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. 2022 Aug 8;30(1):40–53. doi: 10.1177/09727531221115899

The Distribution of Lifestyle Risk Factors Among Patients with Stroke in the Indian Setting: Systematic Review and Meta-Analysis

Biji P Varkey 1, Jaison Joseph 2, Abin Varghese 3, Suresh K Sharma 4, Elezebeth Mathews 5,✉, Manju Dhandapani 6, Venkata Lakshmi Narasimha 7, Radha Kuttan 8, Saleena Shah 9, Surekha Dabla 10, Sivashanmugam Dhandapani 11
PMCID: PMC10259149  PMID: 37313337

Abstract

Background

The burden of stroke is increasing in India, but there is limited understanding of the distribution of reported risk factors in the Indian setting. It is vital to generate robust data on these modifiable risk factors to scale up appropriate strategies for the prevention of cerebrovascular diseases in this setting.

Summary

The objective of this study is to estimate the overall proportion of life style risk factors of patients with stroke in the Indian setting. We searched PubMed and Google Scholar and relevant studies published till February 2022 were included. The risk of bias assessment was considered for the study selection criterion in the meta-analysis. The publication bias was evaluated by funnel plots and Egger’s test. We identified 61 studies in the systematic review and after quality assessment, 36 studies were included for meta-analysis. Random effect model was used due to the significant inconsistency among the included studies (I2 > 97%). The mean age of the participants was 53.84±9.3 years and patients with stroke were predominantly males (64%). Hypertension (56.69%; 95% CI: - 48.45 – 64.58), obesity (36.61%; 95% CI: - 19.31 – 58.23), dyslipidemia (30.6%; 95% CI: - 22 – 40.81) and diabetes mellitus (23.8%; 95% CI: - 18.79 – 29.83) are the leading intermediate conditions associated with stroke. The Physical inactivity - 29.9% (95% CI: - 22.9 – 37.1), history of tobacco use (28.59 %; 95% CI: - 22.22 – 32.94) and alcohol use (28.15 %; 95% CI: - 20.49 – 37.33) were reported as the behavioral risk factors for stroke in this setting.

Key Messages

The current meta-analysis provides robust estimates of the life style related risk-factor of stroke in India based on the observational studies conducted from 1994 to 2019. Estimating the pooled analysis of stroke risk factors is crucial to predict the imposed burden of the illness and ascertain the treatment and prevention strategies for controlling the modifiable risk factors in this setting.

Keywords: Stroke, Lifestyle, Risk factors, Patients, India

Introduction

Cardiovascular diseases accounted for the majority of deaths globally [18·6 million (17·1–19·7)] in both sexes combined in 2019, amongst which stroke is the second leading cause of death, 3·33 million (3·04–3·62) stroke deaths in males and 3·22 million (2·86–3·54) deaths in females. 1 Disability-adjusted life years (DALYs) for cardiovascular diseases were 393 million (95% UI 368–417) and 143 million (95% UI 133–153) for stroke, making stroke the third-leading cause of disease burden. 2 In India, cardiovascular diseases attributed the highest percent of total death for all ages in which stroke was the fifth leading cause of death in the year 2016, with a mean percent change in the number of DALYs of 52.9% (40.4–66.7) between 1990 and 2016. 3 Within India, a wide variation in the burden of stroke was observed across the states. To cite, a recent meta-analysis reported a one-month case fatality rate of stroke varied from 41.08% to 42.06% in the urban population and 18% to 46.3%.in the rural population. 4 This wide variation could be because of the variability in the distribution of risk factors in the population, effectiveness of health services in preventive, curative, and rehabilitative services, and data availability. 5

The available empirical data reported the risk factors for stroke, such as sociodemographic, behavioral, anthropometric, clinical, and biochemical, from multiple settings in India.6, 7 The significant lifestyle-related risk factors include hypertension, diabetes, hyperlipidemia, obesity, smoking, heart disorders, congestive cardiac failure, atrial fibrillation, left ventricular hypertrophy), and so on, and the burden of each risk factor remains unknown. 8 The global burden of disease study (1990–2016) reported a gross variation in the risk factors for cardiovascular disease across the states of India. 9 It is also evident that South Asians, including Indians, are highly vulnerable to cardiovascular disease because of their cardiometabolic risk profile and ethnically mediated cardiometabolic dysfunction. 10

The burden of stroke is increasing in India, but there is scanty evidence on the systematic understanding of the distribution of its lifestyle risk factors in the Indian setting. It is vital to generate robust data on these risk factors to scale-up appropriate strategies for preventing cerebrovascular diseases in this setting.

Objective

The objective of this study was to estimate the overall proportion of lifestyle risk factors of patients with stroke in the Indian setting.

