Analysing the interlinkage between diarrhoea and drinking water faecal contamination in India

Contaminated drinking water is one of the world's largest problems considering its impact on the environment and health. In India, the contamination of water ranges from bacteriological to issues of natural geogenic origin (Chattopadhyay & Thiruvananthapuram 2018; Bajpai et al. 2019; Kumar & Tortajada 2020). Surface as well as groundwater (GW) sources are getting affected by environmental pollution. As per the National Jal Jeevan Mission website of the Ministry of Jal Shakti, Government of India, only around 57% of households in India are connected to tap water supply till January 2023 which is a big shift from the earlier piped water connectivity of 45% in December 2021 and 17% in August 2019 (NJJM 2022). Chakravarty et al. (2021) in their study found that even 81% coverage of piped water supply in Delhi was not reliable in maintaining the quantity or quality of drinking water. Singh et al. (2022) studied the microbial contamination of handpumps in Uttar Pradesh and found that 154 out of 354 (47%) handpumps in the study area were microbiologically contaminated. Subbaraman et al. (2013) in their study on urban slums in Mumbai highlight the issues of microbial contamination of the informal water distribution system due to the lack of legal access to the governmental water supplies. George et al. (2021) found that many treated water samples supplied through piped water were bacteriologically contaminated at the consumers' end. A study conducted by Ananth et al. (2018) in Trivandrum on 449 urban households depending on open wells as a source of drinking water reveals that 73% of wells were contaminated with coliform bacteria. Das et al. (2022) studied the microbial contamination of 43 open wells on Mudukudru island of Udupi and found that 75% of wells were bacteriologically contaminated. Dhawde et al. (2018) highlight the microbial/chemical contamination of local drinking water supplies such as borewells and handpumps. Thus, access to safe drinking water has become one of the challenging problems in India resulting in the chances of potential adverse health effects due to microbial contamination of drinking water.

The Ministry of Jal Shakti, Government of India (GoI) in its report for 2021 highlights that the most common and widespread health risks associated with drinking water in India are of biological/bacteriological origin (NJJM 2022). Bacterial species such as Escherichia coli, Proteus vulgaris, Micrococcus luteum, Pseudomonas aeruginosa, Klebsiella sp, Alcaligenes faecalis, Staphylococcus aureus, Bacillus cereus, Enterobacter aerogenes, and Streptococcus lactis are generally found in Indian drinking water sources (Suthar et al. 2009). Waterborne diseases caused due to microbial contamination or unsafe drinking water include cholera, hepatitis A and E, typhoid, diarrhoea, etc. Diarrhoea is the most common infectious disease with nearly 1.7 billion cases of childhood diarrhoea every year and is caused mostly by unsafe drinking water (Ashbolt 2004; Thapar & Sanderson 2004; Baldi et al. 2009; Kaiser & Surawicz 2012). Non-potable water is the major cause of these diseases which has been reported by Black et al. (2003) and Mallick et al. (2020). In 2019, around 1.53 million people lost their lives due to the disease of diarrhoea (GBD 2020). The disease burden of diarrhoea in India is the fourth highest and the major reasons for this are environmental changes, unsafe water and improper sanitation as reported in the global burden of disease study by GBD (2020).

Recent literature on a global level has reported a positive correlation between improved water supply systems and a reduction in diarrhoea cases. Majuru et al. (2011) conducted a study on three small communities for water supply reliability in understanding diarrhoea among under-five children in Limpopo Province in South Africa. The study reported a 57% reduction in under-five children diarrhoea with improved sanitation and water quality in the two communities with water supply system interventions compared to the third controlled community which did not receive any intervention. Gunther & Fink (2010) carried out the analysis of demographic and health survey data from 70 countries and estimated that better water facilities reduce the probability of diarrhoea by 7%. A review conducted on the articles published between 1970 and February 2016 on diarrhoea prevalence and its association with clean drinking water facilities in low and middle-income countries highlights that sanitation and safe drinking water help to reduce the risk of diarrhoea by around 61% compared to baseline unimproved water systems (Wolf et al. 2018). Various studies of on-plot water access were reviewed in July 2013 on a global level without any limits on publication date, location and language by Overbo et al. (2016). The review found less incidence of diarrhoea with a decrease in the distance between water sources from a household. Fewer diarrhoea cases were noticed in this study in households with on-plot water access. Thus, most of the global studies established a clear interlinkage between drinking water safety and access with lower diarrhoea cases.

