Objective The study contextualises the spatial heterogeneity and associated drivers of open defecation (OD) in India.
Design The present study involved a secondary cross-sectional survey data from the fifth round of the National Family Health Survey conducted during 2019–2021 in India. We mapped the spatial heterogeneity of OD practices using LISA clustering techniques and assessed the critical drivers of OD using multivariate regression models. Fairlie decomposition model was used to identify the factors responsible for developing OD hot spots and cold spots.
Setting and participants The study was conducted in India and included 636 699 sampled households within 36 states and union territories covering 707 districts of India.
Primary and secondary outcome measures The outcome measure was the prevalence of OD.
Results The prevalence of OD was almost 20%, with hot spots primarily located in the north-central belts of the country. The rural–urban (26% vs 6%), illiterate-higher educated (32% vs 4%) and poor-rich (52% vs 2%) gaps in OD were very high. The odds of OD were 2.7 and 1.9 times higher in rural areas and households without water supply service on premises compared with their counterparts. The spatial error model identified households with an illiterate head (coefficient=0.50, p=0.001) as the leading spatially linked predictor of OD, followed by the poorest (coefficient=0.31, p=0.001) and the Hindu (coefficient=0.10, p=0.001). The high-high and low-low cluster inequality in OD was 38%, with household wealth quintile (67%) found to be the most significant contributing factor, followed by religion (22.8%) and level of education (6%).
Conclusion The practice of OD is concentrated in the north-central belt of India and is particularly among the poor, illiterate and socially backward groups. Policy measures should be taken to improve sanitation practices, particularly in high-focus districts and among vulnerable groups, by adopting multispectral and multisectoral approaches.
- Public health
- Risk Factors
- Geographical mapping
Data availability statement
The dataset analysed during the current study are available in the Demographic and Health Surveys (DHS) repository, https://dhsprogram.com/data/available-datasets.cfm.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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STRENGTHS AND LIMITATIONS OF THIS STUDY
The study uses a nationally representative sample of households.
The prevalence of open defecation was estimated based on participant self-reporting, which may be influenced by social and cultural settings.
The cross-sectional research design checks to establish a cause-and-effect relationship between open defecation practice and selected explanatory variables.
‘Open defecation (OD)’ is a term used to describe the practice of defecating in public areas, such as fields, forests, shrubs or bodies of water.1 The widespread practice of OD in low-income and middle-income countries (LMICs), such as India, is a significant obstacle to achieving Sustainable Development Goal 6 (SDG-6) by 2030. At the same time, as the 17 SDGs are interconnected and interdependent, the failure of SDG-6 will affect the other SDGs. In particular, OD practice is associated with a range of negative outcomes, particularly the burden of multimorbidities and premature mortality. Under-5 child undernourishment is a consequence that is positively linked with this practice.2 3 In fact, as many as 44 million under-5 children are stunted due to the adverse impact of OD practices.4 Households (HHs) that engage in OD practices are at a higher risk of microbial infection and diarrhoea,5 6 while hot spots of OD practices are also more susceptible to waterborne and vectorborne diseases.6 7 Previous study highlights sanitation and hygiene poverty is linked to 10% of premature deaths in India.8 9 Moreover, women who practise OD are reported to have a higher risk of violence from outsiders.10 11
