Abstract
Background and Objectives:
Data on community-level knowledge and attitudes toward stroke are limited in India, particularly in Kerala. This study assessed knowledge and attitudes toward stroke and identified associated factors among adults aged ≥18 years in Kerala.
Methods:
In this community-based cross-sectional study, 576 adults (mean age: 43 years; 44% male) were selected through multistage cluster sampling. Data on stroke knowledge and attitudes were collected using a pretested structured interview schedule. Log-binomial regression was used to determine factors associated with higher knowledge and positive attitudes.
Results:
Overall, 53.3% identified the brain as the organ affected by stroke, and 26.6% correctly recognized the treatment window (<4.5 hours). High knowledge was observed in 48.9% of participants and was significantly associated with older age (P < 0.001), female sex (P = 0.014), urban residence (P < 0.001), college education (P = 0.022), health insurance (P = 0.002), family history of stroke (P = 0.025), and receiving information from academic sources (P = 0.015) or interpersonal communication (P < 0.001). A positive attitude was reported by 44.4% and was more common among females (P = 0.016), rural residents (P = 0.030), college-educated adults (P = 0.022), people with a family history of stroke (P = 0.007), and those informed through academic sources (P = 0.028).
Conclusions:
Key gaps persisted in recognizing stroke symptoms and the treatment window. Knowledge and attitudes were better among females and educated individuals, highlighting the need for targeted education in this population.
Keywords: Stroke, knowledge, attitude, Kerala, community health, risk factors, stroke literacy, public health education
Introduction
Stroke is the second leading cause of death and the third leading cause of combined death and disability worldwide, accounting for nearly seven million deaths annually.[1] Between 1990 and 2021, global incident strokes increased by 70%, deaths by 44%, and disability-adjusted life years by 32%, with nearly 87% of deaths occurring in low- and middle-income countries (LMICs).[1,2] Without effective prevention and timely care, the global stroke burden is projected to rise further, particularly in LMICs.[3]
In India, stroke remains a major public health challenge, with incidence ranging from 105 to 152 per 100,000 population and wide regional variation in prevalence.[4] Early mortality is high, with case fatality rates exceeding 40% in some settings.[5] Kerala, despite its relatively advanced healthcare system, has experienced a steady rise in stroke incidence and mortality, largely driven by population aging, lifestyle changes, and improved detection.[4,5,6] Ischemic stroke accounts for the majority of cases, and delays in seeking care continue to contribute to adverse outcomes.[6,7]
Despite the growing burden,[8] community awareness of stroke remains suboptimal. Previous studies in India have reported moderate knowledge of stroke risk factors but poor recognition of warning symptoms and appropriate emergency response.[9,10,11,12] Even among community health workers in Kerala, knowledge gaps persist despite generally favorable attitudes, indicating a knowledge–attitude disconnect.[13] Online surveys in Kerala have reported better recognition of classic symptoms but limited awareness of thrombolysis and less typical symptoms, raising concerns regarding representativeness.[14] Registry data also highlight a mismatch between the high prevalence of vascular risk factors and low public awareness.[15]
Improving stroke knowledge and fostering positive attitudes are essential for reducing prehospital delay and improving outcomes. Evidence suggests that awareness of risk factors, warning signs, and timely response leads to earlier hospital arrival, better access to treatment, and improved survival.[16,17] However, most studies in Kerala have focused on specific groups or online populations, with limited data from the general community. Many investigations have assessed only knowledge while neglecting attitudes and intended emergency responses, such as calling Emergency Medical Services or arriving at the hospital promptly. Therefore, this study aimed to assess knowledge and attitudes regarding stroke and to identify associated factors among adults aged 18 years and older in Kerala, India.
Methods
Study design and population
We conducted a cross-sectional analytical study in 2024 among adults in Kerala, a southwestern coastal state of India with a population of 33.4 million.[18] It has fourteen districts. Adults aged 18 years and above who had resided in Kerala for the past year were eligible. Individuals with cognitive impairment or temporary residence (less than one year) were excluded.
Sample size and sampling strategy
The sample size was calculated assuming a prevalence of stroke knowledge of 56.7% reported previously,[19] with 5% absolute precision and a 95% confidence interval (CI). The minimum required sample size was 378. A design effect of 1.5 was used to adjust for the cluster sampling design, resulting in a sample size of 566. To recruit an equal number of participants across the 24 clusters, the final sample size was 576 (24 × 24). Sample size estimation was performed using OpenEpi (version 3.0).
