Prevalence, distribution, and impact of mild cognitive impairment in Latin America, China, and India: a 10/66 population-based study.
ABSTRACT Rapid demographic ageing is a growing public health issue in many low- and middle-income countries (LAMICs). Mild cognitive impairment (MCI) is a construct frequently used to define groups of people who may be at risk of developing dementia, crucial for targeting preventative interventions. However, little is known about the prevalence or impact of MCI in LAMIC settings.
Data were analysed from cross-sectional surveys established by the 10/66 Dementia Research Group and carried out in Cuba, Dominican Republic, Peru, Mexico, Venezuela, Puerto Rico, China, and India on 15,376 individuals aged 65+ without dementia. Standardised assessments of mental and physical health, and cognitive function were carried out including informant interviews. An algorithm was developed to define Mayo Clinic amnestic MCI (aMCI). Disability (12-item World Health Organization disability assessment schedule [WHODAS]) and informant-reported neuropsychiatric symptoms (neuropsychiatric inventory [NPI-Q]) were measured. After adjustment, aMCI was associated with disability, anxiety, apathy, and irritability (but not depression); between-country heterogeneity in these associations was only significant for disability. The crude prevalence of aMCI ranged from 0.8% in China to 4.3% in India. Country differences changed little (range 0.6%-4.6%) after standardization for age, gender, and education level. In pooled estimates, aMCI was modestly associated with male gender and fewer assets but was not associated with age or education. There was no significant between-country variation in these demographic associations.
An algorithm-derived diagnosis of aMCI showed few sociodemographic associations but was consistently associated with higher disability and neuropsychiatric symptoms in addition to showing substantial variation in prevalence across LAMIC populations. Longitudinal data are needed to confirm findings-in particular, to investigate the predictive validity of aMCI in these settings and risk/protective factors for progression to dementia; however, the large number affected has important implications in these rapidly ageing settings.
- [Show abstract] [Hide abstract]
ABSTRACT: Cognitive decline and dementia are an important problem affecting quality-of-life in elderly and their caregivers. There is regional variation in prevalence of cognitive decline as well as risk factors from region to region. The aim was to determine the prevalence of dementia and cognitive decline and its various risk factors in the elderly population of more than 60 years in Eastern Uttar Pradesh (India). A camp-based study was conducted on rural population of Chiraigaon block of Varanasi district from February 2007 to May 2007. Block has 80 villages, of which 11 villages were randomly selected. Eleven camps were organized for elderly people in 11 randomly selected villages on predetermined dates. A total of 728 elderly persons of age >60 years were examined, interviewed and data thus collected was analyzed. Elderly who got Hindi-mini-mental state examination (HMSE) score developed by Ganguli based on the Indo-US Cross-National Dementia Epidemiology Study) score ≤23 were evaluated further and in those with confirmed cognitive and functional impairment, diagnosis of dementia was assigned according to Diagnostic and Statistical Manual for Mental Disorder fourth edition criteria after ruling out any psychiatric illness or delirium. Based on International Classification of Diseases-10 diagnostic criteria sub-categorization of dementia was done. Mean, median and 10(th) percentile of HMSE of the study population were 23.4, 24 and 17, respectively. About 14.6% elderly had scored <17. 42.9% of rural elderly population had HMSE score <23, 70.6% <27 and 27.7% between 23 and 27. Literate people had statistically significant higher mean HMSE score (26.1 ± 3.9) than illiterate people (22.9 ± 4.9). Other risk factors were female gender, malnutrition, and obesity. Prevalence of dementia was 2.74%; in male 2.70% and in female 2.80%. Most common type of dementia was Alzheimer (male 1.5%, female 1.5%) followed by vascular (male 1.2%, female 0.6%) and others 0.6% (male 0%, female 0.6%). Study showed that a very high percentage of rural elderly attending health camps had poor cognitive function score; though the prevalence of dementia was relatively low. Alzheimer dementia was most common, followed by vascular dementia, which was predominant in males. Illiteracy, age, and under-nutrition were the most important risk factors for poor cognitive function. Our study suggest that cut-off of HMSE score should be 17 (10(th) percentile) for illiterate population.Indian journal of psychiatry. 10/2014; 56(4):365-70.
- [Show abstract] [Hide abstract]
ABSTRACT: Data on the prevalence of and risk factors for suicide ideation among older people in developing countries is lacking.Objective This study aimed to estimate if dementia and other mental disorders are associated with suicide ideation among the older people controlling for demographic and other suspected risk factors.Methods We report on the Mexican study of dementia, part of the 10/66 international dementia research group, a series of cross-sectional population-based surveys in low and middle income countries. A survey was conducted to all residents aged 65 years and older from urban and rural catchment areas in Mexico City and Morelos (January 2006 to June 2007).ResultsAfter 18 months of field work, a total of 2003 completed interviews were obtained, with a response rate of 85.1%. We found a lifetime prevalence of suicide ideation of 13.5% and a 2-week prevalence of 4.2%. The common factors associated with both lifetime and 2-week prevalence were having a large number of physical disorders (lifetime prevalence ratio = PR and 95% confidence interval = CI; PR = 2.23, CI = 1.63–3.06), depression (PR = 1.92, CI = 1.36–2.70) and anxiety (PR = 2.23, CI = 1.68–2.97) and screening positive for psychosis (PR = 1.64, CI = 1.15–2.34).Conclusion Dementia plays a minor role on suicide ideation after the other aforementioned variables were taken into account and its effect, if any, could be concentrated among those elders with lower severity scores of dementia. These results show the great challenges that Mexico faces in providing services for the older people with suicidality. As the population in the country ages, suicidality will constitute an additional challenge to the healthcare system. Copyright © 2014 John Wiley & Sons, Ltd.International Journal of Geriatric Psychiatry 05/2014; 30(3). · 3.09 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: In India, “non-notified” slums are not officially recognized by city governments; they suffer from insecure tenure and poorer access to basic services than “notified” (government-recognized) slums. We conducted a study in a non-notified slum of about 12,000 people in Mumbai to determine the prevalence of individuals at high risk for having a common mental disorder (i.e., depression and anxiety), to ascertain the impact of mental health on the burden of functional impairment, and to assess the influence of the slum environment on mental health. We gathered qualitative data (six focus group discussions and 40 individual interviews in July-November 2011), with purposively sampled participants, and quantitative data (521 structured surveys in February 2012), with respondents selected using community-level random sampling. For the surveys, we administered the General Health Questionnaire-12 (GHQ) to screen for common mental disorders (CMDs), the WHO Disability Assessment Schedule 2.0 (WHO DAS) to screen for functional impairment, and a slum adversity questionnaire, which we used to create a composite Slum Adversity Index (SAI) score. Twenty-three percent of individuals have a GHQ score ≥5, suggesting they are at high risk for having a CMD. Psychological distress is a major contributor to the slum’s overall burden of functional impairment. In a multivariable logistic regression model, household income, poverty-related factors, and the SAI score all have strong independent associations with CMD risk. The qualitative findings suggest that non-notified status plays a central role in creating psychological distress—by creating and exacerbating deprivations that serve as sources of stress, by placing slum residents in an inherently antagonistic relationship with the government through the criminalization of basic needs, and by shaping a community identity built on a feeling of social exclusion from the rest of the city.Social Science [?] Medicine 10/2014; 119:155-169. · 2.56 Impact Factor
Prevalence, Distribution, and Impact of Mild Cognitive
Impairment in Latin America, China, and India: A 10/66
Ana Luisa Sosa1., Emiliano Albanese2., Blossom C. M. Stephan3, Michael Dewey4, Daisy Acosta5,
Cleusa P. Ferri4, Mariella Guerra6, Yueqin Huang7, K. S. Jacob8, Ivonne Z. Jime ´nez-Vela ´zquez9, Juan J.
Llibre Rodriguez10, Aquiles Salas11, Joseph Williams12, Isaac Acosta1, Maribella Gonza ´lez-Viruet13,
Milagros A. Guerra Hernandez14, Li Shuran7, Martin J. Prince4, Robert Stewart4*
1National Institute of Neurology and Neurosurgery, Autonomous National University of Mexico, Mexico City, Mexico, 2Laboratory of Epidemiology, Demography and
Biometry, National Institute on Aging, Bethesda, Maryland, United States of America, 3University of Cambridge, Cambridge, United Kingdom, 4King’s College London
(Institute of Psychiatry), London, United Kingdom, 5Universidad Nacional Pedro Henriquez Uren ˜a (UNPHU), Internal Medicine Department, Geriatric Section, Santo
Domingo, Dominican Republic, 6Universidad Peruana Cayetano Heredia, Instituto de la Memoria y Desordenes Relacionados, Peru, 7Peking University, Institute of Mental
Health, Beijing, China, 8Christian Medical College, Vellore, India, 9UPR, School of Medicine, San Juan, Puerto Rico, 10Medical University of Havana, Cuba, 11Medicine
Department, Caracas University Hospital, Faculty of Medicine, Universidad Central de Venezuela, Caracas, Venezuela, 12Institute of Community Health, Voluntary Health
Services, Chennai, India, 13Psy D Program Carlos Albizu University, San Juan, Puerto Rico, 14Policlinico Universitario 27 de Noviembre, Havana, Cuba
Background: Rapid demographic ageing is a growing public health issue in many low- and middle-income countries
(LAMICs). Mild cognitive impairment (MCI) is a construct frequently used to define groups of people who may be at risk of
developing dementia, crucial for targeting preventative interventions. However, little is known about the prevalence or
impact of MCI in LAMIC settings.
