Environmental determinants and child morbidity across three informal settlements in Mumbai: a comparative cross-sectional study

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We compared environmental exposures and child morbidity across three informal settlements in Mumbai with distinct urban contexts: Gautam Nagar, Dharavi, and Govandi. A comparative cross-sectional survey was conducted between January and December 2024 among 500 households with an index child aged 0–8 years. Caregivers reported morbidity in the preceding six months, and trained fieldworkers collected anthropometric measurements to assess nutritional status. Multivariable regression models adjusted for child age, sex, household size, and caregiver education, with outcome-specific exposures included where appropriate. Severe sanitation deficits and widespread perception of poor water quality were observed across all settlements, while key hazards differed by location, including near-universal asbestos roofing in Gautam Nagar and landfill proximity in Govandi. Compared with Gautam Nagar, children in Govandi had higher adjusted prevalence of pneumonia and stunting and lower prevalence of reported waterborne disease. Nutritional indicators were also poorest in Govandi. These findings demonstrate marked intra-urban heterogeneity in environmental risk and child morbidity within a single metropolitan system. Urban health strategies that treat informal settlements as homogeneous units may overlook important spatially differentiated vulnerabilities. Figures Figure 1 Figure 2 Figure 3 Introduction Rapid urbanisation has produced extensive informal settlements across South Asian cities, where structural deficits in water, sanitation, housing and air quality contribute to disproportionate child morbidity and mortality [ 1 , 2 ]. India hosts nearly 100 million slum residents despite substantial national development gains [ 3 ]. In Mumbai, informal settlements house a large share of the population yet remain underserved by municipal systems and exposed to cumulative environmental hazards [ 4 ]. Children are particularly vulnerable owing to developmental susceptibility, immature immune systems and higher exposure relative to adults [ 5 , 6 ]. Environmental determinants account for a substantial share of child disease burden in slum communities, especially waterborne diseases and respiratory infections [ 7 – 9 ]. However, most studies examine single settlements using descriptive methods, limiting comparability and obscuring differences within cities [ 10 ]. Even within the same urban system, environmental conditions vary by geography, infrastructure, housing materials and density [ 11 , 12 ]. Such intra-urban heterogeneity may produce distinct morbidity patterns, challenging the assumption that slum populations face uniform risks. From an urban health perspective, informal settlements are not merely spaces of material deprivation but socially and politically produced environments shaped by land tenure regimes, infrastructure allocation, environmental zoning and municipal governance. Concepts from urban political ecology and structural violence suggest that spatially differentiated exposure to hazards reflects underlying patterns of power and exclusion. Examining variation across settlements within a single city therefore provides insight into how structural urban arrangements translate into uneven child health risks. A further limitation is the predominance of bivariate analysis without adjustment for confounding [ 13 ]. Multivariable approaches are needed to better assess associations between settlement context and child health. Mumbai provides a useful setting for comparison. Gautam Nagar is characterised by dense housing and extensive asbestos roofing near the airport. Dharavi is a long-established, high-density settlement with complex water and sanitation arrangements [ 14 ]. Govandi borders the Deonar landfill and is exposed to waste-related atmospheric pollution [ 15 ]. These settlements also differ socially and economically, dimensions that may shape health outcomes. This study compares environmental exposures and child morbidity across these three settlements using standardised data collection and multivariable analysis. We aim to assess whether settlement of residence is associated with distinct child health patterns after adjustment for available demographic confounders, recognising that causation cannot be inferred and that unmeasured factors may contribute to observed differences. Methods Study design and setting This comparative cross-sectional study examined environmental determinants and child morbidity across three informal settlements in Mumbai selected to represent contrasting environmental exposure profiles: Gautam Nagar (near the international airport in Andheri East), Dharavi (centrally located between the Western and Central railway lines), and Govandi (adjacent to the Deonar municipal landfill in the eastern periphery). The settlements were chosen to permit comparison of distinct environmental contexts within the same metropolitan area. Study population and sampling Households with at least one child aged 0–8 years were eligible. Within each settlement, systematic random sampling was conducted along mapped residential corridors within predefined geographic zones. Every 5th household in Gautam Nagar and every 7th household in Dharavi and Govandi was approached, reflecting differences in settlement density. Random starting points were generated. Where a selected household had no eligible child or declined participation, the next eligible household was approached. If more than one eligible child was present, the youngest was designated the index child. A total of 500 households were enrolled (Gautam Nagar n = 170; Dharavi n = 165; Govandi n = 165). Household income and detailed asset indices were not collected due to concerns about respondent sensitivity and data reliability in settings facing eviction threats. As a result, settlement of residence likely captures both environmental and unmeasured socioeconomic differences. Findings should therefore be interpreted as reflecting contextual settlement-level variation rather than isolated environmental exposures. Data collection procedures Between January and December 2024, trained field workers administered structured questionnaires to primary caregivers. Interviews assessed household demographics, housing structure and materials, water access and treatment, sanitation, indoor smoking exposure, and child morbidity in the preceding six months. Interviews were conducted in Hindi or Marathi. Morbidity outcome definitions Waterborne disease (composite) included caregiver-reported diarrhoea, typhoid, or other illness attributed to contaminated water within six months. Diarrhoea was defined as three or more loose stools in 24 hours. Pneumonia and typhoid were based on caregiver recall of practitioner diagnosis. Prolonged cough was defined as cough lasting at least two weeks. Fever referred to any reported episode. Anthropometric measurements Weight and length or height were measured using calibrated equipment according to WHO standards. Height-for-age z-scores (HAZ) were calculated using WHO growth references; stunting was defined as HAZ < − 2 SD. Body mass index (BMI) and mid-upper arm circumference (MUAC) were also recorded. Statistical analysis Descriptive statistics summarised exposures and outcomes by settlement. Multivariable logistic regression estimated adjusted odds ratios for waterborne disease, pneumonia, and stunting, controlling for child age, sex, household size, and caregiver education, with outcome-specific exposures included where appropriate. Modified Poisson regression with robust standard errors generated adjusted prevalence ratios for common outcomes. Multivariable linear regression assessed associations between settlement and continuous nutritional indicators. Statistical significance was set at p < 0.05. Given that only three settlement-level clusters were included, multilevel modeling was not statistically appropriate. Settlement was therefore modeled as a fixed categorical exposure. Robust standard errors were used in modified Poisson regression to mitigate potential variance underestimation. Ethical considerations The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of the Albert-Ludwigs-Universität Freiburg, Germany (Reference: 2019/0521). Written informed consent was obtained from all participating caregivers, and parents or legal guardians provided consent for children under 18 years. All data were anonymised prior to analysis. Results Study population characteristics A total of 500 households were surveyed across the three settlements (Table 1 ). The sample comprised 267 male (53.4%) and 233 female (46.6%) index children. Mean child age was comparable across sites: 4.16 years (SD 2.1) in Gautam Nagar, 4.26 years (SD 2.0) in Dharavi and 4.09 years (SD 2.2) in Govandi (p = 0.78). The sex distribution did not differ significantly between settlements (p = 0.64). Mean household size ranged from 5.45 members in Dharavi to 5.67 in Govandi (p = 0.52). Caregiver education levels were broadly comparable, though a somewhat higher proportion of caregivers in Dharavi reported secondary education completion. Table 1 Demographic, housing, environmental and WASH characteristics by settlement Variable Gautam Nagar (n = 170) Dharavi (n = 165) Govandi (n = 165) p-value Demographics Mean child age, years (SD) 4.16 (2.1) 4.26 (2.0) 4.09 (2.2) 0.78 Male sex, n (%) 93 (54.7) 85 (51.5) 89 (53.9) 0.84 Mean household size (SD) 5.53 (1.8) 5.45 (1.7) 5.67 (1.9) 0.52 Caregiver education, n (%) No formal education 48 (28.2) 37 (22.4) 52 (31.5) 0.16† Primary completed 74 (43.5) 68 (41.2) 72 (43.6) Secondary or above 48 (28.2) 60 (36.4) 41 (24.8) Housing characteristics Asbestos roofing, n (%) 164 (96.5) 19 (11.5) 7 (4.2) < 0.001 No separate kitchen, n (%) 152 (89.4) 125 (75.8) 138 (83.6) 0.003 No ventilation opening, n (%) 86 (50.6) 67 (40.6) 90 (54.5) 0.03 Home ownership, n (%)‡ 84 (49.4) 108 (65.5) 57 (34.5) < 0.001 Cooking fuel, n (%) LPG 139 (81.8) 145 (87.9) 134 (81.2) 0.31† Kerosene 22 (12.9) 14 (8.5) 21 (12.7) Wood/biomass/other 9 (5.3) 6 (3.6) 10 (6.1) Indoor environmental exposures Indoor smoking, n (%) 152 (89.4) 119 (72.1) 125 (75.8) < 0.001 Water and sanitation External water source, n (%) 111 (65.3) 92 (55.8) 111 (67.3) 0.07 Poor water quality perception, n (%) 163 (95.9) 155 (93.9) 159 (96.4) 0.49 Household water treatment, n (%) 62 (36.5) 75 (45.5) 35 (21.2) < 0.001 Treatment method§ Boiling 34 (54.8) 42 (56.0) 19 (54.3) 0.89† Cloth filtering 20 (32.3) 22 (29.3) 12 (34.3) Commercial purifier 8 (12.9) 11 (14.7) 4 (11.4) Uncovered water storage, n (%) 65 (38.2) 61 (37.0) 72 (43.6) 0.41 In-house toilet facility, n (%) 0 (0.0) 1 (0.6) 4 (2.4) 0.04 Regular waste collection, n (%) 43 (25.3) 52 (31.5) 38 (23.0) 0.18 SD, standard deviation; LPG, liquefied petroleum gas; WASH, water, sanitation and hygiene. p-values from Pearson chi-square test (categorical), Fisher’s exact test (expected cell count < 5) or one-way ANOVA (continuous). †p-value