This document describes the methodology used in the Atlas of Variations in Oncologic Surgery Hospitalization. It defines the key elements of the analysis: - The numerator is oncologic surgery admissions for 9 cancers from 2005-2007 identified using hospital discharge and day surgery registries. - The denominator is the population in each healthcare area from 2005-2007 census data. 180 healthcare areas across Spain are the units of analysis. - Rates of oncologic surgery admissions are calculated and compared across areas to identify variations. Additional analyses examine associations with demographic and healthcare supply factors. Assignment rules are used to geographically assign cases to a healthcare area based on address data in order to calculate standardized rates and

1. Methodology of the Atlas VPM 6
METHODOLOGY OF THE ATLAS OF VARIATIONS IN
ONCOLOGIC SURGERY HOSPITALISATION
The basic architecture of variations measurement
The focus of analysis of geographic variations in practice is on the healthcare
exposure experienced for a population living in a defined geographical area.
Thus, the basic methodological approach to measuring these variations is
ecologic, and relies on a simple rate in which four elements should be defined:
- the numerator (the clinical event of interest: hospital admission, surgical
procedure, diagnosis test, etc.);
- the denominator (population exposed -usually determined by the place of
residence and the time period under scrutiny);
- the “unit” of analysis (typically administrative health care demarcations,
geographically defined around hospital services), and;
- the timeframe (the period of time when the events in the numerator occur).
These four elements allow the estimation of the average annual rate for the
event of study, along a period of time, in a certain area. Once the rate is
estimated for each of the geographical areas under study, several analyses are
carried out:
• Comparison of the rates with a view to detect significant variations. This can
be done by estimating: a) sex and age-standardised rates which can then be
compared across areas; b) statistics of variation based on the standardized
rates (Extremal Quotient, Coefficient of Variation, etc.); c) statistics of
variation based on expected cases (Standardized Utilization Ratios,
Component of Systematic Variation, Empirical Bayes statistic, etc); and d)
the relative variation between an event of interest and other one known as a
low variation and non supply-sensitive procedure (e.g. hip-fracture repair).
• A series of ecological secondary analyses aimed to assess eventual
associations between the rates and certain demand variables (age, sex,
deprivation, etc.), supply variables (beds, health care staff, tertiary care,
teaching hospital, etc), and the effect of the Autonomous Community (AC)
level, a proxy of regional healthcare policies in the Spanish National Health
Service (SNS).
The following paragraphs will review all those aspects describing the actual
methodology implemented in the production of the Atlas, specifically that for the
Atlas of Variations in Oncologic Surgery across the National Health Service
(SNS).1
1
Oliva G, Allepuz A, Kotzeva A, Tebé C, Bernal Delgado E, Peiró S, et al. Variaciones en
hospitalizaciones por cirugía oncológica en el Sistema Nacional de Salud. Atlas Var Pract Med
Sist Nac Salud. 2009; 3(2):241-72.

2. The numerator: Oncologic surgery hospital admissions
A total of 9 oncologic conditions causing admission for surgical intervention in
SNS hospitals were included in this Atlas: breast, bladder, colorectal, uterus,
larynx, lung, stomach and oesophagus. The selection was based on their
impact on morbidity and/or mortality in the Spanish population.
The discharge dataset (hospital administrative database, CMBD) was the
source for case detection and inclusion, together with the case-day surgery
registries (CMA) available in the 16 Autonomous Communities (AACC)
participating in the project. The CMBD registers all hospitalisations in the SNS
hospital network though in the Basque country and Murcia it also includes
private hospitals’ data. In turn, the Catalonian CMBD includes data from all
hospitals integrated in the public utilisation network (Xarxa Hospitalaria
d’Utilització Pública – XHUP) regardless of public or private ownership. All
cases discharged between 2005 and 2007 were used in the analysis.
CMBD and CMA provided the required clinical information (cause of admission,
other diagnoses, surgical procedures) as well as administrative information
(age, sex and address) for each of the cases. Diagnoses and procedures are
coded following the International Classification of Diseases 9th revision clinically
modified (ICD9-CM). Case selection followed the criteria summarised in table 1.
One more issue to be addressed in defining the numerator for the purpose of
this Atlas, deals with the decision on using either patient or admissions in
counting the total number of cases. In this case admissions had to be adopted
as basis for calculations; therefore variations in utilisation of surgical procedures
eventually detected might be affected by existing variations in readmission
rates. As a result, the dominant pattern in the management of individual
patients should be included in the range of explanatory factors underlying the
variation found. An attempt to account for this effect was made by analysing the
average admission rate for the same group of surgical procedures in each
hospital during the period of study.
The denominator: exposed population
2005 to 2007 census update (registered by the National Institute of Statistics,
INE) was the source of information to determine the population under exposure.
Municipalities’ population was disaggregated into 18 five-year age groups (from
0 to 4 years old to 85 and over) and gender. The three census updates were
merged to build the person-time observation data. Some of the analyses were
limited to male, female or elderly population (aged 60 and over).
The census basic information unit, municipality, creates some difficulties for the
estimation of health area populations when it comes to cities which, due to their
size and density, are divided into several healthcare demarcations. Those
hindrances were overcome by resorting to other sources of data such as local
population information systems or regional health identity card databases. This
way, intra-municipality areas were set out when needed.
2

