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ORIGINAL ARTICLE
Year : 2013  |  Volume : 2  |  Issue : 3  |  Page : 171-176

Prevalence of cognitive impairment and related factors among elderly: A population-based study


1 Department of Community Medicine, School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India
2 Department of Community Medicine, Indira Gandhi Medical College, Shimla, Himachal Pradesh, India

Date of Web Publication29-Aug-2013

Correspondence Address:
Deepak Sharma
School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, New Delhi
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2277-8632.117182

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  Abstract 

Background: Older persons are at a risk of developing cognitive impairment, which is often considered a precursor to more serious conditions, such as dementia, depression or even Alzheimer's disease. Mini Mental State Exam (MMSE), a cognitive screening tests rated on a 30-point scale is most widely used to study cognitive measures.
Aims and Objectives: The aim of our study was to determine the prevalence of cognitive impairment among older adults, to describe the pattern of cognitive impairment in rural and urban elderly population and to investigate the influence of socio-demographic and other variables on it.
Materials and Methods: A cross-sectional study was carried out between January 2010 and July 2010, in urban and rural areas of Shimla district of Himachal Pradesh. Four hundred elderly people were included in the study. Cognitive levels were assessed with the MMSE scale (cut-off score 23). Data were analysed with SPSS 17.0 software for windows.
Results: The prevalence of cognitive impairment was 3.5%. It was higher in rural (2.3%) than in urban population (1.3%), with a rural/urban prevalence ratio (PR) of 1.8 (95%CI 0.6-5.7). In the logistic regression model, old-old, illiterate and widowed showed a higher probability of cognitive impairment. It was not associated with use of alcohol, cigarette smoking or under nutrition.
Conclusion: Knowing the prevalence rate of cognitive impairment in elderly, together with the associated factors may inform policy makers and aid in designing better geriatric friendly health services. When planning elderly health services priority should be given to the elderly who are old-old, widowed and those who are illiterate.

Keywords: Cognitive impairment, elderly, Mini mental state examination


How to cite this article:
Sharma D, Mazta SR, Parashar A. Prevalence of cognitive impairment and related factors among elderly: A population-based study. J NTR Univ Health Sci 2013;2:171-6

How to cite this URL:
Sharma D, Mazta SR, Parashar A. Prevalence of cognitive impairment and related factors among elderly: A population-based study. J NTR Univ Health Sci [serial online] 2013 [cited 2019 Dec 13];2:171-6. Available from: http://www.jdrntruhs.org/text.asp?2013/2/3/171/117182


  Introduction Top


The current census (2011) has highlighted that the India age structure is undergoing rapid changes. India's elderly population has already crossed 100-million mark during 2011. It is expected that this number will increase to more than 300 million by 2050. [1] The dramatic aging of the Indian population will result in substantially increased numbers of elderly individuals suffering from cognitive impairment. There is a need to strengthen geriatric care services in the existing public health system so that the increasing care demands of the elderly can be met.

Cognition is a combination of skills that include attention, learning, memory, language, visuospatial skills, and executive function, such as decision making, goal setting, planning, and judgment. Older adults are the population most at risk for cognitive impairment. [2] Establishing an early diagnosis enables elderly and their family members adjust to the diagnosis and prepare for the future in an appropriate way. [3],[4] Mini Mental State Examination (MMSE) is one of the oldest and most widely used to study cognitive measures. [5] Although there is growing literature on research on elderly, very few studies have focused on this growing public health concern in elderly as a study outcome in this part of the country. With this background, a community-based descriptive cross-sectional study was conducted among older persons aged 60 years and above.

Objectives

  1. To determine the prevalence of cognitive impairment among elderly.
  2. To describe the pattern of cognitive impairment in rural and urban elderly population.
  3. To investigate the influence of socio-demographic and other variables on cognitive impairment.



