Seasonality in TB notification in Nigera: Reality or myth?
Bibliographic record
Abstract
Seasonal fluctuations in tuberculosis (TB) notifications have been identified and reported in a number of countries. Nigeria remains one of the 22 TB high-burden countries (HBCs) in the world, and the notification of TB cases in the country over the years has shown a definite pattern that suggests seasonal variation. Previous studies conducted in India, Japan, Mongolia, the Netherlands, Russia, Spain, the United Kingdom and the United States have evaluated the seasonality of TB notification. However, in Nigeria, there has been no systematic study to establish that this pattern is not just a myth. This study seeks to establish the seasonal variations suggested by the trend pattern of TB case notification (all forms of TB) in Nigeria over the past ten years. The yearly TB notification data in Nigeria from 2004 to 2013 was examined for seasonal fluctuations by plotting the quarterly notification figures for the years under review. A rapid trend analysis was done based on the amplitude of the fluctuating curves. Standardization was done by zones. The trend analysis showed a spike in the first quarter of the year for the ten-year period studied (with the exception of 2005 and 2011). This quarter is generally characterized by the dusty, dry harmattan wind in most parts of the country, particularly the northern region. The curves generally plummeted in the third quarter and remained in that neighborhood for the rest of the year. The differences in case notification between the first and last quarter for the ten-year period ranged from 347 to 4230 cases notified. The result of this trend analysis when standardized by zones for the six zones of the country was similar to the overall result for the country. According to the results of this study, there is evidence to suggest that there are seasonal variations in notification of TB cases across the four quarters of the year. This has significant implications for TB control strategies. Further investigation of the reasons for seasonal variations may help to identify risk factors. Also, planning and forecast of TB commodities to order cannot be based on experience from the preceding quarters, but must rather be based on reports from the same quarter in the previous year. Allocation of resources may also have to be intensified during the peak periods in order to adequately control the disease at these periods. Further investigation is required to unmask the reasons for the seasonal variations in TB notification in Nigeria.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".