Impact of Water Related Disasters on Water Related Infectious Disease Risk among Older Adults in India

Impact of Water Related Disasters on Water Related Infectious Disease Risk among Older Adults in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Water Related Disasters on Water Related Infectious Disease Risk among Older Adults in India Anurag Yadav, Md Juel Rana, Margubur Rahaman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8263267/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Discover Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Climate change has intensified the frequency and severity of water-related disasters (WRDs), including floods and cyclones, heightening the burden of water-related infectious diseases (WRIDs) among vulnerable populations. Older adults, who experience age-related frailty and high chronic disease burden, may face disproportionate risks; however, evidence linking WRD exposure to WRIDs in India remains sparse. This study assessed the association between WRD exposure and WRID prevalence among older adults. The study analysed nationally representative data from 72,250 adults aged ≥ 45 years from the Longitudinal Ageing Study in India (LASI) Wave 1. Descriptive statistics, Pearson’s chi-square tests, multivariable logistic regression, and spatial analyses were performed. Overall, 21.3% of older adults reported at least one WRID, with higher prevalence in WRD-exposed individuals (28.9%). WRD exposure was significantly associated with increased WRID risk (AOR 1.28; 95% CI 1.22–1.35). Coastal/island regions showing markedly higher odds of WRIDs (AOR 6.68; 95% CI 3.42–13.07) than arid counterparts. Unsafe water, sanitation and hygeine (WASH) practices significantly linked with WRIDs (AOR 1.12; 95% CI 1.07–1.17). Tribal and poorest households were more likey vulnerable to WRIDs. Chronic illnesses, Activities of Daily Living (ADL) limitations, and open defecation practice (AOR 1.40; 95% CI 1.34–1.46) were risk factors of WRIDs. Urban residence showing lower likelihood of WRIDs (AOR 0.66; 95% CI 0.63–0.69) than rural counterparts. WRD exposure substantially increases WRID vulnerability. Strengthening climate-resilient WASH systems and integrating disaster-sensitive health strategies into geriatric care are essential to mitigate disease risks and advance progress toward SDG 3 targets. Climate change Elderly Environmental Extreme Infectious Diseases SDG-3 Figures Figure 1 Figure 2 Introduction Climate change is redefining the patterns, intensity, and geographic spread of water-related disasters (WRDs), substantially amplifying socio-economic, public health, and environmental vulnerabilities across affected regions ( 1 ). Specifying the consequence of WRDs like floods, cyclones or others on public health, the increasing frequency severely undermines core public health infrastructure—damaging and disrupting essential water, sanitation, hygiene, and healthcare services ( 2 ). Such system failures create fertile conditions for outbreaks of water- and vector-borne diseases as contaminated water sources and stagnant environments become widespread ( 3 ). These risks are further intensified by damage to healthcare facilities and reduced accessibility to medical services during and after disasters, compounding the overall health burden on affected populations ( 4 ). The Global Burden of Diseases (GBD-2019) estimates more than 1.6 million death occurred due to unsafe water, sanitation and hygience (WASH) practices in 2019. This translates to 23 deaths per 100,000 population directly linked to diseases associated with inadequate WASH. Among WASH-related causes, diarrhoeal diseases remain the leading contributor, accounting for over 1 million deaths in 2019 ( 5 , 6 ). Existing regional studies highlighted that the water- and vector-borne diseases have been proportionally increasing with rising frequency and severity of the WRDs ( 7 , 8 ). In WRDs prone regions, vulnerable groups like children, pregnant women, elderly population, and socio-economically marginalize groups are more vulnarable to water and vector borne diseases ( 9 , 10 ). Empirical evidence found that exposure to WRDs is positively associate with water-related infectious diseases (WRIDs) ( 11 , 12 ). For instance, studies across the globe founds that extreme climatic events like flood or cyclones are positively associated with the elevated risk of communicable diseases through several socio-economic pathways ( 13 – 15 ). Similarly exposure to intense rainfall and tropical storms found to be linked with elevated risks of diarrhoeal and vector-borne infections in the United States ( 16 , 17 ). Similarly, recurrent flooding contributes to a growing burden of diarrhoea and malaria observed in the South Asia ( 18 ). Furthermore, these evidences concludes children and elderly are more vulnarable to such WRIDs ( 19 , 20 ). India faces a substantial burden of WRIDs, with nearly two-thirds of the population exposed to water-related disasters (WRDs) and more than 37 million WRID cases annually. The economic toll is considerable, with WRIDs costing over US $ 600 million and resulting in the loss of more than 70 million workdays each year ( 21 – 23 ). A national level study based on National Family Health Survey-5 suggest the incresing exposure reoccuring flood increased the risk of WRID among children in India ( 24 ). Additionally, a review study carried out by walika et al. (2013) founds that burden of the WRIDs increased in the face of disasters like cyclone or flood ( 25 ). A similar study from India founds that risk of the dengue is positively associated with global climatic phenomenon like Oneanic Nino Index (ONI) ( 26 ). Evidence from subnational study shows that frequent flooding situtaion results in increased number of WRIDs during post flood condition ( 27 ). However, older adults—who exhibit heightened physiological vulnerability and reduced coping capacity—remain critically underexamined in the environment and public health literature. A recent national study reported that 4% of adults aged 45 and above years self-reported disaster-related health problems ( 28 ), yet the study did not assess WRD-specific exposure or WRID outcomes. Similarly, analyses using LASI data examined patterns of water-borne diseases among older adults ( 22 ), but again excluded WRD exposure, overlooking a major climate-sensitive disease pathway. Existing geriatric WRID studies have relied mainly on socio-demographic or health indicators, largely ignoring the intensifying influence of climate-driven WRDs. Consequently, the relationship between WRD exposure and WRID risk among older adults in India remains empirically unexplored in India using nationally representive survey dataset. The present study seeks to fill this gap by integrating community-level disaster exposure with individual-level morbidity to quantify the effect of WRDs on WRID risk among older adults. This evidence is essential for guiding climate-resilient healthy ageing policies and advancing India’s progress toward SDG 3 targets—particularly 3.3 (ending infectious diseases) and 3.9 (reducing illness from unsafe water and environmental hazards). Data and method 2.1 Data 2.1.1 Data source In the present study, we utilized both individual- and household-level datasets from the Longitudinal Ageing Study in India (LASI) Wave-1 to evaluate the association between water-related disasters (WRDs) and the prevalence of water-related infectious diseases (WRIDs). The LASI survey, conducted in 2017–18, collected data from individuals aged 45 years and above across all states and union territories of India, except Sikkim. Spatial distribution maps depicting WRD exposure and WRID prevalence were generated using official shapefiles obtained from the Survey of India ( 29 ). 2.1.2 Study design The present study adopts the cross sectional design using the nationally representative data from Longitudnal Ageing Survey of India (LASI), conducted during 2017-18. The study uses the individual level data for the fullfillment of the aim ( 29 ). 2.1.3 Study sample The present study utilized the individual data from Longitudnal Ageing Survey of India, 2017-18, which uses a multistage stratified random sampling technique to collect the data regarding various social behaviour, economic condition, and health status indicators. The survey adopts three stage sampling design in rural areas and four stage sampling design in urban areas to collect the data of 72,250 older adults from 42,949 households India ( 29 ). The data was collected and managed by International Institute for Population Sciences, Mumbai (IIPS), in collaboration of Harvard T.H. Chan School of Public Health and University of Southern California. The project was funded by Ministry of Health and Family Welfare (MoHFW), GoI, National Institute of Ageing (NIA), U.S. National Insitute of Health, and United National Population Fund (UNFPA), India. Highlight here how you marge the two dataset to make data for such analyses. 2.1.4 Outcome variable The primary outcome was any water-related infectious disease (WRID) reported by the respondent. Participants were asked: “In the past two years, have you had any of the following diseases?”—malaria, diarrhea, typhoid, chikungunya, or dengue ( 30 , 31 ). Responses were recorded in a binary format (1 = yes, 0 = no). Using these five items, a composite variable was constructed: respondents who reported yes to any of the five diseases were coded as 1 (any WRID), and those who reported no to all were coded as 0. 2.1.5 Key explanatory variable The key explanatory variable in this study was exposure to WRDs. Information on disaster exposure was obtained from community heads or household representatives in response to the question: “In the last five years, has your health been severely affected by disasters such as floods, landslides, extreme cold or hot weather, cyclones/typhoons, droughts, earthquakes, tsunamis, or any other natural calamities?” Responses were recorded in a binary format (1 = yes, 0 = no). For this analysis, exposure to floods, cyclones/typhoons, and tsunamis was classified as water-related disaster exposure ( 13 , 31 ). A composite WRD exposure index was then constructed, wherein individuals reporting exposure to any one of these disasters were coded as 1 (WRD exposed), and those reporting no exposure were coded as 0 (not exposed). 