Socioeconomic Disparities and Air Pollution Exposure: a Global Review
Curr Envir Health Rpt
Socioeconomic Disparities and Air Pollution Exposure: a Global Review
Anjum Hajat 0 1
Charlene Hsia 0 1
Marie S. O'Neill 0 1
Anjum Hajat 0 1
0 Departments of Environmental Health Sciences and Epidemiology, University of Michigan , 6623 SPH Tower 1415 Washington Heights, Ann Arbor, MI 48109-2029 , USA
1 Department of Environmental and Occupational Health Sciences, University of Washington , 4225 Roosevelt Way NE, Seattle, WA 98105 , USA
The existing reviews and meta-analyses addressing unequal exposure of environmental hazards on certain populations have focused on several environmental pollutants or on the siting of hazardous facilities. This review updates and contributes to the environmental inequality literature by focusing on ambient criteria air pollutants (including NOx), by evaluating studies related to inequality by socioeconomic status (as opposed to race/ethnicity) and by providing a more global perspective. Overall, most North American studies have shown that areas where low-socioeconomic-status (SES) communities dwell experience higher concentrations of criteria air pollutants, while European research has been mixed. Research from Asia, Africa, and other parts of the world has shown a general trend similar to that of North America, but research in these parts of the world is limited.
Environmental justice; Environmental inequality; Criteria air pollutants; Air pollution; Socioeconomic status; Social disadvantage
Department of Epidemiology, University of Washington, 4225
Roosevelt Way NE, Seattle, WA 98105, USA
Several review articles related to inequalities in environmental
hazards have been conducted over the years [
reviews focus on a variety of important topics including the
following: understanding the origins of environmental
], the policy implications of environmental justice
(EJ) research [
], the interaction between the EJ advocacy
movement and the research agenda [
], a methodological
critique of the research [
], and finally the issue of whether
environmental inequalities in the US disproportionately
impact racial/ethnic minorities or populations of low
socioeconomic status (SES) [
]. The reviews of the existing body of
research clearly highlight both the sheer volume of work
around environmental inequalities and the complexity of the
Although the terms EJ and environmental inequality are
often used interchangeably in the literature, they do have
distinct meanings. The concept of justice is normative, involving
value judgments that can vary over place and time, while
equality can be measured empirically and directly compared
[10••, 11]. Inequalities can be defined across other domains
such as process (equal access to the environmental
decisionmaking process) and opportunity (equal opportunity to reduce
or avoid exposures). These concepts, being difficult to
measure, are not often found in empirical research (see Marshall
[12••] and Clark [
] for papers that move beyond
Beyond issues of fairness, environmental inequality
research has important health implications. Several reviews
focus on the relationship between environmental inequality
and health [
]. The triple jeopardy hypothesis states that
low-SES communities face (1) higher exposure to air
pollutants and other environmental hazards and (2) increased
susceptibility to poor health (primarily as a result of more
psychosocial stressors, such as discrimination and chronic stress,
fewer opportunities to choose health-promoting behaviors and
poorer health status) resulting in (3) health disparities that are
driven by environmental factors [
The purpose of this paper is to review empirical data in the
environmental inequality literature from the past 10 years and
to broaden the scope of previous reviews by including
research from around the globe. We define environmental
inequality as the distribution of air pollution across different
socioeconomic groups and focus on papers that address this
issue, rather than the process or opportunity domains. Our
review focuses exclusively on one important environmental
hazard, air pollution, and will only review research related to
the distribution of air pollutants by SES. We recognize that
some researchers will think that the exclusion of research on
environmental inequalities by race/ethnicity is a limitation of
this work. However, racial/ethnic composition of populations
is highly diverse, worldwide, as is patterning of
socioeconomic factors by race/ethnicity. Further, some countries do not
routinely record race/ethnicity in health data. Additionally,
interpretation and conceptualization of research on race/
ethnicity can be challenging [
]. Because of these factors
and because we recognize that EJ is emerging as a critical
issue in nations around the world, we decided to emphasize
socioeconomic factors and not address race/ethnicity to allow
for a more inclusive and generalizable global perspective.
