Increased Incidence of Tuberculosis in Zimbabwe, in Association with Food Insecurity, and Economic Collapse: An Ecological Analysis
Bibliographic record
Abstract
BACKGROUND: Zimbabwe underwent a socioeconomic crisis and resultant increase in food insecurity in 2008-9. The impact of the crisis on Tuberculosis (TB) incidence is unknown. METHODS: Prospective databases from two mission hospitals, which were geographically widely separated, and remained open during the crisis, were reviewed. RESULTS: At the Howard Hospital (HH) in northern Zimbabwe, TB incidence increased 35% in 2008 from baseline rates in 2003-2007 (p<0.01) and remained at that level in 2009. Murambinda Hospital (MH) in Eastern Zimbabwe also demonstrated a 29% rise in TB incidence from 2007 to 2008 (p<0.01) and remained at that level in 2009. Data collected post-crisis at HH showed a decrease of 33% in TB incidence between 2009 to 2010 (p<0.001) and 2010/2011 TB incidence remained below that of the crisis years of 2008/2009 (p<0.01). Antenatal clinic HIV seroprevalence at HH decreased between 2001(23%) to 2011(11%) (p<0.001). Seasonality of TB incidence was analyzed at both MH and HH. There was a higher TB incidence in the dry season when food is least available (September-November) compared to post harvest (April-June) (p<0.001). CONCLUSION: This study suggests that an epidemic of TB mirrored socioeconomic collapse and recovery in Zimbabwe. The seasonal data suggests that food security may have been associated with TB incidence both annually and during the crisis in this high HIV prevalence country.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".