Why was the cohort set up?
Infectious diseases are still a major concern in developing countries, causing up
to 60% of the deaths in low-income countries. However, non-communicable diseases (NCDs)
and their associated risk factors are becoming a major public health issue in the
developing world where between 38% (low-income) and 80% (upper middle-income) of deaths
are attributable to NCDs.
1
,
2
Major common risk factors include raised blood pressure, elevated blood glucose, obesity,
low physical activity, unhealthy diet habits, smoking and alcohol consumption. Many
risks are associated with lifestyle and have rapidly changed over recent decades,
some driven by urbanization.
3
,
4
Environmental changes as well as population flow have led to an increase in urbanization.
The degree of urbanization is closely linked to distinctive features of the health
profile of rural and urban participants, as well as of rural-to-urban migrants. Internal
migration led to an ‘urbanization’ of the individuals who were born in rural settings
and causes a change in their lifestyle habits. Low physical activity, for example,
is linked to urban settings whereas agriculture, the main labour in the rural area,
implies high physical activity.
Within-country migration is a complex phenomenon and can be driven by many reasons,
including but not limited to the aspiration to seek better socioeconomic standards
or living conditions, war displacement and natural disasters. The PERU MIGRANT Study
(PEru's Rural to Urban MIGRANTs Study) was conducted to identify the impact of rural-to-urban
migration on selected cardiometabolic outcomes, e.g. obesity, type-2 diabetes and
hypertension. The study includes rural-to-urban migrants as well as rural and urban
non-migrant groups.
5
Because of political violence between the 1970s and 1990s, approximately 120 000 families
moved from rural highlands to urban coastal settlements in Peru.
6
,
7
This scenario offered an opportunity to study rural-to-urban migration without the
potential bias introduced by socioeconomic improvement, thus decreasing the impact
of selection bias.
The research questions addressed by the PERU MIGRANT Study were:
Is there a difference in specific cardiovascular disease (CVD) risk factors in rural-to-urban
migrants compared with those rural or urban dwellers who did not migrate?
Do CVD risk factor patterns among migrant populations vary by:
length of residence in urban environment?
lifetime exposure to urban environment?
age at first migration?
We considered analysing the exposure to the urban environment as years and percentage
of life exposure. Thus, the length of urban residence was considered as the number
of years that the rural-to-urban migrants reported to had lived in an urban setting.
On the other hand, lifetime exposure was the percentage obtained by dividing the number
of years lived in an urban area by participant’s current age in years.
Settings
The PERU MIGRANT Study was conducted in Lima, an urban sea-level setting, and Ayacucho,
a rural high-altitude setting located at 2761 m above sea level. The reason for choosing
these sites was for convenience. The research team had conducted other studies previously
in these settings, or were close to researchers who had worked there previously. Ayacucho
is an Andean department considered one of the areas most affected by political violence
that occurred between 1970 and 1980. About 50% of all deaths caused by terrorism occurred
in this area.
7
Approximately 11% of the total migrants to Lima were from Ayacucho.
8
These figures made Ayacucho the leading source of rural-to-urban migrants to Lima.
We selected the village of San Jose de Secce, in the district of Santillana, province
of Huanta, in Ayacucho as the rural study site. In San Jose, 50% of the population
is considered extremely poor, with only 5% of residents having direct access to potable
water. In addition, the literacy rate is around 60% and the main language is Quechua.
9
The peri-urban shanty town called Las Pampas de San Juan de Miraflores, in the south
of Lima, was chosen as the urban study site. The definition we consider for ‘urban’
is having approximately 100 houses clustered. The population in this area is ‘extremely
poor’ and up to 20% is ‘poor’. Literacy rate is 79% and the main language is Spanish.
Both sites represent the urban, migrant and rural populations due to the number of
participants and environmental characteristics. Funding for the baseline assessment
was provided by the Wellcome Trust. The first follow-up assessment was partly funded
by the Universidad Peruana Cayetano Heredia. The second follow-up round was partly
funded by the GloCal Health Fellowship Program from the University of California Global
Health Institute.
Who is in the cohort?
In 2007, adult subjects from San Jose de Secce rural group were identified after a
census was made. The 2006 updated Las Pampas de San Juana de Miraflores census was
used to identify urban residents who were born in Lima (urban dwellers) and who were
born in Ayacucho (rural-to-urban migrants). Participants were recruited using a single-stage
random sampling technique that was sex- and age-stratified (30–39, 40–49, 50–59 and
≥60 years) using name, address and national identification number. Men and women ≥
30 years old and permanent residents were considered eligible for the study. Pregnant
women and those with mental conditions that would have prevented completion of the
study procedures were excluded.
