https://orcid.org/0000-0003-2365-4165
Figures
Abstract
In India, the share of households with access to an improved private toilet has increased considerably over the past few decades. However, there are other types of toilets that households often rely on, such as unimproved toilets or shared toilets. And in many cases, households in India still do not have a toilet. This paper provides sub-national prevalence estimates for each of these toilet types across India’s 543 Parliamentary Constituencies (PCs) in 2016 and 2021 and highlights the PCs where the prevalence either increased or decreased. We used a Monte Carlo Markov Chain procedure to derive these estimates. Overall, we found considerable variation between PCs for each of the four toilet types. We also found that in the majority of PCs, the share of no-toilet households decreased by more than 9.99 percentage points between 2016 and 2021, while the share of improved private toilets increased by more than 9.99 percentage points over the same period. The PC-level prevalence of unimproved and shared toilets was similar in 2016 and 2021. Lessons from high-performing PCs should be studied and applied to PCs where the prevalence of no-toilet households remains high. Furthermore, PCs where the share of unimproved toilets remains high should implement policies to help households transition to improved toilets. This is especially important in mountainous areas. In areas where households rely on shared toilets, such as dense urban settlements, policy makers should implement strategies for ensuring these facilities are kept clean and well-maintained.
Citation: Jain A, Kim R, Subramanian SV (2025) Analyzing changes in types of household sanitation among 543 Parliamentary Constituencies between 2016 and 2021 in India. PLOS Water 4(8): e0000409. https://doi.org/10.1371/journal.pwat.0000409
Editor: Eugene Appiah-Effah, Kwame Nkrumah University of Science and Technology, GHANA
Received: February 13, 2025; Accepted: June 27, 2025; Published: August 5, 2025
Copyright: © 2025 Jain et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The dataset analysed during the current study are available in the DHS website: https://dhsprogram.com/data/available-datasets.cfm.
Funding: This work was supported by the Bill & Melinda Gates Foundation, INV-002992. The funder had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Inadequate access to sanitation has long been a major public health issue throughout India. In 1993, 70% of people lived in households without a toilet [1]. National programs such as the Total Sanitation Campaign, launched in 1999, helped households build toilets by providing financial incentives. As a result, the share of people without a household toilet fell to 56% by 2006 [1]. Then, in 2014, the India’s government launched Swachh Bharat Abhiyan (SBA), an expanded version of the Total Sanitation Campaign. This program improved the pace of household toilet coverage. By 2021, seven years into the program, 17.8% of people throughout India did not have a toilet [1], a dramatic reduction from 1993.
However, simply counting those who do or do not have a toilet fails to acknowledge the various other types of sanitation people often rely on [2]. This range of sanitation types is captured by the World Health Organization’s (WHO) sanitation ladder. ‘Safely managed’ toilets are at the top, and include improved private household toilets where the waste is hygienically disposed either on or off site [3]. Below ‘safely managed’ toilets are ‘limited’ toilets, which are improved toilets that are shared between two or more households [3]. This is followed by ‘unimproved’ toilets, which include pit latrines without a slab, hanging latrines, or bucket latrines [3]. Finally, open defecation sits at the bottom of the JMP’s sanitation ladder, and is most commonly a consequence of a household not having a toilet [1,3].
The WHO provides national estimates for the share of people using the various types of toilets in each country. For instance, in 2015, 43% of Indians (approximately 570 million people) did not have ‘safely managed’ toilets [3]. Nearly 11% of people throughout India were using ‘limited’ toilets, while 3% were using ‘unimproved’ toilets [3].
What remains less understood, however, is the extent to which the reliance on these various toilet types varies within countries. Filling this gap is particularly important in India where previous studies have shown considerable geographic variation in sanitation outcomes [1,4–6]. A previous study has examined the urban versus rural distribution of household toilet type using data from 76th round of the National Sample Survey Office from 2018 [7]. However, more recent information on variations in household toilet types across smaller geographic units is less known. For instance, no prior studies have examined these variations across India’s Parliamentary Constituencies (PCs). Variations in SBA program implementation, water availability, soil type, and topography are some of the geographic factors that could shape the distribution of household toilet types across PCs in India [7–10].