from chi-square test for overall distribution across categories. ‡Home ownership estimates may be imprecise owing to respondent reluctance to disclose tenure status in settlements facing eviction threats. §Percentages calculated among those reporting any water treatment (Gautam Nagar n = 62; Dharavi n = 75; Govandi n = 35). Housing characteristics and environmental exposures Housing structural characteristics differed markedly across settlements (Table 1 , Supplementary Figure S1 ). Gautam Nagar demonstrated the highest prevalence of asbestos-containing corrugated roofing at 96.5%, compared with 11.5% in Dharavi and 4.2% in Govandi (p < 0.001). The absence of a dedicated kitchen space was widespread: 89.4% of households in Gautam Nagar, 75.8% in Dharavi and 83.6% in Govandi lacked separate cooking areas (p = 0.003). Indoor smoking was highly prevalent across all settlements (p 80%). Ventilation was limited, with approximately 40–55% of dwellings lacking external openings. Home ownership was highest in Dharavi (estimated 65%), intermediate in Gautam Nagar (approximately 50%) and lowest in Govandi (approximately 35%). Water access and sanitation Water access patterns indicated structural deprivation across all three settlements (Table 1 ). External communal water sources were utilised by 65.3% of Gautam Nagar households, 55.8% in Dharavi and 67.3% in Govandi (p = 0.07). Perception of poor water quality exceeded 93% in all settlements (p = 0.49). Household water treatment was practised by 36.5% in Gautam Nagar, 45.5% in Dharavi and 21.2% in Govandi (p < 0.001). The predominant treatment method was boiling (58%), followed by cloth filtering (31%) and commercial water purifiers (11%). Water storage in uncovered containers was reported by approximately 40% of households across all settlements. Sanitation deprivation was near-universal. No household in Gautam Nagar possessed an in-house toilet facility, compared with 0.6% in Dharavi and 2.4% in Govandi. The overwhelming majority relied on shared community toilet blocks. Formal waste collection was reported as irregular or absent by more than 70% of respondents in all three settlements. Child health outcomes: descriptive analysis Morbidity patterns, based on caregiver recall over the preceding six months, reflected both shared and differential features (Table 2 , Supplementary Figure S2 ). Any waterborne disease (composite) was most frequently reported in Gautam Nagar (62.9%) and Dharavi (60.0%), and less commonly in Govandi (47.3%) (p = 0.008). Diarrhoea prevalence was similar across sites (32.7–35.8%, p = 0.82). Table 2 Child morbidity outcomes (preceding 6 months) by settlement Health outcome Gautam Nagar (n = 170) Dharavi (n = 165) Govandi (n = 165) p-value Any waterborne disease†, n (%) 107 (62.9) 99 (60.0) 78 (47.3) 0.008 Diarrhoea, n (%) 58 (34.1) 59 (35.8) 54 (32.7) 0.82 Typhoid fever, n (%) 35 (20.6) 30 (18.2) 25 (15.2) 0.41 Pneumonia‡, n (%) 19 (11.2) 20 (12.1) 38 (23.0) 0.003 Cough ≥ 2 weeks, n (%) 45 (26.5) 52 (31.5) 68 (41.2) 0.01 Fever (any), n (%) 118 (69.4) 99 (60.0) 104 (63.0) 0.15 p-values from chi-square test. †Composite of diarrhoea, typhoid and other caregiver-reported water-attributed illness. ‡Based on caregiver recall of health practitioner diagnosis. Respiratory morbidity demonstrated a different gradient. Caregiver-reported pneumonia prevalence was highest in Govandi at 23.0%, compared with 11.2% in Gautam Nagar and 12.1% in Dharavi (p = 0.003). Prolonged cough (≥ 2 weeks) followed a similar pattern (p = 0.01). Fever and typhoid were more evenly distributed. Nutritional status Nutritional indicators revealed substantial variation across settlements (Table 3 , Fig. 3). Govandi demonstrated the poorest nutritional profile, with a stunting prevalence of 54.6% compared with 37.0% in Dharavi and 28.8% in Gautam Nagar (p < 0.001). Mean BMI was lowest in Govandi (13.97 kg/m², SD 1.5) and highest in Gautam Nagar (15.10 kg/m², SD 1.8) (p < 0.001). Mean MUAC followed a gradient from 13.19 cm in Gautam Nagar to 12.57 cm in Govandi (p < 0.001). Table 3 Nutritional status indicators by settlement Indicator Gautam Nagar (n = 170) Dharavi (n = 165) Govandi (n = 165) p-value Mean BMI, kg/m² (SD) 15.10 (1.8) 14.42 (1.6) 13.97 (1.5) < 0.001 Mean MUAC, cm (SD) 13.19 (1.2) 13.12 (1.1) 12.57 (1.3) < 0.001 Stunting (HAZ < − 2 SD), n (%) 49 (28.8) 61 (37.0) 90 (54.6) < 0.001 BMI, body mass index; MUAC, mid-upper arm circumference; HAZ, height-for-age z-score; SD, standard deviation. p-values from chi-square test (categorical) or ANOVA (continuous). Multivariable logistic regression analysis Multivariable logistic regression demonstrated that settlement of residence remained associated with child health outcomes after adjustment for measured demographic and environmental covariates (Table 4 , Fig. 2), although residual confounding by unmeasured socioeconomic factors cannot be excluded. Table 4 Multivariable logistic regression: factors associated with child morbidity outcomes Variable Waterborne disease aOR (95% CI) Pneumonia aOR (95% CI) Stunting aOR (95% CI) Settlement Gautam Nagar 1.00 (ref) 1.00 (ref) 1.00 (ref) Dharavi 0.91 (0.57–1.46) 1.13 (0.57–2.23) 1.47 (0.92–2.35) Govandi 0.54 (0.34–0.87)* 2.38 (1.28–4.42)** 2.95 (1.86–4.68)*** Child age (years) 0.94 (0.85–1.04) 0.88 (0.77–1.01) 1.12 (1.01–1.24)* Male sex 1.18 (0.79–1.76) 1.42 (0.84–2.40) 1.08 (0.72–1.62) Household size 1.05 (0.94–1.18) 1.02 (0.89–1.17) 1.09 (0.97–1.22) Water treatment (yes) 0.62 (0.41–0.95)* — — Indoor smoking (yes) — 1.58 (0.86–2.89) — Waterborne disease (yes) — — 1.54 (1.02–2.33)* N 500 500 500 aOR, adjusted odds ratio; CI, confidence interval; ref, reference category. *p < 0.05, **p < 0.01, ***p < 0.001. Models additionally adjusted for caregiver education. — indicates variable not included in model. For the composite waterborne disease outcome, residence in Govandi was associated with lower odds compared with Gautam Nagar (aOR 0.54, 95% CI 0.34–0.87, p = 0.01). Dharavi did not differ significantly (aOR 0.91, 95% CI 0.57–1.46, p = 0.70). Children in Govandi had higher odds of caregiver-reported pneumonia compared with Gautam Nagar (aOR 2.38, 95% CI 1.28–4.42, p = 0.006). Dharavi did not differ significantly (aOR 1.13, 95% CI 0.57–2.23, p = 0.73). For stunting, residence in Govandi was associated with substantially elevated odds (aOR 2.95, 95% CI 1.86–4.68, p < 0.001). Dharavi showed a non-significant elevation (aOR 1.47, 95% CI 0.92–2.35, p = 0.11). Household water treatment was independently associated with reduced odds of waterborne disease (aOR 0.62, 95% CI 0.41–0.95), and waterborne disease was associated with increased odds of stunting (aOR 1.54, 95% CI 1.02–2.33). Prevalence ratios from modified Poisson regression Modified Poisson regression provided adjusted prevalence ratios as a more conservative alternative to odds ratios (Table 5 , Supplementary Figure S3 ). The adjusted prevalence ratio for pneumonia comparing Govandi to Gautam Nagar was 1.98 (95% CI 1.21–3.24), and for stunting 1.84 (95% CI 1.40–2.42). For waterborne disease, the aPR was 0.76 (95% CI 0.61–0.95). Table 5 Adjusted prevalence ratios for child health outcomes by settlement (modified Poisson regression) Settlement Waterborne disease aPR (95% CI) Pneumonia aPR (95% CI) Stunting aPR (95% CI) Gautam Nagar 1.00 (ref) 1.00 (ref) 1.00 (ref) Dharavi 0.96 (0.79–1.16) 1.08 (0.62–1.89) 1.27 (0.95–1.71) Govandi 0.76 (0.61–0.95)* 1.98 (1.21–3.24)** 1.84 (1.40–2.42)*** aPR, adjusted prevalence ratio; CI, confidence interval; ref, reference category. *p < 0.05, **p < 0.01, ***p < 0.001. Modified Poisson regression with robust standard errors. Models adjusted for child age, sex, household size, caregiver education and outcome-specific exposures. Linear regression for nutritional outcomes Multivariable linear regression confirmed associations between settlement and continuous nutritional indicators (Table 6 ). Compared with Gautam Nagar, residence in Govandi was associated with lower mean BMI (β − 1.13 kg/m², 95% CI − 1.52 to − 0.74, p < 0.001) and MUAC (β − 0.62 cm, 95% CI − 0.88 to − 0.36, p < 0.001). Dharavi showed intermediate BMI deficits (β − 0.68 kg/m², 95% CI − 1.07 to − 0.29) but no significant difference in MUAC. Models explained 15.2% and 11.8% of variance in BMI and MUAC. Table 6 Multivariable linear regression: nutritional status indicators Variable BMI (kg/m²) β (95% CI) MUAC (cm) β (95% CI) Settlement Gautam Nagar 0.00 (ref) 0.00 (ref) Dharavi −0.68 (− 1.07 to − 0.29)** −0.07 (− 0.33 to 0.19) Govandi −1.13 (− 1.52 to − 0.74)*** −0.62 (− 0.88 to − 0.36)*** Child age (years) 0.18 (0.08 to 0.28)** 0.22 (0.16 to 0.29)*** Male sex 0.12 (− 0.18 to 0.42) 0.08 (− 0.12 to 0.28) Waterborne disease (yes) −0.34 (− 0.64 to − 0.04)* −0.18 (− 0.38 to 0.02) N 500 500 Adjusted R² 0.152 0.118 BMI, body mass index; MUAC, mid-upper arm circumference; β, regression coefficient; CI, confidence interval; ref, reference category. *p < 0.05, **p < 0.01, ***p < 0.001. Models additionally adjusted for household size and caregiver education. Interaction and sensitivity analyses No significant interaction was detected between settlement and water treatment for waterborne disease (p = 0.34), nor between settlement and indoor smoking for pneumonia (p = 0.52). Stratified analyses by age group (0–2, 3–5, 6–8 years) demonstrated broadly consistent patterns, with Govandi showing elevated pneumonia and stunting risk across strata. The association between Govandi residence and pneumonia was strongest in children aged 0–2 years (aOR 3.12, 95% CI 1.24–7.85). Discussion Principal findings This comparative analysis demonstrates that environmental determinants of child health in Mumbai’s informal settlements exhibit both common structural deprivation and site-specific risk patterns associated with distinct morbidity outcomes. After adjustment for available demographic confounders, children in Govandi had approximately 2.4-fold higher odds of caregiver-reported pneumonia and 3-fold higher odds of stunting compared with Gautam Nagar, whilst showing lower odds of reported waterborne disease. These associations were consistent across logistic and modified Poisson regression approaches, lending confidence to the overall pattern. The lower reported prevalence of waterborne disease in Govandi despite poorer water treatment coverage warrants cautious interpretation. Possible explanations include differential caregiver recall, variation in health-seeking behaviour and diagnostic access, or competing morbidity patterns in which respiratory illness predominates. Under-reporting in more socioeconomically marginalised contexts cannot be excluded. These findings therefore should not be interpreted as evidence of superior water conditions in Govandi but rather as reflecting complex interactions between exposure, reporting and access to care. The central contribution of this study is to show that intra-city heterogeneity within informal settlements matters for intervention design. Although sanitation deficits and poor water access were pervasive across all three sites, the morbidity profiles were not uniform. Settlement