record

VenueCENTRAL ASIAN JOURNAL OF MEDICAL AND NATURAL SCIENCES · 2021
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsMedicineTuberculosisLogistic regressionHuman immunodeficiency virus (HIV)Cross-sectional studyPublic healthDescriptive statisticsEnvironmental healthFamily medicineInternal medicinePathology
DOInot available

Abstract

fetched live from OpenAlex

Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) co-infection form a very serious public health menace in Nigeria. Among patients confirmed to have been infected with either of the disease, co-infection with the other is highly prevalent. However, comprehensive studies focusing on the distributions and correlates of TB/HIV co-infections among Patients attending TB clinics in Ibadan are lacking in the literature. The objective of this study was to determine the prevalence and correlates of TB/HIV co-infection among patients suspected to be TB positive at various health facilities offering TB/HIV Collaboration Service (THCS) in Ibadan. A descriptive cross-sectional study was carried out among 500 TB/ HIV clinic attendees in Ibadan, Nigeria. A simple random sampling method was used to select 8 TB clinics in Ibadan from the list of all clinics offering THCS in Ibadan. An interviewer administered questionnaire was used to elicit information on TB/HIV status, risk factors and knowledge of HIV and TB from all participants who consented to be interviewed. Descriptive statistics, Chi-square test and logistic regression were used for data analysis at 5% level of significant. Mean age of the patients was 33.98±13.15 years. The overall prevalence of TB/HIV co-infection among the participants was found to be (41.6%). Prevalence of TB/HIV co-infection were highest (11.2% and 14.8%) among participants in age group 20-29 years and 30-39 years respectively. More females (25.2%) than males (16.4%) had been infected with TB/HIV co- infection. While the prevalence of TB/HIV co-infection were respectively 2.0%, 6.6% 18.4% and 14.6% among participants with no formal education, Primary education, Secondary education and Tertiary education, the prevalence was 20.6% and 16.4% among the married and the unmarried respectively. Results of the Chi-square test show that TB/HIV co-infection was found to be associated with History of the use of TB and HIV drugs defaults, Multiple sex partners, Paid sex, Marital status and occupation of participants. Also, Multiple sex partners (OR = 6.0, 95% CI: 2.4-15.0), Extra-vaginal intercourse (OR= 0.3, 95% CI: 0.1- 0.8) and Paid sex (OR= 0.1, 95% CI: 0.5-0.7) were found to be associated with TB/HIV co-infection among the participants. The study revealed that a higher prevalence of co-infection was observed among 10-49 years age group. This implies that the productive age group bears the brunt of TB/HIV co-infection. It was also found that participants with multiple sex partner (OR=6.01) those whose partners are residing with them(OR=1.45) and those with formal education(OR=1.59) are more likely to have TB/HIV co-infection while those with History of anti-TB drug default(OR=0.54), History of anti-retroviral drug default(OR=0.49), those who practice Extra-vaginal intercourse(OR=0.346) and paid sex(OR=0.19) are less likely to be TB/HIV co-infected. TB/HIV control programs that educate people on the prevalence and focus on these subgroups are likely to decrease the joint burden of TB and HIV

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.320
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCENTRAL ASIAN JOURNAL OF MEDICAL AND NATURAL SCIENCES→Same topicTuberculosis Research and Epidemiology→French-language works237,207→