Therefore, this paper aims to provide a spatially granular analysis of sanitation progress to inform targeted policy interventions. As such, this paper examines changes in rates of no-toilet households, unimproved toilets, shared toilets, and improved private toilets use between Parliamentary Constituencies (PCs) throughout India between 2016 and 2021. There are 543 PCs throughout India, each being represented by a Member of Parliament (MP) in India’s lower house of parliament, the Lok Sabha. This makes PCs an important geographic unit to examine given that each one represents a unique set of constituents at the national government level [11]. This paper elucidates changes in sanitation outcomes between these regions since the launch of Swachh Bharat Abhiyan.
Methods
Data source
Data from the fourth and fifth rounds of India’s National Family Health Survey (NFHS) were used for this study. NFHS-4 was conducted in 2016, and NFHS-5 was conducted in 2021. All NFHS rounds are conducted with a representative household sample, and the surveys are designed to capture data pertaining to population health and nutrition. Furthermore, the NFHS, like all Demographic and Health Surveys, uses a two-stage sampling process. Primary Sampling Units (PSUs), villages in rural communities and wards in urban communities, are selected using probability proportional to size within each district and state. Next, households are selected from each PSU. A full description of the sampling strategy is provided in the NFHS report [12].
Outcome definition
The four sanitation outcomes included in this study were no toilet, unimproved toilet, improved shared toilet (hereafter referred to as shared toilet), and improved private toilet (hereafter referred to as private toilet). During the survey, respondents were asked what type of toilet facility members of their household usually use out of 12 total options. These options were coded into the four outcomes in the following way. No toilet was its own response option, which we used for the first outcome. Those who responded that they usually use a toilet in which the waste is flushed to somewhere else (such as open drains or open water bodies), a pit latrine without a slab, a dry toilet, or “other” were coded as having an unimproved toilet. People who shared a flush toilet piped to a sewer system, a toilet that is flushed to a septic tank, a toilet flushed to a pit latrine, a toilet that flushes to an unknown location, a ventilated pit latrine, a pit latrine with a slab, or a composting toilet with another family were coded as using an improved shared toilet. Those who used any of the above toilet types but did not share it with any other households were coded as using a private toilet.
Analysis
Combining PSU and PC boundaries.
Unlike districts, PC boundaries have not changed within India since 2002. This makes them a useful subnational level to analyze from a policy perspective given that trends can be seen within a given PC over time.
We followed the same methodology as outlined by Swaminathan et al. and Kim et al. to link PC boundaries with the NFHS data [11,13]. Overall, this method relies on first ascertaining the GPS coordinates for each PSU. These data are available upon request from the DHS. These coordinates can then be combined with the PC boundary shapefiles, which are available for the Community Created Maps of India (available: http://projects.datameet.org/maps/). Based on this map, PSUs were assigned to their correct PC. The unique PC identifier was then assigned to each observation in the NFHS dataset based on the PSU that the observation is in. There were 172 PSUs that could not be joined in 2021, which corresponded to 3,553 households. In 2016, 152 PSUs could not be joined corresponding to 3,189 households. A complete description of the number of PCs, PSUs, and households by state/UT is provided in Table 1 below.
Estimating percent prevalence and standard deviation of sanitation outcomes by PC.
Estimates for each outcome by PC were derived using a four-level multilevel model. These hierarchical models first produce precision-weighted estimates at the PSU level, which can then be used to estimate outcome prevalence at the PC level [14,15].
The model took the basic form of , where
is the log odds of the outcome for household i, and the random effects are the residual differentials for PSUs
districts
), and states
). This model nesting structure accounts for the various factors at the cluster, district, and state levels that might be associated with household toilet type [1,16]. This analysis was conducted using the runmlwin command in Stata 18 [17] using a Monte Carlo Markov Chains which performs better than other quasi-likelihood procedures that often produce downward biased variance estimates [18,19]. We varied the chain length and burn-ins for each outcome to achieve a minimum effective sample size (ESS) of 250, the number of independent samples equivalent to the number of dependent MCMC samples [20,21]. The full MCMC output is presented in S1 and S2 Table. The
,
,
, and
values were used to calculate the PSU-level prevalence estimates for each outcome using
PSU-level estimates were then averaged by states and PCs to derive the prevalence for each outcome for these two geographic levels.
