Association between chronic conditions and health-related quality of life: differences by level of urbanization in Peru
Association between chronic conditions and health-related quality of life: differences by level of urbanization in Peru
Alvaro Taype-Rondan 0 1 2 3 4 5 6 7 8 9
Elizabeth Sarah Abbs 0 1 2 3 4 5 6 7 8 9
Maria Lazo-Porras 0 1 2 3 4 5 6 7 8 9
William Checkley 0 1 2 3 4 5 6 7 8 9
Robert H. Gilman 0 1 2 3 4 5 6 7 8 9
Liam Smeeth 0 1 2 3 4 5 6 7 8 9
J. Jaime Miranda 0 1 2 3 4 5 6 7 8 9
Antonio Bernabe-Ortiz 0 1 2 3 4 5 6 7 8 9
Liam Smeeth 0 1 2 3 4 5 6 7 8 9
0 William Checkley
1 Elizabeth Sarah Abbs
2 & Antonio Bernabe-Ortiz
3 Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine , London , UK
4 Department of International Health, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD , USA
5 Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University , Baltimore, MD , USA
6 School of Medicine and Public Health, University of Wisconsin , Madison, WI , USA
7 CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia , Armenda ́riz 497, Miraflores, 18 Lima , Peru
8 J. Jaime Miranda
9 Robert H. Gilman
Purpose To evaluate the role of urbanization as an effect modifier for the association between specific chronic conditions and number of conditions with health-related quality of life (QOL). Methods We analyzed cross-sectional data from the CRONICAS Cohort Study conducted in Lima (highly urbanized), Tumbes (semi-urban), as well as rural and urban sites in Puno. Exposures of interest were chronic bronchitis, depressive mood, hypertension, type 2 diabetes, and a composite variable aggregating the number of chronic conditions (the four exposures plus heart disease and stroke). QOL outcomes were assessed with EuroQol's EQ-5D visual analogue scale (EQ-VAS). We fitted linear regressions with robust variance to evaluate the associations of interest. Study site was assessed as a potential effect modifier using the likelihood-ratio (LR) test. Results We evaluated data on 2433 subjects: 51.3% were female, mean age was 57.2 years. Study site was found to be an effect modifier only for the association between depressive mood and EQ-VAS score (LR test p 0.001). Compared to those without depressive mood, participants with depressive mood scored -13.7 points on the EQ-VAS in Lima, -7.9 in urban Puno, -11.0 in semi-urban Tumbes, and -2.7 in rural Puno. Study site was not found to be an effect modifier for the association between the number of chronic conditions and EQ-VAS (LR test p = 0.64). Conclusion The impact of depressive mood on EQ-VAS was larger in urban than in rural sites, while site was not an effect modifier for the remaining associations.
Health-related quality of life; Multiple chronic conditions; Burden of disease; Depressive mood
Health-related quality of life (QOL) is defined as ‘‘an
individual’s or a group’s perceived physical and mental health
over time’’ [
]. QOL is worse in patients with chronic
noncommunicable conditions, who may face a multitude of
clinical, psychosocial, and economic challenges [
Consequently, poorer QOL scores are observed with each
additional chronic condition [
]. Although not all
chronic conditions affect QOL in the same magnitude, the
list of conditions notably affecting QOL includes chronic
pain disorders and psychiatric conditions [
4, 12, 13
The impact of chronic conditions on QOL could be
altered by urbanization level. In rural areas, barriers to
healthcare access may challenge clinical follow-up and
disease management [
], translating to lower QOL
scores than those in urban sites with more access to
medical care. On the other hand, individuals in rural areas do
have higher indices of social support [
] which could
promote better QOL and thus balance the effect of lower
access to healthcare services in these areas. In addition,
differences in culture and customs between areas of
different urbanization levels could further modify how
chronic conditions impact health-related QOL.
Understanding how chronic conditions affect QOL,
particularly across different cultural contexts and regions
might help to evaluate the relative impact of chronic
conditions, prioritize conditions within limited-resources sites,
and design site-specific interventions to improve QOL
across different contexts [
]. To date, the majority of
studies assessing the association between chronic
conditions and QOL have been conducted in urban areas, and we
could not find studies exploring how urbanization modifies
the chronic condition-QOL association.
Therefore, this study uses data from four sites in Peru
with varying degrees of urbanization, with the objective to
evaluate the role of urbanization as an effect modifier for
the association between specific chronic conditions and
number of conditions with health-related QOL.