A multistage cluster sampling approach was adopted.[20] One district each from the northern, central, and southern regions of Kerala was randomly selected. Within each district, two urban localities (municipalities/corporations) and two rural localities (panchayats) were randomly selected. From each locality, two wards were randomly selected (a ward is the smallest administrative unit in Kerala), yielding twenty-four wards (clusters) [Figure 1]. In each cluster, the center of the ward was identified with the help of local resource persons [ward member, Accredited Social Health Activist (ASHA), or health worker]. The first household and subsequent households were selected using the World Health Organization (WHO) cluster survey protocol.[21] Within each household, one eligible adult was chosen using the Kish method.[22] In total, twenty-four adults were recruited per cluster, yielding 576 participants.
Figure 1.
Flowchart depicting the selection of study participants
Data collection procedures
Data collection was performed by the lead author (SSS) through household visits. A participant information sheet detailing the study purpose, procedures, risks, and confidentiality protections was provided to each participant. Written informed consent was obtained before participation. Interviews were conducted privately in participants’ homes, in Malayalam, the local language, using a structured pretested interview schedule. Each interview lasted approximately 30–40 minutes.
The interview schedule was developed using validated tools employed in stroke awareness research,[23,24] culturally adapted for the Kerala context, and pilot-tested for clarity and reliability. It comprised three sections: (1) sociodemographic variables including age, gender, marital status, religion, education, occupation, socioeconomic status, health insurance coverage, and family history of stroke; (2) questions assessing stroke knowledge included the affected organ, stroke subtypes (ischemic/hemorrhagic), risk factors (e.g., smoking, diabetes, alcohol use, heart disease, obesity, physical inactivity), warning signs covering classic symptoms such as facial drooping, speech difficulty, and limb weakness (arm or leg weakness), as well as other symptoms such as sudden vision loss, dizziness, and severe headache; long-term disabilities (e.g., one-sided paralysis, difficulty speaking, reading, writing, or remembering), the golden treatment window, and sources of stroke-related information (mass media, online sources, etc.); and (3) questions related to attitudes toward stroke, such as urgency of treatment, emergency help-seeking behaviors, and preventive measures. The questionnaire also included items assessing intended responses during a suspected stroke event, such as calling for help and making an immediate hospital visit.
Knowledge regarding stroke
Knowledge was assessed using 29 items covering risk factors, warning signs, prevention, complications, and treatment. Each correct response was scored as “1,” and each incorrect or “don’t know” as “0,” yielding a total score ranging from 0 to 29.
Attitude regarding stroke
Attitude was measured using 14 items assessing beliefs and perceptions related to stroke prevention, recognition, and management. Each correct attitude response received a score of “1,” and each incorrect or “don’t know” response received a score of “0,” giving a total score ranging from 0 to 14.
Quality assurance
Data were reviewed daily for completeness and accuracy. Interviews were conducted solely by the lead author to ensure consistency. Questionnaires were coded, anonymized, and entered into a secure database.
Ethical considerations
Ethical approval was obtained from the Institutional Ethics Committee. All participants provided written informed consent (Ref no.ECASM-AIMS-2024-595). Participation was voluntary, and respondents could withdraw at any stage without penalty. Confidentiality was strictly ensured by anonymizing responses and restricting database access to the investigator. The study complied with the principles of the Declaration of Helsinki.[25]
Statistical analysis
Statistical analyses were performed using Stata version 14 (StataCorp LLC, College Station, TX, USA). Descriptive statistics (frequencies, percentages, means, and standard deviations) summarized sociodemographic characteristics and knowledge and attitude levels. For analysis, the mean knowledge score was used as the cutoff, with participants scoring at or above the mean classified as having high knowledge and those scoring below the mean classified as having low knowledge. Similarly, the mean attitude score served as the cutoff, with scores at or above the mean indicating a positive attitude and scores below the mean indicating a negative attitude. Associations between sociodemographic factors and positive knowledge or attitude were first examined using bivariate analysis to obtain unadjusted prevalence ratios (UPRs) with 95% CIs. Variables with P < 0.20 in bivariate analysis were included in multivariable models. Log-binomial regression was then used to derive adjusted prevalence ratios (APRs) with 95% CIs to identify independent predictors of knowledge and attitude. A P value of less than 0.05 was considered statistically significant.
