Global burden and trends of neglected tropical diseases from 1990 to 2019
Bibliographic record
Abstract
BACKGROUND: An updated analysis of neglected tropical diseases (NTDs) from a global perspective is missing from the literature. We aimed to assess the global burden and trends of NTDs from 1990 to 2019. METHODS: Yearly incident case, mortality and disability-adjusted life years (DALYs) data for NTDs were extracted from the Global Burden of Disease Study 2019 (GBD 2019) based on global, regional, country, social development index (SDI), age and sex categories. The age-standardized rate (ASR) and number of incident cases, mortality and DALYs were computed from 1990 to 2019. The estimated annual percentage change (EAPC) in the ASR was calculated to quantify the changing trend. RESULTS: Globally, the age-standardized incidence rate (ASIR) and the number of incident cases of total NTDs increased between 1990 and 2019, whereas the age-standardized mortality rate (ASMR), mortality, age-standardized DALY rate and DALYs of total NTDs decreased. Although tropical Latin America, South Asia, Southeast Asia and Oceania had the highest ASIR for total NTDs in 2019, tropical Latin America was the only region to experience a decreasing trend in ASIR from 1673.5 per 100 000 in 2010 to 1059.2 per 100 000 in 2019. The middle, high-middle and high SDI regions experienced increasing ASIR trends between 1990 and 2019, whereas the low-middle SDI region remained stable, and the low SDI region presented a decreasing trend. Children and older adults were vulnerable to dengue, rabies and leishmaniasis (cutaneous and mucocutaneous). Females had a higher ASIR but a lower ASMR and age-standardized DALY rate than males. CONCLUSIONS: NTDs still represent a serious problem for public health, and the increasing ASIR and incident cases globally may require more targeted strategies for prevention, control and surveillance, especially among specific populations and endemic areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".