Methods and Findings: Data were analysed from cross-sectional surveys established by the 10/66 Dementia Research
Group and carried out in Cuba, Dominican Republic, Peru, Mexico, Venezuela, Puerto Rico, China, and India on 15,376
individuals aged 65+ without dementia. Standardised assessments of mental and physical health, and cognitive function
were carried out including informant interviews. An algorithm was developed to define Mayo Clinic amnestic MCI (aMCI).
Disability (12-item World Health Organization disability assessment schedule [WHODAS]) and informant-reported
neuropsychiatric symptoms (neuropsychiatric inventory [NPI-Q]) were measured. After adjustment, aMCI was associated
with disability, anxiety, apathy, and irritability (but not depression); between-country heterogeneity in these associations
was only significant for disability. The crude prevalence of aMCI ranged from 0.8% in China to 4.3% in India. Country
differences changed little (range 0.6%–4.6%) after standardization for age, gender, and education level. In pooled estimates,
aMCI was modestly associated with male gender and fewer assets but was not associated with age or education. There was
no significant between-country variation in these demographic associations.
Conclusions: An algorithm-derived diagnosis of aMCI showed few sociodemographic associations but was consistently
associated with higher disability and neuropsychiatric symptoms in addition to showing substantial variation in prevalence
across LAMIC populations. Longitudinal data are needed to confirm findings—in particular, to investigate the predictive
validity of aMCI in these settings and risk/protective factors for progression to dementia; however, the large number
affected has important implications in these rapidly ageing settings.
Please see later in the article for the Editors’ Summary.
PLoS Medicine | www.plosmedicine.org1February 2012 | Volume 9 | Issue 2 | e1001170
Citation: Sosa AL, Albanese E, Stephan BCM, Dewey M, Acosta D, et al. (2012) Prevalence, Distribution, and Impact of Mild Cognitive Impairment in Latin America,
China, and India: A 10/66 Population-Based Study. PLoS Med 9(2): e1001170. doi:10.1371/journal.pmed.1001170
Academic Editor: Sam Gandy, Mount Sinai School of Medicine, United States of America
Received September 15, 2010; Accepted December 20, 2011; Published February 7, 2012
Copyright: ? 2012 Sosa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The 10/66 Dementia Research Group population based surveys were supported by: the Wellcome Trust (UK) (GR066133); the World Health
Organization; the US Alzheimer’s Association (IIRG – 04 – 1286); and the Fondo Nacional de Ciencia Y Tecnologia, Consejo de Desarrollo Cientifico Y Humanistico,
Universidad Central de Venezuela (Venezuela). RS is funded by the NIHR Specialist Biomedical Research Centre for Mental Health at the South London and
Maudsley NHS Foundation Trust and Institute of Psychiatry, King’s College London. The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
Competing Interests: The 10/66 Dementia Research Group (DRG) works closely with Alzheimer’s Disease International (ADI), the non-profit federation of 77
Alzheimer associations around the world. ADI is committed to strengthening Alzheimer associations worldwide, raising awareness regarding dementia and
Alzheimer’s disease, and advocating for more and better services for people with dementia and their caregivers. ADI is supported in part by grants from
GlaxoSmithKline, Novartis, Lundbeck, Pfizer, and Eisai. Concerning the relationship with ADI, the 10/66 Dementia Research Group is an autonomous research
network administered from the Institute of Psychiatry, King’s College London. Its relationship with Alzheimer’s Disease International is primarily around research
dissemination; the 10/66 project website is hosted on the ADI server, and the cost of developing the site was met by ADI. 10/66 routinely makes a report of
ongoing projects to the ADI Council, and have provided training at ADI’s Alzheimer Universities. 10/66 have not received funding from ADI to conduct research,
and ADI has no influence upon or input into 10/66 published research outputs. Martin Prince (but not the 10/66 DRG per se), through IoP/ KCL has received three
small grants from ADI to fund researchers based at IoP/ KCL to work on the 2009, 2010, and 2011 World Alzheimer reports (not part of the present study). MD is a
paid statistical reviewer for PLoS Medicine. All other authors have declared that no competing interests exist.
Abbreviations: aMCI, amnestic mild cognitive impairment; CERAD, Consortium to Establish a Registry for Alzheimer’s Disease; CSI ‘‘D’’, Community Screening
Instrument for Dementia; GMS, Geriatric Mental State; LAMIC, low- and middle-income country; MCI, mild cognitive impairment; NPI-Q, neuropsychiatric
inventory; PR, prevalence ratio; SD, standard deviation; WHODAS-12, 12-item World Health Organization disability assessment schedule; ZINB, zero-inflated
negative binomial regression
* E-mail: firstname.lastname@example.org
. These authors contributed equally to this work.
Mild Cognitive Impairment in LAMIC Settings
PLoS Medicine | www.plosmedicine.org2February 2012 | Volume 9 | Issue 2 | e1001170
Ageing  and the health transition in low- and middle-income
countries (LAMICs) are responsible for an unprecedented increase
in the prevalence and societal impact of noncommunicable
diseases, including dementia . Large numbers of people with
dementia currently live in LAMICs [3,4] with prevalence
estimates comparable to those of the Western world . At
present, disease-modifying drugs are not available  and
symptomatic medications have been found to have only modest
benefit . Primary prevention of dementia is therefore of great
Mild cognitive impairment (MCI) is an intermediate state
between normal cognitive ageing and dementia . Identification
of MCI is thought to be crucial to early intervention. Indeed, in
some studies MCI is associated with an increased risk of dementia
, as well as with future disability  and mortality . Such
associations, however, do vary according to the nature of the
sample (clinical versus population-based), the case definition of
MCI applied, the assessment procedures used for operationalizing
component criteria [13–15], and, potentially, the cultural
background of participants [16,17]. A recent review also suggested
that MCI is associated with neuropsychiatric symptoms, cited as
being of potential importance for defining subgroups at higher risk
of developing dementia in the future .
In community-dwelling older adults the prevalence of amnestic
MCI (aMCI), defined according to Petersen’s revised criteria ,
ranges between 2.1%  and 11.5%  and is most commonly
found to be around 3%–5% [21–33] with few exceptions in older
samples [20,34–36]. Reports of the community prevalence of
aMCI have been predominantly derived from European and
North American populations. To our knowledge, very few
population-based studies have been published from LAMICs
and those from Asia are controversial. Specifically, estimates of
aMCI prevalence were similar to those found in Western countries
in Kolkata, India (6%)  and in Chongqing, China (4.5%) ,
but higher prevalences were reported by Lee and colleagues in
Malaysia (15.4%)  and by Kim et al. in South Korea (9.7%)
Estimating the population prevalence of MCI in LAMICs is a
public health priority as rapid demographic ageing is predicted to
result in a large majority of people residing in these regions being
at risk of dementia and cognitive decline. If so, this will have
significant implications with regard to social support and future
health care costs, especially as systems are not in place to cope with
increased neurodegenerative disease and health resources at
present are already extremely limited.
In this study, using data from the cross-sectional phase of the
10/66 Dementia Research Group (DRG) programme on
dementia, noncommunicable diseases and ageing in LAMICs
, we operationalized the Mayo Clinic–defined aMCI 
construct and then estimated the prevalence of this condition in
eight LAMICs, in addition to its sociodemographic correlates and
associations with disability and neuropsychiatric symptoms.
Written informed consent, or witnessed oral consent in case of
illiteracy, or next of kin written agreement in case of incapacity,
was obtained from all participants. The appropriate Research
Ethics Committees at King’s College London and at all local
countries approved the study protocol and the consent procedures.
The 10/66 study has been described previously . In brief,
the study consisted of a series of cross-sectional one-phase
geographic catchment area surveys, carried out in eight urban
and rural sites in Peru, Mexico, China, and India, and in three
urban sites in Cuba, the Dominican Republic, and Venezuela,
between January 2003 and November 2007. The target sample
size was 2,000 participants per country, in order to allow
estimation of a typical dementia prevalence of 4.5% (SE 0.9%)
with 80% power. All community-resident individuals aged 65+ y
were eligible for inclusion. Using a process of full household
enumeration, all residents aged 65+ y within catchment areas were
approached by means of door-knocking and a reliable informant
was required for inclusion. Being younger than 65 y was the only
exclusion criteria, and weighted sampling procedures were not
All participants completed the 10/66 standardized assessment at
their place of residence. This consisted of participant and
informant interviews and a physical examination, described in
full elsewhere in an open-access publication . Participant
interviews included questionnaire measures of sociodemographic
status, education and childhood environment, social networks and
support, self-report measures of common physical disorders, health
service use, and lifestyles (smoking, alcohol intake, diet, exercise),
in addition to a fully structured diagnostic interview for mental
disorder (Geriatric Mental State [GMS], described below).