Results
Study sample characteristics are presented in Table 1. The median age was 43 years; 305 (52.9%) were aged ≥43 years, and 271 (47.1%) were aged 18–42 years. Females accounted for 323 (56.1%) participants and males for 253 (43.9%). Urban and rural residents were equally represented (288; 50.0% each). Most participants were Hindus (284; 49.3%) and currently married (363; 63.0%). A high proportion had completed high school or above (511; 88.7%). Government/private employment was reported by 190 (33.0%), whereas 98 (17.0%) were unemployed and 96 (16.7%) were homemakers. Most belonged to the above poverty line (APL) category (355; 61.6%), 296 (51.4%) had health insurance, and 100 (17.4%) reported a family history of stroke.
Table 1.
Baseline characteristics of participants of the study (N=576)
| Variables | Number (%) | |
|---|---|---|
| Age, Median (Q1–Q3): 43 (33–52) | ||
| 18–42 years ≥43 years |
271 (47.1) 305 (52.9) |
|
| Gender | ||
| Male Female |
253 (43.9) 323 (56.1) |
|
| Residence | ||
| Urban Rural |
288 (50.0) 288 (50.0) |
|
| Religion | ||
| Hindu Christian Muslim |
284 (49.3) 160 (27.8) 132 (22.9) |
|
| Marital status | ||
| Never married Currently married Widow/er Divorced/Separated |
106 (18.4) 363 (63.0) 80 (13.9) 27 (04.7) |
|
| Education | ||
| Professional or honors degree Graduate and above High school, plus two/pre-degree Middle school Primary school |
31 (05.4) 237 (41.1) 274 (47.6) 31 (05.4) 03 (00.5) |
|
| Occupation | ||
| Govt/Private Job Farmer Professional Unemployed Student Housewife Others [Business-32, Retired-38, Stock trader-1] |
190 (33.0) 49 (08.5) 18 (03.1) 98 (17.0) 54 (09.4) 96 (16.7) 71 (12.3) |
|
| Economic status | ||
| Above poverty line (APL) Below poverty line (BPL) |
355 (61.6) 221 (38.4) |
|
| Status of insurance policy | ||
| Yes No |
296 (51.4) 280 (48.6) |
|
| Family history of stroke | ||
| Yes No |
100 (17.4) 476 (82.6) |
As shown in Table 2, among the 576 participants, 53.3% (95% CI: 49.1–57.4; n = 307) correctly identified the brain as the organ affected by stroke, and a majority recognized major risk factors such as high blood pressure (83.0%, n = 478), high cholesterol (78.0%, n = 449), and physical inactivity (77.4%, n = 446). Knowledge of symptoms was mixed; weakness, including limb weakness/numbness (85.1%, n = 490), and speech difficulty (81.8%, n = 471) were well recognized, whereas dizziness (40.8%, n = 235), memory loss (38.0%, n = 219), and visual symptoms (59.7%, n = 344) were less well known. Only 26.6% (n = 153) knew the correct treatment window (<4.5 hours).
Table 2.
