Awareness, treatment, and control of hypertension in adults aged 45 years and over and their spouses in India: A nationally representative cross-sectional study

Sanjay K Mohanty; Sarang P Pedgaonkar; Ashish Kumar Upadhyay; Fabrice Kämpfen; Prashant Shekhar; Radhe Shyam Mishra; Jürgen Maurer; Owen O’Donnell

doi:10.1371/journal.pmed.1003740

. 2021 Aug 24;18(8):e1003740. doi: 10.1371/journal.pmed.1003740

Awareness, treatment, and control of hypertension in adults aged 45 years and over and their spouses in India: A nationally representative cross-sectional study

Sanjay K Mohanty ^1,^*, Sarang P Pedgaonkar ², Ashish Kumar Upadhyay ³, Fabrice Kämpfen ⁴, Prashant Shekhar ³, Radhe Shyam Mishra ³, Jürgen Maurer ⁵, Owen O’Donnell ⁶

Editor: Sanjay Basu⁷

PMCID: PMC8425529 PMID: 34428221

Abstract

Background

Lack of nationwide evidence on awareness, treatment, and control (ATC) of hypertension among older adults in India impeded targeted management of this condition. We aimed to estimate rates of hypertension ATC in the older population and to assess differences in these rates across sociodemographic groups and states in India.

Methods and findings

We used a nationally representative survey of individuals aged 45 years and over and their spouses in all Indian states (except one) in 2017 to 2018. We identified hypertension by blood pressure (BP) measurement ≥140/90 mm Hg or self-reported diagnosis if also taking medication or observing salt/diet restriction to control BP. We distinguished those who (i) reported diagnosis (“aware”); (ii) reported taking medication or being under salt/diet restriction to control BP (“treated”); and (iii) had measured systolic BP <140 and diastolic BP <90 (“controlled”). We estimated age–sex adjusted hypertension prevalence and rates of ATC by consumption quintile, education, age, sex, urban–rural, caste, religion, marital status, living arrangement, employment status, health insurance, and state. We used concentration indices to measure socioeconomic inequalities and multivariable logistic regression to estimate fully adjusted differences in these outcomes. Study limitations included reliance on BP measurement on a single occasion, missing measurements of BP for some participants, and lack of data on nonadherence to medication.

The 64,427 participants in the analysis sample had a median age of 57 years: 58% were female, and 70% were rural dwellers. We estimated hypertension prevalence to be 41.9% (95% CI 41.0 to 42.9). Among those with hypertension, we estimated that 54.4% (95% CI 53.1 to 55.7), 50.8% (95% CI 49.5 to 52.0), and 28.8% (95% CI 27.4 to 30.1) were aware, treated, and controlled, respectively. Across states, adjusted rates of ATC ranged from 27.5% (95% CI 22.2 to 32.8) to 75.9% (95% CI 70.8 to 81.1), from 23.8% (95% CI 17.6 to 30.1) to 74.9% (95% CI 69.8 to 79.9), and from 4.6% (95% CI 1.1 to 8.1) to 41.9% (95% CI 36.8 to 46.9), respectively. Age–sex adjusted rates were lower (p < 0.001) in poorer, less educated, and socially disadvantaged groups, as well as for males, rural residents, and the employed. Among individuals with hypertension, the richest fifth were 8.5 percentage points (pp) (95% CI 5.3 to 11.7; p < 0.001), 8.9 pp (95% CI 5.7 to 12.0; p < 0.001), and 7.1 pp (95% CI 4.2 to 10.1; p < 0.001) more likely to be aware, treated, and controlled, respectively, than the poorest fifth.

Conclusions

Hypertension prevalence was high, and ATC of the condition were low among older adults in India. Inequalities in these indicators pointed to opportunities to target hypertension management more effectively and equitably on socially disadvantaged groups.

In a cross-sectional study, Sanjay K Mohanty and colleagues investigate the awareness, treatment, and control of hypertension amongst adults aged 45 years and over and their spouses in India.

Author summary

Why was this study done?

We found only one study that reported estimated rates of awareness, treatment, and control (ATC) of hypertension in India using a nationally representative sample covering all states, but that study was restricted to adults aged 15 to 49 years.
Another study estimated rates of hypertension ATC among older adults, but that study covered only 6 states.
This study aimed to provide nationally representative estimates of hypertension ATC in the older population of India and to describe differences in these indicators of hypertension management across sociodemographic groups and states.

What did the researchers do and find?

We used a nationally representative sample of adults aged 45 years and over and their spouses covering all states (except one) of India in 2017 to 2018.
We used measured blood pressure (BP) and self-reported diagnosis and treatment for high BP to estimate hypertension prevalence and the percentages of those with hypertension who were aware of their condition, treated for it, and had achieved BP control.
We found that a slight majority of those with hypertension were aware of their condition, around half were being treated, and less than a third had controlled their BP. While these rates indicated substantial gaps in hypertension management among the older population of India, they were higher than estimates previously obtained from samples restricted to, or including, younger people.
We found substantial variation in the indicators of hypertension management across states. Older Indians who were poorer, less educated, socially disadvantaged, male, rural, and working were less likely to be aware, treated, and to have achieved BP control.

What do these findings mean?

Hypertension prevalence is high in India, particularly in the older population. In this critical population group, low rates of ATC point to deficiencies in diagnosis and management of the condition and in the prevention of cardiovascular diseases (CVDs).
Effectively addressing these deficiencies requires subtle targeting of interventions that balances attention to prevalence, which is higher in the high-income states and socioeconomically advantaged groups, with attention to gaps in ATC, which are greater in the low- or middle-income states and disadvantaged groups.

Introduction

Hypertension is a major risk factor for cardiovascular diseases (CVDs) [1,2] that accounted for 44% of the 42 million deaths related to noncommunicable diseases (NCDs) globally in 2019 [3]. Better management of hypertension is critical to accelerating progress toward the Sustainable Development Goal target of a one-third reduction in premature NCD-related mortality by 2030 [4]. To maximize the impact on global health, improvements in care for hypertension need to occur where prevalence is increasing most rapidly, such as in South Asia [5], and where populations are largest, such as in India, which accounts for 18% of global population [6]. These improvements generally need to be targeted on middle-aged and older adults who are at greatest risk.

India is undergoing demographic and epidemiological transitions, as well as economic growth and urbanization, which make targeted, effective hypertension management a population health imperative. The older population aged 45 years and over has been growing twice as fast as the overall population [7], and the NCD share of disability-adjusted life years (DALYs) reached 58% in 2019 [3]. In 2012 to 2014, one quarter of the adult population was estimated to have hypertension, and the prevalence was almost twice as high among older adults aged 65+ [8]. Prevalence varied widely across Indian states and was found to be higher in wealthier groups [8]. The median age of onset of hypertension is estimated to have declined from 61 years in 2004 to 55 years by 2018 [9]. Hypertension is the main risk factor of CVD in the country [10] and accounted for 35.5% of DALYs in 2019, compared with 26.8% globally [3].

The Government of India was quick to adopt the World Health Organization (WHO) Global Action Plan for the Prevention and Control of NCDs [11], targeted a 25% reduction in hypertension prevalence between 2013 and 2025 [12], and committed to population-based screening and management for hypertension and other NCD risk factors [12]. Monitoring the success of such programs, and targeting them effectively, requires evidence on rates of diagnosis, treatment, and blood pressure (BP) control among those with hypertension. However, to our knowledge, there were no estimates of hypertension awareness, treatment, and control (ATC) obtained from a nationally representative sample of the middle-aged and older population throughout the whole of India. Estimates from a nationally representative sample of the population aged 18 to 49 [13] and from samples that included older adults but did not have nationwide coverage [14–20] did not permit, without imposing strong assumptions, evaluation of hypertension management across the country in the age group most prone to the condition and its related risks. Lack of evidence on variation in hypertension ATC among middle-aged and older (hereafter, “older”) adults across states and sociodemographic groups limited assessment of equity and effectiveness of hypertension care and constrained ability to target interventions on the subpopulations of older adults that have the greatest potential to gain.

This study aimed to estimate levels of hypertension ATC in the older population of India and to assess inequalities in these indicators of hypertension management across states and sociodemographic groups.

Methods

Study design and participants

We used data from the Longitudinal Ageing Study in India (LASI) conducted from April 2017 to December 2018. The study used stratified, multistage probability cluster random sampling to select 42,949 households (S1 Text) [21]. In these households, all individuals aged 45 years and over and their spouses were targeted for interview. The survey was conducted in all states and union territories (hereafter, “states”), but data from Sikkim were not available for this study. The sample was representative of the non-institutionalized population aged 45 years and over (and spouses) at the state level, as well as nationally. The response rate was 95.8% at the household level and 87.3% at the individual level [21]. LASI obtained ethical approval from the Health Ministry’s Screening Committee (Government of India) and the Institutional Review Boards at the International Institute for Population Sciences (IIPS) and its collaborating institutions. Written informed consent was obtained from all study participants. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline in reporting the study (S1 Checklist). We did not prespecify the statistical analysis.