Power calculations were derived using conservative estimates of the prevalence of
major risk factors in the areas of Huaraz (urban, Andes) and Ingenieria (urban, Lima).
The baseline survey, conducted in 2007–08, was designed to include 1000 participants:
200 born in Ayacucho who have always lived in rural areas, 600 rural-to-urban migrants
born in Ayacucho and living in Pampas de San Juan de Miraflores in Lima and 200 urban
participants who have always lived in urban areas. Comparing the Lima with the Andes
group, at least 200 people in each group would give a power of 80% and a significance
level of 5% to detect a difference in the prevalence of hypertension (33% vs 19.5%),
hypercholesterolaemia (22.7% vs 10.6%) and diabetes (7.6% vs 1.3%). More rural-to-urban
individuals were included to have further information from this group, because additionally,
this group was expected to be divided into two groups to be analysed according to
migration surrogates.
A total of 1606 dwellers were invited to participate in the study. The general response
rate at enrolment was 73.2% (1176/1606) and the overall response rate at completion
was 61.6% (989/1606). Response rate was the highest in the rural group (84.8%), and
the corresponding figures were 56.8% and 77.7% for urban and migrant groups, respectively.
Further details about sample size and sample enrolment at baseline are available elsewhere.
5
Characteristics of the individuals who refused to participate in the baseline assessment,
and reasons for refusal, have been previously published,
5
including having access to health care and thus not needing the health evaluation
provided by the study, and logistical issues e.g. time constraints due to work or
travel.
5
Before participation, an informed consent was signed by the participant. The protocol
of the study was approved by the institutional review board of the Universidad Peruana
Cayetano Heredia.
How often have they been followed up?
Five years after the baseline assessment, in 2012–13, the participants of the PERU
MIGRANT Study were re-contacted for the first follow-up. A second follow-up assessment
was completed in June 2016. The response rates for the first and second follow-up
rounds were 93.8% and 85.6%, respectively (Figure 1
).
Figure 1
Number of participants in each round of the PERU MIGRANT Study. The percentages for
follow-up rates do not include deaths in each group.
The first follow-up evaluation aimed to revisit all 989 participants recruited at
baseline. In this round, 33 deaths were recorded and 61 participants were lost to
follow-up (Figure 1). Sociodemographic characteristics of participants re-contacted
or lost to follow-up at the first follow-up round are presented in Table 1 and Table
2.
Table 1
Characteristics of PERU MIGRANT Study participants at first follow-up round: deaths,
lost-to-follow up and re-contacted
Re-contacted participants
Lost-to- follow-up participants
Dead N = 33
Alive N = 895
Overall N = 61
Migration status
Rural
10 (30.3)
191 (21.3)
0 (0.0)
Migrant
14 (42.4)
526 (58.8)
49 (80.1)
Urban
9 (27.3)
178 (19.9)
12 (19.9)
Age (years)
30–39
3 (9.1)
259 (28.9)
20 (32.8)
40–49
1 (3.0)
263 (29.4)
18 (29.6)
50–59
7 (21.2)
252 (28.2)
13 (21.4)
60+
22 (66.7)
121 (13.5)
10 (16.4)
Sex
Male
22 (66.7)
413 (46.1)
32 (52.5)
Female
11 (33.3)
482 (53.9)
29 (47.5)
Education
Primary or less
24 (72.7)
430 (48.4)
26 (42.6)
Some secondary or more
9 (27.3)
463 (51.6)
35 (57.4)
Asset index
Lowest
19 (57.8)
313 (35.0)
15 (24.6)
Middle
8 (24.2)
294 (32.9)
25 (40.9)
Highest
6 (18.1)
288 (32.2)
21 (34.4)
The entries in parentheses refer to the corresponding percentages (%).
Table 2
Characteristics of PERU MIGRANT Study participants at first follow-up round, i.e.
