A cross-sectional study to assess the prevalence of cognitive impairment and its associated factors among the elderly in Kaniyambadi block, Vellore

A cross-sectional study to assess the prevalence of cognitive impairment and its associated factors among the elderly in Kaniyambadi block, Vellore | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Public Health Challenges This is a preprint and has not been peer reviewed. Data may be preliminary. 18 March 2025 V1 Latest version Share on A cross-sectional study to assess the prevalence of cognitive impairment and its associated factors among the elderly in Kaniyambadi block, Vellore Authors : Manoj Jacob Dhinagar 0009-0000-3497-6237 [email protected] , Vinod Joseph Abraham , and Zacharia Mathew Authors Info & Affiliations https://doi.org/10.22541/au.174227235.53165157/v1 Published Public Health Challenges Version of record Peer review timeline 326 views 208 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study aims to determine the prevalence of mild cognitive impairment and major neurocognitive disorder among adults aged greater than or equal to 60 in Kaniyambadi block, Vellore and the factors associated with cognitive impairment. Settings and Design: A community based cross sectional study was conducted on 360 adults greater than or equal to the age of 60 residing in Kaniyambadi block, Vellore. Methods and Material: A semi-structured interviewer-based questionnaire was administered to the participant. Their subjective and objective cognitive abilities was assessed along with their ability to perform their activities of daily living. The participants were also screened for depression. Statistical analysis used: Univariate analysis was done using measures of central tendencies and proportions. Bivariate analysis was done using Chi square test and logistic regression was also performed. Results: The prevalence of mild cognitive impairment among adults aged more than or equal to 60 residing in Kaniyambadi block was 20% (95% CI 15.9 - 24.5). The prevalence of major neurocognitive disorder in the same population was 4.4% (95% CI 2.5 - 7.1). and the prevalence of depression was 18.9% (95% CI 14.9 -23.3). Age greater than or equal to 70 (AOR 2.24 [1.38 – 3.64]), no formal education (AOR 2.62 [1.52 – 4.48]) and depression (AOR 3.64 [1.90 – 6.99]) were found to be statistically significantly associated with cognitive impairment. Conclusions: The overall prevalence of mild cognitive impairment and major neurocognitive disorder in Kaniyambadi block was found to be similar to the prevalence in other parts of the nation. Adults aged more than 70 and those with no formal education are at greater risk of developing cognitive impairment. Since depression is also associated with cognitive impairment, it is imperative to screen the elderly with depression and other psychiatric illnesses for cognitive impairment and those with cognitive impairment for depression A cross-sectional study to assess the prevalence of cognitive impairment and its associated factors among the elderly in Kaniyambadi block, Vellore Abstract: Aims: This study aims to determine the prevalence of mild cognitive impairment and major neurocognitive disorder among adults aged greater than or equal to 60 in Kaniyambadi block, Vellore and the factors associated with cognitive impairment. Settings and Design: A community based cross sectional study was conducted on 360 adults greater than or equal to the age of 60 residing in Kaniyambadi block, Vellore. Methods and Material: A semi-structured interviewer-based questionnaire was administered to the participant. Their subjective and objective cognitive abilities was assessed along with their ability to perform their activities of daily living. The participants were also screened for depression. Statistical analysis used: Univariate analysis was done using measures of central tendencies and proportions. Bivariate analysis was done using Chi square test and logistic regression was also performed. Results: The prevalence of mild cognitive impairment among adults aged more than or equal to 60 residing in Kaniyambadi block was 20% (95% CI 15.9 - 24.5). The prevalence of major neurocognitive disorder in the same population was 4.4% (95% CI 2.5 - 7.1). and the prevalence of depression was 18.9% (95% CI 14.9 -23.3). Age greater than or equal to 70 (AOR 2.24 [1.38 – 3.64]), no formal education (AOR 2.62 [1.52 – 4.48]) and depression (AOR 3.64 [1.90 – 6.99]) were found to be statistically significantly associated with cognitive impairment. Conclusions: The overall prevalence of mild cognitive impairment and major neurocognitive disorder in Kaniyambadi block was found to be similar to the prevalence in other parts