Physical examinations included measures of resting blood
pressure, anthropometric measures, and a structured neurological
examination. A battery of cognitive assessments was administered
(described below) and an informant interview included structured
questionnaires on cognitive decline and neuropsychiatric symp-
toms (both described below), as well as questions on care
arrangements, caregiver strain and distress, financial implications
of caregiving, and support received. The 10/66 study protocol was
translated into Spanish, Tamil, and Mandarin, and minor
adaptations were made by local clinicians fluent in English.
Validation statistics for the assessments and procedures have been
published . The protocol included the GMS Examination
[42,43], an informant interview on all participants, a neurological
examination, and a neuropsychological battery that comprised the
(1) The participant interview section of the Community
Screening Instrument for Dementia (CSI ‘‘D’’) . This was
developed as a screening instrument for dementia for use in cross-
cultural settings in combination with the informant interview. The
cognitive assessment covers multiple domains, including orienta-
tion to time and place, language, memory, praxis, and abstract
thinking. It deliberately excludes literacy-dependent items. A
memory subscale was derived from the CSI ‘‘D’’ using the items
addressing immediate and delayed recall of a three word list, recall
of the name of the interviewer, and recall of five elements of a
short story (logical memory). (2) The Modified Consortium to
Establish a Registry for Alzheimer’s Disease (CERAD) ten-word-
list learning task . Six words: butter, arm, letter, queen, ticket,
and grass were taken from the original CERAD battery English
language list. Pole, shore, cabin, and engine were replaced with
corner, stone, book, and stick, which were deemed more culturally
appropriate for all sites in the 10/66 pilot phase (a wider sample
that included the survey sites). In the learning phase, the list is read
to the participant. Next, the participant is asked to immediately
recall the words that they remember. This process is repeated
Mild Cognitive Impairment in LAMIC Settings
PLoS Medicine | www.plosmedicine.org3February 2012 | Volume 9 | Issue 2 | e1001170
three times, giving an immediate word list memory score, with a
maximum total of 30. After a 5-min delay, the participant is again
asked to recall the ten words with encouragement but no cues,
giving a word list delayed recall score with a maximum total score
Demographic correlates analyzed against aMCI were age,
gender, education, and number of assets. Participants’ gender and
stated age were recorded. Age was confirmed by the interviewer
from official documentation and informant report, and any
discrepancies resolved through further questions and clarification
and, ultimately, by consensus within the research team. Illiteracy
(inability to read and/or write), level of education (none/did not
complete primary/completed primary/secondary/tertiary), and
number of household assets (car, television, refrigerator, telephone,
plumbed toilet, water, and electricity mains) were also recorded.
The impact of aMCI was quantified through investigating
associations with disability and neuropsychiatric symptoms.
Participant interviews included the 12-item WHO disability
assessment schedule (WHODAS-12) , which assesses five
activity-limitation domains (communication, physical mobility,
self-care, interpersonal interaction, life activities and social
participation). Two questions with scores ranging from 0 (no
difficulty) to 4 (extreme difficulty) cover each domain, and the
global standardized score ranges from 0 (not disabled) to 100
(maximum disability). Details on the WHODAS 2.0 validity and
psychometric properties can be found elsewhere [47,48]. The
informant interview, as well as administering structured CSI ‘‘D’’
questions (regarding decline in memory or intelligence, activities of
daily living, social and occupational functioning used for dementia
diagnoses—summarized below and applied as an exclusion
criteria), also included the neuropsychiatric inventory (NPI-Q)
, and the following binary symptom categories were selected
for analyses of associations with aMCI: depression, anxiety,
For analyses of associations of aMCI with disability and
neuropsychiatric symptoms, the following covariates available in
the dataset were used for adjusted models in addition to the four
sociodemographic variables described above: depression (GMS),
self-reported limiting physical impairments (arthritis, visual
difficulties, hearing difficulties, respiratory disorders, heart prob-
lems, gastrointestinal problems, fainting episodes, limb paralysis,
skin disorders), self-reported hypertension, self-reported stroke,
psychotic disorder (GMS), self-reported regular pain.
Case Definition of aMCI
Mayo Clinic–defined aMCI was diagnosed on the basis of the
following criteria: (1) objective memory impairment beyond that
expected for age; (2) subjective memory complaint; (3) no, or only
mild impairment in core activities of daily living, and (4) no
dementia. Each criterion was operationalized as follows.
Objective memory impairment.
score was created using results from the memory subscale of the
CSI ‘‘D’’ , immediate and delayed word recall scores from the
modified CERAD ten-word list . For all tasks impaired
performance was defined as a score 1.5 standard deviation (SD) or
more below the mean adjusted for age and education. The 1.5-SD
definition stems from that applied to define ‘‘abnormal memory
performance’’ by Peterson et al. in 1999 , and has been recently
recommended also by a National Institute on Aging-Alzheimer’s
Association workgroup . Operationalization of MCI in other
population-based studies has consistently followed this definition
[25,33,52,53], which has also been used to define other constructs
such as ‘‘Cognitive Impairment No Dementia’’ . The CERAD
word list has been used in previous research . Although, there
A composite memory
have been controversies surrounding the MCI entity itself [55–58],
they have not to our knowledge focused on the 1.5-SD threshold.
Norms were derived from controls without dementia from the 24-
centre 10/66 pilot study, which had found minimal geographic
variation . Participants were excluded if hearing impairment
had prevented cognitive assessment.
Subjective memory impairment.
from 0 to 6 was created by summing item scores from relevant
questions in the GMS including: (1) Have you had any difficulty
with your memory (0, no; 1, yes)? (2) Have you tended to forget
names of your family or close friends/where you have put things
(for each question: 0, no/transient; 1, noticed most days per week;
2, noticed daily)? (3) Do you have to make more efforts to
remember things than you used to (0, no; 1, yes)? Using this scale,
subjective memory impairment was defined as present when an
individual scored three or more: the definition that has been used
in all previous research to use this scale [59,60].
Normal activities of daily living/instrumental activities
of daily living.
On the basis of responses from the CSI ‘‘D’’
informant interview, normal activities of daily living (ADL)/
instrumental activities of daily living (IADLs) were defined as very
mild or no impairment in either carrying out household chores,
pursuing hobbies, using money, feeding, dressing, or toileting. The
definition of impairment did not include problems arising only
from physical impairments.
Diagnoses of dementia were applied using the
10/66 dementia algorithm and Diagnostic and Statistical Manual
of Mental Disorders IV (DSM-IV) criteria . Participants
meeting either criterion were excluded from the analyzed sample
(both aMCI cases and controls).
An ordinal scale ranging
Analyses were carried out on the 10/66 data archive release 2.1.
All analyses used STATA version 10.1 . As mentioned above,
participants with dementia were excluded from all analyses as has
been standard practice in MCI epidemiological research. Sample
characteristics across countries were described including age,
gender, education, number of household assets, global disability
scores (WHODAS-12) , and NPI-Q symptoms .
In order to determine the potential impact of aMCI we assumed
that, while both activities of daily living (ADLs) and instrumental
activities of daily living (IADLs) would be expected to be intact in
people with aMCI, subtle functional impairment may already be
present as well as possibly nonspecific and mild behavioral and
psychological symptoms of dementia (BPSD) . Zero-inflated
negative binomial regression (ZINB) count models were used to
assess the association between aMCI and WHODAS-12 disability
and NPI-Q scores using identical models to those previously
reported for these samples . We used zero-inflated models to
deal with skewness in the distribution of the scores characterized
by excessive zeros (inflation). The model distinguishes a group
whose members have always zero counts (referred to as ‘‘certain
zero’’), from one in which members have either zero or positive
counts. ZINB includes a logistic part to model the probability that
a zero comes from the first group versus the second group and a
negative binomial part to model the counts within the second
group. Log-scale coefficients were exponentiated and 95%
confidence intervals back-transformed. We determined the
appropriateness of the ZINB model against a standard negative
binomial model using the Vuong test postestimation and adjusted
for the relevant covariates listed above, followed by Poisson
regression models to generate prevalence ratios for NPI-Q
symptoms as binary-dependent variables. ZINB models were
further compared to zero-inflated Poisson models and in every
Mild Cognitive Impairment in LAMIC Settings
PLoS Medicine | www.plosmedicine.org4February 2012 | Volume 9 | Issue 2 | e1001170
country the test of the dispersion parameter (labelled alpha in
Stata and theta by some other sources) was significant at the 0.001
level, indicating ZINB as more appropriate in all cases.