Study design and sites
This is a secondary analysis using data derived from the
CRONICAS Cohort Study, conducted in four diverse sites
in Peru: (1) Las Pampas de San Juan de Miraflores, a highly
urbanized site in Lima, Peru’s capital, located at sea level
with *15,000 people/km2. (2) Urban Puno, a city with
approximately 150,000 inhabitants located at 3825 m
above sea level in the Peruvian Andes. (3) Semi-urban
costal site in Tumbes, located at sea level in north Peru
near the Equator, composed of a group of communities
with about 20,000 people spread over 80 km2. (4) Rural
Puno, composed of a group of rural villages surrounding
urban Puno [
At baseline in 2010, the CRONICAS Cohort Study
recruited individuals with a minimum age of 35 years who
were full-time residents of the selected population sites. A
single random selection of subjects was performed at each
site, stratified by sex and age, using the most updated
census data available. Only one participant per household
was enrolled, as detailed elsewhere [
]. The first and
second follow-up assessments were completed, on average,
15 and 30 months after baseline. For the present study, we
only included participants who completed the second
follow-up as the outcome of interest was evaluated in this
assessment, and included those who had provided all the
variables of interest.
A detailed questionnaire was administrated by trained
fieldworkers including socio-demographic characteristics,
self-report of chronic conditions, and the Center for
Epidemiological Studies Depression Scale [
Systolic and diastolic blood pressures were measured
three times using an automatic monitor OMRON HEM-780
after 5 min of resting period, and the mean of the last two
measurements were used. In addition, fasting blood glucose
samples were obtained and processed using an enzymatic
colorimetric method (GOD-PAP; Modular
P-E/RocheCobas, Grenzach-Whylen, Germany) and analyzed at an
off-site facility in Lima, following standard laboratory
techniques and quality control procedures as detailed
Outcome: quality of life
QOL was assessed with the EuroQol-5D (EQ-5D)
questionnaire, which includes the visual analogue scale
(EQVAS) and a 5-dimensions questionnaire. We used EQ-VAS
scores as our main outcome. It assesses perceived QOL by
asking participants to rank their current state of wellness
from zero, worst imaginable health, to 100, best imaginable
The 5-dimension questionnaire was our secondary
outcome. It evaluates QOL across five health status
dimensions: mobility, self-care, usual activities, pain/discomfort,
and anxiety/depression. Within each dimension, severity is
evaluated with three categories: no problems, some
problems, and extreme problems [
]. For our analysis,
each dimension was dichotomized as ‘‘no problems’’ and
‘‘some or extreme problems.’’
We decided not to use the EQ-5D tariffs validated for
other Latin American countries because these have not
been validated for rural settings where EQ-5D items could
have a different impact.
Exposure: chronic conditions
We included the most prevalent chronic conditions
collected in the CRONICAS Cohort Study. Exposures of
interest were chronic bronchitis, depressive mood,
hypertension (HTN), type 2 diabetes (T2D), and, separately, a
composite variable aggregating the number of chronic
conditions (the four exposures plus heart disease and
stroke). Heart disease and stroke were uncommon in rural
Puno, so were not included as single exposures in the
The rationale for the selection of particular chronic
conditions was the availability of objective measurements,
validated instruments, as well as conditions with less recall
bias. The operational definition of each chronic condition
was based in previous references, as detailed in Table 1.
Effect modifier: study site
Study site, which refers to the site where participants lived,
was categorized according to gradient of urbanization as
follows: highly urban Lima, urban Puno, semi-urban
Tumbes, and rural Puno.
Other variables included in the analyses were sex, age
(categorized as \45, 45–54, 55–64, and C65 years),
educational level (primary or less: 0–6 years, secondary:
7–11 years, and superior: C12 years), and wealth index (in
tertiles). The wealth index was constructed based on
household income, assets, and household facilities as
suggested elsewhere, with higher scores indicating greater
levels of wealth [
For descriptive analysis, means, standard deviations
(±SD), absolute and relative frequencies were used. Chi
square and ANOVA tests were used to compare the four
sites with respect to sex, age, educational level, wealth
index, number of chronic conditions, specific chronic
conditions, EQ-VAS, and EQ-5D dimensions.