of the nation. Adults aged more than 70 and those with no formal education are at greater risk of developing cognitive impairment. Since depression is also associated with cognitive impairment, it is imperative to screen the elderly with depression and other psychiatric illnesses for cognitive impairment and those with cognitive impairment for depression Key-words: cognitive impairment, elderly, rural India, depression, dementia Key Messages: Cognitive impairment is an emerging public health problem that is affecting many elderly people in the population. Early identification and cognitive rehabilitation of those affected with mild cognitive impairment can help slow the progress to major neurocognitive decline. TITLE PAGE Main Author : Dr. Manoj Jacob Dhinagar MD, PGDMLE Assistant Professor, Department of Community Medicine, Christian Medical College, Vellore Email ID: [email protected] Co-Authors : Dr. Vinod Joseph Abraham, MD MPH Professor, Department of Community Medicine, Christian Medical College, Vellore [email protected] Dr. Zacharia Mathew, DNB Assistant Professor, Department of Internal Medicine, Christian Medical College, Vellore [email protected] Funding Statement : This Study was funded by the Internal Fluid Research Grant, Research Office, Christian Medical College, Vellore Conflict of Interest Statement : No conflicts of interest for any of the authors Ethical Statement: Approved by the Institutional Review Board, Christian Medical College (IRB Min NO 14374, Dec 8 th , 2021) and the study adhered to their guidelines. Informed consent was obtained from every study participant by the authors. Data Availability Statement: Data can be obtained from the corresponding author based on a credible request Introduction: Cognitive impairment is an emerging concept that has entered public discourse during the latter half of the 20th century. There has been increased interest in identifying the boundary between the low functioning normal ageing adult and the onset of dementia. Cognitive impairment refers to a decline in the ability to think and process information. This can include difficulties with memory, problem-solving, attention, and other mental processes. (1) Cognitive impairment can be caused by a variety of factors, including brain injury, stroke, aging, or certain medical conditions. It can range from mild to severe and can affect a person’s ability to function independently in daily life. Mild cognitive impairment (MCI) is a syndrome characterised by cognitive decline greater than expected for an individual’s age and level of education, but which does not significantly interfere with daily activities. (2) Individuals with MCI may have trouble remembering recent events or names but can still perform routine tasks and take care of themselves. Some people with mild cognitive impairment appear to remain stable or may return to normal over time, but within 5 years, more than half seem to develop dementia. Thus, mild cognitive impairment can be considered a risk factor for dementia, and its identification could lead to secondary prevention by controlling risk factors like vascular disease, hypertension among others. When acquired cognitive impairment becomes severe enough to impair social and/or occupational functioning, dementia is typically diagnosed. While the term dementia was used commonly in common parlance and in medical literature, it is now referred to as major neurocognitive disorder. The DSM-5 diagnostic criterion for major neurocognitive disorder, which corresponds to dementia, is the presence of substantial impairment in one or (typically) more cognitive domains. The impairment must be severe enough to impede independence in daily activities which differentiates major neurocognitive disorder from mild cognitive impairment where independence in daily activities is retained by the individual. (3) Neurocognitive disorders, particularly major neurocognitive disorders (dementias), have devastating effects on patients, their families, the health care system, and the economy. (4) The costs of health care and uncompensated caregiving for individuals with dementia are high and increasing. Additionally, family caregivers experience elevated levels of emotional stress, depression, and health issues as a result of providing care for individuals with cognitive impairment. Being an emerging field, any forward step in our understanding of cognitive impairment is essential in further improving these strategies to alleviate the impact of cognitive impairment. This study aims to assess the prevalence of cognitive impairment, both mild cognitive impairment and major neurocognitive disorder, to determine the prevalence of depression among adults aged 60 years