A decade beyond the economic recession: A study of health‐related lifestyles in urban and rural Spain (2006–2017)

Abstract

The 2008 economic recession may have affected health‐related indicators differently depending on the living environment. We analyze health‐related indicators in Spain using data from four Spanish health surveys (2006, 2011, 2014, and 2017, 95 924 individuals aged ≥16 years). In 2006–2011, physical activity decreased among men and women, while in 2006–2017, physical activity only decreased among urban women. Daily vegetable intake, except in rural women, increased in 2006–2011 but decreased in 2006–2017 in all groups. Smoking decreased among urban women in 2006–2011 and 2006–2014 but only decreased among men, and even increased among rural women, in 2006–2017. In 2006–2017, obesity increased among men and urban women, good self‐rated health status increased in all groups and flu vaccination declined. Blood pressure and cholesterol control decreased in urban women in 2006–2011 but increased in 2006–2017 in all groups, as well as mammographic and cytological control. Our findings highlight the differential impact of the economic recession on health‐related lifestyles according to sex and place of residence, underscoring the need for targeted health policies to address evolving health disparities over time.

Full text

RESEARCH ARTICLE
A decade beyond the economic recession: A study of
health-related lifestyles in urban and rural Spain (2006–2017)
Jesús García-Mayor PhD | Antonio Moreno-Llamas PhD |
Ernesto De La Cruz Sánchez PhD
Public Health and Epidemiology Research
Group, San Javier Campus, University of
Murcia, San Javier, Spain
Correspondence
Ernesto De La Cruz Sánchez, Public Health and
Epidemiology Research Group, San Javier
Campus, University of Murcia, C/ Santa Alicia,
s/n, San Javier, Murcia 30720, Spain.
Email: erneslacruz@um.es
Abstract
The 2008 economic recession may have affected health-related indicators differently
depending on the living environment. We analyze health-related indicators in Spain
using data from four Spanish health surveys (2006, 2011, 2014, and 2017, 95 924
individuals aged ≥16 years). In 2006–2011, physical activity decreased among men
and women, while in 2006–2017, physical activity only decreased among urban
women. Daily vegetable intake, except in rural women, increased in 2006–2011 but
decreased in 2006–2017 in all groups. Smoking decreased among urban women in
2006–2011 and 2006–2014 but only decreased among men, and even increased
among rural women, in 2006–2017. In 2006–2017, obesity increased among men
and urban women, good self-rated health status increased in all groups and flu vaccina-
tion declined. Blood pressure and cholesterol control decreased in urban women in
2006–2011 but increased in 2006–2017 in all groups, as well as mammographic and
cytological control. Our findings highlight the differential impact of the economic reces-
sion on health-related lifestyles according to sex and place of residence, underscoring
the need for targeted health policies to address evolving health disparities over time.
KEYWORDS
diet, economic recession, physical activity, preventive health care, self-perceived health,
smoking
Key points
•In the long term (2006–2017), health indicators varied by place of residence and sex, includ-
ing decreased physical activity in urban women, increased obesity in urban and rural men
and urban women, and higher smoking rates in rural women.
•The socioeconomic and political context affected health indicator trends: in contrast to long-
term period (2006–2017), short-term period (2006–2011) showed variations in key indica-
tors, including physical activity, vegetable intake, blood pressure, cholesterol, and smoking.
•Surveillance and monitoring trends of health-related indicators according to sex and place of
residence are important to avoid health disparities.
Received: 26 January 2023 Revised: 9 October 2023 Accepted: 19 October 2023
DOI: 10.1111/nhs.13063
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any
medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2023 The Authors. Nursing & Health Sciences published by John Wiley & Sons Australia, Ltd.
700 Nurs Health Sci. 2023;25:700–711.
wileyonlinelibrary.com/journal/nhs

1|INTRODUCTION
Ensuring equitable health is a current public health policy challenge:
avoiding exposure to disease and managing prevention strategies
effectively lead to lower health care costs and greater population
well-being (Avanzas et al., 2017). Major noncommunicable diseases
have an unequal population distribution according to geographic
characteristics due to a myriad of complex and interrelated factors
(Voigt et al., 2019). People differ in health according to socioeconomic,
demographic, and geographical factors, leading to long-term health
outcomes inequalities (Debiasi & Dribe, 2020).
Rural areas are characterized by growing geographical isolation
(Williams et al., 2022). The population exodus from these rural areas
to cities and suburbs is leading to a decrease in infrastructure and
basic services due to economic globalization, emigration, and aging
(Williams et al., 2022). The lack of public resources in rural areas
increases the need to cover the large distance to receive medical care
and access basic public services, which, together with the existence of
transportation barriers, hinder their accessibility for rural inhabitants
(World Health Organization, 2021). On the other hand, in the urban
environment, there may be more incentives to make use of certain
public services (Murimi & Harpel, 2010). Furthermore, the difficulties
faced by rural residents not only affect their health through the use of
medical services but may also compromise health-related lifestyle
behaviors (Gray et al., 2019; Moreno-Llamas et al., 2021).
2|BACKGROUND
Urban residents are generally considered to be in poorer health than
those in rural areas due to exposure to city stress, air pollution, and
higher consumption of psychoactive substances (O'Reilly et al., 2007).
However, in some developed countries, mortality rates and poor
health are higher in rural areas (Kulshreshtha et al., 2014; Monnat &
Beeler Pickett, 2011). The health-related lifestyle behaviors and
access to health services may be responsible for the higher mortality
rates and poor health observed in these regions (Alston et al., 2017;
Chandak et al., 2019; De la Cruz-Sánchez & Aguirre-G
omez, 2014;
Lindroth et al., 2014). In some European countries, smoking rates are
higher in urban than in nonurban areas and are associated with urban
population density (Bommelé et al., 2022; Idris et al., 2007). However,
internationally, there is a higher likelihood of hazardous alcohol con-
sumption and alcohol-related harm in rural areas (Friesen et al., 2022).
In addition, rural inhabitants document lower leisure-time physical
activity (De la Cruz-Sánchez & Aguirre-G
omez, 2014) and worse
eating habits due to the need to cover greater distances to purchase
food, as well as limited food resources in their households (Dean &
Sharkey, 2011). The rural patterns of physical activity and diet may
contribute to the rising body mass index in rural areas being the main
driver of the global obesity epidemic in the adult population (Bixby
et al., 2019). Similarly, according to preventive health care use, rural
women are more vulnerable to the risk of breast cancer diagnosis
compared with urban women (Chandak et al., 2019), and there is
evidence that women in rural areas report a lack of attendance for
cytological check (Allen-Leigh et al., 2017).
In Spain, some initiatives have been enacted to improve the situa-
tion of rural areas over the last two decades, such as Law 45/2007 for
the sustainable development of the rural environment, which promotes
access to quality basic services and guarantees the provision of quality
specialized health care to all rural areas (Boletín Oficial del Estado
(BOE), n.d.). But in the same period, European countries experienced an
economic recession, a period with important changes that affected both
the economy and social structures (Escolar-Pujolar et al., 2014).
The economic crisis significantly impacted the proportion of mental
health-related illnesses, rates of suicides, public spending on health ser-
vices, and the social dimension of health problems mainly in countries
that adopt strict austerity measures such as Greece or Spain (Backhaus
et al., 2022). In addition, the impact of the economic recession on eco-
nomic activity and employment in Spain resulted in the percentage of
people at risk of poverty increasing more substantial compared with the
Eurozone mean, although, in the long-term, these differences have been
narrowing (Lacuesta & Anghel, 2020). The economic crisis not only
revealed significant disparities in economic and social consequences in
the different European countries but also territorial disparities within
countries (Groot et al., 2011). The growth of the economies during the
years before the economic recession was most pronounced in urban
areas with capital metro regions; however, these urban areas were the
hardest hit during the economic crisis and that which experienced the
sharpest contractions in employment (Dijkstra et al., 2015). In Spain,
the onset of the economic crisis had a different impact on the percent-
age of people living in poverty in rural and urban areas; a more pro-
nounced increase was observed in urban areas (Eurostat, 2019).
Therefore, the economic recession may have influenced health, lifestyle
behaviors, and preventive health care use in the middle and long term
differently depending on the territorial environment.
Studies addressing the effects on health-related indicators of the
economic recession in rural and urban areas during the last decade are
scarce. Monitoring changes in health-related indicators among urban
and rural population contribute to promoting equity in health policies.
Here we aim to estimate the trends in self-rated health status, body
mass index (BMI), health-related lifestyle behaviors, and preventive
health care use before, during, and after the 2008 economic crisis
(i.e., from 2006 to 2017) based on the place of residence in Spain.
3|METHODS
3.1 |Data sources
We employed microdata of the Spanish population (aged ≥16 years)
from the Spanish National Health Surveys (SNHS) in 2006
(n=29 478) (data collected from June 2006 to June 2007), 2011
(n=20 884) (data collected from July 2011 to June 2012), and
2017 (n=22 903) (data collected from October 2016 to October
2017), and the 2014 European Health Interview Survey (EHIS) for
Spain (n=22 659) (data collected from January 2014 to February
GARCÍA-MAYOR ET AL.701

