The British Journal of Psychiatry (2009)
195, 510–515. doi: 10.1192/bjp.bp.109.064055
Late-life depression in Peru, Mexico
and Venezuela: the 10/66 population-based study
Mariella Guerra, Cleusa P. Ferri, Ana Luisa Sosa, Aquiles Salas, Ciro Gaona,
Victor Gonzales, Gabriela Rojas de la Torre and Martin Prince
Background
The proportion of the global population aged 60 and over is
increasing, more so in Latin America than any other region.
Depression is common among elderly people and an
important cause of disability worldwide.
Aims
To estimate the prevalence and correlates of late-life
depression, associated disability and access to treatment in
five locations in Latin America.
Method
A one-phase cross-sectional survey of 5886 people aged 65
and over from urban and rural locations in Peru and Mexico
and an urban site in Venezuela. Depression was identified
according to DSM–IV and ICD–10 criteria, Geriatric Mental
State–Automated Geriatric Examination for Computer
Assisted Taxonomy (GMS–AGECAT) algorithm and EURO–D
cut-off point. Poisson regression was used to estimate the
independent associations of sociodemographic
characteristics, economic circumstances and health status
with ICD–10 depression.
Results
For DSM–IV major depression overall prevalence varied
between 1.3% and 2.8% by site, for ICD–10 depressive
The prevalence of late-life depression has been extensively studied
in high-income countries in the European, North American and
Asian Pacific regions.1,2 The main influence on prevalence seems
to be the criterion used to make the diagnosis. In late life, depressive disorder diagnosed according to rigorous research criteria
such as DSM–IV3 major depression and ICD–104 depressive
episode is uncommon, with a weighted mean prevalence of only
1.8%; when all those with clinically relevant symptoms are
included, the weighted mean prevalence rises to 13.3%.1 The
relative lack of epidemiological data from low- and middleincome countries is particularly striking – in the two systematic
reviews, all of the 34 publications covering the period 1989–
1996 and the 122 papers covering the period 1993–2004 described
research carried out in high-income countries. Three more recent
publications report an unusually high prevalence of late-life
depression according to clinical diagnostic criteria. The prevalence
of DSM–IV major depression in Nigeria was 7.1%5 and that of
ICD–10 depressive episode in Brazil was 19.2%.6 In both settings,
depression was associated with high levels of social disability and
impaired quality of life. In a study from Peru published as an
institutional report, the prevalence of DSM–IV major depression
was 9.8%.7 More research is needed to determine whether these
findings are typical of lower-income regions in general. In the
multicentre SABE (Salud, Bienstar y Envejecimiento en America
Latina y el Caribe) study the prevalence of clinically significant
depression (a score of 11 or more on the Geriatric Depression
Scale) in the six Latin American capital cities ranged from
16.5% to 30.1% in women and from 11.8% to 19.6% in men.8
510
episode between 4.5% and 5.1%, for GMS–AGECAT
depression between 30.0% and 35.9% and for EURO–D
depression between 26.1% and 31.2%; therefore, there was
a considerable prevalence of clinically significant depression
beyond that identified by ICD–10 and DSM–IV diagnostic
criteria. Most older people with depression had never
received treatment. Limiting physical impairments and a past
history of depression were the two most consistent
correlates of the ICD–10 depressive episode.
Conclusions
The treatment gap poses a significant challenge for Latin
American health systems, with their relatively weak primary
care services and reliance on private specialists; local
treatment trials could establish the cost-effectiveness of
mental health investment in the government sector.
Declaration of interest
The 10/66 Dementia Research Group works closely with
Alzheimer’s Disease International, the non-profit federation
of 77 Alzheimer associations around the world.
Alzheimer’s Disease International is supported in part
by grants from GlaxoSmithKline, Novartis, Lundbeck, Pfizer
and Eisai.
This is broadly consistent with prevalences reported in two
cross-national comparisons of late-life depression in Europe: the
ten country SHARE (Survey of Health, Ageing and Retirement
in Europe) study9 in which a validated cut-off point was applied
to the EURO–D depression scale10, and the EURODEP study11 in
which depression was diagnosed using the Geriatric Mental State–
Automated Geriatric Examination for Computer Assisted Taxonomy (GMS–AGECAT).12,13
Using a common methodology across all locations, we set out
to estimate the prevalence of late-life depression in three urban
and two rural locations in three Latin American countries (Mexico,
Peru and Venezuela) according to four different diagnostic criteria,
(DSM–IV major depression, ICD–10 depressive episode, GMS–
AGECAT and EURO–D depression). We also aimed to assess
levels of disability associated with ICD–10 depressive episode
and subsyndromal depression, and to describe access to treatment.
Finally we aimed to estimate the independent associations of
sociodemographic characteristics, economic circumstances and
health status with ICD–10 depressive episode in each site.
Method
Setting and study design
The full 10/66 study protocol has been published elsewhere.14 A
one-phase cross-sectional population-based survey has been
conducted of all those over 65 years old from defined catchments
areas for each of the five locations. For urban catchment areas,
predominantly middle-class or professional areas with high-
Late-life depression in Peru, Mexico and Venezuela
income earners were avoided. Rural catchment areas were defined
by low population density and traditional agrarian lifestyle. The
total target sample was 2000 for each country. In Peru and Mexico,
we sampled from urban and rural districts, whereas in Venezuela
we sampled from an urban district only. In Peru the urban
catchment areas comprised two districts (Lima Cercado and San
Miguel) in the capital city, Lima. The rural catchment areas
included six districts (Cerro Azul, Imperial, Nuevo Imperial,
Quilmana, San Luis and San Vicente) in the coastal province of
Canete. The main economic activities are fishing and agriculture.
In Mexico the urban catchment areas were six districts in the
suburb of Tlalpan in the south of Mexico City. The rural
catchment areas included nine villages in the north of the state
of Morelos, a mountainous district 70 km from Mexico City.
The main economic activity is agriculture. In Venezuela, we
sampled an urban district, Caricuao in the south west of the
capital city, Caracas, where most participants came from low- or
middle-income backgrounds and lived in social housing
apartments.