and above and the factors that are associated with cognitive impairment among the adults aged 60 or above who are currently living in Kaniyambadi block, Vellore, South India. Subjects and Methods: The study was conducted in Kaniyambadi block, which is a revenue block located in Vellore district, Tamil Nadu. The block is predominantly rural and consists of 88 villages with a population of about 1,20,000 people. The study was a community-based cross-sectional study carried out between April 2021 and June 2022. The study included adults aged over 60 years and excluded any adults previously diagnosed with any psychiatric illness. The sample size was calculated using the standard formula for sample size calculation for cross sectional studies using prevalence of mild cognitive impairment among elderly found to be 8.8% in a previous study done in a similar setting (27) and an absolute precision of 3. The required sample size was 357 and the sample size attained was 360. Multi-stage cluster sampling method was selected as the sampling method for this study. In the first stage, 18 individual villages in Kaniyambadi block were selected as clusters using probability proportional to size (PPS). In the second stage, 20 individuals fitting the inclusion criteria from each of the selected villages, were selected by simple random sampling. A total of 360 adults above the age of 60 were selected using a table of random numbers and listed in order. The study tools used included a questionnaire regarding the demographic, socio-economic, comorbidity, living status and habits of the participant, AD8 questionnaire, Montreal Cognitive Assessment (MoCA), Everyday Abilities Scale for India (EASI), Geriatric Depression Scale – 15(GDS-15). As per the DSM V Criteria, to classify an individual as Mild or Major Neurocognitive Disorder, there needs to be a subjective and objective decline in the cognitive capabilities of the individual. In this study, the AD8 Dementia Screening tool was used for subjective estimation of an individual’s cognitive function. (6) (7) The AD8 consists of 8 questions with the answers being either Yes/No. The cut-off used in this study was 0 – 1: Normal cognition, 2 or greater: Cognitive impairment is likely to be present. For the objective assessment of cognitive impairment, the MOCA-T test was used. The Montreal Cognitive Assessment Test was validated as a tool for the detection of MCI in 2005. It was found to have a sensitivity of 90% in detecting MCI and was more sensitive than the Mini-Mental State Examination (MMSE) which is the most widely used tool for cognitive assessment. (8) (9) The MoCA also eliminates the high ceiling effects and educational bias of the MMSE. (10) The domains tested by MoCA include visuospatial/executive, naming, attention, language, abstraction, delayed recall assessed by the Memory Index Score (MIS) and orientation. The maximum possible score is 30 and an extra mark is given if the participant has less than or equal to 12 years of education. The MoCA-T was also tested in Tamil Nadu in 2022 and found to have an optimal cutoff of 24 which accommodated a sensitivity of 88.9% and a specificity of 77.9%. (11) In this study, a score of 24 or more was used to imply no cognitive impairment whereas a score of 23 or less implied the presence of cognitive impairment. Activities of Daily Living was assessed using the Everyday Abilities Scale for India (EASI) tool. (12) A cut-off of 3 or more was used to indicate impaired activities of daily living. Depression was screened in this study using the Geriatric Depression Scale-15. This short-form version has been found to have a Sensitivity of 80% and specificity of 75% with a cutoff of 5/6(13). The Tamil version has been validated in Puducherry and has a similar sensitivity (14). A score of 1 was given for each answer suggesting depression and a cut-off of 5 or more suggested the presence of depression in the participant. Presence of subjective and objective cognitive decline without any impairment of ADL was considered positive for mild cognitive impairment. Presence of subjective and objective cognitive decline with impairment of ADL was considered major neurocognitive disorder. The researchers visited the household of each eligible individual as in the list and interviewed the selected participant and their primary caregiver, if available or needed. An auxiliary health worker accompanied the investigator during these interviews to help develop rapport with the participants and assist in establishing a favourable environment prior to administering the questionnaire. Informed consent was obtained from the study participant after being provided the requisite information regarding the study. If