Materials and Methods

Search Strategy and Selection Criteria

This systematic review is reported following the PRISMA checklist. 11 We searched PubMed and Google Scholar, and relevant studies published till February 2022 were included. We used the combination of Medical Subject Headings (MeSH) and keywords of the following search concepts: “stroke,” “risk factors,” “patients,” and “India.” The details of the search strategy in PubMed are given as supplementary material 1. The data search was carried out by two investigators (BPV and MD). The archives of relevant Indian journals were reviewed for maximum inclusion of available studies. No attempts are made to acquire grey/unpublished literature considering the inherent conflict of interest, which might increase the risk of bias. The screening was performed by two investigators (RK and SS) who further appraised the full texts of appropriated records to reach a common consensus regarding the inclusion and exclusion of individual studies.

Inclusion and Exclusion Criteria

Observational studies, both hospital and community-based stroke registry studies, conducted in the Indian setting reporting the risk factors of various types of strokes and published in the English language were included. Stroke registries are observational databases focusing on the clinical information and outcomes of stroke patients. Stroke is a chronic disease with an acute event, so the hospitalization rate is high. As we were not estimating any incidence or prevalence of stroke, we also included hospital-based clinical studies recognizing its limitations and inherent biases.

Studies were included if participants had a confirmed history of stroke as defined by the World Health Organization (WHO) 12 or as defined according to clinical criteria or confirmed by imaging. Global or Indian studies that exclusively estimated the prevalence, incidence, and mortality data among patients with stroke were excluded. Besides, studies with inadequate data, published as editorials or letters to the editor, conference abstracts, expert opinion, or suggestions were excluded. The lifestyle risk factors for stroke were operationally defined as the conditions and behaviors that increases the chances of an individual to have, develop, or be adversely affected by a disease process. In this study, this was categorized into behavioral risk factors and intermediate conditions. The data on the nonmodifiable risk factors of stroke, such as age, previous history of stroke, and family history of stroke, were not estimated.

Data Extraction

The data extraction was done based on the following study characteristics: author (year of publication)/study region, period of study (year), types of stroke, mean age, gender, sample size, and risk factors (hypertension, diabetes mellitus, tobacco use, alcohol use, dyslipidemia, and others). Three investigators (BPV, JJ, and AV) were involved in the data extraction after reading and discussing the full-text version of the shortlisted publications based on the eligibility criteria. The extracted data were cross-verified by the author VLN and SD. A mutual consensus resolved disagreements between the authors (BV, JJ, AV, VLN, and SD).

Quality Assessment

The Joanna Briggs Institute (JBI) critical appraisal checklist was used for the risk of bias assessment (available from https://synthesismanual.jbi.global). The assessment of each study was done based on whether it met the following eight conditions: (a) Were the criteria for inclusion in the sample clearly defined? (b) Were the study subjects and the setting described in detail? (c) Was the exposure measured validly and reliably? (d) Were objective and standard criteria used to measure the condition? (e) Were confounding factors identified? (f) Were strategies to deal with confounding factors stated? (g) Were the outcomes measured validly and reliably? (h) Was appropriate statistical analysis used? Each study was graded as 1–Yes; 0–No; UC–Unclear after rigorous review. The total scores of the included studies were considered for the study selection criterion in the meta-analysis. Three review authors independently assessed the risk of bias in the included studies (SKS, EM, and JJ). This process was performed iteratively. First, each author reviewed the studies and made the risk of bias assessment based on the criteria. A third independent reviewer (SSD) addressed discrepancies in the quality scoring of two reviewers. Disagreements were resolved by group consensus.

Statistical Analysis

The R software was used to perform this meta-analysis, and the pooled estimate of the distribution of lifestyle risk factors of stroke was estimated using inverse variance weighting methods. Assuming the significant inconsistency among the studies, a random-effects meta-analysis model was used, and I2 statistics were calculated to measure heterogeneity among studies. The heterogeneity was considered mild, moderate, or high when the I2 values were from 25% to 50%, 51% to 75%, and >75%, respectively. The funnel plot and Egger’s test were used to assess the potential publication bias.

Results

Identification of Studies

The database search identified 1691 reports: 1130 were excluded based on title and abstract screening, and after eliminating duplicates, 561 articles were retrieved for detailed evaluation, and 500 were excluded for the reasons summarized in Figure 1. Finally, 61 eligible articles were identified after applying the inclusion and exclusion criteria and included in the systematic review, out of which 36 were included in the meta-analysis based on the risk of bias assessment.

Figure 1. Flowchart of Search Strategy and Selection Process.

Figure 1.

