Decoding the Hypertension Care Cascade and Unlocking Influential Factors Among Indian Adolescents: Evidence from Nationally representative Cross-sectional survey

doi:10.21203/rs.3.rs-4037998/v1

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Decoding the Hypertension Care Cascade and Unlocking Influential Factors Among Indian Adolescents: Evidence from Nationally representative Cross-sectional survey

https://doi.org/10.21203/rs.3.rs-4037998/v1

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published 31 Jul, 2024

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Adolescent hypertension in India is an emergent public health concern due to a combination of increasing obesity, poor nutrition, sedentary lifestyles, and genetics with lack of programmatic focus on regular screening secondary to reduced perceived risk, both amongst individuals and healthcare providers. This study aims assess the hypertension care cascade and their determinants among older adolescents in India. We used data from the demographic and health surveillance (DHS) comprising India’s National Family Health Survey Fifth Round (2019–2021). The hypertension prevalence, proportions of aware, on treatment, and under control among adolescents in the age group of 15–19 years were reported, along with (95% CI) and statistically significance (p < 0.05). The prevalence of hypertension among 204054 older adolescents (15–19 years) was 5.08% (95% CI: 4.94–5.23%) wherein 42.26% (95% CI: 40.69–43.64%) were aware of their condition while 43.70% (95% CI: 41.73–45.70%) of those aware were receiving treatment, and 85.88% (95% CI: 83.83–87.71%) of those achieved blood pressure control. Overall, there were nearly 60% newly diagnosed hypertension cases detected on screening. In conclusion, nearly five in every hundred older adolescents in India are clinically hypertensive with significantly higher odds linked with obesity and male gender. Awareness and utilization of antihypertensive treatment was lower than the classical rule of halves signifying deficiencies in hypertension screening and management strategies for older adolescents within existing public health policy.

Health sciences/Health care

Health sciences/Risk factors

Health sciences/Health care/Disease prevention

Adolescents

Hypertension

Care cascade

Treatment seeking behaviour

What is known about the topic:

Hypertension among adolescents is rising globally due to factors like obesity, sedentary lifestyles, and poor nutrition.
The prevalence of adolescent hypertension in India varies widely, but there's a lack of comprehensive data.
Effective management includes lifestyle changes and, if necessary, antihypertensive medication.
The classical "rule of halves" is used as a benchmark for evaluating deficiencies in awareness, treatment, and control of hypertension among adults.

What this study adds:

Nearly five in hundred older adolescents in India have hypertension with a majority being unaware of their condition.
Health system deficiencies: despite availability of effective treatment, less than half of previously diagnosed adolescent hypertension cases receive treatment, signalling critical shortcomings in screening and management protocols.

Hypertension, or high blood pressure, is a multifactorial condition that is characterized by contributions from genetic, physiological and environment exposures [1]. Hypertension is increasingly being recognized as a public health problem that is not restricted to adults but also affecting the paediatric population, especially among adolescents, associated with long-term detrimental cardiovascular health implications especially when left untreated [2]. Adolescents worldwide are increasingly susceptible to the development of hypertension due to increasing childhood obesity from lower physical activity, reduced leisure time physical activity, sedentarism, and greater consumption of unhealthy high salt fast food consumption [3, 4]. Adolescents with elevated blood pressure are further at risk of uncontrolled hypertension due to lack of regular screening by healthcare professionals resulting in delayed detection and suboptimal management of the condition [5].

The global prevalence of hypertension in adolescents varies from 0.70% in Bangladesh [6], 5.5% in Africa[7], 4.6% in United states [8] and 2.9% in China[9]. The prevalence of hypertension among adolescents in India ranges from as low as 2–21.5% suggestive of lack of representativeness in most studies [3]. A systematic review and meta-analysis estimated the pooled prevalence of hypertension among adolescents (10–19 years old) in India as 7.6%, with considerable inter-study heterogeneity [10]. The prevalence of elevated blood pressure was 35.1% among children aged 10–12 years and 25.1% among children above 13 years of age [11].

Adolescent hypertension can be effectively managed with lifestyle changes especially through weight loss in obese individuals through healthy diet characterized wholesome, low-sodium meals, absence of sugar sweetened beverages, plenty of fresh fruits, and vegetables, apart from regular physical activity [12]. However, in adolescents with hypertension, which is symptomatic, or in absence of modifiable risk factors such as obesity, the presence of left ventricular hypertrophy, evidence of kidney disease or diabetes, and failure to attain blood pressure control despite a trial of lifestyle modifications, need initiation with antihypertensive medications [13].

According to the rule of halves for hypertension, half the people with high blood pressure (BP) are not aware, half of those aware are on treatment, and half on those on treatment attain controlled blood pressure levels due to medication nonadherence and therapeutic inertia [14]. In a a national level survey from India, hypertension prevalence among individuals aged up-to 49 years was estimated as 22.8% with 52.06% newly diagnosed cases highlighting the need for targeted screening, community-based interventions, and strengthening of primary care in India. Further, 57.72% participants were aware of their hypertensive status, of which 43.35% were taking antihypertensive medication and 73.7% had achieved controlled blood pressure levels [15]. Previous studies have observed deficiencies in every stage of the hypertension cascade amongst older adults in India through the stages of awareness, management, and control [16]. This hypertension care cascade is anticipated to be further compromised amongst older adolescents considering the lack of programmatic focus on regular screening in this age-cohort due to reduced perceived risk both amongst individuals and healthcare providers [15, 17]. Further, there is considerable lack of evidence regarding the hypertension status amongst adolescents in India which is needed to strengthen health policy towards improving services especially focused on older adolescents (15–19 years) who may share several common modifiable behavioural risk factors with adults.

This study was thus conducted to assess the hypertension care cascade and their determinants among older adolescents from nationally representative data.

Data Source:

The study was conducted on demographic and health surveillance (DHS) data from India’s National Family Health Survey Fifth Round (2019–2021). NFHS-5 is a household survey with two-stage cluster sampling design covering each district in 29 states and 7 union territories of India. In the first stage, primary sampling units (PSUs), i.e., villages in rural areas and census enumeration blocks (CEBs) in urban areas, were selected using the probability proportional to size (PPS) technique. A list of households was created by mapping and household listing in each selected PSU. In the second stage, researchers selected a fixed number of 22 households per cluster (i.e., PSUs) using an equal probability systematic selection. Men and women questionnaires collected information from candidates aged 15–54 and 15–49 years, respectively. Two sets of questionnaires (district and state module) were used for women while men had just one questionnaire (state module only) [18]. The sample size obtained for the current study is explained in Fig. 1.