Results
Sample characteristics
This study analyzed 28,372 households in 598,320 PSUs and 543 PCs in 2016. Approximately 37.7% (95% CI: 37.3 – 38.0) of households had no toilet, 4.4% (95% CI: 4.3 – 4.6) had an unimproved toilet, 7.8% (95% CI: 7.6 – 7.9) had a shared toilet, and 48.4% (95% CI: 48.0 – 48.7) had a private toilet. In 2021, we analyzed 633,146 households in 29,998 PSUs and 543 PCs. In our sample, 17.4% (95% CI: 17.1 – 17.6) of households had no toilet, 3.2% (95% CI: 3.1 – 3.3) had an unimproved toilet, 7.0% (95% CI: 6.9 – 7.1) had a shared toilet, and 70.9% (95% CI: 70.6 – 71.1) had a private toilet. These results are presented in Table 2.
The median PC prevalence of no-toilet households decreased from 40.1% in 2016 to 16.4% in 2021. The interquartile range (IQR) in the PC-level prevalence of no-toilet households decreased from 49.1 to 24.7 over the same period. The median PC prevalence of unimproved toilets increased slightly from 1.1% in 2016 to 1.2% in 2021, while the IQR decreased from 2.5 to 2.0 over the same period. The median PC prevalence of shared toilets increased from 5.7% in 2016 to 6.5% in 2021, and the IQR decreased from 7.3 to 6.6 over the same period. Finally, the PC prevalence of private toilets increased from 45.2% in 2016 to 71.3% in 2021, while the IQR decreased from 35.1 to 19.8 over the same period. These results are presented in Figs 1 and Fig 2A, Fig 2B, Fig 2C, and Fig 2D. Additionally, there was a considerable degree of variation at the cluster level for each of the outcomes, indicating that there is variation in each outcome within PCs as well. This is demonstrated by the variance estimates presented in S1 and S2 Table. A comparison of crude estimates and the MCMC estimates is provided in S3 Table highlighting the goodness-of-fit of the MCMC procedure.
Note: The upper and lower whiskers represent minimum and maximum values, respectively. The upper outline of the box is the 75th percentile and the lower outline is the 25th percentile. The solid line in the middle of the box is the median (50th percentile).
Note: Decile cutoffs are based on 2016 prevalence values to highlight changes in mean prevalence by PC for each outcome. These maps were created using R. The PC shapefiles were obtained upon request from Bharatmaps (https://bharatmaps.gov.in/bharatmaps/). The PC boundaries were delineated by The Election Commission of India based on the 2008 Delimitation Commission.
Changes in PC mean of household toilet types
The share of no-toilet households decreased by more than 9.99 percentage points in 369 PCs between 2016 and 2021. The share of no-toilet households decreased between 4.99 and 9.99 percentage points in 56 PCs, and between 2.49 and 4.99 percentage points in 23 PCs. The share of no-toilet households changed between -2.49 and 2.49 percentage points in 84 PCs and increased between 2.49 and 4.99 percentage points in five PCs. The share of no-toilet households increased between 4.99 and 9.99 percentage points in four PCs, and it increased more than 9.99 percentage points in two PCs. These results are presented in Fig 3A.
Note: These maps were created using R. The PC shapefiles were obtained upon request from Bharatmaps (https://bharatmaps.gov.in/bharatmaps/). The PC boundaries were delineated by The Election Commission of India based on the 2008 Delimitation Commission.
The share of unimproved toilets decreased by more than 9.99 percentage points in 23 PCs between 2016 and 2021. The share of unimproved toilets decreased between 4.99 and 9.99 percentage points in 25 PCs, and between 2.49 and 4.99 percentage points in 44 PCs. The share of unimproved toilets changed between -2.49 and 2.49 percentage points in 391 PCs and increased between 2.49 and 4.99 percentage points in 47 PCs. The share of unimproved toilets increased between 4.99 and 9.99 percentage points in ten PCs, and it increased more than 9.99 percentage points in three PCs. These results are presented in Fig 3B.
