Non-communicable diseases (NCDs) have emerged as the leading cause of morbidity and mortality worldwide, accounting for 71% of global deaths annually (1). While traditionally associated with affluence, the epidemiological transition has shifted the burden of NCDs disproportionately to low and middle-income countries (LMICs), where nearly 85% of premature deaths from NCDs now occur (2). Within these countries, urban slum populations represent a particularly vulnerable group facing a complex "dual burden" of infectious diseases and NCDs (3).

Urban slums, characterized by overcrowding, inadequate housing, limited access to healthcare services, and poor sanitation, house approximately one-third of the urban population in many developing countries (4). In India alone, over 65 million people reside in urban slums, while in Bangladesh, approximately 33% of the urban population lives in slum conditions (5). These settlements, initially viewed as transitional spaces, have become permanent fixtures of urban landscapes, creating unique health challenges for residents.

The epidemiological profile of urban slum populations is undergoing a rapid transformation. While infectious diseases remain prevalent, NCDs including cardiovascular diseases, diabetes, chronic respiratory diseases, and cancer are increasing at an alarming rate (6). This health transition is driven by multiple factors including urbanization, lifestyle changes, dietary transitions, and socioeconomic conditions specific to slum environments (7).

Key modifiable lifestyle risk factors for NCDs among urban slum populations include tobacco use, harmful alcohol consumption, insufficient physical activity, and unhealthy dietary patterns (8). Additionally, intermediate risk factors such as hypertension, dyslipidemia, raised blood glucose, and obesity contribute significantly to NCD development (9). The clustering of multiple risk factors often observed in these populations further amplifies their vulnerability to NCDs (10).

Despite their clear vulnerability, urban slum populations remain underrepresented in epidemiological research and health surveillance systems (11). This research gap impedes the development of targeted interventions and appropriate health policies for NCD prevention and control in these communities. Furthermore, the unique social determinants of health operating within slum environments require specialized approaches that address both proximal risk factors and their underlying causes (12).

This study aims to conduct a comprehensive epidemiological assessment of lifestyle risk factors contributing to NCDs among urban slum populations. By identifying the prevalence, patterns, and social determinants of these risk factors, this research seeks to inform the development of contextually appropriate preventive interventions and policy frameworks to address the growing burden of NCDs in these vulnerable communities.

Study Design and Setting: A community-based cross-sectional study was conducted between January and June 2024 in three purposively selected urban slums located in metropolitan areas. The selection of slum settlements was based on population density, socioeconomic diversity, and administrative accessibility. Each selected settlement had been formally recognized as a slum area by municipal authorities for at least five years preceding the study.

Sample Size and Sampling Procedure: The sample size was calculated using the formula n = Z²p(1-p)/d², where Z = 1.96 at 95% confidence level, p = anticipated prevalence of NCD risk factors (taken as 50% to yield maximum sample size), and d = precision (5%). Accounting for a 10% non-response rate, the final calculated sample size was 422 participants. However, to increase statistical power and account for subgroup analyses, the target sample was increased to 1,250 participants.

A multi-stage stratified random sampling technique was employed. First, each slum area was divided into clusters based on geographical boundaries. Second, households within selected clusters were randomly selected using a systematic random sampling approach. Finally, one eligible adult (aged ≥18 years) from each selected household was recruited using the Kish selection method to ensure unbiased participant selection within households.

Eligibility Criteria: Inclusion criteria comprised: (1) adults aged 18 years and above; (2) permanent residents who had lived in the selected slum for at least six months prior to the survey; and (3) mentally capable of providing informed consent. Exclusion criteria included pregnant women, individuals with severe physical or mental disabilities preventing participation, and temporary residents.

