Abstract
Background:
Dementia is a major cause of disability among older adults. The global burden of dementia is expected to triple by 2050, with developing countries, witnessing a sharp increase. Early detection and management through screening programs can mitigate the progression of cognitive decline.
Objectives:
This study aimed to determine the prevalence and predictors of cognitive impairment among older adults residing in rural areas.
Methods:
An analytical cross-sectional study was conducted among older individuals residing in rural communities. A three-stage cluster sampling method was employed to select 350 participants. Cognitive screening was performed using the Hindi Mental State Examination, with a score ≤23 indicating cognitive impairment. Data on socio-demographics, clinical parameters, and functional abilities were collected and analyzed using SPSS version 26.
Results:
The prevalence of cognitive impairment (CI) was 24.9% (n = 87), with the majority presenting with mild CI (87.4%). CI was significantly higher among females (33.9%) (AOR: 3.2, 1.7–6.01). Key predictors of CI included advanced age (AOR: 3.4, 1.8–6.7), widowhood (AOR: 2.2, 1.5–4.1), and functional limitations [activities of daily living and instrumental ADL (ADL and IALD)] (AOR: 9.71 and 11.22, respectively). A significant positive correlation was found between overall Hindi mental state examination (HMSE) score and anthropometric measures, with the strongest association observed with height (r = 0.47, P < 0.001), followed by weight (r = 0.37, P < 0.001).
Conclusion:
Cognitive impairment is the highly prevalent among the elderly in rural areas, with multiple socio-demographic and behavioral factors contributing to its occurrence. Implementing routine cognitive screening and promoting physical activity, social engagement, and healthy lifestyle practices are essential for early detection and intervention.
Keywords: Cognitive impairment, elderly, Hindi mental state examination (HMSE), prevalence, rural health, screening
INTRODUCTION
Dementia is a leading cause of disability among older adults, posing a significant challenge to individuals, families, and healthcare systems globally. According to the World Health Organization (WHO), approximately 50 million people are living with dementia worldwide, with nearly 10 million new cases emerging annually.[1] Projections indicate that the global burden will triple by 2050, largely driven by population aging.[2] This trend is mirrored in developing countries, including South Asia, where the prevalence of dementia was estimated at 1.9% in 2005, with forecasts suggesting the number of cases could reach 7.5 million by 2040.[3] In India, a systematic review and meta-analysis encompassing 20 studies reported a prevalence of 20 cases per 1,000 individuals, underscoring the growing public health burden posed by cognitive disorders.[4] Cognitive impairment (CI), which includes conditions ranging from mild cognitive impairment (MCI) to severe dementia, profoundly influences the daily living activities and overall quality of life for older adults. It can lead to difficulties in memory, language, thinking, and judgment, affecting their ability to perform everyday tasks such as managing finances, taking medications, and maintaining personal hygiene.
In addition to individual suffering, the burden of CI reverberates through families and caregivers, exerting considerable pressure on healthcare infrastructure. With no cure currently available, early detection and management are crucial.[5] Screening for CI enables healthcare providers to identify affected individuals early, allowing for timely interventions that can slow disease progression and improve quality of life. Furthermore, preventive strategies targeting modifiable risk factors—such as hypertension, diabetes, physical inactivity, poor dietary practices, and smoking—have demonstrated potential in mitigating the onset or progression of cognitive decline.[6] Public health strategies that emphasize the modification of these risk factors are therefore essential to alleviate the growing burden of CI.
The need for cognitive screening is particularly urgent in India, given the rising prevalence of non-communicable diseases, including hypertension and diabetes, compounded by factors such as sedentary lifestyles, rapid urbanization, and environmental pollution.[7] Moreover, India’s vast socio-cultural, geographic, and economic diversity contributes to wide regional variation in the prevalence of cognitive impairment—ranging from 3.5% in Himachal Pradesh[8] and 6.5% in Kashmir,[9] to 27.3% in Punjab[10] and 26.06% in Kerala.[11] These disparities highlight the influence of contextual factors such as education, healthcare access, comorbidities, awareness, and underscore the urgent need for more region-specific studies. The challenge is more pronounced in rural areas, as they often lack adequate healthcare resources and awareness about cognitive health, leading to underdiagnosis and delayed treatment. Instituting routine cognitive screening programs in rural areas can facilitate early identification of cognitive deficits, fostering timely interventions and support for affected individuals and their families. With this backdrop, this study was undertaken among older adults (>60 years) residing in rural communities, with the objectives of determining the prevalence of cognitive impairment and identifying its key predictors.