2.1.6 Other covariates In line with prior studies ( 22 ), the analysis controlled for a range of covariates capturing socio-demographic characteristics and daily life conditions that could influence the association between water-related WRD exposure and WRIDs. Socio-demographic variables included age (45–54, 55–64, 65–74, ≥ 75 years), years of schooling (no schooling, 12 years), social group (Scheduled Tribe (SC), Scheduled Caste (ST), Other Backward Class, Others), household wealth quintile (poorest, poorer, middle, richer, richest), place of residence (rural or urban), and geographical region (arid, plains, plateau, hill/mountain, or island/coastal). Daily life condition variables comprised drinking water treatment (yes/no), open defecation practices (yes/no), housing dampness (yes/no), chronic illness (none, one, two, or more than two), and activities of daily living (ADL) limitations (none, one, two, or more than two). These covariates were included in multivariable analyses to adjust for potential confounding and to better isolate the independent effect of water-related disaster exposure on the prevalence of WRIDs (Table S2). 2.2 Statistical analysis In the present study, both the descriptive and inferential statistics has been used, where proportion distribution of the sample was calculated across different socio-demographic and daily life conditition covarites. The study also utilizes the Chi square to find out the prevalence of WRIDs across different covariates. Futher, both bivarite and muvariate logistic regression model was used to analyse the risk factors associated with the prevalenc of WRIDs. $$\:\text{log}\left(\frac{p}{1-p}\right)=\:{\beta\:}^{0}+\:{\beta\:}^{1}{X}^{1}+\:{\beta\:}^{2}{X}^{2}+\:\dots\:\:+\:\beta\:ₖXₖ$$ Where, p denotes the predicted probability of having a WRID, β 0 represents the intercept term, and β1,β2,…,β k ​ are the regression coefficients corresponding to the socio-demographic and daily life condition covariates X 1 , X 2 , …,X k ( 22 , 32 ). Multicollinearity among independent variables was evaluated using the Variance Inflation Factor (VIF). Model discrimination was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), obtained through the lroc command. All analyses were carried out in Stata version 17.0. Result 3.1 Background characteristics of sampled population About 11.2% of the study participants reported exposure to any kind of WRDs. Most of the study population were aged 45–54 years (41.0%). Similarly, the percentage of individulas was higher from OBC (46.4%) social group, and with no educational background (55.4%) in disaster exposed group. Additionally, less individuals have reported the use of treated drinking water (32.5%) with more participation in open defecation practices (31.4%) among disaster exposed group. In addition to that, the percentage of individuals reporting damp housing condition (24.2%), with more that two ADL (5.9%) was higher among disaster exposed group as compared to individuals from disaster unexposed group. A higher percentage individuals among disaster exposed group belongs to plain regions and majority of them from rural background (Table 1 ). Table 1 Background characterictics of sampled population, LASI 2017-18 Variable Category Disaster Exposed (%) [95% CI] Disaster Unexposed (%) [95% CI] Age (years) 45–54 41.0 [40.0, 42.1] 41.6 [41.2, 42.0] 55–64 27.2 [26.2, 28.2] 27.3 [27.0, 27.7] 65–74 24.0 [23.0, 24.9] 23.6 [23.3, 24.0] 75+ 7.8 [7.2, 8.4] 7.5 [7.3, 7.7] Years of Schooling No schooling 55.1 [54.0, 56.2] 44.9 [44.5, 45.2] 12 4.2 [3.8, 4.7] 6.1 [5.9, 6.3] Social Group ST 10.4 [9.8, 11.1] 18.1 [17.8, 18.4] SC 18.2 [17.4, 19.1] 16.8 [16.5, 17.1] OBC 46.4 [45.3, 47.5] 36.7 [36.3, 37.1] Others 24.9 [24.0, 25.9] 28.4 [28.1, 28.8] Wealth Quantile Poorest 19.9 [19.0, 20.8] 19.5 [19.2, 19.8] Poorer 20.5 [19.6, 21.4] 20.0 [19.7, 20.3] Middle 18.5 [17.7, 19.4] 20.3 [20.0, 20.6] Richer 20.2 [19.3, 21.1] 20.3 [20.0, 20.7] Richest 21.0 [20.1, 21.9] 19.8 [19.5, 20.1] Drinking Water Treatment Yes 32.5 [31.5, 33.6] 43.2 [42.8, 43.6] No 67.5 [66.4, 68.5] 56.8 [56.4, 57.2] Open Defecation No 68.6 [67.6, 69.6] 82.0 [81.7, 82.3] Yes 31.4 [30.4, 32.4] 18.0 [17.7, 18.3] Housing Dampness No 75.8 [74.9, 76.8] 80.1 [79.8, 80.4] Yes 24.2 [23.6, 25.3] 19.9 [18.2, 20.5] Chronic Illness None 56.2 [55.1, 57.3] 55.2 [54.9, 55.6] One 26.9 [25.9, 27.9] 26.7 [26.4, 27.1] Two 11.9 [11.2, 12.7] 12.4 [12.2, 12.7] More than two 5.0 [4.6, 5.5] 5.6 [5.4, 5.8] ADL Limitations None 83.9 [83.1, 84.7] 86.7 [86.4, 86.9] One 7.1 [6.6, 7.7] 6.1 [5.9, 6.2] Two 3.1 [2.7, 3.5] 3.1 [3.0, 3.2] More than two 5.9 [5.4, 6.5] 4.2 [4.0, 4.3] Residence Rural 79.9 [79.0, 80.7] 79.9 [79.0, 80.7] Urban 20.1 [19.3, 21.0] 20.1 [19.3, 21.0] Geographical Region Arid region 0.4 [0.3, 0.6] 4.2 [4.1, 4.4] Plains 31.0 [29.9, 32] 25.0 [24.7, 25.3] Plateau 13.7 [13, 14.5] 19.3 [19.0, 19.7] Hill/Mountains 26.3 [25.3, 27.2] 24.1 [23.7, 24.3] Island/Coastal 28.6 [27.7, 29.6] 27.4 [27.1, 27.8] Sample Size (n) 7,944 (11.2%) 63,190 (88.8%) Note: 95% Confidence Interval in parenthesis A higher percentage of individuals in Bihar were found to be exposed to water-related disasters (WRDs), accounting for 32.06%, followed by Jammu and Kashmir (29.3%), Manipur (28.0%), Madhya Pradesh (26%), and Tamil Nadu (18.5%) (Fig. 1 .a). The prevalence of water-related infectious diseases (WRIDs) was highest in Chhattisgarh at 49.6%, followed by Bihar (47.2%), Madhya Pradesh (46.6%), Rajasthan (46.2%), and Haryana (40.8%) (Fig. 1 .b). 3.2 Variations in prevelence of WRIDs by WRD exposure About 21.3% older adults they suffered any kind of WRIDs in India, which was observed high among rural residence (25.1%), followed by the 65–74 aged group (22.8%). Additionally, higher prevelence of WRIDs was found among STs (24.5, p < 0.001) compared to their counterpart. With increasing education and household wealth quantile, the prevalence of WRIDs observed to be decreased. Among disaster exposed group, the prevelnce was significantly higher among the individuals with history of two chronic illness (32.0, p < 0.05) than their counterpart with no history of chronic illness. Similarly, the prevelence was higher among individuals with two ADL limitations (26.7, p < 0.001) than the individuals with no limitations. Other risk factors like increased participation in open defectation practices (31.1, p < 0.001) or living in damp housing condition (24.9, p < 0.001) were leading to higher prevalence of WRIDs among both disaster exposed or unexposed group. Utilization of untreated drinking water (31.2, p < 0.001) was significantly varied the prevelance of WRIDs along with the geographical belonging of disaster exposed individuals (Fig. 2 ). Individuals belonging to plains region (36.3, p < 0.001) were suffering from higher burden of WRIDs compared to their counterparts living in other geographical regions (Table S1 ). 3.3 Adjusted likelihoods of WRIDs by WRD exposure and other covariates The Multivariate logistic regression model demonstrated acceptable discriminatory power, with an AUC of 0.73 (Figure S2). The study outcome suggests that exposure to disaster increase the risk of WRIDs. Individuals who were exposed to WRDs were 28% (AOR: 1.28, 95% CI: 1.22, 1.35) more likely to report WRIDs than their counterpart who were not exposed to disaster (Table 2 ). Education and socio-economic richness act as protective factors of WRIDs (Table 2 ). For instance, tribals and economically poor older-adults were more likely to suffer with WRIDs than their counterparts. Contrary, having a history of multiple chronic illnesses and multiple ADL limitations found as risk factors of WRIDs. Individuals having a history of more than two chronic illnesses being at a 25% (AOR: 1.25, 95% CI: 1.15, 1.36) higher risk of WRIDs. Older adults bearing the two or more ADL limitations were at 23% (AOR: 1.23, 95% CI: 1.14, 1.33) higher risk of WRIDs compared to their counterparts without any ADL limitation. Lack of healthy public health practice like using untreated drinking water, practicing open defecation, or residing in damp housing conditions were significant predictors of the WRIDs. Individuals utilizing untreated drinking water were 12% (AOR: 1.12, 95% CI: 1.07, 1.17) more likely to report WRIDs compared to their counterparts using treated drinking water. The adjusted likelihood of WRIDs was 40% (AOR: 1.40, 95% CI: 1.34, 1.46) more likely among the individual who engaged in open defecation practices compared to their counterparts practicing safe defecation. Individuals belonging to the urban areas have a 34% (AOR: 0.66, 95% CI: 0.63, 0.69) lower risk of WRIDs compared to their counterparts from rural areas. Table 2 Unadjusted and adjusted Odds ratio of WRIDs among older-adults aged 45 and above, India, LASI (2017-18) Variable Category (Reference) Unadjusted Odds Ratio (95% CI) Adjusted Odds Ratio (95% CI) Disaster Exposure No (Ref) 1.00 1.00 Yes 1.54*** (1.46, 1.61) 1.28*** (1.22, 1.35) Age (years) 45–54 (Ref) 1.00 1.00 55–64 1.07** (1.03, 1.12) 0.99 (0.95, 1.03) 65–74 1.10*** (1.05, 1.15) 0.99 (0.95, 1.04) 75+ 1.11** (1.04, 1.19) 0.96 (0.90, 1.03) Place of Residence Rural (Ref) 1.00 1.00 Urban 0.51*** (0.49, 0.53) 0.66*** (0.63, 0.69) Year of Schooling No schooling (Ref) 1.00 1.00 12 years 0.40*** (0.36, 0.44) 0.63*** (0.57, 0.69) Social Group ST (Ref) 1.00 1.00 SC 0.80*** (0.74, 0.85) 0.73*** (0.68, 0.78) OBC 0.74*** (0.69, 0.78) 0.76*** (0.72, 0.82) Others 0.63*** (0.59, 0.67) 0.75*** (0.70, 0.80) Chronic Illness None (Ref) 1.00 1.00 One 0.87*** (0.82, 0.92) 1.19*** (1.14, 1.24) Two 0.70*** (0.62, 0.79) 1.19*** (1.12, 1.26) >Two 0.90* (0.82, 0.99) 1.25*** (1.15, 1.36) ADL Limitations None (Ref) 1.00 1.00 One 1.08*** (1.03, 1.12) 1.19*** (1.11, 1.27) Two 0.99 (0.94, 1.04) 1.06 (0.97, 1.17) >Two 0.98 (0.90, 1.06) 1.23*** (1.14, 1.33) Wealth Quantile Poor (Ref) 1.00 1.00 Poorer 1.07* (1.01, 1.12) 1.11** (1.05, 1.17) Middle 0.87*** (0.82, 0.91) 0.92** (0.87, 0.98) Richer 0.86*** (0.81, 0.91) 0.96 (0.90, 1.01) Richest 0.80*** (0.76, 0.85) 0.96 (0.90, 1.02) Drinking Water Treatment Yes (Ref) 1.00 1.00 No 1.49*** (1.44, 1.55) 1.12*** (1.07, 1.17) Open Defecation No (Ref) 1.00 1.00 Yes 1.83*** (1.76, 1.90) 1.40*** (1.34, 1.46) Housing Dampness None (Ref) 1.00 1.00 Yes 1.26*** (1.20, 1.33) 1.19*** (1.13, 1.25) Geographical Region Arid (Ref) 1.00 1.00 Plains 5.55*** (2.85, 10.82) 4.89*** (2.50, 9.56) Plateau 4.80*** (2.46, 9.37) 4.54*** (2.32, 8.88) Hills/Mountains 6.56*** (3.36, 12.79) 6.04*** (3.09, 11.81) Island/Coastal 7.83*** (4.02, 15.26) 6.68** (3.42, 13.07) Note: p-value *<0.05; **<0.01; ***0.0001 The geographical belonging of individuals has emerged as a significant predictor of WRIDs, where individuals residing in coastal/island region has the 6.68-time higher odds (AOR: 6.68, 95% CI: 3.42, 13.07) of WRIDs compared to the individuals residing in the arid region (Table 2 ). Discussion This study makes two key contributions to the growing body of evidence at the intersection of environmental change, population ageing, and public health in India. First, it systematically maps water-related disasters (WRDs) across an expanding ageing population, offering one of the few national-scale assessments focused specifically on older adults. Second, it examines the association between exposure to WRDs and WRIDs among older individuals, adjusting