2015). We considered the use of four health surveys from Spain to
compare health-related indicators before (2006), during (2011), and
after (2014 and 2017) the period of economic recession. The surveys are
carried out by the Spanish Ministry of Health and Social Policy in collabo-
ration with the National Institute of Statistics and follow the same
methodological process: these surveys are divided into 50 provincial sub-
samples and use stratified cluster sampling that considers, firstly, the
census sections; secondly, households; and thirdly, one individual
from each household. Census sections are selected within each stra-
tum with a probability proportional to their size, while households and
individuals are randomly selected to ensure representative samples by
age and sex (established from the latest available official census).
Response rates were 94.1% in 2006, 89.6% in 2011, 74.6% in 2014,
and 74.0% in 2017. More details about these surveys could be found
elsewhere (Ministerio de Sanidad Consumo y Bienestar Social, n.d.).
Because this work includes the use of anonymized data belonging
to secondary databases, there are no participants who may be
exposed to the risk of harm or discomfort, as well as no personal data
as established in the EU Regulation 2016/679 and Royal Decree-Law
5/2018 regarding the processing of personal data and the free move-
ment of data. Therefore, all research activities elaborated in this work
comply with the legal laws of the European Union and Spain and with
ICMJE ethical research guidelines.
3.2 |Measures
Participants were asked about their leisure-time physical activity
habits and were classified as active (i.e., (1) engage in light physical
activity such as walking, gardening, gentle gymnastics, low exertion
games and the like; (2) moderate physical activity such as cycling,
gymnastics, aerobics, running, swimming; or (3) intense physical activ-
ity such as soccer, basketball, cycling or competitive swimming, judo,
karate or the like) or inactive (i.e., leisure time is spent in an almost
completely sedentary manner: reading, watching television, going to
the movies, etc.). This question has been used throughout the histori-
cal series of national health surveys, so it allows the assessment of
secular trends. In addition, it has previously been validated as an
instrument to be used in large-scale studies (Moreno-Llamas
et al., 2020). Tobacco use was also self-reported, based on the follow-
ing question “Can you tell me if you currently smoke?”The question
had four options: (a) daily smokers, (b) occasional smokers, (c) former
smokers, and (d) never smokers. Smoking was defined as people being
daily or occasional smokers. Alcohol use was also self-reported and
based on use in the last 2 weeks (yes or no). We also considered four
diet patterns based on food frequency during the last week, which
could determine the risk of all-cause mortality (Alvarez-Alvarez
et al., 2018): daily fruit intake, daily vegetable intake, daily pastries
and sweets intake, and daily soft drink intake. Dietary patterns were
dichotomized (yes or no).
As health outcomes, self-rated health status in the last 12 months
was evaluated as (1) very good, (2) good, (3) fair, (4) bad, or (5) very
bad. Based on these five categories, the self-rated health status was
categorized as good (good or very good) or poor (fair, bad, or very
bad) (Manor et al., 2000). In addition, we also reported BMI. This vari-
able was classified according to World Health Organization (World
Health Organization, 2000) as overweight/obesity (BMI ≥25 kg/m
2
)
and obesity (BMI ≥30 kg/m
2
), ranges currently used in epidemiological
studies on obesity based on BMI (Caballero, 2019).
We also considered factors related to preventive health care use:
flu vaccination in the previous year, blood pressure check at least
once in a lifetime, cholesterol profile check at least once in a lifetime,
and, specifically for women, mammography and cytology at least once
in a lifetime. All variables were dichotomized (yes or no).
Sociodemographic variables considered were sex, age, residence
place, employment status (working, unemployed, retired, homemaker
or other situation), marital status (single, married or other situation),
social class, and educational attainment. The population size of munic-
ipalities is the indicator used by the national health surveys to classify
the population into urban and rural areas: we dichotomized between
<10 000 inhabitants (rural places) and ≥10 000 inhabitants (urban
places), which is also the definition used in Spain for the harmonized
definition of cities and rural areas (Dijkstra & Poelman, 2014). Social
class was based on the 2012 proposal of the Working Group on
Social Determinants of Health of the Spanish Society of Epidemiology
(Domingo-Salvany et al., 2013): high social class (class of service: I–II),
middle social class (intermediate class: III), and low social class (work-
ing class: IV-VI). Educational attainment was classified according to
the International Standard Classification of Education (ISCED, 2012):
high education (short-cycle tertiary education or bachelor's, master's
or doctoral level: levels 5–8), middle education (upper secondary edu-
cation or post-secondary nontertiary education: levels 3 and 4), and
primary or no education (less than primary, primary education, or
lower secondary education: levels 0–2).
3.3 |Statistical analysis
We described the sociodemographic variables according to sex and
survey year (Table S1). Secondly, the sociodemographic characteristics
of the population by sex, place of residence, and survey year were
described. To estimate the differences in the proportions of the socio-
demographic characteristics between each of the survey years, by place
of residence and sex, we used the post hoc testing following Pearson's
chi-square test (χ
2
test) with Bonferroni correction. Subsequently, we
calculated age-adjusted prevalences (%) and 95% confidence intervals
(95% CI) of health-related indicators (i.e., health-related lifestyle behav-
iors, self-rated health status, weight status, and preventive health care
use) by sex, place of residence, and survey year. These sex-specific age-
adjusted prevalences were conducted from a direct method of stan-
dardization considering the 2006 urban population as a reference. Then,
for the study of the magnitude of the effect on the probability of the
health-related indicators analyzed over time, multivariate logistic regres-
sion models were fitted, estimating odds ratios (OR) and 95% CI, using
as dependent variables the health-related indicators and independent
variable the survey year. The reference for the survey year variable was
the period before the economic crisis (2006; OR =1). These analyses
were stratified by sex and place of residence and adjusted for
702 GARCÍA-MAYOR ET AL.