We generated linear regression models with robust
variance to calculate coefficients (b) and their 95%
confidence intervals (95% CI), to evaluate the association
between five exposures: HTN, chronic bronchitis,
depressive mood, T2D, and number of chronic conditions; and
EQ-VAS scores as a numeric outcome. These associations
were calculated for the overall population, and for each
study site. To answer our research question, study site was
assessed as a potential effect modifier using the
likelihoodratio (LR) test.
For our secondary analyses, we generated Poisson
regression models with robust variance to calculate
prevalence ratios (PR) and 95% CI to evaluate the
association between number of chronic conditions and each of
the five EQ-5D dimensions for each of the study sites. Both
linear and Poisson models were adjusted for sex, age,
educational level, and wealth index.
Due to high illiteracy rates, especially in rural areas, all
participants provided verbal informed consent. The study
was approved by the Institutional Review Boards at
Universidad Peruana Cayetano Heredia and A.B. PRISMA,
in Lima, Peru, and at the Johns Hopkins Bloomberg School
of Public Health in Baltimore, USA.
At baseline, 3601 participants were recruited. Of these, 38
died and 865 were lost during follow-up; thus, only 2698
(74.6%) participants were included in the current study.
We further excluded 265 participants with incomplete
chronic disease and/or QOL information. Therefore, our
current analysis includes data from 2433 participants
(67.6% of the original sample enrolled). Participants
included in the analysis were more likely to be from Lima
and Tumbes, be younger, have a secondary-school
education, and be from the highest wealth index, compared to
those not included (Supplementary Table 1).
Of the 2433 participants included, 891 (36.6%) were
from Lima, 357 (14.7%) from urban Puno, 890 (36.6%)
from Tumbes, and 295 (12.1%) from rural Puno. Mean age
was 57.2 (SD ± 12.3) years, and 1248 (51.3%) were
female (Table 2).
In terms of single chronic conditions, 573 participants
(23.6%) had HTN, 309 (12.7%) had chronic bronchitis, 309
(12.7%) had depressive mood, 254 (10.4%) had T2D, 131
(5.4%) had heart disease, and 20 (0.8%) had a history of
stroke. Chronic bronchitis and depressive mood were more
prevalent in rural Puno. When chronic conditions were
aggregated, 1152 (47.3%) participants had C1 chronic
condition. The number of chronic conditions and
Any of the following: systolic blood pressure (SBP) C140 mmHg, or diastolic blood pressure (DBP) C90 mmHg, or
self-report of physician diagnosis and current use of antihypertensive drugs; according to the seventh report of the Joint
National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure [
Presence of phlegm production on most days for at least 3 months a year, in the last 12 months, similar to previous
A score of C23 in the Spanish-validated version of the Center for Epidemiological Studies Depression Scale [
Any of the following conditions: fasting blood glucose C126 mg/dL, or self-report of physician diagnosis and current use
of diabetes medication; according to the World Health Organization definitions [
Self-report physician diagnosis of heart failure or previous myocardial infarction
Self-report of previous stroke, diagnosed by a physician
prevalence of individual chronic conditions varied across
sites, except for stroke. Mean EQ-VAS score was highest
in Lima, and lowest in rural Puno. The prevalence of
having some or extreme problems in most of the EQ-5D
dimensions was lower in Tumbes and higher in rural Puno
Association between chronic conditions and EQ
When evaluating the relationship between specific chronic
conditions and EQ-VAS score (Table 3), we found that
participants with depressive mood and chronic bronchitis
had significantly lower EQ-VAS scores than those without
these conditions, while EQ-VAS scores in participants with
HTN and T2D were not different compared with those
without these conditions.
Study site was an effect modifier of the association
between having depressive mood and EQ-VAS score (LR
test p \ 0.001), but it was not found to be an effect
modifier for the association between EQ-VAS score and having
HTN, chronic bronchitis, and T2D. Therefore, compared to
those without depressive mood, participants with
depressive mood scored -13.7 points on the EQ-VAS in Lima,
-7.9 in urban Puno, -11.0 in semi-urban Tumbes, and
-2.7 in rural Puno.
Across all sites, EQ-VAS score decreased with each
additional chronic condition (Fig. 1). In the adjusted linear
regression, EQ-VAS score was 3.4 points lower for each
additional chronic condition. Study site was not found to be
an effect modifier in this association (LR test p = 0.64)
Association between number of chronic conditions and EQ-5D dimensions
In multivariable models, we found that the number of
chronic conditions was associated with all EQ-5D
dimensions in the sample as a whole. The highest PR was for
‘‘self-care’’ (PR = 1.98, 95% CI 1.57–2.49), followed by
‘‘anxiety/depression’’ (PR = 1.67, 95% CI 1.53–1.81).