Data Collection Tools and Procedures:

Data was collected using a modified version of the WHO STEPS instrument for NCD risk factor surveillance. The survey instrument consisted of three components:

1. STEP 1: A structured questionnaire to collect information on sociodemographic characteristics (age, gender, education, occupation, monthly income, marital status), behavioural risk factors (tobacco use, alcohol consumption, dietary habits including fruit and vegetable intake, physical activity), and self-reported NCDs (diabetes, hypertension, cardiovascular diseases).
2. STEP 2: Physical measurements including height, weight, waist circumference, and blood pressure. Height was measured using a portable stadiometer with participants standing barefoot with shoulders in a normal position. Weight was measured using a calibrated digital scale with participants wearing light clothing. Blood pressure was measured using automated digital blood pressure monitors with appropriate cuff sizes. Three readings were taken at 5-minute intervals, and the average of the second and third readings was recorded.
3. STEP 3: Optional blood glucose measurements were performed on a subsample (25% of participants) using point-of-care testing with calibrated glucometers after obtaining additional consent.

The questionnaire was translated into local languages and back-translated to ensure accuracy. A pilot study was conducted among 50 individuals (not included in the final analysis) to validate the instrument in the local context and train the data collectors.

Variables and Measurements:

The following NCD risk factors were assessed:

1. Current tobacco use: Defined as daily or occasional use of any smoked or smokeless tobacco products.
2. Insufficient fruit and vegetable intake: Consumption of fewer than five combined servings of fruits and vegetables per day.
3. Physical inactivity: Less than 150 minutes of moderate-intensity physical activity or 75 minutes of vigorous-intensity physical activity per week, as per WHO recommendations.
4. Harmful alcohol consumption: Defined as consumption of ≥5 standard drinks for men or ≥4 standard drinks for women on any single occasion in the past 30 days.
5. Overweight and obesity: Categorized based on BMI calculations (weight in kg/height in m²) as underweight (<18.5 kg/m²), normal (18.5-24.9 kg/m²), overweight (25.0-29.9 kg/m²), and obese (≥30.0 kg/m²).
6. Central obesity: Defined as waist circumference ≥90 cm for men and ≥80 cm for women, as per South Asian criteria.
7. Hypertension: Defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg, or self-reported use of antihypertensive medication.
8. Raised blood glucose: Random blood glucose ≥140 mg/dL or self-reported diagnosis of diabetes by a healthcare professional.

Additionally, clustering of risk factors was assessed by counting the number of risk factors present in each participant (0-7).

Quality Assurance: Ten trained field investigators collected data under the supervision of two field supervisors. All investigators underwent a standardized three-day training program covering interview techniques, physical measurement protocols, and ethical considerations. Equipment was calibrated daily, and 10% of interviews were repeated by supervisors to ensure reliability. Double data entry was performed to minimize errors.

Data Analysis: Data was analysed using SPSS version 26.0. Descriptive statistics were presented as frequencies, percentages, means, and standard deviations. Chi-square tests were used to assess associations between categorical variables. Multivariate logistic regression analysis was performed to identify factors associated with the clustering of NCD risk factors, adjusting for potential confounders. Statistical significance was set at p<0.05 with 95% confidence intervals reported for all relevant estimates.

Sociodemographic Characteristics: Table 1 presents the sociodemographic characteristics of the study participants. A total of 1,248 individuals participated in the study, yielding a response rate of 99.8%. The mean age of participants was 38.4 (±12.7) years, with a slight female predominance (53.7%). Nearly half (48.3%) of the participants had either no formal education or primary education only. Regarding occupation, 34.6% were engaged in unskilled labour, 23.4% in skilled/semi-skilled work, while 28.9% were homemakers. The majority (72.1%) reported a monthly household income below 15,000 INR.

Table 1: Sociodemographic characteristics of study participants (N=1,248)

Prevalence of Behavioural Risk Factors: The prevalence of behavioural risk factors for NCDs is presented in Table 2. Tobacco use was reported by 42.3% of participants, with significantly higher prevalence among males (68.7%) compared to females (19.4%). Smokeless tobacco use (28.7%) was more common than smoking (23.1%), with 9.5% reporting both forms. Harmful alcohol consumption was reported by 18.2% of participants, with males (34.6%) showing significantly higher consumption than females (4.2%).

Physical inactivity was observed in 37.8% of participants, with a higher proportion of females (45.7%) being physically inactive compared to males (28.7%). Inadequate fruit and vegetable consumption was nearly universal at 89.7%, with no significant gender difference. Only 10.3% of participants reported consuming the recommended five or more servings of fruits and vegetables daily.