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Characteristics of the Studies Included in the Systematic Review

We have included 61 studies in our systematic review, conducted across various states in India, estimating the various lifestyle risk factors for stroke (Table 1).13–73 Out of 61, 56 studies were conducted and 58 were published after 2000. In 51 studies, patients were enrolled from the hospital-based stroke registries (HBSR) maintained in various treatment settings across India, while in 10 studies, the enrollment was using population-based stroke registries (PBSR). The sample size of included studies varied from 32 to 4989. The number of patients with ischemic stroke and hemorrhagic stroke in the included studies ranged from 19 to 3260 and 3 to 1656, respectively. Other types of strokes reported in a few studies varied from 8 to 271.

Table 1. Characteristics of the Included Studies in the Systematic Review (N = 61).

Author (Year of Publication)/Study Region Period of Study (Year) Study Type Sample Size Type of stroke JBI Score
Ischemic Hemorrhagic Other
Singla et al. (2022)/Punjab 13 2010–2013 PBSR 2948 1890 787 271 7
Jayadevappa and Ravishankar (2021)/Karnataka 14 2013–2014 HBSR 230 200 30 NA 5
Ram et al. (2021)/Across India 15 2016–2017 HBSR 526 299 98 129 4
Rathore et al. (2021)/Rajastan 16 2019 HBSR 100 72 28 NA 4
Kumar et al. (2021)/Uttarakhand 17 2018–2019 HBSR 48 39 9 NA 5
Ahmed et al. (2020)/Uttarakhand 18 2019–2020 HBSR 129 122 07 NA 3
Prabhakar et al. (2020)/Telengana 19 2016–2017 PBSR 144 NM NM NM 8
Kaur et al. (2020)/Rajasthan 20 2015–2016 HBSR 360 290 70 NA 5
Somasundaran and Potty (2020)/Kerala 21 2014–2015 HBSR 464 335 129 NA 3
Moond et al. (2020)/New Delhi 22 2014–18 HBSR 160 160 NA NA 3
Karri and Ramasamy (2019)/Tamil Nadu 23 2014–2017 HBSR 186 186 NA NA 5
Patel and Vagadiya (2019)/Gujarat 24 2014 HBSR 46 46 NA NA 4
Panwar et al. (2019)/Madhya Pradesh 25 2013–2014 HBSR 50 39 11 NA 5
Muralidharan et al. (2019)/Kerala 26 2018–2019 HBSR 200 173 27 NA 4
Rajan et al. (2019)/Karnataka 27 2013–2014 PBSR 150 52 05 93 4
Behera and Mohanty (2019)/Odisha 28 2018–2019 HBSR 796 481 315 NA 5
Pathak et al. (2018)/New Delhi 29 2012–2014 HBSR 268 169 60 39 5
Hussain et al. (2018)/Meghalaya 30 2016–2017 HBSR 150 76 62 12 3
Sylaja et al. (2018)/Across India 31 2012–2014 HBSR 2066 NM NM NM 5
Diwan et al. (2018)/Maharashtra 32 2016–2017 HBSR 70 35 35 NA 3
Kaur et al. (2017)/Punjab 33 2011–2013 PBSR 4989 3260 1656 47 8
Kabi et al. (2017)/Odisha 34 2014–2016 HBSR 367 218 149 NA 3
Chandran et al. (2017)/Kerala 35 2010 PBSR 40 37 03 NA 3
Mahanta et al. (2018)/Assam 36 2013–2015 HBSR 450 163 287 NA 7