The WHO highlights that eradicating OD is a key priority in LMICs, including India.12 Other international organisations, such as the World Bank, UNICEF and USAID, are also providing consistent financial and technical assistance to eliminate the practice of OD.8 12 The efforts of these international and national non-profit organisations have contributed significantly to the improvement of sanitation coverage in LMICs.8 In spite of significant progress in sanitation facilities over the past two decades, it remains disheartening to note that a staggering 494 million individuals worldwide, primarily in sub-Saharan Africa and South and Central Asia, continued to engage in the practice of OD as of 2020.8 13 In the South Asian belt, India displays a high rate of OD practice (16%) compared with both the regional average (12%) and the global average (6%).8To address these persisting challenges, the government of India continues to prioritise sanitation and hygiene interventions. It focuses on building toilets and promoting behaviour change through awareness campaigns, education and community engagement. Efforts are also being made to ensure sustained usage of toilets and the proper management of waste disposal. These programmes have yielded remarkable results, particularly in the reduction of OD; between 2000 and 2020, India witnessed a substantial decline in the proportion of individuals practising OD. The prevalence dropped from a staggering 74% in 2000 to 16% in 2020.14 15 Despite these measures, challenges persist in certain pockets, particularly in remote areas and marginalised communities, where the practice continues to be more prevalent among disadvantaged groups such as the impoverished, less educated and socially marginalised individuals.16
In response to addressing the regional and socioeconomic disparities in improved sanitation coverage, there is a need to examine small-area level variation and associated predictors.16–18 Previous studies have explored regional-level, HH-level and individual-level factors associated with OD practices to contextualise the problem of OD in India.16–21 A significant regional disparity in sanitation practices results in a wide gap in improved sanitation coverage between economically advanced and underdeveloped regions in India.16 The educational and economic status were found to be key individual and HH-level drivers of open defection in India and elsewhere.13 17 21 Many studies have examined the religious and cultural divide in sanitation practices and have highlighted that deeply ingrained religious beliefs, caste system and associated traditional practices also limit improvements in sanitation practices.18–22 There is a substantial body of literature on the regional, HH and individual determinants of OD practices in India, while studies that focus on the spatial heterogeneity and predictors of OD using district-level data are limited. Most studies that have used spatial measures in India have attempted to contextualise the geographical clustering of water, sanitation and hand washing practices using composite indices23–25 and have explored the spatial heterogeneity of OD practices in northern India.17 26 However, the factors accountable for the spatial disparity in OD practice in India remain unanswered.17 23–26 Additionally, the substantial regional gap in OD practices between the north and south of India is well documented,16 18 26 but the factors contributing to this north-south divide remain unexplored. Therefore, the present study aims to fill the existing research gap by contextualising the spatial heterogeneity of OD practices in India. This study has two primary objectives: first, to examine the small area variation in OD and related predictors across India, and second, to measure the inequality in OD practices between hot spot and cold spot zones and identify significant factors contributing to this inequality.
In 2019, the Prime Minister of India declared India as OD Free (ODF) country. However, the NFHS-5 findings say something else. The present study is of utmost importance as it aims to fill the knowledge gap in India’s small-area variation of OD practice. Identifying the hot spots and factors responsible for spatial unevenness can provide invaluable insights to policymakers and sanitation programme directors to consider a more localised approach to programme and policy interventions. This study can also help re-evaluate existing policies and introduce new strategies to eliminate the OD practice by specifying the hot spots zone.
Moreover, the inequality measure between hot spot and cold spot clusters will assist policy-makers in eradicating OD practices from hot spot zones by focusing on factors responsible for cluster-level inequality. The study’s findings will be instrumental in guiding the development of multisectoral policies and programmes to achieve spatial equality in improved sanitation coverage in India. OD may be a strong social and cultural factor along with the economic factor. These factors may be spatially related and diffused over spaces. Therefore, the present study used the LISA analyses and spatial lag and error model to examine the current spatial heterogeneity and predictors, providing valuable insights and recommendations to achieve the goal of an ‘ODF India’ by 2030.