of residence functioned not simply as a geographic label but as a marker of differentiated risk environments within the same urban system. Importantly, settlement should be understood as a composite structural exposure encompassing environmental conditions, housing quality, tenure security, infrastructure provision and socioeconomic stratification. The associations observed likely reflect this bundled contextual effect rather than isolated hazards such as landfill proximity or roofing material alone. Caution is warranted in interpreting these associations as evidence that specific environmental exposures—such as proximity to the Deonar landfill—are causally responsible. Settlement of residence is also correlated with socioeconomic position, housing precarity, dietary patterns, healthcare access and intergenerational deprivation. Our models adjusted for child age, sex, household size and caregiver education, but substantial unmeasured determinants remain, as reflected in the modest explained variance for nutritional outcomes. Comparison with existing literature Elevated child morbidity linked to infrastructural deficits has been documented in informal settlements in Nairobi, Lagos and Dhaka [ 18 – 21 ], supporting the view that slums function as micro-environmental risk zones rather than a uniform category of deprivation [ 22 ]. Few studies, however, have compared multiple settlements within the same city using multivariable methods. By holding the broader metropolitan context constant, this study highlights meaningful health variation within a single urban system. The elevated respiratory morbidity in Govandi is consistent with evidence from landfill-adjacent and combustion-associated environments [ 23 , 24 ]. The adjusted prevalence ratio of 1.98 for pneumonia aligns with findings from Accra and Delhi examining waste-site proximity and childhood respiratory conditions [ 25 , 26 ]. The stronger association in children aged 0–2 years is consistent with evidence that younger children are particularly susceptible to air pollution effects due to developmental vulnerability [ 27 ], although these subgroup estimates remain exploratory. The higher stunting prevalence in Govandi (54.6%) and adjusted odds ratio of 2.95 suggest chronic nutritional deficits that may reflect cumulative environmental and socioeconomic disadvantage. This pattern is potentially consistent with the environmental enteropathy hypothesis [ 28 , 29 ], and the observed association between waterborne disease and stunting (aOR 1.54) supports this pathway. At the same time, differential income, dietary quality or food insecurity may underlie these differences, and these were not directly measured. The near-universal asbestos roofing in Gautam Nagar is notable. Although no direct association with current child morbidity was observed, asbestos is classified as a Group 1 carcinogen with no safe exposure threshold [ 30 ], and childhood exposure carries elevated lifetime risk [ 31 ]. Documenting this exposure prevalence is itself relevant for urban environmental health policy. The protective association between household water treatment and waterborne disease (aOR 0.62) aligns with systematic review evidence on point-of-use water interventions [ 32 ]. Lower treatment rates in Govandi indicate potential for targeted WASH interventions, although cloth filtering does not constitute effective microbiological treatment. Strengths and limitations A key strength is the comparative design across three settlements within the same metropolitan area, allowing assessment of intra-urban variation while holding broader regional factors constant. The use of logistic regression, modified Poisson regression and linear regression provides convergent evidence, and standardised anthropometry strengthens the nutritional findings. Limitations include the cross-sectional design, reliance on caregiver-reported morbidity without clinical verification, particularly for pneumonia, which was based on caregiver recall of practitioner diagnosis and may therefore reflect healthcare access and diagnostic opportunity as much as underlying incidence. Absence of objective environmental measurements, and likely residual confounding by unmeasured socioeconomic variables. The hierarchical data structure was not modelled using multilevel methods due to the small number of settlement units. The purposive selection of settlements limits generalisability, and findings should be interpreted as hypothesis-generating. The study was not designed to provide representative estimates for all informal settlements in Mumbai but rather to compare three contrasting contexts within a single metropolitan system. Implications for policy and future research These findings caution against treating informal settlements as a monolithic category in urban health policy. While citywide improvements in water and sanitation remain essential, differentiated intervention strategies may be required where distinct environmental risk profiles are evident. In landfill-adjacent settlements such as Govandi, interventions may include strengthened air quality monitoring, mitigation of waste combustion exposure, and integration of respiratory screening into primary care outreach. In settlements with widespread asbestos roofing, environmental remediation and safe material replacement should be prioritised irrespective of short-term morbidity findings. Nutritional supplementation and food security initiatives may also require spatial targeting where chronic undernutrition is concentrated. In Govandi, the consistent pattern of elevated respiratory morbidity and stunting suggests the need for targeted air quality mitigation and nutritional support. In Gautam Nagar, widespread asbestos exposure warrants remediation independent of short-term morbidity findings. More broadly, this study adds to urban health research by demonstrating that intra-city comparison can reveal substantial heterogeneity masked by aggregate “slum” classifications. Future research should incorporate objective environmental monitoring, comprehensive socioeconomic assessment, and longitudinal designs to clarify causal pathways. Multilevel analytical approaches would further strengthen understanding of how neighbourhood-level conditions interact with household-level determinants. Conclusion This comparative analysis demonstrates substantial intra-urban heterogeneity in environmental exposures and child morbidity across three informal settlements within Mumbai. While structural deficits in water and sanitation were widespread, settlement of residence remained associated with differential respiratory morbidity and chronic undernutrition after adjustment for measured demographic factors. These findings underscore the importance of moving beyond aggregated “slum” classifications toward spatially disaggregated urban health assessment. Although causality cannot be inferred and residual socioeconomic confounding is likely, the observed patterns suggest that uniform urban health interventions may be insufficient without attention to settlement-specific environmental and structural conditions. Longitudinal studies incorporating objective environmental monitoring and detailed socioeconomic measurement are needed to clarify causal pathways and inform targeted policy responses. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of the Albert-Ludwigs-Universität Freiburg, Germany (Reference: 2019/0521). Written permission was obtained from the Municipal Corporation of Greater Mumbai’s public health department and community leaders. Written informed consent was obtained from all participating caregivers. Parents or guardians provided consent for participation of children under 18 years. Consent for publication All participants consented to the publication of anonymised data. No individually identifying information is presented. Availability of data and materials The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding This research was conducted as part of a Master’s degree programme at the University of Freiburg, Germany. No external funding was received. Authors’ contributions VK conceived and designed the study, collected and analysed the data, and drafted the manuscript. PAM provided supervision, guided data collection methods and contributed to manuscript revision and statistical analysis. Both authors read and approved the final manuscript. Acknowledgements The authors thank Mr Kiran Sonawane for thesis coordination and field support in India, Ms Vidya Bhalerao for community advocacy assistance, Dr Sonia Diaz Monsalve for programme coordination support, and Dr Peter Asaga for technical advice. We are grateful to the child leaders and community members in all three settlements for their participation and to the Urban Social Health Activists and Primary Health Centre staff who facilitated community access and provided health profile data. Generative AI Statement Generative artificial intelligence (AI) was used to assist in the creation of the graphic abstract for this manuscript. The AI tool was employed solely for visual illustration purposes. The authors reviewed, edited, and approved the final graphic to ensure accuracy and alignment with the study findings. No AI tools were used in the analysis, interpretation of data, or writing of the scientific content of this manuscript. References UN-Habitat. World Cities Report 2022: Envisaging the Future of Cities. United Nations Human Settlements Programme; 2022. Ezeh A, Oyebode O, Satterthwaite D, et al. The history, geography, and sociology of slums and the health problems of people who live in slums. 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Lancet. 2020;396(10258):1223–49. Lilford RJ, Oyebode O, Satterthwaite D, et al. Improving the health and welfare of people who live in slums. Lancet. 2017;389(10068):559–70. Snyder RE, Jaimes G, Riley LW, Faerstein E, Corburn J. A comparison of social and spatial determinants of health between formal and informal settlements in a large metropolitan setting in Brazil. J Urban Health. 2014;91(3):432–45. Weimann A, Oni T. A systematised review of the health impact of urban informal settlements and implications for upgrading interventions in South Africa, a rapidly urbanising middle-income country. Int J Environ Res Public Health. 2019;16(19):3608. Corburn J, Sverdlik A. Slum upgrading and health equity. Int J Environ Res Public Health. 2017;14(4):342. Fink G, Günther I, Hill K. The effect of water and sanitation on child health: evidence from the demographic and health surveys 1986–2007. Int J Epidemiol. 2011;40(5):1196–204. Nijman J. Against the odds: slum rehabilitation in neoliberal Mumbai. Cities. 2008;25(2):73–85. Deonar dumping ground. Mumbai’s growing toxic mountain. Econ Polit Wkly. 2016;51(8):7–8. WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards: Length/Height-for-Age, Weight-for-Age, Weight-for-Length, Weight-for-Height and Body Mass Index-for-Age: Methods and Development. World Health Organization; 2006. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702–6. Abuya BA, Ciera J, Kimani-Murage E. Effect of mother’s education on child’s nutritional status in the slums of Nairobi. BMC Pediatr. 