Deprivation, Healthcare Accessibility and Satisfaction: Geographical Context and Scale Implications
Appl. Spatial Analysis
Deprivation, Healthcare Accessibility and Satisfaction: Geographical Context and Scale Implications
Pablo Cabrera-Barona 0 1
Thomas Blaschke 0 1
Gabriel Gaona 0 1
Gabriel Gaona 0 1
0 Universidad Regional Amazónica IKIAM , Vía Muyuna, Kilómetro 7, Tena , Ecuador
1 Interfaculty Department of Geoinformatics - Z_GIS, University of Salzburg , Schillerstraße 30, 5020 Salzburg , Austria
Indices explaining health phenomena are important tools for identifying and investigating health inequalities and to support policy making. Some of these indices are expressed at area-level, and the investigation of the areal influences of these indices on individual health outcomes have scale and geographical contextual implications that need to be assessed. In this study we calculated two area-level indices: one deprivation index and one index of healthcare accessibility. Using multilevel modelling, we calculated the area-level influences of these indices on an individual-level index of healthcare satisfaction considering three kinds of areas or contexts: a context of deprivation, a context of healthcare accessibility and a context combining the two characteristics of healthcare accessibility and deprivation. We evaluated two kinds of geographical problems using the statistical results of these area-level influences: the modifiable areal unit problem (MAUP) and the uncertain geographic context problem (UGCoP). Regarding the MAUP we evaluated the scale effects at two scales: census blocks and census tracts. Regarding the UGCoP we evaluated the differences in areal influences between the three kinds of contexts for both scales. The case study area was the city of Quito, Ecuador. The results of the performed analyses showed no severe MAUP and UGCoP, and revealed important evidence of the area-level influence of deprivation and healthcare accessibility on healthcare satisfaction.
The choice of the reporting units for communicating health-related information has
methodological and practical implications for information users
(Hagenlocher et al.
2014; Nelson and Brewer 2015)
. Health analyses with implications in decision making
have widely used measures of healthcare accessibility and social deprivation
(Bell et al.
2007; Bissonnette et al. 2012; Boyle et al. 2001; Cabrera-Barona et al. 2015; Carstairs
1995; Crooks and Schuurman 2012; Delamater 2013; Havard et al. 2008; Hiscock et al.
2008; Lalloué et al. 2013; Luo 2014; Wan et al. 2013)
. There are different indicators
that can be used to represent deprivation and health.
Townsend et al. (1988)
demonstrated a strong association between health indicators and deprivation indicators and
also between a composite health index and a composite deprivation index. Composite
indices are measures constructed by aggregating different indicators, and represent
multidimensional phenomena, i.e. healthcare accessibility or deprivation. Several
studies analyze the effects on health with simple indicators, and little attention has been paid
to the analyses of health effects using composite indices. This study does not use simple
indicators, but rather composite indices: two area-level indices (one index of
deprivation and one of healthcare accessibility) and one individual-level index of healthcare
The term deprivation is used to name composite indices representing social and
. These indices are useful for identifying
inequalities in health outcomes
(Cabrera-Barona et al. 2015; Boyle et al. 2001; Havard
et al. 2008; Pampalon et al. 2009; Townsend 1987)
. Because deprivation indices are
commonly based on census areas, these indices are also place-specific measures related
(Bell et al. 2007)
Assessing the access to primary healthcare services is fundamental for decision
making in the operation of a health system. Accessibility to healthcare services can be
evaluated considering geographical distances between people’s households and
healthcare services and also considering the healthcare opportunities in terms of the
number of healthcare services a person can choose from
(Delamater 2013; Penchansky
and Thomas 1981)
. This combination of geographical access and healthcare
opportunities is called spatial accessibility (Guagliardo 2004) and can be calculated using a
gravity-based model, which is based on the number of opportunities (e.g. number of
healthcare services), distances to these opportunities, and a gravity decay coefficient
representing the distance decay
The measures of healthcare accessibility derived from gravity-based models are
measures of potential physical accessibility to healthcare.
Joseph and Phillips (1984)
suggested that these measures of potential accessibility to healthcare are easy to
interpret, but that it is also important to evaluate the realized access to healthcare.