The survey was carried out with the support of the Ministry of Health and Family Welfare (MoHFW), overseen by the International Institute for Population Sciences (IIPS) in Mumbai, and executed by a consortium of survey organizations and Population Research Centres. Technical guidance for NFHS-5 was furnished by the International Classification of Functioning, Disability and Health (ICF), USA [18].

Measurement of BP

All participants aged 15 years or more had their BP measured three times, with a five-minute gap between readings, using an Omron BP monitor (OMRON, Kyoto, Japan) [18]. The systolic and diastolic blood pressure of the individual was defined as the average of the second and third blood pressure reading after discarding the initial value [19].

Dependent variables

Prevalence of hypertension:

The prevalence of hypertension among adolescents was calculated by considering numerators satisfying either of following three conditions: i) The number of individuals (15–19 years) with systolic blood pressure (SBP) of ≥ 140 mm Hg or a diastolic blood pressure (DBP) of ≥ 90 mm Hg, ii) or if a doctor or other health professionals in the past identified high BP on two or more occasions and, iii) if the individual has been currently using anti-hypertensive medication to lower the blood pressure. The denominator was the total number of adolescents in the sample (15–19 years) whose blood pressure readings were available.

Awareness of Hypertension: Individuals who responded yes to the following questions were considered as being aware of their hypertensive status: Told had high BP on two or more occasions by the doctor or other health professionals. OR were taking anti-hypertensive medications.

On treatment: Individuals who responded yes to the following statement were on hypertension treatment: Currently taking a prescribed medicine to lower BP.

Under Control

Individuals who were currently taking antihypertensive medication and were detected with SBP < 140 mmHg and DBP < 90 mmHg on screening were classified as having controlled hypertension.

Independent variables:

Socio-demographic variables included sex, marital status, education. Anthropometric factors included waist-hip-ratio, and BMI (kg/m²), community level factors included religion, wealth index, place of residence (urban/rural), geographical region, health-seeking behavior included health insurance coverage, type of healthcare facility visited (public/private/other), lifestyle factors included tobacco or alcohol consumption and diabetes as comorbidity.

The region was classified as North (Jammu & Kashmir, Himachal Pradesh, Punjab, Chandigarh, Uttarakhand, Haryana, NCT of Delhi, Rajasthan, and Ladakh); Central (Uttar Pradesh, Madhya Pradesh, and Chhattisgarh); East (Bihar, West Bengal, Jharkhand, and Odisha); Northeast (Sikkim, Arunachal Pradesh, Nagaland, Manipur, Mizoram, Tripura, Meghalaya, and Assam); West (Gujarat, Dadra & Nagar Haveli, Daman & Diu, Maharashtra, and Goa); South (Andhra Pradesh, Karnataka, Lakshadweep, Kerala, Tamil Nadu, Puducherry, Andaman & Nicobar Islands, and Telangana) [20].

Waist-hip-ratio was as low (cut-off value ≤ 0.9 for males and ≤ 0.8 for females) and high (cut-off value > 0.9 for male and > 0.8 for females) based on WHO recommendation [21].

Age- and sex-specific BMI values were used to categorize adolescents as underweight, normal, overweight or obese, as recommended by CDC [22]. The age-specific cut-off values for each BMI category were determined based on the growth charts for girls and boys separately [23, 24].

Data analysis

All the values of the variables were checked for their plausibility. We accessed the household member-recoded file (IAPR7ADT) with individual-level data. Appropriate weights were applied throughout the analysis for calculating the adjusted proportions and their 95% confidence interval (CI) using “svy” prefix.

The hypertension prevalence, proportions of aware, on treatment, and under control among adolescents in the age group of 15–19 years were reported, along with 95% confidence intervals (95% CI). Variables with a statistically significant association (p < 0.05) in bivariate analysis were included in the regression model. Multiple logistic regression was performed to find the predictors for hypertension awareness, treatment seeking, and its control. Such as linearity with logit, independence of observation, multicollinearity. No influential outliers were detected. Goodness of fit for each model was assessed using the Hosmer-Lemeshow test [25].The analysis was performed in STATA version 15.1 (Stata Corp., College Station, TX, USA).

Ethical considerations

This study is a secondary data analysis of publicly available NFHS-5 data. All the respondents who took the survey provided their voluntary written and informed consent. NFHS-5 received ethical clearance from the ethical review board of the International Institute of Population Sciences (IIPS), Mumbai, India. Permission was also obtained from IIPS to conduct this analysis [18].

The sociodemographic and health related characteristics of the participants stratified by their hypertension awareness, treatment status, and control status are reported in Table 1.

There were 10,862 older adolescents that were identified as hypertensive resulting in a prevalence rate of 5.08% (95% CI: 4.94% to 5.23%). Among the hypertension cases, awareness of their hypertensive status was observed in 42.26% (95% CI: 40.69%, 43.64%) participants, whereas 43.70% (95% CI: 41.73% to 45.70%) of those aware of their hypertension status were receiving antihypertensive treatment, of which 85.88% (95% CI: 83.83% to 87.71%) attained blood pressure control. Among the adolescent population the mean (SD) age of hypertensive adolescents was 17.19 (0.02) years and 50.78% (95% CI: 49.51% to 52.06%) were males.

Table 2 reports the distribution of both incident cases detected on screening [59.48% (95% CI: 58.01, 60.94)] and old cases [40.52% (95% CI: 39.06, 41.99)]. Among males, 70.51% (95% CI: 68.90, 72.07) were incident cases. A higher proportion of newly diagnosed or incident cases were observed among who were either overweight [64.78% (95% CI: 58.47, 70.63)] or obese [73.10% (65.23, 79.74)]. Further, adolescents residing in the urban areas [62.14% (95% CI: 59.50, 64.71)] or those belonging to richest wealth quintile [63.90% (95% CI: 60.85, 66.85)] had a majority of incident cases. The proportion of incident cases was notably elevated among those who consumed either tobacco [70.90% (95% CI: 66.19, 75.21)] or alcohol [72.99% (95% CI: 65.55, 79.33)]. Adolescents with diabetes had a higher proportion of previously diagnosed hypertension cases [87.41% (95% CI: 42.03, 91.35)] whereas the non-diabetic adolescents had a higher proportion of newly diagnosed hypertension cases [61.85% (95% CI: 60.57, 63.12)].