Material and methods
The study focused on India, the world’s most populous country with a population of 1.42 billion. It consists of 28 states and 8 union territories, each of which is further divided into districts. These districts are then subdivided into census enumeration blocks (CEBs) in rural areas and wards in urban areas. Since independence, the government of India has introduced numerous sanitation programmes, including the ‘Clean India Mission’, to achieve improved sanitation and hygienic practices for all by 2030. While there has been significant progress in improving sanitation coverage, the progress varies due to the vast geographical expanse and diverse sociocultural and livelihood conditions across the country.15
The study used data from the fifth round of the National Family Health Survey (NFHS-5), conducted between 2019 and 2021. The NFHS-5 was a nationally representative cross-sectional survey administered by the Ministry of Health and Family Welfare (MoHFW) of the Government of India. The MoHFW designated the International Institute of Population Sciences, Mumbai, as a nodal surveying agency. Samples from the districts were collected to represent the national scenario; from each district, primary sampling units for rural areas and CEBs for urban areas were selected, adopting probability proportional to sample size approach, and finally, HHs were chosen prior to systematic random sampling method. The survey included a sample size of 664 972 HHs, using a two-stage stratified sampling approach, of which 653 144 were inhabited. The NFHS-5 completed 636 699 interviews from occupied houses with a 98 percent response rate. The NFHS-5 report includes a detailed sampling strategy.15 By using the robust and reliable dataset generated by the NFHS-5, this study benefits from a solid foundation of nationally representative data, ensuring that the findings can be generalised to the entire population of India. The MoHFW’s involvement in conducting the survey further enhances the credibility and validity of the data used in the study.15
This study’s primary outcome of interest was OD, which indicates HH defecates in open spaces such as fields, woodland, and shrubs, near water bodies, and other open spaces. During the survey, the surveyor asked respondents about the HH sanitation facility—‘What kind of toilet facility do members of your household usually use?’.Responses were several categories like ‘flush or pour-flush toilet,’ ‘pit latrine’ and ‘no facility/use open space or field’.15 The responses were recoded into binary format to generate our outcome variable, OD. The OD variable was coded ‘yes’ if any HH practised OD and ‘no’ otherwise.15
Several previous research studies in different settings and current data have explored a set of predictors19–21 25 for accelerating OD including the age (less than 45 years, 45 years and above) and sex (male and female) of the family’s head of the HH, year of schooling (illiterate, 1–5 years, 6–10 years and 11 years and more), the family’s geographic location (urban or rural), their social status (scheduled caste (SC), scheduled tribe (ST), other backward class, forward caste), religious affiliation (Hindu, Muslim, Christian, others), HH wealth quintiles (poorest, poor, middle, richer and richest), the family’s housing arrangement (nuclear family vs extended family), type of house (kutcha, semipucca and pucca), quality of drinking water (improved or unimproved) and the length of their walk to the nearest water tap (on-premise or out of premises).
It is important to note that the HH wealth index is used as a proxy variable to measure living standards.15 The wealth quintile was determined by the ownership of HH assets (eg, refrigerator, washing machine, television, radio, car, motorcycle or bicycle), housing types (eg, kind of wall, roof and floor), and access to essentials (eg, drinking water and sanitation). A detailed description of these variables is given in online supplemental file 1.
The study begins with a descriptive analysis of the variables of interest. The prevalence of OD practice (%) by chosen variables was shown using a bivariate analysis, which included the Pearson χ2 test. Furthermore, a binary logistic regression model was used to assess the significant indicators of OD, and the adjusted AOR was then calculated.
Further, the present study applied spatial analysis tools and techniques in the second stage to access the geographical clustering of OD using GeoDa software.27 Univariate and bivariate Moran’s I statistics (LISA), including spatial significance and a set of spatial regression models, were employed to draw the spatial conclusion.28 Online supplemental file 2 discusses univariate and bivariate Moran’s I statistics (LISA) in detail. However, the result of Moran’s I for OD was statistically significant; we adopted spatial lag and error models to explore the spatial association. As the name implies, the spatial lag model (SLM) postulates that the units under study are reliant on and lagging relative to one another regarding their local geographical proximity. In contrast, the spatial error model (SEM) takes into account the impact of exogenous factors that are not factored into the model but might have a major bearing on the results. The Akaike information criterion (AIC) values were computed to measure the best-fit model. Due to its low AIC values, the SEM was deemed to be the most appropriate model for this investigation.