The realized access to healthcare is equivalent to the use of healthcare services. The use
of healthcare services can be interpreted using a specific health outcome: the
satisfaction of healthcare services users
. Additionally, a population’s
characteristics (e.g. the population’s levels of deprivation) are also factors influencing
(Aday and Andersen 1974; Andersen 1995)
The healthcare satisfaction is the patient’s evaluation of the received healthcare service
, and can be evaluated by measuring the patient’s reported experience of the
healthcare service, as well as by some characteristics of the service, e.g. number of
physicians in service
(Pascoe 1983; Bleich et al. 2009; Hekkert et al. 2009)
Deprivation can be associated with healthcare accessibility and with healthcare
(Butler et al. 2013)
. Although individual-level characteristics have an
influence on healthcare satisfaction
(Hekkert et al. 2009)
, analyses of public health must
also focus on the issue of place-related health. The place or local neighborhood
influences health outcomes. The neighborhood is also referred to as the context of
the individuals, and, from a geographical perspective, it can be represented in
welldefined areas such as census blocks. The context can be represented by socio-economic
features and access to urban facilities.
A key issue arises from the presented concepts: the contexts of deprivation and
healthcare accessibility are likely to influence an individual-level health outcome such
as healthcare satisfaction. To represent these contexts we used the two area-level
indices of deprivation and healthcare accessibility. The area-level influence of these
indices on healthcare satisfaction can be studied, to better understand the place-people
dynamics of public health, with important implications in policy making. The
geographical variations in health exist at many scales, from global to local
(Boyle et al.
. Neighborhoods or contexts to express health-related measures can be defined in
different ways. However, the most practical way is to use census areas such as census
blocks and census tracts
(Flowerdew et al. 2008)
Despite the important role that the area-based measures of deprivation and
healthcare accessibility play for health policy making, little attention has been paid to
evaluating the contextual and scale implications of these measures, and only few
studies have addressed this issue
(Cabrera-Barona et al. 2016b; Dumedah et al. 2008;
Haynes and Gale 2000; Schuurman et al. 2007; Wei et al. 2017)
. The phenomenon of
the modifiable areal unit problem (MAUP) usually causes the results of statistical
analyses to differ according to the scale and size of the spatial reporting units
et al. 2010; Openshaw 1984)
. The effects of MAUP have been analyzed using several
approaches, generally based on correlation and regression methods
Fotheringham and Wong 1991; Openshaw 1984; Pietrzak 2014; Soobader et al. 2001)
and these kinds of analyses have typically been applied to census indicators
(Flowerdew 2011; Soobader et al. 2001)
There is another kind of problem less discussed than MAUP: the uncertain
geographic context problem (UGCoP) identified by Kwan (2012a). This problem refers to
the uncertainty of the effects of area-based measures due to the different delineation of
the units of analysis, and to the uncertainty of the deviation from the relevant
geographic context that is, in principle, unknown
(Diez-Roux and Mair 2010)
uncertainties are spatial but also temporal.
In the case of the UGCoP, different kinds of spatial units influence individual-level
outcomes in different ways, while in the case of the MAUP, different configurations of the
spatial units causes different aggregation levels of the individuals, which means different
statistical results. For this reason, the zoning effect in the MAUP is also known as the
aggregation effect when changing the divisions of a study area, even at the same scale.
The other UGCoP uncertainty is related to the time mismatches that can occur when
relating the individuals with their geographical contexts. In other words, the
individuals’ dynamics are very complex and the characteristics of these individuals are not
only influenced by contextual effects of the neighborhoods where they live, but also by
other neighborhoods that they visit at different times
In general, these spatial and temporal uncertainties have been identified when
areabased measures (e.g. deprivation) were used to explain individual behaviors or
outcomes (e.g. satisfaction with healthcare)
. After defining the nature of the
contexts and the individuals, the contextual effects on health outcomes can be evaluated
using multilevel models
(Kwan 2012a; Kwan 2012b)
The UGCoP is especially relevant in this study because healthcare satisfaction is
measured considering people’s perceptions out-of-healthcare services. To create the
index of healthcare satisfaction we used data extracted from interviews conducted in
households. We thus have a measure of actual access to healthcare in a context of
potential spatial access to healthcare. Additionally, the unknown optimal spatial
configuration of deprivation areas could differ from the spatial configuration of census
blocks and census tracts traditionally used to express deprivation.