Chandana and Kalyani (2017)/Andhra Pradesh 37 2016–2017 HBSR 50 30 11 9 4
Jacob and Kulkarni (2017)/Karnataka 38 2012–2013 PBSR 53 NM NM NM 3
Kumar and Rai (2017)/Uttar Pradesh 39 NM HBSR 100 NM NM NM 5
Manorenj et al. (2016)/Telengana 40 2015–2016 HBSR 100 76 24 NA 7
Nayak et al. (2016)/Madhya Pradesh 41 2011–2013 HBSR 104 104 NA NA 6
Huliyappa and Kotrabasappa (2016)/Karnataka 42 2013–2014 HBSR 52 NM NM NA 5
Khan et al. (2015)/Chhattisgarh 43 2014 HBSR 281 190 91 NA 2
Jadhav and Bondarde (2015)/Maharashtra 44 2011–2013 HBSR 40 22 10 8 4
Subha et al. (2015)/Kerala 45 2013 HBSR 100 71 29 NA 8
Vaidya et al. (2015)/Gujarat 46 2012–2013 HBSR 175 175 NA NA 4
Kawle et al. (2015)/Maharashtra 47 2012–2014 HBSR 104 104 NA NA 6
Shravani et al. (2015)/Karnataka 48 2010–2011 HBSR 100 74 26 NA 7
Renjen et al. (2015)/New Delhi 49 2004–2006 HBSR 244 244 NA NA 7
Jan et al. (2015)/Jammu & Kashmir 50 2011 HBSR 209 NM NM NA 4
Gupta et al. (2014)/Chandigarh 51 NM HBSR 73 73 NA NA 5
Kapoor et al. (2014)/Himachal Pradesh 52 2012–2013 HBSR 32 19 13 NA 3
Sorganvi et al. (2014)/Karnataka 53 NM HBSR 100 NM NM NM 8
Dash et al. (2014)/New Delhi 54 2005–2010 HBSR 440 440 NA NA 6
Kulshrestha and Vidyanand (2013)/Uttar Pradesh 55 2011–2012 HBSR 157 112 45 NA 5
Singh et al. (2013)/Punjab 56 2006–2011 HBSR 1156 838 318 NA 8
Deoke et al. (2012)/Maharashtra 57 NM HBSR 101 86 15 NA 8
Narayan et al. (2012)/Telengana 58 2002–2010 HBSR 428 NA NA 428 4
Kumar et al. (2011)/Karnataka 59 1998–2008 HBSR 109 84 25 NA 6
Raju et al. (2010)/Punjab 60 2008–2010 HBSR 162 125 37 NA 3
Kalita et al. (2009)/Uttar Pradesh 61 2004–2006 HBSR 198 198 NA NA 6
Nagaraja et al. (2009)/Karnataka 62 2005 HBSR 1174 797 148 NA 5
Sridharan et al. (2009)/Kerala 63 2005 PBSR 541 311 61 169 7
Dalal et al. (2008)/Maharashtra 64 2005–2006 PBSR 456 366 81 9 4
Lipska et al. (2007)/Kerala 65 2002 HBSR 214 97 NM 117 8
Dalal (2006)/Across India 66 2002–2004 HBSR 2162 1656 461 45 5
Bhattacharya et al. (2005)/West Bengal 67 1992–1998 PBSR 128 NM NM NM 8
Pandiyan et al. (2005)/Tamil Nadu 68 2003–2004 HBSR 402 NM NM NM 6
Mehndiratta et al. (2004)/New Delhi 69 1988–1997 HBSR 127 109 18 NA 5
Kaul et al. (2000)/Telengana 70 2000–2001 HBSR 893 893 NA NA 5
Nayak et al. (1997)/Kerala 71 1988–1994 HBSR 177 125 9 43 6
Razdan et al. (1989)/Jammu and Kashmir 72 1986 PBSR 91 NM NM NM 2
Chopra and Prabhakar (1979)/Chandigarh 73 1970–1977 HBSR 251 109 64 78 3
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Abbreviations: NA, not applicable; NM, not mentioned; PBSR, population-based stroke registry; HBSR, hospital-based stroke register; JBI Score, JBI critical appraisal checklist for analytical cross-sectional studies (score range: 0–8).