Our focus on air pollution is further limited to the criteria
air pollutants which are monitored and regulated by the US
Environmental Protection Agency (EPA) and governmental
agencies in other nations. Air quality standards for
concentrations of particulate matter (PM, both particles <2.5 μm in
aerodynamic diameter, PM2.5, and <10 μm in aerodynamic
diameter, PM10), carbon monoxide (CO), nitrogen dioxide
(NO2), ozone (O3), sulfur dioxide (SO2), and lead in outdoor
air are set by the World Health Organization and individual
governments around the world [
]. They are based on a
review of the scientific evidence and are established to allow
for an adequate margin of human health and safety, in light of
the numerous health effects of criteria air pollutant exposure
on human health [
A systematic review was conducted to identify all published
studies on SES and ambient air pollution exposure. First, a
literature search was performed using Science Direct, Web of
Science, Google Scholar, and PubMed for the following
keywords: Bsocioeconomic injustice and air pollution,^
Benvironmental justice and air pollution,^ Benvironmental
inequity and air pollution,^ Bsocioeconomic status and air
pollution,^ and Bdisparity and air pollution and environment.^
These keywords yielded a total of 440 published papers after
removing duplicates across databases.
We excluded papers from this review if (1) they were
published prior to 2005, (2) they were mainly focused on
quantifying the association between air pollution and a health
outcome, with little attention to inequalities in exposure, (3) they
did not conduct an empirical analysis (i.e., provided a
framework or conceptual model), (4) they evaluated air pollutants
other than the criteria air pollutants (e.g., hazardous air
pollutants (HAPs), black carbon), (5) they used traffic-related
metrics as a proxy for air pollution (e.g., distance to road, traffic
density), (6) they combined several air pollutants (criteria and
non-criteria) into an index without providing data on the
individual ambient air pollutants themselves, and/or (7) they used
only race/ethnicity classifications and not other
socioeconomic factors to evaluate inequality. All papers were screened and
reviewed by two study authors.
Ultimately, 37 studies met the inclusion criteria and were
included in this review. Studies were organized by geographic
location, with 22 North American studies, 10 European studies,
and 5 studies from New Zealand, Asia, and Africa. Of these,
some evaluated both criteria and non-criteria air pollutants and
some evaluated both race/ethnicity and SES. Findings related
to the non-criteria air pollutants and race/ethnicity are not
described in the tables or text of this paper.
Given the differing methods used to assess the association
between SES and air pollution, we did not attempt to quantify
the overall magnitude of effect. Instead, we focused on
describing the directionality of results to better understand if an overall
trend emerges from the literature. We also discuss
methodological issues, such as the analytic techniques employed to assess
the association between SES and air pollution and the unit of
analysis chosen by researchers. Furthermore, the different
approaches used in air pollution exposure assessment and the
types of SES metrics used are also discussed.
The North American studies are outlined in Table 1. In
general, these studies show a consistent finding: lower-SES
individuals and communities are exposed to higher concentrations
of criteria air pollutants. Comparison of magnitude of effects
is difficult given differences across studies, but in those
studies that used similar data sources and methods, we see
relatively small increases in pollutant exposures associated with
lower SES. For example, PM2.5 concentrations were 0.14, 0.2,
0.47, and 0.9 μg/m3 higher in census tracts in North Carolina
], in the northeast USA [
], in six US cities [28•], and in
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selected census tracts around the USA [
], respectively, with
an approximately 15 % greater population of persons with less
than a high school education. For context, the EPA PM2.5
standard is 12 μg/m3 and the WHO guidelines aim for
A few exceptions to this pattern were seen. In New York
City (NYC), Toronto, and Montreal, some SES indicators
showed the opposite association: higher-SES census tracts
had higher concentrations of pollutants [28•, 30–32]. In
NYC, a borough-specific analysis revealed that the Bronx
and Staten Island had these patterns [
], which is similar to
what was found in the Multi-Ethnic Study of Atherosclerosis
cohort among study participants who lived in the southern
Bronx and northern Manhattan [28•]. These results may
reflect the fact that these cities developed in such a way that
high-SES individuals clustered around busy roadways which
often run near rivers and lakes offering more scenic views and
better access to urban amenities.
