NCDs are chronic illnesses, which do not have an infectious cause, with multiple genetic, physiological, environmental and behavioural factors that contribute to their development. Non-communicable conditions encompass a broad spectrum of pathologies, prominent among which are cardiovascular disorder, hypertension, diabetes mellitus, malignancies, cerebrovascular accidents, CKD, chronic obstructive pulmonary disease COPD, asthma and NAFLD, and other chronic systemic diseases [1]. According to global estimates in 2021, nearly 75% of non-pandemic deaths were attributed to NCDs resulting in an estimated 43 million deaths. In total, 18 million premature deaths were observed (under 70 years of age), of which 82% were due to low- and middle-income countries (LMICs). Modifiable lifestyle choices and environmental exposures such as tobacco consumption, physical inactivity, poor dietary patterns, harmful alcohol intake, and outdoor air pollution serve as primary drivers of this [2]. In response to this growing global crisis, the United Nations (UN) added NCD prevention to the 2030 Agenda for Sustainable Development Goals, specifically Target 3.4 under SDG 3, which mandates a one-third reduction in early NCD-related mortality by the year 2030 [1]. Non-communicable diseases claim approximately 5.87 million lives annually across India, contributing to nearly six out of every ten deaths nationwide, making the country a key contributor to the overall burden of NCDs in WHO South-East Asia Region [3]. An epidemiological 'tsunami' has occurred nationally with NCD risk factors rapidly accelerating, which has created a chronic disease emergency in both rural and urban settings. In addition, public health strategies are increasingly integrating chronic condition management while simultaneously addressing infectious illnesses, maternal health, and paediatric healthcare are active priorities, undernutrition being a priority. Despite this, the entrenched socioeconomic inequalities remain a barrier to effective care provision, especially in rural areas [4]. National epidemiological surveys, such as the National Family Health Survey-5 (NFHS-5, 2019-21) and National NCD Monitoring Survey (NNMS) suggest that there is a significant burden of hypertension, diabetes, and obesity both in urban and rural areas [5]. Adults aged 18 to 69 years were found to have a high prevalence of behavioural risk factors, such as current tobacco use (32.8%), alcohol intake (15.9%), as well as low consumption of fruit and vegetable (98.4%) and low physical activity (41.3%), in the NNMS 2017–18 data [6]. According to WHO estimates, some 1.28 billion adults globally (or nearly two-thirds of those aged 30–79) are also suffering from hypertension. Even with available therapies, effective target BP control is reported to be only about 21% of patients with the disease worldwide [7]. In India, the NFHS-5 2019-21 estimates the prevalence of hypertension to be 24-30% [8] highlighting a serious public health problem. Assessing the burden and spread of NCD risk determinants across rural communities is critical for optimizing public health initiatives like the National Programme for Prevention and Control of Non-Communicable Diseases (NP-NCD). It is critical in the context of limited access and constrained resources at primary health care facilities in rural areas, which often makes early screening, ongoing monitoring and follow-up difficult. Therefore, the burden of hypertension along with the psychological, physical and metabolic risk factors was assessed to create a baseline of the chronic disease risk in rural Lucknow.