Description of Studies Included the Meta-Analysis

The characteristics of the studies included in the meta-analysis are summarized in Table 2. The meta-analysis included 36 studies, out of which two studies31, 66 were multicentric and the remaining included data from various states of the Indian subcontinent (North: 13,13, 17, 20, 29, 33, 39, 49, 51, 54–56, 69, 61 South: 14,14, 19, 23, 40, 45, 48, 53, 59, 62, 63, 65, 68, 70, 71 East: 3,28, 36, 6 West: 2,47, 57 and Central: 225, 41). The majority of the studies were published in the past decade.13, 14, 17, 19, 20, 23, 25, 28, 29, 31, 36, 39–41, 45, 47–49, 51, 53–57, 59 Overall, 18,315 stroke patients were included, in which the predominant proportion of subjects were males (64%) compared to females (36%). The mean age of the participants was 53.84 ± 9.3 years.

Table 2. Lifestyle-Related Risk Factors of Stroke Based on the Included Studies in the Meta-Analysis (N = 36).

Author(Year of Publication)/Study Region Mean Age ± SD(Years) Male/Female Sample Size# HTN DM Tobacco Use Alcohol Use Dyslipidemia IHD/CAD RHD/VHD AF Others
Singla et al. (2022)/Punjab 13 NM 1811/1137 (2948) 1736 1506 777 417 887 347 203 58 184 Postpartum-7
Jayadevappa and Ravishankar (2021)/Karnataka 14 59.6 ± 6.1 140/60 200 32 12 44 80 132 NM NM NM D & H-120, T & A-80
Kumar et al. (2021)/Uttarakhand 17 55.25 ± 1.32 44/4 48 33 NM 24 15 NM NM NM NM Hyperhomocysteinemia-12
Prabhakar et al. (2020)/Telengana 19 61.63 100/44 144 92 24 53 83 NM NM NM NM Obesity-50
Kaur et al. (2020)/Rajastan 20 60.46 ± 14.84 217/143 360 189 68 73 NM 93 27 – – Anemia-29
Karri and Ramasamy (2019)/Tamil Nadu 23 38.9 ± 5.74 137/49 186 69 55 92 88 105 29 NM NM Hyperhomocysteinemia-23
Panwar et al. (2019)/Madhya Pradesh 25 31.70 ± 7.42 29/21 50 16 12 20 16 NM 1 NM NM Overweight-8 Homocystinaemia-6
Behera and Mohanty (2019)/Odisha 28 61.4 ± 13.1 287/194 481 336 57 140 67 249 1 2 3 D & H-46, CKD-105, Anemia-128
Pathak et al. (2018)/New Delhi 29 50.3 200/60 260 169 60 88 64 NM NM NM NM Myocardial infarction-38
Sylaja et al. (2018)/Across India 31 58.3 ± 14.7 1389/699 2066 1257 737 668 707 298 349 115 82 NA
Kaur et al. (2017)/Punjab 33 59 ± 15 3124/1865 (4989) 3330 2849 1564 573 1835 631 344 53 352 Postpartum-13
Mahanta et al. (2018)/Assam 36 54.3 ± 13 273/177 450 163 52 319 242 NM 3 1 4 NM
Kumar and Rai (2017)/Uttar Pradesh 39 NM 59/41 100 51 12 36 4 NM 4 3 4 NM
Manorenj et al. (2016)/Telengana 40 54 69/31 100 83 34 43 61 59 13 NM NM Obesity-21, Physical inactivity-28
Nayak et al. (2016)/Madhya Pradesh 41 NM 73/31 104 70 29 8 11 NM 5 NM NM NM
Subha et al. (2015)/Kerala 45 65.30 ± 12.80 55/45 100 83 49 41 21 24 48 10 10 Physical inactivity-37
Kawle et al. (2015)/Maharashtra 47 NM 73/31 104 70 29 8 11 NM 5 NM NM NM
Shravani et al. (2015)/Karnataka 48 50 73/27 100 70 27 24 20 22 15 NM NM NM
Renjen et al. (2015)/New Delhi 49 57.1 ± 1.7 165/79 244 139 85 95 NM 58 44 NM 13 NM
Gupta et al. (2014)/Chandigarh 51 59.9 ± 11.2 49/24 73 64 42 21 NM 63 17 NM 4 Obesity-40
Sorganvi et al. (2014)/Karnataka 53 62.8 59/41 100 62 38 49 32 66 NM NM NM Obesity-84
Dash et al. (2014)/New Delhi 54 38.9 ± 7.1 NM 440 196 61 42 42 115 24 56 29 Cardiomyopathy-12
Kulshrestha and Vidyanand (2013)/Uttar Pradesh 55 NM 92/65 157 26 14 6 NM 9 NM 1 1 D & H-23, Anemia-54
Singh et al. (2013)/Punjab 56 57.3 ± 13.8 650/506 1156 734 454 246 832 220 NM NM 96 NM
Deoke et al. (2012)/Maharashtra 57 59.30 ± 12.44 65/36 101 47 23 61 20 NM 14 NM NM Physical inactivity-25, Overweight-16
Kumar et al. (2011)/Karnataka 59 NM 74/35 109 79 59 76 53 56 NM NM NM Obesity-53, Homocystienemia-7,
Kalita et al. (2009)/Uttar Pradesh 61 53.5 ± 15.9 162/36 198 109 49 61 57 23 NM NM NM Overweight-57
Nagaraja et al. (2009)/Karnataka 62 54.5 ± 17.0 787/387 1174 563 271 383 295 NM NM NM 114 D & H-217
Sridharan et al. (2009)/Kerala 63 67 (Median) 262/279 541 450 271 70 NM 138 NM NM 42 NM
Lipska et al. (2007)/Kerala 65 NM 141/73 214 102 47 79 NM 66 NM NM NM NM
Dalal (2006)/Across India 66 NM 1576/586 2162 611 88 NM NM NM 82 NM NM D & H-391, D & H and CAD-154
Bhattacharya et al. (2005)/West Bengal 67 61 60/68 128 40 18 72 NM NM 17 NM NM NM
Pandiyan et al. (2005)/Tamil Nadu 68 61.7 ± 13.4 265/137 402 289 200 95 NM 105 136 14 13 Anemia-40
Mehndiratta et al. (2004)/New Delhi 69 31.97 61/66 127 25 8 29 5 36 9 16 NM Homocystenemia-1
Kaul et al. (2000)/Telengana 70 56.9 NM 893 553 250 25 NM NM NM NM NM NM
Nayak et al. (1997)/Kerala 71 34.7 ± 8 135/42 177 32 13 64 30 18 NM NM NM NM
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Note:# Calculated based on the analysis of risk factors.

Abbreviations: PHo/S, previous history of stroke; FHo/S, family history of stroke; NM, not mentioned; HTN, hypertension; DM, diabetic mellitus; CAD/IHD, coronary artery disease/ischemic heart disease; CKD, chronic kidney disease; AF, atrial fibrillation; RHD/VHD, rheumatic heart disease/valvular heart disease; D & H, diabetes and hypertension; T & A, tobacco and alcohol use.