Other North American studies found differences by
pollutant. For example, high-poverty clusters in Los Angeles had
similar NO2 and PM2.5 concentrations compared to
lowpoverty clusters but had higher concentrations of other
]. These pollutant-specific results were also seen in a
study from Montreal, where higher NO2 concentrations were
associated with low-income populations, but no differences
across populations were found for PM2.5, CO, and NOx [
Several North American studies also found that higher SES
groups are exposed to higher concentrations of O3 compared
to lower-SES groups [
11, 26, 35, 36
]. This is likely because of
the scavenging of O3 by nitric oxide (NO) which can result in
lower O3 levels near roadways (where low-income
populations are more likely to live) and higher levels further away
from them. However, two US studies found O3 levels to be
higher among low-SES groups [
Although research from other parts of the world is limited,
studies from New Zealand (NZ), Asia, and Africa also showed
negative associations between SES and air pollutants
(Table 2). The three studies from NZ found that low-income
and high-deprivation neighborhoods had higher
concentrations of PM10 compared to higher-SES areas [
lone study to address air pollution inequalities in Africa was
from Ghana [42•]. That study found that community SES was
inversely associated with both PM2.5 and PM10. Lastly, a
study from Hong Kong explored a municipality with a strong
social safety net which had direct bearing on air pollution
inequalities [43•]. The government of Hong Kong provides
public housing for low-income residents, while higher-income
families obtain housing through the private housing market.
Among those living in private housing, the lower-SES
population had higher exposure to PM10 compared to the high-SES
population. No such inequality was found for residences of
public housing. The authors indicate that similar results were
found for several other air pollutants. The differential location
of public housing facilities appears to be reducing residents’
exposure to traffic-related air pollution.
Findings in the European literature were quite mixed
(Table 3). Several studies found non-linear patterns of
]. In Strasbourg, France, only the high-SES quintile
had lower NO2 concentrations, compared to the other four
quintiles that had similar concentrations [
]. Similarly, a
European-wide analysis uncovered non-linear trends where
middle-income populations had lower PM10 concentrations
compared to both higher- and lower-income groups,
depending on if analyses focused on Eastern or Western Europe [
In London, some high-SES groups had similar NOx
concentrations to low-SES groups when using a small-area SES
]. Other studies found that the choice of SES metric was
relevant to findings, where some SES measures were
positively associated with air pollution and others negatively [
]. A pilot study of several cities in the Czech Republic
found pollutant-specific results: smaller cities with larger
low-SES populations had higher PM10 and SO2
concentrations, while larger cities with larger high-SES populations
had higher concentrations of NO2 [
]. Lastly, a Spanish study
of pregnant women found no association between
individuallevel SES and NO2 [
A few European studies from England and Sweden found
patterns of inequality similar to those seen in the USA
]. Two UK-based studies found that low-SES groups
were exposed to worse air quality [
], and a study of a
city in Sweden found that low-income children were exposed
to higher levels of NO2 compared to children from
higherincome families . Patterns similar to those seen in New
York and Toronto were also seen in the Netherlands, where
low-SES groups were exposed to better air quality compared
to high-SES groups [
As described above, results from air pollution inequality
studies vary depending on place. The methodological approaches
used can also result in differences in findings. Previous
authors have discussed how some methodological approaches in
such studies can yield higher-quality research while avoiding
common limitations [
The appropriate unit of analysis and the accompanying
modifiable areal unit problem (MAUP) in environmental
inequality studies have been discussed [
1, 17, 54, 55
refers to the situation where using different units of analysis
results in contradictory findings. Several scholars advocate for
using smaller levels of geography in order to improve
reliability and accuracy of the study [
]. Very few studies in this
review rely exclusively on larger geographic units such as
], cities [
], or regions within a nation [
Most of the studies use something similar to or smaller than
a US census tract. A few studies use very small geographic
New Zealand, Asian, and African studies of air pollution-SES inequalities
Location: NZ New Zealand; SES indicators: NSES index neighborhood SES/deprivation index; analytic method: OLS ordinary least squares regression,
RE random effects/hierarchical model; results: ↓ higher-SES areas/groups associated with lower pollutant concentrations, ↑ higher-SES areas/groups
associated with higher pollutant concentrations, — null association, * private housing only, ** public housing only
areas such as parcel data [
], building of residence [
British postcode (mean of 14 households) [
Some statistical methods used in environmental inequality
research may produce biased findings [
]. Although a variety
of methods are used to evaluate inequality, many researchers
use a regression-based approach to quantify the magnitude and
direction of the inequality. In the studies reviewed here, air
pollution is the outcome or dependent variable. Ordinary least
squares (OLS) regression (i.e., linear regression) assumes that
outcomes are independent. Since air pollution often displays a
pattern of spatial autocorrelation, it is important to evaluate
spatial autocorrelation and use a spatial analytic technique if
autocorrelation is present. This will ensure that the
independence of observations assumption is not violated.
