Spatial analysis of hypertension’s awareness, treatment and control in India: evidence from national family health survey 2019-21

Abstract

Background

Being a primary contributor to cardiovascular diseases (CVDs), Hypertension is the leading cause of morbidity and mortality worldwide. Globally, around 4 billion people were estimated to have hypertension in 2019, resulting in 10 million deaths. However, the present study has been designed to explore hypertension awareness, control, and treatment dimensions in India.

Data & methods

Data were drawn from 1,551,191 respondents from the fifth round of India’s National Family Health Survey. Hypertension, by the World Health Organization, is defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg. The binary Logistic regression statistical method is employed to analyze the data, and the geospatial method—spatial autocorrelation has been used to explore the spatial relationship between variables of hypertension and spatial units.

Results

The results show a positive correlation between higher wealth position, urban residency, male gender, older age, and higher education levels with better knowledge and treatment of hypertension. On the other hand, people from worse socioeconomic origins, those living in rural areas, and members of particular social and religious groups have lower awareness and treatment levels. The findings also show that urban males are more likely to have managed hypertension, indicating that socioeconomic and geographic factors play a major role in the management of hypertension.

Conclusion

The study’s results highlight the urgent need for focused public health initiatives to address hypertension knowledge, management, and treatment differences. Better access to healthcare is necessary for the efficient management of hypertension, especially in underserved areas and among lower socioeconomic groups. Furthermore, public health initiatives should emphasize raising awareness and improving treatment compliance, drawing on information from socioeconomic and spatial assessments to guide the creation of policies and programs.

Introduction

Hypertension (or high blood pressure) is the leading cause of morbidity and mortality worldwide, serving as the primary contributor to cardiovascular disease (CVD) [35]. According to quantitative evidence, hypertension is the most well-known and significant modifiable risk factor for cardiovascular disease and mortality [14, 54]. Noncommunicable diseases (NCDs), also known as chronic diseases, are not transmitted from person to person. These illnesses take a long time to develop and do not cause symptoms in their early stages. One of the most serious concerns about coronary heart disease is hypertension [39]. Globally, approximately 4 billion people were estimated to have hypertension in 2019, resulting in 10 million deaths [8]. Hypertension is a major public health problem with a global distribution and is the most common cardiovascular disease (CVD) risk factor [13]. The prevalence of hypertension remained stable or decreased in developed countries while increasing in developing countries [25]. The There are no significant cross-sectional differences between developed and developing countries in terms of hypertension prevalence, awareness, treatment, and control, with the exception of a lower mean prevalence among men in developing countries compared to developed countries. Cardiovascular disease (CVD) is the primary cause of death in developing countries [48]. Some of the greatest challenges in global health are predicting how the cardiovascular risk profile of populations from low- and middle-income countries will develop and taking timely action to modulate their transition, particularly in the Sub-Saharan African countries at the earliest stage of the epidemiological transition [31, 53]. Additionally, the GBD Study (2019) calculated that 33 million Disability-Adjusted Life Years (DALYs) and nearly 1.6 million deaths in India were attributed to high systolic blood pressure alone. One of the main risk factors for morbidity and mortality worldwide is hypertension [9]. The World Health Organization claims that. Hypertension is the leading cause of avoidable deaths and diseases in India. To enhance the contextual relevance and policy significance of the study, it is recommended that the authors integrate nationally representative statistics on hypertension from the National Family Health Survey (NFHS-5) and the Global Burden of Disease (GBD) Study. According to NFHS-5 (2019–21), approximately 21% of women and 24% of men aged 15 and above were found to be hypertensive in India. Furthermore, the GBD Study (2019) estimated that high systolic blood pressure alone contributed to over 1.6 million deaths and 33 million Disability-Adjusted Life Years (DALYs) in India. Globally, hypertension is a leading risk factor for morbidity and mortality. According to the World Health Organization. According to the most recent data available, it is anticipated that 1.13 billion people globally would suffer from hypertension in 2021 [22]. It is estimated that 26.1 percent of people have hypertension overall. Given the large number of people afflicted and its significant contribution to the worldwide burden of disease, examining the prevalence of hypertension is important [19].