Measurements
Depression
The diagnosis of depression was determined according to four
different criteria, all derived from the same comprehensive
structured clinical interview, the Geriatric Mental State (GMS).12
The prevalence of each condition was determined with respect
to the past month.
Depression of clinical significance. The Geriatric Mental State is
supported by AGECAT,13 a diagnostic algorithm based on clinical
principles. For the community B3 version of the GMS this generates four syndrome clusters: organicity (dementia); schizophrenia
and related paranoia; depression; and anxiety neurosis. A
diagnostic confidence level is provided for each syndrome, ranging
from 0 (no symptoms) to 5 (very severely affected). Level three
and greater represent a degree of severity warranting professional
intervention and levels one and two subcases. These stage one
diagnoses are then organised into a single stage two diagnosis
on the basis of precedence determined by a hierarchically
structured algorithm. In the 10/66 pilot study15 sensitivity was
consistently higher for the stage one than for the stage two
depression diagnosis, against the Montgomery–Åsberg Depression
Scale rating. This is explained by the tendency of the AGECAT
hierarchical system to overdiagnose organicity in lower-income
settings, and then to accord this precedence over depression in
the hierarchical determination. For this reason, we used the stage
one GMS–AGECAT depression syndrome for this analysis – this is
subsequently referred to as ‘GMS–AGECAT depression’.
The EURO–D10 is a symptom scale originally developed to
compare symptoms of late-life depression across 11 European
countries in the EURODEP consortium. It is derived from the
GMS and covers 12 symptom domains: depressed mood,
pessimism, suicidality, guilt, sleep, interest, irritability, appetite,
fatigue, concentration, enjoyment and tearfulness. Each item is
scored 0 (symptom not present) or 1 (symptom present), and
item scores are summed to produce a scale with a minimum score
of 0 and a maximum of 12. Its cross-cultural measurement
properties have been extensively investigated, in the European
EURODEP and SHARE studies, as well as in the 10/66 pilot
studies in Latin America, India and China.15–17 Although the scale
has moderately high internal consistency (standardized alpha
value varied from 0.58 to 0.80),17 two underlying factors are
reliably identified (affective suffering and motivation). In the
EURODEP study the optimum cut-off point was 54 for the
identification of DSM–IV major depression and GMS–AGECAT
depression,17 and this cut-off point was applied in the subsequent
SHARE study of the prevalence of late-life depression across
Europe. Those scoring four or more on the scale are subsequently
described as cases of ‘EURO–D depression’. We used the term
‘subsyndromal depression’ to describe cases of EURO–D depression, with symptoms that did not meet criteria for ICD–10
depressive episode.
Diagnostic criteria for depression. Diagnoses of DSM–IV major
depression and ICD–10 depressive episode were both derived
using a computerised algorithm applied to GMS. The ICD–10
divides depressive episodes into mild, moderate and severe.
The DSM–IV, but not ICD–10, specifies that symptoms should
be severe enough to cause ‘clinically significant distress or
impairment’ and excludes a diagnosis of major depression if the
symptoms are better accounted for by bereavement.
Other health conditions
Other recorded health conditions were: dementia according to the
10/66 dementia diagnosis algorithm;18 self-reported stroke; and
having 3 or more of 11 limiting physical impairments (arthritis
or rheumatism; eyesight problems; hearing difficulty or deafness;
persistent cough; breathlessness, difficulty breathing or asthma;
high blood pressure; heart trouble or angina; stomach or intestine
problems; faints or blackouts; paralysis, weakness or loss of one leg
or arm; skin disorders such as pressure sores, leg ulcers or severe
burns).
Disability
We used the World Health Organization Disability Assessment
Schedule II (WHODAS–II)19 to measure limitation and participation restriction. It was developed by the WHO as a culture-fair
assessment tool for use in cross-cultural comparative
epidemiological and health services research.
Sociodemographic status
Participants’ ages were established from participant and informant
report and an official identification document; when the
information was not clear or there was a discrepancy, an event
calendar was used. Information was also collected on marital
status, education (none, did not complete primary, completed
primary, secondary, tertiary) and social support (we used ‘never
or less than monthly contacts’ with family and friends as a proxy
measure of lack of social support).
Socioeconomic circumstances
This was assessed using different measures. It was determined
whether participants received any income, any pension, proxy
measure of household income (sum of total income for households with more than one participant); a household assets index
was calculated (number of assets in the household categorised:
0–2; 3–5 and 6 or more assets); and food insecurity was assessed
by the question ‘Do you ever go hungry because there is not
enough food to eat?’.
Ethics
Participants were recruited following informed signed consent.
People with dementia who lack capacity for consent were recruited
on the basis of a relative’s signed agreement. Illiterate persons were
read the information sheet and consent form, and invited to
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Guerra et al
express their consent verbally, which was witnessed. Studies were
approved by local ethical committees as well as by the ethical
committee of King’s College London (College Research Ethics
Committee).
Statistical analysis
We recorded the sociodemographic characteristics, economic
circumstances and health status of the sample in each site. We
identified the prevalence of depression in each setting for each
of the four outcomes: EURO–D depression, GMS–AGECAT
depression, ICD–10 depressive episode and DSM–IV major
depression, by age and gender, with 95% confidence intervals
adjusted for household clustering. We determined levels of
disability (mean WHODAS–II global disability scores and the
proportion reporting 15 or more disability days in the past
month) according to depression status, dividing each sample into
those with no depression (neither ICD–10 depressive episode nor
EURO–D depression), subsyndromal depression (EURO–D
depression not confirmed as depressive episode by ICD–10),
and ICD–10 depressive episode. We then estimated the effects of
subsyndromal depression and ICD–10 depressive episode on the
proportion reporting 15 or more disability days (severe disability)
using Poisson regression, adjusting for age, gender, 10/66
dementia and number of limiting physical impairments. The
population attributable prevalence fraction for subsyndromal
depression and ICD–10 depressive episode was then estimated
using the STATA for Windows 10.1, which aflogit command
estimates the attributable fraction from within the Poisson
regression framework, thus enabling confounders to be taken into
account. Population attributable prevalence fractions when
calculated from prevalence ratios in cross-sectional studies
represent the proportion of prevalent severe disability that could
theoretically be avoided if the exposure could be removed from
the population, taking into account the effect of the exposure
on both incidence and duration of the severe disability state,
assuming a causal relationship estimated free of confounding.