Based on the concepts and approaches presented, the overall goal of this research is
to analyze whether important scale effects of MAUP and effects of the UGCoP exist
when analyzing the influence of area-based measures of deprivation and healthcare
accessibility on individual healthcare satisfaction at two different scales: census blocks
and census tracts. We also aim to evaluate the area-level influence of deprivation and
healthcare accessibility on healthcare satisfaction.
A census tract is the geographical unit created by aggregating census blocks. The
capital city of Ecuador, Quito, is used as the case study area. The remainder of the paper
is organized as follows: the next section explains how the health-related indices were
constructed. In the same section, the multilevel modelling used in the analyses is
explained. The outcomes of the indices calculations and the findings of the multilevel
calculations are shown in the results section. The paper concludes with a discussion
including decision making implications, importance of the findings, limitations of the
study and future research directions.
Two area-level indices were calculated: a deprivation index and an index of healthcare
accessibility. These two area-level indices were calculated at two different scales:
census blocks and census tracts. An individual-level index of healthcare satisfaction
was also calculated. The area-level influences of deprivation and healthcare
accessibility on healthcare satisfaction were calculated using multilevel models. We used these
calculations to evaluate the MAUP and the UGCoP.
Calculation of the Deprivation Index
When constructing deprivation indices, there are methodological and operative issues that
need to be considered, such as possible outliers in the data, weighting techniques or
appropriate census measures
. To develop the deprivation index a
multicriteria decision analysis approach
(Boroushaki and Malczewski 2008; Saaty 1977)
applied, and the guidelines proposed by the Organisation for Economic Co-operation and
Development (OECD 2008) for constructing an index were adhered to.
Ten indicators were used to compose the deprivation index. These indicators were
extracted from the 2010 Ecuadorean Population and Housing Census, represent social
and material disadvantages in Quito, and can be related to health issues
(CabreraBarona et al. 2016b). The conceptual reference for selecting the indicators used to
construct the deprivation index (Table 1) is based on the human-rights approach of the
good living standards
(Cabrera-Barona et al. 2015; Mideros 2012)
. A good living that
includes a healthy life is achieved when basic human rights are ensured through the
access to basic services and welfare conditions, such as access to clean water or
education. When access to these services is limited, then people have a lower living
standard, and are thus more deprived. Additionally, the chosen indicators have an
affinity with the material and social dimensions that can be related to health
(Carstairs 1995; Pampalon et al. 2009; Pasetto et al. 2010; Townsend 1987)
. To detect
possible multicollinearities of the indicators, the variance inflation factors (VIFs)
between indicators were calculated (OECD 2008). All the VIFs obtained were lower
than five and we can therefore confirm that all ten indicators can be used to construct
the deprivation index.
The indicators composing a deprivation index need to be weighted in order
to assign relative importance to the different conditions represented by these
indicators. A common method to calculate weights of indicators of a
deprivation index is principal components
(Pampalon et al. 2009)
. However, this
method does not reflect knowledge and judgments from experts and policy
makers. An alternative method is the analytical hierarchy process
. The AHP is a multi-criteria decision analysis method that supports the
creation of weights based on experts´ judgments, and evaluates the reliability of
these weights by applying a consistency test. The key aspect of the AHP is the
construction of a pairwise comparison matrix
(Boroushaki and Malczewski
2008; Saaty 1977; Saaty 1987)
. This is a n × n matrix where n is the number
of indicators and its inputs are the Saaty’s scales (Table 2) and can be defined as a matrix
A: percent of the population that have a long-term disability (for more than one year)
B: percent of the population that does not have any level of formal education or instruction
C: percent of the population that has no public social/health insurance
D: percent of the population that works in unpaid jobs
E: percent of households with four or more persons per dormitory
F: percent of households without access to drinking water from the public system
G: percent of households without access to the sewerage system
H: percent of households without access to the public electricity grid
I: percent of households without garbage collection service
J:distance (meters)to the nearest primary healthcare service
Very strong importance
X with the elements aij (Chen et al. 2010):
The matrix has the property of reciprocity:
aij ; i; j ¼ 1; 2; 3; …; n
aij ¼ aij:
The matrix is normalized and the weights of the indicators are computed as:
wi ¼ ∑n ∑nj¼1bij
; i; j ¼ 1; 2; 3; …; n
The term bij represents the elements of the normalized matrix.