Finally, the present study performed the Fairlie decomposition model to contextualise the high-high clustering (hot spot) and low-low clustering (cold spot) inequality in the practice of open defection. The Blinder-Oaxaca decomposition method has been extensively used in the fields of economics and health to identify and measure disparities between two groups.29 30 This method uses ordinary least squares to decompose the disparity in mean outcomes between two groups by using their additive separability. However, this method is not applicable when the outcome variable is binary, which is the case for our model in this paper. To overcome this limitation, the present study used a binary-model-appropriate version of the Blinder-Oaxaca approach, developed by Fairlie,31 to decompose the high-high clustering (hot spot) and low-low clustering (cold spot) disparity in OD practices. In contrast to the Blinder-Oaxaca approach, the Fairlie technique decomposes the difference between the mean of projected probabilities rather than the mean of outcomes.31 32 The Fairlie decomposition method uses logit regression estimates, and we used the Fairlie package, which supports non-linear decomposition for binary dependent variables.33 Finally, the study used STATA 14.1 statistical software to perform the study analyses. Detailed information about these independent variables is given in online supplemental file 1.
Patient and public involvement
No patient involved.
Online supplemental table 1 depicts the characteristics of the sampled HH. In a total of 636 699 HHs, the majority of HH heads were male and had a secondary level of education. Likewise, a substantial proportion of the HHs belong to rural counterparts (74.8%), Hindu religion (75.7%) and the poorest wealth quintile (23.4%). Concerning the HH structure, more than half (54.8%) of the HHs are pucca, and nearly 95% of HHs have improved sources of drinking water.
Level and patterns of OD practices by socioeconomic backgrounds
The prevalence of OD practices was considerably higher among kutcha HHs (48.6%), HHs with illiterate heads (31.7%) and HHs without water connectivity on their premises (29.6%) (table 1). The disparity in OD practices between the poorest and the richest HHs was substantial (52% vs 0.1%). Similarly, rural HHs were more inclined towards OD practices than their urban counterparts (25.9% vs 6.1%).
The likelihood of OD practice was 81% less likely (OR 0.19; 95% CI 0.18 to 0.20) among HHs with higher educated heads than illiterate HH heads counterparts (table 1). Similarly, participants belonging to the richest HH wealth quintile had the lowest probability of OD practice (OR 0.03; 95% CI 0.02 to 0.08) than their counterparts. The odds of OD practices were almost three times higher in rural areas (OR 2.71; 95% CI 2.65 to 2.80) than the urban counterpart. Similarly, SC and Hindu HHs had a higher probability of OD practice. The pucca HHs were 69% (OR 0.31; 95% CI 0.30 to 0.31) less likely to practice OD compared with kutcha HHs. The probability of OD practice was nearly two times higher in HHs having no water facilities within the premises (OR 1.95; 95% CI 1.92 to 1.97).
Univariate and bivariate LISA outcomes
The high-high clusters (hot spots) are predominantly found in Bihar, Jharkhand, Chhattisgarh, Uttar Pradesh and Madhya Pradesh, Odisha, Maharashtra and Tamil Nadu, comprising 165 districts in India (figure 1). The Univariate Moran’s I value is 0.72, which indicates significant spatial autocorrelation between districts (figure 1).
Table 2 shows OD practice and its key determinants in India using univariate and bivariate Moran’s I value. The coefficient of spatial autocorrelation values demonstrates that HHs belonging to Hindu and poorest wealth quintile are spatially related to OD practice across districts, followed by ST HHs, illiterate head of the house, and water out of premises.
Furthermore, the clustering of OD practice with its covariates has been incorporated using bivariate LISA and significance maps in online supplemental figure 1. Maps 1a and 1b show the hot spots and cold spots for the age of HH heads ≥45 years and female HH heads, respectively. Likewise, Map 1c depicted that around 107 districts comprised the hot spots concerning a higher proportion of illiterate HH heads and a higher prevalence of OD practice particularly found in districts of Uttar Pradesh, Bihar and Andhra Pradesh. Maps 1d and 1e show the spatial clusters of OD correlated Hindu and ST HHs, respectively. Map 1f reveals that hot spots zone concerning a higher percentage of poorest HHs and higher prevalence of OD practice was found across 114 districts, predominantly located in Bihar, Jharkhand, Chhattisgarh, Uttar Pradesh, Orissa and some parts of West Bengal. In addition, Maps 1g, 1h, 1i, 1j and 1k show the hot spots for the nuclear family, kutcha house, unimproved drinking water source, water out of HH premises and rural residence, respectively.