On adjusted analysis, females when compared to males [aOR= 0.80 (0.71, 0.90)] and those with health insurance [aOR= 0.92 (0.85, 0.99)] had lower odds of being hypertensive. On the other hand, adolescents with diabetes compared to non-diabetes [aOR= 10.14 (7.92, 12.98)], higher compared to lower waist-hip ratio [aOR= 1.14 (1.05, 1.23)], obese compared to underweight [aOR= 2.30 (1.82, 2.91)] and those currently not living with their partner [aOR= 1.99 (1.79, 4.96)] had higher odds of being hypertensive (Table 3, model 1).

Factors that were independently associated with awareness of existing hypertension status in adolescents included, female gender [aOR= 2.34 (1.89, 2.90)], those not living with partner [aOR= 4.06 (1.10, 15.05)], belonging to the richest wealth quintile [aOR= 1.42 (1.09, 1.85)], those living in rural residence [aOR= 1.13 (0.93, 1.36)], having diabetes [aOR= 13.74 (7.80, 24.23)] and resided in northeast [aOR= 1.57 (1.21, 2.03)] or eastern parts of India [aOR= 1.94 (1.54, 2.45)] had higher awareness of their hypertensive status compared to the reference categories. Whereas obese adolescents [aOR= 0.35 (0.22, 0.54)] and those smoking tobacco [aOR= 0.58 (0.35, 0.94)] had significant reduced awareness of their hypertension status indicative of being undiagnosed. Moreover, awareness was also less among individuals with higher hip-to-waist ratio [aOR= 0.95 (0.83, 1.10)] and among those who consume alcohol [aOR= 0.66 (0.32, 1.33)], however, these values were not statistically significant (Table 3, model 2).

Factors that were independently associated with intake of antihypertensive drugs among adolescents aware of their hypertensive status included, those with a higher waist-hip ratio [aOR= 1.26 (1.03, 1.55)], living in western [aOR= 2.63 (1.63, 4.27)] or northeast parts [aOR= 2.06 (1.45, 2.92)] of India had significantly higher odds of being on treatment for the management of hypertension. However, adolescents with diabetes [aOR= 0.21 (0.12, 0.36)], tobacco smokers [aOR= 0.49 (0.21, 1.11)], residing in central parts of India [aOR= 0.76 (0.57, 0.99)] had significantly lower odds of being on antihypertensive treatment (Table 3, model 3).

Factors that were independently associated with attainment of blood pressure control among the adolescents with hypertension that were taking antihypertensive medications included those with a lower waist-to-hip ratio (Table 3, model 4).

Table 4 describes the prevalence of hypertension and proportions of treatment cascade variables (aware, on treatment and under control) across all the states and union territories of India. The highest prevalence of hypertension was observed in the Northeastern states including Sikkim (12.60%), Arunachal Pradesh (9.81%) and Mizoram (9%), whereas, the lowest prevalence was observed in Lakshadweep and Kerala. States with the highest awareness levels of their high blood pressure status were Bihar (64.30%) and Jammu & Kashmir (59.64%), while the lowest awareness levels were observed in Chandigarh (14.48%). Meghalaya (81.20%) reported the highest percentage of hypertensive adolescents taking blood pressure medication (Figure 2).

The present study findings suggest that hypertension among older adolescents in India is a neglected public health priority wherein less than half of the cases are diagnosed and less than half of those diagnosed are treated with antihypertensive medication despite its high effectiveness in controlling blood pressure levels in this vulnerable population. However, the prevalence of hypertension amongst older adolescents in India observed in this study (5.08%) is lower compared to a systematic review by Daniel et. al. (2020) which estimated 7.6% (95% CI: 6.1 to 9.1%) hypertensive adolescents in the 10–19 age group although there were considerable heterogeneity in the included studies [10]. The comprehensive national nutrition survey (CNNS) reported a high prevalence of hypertension (25.1%) in children aged > = 13 years. The nearly five times higher prevalence reported in the CNNS compared to this study can partially be explained by the application of the American Academy of Paediatrics (AAP) definition and divergence in the blood pressure measurement method [11]. In contrast, the prevalence of young hypertension (15–39 years) in the fourth round of the national family health survey was 13.8% [26]. These discrepant findings indicate there is need for incorporation of standardized evidence-based guidelines for measuring hypertension among adolescents within India’s national program for control of noncommunicable diseases (NPNCD), and the India National Hypertension Control Initiative [27, 28].

In this study, adolescents with obesity and a higher waist-hip-ratio were more likely to have hypertension. Adolescents aged > = 13 years in the CNNS study with obesity (high BMI) also had two times higher odds of having hypertension [11]. Furthermore, in this study, adolescents with diabetes, also had higher odds of having hypertension, a finding consistent with the CNNS study, suggesting the shared common risk factors mediating the risk of both these conditions [11].

This study highlights gender disparities in health awareness among adolescents. While hypertension prevalence was greater among males, females exhibited significantly higher awareness of their hypertensive condition. This observation aligns well with a study on young adults investigating gender variances in hypertension prevalence and awareness [29].The higher health consciousness among women may account for this trend, as they tend to be more proactive in seeking health-related information [30].

However, adolescents with obesity despite greater awareness of their hypertension status were less likely to avail anti-hypertensive treatment and had poor blood pressure control. Studies conducted in China [31], Kenya [32] and Israel [33] have also observed reduced control of hypertension among obese adolescents, a phenomenon that can be attributed to both medication non-adherence from lack of initiation of regular antihypertensive treatment, and insufficient lifestyle modifications especially healthy diet and exercise although the latter was not assessed in the present study [34]. Similarly, in this study, adolescents with higher waist-to-hip ratio despite antihypertensive treatment had suboptimal blood pressure control. Previous studies also suggest a significant association of WHR with increased blood pressure in adolescents even with normal BMI [35–38].

In this study, adolescents reporting the consumption of tobacco or alcohol did not have elevated risk of hypertension. However, previous studies have suggested that adolescents with daily consumption of alcohol and tobacco had higher likelihood of being hypertensive [39, 40]. The anomaly could be possibly attributed to the social desirability bias and understating addiction behaviour. Nevertheless, in the present study, adolescents consuming tobacco or alcohol were also less aware of their hypertension status and had also lower likelihood of obtaining antihypertensive treatment indicative of poor health seeking behaviour in presence of addictions, a finding consistent with previous chronic disease research [41]. These findings indicate the need for supportive tobacco cessation and de-addiction service integration within the ambit of India’s adolescent friendly health services including peer support [42]

Adolescents with diabetes comorbidity had greater awareness of their hypertensive status which is likely due to combined medical screening for both these conditions but surprisingly adolescents with diabetes were less likely to be on anti-hypertensive medications suggestive of infrequent contact with the health system and possibly reduced belief in medications. Studies conducted in US and Brazil have highlighted the challenge of poor adherence of anti-hypertensive medications among patients with diabetes [43–45]. A study from Brazil study further explained that lack of access healthcare setting, especially in rural areas, could explain the non-adherence to some extent [46].