The total score of risk of bias assessment according to the JBI critical appraisal checklist for analytical cross-sectional studies is eight, and the individual scores of the included studies in the systematic review ranged from two to eight. We took an arbitrary cutoff based on the mean and the median scores as there is no specified cutoff for the classification of studies for risk of bias. The mean score of the 38 studies was 5.06, and the median score was five. We considered five as a cutoff point. Finally, 36 studies were included in the meta-analysis. Most studies had credible information about the eligibility criteria, study population, setting, and outcome measures. The reporting structure of the measurement of risk factors for stroke and the influence of confounding variables was poorly followed in more than half of the studies. The details of the quality assessment of the studies using the JBI checklist are described in Table 3.

Table 3. Risk of Bias Assessment of Included Studies.

Author (Year of Publication) Study Design Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Total Meta-Analysis
Singla et al. (2022) PBSR 1 1 UC 1 1 1 1 1 7 Included
Jayadevappa and Ravishankar (2021) HBSR 1 1 UC 1 1 0 1 0 5 Included
Kumar et al. (2021) HBSR 1 1 UC 1 1 0 1 0 5 Included
Prabhakar et al. (2020) PBSR 1 1 1 1 1 1 1 1 8 Included
Kaur et al. (2020) HBSR 1 1 UC 1 1 0 1 0 5 Included
Karri and Ramasamy (2019) HBSR 1 1 UC 1 1 0 1 0 5 Included
Panwar et al. (2019) HBSR 1 1 UC 1 1 0 1 0 5 Included
Behera and Mohanty (2019) HBSR 1 1 1 1 0 0 1 0 5 Included
Pathak et al. (2018) HBSR 1 1 1 1 0 0 1 0 5 Included
Sylaja et al. (2018) HBSR 1 1 1 1 0 0 1 0 5 Included
Kaur et al. (2017) PBSR 1 1 1 1 1 1 1 1 8 Included
Mahanta et al. (2018) HBSR 1 1 UC 1 1 1 1 1 7 Included
Kumar and Rai (2017) HBSR 1 1 0 1 1 0 1 0 5 Included
Manorenj et al. (2016) HBSR UC 1 1 1 1 1 1 1 7 Included
Nayak et al. (2016) HBSR 1 1 1 1 1 0 1 0 6 Included
Subha et al. (2015) HBSR 1 1 1 1 1 1 1 1 8 Included
Kawle et al. (2015) HBSR 1 1 1 1 1 UC 1 0 6 Included
Shravani et al. (2015) HBSR 1 1 0 1 1 1 1 1 7 Included
Renjen et al. (2015) HBSR 1 1 0 1 1 1 1 1 7 Included
Gupta et al. (2014) HBSR 1 1 1 1 0 0 1 0 5 Included
Sorganvi et al. (2014) HBSR 1 1 1 1 1 1 1 1 8 Included
Dash et al. (2014) HBSR 1 1 UC 1 1 1 1 0 6 Included
Kulshrestha and Vidyanand (2013) HBSR 1 1 1 1 0 0 1 0 5 Included
Singh et al. (2013) HBSR 1 1 1 1 1 1 1 1 8 Included
Deoke et al. (2012) HBSR 1 1 1 1 1 1 1 1 8 Included
Kumar et al. (2011) HBSR 1 1 1 1 1 UC 1 UC 6 Included
Kalita et al. (2009) HBSR 1 1 1 1 1 UC 1 UC 6 Included
Nagaraja et al. (2009) HBSR 1 1 1 1 0 0 1 0 5 Included
Sridharan et al. (2009) PBSR 1 1 1 1 1 1 1 UC 7 Included
Lipska et al. (2007) HBSR 1 1 1 1 1 1 1 1 8 Included
Dalal (2006) HBSR 1 1 1 1 UC UC 1 UC 5 Included
Bhattacharya et al. (2005) PBSR 1 1 1 1 1 1 1 1 8 Included
Pandiyan et al. (2005) HBSR 1 1 1 1 UC UC 1 1 6 Included
Mehndiratta et al. (2004) HBSR 1 1 1 1 UC UC 1 0 5 Included
Kaul et al. (2000) HBSR 1 1 UC 1 1 0 1 0 5 Included
Nayak et al. (1997) HBSR 1 1 1 1 1 UC 1 0 6 Included
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Note: Mean, 5.06; Median, 5.

Abbreviations: PBSR, population-based stroke registry; HBSR, hospital-based stroke register; UC, unclear.

