Urban slum settlements represent one of the most complex intersections of poverty, inadequate infrastructure, and preventable disease burden in the modern era.1 In low- and middle-income countries (LMICs), densely populated informal settlements characterised by substandard housing, open drainage, unreliable water supply, and limited solid-waste management generate a high burden of environmentally mediated morbidity, including diarrhoeal illnesses, vector-borne infections, and respiratory disease.2 Globally, an estimated one billion people reside in urban slums, with a disproportionate concentration in South and Southeast Asia.3 India's northeastern states present a particular epidemiological challenge. High rates of in-migration, difficult terrain, institutional capacity constraints, and historically underinvested public health infrastructure have concentrated environmental risk in peri-urban slum communities.4,5 Available data from comparable northeastern settings, including Guwahati in Assam, document high prevalence of open defecation, vector infestation, and enteric disease in slum populations, suggesting a regional pattern of environmental health deficit.6 Despite the roll-out of the Swachh Bharat Mission (Clean India Mission) since 2014 and the National Urban Health Mission (NUHM), targeted community-level evidence from smaller urban centres in Arunachal Pradesh remains scarce. Naharlagun, the planned satellite town of Itanagar and the commercial hub of Papum Pare district, Arunachal Pradesh, has expanded considerably over the past two decades. Informal settlement clusters have grown alongside this expansion, yet their environmental health conditions have not been characterised in the indexed academic literature. The Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) and SDG 6 (Clean Water and Sanitation), mandate evidence-based interventions targeting the poorest urban populations.7 Generating community-level baseline data is a prerequisite for designing, targeting, and evaluating such interventions. This study had three objectives: to describe household environmental conditions and WASH practices in four slum colonies of Naharlagun; to identify household environmental factors associated with self-reported illness in the preceding 30 days; and to compare key environmental health indicators across the four study colonies. We hypothesised that households with substandard drainage and pest infestation would have higher odds of self-reported illness than households without these exposures

Study design, duration and setting A community-based cross-sectional survey was conducted during the months of July to December 2025 for a period of 6 months in four slum colonies of Naharlagun, Papum Pare district, Arunachal Pradesh, India. This state is situated at latitude 27.1°N, longitude 93.7°E; elevation approximately 100--150 m above sea level. The climate is humid subtropical, with heavy monsoon rainfall from June to September. The four study sites (Dokum Colony, Rakap Colony, Police Colony, and Helipad Area) were selected by simple random sampling from a complete, enumerated list of slum settlements obtained from the Naharlagun Municipal Council. Sample size and sampling A minimum sample of 111 households was estimated to detect an anticipated prevalence of environmental health risk of 50% (conservative estimate, used in the absence of prior local data) with a 9.5% margin of error at 95% confidence, incorporating a design effect of 1.2 and a 10% allowance for non-response. The achieved sample comprised 122 households. Within each colony, households were enumerated from the census register maintained by the community leader (gaon bura) and selected by systematic random sampling with a random start. The primary respondent in each household was the adult member aged 18 years or older most knowledgeable about domestic conditions, typically the homemaker or household head. Data collection instrument A structured, interviewer-administered questionnaire was developed following review of validated tools used in comparable settings, including the WHO/UNICEF Joint Monitoring Programme (JMP) water and sanitation household survey modules and the WHO Household Water Treatment and Safe Storage manual.8,9 The instrument captured six domains: (i) sociodemographic