Health systems in India show significant geographical diversity across states due to health being considered as a state subject with regard to delivering essential health services. Our findings shows that the pre-existing hypertension awareness levels were higher in Northeastern, Southern and Eastern parts of India. This aligns with the finding of a cross sectional study wherein a higher level of awareness was observed in the Northeastern and eastern parts of India [43]. Our study shows lower levels of treatment in Central and Southern parts of India as compared to Northern parts which is also consistent with the findings of another study where the treatment levels were lower in the central compared to the Northern parts of India [47].

Our study findings have important public health implications for India’s NP-NCD. First, although the prevalence of hypertension among older adolescents in India is significantly lower compared to both young and middle-aged adults (NFHS-5), and older and elderly adults (LASI), a majority of adolescents remain undiagnosed [48, 49]. Unfortunately, screening of hypertension for adolescents in India is not uniformly mandated, neither opportunistically, nor through the community-based screening approach by frontline health workers which is advocated only for adults. To address the neglected burden of hypertension among adolescents, policymakers should undertake early detection of elevated blood pressure through initiation of hypertension screening within school health programmes and sensitizing healthcare providers for opportunistic screening at paediatric OPDs [10]. Second,, high-risk screening should be initiated among adolescents with higher BMI and WHR for lifestyle related risk factors and associated diseases [50].

There are certain limitations of the study. The care continuum exhibits significant variance at the state and district levels, which cannot be fully captured by national mean values. Responses related to lifestyle factors (such as consumption of tobacco and alcohol) were subject to social-desirability bias since these were self-reported. Additionally, this study utilized cross-sectional data and hence, causality cannot be determined. The survey did not examine the individuals' physical activity; thus, we were unable to assess its relationship with blood pressure control.

Nearly five in every hundred older adolescents in India are clinically hypertensive with significantly higher odds associated with obesity and male gender. Awareness and utilization of antihypertensive treatment was lower than the classical rule of halves signifying deficiencies in hypertension screening and management strategies for older adolescents within existing public health policy. Collaborative efforts of healthcare providers, educators, parents, and policymakers is crucial for improving long-term cardiovascular health in this young cohort.

Availability of data and materials: All data used in this analysis can be downloaded directly from the DHS program (after registration) at: https://dhsprogram.com/data/available-datasets.cfm.

Acknowledgments: We would like to express our deepest gratitude to the International Institute for Population Sciences for giving us the NFHS dataset and authorizing us to conduct the research.

Authorship:

Conceived and/or designed the work that led to the submission: SR, SB; acquired data: All Authors, and/or played an important role in interpreting the results: All Authors
Drafted or revised the manuscript: SR, SB
Approved the final version: SB
Agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: SB

Funding: Nil

Ethics statement: This study is a secondary data analysis utilizing NFHS-5 data that is accessible in the public domain. Participants in the survey actively provided their consent. The survey received ethical approval from the International Institute of Population Sciences' ethical review board (IIPS). The Demographic Health Survey (DHS), after assessing the submitted proposal, approved access to the dataset.

Conflict of interest: The authors have declared that no competing interests exist.

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Table 1: Distribution of hypertension, awareness, on treated and under control among adolescents in India (2019-2021)