MATERIAL AND METHODS
Study design and participants
A community based analytical cross-sectional study was conducted from October 2021 to September 2022 among older persons (>60 years) residing in rural areas of Lucknow district.
Eligibility criteria
Participants included in the study were the ones more than 60 years old residing in the area for a minimum of 1 year after obtaining written consent. Participants with the known history of psychiatric illness, Parkinson’s disease, stroke, epilepsy, severe head injury, brain neoplasm, or other neurological disorders, as well as those diagnosed with chronic kidney disease, hepatic encephalopathy, or severe visual or hearing impairment, were excluded to minimize bias and prevent overestimation of the outcome.
Sample size estimation
Sample size was estimated using the formula (N) = (Za/2)2 × P × (1 - P)/(MOE)2. From a study conducted among North Indian older adult population, which used Hindi Mental State Examination (HMSE) score (≤25) to define CI, taking prevalence to be 8.8%[12] at 95% confidence interval, 4% type 1 error rate, and an assigned effect size of 1.6, 350 was determined to be the minimum sample size needed for a 10% non-response rate to estimate the prevalence of CI.
Sampling technique
Three stage cluster sampling was used to select the participants. In stage 1, one block was chosen by random sampling from a cluster of eight rural blocks in the city. In stage 2, out of the seven Primary Health Centers (PHC), 1 PHC was selected randomly. In the third stage, three sub-centers were selected randomly to complete the desired sample size, considering the percentage of the older adult population (8.6%), current death rate, eligibility criteria, and provision of consent by participants. Accredited Social Health Activists (ASHAs) of the selected subcenters were contacted to prepare the list of elder population and from that list subjects were contacted individually by purposive sampling.
Data collection process
A pre-designed and pre-tested semi-structured questionnaire was used for data collection consisting of socio-demographic and socio-economic profile, personal habits, physical and clinical parameters, and screening for cognitive impairment. Principal investigator collected the data.
Assessment tool and operational definition
HMSE[13] developed by Professor (Dr) Mary Ganguli et al. as the Hindi adaptation of Mini Mental State Examination (MMSE) was used for cognitive screening as it is suitable for the illiterate population, with the questions needing reading, writing and arithmetic skill were modified. The tool is 81.3% sensitive and 60.2% specific with inter-rater reliability co-efficient of 0.86.[14] The tool has 10 domains consisting of 22 items with maximum score of 30.
Cognitive Impairment: CI was defined as a score of ≤23 on HMSE.[15] It was further sub-categorized into mild CI (score: 16–23), moderate CI (11–15) and severe CI (<11). Since the study population comprised of older adults residing in rural areas with varied literacy levels, these tools were interviewer-administered by the principal investigator in the local language (Hindi). The questions were translated into Hindi and explained in simple terms to ensure clarity and cultural appropriateness. Responses were recorded based on the participants’ answers and, where necessary, corroborated with caregivers’ inputs to minimize recall bias. ‘Self-maintaining’ and ‘instrumental’ Activity of Daily Living[16] developed by Lawton and Broody, 1969 was used to assess the independent living skills of the subjects.
Data analysis
Data was analyzed using Statistical Package for Social Sciences, version 26 (SPSS-26, IBM, Chicago, USA). Chi-square/Fischer exact test was used to test association between categorical variables. Degree of collinearity between the independent variables was examined by inspection of correlation coefficient (<0.7) and tolerance (cut off value >0.1) and variance inflation factor (VIF) values (cut off value <10). Linearity among continuous independent variables was assessed using Box-Tidwell procedure in the regression model and enter method was used for variable selection. Binary logistic regression was conducted to assess the predictors of CI. Pearson P < 0.05 was considered significant for all statistics.