Lifestyle Risk Factors for Stroke

Stroke being an end-stage disease, we distributed the risk factors for stroke as behavioral risk factors and intermediate conditions (Figure 2). Hypertension (56.69%; 95% CI [48.45, 64.58]; n = 36 studies), obesity (36.61%; 95% CI [19.31, 58.23]; n = 8 studies), dyslipidemia (30.6%; 95% CI [22, 40.81]; n = 23 studies), and diabetes mellitus (23.8%; 95% CI [18.79, 29.83]; n = 35 studies) were identified as the leading intermediate conditions associated with stroke. The presence of various forms of anemia was associated with 13.3% (95% CI [7.0, 21.2]; n = 9 studies) of the patients with stroke. The occurrence of ischemic heart disease or coronary artery diseases was reported in 8.46% (95% CI [5.07, 13.80]; n = 22 studies) of the study subjects, and 2.82% (95% CI [1.19, 6.54]; n = 11 studies) of the participants had rheumatic heart disease or valvular heart disease. An ECG diagnosis of atrial fibrillation was identified for 4.66% (95% CI [2.87, 7.46]; n = 15 studies) of patients. Approximately 4% (3.94%; 95% CI [2.0, 6.0]) of the patients experienced an episode of stroke during postpartum period.

Figure 2. The Distribution of Lifestyle Risk Factors Among Patients with Stroke in India.

Figure 2.

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The physical inactivity–29.9% (95% CI [22.9, 37.1]), history of tobacco use, both smoking and smokeless forms (28.59%; 95% CI [22.22, 32.94]), and alcohol use (28.15%; 95% CI [20.49, 37.33]) were the behavioral risk factors for stroke in this setting.

We used the DerSimonian and Laird method of random-effects models to calculate the pooled estimates as there was a significant heterogeneity in the outcome measures (hypertension–I2 = 98.8%, tau squared = 0.92, Q = 2932.05, P < .001; diabetes–I2 = 97.9%, tau squared = 0.74, Q = 1641.78, P < .001; dyslipidemia–I2 = 97.6%, tau squared = 1.03, Q = 909.22, P < .001; atrial fibrillation–I2 = 90.9%, tau squared = 0.70, Q = 154.50, P < .001; tobacco–I2 = 97.3%, tau squared = 0.09, Q = 1238.27, P < .001; alcohol–I2 = 98.2%, tau squared = 1.02, Q = 1422.63, P < .001; IHD/CAD–I2 = 96%, tau squared = 1.40, Q = 525.33, P < .001; RHD/VHD–I2 = 94.5%, tau squared = 1.50, Q = 182.64, P < .001; obesity–I2 = 94.3%, tau squared = 1.04, Q = 122.53, P < .001; previous history of stroke–I2 = 96%, tau squared = 0.61, Q = 447.43, P < .001; family history of stroke–I2 = 92.6%, tau squared = 0.70, Q = 162.83, P < .001). The pooled analysis of the proportion of individual risk factors and heterogeneity are depicted in supplementary materials (Figure S1.1–S1.9)

With the exception of the proportion of studies that estimated diabetes (P = .01), dyslipidemia (P = .02), alcohol (P = .01), and atrial fibrillation (P = .01), Egger’s test revealed no publication bias in the outcome measures (hypertension: P = .53; tobacco: P = .39; RHD/VHD: P = .52; IHD/CAD: P = .39; previous history of stroke P = .12; family history of stroke P = .44). The funnel plot regarding the publication bias of individual risk factors based on the included studies is presented in supplementary materials (Figure S2.1–S2.9).

Discussion

Reducing the burden of stroke in the Indian population requires the identification of modifiable risk factors, and the current meta-analysis provides an aggregate of the distribution of various lifestyle-related risk factors for patients with stroke in the Indian setting. We identified that hypertension (56.69%), obesity (36.61%), dyslipidemia (30.6%), and diabetes mellitus (23.8%) are the leading intermediate conditions associated with stroke. However, physical inactivity (29.9%), history of tobacco use (28.59%), and alcohol use (28.15%) were reported as the behavioral risk factors for stroke in this setting. Referring to some previous studies, there are conflicting results regarding pooled estimates of the risk factors for stroke across Asian countries. 74 Hypertension remains the most common vascular risk factor for stroke in the Asian population74–76 which is consistent with current study findings.