However, some studies carried out in India on drinking water quality and diarrhoea cases do not go in line with global studies. The protected/safe water supply as an intervention was studied by Sarkar et al. (2013) for the diarrhoea associated with Cryptosporidium species in Vellore, Southern India. The study reported that drinking bottled water as a protected water supply alone was not effective in reducing the risk of diarrhoea. Lakshminarayanan & Jayalakshmy (2015) in their study emphasized that access to safe drinking water not in isolation but with improved case management, environmental sanitation, preventive practices and health promotion like hand washing can help in reducing diarrhoea cases in poor urban areas of India. Nandi et al. (2017) in their study highlighted that many households in India do not have either water or sanitation facilities and hence the problem of waterborne disease is still prevalent. The National Family Health Survey-4 data were studied by Mallick et al. (2020) for 601,509 households in India. The study found that the improved water coverage had no significant impact on diarrhoea prevalence in 2015–2016. Reese et al. (2019) in their study in rural Odisha in India found no significant association between water safety interventions with diarrhoea cases, for example, the village with more than 85% coverage of household and sanitation showed similar diarrhoea cases as the control village (5.3 vs 4.9%). A similar observation was made by Fan & Mahal (2011) who did not find any consistent match between improved water supply and diarrhoea cases. Thus, these studies report a poor correlation between safe water access and decreased diarrhoea cases.

Thus some studies conducted in the Indian context question the positive relationship established between improved water supply and reduced diarrhoea cases on a global level. Also, the studies referred to above highlight interventions such as improved water supply, hand washing and sanitation for reducing diarrhoea incidences. It indicates a presumption that the improved water supply means the guaranteed supply of clean/safe drinking water. Thus, they fail to explain the poor correlation seen in the Indian context which can be associated more with water quality than that of water supply or access. The objective of this paper is to connect microbial water quality and diarrhoea datasets to visualize the similarities or differences in their geographic pattern of occurrence and to establish a relationship if any. The mapping of water sources and bacteriological water quality parameters in surface and GW has been done to find interlinkage with diarrhoea cases. An attempt was also made to study the data of drinking water sources, faecal coliform (FC) and districtwide diarrhoea cases to critically examine the possible tools for the interlinkage. The unavailability and irregularity in the data have come up as the main constraint in the analysis. This paper highlights these flaws and tries to give some suggestions concerning the usefulness of the water quality and diarrhoea-related data for further research.

The methodology of this study is mainly divided into two sections, first, data collection and second, analysis and interpretations.

The study is based on the latest available but pre-pandemic secondary data which have been collected from different Government of India agencies such as the Health Management Information System (HMIS 2017–2019), Central Pollution Control Board (CPCB 2017–2019) and India Census of 2011. The study uses data on diarrhoea cases, drinking water sources and bacteriological water quality (surface and GW).

The data on diarrhoea have been collected at the district level in India. It was obtained from the HMIS which is a web-based monitoring information system of the Ministry of Health & Family Welfare, GoI. The HMIS collects monthly health-related data of around 286 parameters at the district level. These data are also available at the state level and for yearly values. Monthly and yearly data on diarrhoea cases treated inpatients and total inpatient cases available in the public domain for the years 2017–2019 at the district level were used in this study.