Ethical clearance
Ethical approval was taken from Institute’s ethical committee. The study was done in accordance with the principles enshrined in the Declaration of Helsinki. All the those found to be CI on screening were referred to tertiary health care center for further assessment and management.
RESULTS
A total of 350 elderly subjects residing in the rural community from three different sub-centers were included and analyzed in the study [Figure 1].
Figure 1.
Study flow chart. The overall prevalence of cognitive impairment (CI) was observed to be 24.9% (n = 87), of which 87.4%% (n = 76) having mild CI, 11.5% (n = 10) having moderate CI and 1.1% (n = 1) having severe CI. Among male participants (n = 158) 13.9% were screened to have CI with mean HMSE score of 20 (SD: ±3.2) and among female subjects (n = 192) 33.9% had CI (mean HMSE score 20.7 ± 2.7). This difference was found to be statistically significant (P < 0.001). HMSE = Hindi mental state examination
Table 1 describes the background characteristics of the study participants. 78% (273) of the subjects were in the age group of 60–75 years, with females being in the majority (54.9%) and living in a joint family (79.1%). Majority of the participants were unemployed (89.1%) and belonging to class four socioeconomic status (38.3%) as per modified BG prasad classification.
Table 1.
Socio-demographic and socio-economic characteristics of the study subjects (n=350)
| Variables | Cognitive function |
P | |||
|---|---|---|---|---|---|
| Cognitive impairment (n=87) | Normal (n=263) | Total (n=350) | |||
| Age (in years) Mean±SD: 70.7±9.5 |
60–75 | 48 (17.6) | 225 (82.4) | 273 [78] | <0.001 |
| >75 | 39 (50.6) | 38 (49.4) | 77 [22] | ||
| Gender | Male | 22 (13.9) | 136 (86.1) | 158 [45.1] | <0.001 |
| Female | 65 (33.9) | 127 (66.1) | 192 [54.9] | ||
| Marital status | Married | 25 (12) | 184 (88) | 209 [59.7] | <0.001 |
| Widow/Divorced | 62 (43.9) | 79 (56.1) | 141 [40.3] | ||
| Educational level | Illiterate | 80 (33.8) | 157 (66.2) | 237 [66.7] | <0.001 |
| Primary | 0 | 33 (100) | 33 [9.4] | ||
| Middle-high | 4 (8.3) | 44 (91.7) | 48 [13.7] | ||
| Intermediate or above | 3 (9.4) | 29 (90.6) | 32 [9.1] | ||
| Employment status | Retired | 2 (7.7) | 24 (92.3) | 26 [7.4] | 0.001 |
| Working | 2 (5.3) | 36 (94.7) | 38 [10.9] | ||
| Unemployed | 286 (81.7) | 83 (29) | 203 [71] | ||
| Past occupation | Professional | 2 (11.1) | 16 (88.9) | 18 [5.1] | 0.008 |
| Skilled manual | 1 (3.4) | 28 (96.6) | 29 [8.3] | ||
| Unskilled manual | 18 (20.5) | 70 (79.5) | 88 [25.1] | ||
| Agriculture | 26 (31) | 58 (69) | 84 [24] | ||
| Unemployed | 40 (30.5) | 91 (69.5) | 131 [37.4] | ||
| Current occupation | Professional | 0 | 2 (100) | 2 [0.6] | 0.041 |
| Skilled manual | 0 | 7 (100) | 7 [2] | ||
| Unskilled manual | 3 (21.4) | 11 (78.6) | 14 [4] | ||
| Agriculture | 2 (13.3) | 13 (86.7) | 15 [4.3] | ||
| Unemployed | 82 (26.3) | 230 (73.7) | 312 [89.1] | ||
(Row percentage) [Column percentage], Chi-square, P<0.05 significant
Social engagement was assessed by asking questions about praying, if called for advice, able to visit sick friends and have hobbies on the line of yes or no. Majority (69.7%) were vegetarian (who has not consumed meat/meat products/fish/poultry even once in their lifetime),[17] had inadequate physical activity (71.4%), and inadequate social engagement (40.6%) [Table 2].
Table 2.