Hypertension is the leading cause of avoidable deaths and diseases in India. It is a leading risk factor for cardiovascular disease, accounting for 23 percent of total deaths and 32 percent of adult deaths between 2010 and 2013 [18]. According to the Global Burden of Hypertension study, India accounted for 18% of the global burden of 212 million Disability-Adjusted Life Years (DALYs) due to hypertension in 2015 [15]. The burden of hypertension in India is expected to increase significantly in the coming years due to rapid environmental and"lifestyle"changes that emanate from hazardous working conditions and growing social pressures of survival (Imrana et al., [29]. In India, cardiovascular diseases (CVDs) account for nearly half of the deaths due to noncommunicable diseases. (NCDs) [34]. In India, cardiovascular diseases (CVDs) account for nearly half of noncommunicable disease deaths [11]. Access to healthcare in India has been under the spotlight, especially after the 2016 The Global Burden of Disease study ranked India 145th out of 195 countries in terms of healthcare access and quality [17]. Despite significant advances over the last decade, access to healthcare in India is worse than in many other middle-income countries, including India's neighbouring countries [17]. Significant disparities exist across geographic locations within the country [12]. This study aimed to assess health risk behaviours and measure the prevalence, awareness, treatment, and control of hypertension and associated factors among people aged 15 to 49 [21]. The causes of hypertension are numerous and complex. Previous studies using Demographic and Health Survey (DHS) data attempted to identify risk factors for hypertensive status. The most important determinants associated with hypertension were age, gender, education, wealth status, working status, caffeine consumption, place of residence, and so on [41]. The primary factors linked to high blood pressure. One of the main risk factors for coronary heart disease, stroke, and chronic heart disease is elevated blood pressure. The risk of stroke and coronary heart disease is positively connected with elevated blood pressure. Its consequences include heart failure, peripheral vascular disease, renal impairment, retinal haemorrhage, and visual impairment in addition to coronary heart disease and stroke [33]. Both modifiable and non-modifiable risk factors have an impact on hypertension. Physical inactivity, excessive salt intake, obesity, tobacco use, and alcohol consumption are all modifiable variables [42, 52]. Age, gender, education, income, employment status, coffee use, domicile, and so forth were in contrast [41] regarded as unchangeable. Designing preventative tactics requires an understanding of this distinction. Hypertension (high blood pressure) occurs when the pressure in your blood vessels is too high (140/90 mmHg or greater). It is common, but it can be dangerous if not treated. Hypertension is diagnosed when both systolic and diastolic blood pressure readings are ≥140 mmHg or ≥90 mmHg on two separate days. Hypertension means that the heart must work harder than usual to deliver blood to all body parts. This increases the workload on the heart. Blood pressure is the force of blood against the artery walls or blood vessels as it is pumped through the body. Blood pressure helps blood flow to all parts of the body high blood pressure is an important risk factor for cardiovascular disease and causes 7.5 million deaths (12.8 percent of all deaths) each year [38]. According to the global burden of disease study, systolic blood pressure is responsible for the greatest proportion of lost years of life due to premature death, accounting for 212 million years [55]. The presence of spatial autocorrelation is assessed using Moran's I statistic, hotspot analysis, cold spot analysis, and influential observation analysis [46]. Assessing spatial distribution of hypertension prevalence would help identifying highly prevalent areas of Indian districts wise. This gap in the management of hypertension is one of the critical reasons for finding the high and low prevalence of hypertension in India. Present studies have found differences in hypertension prevalence between developed and developing countries, but little research has looked systematically at geographic disparities within India. While Myriad studies have analysed hypertension prevalence, awareness, treatment, and control using NFHS data, there is a lack of spatial analysis to assess district-wise clustering and disparities in hypertension management across India. A more nuanced analysis of these determinants, considering spatial variations, is required, particularly in Overlooked areas. Identifying spatial patterns can help policymakers target high-burden areas more effectively. Academics, researchers, decision-makers, and members of the public health community will use the findings to develop new regional hypertension prevention and control strategies.

Data and methods

Data sources

The present study is entirely based on secondary data sources– National Family Health Survey, 2019–21. The NFHS is an Indian version of the Demographic and Health Survey that provides information on fertility, infant and child mortality, family planning, maternal and children health, reproductive health, nutrition, anemia, utilization, and quality of health and family planning services. The survey was conducted under the aegis of the Ministry of Health and Family Welfare (MoHFW), coordinated by the International Institute for Population Sciences (IIPS), Mumbai, and implemented by a group of survey organizations and the Population Research Canter. The NFHS series provides information on the population, health, and nutrition of India and each state/union territory (UT). The survey is representative not only at the national and state level but also at the district level. It has used a two-stage sample design with villages as the Primary Sampling Units (PSUs) at the first stage (selected with probability proportional to size). These sampling designs for both study rural and an enumeration block for Urban followed by a random selection of 22 households in each PSU at the second stage.

The NFHS-5 was conducted in two phases (phase 1 from 17 June to 30 January 2020 and Phase 2 from 2 January to 30 April 2021) due to the outbreak of the Covid-19 pandemic. It provided information from 707 districts of India, which gathered data from 636,699 households across all 28 states and 8 union territories (UTs) in India.

The present study is based on a total number of 2843,917 observations. Out of the total samples, 1,82,585 belong to Hypertension Awareness (72627 males and 109,958 females), 1,11909 to Hypertension Treatment (45,017 males and 66,892 females), and 3,97,409 to Hypertension Control (1,98,266 males, and 1,99194 females).

To proceed further, the concepts used in the study need to be explained:

Systolic Blood Pressure (SBP) is the degree of force when the heart is pumping (contracting), and Diastolic Blood Pressure (DBP) refers to the degree of force when the heart is relaxed. However, the definition used by the World Health Organization (WHO) and the American Heart Association to treat a person with Hypertension is ‘if a person has Systolic BP ≥ 140 and/or diastolic BP ≥ 90 mmHg (millimetres of mercury). Additionally, an individual is classified as having hypertension if s/he is taking anti-hypertensive medication to lower her/his blood pressure [43] Fig. 1.

Fig. 1.

Data source

Dependent variables

Awareness of hypertension

Individuals who responded yes to the following statement were considered as being aware of their hypertensive status: Told had high BP on two or more occasions by the doctor or other health professionals.

Hypertension treatment

Individuals who responded yes to the following statement were considered to be on hypertension treatment: Currently taking a prescribed medicine to lower BP [6].

