The monetary burden of cysticercosis in Mexico
Bibliographic record
Abstract
BACKGROUND: Taenia solium cysticercosis is a public health and agricultural problem in many low and middle-income countries where health education, sanitation, pig management practices and meat inspection infrastructure are insufficient. Cysticercosis affects both human and animal health and has important economic consequences. Very few studies have been conducted to evaluate the monetary burden of cysticercosis. This study aimed at estimating the 2015 costs associated with cysticercosis in humans and pigs in Mexico. METHODS: The monetary burden of human cysticercosis was estimated based on costs incurred by living with and treating epilepsy and severe chronic headaches associated with neurocysticercosis (NCC). The estimated cost of porcine cysticercosis took into consideration losses due to the reduction in the price of cysticercosis-infected animals. Epidemiologic and economic data were obtained from the published literature, government reports, and setting-specific questionnaires. Latin hypercube sampling methods were employed to sample the distributions of uncertain parameters and to estimate 95% credible regions (95% CRs). All results are reported in 2015 U.S.$. FINDINGS: The overall monetary burden associated with NCC morbidity was estimated at U.S.$215,775,056 (95% CR U.S.$109,309,560 -U.S.$361,924,224), with U.S.$436 (95% CR: U.S.$296 -U.S.$604) lost per patient. If loss of future years of income and productivity due to NCC-associated deaths was included, this value increased by U.S.$54.26 million, assuming that these individuals earned Mexico's median wage salary. An additional U.S.$19,507,171 (95% CR U.S.$5,734,782 -U.S.$35,913,487) was estimated to be lost due to porcine cysticercosis. CONCLUSIONS: This study suggests that T. solium cysticercosis results in considerable monetary losses to Mexico.
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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 | 0.004 |
| 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) | 0.003 | 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".