	Total Hypertensives		Aware		On Treatment		Under Control
	N (10862)	(%; 95% CI) (5.08; 4.94, 5.23)	N (4397)	(%, 95% CI) 42.26; 40.69, 43.64)	N (1913)	(%, 95% CI) 43.70; 41.73, 45.70)	N (1647)	(%, 95% CI) 85.88; 83.83, 87.71)
Socio-demographic factors
*Age (in years)* Mean (SD)	10862	17.19 (0.02)	4397	17.14 (0.03)	1913	17.01 (0.37)	1647	16.98 (0.03)
*Gender*
Male	5559	50.78; (49.51, 52.06)	1726	38.36 (36.41, 40.35)	792	41.46 (38.79, 44.19)	654	39.82 (37.00, 42.71)
Female	5303	49.22 (47.94, 50.49)	2671	61.64 (59.65, 63.59)	1121	58.54 (55.81, 61.21)	993	60.18 (57.29, 63.00)
*Marital Status*
Never Married	9986	90.91 (90.11, 91.64)	3882	86.50 (84.99, 87.87)	1744	90.15 (88.13, 91.86)	1501	90.14 (87.90, 92.00)
Currently Married	859	8.93 (8.20, 9.72)	505	13.21 (11.85, 14.70)	165	9.40 (7.79, 11.31)	142	9.34 (7.59, 11.43)
Widowed/ Separated/ Divorced	15	0.17 (0.08, 0.35)	9	0.29 (0.12, 0.75)	3	0.45 (0.10, 2.06)	3	0.53 (0.11, 2.39)
*Education*
No Education	398	3.53 (3.09, 4.03)	160	3.74 (3.04, 4.58)	69	3.24 (2.50, 4.20)	61	3.38 (2.56, 4.44)
Primary	602	5,48 (4.95, 6.06)	239	5.54 (4.71, 6.50)	106	5.23 (4.27, 6.39)	90	5.08 (4.07, 6.33)
Secondary	9095	82.64 (81.91, 83.84)	3706	83.63 (82.14, 85.02)	1624	86.00 (84.31, 87.70)	1404	86.31 (84.38, 88.04)
Higher	764	8,09 (7.39, 8.85)	282	7.09 (6.15, 8.17)	113	5.44 (4.41, 6.69)	91	5.23 (4.13, 6.60)
Anthropometric Factors
*Waist-to-Hip Ratio*
<=0.9 for males and <=0.8 for females	1954	32.99 (31.32, 34.71] )	872	31.29 (29.04, 33.62)	352	28.39 (25.36, 31.63)	322	29.60 (26.35, 33.06)
Cutoffs > 0.9 for males and >0.8 for females	4154	67.01 (65.29, 68.68)	2023	68.71 (66.38, 70.96)	870	71.61 (68.37, 74.64)	750	70.40 (66.94, 73.65)
BMI (Kg/m2)
Underweight	1032	16.82 (15.71, 17.99)	506	17.31 (15.69, 19.07)	206	15.97 (13.78, 18.43)	184	16.41 (14.04, 19.10)
Normal	4560	74.49 (73.14, 75.80)	2213	76.70 (74.77, 78.52)	958	79.72 (77.05, 82.15)	840	79.61 (76.72, 82.23)
Overweight	130	5.54 (4.88, 6.29)	124	4.21 (3.44, 5.15)	43	3.00 (2.21, 4.31)	36	2.92 (2.01, 4.22)
Obese	188	3.15 (2.66, 3.72)	52	1.78 (1.31, 2.41)	15	1.22 (0.68, 2.19)	12	1.05 (0.53, 2.06)
*Community Level Factors*
*Wealth Index*
Poorest	2452	21.42 (20.34, 22.54)	1012	22.81 (21.10, 24.61)	480	23.73 (21.54, 26.07)	423	24.64 (22.24, 27.20)
Poorer	2623	23.11 (22.03, 24.22)	1114	24.92 (23.21, 26.71)	489	24.71 (22.59, 26.97)	434	25.46 (23.14, 27.93)
Middle	2309	20.90 (19.83, 22.02)	937	20.93 (19.36, 22.58)	397	20.09 (18.08, 22.25)	334	19.68 (17.54, 22.01)
Richer	1886	16.27 (17.22, 19.36)	725	16.16 (14.67, 17.77)	291	16.31 (14.39, 18.40)	240	15.70 (13.69, 17.95)
Richest	1592	16.30 (15.24, 17.42)	609	15.19 (13.57, 16.96)	256	15.17 (13.09, 17.52)	216	14.52 (12.40, 16.93)
*Religion*
Hindu	8052	81.26 (80.08, 82.38)	3176	80.76 (78.93, 82.46)	1339	68.88 (66.00, 71.61)	1186	70.58 (67.48, 73.50)
Muslim	1289	13.62 (12.60, 14.72)	614	14.71 (13.11, 16.46)	289	16.27 (14.16, 18.63)	218	14.74 (12.52, 17.26)
Others	1521	5.12 (4.58, 5.71)	607	4.53 (3.84, 5.35)	285	14.85 (12.68, 17.33)	243	14.68 (12.36, 17.36)
*Residence*
Urban	2553	28.51 (26.95, 30.12)	998	26.27 (23.95, 28.72)	403	25.26 (22.58, 28.14)	347	23.37 (22.50, 28.47)
Rural	8309	71.49 (69.88, 73.05)	3399	73.73 (71.28, 76.05)	1510	74.74 (71.86, 77.42)	1300	74.63 (71.53, 77.50)
*Geographical Region*
North	2139	14.90 (13.94, 15.90)	801	13.04 (11.77, 14.43)	338	17.50 (15.49, 19.71)	269	16.25 (14.14, 18.61)
Central	3419	34.19 (32.66, 35.74)	1289	32.96 (30.63, 35.38)	469	21.81 (19.67, 24.12)	418	22.56 (20.28, 25.02)
East	1683	21.29 (19.90, 22.74)	884	27.95 (25.64, 30.38)	366	19.83 (17.60, 22.26)	336	21.27 (18.81, 23.97)
North East	1597	3.10 (2.79, 3.44)	707	3.91 (3.37, 4.54)	401	23.41 (20.66, 26.41)	336	23.37 (20.39, 26.65)
West	859	11.69 (10.57, 12.92)	256	7.81 (6.63, 9.28)	166	8.23 (6.80, 9.93)	140	7.72 (6.25, 9.50)
South	1165	14.44 (13.66, 16.09)	460	14.29 (12.40, 16.41)	173	9.21 (7.71, 10.98)	148	8,82 (7.34, 10.56)
Health-seeking behavior
*Covered by health Insurance/ Health Scheme*
Yes	4549	39.71 (38.32, 41.12)	1774	37.13 (35.69, 40.02)	831	43.56 (40.85, 46.31)	733	44.34 (41.44, 47.28)
No	6252	60.29 (58.88, 61.68)	2589	62.17 (59.98, 64.31)	1069	56.44 (53.69, 59.15)	902	55.66 (52.72, 58.56)
*Type of healthcare facility visited*
Public	5894	45.81 (44.40, 47.22)	2354	44.73 (42.58, 46.90)	1098	57.76 (54.95, 60.51)	925	56.54 (53.52, 59.51)
Private	4799	52.22 (50.82, 53.63)	1973	53.38 (51.22, 55.53 )	787	40.81 (38.08, 43.60)	696	45.94 (39.00, 44.95)
Others	169	1.97 ([1.60, 2.41)	70	1.89 (1.42, 2.51)	28	1.43 (0.92, 2.20)	26	1.52 (0.96, 2.39)
Lifestyle factors
*Tobacco*
Yes	582	4.18 (3.74, 4.67)	177	3.04 (2.48, 3.72)	73	3.41 (2.60, 4.46)	64	3.32 (2.49, 4.42)
No	10263	95.82 (95.33, 96.26)	4214	96.96 (96.28, 97.52)	1835	96.59 (95.54, 97.40)	1580	96.68 (95.58, 97.51)
*Alcohol*
Yes	216	1.42 (1.16, 1.73)	60	0.95 (0.63, 1.42)	26	1.11 (0.70, 1.74)	23	1.05 (0.67, 1.64)
No	10627	98.58 (98.27, 98.84)	4331	99.05 (98.58, 99.37)	1883	98.89 (0.70, 1.74)	1622	98.95 (98.36, 99.33)
Comorbidity
*Diabetes*
Yes	323	1.07 (2.57, 3.55)	293	6.45 (5.45, 7.62)	48	2.44 (1.76, 3.39)	37	2.19 (1.53, 3.13)
No	10358	96.98 (96.45, 97.43)	4043	93.55 (92.38, 94.55)	1830	97.56 (96.61, 98.24)	1591	97.81 (96.87, 98.47)

Table 2: Distribution of Hypertensive adolescents with respect to old and new cases.