Controlled hypertension

Individuals who were currently taking antihypertensive medication and were detected with SBP < 140 mmHg and DBP < 90 mmHg on screening were classified as having controlled hypertension [51].

Explanatory variable

Socio-economic & demographic variables

There are many independent or explanatory socio-economic and demographic variables to explain the awareness, treatment, and control of hypertension variables. Ages (15–24; 25–29; 30–34; 35–39; 40–44; 45–49; 50–54; 55–59). Sex (male, female), Marital status (currently married, married, widowhood divorce). Numerous studies have shown the role of several variables in explaining hypertension. Socio-economic variables include in India caste is divided into four major groups (scheduled caste, scheduled tribes, another backward caste, and none of them) and religion (Hindu, Muslim, Christian, others), The education categories are defined based on different levels of education completed like (No education; primary, secondary, higher education), wealth index (poorest, poorer; middle, richer; richest), place of residence (urban, rural). Besides sociodemographic variables, we included tobacco use and alcohol consumption, health insurance, and region is also an important factor in defining hypertension prevalence (north-, central, east, northeast, west, south. States) Bihar, Chhattisgarh, Himachal Pradesh, Jammu & Kashmir, Jharkhand, Madhya Pradesh, Odisha, Rajasthan, and Uttar Pradesh, Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, and Tripura. Non-high focus states: Andhra Pradesh, Goa, Gujarat, Haryana, Karnataka, Kerala, Maharashtra, Punjab, Tamil Nadu, Telangana and West Bengal, Andaman & Nicobar Islands, Chandigarh, Dadra & Nagar Haveli, Daman & Diu, Delhi, Ladakh, Lakshadweep and Puducherry [45].

Methods

This section details the dependent and explanatory variables used in the study. Further, it sheds light on the statistical methods performed to meet the requirement of the study’s analysis. The analysis was restricted to a total number of 397460 persons aged 15 = 49 years, for men (198266), and women (199194) separately. For this study, awareness of hypertension, treatment of hypertension, and controlled hypertension were outcome or dependent variables.

Statistical analysis

1. Data Source and Tools: - We accessed the household member-recoded file with the Person file from the Demographic and Health Surveys (DHS) The Program is an internationally recognised initiative that collects and disseminates data on population, health, and nutrition in low- and middle-income countries (LMICs). The United States Agency for International Development (USAID) primarily funds the programme, which has been in operation since 1984. It provides high-quality, nationally representative data to help policymakers, researchers, and development agencies make informed decisions about public health and development policies.

• National Family Health Survey: - The National Family Health Survey (NFHS) is a large-scale, multi-round survey carried out in India to gather comprehensive data on population, health, and nutrition. It is India's version of the Demographic and Health Survey (DHS) and is conducted under the auspices of the Ministry of Health and Family Welfare (MoHFW), Government of India. The survey is carried out by the International Institute for Population Sciences (IIPS), Mumbai, in collaboration with various field agencies, and is funded by international organisations such as USAID, UNICEF, UNFPA, the Bill & Melinda Gates Foundation, and WHO. Since its inception in 1992-93, the NFHS has played an important role in providing reliable and nationally representative data to inform policy decisions, programme implementation, and research on critical health and demographic issues in India. For recoding and cross-tabulation with dependent and independent variables, we used STATA 17 (64-bit). Using the NFHS-5 data, we used GeoDa to generate district-level prevalence maps and evaluate spatial clustering. The National Family Health Survey (NFHS-5) dataset was used for statistical and spatial analysis.

• (b)Spatial Analysis:—through (LISA) Cluster Map, Significant Map, and Local Moran’s I. The Analysis of Spatial Autocorrelation and Moran Clusters. The main aim is to detect spatial patterns. A low Moran’s I could very well mean that this feather described by the indicators under study is poorly spatially autocorrelated. If Moran’s I value is lower than 0.5, it will have a low level of autocorrelation, and if the value is higher than 0.5, the results will be high and positive spatial autocorrelation. This measure helps to understand the distribution of hypertension awareness treatment and control the indicators amid districts displaying clustering and randomness.
• (c)Regression Analysis:—we carried out a binary logistic regression model to examine the association between hypertension awareness, treatment, control, and exposure variables. To identify and relate the demographic, socio-economic, and other variables, multiple binary logistic regression variables were used to estimate odds ratios using their 95% CI Person File. All the exposure variables were tested for possible multicollinearity before putting them into the binary logistic regression model. Bivariate logistic regression analysis is used to examine the differences between the dependent variable and the independent variable.

• • The logistic regression model.

• • Here, πi represents the probability of the outcome of interest, β0 is the intercept, βX represents the coefficients of predictor variables, and ε is the error term.

Model diagnostics: Nagelkerke R² = 0.21 (awareness), 0.19 (treatment), 0.23 (control). Hosmer–Lemeshow test p-values = 0.41, 0.35, and 0.49 respectively (indicating good model fit).

Hosmer–Lemeshow test p-values = 0.41, 0.35, and 0.49 respectively (indicating good model fit).

All models were tested for multicollinearity using VIF; no variable had a VIF >2.5 (Supplementary Table S1).

Note: Odds Ratios (OR) and 95% Confidence Intervals (CI) are shown. *p < 0.05, **p < 0.01, ***p < 0.001.