the caregiver was available, the information regarding the study was provided to them as well. The study questionnaire was then administered by the investigator. Univariate analysis was performed by calculating frequencies and percentages for variables such as socio-demographic details, living status, income, family members, primary caregiver, comorbidities, history of head injury, family history of cognitive impairment or diagnosed psychiatric illness and substance abuse. Bivariate analysis was done using the Chi-square test and odds ratio to determine any statistically significant association and the strength of the associations between cognitive impairment and the various factors studied respectively. Multivariable analysis was also performed between cognitive impairment and the factors found to be significant in the bivariate analysis. Results: Table 1. Baseline Characteristics of the Study Population \tightlist Age 60-64 112 (31.1) \tightlist 65-69 94 (26.1) \tightlist >=70 154 (42.8) \tightlist Gender Male 138 (38) \tightlist Female 222 (62) \tightlist Marital Status Currently Married 194 (53.9) \tightlist Single / Widow / Widower / Separated 166 (46.1) \tightlist Current Occupation Status Currently working 101 (28) \tightlist Unemployed /Retired / Pensioner 259 (72) \tightlist Socio-Economic Status (BG Prasad Classification 2022) Class I -II 110 (30.6) \tightlist Class III -V 250 (69.4) \tightlist Education Status No formal education 163 (45.3) \tightlist Primary or Middle School 147 (40.8) \tightlist High school or higher 50 (13.9) \tightlist Medical Comorbidity No Comorbidity 164 (45.6) \tightlist Comorbidity present 196 (54.4) \tightlist History of head injury in participants Head injury present 349 (97) \tightlist Head injury absent 11 (3) \tightlist History of cognitive impairment in the family of the participants Absent 345 (95.8) \tightlist Present 15 (4.2) \tightlist History of diagnosed psychiatric illness in the participants family Absent 356 (98.9) \tightlist Present 4 (1.1) \tightlist Substance Abuse by the study participants Substance abuse absent 302 (84) \tightlist Substance abuse present 58 (16) \tightlist Depression Depression absent 292 (81) \tightlist Depression present 68 (19) A total of 360 participants were interviewed for this study. The baseline characteristics of the 360 participants who were included in the analysis for this study are included in Table 1. The mean age of the participants was 68.4 and median age was 68. The most common self-reported comorbidity among the participants was hypertension (36.9%) followed by diabetes mellitus (31.9%). 12.8% of the participants suffered from vascular disease which included previous cerebrovascular accidents, current cardiovascular disease or peripheral vascular disease. Commonest substance addictions mentioned by the participants included : Beedi(39 participants, 10.8%), Alcohol(13 participants, 3.6%), Chewing Tobacco (7 participants, 1.9%). The median duration of substance use was 30 years. 56% of the participants are currently still using any of these substances. Table 2. Prevalence of Cognitive Impairment No cognitive impairment 185 51.1 (45.8 – 56.3) Only subjective cognitive decline 13 3.6 (1.9 – 6.1) Only objective cognitive decline 75 20.8 (16.7 -25.4) Mild cognitive impairment 72 20 (15.9 - 24.5) Major neurocognitive disorder 16 4.4 (2.5 - 7.1) TOTAL 360 100 Table 2 shows that out of the 360 participants, 72 (20%) has mild cognitive impairment and 16 (4.4%) had major neurocognitive disorder. 75 participants were found to have an objective cognitive decline based on the MoCA scores.18 participants were found to have impaired activities of daily living (ADL) based on their EASI score. Table 3. Association of various factors with any cognitive impairment Age >= 70 96 [62.3] 58 [37.7] 2.60 [1.69- 4.00] 2.24 [1.38 – 3.64] <0.001 \tightlist <70 80 [38.8] 126[61.2] Gender Female 127 [57.2] 95 [42.8] 2.42 [1.57 – 3.75] 1.47 [0.84 – 2.58] 0.18 \tightlist Male 49 [35.5] 89 [64.5] Socio-Economic Status (SES) CLASS III – V 136[54.4] 114[45.6] 2.08 [1.31 – 3.31] 1.17 [0.67 – 2.05] 0.57 \tightlist CLASS I-II 40[36.4] 70[63.6] Marital status Separated /widowed 99[59.6] 67[40.4] 2.24 [1.47 – 3.42] 1.13 [0.64 – 1.97] 0.67 \tightlist Married 77[39.7] 117[60.3] Primary caregiver Others 34 [64.2] 19 [35.8] 2.07 [1.13-3.80] 1.45 [0.72 – 2.90] 0.30 \tightlist Self 142 [46.3] 165 [53.7] Living arrangements Spouse Absent 98 [59.8] 66 [40.2] 2.24 [1.47 – 3.43] 1.75 [0.79 – 3.87] 0.17 \tightlist Spouse Present 78 [39.8] 118 [60.2] Education No Formal Education 110 [67.5] 53 [32.5] 4.11 [2.65 – 6.41] 2.60 [1.51 – 4.43] <0.001 \tightlist Any Education 66 [33.5] 131 [66.5] History of head injury Head