Variables	Old cases (%, 95% CI) 40.52 (39.06, 41.99)	*New cases (%, 95% CI) 59.48 (58.01, 60.94)**
Socio-demographic factors
*Age (in years)* Mean (SD)	17.19 (1.39)	17.24 (1.37)
*Gender*
Male	29.49 (27.93, 31.10)	70.51 (68.90, 72.07)
Female	49.74 (48.02, 51.46)	50.26 (48.54, 51.98)
*Marital Status*
Never Married	37.56 (36.26, 38.88)	62.44 (61.12, 63.74)
Currently Married	58.08 (54.05, 62.00)	41.92 (38.00, 45.95)
Widowed/ Separated/ Divorced	77.15 (49.69, 92.03)	22.85 (7.97, 50.31)
*Education*
No Education	41.46 (36.08, 47.06)	58.54 (52.94, 63.92)
Primary	38.86 (34.40, 43.51)	61.14 (56.49, 65.60)
Secondary	39.64 (38.27, 41.02)	60.36 (58.98, 61.73)
Higher	34.60 (30.71, 38.82)	65.40 (61.28, 69.29)
Anthropometric Factors
*Waist-to-Hip Ratio*
<=0.9 for males and <=0.8 for females	44.26 (41.55, 47.00)	55.74 (53.00, 58.45)
Cutoffs > 0.9 for males and >0.8 for females	47.92 (45.98, 49.87)	52.08 (50.13, 54.02)
BMI (Kg/m2)
Underweight	48.02 (44.39, 51.66)	51.98 (48.34, 54.61)
Normal	48.10 (46.23, 49.97)	51.90 (50.03, 53.77)
Overweight	35.22 (29.37, 41.53)	64.78 (58.47, 70.63)
Obese	26.90 (20.26, 34.77)	73.10 (65.23, 79.74)
*Community Level Factors*
*Wealth Index*
Poorest	41.18 (38.71, 43.70)	58.82 (56.30, 61.29)
Poorer	41.98 (39.60, 44.39)	58.02 (55.61, 60.40)
Middle	39.20 (36.77, 41.67)	60.80 (58.33, 63.23)
Richer	36.77 (34.11, 39.50)	63.23 (60.50, 65.89)
Richest	36.10 (33.15, 39.15)	63.90 (60.85, 66.85)
*Religion*
Hindu	38.68 (37.26, 40.12)	61.32 (59.88, 62.74)
Muslim	46.15 (42.51, 49.82)	53.85 (50.18, 57.49)
Others	36.47 (33.10, 36.99)	63.53 (60.01, 66.90)
*Residence*
Urban	37.86 (35.29, 40.50)	62.14 (59.50, 64.71)
Rural	39.85 (38.41, 41.31)	60.15 (58.69, 61.59)
*Geographical Region*
North	35.68 (33.13, 38.32)	64.32 (61.68, 66.87)
Central	36.78 (34.50, 39.12)	63.22 (60.88, 65.50)
East	51.48 (48.25, 54.69)	48.52 (45.31, 51.75)
North East	43.25 (39.70, 46.87)	56.75 (53.13, 60.30)
West	26.80 (23.15, 30.79)	73.20 (69.21, 76.85)
South	38.44 (34.66, 42.36)	61.56 (57.64, 65.34)
Health-seeking behavior
*Covered by health Insurance/ Health Scheme*
Yes	38.09 (36.26, 39.96)	61.91 (60.04, 63.74)
No	39.97 (38.35, 41.63)	60.03 (58.37, 61.65)
*Type of healthcare facility visited*
Public	38.96 (37.29, 40.65)	61.04 (59.35, 62.71)
Private	39.68 (37.89, 41.50)	60.32 (58.50, 62.11)
Others	39.52 (31.20, 48.49)	60.48 (51.51, 68.80)
Lifestyle factors
*Tobacco*
Yes	29.10 (24.79, 33.81)	70.90 (66.19, 75.21)
No	39.86 (38.56, 41.18)	60.14 (58.82, 61.44)
*Alcohol*
Yes	27.01 (20.67, 34.45)	72.99 (65.55, 79.33)
No	39.56 (38.28, 40.85)	60.44 (59.15, 61.72)
Comorbidity
*Diabetes*
Yes	87.41 (42.03, 91.35)	12.59 (8.65, 17.97)
No	38.15 (36.88, 39.43)	61.85 (60.57, 63.12)

* Detected on screening during the survey

Table 3: Determinants of treatment-seeking behaviour among older Hypertensive adolescents in India