Distribution of responses to stroke-related knowledge items (N=576)
| Knowledge item | Correctly answered n (%) | Wrongly answered/Don’t know n (%) | ||
|---|---|---|---|---|
| Organ affected by stroke (Brain vs Others) | 307 (53.3%) | 269 (46.7%) | ||
| Pathophysiology: blood clot/bleeding† | 187 (60.9%) | 120 (39.1%) | ||
| Stroke is contagious (Correct: No) | 576 (100.0%) | 0 (0%) | ||
| High blood pressure is a risk factor | 478 (83.0%) | 98 (17.0%) | ||
| Smoking is a risk factor | 295 (51.2%) | 281 (48.8%) | ||
| Diabetes is a risk factor | 282 (49.0%) | 294 (51.0%) | ||
| High cholesterol is a risk factor | 449 (78.0%) | 127 (22.0%) | ||
| Heart disease is a risk factor | 309 (53.7%) | 267 (46.3%) | ||
| Obesity is a risk factor | 238 (41.3%) | 338 (58.7%) | ||
| Excessive alcohol is a risk factor | 298 (51.7%) | 278 (48.3%) | ||
| Old age is a risk factor | 427 (74.1%) | 149 (25.9%) | ||
| Physical inactivity is a risk factor | 446 (77.4%) | 130 (22.6%) | ||
| Psychological stress is a risk factor | 236 (41.0%) | 340 (59.0%) | ||
| An unhealthy diet is a risk factor | 194 (33.7%) | 382 (66.3%) | ||
| ‘God’s will’ is NOT a risk factor | 397 (68.9%) | 179 (31.1%) | ||
| Oral contraceptives are a risk factor | 65 (11.3%) | 511 (88.7%) | ||
| Magic/witchcraft NOT a risk factor | 566 (98.3%) | 10 (1.7%) | ||
| Sudden dizziness is a symptom | 235 (40.8%) | 341 (59.2%) | ||
| Sudden blindness/double vision is a symptom | 344 (59.7%) | 232 (40.3%) | ||
| A sudden, severe headache is a symptom | 328 (56.9%) | 248 (43.1%) | ||
| Sudden onset of memory loss is a symptom | 219 (38.0%) | 357 (62.0%) | ||
| Loss of consciousness/fainting is a symptom | 366 (63.5%) | 210 (36.5%) | ||
| Sudden weakness/numbness/tingling in the arms/legs is a symptom | 490 (85.1%) | 86 (14.9%) | ||
| Sudden difficulty speaking or understanding speech is a symptom | 471 (81.8%) | 105 (18.2%) | ||
| At least one disability that persists long after the stroke is over | 383 (66.49%) | 193 (33.51%) | ||
| Paralysis is a long-term effect | 482 (83.7%) | 94 (16.3%) | ||
| Cognitive impairment is a long-term effect | 280 (48.6%) | 296 (51.4%) | ||
| Visual disability is a long-term effect | 187 (32.5%) | 389 (67.5%) | ||
| Correct window period (<4.5 h) | 153 (26.6%) | 423 (73.4%) |
†Pathophysiology question was asked only among those who answered “brain” (n=307), so percentages are relative to 307
As shown in Table 3, participants demonstrated generally positive attitudes, with most preferring hospital care for stroke (78.47%, 95% CI: 74.9–81.7; n = 452) and calling for help (67.36%, n = 388), whereas self-treatment was largely rejected (82.81%, n = 477). Preventive attitudes were moderate; exercise (57.81%, n = 333) and avoiding sweets (54.69%, n = 315) were recognized as helpful, and most rejected alternative/herbal remedies (79.86%, n = 460; 73.26%, n = 422). A large majority (73.96%, n = 426) supported health education for stroke prevention.
Table 3.
Distribution of participants’ responses to attitude and belief items (N=576)
| Attitude Item | Agree, n (%) | Neutral, n (%) | Disagree, n (%) | |||
|---|---|---|---|---|---|---|
| Visit the hospital first for a stroke | 452 (78.47%) | 39 (6.77%) | 85 (14.76%) | |||
| Call for help during a stroke | 388 (67.36%) | 147 (25.52%) | 41 (7.12%) | |||
| Self-treatment for stroke is acceptable | 27 (4.69%) | 72 (12.50%) | 477 (82.81%) | |||
| Visit PHC for stroke | 360 (62.50%) | 12 (2.08%) | 204 (35.42%) | |||
| Alternative medicine helps with stroke | 16 (2.78%) | 100 (17.36%) | 460 (79.86%) | |||
| Stroke is a serious disease | 256 (44.44%) | 19 (3.30%) | 301 (52.26%) | |||
| Stroke leads to financial/economic burden | 468 (81.25%) | 56 (9.72%) | 52 (9.03%) | |||
| Young people can also get a stroke | 250 (43.40%) | 46 (7.99%) | 280 (48.61%) | |||
| Exercise helps prevent stroke | 333 (57.81%) | 122 (21.18%) | 121 (21.01%) | |||
| Avoiding sweets helps prevent stroke | 315 (54.69%) | 100 (17.36%) | 161 (27.95%) | |||
| Stroke is preventable | 309 (53.65%) | 99 (17.19%) | 168 (29.17%) | |||
| Herbal remedies help with stroke | 49 (8.51%) | 105 (18.23%) | 422 (73.26%) | |||
| Stopping medicines helps recovery | 198 (34.38%) | 76 (13.19%) | 302 (52.43%) | |||
| Health education can prevent stroke | 426 (73.96%) | 77 (13.37%) | 73 (12.67%) |
PHC=Primary healthcare center
Overall, 48.9% reported high knowledge. Table 4 presents the results of bivariate and multiple logistic regression analyses examining factors associated with stroke knowledge. Stroke knowledge was significantly higher among adults aged ≥43 years (APR 1.44; 95% CI: 1.19–1.75; P < 0.001), females (APR 1.23; 95% CI: 1.04–1.44; P = 0.014), and urban residents (APR 1.37; 95% CI: 1.16–1.61; P < 0.001). College-educated participants also had better knowledge (APR 1.24; 95% CI: 1.03–1.50; P = 0.022), as did those with health insurance (APR 1.31; 95% CI: 1.09–1.55; P = 0.002) and those with a family history of stroke (APR 1.23; 95% CI: 1.03–1.47; P = 0.025). Information from interpersonal sources (APR 1.26; 95% CI: 1.05–1.52; P = 0.015) and medical/academic sources (APR 2.10; 95% CI: 1.63–2.72; P < 0.001) strongly predicted higher knowledge.