Behavioural/psychological outcomes, depression, anxiety, apathy,
irritability were modelled separately against aMCI as an
independent variable for illustrative purposes, with no attempt to
adjust given symptoms for the other three, accepting that these are
Prevalence of aMCI was reported for each country by age and
gender and adjusted for household clustering. Direct standardiza-
tion, using the whole sample as the reference population, was used
to compare prevalence estimates across countries after adjustment
for age, gender, and education. For each country associations with
age (continuous variable), gender, education (ordinal variable), and
number of household assets (ordinal variable) on aMCI prevalence
were calculated using mutually adjusted (as appropriate) preva-
lence ratios (PRs), with robust 95% confidence intervals (using the
‘‘robust’’ syntax in Stata to take into account household clustering:
model robust standard errors [64,65]), using Poisson working
To determine the pooled effects for all analyses, the statistical
outputs for each country were combined into fixed-effect meta-
analyses. Random effect models were not used as we wished to
summarise the countries within this study rather than generalise to
a hypothetical population of centres. We then calculated Cochrane
Q heterogeneity and Higgins’ I2(95% CIs). The latter statistics set
the degree of heterogeneity between studies that is not explained
by chance and is expressed as a percentage with values up to 25%,
50%, and over 75% representing mild, moderate, and high
heterogeneity, respectively .
The results were derived from a total of 15,376 participants
aged 65+ and without dementia across the different countries.
Response rates (i.e., participation rates for all potentially eligible
residents within the defined geographic catchments) were higher
than 80% in all countries. Missing data on the variables of interest
were present in less than 1% of the sample. Descriptive data by
country are displayed in Table 1. Age was not evenly distributed
across groups (65–69, 70–74, 75–79, and 80+ y) across countries,
the samples from Venezuela, China, and India being slightly
younger. In all countries more women participated than men.
Educational level was highest in Cuba, and the number of
household assets was lowest in Mexico and India.
In each country there was a statistically significant zero-inflation
in the distributions of WHODAS-12 scores (Vuong test for the
whole sample, z=45.29, p,0.001) that confirmed the better fit of
ZINB over negative binomial alone. Associations between aMCI,
disability, and neuropsychiatric symptoms are summarized in
Table 2 along with meta-analytical fixed-effect method-pooled
estimates, and between-country heterogeneity. After adjustment,
Table 1. Sociodemographic characteristics of participants by country.
RepublicPeruVenezuela MexicoChinaIndia Puerto Rico
Sample size (n)2,6201,7671,7671,8201,8212,0141,8021,765
Response rate (%) 94 958280 8583 83 93
Age, n (%) – MV70141040
65–69 y 738 (28.2)511 (28.9) 538 (30.5)813 (44.7) 537 (29.5)683 (33.9)703 (39.0) 398 (22.6)
70–74 y 739 (28.2)483 (27.3)475 (26.9)450 (24.7) 552 (30.3)634 (31.5)604 (33.5)439 (24.9)
75–79 y582 (22.2)345 (19.5)368 (20.8)320 (17.6) 384 (21.1)417 (20.7)290 (16.1)436 (24.7)
80+y 555 (21.2) 428 (24.2) 386 (21.8)236 (13.0) 348 (19.1)280 (13.9) 201 (11.2)492 (27.9)
Gender – MV020 3300 157
Females, n (%)1,686 (64.4)1,154 (65.3)1,073 (60.7)1,146 (63.0)1,143 (62.8)1,128 (56.0) 974 (54.0)1,183 (67.0)
Educational level, n (%) – MV8 1916 402020
No education54 (2.1) 314 (17.8)103 (5.8)133 (7.3) 459 (25.2)743 (36.9)935 (51.9)47 (2.7)
Some education 548 (20.9)916 (51.8)212 (12.0) 408 (22.4) 802 (44.0)246 (12.2)411 (22.8)313 (17.7)
Complete primary 864 (33.0) 338 (19.1)654 (37.0) 913 (50.2)337 (18.5) 532 (26.4)301 (16.7)356 (20.2)
Complete secondary 681 (26.0)126 (7.1) 486 (27.5)262 (14.4)117 (6.4) 358 (17.8)110 (6.1) 661 (37.5)
Complete tertiary 468 (17.9)66 (3.7) 301 (17.0)92 (5.1) 104 (5.7)135 (6.7) 43 (2.4)383 (21.7)
Three assets or fewer – MV85000140
n (%)67 (2.6) 256 (14.5) 83 (4.7)33 (1.8)373 (20.5) 104 (5.2)918 (51.0) 4 (0.2)
Neuropsychiatric symptoms, n (%) 41 20 11103 163 29 112
Depression117 (4.5) 220 (12.5) 86 (4.9)84 (4.6) 73 (4.0)3 (0.2)139 (7.7) 36 (2.0)
Anxiety158 (6.0)233 (13.2)199 (11.3) 263 (14.5)121 (6.6)7 (0.4)77 (4.3) 101 (5.7)
Apathy 117 (4.5)226 (12.8)93 (5.3)138 (7.7) 165 (9.1)15 (0.7)18 (1.0) 58 (3.5)
Irritability 583 (22.5) 412 (23.3)381 (21.6) 383 (21.3)434 (23.9) 26 (1.3)227 (12.6) 254 (15.2)
WHODAS-12 – MV 111512963 1249
Mean (SD) 9.69 (14.2)13.91 (17.3) 9.36 (14.3)9.18 (13.8) 8.59 (15.3) 5.30 (12.0)17.44 (17.2)12.13 (16.6)
Mean (SD) omitting zeros16.55 (15.2)21.11 (17.3) 15.91 (15.7)16.18 (14.8) 18.03 (17.9) 18.39 (16.1)22.19 (16.4) 21.33 (17.0)
MV, missing values; NPI-Q severity: total severity in neuro-psychiatric inventory.
Mild Cognitive Impairment in LAMIC Settings
PLoS Medicine | www.plosmedicine.org5February 2012 | Volume 9 | Issue 2 | e1001170
disability was significantly higher in aMCI cases compared to the
remainder in Peru, India, and Dominican Republic, although was
lower in China. The pooled fixed-effect model meta-analytical
estimate indicated a positive association with disability although
there was moderate to high heterogeneity in these associations
between countries. After adjustment aMCI cases were more likely
to have informant-rated anxiety, irritability, and apathy symp-
toms, with no significant between-country heterogeneity. Howev-
er, there was no overall association with informant-rated
depression in pooled estimates although the individual prevalence
ratio was significant in Peru.
The prevalence of aMCI ranged from 0.8% in China to 4.3% in
India, and changed very little after direct standardization for age,
gender, and education level, as displayed in Table 3. Adjusted PRs
(95% CI) from Poisson regression models for independent
associations with age, gender, education, and assets are shown in
Table 4. No pooled associations were found with age or education
but there was a modest association with male gender and fewer
assets. Overall little heterogeneity was found between nations in
Using data from a large series of cross-sectional surveys applying
standard sampling and measurements, we estimated the commu-
nity prevalence of Mayo Clinic–defined aMCI in six countries in
Latin America, China, and India. To our knowledge this is the first
study to attempt to make direct comparisons of prevalence
estimates of aMCI across diverse cultures and world regions.
Differences in prevalence between countries were marked and
ranged from 0.8% (China) to 4.3% (India), i.e., greater than five-
fold variation. After direct standardization for age, gender, and
education, using the whole population as the reference, these
differences were not markedly attenuated.
Inconsistencies in aMCI prevalence observed between the 10/
66 study centres are likely to be due to components of the aMCI
diagnosis itself. In a cross-cultural context, these support questions
previously raised concerning its conceptual basis  and/or
operationalization outside clinical settings . However, aMCI
has been reported to be associated with increased mortality in a
prospective study , and differences in aMCI-associated
survival between country sites cannot be excluded as a factor
influencing variation in prevalence. It should be noted that the 10/
66 dementia diagnosis showed much higher sensitivity than the
Diagnostic and Statistical Manual of Mental Disorders IV (DSM-
IV) criteria in both pilot and clinical validation 10/66 studies
[41,61]. Compared to numerous aMCI prevalence reports from
community-based sites in Finland (5.3%) , Italy (4.9%) ,
Japan (4.9%) , the US (6%) , South Korea (9.7%) ,
Malaysia (15.4%) , and India (6%) , both the crude and
adjusted aMCI prevalence reported here are relatively low.
However, the estimates are similar to those reported by the
British MRC CFAS study (2.5%)  and to estimates for aMCI
prevalence in community samples from Southern France (3.2%)
, the US (3.8%) , and Germany (3.1%) . Low aMCI
prevalence in our Latin American sites contrast with the aMCI
prevalence (ranging between 3.8% and 6.3% depending on age)
reported amongst American Caribbean Hispanics . Differen-
Table 2. Association between aMCI and disability (WHODAS-12), and the association between aMCI and neuropsychiatric
symptoms (NPI–Q; depression, anxiety, apathy, and irritability).