	Hypertensives Model 1			Aware Model 2			Treated Model 3			Under Control Model 4
	U OR	A OR	p-value	U OR	A OR	p-value	U OR	A OR	p-value	U OR	A OR	p-value
*Socio-demographic Characteristics*
*Gender*
Male	1	1		1	1		1			1
Female	0.84 (0.81, 0.88)	0.80 (0.71, 0.90)	<0.001	2.93 (2.15, 2.66)	2.34 (1.89, 2.90)	<0.001	0.84 (0.71, 1.01)			1.77 (1.18, 2.66)
*Marital Status*
Never Married	1	1		1	1		1	1		-
Currently Married	1.43 (1.31, 1.56)	1.66 (1.49, 1.84)		2.48 (2.05, 2.99)	1.83 (1.50, 2.23)		0.63 (0.49, 0.82)	0.59 (0.45, 0.77)		-
Widowed/ Separated/ Divorced	2.23 (1.07, 4.65)	1.99 (1.79, 4.96)	<0.001	4.40 (1.13, 17.05)	4.06 (1.10, 15.05)	<0.001	0.36 (0.06, 2.22)	0.62 (0.09, 4.10)	<0.001	-
*Education*
No Education	1			1			1			1
Primary	1.01 (0.87, 1.17)			0.92 (0.66 1.28)			1.09 (0.66, 1.80)			1.15 (0.28, 4.80)
Secondary	0.99 (0.88, 1.13)			0.92 (0.71, 1.19)			1.12 (0.76, 1.64)			0.99 (0.28, 3.53)
Others	1.22 (1.05, 1.41)			0.73 (0.53, 1.00)			0.81 (0.49, 1.31)			0.72 (0.17, 2.93)
Anthropometric Measures
*BMI*
Under-weight	1	1		1	1		1	1		1	1
Normal Weight	1.06 (0.98, 1.16)	1.01 (0.92, 1.12)		1.00 (0.85, 1.17)	0.91 (0.76, 1.08)		1.23 (0.97, 1.56)	1.13 (0.88, 1.45)		0.78 (0.46, 1.33)	0.84 (0.49, 1.42)
Overweight	1.71 (1.46, 1.99)	1.55 (1.29, 1.86)		0.59 (0.43, 0.79)	0.56 (0.41, 0.77)		0.70 (0.44, 1.13)	0.54 (0.32, 0.93)		0.54 (0.20, 1.47)	0.61 (0.23, 1.68)
Obese	2.55 (2.09, 3.11)	2.30 (1.82, 2.91)	<0.001	0.38 (0.26, 0.56)	0.35 (0.22, 0.54)	<0.001	0.64 (0.31, 1.33)	0.53 (0.27, 1.02)	<0.001	0.35 (0.08, 1.49)	0.39 (0.09, 1.66)	0.18
*Waist to Hip Ratio*
<=0.9 for males and <=0.8 for females	1	1		1	1		1	1		1	1
Cutoffs > 0.9 for males and >0.8 for females	1.15 (1.07, 1.23)	1.14 (1.05, 1.23)	0.002	1.17 (1.00, 1.35)	0.95 (0.83, 1.10)	0.13	1.29 (1.04, 1.59)	1.26 (1.03, 1.55)	0.026	0.45 (0.26, 0.79)	0.62 (0.38, 1.02)	0.058
Community level factors
*Wealth Index*
Poorest	1			1	1		1			1
Poorer	1.05 (0.98, 1.13)			1.02 (0.88, 1.19)	1.17 (.98, 1.41)		0.81 (0.65, 1.02)			0.87 (0.52, 1.48)
Middle	1.04 (0.97, 1.12)			0.90 (0.76, 1.05)	1.28 (1.05, 1.57)		0.71 (0.56, 0.90)			0.99 (0.57, 1.73)
Richer	1.02 (0.94, 1.10)			0.73 (0.62, 0.87)	1.18 (0.95, 1.48)		0.86 (0.66, 1.11)			0.68 (0.37, 1.26)
Richest	1.10 (1.01, 1.19)			0.79 (0.66, 0.96)	1.42 (1.09, 1.85)	0.002	0.76 (0.58, 1.01)			0.58 (0.29, 1.14)
*Religion*
Hindu	1			1			1			1
Muslim	1.01 (0.94, 1.09)			1.16 (0.97, 1.38)			1.30 (1.02, 1.68)			0.66 (0.39, 1.11)
Others	1.41 (1.30, 1.54)			0.83 (0.66, 1.03)			1.29 (0.92, 1.82)			0.57 (0.24, 1.34)
*Residence*
Urban	1			1	1		1			1
Rural	0.98 (0.91, 1.04)			1.21 (1.05, 1.40)	1.13 (0.93, 1.36)	0.06	1.13 (0.91, 1.41)			0.99 (0.60, 1.62)
*Geographical Region*
North	1	1		1	1		1	1		1
Central	1.04 (0.96, 1.12)	1.11 (0.99, 1.22)		1.17 (1.00, 1.37)	1.02 (0.84, 1.23)		0.63 (0.49, 0.80)	0.76 (0.57, 0.99)		1.31 (0.79, 2.17)
East	0.85 (0.78, 0.93)	0.85 (0.75, 0.95)		2.12 (1.77, 2.54)	1.94 (1.54, 2.45)		1.10 (0.85, 1.44)	1.06 (0.79, 1.43)		1.57 (0.83, 2.99)
North East	1.20 (1.09, 1.33)	0.91 (0.79, 1.07)		1.94 (1.56, 2.41)	1.57 (1.21, 2.03)		2.83 (2.03, 3.95)	2.06 (1.45, 2.92)		0.96 (0.55, 1.69)
West	0.78 (0.69, 0.87)	0.83 (0.71, 0.98)		0.67 (0.53, 0.86)	0.65 (0.48, 0.88)		2.76 (1.87, 4.09)	2.63 (1.63, 4.27)		0.72 (0.38, 1.38)
South	0.84 (0.76, 0.93)	0.81 (0.70, 0.92)	<0.001	1.17 (0.94, 1.45)	1.19 (0.91, 1.54)	<0.001	0.73 (0.53, 1.01)	0.86 (0.59, 1.25)	<0.001	0.75 (0.37, 1.59)
Health-seeking behavior
*Covered by health Insurance/ Health Scheme*
Yes	0.89 (0.85, 0.94)	0.92 (0.85, 0.99)	0.02	0.87 (0.78, 0.98)	0.89 (0.78, 1.02)	0.06	1.13 (0.94, 1.34)			1.18 (0.78, 1.77)
No	1	1		1	1		1			1
*Type of healthcare facility visited*
Public	1			1			1	1		1
Private	0.99 (0.95, 1.05)			1.08 (0.97, 1.21)			0.81 (0.68, 0.96)	0.80 (0.66, 0.98)		1.40 (0.93, 2.11)
Others	1.01 (0.84, 1.21)			0.97 (0.65, 1.44)			0.81 (0.44, 1.48)	0.92 (0.47, 1.79)	0.44	2.66 (0.59, 11.97)
Lifestyle Factors
*Tobacco*
Yes	1.21 (1.09, 1.35)	0.86 (0.66, 1.11)	0.25	0.59 (0.46, 0.76)	0.58 (0.35, 0.94)	0.01	0.61 (0.40, 0.92)	0.49 (0.21, 1.11)	0.06	1.07 (0.36, 3.19)
No	1	1		1	1		1	1		1
*Alcohol*
Yes	1.35 (1.15, 1.59)	1.07 (0.67, 1.71)	0.77	0.53 (0.33, 0.86)	0.66 (0.32, 1.33)	0.81	0.39 (0.19, 0.82)	1.07 (0.37, 3.14)	0.85	12.75 (2.59, 62.81)	0.62 (0.08, 5.08)	0.06
No	1	1		1	1		1	1		1	1
Comorbidity
*Diabetes*
Yes	8.46 (7.05, 10.16)	10.14 (7.92, 12.98)	<0.001	13.34 (8.36, 21.28)	13.74 (7.80, 24.23)	<0.001	0.29 (0.19, 0.43)	0.21 (0.12, 0.36)	<0.001	0.45 (0.19, 1.07)
No	1	1		1	1		1	1		1

Model 1 Goodness of fit p-value= 0.4771

Model 2 Goodness of fit p-value= 0.6863

Model 3 Goodness of fit p-value= 0.3905

Model 4 Goodness of fit p-value= 0.9471

Table 4: Prevalence of different stages of hypertension treatment cascade across the states and union territories in India (NFHS-5, 2019-21).