Measures

BP was measured using an Omron HEM 7121 automatic digital BP monitor manufactured by Omron Healthcare Vietnam Co. Ltd, Vietnam, on a single home visit after completion of the survey questionnaire and before measurement of any other biomarker. Participants were requested to avoid exercise, food, alcohol, and smoking 30 minutes prior to measurement. Three measurements were taken, with a 1-minute gap between each. We used the average of the last 2 readings. We classified a participant as having hypertension if (a) they had systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg; or (b) they reported ever having been told they had hypertension/high BP, and they reported currently taking medication or being under salt/diet restriction to control BP (S2 Text). We classified participants with hypertension as (a) “aware” if they reported having been diagnosed with hypertension; (b) “treated” if they reported currently taking medication or being under salt/diet restriction to control BP; and (c) “controlled” if they had systolic BP <140 mm Hg and diastolic BP <90 mm Hg using the survey measurement of BP (S2 Text). The BP thresholds used to define hypertension and hypertension control are those specified in the training module of the Government of India National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases, and Stroke [22].

We used household monthly per capita consumption expenditure (MPCE) as the primary indicator of socioeconomic status. This is the measure used to determine a household’s poverty status in India [23]. We deviated from the standard measure by excluding expenditure on healthcare and medicines to avoid giving the false impression that households with such expenditures enjoyed a higher standard of living. We constructed MPCE from detailed reports of items of expenditure and consumption, including goods produced by the household (S3 Text).

Statistical analysis

We used the full sample of individuals aged 45 years and over and their spouses—who could be younger than 45—to estimate hypertension prevalence. We used a subsample of these participants who were identified as having hypertension to estimate rates of ATC (S2 Text, S4 and S5 Tables). We estimated prevalence and these rates nationally, by state, by MPCE quintile group (S3 Text), and by other sociodemographic characteristics (years of schooling, age group, sex, urban–rural, caste, religion, marital status, living arrangement, employment status, and health insurance status). Years of schooling were categorized into 4 groups corresponding to education levels: no school, incomplete primary school (<5 years), primary high school (5 to 9 years), and secondary school and above (≥10 years). We adjusted estimates for age and sex using the age–sex composition of the nationally representative full sample as the reference (S4 Text).

At the state level, we used a scatter plot and a linear regression line to examine the association between hypertension prevalence and economic development measured by net state domestic product (NSDP) per capita [24]. We used the same analysis to examine associations between rates of ATC and NSDP per capita. We have excluded 3 union territories (Dadra and Nagar Haveli, Daman and Diu, and Lakshadweep) from these analyses due to unavailability of NSDP per capita for the reference period 2017 to 2018.

We used a concentration index—the scaled covariance between an outcome and rank in the MPCE distribution [25]—to quantify the degree of socioeconomic inequality along the full distribution of MPCE. This approach avoided loss of information from grouping participants into MPCE groups such as quintiles and making comparisons only between extremes, e.g., richest fifth versus poorest fifth. A positive (negative) concentration index would indicate, for example, higher prevalence among richer (poorer) individuals. We adjusted the concentration indices for age and sex (S4 Text). We used multivariable logistic regressions to estimate fully adjusted marginal effects of MPCE quintile groups, sociodemographic characteristics, and state indicators on the probability of hypertension, and on the probabilities of ATC among those with hypertension. Each marginal effect was averaged over the sample used in the respective regression.

In all analyses, we applied sampling weights and took account of stratification and cluster sampling in the estimation of confidence intervals (CIs). We included in the analysis sample all participants with full item response on BP measurement, reported diagnosis and treatment for hypertension/high BP, MPCE, and all reported covariates. All the analyses were done using Stata 15.0. The Stata do file is shown in S1 Code.

Results

Fig 1 shows our selection of the analysis sample. Of the 72,250 participants interviewed, BP could not be measured for 6,499 (9.0%) because participants did not give consent, they were interviewed by proxy, or they had irritations on both arms that prevented fitting the cuff of the BP monitor. Participants for whom BP could not be measured were richer, better educated, and older, and they were more likely to be urban, Muslim, not working, and belong to a privileged caste (S1 Table). A further 1,324 participants (2.0%) did not report all information on hypertension diagnosis and treatment, MPCE, and sociodemographic characteristics (S2 Table), leaving an analysis sample of 64,427 participants with full item response that was used to estimate hypertension prevalence. In this sample, 28,600 participants were identified as having hypertension and were used to estimate rates of hypertension ATC. Estimates of hypertension prevalence and of rates of ATC obtained from the full item response analysis sample used were very close to estimates obtained from a sample that did not exclude those with missing information on MPCE or any sociodemographic characteristic (S3 Table).

Table 1 shows characteristics of the full analysis sample and estimates of age–sex adjusted hypertension prevalence. The median age of the sample was 57 years (interquartile range [IQR]: 49 to 65). A majority was female (58%). Educational attainment was low: half had no formal schooling. The sample was predominantly rural (70%) and married (76%). Less than half of the participants (47%) were working. Only 21% had health insurance.

Table 1. Participant characteristics and adjusted hypertension prevalence, adults aged 45 years and over and their spouses.

	Participants		Adjusted hypertension prevalence	F statistic
	n	(%)	% (95% CI)	(p-value)
Overall	64,427	100	41.9 (41.0–42.9)
MPCE quintile group
Poorest	11,237	(20.0)	35.3 (33.6–37.0)	25.63 (<0.001)
Poorer	11,514	(20.0)	39.5 (38.0–41.0)
Middle	12,348	(20.0)	41.0 (39.4–42.5)
Richer	14,050	(20.0)	43.4 (41.8–45.0)
Richest	15,278	(20.0)	50.7 (48.2–53.2)
Education
No schooling	29,676	(49.9)	37.0 (36.0–38.0)	72.45 (<0.001)
<5 years	7,309	(11.1)	43.0 (41.1–44.9)
5–9 years	15,240	(21.3)	45.6 (44.2–46.9)
≥10 years	12,202	(17.7)	51.2 (49.1–53.2)
Age
<45 years	6,027	(8.9)	22.3 (18.4–26.3)	164.23(<0.001)
45–54	21,573	(31.7)	34.5 (33.2–35.7)
55–64	18,077	(27.8)	44.2 (42.9–45.6)
65–74	12,994	(21.8)	52.4 (50.6–54.3)
≥75	5,756	(9.8)	54.5 (52.4–56.6)
Sex
Male	27,154	(42.1)	39.6 (38.4–40.9)	45.25 (0.037)
Female	37,273	(57.9)	43.7 (42.8–44.6)	45.25 (0.037)
Location
Rural	42,109	(70.4)	37.8 (36.9–38.7)	230.22 (<0.001)
Urban	22,318	(29.6)	51.8 (50.3–53.4)	230.22 (<0.001)
Caste
Scheduled caste	10,894	(19.7)	39.0 (37.6–40.4)	25.48 (<0.001)
Scheduled tribe	11,231	(8.6)	36.8 (34.1–39.4)
Other Backward Class	24,394	(45.3)	41.9 (40.3–43.5)
Other	17,908	(26.4)	45.9 (44.6–47.3)
Religion
Hindu	47,255	(82.4)	40.8 (39.7–42.0)	14.66 (<0.001)
Muslim	7,675	(11.2)	46.8 (43.6–50.0)
Christian	6,484	(2.9)	43.2 (38.3–48.1)
Other	3,013	(3.5)	51.6 (48.0–55.2)
Marital status
Married	49,632	(76.2)	41.0 (39.9–42.1)	10.77 (<0.001)
Widowed	12,857	(21.2)	45.7 (43.8–47.6)
Other	1,938	(2.7)	38.7 (33.4–44.0)
Living arrangement
Alone	2,097	(3.5)	44.3 (40.6–47.9)	5.60 (0.001)
With spouse	9,329	(15.5)	40.6 (38.4–42.8)
With children	39,522	(59.8)	41.1 (39.7–42.5)
With others	13,479	(21.3)	44.8 (43.3–46.3)
Working status
Working	29,619	(47.2)	38.0 (36.3–39.7)	26.08 (<0.001)
Worked previously	15,845	(25.6)	45.8 (44.5–47.2)
Never worked	18,963	(27.3)	45.0 (43.4–46.6)
Health insurance
No	49,394	(79.0)	41.5 (40.6–42.4)	5.18 (0.023)
Yes	15,033	(21.0)	43.7 (41.8–45.6)	5.18 (0.023)

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Notes: Adjusted for age and sex. Prevalence estimates by age group were adjusted for sex composition only. Estimates by sex were adjusted for age composition only. Unadjusted prevalence rates presented in S6 Table. F statistic for test of equal prevalence across groups defined by each characteristic. Other religion includes Sikh, Buddhist/neo-Buddhist, Jain, Jewish, Parsi, and no religion.