Personal habits of the study subjects (n=350)
| Variables | Cognitive function |
P | |||
|---|---|---|---|---|---|
| Cognitive impairment (n=87) | Normal (n=263) | Total (n=350) | |||
| Food Preference* | Vegetarian | 64 (26.2) | 180 (73.8) | 244 [69.7] | 0.367 |
| Non-vegetarian | 23 (21.7) | 83 (78.3) | 106 [30.3] | ||
| Physical Activity | Inadequate (<150 min/week) | 79 (31.6) | 171 (68.4) | 250 [71.4] | <0.001 |
| Adequate (<150 min/week) | 8 (8) | 92 (92) | 100 [28.6] | ||
| Sleep quality* | Poor | 9 (40.9) | 13 (59.1) | 22 [6.3] | <0.001 |
| Fair | 49 (39.2) | 76 (60.8) | 125 [35.7] | ||
| Good | 29 (14.3) | 174 (85.7) | 203 [58] | ||
| Tobacco consumption* | Never/former | 77 (28.7) | 191 (71.3) | 268 [76.6] | 0.002 |
| Current | 10 (12.2) | 72 (87.8) | 82 [23.4] | ||
| Alcohol consumption* | Never/former | 80 (25.4) | 235 (74.6) | 315 [90] | 0.483 |
| Current | 7 (20) | 28 (80) | 35 [10] | ||
| Social engagement | Inadequate | 64 (45.1) | 78 (54.9) | 142 [40.6] | <0.001 |
| Adequate | 23 (11.1) | 185 (88.9) | 208 [59.4] | ||
(Row percentage) [Column percentage], Chi-square test, P<0.05 significant, *self-reported
Approximately, one fourth (28%) of the participants were overweight and among them 20% had cognitive impairment. Majority of the male (90.5%) as well as female (78.6%) participants in the study had waist-hip ratio in the high range. Overall, 13.7% of senior citizens had inadequate scoring in physical activity of daily living scale, of which 75.0% were screened to have CI. On assessing instrumental activity of daily living, also 13.7% of the participants had inadequate scoring, of which 77.1% came out to have CI [Table 3].
Table 3.
Physical and clinical parameters of the study subjects (n=350)
| Variables | Cognitive function |
P | |||
|---|---|---|---|---|---|
| Cognitive impairment (n=87) | Normal (n=263) | Total (n=350) | |||
| Body Mass Index$ (kg/m2) | Underweight | 21 (38.9) | 33 (61.1) | 54 [15.4] | 0.036 |
| Normal | 44 (24.6) | 135 (75.4) | 179 [51.1] | ||
| Overweight | 20 (20) | 80 (80) | 100 [28.6] | ||
| Obese | 2 (11.8) | 15 (88.2) | 17 [4.9] | ||
| Waist-hip ratio* Male (n=158) |
High (<0.90) | 19 (13.3) | 124 (86.7) | 143 [90.5] | 0.475 |
| Normal (<0.90) | 3 (20) | 12 (80) | 15 [9.5] | ||
| Waist-hip ratio* Female (n=192) | High (<0.85) | 49 (32.5) | 102 (67.5) | 151 [78.6] | 0.430 |
| Normal (<0.85) | 16 (39) | 25 (61) | 41 [21.4] | ||
| Blood Pressure | Abnormal | 65 (26.5) | 180 (73.5) | 245 [70] | 0.268 |
| Normal | 22 (21) | 83 (79) | 105 [30] | ||
| Random Blood Sugar | Abnormal | 9 (20.5) | 35 (79.5) | 44 [12.6] | 0.470 |
| Normal | 78 (25.5) | 228 (74.5) | 306 [87.4] | ||
| ADL# | Inadequate | 36 (75) | 12 (25) | 48 [13.7] | <0.001 |
| Adequate | 51 (16.9) | 251 (83.1) | 302 [86.3] | ||
| IADL& | Inadequate | 37 (77.1) | 11 (22.9) | 48 [13.7] | <0.001 |
| Adequate | 50 (16.6) | 252 (83.4) | 302 [86.3] | ||
(Row percentage) [Column percentage], Chi-square/fisher’s exact test, P<0.05 significant, $As per Asia-Pacific classification, *As per WHO classification, #Activity of Daily Living, &Instrumental Activity of Daily Living
There was a significant positive correlation between overall HMSE score and all anthropometric parameters, with the strongest association observed with height (r = 0.47, P < 0.05). Among cognitive domains, orientation to time and place, recall, and praxis showed moderate positive correlations with weight, height, waist, and hip circumference. Naming showed no significant correlation with any anthropometric measure [Table 4].