deaths, lost-to-follow-up and re-contacted according to study group
Re-contacted participants
Lost-to-follow-up participants
Rural N = 201
Migrant N = 540
Urban N = 185
Overall N = 61
Mortality
10 (5.0)
14 (2.6)
9 (4.8)
–
Age (years)
30–39
20 (9.9)
64 (11.9)
23 (12.4)
60 (98.4)
40–49
59 (29.4)
157 (29.4)
48 (25.9)
1 (1.6)
50–59
43 (21.4)
158 (29.3)
57 (30.8)
–
60+
55 (27.4)
135 (25.0)
47 (25.4)
–
Sex
Male
95 (47.3)
252 (46.7)
88 (47.1)
32 (52.5)
Female
106 (52.7)
288 (53.3)
99 (52.9)
29 (47.5)
Education
None/some
132 (65.6)
168 (31.2)
13 (6.9)
15 (24.6)
Primary complete
30 (14.9)
92 (17.1)
19 (10.2)
11 (18.0)
Some secondary or more
39 (19.4)
279 (51.7)
154 (82.8)
35 (57.4)
Asset index
Lowest
196 (97.5)
108 (20.0)
28 (14.9)
15 (24.6)
Middle
5 (2.5)
233 (43.2)
64 (34.2)
25 (40.9)
Highest
0 (0.0)
199 (36.9)
95 (50.8)
21 (34.4)
The entries in parentheses refer to the corresponding percentages (%).
Updated figures for 2016, after the second follow-up visit, include 57 deaths recorded
and 142 participants lost to follow-up. Accordingly, to date, information from 847
participants, divided into 154 rural dwellers, 520 rural-to-urban migrants and 173
urban individuals, is available. The cumulative mortality after the second follow-up
was 6.7% using 989 as the population’s denominator. Characteristics of re-contacted
participants at the second follow-up round are presented in Table 3.
Table 3
Characteristics of PERU MIGRANT study participants at second follow-up
Re-contacted participants
Rural N = 154
Migrant N = 520
Urban N = 173
Mortality
13 (8.4)
30 (5.8)
14 (8.1)
Age (years)
30–39
–
–
–
40–49
41 (29.5)
147 (30.3)
43 (26.7)
50–59
37 (30.3)
153 (31.5)
53 (32.9)
60+
61 (26.7)
186 (38.7)
65 (40.4)
Sex
Male
63 (40.9)
241 (46.4)
77 (44.5)
Female
91 (59.1)
279 (53.7)
96 (55.5)
Education
None/some
101 (65.6)
164 (31.6)
12 (7.0)
Primary complete
22 (14.3)
90 (17.3)
18 (10.4)
Some secondary or more
31 (20.1)
265 (51.1)
142 (82.6)
Asset index
Lowest
149 (96.8)
103 (19.8)
29 (16.8)
Middle
5 (3.5)
222 (42.7)
60 (34.7)
Highest
0 (0.0)
195 (34.7)
95 (48.6)
The entries in parentheses refer to the corresponding percentages (%).
What has been measured?
The baseline assessment of the PERU MIGRANT Study aimed to identify prevalence of
CVD risk factors and major NCDs. These included: obesity, defined as BMI ≥ 30; hypertension,
considered as the mean of three blood pressure measurements ≥ 140/90, or previous
physician diagnosis or currently receiving treatment for hypertension; type 2 diabetes
mellitus, considered in those with fasting glucose ≥ 126 mg/dl, or previous physician
diagnosis or currently receiving treatment for diabetes. Dyslipidaemia was considered
as total cholesterol ≥ 200 mg/dl, triglycerides ≥ 200 mg/dl, low-density lipoprotein
(LDL) ≥ 160 mg/dl and high-density lipoprotein (HDL) ≤ 40 mg/dl for men and ≤ 50 mg/dl
for women. Cardiovascular diseases (myocar-dial infarction and stroke) were considered
as the self-report of previous diagnosis by a physician. Other risk factors assessed
were: physical activity and tobacco and alcohol consumption.
The follow-up rounds were conducted to study the incidence and risk of those NCDs
and their associated risk factors. In addition, in order to better determine CVD,
the second follow-up included an electrocardiographic evaluation in which signs of
necrosis were considered for diagnosis. Variables collected throughout study rounds
are summarized in Table 4. In brief, information was collected using: face-to-face
questionnaires including sociodemographic variables, lifestyle behaviuors and self-reported
clinical conditions; the clinical evaluation, e.g. anthropometric procedures and electrocardiogram;
and blood samples, e.g. lipid profile, fasting glucose and inflammatory markers, among
others.