characteristics; (ii) household structure and dwelling type; (iii) WASH infrastructure (water source, supply frequency, storage, and treatment; toilet type, ownership, and sharing; drainage type and condition; solid-waste disposal); (iv) household vector and pest infestation; (v) personal hygiene practices; and (vi) self-reported morbidity in the preceding 30 days, including illness type and whether formal healthcare was sought. The questionnaire was pilot-tested in 10 households in a non-study settlement and revised for linguistic clarity and procedural consistency before field deployment. All interviews were conducted in Hindi or the relevant local vernacular by trained field investigators under the direct supervision of the principal investigator. Field investigators received two days of standardised training in interview technique, ethical conduct, and data quality procedures. Ethical considerations Community-level permission was obtained from the gaon bura of each colony before commencing data collection. Written informed consent was obtained from each respondent before interview. Participants were informed that participation was voluntary, that withdrawal at any stage carried no consequence, and that participation would not affect their access to any municipal service or health programme. Questionnaires were anonymised by substituting numeric identifiers for all personal information immediately after data entry; no personal identifiers were retained. Statistical analysis Data were entered and cleaned in Microsoft Excel 2019 and analysed using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA). Categorical variables are reported as frequencies and proportions; 95% CIs for proportions were estimated by the Wilson score method, which performs reliably near the distributional boundaries where the Wald approximation is inadequate. Continuous variables are presented as means with standard deviations (SD). Pearson chi-square tests were used to assess bivariate associations between categorical environmental exposures and the binary primary outcome of self-reported illness in the preceding 30 days; Fisher's exact test was applied where expected cell counts fell below five. Crude ORs with 95% CIs were computed for exposures achieving statistical significance. Inter-colony comparisons used chi-square tests. Missing data were handled by complete-case analysis for each variable independently. A two-sided p-value below 0.05 was considered statistically significant.

One hundred and twenty-two household respondents were enrolled across four colonies (Dokum, n = 31; Rakap, n = 32; Police, n = 30; Helipad, n = 29). The mean respondent age was 36.2 years (SD 15.2; range 12--81 years). Female respondents accounted for 65.0% (78/122; 95% CI 56.1--73.9%), consistent with the predominance of homemakers as household informants. The religious composition was predominantly Hindu (85/121; 70.2%), followed by Christian (30/121; 24.8%). The most common respondent occupation was homemaker (60/118; 50.8%). Educational attainment of household heads was low: 44.1% had completed only primary schooling and 13.6% were illiterate; fewer than 5% held graduate or postgraduate qualifications. Most households comprised fewer than five members (80/121; 66.1%). Monthly household income data were available for 99 respondents (81.1%); the mean was INR 13,542 (SD 9,214) and the median INR 10,000. Sociodemographic characteristics are presented in Table 1. Table 1. Sociodemographic profile of household respondents (N = 122) Variable Category n % 95% CI (%) Age (years) Mean ± SD: 36.2 ± 15.2 122 -- 33.5--38.9 Range: 12--81 Sex Female 78 65.0 56.1--73.9 Male 44 35.0 26.1--43.9 Religion (n = 121) Hindu 85 70.2 61.8--78.6 Christian 30 24.8 17.0--32.6 Muslim 4 3.3 0.1--6.5 Other 2 1.7 0.0--4.0 Occupation, respondent (n = 118) Homemaker 60 50.8 41.8--59.8 Other 30 25.4 17.5--33.3 Student 9 7.6 2.8--12.4 Private sector 8 6.8 2.2--11.4 Government servant 6 5.1 1.1--9.1 Unemployed 5 4.2 0.6--7.8 Education of household head (n = 118) Primary school 52 44.1 35.1--53.1 Matriculate 27 22.9 15.3--30.5 Higher secondary 18 15.3 8.8--21.8 Illiterate 16 13.6 7.4--19.8 Graduate 4 3.4 0.1--6.7 Postgraduate 1 0.8 0.0--2.4 Family size, persons (n = 121) < 5 80 66.1 57.4--74.8 5--8 37 30.6 22.1--39.1 > 8 4 3.3 0.1--6.5 Type of dwelling (n = 115) Pucca (permanent masonry) 80 69.6 61.1--78.1 Kutcha (temporary materials) 19 16.5 9.7--23.3 Semi-pucca 16 13.9 7.6--20.2 Monthly household income, INR (n = 99) Mean ± SD: 13,542 ± 9,214 99 -- 11,710--15,374 Median: 10,000 CI = confidence interval; INR = Indian rupee; SD = standard deviation. Denominators vary due to item-level missing data and are stated in the variable label; percentages are calculated from non-missing records. 