Injury Present 7 [63.6] 4 [36.4] 1.86 [0.53 – 6.48] – 0.32 \tightlist Head Injury Absent 169 [48.4] 180 [51.6] Presence of psychiatric illness in the family Psychiatric Illness Present 1 [25] 3 [75] 0.34 [0.03 – 3.34] – 0.62 \tightlist Psychiatric Illness Absent 175 [49.2] 181 [50.8] Cognitive impairment in the family Cognitive impairment present 13[86.7] 2 [13.3] 7.25 [1.61 – 32.64] – 0.003 \tightlist Cognitive impairment absent 163 [47.2] 182 [52.8] Substance abuse Substance Abuse Present 23[39.7] 35 [60.3] 0.64 [0.36 –1.13] – 0.15 \tightlist Substance Abuse Absent 153 [50.7] 149 [49.3] Comorbidities Present 101[51.5] 95[48.5] 1.26 [0.83- 1.91] – 0.27 \tightlist Absent 75[45.7] 89[54.3] Vascular disease Present 29 [63.0] 17 [37.0] 1.93 [1.02– 3.66] 1.62 [0.78 – 3.39] 0.19 \tightlist Absent 147 [46.8] 167 [53.2] Depression Present 52 [76.5] 16 [23.5] 4.40 [2.40– 8.07] 3.64 [1.90 – 6.99] <0.001 \tightlist Absent 124 [42.5] 168 [57.5] Table 3 shows the association between different risk factors and presence of any cognitive impairment (both mild and major). Among the 360 study participants, age greater than or equal to 70, female gender, socio-economic classification as per BG Prasad scale belonging to Class III or lower, separated from their spouse or widowed, currently not living with a spouse, requiring a primary caregiver, having no formal education, family history of cognitive impairment, presence of vascular disease and presence of depression were all found to be significantly associated with the presence of cognitive impairment. A multiple logistic regression analysis was carried out for all the variables found to be significant in the bivariate analysis. After adjusting for all the factors, age greater than or equal to 70 (AOR 2.24 [1.38 – 3.64]), no formal education (AOR 2.62 [1.52 – 4.48]) and depression (AOR 3.64 [1.90 – 6.99]) were found to be statistically significantly associated with the presence of cognitive impairment. Table 4. Association of various factors with mild cognitive impairment or major neurocognitive disorder Age >= 70 13 [24.1] 41 [75.9] 3.27 [0.85- 12.50] 1.61 [0.35 – 7.25] 0.53 \tightlist <70 3 [8.8] 31 [91.2] Gender Female 14 [21.5] 51 [78.5] 2.88 [0.60 – 14.28] – 0.22 \tightlist Male 2 [8.7] 21 [91.3] Socio-Economic Status (SES) CLASS III – V 15[20.8] 57 [79.2] 3.95 [0.48 – 32.25] – 0.28 \tightlist CLASS I-II 1[6.3] 15 [93.8] Marital status Separated /widowed 11 [21.6] 40[78.4] 1.76 [0.55 – 5.58] – 0.333 \tightlist Married 5 [13.5] 32[86.5] Primary caregiver Others 9 [52.9] 8 [47.1] 10.28 [3.00-35.23] 5.83 [1.39 – 24.38] <0.001 \tightlist Self 7 [9.9] 64 [90.1] Living arrangements Spouse Absent 12 [23.5] 39 [76.5] 2.53 [0.74 – 8.62] 0.99 [0.23 – 4.22] 0.99 \tightlist Spouse Present 4 [10.8] 33 [89.2] Education No Formal Education 14 [24.6] 43 [75.4] 4.71 [0.99 – 22.22] 2.45 [0.42 – 14.08] 0.31 \tightlist Any Education 2 [6.5] 29 [93.5] History of head injury Head Injury Present 0 3 [100] – – 1.00 \tightlist Head Injury Absent 16 [18.8] 69 [81.2] Presence of psychiatric illness in the family Psychiatric Illness Present 0 1 [100] – 1.00 \tightlist Psychiatric Illness Absent 16 [18.4] 71 [81.6] Cognitive impairment in the family Cognitive impairment present 5[62.5] 3 [37.5] 10.45 [2.18 – 50.07] 7.94 [1.18 – 53.13] 0.03 \tightlist Cognitive impairment absent 11 [13.8] 69 [86.2] Substance abuse Substance Abuse Present 0 11 [100] – – 0.20 \tightlist Substance Abuse Absent 16 [20.8] 61 [79.2] Comorbidities Present 7 [14.0] 43 [86.0 ] 0.52 [0.17 -1.56] – 0.24 \tightlist Absent 9 [23.7] 29[76.3] Vascular disease Present 4 [17.4] 19 [82.6] 0.93 [0.26 – 3.23] – 1.00 \tightlist Absent 12 [18.5] 53 [81.5] Depression Present 7 [21.2] 26 [78.8] 1.37 [0.45 – 4.12] – 0.58 \tightlist Absent 9 [16.4] 46 [83.6] Table 4 shows that no formal education, need for a caregiver and family history of cognitive impairment were found to be statistically significantly associated with the presence of major neurocognitive disorder in this study. In a logistic regression model, presence of a caregiver (AOR 5.83 [1.39 – 24.38]) and presence of family history of cognitive impairment (AOR 7.94 [1.18 – 53.13]) were found to be statistically significantly associated with the presence of major neurocognitive disorder. Discussion: The prevalence of Mild Cognitive Impairment (MCI) in this study was 20% (95% CI 15.9 - 24.5). Since, the concept of MCI is an emerging one, there is a paucity of studies looking at its prevalence in different populations. The existence of varied criteria for detection and the presence of numerous tools to screen for MCI lead to large scale variations in the prevalence of MCI. The prevalence of MCI in a study done in the out-patient