for a range of socio-demographic and public-health indicators. By incorporating regional variation in WRID vulnerability, the study extends current understanding of how environmental risks interact with demographic transitions, thereby enriching the broader climate and public-health literature. Coi ncides with several previous global and national studies ( 13 , 16 , 19 , 24 , 34 ), the present study highlights that exposure to WRDs increases the risk of the WRIDs among the elderly population. This increases the risk of the dual burden of disease (communicable and non-communicable diseases), which further affects healthy ageing ( 33 ). The reason behind this elevated risk is the water contamination caused by the onset of the WRDs, which further creates a favourable condition for the proliferation of the pathogens related to the WRIDs ( 35 , 36 ). Disruption or overcrowding at the sanitation services is another important factor which increases the risk of WRIDs. Covariates such as place of residence, year of education, and social group belonging act as protective factors against the risk of WRIDs ( 22 ). Individuals belonging to the urban area shown lower risk of WRIDs compared to individuals from rural areas may be due to variation in disaster frequency and intensity, and public health infrastructures ( 37 , 38 ). Another reason for higher risk among the elderly from the rural areas is their dependency on other household members for daily life activities, which is affected due to the onset of any environmental extremes ( 39 ). Similarly, with the increasing years of education, the risk of WRIDs because of the increasing awareness of hygiene practices and safe drinking water utilization ( 40 ). Different social group belongings of the individuals compared to the ST social group, reduce the risk of acquisition of WRIDs. Higher risk of WRIDs among the ST social group could be because of lower accessibility of safe drinking water and sanitation. Low awareness level about the hygiene practice could be another factor that increases the risk of WRIDs among the ST population ( 22 , 41 ). Individuals having any history of chronic illness or suffering from any type of ADL were at higher risk of WRIDs. The reason behind this is that the physiological capacity of the individuals with a history of chronic illness is degraded, which weakens their immune system ( 42 ). Similarly, the individuals with any type of ADL, whose mobility is already lower, face difficulty in accessing the clean drinking water and sanitation during the exposure of WRDs ( 34 , 43 ). Using untreated drinking water, practicing open defecation, and living in damp housing conditions are positive risk factors associated with the acquisition of WRIDs, which are further exacerbated because of exposure to WRDs ( 44 ). Individuals already using untreated drinking water further face compulsion to use contaminated drinking water, which increases the risk of acquisition of WRIDs. Individuals practicing open defecation or living in damp housing conditions face a higher risk of WRIDs, because these unhygienic practices create favourable condition for the proliferation of pathogens and vectors related to WRIDs ( 21 ). Individuals living in island/coastal areas were also at higher risk of acquisition of WRIDs compared to other geographical belongings because of local climatic conditions ( 45 ). Hot and humid conditions in coastal/island areas act as a supportive factor in outbreak of WRIDs ( 46 ). This existing condition further deteriorates due to frequent exposure to WRDs, reducing access to clean drinking water and sanitation practices ( 6 ). The study findings indicate a strong effect of WRDs exposure on the prevalence of WRIDs among the elderly population. However, the study carries some limitations also, like we have considered flood, cyclone, & tsunami as water-related disaster, which directly involve exposure to water as the main hazard component. However, other disasters like drought, earthquake, & landslide etc., though not classified as WRDs, can indirectly contribute to an elevated risk of WRIDs by affecting water availability, quality, and sanitation infrastructure ( 47 , 48 ). This increased burden of communicable diseases along with the non-communicable diseases affects the societal progress in achieving the sustainable development goal (SDG), which can be further worsened in the face of climate change ( 49 ). So, there is a need for targeted intervention where elderly populations belonging to marginalized sections (lower wealth quintile, scheduled tribe) of society should be given more attention in the condition of any WRDs exposure. Equitable access to clean drinking water and promoting hygienic practices among older populations can further help in reducing this burden and contribute to countries' progress in healthy aging and SDG-3. Conclusion As the country is approaching the third phase of the demographic transition, the elderly population is increasingly facing a dual burden of diseases, where communicable and non-communicable conditions are rising simultaneously. The higher prevalence of WRIDs among disaster-exposed individuals shows that vulnerability among older adults increases with exposure to water-related disasters (WRDs). Frequent episodes of communicable diseases, such as vector-borne, water-borne, and viral infections, can further contribute to the aggravated burden of non-communicable diseases among the elderly by weakening immunity, increasing physiological stress, and worsening pre-existing chronic conditions. The study outcome highlights how the burden of infectious diseases continues to influence the overall health profile of older populations in changing climatic conditions. Existing programs such as the Integrated Disease Surveillance Programme (IDSP), the National Vector Borne Disease Control Programme (NVBDCP), National Viral Hepatitis Control Programme (NVHCP), etc., are designed to address the burden of communicable diseases. However, the situation is likely to become more challenging under changing climatic conditions through expanding risk of WRDs. The study findings highlight the need for strengthening current policies and making them more targeted and responsive to specific risk factors experienced by vulnerable groups, particularly the elderly. Implementing appropriate mitigation strategies and taking appropriate adaptation measures during and after WRD events can help reduce the disease burden and support healthy ageing. Strengthening surveillance, improving preparedness, and designing targeted interventions for high-risk populations can contribute to the country’s progress toward achieving Sustainable Development Goals (SDGs), particularly Goal 3 (Good Health and Well-being) and Goal 10 (Reduced Inequalities). Declarations Competing Interest The author declares that there are no financial or personal conflicts of interest associated with this study. Ethical Statement This present study is based on secondary data of the Longitudinal Ageing Study, wave 1 (LASI-I) 2017-18, which is a nationally representative survey of adults aged 45 years and above. LASI was conducted by the International Institute for Population Sciences (IIPS), Mumbai, in collaboration with the Harvard T. H. Chan School of Public Health and the University of Southern California. Ethical approval for LASI was obtained from the IIPS Institutional Review Board (IRB) and the collaborating institutions. Since the present analysis uses publicly available, de-identified data, no additional ethical approval was required. Informed Consent The dataset used for this study is fully anonymized and contains no personal identifiers. Written informed consent was obtained from all respondents prior to their participation in the LASI survey. For individuals with limited capacity to provide consent, proxy consent procedures were implemented in accordance with the guidelines approved by the Institutional Review Board (IRB). All participants were aged 45 years and above (along with their spouses, irrespective of age), and participation was voluntary. Clinical Trial Registration Not applicable. Funding No external funding was received for this work. Author Contribution AY: Conceptualization, data analysis, and manuscript drafting; MJR: Study supervision, manuscript review and editing; MR: Data analysis, manuscript review and editing. Acknowledgement NA Data Availability The present study utilized secondary data from the Longitudinal Ageing Study in India (LASI) Wave 1 2017-18, which is collected and managed by the International Institute for Population Sciences (IIPS), Mumbai. The LASI dataset is accessible to researchers upon request. Interested individuals may obtain the data by submitting a brief description of the intended study purpose through the IIPS data access portal. Data requests can be submitted at: [https://www.iipsindia.ac.in/content/LASI-data](https:/www.iipsindia.ac.in/content/LASI-data) References Ebi KL, Vanos J, Baldwin JW, Bell JE, Hondula DM, Errett NA, et al. Extreme Weather and Climate Change: Population Health and Health System Implications. Annu Rev Public Health. 2021;42:293–315. Felix KT, Balasubramanian M, Govindarajan PL, Kesav B. Assessing the socioeconomic and environmental determinants of flood vulnerability in India: a panel data approach. Sci Rep. 2025 July;30(1):27762. Saatchi M, Khankeh HR, Shojafard J, Barzanji A, Ranjbar M, Nazari N, et al. Communicable diseases outbreaks after natural disasters: A systematic scoping review for incidence, risk factors and recommendations. Progress Disaster Sci. 2024;23:100334. Abebe YA, Pregnolato M, Jonkman SN. Flood impacts on healthcare facilities and disaster preparedness – A systematic review. Int J Disaster Risk Reduct. 2025;119:105340. WHO. Burden of disease attributable to unsafe drinking-water, sanitation and hygiene: 2019 update [Internet]. 2023 [cited 2025 Nov 12]. Available from: https://www.who.int/publications/i/item/9789240075610?utm_source=chatgpt.com WHO, Water. sanitation and hygiene burden of disease [Internet]. 2025 [cited 2025 Nov 12]. Available from: https://www.who.int/data/gho/data/themes/topics/water-sanitation-and-hygiene-burden-of-disease Chen A, Pokhrel Y, Chen D, Huang H, Dai Z, He B, et al. Impact of tropical cyclones and socioeconomic exposure on flood risk distribution in the Mekong Basin. Commun Earth Environ. 2024;5(1):704. Swarnkar S, Mujumdar P. Increasing Flood Frequencies Under Warming in the West-Central Himalayas. Water Resour Res. 2023;59(2):e2022WR032772. Misra A, White K, Nsutezo SF, Straka W, Lavista J. Mapping global floods with 10 years of satellite radar data. Nat Commun. 