Individual healthcare-seeking pathways for tuberculosis in Nigeria’s private sector during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background Pre-COVID-19, individuals with TB in Nigeria were often underdiagnosed and untreated. Care for TB was mostly in the public sector while only 15% of new cases in 2019 were 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, based on patients’ perspectives. Design/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 behavior and pathways to care during the COVID-19 periods. All surveys and interviews were conducted in May 2021. Results Most participants were male (n=111, 62%), with average age of 37. Half (n=96, 53.4%) sought healthcare within a week of symptoms, while few (n=20, 11.1%) waited over 2 months. TB positive individuals had more health-seeking delays, and TB negative had more provider delays. On average, participants visited 2 providers in Kano and 1.69 in Lagos, with 61 (75%) in Kano and 48 (59%) in Lagos visiting other providers before the recruitment facility. Private providers were the initial encounters for most participants (n=60 or 66.7% in Kano, n-83 or 92.3% in Lagos). Most respondents (164 or 91%) experienced short-lived pandemic-related restrictions, particularly during the lockdowns, affecting access to transportation, and closed facilities. Conclusions This study showed a few challenges in accessing TB healthcare in Nigeria, 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".