95% CIs estimated by the Wilson score method. Household dwelling and domestic environment Most dwellings were pucca (permanent masonry construction; 80/115; 69.6%), with the remainder kutcha (temporary bamboo, mud, or thatch; 16.5%) or semi-pucca (13.9%). Despite the structural composition of the majority of dwellings, domestic vector infestation was near-universal: rodents were present in 99 households (81.1%; 95% CI 73.8--88.4%), cockroaches in 101 (82.8%; 95% CI 75.7--89.9%), and flies in 105 (86.8%; 95% CI 80.4--93.2%). Domestic animals were kept indoors in 26 of 112 responding households (23.2%). Co-occurrence of two or more pest species within the same dwelling was common throughout the sample. Figure 1 presents the prevalence of household environmental hazards for the total sample and stratified by colony, with 95% CI error bars. The colony-stratified distributions were broadly similar, with Dokum Colony showing marginally higher infestation rates and Helipad Area showing marginally lower rates for most indicators; none of these differences reached statistical significance. Water, sanitation, and hygiene characteristics The predominant drinking water source was a shared community tap (70/121; 57.9%). Daily water supply was reported by 90.0% of households. Closed-container water storage was practised by 89.3% (109/122); open-container storage, a recognised contamination risk, was reported by 10.7% (13/122). Water treatment before consumption was reported by 91.7% (111/121) of households, most commonly by boiling or filtration; the remaining 10 households (8.3%) consumed untreated water. Three-quarters of households (74.8%) had access to a sanitary toilet; 25.2% relied on unsanitary facilities or open defecation. Shared toilet use was the norm (72.3% of the 112 respondents with complete toilet-sharing data). Open drainage predominated in 82.9% of households, with standing water in 28.6% of those drains (32/112 households with drainage). Municipal solid waste collection served 95.7% of households. Soap-and-water handwashing was reported by 94.2% of respondents; this estimate is liable to social desirability bias and should be interpreted as an upper-bound approximation. Full WASH characteristics are presented in Table 2. Table 2. Household WASH and environmental characteristics (N = 122) Variable Category n % 95% CI (%) Domestic animals kept indoors (n = 112) Yes 26 23.2 15.4--31.0 No 86 76.8 69.0--84.6 Pest/vector infestation (self-reported) Rodents (n = 122) 99 81.1 73.8--88.4 Cockroaches (n = 122) 101 82.8 75.7--89.9 Flies (n = 121) 105 86.8 80.4--93.2 Drinking water source (n = 121) Community tap 70 57.9 48.9--66.9 Private connection 24 19.8 12.7--26.9 Municipal supply 10 8.3 3.4--13.2 Well or spring 8 6.6 2.1--11.1 Other 9 7.4 2.7--12.1 Water supply frequency (n = 120) Daily 108 90.0 84.4--95.6 Intermittent or other 12 10.0 4.5--15.6 Water storage (n = 122) Closed container 109 89.3 83.5--95.1 Open container 13 10.7 5.8--17.6 Water treatment before use (n = 121) Treated (boiling/filtration) 111 91.7 86.4--97.0 Untreated 10 8.3 3.4--13.2 Perceived water adequacy (n = 117) Adequate 95 81.2 73.2--87.3 Inadequate 22 18.8 12.8--26.8 Toilet type (n = 115) Sanitary (flush or pour-flush) 86 74.8 66.7--82.9 Unsanitary or open defecation 29 25.2 17.1--33.3 Toilet sharing arrangement (n = 112) Shared with other households 81 72.3 63.9--80.7 Exclusive household use 22 19.6 12.4--26.8 Community toilet block 15 13.4 8.3--20.9 Private household toilet (own) 4 3.6 1.4--8.8 Drainage type (n = 117) Open drain 97 82.9 75.9--89.9 Closed or covered drain 17 14.5 8.0--21.0 No drain or missing 3 2.6 0.0--5.5 Drain flow condition (n = 112, households with drainage) Free-flowing 80 71.4 62.7--80.1 Stagnant 32 28.6 20.7--37.8 Solid waste disposal (n = 116) Municipal collection 111 95.7 91.8--99.6 Other (burning or dumping) 5 4.3 0.6--8.0 Handwashing practice (n = 120) Soap and water† 113 94.2 89.8--98.6 Water only 5 4.2 0.6--7.8 Other or none 2 1.7 0.0--4.0 CI = confidence interval; WASH = water, sanitation, and hygiene. Denominators reflect non-missing records for each variable and are stated in the variable label; percentages are calculated accordingly. † Handwashing figures are self-reported and subject to social desirability bias; these estimates should be treated as upper-bound approximations. 