An observational cross-sectional study was carried out across rural adult populations in Lucknow district spanning a 24-month timeframe, from February 2024 through February 2026 Objectives 1. To determine the prevalence of hypertension among adults aged 18–60 years living in the rural areas of Lucknow district. 2. To assess the risk factors associated with hypertension among the study subjects. Inclusion criteria Adult people aged between 18 to 69 years of age residing for at least the last 6 months in rural area of Lucknow district and who had given informed consent. Exclusion criteria Individuals who are not cooperative or refuse to provide the necessary information. Sample size and sampling method To recruit older adults for this investigation, a multi-stage probability sampling method was applied. The sample size was determined with the highest and lowest percentage of global burden of risk linked to NCDs using the formula: p1 = 0.429 (42.9%), maximum proportion of non-communicable disease (CVD) . p2 = 0.048 (4.8%), least proportion of non-communicable disease (DM) 1 e =1.5 (p1- p2), the proportion difference. The design effect of 1.5 was used for the calculation of the sample size. It was presumed that the probability for a type I error (α) will be 5%, or equivalently, the confidence interval would be 95%, and the probability for a type II error (β) would be 10%, which makes the statistical power 90%, resulting in a minimum sample size of 572 participants. Lucknow District was selected for this study which was further divided into eight blocks according to Urban Development Department. Of these eight blocks, two blocks, Kakori and Malihabad, were selected randomly. A list of villages was made from these two blocks, and then two villages from each of these two blocks were selected randomly. In these two villages, the first household was selected randomly using the last digit of a currency note. Starting from the first household of the left side of the road, each household thereafter was selected. From these households, one person aged between 18-69 years was selected randomly. Study tool Information regarding participant background, lifestyle choices, medical background, and anthropometric parameters was gathered using a pre-validated, semi-structured assessment tool. Blood pressure measurements were performed with a standardized digital monitor following WHO standard guidelines. Per established diagnostic thresholds, hypertension was classified as systolic blood pressure of 140 mmHg or higher, diastolic blood pressure of 90 mmHg or higher, or ongoing use of blood pressure-lowering medication [9]. Statistical Evaluation Data were processed using IBM SPSS Statistics software (version 26.0). Categorical variables were summarized using counts and percentages. Relationships between non-continuous parameters were assessed via Pearson's chi-square test, applying a threshold for statistical significance of p less than 0.05

Total number of subjects studied was 572. Majority of the study participants were aged 31–50 years (41.8%), with females forming the majority (65.4%). The predominant religion was Hinduism (66.6%), and most subjects were married (81.5%). A large proportion were illiterate (41.2%), and homemakers constituted the majority occupation group (51.2%). Nearly half of the participants lived in joint families (49.5%). According to the Modified BG Prasad classification, most of the study population belonged to the lower middle class (46.9%). Details given in Table 1. Table 1: Distribution of the Study Participants in terms of Sociodemographic Characteris (N=572) Socio-Demographic Variable No. % Age Group (Years) 18 - 30 230 40.2% 31 – 50 239 41.8% 51 – 69 103 18.0% Gender Male 198 34.6% Female 374 65.4% Religion Hindu 381 66.6% Muslim 191 33.4% Marital status Unmarried 78 13.6% Married 466 81.5% Widow/ Widower 28 4.9% Education Level Illiterate 236 41.2% Just Literate 