2025 July 1;16(1):5762. Yazdi MS, Ardalan MA, Hosseini M, Yousefi Zoshk M, Hami Z, Heidari R, et al. Infectious Diarrhea Risks as a Public Health Emergency in Floods; a Systematic Review and Meta-Analysis. Arch Acad Emerg Med. 2024;12(1):e46. Erickson TB, Brooks J, Nilles EJ, Pham PN, Vinck P. Environmental health effects attributed to toxic and infectious agents following hurricanes, cyclones, flash floods and major hydrometeorological events. J Toxicol Environ Health Part B. 2019;22(5–6):157–71. Lee J, Perera D, Glickman T, Taing L. Water-related disasters and their health impacts: A global review. Progress Disaster Sci. 2020;8:100123. Ahmed Z, Khan AA, Nisar N. Frequency of infectious diseases among flood affected people at district Rajanpur, Pakistan. 2011 Sept;27(4):866–9. Arcari P, Tapper N, Pfueller S. Regional variability in relationships between climate and dengue/DHF in Indonesia. Singap J Trop Geogr. 2007;28(3):251–72. Kouadio IK, Aljunid S, Kamigaki T, Hammad K, Oshitani H. Infectious diseases following natural disasters: prevention and control measures. Expert Rev Anti-infective Therapy. 2012;10(1):95–104. Lynch VD, Shaman J. Waterborne Infectious Diseases Associated with Exposure to Tropical Cyclonic Storms, United States, 1996–2018 - Volume 29, Number 8—August 2023 - Emerging Infectious Diseases journal - CDC. 2023 Aug [cited 2025 Nov 12]; Available from: https://wwwnc.cdc.gov/eid/article/29/8/22-1906_article Parks RM, Anderson GB, Nethery RC, Navas-Acien A, Dominici F, Kioumourtzoglou MA. Tropical cyclone exposure is associated with increased hospitalization rates in older adults. Nat Commun. 2021;12:1545. Ahmed SH, Shaikh TG, Waseem S, Zahid M, Mohamed Ahmed KAH, Ullah I, et al. Water-related diseases following flooding in South Asian countries – a healthcare crisis. Eur J Clin Exp Med. 2024;22(1):232–42. Jamalludin FHB, Ahmed MF, Halder B, Khai TS, Asaduzzaman M. Waterborne diseases in flood compromised WASH conditions in Malaysia: a planetary health perspective. Front Clim [Internet]. 2025 Oct 22 [cited 2025 Nov 12];7. Available from: https://www.frontiersin.org/journals/climate/articles/ 10.3389/fclim.2025.1646753/full Takahashi T, Goto M, Yoshida H, Sumino H, Matsui H. Infectious Diseases after the 2011 Great East Japan Earthquake. J Experimental Clin Med. 2012;4(1):20–3. Goyanka R. Burden of water, sanitation and hygiene related diseases in India: prevalence, health care cost and effect of community level factors. Clin Epidemiol Global Health. 2021;12:100887. Kumar P, Srivastava S, Banerjee A, Banerjee S. Prevalence and predictors of water-borne diseases among elderly people in India: evidence from Longitudinal Ageing Study in India, 2017–18. BMC Public Health. 2022;22(1):993. UN, Health. Water and Sanitation | United Nations in India [Internet]. 2023 [cited 2025 Oct 24]. Available from: https://india.un.org/en/171844-health-water-and-sanitation Saha J, Hussain D, Debsarma D. Exploring the Association Between Floods and Diarrhea among Under-five Children in Rural India. Disaster Med Pub Health Prep. 2024;18:e142. Walika M, De Moitinho M, Castro Delgado R, Arcos González P. Outbreaks Following Natural Disasters: A Review of the Literature. Disaster med public health prep. 2023;17:e444. Pramanik M, Singh P, Kumar G, Ojha VP, Dhiman RC. El Niño Southern Oscillation as an early warning tool for dengue outbreak in India. BMC Public Health. 2020;20:1498. Ifejube OJ, Kuriakose SL, Anish TS, van Westen C, Blanford JI. Analysing the outbreaks of leptospirosis after floods in Kerala, India. Int J Health Geogr. 2024;23(1):11. Rahaman M, Saha A, Das KC, Rana MJ. Self-Reported Health Problems Due to Disasters in India: Evidence from the Longitudinal Ageing Study in India Survey. In: Disaster Risk, Resilient Agriculture and Livelihood. Routledge India; 2024. International Institute for Population Sciences (IIPS), National Programme for Health Care of Elderly, Ministry of Health & Family Welfare, Harvard T. H. Chan School of Public Health, University of Southern California. Longitudinal Ageing Study in India (LASI) [Internet]. Mumbai: International Institute for Population Sciences (IIPS). 2018 p. 632. (Wave 1). Available from: https://www.iipsindia.ac.in/sites/default/files/LASI_India_Report_2020_compressed.pdf Nichols G, Lake I, Heaviside C, Nichols G, Lake I, Heaviside C. Climate Change and Water-Related Infectious Diseases. Atmosphere [Internet]. 2018 Oct 2 [cited 2025 Nov 12];9(10). Available from: https://www.mdpi.com/ 2073-4433/9/10/385. WHO. Introduction to water-related infectious diseases. [Internet]. 2022. Available from: https://www.who.int/docs/librariesprovider2/default-document-library/module_1.1_introduction-wrid_ds_220831.pdf Stoltzfus JC. Logistic Regression: A Brief Primer. Acad Emerg Med. 2011;18(10):1099–104. Verma A, Satapathy P, Venugopal D, Menon SV, Vadia N, Panigrahi R, et al. Monsoon-driven dynamics of infectious diseases: Climatic determinants, outbreak patterns, and public health implications. Clin Infect Pract. 2025;28:100516. Shafii NZ, Saudi ASM, Pang JC, Abu IF, Sapawe N, Kamarudin MKA, et al. Association of Flood Risk Patterns with Waterborne Bacterial Diseases in Malaysia. Water. 2023;15(11):2121. Acosta-España JD, Romero-Alvarez D, Luna C, Rodriguez-Morales AJ. Infectious disease outbreaks in the wake of natural flood disasters: global patterns and local implications. Infez Med. 2024;32(4):451–62. CANN KF THOMAS, DRh SALMONRL, WYN-JONES AP KAYD. Extreme water-related weather events and waterborne disease. Epidemiol Infect. 2013;141(4):671–86. Chaudhuri S, Roy M. Rural-urban spatial inequality in water and sanitation facilities in India: A cross-sectional study from household to national level. Appl Geogr. 2017;85:27–38. Hutton G, Chase C. Water Supply, Sanitation, and Hygiene. In: Mock CN, Nugent R, Kobusingye O, Smith KR, editors. Injury Prevention and Environmental Health [Internet]. 3rd ed. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2017 [cited 2025 Oct 17]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK525207/ Degbey C, Houessionon E, de Brouwer C. Prevalence and Factors Associated with Waterborne Diseases in Couffo, Southwestern Benin: The Case of Aplahoué. Int J Environ Res Public Health. 2025;22(1):58. Pangotra A, Bhadoria A. Community Engagement and Awareness about waterborne diseases to Detect Early Warning Signs & increase Self-reporting. J Epidemiol Foundation India. 2025;3(1):79–82. Biswas S, Adhikary M, Alam A, Islam N, Roy R. Disparities in access to water, sanitation, and hygiene (WASH) services and the status of SDG-6 implementation across districts and states in India. Heliyon. 2024 Sept 30;10(18):e37646. Quiros-Roldan E, Sottini A, Natali PG, Imberti L. The Impact of Immune System Aging on Infectious Diseases. Microorganisms. 2024;12(4):775. Yeboah SIIK, Antwi-Agyei P, Kabo-Bah AT, Ackerson NOB. Water, environment, and health nexus: understanding the risk factors for waterborne diseases in communities along the Tano River Basin, Ghana. J Water Health. 2024;22(8):1556–77. Gerdes ME, Miko S, Kunz JM, Hannapel EJ, Hlavsa MC, Hughes MJ et al. Estimating Waterborne Infectious Disease Burden by Exposure Route, United States, 2014 - Volume 29, Number 7—July 2023 - Emerging Infectious Diseases journal - CDC. 2023 July [cited 2025 Nov 12]; Available from: https://wwwnc.cdc.gov/eid/article/29/7/23-0231_article Hosking R, Smurthwaite K, Hales S, Richardson A, Batikawai S, Lal A. Climate variability and water-related infectious diseases in Pacific Island Countries and Territories, a systematic review. Qureshi A, editor. PLOS Clim. 2023;2(10):e0000296. Ramasamy R, Surendran SN. Global Climate Change and Its Potential Impact on Disease Transmission by Salinity-Tolerant Mosquito Vectors in Coastal Zones. Front Physiol. 2012 June;19:3:198. Levy K, Smith SM, Carlton EJ. Climate Change Impacts on Waterborne Diseases: Moving Toward Designing Interventions. Curr Environ Health Rep. 2018 June;5(2):272–82. Mora C, McKenzie T, Gaw IM, Dean JM, Von Hammerstein H, Knudson TA, et al. Over half of known human pathogenic diseases can be aggravated by climate change. Nat Clim Chang. 2022 Sept;12(9):869–75. Semenza JC, Rocklöv J, Ebi KL. Climate Change and Cascading Risks from Infectious Disease. Infect Dis Ther. 2022;11(4):1371–90. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":469776,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation percentage exposed to water-related disaster (a) and prevalence of water-related infectious diseases (b)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8263267/v1/a778d636a73d5efe62d12221.jpeg"},{"id":98312144,"identity":"2f725667-72fd-4a0a-b234-c4a704bd23a9","added_by":"auto","created_at":"2025-12-16 12:25:18","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":500801,"visible":true,"origin":"","legend":"\u003cp\u003eSocio-demographic variation of water-related infectious diseases amid disaster exposure\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8263267/v1/93a7b1067fe40fa6cc8c9d2a.jpeg"},{"id":107927878,"identity":"a277c3fa-2a88-44dd-b30f-0f9396659193","added_by":"auto","created_at":"2026-04-27 16:05:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1437525,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8263267/v1/98d3678e-6742-4591-82d3-5ca7fff5793f.pdf"},{"id":98435311,"identity":"4d4e2188-6535-4369-b3f5-bdc07a780af5","added_by":"auto","created_at":"2025-12-17 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Specifying the consequence of WRDs like floods, cyclones or others on public health, the increasing frequency severely undermines core public health infrastructure\u0026mdash;damaging and disrupting essential water, sanitation, hygiene, and healthcare services (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Such system failures create fertile conditions for outbreaks of water- and vector-borne diseases as contaminated water sources and stagnant environments become widespread (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These risks are further intensified by damage to healthcare facilities and reduced accessibility to medical services during and after disasters, compounding the overall health burden on affected populations (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Global Burden of Diseases (GBD-2019) estimates more than 1.6\u0026nbsp;million death occurred due to unsafe water, sanitation and hygience (WASH) practices in 2019. This translates to 23 deaths per 100,000 population directly linked to diseases associated with inadequate WASH. Among WASH-related causes, diarrhoeal diseases remain the leading contributor, accounting for over 1\u0026nbsp;million deaths in 2019 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Existing regional studies highlighted that the water- and vector-borne diseases have been proportionally increasing with rising frequency and severity of the WRDs (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In WRDs prone regions, vulnerable groups like children, pregnant women, elderly population, and