2012;12:80. Adedokun ST, Uthman OA. Children who have received no routine polio vaccines in Nigeria: who are they and where do they live? Hum Vaccin Immunother. 2017;13(9):2111–22. Hasan MM, Saha KK, Yunus MF, et al. Prevalence of acute respiratory infections in children under five in Bangladesh: a nationally representative study. BMC Pediatr. 2022;22(1):533. Unger A. Children’s health in slum settings. Arch Dis Child. 2013;98(10):799–805. Riley LW, Ko AI, Unger A, Reis MG. Slum health: diseases of neglected populations. BMC Int Health Hum Rights. 2007;7:2. Nartey VK, Nii O, Hayford EK, Doamekpor LK, Appiah-Brempong E. Effects of proximity to landfill on respiratory health of residents in Accra, Ghana. Int J Environ Res Public Health. 2012;9(11):3939–46. Bates MN, Garrett N, Shoemack P. Investigation of health effects of hydrogen sulfide from a geothermal source. Arch Environ Health. 2002;57(5):405–11. Dhandapani HK, George S, Gopi KS, et al. Air pollution and respiratory health near dumpsites in urban India. Environ Geochem Health. 2018;40(5):2149–62. Mondal D, Paul P. Effects of indoor pollution on acute respiratory infections among under-five children in India: evidence from a nationally representative population-based study. PLoS ONE. 2020;15(8):e0237611. Schwartz J. Air pollution and children’s health. Pediatrics. 2004;113(suppl 4):1037–43. Owino V, Ahmed T, Freemark M, et al. Environmental enteric dysfunction and growth failure/stunting in global child health. Pediatrics. 2016;138(6):e20160641. Humphrey JH. Child undernutrition, tropical enteropathy, toilets, and handwashing. Lancet. 2009;374(9694):1032–5. International Agency for Research on Cancer. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, Volume 100C: Arsenic, Metals, Fibres, and Dusts. IARC; 2012. Reid A, de Klerk NH, Ambrosini GL, Berry G, Musk AW. The risk of lung cancer with increasing time since ceasing exposure to asbestos and quitting smoking. Occup Environ Med. 2006;63(8):509–12. Clasen T, Schmidt WP, Rabie T, Roberts I, Cairncross S. Interventions to improve water quality for preventing diarrhoea: systematic review and meta-analysis. Cochrane Database Syst Rev. 2007;(3):CD004794. Alirol E, Getaz L, Stoll B, Chappuis F, Loutan L. Urbanisation and infectious diseases in a globalised world. Lancet Infect Dis. 2011;11(2):131–41. Luby SP, Agboatwalla M, Feikin DR, et al. Effect of handwashing on child health: a randomised controlled trial. Lancet. 2005;366(9481):225–33. Sverdlik A. Ill-health and poverty: a literature review on health in informal settlements. Environ Urban. 2011;23(1):123–55. Turley R, Saith R, Bhan N, Rehfuess E, Carter B. Slum upgrading strategies involving physical environment and infrastructure interventions and their effects on health and socio-economic outcomes. Cochrane Database Syst Rev. 2013;(1):CD010067. Kjellstrom T, Friel S, Dixon J, et al. Urban environmental health hazards and health equity. J Urban Health. 2007;84(suppl 1):86–97. Supplementary Files GraphicAbstract.png SupplementaryFigurelegends.docx FigureS1EnvironmentalExposures.png FigureS2MorbidityOutcomes.png FigureS3PrevalenceRatios.png SupplementaryTableS1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 24 Feb, 2026 First submitted to journal 23 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8953863","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596510476,"identity":"d9780e1b-029c-4a29-bdd0-45eb01f27347","order_by":0,"name":"Asaga Peter","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYPACCTl5BuYDIIYMsVosjA0b2BJAWniI1VKR2HCAxwDEIqzFnP34w49faiQYG2fkfH51o8aCh4H98NEN+LRY9uQYS8sck2Bm5zm7zTrnGNBhPGlpN/BpMTiQwyAtwSbBxtjeu804hw2oRYLHDL+W888f/5b4B1R5mOeZcc4/YrTcSDCT/NgmIcFwvIf5cW4bEVosZ7wxs2bskzAw7DlmxpzbJ8HDRsgv5vzpj2/++FZXP18i+fHnnG91cvzsh4/hdxgQM0Pjgk0CTOJTDtPC+APCZv5ASPUoGAWjYBSMTAAAwJRGDStEsmkAAAAASUVORK5CYII=","orcid":"","institution":"Universitätsklinikum Freiburg: Albert-Ludwigs-Universitat Freiburg Universitatsklinikum Freiburg","correspondingAuthor":true,"prefix":"","firstName":"Asaga","middleName":"","lastName":"Peter","suffix":""},{"id":596510477,"identity":"fe8d5fb0-2fe1-4b13-b0dd-755eba091121","order_by":1,"name":"Vijeesh Kadukatti","email":"","orcid":"","institution":"Uniklinik RWTH Aachen: Universitatsklinikum Aachen","correspondingAuthor":false,"prefix":"","firstName":"Vijeesh","middleName":"","lastName":"Kadukatti","suffix":""},{"id":596510478,"identity":"e180eb6c-04df-416a-a109-7527661689e2","order_by":2,"name":"Axel Kroeger","email":"","orcid":"","institution":"Albert-Ludwigs-Universitat Freiburg Universitatsklinikum Freiburg","correspondingAuthor":false,"prefix":"","firstName":"Axel","middleName":"","lastName":"Kroeger","suffix":""}],"badges":[],"createdAt":"2026-02-24 07:11:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8953863/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8953863/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103599609,"identity":"31ea6279-ba88-4e11-9040-9ed29a17f61c","added_by":"auto","created_at":"2026-02-27 13:51:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":488407,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Mumbai showing the locations of the three study settlements: Gautam Nagar (Andheri East, western suburbs, near airport), Dharavi (central Mumbai, between Western and Central railway lines) and Govandi (eastern periphery, adjacent to Deonar landfill). Settlement boundaries are indicated in orange.\u003c/p\u003e","description":"","filename":"Figure1studysite.png","url":"https://assets-eu.researchsquare.com/files/rs-8953863/v1/cf59c439e8ca59686af83485.png"},{"id":104398978,"identity":"c45ee1a6-7dd4-47f4-a394-f74abf5a3e36","added_by":"auto","created_at":"2026-03-11 12:04:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":544721,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of adjusted odds ratios (aOR) with 95% confidence intervals for child health outcomes comparing Dharavi and Govandi to Gautam Nagar (reference). (A) Waterborne disease (composite). (B) Pneumonia (caregiver-reported practitioner diagnosis). (C) Stunting (HAZ \u0026lt;−2 SD). Models adjusted for child age, sex, household size, caregiver education and outcome-specific exposures. The dashed vertical line indicates OR = 1 (no association).\u003c/p\u003e","description":"","filename":"Figure2ForestPlot1.png","url":"https://assets-eu.researchsquare.com/files/rs-8953863/v1/7097f8a0cad4930be5da6d04.png"},{"id":103599604,"identity":"0c02dfd5-db93-47ed-86a5-c06637c96ea8","added_by":"auto","created_at":"2026-02-27 13:51:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":137878,"visible":true,"origin":"","legend":"\u003cp\u003eNutritional status indicators among children aged 0–8 years by settlement. (A) Mean body mass index (BMI, kg/m²) with 95% confidence intervals. (B) Stunting prevalence (height-for-age z-score \u0026lt;−2 SD) with 95% confidence intervals. (C) Mean mid-upper arm circumference (MUAC, cm) with 95% confidence intervals. p-values from one-way ANOVA (continuous variables) or chi-square test (stunting prevalence).\u003c/p\u003e","description":"","filename":"Figure3NutritionalIndicators.png","url":"https://assets-eu.researchsquare.com/files/rs-8953863/v1/d6d69d730bafa85cc81b5d11.png"},{"id":104407539,"identity":"d87c781c-521e-4efc-891e-4376ca54fd4b","added_by":"auto","created_at":"2026-03-11 12:38:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2418934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8953863/v1/c4c0f954-aac1-4d9d-8304-1debb3a614d0.pdf"},{"id":103599613,"identity":"c0404c21-243a-49b8-aff5-8bd0a3e7803c","added_by":"auto","created_at":"2026-02-27 13:51:34","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3081239,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-8953863/v1/1528fab58098b5d86b7c4884.png"},{"id":103599606,"identity":"fc9f47d0-5684-4be2-9133-bd46f02a2860","added_by":"auto","created_at":"2026-02-27 13:51:34","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14043,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigurelegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-8953863/v1/3add5c5dbc4a702f1d3a215f.docx"},{"id":103599625,"identity":"a3ef5d8c-0eed-4970-83b0-5b5e7d039713","added_by":"auto","created_at":"2026-02-27 13:51:34","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":113456,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1EnvironmentalExposures.png","url":"https://assets-eu.researchsquare.com/files/rs-8953863/v1/94de305fc418735dfa911112.png"},{"id":104399286,"identity":"dc7c3417-6b49-4c89-89b1-4307d0dfa003","added_by":"auto","created_at":"2026-03-11 12:05:23","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":122757,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2MorbidityOutcomes.png","url":"https://assets-eu.researchsquare.com/files/rs-8953863/v1/3c1ca7b2f94eba98aad2eee3.png"},{"id":103599619,"identity":"d719df68-b891-4b93-ba35-b0cb6a855224","added_by":"auto","created_at":"2026-02-27 13:51:34","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":180726,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3PrevalenceRatios.png","url":"https://assets-eu.researchsquare.com/files/rs-8953863/v1/fc1a77f03f75e41bfe0d5623.png"},{"id":103599626,"identity":"764d035a-b2d7-4ccb-b0e5-fc220103c100","added_by":"auto","created_at":"2026-02-27 13:51:34","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":25387,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8953863/v1/aee9a50a0bf105e350036379.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eEnvironmental determinants and child morbidity across three informal settlements in Mumbai: a comparative cross-sectional study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRapid urbanisation has produced extensive informal settlements across South Asian cities, where structural deficits in water, sanitation, housing and air quality contribute to disproportionate child morbidity and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. India hosts nearly 100\u0026nbsp;million slum residents despite substantial national development gains [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In Mumbai, informal settlements house a large share of the population yet remain underserved by municipal systems and exposed to cumulative environmental hazards [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Children are particularly vulnerable owing to developmental susceptibility, immature immune systems and higher exposure relative to adults [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEnvironmental determinants account for a substantial share of child disease burden in slum communities, especially waterborne diseases and respiratory infections [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, most studies examine single settlements using descriptive methods, limiting comparability and obscuring differences within cities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Even within the same urban system, environmental conditions vary by geography, infrastructure, housing materials and density [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Such intra-urban heterogeneity may produce distinct morbidity patterns, challenging the assumption that slum populations face uniform risks.