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Indian J of Community medicine. 2021 Apr-Jun; 46(2): 236–240.
Published online 2021 May 29. doi: 10.4103/ijcm.IJCM_475_20
PMCID: PMC8281830
PMID: 34321733
Prevalence and Determinants of Cognitive Impairment
and Depression among the Elderly Population in a
Rural Area of North India
Rashmi Kumari, Bhavna Langer, Rajiv Kumar Gupta, Rakesh Bahl, Najma
Akhtar, and Heena Nazir
Author information Article notes Copyright and License information Disclaimer
Address for correspondence: Dr. Rashmi Kumari, Department of Community
Medicine, GMC, Jammu - 180 001, (J and K), India. E-mail: moc.oohay@uliak.imhsar
Copyright : © 2021 Indian Journal of Community Medicine
This is an open access journal, and articles are distributed under the terms of the
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Abstract
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INTRODUCTION
The world is aging rapidly through “demographic transition” and is about to enter a
new paradigm where older people will outnumber the youngsters. This shift is
getting reflected in epidemiological transition in the form of increasing burden of
degenerative diseases. Common among them are neurodegenerative diseases, with
cognitive impairment (CI) and depression being the most common. As per
definition, CI is a transitional stage between normal aging and dementia and it
reflects the clinical situation where a person has memory complaint and objective
evidence of CI but no evidence of dementia.[1] A large proportion of people with
cognitive disability live in low- or middle-income countries (60% in 2001,
estimated to rise to 71% by 2040); it is estimated that the rate of increase over the
decades is only 100% for high-income countries, whereas it is around 300% for
India.[2]
Late-life depression occurring in patients over the age of 60 is another serious
illness of concern. Geriatric depression is considered as both, a disease and risk
factor of other diseases. It is mainly responsible for cognitive dysfunction,
dementia, impaired functional activities of daily living, and quality of life.
Till date, treatment for CI is not available. Therefore, preventive measures taken at
the appropriate time can only help in reducing the burden of disease. Considering
the usefulness of early screening in the elderly population in extending appropriate
care to those at risk, the present study was planned. There is a dearth of literature
related to CI and depression in India at present, especially in North India and more
so in rural areas. Hence, this study was conducted with the objectives to estimate
the prevalence of CI and depression in this population and to identify various
factors associated with them.
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MATERIALS AND METHODS
Setting and study design
This cross-sectional study was conducted in a rural health block, attached as field
practice area to the Postgraduate Department of Community Medicine of a tertiary
care institute in North India. The study was conducted after obtaining permission
from the Institutional Ethical Committee.
Sample size calculation
The sample size for the current study was calculated using the formula N = 4PQ/
L2 at 95% confidence interval, with an allowable error of 20%. Taking the
prevalence of CI and geriatric depression to be 20% each, the sample size was
calculated as 400. Considering the nonresponse rate to be 10%, the final sample
size arrived at 440.
Sampling technique
For the purpose of providing efficient health-care services, the block has been
divided into eight zones. Out of them, one of the zones was selected by a simple
random sampling technique. A list of all the villages falling in that zone was
procured from the health centers, before the start of the study. First, village was
selected by simple random sampling and all the houses falling in that village were
surveyed by the house-to-house visit to collect the data from the elderly
population. Then next adjoining villages were included till the required sample size
was reached.
Data collection
Before the start of the study, the local community leaders of the respective areas
were approached and sensitized about the purpose of the study. All the elderly in
selected areas constituted the sampling frame. Data collection was done by the
house-to-house visit. On reaching the house, after introducing oneself, the reason
of visit and purpose of the study was explained and then verbal informed consent
of the subject was sought. Those who replied in affirmation were included in the
study and those who replied in negative were again requested for participation and
if still not willing, were excluded. Privacy during the interview was ensured by
taking them in a separate room. All the elderly people (aged >60 years) residing in
the surveyed villages, who met the eligibility criteria and were willing to
participate in the study by giving verbal informed consent were interviewed.
Inclusion criteria
All the elderly people (aged >60 years), apparently healthy, who agreed to
participate in the study.
