Economic impact of the episode of dengue fever in the city of Teresina, Piauí, Brazil
Bibliographic record
Abstract
Dengue is considered the most important arboviral internationally. An estimated 390 million people infected annually by the dengue virus and about a quarter expressed some level of clinical severity. In the last decade, Brazil has been contributing to the global burden of dengue in the Western hemisphere, despite investments to control the vector mosquitoes and thus attempting to reduce disease transmission. Objective: To analyze the economic impact of dengue episode from the perspectives of the public payer and society, in outpatients and inpatients served in public and private health sectors and realize systematic literature review about dengue costs in the Americas countries. Methods: This is an economic evaluation in health care, using the method of macro and micro costing, which sets up a cost analysis of an episode of dengue among ambulatory and hospital with suspected dengue cases treated in the public and private sectors. Patients were interviewed at the beginning of the episode of dengue and approximately 30 days after the onset of symptoms. Results and discussion: The results presented were based on interviews of 415 patients included in the study. Of the respondents, 355 (85.5%) were ambulatory and 60 (14.5%) of the group of hospitalized group. Considering the perspective of society, the average cost per unit of outpatient care in the public sector was $ 72 and in the private sector was USD 500. For hospitalized cases, the overall average costs for patients treated in the public sector were about four times higher (270 USD), while in the private sector, about 1.5 times higher than ambulatory patients (835 USD). Considering the prospect of paying assessed only the public sector, where the average cost per outpatient ward was USD 31 and USD 198 hospitals was lower than the assessment value from the perspective of society. Conclusion: Dengue is a high cost in the private and public health care economy as well as to society, beyond that costs vary according to the sector of care (outpatient and inpatient).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".