confounding variables (age, social class, education, employment status
and marital status). Statistical significance was established using the
Wald statistic and set at a p-value < 0.05. Data were analyzed using
SPSS 25.0 (IBM Corp., Armonk, NY, USA).
4|RESULTS
4.1 |Sociodemographic and health-related
characteristics in the study period
Our analysis encompassed four population-based surveys conducted
between 2006 and 2017, comprising a total of 95 924 participants
aged 16 years and above from Spain. In Table 1, in the long term
(period 2006–2017), the percentage of urban men (+4.4%, p< 0.001)
and urban and rural women (+6.3%, p< 0.001 in urban women;
+4.1%, p< 0.001 in rural women) aged ≥65 years increased signifi-
cantly. In general, in the two areas analyzed, there was a decrease in
men (13.8% in urban men; 21.9% in rural men) and women
(15.5% in urban women; 21.3% in rural women) with primary edu-
cation or less (p< 0.001 in all groups) and an increase in low occupa-
tional social class (+6.1% and +16.8% in urban and rural men,
respectively; +4.9% and +16.1% in urban and rural women, respec-
tively; p< 0.001 in all groups). We also showed an increase in unem-
ployment for men (+4.7% in urban men; +7.0% in rural men) and
women (+4% in urban women; +4.1% in rural women) in both areas
(p< 0.001 in all groups), as well as a decrease in married women in
urban (6.7, p< 0.001) and rural areas (4.9%, p< 0.001). In contrast,
the population of married urban men increased (+3.3, p=0.002).
Among men and women in both areas analyzed, daily intake of
fruit, pastries, and sweets and sugary soft drinks, alcohol use and flu
vaccination decreased significantly in the long term (p≤0.002); self-
rated health status, blood pressure, cholesterol, mammography, and
cytological control increased (p< 0.001); and obesity and overweight/
obesity did not change (p> 0.05) (Figures 1and 2). However, differ-
ences by sex and place of residence were also observed in long-term
trends (Figure 1): smoking only declined significantly among men
(5.9%, p< 0.001 in urban men; 4.9%, p< 0.001 in rural men), physi-
cal activity among rural men (+6.6%, p< 0.001 in rural men), and daily
vegetable intake increased among rural dwellers (+8.6%, p<0.001in
rural men; +7.9%, p< 0.001 in rural women). Further details on sex-
specific age-adjusted prevalence values can be found in Table S2.
4.2 |Adjusted odds ratios estimating health-
related indicators before, during, and after the
economic recession
4.2.1 | Health-related lifestyle behaviors
Based on the magnitude of differences over time in health-related
indicators (Tables 2and 3), the ORs adjusted for sociodemographic
variables indicated that the probability increased in physical activity in
rural men in the long term (OR =1.136, 95% CI 1.015–1.271). How-
ever, decreased in urban women (OR =0.943, 95% CI: 0.891–0.997).
In both areas analyzed, a lower daily fruit intake was also observed, as
well as daily vegetable intake, daily pastries and sweets intake, daily
soft drink intake and alcohol use in men (Table 2) and women
(Table 3). Smoking declined significantly among urban and rural men
(OR =0.801, 95% CI 0.747–0.859 and OR =0.877, 95% CI 0.777–
0.990 in urban and rural men, respectively) but was higher among
rural women (OR =1.156, 95% CI 1.008–1.326).
Despite the long-term trends, during 2006–2011, physical activ-
ity decreased in all the groups analyzed, but to a greater extent in
women (OR =0.735, 95% CI 0.679–0.780 and OR =0.670, 95% CI
0.603–0.744 in urban and rural women, respectively); daily vegetable
intake increased, except in rural women (OR =1.080, 95% CI 0.973–
1.199); and we also observed a decrease in urban women smokers in
2006–2011 (OR =0.897, 95% CI 0.836–0.963) and 2006–2014
(OR =0.873, 95% CI 0.815–0.935).
4.3 |Self-rated health status and body mass index
Good self-rated health status increased in all groups analyzed in the
long term (OR =1.242, 95% CI 1.151–1.340 and OR =1.264, 95% CI
1.117–1.432 in urban and rural men, respectively; OR =1.349, 95%
CI 1.268–1.435 and OR =1.330, 95% CI (1.187–1.489) in urban and
rural women, respectively), as well as obesity in men (OR =1.135,
95% CI 1.040–1.239 and OR =1.171, 95% CI 1.019–1.345 in urban
and rural men, respectively) and urban women (OR =1.138, 95% CI
1.050–1.234).
4.4 |Preventive health care use
In both areas analyzed, we report a lower proportion of flu vaccination
and greater blood pressure check and cholesterol check in the long
term in men (Table 2) and women (Table 3). Mammography check and
cytology check were higher in both urban and rural women
(OR =1.299, 95% CI 1.220–1.382 and OR =1.468, 95% CI
1.341–1.640 for mammography in urban and rural women, respec-
tively; OR =1.455, 95% CI 1.361–1.556 and OR =1.617, 95% CI
1.440–1.816 for cytology in urban and rural women, respectively).
Despite long-term trends, in the period 2006–2011, blood pres-
sure check (OR =0.831, 95% CI 0.735–0.940) and cholesterol check
(OR =0.892, 95% CI 0.813–0.979) decreased in urban women.
5|DISCUSSION
We found, in all the groups analyzed, an increase in the good self-
rated health status and preventive health care use, except for the flu
vaccine, which decreased, and a decrease in the daily intake of fruit,
vegetable, pastries, and sweets, as well as sugary soft drink and alco-
hol use in the long term. However, we also found trends in health-
GARCÍA-MAYOR ET AL.703