socio-economically marginalize groups are more vulnarable to water and vector borne diseases (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmpirical evidence found that exposure to WRDs is positively associate with water-related infectious diseases (WRIDs) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). For instance, studies across the globe founds that extreme climatic events like flood or cyclones are positively associated with the elevated risk of communicable diseases through several socio-economic pathways (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Similarly exposure to intense rainfall and tropical storms found to be linked with elevated risks of diarrhoeal and vector-borne infections in the United States (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Similarly, recurrent flooding contributes to a growing burden of diarrhoea and malaria observed in the South Asia (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Furthermore, these evidences concludes children and elderly are more vulnarable to such WRIDs (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndia faces a substantial burden of WRIDs, with nearly two-thirds of the population exposed to water-related disasters (WRDs) and more than 37\u0026nbsp;million WRID cases annually. The economic toll is considerable, with WRIDs costing over US\u003cspan\u003e$\u003c/span\u003e600\u0026nbsp;million and resulting in the loss of more than 70\u0026nbsp;million workdays each year (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). A national level study based on National Family Health Survey-5 suggest the incresing exposure reoccuring flood increased the risk of WRID among children in India (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Additionally, a review study carried out by walika et al. (2013) founds that burden of the WRIDs increased in the face of disasters like cyclone or flood (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). A similar study from India founds that risk of the dengue is positively associated with global climatic phenomenon like Oneanic Nino Index (ONI) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Evidence from subnational study shows that frequent flooding situtaion results in increased number of WRIDs during post flood condition (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). However, older adults\u0026mdash;who exhibit heightened physiological vulnerability and reduced coping capacity\u0026mdash;remain critically underexamined in the environment and public health literature. A recent national study reported that 4% of adults aged 45 and above years self-reported disaster-related health problems (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), yet the study did not assess WRD-specific exposure or WRID outcomes. Similarly, analyses using LASI data examined patterns of water-borne diseases among older adults (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), but again excluded WRD exposure, overlooking a major climate-sensitive disease pathway. Existing geriatric WRID studies have relied mainly on socio-demographic or health indicators, largely ignoring the intensifying influence of climate-driven WRDs. Consequently, the relationship between WRD exposure and WRID risk among older adults in India remains empirically unexplored in India using nationally representive survey dataset. The present study seeks to fill this gap by integrating community-level disaster exposure with individual-level morbidity to quantify the effect of WRDs on WRID risk among older adults. This evidence is essential for guiding climate-resilient healthy ageing policies and advancing India\u0026rsquo;s progress toward SDG 3 targets\u0026mdash;particularly 3.3 (ending infectious diseases) and 3.9 (reducing illness from unsafe water and environmental hazards).\u003c/p\u003e"},{"header":"Data and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Data source\u003c/h2\u003e \u003cp\u003eIn the present study, we utilized both individual- and household-level datasets from the Longitudinal Ageing Study in India (LASI) Wave-1 to evaluate the association between water-related disasters (WRDs) and the prevalence of water-related infectious diseases (WRIDs). The LASI survey, conducted in 2017\u0026ndash;18, collected data from individuals aged 45 years and above across all states and union territories of India, except Sikkim. Spatial distribution maps depicting WRD exposure and WRID prevalence were generated using official shapefiles obtained from the Survey of India (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Study design\u003c/h2\u003e \u003cp\u003eThe present study adopts the cross sectional design using the nationally representative data from Longitudnal Ageing Survey of India (LASI), conducted during 2017-18. The study uses the individual level data for the fullfillment of the aim (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Study sample\u003c/h2\u003e \u003cp\u003eThe present study utilized the individual data from Longitudnal Ageing Survey of India, 2017-18, which uses a multistage stratified random sampling technique to collect the data regarding various social behaviour, economic condition, and health status indicators. The survey adopts three stage sampling design in rural areas and four stage sampling design in urban areas to collect the data of 72,250 older adults from 42,949 households India (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The data was collected and managed by International Institute for Population Sciences, Mumbai (IIPS), in collaboration of Harvard T.H. Chan School of Public Health and University of Southern California. The project was funded by Ministry of Health and Family Welfare (MoHFW), GoI, National Institute of Ageing (NIA), U.S. National Insitute of Health, and United National Population Fund (UNFPA), India. Highlight here how you marge the two dataset to make data for such analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4 Outcome variable\u003c/h2\u003e \u003cp\u003eThe primary outcome was any water-related infectious disease (WRID) reported by the respondent. Participants were asked: \u0026ldquo;In the past two years, have you had any of the following diseases?\u0026rdquo;\u0026mdash;malaria, diarrhea, typhoid, chikungunya, or dengue (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Responses were recorded in a binary format (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no). Using these five items, a composite variable was constructed: respondents who reported yes to any of the five diseases were coded as 1 (any WRID), and those who reported no to all were coded as 0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.1.5 Key explanatory variable\u003c/h2\u003e \u003cp\u003eThe key explanatory variable in this study was exposure to WRDs. Information on disaster exposure was obtained from community heads or household representatives in response to the question: \u003cem\u003e\u0026ldquo;In the last five years, has your health been severely affected by disasters such as floods, landslides, extreme cold or hot weather, cyclones/typhoons, droughts, earthquakes, tsunamis, or any other natural calamities?\u0026rdquo;\u003c/em\u003eResponses were recorded in a binary format (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no). For this analysis, exposure to floods, cyclones/typhoons, and tsunamis was classified as water-related disaster exposure (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). A composite WRD exposure index was then constructed, wherein individuals reporting exposure to any one of these disasters were coded as 1 (WRD exposed), and those reporting no exposure were coded as 0 (not exposed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.1.6 Other covariates\u003c/h2\u003e \u003cp\u003eIn line with prior studies (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), the analysis controlled for a range of covariates capturing socio-demographic characteristics and daily life conditions that could influence the association between water-related WRD exposure and WRIDs. Socio-demographic variables included age (45\u0026ndash;54, 55\u0026ndash;64, 65\u0026ndash;74, \u0026ge;\u0026thinsp;75 years), years of schooling (no schooling, \u0026lt;\u0026thinsp;5, 5\u0026ndash;8, 9\u0026ndash;12, \u0026gt;\u0026thinsp;12 years), social group (Scheduled Tribe (SC), Scheduled Caste (ST), Other Backward Class, Others), household wealth quintile (poorest, poorer, middle, richer, richest), place of residence (rural or urban), and geographical region (arid, plains, plateau, hill/mountain, or island/coastal). Daily life condition variables comprised drinking water treatment (yes/no), open defecation practices (yes/no), housing dampness (yes/no), chronic illness (none, one, two, or more than two), and activities of daily living (ADL) limitations (none, one, two, or more than two). These covariates were included in multivariable analyses to adjust for potential confounding and to better isolate the independent effect of water-related disaster exposure on the prevalence of WRIDs (Table S2).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Statistical analysis\u003c/h2\u003e \u003cp\u003eIn the present study, both the descriptive and inferential statistics has been used, where proportion distribution of the sample was calculated across different socio-demographic and daily life conditition covarites. The study also utilizes the Chi square to find out the prevalence of WRIDs across different covariates. Futher, both bivarite and muvariate logistic regression model was used to analyse the risk factors associated with the prevalenc of WRIDs.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{log}\\left(\\frac{p}{1-p}\\right)=\\:{\\beta\\:}^{0}+\\:{\\beta\\:}^{1}{X}^{1}+\\:{\\beta\\:}^{2}{X}^{2}+\\:\\dots\\:\\:+\\:\\beta\\:ₖXₖ$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cem\u003ep\u003c/em\u003e denotes the predicted probability of having a WRID, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e represents the intercept term, and \u003cem\u003eβ1,β2,\u0026hellip;,β\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e ​ are the regression coefficients corresponding to the socio-demographic and daily life condition covariates \u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e,\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e,\u003cem\u003e\u0026hellip;,X\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Multicollinearity among independent variables was evaluated using the Variance Inflation Factor (VIF). Model discrimination was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), obtained through the lroc command. All analyses were carried out in Stata version 17.