All analyses were conducted using the sampling weights provided in the NFHS-5 dataset to account for the complex survey design, including stratification, clustering, and unequal probabilities of selection. Specifically, we used the individual women's (v005), men's (mv005), and biomarker (hv005) weights, adjusted by dividing by 1,000,000 as recommended in the NFHS-5 documentation. Logistic regression models were estimated using the survey module in Stata, which incorporates weights and adjusts for clustering at the PSU level.

Results and findings

As per India’s National Family and Health Survey 2019–21 estimates, the prevalence of hypertension is 22.6 percent from a total sample of 17,08,241 persons [36]. Seen from the gendered lenses, the figures are found to be higher among males (24.1 percent) compared to females (21.2 percent). Table 1. Sample characteristics of Hypertension by demographic and socioeconomic variables in India, NFHS 2019–21 shows the socio-economic and demographic characteristics of people’s awareness, treatment, and control of hypertension in India. It may be seen that awareness and treatment of hypertension have been estimated higher among male compared to female sample respondents. Further, respondents belonging to urban areas, other social and religious groups, wealthier households, and the country's east, north, and northeast regions have been found to have higher awareness and treatment of hypertension. The lowest levels of hypertension awareness and treatment were found among the respondents who came from Scheduled Tribes, Hindu religion, rural areas, poor and poorest wealth quintiles, and the western region of the country. The results further show the magnitude of controlled hypertension, which was found to be higher among male respondents compared to females hailing from urban areas primarily. It is visible from the table that as the increases, hypertension awareness, treatment, and control also increase, indicating that adults and older persons have higher levels of awareness, treatment, and control of hypertension in India. The difference in hypertension awareness and treatment can also be seen among the people who consume alcohol, tobacco, or smoke. Compared to the respondents who do not consume tobacco, smoke, or drink alcohol, awareness, and treatment were found to be higher among those who consume these substances.

Table 1.

Sample characteristics of Hypertension by demographic and socioeconomic variables in India

Variables	Awareness		Treatment		Control
	Sample size (N)	%	Sample size (N)	%	Sample size (N)	%
Demographic Variables
Sex
Male	68358	5.75	31638	2.77	169930	14
Female	10878	5.92	4903	2.68	24391	12.81
Place of Residence
Urban	23366	6.38	11837	3.43	23366	6.38
Rural	55872	5.49	24706	2..44	55872	5.49
Age
Less than 15	8594	3.63	1951	0.86	8594	3.63
20–24	13176	6.05	2384	1.13	13176	6.05
25–29	18881	8.58	2859	1.3	18881	8.58
30–34	24343	12.72	3396	1.8	24343	12.72
35–39	31465	17.08	4859	2.65	31465	17.08
40–44	34691	22.91	6598	4.68	34691	22.91
45–49	43100	27.8	9910	6.95	43100	27.8
Above 50	20076	33.5	4586	8.34	20076	33.5
Socio-economic variables
Caste
Schedule caste	15861	5.6	6541	2.44	37543	13.48
Scheduled tribe	12528	3.7	5937	1.88	39942	13.77
Other backward class	29013	5.95	13053	2.7	70011	13.79
None of them	17948	6.7	8598	3.41	38368	14.76
Religion
Hindu	56271	5.6	25765	2.63	144993	13.84
Muslim	10233	6.05	5404	3.22	19601	12.56
Christian	6172	6.53	3117	3.99	15849	16.13
Others	6562	8.96	2257	3.18	13883	18.79
Education
No Education	15870	7.19	7202	3.27	40227	29.15
Primary	10783	6.45	5220	3.26	28555	16.56
Secondary	40293	5.24	18862	2.56	98208	12.7
Higher	12261	5.57	5240	2.52	27250	12.42
Wealth Index
Poorest	11777	4.51	5100	1.9	34095	11.38
Poorer	15493	5.08	6612	2.15	39284	12.21
Middle	16710	5.62	7636	2.62	41528	13.81
Richer	17475	6.21	8471	3.21	41045	15.2
Richest	17783	7.35	8724	3.86	38375	16.4
Marital Status
Never Married	8291	1.93	4213	0.99	26648	6.2
Married	67506	7.28	30476	342	159248	16.78
Widowed	2638	9.74	1452	5.81	6239	23.31
Divorced	330	6.04	150	3.11	842	16.82
Other variables
Smoke or Tobacco
No	60311	5.76	27461	2.73	133551	13
Yes	18889	5.82	9066	2.87	60644	19.06
Drinking Alcohol
No	68387	5.72	31344	6.72	154622	12.79
Yes	10804	6.29	5179	3.25	39533	23.85
Health Insurance
No	46330	5.91	19921	2.63	107580	13.6
Yes	32480	5.58	16444	2.93	85842	14.18
Region
North	18903	6.74	7453	2.54	38393	14.21
Central	16227	5.70	5456	1.76	45110	13.82
East	13556	6.48	5439	2.98	25494	11.54
North East	13398	6.21	6891	4.00	31995	13.34
West	4724	3.38	3512	2.54	18277	13.24
South	12430	6.10	7792	3.75	35057	16.64
Total	79238	5.78	34352	2.71	194326	13.84