Finally, we used a Poisson regression model (adjusted for
household clustering) to estimate the independent associations
of the following factors with ICD–10 depressive episode:
(a) sociodemographic characteristics – age, gender, marital status
and level of education social support;
(b) never or less than monthly contact with family and with
friends;
(c) economic circumstances – household income and food
insecurity; the effect of other correlated indicators of socioeconomic position (receiving any income, yes/no; receiving
any pension, yes/no; and numbers of household assets) was
tested by substituting household income with each of them
in turn;
(d) health status – 10/66 dementia, number of physical impairments, past depression and stroke.
Results
General characteristics
The numbers of participants interviewed in each site was 1933 for
Peru (urban, 1381; rural, 552); 2002 for Mexico (urban, 1002;
rural, 1000) and 1951 for Venezuela (urban only). Response rates
were 80% and above in all locations (80%, 88%, 84%, 86% and
80% respectively).
Online Table DS1 describes the general characteristics of the
samples by country. Age distributions were similar for each site,
512
other than Venezuela where there was a relative preponderance
of younger participants. Nearly two-thirds of participants were
female in each site, other than in rural Peru (53.4%). Levels of
education were lower in rural than in urban locations, especially
rural Mexico where 83.7% of the participants had minimal or
no education. Participants in rural locations also reported fewer
household assets and a higher prevalence of food insecurity.
Whereas two-thirds or more of participants across all locations
reported receiving some kind of income, pension coverage was
especially low in rural Mexico (25.4%). For a high proportion
of the participants social contact was limited to family members;
only a small proportion (from 5.4% in urban Peru to 8.6% in
rural Peru) had less than monthly or no contact with family
members, but a much higher proportion had less than monthly
or no contact with friends (13.1% in rural Peru to 59.8% in rural
Mexico). Half or more of the participants reported at least one
limiting physical impairment and nearly one in five reported three
or more. A past history of depression was especially common in
urban Peru (34.9%), in other locations the prevalence varied
between 13.6% and 18.1%. The prevalence of self-reported stroke
was lower in rural Peru (3.6%) than other locations (5.7% to
8.2%). Dementia prevalence varied from 6.7% in rural Peru to
11.1% in rural Mexico.
Prevalence of depression
The prevalence of depression was consistently much higher for
EURO–D and GMS–AGECAT depression than for DSM–IV major
depression and ICD–10 depressive episode (Table DS2). For
EURO–D depression overall prevalence varied between 26.1%
and 31.2% by location, for GMS–AGECAT depression between
30.0% and 35.9%, for DSM–IV major depression between 1.3%
and 2.8% and for ICD–10 depressive episode between 4.5% and
5.1%. There was therefore relatively little variation in the prevalence of depression between locations, according to any of the
criteria. Prevalence according to DSM–IV and ICD–10 diagnostic
criteria was a little lower in rural compared with urban locations,
but the same trend was not seen for EURO–D or GMS–AGECAT
depression. In all locations, for all or most criteria the prevalence
of depression was higher in women than among men, and varied
somewhat with age, but with no consistent trend. Given the much
higher prevalence of EURO–D depression compared with that of
ICD–10 depressive episode, we explored the phenomenology of
these two categories, empirically, by comparing the frequency of
individual EURO–D symptoms and mean EURO–D total scores
according to depression status. The mean total EURO–D scores
were 1.0 (s.d. = 1.1) for those with neither condition, 5.3
(s.d. = 1.4) for those with subsyndromal depression (EURO–D
depression cases not meeting ICD–10 criteria) and 7.1
(s.d. = 1.8) for those with ICD–10 depressive episode. Symptom
prevalences were similar between subsyndromal depression and
ICD–10 depressive episode cases for most EURO–D symptoms
(depression, pessimism, wishing death, guilt, sleep disturbance,
irritability and tearfulness). However, loss of interest (56% v.
22%), loss of appetite (49% v. 30%), fatigue (88% v. 56%), loss
of concentration (57% v. 35%) and loss of enjoyment (54% v.
18%) were all considerably more common among those meeting
ICD–10 criteria (Table DS3).
Impact of depression
For those with subsyndromal depression, levels of disability were
intermediate between those with no depression and those with
ICD–10 depressive episode, according to both mean WHODAS–
II global disability scores and the proportion with severe disability
as indicated by having reported 15 or more disability days in the
Late-life depression in Peru, Mexico and Venezuela
past month. After adjusting for age, gender, number of limiting
physical impairments and dementia both subsyndromal
depression and ICD–10 depressive episode were independently
associated with the prevalence of severe disability, in each country.
Those with subsyndromal depression were one and a half to two
times more likely to report severe disability. The population
attributable prevalence fractions suggest that 13.6% of the
prevalence of severe disability in Peru, 32.4% in Mexico and
31.7% in Venezuela could be independently attributed to
depression. In each country, given the higher prevalence of
subsyndromal compared with ICD–10 depression, a similar or
greater proportion of severe disability prevalence was attributed
to subsyndromal depression compared with ICD–10 depressive
episode (Table 1).
Treatment of depression
Most of those with an ICD–10 depressive episode did not report
having ever received in-patient or out-patient treatment for
depression: 75.9% in urban Peru, 81.2% in rural Peru, 87.2% in
urban Mexico, 95.6% in rural Mexico and 80.4% in Venezuela.
The proportions that had visited any healthcare provider in the
past 3 months were 69.0%, 62.5%, 80.9%, 66.7% and 75.7%
respectively.