TABLE 1 Descriptive analysis of sociodemographic variables by sex, place of residence, and survey year (2006, 2011, 2017 SNHS, and 2014 EIHS for Spain).
Men Women
2006 2011 2014 2017 2006 2011 2014 2017
Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban
N(%) N(%) N(%) N(%) N(%) N(%) N(%) N(%) N(%) N(%) N(%) N(%) N(%) N(%) N(%) N(%)
Age groups
16–24 211 (6.4) 837 (10) 142 (5.9) 630 (8.8) 124 (4.8) 571 (7.2) 159 (6.1) 584 (7.4) 247 (5.2) 873 (6.7) 144 (5.6) 617 (7.1) 122 (4.6) 614 (6.4) 136 (5) 580 (6.0)
25–34 443 (13.5) 1358 (16.2) 304 (12.6) 1038 (14.5) 227 (8.8) 956 (12.1) 211 (8.1) 814 (10.3) 590 (12.5) 2067 (15.7) 293 (11.4) 1122 (12.8) 245 (9.3) 1076 (11.2) 210 (7.7) 1014 (10.5)
35–44 624 (19) 1846 (22.1) 462 (19.1) 1562 (21.8) 488 (18.9) 1747 (22.2) 403 (15.5) 1596 (20.2) 810 (17.2) 2654 (20.2) 405 (15.8) 1522 (17.4) 461 (17.5) 1863 (19.5) 424 (15.6) 1726 (17.8)
45–55 529 (16.1) 1401 (16.8) 407 (16.8) 1345 (18.8) 526 (20.4) 1501 (19) 492 (18.9) 1520 (19.2) 681 (14.5) 2242 (17.1) 367 (14.3) 1455 (16.6) 439 (16.7) 1610 (16.8) 454 (16.7) 1648 (17)
56–64 459 (14) 1124 (13.4) 380 (15.7) 1086 (15.2) 445 (17.2) 1254 (15.9) 521 (20) 1349 (17.1) 753 (16) 1894 (14.4) 350 (13.6) 1357 (15.5) 383 (14.6) 1487 (15.5) 447 (16.4) 1592 (16.4)
≥65 1021 (31.1) 1792 (21.4) 722 (29.9) 1501 (21) 770 (29.8) 1854 (23.5) 816 (31.4) 2034 (25.8) 1625 (34.5) 3397 (25.9) 1006 (39.2) 2667 (30.5) 977 (37.2) 2919 (30.5) 1052 (38.6) 3121 (32.2)
Occupational social class
High social
class (I-II)
383 (11.8) 1866 (22.6) 274 (11.4) 1488 (21.2) 295 (11.5) 1736 (22.3) 253 (9.8) 1641 (21) 548 (11.9) 2683 (21.1) 281 (11.6) 1653 (20) 305 (12) 1977 (21.4) 254 (9.8) 1857 (19.9)
Middle social
class (III)
953 (29.3) 2014 (24.4) 307 (12.8) 1375 (19.5) 325 (12.7) 1619 (20.8) 373 (14.5) 1561 (20) 1377 (29.9) 3068 (24.1) 368 (15.2) 1709 (20.7) 362 (14.2) 1921 (20.8) 412 (15.9) 1901 (20.4)
Low social
class (IV-VI)
1916 (58.9) 4364 (52.9) 1819 (75.8) 4171 (59.3) 1937 (75.8) 4439 (57.0) 1953 (75.7) 4606 (59) 2679 (58.2) 6970 (54.8) 1773 (73.2) 4910 (59.4) 1877 (73.8) 5329 (57.8) 1927 (74.3) 5561 (59.7)
Educational attainment
High education 248 (7.6) 1605 (19.3) 181 (7.5) 1209 (16.9) 236 (9.1) 1613 (20.5) 224 (8.6) 1581 (20) 388 (8.3) 2211 (16.9) 184 (7.2) 1334 (15.3) 341 (13) 2070 (21.6) 318 (11.7) 2067 (21.4)
Middle education 1138 (34.7) 3487 (41.9) 1233 (51.1) 4154 (58.2) 1233 (47.8) 4117 (52.2) 1446 (55.6) 4340 (55) 1342 (28.6) 4650 (35.6) 1204 (47) 4696 (53.8) 994 (37.8) 4049 (42.3) 1267 (46.5) 4514 (46.6)
Primary education
or less
1890 (57.7) 3227 (38.8) 999 (41.4) 1777 (24.9) 1111 (43.1) 2153 (27.3) 932 (35.8) 1976 (25) 2956 (63.1) 6199 (47.5) 1174 (45.8) 2692 (30.9) 1292 (49.2) 3450 (36.1) 1138 (41.8) 3100 (32)
Employment status
Working 1763 (53.8) 4976 (59.7) 916 (40.1) 3372 (48.8) 1109 (43) 3880 (49.2) 1195 (45.9) 4027 (51) 1530 (32.6) 5355 (40.9) 830 (36.0) 3451 (42.6) 950 (36.2) 3989 (41.7) 910 (33.4) 3787 (39.1)
Unemployed 138 (4.2) 496 (6.0) 272 (11.9) 848 (12.3) 302 (11.7) 960 (12.2) 292 (11.2) 844 (10.7) 267 (5.7) 939 (7.2) 229 (9.9) 817 (10.1) 249 (9.5) 1076 (11.2) 266 (9.8) 1085 (11.2)
Retired 1266 (38.6) 2347 (28.2) 929 (40.7) 2157 (31.2) 1009 (39.1) 2499 (31.7) 918 (35.3) 2288 (29) 1381 (29.4) 3046 (23.3) 1083 (47) 3158 (38.9) 1251 (47.6) 3827 (40) 844 (31) 2558 (26.4)
Homemaker 7.0 (0.2) 7.0 (0.1) 118 (5.2) 319 (4.6) 80 (3.1) 279 (3.5) 7.0 (0.3) 18 (0.2) 1342 (28.6) 3169 (24.2) 120 (5.2) 461 (5.7) 118 (4.5) 410 (4.3) 545 (20) 1586 (16.4)
Other 105 (3.2) 509 (6.1) 48 (2.1) 217 (3.1) 80 (3.1) 265 (3.4) 190 (7.3) 720 (9.1) 177 (3.8) 592 (4.5) 43.0 (1.9) 221 (2.7) 59 (2.2) 267 (2.8) 158 (5.8) 665 (6.9)
Marital status
Single 1064 (32.4) 2713 (32.5) 780 (32.3) 2306 (32.2) 744 (28.8) 2301 (29.2) 789 (30.3) 2205 (28.0) 792 (16.8) 2856 (21.8) 502 (19.6) 2199 (25.2) 473 (18) 2259 (23.6) 460 (16.9) 2249 (23.3)
Married 1875 (57.1) 4797 (57.5) 1370 (56.7) 4103 (57.3) 1535 (59.5) 4738 (60.1) 1510 (58.1) 4796 (60.8) 2825 (60.1) 7234 (55.3) 1339 (52.2) 4167 (47.7) 1401 (53.5) 4648 (48.6) 1500 (55.2) 4658 (48.2)
Other 342 (10.4) 827 (9.9) 266 (11) 746 (10.4) 300 (11.6) 839 (10.6) 302 (11.6) 883 (11.2) 1086 (23.1) 3000 (22.9) 723 (28.2) 2362 (27.1) 747 (28.5) 2652 (27.7) 758 (27.9) 2754 (28.5)
Note: Residence place (urban and rural areas) was estimated from the number of inhabitants of the municipalities of residence, according to the population census of each year.
704 GARCÍA-MAYOR ET AL.

related indicators that differ by place of residence and sex during the
analyzed period: in the long term, we observed a decrease in physical
activity in urban women and an increase in rural men; an increase in
obesity in urban and rural men and urban women; and a decrease
in smoking in men and an increase in rural women. In addition, we
observed an effect of the socioeconomic and political context on the
trends in the health-related indicators analyzed: despite long-term
trends, in the period 2006–2011, physical activity decreased in all
groups analyzed; daily vegetable intake increased, except for rural
women; and blood pressure check and cholesterol check decreased in
urban women. Furthermore, smoking decreased in urban women
in 2006–2011 and 2006–2014.
Compared with before the economic downturn, we comple-
ment studies showing an improvement in self-rated health status
during the economic recession in Spain (Regidor et al., 2014), also
showing an increase after the economic recession in all groups
analyzed. The trends in self-rated health status could be related to
lower smoking among men, lower alcohol use, and discretionary
food intake, and higher use of preventive health services in the
study period. Moreover, these improved health-related indicators
observed here could contribute to the results showing a decline in
mortality rates during and after the crisis period (Regidor
et al., 2019).
Nevertheless, in the long term, we observed a decrease in the
daily fruit and vegetable intake, recommended indicators for prevent-
ing chronic diseases such as cardiovascular disease, coronary heart
disease, cancer, and stroke, as well as all-cause mortality (Aune
et al., 2018). These long-term trends may have influenced the increase
in obesity observed in certain population groups analyzed. Some stud-
ies warn that rural inhabitants living in industrialized countries have
increased their BMI more exponentially than urban inhabitants in the
last decades, suggesting an integrated approach to rural dietary pat-
terns that improve access to healthy and fresh food (Bixby
et al., 2019). However, we observed a significant increase in obesity
among both urban and rural men and urban women, with the rate of
increase ranging from 14% to 17% for these population groups.
FIGURE 1 Sex-specific age-adjusted prevalence of health-related lifestyles by place of residence and sex from 2006 to 2017 (2006, 2011,
2017 SNHS, and 2014 EIHS for Spain). Urban men (a); rural men (b); urban women (c); rural women (d). Error bars indicate 95% CI.
GARCÍA-MAYOR ET AL.705

Therefore, an integrated approach that considers the increase in obe-
sity among urban and rural Spanish populations seems necessary.
In the rise of obesity, it is important to consider also the trends in
physical activity observed during and after the economic downturn, as
well as the different trends according to the place of residence. Thus,
a clear decrease in physical activity was found during the economic
recession in all groups, mainly among women, which could be related
to available leisure time and working hours (Kulic et al., 2021). In this
context, the economic recession could have conditioned many unem-
ployed or homemaker women to work to compensate for the loss of
family income in Spain, which led to significant changes in their leisure
time due to additional responsibilities (Legazpe & Davia, 2019). This
increased demand and workload on women has also been observed
recently in the COVID-19 (coronavirus disease of 2019) pandemic in
Germany (Mutz & Reimers, 2021). In addition, after the economic
recession, a long-term decline in physical activity among urban
women was also observed. Based on European literature, our results
in urban populations in the long term could be associated with the
effects of urbanization on total physical activity in this population
(Boakye et al., 2023).
Our results reinforce, considering the long-term reduction of smok-
ing among urban and rural men, and in urban women in the short and
middle term, that smoking cessation policies (Law 28/2005 and Law
42/2010) or tobacco tax increases were effective in reducing smoking
in Spain (Kelly et al., 2018; Villalbi et al., 2019). However, these policies
may not be as effective, across all population groups or over time, given
the increase in smoking among rural women, and the absence of signifi-
cant differences among urban women in the long term.
In preventive health care use, blood pressure check and choles-
terol check declined in the period 2006–2011 only in urban women,
which could be related to the austerity measures adopted during the
economic recession and the financial difficulties of the period ana-
lyzed in Spain (Oliva et al., 2018); and/or, as has been observed in
other parts of the world, the impact of lack of accessibility and
supplier-level barriers (Loftus et al., 2018) experienced to a greater
extent by this population group. However, in the long term, the
FIGURE 2 Sex-specific age-adjusted prevalence of self-rated health status, body mass index, and preventive health care use by place of
residence and sex from 2006 to 2017 (2006, 2011, 2017 SNHS, and 2014 EIHS for Spain). Urban men (a); rural men (b); urban women (c); rural
women; (d) error bars indicate 95% CI.
706 GARCÍA-MAYOR ET AL.