Source: NFHS-5, 2019–21

Spatial analysis

The Global Moran I Statistics is used to understand the whether there is spatial auto-correlation are pervasive among the regional distribution of the selected variables. Additionally, the Local indicator for spatial association (LISA) measure of local Moran’s I is used to identify the presence or absence of significant spatial clusters or outliers for each geographical unit such as districts in the data set.The spatial analysis has been performed with the help of the spatial autocorrelation technique. This technique is usually used to elucidate the degree to which one spatial unit is similar to or different from its neigh bouring spatial unit [16]. Global Moran’s I statistics was used to understand whether there was a spatial autocorrelation among the regional patterns. The value of Moran’s I range from −1 to + 1, where the positive value indicates spatial clustering, the negative value shows no clustering and a neutral or zero value indicates no or random spatial clustering between the variables [1, 2]. Local Moran’s I range + 1 (indicating high-high or low-low clusters) through 0 (indicating random pattern) to −1 (indicating high-low or low–high outliers) [5, 50]. Local indicator of Spatial association (LISA) measure of local Moral’s I is used to identify the presence or absence of significant spatial clusters or outliers for each spatial unit. The researchers used the GeoDa software package to perform the spatial analysis. In the present study, If the value of Moran’s I is lower than 0.05, there is a low level of spatial autocorrelation, while a value of more than 0.5 shows a higher or positive clustering. Prior research has recognised the prevalence of localised clustering in chronic conditions such as hypertension, but it has not thoroughly examined the spatial clustering characteristics of their control rates [26]. Few studies using worldwide data including all urban or rural areas have documented variations in blood pressure regulation between urban and rural settings ([10, 30], Wu et al.).

The maps were represented by a five-colour scheme, with high-high assigned as red (hotspot), low-low as blue (cold spot), low–high as light blue, high-low as pink, and random patterns shown as white [4].

Figure 2(a). Individual and socio-economic factors, including education, wealth, marital status, religion, caste, human behaviour or lifestyle; these factors, were found to be determinants in the spatial analysis of HT in Thailand as discussed by [28]. The relationship of the NTLdata to population size was similar to represents the Global Moran’s I value for the variables of hypertension’s awareness and educational level, and wealth index of males and females. The Global Moran’s I value was 0.557, which signifies a high spatial autocorrelation and a significant positive association of hypertension awareness with the educational level and wealth index among the districts of India. Conversely.

Fig. 2.

Global Moran’s I (a) and Univariate LISA Cluster Map (b) of Hypertension Awareness in the districts of India

Figure 2(b). shows a univariate LISA cluster map of hypertension in the country's districts. Out of the 707 districts, 104 were found as hotspots, which symbolizes that these districts had a higher prevalence of hypertension awareness than their neighboring districts. The hotspot districts were clustered in Jammu and Kashmir, Uttarakhand, Arunachal Pradesh, Chhattisgarh, Odisha, and Meghalaya.

Figure 3(a). The value of Global Moran’s I and LISA cluster map for the variable of treatment of hypertension is presented which reveals that with a value of 0.557, there is a high spatial autocorrelation and a significant positive association of hypertension treatment among the districts of India. There are 92 districts out of a total of 707 districts in the country where there was high clustering. These 92 districts are shown as hotspot districts in red. The number of districts with low clustering for the hypertension treatment is 152, as shown in blue on the map.

Fig. 3.

Global Moran’s I (a) and Univariate LISA Cluster Map (b) of Hypertension Treatment in the districts of India

Figure 3(b) Displays a univariate LISA cluster map of hypertension across the country's districts. Out of 707 districts, 92 were identified as hotspots, including Gujarat, parts of Maharashtra, Tamil Nadu, Telangana, and Andhra Pradesh. Figure B shows that the maximum hypertension treatment hotspot districts in Southern India have a value of High-High. There are 152 districts in hypertension treatment that are classified as low-low. Which is shown in blue on the India map. According to the LISA cluster map, 152 districts, including Punjab, Haryana, the Northern part of Rajasthan, all districts of Jammu and Kashmir, Himachal Pradesh, and Uttarakhand, receive lower treatment.

Figure 4(a). The results of the Global Moran’s I and LISA clustering for the variable of hypertension’s control have been given and b, respectively. A value of 0.634 for the Global Moran’s I reveal the existence of high spatial autocorrelation among the districts of India. Of the 707 districts in India, 92 districts have high spatial clustering of hypertension control compared to 152 districts with low clustering of hypertension control.

Fig. 4.

Global Moran’s I (a) and Univariate LISA Cluster Map (b) of Hypertension Control in the districts of India

Figure 4(b). According to NFHS-5, the Univariate LISA cluster Hypertension Control is High-High, encompassing 92 of India's 707 districts. Almost all of India's southern states, as well as low-low clustering in 152 districts, are undergoing hypertension treatment; this is also true in the northern states.

Table 2 Predictors of hypertension’s awareness, treatment and control

Table 2.