department of a secondary hospital in Vellore was 36.5%. However, this study used the Mini-Mental State Examination (MMSE) and the Vellore Screening Instruments for Dementia (VSID) to screen for MCI and MNCD. (15) The 10/66 dementia research group found the crude prevalence of MCI in Vellore to be 4.3% while using the Community Screening Instrument for Dementia (CSI-D) tool. (16) A community-based study involving 185 participants above the age of 60 in Coimbatore also using the MOCA-T tool and identical cut-offs as that which was used in the present study, found the prevalence of MCI in that population to be 35.1%. (17) The prevalence of dementia or Major Neurocognitive Disorder (MNCD) in this study was 4.4% (95% CI 2.5 - 7.1). The prevalence of dementia in the previously mentioned study done in an out-patient department in Vellore was 1.5% (15) The presence of multiple diagnostic criteria also cause a wide variation in the prevalence of dementia. Different criteria have led to the prevalence of dementia to range from 0.8% to 63.2% in Kaniyambadi block, Vellore based on different tools. (18) 3.6 % of the study participants were found to have only subjective cognitive decline and 20.8% had objective cognitive decline without any subjective decline. Both groups had no restriction in activities of daily living. A study in Kerala which used the Malayalam version of the Addenbrooke’s Cognitive Examination (m-ACE) showed the prevalence of participants with subjective cognitive decline to be 13.6% and objective cognitive decline to be 11.03%. (19) The larger numbers of participants with objective cognitive decline could be as a result of the difference in the tools used and the levels of education between the populations. In the bivariate analysis, having no formal education was found to be significantly associated with the presence of any cognitive impairment with an odds ratio of 4.11[2.65 – 6.41]. This was confirmed by the logistic regression analysis where no formal education was still significantly associated with cognitive impairment (AOR 2.62 [1.52 – 4.48]). This is similar to the findings from studies done in comparable settings where low levels of education were found to be significantly associated with cognitive impairment. (20). Age greater than 70 was also found to be significant in the logistic regression analysis with an adjusted odds ratio of 2.24 [1.38 – 3.64]. Older age was found to be significantly associated with cognitive impairment, both mild and major neurocognitive disorders in another study done in Vellore which was mentioned above. (15) Age has been found to be a significant factor associated with greater risk of developing cognitive impairment in multiple settings including rural areas in North India and Andhra Pradesh as well. (27,21) Depression was found to be significantly associated with cognitive impairment in the logistic regression analysis with an adjusted odds ratio of 3.64 [1.90 – 6.99] . The association of depression and cognitive impairment has also been well-studied and this finding is in line with the consensus opinions and findings regarding this association. (22) Depression was also found to be significantly associated with cognitive impairment in the Indian context in a study done in Guwahati. (23) Other factors found to be significantly associated with cognitive impairment in the bivariate analysis in this study included female gender (OR 2.42 [1.57 – 3.75]), socio-economic classification as per BG Prasad scale belonging to Class III or lower (OR 2.08 [1.31 – 3.31]), separated from their spouse or widowed (OR 2.24 [1.47 – 3.42]), currently living alone (OR 2.69 [1.38 - 6.66]), requiring a primary caregiver (OR 2.07 [1.13-3.80]), family history of cognitive impairment (OR 7.25 [1.61 – 32.64]), presence of vascular disease ( OR 1.93 [1.02– 3.66]). However, these factors were found to not be significant in the multivariable analysis. The presence of medical comorbidities like diabetes and hypertension were found to not be significantly associated with cognitive impairment which was similar to the findings in other studies (15, 24). The association and biological relation of diabetes and hypertension to the presence of cognitive impairment is yet to be completely ascertained and further studies are needed. History of previous head injury and history of psychiatric illness in the family were not found to be significantly associated with cognitive impairment in this study. This could be due to the low numbers of study participants who had history of head injury in the past or had family members with cognitive impairment. History of smoking has been found to be positively associated with cognitive decline (25), however substance