Table 2: Prevalence of behavioural risk factors for NCDs by gender (N=1,248)

Physical Measurements and Metabolic Risk Factors: Table 3 presents the prevalence of anthropometric and metabolic risk factors. The overall prevalence of overweight (BMI 25.0-29.9 kg/m²) was 22.6%, while obesity (BMI ≥30 kg/m²) was 8.6%. Females showed a significantly higher prevalence of overweight and obesity (35.5%) compared to males (26.8%). Central obesity was present in 41.3% of participants, again with higher prevalence among females (56.1%) than males (24.2%).

Hypertension was detected in 28.4% of participants, with 13.7% previously diagnosed and 14.7% newly detected during the study. The prevalence increased with age, from 12.6% in the 18-30 age group to 56.5% in those above 60 years. Among the subsample tested for blood glucose (n=312), 16.3% had elevated random blood glucose levels, with 7.7% previously diagnosed with diabetes and 8.6% newly detected with hyperglycemia.

Table 3: Prevalence of anthropometric and metabolic risk factors by gender

*Central obesity defined as waist circumference ≥90 cm for men and ≥80 cm for women

Clustering of NCD Risk Factors: The clustering of NCD risk factors among participants is shown in Table 4. Only a small proportion (1.4%) of participants had no risk factors, while 58.7% had three or more risk factors. The mean number of risk factors per participant was 3.16 (±1.24). A higher proportion of males (64.2%) had three or more risk factors compared to females (53.9%).

Table 4: Clustering of NCD risk factors by gender

*Risk factors included: tobacco use, harmful alcohol consumption, insufficient physical activity, inadequate fruit/vegetable intake, overweight/obesity, hypertension, and hyperglycemia (for subsample)

Sociodemographic Factors Associated with Clustering of Risk Factors: Table 5 presents the adjusted odds ratios (AOR) for factors associated with the clustering of three or more NCD risk factors. After adjusting for covariates, males had significantly higher odds of risk factor clustering compared to females (AOR 1.62, 95% CI 1.26-2.08). Age showed a significant association, with individuals aged 46-60 years (AOR 2.43, 95% CI 1.78-3.31) and those above 60 years (AOR 3.05, 95% CI 1.92-4.84) having higher odds compared to the youngest age group.

Lower educational attainment was significantly associated with risk factor clustering. Individuals with no formal education (AOR 2.38, 95% CI 1.64-3.46) and primary education (AOR 1.84, 95% CI 1.24-2.73) had higher odds compared to those with higher secondary education and above. Monthly household income showed an inverse association, with lower income groups having higher odds of risk factor clustering. Occupation was also significantly associated, with unskilled labourers (AOR 1.79, 95% CI 1.18-2.72) showing higher odds compared to service/salaried employees.

Table 5: Factors associated with clustering of three or more NCD risk factors (N=1,248)

*AOR: Adjusted Odds Ratio, adjusted for all variables in the table

This comprehensive epidemiological assessment reveals a high burden of lifestyle risk factors for NCDs among urban slum populations, with significant implications for public health planning and interventions. Our findings highlight the complex interplay between sociodemographic factors and NCD risk profiles in these vulnerable communities, challenging the traditional notion that NCDs primarily affect affluent populations.

The alarmingly high prevalence of tobacco use (42.3%) in our study population exceeds the national average of 28.6% reported in the Global Adult Tobacco Survey (GATS) for India (13). This elevated prevalence is consistent with findings from other urban slum studies in Mumbai (54%) and Dhaka (36%), indicating that tobacco use remains a critical public health challenge in slum settings (14,15). The gender disparity in tobacco use patterns, with males showing significantly higher prevalence, reflects wider sociocultural norms and targeting of tobacco products toward specific demographic groups (16).

Our findings regarding insufficient fruit and vegetable consumption (89.7%) are particularly concerning but align with previous studies in urban slums of Pune (83.8%) and Burdwan (78%) (17,18). This near-universal inadequacy in healthy food consumption reflects both accessibility and affordability barriers in slum environments, where nutritious foods often cost more than energy-dense alternatives (19). The double burden of malnutrition is evident, with 12.2% of participants being underweight while 31.2% were overweight or obese, highlighting the nutritional transition occurring in these communities.