Factors associated with depression
Table 2 presents the crude and adjusted prevalence ratios for the
association of sociodemographic characteristics, economic
circumstances and health status with ICD–10 depressive episode,
in each location. After adjusting for other covariates, there was a
trend towards a lower prevalence of depression with increasing
age in Venezuela only. Univariate associations between female
gender and depression in all three urban locations were no longer
statistically significant after adjustment; closer inspection of the
models suggested that the main confounders were living without
a partner, limited social support, physical impairment and, for
urban locations only, level of education. There was a trend
towards a protective effect of higher levels of education in all
locations other than rural Mexico; however, this effect was
statistically significant only in urban Peru. Of the indicators of
socioeconomic status, food insecurity was strongly positively
associated with depression in Mexico, with non-significant trends
in Peru and Venezuela. Receiving any income was inversely
associated with depression in rural Mexico only. Neither receiving
a pension, nor number of household assets was associated with
Table 1
depression. Limited contact with family was associated with
depression in urban Mexico only. For limited contact with friends
there was a trend towards a univariate association with depression
in all locations, but after adjustment the effect was statistically
significant only in rural Mexico. Increasing numbers of physical
impairments and a past history of depression were consistently
associated with an increased prevalence of depression in all
locations.
Discussion
In our study of late-life depression in three Latin American
countries, the prevalence depends mainly upon the diagnostic
criterion, with little variation evident between locations. The most
restrictive criterion, DSM–IV major depression, returns the lowest
prevalence with ICD–10 depressive episode only slightly more
inclusive. The DSM–IV, but not ICD–10, specifies that symptoms
should be severe enough to cause ‘clinically significant distress or
impairment’ and excludes a diagnosis of major depression if the
symptoms are better accounted for by bereavement. The
prevalence of EURO–D and GMS–AGECAT depression is around
6 times higher than that of ICD–10 depressive episode and 15 times
higher than that of DSM–IV major depression. These findings are all
consistent with earlier reviews based largely on European and North
American data.15 The prevalence of the broader EURO–D and
GMS–AGECAT depression is also consistent with that recorded
for the Geriatric Depression Scale in the Latin American SABE
study8 and with the relatively high prevalence of EURO–D
depression recorded in the three ‘Latin’ countries (France, Spain
and Italy) in the European SHARE study.9
Given the large discrepancy between the prevalence of late-life
depression according to ICD–10 and DSM–IV clinical criteria, and
the broader EURO–D and GMS–AGECAT depression syndromes,
it was important to establish whether the subsyndromal cases
detected by EURO–D but not meeting ICD–10 criteria were
indeed ‘clinically significant’. The strong and independent association between subsyndromal depression and an established criterion
of relatively severe disability, 15 or more disability days in the past
month, would appear to indicate that the DSM–IV and ICD–10
diagnostic criteria might indeed be missing a substantial proportion of clinically significant cases. We have also demonstrated, as
others have shown with respect to younger adults,20,21 that
the true population burden of depression is significantly
underestimated by these very narrow diagnostic definitions.
Disability (WHODAS–II global disability scores and 15 or more disability days), by depression status, in each country
Severe disability (15 or more disability days in the past month)
n (%)
WHODAS–II score
Mean (s.d.)
Proportion (%)
Adjusted prevalence
ratio (95% CI)
Population attributable
prevalence fraction (95% CI)
Peru
Not depressed
EURO–D depression only
ICD–10 depressive episode
1346 (71.5)
434 (23.0)
103 (5.5)
MD = 12
8.0 (14.2)
16.1 (17.9)
27.8 (22.4)
MD = 444
226/1048 (21.6)
114/326 (35.0)
26/71 (36.6)
n = 1444
1 (ref)
1.56 (1.29–1.89)
1.53 (1.09–2.14)
–
10.6 (7.3–13.7)
3.0 (1.3–4.7)
Mexico
Not depressed
EURO–D depression only
ICD–10 depressive episode
1409 (71.1)
482 (24.3)
92 (4.6)
MD = 3
7.0 (13.8)
15.1 (20.6)
28.3 (23.4)
MD = 22
87/1393 (6.2)
77/479 (16.1)
28/90 (31.1)
n = 1961
1 (ref)
2.15 (1.57–2.94)
3.41 (2.31–5.02)
–
18.5 (11.4–25.1)
13.9 (10.0–17.7)
Venezuela
Not depressed
EURO–D depression only
ICD–10 depressive episode
1357 (70.3)
466 (24.1)
107 (5.5)
MD = 119
6.6 (11.4)
16.9 (17.7)
34.1 (26.1)
MD = 625
49/893 (5.5)
43/358 (12.0)
24/87 (27.6)
n = 1330
1 (ref)
2.01 (1.33–3.03)
4.34 (2.48–7.60)
–
15.5 (6.7–23.5)
16.2 (10.6–21.4)
MD, participants with missing data.
513
514
0.85 (0.44–1.65)
b
Number of assets (per asset)
a. Adjusted for all other variables in the top part of the table.
b. When substituted for household income in the above model.