Table 4.
Factors associated with knowledge of stroke among adults in Kerala: Results of bivariate and multiple-logistic regression analyses (N=576)
| Variables | High knowledge of stroke, n/N (%) | UPR (95% CI) | P-value | APR (95% CI) | P-value | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Age | ||||||||||
| ≥ 43 years 18–42 years |
173/305 (56.7) 109/271 (40.2) |
1.43 (1.18–1.72) Reference |
<0.001 - |
1.44 (1.19–1.75) Reference |
<0.001 - |
|||||
| Gender | ||||||||||
| Female Male |
173/323 (53.6) 109/253 (43.1) |
1.24 (1.04–1.48) Reference |
0.014 - |
1.23 (1.04–1.44) Reference |
0.014 - |
|||||
| Residence | ||||||||||
| Urban Rural |
160/288 (55.6) 122/288 (42.4) |
1.31 (1.11–1.55) Reference |
0.002 - |
1.37 (1.16–1.61) Reference |
<0.001 - |
|||||
| Religion | ||||||||||
| Christian Muslim Hindu |
79/160 (49.4) 66/132 (50.0) 137/284 (48.2) |
1.02 (0.84–1.25) 1.04 (0.84–1.28) Reference |
0.818 0.737 - |
- - - |
- - - |
|||||
| Marital status | ||||||||||
| Ever marrieda Never married |
238/470 (50.6) 44/106 (41.5) |
1.22 (0.96–1.56) Reference |
0.109 - |
0.95 (0.74–1.21) Reference |
0.660 - |
|||||
| Education category | ||||||||||
| College educatedb School educatedc |
155/268 (57.8) 127/308 (41.2) |
1.40 (1.19–1.66) Reference |
<0.001 - |
1.24 (1.03–1.50) Reference |
0.022 - |
|||||
| Occupation | ||||||||||
| Employedd Unemployede Student |
149/290 (51.4) 123/232 (53.0) 18/54 (33.3) |
1.46 (0.98–2.17) 1.59 (1.07–2.36) Reference |
0.061 0.022 - |
Omitted due to collinearity | ||||||
| Economic statusf | ||||||||||
| APL BPL |
196/355 (55.2) 86/221 (38.9) |
1.42 (1.17–1.72) Reference |
<0.001 - |
Omitted due to collinearity | ||||||
| Insurance | ||||||||||
| Yes No |
171/296 (57.8) 111/280 (39.6) |
1.46 (1.22–1.73) Reference |
<0.001 - |
1.31 (1.09–1.55) Reference |
0.002 - |
|||||
| Family history of stroke | ||||||||||
| Yes No |
63/100 (63.0) 219/476 (46.0) |
1.37 (1.14–1.64) Reference |
0.001 - |
1.23 (1.03–1.47) Reference |
0.025 - |
|||||
| Source of information about stroke | ||||||||||
| Interpersonal Sourcesg Medical/academic Sourcesh Mass mediai |
166/301 (55.1) 18/20 (90.0) 98/255 (38.4) |
1.44 (1.19–1.73) 2.34 (1.89–2.90) Reference |
<0.001 <0.001 - |
1.26 (1.05–1.52) 2.10 (1.63–2.72) Reference |
0.015 <0.001 - |
|||||
n: Number of participants in that category with the positive attitude, N: Total number of participants in that category, (%): Proportion within the category, UPR: Unadjusted prevalence ratio, APL: Above poverty line, BPL: Below poverty line, CI: Confidence interval, APR: Adjusted prevalence ratio. a: Included currently married, widow/er, separated, divorced, b: Included graduates and above, c: Included primary, middle, high school, and Plus-2/pre-degree, d: Included government/private job, professional job, farmers, business, and stock trader, e: Included unemployed, housewife, and retired, f: Economic status of the participants based on the ration card category held (APL/BPL), g: Interpersonal sources included friends/relatives, patients with stroke, other health workers, h: Medical books, doctors, i: Newspaper/magazine, social media, radio/TV
Overall, a positive attitude was reported by 44.4%. In the adjusted analysis [Table 5], females reported significantly more positive attitudes (APR 1.31; 95% CI: 1.05–1.62; P = 0.016), and rural residents had more favorable attitudes than urban residents (APR 1.22; 95% CI: 1.02–1.47; P = 0.030). College-educated individuals had better attitudes (APR 1.25; 95% CI: 1.03–1.55; P = 0.022) than their less educated counterparts. A family history of stroke was also a significant determinant (APR 1.31; 95% CI: 1.08–1.59; P = 0.007). Participants relying on medical/academic sources had significantly more positive attitudes (APR 1.48; 95% CI: 1.04–2.09; P = 0.028).
Table 5.
Factors associated with attitude of stroke among adults in Kerala: Results of bivariate and multiple-logistic regression analyses (N=576)
| Variables | Positive attitude of stroke, n/N (%) | UPR (95% CI) | P-value | APR (95% CI) | P-value | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Age | ||||||||||