Table 4
Information collected at each study round, the PERU MIGRANT Study
Phase
Information collected
Baseline 2007–08
Laboratory
Fasting glucose, fasting insulin, glycosylated haemoglobin, lipid profile (total cholesterol,
HDL-c, triglycerides), high ultrasensitive C-reactive protein, self-reported socioeconomic
position
Clinical examination
Anthropometric measurements: weight, waist and hip circumferences, skinfolds (subcapsular,
biceps, triceps and supra-iliac), blood pressures
Questionnaire
Self-reported NCDs (myocardial infarction, stroke, type 2 diabetes mellitus, dyslipidaemia),
alcohol consumption, smoking status, mental health (12-item general health questionnaire),
medication (specially antihypertensive and hypoglycaemic agents), socioeconomic factors,
physical activity level (IPAQ), a social capital instrument validated in Peru, acculturation
scale, Rose angina questionnaire
First follow-up 2012–13
Clinical examination
Anthropometric measurements: weight, height, waist and hip circumferences, blood pressures
Questionnaire
Self-reported NCDs, alcohol consumption, smoking status, mental health (12-item general
health questionnaire), medication (specially antihypertensive and hypoglycaemic agents),
verbal autopsy and death certificates if available
Second follow-up 2015–16
Laboratory
Fasting glucose
Clinical examination
Weight, body composition (bioimpedance) and blood pressure measurements, assessment
using electrocardiogram (ECG)
Questionnaire
Questions related to the occurrence of cardiovascular diseases and/or risk factors
such as previous diagnosis of myocardial infarction, hypertension, stroke, diabetes,
physical activity level (IPAQ); plus self-rated status of health, medication and the
patient health questionnaire (PHQ-9; mental health status)
Personal histories of cardiovascular diseases and other non-communicable diseases
were self-reported.
What has it found? Key findings and publications
Major findings from the baseline assessment identified a clear pattern of differences
in cardiovascular risk factors according to migration status.
10
The length of urban residence had a robust impact on the prevalence of obesity in
rural-to-urban migrants: 12% higher obesity prevalence was observed for each additional
10-year period of urban residence [95% confidence interval (CI) 6‐18].
11
At baseline, prevalences of overweight, obesity and low physical activity were higher
in the urban and migrant groups, relative to the rural group (P for trend = 0.001).
12
,
13
Predictably, urban participants were almost 33 times more likely to have low physical
activity [odds ratio (OR) 32.98; 95% CI 11.02‐98.63].
Hypertension prevalence was higher in the urban (29%) and migrant (16%) groups; however;
the difference in prevalence between the migrant and rural groups (11%) was not significant.
10
On the other hand, the overall prevalence of diabetes was 4.5% with a significant
difference between groups (0.8%, 2.8% and 6.3% for rural, migrant and urban groups,
respectively, P < 0.01).
14
Higher odds of impaired fasting glucose, metabolic syndrome and diabetes were found
in participants who migrated at age ≥ 12 years vs their peers who migrated at younger
ages.
14
A suboptimal control rate of hypertension was found in 95% of the hypertensive participants
and 100% of those with diabetes, considering controlled those with blood pressure
and HbA1c normal levels. For either or both conditions, treatment rates were higher
in the urban than the migrant and rural groups, with a total of only 40% currently
on medication.
14
Data from the first follow-up round addressed four main issues: all-cause and specific-cause
mortality;
15
hypertension incidence;
16
obesity incidence;
17
and low HDL-cholesterol as a cardiovascular risk factor.
18
In both follow-up rounds, mortality data were collected through verbal autopsy and
death certificates when available. In addition, in the second follow-up, since we
could not re-contact all the participants from the baseline, we requested information
from the national death records.
Of the 33 deaths recorded in the first follow-up evaluation, nine were due to CVDs
and eight due to cancer of unknown aetiology. Other causes included sepsis, accidental
injuries and asthma, among others. In six cases, cause of death was undetermined.
Men, older participants and individuals with hypertension, as well as those with lower
education levels or a low assets index, were more likely to have died. There was a
trend towards lower CVD mortality in migrant and rural dwellers, relative to urban
counterparts. As such, urban dwellers were at higher risk of all-cause mortality compared
with rural dwellers.
15
Regarding hypertension, the rural group showed greater risk of developing hypertension,
when compared with their urban counterparts, and central obesity explained most of
the new hypertension cases observed across study groups.
16
Relative to rural dwellers, the urban and migrant groups showed greater incidence
of obesity. Migrant and urban participants showed an 8- and 9.5-fold higher incidence
ratio of obesity compared with the rural group, respectively. Central obesity was
the highest in the migrant group and its incidence ratio was associated with a higher
assets index.
17
Finally, individuals with non-isolated low HDL-cholesterol had a 2- to 3-fold higher
risk of CVD, including fatal stroke and myocardial infarction, at the first follow-up
assessment. Furthermore, lower levels of HDL-cholesterol were found in the rural group
compared with their migrant and urban counterparts.
18
What are the main strengths and weaknesses?
The PERU MIGRANT Study followed three well-defined population groups: rural dwellers,
rural-to-urban migrants and urban participants. The strength of the PERU MIGRANT Study
does rely in its well-defined population groups: rural, urban and rural-to-urban migrants.