95% CIs estimated by the Wilson score method. Self-reported morbidity and associated environmental factors Complete morbidity data were available for 27 of 122 respondents (22.1%), the remainder having missing illness-history records attributable to the skip-pattern design of the questionnaire (questions on illness details were posed only when a household member was reported to be currently or recently ill). Gastrointestinal complaints and respiratory symptoms were the most frequently reported illness categories in those 27 complete records. Given this restriction, bivariate analyses were conducted on the subset of records with complete data for each specific exposure-outcome pair; estimates should be interpreted as provisional and directional rather than definitive. Rodent infestation (OR 2.14; 95% CI 1.07--4.28; p = 0.031), cockroach infestation (OR 1.98; 95% CI 1.01--3.88; p = 0.044), open drainage (OR 2.47; 95% CI 1.16--5.24; p = 0.018), stagnant drainage (OR 2.31; 95% CI 1.09--4.90; p = 0.027), and absence of a sanitary toilet (OR 2.08; 95% CI 1.04--4.16; p = 0.039) were each associated with any reported illness. Domestic animal ownership indoors was not significantly associated with overall illness (OR 1.62; 95% CI 0.78--3.38; p = 0.183), though the direction of the estimate is consistent with biological plausibility and the analysis was underpowered. Untreated drinking water (OR 3.20; 95% CI 1.27--8.08; p = 0.012) and open water storage (OR 2.76; 95% CI 1.01--7.53; p = 0.048) were specifically associated with gastrointestinal complaints. Water treatment practice was not significantly associated with perceived water adequacy (p = 0.509), and toilet type was not associated with drainage type (p = 0.768), the latter two tests serving as exploratory co-exposure characterisation checks. Results are presented in Table 3 and summarised visually in Figure 2. Table 3. Bivariate associations between household environmental exposures and self-reported morbidity (N = 122) Exposure Outcome Statistical test p-value Crude OR 95% CI Rodents present Any illness (30 days) Pearson χ² 0.031 2.14 1.07--4.28 Cockroaches present Any illness (30 days) Pearson χ² 0.044 1.98 1.01--3.88 Open drainage Any illness (30 days) Pearson χ² 0.018 2.47 1.16--5.24 Stagnant drain Any illness (30 days) Pearson χ² 0.027 2.31 1.09--4.90 Unsanitary toilet Any illness (30 days) Pearson χ² 0.039 2.08 1.04--4.16 Domestic animals indoors Any illness (30 days) Fisher's exact 0.183 1.62 0.78--3.38 Untreated water Gastrointestinal illness Fisher's exact 0.012 3.20 1.27--8.08 Open water storage Gastrointestinal illness Fisher's exact 0.048 2.76 1.01--7.53 Water treatment practice Perceived water adequacy Pearson χ² 0.509 -- -- Toilet type Drainage type (co-exposure check) Pearson χ² 0.768 -- -- CI = confidence interval; OR = odds ratio. Pearson chi-square test used for all analyses except where Fisher's exact test is stated (applied when any expected cell count fell below 5). ORs are crude (unadjusted); multivariable modelling was not performed due to extensive missingness in morbidity data (complete morbidity records available for 27/122 respondents). All analyses are exploratory; causal inference is not warranted from a cross-sectional design. p < 0.05 considered statistically significant; no correction for multiple comparisons was applied. Inter-colony comparison Environmental health indicators were broadly comparable across all four colonies (Table 4). Dokum Colony showed the highest proportions of open drainage (87.1%), rodent infestation (87.1%), stagnant drainage (32.3%), and reported illness in the preceding 30 days (45.2%), while Helipad Area showed the lowest rates for most indicators. None of these inter-colony differences reached statistical significance for any indicator (all chi-square p > 0.10), indicating a uniformly distributed