Results from the Logistic regression model showing factors associated with hypertension in India

Variables	Odd ratio (95% confidence interval)
	Awareness	Treatment	Control
Age
Less than 15 ®
20–24	1.69*** [1.59,1.80]	1.16** [1.06,1.27]	1.75*** [1.68,1.82]
25–29	1.97*** [1.84,2.11]	1.20*** [1.09,1.33]	2.52*** [2.41,2.63]
30–34	2.41*** [2.25,2.59]	1.63*** [1.47,1.81]	3.89*** [3.72,4.07]
35–39	3.07*** [2.85,3.30]	2.41*** [2.18,2.66]	5.46*** [5.21,5.71]
40–44	4.33*** [4.03,4.65]	4.34*** [3.94,4.79]	7.79*** [7.44,8.15]
45–49	5.77*** [5.36,6.21]	6.65*** [6.02,7.34]	10.16*** [9.71,10.64]
More than 50	8.41*** [7.75,9.13]	9.90*** [8.88,11.03]	11.03*** [10.48,11.61]
Sex
Male ®
Female	1.57*** [1.53,1.61]	1.33*** [1.28,1.38]	0.69*** [0.68,0.70]
Place of Residence
Urban ®
Rural	1.01 [0.96,1.06]	0.94** [0.89,0.98]	0.96* [0.94,0.99]
Religion
Hindu ®
Muslim	1.14*** [1.07,1.20]	1.23*** [1.16,1.31]	1.01 [0.98,1.05]
Christian	1.18*** [1.07,1.30]	1.14** [1.03,1.25]	1.05 [0.99,1.10]
Others	1.64*** [1.52,1.78]	1.19*** [1.08,1.30]	1.41*** [1.35,1.47]
Caste
Scheduled caste®
Scheduled tribe	0.72*** [0.67,0.76]	0.82*** [0.77,0.88]	1.10*** [1.07,1.14]
Other backward class	1.03 [0.99,1.07]	0.97 [0.93,1.02]	0.97* [0.95,0.99]
None of them	1.04 [1.00,1.09]	1.12*** [1.06,1.18]	1.01 [0.98,1.04]
Wealth Index
Poorest ®
Poorer	1.15*** [1.10,1.21]	1.11** [1.04,1.18]	1.08*** [1.05,1.11]
Middle	1.28*** [1.22,1.35]	1.27*** [1.19,1.35]	1.19*** [1.16,1.22]
Richer	1.39*** [1.32,1.47]	1.51*** [1.41,1.61]	1.30*** [1.26,1.35]
Richest	1.55*** [1.46,1.66]	1.79*** [1.67,1.93]	1.38*** [1.33,1.43]
Education
No Education ®
Primary	1.06** [1.02,1.10]	1.13*** [1.07,1.19]	1.05*** [1.02,1.08]
Secondary	1.18*** [1.14,1.22]	1.16*** [1.11,1.22]	1.03** [1.01,1.06]
Higher	1.23*** [1.17,1.29]	1.11** [1.04,1.19]	0.94*** [0.91,0.97]
Marital Status
Never married ®
Married	1.76*** [1.68,1.85]	1.37*** [1.27,1.47]	1 [0.97,1.03]
Widowed	1.78*** [1.65,1.92]	1.59*** [1.44,1.77]	1.19*** [1.13,1.26]
Divorced	1.61*** [1.34,1.94]	1.31* [1.01,1.69]	1.04 [0.92,1.17]
Alcohol Consumption
No ®
Yes	1.11*** [1.06,1.15]	1.08** [1.02,1.14]	1.34*** [1.31,1.37]
Tobacco Consumption
No ®
Yes	0.85*** [0.82,0.88]	0.79*** [0.75,0.83]	0.87*** [0.85,0.88]
Health Insurance
No ®
Yes	0.90*** [0.88,0.93]	0.99 [0.95,1.02]	0.97*** [0.95,0.98]
Region
North ®
Central	1.02 [0.96,1.08]	0.88*** [0.83,0.93]	1.13*** [1.10,1.17]
East	1.22*** [1.14,1.30]	1.51*** [1.42,1.61]	0.90*** [0.86,0.93]
North East	1.21*** [1.13,1.30]	2.10*** [1.96,2.26]	1.01 [0.97,1.06]
West	0.49*** [0.46,0.52]	1.01 [0.94,1.08]	0.89*** [0.85,0.93]
South	0.86*** [0.81,0.92]	1.42*** [1.35,1.50]	1.12*** [1.08,1.16]

Source: NFHS-5, 2019–21

*p < 0.05, **p < 0.01, ***p < 0.001

To examine the associated factors that make differences in the prevalence of hypertension awareness, treatment, and control when cross-classified with the background socio-economic and demographic characteristics of the respondents, the results of binary logistics regression have been presented in.

Table 2(a). Awareness of Hypertension

It has been statistically evident that the independent variables, viz., increasing age and quintiles of wealth, have significant associations with the dependent variable of awareness of hypertension. The awareness of hypertension among persons aged 50 and above is about eight times [OR:8.41 (7.75–9.13)] compared to those of the age group of fewer than 15 years. Compared to the poorest wealth quintile, respondents belonging to the richest wealth quintile are 1.55 times more (CI: 1.46–1.66) likely to have awareness of hypertension. Similarly, a respondent with the highest educational attainment of higher education is more likely to be aware of hypertension than a person with no education. Compared with the northern region of India, respondents from the eastern and north-eastern regions are 1.22 times (CI: 1.14—1.30) and 1.21 times (CI: 1.13—1.30) more likely to be aware of hypertension. Furthermore, respondents consuming tobacco or smoking and drinking alcohol are 1.11 times (CI: 1.06—1.15) and 0.85 times (CI: 0.82—0.88), respectively, more likely to have an awareness of hypertension.