MPCE, monthly per capita consumption expenditure.

We estimated hypertension prevalence to be 41.9% (95% CI 41.0 to 42.9) among adults aged 45 years and over and their spouses in India. Prevalence increased strongly with age and was higher for females (43.7%: 95% CI 42.8 to 44.6) than for males (39.6%: 95% CI 38.4 to 40.9) (adjusted for age). Adjusted for age and sex, estimated prevalence increased monotonically in moving from the poorest fifth (35.3%: 95% CI 33.6 to 37.0) to the richest fifth (50.7%: 95% CI 48.2 to 53.2). A positive concentration index of 0.061 (95% CI 0.048 to 0.073) confirmed that richer individuals with higher MPCE were more likely to have hypertension (Table 2). Prevalence was estimated to increase from 37.0% (95% CI 36.0 to 38.0) among the least educated to 51.2% (95% CI 49.1 to 53.2) among the most educated. Prevalence was higher in urban areas (51.8%: 95% CI 50.3 to 53.4) than in rural areas (37.8%: 95% CI 36.9 to 38.7). It was also higher among those in the privileged “other castes” and those not working.

Table 2. Adjusted concentration indices for hypertension and for ATC among those with hypertension, adults aged 45 years and over and their spouses.

	Concentration index (95% CI)	n
Hypertension	0.061 (0.048–0.073)	64,427
Awareness	0.072 (0.056–0.089)	28,600
Treatment	0.077 (0.061–0.093)	28,600
Control	0.055 (0.043–0.067)	28,600

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Notes: A concentration index is a scaled covariance between an outcome and rank in the distribution of MPCE. Adjusted for age and sex. Concentration curves presented in S1 Fig.

ATC, awareness, treatment, and control; MPCE, monthly per capita consumption expenditure.

Table 3 shows adjusted percentages of adults aged 45 years and over and their spouses with hypertension who (a) were aware of their condition; (b) were under treatment for it; and (c) had their BP under control. Overall, we estimated that 54.4% (95% CI 53.1 to 55.7) were aware, 50.8% (95% CI 49.5 to 52.0) were treated, and only 28.8% (95% CI 27.4 to 30.1) had achieved control. There was an 18 percentage points (pp) gap between the richest fifth and the poorest fifth in awareness. The rich–poor gaps were 19 pp and 13 pp for treatment and control, respectively. Concentration indices for ATC were all positive: 0.072 (95% CI 0.056 to 0.089), 0.077 (95% CI 0.061 to 0.093), and 0.055 (95% CI 0.043 to 0.067), respectively (Table 2). This confirmed pro-rich inequalities in hypertension ATC. Gaps between the most and least educated groups were 18 pp, 19 pp, and 13 pp for rates of hypertension ATC, respectively.

Table 3. Adjusted percent aware, treated, and controlled among those with hypertension, adults aged 45 years and over and their spouses.

(n = 28,600)
	Awareness		Treatment		Control
	% (95% CI)	F statistic (p-value)	% (95% CI)	F statistic (p-value)	% (95% CI)	F statistic
	% (95% CI)	F statistic (p-value)	% (95% CI)	F statistic (p-value)	% (95% CI)	(p-value)
Overall	54.4 (53.1–55.7)		50.8 (49.5–52.0)		28.8 (27.4–30.1)
MPCE quintile group
Poorest	43.6 (41.0–46.1)	38.64 (<0.001)	40.0 (37.6–42.4)	42.45 (<0.0010)	22.0 (19.8–24.2)	16.57 (<0.001)
Poorer	49.5 (47.0–52.0)		45.3 (42.7–47.9)		24.4 (22.6–26.3)
Middle	54.9 (52.6–57.3)		50.9 (48.5–53.2)		28.1 (26.4–29.9)
Richer	59.0 (56.9–61.1)		55.4 (53.3–57.5)		32.1 (29.9–34.3)
Richest	61.7 (59.2–64.1)		59.0 (56.7–61.3)		34.9 (32.0–37.7)
Education
No schooling	47.0 (45.2–48.8)	57.27 (<0.001)	43.2 (41.5–45.0)	62.29 (<0.001)	24.3 (22.9–25.7)	16.14 (<0.001)
<5 years	55.2 (52.2–58.2)		51.3 (48.3–54.3)		28.8 (26.3–31.3)
5–9 years	59.5 (57.4–61.7)		56.6 (54.4–58.8)		31.3 (29.1–33.5)
≥10 years	65.4 (62.6–68.3)		62.2 (59.5–64.8)		37.6 (34.2–40.9)
Age
<45 years	40.9 (31.2–50.5)	11.84 (<0.001)	37.6 (28.7–46.6)	9.99 (<0.001)	24.6 (18.6–30.6)	0.92 (0.449)
45–54	48.2 (45.8–50.7)		44.7 (42.3–47.1)		26.8 (24.8–28.8)
55–64	55.2 (52.8–57.7)		52.0 (49.5–54.5)		29.8 (27.5–32.0)
65–74	59.5 (57.0–61.9)		55.9 (53.4–58.4)		30.3 (27.3–33.3)
≥75	59.2 (56.3–62.1)		54.5 (51.5–57.5)		28.7 (25.6–31.9)
Sex
Male	48.2 (46.3–50.0)	89.76 (<0.001)	44.5 (42.7–46.2)	98.57 (<0.001)	24.3 (23.1–25.6)	69.94 (<0.001)
Female	58.8 (57.3–60.4)	89.76 (<0.001)	55.3 (53.7–56.9)	98.57 (<0.001)	31.9 (30.2–33.7)	69.94 (<0.001)
Location
Rural	49.4 (47.8–51.0)	128.63 (<0.001)	45.6 (44.0–47.2)	144.44 (<0.001)	25.4 (24.1–26.6)	44.89 (<0.001)
Urban	63.1 (61.2–65.1)	128.63 (<0.001)	59.9 (58.1–61.8)	144.44 (<0.001)	34.7 (32.3–37.1)	44.89 (<0.001)
Caste
Scheduled caste	52.0 (49.5–54.4)	50.29 (<0.001)	48.0 (45.4–50.5)	57.27 (<0.001)	26.4 (24.2–28.6)	31.34 (<0.001)
Scheduled tribe	36.0 (32.4–39.5)		32.2 (28.9–35.5)		16.9 (14.3–19.5)
Other Backward Class	54.2 (52.4–56.0)		50.9 (49.1–52.7)		29.6 (27.3–31.8)
Others	60.8 (59.0–62.7)		57.1 (55.2–59.0)		31.9 (30.3–33.6)
Religion
Hindu	53.2 (51.7–54.6)	8.95 (<0.001)	49.5 (48.1–51.0)	9.26 (<0.001)	28.4 (26.9–30.0)	1.98 (0.115)
Muslim	60.2 (57.4–63.0)		56.7 (53.9–59.4)		31.5 (28.9–34.2)
Christian	53.6 (48.7–58.5)		50.5 (45.7–55.3)		26.7 (22.6–30.7)
Others	60.7 (56.4–65.1)		57.1 (52.8–61.3)		28.1 (24.8–31.4)
Marital status
Married	55.0 (53.3–56.8)	4.94 (0.007)	51.6 (49.9–53.3)	4.79 (0.008)	29.6 (28.0–31.1)	6.58 (0.001)
Widowed	53.4 (51.3–55.5)		49.4 (47.3–51.5)		27.3 (25.3–29.4)
Other	45.0 (38.6–51.4)		42.6 (36.6–48.6)		21.4 (16.7–26.1)
Living arrangement
Alone	49.7 (45.2–54.3)	2.94 (0.032)	46.2 (41.7–50.7)	3.10 (0.026)	25.2 (21.2–29.2)	2.51 (0.057)
With spouse	53.2 (50.4–56.0)		49.9 (47.0–52.7)		27.9 (25.7–30.1)
With children	55.6 (53.9–57.4)		52.1 (50.4–53.9)		30.2 (28.3–32.1)
With others	53.1 (51.1–55.2)		49.3 (47.3–51.2)		27.1 (25.0–29.2)
Working status
Working	44.2 (42.1–46.3)	87.58 (<0.001)	40.9 (38.9–42.9)	83.12 (<0.001)	23.6 (22.0–25.3)	30.22 (<0.001)
Worked previously	59.1 (57.0–61.1)		54.9 (52.9–56.9)		30.1 (28.2–32.0)
Never worked	63.5 (61.1–65.9)		59.9 (57.3–62.5)		34.0 (30.8–37.3)
Health insurance
No	54.3 (52.9–55.8)	0.02 (0.876)	50.6 (49.2–52.1)	0.20 (0.652)	28.8 (27.3–30.3)	0.05 (0.815)
Yes	54.5 (52.3–56.8)	0.02 (0.876)	51.2 (49.0–53.4)	0.20 (0.652)	28.6 (26.7–30.4)	0.05 (0.815)

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Notes: Adjusted for age and sex. Estimates by age group were adjusted for sex composition only. Estimates by sex were adjusted by age composition only. Unadjusted estimates presented in S6 Table. F statistic for test of equal rates across groups defined by each characteristic. Number of participants in each sociodemographic category presented in S4 Table.

