Prevalence and burden of no-toilet households in India: an analysis of 261,746 households in 36 states/Union Territories in 2022–2023
Bibliographic record
Abstract
India's Swachh Bharat Abhiyan was a nation-wide program aimed at providing households with toilets to eliminate open defecation. Between 2016 and 2021, millions of households gained access to a toilet. However, as of 2021, over 238 million people still did not have a toilet, and there was considerable variation in this outcome between India's states and Union Territories. We update the estimates on the number of no-toilet households in India using India's Household Consumption Expenditure Survey from 2022 to 2023. We find that 12.5% of India's households, most of which are in rural communities, still have no toilet. This amounts to over 162 million people still living without a toilet. Over 70% of those without a toilet live in just six states, and the lowest socioeconomic status households are the least likely to have a toilet. This emphasizes the need for policy makers to target states and socioeconomic groups that are lagging. Furthermore, policy makers should understand that a household's toilet status can fluctuate because of adverse climate events, such as flooding. This evidence highlights the need for accurate and up-to-date data on no-toilet households throughout India.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".