The share of shared toilets decreased by more than 9.99 percentage points in 15 PCs between 2016 and 2021. The share of shared toilets decreased between 4.99 and 9.99 percentage points in 50 PCs, and between 2.49 and 4.99 percentage points in 53 PCs. The share of shared toilets changed between -2.49 and 2.49 percentage points in 338 PCs and increased between 2.49 and 4.99 percentage points in 68 PCs. The share of shared toilets increased between 4.99 and 9.99 percentage points in 18 PCs, and it increased more than 9.99 percentage points in one PC. These results are presented in Fig 3C.
The share of private toilets decreased by more than 9.99 percentage points in one PC between 2016 and 2021. The share of private toilets decreased between 4.99 and 9.99 percentage points in four PCs, and between 2.49 and 4.99 percentage points in 43 PCs. The share of private toilets changed between -2.49 and 2.49 percentage points in 43 PCs and increased between 2.49 and 4.99 percentage points in 14 PCs. The share of private toilets increased between 4.99 and 9.99 percentage points in 46 PCs, and it increased more than 9.99 percentage points in 435 PCs. These results are presented in Fig 3D.
Correlation between baseline prevalence and change
There was a negative relationship between the baseline prevalence of each outcome in 2016 and the PC-level change that occurred between 2016 and 2021. For instance, the PCs with the highest prevalence of no-toilet households in 2016 experienced the greatest decreases by 2021. This was true for unimproved and shared toilets, too. PCs with the lowest prevalence of private toilets in 2016 and the largest increases in coverage by 2021. These results are presented in Fig 4A, Fig 4B, Fig 4C, and Fig 4D.
(A) No toilets, (B) unimproved toilets, (C) shared toilets, (D) private toilets.
Discussion
This paper had three salient findings. First, the PC-level prevalence of no-toilet households decreased while the PC-level prevalence of private toilets increased between 2016 and 2021. There was no noticeable changes in the prevalence of PC-level unimproved toilets or shared toilets use during that period. Second, between 2016 and 2021, most PCs experienced a decrease in the prevalence of no-toilet households of more than 9.99 percentage points, and most PCs experienced an increase in private toilet prevalence greater than 9.99 percentage points. Most PCs experienced a change between -2.49 and 2.49 percentage points in terms of unimproved and shared toilet prevalence between 2016 and 2021. Third, PCs with the highest prevalence of households with no toilets, unimproved toilets, and shared toilets in 2016 experienced the greatest declines in prevalence by 2021. PCs with the lowest prevalence of private toilets in 2016 and the largest increases in coverage by 2021.
There are two data limitations with this study. First, the NFHS survey only asks respondents about their household toilet access. Thus there was no information about the quality of toilets that respondents use when they are outside of the home. Future sanitation-specific surveys are needed to better understand the kinds, and quality, of toilets people have access to in all the places they live, work, and play. This is particularly important in work and school settings where sanitation quality has been found to be poor [22,23]. Second, there were 172 PSUs that could not be spatially joined in 2021 and 152 PSUs that could not be spatially joined in 2016. Thus, the household-level data from these PSUs is not included in this study. However, it is unlikely that these would change the overall findings as the omitted households represent a very small fraction of the total sample.
Our results are policy relevant for several reasons. First, our findings underscore the importance of monitoring sanitation outcomes sub-nationally, especially at the PC level so that MPs can be directly responsive to the needs of their constituents. The JMP only reports the prevalence for the four sanitation outcomes at the national level. As such, there is no understanding of how or where these sanitation outcomes cluster. For example, while the JMP shows that the prevalence of unimproved toilet use was just 2% in 2020 throughout India [3], we find that this prevalence was much higher in several mountainous states and Union Territories. For example, in 2021, the PC-level prevalence of unimproved toilet use in Ladakh was 79.1% 18.8% in Jammu & Kashmir, 13.3% in Tripura, and 26.2% in Assam. Similar findings have been found in Nepal, too [24,25]. Geographical and topographical conditions often make sanitation interventions difficult in these areas [26]. Furthermore, each of these four states are prone to flooding, a limiting factor in the implementation of improved toilets [27–29]. Climate change will likely exacerbate flooding, thereby threatening sewerage and septic systems [30]. Moving forward, future iterations of SBA need to be more geographically and contextually responsive to the specific conditions so that households do not have to rely on unimproved toilets. This includes using novel technologies and appropriate toilet designs to account for the topographical features. Additionally, long-term monitoring of household sanitation status should be a core feature of SBA as households might have to rely on unimproved toilets in the wake of extreme weather events [31].