78 13.7% Primary School 70 12.2% Middle School 68 11.9% High School 80 14.0% Graduate & above 40 7.0% Occupation None 63 11.0% Home maker 293 51.2% Labourer 86 15.0% Cultivation 76 13.3% Business 6 1.0% Independent profession 37 6.5% Service 11 1.9% Family Type Nuclear 238 41.6% Joint 283 49.5% Three Generation 51 8.9% Modified B G Prasad Socio-Economic Status Upper Class (I) 18 3.1% Upper Middle Class (II) 88 15.4% Middle Class (III) 84 14.7% Lower Middle Class (IV) 268 46.9% Lower Class (V) 114 19.9% A majority of participants were engaged in walking for at least 30 minutes/ 5 days a week (83.57%) followed by sedentary lifestyle (14.51%). The majority of the adults were engaged in moderate physical activity, accounting for 64.5% of the study population. 22.0% of the study subjects were current smokers, 21.2% reported having ever used and currently using smokeless tobacco & 14.2% reported having ever consumed and currently using alcohol. Major proportion of participants were non-vegetarian (54.9%). The majority of participants (64.7%) reported having a moderate daily salt intake. The majority of participants were overweight (44.6%), followed by those with normal BMI (34.3%). Obesity was observed in 12.9% of the study subjects, while 8.2% were underweight. The majority of participants (78.3%) were classified as at risk based on their waist–hip ratio. Most participants were normoglycemic (83.2%), while 16.8% were hyperglycaemic, reflecting the presence of diabetes among a considerable proportion of the study population. Details are given in Table 2. Out of the 572 subjects studied majority of the participants were normotensive (68.0%), while nearly one-third of the study population were found to be hypertensive (32.0%). Details are given in Table 3. The presence of hypertension exhibited a highly significant age gradient, ranging from 17.4% among people between the ages of 18 to 30 to 55.3% in people aged between 51 and 69 (χ² = 50.091, p < 0.001). Hypertension was significantly more common among males than females (46.0% vs. 24.6%; χ² = 27.149, p < 0.001). A significant association was also observed with marital status, with the highest prevalence among widow/widowers (60.7%), followed by married (32.0%) and unmarried participants (21.8%) (χ² = 14.344, p = 0.001). Hypertension was significantly more prevalent among illiterate participants (48.7%) and those educated up to high school (41.3%) than among graduates and above (15.0%) (χ² = 84.198, p < 0.001). Occupation showed a significant association, with the highest prevalence among individuals in independent professions (75.7%) (χ² = 47.886, p < 0.001). Higher prevalence was observed in three-generation families (45.1%) (χ² = 17.132, p < 0.001) and among the lower socio-economic class (40.4%) (χ² = 16.176, p = 0.003) showing a significant association. Details given in Table 4. Table 2: Distribution of the Study Participants According to Behavioural and Metabolic Risk Factors (N=572) Behavioural & Metabolic Risk factors No. % Physical activity Sedentary Lifestyle 83 14.51% Walking For At least 30 Minutes/ 5 Days A Week 478 83.57% Others (Yoga/Meditation) 11 1.92% Work related Physical activity Light 117 20.5% Moderate 369 64.5% Vigorous 86 15.0% Smoking Yes 126 22.0% No 446 78.0% Smokeless Tobacco Yes 121 21.2% No 451 78.8% Alcohol Yes 81 14.2% No 491 85.8% Dietary habits Non-Vegetarian 314 54.9% Vegetarian 258 45.1% Salt intake Not sure 23 4.0% Low 64 11.2% Moderate 370 64.7% High 115 20.1% BMI Normal 196 34.3% Underweight 47 8.2% Overweight 255 44.6% Obese 74 12.9% WHR Normal 124 21.7% At Risk 448 78.3% Diabetes Mellitus Yes 476 83.2% No 96 16.8% Table 3: Prevalence of Hypertension Among the Study Participants (N=572) Hypertension No. % Absent 389 68.0% Present 183 32.0% Table 4: Association of Hypertension with Sociodemographic risk factors (N=572) Sociodemographic Risk Factors Hypertension Significance No Yes No. % No. % Age