results are encouraging because the use of preventive health services
increased in all groups analyzed, except for flu vaccination. In this
study, we analyzed the Spanish population aged 16 and over. How-
ever, our results are related to the decreasing trends in the population
aged 65 years and older in the period 2009–2014 for flu vaccination
in Spain (Dios-Guerra et al., 2017). This decrease could be due to the
loss of confidence in vaccination against influenza after the 2009
(H1N1) pandemic, as observed in Italy (Pariani et al., 2015).
5.1 |Limitations
To our knowledge, this is the first study that has investigated time
trends in these outcomes by residence place after the economic
recession in Spain. Nevertheless, the absence of longitudinal follow-
up of the same population in the survey design prevents us from
establishing a cause–effect relationship in the present study. Conse-
quently, the data presented herein should be interpreted within the
context of a comparative analysis employing various cross-sectional
surveys conducted at different time periods but utilizing a similar
methodology. In addition, there may be a recall bias due to the use of
self-report measures to assess outcomes. It should be noted that alco-
hol use is based on regular use and not on excessive use due to the
characteristics of the surveys. However, this assessment may be
appropriate because any amount of alcohol is related to all-cause mor-
tality, specifically cancer (GBD 2016 Alcohol Collaborators, 2018). It is
important to acknowledge that the variables pertaining to preventive
health care utilization may not accurately reflect the prevailing pre-
ventive practices of the population at present. However, it is worth
noting that these indicators are standardized across various health
TABLE 2 Logistic regression (OR and 95% CI) of health-related indicators according to the place of residence among men (SNHS 2006, 2011,
2017, and EHIS 2014).
Physical activity Daily fruit intake Daily vegetable intake
Daily pastries and
sweets intake Daily soft drink
Urban men
2011 0.825 (0.770–0.884)*** 0.762 (0.711–0.817)*** 1.161 (1.085–1.242)*** 0.736 (0.685–0.790)*** 0.718 (0.652–0.790)***
2014 1.209 (1.130–1.295)*** 0.755 (0.706–0.808)*** 1.059 (0.992–1.331) 0.658 (0.614–0.706)*** 0.687 (0.625–0.755)***
2017 1.043 (0.975–1.116) 0.710 (0.663–0.760)*** 0.898 (0.840–0.960)** 0.641 (0.597–0.688)*** 0.562 (0.508–0.621)***
Rural men
2011 0.830 (0.742–0.930)*** 0.866 (0.769–0.976)*1.217 (1.088–1.362)** 0.754 (0.671–0.846)*** 0.817 (0.689–0.968)*
2014 1.111 (0.994–1.241) 0.781 (0.696–0.879)*** 0.992 (0.889–1.106) 0.757 (0.677–0.847)*** 0.691 (0.581–0.822)***
2017 1.136 (1.015–1.271)*0.757 (0.674–0.851)*** 0.634 (0.566–0.711)*** 0.599 (0.533–0.672)*** 0.581 (0.484–0.698)***
Smoking Alcohol use Good SRHS Overweight/Obesity Obesity
Urban men
2011 0.897 (0.836–0.963)*** 0.763 (0.711–0.818)*** 1.312 (1.212–1.420)*** 1.137 (1.059–1.221)*** 1.186 (1.083–1.298)***
2014 0.873 (0.815–0.935)*** 0.672 (0.628–0.719)*** 1.276 (1.183–1.377)*** 0.965 (0.902–1.032) 1.044 (0.956–1.140)
2017 0.801 (0.747–0.859)*** 0.671 (0.627–0.718)*** 1.242 (1.151–1.340)*** 1.047 (0.977–1.211) 1.135 (1.040–1.239)**
Rural men
2011 0.937 (828–1.059) 0.890 (0.789–1.005) 1.464 (1.286–1.666)*** 1.127 (0.993–1.278) 1.198 (1.038–1.382)*
2014 0.906 (0.804–1.021) 0.715 (0.637–0.802)*** 1.264 (1.118–1.430)*** 0.945 (0.839–1.065) 1.184 (1.033–1.357)*
2017 0.877 (0.777–0.990)*0.719 (0.640–0.808)*** 1.264 (1.117–1.432)*** 0.945 (0.845–1.077) 1.171 (1.019–1.345)*
Flu vaccination Blood pressure check Cholesterol check
Urban men
2011 0.612 (0.557–0.671)*** 1.012 (0.897–1.142) 1.016 (0.922–1.120)
2014 0.565 (0.517–0.618)*** 1.702 (1.488–1.946)*** 2.698 (2.399–3.035)***
2017 0.557 (0.510–0.609)*** 2.659 (2.278–3.104)*** 4.901 (4.246–5.658)***
Rural men
2011 0.618 (0.531–0.720)*** 1.066 (0.848–1.340) 0.980 (0.823–1.166)
2014 0.656 (0.567–0.758)*** 1.554 (1.207–2.000)*** 2.349 (1.901–2.902)***
2017 0.599 (0.483–0.648)*** 2.612 (1.936–3.523)*** 3.759 (2.934–4.816)***
Note: Overweight/Obesity: body mass index ≥25 kg/m
2
. Obesity: body mass index ≥30 kg/m
2
.
Abbreviation: SHRS, self-rated health status.
*<0.05;
**≤0.01;
***<0.001.
GARCÍA-MAYOR ET AL.707