0.86 (0.58–1.29)
0.61 (0.42–0.88)
0.80 (0.47–1.37)
1.04 (0.62–1.75)
0.86 (0.43–1.74)
1.07 (0.55–2.07)
0.93 (0.61–1.42)
1.18(0.56–2.51)
1.29(0.63–2.64)
1.22 (0.64–2.36)
0.61 (0.36–1.04)
0.88 (0.40–1.91)
1.16 (0.27–4.93)
1.20 (0.42–3.41)
0.57 (0.21–1.53)
0.88 (0.40–1.93)
0.86 (0.56–1.34)
0.85 (0.57–1.28)
Receives any pensionb
1.11 (0.47–2.60)
2.34 (1.26–4.37)
0.84 (0.53–1.32)
0.70 (0.46–1.05)
0.88 (0.74–1.06)
1.23 (0.57–2.64)
0.43 (0.24–0.76)
0.39 (0.22–0.68)
2.00 (1.00–4.00)
1.78 (0.86–3.66)
1.51(0.59–3.92)
1.41 (0.57–3.50)
3.17 (0.46–21.70) 2.33 (1.19–4.56)
0.84 (0.21–3.43)
0.91 (0.32–2.57)
10.78 (4.25–27.38)
0.83 (0.53–1.29)
Receives any incomeb
1.29 (0.63–2.65)
1.50 (0.82–2.75)
10/66-diagnosed dementia
0.71 (0.44–1.13)
3.11 (1.97–4.89)
1.77 (1.05–2.98)
3.20 (2.07–4.96)
1.77 (0.80–3.91)
1.92 (0.83–4.43)
0.89 (0.28–2.85)
3.26 (0.68–15.56) 0.95 (0.31–2.94)
6.11 (1.89–19.8)
1.15 (0.57–2.32)
Stroke
1.24 (0.63–2.43)
3.58 (2.50–5.12)
7.0 (4.83–10.14)
3.37 (2.13–5.33)
3.80 (2.02–7.15)
1.84 (1.18–2.85)
2.26 (1.48–3.46)
4.60 (2.65–8.00)
2.52 (1.40–4.53)
2.62 (1.65–4.16)
3.01 (1.99–4.55)
2.20 (0.88–5.51)
3.93 (1.73–8.94)
4.56 (1.35–15.36) 3.66 (2.10–6.38)
3.16 (1.82–5.49)
4.74 (2.95–7.60)
Past history of depression
2.15 (1.59–2.91)
2.06 (1.99–3.39)
Limiting physical impairments
6.95 (2.67–18.11)
1.46 (0.96–2.20)
0.49 (0.18–1.33)
0.98 (0.46–2.09)
1.39 (0.94–2.08)
1.93 (1.04–3.60)
0.97 (0.23–4.00)
0.78 (0.19–3.18)
1.65 (0.86–3.16)
1.17 (0.63–2.17)
2.05 (1.08–3.90)
2.76 (1.37–5.56)
1.49 (0.83–2.70)
0.68 (0.12–3.88)
0.92 (0.13–6.34)
1.96 (1.29–2.96)
1.65 (0.48–5.69)
1.00 (0.40–2.55)
0.94 (0.35–2.51)
Contact with family (5monthly or none)
Contact with friends (5monthly or none)
1.15 (0.73–1.83)
1.43 (0.64–3.22)
1.57 (0.71–3.45)
Food insecurity
0.76 (0.10–5.66)
1.01 (0.84–1.23)
1.54 (0.81–2.92)
0.94 (0.52–1.70)
2.03 (0.99–4.15)
2.31 (1.12–4.74)
2.43 (0.89–6.63)
3.60 (1.62–7.98)
1.17 (0.45–3.06)
0.79 (0.61–1.03)
1.05 (0.88–1.26)
1.47 (0.43–5.07)
2.54 (1.46–4.42)
0.58 (0.38–0.87)
0.58 (0.40–0.86)
1.04 (0.77–1.40)
0.87 (0.68–1.13)
0.77 (0.36–1.62)
0.75 (0.39–1.45)
1.01 (0.63–1.62)
Household income (per quarter)
0.76 (0.61–0.95)
0.70 (0.58–0.85)
1.08 (0.89–1.32)
0.88 (0.74–1.06)
1.11 (0.79–1.55)
1.12 (0.81–1.56)
0.97 (0.75–1.25)
0.83 (0.65–1.07)
0.66 (0.31–1.40)
1.14 (0.68–1.91)
Education level (per level)
0.60 (0.32–1.11)
0.67 (0.45–0.99)
0.48 (0.23–1.03)
0.43 (0.22–0.82)
0.88 (0.45–1.70)
0.77 (0.43–1.35)
0.72 (0.24–2.18)
0.76 (0.46–1.24)
0.54 (0.35–0.83)
Married or cohabiting
1.01 (0.38–2.68)
0.96 (0.93–0.99)
1.01 (0.98–1.04)
1.59 (1.02–2.46)
0.73 (0.36–1.47)
1.02 (0.97–1.07)
1.02 (0.98–1.07)
1.32 (0.73–2.38)
1.91 (0.83–4.40)
0.98 (0.94–1.03)
1.02 (0.98–1.05)
2.14 (1.04–4.39)
0.68 (0.24–1.93)
0.98 (0.90–1.06)
0.98 (0.92–1.04)
0.87 (0.33–2.29)
1.07 (0.66–1.75)
1.86 (1.16–2.97)
0.99 (0.96–1.02)
1.01 (0.99–1.03)
Female gender
Adjusteda
Unadjusted
Adjusteda
Unadjusted
Adjusteda
Unadjusted
Adjusteda
Unadjusted
Adjusteda
Unadjusted
Peru: rural
Prevalence ratio (95% CI)
Peru: urban
Prevalence ratio (95% CI)
Age (per year)
Table 2
Factors associated with ICD–10 depressive episode in each location
Mexico: urban
Prevalence ratio (95% CI)
Mexico: rural
Prevalence ratio (95% CI)
Venezuela
Prevalence ratio (95% CI)
Guerra et al
In the World Mental Health Surveys there was a considerable
treatment gap for all non-psychotic mental disorders, greater for
high-income than for low- and middle-income countries.22 For
depression among adults, 29.3% of individuals in high-income
countries and 8.1% of individuals in low- and middle-income
countries were receiving treatment. Unfortunately, we only
collected data on lifetime treatment, but still found that the large
majority of people with ICD–10 depressive episode (75.9% to
95.6% by location) had never received treatment. The proportion
receiving treatment for the current episode would obviously
be significantly lower. Despite this, the majority of those with
ICD–10 depressive episode had consulted a healthcare provider
in the past 3 months, signifying that in many cases the opportunity for depression to be diagnosed and treated was there, but
had been missed.
To date there have been no prospective studies of potential
aetiological factors for late-life depression in low- and middleincome countries. There have, however, been a large number of
well-designed cohort studies carried out in Europe and North
America, the findings from which have been subject to systematic
review2,23 and quantitative meta-analysis.23 There is strong and
fairly consistent evidence to support an increased risk for incident
depression associated with female gender, disability, prior
depression, bereavement and sleep disturbance.
We are limited in the inferences that we can make from our
findings because of the cross-sectional design; associations may
have been inflated because of information bias and we cannot
determine direction of causality. Nevertheless our findings are
broadly consistent with those from incidence studies in highincome countries; limiting physical impairments and a past
history of depression were the two factors most consistently
correlated with the prevalence of ICD–10 depressive episode.