\u003c/p\u003e \u003cp\u003eFrom an urban health perspective, informal settlements are not merely spaces of material deprivation but socially and politically produced environments shaped by land tenure regimes, infrastructure allocation, environmental zoning and municipal governance. Concepts from urban political ecology and structural violence suggest that spatially differentiated exposure to hazards reflects underlying patterns of power and exclusion. Examining variation across settlements within a single city therefore provides insight into how structural urban arrangements translate into uneven child health risks.\u003c/p\u003e \u003cp\u003eA further limitation is the predominance of bivariate analysis without adjustment for confounding [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Multivariable approaches are needed to better assess associations between settlement context and child health.\u003c/p\u003e \u003cp\u003eMumbai provides a useful setting for comparison. Gautam Nagar is characterised by dense housing and extensive asbestos roofing near the airport. Dharavi is a long-established, high-density settlement with complex water and sanitation arrangements [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Govandi borders the Deonar landfill and is exposed to waste-related atmospheric pollution [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These settlements also differ socially and economically, dimensions that may shape health outcomes.\u003c/p\u003e \u003cp\u003eThis study compares environmental exposures and child morbidity across these three settlements using standardised data collection and multivariable analysis. We aim to assess whether settlement of residence is associated with distinct child health patterns after adjustment for available demographic confounders, recognising that causation cannot be inferred and that unmeasured factors may contribute to observed differences.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis comparative cross-sectional study examined environmental determinants and child morbidity across three informal settlements in Mumbai selected to represent contrasting environmental exposure profiles: Gautam Nagar (near the international airport in Andheri East), Dharavi (centrally located between the Western and Central railway lines), and Govandi (adjacent to the Deonar municipal landfill in the eastern periphery). The settlements were chosen to permit comparison of distinct environmental contexts within the same metropolitan area.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population and sampling\u003c/h3\u003e\n\u003cp\u003eHouseholds with at least one child aged 0\u0026ndash;8 years were eligible. Within each settlement, systematic random sampling was conducted along mapped residential corridors within predefined geographic zones. Every 5th household in Gautam Nagar and every 7th household in Dharavi and Govandi was approached, reflecting differences in settlement density. Random starting points were generated. Where a selected household had no eligible child or declined participation, the next eligible household was approached. If more than one eligible child was present, the youngest was designated the index child. A total of 500 households were enrolled (Gautam Nagar n\u0026thinsp;=\u0026thinsp;170; Dharavi n\u0026thinsp;=\u0026thinsp;165; Govandi n\u0026thinsp;=\u0026thinsp;165).\u003c/p\u003e \u003cp\u003eHousehold income and detailed asset indices were not collected due to concerns about respondent sensitivity and data reliability in settings facing eviction threats. As a result, settlement of residence likely captures both environmental and unmeasured socioeconomic differences. Findings should therefore be interpreted as reflecting contextual settlement-level variation rather than isolated environmental exposures.\u003c/p\u003e\n\u003ch3\u003eData collection procedures\u003c/h3\u003e\n\u003cp\u003eBetween January and December 2024, trained field workers administered structured questionnaires to primary caregivers. Interviews assessed household demographics, housing structure and materials, water access and treatment, sanitation, indoor smoking exposure, and child morbidity in the preceding six months. Interviews were conducted in Hindi or Marathi.\u003c/p\u003e\n\u003ch3\u003eMorbidity outcome definitions\u003c/h3\u003e\n\u003cp\u003eWaterborne disease (composite) included caregiver-reported diarrhoea, typhoid, or other illness attributed to contaminated water within six months. Diarrhoea was defined as three or more loose stools in 24 hours. Pneumonia and typhoid were based on caregiver recall of practitioner diagnosis. Prolonged cough was defined as cough lasting at least two weeks. Fever referred to any reported episode.\u003c/p\u003e\n\u003ch3\u003eAnthropometric measurements\u003c/h3\u003e\n\u003cp\u003eWeight and length or height were measured using calibrated equipment according to WHO standards. Height-for-age z-scores (HAZ) were calculated using WHO growth references; stunting was defined as HAZ\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;2 SD. Body mass index (BMI) and mid-upper arm circumference (MUAC) were also recorded.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics summarised exposures and outcomes by settlement. Multivariable logistic regression estimated adjusted odds ratios for waterborne disease, pneumonia, and stunting, controlling for child age, sex, household size, and caregiver education, with outcome-specific exposures included where appropriate. Modified Poisson regression with robust standard errors generated adjusted prevalence ratios for common outcomes. Multivariable linear regression assessed associations between settlement and continuous nutritional indicators. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Given that only three settlement-level clusters were included, multilevel modeling was not statistically appropriate. Settlement was therefore modeled as a fixed categorical exposure. Robust standard errors were used in modified Poisson regression to mitigate potential variance underestimation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of the Albert-Ludwigs-Universit\u0026auml;t Freiburg, Germany (Reference: 2019/0521). Written informed consent was obtained from all participating caregivers, and parents or legal guardians provided consent for children under 18 years. All data were anonymised prior to analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy population characteristics\u003c/h2\u003e \u003cp\u003eA total of 500 households were surveyed across the three settlements (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The sample comprised 267 male (53.4%) and 233 female (46.6%) index children. Mean child age was comparable across sites: 4.16 years (SD 2.1) in Gautam Nagar, 4.26 years (SD 2.0) in Dharavi and 4.09 years (SD 2.2) in Govandi (p\u0026thinsp;=\u0026thinsp;0.78). The sex distribution did not differ significantly between settlements (p\u0026thinsp;=\u0026thinsp;0.64). Mean household size ranged from 5.45 members in Dharavi to 5.67 in Govandi (p\u0026thinsp;=\u0026thinsp;0.52). Caregiver education levels were broadly comparable, though a somewhat higher proportion of caregivers in Dharavi reported secondary education completion.\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\u003eDemographic, housing, environmental and WASH characteristics by settlement\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eGautam Nagar (n\u0026thinsp;=\u0026thinsp;170)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDharavi (n\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGovandi (n\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDemographics\u003c/em\u003e\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 \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean child age, years (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.16 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.26 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.09 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93 (54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85 (51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89 (53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean household size (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.53 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.45 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.67 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaregiver education, n (%)\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary completed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousing characteristics\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsbestos roofing, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo separate kitchen, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e152 (89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125 (75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138 (83.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo ventilation opening, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome ownership, n (%)\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108 (65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooking fuel, n (%)\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145 (87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134 (81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKerosene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWood/biomass/other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndoor environmental exposures\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndoor smoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e152 (89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125 (75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWater and sanitation\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal water source, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111 (65.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92 (55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111 (67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor water quality perception, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163 (95.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155 (93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e159 (96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold water treatment, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75 (45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment method\u0026sect;\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoiling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (56.