ZINB (95% CI)AdjustedaPRs (95% CI)
Individual study site estimates
Cuba0.93 (0.74–1.19)0.96 (0.23–3.93)1.74 (0.77–3.94)1.66 (0.59–4.67)0.84 (0.44–1.57)
Dominican Republic1.49 (1.08–2.06)1.04 (0.47–2.30)1.75 (1.00–3.05)1.54 (0.76–3.12) 0.98 (0.52–1.82)
Peru1.51 (1.17–1.94) 2.14 (1.01–4.54)1.54 (0.89–2.65)1.38 (0.57–3.33)1.28 (0.83–1.96)
Venezuela0.92 (0.53–1.60)2.14 (0.47–9.74)2.49 (1.40–4.42)3.59 (1.94–6.65)1.74 (1.06–2.86)
Mexico1.12 (0.78–1.62)1.07 (0.35–3.29) 1.59 (0.76–3.31) 0.79 (0.35–1.82) 1.11 (0.73–1.69)
0.67 (0.45–0.99) NCNC10.2 (1.40–74.5) 9.90 (2.57–38.0)
India1.20 (1.03–1.40)0.69 (0.31–1.53)0.81 (0.25–2.57)1.18 (0.13–10.8)1.27 (0.82–1.98)
Puerto Rico1.05 (0.87–1.27) 2.60 (0.90–7.54)1.85 (0.98–3.49)1.68 (0.65–4.34)1.04 (0.61–1.76)
Pooled meta-analysis (fixed-effect
Combined estimate1.13 (1.04–1.23) 1.31 (0.91–1.89)1.75 (1.37–2.25)1.83 (1.33–2.51)1.24 (1.03–1.49)
Test for heterogeneity p-value0.0080.3440.7530.0910.058
I2Higgins (95% CI) 63% (20–83) 11% (0–74)0% (0–71) 43% (0–75)49% (0–77)
Association between aMCI and disability is measured by exponentiated coefficients from a zero inflated binomial model and representing the increase in disability
of aMCI participants compared to normal. Zero inflation fitted using age, gender, educational level, number of household assets, depression, arthritis, visual problems,
hearing problems, cough and breathing problems, heart problems, gastrointestinal problems, fainting, limb and skin problems, hypertension and stroke. The
association between aMCI and neuropsychiatric symptomsis measured by the risk ratio from a regression using a Poisson working model and model robust
standard errors, and representing the risk for having the symptom in aMCI participants compared to normal.
aAdjusted for age, gender, and educational level, number of household assets and of physical limiting impairments, psychosis, and stroke.
bDepression and irritability were additionally adjusted for pain. The four NPI–Q symptoms are all associated but in the four models presented in the table we have not
adjusted each of them for the other three.
cChina was not adjusted for psychosis
dThe pooled fixed-effect model meta-analytical estimate for depression and anxiety were done without China.
NC, not calculable due to zero cell sizes.
Mild Cognitive Impairment in LAMIC Settings
PLoS Medicine | www.plosmedicine.org6February 2012 | Volume 9 | Issue 2 | e1001170
tial mortality may explain these differences, but a potential role of
the environment and lifestyle in the increased risk of MCI amongst
Hispanic immigrants in North America cannot be excluded.
Crude aMCI prevalence in India (4.3%) is similar to the figure
described by Das and colleagues in Kolkata . Prevalence in
China was the lowest (0.6%), similar only to that described in the
VITA study in Vienna  and markedly lower than that reported
in a recent study from Chongqing (4.5%) . Overall, the results
suggest that there is very little consistency in prevalence of aMCI
across world regions. When considered between studies, this may
well reflect diagnostic issues arising from a lack of specific criteria
for the operationalization of MCI (i.e., cognitive batteries and
specific cut-off scores for impairment) as well as unmeasured
differences and cultural variations potentially relevant for some
components of the aMCI construct (such as subjective memory
impairment, as described below). The objective for the analyses
here was to standardize the assessments as much as possible in
order to gain a clearer idea of international variation. The fact that
substantial heterogeneity remains suggests important variation in
constructs underlying the definition. These will be considered
Female gender, increased age, lower education, and lower
socioeconomic status are associated with dementia  and have
beendescribed inassociationwithMCI.In ourstudy,however,
across study sites, with no between-country heterogeneity in this
respect. It is important to bear in mind that age- and education-
standardised normative data were used to define aMCI and the lack
of association supports the robustness of the norms, although for
education, it might also reflect lower variance in the exposure or
weaker underlying associations between education and other risk
factor profiles in these samples. Lower socioeconomic status
remained associated with aMCI and this may be an additional
marker, beyond education, of relevant social disadvantage. The
observed association with male gender contrasts with the higher
reported age-adjusted prevalence of dementia in women compared
to men , but could reflect the effect of dementia case exclusion
consistent with Mayo Clinic Study of Aging reports that women
experience a transition from normal cognition directly to dementia
at a later age but more abruptly .
As described earlier, a key consideration with aMCI applied as a
construct in international research is its cross-cultural validity. An
advantage of the 10/66 study was that identical measures were
taken and identical algorithms applied for diagnosis across the study
sites and the protocols for cognitive assessments in the 10/66 study
were the result of a long and painstaking process of development
and validation . However, a construct such as subjective
memory impairment is potentially subject to cultural influences and
Table 3. Prevalence of aMCI by country, gender, and age group.
aMCI Prevalence, % (95% CI)
65–69 y 70–74 y75–80 y80+ +y All Age Groups All Age Groups
Cuba (n) 738739 582 5551.8 (1.3–2.3)1.5 (1.0–1.9)
Males 1.5 (0.0–3.0)1.8 (0.2–3.4)0.0 (0.0–0.0) 1.7 (20.2 to 3.6)——
Females2.7 (1.3–4.2)2.6 (1.1–4.0)1.6 (0.3–2.9)0.8 (20.1 to 1.7)——
511483345 4281.4 (0.9–2.0)1.3 (0.7–1.8)
Males1.7 (20.2 to 3.6)2.2 (0.0–4.4)2.7 (20.4 to 5.7)2.9 (0.1–5.7)——
Females0.9 (20.1 to 1.9)1.7 (0.2–3.1)0.4 (20.4 to 1.3)0.7 (20.3 to 1.7)——
Peru (n) 5384753683863.1 (2.3–3.9)2.6 (1.9–3.3)
Males5.4 (2.1–8.6)2.7 (0.3–5.1)2.1 (20.3 to 4.5)4.4 (1.4–7.4)——
Females2.3 (0.7–3.8)1.7 (0.2–3.2)3.6 (1.1–6.0)3.4 (0.9–5.9)——
Venezuela (n) 813450 320236 1.2 (0.7–1.7)1.0 (0.7–1.4)
Males1.3 (0.0–2.6)0.0 (0.0–0.0) 2.6 (20.3 to 5.5)0.0 (0.0–0.0)——
Females1.6 (0.5–2.7)1.4 (0.0–2.9)1.5 (20.2 to 3.1)0.0 (0.0–0.0)——
Mexico (n)5375523843483.2 (2.4–4.1)2.8 (2.0–3.6)
Males3.7 (0.8–6.7)4.3 (1.5–7.0)5.1 (1.6–8.6)4.0 (0.8–7.2)——
Females1.3 (0.2–2.5)4.1 (2.0–6.2)3.9 (1.4–6.5)1.0 (20.4 to 2.4)——
China (n) 6836344172800.8 (0.4–1.2)0.6 (0.3–0.9)
Males1.0 (20.1 to 2.1)0.4 (20.3 to 1.1)1.7 (20.2 to 3.6)0.0 (0.0–0.0)——
Females1.3 (0.2–2.4)0.6 (20.2 to 1.4)0.8 (20.3 to 2.0)0.7 (20.6 to 2.0)——
India (n) 703604 290201 4.3 (3.3–5.2)4.6 (3.7–5.4)
Males 7.0 (4.1–9.9)3.8 (1.5–6.1) 4.8 (1.3–8.3)1.0 (21.0 to 2.9)——
Females3.3 (1.5–5.0) 4.4 (2.2–6.6)5.6 (1.8–9.5) 1.1 (21.1 to 3.2)——
Puerto Rico (n) 3984394364923.9 (3.0–4.8)3.0 (2.2–3.8)
Males 3.9 (0.1–7.8) 5.5 (1.7–9.2) 4.1 (0.8–7.3)5.5 (2.2–8.9)——
Females 4.4 (2.1–6.8)3.4 (1.3–5.5) 3.5 (1.3–5.6)2.3 (0.6–3.9)——
aDirect standardization for age gender and educational level using the whole sample as the standard population.
Mild Cognitive Impairment in LAMIC Settings
PLoS Medicine | www.plosmedicine.org7February 2012 | Volume 9 | Issue 2 | e1001170
may underlie between-site variation. For example, between sites,
people with objectively lower performance on cognitive assessments
may be more or less likely to admit to memory difficulties. Since this
is a component of the most commonly used definitions of aMCI/
MCI, these cultural variations may be reflected in differing
prevalences. However, despite the differences in prevalences of
aMCI between sites, associations with disability were relatively
consistent, providing support for the cross-cultural applicability of
the aMCI construct. They did not suggest, for example, that only
more severe forms of aMCI were being identified in China where
prevalence was lowest, compared to India where it was highest
(particularly since disability was lower rather than higher in China
in those with aMCI compared to the remainder of the sample).