MPCE, monthly per capita consumption expenditure.

Among those with hypertension, males were less likely than females to be aware, treated, and controlled. Rates of ATC were also lower for scheduled tribes, rural dwellers, the employed, those living alone, and those not married or widowed. Younger study participants were less likely to have been diagnosed and treated, but they were not less likely to have achieved BP control. Hindus and Christians were also less likely than those of other religions to be aware and treated. There were no differences in ATC by health insurance status.

Using the full analysis sample that includes participants without hypertension, we estimated that 19.1% (95% CI 18.5 to 19.8) had measured high BP (≥140/90 mm Hg) but had not been diagnosed with hypertension (S7 Table). This prevalence of undiagnosed hypertension was higher among both the poorest and the richest compared with those in the middle. It was higher for the least educated compared to those with more education. The prevalence of untreated hypertension (BP ≥140/90 mm Hg and not on medication/diet) was 20.7% (95% CI 20.0 to 21.3) in the full sample and was not related to MPCE or education (S7 Table). We estimated that 29.9% (95% CI 29.2 to 30.6) of the population had uncontrolled hypertension, and the prevalence was higher for richer and better educated groups (S7 Table).

Fig 2 shows variation across states in adjusted hypertension prevalence and in rates of ATC among those with hypertension. Prevalence exceeded the national average of 42% in 28 of the 35 states and varied from 31.3% (95% CI 29.2 to 33.5) in Uttar Pradesh to 66.0% (95% CI 61.3 to 70.6) in Lakshadweep. Awareness among those with hypertension varied from 27.5% (95% CI 22.2 to 32.8) in Nagaland to 75.9% (95% CI 70.8 to 81.1) in Jammu and Kashmir. Treatment among those with hypertension varied from 23.8% (95% CI 17.6 to 30.1) in Nagaland to 74.9% (95% CI 69.8 to 79.9) in Jammu and Kashmir. In 22 out of 35 states, the estimated percentage of hypertension cases with controlled BP was less than the national average (28.8%). The percentage with controlled BP varied from 4.6% (95% CI 1.1 to 8.1) in Nagaland to 41.9% (95% CI 36.8 to 46.9) in Goa.

S2 Fig presents the adjusted percent treated among those aware of hypertension and adjusted percent controlled among those treated, adults aged 45 years and over, and their spouses in India.

Fig 3 shows that hypertension prevalence was positively associated with NSDP per capita. On average, rates of ATC were also higher in the high-income states. Consequently, states that had higher hypertension prevalence tended to have higher rates of ATC, although the correlation with rates of control was not significant (S10 Table). The positive association with state domestic product (SDP) per capita was sequentially stronger for awareness (R-squared = 0.227), treatment (R-squared = 0.239), and control (R-squared = 0.271). Jammu and Kashmir stands out for having achieved high rates of ATC despite having low SDP per capita, while Arunachal Pradesh, Chhattisgarh, and Nagaland performed even worse than would be expected on the basis of the low level of development in each of these states. Delhi underperformed, particularly on control of hypertension, relative to the linear prediction from its high SDP per capita.

Fig 4 shows adjusted concentration indices for hypertension and for ATC among those with hypertension by state, ordered from the lowest (most prevalent among poor) to the highest (most prevalent among rich) concentration index for hypertension. In all except 3 states, the point estimate of this index is positive, indicating a disproportionate concentration of hypertension among the economically better off, although most of the 95% CIs include zero, which is consistent with no inequality. With only a few exceptions, the point estimates of the concentration indices for ATC are positive, which indicates that the better off were more likely to have their hypertension diagnosed, treated, and controlled in almost all states. Many states with little or no inequality in the distribution of hypertension had inequalities in ATC that favored the better off. States with greater socioeconomic inequality in prevalence tended to have greater inequality in awareness (Spearman rank correlation ρ = 0.444, p-value = 0.008) and treatment (Spearman rank correlation ρ = 0.416, p-value = 0.013).

Fig 5 shows average marginal effects from multivariable logistic regressions for each outcome. Controlling for sociodemographic characteristics and state, the prevalence of hypertension was estimated to be 6.1 pp (95% CI 3.0 to 9.2) higher in the richest fifth of the population than in the poorest fifth. In the other MPCE quintile groups, prevalence was estimated to be about 2.3 to 2.5 pp higher than in the poorest fifth. The education gradient in hypertension prevalence remained strong after controlling for other characteristics and state, with a difference of 6.9 pp (95% CI 4.5 to 9.3) between the highest and lowest education groups. Greater prevalence among older adults, women, urban dwellers, and the nonemployed also remained after controlling for other characteristics and state. Adding controls did not eliminate socioeconomic inequalities in ATC. For example, the difference between the richest fifth and the poorest fifth was estimated to be 8.5 pp (95% CI 5.3 to 11.7), 8.9 pp (95% CI 5.7 to 12.0), and 7.1 pp (95% CI 4.2 to 10.1) for ATC, respectively. These outcomes all remained higher among females, urban dwellers, and the nonemployed after conditioning on other characteristics. Awareness and treatment, but not control, were higher among older adults holding other characteristics fixed.

Discussion

In India, hypertension is the main risk factor for CVD [10], accounts for the largest disease burden of any condition [3], and is substantially more prevalent among older adults [8]. Yet, to our knowledge, there were no nationally representative and state-specific estimates of hypertension ATC for the older Indian population. This study estimated high prevalence of hypertension and found substantial gaps in its diagnosis, treatment, and control in the older population of India in 2017 to 2018. We estimated that 42% of adults aged 45 years and over and their spouses had hypertension, while only half of those with the condition had been diagnosed and treated, and less than one-third had achieved BP control.

We found substantial geographic and socioeconomic inequalities in the prevalence of hypertension and in its diagnosis, treatment, and control. The states with the highest prevalence included those most advanced in the demographic transition, such as Kerala, and the high-income states, such as Goa and the capital Delhi. While prevalence tended to be lower in the low- or middle-income states and it was relatively low in all 4 states with the highest rates of poverty (Bihar, Jharkhand, Madhya Pradesh, and Uttar Pradesh) [21]—these states also achieved lower rates of ATC. Consequently, we would expect cross-state variation in CVDs arising from uncontrolled hypertension to have been less strongly associated with economic development than was hypertension itself. Those living in poorer states were at lower risk of hypertension. If they were hypertensive, however, they were also more vulnerable to diseases associated with the risk factor because they were less likely to have been diagnosed, treated, and controlled. However, the examples of Jammu and Kashmir and West Bengal demonstrated that higher rates of ATC could be achieved even if lower incomes constrained available resources. The reasons why some states performed better than others at similar levels of economic development deserve further attention. Previous estimates of ATC rates in the older Indian population that used data on these outcomes for older people in only 6 states [17] relied on the strong assumption that the patterns observed in those states would hold in other states. We showed substantial variation across states in rates of ATC and in the socioeconomic gradient of each outcome.

The sociodemographic patterns among individuals were consistent with the patterns observed across states. While socioeconomically disadvantaged individuals were less likely to have hypertension, if they did have it, they were less likely to have been diagnosed and treated and to have achieved BP control. Among those with hypertension, only 42% of those in the poorest fifth of the sample had been diagnosed, while 62% of the richest fifth were diagnosed. Consequently, the socioeconomic gradient observed in the prevalence of hypertension partly reflected the lower propensity of poorer people to be diagnosed. This pattern is consistent with the finding that states with greater socioeconomic inequality in awareness also tended to have greater inequality in prevalence.

Among those with hypertension, almost 60% of those in the richest fifth were receiving treatment, while only 40% of those in poorest fifth were treated. Differences in the propensity to be diagnosed and treated imply that the higher prevalence of hypertension observed in richer and better educated groups overstated socioeconomic inequality in exposure to the risk of CVDs that is associated with unmanaged hypertension.

Rates of hypertension ATC were lower not only for poorer and less educated study participants but also for those who were relatively younger (not control), male, rural dwellers, widowed or unmarried, and working. These characteristics define the profile of older Indian adults who appear to have least access to diagnosis, medication, and knowledge of how to control hypertension. The same characteristics identify those with the lowest estimated hypertension prevalence, which further suggests that apparent differences in prevalence partly reflect differences in the likelihood of being diagnosed and treated and that patterns of prevalence should not exclusively determine the targeting of hypertension management interventions.