Table 2(b). Treatment of Hypertension

A statistically significant finding observes that, when compared to the age group of below 15 years, the respondents who are in the age brackets of 45–49 years [OR: 6.65 (6.02—7.34)], and 50 and above years [OR: 6. 9.90 (8.88—11.03)]. Respondents residing in an urban area are more likely to take prescribed medicine to lower their blood pressure than rural area respondents. Respondents belonging to a religious group of Muslims [OR: 1.14 (1.07–1.20)] and other than Hindu, Muslim, and Christian [OR: 1.64 (1.52–1.78)] are more likely to take treatment for lowering blood pressure than the respondents of Hindu religious group. A statistically significant finding observes that, when compared to the Northern respondents, the respondents residing in Central and Southern regions are likely to take prescribed medicines to lower their blood pressure levels.

Table 2(c). Control of Hypertension

A person who is currently taking antihypertensive medication and was detected with Systolic blood pressure < 140 mmHg and Diastolic blood pressure < 90 mmHg on screening was treated as having controlled hypertension. However, Table 2 reveals that respondents who fall under the age brackets of 45–49 years and 50 and above years are 10.16 times (CI: 9.71–10.64) and 11.03 times (CI: 10.48–11.61) more likely to have controlled hypertension than the respondent of age less than 15 years. Similarly, respondents belonging to the religious group of ‘Other’ are 1.41 times more likely to be in controlled hypertension condition than the respondents of Hindu religion. In the case of social groups, the respondents belonging to the Other Backward Classes (OBC) are less likely to have controlled hypertension than respondents of Scheduled Caste. On the other hand, respondents from Scheduled Tribe (ST) category are more likely to have controlled hypertension than Scheduled Caste respondents. It is essential to highlight that higher educated are less likely to have controlled hypertension as compared to the respondents who are without any formal education. The odds of having controlled hypertension among Widowed respondents is 1.19 times compared to 1.00 times among never-married respondents.

Summary & discussion

We report the Hypertension cascade analysis in the nationally representative sample of 2843,917 from the age group (15–49 years) in India. This nationally representative study from India found low levels of hypertension awareness, treatment, and control. The study focused on the disease's socioeconomic pattern. The gap between the lowest and highest wealth quintile was larger for control (11.38%, 4.55%, 1.9%), but narrower for awareness and treatment [3]. However, the wealth index determined neither treatment nor control of hypertension. The study, based on India’s NFHS-5 data, estimates the prevalence of hypertension awareness, treatment, and control across the districts and socioeconomic and demographic characteristics of the sample respondents of the country [45]. It may be seen that awareness and treatment of hypertension have been estimated higher among male compared to female sample respondents. Further, respondents belonging to urban areas, other social and religious groups, wealthier households, and the country's east, north, and northeast regions have been found to have higher awareness and treatment of hypertension. The lowest levels of hypertension awareness and treatment were found among the respondents who came from Scheduled Tribes, Hindu religion, rural areas, poor and poorest wealth quintiles, and the western region of the country. The findings show significant disparities in hypertension prevalence and management across gender, age groups, socioeconomic status, and geographic regions, highlighting the critical need for targeted public health interventions. This study also tried to examine the associated factors with the help of binary logistic regression analysis. The National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Disease, and Stroke (NPCDCS) of India aims to conduct population-based screening of diabetes mellitus, hypertension, and the three common cancers for all women and men aged 30 years and above among the NPCDCS implemented health facilities, 68.7% of CHCs and 65.9% of DHs had written standard treatment [24, 49]. At the national level, awareness, treatment, and control prevalence was 11.67 percent, 5.45 percent, and 26.81 percent, respectively [32].

Numerous organ systems are significantly impacted by hypertension, and its effects are severe and pervasive. From a cardiovascular perspective, it plays a significant role in peripheral artery disease, heart failure, and myocardial infarction. Acute coronary events are more likely when blood pressure is high because it causes left ventricular hypertrophy and speeds up atherosclerosis. One of the main causes of chronic kidney disease (CKD) in the renal system is long-term hypertension, which also contributes to increasing nephron destruction and end-stage renal disease (ESRD). Cerebrovascular problems are very common; the most significant risk factor for ischemic and hemorrhagic stroke as well as vascular dementia is hypertension. High systolic blood pressure alone was one of the top three risk factors contributing to the overall disease burden in India, accounting for over 1.63 million deaths and 33 million disability-adjusted life years (DALYs), according to the Global Burden of Disease (GBD) Study 2019 [20]. In addition to highlighting the seriousness of hypertension consequences, these numbers also highlight the necessity of focused measures for early detection, prevention, and control. The inclusion of such thorough data emphasises how urgent it is to address hypertension as a national and international health concern.

Three readings of blood pressure were recorded during the survey based on a particular time duration and the mean of last two readings were considered. According to National Non-communicable Disease Monitoring Survey 2017–18 (NNMS) The mean systolic blood pressure (SBP) was 124.1 mm of Hg and diastolic blood pressure (DBP) was 80.9 mm of Hg with a SBP mean of 126.1 mm of Hg among men and 121.8 mm of Hg in women. It is almost similar to that study for measurement of Hypertension. Among the secondary health care facilities, where NPCDCS was implemented, 91.8% CHCs and 96.9% district hospitals provided inpatient services, while 71.2% (CHCs) and 75.2% (DHs) [40]. Studies have found a higher prevalence of hypertension among males compared to females [36]. It is partially possible because of biological differences and behavioral risk factors like relatively higher smoking, drinking alcohol consumption, or physical activities. On the other hand, our findings found that awareness and treatment of hypertension are significantly more prevalent among females as compared to males. Women use public health care facilities more frequently than men, as primary health care in India focuses on maternal and child health. This could be because females are more likely to seek healthcare services and report poor health conditions than their male counterparts [7, 47]. These findings suggest the need for targeted awareness campaigns for males, focusing on preventive health behaviours and early detection of hypertension. We have also found a higher prevalence of hypertension awareness, treatment, and control among people who belong to the wealthiest household than among poor household in terms of their access to essential household assets and amenities [44]. Further, respondent aged more than 35 years and mainly coming from the southern parts of the country havemore controlled hypertension than those aged less than 15 years and other parts of the country, respectively [37].