Associations between aMCI and disability should be viewed with
caution since activities of daily living impairment is an exclusion
criterion for the former. Lower likelihood of reporting difficulties in
China would be unlikely to account for the negative association
observed between aMCI and disability in that site because under-
reporting would have to be differential between those with/without
on the occurrence and characteristics of neuropsychiatric symptoms
that may accompany MCI . While we did not find any
association between aMCI and depressive symptoms, our findings
of a significant association between aMCI and anxiety, apathy, and
irritability are largely consistent with those from the Cardiovascular
Health Study and the Mayo Clinic longitudinal study on aging in
the US [72,73], the Kungsholmen study in Sweden , and a
small study from Thailand . However, it should be borne in
mind that individual behavioural/psychological symptoms were not
mutually adjusted as outcomes and the independence of observed
associations in Table 2 cannot be assumed.
Strengths of the study include the very large sample size and the
wide range of populations sampled in terms of culture, economy,
and population characteristics. Moreover, internal validity was
maintained through rigorously prevalidated and standardised
measurements applied consistently between countries in addition
to common algorithms used to define aMCI. There are some
limitations. The samples were drawn from specific geographic
catchment areas and cannot be assumed to be representative of the
source nation/site. No attempt was made to differentiate urban
and rural status in this analysis because not all sites recruited from
both settings. The study was cross-sectional in design and the
impact of survival cannot be evaluated. Furthermore, within the
aMCI category, participants who had developed this late in life
could not be distinguished from those for whom it was a stable
lifetime trait. Finally, aMCI diagnosis was determined without
clinical judgement, which is difficult to obtain in large population-
based studies and unfeasible in most of our study sites. Although
aMCI was originally derived as a diagnosis for secondary or
tertiary care clinical settings, it is being increasingly applied in
epidemiological research and data from community samples is an
important supplement, particularly if future community-level
interventions are planned to prevent progression to dementia.
Our analysis here is intended to extend this particular evidence
base. Follow-up is currently underway in most 10/66 sites, which
will provide further data on predictive validity.
This is one of the first studies, to our knowledge, to investigate
the prevalence of aMCI in LAMICs, where the large majority of
older people and people with dementia currently live [3,4].
Longitudinal data are needed to clarify further the predictive
validity of the aMCI case-definition applied here and to evaluate
the extent to which it can be applied as a risk marker for further
cognitive decline or dementia. In addition, further evaluation is
needed of the associations with disability and neuropsychiatric
symptoms since our findings do suggest higher than expected
comorbidity and there are large absolute numbers of older people
with aMCI in these rapidly ageing and populous world regions.
Conceived and designed the experiments: ALS MD DA CPF MG YH KSJ
IZJV JJLR AS JW MJP. Performed the experiments: ALS MD DA CPF
MG YH KSJ IZJV JJLR AS JW MJP. Analyzed the data: ALS EA BCMS
MD RS. Wrote the first draft of the manuscript: ALS EA RS. Contributed
to the writing of the manuscript: ALS EA BCMS MD DA CPF MG YH
Table 4. Mutually adjusted (95% CI) for the independent effects of age, gender, education, and assets on aMCI prevalence.
Adjusted PRs (95% CI)a
(Per Year Increment) (Males Versus Females)(More Versus Less Years) (More Versus Less)
Individual study site estimates
Cuba0.97 (0.92–1.02)0.63 (0.33–1.21)0.95 (0.72–1.24)1.52 (1.00–2.30)
Dominican Republic1.03 (0.97–1.09)2.25 (1.04–4.86)1.27 (0.83–1.96)0.82 (0.63–1.06)
Peru1.03 (0.99–1.07) 1.29 (0.75–2.22)1.08 (0.82–1.42) 0.81 (0.64–1.03)
Venezuela0.95 (0.88–1.02)0.79 (0.33–1.90)0.91 (0.55–1.52)0.97 (0.83–1.14)
Mexico1.01 (0.97–1.04)1.57 (0.94–2.60) 1.24 (0.95–1.61)0.81 (0.69–0.95)
China0.97 (0.88–1.06)1.00 (0.40–2.51)0.86 (0.64–1.15)0.80 (0.50–1.27)
India0.97 (0.94–1.01)1.19 (0.74–1.93)1.14 (0.89–1.47)0.85 (0.72–0.99)
Puerto Rico 0.99 (0.95–1.02)1.46 (0.91–2.33)1.04 (0.86–1.26)0.94 (0.70–1.27)
Pooled meta-analysis (fixed-effect
Combined estimate0.99 (0.98–1.01)1.25 (1.01–1.54)1.06 (0.96–1.16)0.88 (0.82–0.95)
Test for heterogeneity0.2090.250.6190.168
Higgins (95% CI) 27% (0–67) 23% (0–64)0% (0–68) 33% (0–70)
aMutually adjusted for age, educational level, gender, and number of assets as appropriate.
Mild Cognitive Impairment in LAMIC Settings
PLoS Medicine | www.plosmedicine.org8February 2012 | Volume 9 | Issue 2 | e1001170
KSJ IZJV JJLR AS JW IA MGV MAGH LS MJP RS. ICMJE criteria for
authorship read and met: ALS EA BCMS MD DA CPF MG YH KSJ
IZJV JJLR AS JW IA MGV MAGH LS MJP RS. Agree with manuscript
results and conclusions: ALS EA BCMS MD DA CPF MG YH KSJ IZJV
JJLR AS JW IA MGV MAGH LS MJP RS.
1. UN, Affairs DoEaS (2006) World population prospects: 2006 revision. New
York: Population Division, UN Secretariat.
2. Mathers CD, Loncar D (2006) Projections of global mortality and burden of
disease from 2002 to 2030. PLoS Med 3: e442. doi:10.1371/journal.pmed.
3. Ferri CP, Prince M, Brayne C, Brodaty H, Fratiglioni L, et al. (2005) Global
prevalence of dementia: a Delphi consensus study. Lancet 366: 2112–2117.
4. Prince M, Jackson JC, Albanese E, Sousa RM, Ferri CP (2009) World Alzheimer
Report. London: King’s College London.
5. Llibre Rodriguez JJ, Ferri CP, Acosta D, Guerra M, Huang Y, et al. (2008)
Prevalence of dementia in Latin America, India, and China: a population-based
cross-sectional survey. Lancet 372: 464–474.
6. Cummings JL (2004) Treatment of Alzheimer’s disease: current and future
therapeutic approaches. Rev Neurol Dis 1: 60–69.
7. Kaduszkiewicz H, Zimmermann T, Beck-Bornholdt HP, van den Bussche H
(2005) Cholinesterase inhibitors for patients with Alzheimer’s disease: systematic
review of randomised clinical trials. BMJ 331: 321–327.
8. Wilcock GK (2004) Primary prevention of dementia. Psychiatry 3: 35–36.
9. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, et al. (1999) Mild
cognitive impairment: clinical characterization and outcome. Arch Neurol 56:
10. Petersen RC, Stevens JC, Ganguli M, Tangalos EG, Cummings JL, et al. (2001)
Practice parameter: early detection of dementia: mild cognitive impairment (an
evidence-based review). Report of the Quality Standards Subcommittee of the
American Academy of Neurology. Neurology 56: 1133–1142.
11. Purser JL, Fillenbaum GG, Pieper CF, Wallace RB (2005) Mild cognitive
impairment and 10-year trajectories of disability in the Iowa Established
Populations for Epidemiologic Studies of the Elderly cohort. J Am Geriatr Soc
12. Hunderfund AL, Roberts RO, Slusser TC, Leibson CL, Geda YE, et al. (2006)
Mortality in amnestic mild cognitive impairment: a prospective community
study. Neurology 67: 1764–1768.
13. Palmer K, Backman L, Winblad B, Fratiglioni L (2003) Detection of Alzheimer’s
disease and dementia in the preclinical phase: population based cohort study.
BMJ 326: 245.
14. Panza F, Capurso C, D’Introno A, Colacicco AM, Capurso A, et al. (2007)
Heterogeneity of mild cognitive impairment and other predementia syndromes
in progression to dementia. Neurobiol Aging 28: 1631-1632; discussion 1633-
15. Stephan BC, Matthews FE, McKeith IG, Bond J, Brayne C (2007) Early
cognitive change in the general population: how do different definitions work?
J Am Geriatr Soc 55: 1534–1540.
16. Arnaiz E, Almkvist O, Ivnik RJ, Tangalos EG, Wahlund LO, et al. (2004) Mild
cognitive impairment: a cross-national comparison. J Neurol Neurosurg
Psychiatry 75: 1275–1280.
17. Xu G, Meyer JS, Huang Y, Chen G, Chowdhury M, et al. (2004) Cross-cultural
comparison of mild cognitive impairment between China and USA. Curr
Alzheimer Res 1: 55–61.