Our estimates of hypertension prevalence in India among adults aged 45 years and over and their spouses are slightly higher than estimates based only on BP measurement obtained from nationally representative data for comparable age groups (S13 Table) [8]. The differences likely stem from our identification of hypertension using reported diagnosis and medication/diet restriction to control BP in addition to measured BP (S13 Table). The higher prevalence we found among older adults located in high-income states and belonging to socioeconomically advantaged groups are consistent with the patterns previously observed in a very large sample of Indian adults aged 18+ [8]. That study could not estimate hypertension ATC, and the only previous nationally representative study of these outcomes was restricted to the 15 to 49 age range [13]. Compared with that study, we estimated a higher rate of hypertension awareness, and we estimated rates of treatment and control that are substantially higher. The differences are partly explained by the facts that we examined a much older age range and found, like other studies [17,18], that awareness and treatment, but not control, increased with age. Like many small, local studies of ATC in India [14–17], we found higher rates in urban areas than in rural areas. We estimated considerably higher rates of ATC than were obtained from a meta-analysis of small-scale Indian studies up to 2013 [15]. The differences may be partly attributable to the fact that we examined an older age group, as well as any improvements in the management of hypertension that have arisen from the NCD initiatives taken by the Indian government since 2013 [12]. Our findings of lower rates of ATC among men and socioeconomically disadvantaged groups are consistent with other Indian studies [18,19].

In 2013, the Government of India adopted a national action plan for prevention and control of NCDs and set an ambitious target to reduce hypertension prevalence [12]. However, the India Hypertension Control Initiative (IHCI) was launched—in 5 states—only in November 2017. An observational study found that BP control achieved by public health facility patients increased from 26% at registration in the IHCI to 60% at follow-up [26]. Our findings can help identify states and sociodemographic groups to be targeted as this program is rolled out over the country. That targeting should balance attention to prevalence, which is higher in the high-income states and socioeconomically advantaged groups, with attention to gaps in ATC, which are greater in the low- or middle-income states and disadvantaged groups.

Low awareness of hypertension in the older population of India signals a pressing need for improved health education and screening. The national NCD action plan [12] stipulates opportunistic screening for hypertension at public health facilities, which needs to be implemented effectively and extended to the more frequently used private clinics. Low rates of treatment and control of hypertension, particularly among poorer individuals and in rural areas, suggest difficulties in accessing and affording primary healthcare. Although low cost generic antihypertensives are widely available, they may not be affordable for poorest people. Non-pharmacological interventions, on the other hand, may be hard to adhere to in the long run. The large interstate variation we found in hypertension ATC presumably arises, in part, from the fact that health is a state responsibility in India. While the central government can issue guidelines and provide financial assistance for certain health services, state governments are largely responsible for the financing and management of facilities, manpower, and drug supply. Greater investment by both national and state governments in hypertension management is infeasible in the short term as the health system is straining to cope with the Coronavirus Disease 2019 (COVID-19) epidemic. But the fact that older adults with chronic conditions, such as hypertension, are most likely to succumb to this illness and need high-cost medical care emphasizes that investment in hypertension screening and management can potentially pay off in the long term through reduced demands on the health system.

The main limitation of this study, which is common to all ATC care cascade studies [14,16,18] and most prevalence studies [8,27,28], is that hypertension status was identified from BP measured on a single occasion. Some of the participants with a high BP reading will have been false positives. This is likely to have pushed the estimates toward overestimation of hypertension prevalence and underestimation of rates of awareness and treatment among those who had hypertension. We could think of no reason for this measurement error to have varied systematically across sociodemographic groups or states, and so it may not have biased estimates of socioeconomic and geographic inequalities in awareness and treatment. BP could not be measured for almost 9% of LASI participants. While this percentage is not especially high for a general purpose population survey of older adults, the fact that BP measurements were slightly less likely to be obtained from some sociodemographic groups (S1 Table) that had higher hypertension prevalence and rates of ATC may have pushed the estimates of population averages toward some underestimation of both prevalence and ATC. Taken together, these two data limitations arising from measurement of BP on a single occasion and systematic differences in non-measurement of BP are likely to have had offsetting effects on the estimate of hypertension prevalence but potentially compounding, downward effects on estimates of ATC. We may have underestimated the extent to which hypertension is diagnosed, treated, and controlled in the older Indian population, on average. However, this potential bias in the estimates of the averages does not call into question the findings that rates of ATC are substantially lower in less privileged socioeconomic groups and differ substantially across states.

A third limitation is that the study did not collect data on medication adherence, and so it is not possible to examine the extent to which this contributed to the low rate of controlled BP. Participants were not asked if they had ever had their BP measured. This prevented us from both examining screening as first step in the hypertension care cascade and distinguishing between the unaware who had never been screened and those unaware despite having had their BP measured. While the main contribution of this study was to add nationally representative evidence on hypertension ATC in the older population of India in which the condition is more prevalent, a limitation is that the sample did not include adults aged below 45, except for spouses. Given India’s large and young population, there is a large absolute number of younger adults with hypertension in the country [8]. Our analysis did not extend to this population, although the age differences we observed would suggest that awareness and treatment, and, possibly, also BP control, may well have been even lower at younger ages. Prenissl and colleagues [13] provided evidence on hypertension ATC at aged 15 to 49, and this study should be viewed as complementary to that one.

A final limitation, which is also common to other care cascade studies, is the presumption of a “treat-to-target” approach to hypertension management with the target set to controlling BP <140/90 mm Hg. Recent guidelines have defined hypertension at lower thresholds [29], aimed for BP control at lower levels [29,30], and advocated a “benefit-based” approach that involves offering hypertension therapy to patients at high CVD risk even if their BP is below the hypertension threshold [29,31,32]. This new approach is based on evidence that even someone with “normal” BP according to the thresholds used in this study could benefit from BP therapy that substantially reduces their elevated CVD risk. It suggests that the evidence we presented may understate gaps that existed in the treatment and control of BP in India [33].

Notwithstanding these limitations, to our knowledge, this study produced the first estimates of hypertension ATC using nationally representative data on older people spread throughout the whole of India. It described socioeconomic and geographic inequalities in ATC that can be used to motivate policy action on hypertension management, target interventions, and monitor their effects.

Supporting information

S1 Checklist. STROBE checklist of items that should be included in reports of cross-sectional studies.

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S1 Text. Sample design.

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S2 Text. Outcome definitions.

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S3 Text. Measurement of MPCE. MPCE, monthly per capita consumption expenditure.

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S4 Text. Age–sex adjustment.

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S1 Fig. Adjusted concentration curves for hypertension cases and ATC among those with hypertension, adults aged 45+ and their spouses in India.

The figure shows concentration curves, which depict relative inequality in an outcome in relation to a measure of socioeconomic status (O’Donnell and colleagues 2008). For example, the curve in the top-left panel traces the cumulative proportion of hypertension cases (y-axis) against the cumulative proportion of participants ranked from the poorest (left) to the richest (right) (x-axis) based on MPCE. The other curves trace the cumulative proportion of participants with hypertension who are aware, treated, and controlled (y-axis) against the cumulative proportion ranked from poorest to richest. Shading around the curves indicates 95% CIs. Each curve lies below the respective 45-degree line, which indicates that there is a disproportionate concentration of hypertension cases among richer participants and that among those with hypertension, ATC are also disproportionately concentrated among the richer participants. ATC, awareness, treatment, and control; MPCE, monthly per capita consumption expenditure.

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S2 Fig. Adjusted percent treated among those aware of hypertension and adjusted percent controlled among those treated, adults aged 45+ and their spouses in India.

The base map can be found at https://globalsolaratlas.info/download/india. Adjusted for age and sex.

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S1 Table. Sample characteristics by whether the BP was measured, adults aged 45+ and their spouse, adults aged 45+ and their spouses.

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S2 Table. Missing observations on BP measurement, diagnosis, treatment and sociodemographic variables, adults aged 45+ and their spouses.

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S3 Table. Estimates of hypertension prevalence and rates of ATC from full item response analysis sample and alternative sample including participants missing on MPCE or any sociodemographic variable, adults aged 45+ and their spouses.

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S4 Table. Number of participants with hypertension by sociodemographic characteristics.

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S5 Table. Number of participants in full analysis sample and with hypertension by state.

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S6 Table. Unadjusted estimates of hypertension prevalence and percent aware, treated, and controlled among those with hypertension by sociodemographic characteristics, adults aged 45+ and their spouses.

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S7 Table. Adjusted prevalence of undiagnosed, untreated, and uncontrolled hypertension by MPCE quintile and education groups, adults aged 45+ and their spouses in India.

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S8 Table. Adjusted hypertension prevalence, and percent aware, treated, and controlled among those with hypertension by state, adults aged 45+ and their spouses in India.

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S9 Table. Unadjusted estimates of hypertension prevalence and percent aware, treated, and controlled among those with hypertension by state, adults aged 45+ and their spouses in India.

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S10 Table. Correlation of rates of ATC each with hypertension prevalence across states, adults aged 45+ and their spouses.

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S11 Table. Adjusted concentration indices for hypertension and ATC among those with hypertension, adults aged 45+ and their spouses in India.

