Cascade Variabilities in TB Case Finding among People Living with HIV and the Use of IPT: Assessment in Three Levels of Care in Cross River State, Nigeria
Bibliographic record
Abstract
TB is the most common cause of morbidity and mortality in people living with HIV/AIDS (PLHIV) as it accelerates the progression of HIV infection. Every PLHIV is at an annual risk of 10% and a lifetime risk of 50% to acquiring TB and TB is responsible for the death of 30-40% PLHIV. We undertook to assess the WHO recommended intensified TB case finding among PLHIVs in three of levels of ART clinics in cross River State, Nigeria. We used quantitative method to review retrospectively collected routine TB and HIV facility data from University of Calabar Teaching hospital, Calabar; Infectious disease Hospital, Calabar and primary Health Centre, Calabar Municipal ART clinics. The study population for intensified case finding and IPT comprised of available records of HIV- positive patients ≥15 years old seen at the selected facilities for clinical care and treatment from January to December 2016 and January to December 2018. A cohort sampling strategy was used to assess the ICF cascade and IPT uptake. Data collection lasted from 15th to 31st November 2019 in the first phase and had extension to February 2020 due to delayed ethical clearance from the University of Calabar Teaching hospital. Quantitative data was analysed using Stata 13.0 to produce descriptive statistics including frequencies and percentages for categorical variables. Of the 326 PLHIVs (115 females) in the records, 311 had their TB screening recorded (95%). 155(50%) were screen positive while 326(210%) were evaluated for TB in the lab, out of which 182 (56%) were TB confirmed. PLHIVs ages 25-29 years were most affected, with more persons being evaluated for TB before the `test and treat` policy. Of the 207 PLHIVs started on IPT, 103 (99%) started before `test and treat` compared to 102(91%) after policy. IPT uptake was highest among ages 25-29 years. This evaluation shows that intensified case finding among PLHIV is feasible and has a high prospect for TB case finding among PLHIVs. However, critical gaps exist- poor documentation and linkages on the clinical and diagnostic arm of the cascade makes it impossible to estimate yield in a cohort and present the situation of weak clinical interphase with people seeking care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".