Towards TB elimination: how are macro-level factors perceived and addressed in policy initiatives in a high burden country?
Bibliographic record
Abstract
BACKGROUND: Notwithstanding extensive general discussion of the effects of upstream forces on health, there has been limited empirical examination, let alone systematic evidence documenting policy responses to such pathways in the area of tuberculosis (TB) management and control. Our study aimed to gain insight into how macro level drivers of TB are perceived by key stakeholders involved in TB management and control in a high-endemic country, and to assess how such concerns are being addressed in policy initiatives in this setting. South Africa was chosen for this case study due to our team's long-standing collaborations there, its very high burden of TB, and its introduction of a strategic plan to combat this disease. METHOD: Semi-structured interviews were conducted with 20 key informants who were purposively selected for their knowledge and expertise of TB in South Africa. South Africa's National Strategic Plan for HIV, TB and STIs 2017-2022 was then reviewed to examine how identified themes from the interviews were reflected in this policy document. RESULTS: A history of colonization, the migrant labour system, economic inequality, poor shelter, health system challenges including TB governance, the HIV epidemic, and pertinent socio-cultural factors were all perceived to be major drivers of the epidemic. Although South Africa's current National Strategic Plan makes a firm discursive commitment to addressing the structural or macro-level drivers of TB, our analysis revealed that this commitment was not clearly reflected in projected budgetary allocations. CONCLUSION: As in many other high burden settings, macro-level drivers of TB are widely recognized. Nonetheless, while micro-level (biomedical and clinical) measures, such as improving diagnostic procedures and investment in more efficacious drugs, are being (and well should be) implemented, our findings showed that macro-level drivers of TB are underrepresented in budgeting allocations for initiatives to combat this disease. Although it could be argued that structural drivers that undermine health-promoting actions are beyond the purview of the health sector itself, we argue that strategic plans to combat TB in high burden settings need more attention to directly considering such drivers to prompt the necessary changes and reduce the burden of this and other such diseases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".