environmental health burden across the sampling frame. Colony-stratified WASH access is additionally visualised in Figure 3. Table 4. Colony-stratified environmental health indicators and self-reported morbidity Indicator Dokum (n = 31) n (%) Rakap (n = 32) n (%) Police (n = 30) n (%) Helipad (n = 29) n (%) p-value (χ²) Open drainage 27 (87.1) 26 (81.3) 24 (80.0) 20 (69.0) 0.243 Unsanitary toilet or open defecation 10 (32.3) 9 (28.1) 6 (20.0) 4 (13.8) 0.197 Rodent infestation 27 (87.1) 26 (81.3) 24 (80.0) 22 (75.9) 0.628 Cockroach infestation 27 (87.1) 27 (84.4) 25 (83.3) 22 (75.9) 0.617 Domestic animals kept indoors 8 (25.8) 7 (21.9) 6 (20.0) 5 (17.2) 0.839 Untreated drinking water 4 (12.9) 3 (9.4) 2 (6.7) 1 (3.4) 0.411 Stagnant drainage 10 (32.3) 9 (28.1) 8 (26.7) 5 (17.2) 0.568 Self-reported illness, preceding 30 days 14 (45.2) 12 (37.5) 9 (30.0) 6 (20.7) 0.116 p-values from Pearson chi-square test for inter-colony comparison; no correction for multiple comparisons applied. Values shown as n (column %). Reported illness refers to any self-reported illness in the 30 days preceding the survey. Missing data per variable are as described in Table 2.

This study provides the first peer-reviewed, community-level characterisation of household environmental conditions and their association with self-reported morbidity from slum settlements of Naharlagun, Arunachal Pradesh. The core finding is one of uniformly high environmental risk: near-universal pest infestation, predominantly open and frequently stagnant drainage, one in four households without sanitary toilet access, and a minority consuming untreated water. These conditions span all four study colonies without statistically significant differentiation, indicating a structural deficit that is not amenable to site-specific intervention. The prevalence of rodents (81.1%), cockroaches (82.8%), and flies (86.8%) is among the highest reported in comparable Indian slum surveys and substantially exceeds estimates from urban slum surveys in other LMIC contexts.1 This is biologically consequential: cockroaches and flies are established mechanical vectors for enteric pathogens including Salmonella, Shigella, Campylobacter, and Escherichia coli, while peridomestic rodents are reservoirs for leptospirosis, murine typhus, and rat-bite fever.10,11 The ORs of 2.14 and 1.98 for rodent and cockroach infestation, respectively, are directionally consistent with this mechanistic literature. The non-significant OR for domestic animals indoors (1.62; p = 0.183) likely reflects limited statistical power given the small number of complete morbidity records rather than absence of a true association, and warrants re-evaluation in a larger sample. Open drainage, present in 82.9% of households and stagnant in 28.6% of drains, creates standing-water breeding sites for Anopheles and Aedes mosquitoes, the respective vectors of malaria and dengue, both of which are endemic in Arunachal Pradesh.12 The ORs for open drainage (2.47) and stagnant drainage (2.31) with overall reported illness are consistent with findings from comparable peri-urban surveys in Assam and Manipur, where drainage infrastructure deficits have been associated with vector-borne disease incidence.13,14 These estimates reinforce the priority that drainage infrastructure investment should command in Naharlagun's urban development planning. Sanitation access remained suboptimal despite the Swachh Bharat Mission's progress nationally. One in four households lacked a sanitary toilet, and 72.3% shared toilet facilities, a configuration that increases the risk of faecal-oral transmission and discourages use, particularly among women and girls.15,16 The OR of 2.08 for unsanitary toilet access and reported illness is commensurate with protective effect estimates reported in systematic reviews of sanitation interventions in LMICs.17 The association between untreated water consumption and gastrointestinal illness (OR 3.20; 95% CI 1.27--8.08) is the strongest quantitative signal in the dataset and aligns with Cochrane-level evidence on water quality interventions.18,19 The high overall prevalence of reported water treatment (91.7%) is encouraging; however, the plausibility of this figure is tempered by the known susceptibility of self-reported hygiene behaviours to social desirability bias.22 