  Materials and Methods Top


This was a descriptive cross-sectional study conducted among rural and urban elderly population aged 60 years and above residing in Shimla hills located in North India. The study was done over a period of 7 months (January-July 2010). The sample size required for a valid statistical analysis was calculated assuming that 5% of the elderly population were suffering from cognitive impairment (results from a pilot study carried out in the study area). Considering confidence limits of 3%, confidence level of 95%, a sample size of 200 individuals was worked out (200 study subjects in urban and 200 in rural area). The study sample was obtained by multistage simple random sampling. In urban area, five wards were selected out of the twenty five wards by simple random sampling. Forty older persons were selected from each ward. Similarly, in rural area five subcenter villages were selected out of the 15 subcenter villages. Forty older persons were selected from each subcenter village.Informed consent was sought from all the study participants. They were subjected to a pretested form for collecting socio-demographic and morbidity information. Subjects were asked whether they are current smokers or had ever smoked in the past. Participants were also asked whether there were current alcoholics or had consumed alcohol in the past. Anthropometry was used since it is an important method of assessing nutritional status in older persons. [6] Body weight was measured on the weighing scale and recorded to the nearest 0.5 kg. Height was measured to the nearest 0.5 cm. Body mass index (BMI) was calculated as: Weight (kg)/height (m) 2 and was classified according to WHO guidelines.

For evaluation of cognitive impairment, MMSE scale was administered to all the study subjects. Worldwide, the most widely accepted and frequently used cut-off score for the MMSE is 23, with scores of 23 or lower indicating the presence of cognitive impairment. [5] This study utilized similar standardized cut off for classifying an elderly suffering from cognitive impairment.

Statistical analysis was performed using Statistical Package for the Social Sciences (SPSS for Windows 17.0) software. MMSE score was presented as mean + SD and median. The rural/urban prevalence ratios (PR) of cognitive impairment were used to compare the prevalence in both settings. The association of socio-demographic variables with cognitive impairment was examined by logistic regression analysis from which the odds ratios and 95% confidence intervals were analyzed. In this study, age groups used were 60-64, 65-69, 70-74, 75-79, 80-84, 85 and above. The study was approved by the Ethics Committee, Indira Gandhi Medical College, Shimla.


  Results Top


The median age of sample subjects was 68.0 years (urban = 68, rural = 69), with age ranging from 60 to 90 years. Most of them were married (72.5%) and only one 27.5% were leading widowed life. Nearly half of the study subjects (49%) were literate. Significantly, illiteracy was more among the rural older persons (65.0%) as compared to urban elderly (34.5%). Among the study subjects, 18.8% were currently consuming alcohol or were ex-alcoholics and 35.3% were current or ex-smokers. Eighteen (4.5%) of the older subjects were found under nourished; of which 14 (7%) were from rural area and the remaining four (2%) from urban area. Most of them were suffering from multiple chronic illnesses (84%) with the most common among them being musculoskeletal problems followed by hypertension and dental problems.

Regarding MMSE scores, in both the urban and rural strata the mean score decreased with increasing age. Educated individuals had higher MMSE scores (both mean and median scores). Widowed elderly had lower MMSE score as compared to married individuals [Table 1]. Based on the screening criteria, 14 study subjects (3.5%) had cognitive impairment. Among them five were from urban area (1.3%) and the remaining nine (2.3%) from rural area. There was higher prevalence of cognitive impairment in rural compared to urban populations. (Prevalence ratio 1.8, 95% CI (0.6-5.7), although it was not found to be statistically significant. The median MMSE score was 25, with the score ranging from 18 to 29. Regarding cognitive impairment, a comparison of urban and rural strata showed that in both areas, it increased steeply with age (prevalence ratio = 5.4 and decreasing with education level (Prevalence ratio = 0.9). The only significant socio demographic variable in this strata comparison was marital status. Widowed elderly in urban settings (prevalence ratio = 10.8) were more cognitively impaired as compared to rural counterparts (prevalence ratio = 6.0) [Table 2].
Table 1: Distribution of Mmse Scores According to Socio-Demographic Variables

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Table 2: Prevalence of Cognitive Impairment in Rural and Urban Populations by Sociodemographic Characteristics