Study site was not an effect modifier for these associations
Depressive mood and chronic bronchitis, but not HTN nor
T2D, were associated with lower EQ-VAS scores. Site was
not an effect modifier for the association between EQ-VAS
score and having HTN, chronic bronchitis, and T2D.
However, site was an effect modifier of the association
between EQ-VAS and depressive mood: the EQ-VAS
score was lower in people with depressive mood than in
those without depressive mood, but this difference was
larger in Lima than in rural Puno with a clear pattern of a
gradient in the magnitudes of difference in EQ-VAS scores
observed across the range of urbanization profiles.
Regarding the number of chronic conditions, it was
inversely associated with EQ-VAS scores. The association
between number of chronic conditions and EQ-VAS scores
was similar across all study sites.
Chronic conditions and EQ-VAS score
Depressive mood was the chronic condition that seemed to
have the larger impact in QOL, similar to studies in Finland
], Canada [
], Spain [
], and the United States [
However, since these studies were also cross-sectional, it is
possible that, at least in some cases, depressive mood could
be the cause rather than the consequence of low QOL.
Thus, longitudinal approaches are needed to identify the
real impact of depressive mood in QOL.
Chronic bronchitis also had a high impact on QOL, as
found in studies that assessed chronic pulmonary
conditions (chronic bronchitis, asthma, COPD) [
7, 29, 30
a p values were calculated using v2 or ANOVA tests
which is attributed to the impact of chronic cough,
shortness of breath, and phlegm production [
suggests that chronic bronchitis should be prioritized in
primary care programs in our sites.
EQ-VAS score in persons with T2D was on average 1.3
points lower than in those without T2D. Conversely, in a
population study in Korea, EQ-VAS score was 4.6 points
lower in persons who self-reported T2D than those who did
]. This variation may be due to our inclusion of not
only self-reported T2D cases, but also of those with
abnormal point-of-care fasting blood glucose. As expected,
the very nature of having T2D or hypertension, be it a short
duration with the condition or having them without
complications, could explain the little impact of these
conditions on QOL found in our study [
]. To evaluate this
hypothesis, we made a post hoc linear regression in our
population, and found that those receiving a prescription
for T2D medications (i.e., those with a T2D diagnosis)
scored 2.6 points lower on the EQ-VAS than those not on
hypoglycemic agents (p = 0.10). This suggests that QOL
was diminished among participants who were aware of
their diagnosis, although other variables such as aware of
We used linear regression models with robust variances. All models were adjusted for sex, age, education status, and wealth index. Stroke and
heart disease were not evaluated due to the small number of cases
Significant associations (p \ 0.05) are bolded
a Likelihood-ratio test to assess the potential effect modifier of study site
LR test pa
For each additional chronic condition, our participants
showed a 3.4-unit reduction in their EQ-VAS score. This
reduction is substantial, since data from a 5-year cohort
population study of elderly Italians found that one unit
decrease in EQ-VAS was associated to a 1% increase in
mortality and hospitalization rates [
]. As such,
strategies to improve QOL in persons with chronic
conditions are needed. Increasing healthcare access for
physician visits, incorporating disease self-management
], and designing individualized patient regimen
with consideration of treatment side effects [
potentially decrease treatment burden and therefore
All models were adjusted for sex, age, education status, and wealth index
a Likelihood-ratio test to assess the potential effect modification of study site
EQ-VAS score was 3.4 points lower for each additional
chronic condition. In comparison, a previous study in the
United Kingdom found that EQ-VAS score was 5.9 points
lower for each additional chronic condition [
]. This study
evaluated six chronic conditions: HTN, T2D,
cerebrovascular disease, ischemic heart disease, COPD, and asthma,
but it did not evaluated depressive mood, which had the
highest impact in QOL in our study. Thus, it is possible that
the EQ-VAS score per chronic condition would be even
lower in this UK study if depressive mood had been
included. Then, there could be a great difference between
this study and ours in terms of QOL affectation by chronic
conditions. This could be explained by the severity of the
United Kingdom subjects, who were registered with
general practitioners who probably see patients with more
advanced complications of their chronic diseases.