Spatially associated key drivers of OD practice
Table 3 shows the relationship between OD practice and its drivers using OLS, SLM and SEM. The study consistently identified illiterate HH heads, belonging to the poorest wealth quintile, and being Hindu as crucial factors of inequality in open defection practice, demonstrating significant relationships across all models tested. The SEM was identified as the optimal model for all predictors in the study, as its AIC value was the lowest among all models (AIC: 4849.59; error lag (λ): 0.78; adjusted R2: 0.81). The SEM analysis indicated that the variable of the illiterate HH head had the highest coefficient (0.50 (<0.001)) and was the strongest predictor of the outcome variable. Specifically, a 10-point increase in the proportion of illiterate individuals was associated with a 5.0-point increase in OD practice. The poorest HH wealth quintile was the second-strongest predictor, with a coefficient of 0.31 (p<0.001), followed by identifying as Hindu, with a coefficient of 0.10 (p<0.001).
Online supplemental table 2 denotes the gap in OD practice between high-high (hot spot) and low-low (cold spot) clusters was 35%, explained 59% of the total inequality. The probability of OD in high-high clusters was 0.38, considerably higher than in low-low (cold spot) clusters (0.027).
Table 4 presents the effect and contribution of each predictor variable in the hot spot-cold spot difference of OD practice. Results indicate that the HH wealth quintile was the key contributor explaining about 67% of the difference in OD practice between high-high and low-low clusters, followed by religion (22.8%), education level of the HH head (6%) and distance from the water source (2.7%).
This study aimed to investigate the spatial patterns and underlying factors that influence OD practices in India, using data from the most recent National Family Health Survey (2019–2021). Consistent with previous WHO assessments, the results showed that a significant percentage of the population still practised OD. The WHO has called the practice of OD a ‘major public health problem’ worldwide, with 6% of the world’s open defecators living in India.8 OD has been a long-standing issue in India, with the 2011 census estimating that almost half of all HHs (49.8%) practised this behaviour.19 34 Nevertheless, the situation has improved significantly over the past decade, with the most recent National Family Health Survey (2019–2021) indicating that only 19% of HHs are still engaged in OD.15
Despite efforts by the government and various organisations to improve access to basic sanitation, the issue of OD remains a significant public health concern. Compared with other South Asian countries, India has a higher prevalence of OD, which poses a risk to public health and contributes to health vulnerabilities.4 8 Spatial analysis has revealed that hot spots of OD practice are concentrated in several regions across India, including the northern plain, central region, eastern region and some parts of Maharashtra and Tamil Nadu. The majority of high-high clusters or hot spots are concentrated in the socioeconomically underprivileged districts.23 35 However, despite the implementation of various programmes and special funds allocated to backward states, the gap in public health outcomes between backward and forward states remains substantial. The coverage of basic public health services, including safe drinking water, access to latrines, and electricity, is far from the SDG targets, the national average and the regional average for South Asia.