Exclusion criteria
All those elderly people (aged >60 years) who (a) were sick with any acute illness,
(b) had some known neurodegenerative disease or psychiatric condition, (c) were
taking psychotropic medication, (d) were having communication difficulties due to
reasons of hearing loss or language barrier and who did not give consent or were
not available even after visiting the household twice.
Study tool
The questionnaire used for the study purpose comprised of three parts: (a)
sociodemographic details (b) scale for Mini-Mental State Examination (MMSE)[3]
and (c) Geriatric Depression Scale (GDS).[4] The sociodemographic details
included age, gender, literacy status, socioeconomic status, marital status, type of
family, memory complaints, family history of dementia, and any chronic
comorbidity. Modified Uday–Pareek Scale was employed to assess their
socioeconomic status.
Mini-Mental State Examination
Assessment of cognitive function was done by applying the standardized Mini-
Mental State Examination (MMSE) of Folstein. MMSE score ranges from 0 to 30,
with lower scores indicating increasing severity of CIs in the domains of
orientation, memory, attention, and executive functions. CI was classified as
follows: scores between 24 and 30 indicate no CI, 18–23 as mild CI, and 0–17 as
severe CI.
Geriatric Depression Scale
Depression was assessed with the Geriatric Depression Scale (GDS), a 15-point
scale questionnaire, specifically developed for screening depressive symptoms in
elderly populations. Subjects scoring >5 were defined as having depressive
symptoms. Further, a score ranging between 6 and 10 indicates mild depression
and a score of 11–15 as severe depression.
Statistical analysis
Data thus collected was compiled and analyzed using PSPP (Free open access
software). The prevalence of CI and depression was calculated in percentages (%).
Univariate analysis was done by employing the Chi-square test to find the
statistical significance of the association of different variables with CI and geriatric
depression. Correlation between CI and geriatric depression was calculated using
Pearson's correlation coefficient. To determine the independent association of
variables, those variables which were found to be significant on univariate analysis
were entered into the logistic regression model. A value of P < 0.05 was taken as
statistically significant.
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RESULTS
A total of 443 elderly were interviewed, out of which 18 subjects did not give
answers to all the questions. Hence, after excluding the incompletely filled
questionnaires, the final analysis was done on 425 subjects. The mean age of the
study population was 67.47 ± 6.43 years. Females constituted 56.47% of the study
subjects. About 47.29% of the study population was illiterate and only 2.59% of
the subjects were educated beyond the higher secondary level. A larger proportion
of participants belonged to the middle class of socioeconomic status (86.36%). The
prevalence of CI was found to be 36% (153/425) and geriatric depression 29.1%
(124/425). However, the coexistence of both the disorders was seen in 16%
(68/425) of the subjects. The distribution and prevalence of CI and geriatric
depression among participants according to sociodemographic variables is detailed
in Table 1.
Table 1
Association of cognitive impairment and geriatric depression with
sociodemographic variables (n=425)
Variables Frequency, n (%) Cognive impairment, n (%) Geriatric depression, n (%)
P
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Variables Frequency, n (%) Cognive impairment, n (%) Geriatric depression, n (%)
P
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,'
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P
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P
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Variables Frequency, n (%) Cognive impairment, n (%) Geriatric depression, n (%)
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Variables Frequency, n (%) Cognive impairment, n (%) Geriatric depression, n (%)
(1&&1$"
4
2
P
5&&/&'/
# P<0.05 taken as significant, *Single includes unmarried, widow, divorcee, **For analysis
purpose, socioeconomic status has been grouped into three classes
Variables which had a statistically significant association with CI were literacy,
marital status, type of family, presence of memory complaints, any chronic
comorbidity, and presence of any stress in the family (P < 0.05). At the same time,
geriatric depression was significantly associated with age, literacy, socioeconomic
status, marital status, presence of memory complaints, any chronic comorbidity,
and presence of any stress in the family (P < 0.05) [Table 1]. However, gender and
family history of dementia had shown no statistically significant association with
either of these disorders (P > 0.05).
CI and geriatric depression strongly correlated with each other (r = −0.252, P <
0.001). The distribution of the study population according to the severity of CI and
geriatric depression is shown in Figures Figures11 and and22.
