Prevalence of and risk factors associated with Cryptosporidium infection in an underdeveloped rural community of southwest China
Bibliographic record
Abstract
Cryptosporidium spp. is an important intestinal protozoan causing diarrhea in humans, livestock, and wild animals. Cryptosporidium infection remains a major public health issue, but its epidemiology in humans is still unclear, particularly in rural China. This study was designed to determine the prevalence of and risk factors associated with Cryptosporidium infection in a rural southwestern Chinese community. A community-based cross-sectional survey was conducted among 687 residents of a small town in a Yi autonomous prefecture of southwest China in 2014. Blood samples were examined using a broad set of quality-controlled diagnostic methods for hepatitis B virus (HBV) and human immunodeficiency virus (HIV). Stool specimens were processed using the modified acid-fast staining method, and microscopically examined for Cryptosporidium infection. Univariable and multivariable analyses were performed to determine the risk factors associated with Cryptosporidium infection. The majority of the participants were Yi people with poor living conditions and unsatisfactory hygiene habits, and the study area was of very low socioeconomic status. Of the 615 individuals included in the analysis, 14 (2.3%) were HIV positive, 51 (8.3%) were infected with HBV, and 74 (12.0%) had Cryptosporidium infection. The prevalences of HIV/HBV, HIV/Cryptosporidium, and HBV/Cryptosporidium co-infections were 0.3%, 0.3%, and 1.8%, respectively. The prevalence of HBV infection was higher in individuals with Cryptosporidium infection (χ 2 = 5.00, P = 0.03). Owning livestock or poultry was an important risk factor for Cryptosporidium infection (aOR = 2.27, 95% CI: 1.01–5.08, P < 0.05). Cryptosporidium infection was significantly associated with HBV infection (aOR = 3.42, 95% CI: 1.47–7.92, P < 0.01), but not with HIV infection (aOR = 0.57, 95% CI: 0.07–4.39, P = 0.59). The prevalence of Cryptosporidium infection was high in the rural area of southwestern China that was investigated, and there was a significant association between HBV infection and Cryptosporidium infection. Further investigations are needed to determine the significance of Cryptosporidium infection in patients infected with HBV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".