The scales are obtained from experts´ interviews. Experts evaluate the contribution
of all the indicators to the deprivation index by comparing them to each other and
assigning values from Saaty’s scales to the comparisons. For instance, one expert can
conclude that having no access to clean water is very strongly more important than not
having insurance. In this example, the scale of 7 is assigned (Table 2). Intermediate
values of the scales (2,4,6,8) can been used to represent Bthresholds^ judgments, and
reciprocals (inverse values) of the scales can also be used. For this study we consulted
13 experts of the fields of geography, health and urban studies.
As was mentioned before, one of the advantages of the AHP is to test the
consistency of the obtained weights. This test is performed by calculating a consistency index
(CI) and a consistency ratio (CR). The CI is calculated with the equation
CR ¼ RI
Where λmax is a function of the weights´ vector and the pairwise comparison matrix.
The CR evaluates the likelihood that the judgments were obtained randomly
The random index (RI) is the consistency index of a randomly generated matrix and
depends of the number of indicators used
(Cabrera-Barona et al. 2015; Boroushaki and
. In this case a value of 1.49 was assigned to RI
and Malczewski 2008)
because ten indicators were used to construct the
deprivation index. If the CR is lower than 0.10 then it can be concluded that
the weights calculated are consistent, thus the experts´ judgments are consistent.
Table 3 shows the pairwise comparison matrix, the obtained weights for each
indicator, and the calculated CR. The capital letter representing each indicator is
equivalent to the capital letters assigned for each indicator in Table 1.
The indicators were standardized by applying a min-max normalization to render
them comparable (OECD 2008):
Where vi is the value of one indicator in a unit area i, and vmin and vmax refers to the
minimum and maximum values of the indicator respectively.
Finally, a weighted additive aggregation (OECD 2008) of all the ten indicators was
applied to each of the census areas:
Where Di represents the deprivation index in a census area i, and wi represents the weight
of the indicator vi norm. To facilitate interpretation, the deprivation index was normalized
using the min-max normalization. Values closer to 1 represent higher deprivation.
Calculation of the Index of Healthcare Accessibility
The index of healthcare accessibility was constructed by integrating concepts of the
and gravity models
(Hansen 1959; Crooks and Schuurman
2012; Guagliardo 2004)
The accessibility in a unit area i (e.g. a census block) can be expressed in the
following generalized measure of accessibility
Where Sj refers to the opportunities in the area j, and f(cij) represent a function of
travel cost from unit area i to area j.
The proposal of
can be expressed with the following formula
Ai ¼ ∑ jS j f cij
Ai ¼ ∑j d j
The term d βj is the function of travel cost. dj is the Euclidean distance from i to j and
β is the gravity decay coefficient. The β value can be defined considering that the use of
healthcare services is inversely proportional to the distance dj
(Schuurman et al. 2010)
In this case β = −1 and the travel cost function d βj is then expressed d1j. In the case of
healthcare accessibility the opportunities are known as a supply capacity Sj that can be
represented by different kinds of indicators, e.g. number of beds or number of
healthcare professionals. For this study, the indicator available and chosen to represent
Sj was the number of healthcare professionals in the primary healthcare service.
The gravity-based equation above is only adjusted to supply and not to demand. To
correct this problem
Joseph and Bantock (1982)
proposed the incorporation of
population demand, understood as the sum of the populations that may access healthcare
services, who are also affected by the travel cost function:
D j ¼ ∑k Pk * dkj
We used geographic information systems to define which populations to include in
the equation above, and analyzed the distances from each census block centroid to the
nearest primary healthcare service. We found that the average distance was 668 m, and
that most of the centroids were located less than 1000 m from the nearest primary
healthcare service. With these criteria we defined a maximum threshold of 1200 m and
additional thresholds of 300 and 600 m. The population demand is then formulated as
D j ¼
Pk 300 þ
Pk 600 þ
Adjusting the simple gravity model to the population factor, and using the travel cost
function considering the maximum distance threshold, we obtained the following
improved gravity model:
Ai ¼ ∑j
S j* 1200
The improved gravity model does not consider competition among the different
primary healthcare services, and, consequently, its use can lead to an overestimation of
the accessibility of some services
. To overcome this limitation, we added
the probability of people’s selection of a healthcare service out of other available
services to the gravity model. This probability is known as the Huff model
and for our study can be expressed as follows:
Where probi is the probability of population in location i visiting a primary
healthcare service j. Ss represents the number of healthcare professionals of the primary
healthcare services found inside the maximum distance threshold of 1200 m from a
The last step of the proposed method of measuring healthcare accessibility is to join
the improved gravity-based model and the Huff model to create the index of healthcare
The index was normalized using min-max normalization. Values closer to 1
represent higher accessibility. The calculation of the index of healthcare accessibility was
automatized by a tool generated with Python 2.7, using the packages for scientific
computing NumPy, ArcPy and OS.