Tables Tables22 and and33 depict multivariate analysis of CI and geriatric
depression, respectively. Variables which were found to have independent
significant association with CI were literacy, memory complaints, and geriatric
depression. For geriatric depression, independent association was seen for literacy,
socioeconomic status, memory complaints, stress in the family, and presence of CI.
Table 2
Logistic regression analysis of cognitive impairment
Variable BSE P
value
Exp(B) 95% CI for Exp(B)
7
% &$ 7
," 7
011$"
,$ $"&
%&1$" 7
Variable BSE P
value
Exp(B) 95% CI for Exp(B)
,*'
8 '& 7
9&&
# P<0.05 taken as significant. CI: Confidence interval, SE: Standard error
Table 3
Logistic regression analysis of geriatric depression
Variable BSE P
value
Exp(B) 95% CI for Exp(B)
7
% &$
Variable BSE P
value
Exp(B) 95% CI for Exp(B)
," 7
011$" 7
,$ $"& 7
%&1$" 7
,*'
9&:+$$& 7
9&&
# P<0.05 taken as significant. CI: Confidence interval, SE: Standard error
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DISCUSSION
The prevalence of CI in the current study was found to be 36%. A slightly lesser
prevalence of 31% was reported in a study conducted by Kumar and Sudhakar in
South India.[5] However, a quite lesser prevalence of 10%, 8.8%, and 11.5% was
reported by Konda et al.,[6] Sengupta et al.[7] and Shaji et al.[8] et al.,
respectively, in their studies. Few studies conducted outside India also reported a
comparable prevalence rate of CI, i.e., 30% in China[9] and 33% in Australia.[10]
A systematic review of literature from Europe revealed the prevalence to range
from 8% to 34%, but these data were inclusive of diverse ethnic population.[11]
Literacy, marital status, type of family, presence of memory complaints, any
chronic co-morbidity, and presence of any stress in the family were found to be
significantly associated with CI on univariate analysis. However, the variables
which had an independent association with CI on multivariate analysis were
literacy, presence of memory complaints, and depression. Konda et al. reported
higher age, illiteracy, and bed ridden for the past 6 months as the independent
correlates of CI.[6] Few other studies also have reported illiteracy or no formal
education to be associated with CI and increased risk of dementia.[12,13] In a
similar vein, Kumar and Sudhakar in their study concluded that age, gender,
literacy, and economic status to be significant factors associated with CI.[5]
A study from Mexico revealed that as the cognitive status of the elderly individual
is impaired, depression levels also increase.[13] Thus, early detection of depressive
symptoms in elderly people with CI is crucial to take preventive and early
rehabilitative measures.
The prevalence of depression in our study population was found to be 29.1%.
Majority of respondents had mild type of depression. Surprisingly, a study
conducted in Mexico also reported a similar rate of prevalence of depression
(29.1%).[13] The results are also congruent with a study conducted by Sangma et
al.[14] Higher prevalence rates of 35.5%, 42.7%, and 44.8% were reported by
others.[5,15,16] A slightly lesser prevalence of 23% was reported in a study
conducted among the South Indian geriatric urban population by Konda PR et al.
[17] The use of different instruments to measure the geriatric depression is likely to
be one of the reasons for variation in prevalence rates of depression from country
to country and even among different regions within a single country.
On multivariate analysis, various independent risk factors found to be associated
with geriatric depression were literacy, socioeconomic status, presence of memory
complaints, stress in the family, and CI. The association between illiteracy and
depression was also reported by various other authors.[18,19] Variables such as
female gender, nuclear family, being widowed, unemployed status, low
socioeconomic status, and illness of self/family members were reported to be
significantly associated with depression by Bhuvneshkumar et al.[16] In a meta-
analysis by Cole and Dendukuri, using multivariate techniques, risk factors
identified for depression were medical illness, poor health status, and bereavement.
[20]
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CONCLUSIONS
Mental health problems, especially CI and geriatric depression are the leading
health problems, putting a lot of disease burden for the society and health-care
delivery system, in the developing countries, most of which are ill-prepared for
fulfilling such demands. Hence, strengthening of the existing health-care system,
especially geriatric health services is the need of the hour.
Limitations
The cross-sectional design of the study limits our capability to find out the
temporal associations.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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