Number of chronic conditions and EQ-VAS score per site
Average EQ-VAS score was higher in Lima and
semiurban Tumbes, intermediate in urban Puno, and lower in
rural Puno. This linearly reflects the degree of urbanization,
with the exception of semi-urban Tumbes, with values
similar to urban Lima. It is possible that semi-urban
Tumbes dwellers have strong social or familiar support,
similar to those in rural areas as described elsewhere [
while at the same time having better healthcare access,
similar to urban areas. This could also explain the low
depressive mood prevalence in this semi-urban site.
Lower QOL values in the rural site could be due to less
education, poor economic status, and limited access to
health services; all increasing their risk of developing
severe health complications [
]. In addition, people
living in rural areas are more likely to be involved in
manual labor and may require more physical skills to
perform their usual activities; thus, the decrease of physical
capabilities in rural dwellers could have a greater impact
on subjective wellbeing than in urban dwellers. Such
regional observations would suggest that a higher number
of chronic diseases would more dramatically affect QOL in
rural than urban sites. However, our results show that
EQVAS scores per number of chronic conditions were similar
across studied sites.
This is probably because the average EQ-VAS score in
those without chronic conditions was higher in Lima than
in rural Puno (75 and 62 points, respectively). Due to the
subjective nature of the EQ-VAS assessment, the same
condition could cause a larger decrease in QOL in Lima
than in rural Puno [
]. If this is true, it would be incorrect
to compare linear regression coefficients with EQ-VAS;
therefore, future studies should evaluate this hypothesis
comparing EQ-VAS with other more objective QOL
questionnaires. Another potential explanation is that rural
dwellers may have a higher emotional resilience, which
could diminish the subjective effect of chronic conditions
on their QOL, as seen in the lower impact of depressive
mood on EQ-VAS in rural sites. This should be more
thoroughly studied to better understand what factors
predict and define QOL for people living in rural versus urban
However, few studies have evaluated the differences of
social support, familial support, healthcare access,
education, economic status, and labor characteristics, across
Peruvian sites, and especially in sites with a heterogeneity
of urbanization degree patterns. Moreover, there is a
paucity of information about the impact of these variables in
objective and subjective QOL. Understanding what levels
or profiles of urbanization are directly linked with be
deeply studied in order to understand QOL across different
sites warrants further evaluation.
Number of chronic conditions and EQ-5D dimensions
The most prevalent EQ-5D dimension was pain and the
least prevalent was self-care, similar to previous studies
9, 11, 43
]. All dimensions but anxiety/depression had a
higher prevalence in the rural site, while the lowest
prevalence across all EQ-5D dimensions was observed in
semi-urban Tumbes. This could relate to the rural
contextual features and the Tumbes familiar/social support
Strength and limitations
This study compared the association of multiple and
specific chronic conditions and QOL within urban and rural
sites. Its results might help us to understand how
healthrelated QOL varies among intra-national sites.
Limitations of our study are as follows: (1) not all
chronic conditions with chief impact on QOL were
included, such as cancer, chronic pain, and arthritis [
that the inclusion of these conditions could increase the
impact of number of chronic conditions on EQ-VAS. (2)
Some of our diagnostic criteria for chronic conditions are
not the current gold standard, such as blood pressure
measure in a single day for HTN, and the evaluation of a
single fasting blood glucose sample for T2D. In addition,
our chronic bronchitis diagnosis was based on symptoms
presented during the last 12 months while other studies
required symptom persistence for at least 2 years to
consider a diagnosis of chronic bronchitis . These
evaluations could be over-estimating the prevalence of some
chronic conditions, although they could be more accurate
than the self-report used in similar previous studies
]. (3) Diminished sample representativeness,
especially in older and poor participants, could be
underestimating the association, since these subjects could have a
greater QOL decrease due to insufficient care of their
chronic conditions. (4) Low sample size, especially in rural
Puno, which could affect the extrapolation of our results to
Depressive mood, chronic bronchitis, and the number of
chronic conditions were related to lower EQ-VAS scores.
The associations between HTN, chronic bronchitis, T2D,
and number of chronic conditions with EQ-VAS score
were similar across sites with different levels of
urbanization, while depressive mood had a larger impact on
EQ-VAS score for participants living in urban sites than
those living in rural sites. This suggests that urbanization
could influence the impact that some chronic conditions
have on health-related QOL, and warrants attention to
context when evaluating and comparing QOL across
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://crea
tivecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
1. Zahran , H. S. , Kobau , R. , Moriarty , D. G. , Zack , M. M. , Holt , J. , Donehoo , R. , et al. ( 2005 ). Health-related quality of life surveillance-United States, 1993 - 2002 . MMWR: Surveillance Summaries, 54 ( 4 ), 1 - 35 .