Calculation of the Index of Healthcare Satisfaction
We used the index of composite healthcare satisfaction proposed by Cabrera-Barona
et al. (2016a). This index uses three indicators: waiting time to receive healthcare
attention after arriving at the primary healthcare service (T), the quality of the attention
received, as reported by the patient (Q), and the opportunities (supply capacity) offered
by the service (S).
The indicators T and Q were extracted from a survey carried out in the city of Quito
in 2014. A total of 231 survey responses were used for this study (error margin ±6, 95%
T was measured in hours and Q was measured using a 1–5 score, where 5 means a
person received excellent healthcare attention at their last visit to a primary healthcare
service. Cabrera-Barona et al. (2016a) used a categorical score for the indicator S,
measuring if a person attended a primary healthcare service, a basic hospital, or a
specialized hospital. Because this study is applied to primary healthcare services, we
used the number of healthcare professionals in the healthcare service.
The indicators were standardized by applying the min-max normalization.
The values used to weight the indicators were generated by the application of
the AHP. The index of healthcare satisfaction (HCS) is calculated by
aggregating the weighted normalized indicators. Thus, the equation that describes HCS
is as follows:
HCS ¼ 0:49ðQÞ þ 0:20ðSÞ−0:31 ðT Þ
We thereby obtained 231 index values of healthcare satisfaction that were
geolocated in the census blocks and census tracts, which could then be linked to the
arealevel values of deprivation and healthcare accessibility.
Area-Level Influences of the Indices of Deprivation and Healthcare Accessibility on Individual Healthcare Satisfaction
Multilevel models were applied to evaluate the index of healthcare satisfaction as a
response to the indices of deprivation and healthcare accessibility. These two latter
indices represent the context or Bneighborhood^ that influences individual healthcare
satisfaction (Fig. 1).
The individual-level index of healthcare satisfaction is spatially nested in the
contexts of deprivation and healthcare accessibility. Multilevel models allow the
identification of nested (random) effects when explaining an individual-level
(level-1) variable. For the case of this study, a basic two-level model can be
applied. This basic model is also named unconditional and it does not include
additional individual measures. The level-1 equation of this model is
Where Yij is the level-1 dependent variable in area j, β0j is the level-1 intercept and rij
represents the level-1 error (individual-level residual).
The area-level (level-2) equation of the unconditional model is constructed using the
level-1 intercept β0j as the dependent variable of the level-2 equation
Where β0 is the overall mean across areas (fixed effect) and u0j is the effect of area j
on the dependent variable Yij. u0j is a random term at level-2 and can be considered as
the level-2 residual.
The combination of the basic level-1 and level-2 equations is called the totally
unconditional model (null model) and it is useful to describe the variance of an
individual-level variable between areas
. We use the variance partition
coefficient (VPC) as the measure to evaluate the variances of the individual-level
measure (index of healthcare satisfaction) in relation to the area-level measures
(healthcare accessibility and deprivation indices). The VPC measures the proportion
of the total variance of the individual-level variable that is due to differences between
census blocks (or census tracts). The VPC is calculated by dividing the variance of the
intercept of the multilevel model by the total variance of the model.
In conclusion, an index value of healthcare satisfaction i in an area j (census block or
census tract) with values of healthcare accessibility, or deprivation, or both, can be
Y ij ¼ β0 þ u0 j þ rij
Three kinds of multilevel models were then executed: i) one considering only the
deprivation index (context of deprivation), ii) a second considering only the healthcare
accessibility index (context of healthcare accessibility), and iii) a third one considering
both area-level indices (context of deprivation and healthcare accessibility interaction).
These three models were calculated for census blocks and for census tracts.
Because both measures of deprivation and healthcare accessibility include an
indicator related to distances to primary healthcare services, the assumption of the statistical
independence of the area-level measures can be infringed. To ensure this statistical
independence, we applied an orthogonal transformation to the index of healthcare
accessibility before running the multilevel models.
Finally, to evaluate the significance of areal influences two tests were performed: the
likelihood ratio (LR) test and the Wald test. Both tests compare the unconditional
model with a null single-level model.