However, the comparisons of the aggregate estimate of the lifestyle risk factors of stroke in this setting should be interpreted based on several contextual factors. First, the high or low frequencies of the occurrence of the risk factors for the noncommunicable disease of a country need to be considered while estimating the specific risk factors for stroke in other countries. For example, a high prevalence of hypertension is seen in Mongolia and Pakistan, which is low in Korea and Singapore. 74 Therefore, a countrywide comparison would be made based on the magnitude of the risk factors predisposing stroke. Second, most of the information on the risk factors among stroke patients was derived from HBSR studies in which data were collected at differing time points with varying definitions limiting its generalization for the estimation of risk factors across countries.77, 78 It is worth noting that estimating stroke risk based on valid risk scoring systems is of pivotal importance for better understanding risk factors to maximize the efficacy of risk reduction efforts. 79

Currently, the burden of stroke is increasing in India, 80 and the findings of this meta-analysis reflect a comprehensive report on the trends of risk factors for stroke in India over a long period. Although several HBSR and PBSR studies were conducted in different parts of India, there is a dearth of evidence of a systematic summary of the risk factor profile of stroke in this setting. This study provides robust estimates of the lifestyle risk factor of stroke in India from 1994 to 2019. The mean age of the participants was 53.84 ± 9.3 years, and a predominant proportion of subjects were males (64%). In contrast to our findings, earlier epidemiological studies in India have found hypertension, diabetes, and cigarette smoking as the leading lifestyle risk factors for stroke. 81 One of the reasons for this change in the risk factor profile for stroke might be because of the varied epidemiological transition among the different states of India. 82 The current findings emphasize that, although the proportion of risk factors for stroke varied considerably across the states of India, the prevalence of hypertension remains the pivotal risk factor across all state groups since 1994. The burden of stroke in the developing world is likely to increase substantially, partly because of ongoing demographic changes, including the aging of the population and health transitions in these countries. 83

The current meta-analysis indicates an urgent need for controlling the vascular and lifestyle risk factors of stroke by focusing more on the public campaign to build the protective factors against cerebrovascular diseases in this setting. There was a significant inconsistency among the included studies as the level of heterogeneity was high (I2 > 94%). The risk of bias assessment of the included studies has implications for the generalization of our findings. Therefore, we exclusively selected stroke epidemiological studies with a low risk of bias conducted in the Indian setting. The current findings provide an evidence base to successfully meet the challenges while devising appropriate strategies to curtail the strategies targeted for risk factor modification.

Strength and Limitations

The primary uniqueness of this study is the novelty of a meta-analysis reflecting the pooled estimate of the proportion of various risk factors of stroke from an Indian perspective. There are certain limitations to generalizing our findings. The results are purely based on observational studies with methodological limitations, such as sampling bias and respondent bias. The level of heterogeneity of the included studies was high because of differences in the study contexts. There might be a chance of contamination of the study subjects, as our estimation is based on the pooled analysis of both hospital and population-based studies, including stroke register studies. The data on clustering risk factors for stroke were not estimated as it was poorly reported in most studies. Despite the limitations, the current meta-analysis provides robust estimates of the lifestyle-related risk factor of stroke in India based on the observational studies conducted from 1994 to 2019.

Conclusion

The present meta-analysis elucidates the overall estimates of lifestyle risk factors for patients with stroke in India. Estimating the pooled analysis of stroke risk factors is crucial to predict the imposed burden of the illness and ascertain the treatment and prevention strategies for controlling the modifiable risk factors in this setting.

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Supplemental Material for The Distribution of Lifestyle Risk Factors Among Patients with Stroke in the Indian Setting: Systematic Review and Meta-Analysis by Biji P. Varkey, Jaison Joseph, Abin Varghese, Suresh K. Sharma, Elezebeth Mathews, Manju Dhandapani, Venkata Lakshmi Narasimha, Radha Kuttan, Saleena Shah, Surekha Dabla and Sivashanmugam Dhandapani, in Annals of Neurosciences

Acknowledgement

Elezebeth Mathews would like to thank DBT, India for the Clinical and Public Health Early Career Fellowship (grant number IA/CPHE/17/1/503345).

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Elezebeth Mathews is supported by a Clinical and Public Health Early Career Fellowship (grant number IA/CPHE/17/1/503345) from the DBT India Alliance/Welcome Trust-Department of Biotechnology, India Alliance (2018–2023).

Authors’ Contribution

BPV: conceptualization, data search, data extraction; JJ: data extraction, risk of bias assessment and manuscript drafting; AV: data extraction; EM: risk of bias assessment and intellectual revision including manuscript drafting; MD: data search and editing; RK and SS: screening of the potential references according to the inclusion and exclusion criteria; VLN and SD: cross-verification of data extraction; SKS: risk of bias assessment; SSD: risk of bias assessment (third independent reviewer).

Statement of Ethics

Not applicable.

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Supplementary Materials

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Supplemental Material for The Distribution of Lifestyle Risk Factors Among Patients with Stroke in the Indian Setting: Systematic Review and Meta-Analysis by Biji P. Varkey, Jaison Joseph, Abin Varghese, Suresh K. Sharma, Elezebeth Mathews, Manju Dhandapani, Venkata Lakshmi Narasimha, Radha Kuttan, Saleena Shah, Surekha Dabla and Sivashanmugam Dhandapani, in Annals of Neurosciences

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