2. Naylor , C. , Parsonage , M. , McDaid , D. , Knapp , M. , Fossey , M. , & Galea , A. ( 2012 ). Long-term conditions and mental health: The cost of co-morbidities . London: The King's Fund.
3. Hajian-Tilaki , K. , Heidari , B. , & Hajian-Tilaki , A. ( 2016 ). Solitary and combined negative influences of diabetes, obesity and hypertension on health-related quality of life of elderly individuals: A population-based cross-sectional study . Diabetes & Metabolic Syndrome , 10 , S37 - S42 .
4. Brettschneider , C. , Leicht , H. , Bickel , H. , Dahlhaus , A. , Fuchs , A. , Gensichen , J. , et al. ( 2013 ). Relative impact of multimorbid chronic conditions on health-related quality of life-results from the MultiCare Cohort Study . PLoS ONE , 8 ( 6 ), e66742 .
5. Banegas , J. R. , Lo´ pez -Garc´ıa, E., Graciani , A. , Guallar-Castillo´n, P. , Gutierrez-Fisac , J. L. , Alonso , J. , et al. ( 2007 ). Relationship between obesity, hypertension and diabetes, and health-related quality of life among the elderly . European Journal of Cardiovascular Prevention & Rehabilitation , 14 ( 3 ), 456 - 462 .
6. van Nispen, R. M. , de Boer , M. R. , Hoeijmakers , J. G. , Ringens , P. J. , & van Rens, G. H. ( 2009 ). Co-morbidity and visual acuity are risk factors for health-related quality of life decline: Fivemonth follow-up EQ-5D data of visually impaired older patients . Health and Quality of Life Outcomes , 7 ( 1 ), 1 .
7. Hunger , M. , Thorand , B. , Schunk , M. , Do¨ring, A. , Menn , P. , Peters , A. , et al. ( 2011 ). Multimorbidity and health-related quality of life in the older population: Results from the German KORAAge study . Health and Quality of Life Outcomes , 9 ( 1 ), 1 .
8. Saarni , S. I. , Ha¨rka¨nen, T., Sintonen , H. , Suvisaari , J. , Koskinen , S. , Aromaa , A. , et al. ( 2006 ). The impact of 29 chronic conditions on health-related quality of life: a general population survey in Finland using 15D and EQ-5D . Quality of Life Research , 15 ( 8 ), 1403 - 1414 .
9. Heyworth , I. T. , Hazell , M. L. , Linehan , M. F. , & Frank , T. L. ( 2009 ). How do common chronic conditions affect health-related quality of life? British Journal of General Practice , 59 ( 568 ), e353 - e358 .
10. Wee , H.-L., Cheung , Y.-B. , Li , S.-C. , Fong , K.-Y., & Thumboo , J. ( 2005 ). The impact of diabetes mellitus and other chronic medical conditions on health-related Quality of Life: Is the whole greater than the sum of its parts? Health and Quality of Life Outcomes, 3(1), 1 .
11. Agborsangaya , C. B. , Lahtinen , M. , Cooke , T. , & Johnson , J. A. ( 2014 ). Comparing the EQ-5D 3L and 5L: Measurement properties and association with chronic conditions and multimorbidity in the general population . Health and Quality of Life Outcomes , 12 ( 1 ), 1 .
12. da Mata , A. R. , Alvares , J. , Diniz , L. M. , Ribeiro da Silva, M. R. , da Alvernaz Dos Santos, B. R. , Guerra Junior , A. A. , et al. ( 2016 ). Quality of life of patients with diabetes mellitus types 1 and 2 from a referal health centre in Minas Gerais, Brazil . Expert Review of Clinical Pharmacology. doi:10.1586/17512433 . 2016 . 1152180 .
13. Alonso , J. , Angermeyer , M. , Bernert , S. , Bruffaerts , R. , Brugha , T. , Bryson , H. , et al. ( 2004 ). Disability and quality of life impact of mental disorders in Europe: Results from the European Study of the Epidemiology of Mental Disorders (ESEMeD) project . Acta Psychiatrica Scandinavica , 109 ( s420 ), 38 - 46 .