surveys and can offer valuable insights into the extent of national cov-
erage during the specified time period. Finally, due to the limitation of
the health surveys used, it has not been possible to consider the com-
plexity and the diversity of the demographic and economic structures
of each environment.
6|CONCLUSIONS
Our findings highlight the differential impact of the economic reces-
sion on health-related lifestyles according to sex and place of resi-
dence, underscoring the need for targeted health policies to address
evolving health disparities over time. Health policies that consider
health-related indicators by place of residence, addressing disparities
over time, are needed to promote healthier and more equitable living
environments. Future research directions could consider the lifestyle
and health of people living in both urban and rural locations by age
and socioeconomic groups. It is also a potential line of research to
consider the characteristics of urban and rural municipalities (in terms
of resources, accessibility, or environmental features) and their influ-
ence on health and lifestyle.
7|RELEVANCE AND IMPLICATIONS
Based on long-term trends, the fact that smoking among rural women
increased significantly and has not declined significantly among urban
women may indicate the measures implemented are not enough in
TABLE 3 Logistic regression models (OR and 95% CI) of the health-related indicators according to the place of residence among women
(SNHS 2006, 2011, 2017, and EHIS 2014).
Physical activity Daily fruit intake Daily vegetable intake
Daily pastries and
sweets intake Daily soft drink
Urban women
2011 0.735 (0.679–0.780)*** 0.636 (0.595–0.679)*** 1.127 (1.064–1.194)*** 0.722 (0.678–0.768)*** 0.669 (0.605–0.741)***
2014 0.992 (0.938–1.050) 0.686 (0.643–0.731)*** 1.062 (1.005–1.222)*0.664 (0.626–0.705)*** 0.611 (0.553–0.679)***
2017 0.943 (0.891–0.997)*0.674 (0.632–0.718)*** 0.938 (0.888–0.992)*0.639 (0.602–0.679)*** 0.647 (0.585–0.714)***
Rural women
2011 0.670 (0.603–0.744)*** 0.772 (0.682–0.874)*** 1.080 (0.973–1.199) 0.694 (0.622–0.775)*** 0.829 (0.684–1.005)
2014 0.910 (0.821–1.008) 0.679 (0.603–0.764)*** 0.835 (0.755–0.924)*** 0.755 (0.679–0.838)*** 0.854 (0.708–1.029)
2017 0.966 (0.871–1.071) 0.680 (0.603–0.766)*** 0.675 (0.609–0.747)*** 0.619 (0.556–0.689)*** 0.669 (0.547–0.819)***
Smoking Alcohol use Good SRHS Overweight/Obesity Obesity
Urban women
2011 0.922 (0.859–0.990)*0.755 (0.711–0.802)*** 1.314 (1.231–1.402)*** 1.004 (0.941–1.072) 1.174 (1.076–1.279)***
2014 0.839 (0.783–0.898)*0.644 (0.607–0.683)*** 1.339 (1.258–1.424)*** 0.996 (0.936–1.059) 1.103 (1.106–1.196)*
2017 0.941 (0.879–1.007) 0.699 (0.660–0.741)*** 1.349 (1.268–1.435)*** 1.022 (0.961–1.088) 1.138 (1.050–1.234)**
Rural women
2011 1.020 (0.886–1.175) 0.748 (0.668–0.837)*** 1.245 (1.108–1.400)*** 1.051 (0.932–1.186) 1.118 (0.961–1.301)
2014 1.013 (0.883–1.164) 0.615 (0.550–0.688)*** 1.307 (1.607–1.463)*** 0.862 (0.770–0.966)*1.008 (0.874–1.163)
2017 1.156 (1.008–1.326)*0.653 (0.584–0.730)*** 1.330 (1.187–1.489)*** 0.974 (0.869–1.091) 1.081 (0.939–1.246)
Flu Vaccination Blood Pressure check Cholesterol check Mammographic Check Cytological Check
Urban women
2011 0.762 (0.704–0.825)*** 0.831 (0.735–0.940)*** 0.892 (0.813–0.979)*** 1.175 (1.101–1.253)*** 1.194 (1.114–1.279)***
2014 0.722 (0.670–0.778)*** 1.664 (1.442–1.920)*** 2.976 (2.634–3.362)*** 1.261 (1.185–1.342)*** 1.332 (1.246–1.424)***
2017 0.622 (0.577–0.671)*** 2.312 (1.976–2.704)*** 5.507 (4.737–6.401)*** 1.299 (1.220–1.382)*** 1.455 (1.361–1.556)***
Rural women
2011 0.736 (0.641–0.846)*** 1.068 (0.819–1.393) 0.966 (0.811–1.150) 0.966 (0.891–1.114) 1.175 (1.044–1.322)**
2014 0.602 (0.527–0.688)*** 1.983 (1.442–2.727)*** 3.567 (2.772–4.591)*** 1.211 (1.086–1.352)** 1.313 (1.712–1.472)***
2017 0.575 (0.503–0.658)*** 2.532 (1.801–3.560)*** 4.836 (3.653–6.401)*** 1.468 (1.341–1.640)*** 1.617 (1.440–1.816)***
Note: Overweight/Obesity: body mass index ≥25 kg/m
2
. Obesity: body mass index ≥30 kg/m
2
.
Abbreviation: SHRS, self-rated health status.
*<0.05;
**≤0.01;
***<0.001.
708 GARCÍA-MAYOR ET AL.

some population groups. Therefore, health policies should consider
the different trends in smoking according to the degree of urbaniza-
tion and suggest effective policies to promote smoking cessation
(or non-initiation) mainly among women.
On the other hand, given the decline in physical activity among
urban women in the long term, it is also important that urban mobility
policies include urbanization plans that increase walking and cycling
both to work and for leisure activities. These measures can help cre-
ate active, healthier, and more livable communities in Europe
(Nieuwenhuijsen, 2020). In addition, it is important to consider that
women may have more difficulties to practice leisure physical activity
due to gender inequalities in living and working conditions, especially
in a context where the economy is suffering (Mutz & Reimers, 2021).
Our results suggest an integrated approach to prevent and con-
trol the increase in obesity in the Spanish population. It is important
to consider the decreasing trends in the daily consumption of fruit
and vegetable observed, together with the monitoring of other
healthy eating habits and physical activity, due to the implication in
the risk of obesity (García-Mayor et al., 2022). Based on it, we pro-
pose the need for comprehensive strategies that encourage the moti-
vation of families toward these behaviors, the creation of healthy
environments, and the promotion of health education from a global
perspective.
Finally, considering the decline in flu vaccination, it is suggested
some of the factors that stand out for not getting flu vaccination are
the lack of recommendations and the fact that the flu vaccine is con-
sidered unnecessary, with physicians being the main source of infor-
mation, followed by the traditional media and the public
administration (Prada-García et al., 2022). Therefore, physicians must
be properly informed about flu vaccination so that they can actively
educate their patients. In addition, some studies from Europe, con-
ducted in a context after the period analyzed, indicate that the
COVID-19 pandemic had no positive effect on the flu vaccination
acceptance rate, generating discrepancies among health workers
(Gagneux-Brunon et al., 2021). Therefore, loss of confidence in flu
vaccination remains an ongoing problem that requires further analysis
of concerns about this vaccine in different population groups.
AUTHOR CONTRIBUTIONS
Jesús García-Mayor: Conceptualization; investigation; funding
acquisition; writing –original draft; methodology; validation; visualiza-
tion; writing –review and editing; formal analysis; data curation.
Antonio Moreno-Llamas: Conceptualization; investigation; funding
acquisition; methodology; validation; visualization; formal analysis;
data curation. Ernesto De La Cruz Sánchez: Conceptualization;
investigation; funding acquisition; writing –original draft; methodol-
ogy; validation; visualization; writing –review and editing; formal
analysis; data curation; supervision.
ACKNOWLEDGMENTS
The authors thank the Spanish Ministry of Health, Consumption and
Social Welfare for the availability of data from the Spanish National
Health Survey and the European Health Interview Survey. The
analyses and content of this work are the sole responsibility of the
authors who sign it.
CONFLICT OF INTEREST STATEMENT
The authors declare that there is no conflict of interest or financial
financing of any kind.
DATA AVAILABILITY STATEMENT
Data sharing is not applicable to this article as no new data were cre-
ated or analyzed in this study.
ORCID
Jesús García-Mayor https://orcid.org/0000-0002-7212-1974
Antonio Moreno-Llamas https://orcid.org/0000-0003-0168-4806
Ernesto De La Cruz Sánchez https://orcid.org/0000-0002-4718-
7058
REFERENCES
Allen-Leigh, B., Uribe-Zúñiga, P., Le
on-Maldonado, L., Brown, B. J.,
Lörincz, A., Salmeron, J., & Lazcano-Ponce, E. (2017). Barriers to HPV
self-sampling and cytology among low-income indigenous women in
rural areas of a middle-income setting: A qualitative study. BMC Can-
cer,17(1), 734. https://doi.org/10.1186/s12885-017-3723-5
Alston, L., Peterson, K. L., Jacobs, J. P., Allender, S., & Nichols, M. (2017).
Quantifying the role of modifiable risk factors in the differences in car-
diovascular disease mortality rates between metropolitan and rural
populations in Australia: A macrosimulation modelling study. BMJ
Open,7(11), e018307. https://doi.org/10.1136/bmjopen-2017-
018307
Alvarez-Alvarez, I., Zazpe, I., Pérez de Rojas, J., Bes-Rastrollo, M., Ruiz-
Canela, M., Fernandez-Montero, A., Hidalgo-Santamaría, M., &
Martínez-González, M. A. (2018). Mediterranean diet, physical activity
and their combined effect on all-cause mortality: The Seguimiento
Universidad de Navarra (SUN) cohort. Preventive Medicine,106,45–
52. https://doi.org/10.1016/j.ypmed.2017.09.021
Aune, D., Keum, N., Giovannucci, E., Fadnes, L. T., Boffetta, P.,
Greenwood, D. C., Tonstad, S., Vatten, L. J., Riboli, E., & Norat, T.
(2018). Dietary intake and blood concentrations of antioxidants and
the risk of cardiovascular disease, total cancer, and all-cause mortality:
A systematic review and dose-response meta-analysis of prospective
studies. The American Journal of Clinical Nutrition,108(5), 1069–1091.
https://doi.org/10.1093/ajcn/nqy097
Avanzas, P., Pascual, I., & Moris, C. (2017). The great challenge of the pub-
lic health system in Spain. Journal of Thoracic Disease,9(Suppl 6),
S430–S433. https://doi.org/10.21037/jtd.2017.04.59
Backhaus, I., Hoven, H., Di Tecco, C., Iavicoli, S., Conte, A., & Dragano, N.
(2022). Economic change and population health: Lessons learnt from
an umbrella review on the great recession. BMJ Open,12(4), e060710.
https://doi.org/10.1136/bmjopen-2021-060710
Bixby, H., Bentham, J., Zhou, B., Di Cesare, M., Paciorek, C. J.,
Bennett, J. E., Taddei, C., Stevens, G. A., Rodriguez-Martinez, A.,
Carrillo-Larco, R. M., Khang, Y.-H., Soric, M., Gregg, E. W.,
Miranda, J. J., Bhutta, Z. A., Savin, S., Sophiea, M. K., Iurilli, M. L. C.,
Solomon, B. D., & Collaboration, N. C. D. R. F. (2019). Rising rural
body-mass index is the main driver of the global obesity epidemic in
adults. Nature,569(7755), 260–264. https://doi.org/10.1038/s41586-
019-1171-x
Boakye, K., Bovbjerg, M., Schuna, J., Jr., Branscum, A., Varma, R. P.,
Ismail, R., Barbarash, O., Dominguez, J., Altuntas, Y., & Anjana, R. M.
(2023). Urbanization and physical activity in the global prospective
urban and rural epidemiology study. Scientific Reports,13(1), 290.
GARCÍA-MAYOR ET AL.709