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCloth filtering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial purifier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncovered water storage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-house toilet facility, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular waste collection, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eSD, standard deviation; LPG, liquefied petroleum gas; WASH, water, sanitation and hygiene.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003ep-values from Pearson chi-square test (categorical), Fisher\u0026rsquo;s exact test (expected cell count\u0026thinsp;\u0026lt;\u0026thinsp;5) or one-way ANOVA (continuous).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e\u0026dagger;p-value from chi-square test for overall distribution across categories.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e\u0026Dagger;Home ownership estimates may be imprecise owing to respondent reluctance to disclose tenure status in settlements facing eviction threats.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e\u0026sect;Percentages calculated among those reporting any water treatment (Gautam Nagar n\u0026thinsp;=\u0026thinsp;62; Dharavi n\u0026thinsp;=\u0026thinsp;75; Govandi n\u0026thinsp;=\u0026thinsp;35).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHousing characteristics and environmental exposures\u003c/h2\u003e \u003cp\u003eHousing structural characteristics differed markedly across settlements (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Gautam Nagar demonstrated the highest prevalence of asbestos-containing corrugated roofing at 96.5%, compared with 11.5% in Dharavi and 4.2% in Govandi (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The absence of a dedicated kitchen space was widespread: 89.4% of households in Gautam Nagar, 75.8% in Dharavi and 83.6% in Govandi lacked separate cooking areas (p\u0026thinsp;=\u0026thinsp;0.003). Indoor smoking was highly prevalent across all settlements (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Cooking fuel was predominantly liquefied petroleum gas in all three settlements (\u0026gt;\u0026thinsp;80%). Ventilation was limited, with approximately 40\u0026ndash;55% of dwellings lacking external openings. Home ownership was highest in Dharavi (estimated 65%), intermediate in Gautam Nagar (approximately 50%) and lowest in Govandi (approximately 35%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eWater access and sanitation\u003c/h2\u003e \u003cp\u003eWater access patterns indicated structural deprivation across all three settlements (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). External communal water sources were utilised by 65.3% of Gautam Nagar households, 55.8% in Dharavi and 67.3% in Govandi (p\u0026thinsp;=\u0026thinsp;0.07). Perception of poor water quality exceeded 93% in all settlements (p\u0026thinsp;=\u0026thinsp;0.49). Household water treatment was practised by 36.5% in Gautam Nagar, 45.5% in Dharavi and 21.2% in Govandi (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The predominant treatment method was boiling (58%), followed by cloth filtering (31%) and commercial water purifiers (11%). Water storage in uncovered containers was reported by approximately 40% of households across all settlements.\u003c/p\u003e \u003cp\u003eSanitation deprivation was near-universal. No household in Gautam Nagar possessed an in-house toilet facility, compared with 0.6% in Dharavi and 2.4% in Govandi. The overwhelming majority relied on shared community toilet blocks. Formal waste collection was reported as irregular or absent by more than 70% of respondents in all three settlements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eChild health outcomes: descriptive analysis\u003c/h2\u003e \u003cp\u003eMorbidity patterns, based on caregiver recall over the preceding six months, reflected both shared and differential features (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Any waterborne disease (composite) was most frequently reported in Gautam Nagar (62.9%) and Dharavi (60.0%), and less commonly in Govandi (47.3%) (p\u0026thinsp;=\u0026thinsp;0.008). Diarrhoea prevalence was similar across sites (32.7\u0026ndash;35.8%, p\u0026thinsp;=\u0026thinsp;0.82).\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\u003eChild morbidity outcomes (preceding 6 months) by settlement\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGautam Nagar (n\u0026thinsp;=\u0026thinsp;170)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDharavi (n\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGovandi (n\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny waterborne disease\u0026dagger;, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107 (62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78 (47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiarrhoea, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59 (35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyphoid fever, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePneumonia\u0026Dagger;, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u0026thinsp;\u0026ge;\u0026thinsp;2 weeks, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever (any), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118 (69.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003ep-values from chi-square test. \u0026dagger;Composite of diarrhoea, typhoid and other caregiver-reported water-attributed illness. \u0026Dagger;Based on caregiver recall of health practitioner diagnosis.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRespiratory morbidity demonstrated a different gradient. Caregiver-reported pneumonia prevalence was highest in Govandi at 23.0%, compared with 11.2% in Gautam Nagar and 12.1% in Dharavi (p\u0026thinsp;=\u0026thinsp;0.003). Prolonged cough (\u0026ge;\u0026thinsp;2 weeks) followed a similar pattern (p\u0026thinsp;=\u0026thinsp;0.01). Fever and typhoid were more evenly distributed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNutritional status\u003c/h2\u003e \u003cp\u003eNutritional indicators revealed substantial variation across settlements (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;3). Govandi demonstrated the poorest nutritional profile, with a stunting prevalence of 54.6% compared with 37.0% in Dharavi and 28.8% in Gautam Nagar (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mean BMI was lowest in Govandi (13.97 kg/m\u0026sup2;, SD 1.5) and highest in Gautam Nagar (15.10 kg/m\u0026sup2;, SD 1.8) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mean MUAC followed a gradient from 13.19 cm in Gautam Nagar to 12.57 cm in Govandi (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutritional status indicators by settlement\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGautam Nagar (n\u0026thinsp;=\u0026thinsp;170)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDharavi (n\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGovandi (n\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean BMI, kg/m\u0026sup2; (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.10 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.42 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.97 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean MUAC, cm (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.19 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.12 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.57 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStunting (HAZ\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;2 SD), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90 (54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eBMI, body mass index; MUAC, mid-upper arm circumference; HAZ, height-for-age z-score; SD, standard deviation. p-values from chi-square test (categorical) or ANOVA (continuous).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable logistic regression analysis\u003c/h2\u003e \u003cp\u003eMultivariable logistic regression demonstrated that settlement of residence remained associated with child health outcomes after adjustment for measured demographic and environmental covariates (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;2), although residual confounding by unmeasured socioeconomic factors cannot be excluded.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression: factors associated with child morbidity outcomes\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\u003eWaterborne disease aOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePneumonia aOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStunting aOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement\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 \u003cp\u003eGautam Nagar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDharavi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.57\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.57\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47 (0.92\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovandi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.34\u0026ndash;0.87)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.38 (1.28\u0026ndash;4.42)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.95 (1.86\u0026ndash;4.68)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.85\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.77\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (1.01\u0026ndash;1.24)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (0.79\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 (0.84\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.72\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (0.94\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.89\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.97\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater treatment (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62 (0.41\u0026ndash;0.95)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndoor smoking (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58 (0.86\u0026ndash;2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterborne disease (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54 (1.02\u0026ndash;2.33)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eaOR, adjusted odds ratio; CI, confidence interval; ref, reference category. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Models additionally adjusted for caregiver education. \u0026mdash; indicates variable not included in model.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the composite waterborne disease outcome, residence in Govandi was associated with lower odds compared with Gautam Nagar (aOR 0.54, 95% CI 0.34\u0026ndash;0.87, p\u0026thinsp;=\u0026thinsp;0.01). Dharavi did not differ significantly (aOR 0.91, 95% CI 0.57\u0026ndash;1.46, p\u0026thinsp;=\u0026thinsp;0.70).\u003c/p\u003e \u003cp\u003eChildren in Govandi had higher odds of caregiver-reported pneumonia compared with Gautam Nagar (aOR 2.38, 95% CI 1.28\u0026ndash;4.42, p\u0026thinsp;=\u0026thinsp;0.006). Dharavi did not differ significantly (aOR 1.13, 95% CI 0.57\u0026ndash;2.23, p\u0026thinsp;=\u0026thinsp;0.73).