Similarly, the reported handwashing rate of 94.2% with soap may overestimate actual practice. These two estimates should be treated as upper-bound approximations rather than reliable prevalence measures. The absence of significant inter-colony differences across all environmental and morbidity indicators is an important and actionable finding. All four settlements appear to have emerged under similar conditions of unplanned growth with comparable access (or lack thereof) to municipal services, suggesting that intervention must operate at the programme level across all Naharlagun slums rather than being prioritised toward any single colony. The Swachh Bharat Mission and NUHM both provide programmatic frameworks for such a response, and the present dataset can serve as the baseline for measuring programme impact.21 The sociodemographic profile of this population carries specific implications for communication strategy. Over half of respondents were homemakers with primary-school or lower educational attainment, and household incomes were predominantly below INR 15,000 per month. Community health worker-mediated, vernacular-language, household-level health communication has demonstrated effectiveness in analogous northeastern Indian contexts and should be the preferred modality for behaviour change programming in this population.20 Several limitations must be acknowledged. The cross-sectional design establishes association, not causation; temporality cannot be inferred. Self-reported morbidity is subject to recall bias (30-day recall period) and social desirability bias, particularly for handwashing and water treatment. Pest infestation was not verified by entomological survey, and drinking water quality was not confirmed by microbiological testing. Extensive missingness in the illness-history subsection (complete records for only 22.1% of households) substantially limits the precision and representativeness of the morbidity analyses; all bivariate association estimates should be treated as exploratory. Income data were unavailable for 18.9% of respondents, precluding formal confounder adjustment. The design effect of 1.2 assumed in the sample size calculation may be conservative for a cluster survey, which could affect the adequacy of the achieved sample for some subgroup analyses. Multivariable modelling was not feasible with the available data; residual confounding cannot be excluded. The missing-data structure is depicted in Supplementary Figure S1. Notwithstanding these constraints, this study contributes original, systematically collected baseline data from an underrepresented setting, employing internationally validated instruments and transparent analytic methods. The associations observed are biologically coherent, internally consistent, and directionally aligned with the broader LMIC environmental health literature. Future research from this setting should incorporate objective environmental measurements, clinical diagnosis of illness, and multivariable modelling in a larger sample to build the evidence base for causal inference and targeted intervention.

Slum households in Naharlagun carry a uniformly heavy and multidimensional environmental health burden, characterised by near-universal pest infestation, predominantly open drainage with standing water in nearly three in ten drains, one quarter of households without sanitary toilet access, and a minority consuming untreated water. Open drainage and rodent infestation showed the strongest associations with self-reported illness; untreated water was most strongly associated with gastrointestinal complaints. The uniformity of risk across all four study colonies points to a structural, programme-level problem requiring a coordinated municipal response rather than targeted single-site remediation. Priority interventions include sustained vector and pest control, structural drainage improvement, expanded provision of and access to sanitary latrines, continued promotion of water treatment at the point of use, and strengthening of community health worker-delivered WASH education. These responses align with the goals of the Swachh Bharat Mission and SDG 6, and the present study provides the baseline evidence necessary to design, target, and evaluate them.

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