14. Health UDo , Services H . National Advisory Committee on Rural Health and Human Services . ( 2012 ). The 2006 Report to the Secretary: Rural Health and Human Service Issues .
15. Eberhardt , M. S. , & Pamuk , E. R. ( 2004 ). The importance of place of residence: Examining health in rural and nonrural areas . American Journal of Public Health , 94 ( 10 ), 1682 - 1686 .
16. Weeks , W. B. , Wallace , A. E. , Wang , S. , Lee , A. , & Kazis , L. E. ( 2006 ). Rural-urban disparities in health-related quality of life within disease categories of veterans . The Journal of Rural Health , 22 ( 3 ), 204 - 211 .
17. Mola , D. , Loret , C. , Stanojevic , S. , Ruiz , P. , Gilman , R. H. , Smeeth , L. , et al. ( 2012 ). The effect of rural-to-urban migration on social capital and common mental disorders . Social Psychiatry and Psychiatric Epidemiology , 47 ( 6 ), 967 - 973 .
18. Chen , A. , Jacobsen , K. H. , Deshmukh , A. A. , & Cantor , S. B. ( 2015 ). The evolution of the disability-adjusted life year (DALY) . Socio-Economic Planning Sciences , 49 , 10 - 15 .
19. Miranda , J. J. , Bernabe-Ortiz , A. , Smeeth , L. , Gilman , R. H. , Checkley , W. , & Group CCS. ( 2012 ). Addressing geographical variation in the progression of non-communicable diseases in Peru: The CRONICAS cohort study protocol . British Medical Journal Open , 2 ( 1 ), e000610 .
20. Julian , L. J. , Gregorich , S. E. , Tonner , C. , Yazdany , J. , Trupin , L. , Criswell , L. A. , et al. ( 2011 ). Using the Center for Epidemiologic Studies Depression Scale to screen for depression in systemic lupus erythematosus . Arthritis Care & Research , 63 ( 6 ), 884 - 890 .
21. EuroQol. EQ- 5D -3L: EuroQol Retrieved August , 6 , 2016 from http://www.euroqol.org/eq-5d -products/eq-5d-3l.html.
22. Janssen , M. , Pickard , A. S. , Golicki , D. , Gudex , C. , Niewada , M. , Scalone , L. , et al. ( 2013 ). Measurement properties of the EQ-5D5L compared to the EQ-5D-3L across eight patient groups: A multi-country study . Quality of Life Research , 22 ( 7 ), 1717 - 1727 .
23. Chobanian , A. V. , Bakris , G. L. , Black , H. R. , Cushman , W. C. , Green , L. A. , Izzo , J. L. , Jr. , et al. ( 2003 ). The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: The JNC 7 report . JAMA, 289 ( 19 ), 2560 - 2571 .
24. de Oca, M. M. , Halbert , R. J. , Lopez , M. V. , Perez-Padilla , R. , Ta´lamo, C. , Moreno , D. , et al. ( 2012 ). Chronic bronchitis phenotype in subjects with and without COPD: The PLATINO study . European Respiratory Journal. doi:10.1183/09031936 .00141611.
25. Consultation W. Definition, diagnosis and classification of diabetes mellitus and its complications . Part; 1999 .
26. Howe , L. D. , Galobardes , B. , Matijasevich , A. , Gordon , D. , Johnston , D. , Onwujekwe , O. , et al. ( 2012 ). Measuring socio-economic position for epidemiological studies in low-and middle-income countries: A methods of measurement in epidemiology paper . International Journal of Epidemiology. doi:10 .1093/ije/dys037.
27. Delgado-Sanz , M. C. , Prieto-Flores , M.-E., Forjaz , M. J. , Ayala , A. , Rojo-Perez , F. , Fernandez-Mayoralas , G. , et al. ( 2011 ). Influence of chronic health problems in dimensions of EQ-5D: Study of institutionalized and non-institutionalized elderly . Revista Espan˜ola de Salud Pu´blica, 85 ( 6 ), 555 - 568 .
28. Ko , Y. , & Coons , S. J. ( 2006 ). Self-reported chronic conditions and EQ-5D index scores in the US adult population . Current Medical Research and Opinion , 22 ( 10 ), 2065 - 2071 .