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S12 Table. Adjusted marginal effect for hypertension and ATC among those with hypertension, adults aged 45+ and their spouses in India.

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S13 Table. Comparison of estimates of hypertension prevalence by age and sex with those in Geldsetzer and colleagues.

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S14 Table. Numbers of participants by age and sex used to estimate prevalence rates presented in S13 Table.

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S1 Code. Stata code for computation of prevalence and ATC of hypertension.

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Abbreviations

ATC: awareness treatment, and control
BP: blood pressure
CI: confidence interval
COVID-19: Coronavirus Disease 2019
CVD: cardiovascular disease
DALY: disability-adjusted life year
IHCI: India Hypertension Control Initiative
IIPS: International Institute for Population Sciences
IQR: interquartile range
LASI: Longitudinal Ageing Study in India
MPCE: monthly per capita consumption expenditure
NCD: noncommunicable disease
NDSP: net state domestic product
pp: percentage points
SDP: state domestic product
STROBE: Strengthening the Reporting of Observational Studies in Epidemiology
WHO: World Health Organization

Data Availability

Data are publicly available and can be accessed by registering at https://iipsindia.ac.in/sites/default/files/LASI_DataRequestForm_0.pdf. Data will be made available to the researchers meeting the criteria for access to confidential data. We are enclosing the do files used for tabulation and graph as additional file.

Funding Statement

This was supported by funding from the Swiss National Science Foundation (https://www.snf.ch) and the Swiss Agency for Development and Cooperation (https://www.eda.admin.ch/deza/en/home.html) through the Swiss Programme for Research on Global Issues for Development (http://www.r4d.ch) grant 400640_160374: “Inclusive social protection for chronic health problems” (https://r4d-ncd.org) (JM). The content of the Article is solely the responsibility of the authors and does not necessarily represent the views of the Swiss National Science Foundation or the Swiss Agency for Development & Cooperation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1003740.r001

Decision Letter 0

Callam Davidson

20 May 2021

Dear Dr Mohanty,

Thank you for submitting your manuscript entitled "Awareness, treatment, and control of hypertension in India: a nationally representative cross-sectional study of older adults aged 45+" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff as well as by an academic editor with relevant expertise and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by May 24 2021 11:59PM.

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Callam Davidson

Associate Editor

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1003740.r002

Decision Letter 1

Callam Davidson

16 Jun 2021

Dear Dr. Mohanty,

Thank you very much for submitting your manuscript "Awareness, treatment, and control of hypertension in India: a nationally representative cross-sectional study of older adults aged 45+" (PMEDICINE-D-21-02188R1) for consideration at PLOS Medicine.

Your paper was evaluated by an associate editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to invite you to submit a revised version that addresses the reviewers' and editors' comments fully. You will appreciate that we cannot make a decision about publication until we have seen the revised manuscript and your response, and we expect to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We hope to receive your revised manuscript by Jul 07 2021 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

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Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

Please let me know if you have any questions, and we look forward to receiving your revised manuscript.

Sincerely,

Callam Davidson,

Associate Editor,

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Please can the authors clarify their response ('No - some restrictions will apply) to the Data Availability question in the introductory form, as it appears that the data used are publicly available.

Please add a completed STROBE/RECORD checklist as a supplementary file, labelled "S1_STROBE_Checklist" or similar and referred to as such in the Methods section.

In the checklist, please refer to individual items by section (e.g., "Methods") and paragraph number, not by line or page numbers as these generally change in the event of publication.

Please update the manuscript to include line numbering in the margins.

Please update the manuscript title such that the age range studied comes before the colon (e.g. Awareness, treatment, and control of hypertension in adults aged 45+ in India: a nationally representative cross-sectional study)

Please add a new final sentence to the "Methods and findings" subsection of your abstract, which should begin "Study limitations include..." or similar and should quote 2-3 of the study's main limitations.

Please add basic sample demographic information to the abstract (median age and % male/female at a minimum).

Please present conclusions in the past tense (e.g. in "Conclusion" section of abstract - 'Hypertension prevalence was high' rather than 'is high'. Please check throughout manuscript.

Please change the style of all in-text citations. These should be in square brackets and occur before punctuation.

Please remove the information on funding and data availability from the main text. In the event of publication, this information will appear in the article metadata via entries in the submission form.

Please remove the section 'Role of the funding source' from the manuscript.

Please add 1-2 sentences at the beginning of the discussion summarising why the study was performed followed by the major findings.

The discussion sentence introduces new data that is not described in the results section text (notably Spearman rank correlation rho and associated p values). Please ensure that any results discussed in the discussion section are first described adequately in the results section.

In the final paragraph of the discussion, please update the first sentence to include '...to our knowledge, this study produced the first estimates of hypertension ATC...'

Please ensure references do not contain bold or italicised text, and please note that references should list the first six authors followed by et al. where appropriate.

If an analysis plan was prepared prior to the study, please could this be provided in the supplementary materials.

Please confirm that the maps used to produce Figure 3 and supplementary figure D2 are appropriate for publication under a Creative Commons CC-BY licence.

In tables that state P values, please update any P = 0.000 values to P < 0.001.

Comments from the reviewers:

Reviewer #1: This is a cross-sectional analysis of hypertension "care cascades" using nationally representative data from older adults in India collected in the most recent LASI cohort wave. The study is well conducted, advances knowledge on the epidemiology of hypertension care in India, and is well-written in its framing relative to prior literature. I think the paper would be a good fit at PLOS Medicine but also could be improved through several suggested revisions.

1. The authors should provide clinical justification for why they have selected a BP threshold of <140/90 to define hypertension and hypertension control. I am not familiar with hypertension management guidelines in India, but this is a controversial topic and major guidelines including the ACC/AHA and WHO-PEN now recommend more strict thresholds in individuals with higher cardiovascular risk. Please see my comment below on limitations for more on my perspective on this issue.

2. I agree with primarily presenting sex- and age-adjusted estimates in the state-level data. However, the methods for age-sex adjustments for hypertension prevalence, awareness, treatment, and control is not 100% clear to me. Am I correctly inferring that the authors ran the logistic regression models with state fixed effects as well as fixed effects for sex + age groups (categorical rather than continuous age), and then used "average adjusted predictions" for each state?

3. The authors mention that they are enclosing their Stata .do files. This would be useful and welcome. I do not see the files in the submission, however, so I have been unable to personally review any statistical code.

4. Figure 3: would strongly urge the others to redraw the heatmap using color gradients rather than color tertiles for proportions by state. Much granularity of information is lost using the tertiles.

5. This is a stylistic suggestion, but I would recommend moving the table of odds ratios (Table 4) to the appendix and instead present a table of average marginal effects from the logistic models. Such a table would be much easier for readers to interpret the absolute differences in proportions within categories of covariates. Additionally, in place of a table, I would favor using forestplots to present regression outputs visually; in Stata, "coefplot" is a very nice package to do this, if the authors would like to pursue this route.

6. I would request that the authors add a supplementary appendix specifying the precise survey questions that were used in LASI to define awareness and treatment. This can be a simple table and will help readers understand the numerators for the cascades as you define them.

7. Some states had higher proportions of individuals controlled than treated. I believe that this is because the authors have not defined the hypertension cascades to be conditional, i.e., achieving one step was not necessarily contingent on the prior step. For example, an individual could have reported a prior diagnosis of hypertension, not be receiving treatment, and yet still have a BP level under the controlled threshold. Such unconditional cascades are reasonable (and my own personal preference in these analyses), though I believe the Prenissl India study used conditional cascades. Since the finding of % control > % treated appears a bit unanticipated, especially to a reader less familiar with the definitional nuances of these studies, the authors may wish to add 1-2 lines in either a caption or main text describing why this finding can occur.

8. The authors have defined hypertension treatment strictly as pharmacological treatment. Why? Non-pharmacological therapy is effective for lowering blood pressure in clinical trials, and, LASI also had at least one question specifically on lifestyle changes for treatment of blood pressure (diet/salt focused). I believe the authors should either (1) specifically state why they opted to exclude non-pharmacological treatment in their estimates, (2) include diet as "treatment" in the main analysis, or (3) generate a sensitivity analysis in which diet treatment was also considered "treatment."

9. I wonder if a missing opportunity in this manuscript is at least exploring some state-level characteristics that are associated with hypertension prevalence and ATC. This need not be a complex formal analysis. For example, the authors could consider simply plotting proportions by state-level GDP or some other marker of economic development (as in the Prenissl paper). The authors comment on this already in the discussion (second paragraph, bottom of page 10).

10. The supplementary tables E11 and E12 are very useful in helping readers locate this study in the context of the prior Geldsetzer paper in JAMA IM. Thanks for including it.

11. I can think of a few additional limitations that the authors may wish to mention:

* I agree that measurement error is probably the biggest limitation of this and other cross-sectional assessments of hypertension. I might just also add text that the hypertension prevalence is likely overestimated. This idea is suggested but not specifically stated.