The finding of a strong and consistent cross-sectional association between depression and physical impairment is, in any case,
of practical significance. A recent comprehensive review of interactions between physical and mental health provided strong evidence that, for many chronic physical diseases, comorbidity with
depression complicates help-seeking, diagnosis and treatment
(particularly through reduced adherence), and hence influences
the prognosis of the physical health condition.24
The strength of the association with past history of depression is
of obvious concern given the extent of lifetime under-identification
and treatment apparent in all of the locations. The prevalence of
depression was higher among women in all settings in nearly all
age groups and for all diagnostic criteria, but this effect was
confounded; women were less likely than men to be living with
a partner, and were more likely to have a past history of
depression, and to have limiting physical impairments. A similar
pattern was reported in the SABE study.8
There have been many reports from cross-sectional community
surveys, from a variety of cultures, of associations between late-life
depression and disadvantage with respect to educational level,
occupational social class and income.2 These are highly correlated
variables, and it is difficult to determine the effect of one
independent of the others. The possibility of reverse causality also
needs to be considered – those whose adult life has been scarred by
depression may experience lifelong occupational and economic
disadvantage. In our study, there was very little evidence for an
association between socioeconomic position and late-life
depression prevalence. Level of education seemed to be more
relevant than income or wealth. However, food insecurity was
strongly positively associated with depression in Mexico, with
non-significant trends in Peru and Venezuela.
Some of the heterogeneity in associations across locations in
the current analysis may be explained by limited statistical power;
Late-life depression in Peru, Mexico and Venezuela
a clearer picture of the pattern of correlations with late-life
depression will be obtained when findings on associations across
the full set of 10/66 research centres in Latin America, India,
China and Africa are subjected to quantitative meta-analysis.
More robust information regarding aetiology will become
available when the incidence phase of the project, now underway
in most locations, is completed.
Implications
In summary, our cross-sectional surveys suggest that the
prevalence of late-life depression in Peru, Mexico and Venezuela
is similar to that seen in Europe. There exists a considerable
prevalence of clinically significant depression beyond that
identified by the current ICD–10 and DSM–IV diagnostic criteria.
Both ICD–10 depressive episode and subsyndromal depression are
independently associated with disability, and a similar or even
greater proportion of severe disability at population level may
arise from subsyndromal cases. The large majority of older people
with depression, even those meeting the more exacting DSM–IV
and ICD–10 criteria, have never received treatment. Evidence
suggests that there are effective treatments available for late-life
depression,25 although no trials have been carried out on older
people in Latin America or other low- and middle-income
countries settings. The extent of the current treatment gap poses
a significant challenge for Latin American healthcare systems, with
their relatively weak primary care services and a traditional
reliance on private specialists – local treatment trials may help
to establish the cost-effectiveness of investment and training in
the government sector.
Mariella Guerra, MD, Universidad Peruana Cayetano Heredia, Institute de la
Memoria y Desordenes Relacionadas, Lima, Peru and Centre for Public Mental Health,
Health Service and Population Research Department, Institute of Psychiatry, King’s
College London, UK; Cleusa P. Ferri, PhD, Centre for Public Mental Health, Health
Service and Population Research Department, Institute of Psychiatry, King’s College
London, UK; Ana Luisa Sosa, MD, The Cognition and Behavior Unit, National Institute
of Neurology and Neurosurgery of Mexico, Autonomous National University of
Mexico, Mexico City, Mexico; Aquiles Salas, MD, Medicine Department, Caracas
University Hospital, Faculty of Medicine, Universidad Central de Venezuela, Caracas,
Venezuela; Ciro Gaona, MD, Clı́nica Loira, Caracas, Venezuela; Victor Gonzales,
MD, Institute de la Memoria y Desordenes Relacionadas, Lima, Peru; Gabriela Rojas
de la Torre, BSc, The Cognition and Behavior Unit, National Institute of Neurology
and Neurosurgery of Mexico, Autonomous National University of Mexico, Mexico City,
Mexico; Martin Prince, MD, Centre for Public Mental Health, Health Service and
Population Research Department, Institute of Psychiatry, King’s College London, UK
Correspondence: Mariella Guerra, Alzheimer’s Association, Avda. Araquipa
3845, Miraflores, Lima 18, Peru. Email: [email protected]
First received 2 Feb 2009, final revision 21 May 2009, accepted 17 Jun 2009
Funding
This study was funded by the US Alzheimer Association (IIRG-04-1286-Mexico and Peru)
and by the Fondo Nacional de Ciencia y Tecnologia, Consejo de Desarrollo Cientifico y
Humanistico, and Universidad Central de Venezuela (Venezuela).