3. Methodology of the Atlas VPM 6
No exclusions have been applied for the denominator. It is worth noting that
though the SNS’s wide coverage allows for equivalence between census and
protected population, there is some degree of mismatching due to the people
insured by public servants’ mutuality schemes (MUFACE, MUGEJU, ISFAS and
others). This population is included in the denominator as part of the census
figures, however, only the cases served in public hospitals would be included in
the numerator. Since the majority of public servants opt for private provision,
this factor might bias the rates due to the relatively different weight of civil
servant populations across AACC and healthcare areas.
Table 1. Inclusion and exclusion criteria applied in retrieving cases of oncologic surgery
Main Diagnosis Procedures Observations
Breast 174.*; 233.0 85.20; 85.21; 85.22; 85.23; 85.25; Only women cases of breast cancer. Excluded cases
85.33; 85.34; 85.35; 85.36; 85.41; referred to other centre
85.42; 85.43; 85.44; 85.45; 85.46;
85.47; 85.48;
Colorectal 153.*; 154.0; 154.1; 45.72; 45.73; 45.74; 45.75; 45.76; Excluded cases referred to other centre
154.8; 230.3; 230.4; 45.79; 45.8; 46.10; 46.11; 46.13; Codes 46.1x y 46.2x only considered when coded
46.14; 46.20; 46.22; 46.23; 48.4*; together with the aforementioned codes (solo coding
48.5; 48.6*; Is referred to palliative interventions, excluded in this
Atlas).
60.21; 60.29; 60.3; 60.4; 60.5;
Prostate 185; 233.4; 60.61; Excluded cases referred to other centre
236.5 60.62; 60.69
Uterus 179; 180.*; 182.*; 68.3; 68.4; 68.5*; 68.6; 68.7; 68.8;
Excluded cases referred to other centre
233.1; 233.2; 68.9
Stomach 151.*; 230.2 43.0; 43.4*; 43.5; 43.6; 43.7; 43.8*; Excluded cases referred to other centre
43.9; 43.1*; 44.3*
Esophagus 150.* 230.1 42.4*; 42.32; 42.33; 42.39; 43.99; Excluded cases referred to other centre.
42.1*; 42.5 -42.69; 43.1* Codes 42.1*, 42.51-42.69 y 43.1* must go together with
42.4* (the opposite is referred to palliative intervention.)
Larynx 161.*; 146.4; 148.2; 30.09; 30.1; 30.2*; 30.3; 30.4 Excluded cases referred to other centre.
230.0; 231.0 Code 31.1 (temporal tracheotomy) is not considered a
palliative intervention; thus included
Codes 30.3 and 30.4, permanent tracheotomy, is
excluded, since is considered a palliative treatment.
Lung 162.2 - 162.9; 32.28; 32.29; 32.3; 32.4; 32.5; 32.6; Excluded cases referred to other centre.
231.2 32.9; 34.01; 34.03; 34.04; 34.05; Trachea and pleura cancers are excluded from the
34.09 defintion
Bladder 188.*; 233.7 57.4*; 57.5*; 57.6; 57.7*; Excluded cases referred to other centre.
56.71 - 56.79. Codes 56.71 a 56.79 go together with radical cystectomy
and are considered palliative care.
[*] denotes the inclusion of all 4th and/or 5th digits following the root code.