A frequent and potential limitation of migration studies rests in the self-selection
of the migrant participants due to better socioeconomic standards. Therefore, a strength
of this cohort is that the migrants moved to urban settings due to political violence
events, reducing the risk of socioeconomic selection bias. Finally, we re-contacted
most of the initial study sample, particularly those in rural settings. Having completed
two extensive follow-ups over an 8-year period, this cohort of rural-to-urban migrants
and non-migrants is an asset for studies arising from low-income settings.
Still, the PERU MIGRANT Study has several limitations. First, the sample size is rather
small and statistical power for some analyses is restricted. Second, most of the refusals
at baseline were observed in the oldest old and in male participants; however, the
final cohort studied included similar proportions of sex and age, minimizing the selection
bias. Furthermore, urban individuals who rejected participation in the study had higher
education levels, compared with those enrolled in that group. This fact could be associated
with a low socioeconomic status and less access to health care adding selection bias.
5
Finally, neither at baseline nor at the first follow-up round did we collect information
about dietary patterns. Nevertheless, at the second follow-up a questionnaire to assess
fat intake (e.g. low or high) was included.
19
This caveat could be overcome with the use of assumptions in interpreting results.
For example, relative to rural participants, their urban fellows would consume more
fat/energy-dense foods, and so would migrants.
20
In the future, further funding would enable a full assessment of markers in blood
samples to complement the panel obtained at baseline, as well as measurements of fasting
glucose obtained during the second follow-up, allowing for more comprehensive time
variation analysis of glucose, lipid profiles, HbA1C and inflammatory markers.
Can I get hold of the data? Where can I find out more?
Baseline data can be freely accessed online at [https://figshare.com/articles/PERU_MIGRANT_Study_Baseline_dataset/3125005].
If interested in establishing collaborations, conducting analyses or having further
information regarding the PERU MIGRANT Study, please send an e-mail to our research
centre at [cronicas@oficinas-upch.pe]. Should you wish to use our data, please contact
us with a brief analysis plan: title, research question, general or specific objectives,
main variables and statistical analysis plan. Further information about our research
group can be found at [http://en.cronicas-upch.pe/].
Funding
The establishment of the PERU MIGRANT Study was funded through a Wellcome Trust Master
Research Training Fellowship and a Wellcome Trust PhD Studentship to J.J.M. (074833/Z/04/A).
The first follow-up evaluation was funded by Universidad Peruana Cayetano Heredia
(Fondo Concursable No. 20205071009). The second follow-up evaluation was funded by
the National Heart, Lung, and Blood Institute, National Institutes of Health, through
the GloCal Health Fellowship Program from the University of California Global Health
Institute. R.M.C-L., A.B-O., J.J.M. and the CRONICAS Centre of Excellence in Chronic
Diseases were supported by Federal Funds from the United States National Heart, Lung,
and Blood Institute, National Institutes of Health, Department of Health and Human
Services, under contract No. HHSN268200900033C. L.S. is a Wellcome Trust Senior Clinical
Fellow (098504/Z/12/Z), and A.B-O. is a Wellcome Trust Research Training Fellow in
Public Health and Tropical Medicine (103994/Z/14/Z).
Conflict of interest: The authors declare no conflict of interest.
PERU MIGRANT Study profile in a nutshell
The cohort was established to study cardiovascular diseases and associated risk factors
in three population groups in Peru: rural, urban and rural-to-urban migrants. The
PERU MIGRANT Study’s posed hypothesis was that the occurrence and progression of cardiovascular
disease and their risk factors would be different among these groups.
Peru offers an unusual scenario to study rural-to-urban migration: a lot of migration
happened in response to violence rather than economic issues, with a reduced likelihood
of selection effects.
The study was conducted in Ayacucho, a rural site, 2761 m above sea level, and in
Lima, an urban site at sea level. The baseline assessment was in 2007–08, with 989
participants aged ≥ 30 years.
There have been two follow-up evaluations conducted in 2012–13 and 2015–16. Out of
the 989 people enrolled at baseline, 57 deaths were recorded and 142 participants
were lost to follow-up, considering both study rounds. Although the second follow-up
has been completed, the PERU MIGRANT is an ongoing cohort. We expect to re-contact
the participants in the near future.
Information collected throughout baseline, first and second follow-up rounds includes
participants’ sociodemographic, behavioural and medical history and clinical data.
Blood samples were taken at baseline and at the second follow-up evaluation only.
Baseline data are available online at [https://figshare.com/articles/PERU_MIGRANT_Study_Baseline_dataset/3125005].
Collaboration proposals or requests for further information should be e-mailed to
[cronicas@oficinas-upch-pe].