Household water insecurity will complicate the ongoing COVID-19 response: Evidence from 29 sites in 23 low- and middle-income countries
Bibliographic record
Abstract
In March 2020, the World Health Organization (WHO) issued a set of public guidelines for Coronavirus Disease 2019 (COVID-19) prevention measures that highlighted handwashing, physical distancing, and household cleaning. These health behaviors are severely compromised in parts of the world that lack secure water supplies, particularly in low- and middle-income countries (LMICs). We used empirical data gathered in 2017-2018 from 8,297 households in 29 sites across 23 LMICs to address the potential implications of water insecurity for COVID-19 prevention and response. These data demonstrate how household water insecurity presents many pathways for limiting personal and environmental hygiene, impeding physical distancing and exacerbating existing social and health vulnerabilities that can lead to more severe COVID-19 outcomes. In the four weeks prior to survey implementation, 45.9% of households in our sample either were unable to wash their hands or reported borrowing water from others, which may undermine hygiene and physical distancing. Further, 70.9% of households experienced one or more water-related problems that potentially undermine COVID-19 control strategies or disease treatment, including insufficient water for bathing, laundering, or taking medication; drinking unsafe water; going to sleep thirsty; or having little-to-no drinking water. These findings help identify where water provision is most relevant to managing COVID-19 spread and outcomes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".