Many of the studies reviewed do use a spatial regression
approach to evaluate the association between SES and air
pollution: both spatial generalized additive models (GAM) and
spatial autoregressive (SAR) models (i.e., spatial lag or spatial
error models) were popular choices. In addition, a few papers
used a hierarchical or random effects model that accounted for
between neighborhood correlations [
] and, in some cases,
specified a spatial covariance structure [42•, 56]. A few studies
use both spatial and aspatial approaches to underscore
differences across models and find that parameter estimates from
OLS models tend to overestimate the magnitude of effect
compared to spatial approaches (i.e., GAMs or SAR) [28•, 30, 45,
57]. One study compared aspatial multilevel models to a spatial
approach and found little difference between the two [28•].
Unfortunately, among studies using regression methods, many
do not use methods that account for the clustering of air
pollutants across space [11, 26, 29, 32, 35–38, 43•, 44, 46, 49, 50, 52,
58, 59]. Furthermore, these same studies do not report the
degree of autocorrelation present in the data, so it is unclear if
their choice of model is justified.
Regardless of the use of spatial or aspatial regression
approaches for addressing autocorrelation, the issue of adjusting
for additional confounders is an important one. It seems
plausible that factors such as population density and land use
could be important confounders of the air pollution-SES
association. However, only a few studies adjust for potential
confounders [11, 27, 28•, 35, 40, 42•, 52], leaving parameter
estimates subject to bias. The amount of bias will depend on the
number and strength of the confounders adjusted for. In the few
studies that provide data for both adjusted and unadjusted
models, it appears that controlling for several confounders
attenuates the parameter estimates [28•, 52]. We recognize that
confounders may be specific to the study population at hand;
thus, future research should explore this issue on a case-by-case
basis. Exploring the possibility of potential confounders may
result in future environmental inequality studies that provide a
less-biased measure of the magnitude of effect.
A related issue pertains to whether air pollution inequality
studies pool data (or combine effect estimates) across
locations or conduct stratified analyses. A few papers reviewed
here provide examples of pooling data within the context of
a single study, and all show that pooled analyses tend to mask
potentially important patterns found in stratified models [28•,
46, 52, 57]. For example, data from an English study show
that in the cities of Leeds and London, PM10 and NO2
concentrations increase as SES declines, whereas the association
is similar for SES groups in Liverpool and Bristol [
patterns were masked in the country-wide analyses.
Understanding the locality-specific patterns will be relevant
for policy makers and those considering interventions to
reduce the health effects of air pollution.
A few studies have begun using inequality metrics such as
the concentration index, Atkinson index, and the slope index of
inequality to quantify the inequality present in the data [12••,
13, 53, 60, 61]. These metrics were first developed by
econometricians to assess inequality in income across populations but
have since been applied to health and environmental studies
]. Inequality metrics are useful in order to directly
compare inequality across groups but may also be useful in
assessing high-risk individuals within a population of interest.
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Furthermore, these metrics show much promise in quantifying
inequality across time, e.g., before and after a policy is
implemented [10••]. The studies using inequality metrics in this
review were all cross-sectional in nature. We hope that future
studies will apply these metrics to health effects studies to better
understand if inequality in the distribution of air pollution is
related to environmental health disparities.
Overall, air pollution inequality studies have become more
analytically sophisticated over time. Given the important policy
ramifications of this work, this is a welcomed development.
Air Pollution Exposure Assessment
Air pollution exposure assessment has evolved over the past
several decades. The move from between-city to within-city
estimation has allowed for a reduction in measurement error and the
identification of significant variability of air pollution within small
geographic areas [
]. The ability to predict air pollution at fine
spatial resolution may also be useful for explaining the mixed
results seen previously. That is, the ability to incorporate
finescale variability in air pollution across space may allow
researchers to unmask some of the homogeneity seen in past
studies, creating a more nuanced picture of the air pollution-SES
association. Furthermore, the advances in exposure assessment
may also help us better understand the differing patterns of SES
by pollutant, i.e., O3 vs NO2, which have different spatial
Most of the studies reviewed here used either dispersion
models, land use regression (LUR) models, or a hybrid
approach which combines a variety of techniques such as LUR
and geostatistical interpolation (e.g., kriging) to predict air
pollution at unmeasured locations (all except [
29, 35, 37, 49
few studies use proximity-based or proximity-weighted
29, 35, 37, 47, 49, 53
]. In most cases where these
approaches were taken, collecting additional data was not
feasible because these studies were interested in providing an
assessment of air pollution inequality for the entire nation.