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Background characteristics of sampled population\u003c/h2\u003e \u003cp\u003eAbout 11.2% of the study participants reported exposure to any kind of WRDs. Most of the study population were aged 45\u0026ndash;54 years (41.0%). Similarly, the percentage of individulas was higher from OBC (46.4%) social group, and with no educational background (55.4%) in disaster exposed group. Additionally, less individuals have reported the use of treated drinking water (32.5%) with more participation in open defecation practices (31.4%) among disaster exposed group. In addition to that, the percentage of individuals reporting damp housing condition (24.2%), with more that two ADL (5.9%) was higher among disaster exposed group as compared to individuals from disaster unexposed group. A higher percentage individuals among disaster exposed group belongs to plain regions and majority of them from rural background (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBackground characterictics of sampled population, LASI 2017-18\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDisaster Exposed (%) [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisaster Unexposed (%) [95% CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.0 [40.0, 42.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.6 [41.2, 42.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.2 [26.2, 28.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.3 [27.0, 27.7]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.0 [23.0, 24.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.6 [23.3, 24.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.8 [7.2, 8.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.5 [7.3, 7.7]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears of Schooling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.1 [54.0, 56.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.9 [44.5, 45.2]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.7 [9.1, 10.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.3 [11.1, 11.6]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.2 [15.4, 17.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.2 [18.9, 19.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.8 [14.0, 15.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.6 [18.2, 18.9]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.2 [3.8, 4.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.1 [5.9, 6.3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.4 [9.8, 11.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.1 [17.8, 18.4]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.2 [17.4, 19.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.8 [16.5, 17.1]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.4 [45.3, 47.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.7 [36.3, 37.1]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.9 [24.0, 25.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.4 [28.1, 28.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Quantile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.9 [19.0, 20.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.5 [19.2, 19.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.5 [19.6, 21.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0 [19.7, 20.3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.5 [17.7, 19.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.3 [20.0, 20.6]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.2 [19.3, 21.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.3 [20.0, 20.7]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.0 [20.1, 21.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.8 [19.5, 20.1]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking Water Treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.5 [31.5, 33.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.2 [42.8, 43.6]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.5 [66.4, 68.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.8 [56.4, 57.2]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOpen Defecation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.6 [67.6, 69.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.0 [81.7, 82.3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.4 [30.4, 32.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.0 [17.7, 18.3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousing Dampness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.8 [74.9, 76.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.1 [79.8, 80.4]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.2 [23.6, 25.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.9 [18.2, 20.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic Illness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.2 [55.1, 57.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.2 [54.9, 55.6]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.9 [25.9, 27.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.7 [26.4, 27.1]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.9 [11.2, 12.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.4 [12.2, 12.7]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than two\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.0 [4.6, 5.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.6 [5.4, 5.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eADL Limitations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.9 [83.1, 84.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.7 [86.4, 86.9]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.1 [6.6, 7.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.1 [5.9, 6.2]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.1 [2.7, 3.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.1 [3.0, 3.2]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than two\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.9 [5.4, 6.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.2 [4.0, 4.3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.9 [79.0, 80.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.9 [79.0, 80.7]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.1 [19.3, 21.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.1 [19.3, 21.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographical Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArid region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4 [0.3, 0.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.2 [4.1, 4.4]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.0 [29.9, 32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.0 [24.7, 25.3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.7 [13, 14.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.3 [19.0, 19.7]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHill/Mountains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.3 [25.3, 27.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.1 [23.7, 24.3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsland/Coastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.6 [27.7, 29.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.4 [27.1, 27.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSample Size (n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,944 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63,190 (88.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: 95% Confidence Interval in parenthesis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA higher percentage of individuals in Bihar were found to be exposed to water-related disasters (WRDs), accounting for 32.06%, followed by Jammu and Kashmir (29.3%), Manipur (28.0%), Madhya Pradesh (26%), and Tamil Nadu (18.5%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.a). The prevalence of water-related infectious diseases (WRIDs) was highest in Chhattisgarh at 49.6%, followed by Bihar (47.2%), Madhya Pradesh (46.6%), Rajasthan (46.2%), and Haryana (40.8%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Variations in prevelence of WRIDs by WRD exposure\u003c/h2\u003e \u003cp\u003eAbout 21.3% older adults they suffered any kind of WRIDs in India, which was observed high among rural residence (25.1%), followed by the 65\u0026ndash;74 aged group (22.8%). Additionally, higher prevelence of WRIDs was found among STs (24.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to their counterpart. With increasing education and household wealth quantile, the prevalence of WRIDs observed to be decreased. Among disaster exposed group, the prevelnce was significantly higher among the individuals with history of two chronic illness (32.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than their counterpart with no history of chronic illness. Similarly, the prevelence was higher among individuals with two ADL limitations (26.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than the individuals with no limitations. Other risk factors like increased participation in open defectation practices (31.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) or living in damp housing condition (24.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were leading to higher prevalence of WRIDs among both disaster exposed or unexposed group. Utilization of untreated drinking water (31.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was significantly varied the prevelance of WRIDs along with the geographical belonging of disaster exposed individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Individuals belonging to plains region (36.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were suffering from higher burden of WRIDs compared to their counterparts living in other geographical regions (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Adjusted likelihoods of WRIDs by WRD exposure and other covariates\u003c/h2\u003e \u003cp\u003eThe Multivariate logistic regression model demonstrated acceptable discriminatory power, with an AUC of 0.73 (Figure S2). The study outcome suggests that exposure to disaster increase the risk of WRIDs. Individuals who were exposed to WRDs were 28% (AOR: 1.28, 95% CI: 1.22, 1.35) more likely to report WRIDs than their counterpart who were not exposed to disaster (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Education and socio-economic richness act as protective factors of WRIDs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For instance, tribals and economically poor older-adults were more likely to suffer with WRIDs than their counterparts. Contrary, having a history of multiple chronic illnesses and multiple ADL limitations found as risk factors of WRIDs. Individuals having a history of more than two chronic illnesses being at a 25% (AOR: 1.25, 95% CI: 1.15, 1.36) higher risk of WRIDs. Older adults bearing the two or more ADL limitations were at 23% (AOR: 1.23, 95% CI: 1.14, 1.33) higher risk of WRIDs compared to their counterparts without any ADL limitation. Lack of healthy public health practice like using untreated drinking water, practicing open defecation, or residing in damp housing conditions were significant predictors of the WRIDs. Individuals utilizing untreated drinking water were 12% (AOR: 1.12, 95% CI: 1.07, 1.17) more likely to report WRIDs compared to their counterparts using treated drinking water. The adjusted likelihood of WRIDs was 40% (AOR: 1.40, 95% CI: 1.34, 1.46) more likely among the individual who engaged in open defecation practices compared to their counterparts practicing safe defecation. Individuals belonging to the urban areas have a 34% (AOR: 0.66, 95% CI: 0.63, 0.69) lower risk of WRIDs compared to their counterparts from rural areas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnadjusted and adjusted Odds ratio of WRIDs among older-adults aged 45 and above, India, LASI (2017-18)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory (Reference)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnadjusted Odds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted Odds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisaster Exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54*** (1.46, 1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28*** (1.22, 1.