Malaria surveillance and use of evidence in planning and decision making in Kilosa District, Tanzania
Bibliographic record
Abstract
Background: Since 2001, Tanzania has been making concerted efforts to strengthen its Integrated Disease Surveillance and Response system. In this system, malaria is one of the priority diseases that are to be reported monthly. The objectives of this study were to (i) assess malaria surveillance system at facility and district levels to identify key barriers, constraints and priority actions for malaria surveillance strengthening; and (ii) to explore the use of evidence in health planning and decision making at these levels.Methods: The study was carried in Kilosa District in central Tanzania, during October 2012 and involved health facility workers and members of the district health management team. The existing information system on malaria was evaluated using a structured questionnaire and check list. Data collection also involved direct observations of reporting and processing, assessment of report forms and reports of processed data.Results: Three district officials and 17 health facility workers from both public and private health facilities were interviewed. Of the 17 informants, 15 were familiar with disease surveillance functions. A good percentage (47%, 8/17) received training on disease surveillance during the previous two years. Public transport and motorcycles were the main means of reporting epidemiological information from facility to district level. Most of the health facilities (93%, 14/15) faced difficulties in submitting reports due to lack of resources and feedback from the district authority. Analysis of malaria data was reported in 52.9% (9/17) of the facilities, but limited to malaria incidence per age groups. Challenges in data analysis included unavailability of compilation books; lack of computers; poor data storage; incomplete recording; lack of adequate skills for data analysis; and increase in workloads. Data at both facility and district levels were mainly used for quantification and forecasting of drug requirements.Conclusion: Malaria surveillance system in Kilosa district is weak and utilization of evidence for planning and decision making is poor. Capacity strengthening on data analysis and utilization should be given a priority at both facility and district levels of the health systems in Tanzania.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".