| ≥43 years 18–42 years |
147/305 (48.2) 109/271 (40.2) |
1.20 (0.99–1.44) Reference |
0.057 - |
1.12 (0.91–1.37) Reference |
0.286 - |
|||||
| Gender | ||||||||||
| Female Male |
165/323 (51.1) 91/253 (36.0) |
1.42 (1.17–1.73) Reference |
<0.001 - |
1.31 (1.05–1.62) Reference |
0.016 - |
|||||
| Residence | ||||||||||
| Rural Urban |
142/288 (49.3) 114/288 (39.6) |
1.25 (1.04–1.50) Reference |
0.020 - |
1.22 (1.02–1.47) Reference |
0.030 - |
|||||
| Religion | ||||||||||
| Christian Muslim Hindu |
82/160 (51.2) 59/132 (44.7) 115/284 (40.5) |
1.27 (1.02–1.56) 1.10 (0.87–1.40) Reference |
0.025 0.413 - |
1.21 (0.99–1.48) 1.21 (0.96–1.52) Reference |
0.062 0.116 - |
|||||
| Marital status | ||||||||||
| Ever marrieda Never married |
216/470 (46.0) 40/106 (37.7) |
1.22 (0.94–1.58) Reference |
0.143 - |
1.22 (0.84–1.76) Reference |
0.291 - |
|||||
| Education category | ||||||||||
| College educatedb School educatedc |
137/268 (51.1) 119/308 (38.6) |
1.32 (1.10–1.59) Reference |
0.003 - |
1.25 (1.03–1.55) Reference |
0.022 - |
|||||
| Occupation | ||||||||||
| Unemployedd Student Employede |
115/232 (49.5) 23/54 (42.6) 118/290 (40.7) |
1.22 (1.01–1.47) 1.05 (0.75–1.47) Reference |
0.042 0.792 - |
Omitted due to collinearity | ||||||
| Economic statusf | ||||||||||
| Above poverty line Below poverty line |
174/355 (49.0) 82/221 (37.1) |
1.32 (1.08–1.62) Reference |
0.007 - |
Omitted due to collinearity | ||||||
| Insurance | ||||||||||
| Yes No |
148/296 (50.0) 108/280 (38.6) |
1.30 (1.08–1.56) Reference |
0.006 - |
1.13 (0.90–1.40) Reference |
0.300 - |
|||||
| Family history of stroke | ||||||||||
| Yes No |
58/100 (58.0) 198/476 (41.6) |
1.39 (1.14–1.70) Reference |
0.001 - |
1.31 (1.08–1.59) Reference |
0.007 - |
|||||
| Source of information about stroke | ||||||||||
| Interpersonal Sourcesg Medical/academic Sourcesh Mass mediai |
148/301 (49.2) 14/20 (70.0) 94/255 (36.9) |
1.33 (1.09–1.63) 1.90 (1.37–2.64) Reference |
0.004 <0.001 - |
1.20 (0.98–1.47) 1.48 (1.04–2.09) Reference |
0.084 0.028 - |
|||||
n: Number with positive attitude, N: Total participants, (%): Proportion, UPR: Unadjusted prevalence ratio, CI: Confidence interval, APR: Adjusted prevalence ratio. a–i: Indicate sociodemographic and information-source categories as defined in the notes. a: Included currently married, widow/er, separated, divorced, b: Included graduates and above, c: Included primary, middle, high school, and Plus-2/Pre-degree, d: Included government/private job, professional job, farmers, business, and stock trader, e: Included unemployed, housewife, and retired, f: Economic status of the participants based on the ration card category (APL/BPL), g: Interpersonal sources included friends/relatives, patients with stroke, other health workers, h: Medical books, doctors, i: Newspaper/magazine, social media, radio/TV
Discussion
In this community-based cross-sectional study from Kerala, we found substantial gaps in stroke knowledge and attitudes despite the state’s high literacy and strong health indicators. Awareness of the affected organ closely mirrored earlier evidence: a prior Indian study reported that 56.7% of adults recognized the brain as the organ involved in stroke,[19] and our findings were comparable, with 53.3% demonstrating correct identification. Although many participants were aware of major risk factors such as hypertension and high cholesterol, awareness of several important symptoms, particularly dizziness, memory loss, and vision changes, remained low. Only one-quarter correctly identified the thrombolysis treatment window of <4.5 hours, similar to earlier Indian studies reporting inadequate public understanding of acute stroke care.[9,10,11,14] These findings highlight a concerning gap because timely symptom recognition and rapid hospital presentation are essential for reducing stroke mortality and disability.[1,2,3] Despite moderate conceptual awareness, gaps in symptom recognition and emergency response highlight a persistent knowledge–action gap that may contribute to delays in stroke care.[26,27]
