Prevalence of Cryptosporidium Infection in the Global Population: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Cryptosporidium is a protozoan parasite that can infect both humans and animals and cause cryptosporidiosis. We aimed to estimate the global prevalence of Cryptosporidium infection. METHODS: In this study, Web of Science, Medline and PubMed were searched for relative articles, published between January 1, 1960 and January 1, 2018. Included articles were restricted to English language and that sample size of articles was not less than 50. Studies with no information on the study period, location, method of diagnosis, sample size and number of infected people were excluded. Studies about outbreak, laboratory report or immunocompromised population were excluded as well. The quality of the included publications was assessed. The prevalence of cryptosporidiosis was estimated by DerSimonian-Laird random-effects model, after converting by the Freeman-Tukey type double arcsine transformation. FINDINGS: From 13,064 publications selected by literature search, 221 studies were included in the meta-analysis. The global pooled prevalence of Cryptosporidium infection was 7.6 % (95% CI: 6.9-8.5). The highest estimated prevalence of Cryptosporidium infection was in Mexico (69.6%, 95% CI 66.3-72.8), Nigeria (34.0%, 95% CI 12.4-60.0), Bangladesh (42.5%, 95% CI 36.1-49.0) and Republic of Korea (8.3%, 95% CI 4.4-13.2) among general residents, patients, school children and healthy population, respectively. The estimated prevalence was high in people from low-income country, people with gastrointestinal symptoms, people younger than 5 years old and residents not living in urban areas. CONCLUSIONS: These estimates indicate the substantial prevalence of Cryptosporidium infection in the world, which may provide a theoretical basis for the formulation of the prevention strategy about Cryptosporidium.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".