The city of Quito has low levels of deprivation: the average of the index was
0.13 ± 0.09 std. dev. for the census blocks and 0.17 ± 0.13 std. dev. for the census
tracts. However, this does not mean the absence of inequalities in the city. Figure 2
depicts the results of the deprivation index at both scales of analyses: census blocks
(Fig. 2a) and census tracts (Fig. 2b). It is clear that, independently of the scale,
deprivation is concentrated in the peripheries of the city such as in the extreme
northwest, the extreme south-east and the extreme south-west.
The index of healthcare accessibility has an average of 0.04 ± 0.06 std. dev. for the
census blocks and 0.06 ± 0.07 std. dev. for the census tracts. The results of this index
must be interpreted cautiously because medium scores of gravity-based models of
healthcare accessibility may range around 0.001
(Luo 2014; Luo and Qi 2009)
Figure 3 geographically illustrates the index of healthcare accessibility: accessibility
scores higher than 0.001 show that the city of Quito has a good geographical access to
healthcare services in most of its census blocks or census tracts. This means that the city’s
population has a reasonable access to primary healthcare services from a spatial point of
The index of healthcare satisfaction has a mean of 0.46 ± 0.16 std. dev. Results
showed that most interviewed people experienced fair satisfaction with healthcare
(satisfaction scores between 0.36 and 0.50). Only few people were determined to have
low (< 0.30) and high (> 0.58) healthcare satisfaction: 11% and 7% respectively.
Table 4 shows the three types of multilevel models calculated for census blocks as
well as for census tracts. The first type of model considers a context of deprivation; the
Intercept Std. Level 1 Level 2
errors variance variance
Census Deprivation 34.04
blocks Healthcare accessibility 34.51
second type of model considers a context of healthcare accessibility; the third type of
model considers both kinds of contexts, deprivation and healthcare accessibility.
The results of the fixed effects of the models are the intercepts and the standard
errors. The intercepts are similar for all models and what is interesting is that all the
contexts considered are positively related to individual healthcare satisfaction. This is
concordant with the low levels of deprivation and high levels of healthcare accessibility
of the city. The obtained standard errors mean that the fixed effects are precise
considering a 95% confidence interval.
The random effects are the most interesting part of the multilevel models for this
study because it is possible to measure the effects of the area-level deprivation and
healthcare accessibility on individual healthcare satisfaction. These effects are
summarized in the VPC, LC and Wald test statistics. The obtained variance partition
coefficients (VPC) do not significantly vary between the different kinds of contexts and
between census blocks and census tracts: 25% of the variance of individual
healthcare satisfaction can be attributed to differences between census blocks or
census tracts where deprivation is the area-level feature; 25% of the variance of
individual healthcare satisfaction can be attributed to differences between
healthcare accessibility values of census blocks, and 21% of the variance of
this satisfaction can be attributed to differences between healthcare accessibility values
of census tracts.
Notwithstanding, a small difference can be identified when the context encompasses
both deprivation and healthcare accessibility: in the case of census blocks, 28% of the
variance of healthcare satisfaction can be attributed to differences between census
blocks with both characteristics deprivation and healthcare accessibility, while in the
case of census tracts this variance is lower, at 19%. The LR and Wald tests show the
significance of all these area-level influences on healthcare satisfaction. In the case of
the LR test, all the values obtained are higher than 3.84 (the 5% point of a chi-squared
distribution with 1 degree of freedom), which strongly supports the area-level
influences of this study. The p-values of the Wald test complement the LR test results, and
confirm the high significance of the area-level influences at both scales.
Finally, the Akaike information criterion (AIC) showed a similar performance for all
the models. The lower AIC corresponds to the model considering the deprivation
context at census tract scale.
In this paper we proposed a methodology to examine scale effects of MAUP and effects
of the UGCoP when analyzing the influence of deprivation and healthcare accessibility
on healthcare satisfaction. This study demonstrates that there are no marked variations
in healthcare satisfaction between the different contexts and different scales of analysis.
Therefore, no severe MAUP and UGCoP were found. Interestingly, even when our
main focus was to evaluate MAUP and UGCoP effects, we also found striking
evidence of area-level influence of deprivation and healthcare accessibility on
individual healthcare satisfaction.