State	NITI Aayog Index	Hypertensive Individuals (%, 95% CI)	Aware (%, 95% CI)	On Treatment (%, 95% CI)	Under Control (%, 95% CI)
Andaman & Nicobar Islands	44.74	5.34 (3.49, 8.07)	41.54 (19.60, 67.44)	54.67 (17.48, 87.29)	78.86 (37.41, 95.88)
Andhra Pradesh	69.95	3.65 (2.91, 4.57)	32.22 (23.18, 42.81)	66.76 (47.59, 81.63)	78.66 (51.10, 92.86)
Arunachal Pradesh	33.92	9.81 (8.43, 11.39)	37.54 (31.26, 44.26)	40.39 (31.74, 49.68)	78.40 (65.16, 87.57)
Assam	47.74	4.32 (3.80, 4.90)	57.57 (50.84, 60.03)	74.53 (66.05, 81.49)	82.02 (72.90, 88.55)
Bihar	31.00	5.14 (4.69, 5.64)	64.30 (59.63, 68.72)	46.36 (40.85, 51.97)	97.12 (92.33, 98.95)
Chandigarh	62.52	5.04 (2.88, 8.68)	14.48 (2.09, 57.30)	0.00	0.00
Chhattisgarh	50.70	6.10 (5.45, 6.82)	23.68 (19.23, 28.80)	39.78 (30.38, 49.99)	90.20 (79.63, 95.59)
Dadra and Nagar Haveli & Daman and Diu	66.20	3.96 (2.54, 6.15)	32.71 (16.68, 34.14)	63.91 (27.96, 88.99)	48.99 (12.03, 87.08)
Delhi	49.84	6.25 (5.36, 7.28)	30.41 (23.25, 38.67)	47.09 (33.00, 61.67)	96.74 (79.83, 99.55)
Goa	53.68	6.35 (3.38, 11.63)	59.46 (24.94, 86.62)	81.17 (32.21, 97.51)	49.30 (11.46, 87.95)
Gujarat	63.59	4.06 (3.55, 4.64)	29.29 (23.45, 35.90)	55.11 (44.27, 65.49)	89.60 (72.13, 96.63)
Haryana	49.26	5.36 (4.70, 6.11)	35.72 (29.82, 42.06)	31.91 (23.92, 41.14)	85.41 (71.75, 93.10)
Himachal Pradesh	63.17	5.29 (3.82, 7.26)	31.97 (23.72, 41.54)	37.80 (20.57, 58.78)	100.00
Jammu and Kashmir	46.99	6.96 (5.81, 8.32)	59.64 (50.33, 68.31)	51.3 (38.76, 63.68)	39.19 (26.55, 53.47)
Jharkhand	47.55	5.38 (4.73, 6.11)	47.74 (40.91, 54.66)	29.41 (21.89, 38.25))	95.15 (85.31, 98.51)
Karnataka	57.93	5.00 (4.22, 5.92)	49.75 (40.99, 58.52)	30.17 (21.29, 40.85)	69.03 (42.63, 86.99)
Kerala	82.20	2.69 (2.06, 3.51)	41.15 (29.90, 53.41)	68.74 (48.76, 83.56)	92.00 (60.28, 98.87)
Lakshadweep	51.87	2.69 (1.44, 4.97)	35.47 (12.03, 68.85)	67.15 (15.62, 95.76)	100.00
Madhya Pradesh	36.72	4.46 (4.07, 4.87)	27.54 (23.42, 32.08)	45.82 (36.85, 55.07)	97.43 (91.85, 99.22)
Maharashtra	69.14	4.76 (4.18, 5.43)	27.55 (22.30, 33.51)	75.63 (65.21, 83.71)	77.00 (64.04, 86.29)
Manipur	34.26	5.47 (4.22, 7.05)	33.69 (22.97, 46.40)	36.62 (21.89, 54.36)	94.24 (67.36, 99.24)
Meghalaya	43.05	6.81 (5.70, 8.11)	57.42 (47.90, 66.43)	81.20 (68.16, 89.71)	92.54 (84.34, 96.62)
Mizoram	75.77	9.00 (6.62, 12.14)	38.60 (28.48, 50.00)	58.77 (38.01, 76.81)	86.17 (64.99, 95.44)
Nagaland	27.00	5.14 (3.82, 6.88)	45.45 (32.04, 59.56)	26.55 (9.06, 56.72)	75.90 (29.86, 95.88)
Odisha	44.31	4.16 (3.62, 4.79)	26.19 (21.25, 33.14)	42.46 (31.07, 54.70)	79.62 (60.34, 90.94)
Puducherry	50.83	5.19 (3.22, 8.27)	28.41 (14.99, 47.18)	48.09 (13.73, 34.36)	97.71 (79.73, 99.79)
Punjab	58.08	7.30 (6.46, 8.24)	30.75 (25.57, 36.47)	31.30 (22.79, 41.28)	90.36 (75.13, 96.68)
Rajasthan	41.33	4.54 (4.10, 5.02)	42.16 (37.40, 47.08)	49.80 (42.09, 57.51)	86.57 (77.83, 92.21)
Sikkim	55.53	12.60 (9.09, 17.21)	38.37 (26.35, 51.99)	44.87 (23.23, 68.65)	100.00
Tamil Nadu	72.42	5.64 (4.92, 6.44)	39.06 (31.72, 46.94)	21.30 (14.11, 30.84)	83.64 (59.01, 94.78)
Telangana	69.96	4.76 (4.11, 3.51)	36.23 (29.41, 43.66)	50.22 (39.78, 60.64)	89.04 (77.22, 95.12)
Tripura	70.16	5.02 (3.56, 7.03)	50.85 (39.50, 62.11)	46.86 (30.05, 64.40)	89.95 (66.91, 97.54)
Uttarakhand	44.21	6.10 (5.15, 7.22)	26.46 (18.45, 36.40)	36.04 (22.99, 51.53)	81.75 (51.74, 94.93)
Uttar Pradesh	30.57	6.29 (5.95, 6.65)	45.65 (42.69, 48.65)	30.72 (27.03, 34.67)	86.09 (80.78, 90.11)
West Bengal	-	3.53 (2.96, 4.21)	54.50 (45.56, 63.16)	56.52 (43.78, 68.44)	73.82 (56.50, 85.96)

There is NO conflict of interest to disclose.

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Decoding the Hypertension Care Cascade and Unlocking Influential Factors Among Indian Adolescents: Evidence from Nationally representative Cross-sectional survey

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Measurement of BP

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Prevalence of hypertension:

Independent variables:

Data analysis

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