Second, there remain areas within these PCs where progress towards improving sanitation outcomes has been slow. For example, we show that India has made considerable progress in improving the prevalence of private toilet use while reducing the prevalence of no-toilet households. This is evident from the maps we show comparing PCs in 2016–2021. Political will is an important determinant of improving access to private household toilets in India [32], and it is possible that the PCs with the greatest improvements in private toilet coverage had the most conducive political environments. Future research should examine these as cases of positive deviance. However, Bihar, Uttar Pradesh, Jharkhand, Madhya Pradesh, and West Bengal still have considerable between-PSU variation in the prevalence of no-toilet households. This could be due to the fact that India’s poorest households have struggled to take advantage of SBA as they cannot afford the upfront cost of toilet construction [33]. Therefore, decreasing the burden of no-toilet households will require policies to change their payment structures so that poor households can more easily access funds for toilet construction. For example, SBA could provide 50% of the funds needed for toilet construction upfront. This installment-based payment mechanism is used as a part of the Indira Awas Yojna, India’s program aimed at helping India’s poorest build proper homes. Additionally, the NFHS data only allow us to estimate the prevalence of these outcomes when people are at home. Yet, people need access to safe sanitation in all the places where the live, study, play, work, rest, and seek care [34]. Studies from India show far from complete access to toilets in the workplace, a problem felt acutely by women [23,35,36]. And while SBA claims to have built toilets in every school in India, more recent reports by the Comptroller and Auditor General of India suggest that only one third of schools have a toilet [37]. Thus, our findings suggest that monitoring sanitation outcomes at the subnational level should be done for all the places that people live their daily lives.
Across most PCs, we found very small changes in the proportion of people using shared toilets between 2016 and 2021. However, five PCs that represent Mumbai, one PC from Delhi, one PC representing Kolkata, a PC representing Jaipur, and one from Hyderabad were among the PCs with the highest prevalence of shared toilets. In fact, India’s government has built over 600,000 shared toilets since the launch of SBA [38]. This is reflective of the fact that shared toilet facilities are important in densely populated urban areas as households might not have sufficient space for a private toilet [39]. In India, studies have shown that even households in rural areas lack sufficient dwelling space for private toilets [40]. As such, we found that more rural PCs such as Sitamarhi in Bihar and Bijnor in Uttar Pradesh had a higher prevalence of shared toilet use. More people will rely on these facilities for their sanitation needs over time. Therefore, future versions of SBA need to ensure that these shared facilities are properly maintained over time as many current shared toilet facilities are in disrepair and thus unusable [41]. Water availability, cleanliness, handwashing stations, gender-separated entrances, lockable doors, proper lighting, and professional maintenance are all factors that users value in shared sanitation facilities [42–44]. Regular cleaning and maintenance is essential for preventing the spread of disease at these facilities, an issue that was highlighted during the height of the global COVID-19 pandemic.
Supporting information
S1 Table. Output estimates of models used to derive district-level prevalence of each household toilet outcome in 2016.
https://doi.org/10.1371/journal.pwat.0000409.s001
(DOCX)
S2 Table. Output estimates of models used to derive district-level prevalence of each household toilet outcome in 2021.
https://doi.org/10.1371/journal.pwat.0000409.s002
(DOCX)
S3 Table. Pearson correlation value (p-value) between Markov Chain Monte Carlo estimates and crude estimates for the district-level prevalence for toilet types in 2016 and 2021.
https://doi.org/10.1371/journal.pwat.0000409.s003
(DOCX)
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