Individual journeys to tuberculosis care in Nigeria’s private sector during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Pre-COVID-19, individuals with tuberculosis (TB) in Nigeria were often underdiagnosed and untreated. TB services were mostly in the public sector with only 15% of new cases in 2019 reported from the private sector. Reports highlighted challenges in accessing care in the private sector, which accounted for 67% of all initial care-seeking. Our study examined patients' health seeking pathways for TB in Nigeria's private sector and explored any changes to care pathways during COVID-19. METHODS: We conducted 180 cross-sectional surveys and 20 in-depth interviews with individuals having chest symptoms attending 18 high-volume private clinics and hospitals in Kano and Lagos States. Questions focused on sociodemographic characteristics, health-seeking behaviour, and pathways to care during the COVID-19 pandemic. All surveys and interviews were conducted in May 2021. RESULTS: Most participants were male (111/180), with an average age of 37. Half (96/180) sought healthcare within a week of symptoms, while few (20/180) waited over 2 months. Individuals testing positive for TB had more health-seeking delays, and those testing negative for TB had more provider delays. On average, participants visited two providers in Kano and 1.69 in Lagos, with 61 of 180 in Kano and 48 of 180 in Lagos visiting other providers before the recruitment facility. Private providers were the initial encounters for most participants (60/180 in Kano, 83/180 in Lagos). Most respondents (164/180) experienced short-lived pandemic-related restrictions, affecting access to transportation, and closed facilities. CONCLUSIONS: This study showed a few challenges in accessing TB care, necessitating continued investment in healthcare infrastructure and resources, particularly in the private sector. Understanding the different care pathways and delays in care provides opportunities for targeted interventions to improve deployment of services closer to where patients first seek care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".