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Data supplement
Table DS1
General characteristics of the sample
n (%)
Peru – urban
(n = 1381)
Peru – rural
(n = 552)
Mexico – urban
(n = 1002)
Mexico – rural
(n = 1000)
Venezuela
(n = 1951)
Age, years
65–69
70–74
75–79
80+
375
352
298
355
Gender, female
888 (64.3)
295 (53.4)
665 (66.4)
602 (60.2)
Marital status
Single
Married
Widow
Separated/divorced
145
784
367
75
(10.6)
(57.2)
(26.8)
(5.5)
68
308
157
18
(12.3)
(55.9)
(28.5)
(3.3)
63
470
394
75
(6.3)
(46.9)
(39.4)
(7.5)
42
538
371
48
(4.2)
(53.8)
(37.1)
(4.8)
189
985
548
267
(9.7)
(48.2)
(28.3)
(13.8)
Education level
None
Minimal
Primary completed
Secondary completed
Tertiary
37
90
460
481
305
(2.7)
(6.5)
(33.5)
(35.0)
(22.2)
84
141
267
36
16
(15.4)
(25.9)
(49.1)
(6.6)
(2.9)
227
353
229
99
92
(22.7)
(35.4)
(22.9)
(9.9)
(9.2)
327
510
122
25
16
(32.7)
(51.0)
(12.2)
(2.5)
(1.6)
156
450
973
269
96
(8.0)
(23.2)
(50.1)
(13.8)
(4.9)
(27.2)
(25.5)
(21.6)
(25.7)
179
141
101
131
(32.4)
(25.5)
(18.3)
(23.7)
245
329
205
223
(24.4)
(32.8)
(20.5)
(22.3)
299
252
221
228
(29.9)
(25.2)
(22.1)
(22.8)
860
461
340
290
(44.1)
(23.6)
(17.4)
(14.9)
1252 (64.2)
Receives any income
1012 (74.4)
388 (70.8)
858 (85.5)
682 (68.3)
1413 (74.8)
Receives any pension
908 (65.7)
357 (64.7)
729 (72.7)
254 (25.4)
1153 (59.1)
5 (0.4)
61 (4.4)
1315 (95.2)
38 (6.9)
343 (62.1)
171 (31.0)
12 (1.2)
150 (15.0)
840 (83.8)
213 (21.3)
518 (51.8)
269 (26.9)
35 (1.8)
9 (0.5)
1907 (97.7)
Assets, n
0–2
3–5
6+
Food insecurity
Plumbed bathroom in the household
63 (4.6)
74 (13.5)
39 (3.9)
85 (8.6)
111 (5.9)
1375 (99.6)
399 (72.4)
887 (88.6)
351 (35.1)
1913 (99.7)
Contact with family, never/less than monthly
67 (5.4)
44 (8.6)
80 (8.5)
55 (5.9)
140 (7.6)
Contact with friends, never/less than monthly
451 (32.8)
72 (13.1)
565 (56.4)
597 (59.8)
541 (28.1)
Limiting physical impairments
0
1–2
3+
607 (44.0)
549 (39.8)
224 (16.2)
280 (50.8)
231 (41.9)
40 (7.3)
453 (45.2)
392 (39.0)
158 (15.8)
382 (38.2)
433 (43.3)
185 (18.5)
756 (38.7)
703 (36.0)
492 (25.2)
Past depression
Stroke
10/66 dementia
479 (34.9)
112 (8.2)
121 (8.8)
86 (15.6)
20 (3.6)
37 (6.7)
181 (18.1)
67 (6.7)
104 (10.4)
148 (14.8)
74 (7.4)
111 (11.1)
264 (13.6)
137 (7.1)
119 (6.1)
1
2
Table DS2
Prevalence of depression in each location, according to different criteria, stratified by age and gender
DSM–IV major depression prevalence,
% (95% CI)
ICD–10 depressive episode prevalence,
% (95% CI)
Age groups
Female
Male
Peru – urban
65–69
70–74
75–79
80 +
Total
5.3 (2.5–8.0)
8.2 (4.5–11.8)
10.0 (5.7–14.3)
7.5 (3.9–11.0)
7.5 (58–9.3)
3.6 (0–7.1)
1.5 (0–3.6)
4.6 (0.6–8.6)
6.4 (2.3–10.5)
4.1 (2.3–5.8)
Peru – rural
65–69
70–74
75–79
80 +
Total
1.0 (0–3.0)
6.0 (0.8–11.3)
1.9 (0–5.7)
1.7 (0–5.1)
2.7 (0.8–4.6)
2.5 (0–6.1)
6.9 (0.1–13.6)
2.1 (0–6.3)
1.4 (0–4.2)
3.1 (1.0–5.2)
Mexico – urban
65–69
70–74
75–79
80 +
Total
4.3 (1.4–7.3)
5.2 (2.2–8.2)
6.3 (2–10.7)
7.7 (3.3–12.1)
5.7 (3.9–7.5)
0
4.2 (0.5–7.9)
2.5 (0–6.1)
2.5 (0–6.0)
2.7 (0.9–4.4)
Mexico – rural
65–69
70–74
75–79
80 +
Total
5.6 (2.3–8.8)
4.7 (1.3–8.1)
3.0 (0.1–5.9)
6.5 (2.1–11.0)
5.0 (3.2–6.8)
2 (0–4.7)
2.9 (0–6.2)
3.4 (0–7.4)
6.6 (1.8–11.4)
3.8 (1.9–5.6)
Venezuela
65–69
70–74
75–79
80 +
Total
4.7 (2.9–6.6)
7.7 (4.6–10.8)
6.0 (2.8–9.2)
5.9 (2.6–9.2)
5.8 (4.5–7.1)
3.6 (1.5–5.7)
4.0 (1.01–6.9)
3.2 (0–6.4)
4.6 (0.1–9.1)
3.7 (2.3–5.1)
Total
Female
Male
6.3 (5.0–7.6)
2.3
1.8
2.6
3.3
2.5
(0.5–4.1)
(0–3.6)
(0.3–4.9)
(0.9–5.7)
(1.5–3.5)
2.7 (0–5.7)
0.7 (0–2.2)
0.9 (0–2.8
2.8 (0–5.6)
1.8 (0.6–3.0)
2.9 (1.5–4.3)
1.2
1.9
1.7
1.0
0
(0–3.6)
(0–5.7)
(0–5.1)
(0.1–2.2)
1.3 (0–3.9)
3.4 (0–8.3)
2.1 (0–6.3)
1.4 (0–4.2)
1.9 (0.2–3.6)
4.7 (3.4–6.0)
