Diarrheal disease and associations with water access and sanitation in Indigenous Shawi children along the Armanayacu River basin in Peru
Bibliographic record
Abstract
INTRODUCTION: Diarrheal disease, particularly in children under 5 years old, remains a global health challenge due to its high prevalence and chronic health consequences. Public health interventions that reduce diarrheal disease risk include improving access to water, sanitation, and hygiene. Although Peru achieved the 2015 Millennium Development Goal (MDG) indicators for water access, less progress was achieved on sanitation. Furthermore, many Indigenous Peoples were overlooked in the MDG indicators, resulting in a prioritization of Indigenous Peoples in the 2030 Sustainable Development Goals (SDGs). This study aimed to estimate the prevalence of childhood diarrhea, characterize access to water and sanitation, and determine the association of childhood diarrhea with water access and sanitation indicators in 10 Shawi Indigenous communities along the Armanayacu River in the Peruvian Amazon. METHODS: A cross-sectional survey (n=82) that captured data on diarrheal disease, sociodemographic variables, and water and sanitation exposures was conducted in 10 Shawi communities. Nutritional status of children under 5 was also assessed via physical examination. Descriptive and comparative statistics were conducted. RESULTS: A small proportion (n=7; 8.54%) of participating children reported an episode of diarrhea in the previous month. Almost half (46.30%) of participating children had stunting, wasting, or both. Although not statistically significant, children living in households that used latrines were 4.29 times (95% confidence interval (CI) 1.01-18.19) more likely to report an episode of diarrhea than children living in households that practiced open defecation. Although not statistically significant, children living in households that used water treatment methods were 4.25 times (95%CI 0.54-33.71) more likely to report an episode of diarrhea than children living in households that did not. CONCLUSION: The prevalence of childhood diarrhea was lower for Shawi than for other Amazon areas. The higher prevalence of childhood diarrhea in households that used latrines and water treatments warrants further investigation into local risk and protective factors. These Shawi communities scored low for the WHO/UNICEF Joint Monitoring Programme indicators for water and sanitation, indicating that they should be prioritized in future water, sanitation, and hygiene initiatives. Research will be required to understand and incorporate local Indigenous values and cultural practices into water, sanitation, and hygiene initiatives to maximize intervention uptake and effectiveness.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".