According to the findings of the multivariable analysis used in this research, there is a significant association between OD practices and various demographic, socioeconomic and regional factors. The present study’s findings are consistent with those of past research36–38 that showed OD practices are more prevalent in families with lower levels of education. According to the results of the decomposition analysis, the degree of education of the HH heads has a significant impact on the extent of OD in hotspot areas. As a result of an increased understanding of the adverse consequences of OD and the value of having sanitary facilities brought about by higher education, we noticed that the usage of improved latrines increases. The finding is backed up by earlier studies from Ethiopia,36 Ghana37 and India.19 Additionally, persons with higher levels of education usually lead better lifestyles and have better employment, both of which are positively connected to better hygiene.23 With higher levels of education comes a more extraordinary ability for a family to maintain a standard of living, which is related to their ability to construct latrines.19 According to the findings of the study, there are considerable differences between urban and rural disparity in terms of access to toilets, with one in three rural HHs preferring OD. In accordance with the study’s findings, rural inhabitants are more likely to practise OD due to a mix of customs, traditional behaviour, ignorance and lack of resources.19 Routray et al 39 realise that people choose to retain their traditional open-defecation practices in open places, that is, bushes, marshes and fields, even when they have access to their toilets.39
Based on the results of the multivariate and decomposition analyses, the researchers concluded that there is a significant relationship between OD habits and socioreligious affiliation and that religion plays a significant role in explaining the variations in OD habits across hotspot and coldspot locations. Hindu families, especially ST and SC HHs, had a greater incidence of OD. The results are in line with a past study, which revealed that economic challenges did not significantly affect OD practices in rural Hindu families in India but rather had a bigger influence on beliefs, rituals and ambitions concerning purity.19 Cultural and religious beliefs may influence sanitation practices, with some communities viewing OD as a more acceptable practice than using a latrine.18 39 People frequently reject indoor latrines because of these cultural considerations because they perceive them as ritually dirty and filthy. For instance, in some Hindu communities, bathing in the river is an important purification ritual, which can lead to OD near rivers. In some Muslim cultures, the use of water for cleansing after defecation is emphasised, which can affect the design and use of latrines.
The study also found a strong association between HH wealth and OD practices, with families in the quintile with the lowest wealth having greater odds of doing so. The results of other research conducted in India19 24 25 and other low-income and middle-income nations13 36 37 support this conclusion. The decomposition suggests that disparities in wealth are mostly responsible for the difference between high and low rates of OD in the districts of India. Poorer families need more funds to build and maintain latrines, or they might put other fundamental necessities ahead of sanitation.22 In addition, this could be due to the combined effects of economic and sociocultural deprivation.16 40 Furthermore, OD was more prevalent in low-income HHs with kutcha houses. Usually, the residents of the kutcha dwelling are more eager to build the pucca bedroom first, followed by the other necessary facilities (such as an upgraded latrine and kitchen). Moreover, in India, the lack of access to water within the house may be a barrier to toilet acceptability. Similar to previous studies,41 the present study explored that the HHs collecting water from outside the HH premises were more likely to defecate in open spaces. Inadequate or unclean water supply can increase the likelihood of OD. In areas with no access to clean water, people may resort to OD as a means to prevent further contamination of the limited available water sources. Inadequate water supply can also make it challenging to maintain proper hygiene practices, leading to the spread of diseases.
After independence, the Government of India introduced many sanitation programmes to accelerate sanitation coverage like ‘Central Rural Sanitation Programme’ in 1986; ‘Total Sanitation Campaign’ (TSC) in 1999; ‘Nirmal Bharat Abhiyan’ in 2012; ‘Swachh Bharat Mission’ in 2014. The general objectives of the entire sanitation programme were to provide improved sanitation facilities, a hygienic environment in rural and urban areas, and to remove OD. The intervention of community-led total sanitation plays a significant role in this regard. Under this programme, toilet facilities expanded to 23.4 percentage points in India, and along with this, OD decreased by nearly ten percentage points in India.42 Literally another great initiation was the Clean India Mission (Swachha Bharat) for rural India. By implementing this programme, the toilet increased by 6.8%–10.4%, and apart from this, OD was reduced by 7.3%–7.8% points. This curriculum has a considerable beneficial influence on both adult’s and children’s understanding of hygiene.43 However, there are many difficulties with its construction. The reimbursement strategy of this project, which requires HHs to instal toilets with their own investment and then claim subsidies, does not meet impoverished HHs.40 The government-subsidised latrines (facilitated by Non-governmental organizations (NGOs) under TSC) largely remain unused and rejected. OD problems existed in even the official ODF certified villages. While the provision of toilets is an essential step towards eliminating OD, it is not sufficient on its own. Changing people’s attitudes towards OD requires a multifaceted approach that addresses both personal and contextual factors. Despite the availability of public toilets, some people continue to prefer defecating in the open due to misconceptions about the health benefits of open fields over latrines.19 Thus, increasing accessibility alone cannot solve the issue. Rather, it is necessary to promote behavioural change and raise awareness about the importance of proper sanitation and hygiene practices, particularly in aspirational districts across the country. Such measures might include community-led programmes, public awareness campaigns and educational initiatives emphasising the benefits of using toilets and the dangers of OD. Our findings highlight wealth and education responsible factors of OD practice. Therefore, by combining education, skill development, economic incentives and effective governance, it is possible to combat OD practices in socioeconomic backward districts in India. This holistic approach addresses the underlying causes and encourages sustainable behaviour change and community economic empowerment.