Table 4.
Correlation between anthropometric measurements and domains of HMSE (n=350)
| Domain | Weight | Height | Waist circumference | Hip circumference |
|---|---|---|---|---|
| Orientation to time | 0.39* | 0.54* | 0.38* | 0.30* |
| Orientation to place | 0.28* | 0.45* | 0.26* | 0.14* |
| Registration | 0.20* | 0.09 | 0.09 | 0.09 |
| Attention | 0.24* | 0.25* | 0.12* | 0.09 |
| Recall | 0.25* | 0.24* | 0.22* | 0.20 |
| Naming | −0.002 | −0.03 | −0.01 | −0.05 |
| Repetition | 0.11* | 0.07 | 0.01 | 0.10 |
| Visuo-spatial command | 0.10 | 0.17* | 0.06 | −0.10 |
| Sentence | 0.13* | 0.09 | 0.06 | 0.08 |
| Praxis | 0.33* | 0.42* | 0.27* | 0.18* |
| Overall score | 0.37* | 0.47* | 0.30* | 0.22* |
Pearson correlation coefficient, *P<0.05 significant. HMSE=Hindi mental state examination
Binary logistic regression was performed to ascertain the effect of age, gender, marital status social engagement, body mass index (BMI), and activity of daily living on CI status. The logistic regression model was statistically significant (X2 (17.9), P value = 0.65). The model explained 47.2% (Negelkerke R square) of variance. Older age (>75 years), female gender, being widowed or divorced, inadequate social engagement, inadequate activity of daily living (self-maintaining as well as instrumental) was significantly associated with the risk of CI [Table 5].
Table 5.
Predictors of cognitive impairment among study subjects (n=350)
| Variable | Crude’s Odd’s ratio* | Value | Adjusted Odd’s ratio# 95% CI |
P | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Lower | Upper | |||||||||
| Age | 60–75 | Ref | ||||||||
| >75 | 4.8 | 3.48 | 1.81 | 6.69 | <0.001 | |||||
| Gender | Male | Ref | ||||||||
| Female | 3.1 | 3.26 | 1.77 | 6.01 | 0.01 | |||||
| Marital status | Married | Ref | ||||||||
| Widow/divorced | 3.8 | 2.19 | 1.48 | 4.09 | <0.001 | |||||
| Social engagement | Adequate | Ref | ||||||||
| Inadequate | 6.6 | 3.26 | 1.19 | 10.63 | 0.049 | |||||
| BMI | Obese | Ref | ||||||||
| Overweight | 4.7 | 9.3 | 0.21 | 14.8 | 0.24 | |||||
| Normal | 2.5 | 1.5 | 0.31 | 7.7 | 0.57 | |||||
| Underweight | 1.9 | 3.4 | 1.02 | 11.8 | 0.04 | |||||
| ADL | Adequate | Ref | ||||||||
| Inadequate | 14.7 | 9.71 | 2.31 | 40.7 | 0.002 | |||||
| IADL | Adequate | Ref | ||||||||
| Inadequate | 16.9 | 11.22 | 2.84 | 44.32 | 0.001 | |||||
*Univariate binary logistic regression, #Multivariate binary logistic regression, P<0.05 significant, Outcome variable [Cognitive Impairment (No/Yes)]. ADL=Activity of Daily Living, IADL=Instrumental Activity of Daily Living. BMI=Body Mass Index
DISCUSSION
This study found that the prevalence of CI among the elderly population was 24.9%. Key predictors of CI included malnutrition, inadequate social engagement, and impairments in activities of daily living (ADL). Gender predilection was evident, with odds of having cognitive impairment was almost three times more (P value: 0.01) in females as compared to their male counterparts. Epidemiological studies suggests that this gender disparity (especially in dementia and Alzheimer disease) may result from a combination of longer life expectancy in females and selective attrition of men due to early mortality is cited to be one of the reasons.[18] Additionally, sex hormones, especially oestrogen is proposed to have protective effect on brain health and hence post-menopausal hormonal changes affects the cognition more in females.[19,20] Females living in rural areas often experience limited access to mental health services, delayed or inadequate treatment of physical disorder and are less likely to engage in cognitively stimulating activities, further elevating their risk. Older age (>75 years) was strongly associated with CI (AOR: 3.48, P < 0.001) in line with previous studies that have shown age to be a predominant risk factor for cognitive decline owing to the age-related brain changes, such as reductions in brain volume, neuronal loss, and synaptic degeneration, contribute to this decline, which is often exacerbated by comorbidities like hypertension and diabetes.[21,22] Additionally, older individuals with low educational attainment were more likely to have CI (P < 0.001), reflecting the cognitive reserve hypothesis, which posits that higher education and intellectual engagement protect against cognitive decline.