4. The unit of analysis: healthcare areas
180 geographical units make part of the analysis in this Atlas edition. They
accounted for an average annual population of 36,664,474 along the period of
study, according to the municipal census.
The territorial division adopted in the Atlas follows the health circumscriptions
created by each of the regional health administrations; these circumscriptions
hold the added value of defining catchment areas for hospitals. Tables 2 and 3
provide some of the relevant characteristics of these healthcare areas. Table 3
also shows population size data for the age subgroup 60 and over; these data
are useful in describing the minimum size of geographical populations dealt with
in the analysis, an issue of interest in assessing the rates’ stability.
Table 2. Some of the relevant characteristics of healthcare areas
Innhabitants*
CCAA Healthcare area
Menor Mayor
Andalucía 32 65.278 710.956
Aragón 8 55.560 377.592
Asturias 8 34.543 321.398
Baleares 3 80.912 747.728
Canarias Islas 7 10.078 796.933
Cantabria 3 86.238 299.729
Cataluña 37 3.713 1.562.825
Castilla la Mancha 8 79.473 398.443
Castilla León 11 91.364 354.788
Com. Aut. Vasca 7 203.690 419.983
Com. Valenciana 22 50.222 345.055
Extremadura 8 49.232 258.905
Galicia 16 33.000 487.281
La Rioja 1 286.981 286.981
Murcia 6 55.921 490.120
Navarra 3 61.900 427.503
(*) Average annual 2002-2004
4

5. Methodology of the Atlas VPM 6
Table 3. Some of the relevant characteristics of healthcare areas
Women Men Aged over 60 Total
Minimum 1.798 1.915 942 3.713
Percentile 5th 16.819 16.953 7.697 33.772
Percentile 25th 42.699 40.762 20.516 84.143
Percentile 50th 80.694 81.991 37.043 160.926
Percentile 75th 139.717 136.607 60.369 274.923
Percentile 95th 234.392 222.731 100.041 459.531
Maximum 825.542 737.283 413.953 1.562.825
Population 18.565.751 18.098.723 8.044.232 36.664.474
Average Population 2004-2006.
Geographical assignment of cases
As explained above, the focus of the analysis is on the experiences of the
populations residing in a well defined geographical unit during a certain period
of time. Thus, the analyses conducted compares the hospital admission
experience of populations living in different territories, rather than hospital
admission patterns, though they will obviously be closely interrelated. In this
sense, cases assignment to the corresponding area becomes a central issue.
Four AACC record address data following the INE’s municipalities coding
(Catalonia, the Basque Country, Valencia and Galicia).The other 12 AACC use
postcodes to record residence data. Hence the vast majority of admissions
contained in the CMBD could be confidently assigned to a certain area.
However, coding quality of this type of data (by any of the two procedures)
varies widely with the percentage of records with incomplete address field -
raising over 10% of the total in some CCAA. This circumstance has advised the
implementation of a set of assignment rules aimed to minimise the number of
records missed by the analysis:
- Cases presenting complete address coding were assigned to the healthcare
area that includes the corresponding municipality or postcode;
- Cases with incomplete address coding but including at least the digits for
province of the corresponding municipality or postcode system, were
reassigned to the catchment area of the hospital where the patient was
admitted, whenever the hospital was located in the province identified by the
incomplete code;
- Cases with incomplete address coding, where province digits did not match
the hospital’s, were excluded from analysis.
CMBD originated in certain CCAA deserve further consideration:

6. - Catalonia’s CMBD does not include the address code; instead, there is
direct assignment of each discharge to a particular sector and healthcare
area (GTS). Therefore, residents in other AACC, together with foreign
residents and unknown addresses cannot be reassigned;
- Navarra, Valencia and Murcia provided area assigned information (based on
health identity cards) in addition to the address codes included in the CMBD
database. Priority was given to the former, whenever inconsistencies
between the two data sources were detected;
- Murcia and Castilla-La Mancha contribute to the project AVPM-SNS with a
restricted version of their CMBD which strictly includes the data required for
the elaboration of each of the atlases, instead of the complete database
submitted by the other CCAA. Though the assignment rules applied were
identical, the results obtained from them might not be strictly comparable
with those based on complete versions of the CMBD, as contributed by the
rest of CCAA.
Table 4 describes the results in assignment of cases to place of residence for
the period 2002 to 2004 (the most recent where assignment was checked). The
number of discharges with incomplete address code requiring reassignment
ranged from 0.6% in Galicia to 48% in La Rioja (impeding disaggregation into
its 2 health administrative circumscriptions). The ability to reassign patients –
either directly or following the described set of rules- allowed reach very high
values of assignment, between 93.2% in Cantabria and 99.3% in Galicia.
Table 4. Assignment of cases to place of residence for the period 2002 to 2004
CCAA Total Incomplete Total
Unknown Residence Reasigned
CMBD Residence Assigned
n % n % n % %
Andalucía 2.007.876 349.468 17,4 21.941 1,1 315.720 15,7 96,9
Aragón 427.196 34.300 8 1.658 0,4 21.114 4,9 96,4
Asturias 361.857 50.569 14 1.190 0,3 47.440 13,1 98,8
Baleares 245.040 29.332 12 396 0,2 26.699 10,9 97,1
Canarias 340.716 16.284 4,8 8.516 2,5 15.625 4,6 97,2
Cantabria 155.567 35.733 23 968 0,6 29.032 18,7 93,2
Cataluña 2.177.234 28.156 1,3 19.884 0,9 325 0 97,8
Castilla la Mancha 109.728 4.972 4,5 201 0,2 2.533 2,3 97,6
Castilla León 684.925 93.993 13,7 2.384 0,3 75.258 11 96,9
Com. Aut. Vasca 711.768 25.424 3,6 4.354 0,6 9.892 1,4 97,1
Com. Valenciana 1.248.945 32.924 2,6 14.250 1,1 9.666 0,8 96,6
Extremadura 316.017 19.562 6,2 660 0,2 15.000 4,7 98,2
Galicia 707.561 3.909 0,6 823 0,1 133 0 99,3
La Rioja 80.389 38.893 48,4 271 0,3 35.572 44,2 95,5
Murcia 62.981 1.121 1,8 1 0 23 0 98,3
Navarra 191.201 2.867 1,5 3.552 1,9 195 0,1 96,7
6

7. Methodology of the Atlas VPM 6
Rates and variation statistics
Crude, specific and standardised rates
Crude Rates are obtained by affiliating all discharges to the populations of
origin, and represent the number of discharges generated along the period
2005-2007, over the annual average registered population multiplied by 3. As a
rule, all rates are calculated as per 10,000 inhabitants and year.2
Since age and sex are potential determinants for morbidity, differences in age
and sex distribution across populations might explain differences in the
corresponding intervention rates. In order to control such effect, age and sex-
standardized rates were calculated in each area by using the direct method,
being 2001 Spanish population, the standard population of reference.
Standardised rates would represent the rate that each of the areas would have -
should all of them have the same age and sex structure of the Spanish
population. 95% confidence intervals were calculated for each of the
standardised rates.
Expected cases and the standardised utilisation ratio (SUR)
The statistical power of the calculations detailed above is subject to variations
depending on the size of the populations and the number of interventions.
Therefore, the error estimation linked to those rates can become significant
when dealing with clinical procedures with low number of interventions and/or
with small populations of origin. In these situations a better option to be used is
the standardised utilisation ratio (SUR), a parameter quite similar to the well
known standardised mortality ratio (SMR). Calculations require estimating the
number of expected cases in each of the areas and contrasting (by using a
ratio) with the observed number of interventions.
Expected cases are obtained by using the indirect method; thus applying the
overall standardised age and sex-specific rates to the corresponding sub-
population in each area. Expected cases would represent the expected
utilisation if the different territories’ sub-populations homogenised their
hospitalisation levels according to overall rates. Unlike the direct method, SUR
does not allow for comparison across areas, because constant specific rates
(those in the overall population) are applied onto the population pyramids in
each area, being needed to take into account differences in age and sex
structures across areas. However it allows for the comparison of each of the
areas against a global pattern, in this case the overall population of the areas
included in the study; therefore, it can be interpreted as a “relative risk”.
2
It is important to keep in mind how discharges rather than persons are computed for these
calculations; nevertheless, the equivalence discharge-person is expected to be high for the
majority of the procedures under study –except bladder cancer surgery and to a lesser extent
breast and larynx cancer related surgeries. Re-interventions’ relevance (i.e. discharge-person
equivalence) was assessed whenever it was from the same hospital fulfilling the criteria outlined
in table 1 (same tumour plus some of the corresponding surgical procedures) during the 3 years
of the study period.