Group (Years) 18 – 30 190 82.6% 40 17.4% chi sq=50.091, p<0.001 31 – 50 153 64.0% 86 36.0% 51 – 69 46 44.7% 57 55.3% Gender Male 107 54.0% 91 46.0% chi sq=27.149, p<0.001 Female 282 75.4% 92 24.6% Religion Hindu 254 66.7% 127 33.3% chi sq=0.942, p=0.332 Muslim 135 70.7% 56 29.3% Marital status Unmarried 61 78.2% 17 21.8% chi sq=14.344, p=0.001 Married 317 68.0% 149 32.0% Widow/ Widower 11 39.3% 17 60.7% Education Level Illiterate 121 51.3% 115 48.7% chi sq=84.198, p<0.001 Just Literate 67 85.9% 11 14.1% Primary School 70 100.0% 0 0.0% Middle School 50 73.5% 18 26.5% High School 47 58.8% 33 41.3% Graduate & above 34 85.0% 6 15.0% Occupation None 40 63.5% 23 36.5% chi sq=47.886, p<0.001 Home maker 218 74.4% 75 25.6% Labourer 58 67.4% 28 32.6% Cultivation 47 61.8% 29 38.2% Business 6 100.0% 0 0.0% Independent profession 9 24.3% 28 75.7% Service 11 100.0% 0 0.0% Family Type Nuclear 146 61.3% 92 38.7% chi sq=17.132, p<0.001 Joint 215 76.0% 68 24.0% Three Generation 28 54.9% 23 45.1% Modified B G Prasad Socio-Economic Status Upper Class (I) 18 100.0% 0 0.0% chi sq=16.176, p=0.003 Upper Middle Class (II) 53 60.2% 35 39.8% Middle Class (III) 61 72.6% 23 27.4% Lower Middle Class (IV) 189 70.5% 79 29.5% Lower Class (V) 68 59.6% 46 40.4% Hypertension was significantly associated with lifestyle and metabolic risk factors. Participants with a sedentary lifestyle had a significantly higher prevalence of hypertension (56.6%) than those who engaged in regular walking (28.5%) or yoga/meditation (0%) (χ² = 31.078, p < 0.001). Participants performing light work showed a higher prevalence than those involved in moderate or vigorous physical activity (χ² = 9.705, p = 0.008). Hypertension was significantly more common among smokers (54.0%), users of smokeless tobacco (61.2%), alcohol consumers (49.4%), and individuals with high salt intake (59.1%) (all p < 0.001). The prevalence of hypertension increased significantly with increasing BMI, with the highest prevalence observed among obese and overweight participants (χ² = 8.524, p = 0.036). Individuals with an at-risk waist–hip ratio had significantly higher hypertension than those with a normal ratio (35.7% vs. 18.5%; p < 0.001). Diabetes mellitus showed a strong association, with hypertension affecting 70.8% of diabetics compared to 24.2% of non-diabetics (χ² = 79.987, p < 0.001). Details given in Table 5. Table 5: Association of Hypertension with Behavioural & Metabolic Risk factors (N=572) Behavioural & Metabolic Risk factors Hypertension Significance No Yes No. % No. % Physical activity Sedentary Lifestyle 36 43.4% 47 56.6% chi sq=31.078, p<0.001 Walking For At least 30 Minutes/ 5 Days A Week 342 71.5% 136 28.5% Others (Yoga/Meditation) 11 100.0% 0 0.0% Work related Physical activity Light 66 56.4% 51 43.6% chi sq=9.705, p=0.008 Moderate 265 71.8% 104 28.2% Vigorous 58 67.4% 28 32.6% Smoking Yes 58 46.0% 68 54.0% chi sq=35.867, p<0.001 No 331 74.2% 115 25.8% Smokeless Tobacco Yes 47 38.8% 74 61.2% chi sq=59.992, p<0.001 No 342 75.8% 109 24.2% Alcohol Yes 41 50.6% 40 49.4% chi sq=13.115, p<0.001 No 348 70.9% 143 29.1% Dietary habits Non-Vegetarian 205 65.3% 109 34.7% chi sq=2.368, p=0.124 Vegetarian 184 71.3% 74 28.7% Salt intake Not sure 17 73.9% 6 26.1% chi sq=48.802, p<0.001 Low 47 73.4% 17 26.6% Moderate 278 75.1% 92 24.9% High 47 40.9% 68 59.1% BMI Normal 145 74.0% 51 26.0% chi sq=8.524, p=0.036 Underweight 36 76.6% 11 23.4% Overweight 163 63.9% 92 36.1% Obese 45 60.8% 29 39.2% WHR Normal 101 81.5% 23 18.5% chi sq=13.153, p<0.001 At Risk 288 64.3% 160 35.7% Diabetes Mellitus Yes 28 29.2% 68 70.8% chi sq=79.987, p<0.001