\u003c/p\u003e \u003cp\u003eFor stunting, residence in Govandi was associated with substantially elevated odds (aOR 2.95, 95% CI 1.86\u0026ndash;4.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Dharavi showed a non-significant elevation (aOR 1.47, 95% CI 0.92\u0026ndash;2.35, p\u0026thinsp;=\u0026thinsp;0.11). Household water treatment was independently associated with reduced odds of waterborne disease (aOR 0.62, 95% CI 0.41\u0026ndash;0.95), and waterborne disease was associated with increased odds of stunting (aOR 1.54, 95% CI 1.02\u0026ndash;2.33).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence ratios from modified Poisson regression\u003c/h2\u003e \u003cp\u003eModified Poisson regression provided adjusted prevalence ratios as a more conservative alternative to odds ratios (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Supplementary Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The adjusted prevalence ratio for pneumonia comparing Govandi to Gautam Nagar was 1.98 (95% CI 1.21\u0026ndash;3.24), and for stunting 1.84 (95% CI 1.40\u0026ndash;2.42). For waterborne disease, the aPR was 0.76 (95% CI 0.61\u0026ndash;0.95).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted prevalence ratios for child health outcomes by settlement (modified Poisson regression)\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\u003eSettlement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWaterborne disease aPR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePneumonia aPR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStunting aPR (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\u003eGautam Nagar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDharavi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.79\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 (0.62\u0026ndash;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27 (0.95\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovandi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76 (0.61\u0026ndash;0.95)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.98 (1.21\u0026ndash;3.24)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84 (1.40\u0026ndash;2.42)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eaPR, adjusted prevalence ratio; CI, confidence interval; ref, reference category. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Modified Poisson regression with robust standard errors. Models adjusted for child age, sex, household size, caregiver education and outcome-specific exposures.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLinear regression for nutritional outcomes\u003c/h2\u003e \u003cp\u003eMultivariable linear regression confirmed associations between settlement and continuous nutritional indicators (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Compared with Gautam Nagar, residence in Govandi was associated with lower mean BMI (β\u0026thinsp;\u0026minus;\u0026thinsp;1.13 kg/m\u0026sup2;, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;1.52 to \u0026minus;\u0026thinsp;0.74, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and MUAC (β\u0026thinsp;\u0026minus;\u0026thinsp;0.62 cm, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.88 to \u0026minus;\u0026thinsp;0.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Dharavi showed intermediate BMI deficits (β\u0026thinsp;\u0026minus;\u0026thinsp;0.68 kg/m\u0026sup2;, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;1.07 to \u0026minus;\u0026thinsp;0.29) but no significant difference in MUAC. Models explained 15.2% and 11.8% of variance in BMI and MUAC.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable linear regression: nutritional status indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \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\u003eBMI (kg/m\u0026sup2;) β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMUAC (cm) β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGautam Nagar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDharavi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.68 (\u0026minus;\u0026thinsp;1.07 to \u0026minus;\u0026thinsp;0.29)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.07 (\u0026minus;\u0026thinsp;0.33 to 0.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovandi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.13 (\u0026minus;\u0026thinsp;1.52 to \u0026minus;\u0026thinsp;0.74)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.62 (\u0026minus;\u0026thinsp;0.88 to \u0026minus;\u0026thinsp;0.36)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18 (0.08 to 0.28)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22 (0.16 to 0.29)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12 (\u0026minus;\u0026thinsp;0.18 to 0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08 (\u0026minus;\u0026thinsp;0.12 to 0.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterborne disease (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.34 (\u0026minus;\u0026thinsp;0.64 to \u0026minus;\u0026thinsp;0.04)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.18 (\u0026minus;\u0026thinsp;0.38 to 0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eBMI, body mass index; MUAC, mid-upper arm circumference; β, regression coefficient; CI, confidence interval; ref, reference category. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Models additionally adjusted for household size and caregiver education.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eInteraction and sensitivity analyses\u003c/h2\u003e \u003cp\u003eNo significant interaction was detected between settlement and water treatment for waterborne disease (p\u0026thinsp;=\u0026thinsp;0.34), nor between settlement and indoor smoking for pneumonia (p\u0026thinsp;=\u0026thinsp;0.52). Stratified analyses by age group (0\u0026ndash;2, 3\u0026ndash;5, 6\u0026ndash;8 years) demonstrated broadly consistent patterns, with Govandi showing elevated pneumonia and stunting risk across strata. The association between Govandi residence and pneumonia was strongest in children aged 0\u0026ndash;2 years (aOR 3.12, 95% CI 1.24\u0026ndash;7.85).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal findings\u003c/h2\u003e \u003cp\u003eThis comparative analysis demonstrates that environmental determinants of child health in Mumbai\u0026rsquo;s informal settlements exhibit both common structural deprivation and site-specific risk patterns associated with distinct morbidity outcomes. After adjustment for available demographic confounders, children in Govandi had approximately 2.4-fold higher odds of caregiver-reported pneumonia and 3-fold higher odds of stunting compared with Gautam Nagar, whilst showing lower odds of reported waterborne disease. These associations were consistent across logistic and modified Poisson regression approaches, lending confidence to the overall pattern.\u003c/p\u003e \u003cp\u003eThe lower reported prevalence of waterborne disease in Govandi despite poorer water treatment coverage warrants cautious interpretation. Possible explanations include differential caregiver recall, variation in health-seeking behaviour and diagnostic access, or competing morbidity patterns in which respiratory illness predominates. Under-reporting in more socioeconomically marginalised contexts cannot be excluded. These findings therefore should not be interpreted as evidence of superior water conditions in Govandi but rather as reflecting complex interactions between exposure, reporting and access to care.\u003c/p\u003e \u003cp\u003eThe central contribution of this study is to show that intra-city heterogeneity within informal settlements matters for intervention design. Although sanitation deficits and poor water access were pervasive across all three sites, the morbidity profiles were not uniform. Settlement of residence functioned not simply as a geographic label but as a marker of differentiated risk environments within the same urban system. Importantly, settlement should be understood as a composite structural exposure encompassing environmental conditions, housing quality, tenure security, infrastructure provision and socioeconomic stratification. The associations observed likely reflect this bundled contextual effect rather than isolated hazards such as landfill proximity or roofing material alone.\u003c/p\u003e \u003cp\u003eCaution is warranted in interpreting these associations as evidence that specific environmental exposures\u0026mdash;such as proximity to the Deonar landfill\u0026mdash;are causally responsible. Settlement of residence is also correlated with socioeconomic position, housing precarity, dietary patterns, healthcare access and intergenerational deprivation. Our models adjusted for child age, sex, household size and caregiver education, but substantial unmeasured determinants remain, as reflected in the modest explained variance for nutritional outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eComparison with existing literature\u003c/h2\u003e \u003cp\u003eElevated child morbidity linked to infrastructural deficits has been documented in informal settlements in Nairobi, Lagos and Dhaka [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], supporting the view that slums function as micro-environmental risk zones rather than a uniform category of deprivation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Few studies, however, have compared multiple settlements within the same city using multivariable methods. By holding the broader metropolitan context constant, this study highlights meaningful health variation within a single urban system.\u003c/p\u003e \u003cp\u003eThe elevated respiratory morbidity in Govandi is consistent with evidence from landfill-adjacent and combustion-associated environments [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The adjusted prevalence ratio of 1.98 for pneumonia aligns with findings from Accra and Delhi examining waste-site proximity and childhood respiratory conditions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The stronger association in children aged 0\u0026ndash;2 years is consistent with evidence that younger children are particularly susceptible to air pollution effects due to developmental vulnerability [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], although these subgroup estimates remain exploratory.\u003c/p\u003e \u003cp\u003eThe higher stunting prevalence in Govandi (54.6%) and adjusted odds ratio of 2.95 suggest chronic nutritional deficits that may reflect cumulative environmental and socioeconomic disadvantage. This pattern is potentially consistent with the environmental enteropathy hypothesis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and the observed association between waterborne disease and stunting (aOR 1.54) supports this pathway. At the same time, differential income, dietary quality or food insecurity may underlie these differences, and these were not directly measured.