Physical inactivity (37.8%) appears somewhat lower than reported in some comparable studies (40-48.9%), possibly due to occupational physical activity among slum dwellers, many of whom engage in physically demanding manual labour (20). However, the significantly higher prevalence of inactivity among females (45.7% vs. 28.7% in males) points to gender-specific barriers including limited recreational spaces, safety concerns, and cultural norms that may restrict women's mobility and participation in leisure-time physical activities (21).

The prevalence of hypertension (28.4%) in our study, while concerning, is slightly lower than reported in some urban slum studies from Mumbai (39%) and Delhi (35%), but comparable to findings from slums in West Bengal (26.4%) (22,23). More alarming is the high proportion of undiagnosed hypertension (51.8% of all hypertensive cases), highlighting significant gaps in awareness and healthcare access among slum residents. Similarly, the substantial proportion of undiagnosed hyperglycemia (52.8% of all cases) underscores the need for improved screening and early detection programs in these underserved communities (24).

The clustering of multiple risk factors presents perhaps the most significant concern, with 58.7% of participants having three or more risk factors for NCDs. This clustering phenomenon increases the risk of developing NCDs exponentially rather than additively and has been reported in other slum studies, though with varying prevalence rates (25). The observed sociodemographic gradient in risk factor clustering, with higher odds among males, older age groups, those with lower education, and lower income, emphasizes how social determinants fundamentally shape NCD risk profiles in urban slum settings (26).

Education emerged as a particularly strong predictor of risk factor clustering, with those having no formal education showing 2.38 times higher odds compared to those with higher secondary education or above. This finding aligns with the Social Determinants of Health framework and underscores the importance of education as a fundamental enabler of health literacy, health-seeking behaviour, and adoption of healthier lifestyle practices (27). Income showed a similar gradient, with the lowest income group having 2.24 times higher odds of risk factor clustering compared to the highest income group, reflecting how economic constraints limit health-promoting choices in resource-poor settings.

From a policy perspective, our findings call for context-specific, multi-level interventions addressing both proximal risk factors and their underlying determinants. Tobacco control measures need strengthening in slum areas, with particular attention to smokeless tobacco products widely used in these settings (28). Improving access to affordable fruits and vegetables through urban farming initiatives, community gardens, and subsidized healthy food programs could address the nearly universal inadequacy in consumption (29). Creating safe, accessible spaces for physical activity, particularly for women, and developing gender-sensitive health promotion strategies could help address the observed disparities in physical inactivity (30).

Beyond individual-level interventions, structural approaches addressing the fundamental causes of NCDs in slum settings are essential. This includes improving formal education access, enhancing livelihood opportunities, strengthening primary healthcare systems with a focus on NCD prevention and control, and implementing health-promoting urban planning and policies (31). The high prevalence of undiagnosed hypertension and hyperglycemia calls for improved screening programs integrated into community-based healthcare delivery models that overcome traditional barriers to care in slum settings (32).

This study has several strengths, including its comprehensive assessment of multiple risk factors, large sample size, and examination of clustering patterns. However, limitations include its cross-sectional design, which precludes causal inferences, and potential recall bias in self-reported behaviours. The study's focus on three urban slums may limit generalizability to all slum settings, which can be heterogeneous in their characteristics. Additionally, the reliance on random blood glucose rather than fasting blood glucose or HbA1c for diabetes assessment may have affected the accuracy of hyperglycemia estimates (33).

Future research should explore longitudinal trends in NCD risk factors in urban slums, evaluate intervention effectiveness in these specific contexts, and investigate the complex interplay between infectious diseases and NCDs in these populations. Qualitative studies exploring barriers and facilitators to healthy behaviours in slum settings would complement quantitative assessments and inform more nuanced intervention approaches (34,35).

In conclusion, this study provides comprehensive evidence of the high burden of modifiable NCD risk factors in urban slum populations, with substantial clustering of multiple risk factors. The observed sociodemographic patterning of risk factors underscores how social determinants fundamentally shape NCD risks in these vulnerable communities. Addressing this growing public health challenge requires integrated approaches that combine individual-level interventions with structural policies addressing the underlying determinants of health in urban slum environments.

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