29. Uchmanowicz , B. , Panaszek , B. , Uchmanowicz , I. , & Rosinczuk , J. ( 2016 ). Sociodemographic factors affecting the quality of life of patients with asthma . Patient Preference and Adherence , 10 , 345 - 354 .
30. Martinez Rivera , C. , Costan Galicia , J. , Alcazar Navarrete , B. , Garcia-Polo , C. , Ruiz Iturriaga , L. A. , Herrejon , A. , et al. ( 2016 ). Factors associated with depression in COPD: A multicenter study . Lung , 194 ( 3 ), 335 - 343 .
31. Kanervisto , M. , Saarelainen , S. , Vasankari , T. , Jousilahti , P. , Heistaro , S. , Helio¨vaara, M. , et al. ( 2010 ). COPD, chronic bronchitis and capacity for day-to-day activities: Negative impact of illness on the health-related quality of life . Chronic Respiratory Disease , 7 ( 4 ), 207 - 215 .
32. Golicki , D. , Dudzin´ska, M. , Zwolak , A. , & Tarach , J. S. ( 2015 ). Quality of life in patients with type 2 diabetes in Poland-comparison with the general population using the EQ-5D questionnaire . Advances in Clinical and Experimental Medicine: Official Organ Wroclaw Medical University, 24 ( 1 ), 139 - 146 .
33. Alcubierre , N. , Rubinat , E. , Traveset , A. , Martinez-Alonso , M. , Hernandez , M. , Jurjo , C. , et al. ( 2014 ). A prospective cross-sectional study on quality of life and treatment satisfaction in type 2 diabetic patients with retinopathy without other major late diabetic complications . Health and Quality of Life Outcomes , 12 ( 1 ), 1 .
34. Doll , H. , & Miravitlles , M. ( 2005 ). Health-related QOL in acute exacerbations of chronic bronchitis and chronic obstructive pulmonary disease . Pharmacoeconomics , 23 ( 4 ), 345 - 363 .
35. Choi , Y. J. , Lee , M. S. , An , S. Y. , Kim , T. H., Han, S. J. , Kim , H. J. , et al. ( 2011 ). The relationship between diabetes mellitus and health-related quality of life in Korean adults: The fourth Korea National Health and Nutrition Examination Survey ( 2007 - 2009 ). Diabetes & Metabolism Journal , 35 ( 6 ), 587 - 594 .
36. Chung , J. O. , Cho , D. H. , Chung , D. J. , & Chung , M. Y. ( 2014 ). An assessment of the impact of type 2 diabetes on the quality of life based on age at diabetes diagnosis . Acta Diabetologica , 51 ( 6 ), 1065 - 1072 .
37. Mehta , Z. , Cull , C. , STratton , I. , & Yudkin , J. ( 1999 ). Quality of life in type 2 diabetic patients is affected by complications but not by intensive policies to improve blood glucose or blood pressure control (UKPDS 37) . Diabetes Care , 22 ( 7 ), 1125 .
38. Cavrini , G. , Broccoli , S. , Puccini , A. , & Zoli , M. ( 2012 ). EQ-5D as a predictor of mortality and hospitalization in elderly people . Quality of Life Research , 21 ( 2 ), 269 - 280 .
39. Musekamp , G. , Bengel , J. , Schuler , M. , & Faller , H. ( 2016 ). Improved self-management skills predict improvements in quality of life and depression in patients with chronic disorders . Patient Education and Counseling , 99 ( 8 ), 1355 - 1361 .
40. May , C. , Montori , V. M. , & Mair , F. S. ( 2009 ). We need minimally disruptive medicine . The BMJ , 339 , b2803 .
41. Zarzycka , D. , Slusarska , B. , Marcinowicz , L. , Wronska , I. , & Ko´zka, M. ( 2014 ). Assessment of differences in psychosocial resources and state of health of rural and urban residents-based on studies carried out on students during examination stress . Annals of Agricultural and Environmental Medicine , 21 ( 4 ), 882 - 887 .
42. Rabin , R. ( 2001 ). Charro Fd. EQ-SD: A measure of health status from the EuroQol Group . Annals of Medicine , 33 ( 5 ), 337 - 343 .
43. Parker , L. , Moran , G. M. , Roberts , L. M. , Calvert , M. , & McCahon , D. ( 2014 ). The burden of common chronic disease on health-related quality of life in an elderly community-dwelling population in the UK . Family Practice , 31 ( 5 ), 557 - 563 .