\u003c/p\u003e \u003cp\u003eThe near-universal asbestos roofing in Gautam Nagar is notable. Although no direct association with current child morbidity was observed, asbestos is classified as a Group 1 carcinogen with no safe exposure threshold [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and childhood exposure carries elevated lifetime risk [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Documenting this exposure prevalence is itself relevant for urban environmental health policy.\u003c/p\u003e \u003cp\u003eThe protective association between household water treatment and waterborne disease (aOR 0.62) aligns with systematic review evidence on point-of-use water interventions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Lower treatment rates in Govandi indicate potential for targeted WASH interventions, although cloth filtering does not constitute effective microbiological treatment.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eA key strength is the comparative design across three settlements within the same metropolitan area, allowing assessment of intra-urban variation while holding broader regional factors constant. The use of logistic regression, modified Poisson regression and linear regression provides convergent evidence, and standardised anthropometry strengthens the nutritional findings.\u003c/p\u003e \u003cp\u003eLimitations include the cross-sectional design, reliance on caregiver-reported morbidity without clinical verification, particularly for pneumonia, which was based on caregiver recall of practitioner diagnosis and may therefore reflect healthcare access and diagnostic opportunity as much as underlying incidence. Absence of objective environmental measurements, and likely residual confounding by unmeasured socioeconomic variables. The hierarchical data structure was not modelled using multilevel methods due to the small number of settlement units. The purposive selection of settlements limits generalisability, and findings should be interpreted as hypothesis-generating. The study was not designed to provide representative estimates for all informal settlements in Mumbai but rather to compare three contrasting contexts within a single metropolitan system.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eImplications for policy and future research\u003c/h2\u003e \u003cp\u003eThese findings caution against treating informal settlements as a monolithic category in urban health policy. While citywide improvements in water and sanitation remain essential, differentiated intervention strategies may be required where distinct environmental risk profiles are evident. In landfill-adjacent settlements such as Govandi, interventions may include strengthened air quality monitoring, mitigation of waste combustion exposure, and integration of respiratory screening into primary care outreach. In settlements with widespread asbestos roofing, environmental remediation and safe material replacement should be prioritised irrespective of short-term morbidity findings. Nutritional supplementation and food security initiatives may also require spatial targeting where chronic undernutrition is concentrated.\u003c/p\u003e \u003cp\u003eIn Govandi, the consistent pattern of elevated respiratory morbidity and stunting suggests the need for targeted air quality mitigation and nutritional support. In Gautam Nagar, widespread asbestos exposure warrants remediation independent of short-term morbidity findings.\u003c/p\u003e \u003cp\u003eMore broadly, this study adds to urban health research by demonstrating that intra-city comparison can reveal substantial heterogeneity masked by aggregate \u0026ldquo;slum\u0026rdquo; classifications. Future research should incorporate objective environmental monitoring, comprehensive socioeconomic assessment, and longitudinal designs to clarify causal pathways. Multilevel analytical approaches would further strengthen understanding of how neighbourhood-level conditions interact with household-level determinants.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis comparative analysis demonstrates substantial intra-urban heterogeneity in environmental exposures and child morbidity across three informal settlements within Mumbai. While structural deficits in water and sanitation were widespread, settlement of residence remained associated with differential respiratory morbidity and chronic undernutrition after adjustment for measured demographic factors. These findings underscore the importance of moving beyond aggregated \u0026ldquo;slum\u0026rdquo; classifications toward spatially disaggregated urban health assessment. Although causality cannot be inferred and residual socioeconomic confounding is likely, the observed patterns suggest that uniform urban health interventions may be insufficient without attention to settlement-specific environmental and structural conditions. Longitudinal studies incorporating objective environmental monitoring and detailed socioeconomic measurement are needed to clarify causal pathways and inform targeted policy responses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of the Albert-Ludwigs-Universit\u0026auml;t Freiburg, Germany (Reference: 2019/0521). Written permission was obtained from the Municipal Corporation of Greater Mumbai\u0026rsquo;s public health department and community leaders. Written informed consent was obtained from all participating caregivers. Parents or guardians provided consent for participation of children under 18 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants consented to the publication of anonymised data. No individually identifying information is presented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted as part of a Master\u0026rsquo;s degree programme at the University of Freiburg, Germany. No external funding was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVK conceived and designed the study, collected and analysed the data, and drafted the manuscript. PAM provided supervision, guided data collection methods and contributed to manuscript revision and statistical analysis. Both authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Mr Kiran Sonawane for thesis coordination and field support in India, Ms Vidya Bhalerao for community advocacy assistance, Dr Sonia Diaz Monsalve for programme coordination support, and Dr Peter Asaga for technical advice. We are grateful to the child leaders and community members in all three settlements for their participation and to the Urban Social Health Activists and Primary Health Centre staff who facilitated community access and provided health profile data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerative AI Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenerative artificial intelligence (AI) was used to assist in the creation of the graphic abstract for this manuscript. The AI tool was employed solely for visual illustration purposes. The authors reviewed, edited, and approved the final graphic to ensure accuracy and alignment with the study findings. No AI tools were used in the analysis, interpretation of data, or writing of the scientific content of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUN-Habitat. World Cities Report 2022: Envisaging the Future of Cities. 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Lancet Infect Dis. 2011;11(2):131\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuby SP, Agboatwalla M, Feikin DR, et al. Effect of handwashing on child health: a randomised controlled trial. Lancet. 2005;366(9481):225\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSverdlik A. Ill-health and poverty: a literature review on health in informal settlements. Environ Urban. 2011;23(1):123\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurley R, Saith R, Bhan N, Rehfuess E, Carter B. Slum upgrading strategies involving physical environment and infrastructure interventions and their effects on health and socio-economic outcomes. Cochrane Database Syst Rev. 2013;(1):CD010067.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKjellstrom T, Friel S, Dixon J, et al. Urban environmental health hazards and health equity. J Urban Health. 2007;84(suppl 1):86\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-urban-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jurh","sideBox":"Learn more about [Journal of Urban Health](https://www.springer.com/journal/11524)","snPcode":"11524","submissionUrl":"https://www.editorialmanager.com/jurh","title":"Journal of Urban Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8953863/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8953863/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChildren living in urban informal settlements experience multiple environmental risks, yet conditions and health outcomes are not uniform within cities. We compared environmental exposures and child morbidity across three informal settlements in Mumbai with distinct urban contexts: Gautam Nagar, Dharavi, and Govandi. A comparative cross-sectional survey was conducted between January and December 2024 among 500 households with an index child aged 0\u0026ndash;8 years. Caregivers reported morbidity in the preceding six months, and trained fieldworkers collected anthropometric measurements to assess nutritional status. Multivariable regression models adjusted for child age, sex, household size, and caregiver education, with outcome-specific exposures included where appropriate. Severe sanitation deficits and widespread perception of poor water quality were observed across all settlements, while key hazards differed by location, including near-universal asbestos roofing in Gautam Nagar and landfill proximity in Govandi. Compared with Gautam Nagar, children in Govandi had higher adjusted prevalence of pneumonia and stunting and lower prevalence of reported waterborne disease. Nutritional indicators were also poorest in Govandi. These findings demonstrate marked intra-urban heterogeneity in environmental risk and child morbidity within a single metropolitan system. Urban health strategies that treat informal settlements as homogeneous units may overlook important spatially differentiated vulnerabilities.\u003c/p\u003e","manuscriptTitle":"Environmental determinants and child morbidity across three informal settlements in Mumbai: a comparative cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 13:51:24","doi":"10.21203/rs.3.rs-8953863/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2026-02-24T19:55:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Urban Health","date":"2026-02-24T02:08:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-urban-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jurh","sideBox":"Learn more about [Journal of Urban Health](https://www.springer.com/journal/11524)","snPcode":"11524","submissionUrl":"https://www.editorialmanager.com/jurh","title":"Journal of Urban Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9b1e45ec-b3a1-43a9-af8b-679013c04c63","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T13:51:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 13:51:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8953863","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8953863","identity":"rs-8953863","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}