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;54 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07** (1.03, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.95, 1.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10*** (1.05, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.95, 1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11** (1.04, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.90, 1.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of Residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51*** (0.49, 0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66*** (0.63, 0.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYear of Schooling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo schooling (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79*** (0.75, 0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91** (0.86, 0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;8 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75*** (0.71, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90*** (0.85, 0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u0026ndash;12 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56*** (0.53, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78*** (0.73, 0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40*** (0.36, 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63*** (0.57, 0.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80*** (0.74, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73*** (0.68, 0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74*** (0.69, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76*** (0.72, 0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63*** (0.59, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75*** (0.70, 0.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic Illness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87*** (0.82, 0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19*** (1.14, 1.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70*** (0.62, 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19*** (1.12, 1.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;Two\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90* (0.82, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25*** (1.15, 1.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eADL Limitations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08*** (1.03, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19*** (1.11, 1.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.94, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.97, 1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;Two\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.90, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23*** (1.14, 1.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Quantile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07* (1.01, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11** (1.05, 1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87*** (0.82, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92** (0.87, 0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86*** (0.81, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.90, 1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80*** (0.76, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.90, 1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking Water Treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49*** (1.44, 1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12*** (1.07, 1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOpen Defecation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83*** (1.76, 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40*** (1.34, 1.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousing Dampness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26*** (1.20, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19*** (1.13, 1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographical Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArid (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.55*** (2.85, 10.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.89*** (2.50, 9.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.80*** (2.46, 9.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.54*** (2.32, 8.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHills/Mountains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.56*** (3.36, 12.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.04*** (3.09, 11.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsland/Coastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.83*** (4.02, 15.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.68** (3.42, 13.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNote: p-value *\u0026lt;0.05; **\u0026lt;0.01; ***0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe geographical belonging of individuals has emerged as a significant predictor of WRIDs, where individuals residing in coastal/island region has the 6.68-time higher odds (AOR: 6.68, 95% CI: 3.42, 13.07) of WRIDs compared to the individuals residing in the arid region (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study makes two key contributions to the growing body of evidence at the intersection of environmental change, population ageing, and public health in India. First, it systematically maps water-related disasters (WRDs) across an expanding ageing population, offering one of the few national-scale assessments focused specifically on older adults. Second, it examines the association between exposure to WRDs and WRIDs among older individuals, adjusting for a range of socio-demographic and public-health indicators. By incorporating regional variation in WRID vulnerability, the study extends current understanding of how environmental risks interact with demographic transitions, thereby enriching the broader climate and public-health literature.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCoi\u003c/strong\u003e \u003cp\u003encides with several previous global and national studies (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), the present study highlights that exposure to WRDs increases the risk of the WRIDs among the elderly population. This increases the risk of the dual burden of disease (communicable and non-communicable diseases), which further affects healthy ageing (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The reason behind this elevated risk is the water contamination caused by the onset of the WRDs, which further creates a favourable condition for the proliferation of the pathogens related to the WRIDs (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Disruption or overcrowding at the sanitation services is another important factor which increases the risk of WRIDs. Covariates such as place of residence, year of education, and social group belonging act as protective factors against the risk of WRIDs (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Individuals belonging to the urban area shown lower risk of WRIDs compared to individuals from rural areas may be due to variation in disaster frequency and intensity, and public health infrastructures (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Another reason for higher risk among the elderly from the rural areas is their dependency on other household members for daily life activities, which is affected due to the onset of any environmental extremes (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Similarly, with the increasing years of education, the risk of WRIDs because of the increasing awareness of hygiene practices and safe drinking water utilization (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Different social group belongings of the individuals compared to the ST social group, reduce the risk of acquisition of WRIDs. Higher risk of WRIDs among the ST social group could be because of lower accessibility of safe drinking water and sanitation. Low awareness level about the hygiene practice could be another factor that increases the risk of WRIDs among the ST population (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIndividuals having any history of chronic illness or suffering from any type of ADL were at higher risk of WRIDs. The reason behind this is that the physiological capacity of the individuals with a history of chronic illness is degraded, which weakens their immune system (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Similarly, the individuals with any type of ADL, whose mobility is already lower, face difficulty in accessing the clean drinking water and sanitation during the exposure of WRDs (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Using untreated drinking water, practicing open defecation, and living in damp housing conditions are positive risk factors associated with the acquisition of WRIDs, which are further exacerbated because of exposure to WRDs (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Individuals already using untreated drinking water further face compulsion to use contaminated drinking water, which increases the risk of acquisition of WRIDs. Individuals practicing open defecation or living in damp housing conditions face a higher risk of WRIDs, because these unhygienic practices create favourable condition for the proliferation of pathogens and vectors related to WRIDs (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Individuals living in island/coastal areas were