Boletín Oficial del Estado (BOE). (n.d.). Ley 45/2007,para el desarrollo sos-
tenible del medio rural.https://www.boe.es/eli/es/l/2007/12/13/
45/con
Bommelé, J., Walters, B. H., van Dorsselaer, S., & Willemsen, M. C. (2022).
Outdoor smoking as a nuisance to non-smokers: The case for smoke-
free outdoor public spaces in dense urban areas. Tobacco Prevention &
Cessation,8,8–9. https://doi.org/10.18332/tpc/145502
Caballero, B. (2019). Humans against obesity: Who will win? Advances in
Nutrition,10(suppl_1), S4–S9. https://doi.org/10.1093/advances/
nmy055
Chandak, A., Nayar, P., & Lin, G. (2019). Rural-urban disparities in access
to breast cancer screening: A spatial clustering analysis. The Journal of
Rural Health,35(2), 229–235. https://doi.org/10.1111/jrh.12308
De la Cruz-Sánchez, E., & Aguirre-G
omez, L. (2014). Health related lifestyle
and preventive medical care of rural Spanish women compared to their
urban counterparts. Journal of Immigrant and Minority Health,16(4),
712–718. https://doi.org/10.1007/s10903-013-9911-8
Dean, W. R., & Sharkey, J. R. (2011). Rural and urban differences in the
associations between characteristics of the community food environ-
ment and fruit and vegetable intake. Journal of Nutrition Education and
Behavior,43(6), 426–433. https://doi.org/10.1016/j.jneb.2010.07.001
Debiasi, E., & Dribe, M. (2020). SES inequalities in cause-specific adult
mortality: A study of the long-term trends using longitudinal individual
data for Sweden (1813–2014). European Journal of Epidemiology,
35(11), 1043–1056.
Dijkstra, L., Garcilazo, E., & McCann, P. (2015). The effects of the global
financial crisis on European regions and cities. Journal of Economic
Geography,15(5), 935–949.
Dijkstra, L., & Poelman, H. (2014). Regional Working Paper 2014. A Har-
monised Definition of Cities and Rural Areas: The New Degree of
Urbanisation. European Comission Directorate-General for Regional
and Urban Policy: Working Paper.
Dios-Guerra, C., Carmona-Torres, J. M., L
opez-Soto, P. J., Morales-
Cané, I., & Rodríguez-Borrego, M. A. (2017). Prevalence and factors
associated with influenza vaccination of persons over 65 years old in
Spain (2009-2014). Vaccine,35(51), 7095–7100. https://doi.org/10.
1016/j.vaccine.2017.10.086
Domingo-Salvany, A., Bacigalupe, A., Carrasco, J. M., Espelt, A.,
Ferrando, J., & Borrell, C. (2013). Proposals for social class classifica-
tion based on the Spanish National Classification of occupations 2011
using neo-Weberian and neo-Marxist approaches. Gaceta Sanitaria,
27(3), 263–272. https://doi.org/10.1016/j.gaceta.2012.12.009
Escolar-Pujolar, A., Bacigalupe, A., & San Sebastian, M. (2014). European
economic crisis and health inequities: Research challenges in an uncer-
tain scenario. International journal for equity in health,13, 59. https://
doi.org/10.1186/s12939-014-0059-5
Eurostat, E. U. (2019). People at risk of poverty or social exclusion.
Friesen, E. L., Bailey, J., Hyett, S., Sedighi, S., de Snoo, M. L., Williams, K.,
Barry, R., Erickson, A., Foroutan, F., Selby, P., Rosella, L., & Kurdyak, P.
(2022). Hazardous alcohol use and alcohol-related harm in rural and
remote communities: A scoping review. The Lancet Public Health,7(2),
e177–e187. https://doi.org/10.1016/S2468-2667(21)00159-6
Gagneux-Brunon, A., Detoc, M., Bruel, S., Tardy, B., Rozaire, O.,
Frappe, P., & Botelho-Nevers, E. (2021). Intention to get vaccinations
against COVID-19 in French healthcare workers during the first pan-
demic wave: A cross-sectional survey. The Journal of Hospital Infection,
108, 168–173. https://doi.org/10.1016/j.jhin.2020.11.020
García-Mayor, J., Moreno-Llamas, A., & De la Cruz-Sánchez, E. (2022).
How socioeconomic status affects weight status through health-
related lifestyles: A latent class analysis. European Journal of Cardiovas-
cular Nursing.,22, 730–744. https://doi.org/10.1093/eurjcn/zvac101
GBD 2016 Alcohol Collaborators. (2018). Alcohol use and burden for
195 countries and territories, 1990-2016: A systematic analysis
for the global burden of disease study 2016. Lancet,392(10152),
1015–1035. https://doi.org/10.1016/S0140-6736(18)31310-2
Gray, M. S., Judd, S. E., Sloane, R., Snyder, D. C., Miller, P. E., & Demark-
Wahnefried, W. (2019). Rural-urban differences in health behaviors
and outcomes among older, overweight, long-term cancer survivors in
the RENEW randomized control trial. Cancer Causes & Control,30(4),
301–309. https://doi.org/10.1007/s10552-019-01141-x
Groot, S. P. T., Möhlmann, J. L., Garretsen, J. H., & de Groot, H. L. F.
(2011). The crisis sensitivity of European countries and regions: Styl-
ized facts and spatial heterogeneity. Cambridge Journal of Regions,
Economy and Society,4(3), 437–456.
Idris, B. I., Giskes, K., Borrell, C., Benach, J., Costa, G., Federico, B.,
Helakorpi, S., Helmert, U., Lahelma, E., Moussa, K. M.,
Ostergren, P.-O., Prättälä, R., Rasmussen, N. K., Mackenbach, J. P., &
Kunst, A. E. (2007). Higher smoking prevalence in urban compared to
non-urban areas: Time trends in six European countries. Health &
Place,13(3), 702–712. https://doi.org/10.1016/j.healthplace.2006.
11.001
ISCED. (2012). International standard classification of education: ISCED
2011. United Nations Educational, Scientific and Cultural Organization
(UNESCO), UNESCO Institute for Statistics.
Kelly, B. C., Vuolo, M., Frizzell, L. C., & Hernandez, E. M. (2018). Denorma-
lization, smoke-free air policy, and tobacco use among young adults.
Social Science & Medicine,211,70–77. https://doi.org/10.1016/j.
socscimed.2018.05.051
Kulic, N., Dotti Sani, G. M., Strauss, S., & Bellani, L. (2021). Economic dis-
turbances in the COVID-19 crisis and their gendered impact on unpaid
activities in Germany and Italy. European Societies,23(sup1), S400–
S416.
Kulshreshtha, A., Goyal, A., Dabhadkar, K., Veledar, E., & Vaccarino, V.
(2014). Urban-rural differences in coronary heart disease mortality in
the United States: 1999-2009. Public Health Reports,129(1), 19–29.
https://doi.org/10.1177/003335491412900105
Lacuesta, A., & Anghel, B. (2020). Population at risk of poverty or social
exclusion in Spain, according to the European council definition. Banco
de Espana Article,7, 20.
Legazpe, N., & Davia, M. A. (2019). Women's employment and childcare
choices in Spain through the great recession. Feminist Economics,
25(2), 173–198.
Lindroth, M., Lundqvist, R., Lilja, M., & Eliasson, M. (2014). Cardiovascular
risk factors differ between rural and urban Sweden: The 2009 north-
ern Sweden MONICA cohort. BMC Public Health,14, 825. https://doi.
org/10.1186/1471-2458-14-825
Loftus, J., Allen, E. M., Call, K. T., & Everson-Rose, S. A. (2018). Rural-urban
differences in access to preventive health care among publicly insured
Minnesotans. The Journal of Rural Health,34(Suppl 1), s48–s55.
https://doi.org/10.1111/jrh.12235
Manor, O., Matthews, S., & Power, C. (2000). Dichotomous or categorical
response? Analysing self-rated health and lifetime social class. Interna-
tional Journal of Epidemiology,29(1), 149–157. https://doi.org/10.
1093/ije/29.1.149
Ministerio de Sanidad Consumo y Bienestar Social. (n.d.). Encuesta Nacio-
nal de Salud de España.https://www.sanidad.gob.es/estadEstudios/
estadisticas/encuestaNacional/home.htm
Monnat, S. M., & Beeler Pickett, C. (2011). Rural/urban differences in self-
rated health: Examining the roles of county size and metropolitan adja-
cency. Health & Place,17(1), 311–319. https://doi.org/10.1016/j.
healthplace.2010.11.008
Moreno-Llamas, A., García-Mayor, J., & De la Cruz-Sánchez, E. (2020).
Concurrent and convergent validity of a single, brief question for phys-
ical activity assessment. International Journal of Environmental Research
and Public Health,17(6), 1989. https://doi.org/10.3390/
ijerph17061989
Moreno-Llamas, A., García-Mayor, J., & De la Cruz-Sánchez, E. (2021).
Urban-rural differences in trajectories of physical activity in Europe
from 2002 to 2017. Health & Place,69, 102570. https://doi.org/10.
1016/j.healthplace.2021.102570
710 GARCÍA-MAYOR ET AL.