18. Apostolova LG, Cummings JL (2008) Neuropsychiatric manifestations in mild
cognitive impairment: a systematic review of the literature. Dement Geriatr
Cogn Disord 25: 115–126.
19. Palmer K, Backman L, Winblad B, Fratiglioni L (2008) Mild cognitive
impairment in the general population: occurrence and progression to Alzheimer
disease. Am J Geriatr Psychiatry 16: 603–611.
20. Petersen RC, Roberts RO, Knopman DS, Geda YE, Cha RH, et al. (2010)
Prevalence of mild cognitive impairment is higher in men. The Mayo Clinic
Study of Aging. Neurology 75: 889–897.
21. Busse A, Bischkopf J, Riedel-Heller SG, Angermeyer MC (2003) Subclassifica-
tions for mild cognitive impairment: prevalence and predictive validity. Psychol
Med 33: 1029–1038.
22. Dlugaj M, Weimar C, Wege N, Verde PE, Gerwig M, et al. (2010) Prevalence of
mild cognitive impairment and its subtypes in the Heinz Nixdorf Recall study
cohort. Dement Geriatr Cogn Disord 30: 362–373.
23. Gamaldo AA, Allaire JC, Sims RC, Whitfield KE (2010) Assessing mild
cognitive impairment among older African Americans. Int J Geriatr Psychiatry
24. Ganguli M, Chang CC, Snitz BE, Saxton JA, Vanderbilt J, et al. (2010)
Prevalence of mild cognitive impairment by multiple classifications: The
Monongahela-Youghiogheny Healthy Aging Team (MYHAT) project.
Am J Geriatr Psychiatry 18: 674–683.
25. Ganguli M, Dodge HH, Shen C, DeKosky ST (2004) Mild cognitive
impairment, amnestic type: an epidemiologic study. Neurology 63: 115–
26. Hanninen T, Hallikainen M, Tuomainen S, Vanhanen M, Soininen H (2002)
Prevalence of mild cognitive impairment: a population-based study in elderly
subjects. Acta Neurol Scand 106: 148–154.
27. Jungwirth S, Weissgram S, Zehetmayer S, Tragl KH, Fischer P (2005) VITA:
subtypes of mild cognitive impairment in a community-based cohort at the age
of 75 years. Int J Geriatr Psychiatry 20: 452–458.
28. Kochan NA, Slavin MJ, Brodaty H, Crawford JD, Trollor JN, et al. (2010) Effect
of different impairment criteria on prevalence of ‘‘objective’’ mild cognitive
impairment in a community sample. Am J Geriatr Psychiatry 18: 711–722.
29. Li J, Wang YJ, Zhang M, Xu ZQ, Gao CY, et al. (2011) Vascular risk factors
promote conversion from mild cognitive impairment to Alzheimer disease.
Neurology 76: 1485–1491.
30. Lopez OL, Jagust WJ, DeKosky ST, Becker JT, Fitzpatrick A, et al. (2003)
Prevalence and classification of mild cognitive impairment in the Cardiovascular
Health Study Cognition Study: part 1. Arch Neurol 60: 1385–1389.
31. Manly JJ, Bell-McGinty S, Tang MX, Schupf N, Stern Y, et al. (2005)
Implementing diagnostic criteria and estimating frequency of mild cognitive
impairment in an urban community. Arch Neurol 62: 1739–1746.
32. Meguro K, Ishii H, Yamaguchi S, Ishizaki J, Sato M, et al. (2004) Prevalence
and cognitive performances of clinical dementia rating 0.5 and mild cognitive
impairment in Japan. The Tajiri project. Alzheimer Dis Assoc Disord 18: 3–10.
33. Ritchie K, Artero S, Touchon J (2001) Classification criteria for mild cognitive
impairment: a population-based validation study. Neurology 56: 37–42.
34. Dickerson BC, Sperling RA, Hyman BT, Albert MS, Blacker D (2007) clinical
prediction of alzheimer disease dementia across the spectrum of mild cognitive
impairment. Arch Gen Psychiatry 64: 1443–1450.
35. Rapp SR, Legault C, Henderson VW, Brunner RL, Masaki K, et al. (2010)
Subtypes of mild cognitive impairment in older postmenopausal women: the
Women’s Health Initiative Memory Study. Alzheimer Dis Assoc Disord 24:
36. Yaffe K, Middleton LE, Lui LY, Spira AP, Stone K, et al. (2011) Mild cognitive
impairment, dementia, and their subtypes in oldest old women. Arch Neurol 68:
37. Das SK, Bose P, Biswas A, Dutt A, Banerjee TK, et al. (2007) An epidemiologic
study of mild cognitive impairment in Kolkata, India. Neurology 68: 2019–2026.
38. Lee LK, Shahar S, Chin AV, Mohd Yusoff NA, Rajab N, et al. (2011)
Prevalence of gender disparities and predictors affecting the occurrence of mild
cognitive impairment (MCI). Arch Gerontol Geriatr 54: 185–191.
39. Kim KW, Park JH, Kim MH, Kim MD, Kim BJ, et al. (2011) A nationwide
survey on the prevalence of dementia and mild cognitive impairment in South
Korea. J Alzheimers Dis 23: 281–291.
40. Prince M, Ferri CP, Acosta D, Albanese E, Arizaga R, et al. (2007) The
protocols for the 10/66 dementia research group population-based research
programme. BMC Public Health 7: 165.
41. Prince M, Acosta D, Chiu H, Scazufca M, Varghese M (2003) Dementia
diagnosis in developing countries: a cross-cultural validation study. Lancet 361:
42. Copeland JR, Dewey ME, Griffiths-Jones HM (1986) A computerized
psychiatric diagnostic system and case nomenclature for elderly subjects: GMS
and AGECAT. Psychol Med 16: 89–99.
43. Copeland JR, Prince M, Wilson KC, Dewey ME, Payne J, et al. (2002) The
Geriatric Mental State Examination in the 21st century. Int J Geriatr Psychiatry
44. Hall KS, Hendrie HC, Brittain HM, Norton JA, Jr., Rodgers DD, et al. (1993)
The Development of a dementia screening interview in two distinct languages.
International Journal of Methods in Psychiatric Research 3: 1–28.
45. Welsh KA, Butters N, Mohs RC, Beekly D, Edland S, et al. (1994) The
Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part V. A
normative study of the neuropsychological battery. Neurology 44: 609–614.
46. Ustun TB, Kostanjsek N, Chatterji S () Rehm J Measuring health and disability:
manual for WHO Disability Assessment Schedule (WHODAS 2.0). Geneva:
World Health Organization, In press.
47. Rehm J, U¨stu ¨n TB, Saxena S, Nelson CB, Chatterji S, et al. (1999) On the
development and psychometric testing of the WHO screening instrument to
assess disablement in the general population. Int J Meth Psych Res 8: 110–122.
48. Sousa RM, Dewey ME, Acosta D, Jotheeswaran AT, Castro-Costa E, et al.
(2010) Measuring disability across cultures–the psychometric properties of the
WHODAS II in older people from seven low- and middle-income countries.
The 10/66 Dementia Research Group population-based survey. Int J Methods
Psychiatr Res 19: 1–17.
49. Kaufer DI, Cummings JL, Ketchel P, Smith V, MacMillan A, et al. (2000)
Validation of the NPI-Q, a brief clinical form of the Neuropsychiatric Inventory.
J Neuropsychiatry Clin Neurosci 12: 233–239.
50. Guruje O, Unverzargt FW, Osuntokun BO, Hendrie HC, Baiyewu O, et al.
(1995) The CERAD Neuropsychological Test Battery: norms from a Yoruba-
speaking Nigerian sample. West Afr J Med 14: 29–33.
51. Albert MS, Dekosky ST, Dickson D, Dubois B, Feldman HH, et al. (2011) The
diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommen-
dations from the National Institute on Aging-Alzheimer’s Association work-
Mild Cognitive Impairment in LAMIC Settings
PLoS Medicine | www.plosmedicine.org9February 2012 | Volume 9 | Issue 2 | e1001170
groups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:
52. Larrieu SM, Letenneur LP, Orgogozo JMM, Fabrigoule CP, Amieva HP, et al.
(2002) Incidence and outcome of mild cognitive impairment in a population-
based prospective cohort. Neurology 59: 1594–1599.
53. Palmer K, Wang H-X, Backman L, Winblad B, Fratiglioni L (2002) Differential
evolution of cognitive impairment in nondemented older persons: results from
the Kungsholmen Project. Am J Psychiat 159: 436–442.
54. Graham JE, Rockwood K, Beattie BL, Eastwood R, Gauthier S, et al. (1997)
Prevalence and severity of cognitive impairment with and without dementia in
an elderly population. Lancet 349: 1793.
55. Gauthier S, Touchon J (2005) Mild cognitive impairment is not a clinical entity
and should not be treated. Arch Neurol 62: 1164–1166. discussion 1167.