Reducing OD has numerous implications, including improving public health outcomes, reducing environmental pollution, promoting gender dignity and achieving sustainable development. By reducing OD, the spread of faecalborne diseases can be curtailed, leading to better health outcomes for individuals and communities. Reduced pollution from human waste can also improve environmental conditions and reduce water contamination, leading to improved water quality and reduced incidence of waterborne illnesses. Moreover, reducing OD can help promote gender dignity by ensuring that women and girls have access to safe and private sanitation facilities. Finally, improved sanitation has the potential to contribute to economic development by improving overall health and reducing health-related expenditures. Therefore, reducing OD should be a priority for governments and development organisations working to improve public health, protect the environment, promote gender equity and foster economic development.
The study presents valuable empirical evidence on the spatial inequality in OD practices in India, along with the associated drivers. However, it is essential to acknowledge and address certain limitations that are inherent in the study design. First, due to the use of cross-sectional data, it is challenging to establish causal relationships or definitively determine associations between explanatory factors and OD practices. This implies that caution must be exercised when interpreting the findings in terms of cause and effect. Additionally, the reliance on self-reported data to estimate the prevalence of OD introduces a potential limitation. Self-reported data are subject to biases, including under-reporting or over-reporting by respondents. Therefore, the accuracy of the prevalence estimates could be influenced by these reporting tendencies, which should be taken into account when interpreting the results. Moreover, while the National Family Health Survey (NFHS) is a well-designed survey that provides reliable estimates of maternal and child health indicators, its scope for estimating OD prevalence may have limitations. Further investigation, possibly using complementary data sources such as the Indian census, is warranted to ensure the robustness of the results. However, it is important to note that the census data in India is currently backdated, with the last census conducted in 2011, which may not accurately capture the current scenario of OD. By acknowledging these limitations, future research can build on these findings and provide a more comprehensive understanding of the dynamics and determinants of OD in India.
The secondary analysis of the study confirms that one-fifth of HHs in India were practising OD. Our findings indicate the presence of significant spatial and socioeconomic inequalities in the prevalence of OD. The variations in OD practices based on factors such as place of residence, wealth quintile, educational attainment and religious affiliation were found to be substantial and pronounced. The study revealed an extreme disparity in OD practices between different clusters based on their geographical location. The difference in OD rates between high-high and low-low clusters was substantial. Moreover, the study identified HHs wealth quintile, religion and level of education as significant factors contributing to the observed gaps in OD practices. In the quest to eliminate OD in India, targeting specific regions, socioeconomic groups and communities becomes paramount. By adopting interventions specifically tailored to target regions, socioeconomic groups and communities, India will be able to achieve its goal of becoming ODF.
Data availability statement
The dataset analysed during the current study are available in the Demographic and Health Surveys (DHS) repository, https://dhsprogram.com/data/available-datasets.cfm.
Patient consent for publication
The authors are grateful to the Demographic and Health Surveys (DHS) for providing approval to use the dataset in this study. We would also like to thank anonymous reviewers for their critical input in the manuscript.
Contributors MR and AR designed the study. MR, AR, NGK, and MA extracted the data from all sources, performed the analyses, and drafted the manuscript. MA, AR, and MA critically revised the manuscript. MR, AR, MR, PC, NGK KCD is the guarantor of this work and, as such, had full access to all the data in the study, approved the final version of the manuscript, and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
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Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
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