0.5 (0–1.6)
0.9 (0–2.3)
4 (0.5–7.4)
2.8 (0–5.5)
1.8 (0.8–2.8)
0
0.8 (0–2.5)
0
0
0.3 (0.0–0.9)
4.5 (3.1–5.9)
1.5
0.7
3.0
2.4
1.8
(0–3.2)
(0–2.0)
(0–5.9)
(0–5.2)
(0.7–2.9)
0
0
0
1.9 (0–5.5)
0.5 (0.0–1.2)
5.1 (4.1–6.0)
2.7
3.1
3.7
6.4
3.6
(1.3–4.2)
(1.1–5.2)
(1.2–6.2)
(3–9.8)
(2.6–4.6)
1.6 (0.2–3.1)
1.1 (0–2.7)
1.6 (0–3.9)
1.1 (0–3.4)
1.4 (0.5–2.3)
Total
GMS–AGECAT depression prevalence,
% (95% CI)
Female
Male
EURO–D depression prevalence,
% (95% CI)
Total
Female
Male
Total
30.0(27.6–32.5)
25.9 (20.6–31.3)
34.9 (28.6–41.1)
40.2 (33.0–47.4)
33.2 (26.5–34.9
33.0 (29.7–36.1)
20.9 (13.3–28.5
17.7 (11.0–24.3)
23.1 (15.1–31.2)
28.5 (20.5–36.4)
22.6 (18.8–26.4) 29.2 (26.7–31.7)
2.2 (1.5–3.0)
35.4
38.2
35.2
30.8
34.9
(29.5–41.2)
(31.7–44.6)
(28.3–42.2)
(24.5–37.1)
(31.7–38.1)
17.8 (10.7–25)
20.4 (13.5–27.4)
25.9 (17.5–34.3)
21.3 (14.3–28.2)
21.3 (17.6–25.0)
1.4 (0.4–2.4)
43.0
45.8
45.3
45.8
44.7
(33.1–52.9)
(34.6–56.9)
(31.4–59.1)
(32.7–58.8)
(39.0–50.5)
21.5 (12.2–30.8)
25.9 (14.2–37.5)
33.3 (19.5–47.2)
25 (14.7–35.2)
25.7 (20.3–31.1) 35.9 (31.71–40.0)
28.0
32.5
35.8
32.1
31.5
(19.0–36.9)
(22.1–42.9)
(22.5–49.2)
(19.5–44.8)
(26.1–36.9)
17.7
22.4
20.8
21.1
20.3
1.3 (0.6–2.0)
35.1 (28–42.2)
37 (30.3–43.6)
35.7 (27.3–44.1)
41.9 (33.7–50.2)
37.3 (33.5–41.1)
16.7 (6.9–26.4)
16.9 (10.1–23.8)
26.6 (16.6–36.5)
26.2 (16.5–36)
21.4 (17.0–25.8) 31.9 (28.9–34.9)
28.6
39.0
38.1
36.9
35.5
(22.0–35.3)
(32.3–45.7)
(29.4–46.7)
(28.9–45.0)
(31.8–39.2)
18.3 (8.2–28.4)
20.5 (13.1–27.9)
26.6 (16.6–36.5)
26.2 (16.5–40.0)
22.9 (18.4–27.4) 31.2 (28.3–34.2)
1.3 (0.6–2.0)
43.6
38.9
35.1
37.7
39.4
(36.6–50.7)
(31–46.8)
(26.8–43.3)
(28.9–46.5)
(35.4–43.3)
16.7 (9.3–24.0)
24.3 (15.9–32.6)
24.1 (15–33.3)
20.7 (12.9–28.6)
21.4 (17.3–25.4) 32.2 (29.2–35.2)
34.3
35.6
29.4
30.8
32.9
(27.6–41.1)
(27.8–43.3)
(21.4–37.4)
(22.5–39.2)
(29.1–36.7)
13.7
11.8
20.9
18.3
16.0
(6.9–20.5)
(5.4–18.1)
(12.1–29.7)
(10.7–25.8)
(12.3–19.6)
2.8 (2.1–3.5)
33.8
37.4
41.7
35.5
36.3
(29.7–37.9)
(31.8–43.0)
(35–48.3)
(28.8–42.1)
(33.6–38.9)
22.4 (17.7–27.2)
27.4 (20.6–34.2)
20.2 (13–27.3)
20.7 (12–29.4)
23.0 (19.9–26.2) 31.5 (29.4–33.7)
31.0
33.8
33.5
36.9
33.1
(27.0–35.1)
(28.3–39.3)
(27.1–39.8)
(30.1–43.6)
(30.5–35.7)
19.2
26.3
21.1
24.7
22.0
(14.7–23.7)
(19.7–32.8)
(13.8–28.4)
(15.3–34.1)
(18.9–25.1) 29.1 (27.1–31.2)
(9.1–26.3)
(11.3–33.5)
(8.9–32.7)
(11.4–30.8)
(15.3–25.3) 26.3 (22.5–30.0)
26.1 (23.3–28.9
Table DS3
Frequency of EURO–D symptoms according to depression status
EURO–D symptoms
No depression
Subsyndromal depression
ICD–10 depression
1 Depression, n (%)
1973 (18.4)
2706 (87.1)
788 (87.9)
2 Pessimism, n (%)
1002 (9.4)
1758 (56.7)
603 (67.3)
3 Wishing death, n (%)
286 (2.7)
1025 (33.1)
419 (46.7)
4 Guilt, n (%)
183 (1.7)
399 (12.9)
141 (15.8)
5 Sleep, n (%)
1592 (14.9)
1949 (63.0)
708 (78.9)
222 (2.1)
669 (21.6)
498 (55.5)
1417 (13.2)
1599 (51.5)
453 (50.5)
461 (4.3)
922 (29.7)
443 (49.3)
1582 (14.7)
1731 (55.9)
789 (88.2)
431 (57.2)
6 Loss of interest, n (%)
7 Irritability, n (%)
8 Loss of appetite, n (%)
9 Fatigue, n (%)
10 Concentration, n (%)
741 (7.8)
930 (34.7)
11 Enjoyment, n (%)
173 (1.6)
569 (18.4)
482 (53.8)
12 Tearfulness, n (%)
1230 (11.5)
2128 (68.5)
654 (72.8)
1.0 (1.1)
5.3 (1.4)
7.1 (1.8)
0–3
4–12
3–12
Total score, mean (s.d.)a
Total score, range
a. P50.001 ANOVA.
3
Late-life depression in Peru, Mexico and Venezuela: the 10/66
population-based study
Mariella Guerra, Cleusa P. Ferri, Ana Luisa Sosa, Aquiles Salas, Ciro Gaona, Victor Gonzales, Gabriela
Rojas de la Torre and Martin Prince
BJP 2009, 195:510-515.
Access the most recent version at DOI: 10.1192/bjp.bp.109.064055
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