use was not found to be associated with cognitive impairment in this study. Multiple studies evaluating the association between alcohol and cognitive impairment have yielded inconclusive, ambiguous, and contradictory findings. Alcohol has been found to be significantly associated in certain studies done in India (19,23) but there have been other studies showing no or even a positive effect on cognition with low-moderate levels of alcohol consumption. (26) The presence of a caregiver (AOR 5.83 [1.39 – 24.38]) and presence of family history of cognitive impairment (AOR 7.94 [1.18 – 53.13]) were found to be significantly associated with the presence of major neurocognitive disorder in the multivariable analysis. The probable genetic predisposition for dementia has also been identified in other studies. (27) There is also a possibility that the patients or informers of patients with dementia may not have been able to accurately remember whether their parents or other family members may have had any degree of cognitive impairment. Patients with major neurocognitive disorder also had impairment in their activities of daily living and hence had a greater need for a caregiver to help them with their daily needs. In the bivariate analysis, female gender (1.93 [1.07 – 3.48]), no formal education (2.11 [1.23 – 3.62]), belonging to BG Prasad socio-economic class III or lower (1.89 [1.00 – 3.57]), presence of cognitive impairment in the family (5.42 [1.89 – 15.54]) and presence of vascular disease in the study participant (2.68[1.37 – 5.28]) were found to be significantly associated with the presence of depression in the participant. These findings were similar to the factors found to be associated with depression in similar studies. (28,29,30) Out of these, presence of vascular disease (AOR 2.42(1.18 – 4.98)) and presence of cognitive impairment in the family (AOR 4.25(1.39 – 12.95)) were significant in the multivariable analysis. LIMITATIONS There is a possibility of inaccurate recall by the study participants or their caregivers leading to information bias. This affected questions regarding subjective cognitive decline, previous history of head injury, family history of cognitive impairment and family history of psychiatric illness. This study was done in a cross-sectional manner and hence variables whose lifetime exposure affects the presence of cognitive impairment such as diabetes, hypertension, smoking and alcohol consumption could not be accurately studied. The Montreal Cognitive Assessment has the potential to be influenced by the education status of the participant since it was initially designed in a setting with higher education levels than this study setting. This was countered by using a cut-off more appropriate for the current study. However, there is still a possibility of certain participants being considered to have objective cognitive decline due to low scores in the MoCA test as a result of having no formal education but meeting no other criteria for cognitive impairment. Depression was assessed using the Geriatric Depression Scale -15 (GDS -15) which is a screening tool and does not provide a conclusive diagnosis of depression. Furthermore, the presence of cognitive impairment may have affected the responses provided by the participants for the questions in the Geriatric Depression Scale. CONCLUSIONS Cognitive impairment is an emerging public health problem that needs to be studied in depth in order to improve our understanding of the condition and alleviate the suffering of the individuals with cognitive impairment. This study shows the ever-present burden of cognitive impairment in Kaniyambadi block, Vellore, the different stages of cognitive impairment in the population and the factors associated with it. 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Collection Public Health Challenges Keywords cognitive impairment elderly rural india Authors Affiliations Manoj Jacob Dhinagar 0009-0000-3497-6237 [email protected] Christian Medical College Vellore View all articles by this author Vinod Joseph Abraham Christian Medical College Vellore View all articles by this author Zacharia Mathew Christian Medical College Vellore View all articles by this author Metrics & Citations Metrics Article Usage 326 views 208 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Manoj Jacob Dhinagar, Vinod Joseph Abraham, Zacharia Mathew. A cross-sectional study to assess the prevalence of cognitive impairment and its associated factors among the elderly in Kaniyambadi block, Vellore. Authorea . 18 March 2025. DOI: https://doi.org/10.22541/au.174227235.53165157/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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