A particularly interesting exposure assessment approach
was implemented in Ghana. In light of the lack of government
air pollution monitoring in Ghana, the authors undertook an
extensive mobile monitoring campaign coupled with the
placement of several fixed site monitors and a census of wood and
charcoal stoves along the mobile monitoring route. These data
were combined to produce detailed exposure maps which
showed significant spatial variability both within and between
the neighborhoods under study [42•, 66]. Such extensive efforts
may be required to characterize inequality in less-industrialized
nations where routine ambient air quality monitoring is lacking.
Some studies looked at specific sources of air pollution
(e.g., road versus industrial) [
11, 33, 58
] or components of a
more complex mixture [
]. Source-specific studies may
guide regulations and other interventions which may have a
more direct impact on reducing air pollution inequalities.
SES is a complex construct that has been operationalized with
a variety of different measures, including income, education,
and occupation [
]. SES measures take different forms in
less-industrialized countries where housing type, water and
electricity access, and assets in the form of cattle and
televisions are often used [
]. In terms of area-level measures of
SES, the British have led the way in articulating the need for a
deprivation index, an index composed of several individual
metrics to measure a relative lack of resources along several
dimensions (social, material) [
Many authors agree that using only one indicator of SES
(e.g., income) may not sufficiently capture the broader
construct of SES. For example, some US health studies ask one
question on income or education and assume that item
sufficiently measures (with minimal measurement error) this
relatively complex construct. However, it is also widely
acknowledged that indicators of SES tend to be highly correlated, and
thus, using multiple measures within a single model is not
recommended. SES indices based on principal components
analysis or a similar dimension reduction technique are
intended to address this issue. Fourteen studies in this review
use some sort of SES index [26, 28•, 38–41, 42•, 43•, 44, 45,
47, 48, 56, 59]. As a part of the nationwide multidomain
deprivation index, a few studies from the UK used several
indicators such as number of families receiving income
support or some other means-tested benefit offered by the
government to better capture the concept of income deprivation
]. To date, only air pollution inequality studies from
Canada have not embraced the use of an SES index.
Another important methodological issue with implications
for health effects studies is the use of both individual- and
area-level SES metrics. To better understand the role of SES
as a confounder of the air pollution-health association, data at
both individual and area levels are needed. Only a few studies
have included both levels of data [28•, 44, 51], and all have
found stronger associations with air pollution for area/
neighborhood-level SES compared to individual-level SES.
Because of the relatively limited knowledge based on how
both levels may singly and/or jointly influence air pollution
exposures and associated health outcomes and because
preventive interventions often differ by level, future studies
should evaluate the role of both individual- and area-level
SES metrics in their specific populations.
Much, but not all, of the environmental inequality literature
from North America, NZ, Asia, and Africa, to date, has shown
that low-SES communities face higher concentrations of
criteria air pollutants. The European research, on the other
hand, is quite mixed. Some studies found that SES was
positively associated with air pollution, while others found a
negative association, and still, others found patterns suggesting
similar levels regardless of social class. These results suggest
the need for further, more rigorous examination of the air
pollution-SES association in Europe. Overall, there is a
paucity of environmental inequality research from nations outside
the USA, but the concepts of EJ and inequality are taking hold
around the world, and we anticipate more research in years to
come. In particular, rapidly developing nations like India and
China are understudied assessing if economic development
distributes air pollution unequally across these population
may have sizeable impacts for population health.
Although several methodological advances in this body of
research have occurred, future researchers may want to
consider some methodological areas of particular importance.
First, understanding the spatial structure of the air pollution
data is a critical first step in choosing an analytic approach.
Secondly, researchers may want to explore the possibility of
c on f ou n de r s of t he ai r po l l u t i o n - S E S a s s o c i a t i o n .