The proportion of participants who demonstrated high stroke knowledge aligns with existing Indian data, which indicate that only 40%–60% of the population can correctly identify stroke features or modifiable risk factors.[9,10,11] However, this level remains far lower than that reported in high-income countries, where recognition of stroke warning signs often exceeds 80%.[3] In Kerala, a state experiencing rising stroke mortality and a substantial noncommunicable disease (NCD) burden,[5,6,7,8] these knowledge deficits are particularly concerning. Consistent with previous studies, awareness of hypertension and physical inactivity was reasonably high, yet recognition of less obvious symptoms lagged, mirroring gaps reported in surveys from South India and West Bengal.[11,14,24]
Older adults demonstrated significantly better stroke knowledge than younger adults, a trend consistent with previous Indian and international studies,[10,24] likely due to their greater interaction with healthcare providers and exposure to chronic disease counselling, whereas younger adults may rely more on fragmented or nonvalidated online information.[14] Female participants also showed higher knowledge and more positive attitudes than males, aligning with Kerala-based findings and possibly reflecting women’s strong involvement in community health initiatives such as ASHA networks,[13] in contrast to northern India, where men often exhibit higher awareness due to exposure to health-related information through workplace environments and social interactions.[11,12] Urban residents had higher knowledge levels, consistent with studies from Thiruvananthapuram and Tamil Nadu,[13,14] likely due to better access to tertiary care and health information, but rural residents displayed more positive attitudes, possibly driven by greater trust in community health workers and interpersonal communication networks. Education played a major role, with college-educated individuals exhibiting significantly better knowledge and attitudes, similar to findings from Kolkata and Bengaluru,[11,24] underscoring the need for simplified, localized, and culturally adapted stroke literacy interventions to reach adults with lower educational backgrounds. These findings have important public health implications. Strengthening stroke awareness through community-based interventions, including ASHA worker-led education, NCD clinic counselling, and mass media campaigns, could improve early recognition and timely healthcare seeking.[28] Integrating stroke literacy into existing primary care and NCD programs may help bridge the observed knowledge–action gap and reduce prehospital delays. The finding of better attitudes among rural residents may be explained by stronger community cohesion, greater trust in frontline health workers such as ASHAs, and effective interpersonal communication networks.[29,30,31]
Health insurance status showed a strong association with higher stroke knowledge, likely reflecting more frequent interactions with healthcare providers, NCD clinics, and preventive counselling. Similar findings from Kerala and other Indian states have reported that insured individuals tend to have greater engagement with health services, better uptake of lifestyle advice, and improved NCD awareness.[15,19,24] Evidence from community-based studies in South India also suggests that individuals linked to regular health services exhibit higher recognition of stroke risk factors and warning signs.[9,14] These patterns emphasize the potential of public insurance schemes and state-led NCD programs as platforms for integrating routine stroke education into existing service delivery. A family history of stroke was another strong determinant of both knowledge and attitude, consistent with findings from the Trivandrum Stroke Registry, where households with prior stroke exposure demonstrated better symptom recognition and stronger health-seeking behavior.[15] Similar observations from studies in West Bengal and Hyderabad indicated that experiential learning—through witnessing a stroke event in a family member—enhances awareness and encourages proactive preventive attitudes.[11,24] These findings underscore the importance of family-centered and household-based educational strategies, especially for high-risk families with prior stroke exposure. The high rejection of alternative or herbal remedies observed in this study may reflect Kerala’s higher literacy levels, stronger public health system, and greater reliance on formal healthcare services compared with other regions in India.[32,33]