The spatial analysis has been performed to prepare the maps to understands the spatial variation the prevalence of selected variables. However, the considering the spatial findings from this study, the concerned policy makers can frame the place-specific policies with references to hypertension’s control, treatment and awareness. The present study has primarily focused on the fifth round of the National Family and Health Survey to study comprehensively the different dimensions of hypertension; however, the scope for selection for trends analysis is beyond the study’s subject matter. Local indicator of Spatial association (LISA) measure of local Moral’s I is used to identify the presence or absence of significant spatial clusters or outliers for each spatial unit. The researchers used the GeoDa software package to perform the spatial analysis. In the present study, If the value of Moran’s I is lower than 0.05, there is a low level of spatial autocorrelation, while a value of more than 0.5 shows a higher or positive clustering. The spatial analysis, with the help of Global Moran’s I and LISA Clustering techniques, found a significant variation in the level of awareness, treatment, and control of hypertension across the districts of India (Tabashsum et al., n.d.) our study highlight that out of 707 districts, 104 districts exhibit significantly higher clustering for hypertension awareness, 92 districts for treatment, and 94 districts for control, where the clustering of awareness, treatment, and control was significantly higher and positive [23] as per India’s NFHS −5 data [27]. Southern states have better hypertension control, which can be attributed to increased healthcare access, improved public health infrastructure, and regional dietary habits. Conversely, districts with lower clustering of hypertension management are primarily located in northern and central India, where access to healthcare services remains difficult. These spatial disparities highlight the need for region-specific interventions aimed at improving healthcare infrastructure, training healthcare providers, and raising awareness about hypertension management. Policymakers should consider using geospatial analysis to better allocate resources and enforce targeted public health programmes in high-burden areas.

Strengths of the study to evaluate hypertension awareness, treatment, and control in India using NFHS-5 data, a nationally representative sample. One of its main perks is its large sample size (2.8 million), which enhances the credibility and generalizability of the findings. The study effectively identifies socioeconomic and regional distinctions in hypertension management, providing useful recognition for targeted interventions. Another advantage is the use of spatial analysis techniques (Global Moran's I and LISA clustering), which provide a geographical outlook on hypertension management across districts. The use of binary logistic regression provides additional depth by identifying associated risk factors. Furthermore, the study's discussion of gender differences in healthcare-seeking behaviour, as well as the role of public health policies like NPCDCS, makes it more policy-applicable. However, the study does have some limitations. While it identifies disparities, it does not fully inspect the underlying causal mechanisms. The reliance on NFHS-5 data may lead to recall bias and self-notifying errors. The paper lacks an in-depth discussion of lifestyle factors such as diet and physical activity, which are critical in hypertension management. It also does not consider regional variations in healthcare quality. Regardless of its strong statistical methods, the study could benefit from a longitudinal analysis to identify trends over time.

Concluding remarks

According to NFHS-5 (2019–21), hypertension affects over 22% of individuals in India and has become a major public health concern. According to Muhammad and Bansod [36], the burden is disproportionately greater among older women, city dwellers, and people living in the southern parts of the nation. Significant sociodemographic differences in knowledge and treatment are revealed by this study, with women, the wealthiest households, and urban dwellers showing the best results. Remarkably, only 3% of people with hypertension receive the proper treatment, and only 6% are aware that they have the problem. These results highlight the critical need for focused health promotion and education initiatives to enhance early detection and care. A mixed-method approach should be used in future studies to investigate the obstacles.

Authors’ contributions

1. Priti: Data Curation, Data Analysis, Application of Data & Methodology, Validation of the Results, Writing Original Draft, 2. Rahul Kumar: Conceptualization, Resources, Supervision, Writing-Review and Editing, 3. Dr. Kunal Keshri: Idea for Paper writing, Investigation, Project Administration, 4. Ankit Gupta: Formal Analysis, Maps and Tables Preparation, Visualization, searching for Funding Sources, Seeking knowledge for the Publication, Finalizing the draft for Submission, working on modifications.

Funding

Not applicable.

Data availability

National Family Health Survey-5. https://www.iipsdata.ac.in/datacatalog_detail/1.

Declarations

Ethics approval and consent to participate

This study is based entirely on secondary data from the publicly available National Family Health Survey (NFHS-5, 2019–21). NFHS-5 data were collected under the supervision of the Ministry of Health and Family Welfare (MoHFW), Government of India, and ethical approvals for the survey were obtained from the Institutional Review Boards (IRB) of the International Institute for Population Sciences (IIPS), Mumbai, and other collaborating institutions. Written informed consent was obtained from all participants by the survey teams before data collection. No separate ethical approval was required for this analysis, as it uses anonymized, publicly available data.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.