Methodological improvements in both these areas will
provide more accurate point estimates and standard errors.
Environmental inequality research has implications for health
effects analyses. First, it is important for health researchers to
know if individual- and/or area-level SES confounds the air
pollution-health outcome association. SES, like air pollution,
can be highly variable from place to place, and researchers
should carefully consider what it represents in the context of
health studies. Environmental inequality studies can provide an
in-depth look at one piece of the confounding triangle, but only if
both individual- and area-level SESs are explored. Few studies,
to date, have tackled this question [28•, 44, 51].
More importantly, the question of whether differential
exposure to air pollution is driving environmental health disparities
is relevant from a regulatory and public health perspective. In
the USA, evidence supports that many (but not all) low-SES
communities bear a disproportionate burden of air pollution.
For these communities, it is plausible that differential exposure
to air pollution may be a contributor to higher associations
between air pollution and health than seen in better-off
populations. In some European studies, however, higher air
pollution concentrations were found among higher-SES
populations, but the health effects of air pollution were still distributed
disproportionately among the poor [
]. The observation
that communities where high-SES groups live have higher
concentrations of air pollution does not necessarily mean that the
residents are more exposed. High-SES individuals have access
to more resources that can protect them from increased
exposure, such as private transportation versus public, indoor versus
outdoor work environments, better constructed housing and,
potentially, access to climate control, including filtration, for
indoor environments [
]. Alternatively, environmental
health disparities in Europe could be driven by other
environmental hazards, such as noise, second-hand smoke, or
other work- or housing-related indicators, many of which are
also linked to the social environment and disproportionately
impact the poor [
]. Additional research into the social
distribution of air pollution in Europe will require a rigorous,
areaspecific approach to shed light on what is likely to be a quite
Understanding how environmental inequality is created may
help explain air pollution and inequality research and has
implications for policy. It has been hypothesized that low-SES
communities with limited political power and influence are unable to
stop locally undesirably land uses (LULU), such as factories and
roads, from being built in their communities. That is, poor
communities lack social capital, a necessary prerequisite for
mounting an effective campaign against placing a LULU in one’s
community. On the other hand, it has been suggested that
industry is motivated solely by economic factors: building a LULU on
cheap land is economically prudent. The presence of a LULU
will then result in the decline in property values which makes an
area more accessible for low-SES and minority populations [
]. Both of these theories point to the importance of class- and
race-based residential segregation in creating inequality in air
pollution concentrations across space. It should be noted that
much of the research about causes of environmental inequalities
has focused on the US context. Given the importance of
historical, economic, and social contexts in understanding inequality,
other nations may have very different explanations for why
environmental inequalities exist.
One strength of the environmental inequality literature as
reviewed here is its truly interdisciplinary nature. Researchers
from a diverse set of fields bring their own tools and lenses to
the question of inequality, making this body of research primed
for innovation. In the studies reviewed here, authors were from a
wide array of disciplines including the following: geography,
sociology, economics, epidemiology, urban studies,
environmental health sciences, environmental studies, and civil engineering.
Several open research areas and knowledge gaps exist.
First, very few studies have examined changes in inequality
over time [
]. Since levels of air pollution have declined
over time, particularly in North America, it is of interest to
understand if the unequal distribution of air pollution is
widening or narrowing. Specifically, as air pollution policies and
regulations (both related and unrelated to inequality) are put
into place, it is important to understand if these policies impact
inequality. Another policy-relevant issue is that of which
sources or components are most unequally distributed.
Although a few studies have begun to examine this question,
inequalities may be driven by local sources of pollution, thus
necessitating more research. Finally, although this review did
not specifically address race/ethnicity, understanding how
these factors relate to socioeconomic factors in terms of
location-based variability in air pollution concentrations is
important for EJ.
Research that pursues these and other questions that
directly inform policy changes to enhance environmental quality
and health equity is essential in continuing global efforts to
improve health and provide safe environments for all.
Compliance with Ethics Guidelines
Conflict of Interest Anjum Hajat, Charlene Hsia, and Marie S. O’Neill
declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent Ethical approval:
All procedures performed in studies involving human participants were in
accordance with the ethical standards of the institutional and/or national
research committee and with the 1964 Helsinki declaration and its later
amendments or comparable ethical standards.
Papers of particular interest, published recently, have been
• Of importance
•• Of major importance
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