also at higher risk of acquisition of WRIDs compared to other geographical belongings because of local climatic conditions (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Hot and humid conditions in coastal/island areas act as a supportive factor in outbreak of WRIDs (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). This existing condition further deteriorates due to frequent exposure to WRDs, reducing access to clean drinking water and sanitation practices (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study findings indicate a strong effect of WRDs exposure on the prevalence of WRIDs among the elderly population. However, the study carries some limitations also, like we have considered flood, cyclone, \u0026amp; tsunami as water-related disaster, which directly involve exposure to water as the main hazard component. However, other disasters like drought, earthquake, \u0026amp; landslide etc., though not classified as WRDs, can indirectly contribute to an elevated risk of WRIDs by affecting water availability, quality, and sanitation infrastructure (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). This increased burden of communicable diseases along with the non-communicable diseases affects the societal progress in achieving the sustainable development goal (SDG), which can be further worsened in the face of climate change (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). So, there is a need for targeted intervention where elderly populations belonging to marginalized sections (lower wealth quintile, scheduled tribe) of society should be given more attention in the condition of any WRDs exposure. Equitable access to clean drinking water and promoting hygienic practices among older populations can further help in reducing this burden and contribute to countries' progress in healthy aging and SDG-3.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAs the country is approaching the third phase of the demographic transition, the elderly population is increasingly facing a dual burden of diseases, where communicable and non-communicable conditions are rising simultaneously. The higher prevalence of WRIDs among disaster-exposed individuals shows that vulnerability among older adults increases with exposure to water-related disasters (WRDs). Frequent episodes of communicable diseases, such as vector-borne, water-borne, and viral infections, can further contribute to the aggravated burden of non-communicable diseases among the elderly by weakening immunity, increasing physiological stress, and worsening pre-existing chronic conditions. The study outcome highlights how the burden of infectious diseases continues to influence the overall health profile of older populations in changing climatic conditions. Existing programs such as the Integrated Disease Surveillance Programme (IDSP), the National Vector Borne Disease Control Programme (NVBDCP), National Viral Hepatitis Control Programme (NVHCP), etc., are designed to address the burden of communicable diseases. However, the situation is likely to become more challenging under changing climatic conditions through expanding risk of WRDs. The study findings highlight the need for strengthening current policies and making them more targeted and responsive to specific risk factors experienced by vulnerable groups, particularly the elderly. Implementing appropriate mitigation strategies and taking appropriate adaptation measures during and after WRD events can help reduce the disease burden and support healthy ageing. Strengthening surveillance, improving preparedness, and designing targeted interventions for high-risk populations can contribute to the country\u0026rsquo;s progress toward achieving Sustainable Development Goals (SDGs), particularly Goal 3 (Good Health and Well-being) and Goal 10 (Reduced Inequalities).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interest\u003c/h2\u003e \u003cp\u003eThe author declares that there are no financial or personal conflicts of interest associated with this study.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical Statement\u003c/h2\u003e \u003cp\u003eThis present study is based on secondary data of the Longitudinal Ageing Study, wave 1 (LASI-I) 2017-18, which is a nationally representative survey of adults aged 45 years and above. LASI was conducted by the International Institute for Population Sciences (IIPS), Mumbai, in collaboration with the Harvard T. H. Chan School of Public Health and the University of Southern California. Ethical approval for LASI was obtained from the IIPS Institutional Review Board (IRB) and the collaborating institutions. Since the present analysis uses publicly available, de-identified data, no additional ethical approval was required.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eInformed Consent\u003c/h2\u003e \u003cp\u003eThe dataset used for this study is fully anonymized and contains no personal identifiers. Written informed consent was obtained from all respondents prior to their participation in the LASI survey. For individuals with limited capacity to provide consent, proxy consent procedures were implemented in accordance with the guidelines approved by the Institutional Review Board (IRB). All participants were aged 45 years and above (along with their spouses, irrespective of age), and participation was voluntary.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical Trial Registration\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo external funding was received for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAY: Conceptualization, data analysis, and manuscript drafting; MJR: Study supervision, manuscript review and editing; MR: Data analysis, manuscript review and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNA\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe present study utilized secondary data from the Longitudinal Ageing Study in India (LASI) Wave 1 2017-18, which is collected and managed by the International Institute for Population Sciences (IIPS), Mumbai. The LASI dataset is accessible to researchers upon request. Interested individuals may obtain the data by submitting a brief description of the intended study purpose through the IIPS data access portal. Data requests can be submitted at: [https://www.iipsindia.ac.in/content/LASI-data](https:/www.iipsindia.ac.in/content/LASI-data)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEbi KL, Vanos J, Baldwin JW, Bell JE, Hondula DM, Errett NA, et al. Extreme Weather and Climate Change: Population Health and Health System Implications. Annu Rev Public Health. 2021;42:293\u0026ndash;315.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFelix KT, Balasubramanian M, Govindarajan PL, Kesav B. Assessing the socioeconomic and environmental determinants of flood vulnerability in India: a panel data approach. 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Infect Dis Ther. 2022;11(4):1371\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate change, Elderly, Environmental Extreme, Infectious Diseases, SDG-3","lastPublishedDoi":"10.21203/rs.3.rs-8263267/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8263267/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change has intensified the frequency and severity of water-related disasters (WRDs), including floods and cyclones, heightening the burden of water-related infectious diseases (WRIDs) among vulnerable populations. Older adults, who experience age-related frailty and high chronic disease burden, may face disproportionate risks; however, evidence linking WRD exposure to WRIDs in India remains sparse. This study assessed the association between WRD exposure and WRID prevalence among older adults. The study analysed nationally representative data from 72,250 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years from the Longitudinal Ageing Study in India (LASI) Wave 1. Descriptive statistics, Pearson\u0026rsquo;s chi-square tests, multivariable logistic regression, and spatial analyses were performed. Overall, 21.3% of older adults reported at least one WRID, with higher prevalence in WRD-exposed individuals (28.9%). WRD exposure was significantly associated with increased WRID risk (AOR 1.28; 95% CI 1.22\u0026ndash;1.35). Coastal/island regions showing markedly higher odds of WRIDs (AOR 6.68; 95% CI 3.42\u0026ndash;13.07) than arid counterparts. Unsafe water, sanitation and hygeine (WASH) practices significantly linked with WRIDs (AOR 1.12; 95% CI 1.07\u0026ndash;1.17). Tribal and poorest households were more likey vulnerable to WRIDs. Chronic illnesses, Activities of Daily Living (ADL) limitations, and open defecation practice (AOR 1.40; 95% CI 1.34\u0026ndash;1.46) were risk factors of WRIDs. Urban residence showing lower likelihood of WRIDs (AOR 0.66; 95% CI 0.63\u0026ndash;0.69) than rural counterparts. WRD exposure substantially increases WRID vulnerability. Strengthening climate-resilient WASH systems and integrating disaster-sensitive health strategies into geriatric care are essential to mitigate disease risks and advance progress toward SDG 3 targets.\u003c/p\u003e","manuscriptTitle":"Impact of Water Related Disasters on Water Related Infectious Disease Risk among Older Adults in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 12:25:11","doi":"10.21203/rs.3.rs-8263267/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-16T06:33:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-10T23:26:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87403212891577506271306392016750956030","date":"2026-01-06T07:16:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133168797381167350696244338767976833221","date":"2026-01-02T11:25:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-20T11:54:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226095719920845813917233155977000520907","date":"2025-12-20T11:11:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T10:38:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-11T15:32:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-09T18:13:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2025-12-09T18:08:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32e8c617-5e2b-426e-bf16-38c23cbbeb87","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:03:51+00:00","versionOfRecord":{"articleIdentity":"rs-8263267","link":"https://doi.org/10.1186/s12982-026-01947-6","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2026-04-20 16:00:03","publishedOnDateReadable":"April 20th, 2026"},"versionCreatedAt":"2025-12-16 12:25:11","video":"","vorDoi":"10.1186/s12982-026-01947-6","vorDoiUrl":"https://doi.org/10.1186/s12982-026-01947-6","workflowStages":[]},"version":"v1","identity":"rs-8263267","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8263267","identity":"rs-8263267","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}