The last population census was held in India in the year 2011. It provides district-wise data on percentage sources of drinking such as treated and untreated tap water (India Census 2011). CPCB monitor various water quality parameters through 4,111 monitoring stations across all states and union territories of India. These stations are maintained under the National Water Monitoring Programme (NWMP). It monitors water samples for nine core parameters, 19 general parameters and nine trace metals. In microbiological monitoring, the NWMP provides the yearly average minimum and maximum levels of FC, total coliform and faecal streptococci in most probable number (MPN) per 100 mL. The FC value as a direct representation of the pathogenic/faecal contamination of drinking water has been considered in the analysis (Motohashi 2024). Hence, the FC data for GW and surface water (SW) (major and minor rivers, lakes and ponds) were obtained from the CPCB for the period 2017–2019. This study uses yearly average data of SW monitoring stations (2017: 1,495 Nos., 2018: 1,542 Nos. and 2019: 1,902 Nos.) and GW monitoring stations (2017: 660 Nos., 2018: 714 Nos. and 2019: 724 Nos.) across India for the water quality parameters of faecal coliforms (FC). The number of stations selected in this study is based on the available data on the CPCB website for the selected period. The major focus is to understand the FC levels in SW (rivers, canals, lakes and ponds) and GW in various districts of India for the selected study period.

The district-level diarrhoea data were first studied to understand the outliers and general trends. The data were presented in terms of diarrhoea inpatient cases (DIC) and diarrhoea inpatient rate (DIR) for each district of India. The DIR per 100,000 population was calculated for districts using district population data from the latest India Census held in 2011. To analyse district-level data on diarrhoea, various trends for monthly variation analysis were obtained using Microsoft Excel 365. These data were then used to form district-wise geospatial maps using ArcGIS software version 10.8.2. The district-level administrative map of India showing the location of 709 districts has been added as supplementary Figure S1.1 and Table S1.1. The GW and SW quality data for FC obtained from NWMP do not specify the name of the district but provide the name of the station, state and its latitude and longitude. The NWMP data were then categorized in a district-wise manner using the spatial join tool in ArcGIS to get the district-wise monitoring stations. The max FC level in GW and SW reported in the district was considered for further interpretations.

The statistical analysis of the data was carried out for a mixed effect regression model using IBM SPSS statistics software version 28.0.1. The DIC were considered as dependent whereas FC Max in SW (FCSW), FC Max in GW (FCGW), treated tap water (TTP) and GW as drinking water sources (GW) as variables. The year (Year) and districts (Dist) were considered in the random effect. Also, Panel Data Analysis was performed using R-Stuodio and R-programming for fixed and random effect regression models. Detailed information on the data used (Table S3.1) and statistical analysis have been provided in supplementary information S3. The geospatial maps prepared for diarrhoea cases, rate, water source and FC are then compared for similar occurrence patterns which are then described in discussion sections. The results of these trends have been described for each data source and the analysis of trends has been interpreted for these trends along with the key variables.

The following result shows the data analysis at the overall India level and district level. The various graphical interpretations are supported by the discussion on the probable outcomes of the analysis.

These monsoon months accounted for around 40–42% of yearly cases in India. July 2019 reports the highest monthly 335,329 inpatient cases as shown in Figure 1. The inpatient hospitalization has been seen increasing at the rate of 6.5–8% per year from 2017 to 2019.

The border regions of Andhra Pradesh, Karnataka and Telangana, with around 12 districts, depend on untreated water sources in the range of 50–75% of the total population of these states. To understand the dependency on GW, Figure 4(c) was plotted which shows the percentage usage of GW as a drinking water source at the district level. It gives a clear indication of the states and districts with higher dependency on GW. Figure 4(c) indicates that Bihar, Jharkhand, Odisha, Madhya Pradesh, Chhattisgarh, Uttar Pradesh, West Bengal, Assam, Tripura, Kerala and Nagaland are more than 70% GW dependent on meeting drinking water demands.

FC levels of more than 50 MPN/100 mL (which is a designated best-use water quality criteria for FC as per CPCB without conventional treatment) have been reported by 64 accounting for 9% of total stations. Around 8% of stations (50 out of 685) reported higher than 100 MPN/100 mL FC level. A similar analysis done for the year 2018 reports that 710 out of 771, 92% of stations, reported the occurrence of FC in GW. In 2018, 8% of stations reported more than 50 MPN/100 mL FC and 7% of stations had higher FC than 100 MPN/100 mL. In 2019, 719 out of 768, 94% of stations, reported FC in GW. Around 9% of stations reported higher than 50 MPN/100 mL and 8% of stations reported FC more than 100 MPN/100 mL. Kerala, Odisha, Delhi, West Bengal and Assam have shown high FC levels for three consecutive years from 2017 to 2019. Surprisingly, 13 districts in the state of Bihar started reporting higher FC values in 2019 which was not reported in earlier years, i.e. 2017 and 2018.