* Lower age limit of LASI <45 years cuts off a substantial absolute number of adults with hypertension at younger ages, given that India has a relatively young population age distribution.

* LASI does not assess if a respondent has ever had blood pressure measured (sometimes worded as "ever screened" in cascade papers). This is often a step in the hypertension cascade that is reported when available.

* I mentioned the issue of non-pharmacological therapy above. If the authors do not wish to address this in the analysis, then it should be mentioned as a limitation.

* A conceptual limitation of the present study is that it focuses on the "treat-to-target" approach for hypertension treatment. As the authors may be aware, the field has been moving away from this approach to hypertension treatment, which is the one operationalized in this study. This shift has happened due to evidence that the relative benefits of hypertension therapy are constant across risk levels; thus, individuals with higher baseline risk have much larger absolute benefit irrespective of blood pressure. In other words, even a person with "normal" blood pressure according to traditional BP thresholds may have large benefit from BP therapy. This approach is sometimes referred to as "benefit-based" therapy and has begun to be incorporated into recent guidelines from the ACC/AHA and WHO-PEN.

Reviewer #2: I confine my remarks to statistical aspects of this paper. The general approach is fine, but I do have some questions and issues to resolve before I can recommend publication.

NOTE: Line numbers would have made the review process easier.

p. 4 In addition to "44% of global deaths ...." give a number of deaths, or rate per 100,000 or something.

p. 5 While your results should definitely be useful, it IS possible to estimate these numbers using the analyses that have already been done. Those estimates might be poor, but, for instance, you could estimate the rates for older people in districts where they are not known by adjusting the rates in known districts using the more general results that were gotten in all districts.

p. 6 It would be good to clarify that you can be a, b, and c .... or other combinations. It seems to me that you can be a) alone, a) and b), or a), b) and c). But it would aid clarity to spell this out.

p. 7 Why did you use the concentration index? What does it add to the regression results? It's a pretty unusual statistic, so there should be some reason for using it.

Why was BP unmeasurable on some people? Were these people different from those where it could be measured? Why wasn't multiple imputation used? (I think it should be, but you may have some reason for not doing so)

Figure 3: It would be better to use more colors and to have the percent relate to some quality of the color (e.g. saturation). A very good site on doing this well is ColorBrewer https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3

Peter Flom

Reviewer #3: The authors provide a clear picture of hypertension prevalence and rates of awareness, treatment, and control among older adults in India. The research idea is a valuable extension of existing evidence on younger age groups and highlights the need for better hypertension care among the population segment most at risk of suffering from hypertension and cardiovascular diseases.

However, the definition of their sample needs to be improved. The authors defined participants who have "ever […] been diagnosed as hypertensive" (p. 6) to have hypertension. The survey instrument does not distinguish between hypertension and high blood pressure. Thus, this is a rather broad indicator, which does not reflect clinical guidelines and can capture temporary episodes of high blood pressure. I assume that the inclusion of "having been diagnosed" in the study sample is the reason why in some age groups and states the control rates are larger than the treatment rates (Table E3, Bihar, for example). The authors should base the definition of hypertension on BP and self-reported medication use only.

Additional specific comments:

- Last sentence in first paragraph of introduction (p.4) is too long.

- Average of BP measures (p.6.): were there missing values in either the second or the third measurement? If yes, it needs to be stated how the final BP was computed if only one of the two measurements was available.

- MPCE indicator (p. 6, "Measures"): It is not clear to me why all expenditure on healthcare and medicine were excluded from the calculation. This should briefly be explained.

- Levels of Covariates (p.6, "Statistical Analysis"): The main text, appendix or notes of Table 1 should include an overview on how levels of categories were chosen and which information they include. Eg. what categories were clubbed in the "other" groups, how were years of education cut-offs defined. Furthermore, it should be considered if the categories in "other" really are comparable or should better be listed separately, despite their small sample size.

- Missingness in variables of interest (p. 7, "Results"): Roughly 10% of observations have either a missing value in the BP measurement or another variable. The appendix should include a comparison of the sample characteristics of the samples with and without missings in the variables of interest (BP measurement, ATC variables). A detailed table of the number(%) of missing values in each outcome and covariate should be provided.

- Median age (p.8): IQR should be reported alongside the median.

- Thresholds for comparison (p.9 "Figure 2 shows […] in Haryana"): The threshold for comparison, eg "the rate of awareness exceeded 70% in only three states…" seem arbitrary and are confusing. I suggest deleting these kinds of comparisons.

- Figure 3: The thresholds for the colour coding vary in each panel. Either, the same cut-offs or different colours in each panel should be used. Or it should be made clearer that the focus is on the relative comparison across states, e.g. by removing the prevalence ranges from the legend. The same applies to the other maps of this kind.

- Multivariate regression (p. 10): I assume the authors mean "multivariable logistic regression"

- Presentation of results in Discussion section: The authors present new results in the discussion. The discussion should only briefly summarize the main results (without presenting p-values and CIs). The focus should be on the interpretation of the results and putting them into context.

- Comparison to previous evidence from other studies in Discussion: This is interesting but too detailed. A higher level comparison would be more suitable.

- In several instances, the authors refer to the poverty rates across states. It would be interesting to include this information in figures/tables; eg. by colour coding Figure 4 according to poverty rate quartile.

- Decimal places: The authors should report only two decimal places, also when presenting the concentration index results.

- Oxford comma: The Oxford comma should be used consistently.

Reviewer #4: 1. The authors used some acronyms in the abstract, which can be avoided if not well-known ones like ATC.

2. About 10% of participants were excluded from the study due to some reasons. There may be a possibility that the excluded participants may have different socioeconomic characteristics. Authors may check that the participants included in the study are not different from excluded participants.

3. The initial recommendation of hypertension control is lifestyle modification. If this information is available, authors may think about modifying treatment definition in the cascade of care.

4. "STATA" is a typo. Please have a look: https://www.stata.com/statalist/archive/2010-01/msg00489.html

5. Authors mentioned, "Table 1 shows characteristics of the full analysis sample and estimates of age-sex adjusted hypertension prevalence. The median age of the sample was 57 years." The measure of central tendency gives only a half picture. Please use a measure of dispersion along with a measure of central tendency here and throughout the manuscript.

6. The study's title, all the tables, and figure captions indicate that analysis is only for 45+ aged participants, but about 9% of participants were <45 years as per table 1. Please make it clear.

7. The authors used Spearman rank correlation to measure the relationship between hypertension prevalence, awareness, and treatment. Some other methods may be preferred in such cases.

8. Figure 2 and figure 3 seems duplication of information; authors may choose one of them.

9. In Figure 3, the authors categorized the prevalence of hypertension prevalence and cascade of care, masking the state-level variation. It may be worth to show it using a continuous scale.

10. Authors mentioned, "Table 4 shows estimated OR from a multivariate logistic regression for each outcome." Although it is used as a synonym, there is a difference in multivariable and multivariate regression. Authors may recheck that they used multivariate or multivariable regression.

11. In table 4, the authors categorized age in various age groups, which can lead to loss of information and an increase in type I and II errors. A supplementary analysis is warranted in the case of categorization of continuous data.

12. The authors included caste and religion as independent variables in table 4, but biologically caste and religion have nothing to do with hypertension and its cascade of care. Authors may think about removing caste and religion or may be interested in assessing the impact of all other variables excluding caste and religion in a separate analysis.

13. Authors mentioned, "The discrepancies are partly explained by the facts that we examined a much older age range and found, like other studies." Discrepancy may not be the correct word choice as the differences are obvious with higher age, especially in postmenopausal women.

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2021 Aug 24;18(8):e1003740. doi: 10.1371/journal.pmed.1003740.r003

Author response to Decision Letter 1

8 Jul 2021

Attachment

Submitted filename: Reply_text _R1_07072021.docx

Click here for additional data file.^{(54.3KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003740.r004

Decision Letter 2

Callam Davidson

19 Jul 2021

Dear Dr. Mohanty,

Thank you very much for re-submitting your manuscript "Awareness, treatment, and control of hypertension in adults aged 45+ and their spouses in India: a nationally representative cross-sectional study" (PMEDICINE-D-21-02188R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by three reviewers. I am pleased to say that, provided the remaining editorial and production issues are dealt with, we expect to be able to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We hope to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

Please let me know if you have any questions, and we look forward to receiving the revised manuscript.

Sincerely,

Callam Davidson,

Associate Editor

PLOS Medicine

cdavidson@plos.org

------------------------------------------------------------

Requests from Editors:

Please update ‘aged 45+’ to ‘aged 45 years and over’ throughout the manuscript (including in the title).

Thank you for responding to my previous query regarding your data availability statement, it appears that your data sharing approach aligns with the journal policy. While you have clarified the matter for me, please can you update your response to read ‘No – some restrictions will apply’ as it did in the first revision, as this is more accurate.