This study has several strengths, including a community-based sample selected from northern, central, and southern regions of Kerala, enhancing geographic representation. However, certain limitations should be considered. The cross-sectional design precludes causal inference. The relatively higher proportion of participants from APL households may have resulted in overestimation of stroke knowledge and positive attitudes, thereby limiting generalizability to lower socioeconomic groups. Although multistage cluster sampling was employed, inclusion of participants from only three districts may not fully capture the socioeconomic and healthcare heterogeneity across Kerala. The use of a sample-dependent mean cutoff to classify knowledge and attitude levels may limit comparability with other studies using standardized thresholds. Data collection through face-to-face interviews by a single investigator may have introduced interviewer and social desirability bias. All responses were self-reported and may be subject to recall bias. The sample excluded institutionalized individuals and those with severe illness, potentially underestimating knowledge and attitude gaps. The unexpected finding that a substantial proportion of participants disagreed with stroke being a serious disease may reflect ambiguity in question wording or misinterpretation. Unmeasured factors such as prior exposure to health campaigns or digital literacy may have influenced responses. Additionally, the questionnaire did not incorporate standardized stroke recognition tools such as BE-FAST (Balance, Eyes, Face, Arm, Speech, Time), which could be considered in future studies.
Conclusions
More than half recognized the brain as the organ affected by stroke; however, notable gaps persisted in recognizing key symptoms and the <4.5-hour treatment window, particularly among younger, male, less educated, and uninsured participants. Attitudes were generally positive, with stronger attitudes observed among females, rural residents, those with higher education, individuals with a family history of stroke, and those informed through academic sources. Targeted educational efforts are needed to improve timely recognition and response to stroke.
Author contributions
SSS conceptualized the study, contributed to study design, data collection, statistical analysis, literature review, drafting, and critical revision of the manuscript. VN contributed to the study design, literature review, and critical input to the discussion. JJO contributed to the study design, statistical analysis, literature review, and discussion. KRT contributed to the study design, statistical analysis, literature review, and discussion.
All authors reviewed and approved the final manuscript and agreed to be accountable for all aspects of the work.
Conflicts of interest
There are no conflicts of interest.
Data availability statement
The data generated during this study are not publicly available due to ethical and confidentiality considerations related to community-based data collection. De-identified data may be made available by the corresponding author upon reasonable request and subject to approval by the Institutional Ethics Committee.
Acknowledgements
The authors express their gratitude to all the participants of the study for their invaluable time.
Funding Statement
Nil.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data generated during this study are not publicly available due to ethical and confidentiality considerations related to community-based data collection. De-identified data may be made available by the corresponding author upon reasonable request and subject to approval by the Institutional Ethics Committee.