The variation in FC level of SW (river, canal, lakes and ponds) across different districts of India for the years 2017, 2018 and 2019 are shown in Figure 5(b). The FC data of available 1927 NWMP stations from the NWMP, CPCB database were studied. The IS 10500, India's drinking water quality standards, prescribe the absence of FC (zero value of FC) in drinking water as the permissible limit (IS 10500 2005). Around 95% of NWMP SW monitoring stations reported a non-zero MPN value in the year 2017 and 1,091 out of 1,475 (74%) stations reported more than 50 MPN/100 mL FC. In 2017, 42% of stations reported higher than 1,000 MPN/100 mL FC values (1,000 MPN is the designated FC value after conventional treatment and disinfection as per CPCB). In 2018, 99% of stations reported FC occurrence.

FC value of higher than 50 MPN/100 mL has been reported by 1,131 out of 1,475, i.e. 77% of stations and 42% of stations with more than 1,000 MPN/100 mL. Data for 2019 indicate 97% of stations reporting FC occurrence with 68% of stations reporting higher than 50 MPN/100 mL and 36% of stations reporting higher than 1,000 MPN/100 mL. The analysis showed that the region of the Ganga basin (Figure 5(b)) has a maximum concentration of FC. A total of 123 monitoring stations located in the states of Assam, Bihar, Delhi, Goa, Karnataka, Odisha, Tamil Nadu, Uttar Pradesh and West Bengal showed FC levels of more than 100,000/100 mL. Delhi showed one of the major occurrences of coliform in the river Yamuna. Also, Karnataka reported a maximum concentration of coliform in the river Arkhavathi which is one of the tributaries of the river Cauvery. West Bengal showed around a 150% increase in the average FC level from the year 2017 to 2019. Kamrup, Darrang in Assam, Imphal, Chandel in Manipur, East Khasi Hills in Meghalaya and Dimapur and Mon in Nagaland reported a high occurrence of FC levels than 50 MPN/100 mL.

The statistical analysis using a mixed effect model showed a poor significance between the DIC and FCSW (p = 0.517) and FCGW (p = 0.137) whereas the TTP (p = 0.01) and GW (p < 0.01) showed a significant connection with DIC. Also, the interaction effects of FCSW*TTP (p = 0.354) and FCGW*GW (p = 0.077) were not significantly connected (Table S3.2 and Table S3.3). The Panel Data Analysis using R-programming showed p-values in the fixed effect model for FCSW (p = 0.883) and FCGW (p = 0.185) statistically insignificant (Table S3.4 and Table S3.5) hence both of these analyses were not considered further, shifting focus on the visual comparison of maps for significant patterns.

Visual comparison of Figures 2–5 gives an approximate idea of overlap among the regions with higher diarrhoea inpatient percentage and use of untreated drinking water or more particularly GW as a major source of drinking water. As shown in Figure 3, 48% (11 out of 23) districts of West Bengal, 40% (12 out of 30) districts of Odisha, 30% (eight out of 27) districts of Chhattisgarh and 35% (11 out of 31) districts of Telangana have higher DIR than 500 inpatients per 100,000 population compared to other districts as per 2019 HMIS data. In Karnataka, 40% (12 out of 30) of districts and in Madhya Pradesh 35% (18 out of 51) districts were with more than 500 DIR. The Dangs in Gujarat reported a 1994 DIR in 2019. While linking this high rate with the drinking water sources of these districts, it was noticed that these were districts and states with a higher dependency on untreated GW as a source of drinking water (Figure 4). Figure 4(b) and 4(c) indicate this being a region with more than 50% drinking water sources as untreated or GW shall ideally show more occurrence of diarrhoea.