In the ‘Describe where data can be found’ section of the submission form:

* you state ‘Data is publicly available’ – please update to ‘Data are publicly available’. Please update elsewhere in the manuscript if necessary to ensure data are treated as plural.

* Please add a sentence to the effect of ‘data will be made available to the researchers meeting the criteria for access to confidential data’, if applicable and accurate.

In both the abstract and discussion, please update the sentence ‘Study limitations included reliance in BP’ to ‘Study limitations included reliance on BP’.

Please provide 95% CIs and p values for quantitative results presented in the abstract, as appropriate.

Please consider whether any quantitative data ought to be added to the abstract to support the results detailed on lines 29-32. If you opt to include, please ensure it is presented with 95% CIs and p-values, where appropriate.

The first sentence of the ‘Notes’ under Table 1 (‘Adjusted for age and sex, except age (sex) group estimates adjusted for sex (age) composition’) is confusing, please reword to improve clarity.

In several tables and figures, you need to define the abbreviation MPCE in the legend. Please check and update throughout.

Table 3 still contains p-values of 0.000. Please update these to P<0.001.

Please ensure consistent use of ‘Figure’ (as opposed to ‘Fig’).

On lines 328-329, please report the actual p-values rather than stating p-value<0.05.

The first line of your discussion should be split into two sentences. I would advise placing a full stop after [8] on line 362, then beginning the following sentence ‘Yet, to our knowledge, there were no nationally representative…’ etc.

Please add the date accessed for references 24 and 31.

Please review your references and ensure that initials are not followed by a full stop (as in, for example, reference 32), and also please ensure journal titles are abbreviated consistently (e.g. reference 33 should be ‘Circ Res’.

Comments from Reviewers:

Reviewer #1: I have reviewed the author responses in detail. My concerns have been very thoughtfully addressed, and I have no further critiques.

I wish to commend the authors for making their full statistical code available and for generally completing a very thoughtful revision.

This is a very nicely done paper and the authors should be proud.

Reviewer #2: The authors have addressed my concerns and I now recommend publication.

Reviewer #4: As authors have addressed almost all of my comments, I recommend the acceptance of the paper.

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2021 Aug 24;18(8):e1003740. doi: 10.1371/journal.pmed.1003740.r005

Author response to Decision Letter 2

22 Jul 2021

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PLoS Med. doi: 10.1371/journal.pmed.1003740.r006

Decision Letter 3

Callam Davidson

23 Jul 2021

Dear Dr Mohanty,

On behalf of my colleagues and the Academic Editor, Dr Sanjay Basu, I am pleased to inform you that we have agreed to publish your manuscript "Awareness, treatment, and control of hypertension in adults aged 45 years and over and their spouses in India: a nationally representative cross-sectional study" (PMEDICINE-D-21-02188R3) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

Please also make the following changes to your manuscript when completing your formatting changes:

* Please delete the heading 'Title Page' from the top of your title page

* Please remove the words 'the first' from line 90 in your Author Summary

* Please remove the words 'and growing' from line 110 in your Author Summary

* Please refer to low or middle income states rather than "developing states". Please refer to high income states rather than "developed" states (update as appropriate on lines 115, 117, 336, 342, 411, 412, 456, 477, 479).

* Please update Data Availability Statement to read 'Data can be obtained from https://iipsindia.ac.in/ by registering at https://iipsindia.ac.in/sites/default/files/LASI_DataRequestForm_0.pdf. Data will be made available to the researchers meeting the criteria for access to confidential data. Questions can be directed to datacenter@iipsindia.ac.in'

* Please note that the 'DO' file you refer to in your Data Availability Statement is now a TXT file. Either update the wording to 'we are enclosing the DO file information as a TXT file' or provide as a DO file per the original submission. Please also specify exactly which graph you are referring to in your data availability statement (such that the reader could locate it).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist. STROBE checklist of items that should be included in reports of cross-sectional studies.

(DOC)

Click here for additional data file.^{(93KB, doc)}

S1 Text. Sample design.

(DOCX)

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S2 Text. Outcome definitions.

(DOCX)

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S3 Text. Measurement of MPCE. MPCE, monthly per capita consumption expenditure.

(DOCX)

Click here for additional data file.^{(18.9KB, docx)}

S4 Text. Age–sex adjustment.

(DOCX)

Click here for additional data file.^{(19.8KB, docx)}

S1 Fig. Adjusted concentration curves for hypertension cases and ATC among those with hypertension, adults aged 45+ and their spouses in India.

(TIF)

Click here for additional data file.^{(286.4KB, tif)}

S2 Fig. Adjusted percent treated among those aware of hypertension and adjusted percent controlled among those treated, adults aged 45+ and their spouses in India.

The base map can be found at https://globalsolaratlas.info/download/india. Adjusted for age and sex.

(TIF)

Click here for additional data file.^{(539.2KB, tif)}

S1 Table. Sample characteristics by whether the BP was measured, adults aged 45+ and their spouse, adults aged 45+ and their spouses.

(DOCX)

Click here for additional data file.^{(21.3KB, docx)}

S2 Table. Missing observations on BP measurement, diagnosis, treatment and sociodemographic variables, adults aged 45+ and their spouses.

(DOCX)

Click here for additional data file.^{(20.5KB, docx)}

(DOCX)

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S4 Table. Number of participants with hypertension by sociodemographic characteristics.

(DOCX)

Click here for additional data file.^{(20.1KB, docx)}

S5 Table. Number of participants in full analysis sample and with hypertension by state.

(DOCX)

Click here for additional data file.^{(19.9KB, docx)}

(DOCX)

Click here for additional data file.^{(22.8KB, docx)}

S7 Table. Adjusted prevalence of undiagnosed, untreated, and uncontrolled hypertension by MPCE quintile and education groups, adults aged 45+ and their spouses in India.

(DOCX)

Click here for additional data file.^{(22.4KB, docx)}

S8 Table. Adjusted hypertension prevalence, and percent aware, treated, and controlled among those with hypertension by state, adults aged 45+ and their spouses in India.

(DOCX)

Click here for additional data file.^{(22.2KB, docx)}

S9 Table. Unadjusted estimates of hypertension prevalence and percent aware, treated, and controlled among those with hypertension by state, adults aged 45+ and their spouses in India.

(DOCX)

Click here for additional data file.^{(22.5KB, docx)}

S10 Table. Correlation of rates of ATC each with hypertension prevalence across states, adults aged 45+ and their spouses.

(DOCX)

Click here for additional data file.^{(18.6KB, docx)}

S11 Table. Adjusted concentration indices for hypertension and ATC among those with hypertension, adults aged 45+ and their spouses in India.

(DOCX)

Click here for additional data file.^{(22.2KB, docx)}

S12 Table. Adjusted marginal effect for hypertension and ATC among those with hypertension, adults aged 45+ and their spouses in India.

(DOCX)

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S13 Table. Comparison of estimates of hypertension prevalence by age and sex with those in Geldsetzer and colleagues.

(DOCX)

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S14 Table. Numbers of participants by age and sex used to estimate prevalence rates presented in S13 Table.

(DOCX)

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S1 Code. Stata code for computation of prevalence and ATC of hypertension.

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Attachment

Submitted filename: Reply_text _R1_07072021.docx

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Attachment

Submitted filename: ReplyR2_190721.docx

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Data Availability Statement

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PERMALINK

Awareness, treatment, and control of hypertension in adults aged 45 years and over and their spouses in India: A nationally representative cross-sectional study

Sanjay K Mohanty

Sarang P Pedgaonkar

Ashish Kumar Upadhyay

Fabrice Kämpfen

Prashant Shekhar

Radhe Shyam Mishra

Jürgen Maurer

Owen O’Donnell

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Study design and participants

Measures

Statistical analysis

Results

Fig 1. Flowchart of participant selection.

Table 1. Participant characteristics and adjusted hypertension prevalence, adults aged 45 years and over and their spouses.

Table 2. Adjusted concentration indices for hypertension and for ATC among those with hypertension, adults aged 45 years and over and their spouses.

Table 3. Adjusted percent aware, treated, and controlled among those with hypertension, adults aged 45 years and over and their spouses.

Fig 2. Adjusted hypertension prevalence and percent aware, treated, and controlled among those with hypertension by state, adults aged 45 years and over and their spouses.

Fig 3. The association of NSDP per capita (in US$) with each of hypertension prevalence and ATC.

Fig 4. Adjusted concentration indices for hypertension and ATC among those with hypertension by states, adults aged 45 years and over and their spouses.

Fig 5. Averaged marginal effects on probability of hypertension and on probabilities of ATC among those with hypertension, adults aged 45 years and over and their spouses.

Discussion

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Callam Davidson

Roles

Decision Letter 1

Callam Davidson

Roles

Author response to Decision Letter 1

Decision Letter 2

Callam Davidson

Roles

Author response to Decision Letter 2

Decision Letter 3

Callam Davidson

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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