Investigating Barriers and Challenges to Tuberculosis Service Delivery in Hard-to-Reach Riverine Areas: A Mixed-Methods Study in the Niger Delta Region of Nigeria
Bibliographic record
Abstract
<title>Abstract</title> Background Little is known about the challenges and barriers to tuberculosis (TB) service delivery in hard-to-reach riverine populations in Nigeria. The missing TB cases in such key populations need to be found if the End TB targets are to be met. This study explored perceptions and attitudes related to TB, as well as the level of diagnostic and treatment delays in communities of the riverine Niger Delta.Methods This was an exploratory mixed-methods study carried out in two states (Bayelsa and Delta) situated in the Niger Delta region in Nigeria. It consisted of quantitative surveys of community members and TB patients, FGDs with community members and KIIs with health care workers.Results The questionnaire survey was completed by 597 community members (51.6% female) and 51 TB patients (56.9% female); 73 community members and 15 HCWs participated in FGDs and interviews respectively. Community members’ mean [SD] knowledge and attitude scores were 6.1/10 [2.2] and 4.8/12 [1.9] respectively. Older age (>40y) (p=0.04) and regular income (p<0.001) were independent predictors of TB knowledge. Good TB knowledge (aOR 2.5; 95% C.I. 1.5 – 4.4) and formal education (aOR 5.8; 95% C.I. 1.3 – 25.6) were associated with positive TB attitudes. Patients’ TB knowledge was similar at 6.8/8 [1.5]. Most (98.8%) respondents took >1 hour to access the nearest diagnostic centre. Mean patient-related and health system-related diagnostic delays were 16.3 and 3.7 weeks respectively. Mean treatment delay was found to be 0.5 weeks. Patient-related, financial, cultural and structural barriers were found to delay TB diagnosis and treatment in this environment. Belief in faith healing and herbal remedies, transport barriers, and negative HCW attitudes were prominent themes in FGDs. Problems transporting sputum samples and tracing mobile communities were primary HCW complaints.Conclusions A number of barriers affect TB service delivery in hard-to-reach riverine communities in Nigeria. Our study suggests that an appropriately designed community intervention can improve TB service delivery in these communities. The main focus will be to address information asymmetry between service providers and the community, empower affected communities to find cases, engage informal providers such as patent medicine vendors, and strengthen the health system.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".