To establish more clarity on the interlinkage between diarrhoea, water source and water quality, an attempt was made to list the district with the top 20 DIR in 2019 and to tabulate the water sources and FC levels for these districts in Table 1. It shows that districts Balrampur, Dangs, Kulgam, Bhadrak, Poonch, Karauli, Puri and Rajauri with more than 80% untreated drinking water sources reported more than 1,000 DIR.

The corresponding percentage of use of GW was higher in these districts. Surprisingly, the NWMP did not report the FC levels for most of these districts from 2017 to 2019. The NWMP available station list was studied to find the reasons behind this. It shows that Balrampur, Dangs and Rajauri districts did not have any NWMP monitoring stations at all. Whereas, Bhadrak, Poonch and Kulgam, though having monitoring stations, did not report or test any samples for FC level. The table also reveals that New Delhi with more than 90% treated tap water supply still shows high DIRs of 2,751. This poses a few questions. Is the treated water reaching the consumers' tap safe? Do we have a sufficient water quality monitoring mechanism? Why have important parameters like FC not been monitored in around 130–200 districts in the period 2017–2019? It necessitates studying and understanding the etiological reasons behind diarrhoea to check risk factors other than water quality/contamination.

The results of data analysis of FC contamination in GW and SW indicate an overall high level (more than 50 MPN/100 mL) of bacteriological contamination of SW in most parts of India. GW contamination by FC has also been reported in highly populated (in number) states of India. These SW and GW contaminations are mostly attributed to unsafe sanitation practices as reported by Raman & Muralidharan (2019) and Bindra et al. (2021). The Ganga River basin is showing higher levels of FC contamination in SW. The area of the Ganga River basin is also a major GW basin of India. It has a very high recharge potential of more than 246–300 mm/year (Mondal & Ajaykumar 2022). This indicates that the chances of percolation of bacteriological contamination from surface to GW cannot be ruled out and thus can be considered as the most appropriate justification for the FC contamination of GW in the Ganga basin.

The contamination can be better understood by correlating FC occurrence patterns with diarrhoea trends in India. The diarrhoea occurrence peaks in June and July found in this study contradict the study conducted by Chaurasia et al. (2020) where February was reported as the peak month. This also contradicts the studies conducted by Patel et al. (2019), Saha et al. (2019) and Singh et al. (2021) where the increased bacteriological contamination of water in India was reported during the monsoon season. The increased diarrhoea cases from June to September months can be correlated with the poor water quality and hygiene conditions at the time of monsoon season in some parts of India.

The overlapping of diarrhoea prevalence with FC levels though visibly helpful but very problematic could also be misleading. The FC monitoring results from NWMP of CPCB were available for an average of 400 districts out of 709 districts considered in the study period, i.e. 2017–2019. Thus, many districts with non-reporting of FC value cannot be considered for statistically correlating it with diarrhoea data. Also, it makes overall correlation difficult due to the non-availability of sufficient datasets. Also, there is no uniformity in the GW and SW monitoring stations with a complete absence of stations in around 113 districts of India. This also indicates problems associated with the present monitoring mechanism of the NWMP where these districts have been left out from NWMP coverage. This questions the granularity of water quality data and poses a challenge to NWMP data utilization at the state/country level. Nevertheless, the comparison of the data of 2017, 2018 and 2019 at many places shows similar reported values and data discrepancies in all data sources used in this study. Thus, not only the availability of reported values but also the credibility and realistic datasets is a major problem. Researchers, governmental agencies and NGOs are using these datasets to study, model and predict water quality and health outcomes. These studies and research, if used for policy-making and implementation of certain drinking water and health-related programmes, may lead to serious consequences.

Many researchers have come up with a way of dealing with water quality data discrepancies. Some researchers have used the inverse distance weightage (IDW) interpolation tool to predict the water quality of SW such as a river (Parween et al. 2022; Slathia & Jamwal 2022) and GW (Pande & Moharir 2018; Ahmad et al. 2020; Masood et al. 2022) in non-sampled or nearby regions of monitoring stations. The quality of water is a dynamic entity. It keeps on changing due to its complex interaction with geogenic and anthropogenic factors. Generalizing it to the larger geography by interpolating and projecting general values is ineffectual. Also, some researchers have used the water quality index (WQI) as a tool to interlink water quality with waterborne diseases (Ali & Ahmad 2020; Chauhan et al. 2020; Ajmal et al. 2022; Dutta et al. 2022). WQI is a combined index of many water quality parameters with different weightings. Thus, focusing on one water quality parameter and its interlinkage with the disease cannot be established.