[23] Secondary analysis of LASI[24] showed that early life socio-economic conditions and education explains up to 74% in decreased cognitive core, although in this study both the factors (past occupation status, educational level) were statistically insignificant on bivariate analysis. Functional decline emerged as a critical concern, with individual experiencing impairments in ADL (AOR: 9.7) and instrumental ADL (IADL) (AOR: 11.2) being more likely to have CI. This highlights the cyclical relationship between cognitive impairment and loss of independence, as difficulties, as difficulties in managing everyday activities can further exacerbate cognitive deficits.[25] Malnutrition, a widely prevalent issue in older age[26] was another significant risk factor, with underweight individuals showing a higher risk for CI, aligning with evidence that energy deficiency can impair neural function and dysregulate the central nervous system.[27] Interestingly, while both underweight (AOR: 3.4) and obesity (AOR: 9.3) have been associated with cognitive decline in earlier studies,[28] this study did not find a significant association between overweight status and CI, suggesting that other factors may influence this relationship in rural populations. The finding that widowed or divorced individuals were more likely to experience CI (AOR: 2.19) may be linked to the effects of social isolation and loneliness, both of which have been documented as significant risk factors for cognitive decline and dementia.[11] Also in Indian society, especially in the rural areas widowed or divorced persons are more prone to face social isolation that correlates with cognitive decline. Inadequate social engagement was similarly found to be positively linked with cognitive decline further backing the hypothesis that later-life leisure engagement is considered to expand cognitive reserve, thus providing more resilience to brain damage or neurodegenerative disorder.[29]
Strengths and limitation of the study
The study has several strengths. This is a community-based study on an adequate sample. Use of three-stage cluster sampling enhances the representativeness of the selected area. Comprehensive data collection (house to house survey), including socio-demographic, clinical, and personal habit information, allowed a holistic analysis of predictors.
However, the study has some limitations. The cross-sectional design limits the ability to establish causality between predictors and cognitive impairment. Follow-up studies are crucial to see the pattern of change in cognitive function. Self-reported data on personal habits may be subject to inaccuracies, especially in individuals with early cognitive decline. Participants with known history of head trauma or any neurological, systemic, or psychiatric conditions associated with CI were excluded that can lead to underestimation of the condition. Although diagnosed cases of psychiatric illnesses were included, but undiagnosed cases were missed, which could have created bias in the results. Also, because it was a community-based study other clinical parameters like hemoglobin, vit B12, folate, vit D levels and so on. could not be evaluated. Finally, the study’s focus on a rural setting limits its applicability to urban populations with different socio-economic contexts.
CONCLUSION
The present study observed the prevalence of CI to be 24.9% and identified ageing, relationship status (widowed/divorced status), inadequate social engagement, being underweight, and inadequate scoring on ADL scale as factors associated with cognitive impairment, thus emphasizing on the need for targeted interventions focusing on malnutrition, social engagement, and functional independence to mitigate the growing burden of cognitive impairment among the elderly. Special attention should be given to vulnerable groups, such as older women and socially isolated individuals, through culturally sensitive community-based programs.
The authors attest that there was no use of generative artificial intelligence (AI) technology in the generation of text, figures, or other informational context of this manuscript.
Conflicts of interest
There are no conflicts of interest.
Funding Statement
Nil.
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