The present study of 572 individuals identified the vast majority (68.0%) as normotensive subjects and almost one-third (32.0%) as subjects with hypertension. This high level of prevalence is probably the result of the combined effect of lifestyle and metabolic risk factors (increased sodium intake; high levels of obesity; smoking; chronic stress; and lack of physical activity). Jaswal et al. [10] reported on the contrail being considerably lower, with 93% normotensive and 7% identified as being hypertensive. Hypertension rates demonstrated a marked age-dependent upward trend, rising from 17.4% among 18-to-30-year-olds to 36.0% in the 31–50 cohort, and reaching 55.3% in individuals aged 51 to 69 years. This observation is consistent with the known age-related arterial stiffening, progressive vascular non-compliance and protracted duration of behavioural risk factors. A similar age association was reported by Dadras et al. [11] who found an increase of age from 15.5% in the 18–29 age group to 48.2% among those in the 45–69 age group (p<0.001). "Gender showed a pronounced statistical link with high blood pressure rates (p less than 0.001), where male subjects exhibited a higher prevalence (46.0%) compared to female subjects (24.6%). This divergence likely stems from greater male vulnerability to lifestyle and workplace hazards, including elevated rates of tobacco consumption and regular alcohol intake, more job stress, and more eating out. On the contrary, Dadras et al, [11] reported a slightly higher prevalence rate in females (25.2%) compared to the males (24.9%) but statistically non-significant (p = 0.91). Physical activity was significantly related to blood pressure regulation. People who did not exercise had a significantly higher rate of hypertension (56.6%) than people who walked at least 30 minutes a day 5 or more times per week (28.5%) or people who practiced yoga or meditation regularly (0%) (p < 0.001). Lack of exercise leads to poor heart conditioning, weight gain and increased vascular resistance. An inverse association between low physical activity and hypertension was seen in M S et al. [12] with an OR of 0.617, but this relationship was not statistically significant (p = 0.119). There was substantial association of elevated BP with substance use with hypertension seen among 54.0% of cigarette smokers, 61.2% of smokeless tobacco users and 49.4% of alcohol drinkers, all of which were significantly higher than non-users (p<0.001 for all). Nicotine and alcohol cause hyperactivation of the sympathetic nervous system, endothelial dysfunction and activate persistent vasoconstriction of the arteries. The results are similar to Agrawal et al. [13] who found significant association between hypertension and smoking (p = 0.016), tobacco use (p = 0.017) and alcohol intake (p = 0.033). Likewise, Kakchapati et al. [14] found that alcohol use was an independent risk factor for hypertension (AOR = 1.58, p = 0.019). The Body Mass Index (BMI) was associated with blood pressure with a positive gradient: in obese (39.2%) and overweight (36.1%) subjects (p = 0.036). Similarly, a significant correlation between BMI and hypertension was found by Agrawal et al. [13] (p = 0.013). In addition, central adiposity was a powerful determinant – the prevalence of hypertension was almost double among people with at-risk WHR versus those with normal WHR (35.7% versus 18.5%, p<0.001). This further highlights the contribution of the accumulation of visceral fat in the pathogenesis of metabolic dysregulation and hypertension. However, Agrawal et al. [13] did not find any significant association between WHR and hypertension (OR = 1.376, p = 0.235). Last, significant comorbidity was found between hypertension and diabetes mellitus (p < 0.001). Hypertensive patients had significantly elevated prevalence of DM compared with hypertensive patients (37.2% vs. 7.2%). Several of these associations with a single pathophysiological cluster, such as systemic insulin resistance, central obesity, chronic low-grade inflammation, physical inactivity, and genetic susceptibility. A similar co-occurrence was reported by Kumari et al who found a significant diabetes prevalence among those who were also hypertensive (21.9%, p = 0.009).