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Considering simultaneously the main effects of socio-demographic characteristics on overall cognitive impairment; logistic regression model showed that increasing age (odds ratio = 2.2-22.2), educational level (odds ratio = 6.0), and marital status (odds ratio = 7.8) significantly predict cognitive impairment in elderly. Cognitive impairment was not associated with use of alcohol, cigarette smoking, or under nutrition [Table 3].
Table 3: Distribution of Independent Variables According to Cognitive Impairment (Multivariate Logistic Regression)

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  Discussion Top


In our study, the prevalence of cognitive impairment was 3.5%. Studies done by Chandra et al.,[7] among North Indian elderly; and by Rajkumar [8],[9] Shaji [10] in South Indian elderly have documented nearly similar prevalence. Contrary to our finding a study done by Raina [11] in elderly Kashmiris, reported a slightly higher prevalence (6.5%). Studies conducted in other countries have reported much higher percent of cognitive impairment among elderly (7.5-22.5%). [12],[13],[14] This difference may be a function of a valid regional difference.

The prevalence of cognitive impairment was higher in rural than in urban population. Nunes B in his study among elderly in North Portugal has reported similar finding. [15] Jeffrey N in his study among elderly in Northern Costa Rica, observed area of residence affects cognitive impairment. [16] The difference could be attributed to the urban rural environmental difference. In our study the median age was more for rural elderly as compared to urban elderly.

Logistic regression showed that increasing age, widowhood, and illiteracy increased the risk of cognitive impairment. Our finding is in conformity with studies done across the globe (Yu et al.,[17] , Brayne [18] , O Connor [19] ). Black [20] and Gelder [21] observed that widowed elderly have more severe cognitive impairment as compared to married elderly. Contrary to our finding, Launer [22] observed that education did not affect MMSE performance. It is reasoned that, with increasing age, all humans will develop some degree of decline in cognitive decline. As one gets older, there is a drop in regional brain volume, loss of myelin integrity, cortical thinning, and impaired secretion of neurotransmitters like serotonin, acetylcholine lead to cognitive impairment. Cumulatively these changes give rise to a variety of symptoms associated with aging, such as decreased ability to maintain focus and decreased recalling capability. Also, lower educational attainment has been linked to subsequent development of dementia. More years of education translates into greater cognitive reserve. [23],[24] Pederson observed that the association between education and MMSE performance predominantly reflects genetically mediated cerebral capacity. [25] We did not observe any statistically significant association of gender, residence, income, and morbidity with cognitive decline. Similar to our finding, Tombaugh et al., [26] and Black [20] in their studies among elderly concluded that there is negligible gender and residence differences with regards to MMSE score. Contrary to our finding, Excel [27] in his study observed that women have a better cognitive function than men. Jose [28] reported that cognitive impairment was associated with an increase of morbidity. Also, we found that smoking and alcohol consumption are not a risk factor for cognitive impairment. Similar finding has been reported in study done by Candy WY in Canada elderly population. [29] Contrary to our finding Cervilla JA, reported smoking as a risk factor for incident cognitive impairment. [30] We found no relationship of under nutrition with cognitive impairment. Contrary to our finding, a study done by Goodwin reported that nutritional risk is associated with cognitive impairment in the elderly. [31] The strength of this study is that it is a community-based study with an acceptable sample size. The primary limitation is the cross-sectional study design of the study. The other limitation is that we could not examine the relation of cognitive impairment with food iodine intake. Such an association with cognitive function need to be investigated in future studies.


  Conclusions Top


The findings of this study have implications for societies that are aging. It is important to assess older people for any cognitive impairment. When planning geriatric health care for elderly, priority must be given to older-older, widowed, and illiterate elderly, as they are more vulnerable to impaired cognitive function. Establishing an early diagnosis enables elderly and their family members adjust to the diagnosis and prepare for the future in an appropriate way. Studying associated factors will also guide physicians in developing their judgement and thus improving elderly patient care.

 
  References Top

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    Tables

  [Table 1], [Table 2], [Table 3]


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