56. Petersen RC, Morris JC (2005) Mild cognitive impairment as a clinical entity
and treatment target. Arch Neurol 62: 1160–1163. discussion 1167.
57. Raschetti R, Albanese E, Vanacore N, Maggini M (2007) Cholinesterase
inhibitors in mild cognitive impairment: a systematic review of randomised trials.
PLoS Med 4: e338. doi:10.1371/journal.pmed.0040338.
58. Whitehouse PJ, Moody HR (2006) Mild cognitive impairment: A ‘hardening of
the categories’? Dementia 5: 11–25.
59. Ganguli M, Chandra V, Gilby JE, Ratcliff G, Sharma SD, et al. (1996)
Cognitive test performance in a community-based nondemented elderly sample
in rural India: the Indo-U.S. Cross-National Dementia Epidemiology Study. Int
Psychogeriatr 8: 507–524.
60. Kim JM, Stewart R, Prince M, Shin IS, Yoon JS (2003) Diagnosing dementia in
a developing nation: an evaluation of the GMS-AGECAT algorithm in an older
Korean population. Int J Geriatr Psychiatry 18: 331–336.
61. Prince MJ, de Rodriguez JL, Noriega L, Lopez A, Acosta D, et al. (2008) The
10/66 Dementia Research Group’s fully operationalised DSM-IV dementia
computerized diagnostic algorithm, compared with the 10/66 dementia
algorithm and a clinician diagnosis: a population validation study. BMC Public
Health 8: 219.
62. STATA (2007) Stata Statistical Software: Release 10. LP S, ed. College Station
(Texas): StataCorp. LP 2007.
63. Sousa RM, Ferri CP, Acosta D, Albanese E, Guerra M, et al. (2009)
Contribution of chronic diseases to disability in elderly people in countries with
low and middle incomes: a 10/66 Dementia Research Group population-based
survey. Lancet 374: 1821–1830.
64. Lumley T, Kronmal R, Ma S Relative risk regression in medical research:
models, contrasts, estimators, and algorithms. Technical report 293, UW
Biostatistics Working Paper Series. Available: http://www.bepress.com/uwbiostat/
paper293. Accessed 8 January 2012..
65. G Zou (2004) A modified Poisson regression approach to prospective studies
with binary data. Am J Epidemiol 159: 702–706.
66. Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis.
Stat Med 21: 1539–1558.
67. Ritchie K, Touchon J (2000) Mild cognitive impairment: conceptual basis and
current nosological status. Lancet 355: 225–228.
68. Matthews FE, Stephan BC, Bond J, McKeith I, Brayne C (2007) Operationa-
lization of mild cognitive impairment: a graphical approach. PLoS Med 4: 1615.
69. Tognoni G, Ceravolo R, Nucciarone B, Bianchi F, Dell’Agnello G, et al. (2005)
From mild cognitive impairment to dementia: a prevalence study in a district of
Tuscany, Italy. Acta Neurol Scand 112: 65–71.
70. Busse A, Bischkopf J, Riedel-Heller SG, Angermeyer MC (2003) Mild cognitive
impairment: prevalence and predictive validity according to current approaches.
Acta Neurol Scand 108: 71–81.
71. Fratiglioni L, Winblad B, von Strauss E (2007) Prevention of Alzheimer’s disease
and dementia. Major findings from the Kungsholmen Project. Physiol Behav 92:
72. Lyketsos CG, Lopez O, Jones B, Fitzpatrick AL, Breitner J, et al. (2002)
Prevalence of neuropsychiatric symptoms in dementia and mild cognitive
impairment: results from the cardiovascular health study. JAMA 288:
73. Geda YE, Roberts RO, Knopman DS, Petersen RC, Christianson TJ, et al.
(2008) Prevalence of neuropsychiatric symptoms in mild cognitive impairment
and normal cognitive aging: population-based study. Arch Gen Psychiatry 65:
74. Palmer K, Berger AK, Monastero R, Winblad B, Backman L, et al. (2007)
Predictors of progression from mild cognitive impairment to Alzheimer disease.
Neurology 68: 1596–1602.
75. Muangpaisan W, Intalapaporn S, Assantachai P (2008) Neuropsychiatric
symptoms in the community-based patients with mild cognitive impairment
and the influence of demographic factors. Int J Geriatr Psychiatry 23: 699–703.
Mild Cognitive Impairment in LAMIC Settings
PLoS Medicine | www.plosmedicine.org10February 2012 | Volume 9 | Issue 2 | e1001170
Background. Currently, more than 35 million people
worldwide have dementia, a group of brain disorders
characterized byan irreversible
problem solving, communication, and other ‘‘cognitive’’
functions. Dementia, the commonest form of which is
Alzheimer’s disease, mainly affects older people and,
because more people than ever are living to a ripe old
age, experts estimate that, by 2050, more than 115 million
people will have dementia. At present, there is no cure for
dementia although drugs can be used to manage some of
the symptoms. Risk factors for dementia include physical
inactivity, infrequent participation in mentally or socially
stimulating activities, and common vascular risk factors such
as high blood pressure, diabetes, and smoking. In addition,
some studies have reported that mild cognitive impairment
(MCI) is associated with an increased risk of dementia. MCI
can be seen as an intermediate state between normal
cognitive aging (becoming increasingly forgetful) and
dementia although many people with MCI never develop
dementia, and some types of MCI can be static or self-
limiting. Individuals with MCI have cognitive problems that
are more severe than those normally seen in people of a
similar age but they have no other symptoms of dementia
and are able to look after themselves. The best studied form
of MCI—amnestic MCI (aMCI)—is characterized by memory
problems suchas misplacing
Why Was This Study Done? Much of the expected
increase in dementia will occur in low and middle income
countries (LAMICs) because these countries have rapidly
aging populations. Given that aMCI is frequently used to
define groups of people who may be at risk of developing
dementia, it would be useful to know what proportion of
community-dwelling older adults in LAMICs have aMCI (the
governments plan their future health care and social
support needs. In this cross-sectional, population-based
study, the researchers estimate the prevalence of aMCI in
eight LAMICs using data collected by the 10/66 Dementia
Research Group. They also investigate the association of
aMCI with sociodemographic factors (for example, age,
gender, and education), disability, and neuropsychiatric
depression. A cross-sectional study collects data on a
population at a single time point; the 10/66 Dementia
Research Group is building an evidence base to inform the
development and implementation of policies for improving
the health and social welfare of older people in LAMICs,
particularly people with dementia.
apathy, irritability, and
What Did the Researchers Do and Find? In cross-
sectional surveys carried out in six Latin American LAMICS,
China, and India, more than 15,000 elderly individuals
without dementia completed standardized assessments of
their mental and physical health and their cognitive function.
Interviews with relatives and carers provided further details
neuropsychiatric symptoms. The researchers developed an
algorithm (set of formulae) that used the data collected in
these surveys to diagnose aMCI in the study participants.
Finally, they used statistical methods to analyze the
prevalence, distribution, and impact of aMCI in the eight
LAMICs. The researchers report that aMCI was associated
with disability, anxiety, apathy, and irritability but not with
depression and that the prevalence of aMCI ranged from
0.8% in China to 4.3% in India. Other analyses show that,
considered across all eight countries, aMCI was modestly
associated with being male (men had a slightly higher
prevalence of aMCI than women) and with having fewer
assets but was not associated with age or education.
What Do These Findings Mean? These findings suggest
that aMCI, as diagnosed using the algorithm developed by
the researchers, is consistently associated with higher
disability and with neuropsychiatric symptoms in the
LAMICs studied but not with most sociodemographic factors.
Because prevalidated and standardized measurements were
applied consistently in all the countries and a common
algorithmwas used todefineaMCI,these findingsalsosuggest
that the prevalence of aMCI varies markedly among LAMIC
populations and is similar to or slightly lower than the
prevalence most often reported for European and North
American populations. Although longitudinal studies are now
needed toinvestigate the extent towhich aMCI can beused as
risk marker for further cognitive decline and dementia in these
in LAMICs revealed here potentially has important implications
for health care and social service planning in these rapidly
aging and populous regions of the world.
Additional Information. Please access these Web sites via
the online version of this summary at http://dx.doi.org/10.
N Alzheimer’s Disease International is the international
federation of Alzheimer associations around the world; it
provides links to individual associations, information about
dementia, and links to three World Alzheimer Reports;
information about the 10/66 Dementia Research Group is
also available on this web site
N The Alzheimer’s Society provides information for patients
and carers about dementia, including information on MCI
and personal stories about living with dementia
N The Alzheimer’s Association also provides information for
patients and carers about dementia and about MCI, and
personal stories about dementia
N A BBC radio program that includes an interview with a man
with MCI is available
N MedlinePlus provides links to further resources about MCI
and dementia (in English and Spanish)
Mild Cognitive Impairment in LAMIC Settings
PLoS Medicine | www.plosmedicine.org11February 2012 | Volume 9 | Issue 2 | e1001170