The scale effects remained relatively similar when comparing census blocks and
census tracts. For example, regarding the context of deprivation, a VPC of 0.25 was
found for both census blocks for census tracts. Only the VPCs of the contexts with both
healthcare accessibility and deprivation suggest the existence of MAUP.
In the case of the models performed at census blocks scale, the VPCs are similar,
which can be interpreted as no evidence of UGCoP. In the case of the census tracts, the
VPCs found between the contexts of deprivation and healthcare accessibility mean that
there is practically no UGCoP. However, between these contexts and the context where
deprivation and healthcare accessibility interact, there is a minimal UGCoP.
The population data to construct the deprivation and the healthcare accessibility
indices was obtained from the 2010 Census, and the data to construct the healthcare
satisfaction index was obtained in the year 2014. The results of the multilevel models
showed a minimal temporal Bmismatch^ related to the UGCoP in the study area. This
minimal temporal mismatch could be ascribed to the use of aggregated data to compose
the indices applied, and to the fact that this data was obtained in years relatively close to
each other. However, it is more likely to have a day-to-day stability of the dynamics of
the geographic contexts. This stability can also be presented during longer time frames,
e.g. years. For instance,
Wheaton and Clarke (2003)
identified a lagged effect of
childhood neighborhood disadvantages on early adult mental health. They showed
cumulative neighborhoods´ effects on individuals. Our findings corroborate this
In terms of health policy making, the results of this study are promising: the levels of
healthcare accessibility and deprivation in neighborhoods have a cumulative effect on
individuals´ healthcare satisfaction.
Penchansky and Thomas (1981)
multidimensional accessibility to healthcare can be related to the difficulty of a person
getting to a healthcare service and to the satisfaction of the received service. This means
that accessibility can be divided into two general components: the potential access and
. Potential access can be measured with gravity-based
and realized access can be measured with healthcare
satisfaction. In our study we identified that this potential access to healthcare at
neighborhood level may explain satisfaction with the realized access at individual level. Health
policy makers and urban planners can pay special attention to the spatial distribution of
primary healthcare services in different parts of the city to avoid inequitable access and
possibly future low satisfaction with the services.
Deprivation is a contextual or neighborhood-based measure that is useful in
explaining health outcomes. Indeed, there is broad experience in showing the
relationships of deprivation and different kinds of health outcomes (Boyle et al. 2001;
Barona et al. 2015; Carstairs 1995; Havard et al. 2008; Lalloué et al. 2013). The
importance of studying contextual social disadvantages also lies in the fact that
deprived neighborhoods foster negative views or low satisfaction in individuals living
(Gambaro et al. 2015)
. Local planners and policy makers need to take action in
the deprived areas identified in Quito. The reduction of deprivation in these areas is
fundamental to reduce not only socio-economic inequalities, but also health
inequalities. Thereby, an essential issue is to decide the scale of intervention to reduce
deprivation. The spatial stability of our results in both MAUP scale effects and the
spatial dimension of the UGCoP can support this decision. The satisfaction with
healthcare is not only influenced by the geographical context of the healthcare services
locations, but also by the geographical contexts of where people’s households are
located. Furthermore, the influence is similar at different scales. Health and urban
policy makers can thus be aware that healthcare satisfaction may also be influenced by
deprivation of the neighborhoods where patients live. They can also choose the
reporting unit that best fits their own interests and purposes. In general, health analysts
and decision makers tend to choose areas of analysis with the finest spatial resolution,
e.g. the census blocks
(Cabrera-Barona et al. 2016b)
. In other cases, selecting larger
census areas, e.g. the census tracts, can avoid the difficulty of implementing
interventions to very small areas, and can support the formulation of policies for a larger
population (Séguin et al. 2012). Additionally, a better understanding of the relationships
between social and health measures can be accomplished from analyzing both small
and large areas (Nelson and Brewer 2015), as we did in this research.
Even though no severe MAUP and UGCoP were found, it is essential to be aware
that intrinsic differences exist when analyzing social indices at different scales and in
different geographical contexts. A simple example is the number of people living in an
area. Obvious differences of population between census tracts and census blocks exist
when analyzing a social index.
Schuurman et al. (2007)
found significant differences in
populations classified by deprivation in census blocks and census tracts. We therefore
claim that some health-related processes and relationships may be similar at different
scales and contexts, but all oversimplification in the interpretation of statistical results
should be avoided, and the MAUP and the UGCoP always need to be monitored to
ensure the best possible decision making.