The study conducted by Daniels et al. (2018) in Puri, Odisha shows that bacteriological contamination even at very low concentrations can cause significant diarrhoea morbidity. Mostly, GW is directly consumed without any effective treatment in rural India (Daniels et al. 2018). Thus, the impact of FC-contaminated and untreated GW consumption, in general, can be considered more prominent and possibly have a significant role in diarrhoea prevalence in India. Strikingly, however, districts with improved piped water connectivity (as claimed by NJJM and India Census data) still reported high diarrhoea rates. Also, a majority of districts with low treated tap water access are showing the lowest DIR. This contradicts the general belief that improved pipe water supply lowers the occurrence of diarrhoea cases and goes in line with studies conducted by Mallick et al. (2020), Reese et al. (2019) and Fan & Mahal (2011).

Though the maps developed in this study help in understanding the geospatial distribution of FC and diarrhoea, they fail to make any connections between diarrhoea occurrence, water sources and its bacteriological quality. They show some spatial overlapping and relation between various datasets used in the study. Basic analysis of maps turns up many important contradictions and questions and indicates a lack of a robust interlinking tool, methodology or framework to fill the correlation gap. Thus, the attempt to study the interlinkage between diarrhoea and water quality using HMIS and NWMP data reveals issues related to the veracity of data due to variation, non-granularity and non-representation of a large number (300) districts. This questions the ability and competence of the system to administer huge quantities and high-quality data. It also necessitates researchers not to rely completely on these datasets alone and demands a certain level of ground proofing. However, the groundproofing of large regional data such as those used in this study is impossible, which demands strengthening of the state's mechanism of generating these datasets with true representation of expected values. It is of great importance to capture and report data related to water quality and diseases correctly. It is also important to devise a mechanism of communication between the creators and users of these data using transparent dissemination.

From this study, it is understood that diarrhoea is a continuing problem in India despite major efforts in the improved drinking water supply. The interlinkage between diarrhoea prevalence pattern, water sources and FC contamination is a complex activity. Though the etiological causes highlight the faecal–oral route which attributes to diarrhoea with unsafe drinking water as the major risk factor, states with high diarrhoea occurrence and adequate water supply infrastructure have posed a question on the effectiveness of water treatment, safe quality of water supplied and also on the credibility and accuracy of the data related to water quality and waterborne diseases. The analysis of data showed no interlinkage between the bacteriological water quality, diarrhoea occurrence and its causes. There is an urgent need for a more robust mechanism for strengthening the collection, representation and communication of water quality and health-related datasets. The accurate and reliable data on water quality and waterborne diseases and their interlinkage can be beneficial in controlling the major outbreaks of diseases and planning for future actions. If successful, these data and their analysis may help the policy and decision makers to plan drinking water schemes; water boards and water treatment plant operators to decide the extent of treatment required and health departments to decide the course of action in controlling probable outbreaks of diseases.

The authors would like to acknowledge the IITB-Monash Research Academy, IIT Bombay (India), Monash University (Australia) and the Department of Biotechnology, Government of India for providing administrative and financial assistance for this research. All the data is available in the public domain at https://cpcb.nic.in/nwmp-data/ and https://ghdx.healthdata.org/series/india-health-management-information-system-hmis.

This research is financially supported by the IITB-Monash Research Academy (Project code: IMURA 0954) and the Department of Biotechnology, Government of India.

G.K. conceptualized the whole article, rendered support in data curation and formal analysis, investigated the process, arranged the software, visualized the article, wrote the original draft. S.A. brought the resources, supervised the work, wrote the review and edited the article. P.S. brought the resources, supervised the work, wrote the review and edited the article. M.S. supervised the work, wrote the review and edited the article.

This study is based on secondary data, which is available in the public domain. Therefore, there was no need to obtain ethical clearance for this study.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.