Murimi, M. W., & Harpel, T. (2010). Practicing preventive health: The
underlying culture among low-income rural populations. The Journal of
Rural Health,26(3), 273–282. https://doi.org/10.1111/j.1748-0361.
2010.00289.x
Mutz, M., & Reimers, A. K. (2021). Leisure time sports and exercise activi-
ties during the COVID-19 pandemic: A survey of working parents. Ger-
man Journal of Exercise and Sport Research,51(3), 384–389.
Nieuwenhuijsen, M. J. (2020). Urban and transport planning pathways to
carbon neutral, liveable and healthy cities; a review of the current evi-
dence. Environment International,140, 105661.
Oliva, J., González, B., Barber, P., Peña, L. M., Urbanos, R., & Zozaya, N.
(2018). Crisis econ
omica y salud en España. Consum y Bienestar Soc:
Minist Sanidad.
O'Reilly, G., O'Reilly, D., Rosato, M., & Connolly, S. (2007). Urban and rural
variations in morbidity and mortality in Northern Ireland. BMC Public
Health,7, 123. https://doi.org/10.1186/1471-2458-7-123
Pariani, E., Amendola, A., Piatti, A., Anselmi, G., Ranghiero, A., Bubba, L.,
Rosa, A. M., Pellegrinelli, L., Binda, S., Coppola, L., Gramegna, M., &
Zanetti, A. (2015). Ten years (2004-2014) of influenza surveillance in
northern Italy. Human Vaccines & Immunotherapeutics,11(1), 198–205.
https://doi.org/10.4161/hv.35863
Prada-García, C., Fernández-Espinilla, V., Hernán-García, C., Sanz-
Muñoz, I., Martínez-Olmos, J., Eiros, J. M., & Castrodeza-Sanz, J.
(2022). Attitudes, perceptions and practices of influenza vaccination in
the adult population: Results of a cross-sectional survey in Spain. Inter-
national Journal of Environmental Research and Public Health,19(17), 1–
12. https://doi.org/10.3390/ijerph191711139
Regidor, E., Barrio, G., Bravo, M. J., & de la Fuente, L. (2014). Has health in
Spain been declining since the economic crisis? Journal of Epidemiology
and Community Health,68(3), 280–282. https://doi.org/10.1136/jech-
2013-202944
Regidor, E., Mateo, A., Barrio, G., & de la Fuente, L. (2019). Mortality in
Spain in the context of the economic crisis and austerity policies.
American Journal of Public Health,109(7), 1043–1049. https://doi.org/
10.2105/AJPH.2019.305075
Villalbi, J. R., Suelves, J. M., Martinez, C., Valverde, A., Cabezas, C., &
Fernandez, E. (2019). Smoking control in Spain: Current situation and
priorities. Revista Espanola de Salud Publica,93,e1–e16.
Voigt, M., Ordanovich, D., Viciana Fernandez, F., Ann Cilek, L., Canovas
Balboa, R., & Ramiro Farinas, D. (2019). Urban environment and mor-
tality differentials in Spain. Population Space and Place,25(4), 1–11.
https://doi.org/10.1002/psp.2239
Williams, T., Lakhani, A., & Spelten, E. (2022). Interventions to reduce lone-
liness and social isolation in rural settings: A mixed-methods review.
Journal of Rural Studies,90,76–92.
World Health Organization. (2000). WHO Technical Report Series 894: Obe-
sity: Preventing and Managing the Global Epidemic. World Health
Organization.
World Health Organization. (2021). Primary Health Care on the Road to Uni-
versal Health Coverage: 2019 Global Monitoring Report. World Health
Organization.
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How to cite this article: García-Mayor, J., Moreno-Llamas, A.,
& De La Cruz Sánchez, E. (2023). A decade beyond the
economic recession: A study of health-related lifestyles in
urban and rural Spain (2006–2017). Nursing & Health Sciences,
25(4), 700–711. https://doi.org/10.1111/nhs.13063
GARCÍA-MAYOR ET AL.711