We are aware that this research has some limitations. First, no additional
individuallevel explanatory variables were used. Our index of healthcare satisfaction can
somewhat overcome this limitation due to its multidimensionality; however, healthcare
satisfaction may be influenced by a physician’s characteristics (e.g. listening skills),
or by a patient’s characteristics (e.g. having insurance, car ownership). Healthcare
satisfaction can also be influenced by a Bhealthcare service scale^. For instance, it
may be possible to score the quality of the primary healthcare service as a whole. In this
case, three-level models need to be applied because healthcare satisfaction may be
influenced by the quality of the physician’s attention, the quality of the service, and the
geographical context of where the patient lives. A second limitation is that only census
areas were used to evaluate the MAUP and the UGCoP. Even though most measures of
deprivation and gravity-based models of healthcare accessibility are calculated in
census areas, we highlight that the complex reality represented by these indices is not
limited to these kinds of areas.
Flowerdew et al. (2008)
used census areas as building
blocks to construct alternative neighborhoods to understand area-level influence on
health. We believe that future research can apply the methodology presented in this
study to such alternative neighborhoods. These alternative neighborhoods could also be
used as an additional level 2 unit in multilevel models, and these models could be
compared to different non-hierarchical null models in order to assess scale and zoning
effects of the MAUP.
Alternative delineations of neighborhoods have been applied to represent social and
ecological indices, including deprivation
(Wei et al. 2017)
. These alternative
neighborhoods have been created using different kinds of zoning systems. A comparison of the
deprivation index used in this study between different zoning systems may be useful to
evaluate the MAUP and the UGCoP. Additionally, independently of possible changes
of neighborhood delineation to represent deprivation, it is an important issue to identify
possible changes of census indicators at different time-points
Modifications in these kinds of indicators would affect the construction of a deprivation index
and, consequently, would make the multi-temporal evaluations of the MAUP and the
UGCoP less comparable.
Future studies could exclude indicators A (percent of the population that have a
long-term disability) and J (distance to the nearest primary healthcare service) when
constructing the deprivation index. These indicators already being measures of health,
and infringements of statistical independence can take place when relating the
deprivation index to the healthcare accessibility index. In this study we overcame this
problem by applying orthogonal transformation to the index of healthcare accessibility;
however, future deprivation indices could be composed by indicators B to I of Table 1.
Removing the health domain from deprivation indices can avoid mathematical
coupling, does not dramatically change the measurement of the socioeconomic inequalities
in census areas, and may be a better practice when working with deprivation indices
(Adams and White 2006)
Future research can also use different kinds of indices of deprivation and healthcare
accessibility. The method used to construct the deprivation index can be transferred to
other studies. Depending of the reality of the study area, interests of the researchers, and
availability of information, different indicators to the ones we used to construct the
deprivation index can be used.
It is also recommended that future work uses and compares area-level influences of
different measures of healthcare accessibility (e.g. Euclidean distances, healthcare
services catchment areas). The method to construct our index of healthcare accessibility
can also be transferred to other studies. However, the distances thresholds of the index
need to vary with respect to the reality of other study areas. Another parameter that may
vary is the function of travel cost. Finally, measures of spatial autocorrelation can be
used to describe spatial patterns of area-level effects
(Manley et al. 2006)
. We believe
that future work can use indices of local and global autocorrelation not only to evaluate
spatial patterns of area-level influences, but also the spatial patterns of the indices used
in this study.
The scale effects of MAUP and the effects of the UGCoP were minimal when
analyzing the area-level influence of deprivation and healthcare accessibility on
healthcare satisfaction. We recommend that analyses of scale and the geographical
context should be incorporated in all studies that analyze health phenomena. This study
also added a significant piece of evidence of neighborhood effects in health outcomes: a
striking area-level influence of deprivation and healthcare accessibility on healthcare
satisfaction was determined. We consider this work useful to support policy making, to
reduce social and health inequalities in the study area.
Acknowledgements Open access funding provided by Austrian Science Fund (FWF). The presented work
has been funded by the Ecuadorian Secretary of Higher Education, Science, Technology and Innovation and
the Ecuadorian Institute of Promotion of Human Talent (Scholarship contract No. 375-2012). It has also
partially been funded by the Austrian Science Fund (FWF) through the Doctoral College GIScience (DK W
1237 N23) at the University of Salzburg.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.
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