In rural Lucknow, nearly one-third (32%) of adults demonstrated hypertension. Statistical evaluation confirmed that elevated blood pressure was significantly driven by demographic characteristics, lifestyle factors—such as physical inactivity, tobacco and alcohol use, and excessive salt intake—as well as metabolic markers like obesity, waist-to-hip ratio, and diabetes. Mitigating this expanding non-communicable disease trend demands stronger primary prevention strategies, early screening initiatives, integrated clinical care, and sustained community-level health promotion

1. Operational guidelines National Programme for Prevention and Control of Non-Communicable Diseases (2023-2030). Mohfw.gov.in. 2024. Available from:https://www.mohfw.gov.in/sites/default/files/NP-NCD%20Operational%20Guidelines.pdf 2. World. Noncommunicable diseases [Internet]. Who.int. World Health Organization: WHO; 2024. Available from: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases? 3. Nethan S, Sinha D, Mehrotra R. Non Communicable Disease Risk Factors and their Trends in India. Asian Pac J Cancer Prev. 2017;18(7):2005-10. doi:10.22034/APJCP.2017.18.7.2005 4. Srivastav S, Mahajan H, Goel S, Mukherjee S. Prevalence of risk factors of noncommunicable diseases in a rural population of district Gautam-Budh Nagar, Uttar Pradesh using the World Health Organization STEPS approach. J Family Med Prim Care 2017;6:491-7. 5. Kulothungan V, Mascarenhas L, Das P, Mathur P. District-level epidemiology and sociodemographic determinants of noncommunicable diseases - results the National Family Health Survey −5 (2019–21). Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2024;18(7):103085. 6. Comprehensive National Nutrition Survey, Fact Sheets, accessed on url: https://nhm.gov.in/index1.php?lang=1&level=2&sublinkid=1332&lid=713 7. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet. 2021;398(10304):957–980. 8. National Family Health Survey (NFHS-5) INDIA Report. International Institute for Population Sciences (IIPS) [Internet]. iipsindia.ac.in. [cited 2026 July 30]. Available from: https://iipsindia.ac.in/content/national-family-health-survey-nfhs-5-india-report 9. Misra PJ, Mini GK, Thankappan KR. Risk factor profile for non-communicable diseases among mishing tribes in Assam, India: Results from a WHO STEPs survey. Indian J Med Res 2014;140:370-8. 10. Jaswal P, Kaur P, Sarin J. Predicting 10-year Cardiovascular Risk Using WHO/ISH Risk Prediction Chart among Rural Population in Haryana, India. Indian Journal of Forensic Medicine & Toxicology. 2021;15(2):497-503. 11. Dadras O, Stanikzai MH, Jafari M, Tawfiq E. Risk factors for non-communicable diseases in Afghanistan: insights of the nationwide population-based survey in 2018. Journal of Health Population and Nutrition. 2024;43(1):129. 12. M S, Sunil B N, R V. Prevalence of Non-communicable Disease Risk Factors Among Adults in a Rural Field Practice Area of a Tertiary Care Medical Center in Karnataka. Cureus. 2024;16(8): e67474. 13. Agrawal A, Kumari R. Association of lifestyle factors and non-communicable diseases among patients attending the NCD clinic of a Government Health Facility in Rural Uttar Pradesh, India. International Journal of Community Medicine and Public Health. 2025;12(7):3173–9. 14. Kakchapati S, Neupane R, Baral KS, Shrestha G, Joshi D, Dawkins B, et al. Social determinants and risk factors associated with non-communicable diseases among urban population in Nepal: A comparative study of poor, middle and rich wealth categories of urban population using STEPS survey. PLOS One. 2025;20(5): e0307622. 15. Kumari J, Ray S, Gupta S. Prevalence and Risk Factors of Diabetes Mellitus: A Community-Based WHO STEPwise Approach to Surveillance (STEPS) Survey in Rural Haryana, India. Cureus. 2025;17(7):e89089. 16. Beg S, Ahmad S, Anwar S, Baqar T, Srivastava P, Sharma P. Predictive analysis in medical healthcare: A meta-analysis. International Journal of Research–Granthaalayah. 2024;12(1):9-16. 17. Kazmi SM, Jilani AQ, Ahmad S, Srivastava P, Pandey K,Anwar S. Effects of Excessive Social Media Use on NeurotransmitterLevels and Mental Health: A Neurobiological Meta-Analysis. Era J MedRes 2025;12(1):56–60.