8. Statistics of variation3
In addition to standardized rates and standardized ratios, Atlas VPM-SNS
includes the following statistics:
1. Ratio of Variation (Extremal Quotient, RV), as the ratio of the highest
to the lowest observed rate values. This statistic is widely used and
greatly valued due to its simplicity and intuitive interpretation (a RV of 2
denotes double utilisation). However, this indicator is quite limited due to
its sensitivity to low rates, to differences in population size across areas,
to readmissions and to extreme values; its statistical power is very low
and should any of the areas involved present no interventions –which
could often be the case in small area studies- the statistic will take
infinity as a value. For all these reasons RV is usually substituted by the
ratio of rates in areas in the 95th percentile of variation over those in the
5th percentile (RV95-5); this formulation reduces the impact of extreme
values. It is often presented along with RV75-25 (ratio of percentile 75th to
the 25th) which gives an idea of the variation within the 50% of the
observations central in the distribution.
2. Coefficient of variation (CVu): standard deviation (Su) over the mean
(Yu)
CVu= Su/Yu Where:
Su2 = Σ(Yi-Yu)2/(k-1); Su=√Σ(Yi-Yu)2/(k-1); and
Yu= unweighted mean = ΣYi/k; where Yi = area i mean; k= number of areas
The CVu expresses standard deviation (SD) in mean units; the
advantage of it, compared to simple SD, is the independence from
measure units. It can be interpreted as a relative variation (the higher the
coefficient the more the variation).
3. Weighted Coefficient of Variation (CVw): standard deviation across
areas (Sw) over mean across areas (Yw) , weighted by the size of each
area.
CVw=Sw/Yw where Sw2=Σ[ni(Yi-Yu)]/ (Σni-1); Sw=√[Σ[ni(Yi-Yu)]/ (Σni-1)]
Yi=area i mean; Yu= population mean (=prevalence);
Yw=Σni(Yi-Yu)/Σni= weighted mean; k=number of areas
CVw is similar to the CVu, though it attaches more weight to the areas
with larger population size and behaves better in the presence of
substantial population size differences across areas.
3
A detailed update on variation statistics can be accessed at: Ibañez B, Librero J, Bernal-
Delgado E, Peiró S, González López-Valcárcel B, Martínez N, et al. Is there much variation in
variation? Revisiting statistics of small area variation in health services research. BMC Health
Serv Res. 2009;9:60. [Epub ahead of print]. (Acceded April 2011). Available at:
http://www.biomedcentral.com/1472-6963/9/60
8

9. Methodology of the Atlas VPM 6
4. Systematic component of variation (SCV): It measures the variation in
the deviation of the observed rate to the expected rate, expressed as a
percentage of the latter. This measure derives from a model that
acknowledges two sources of variation: systematic variation (among
areas) and random variation (within each area). Expressed in
mathematical terms
[Σ((Oi-Ei)2/Ei2)-Σ(1/Ei)]/k where
Oi= observed number of surgical procedures in area i
Ei=expected number of surgical procedures in area i as a function of the age and sex
population structure and the specific procedure rates by age and sex (indirect method
adjustment)
k= number of areas
The higher the SCV value the larger the systematic variation (not
attributable to randomness)
5. Empirical Bayes Statistic (EB). As described some paragraphs above,
the Standardised Utilization Ratio (SUR) is an estimator of each area’s
“relative risk”; that is, the surgery utilisation risk compared to the group of
reference (all areas) being highly dependent upon population sizes (its
variance is inversely proportional to the expected number of cases).
Extreme values of SUR are therefore the least precise estimations, since
they correspond to the areas registering very low number of cases;
however they may look dominant in the apparent geographical pattern.
On the other hand the variability in the number of observed interventions
is usually wider than the expected for a Poisson distribution (extra-
variability). With a view to overcome all these issues derived from the
direct use of SUR, several smoothing alternatives have been proposed in
order to reduce the extra-variability.
Based on the assumption of observed cases following a Poisson
distribution, the empirical Bayesian method assumes SUR to be a
random variable described by a log-normal distribution [log (SUR) ~N(m,
s2)]. This model, known as mixed Poisson log-normal, is widely used in
disease mapping literature, becoming the best fit (likelihood) estimation
of the variance of the log-normal distribution for the SUR geographical
pattern. EB is obtained by a quasi likelihood method. For empirical
studies this statistic is extremely robust in detecting variability, showing
stability across areas, particularly in low rates. Despite its scarcely
intuitive interpretation (it should be interpreted in terms of relative
variation) this is the most valuable statistic in appraising variability, and
has been adopted as the variation reference pattern in the Atlas VPM-
SNS publications.

10. 6. Hip fracture repair´s SCV as a reference
The hip fracture repair´s SCV is adopted as a criteria of need-based
variability on the basis that variations in the utilisation of this procedure are
unlikely to be affected by factors other than difference in incidence of femur
fracture. A statistic can then be built as the ratio of a certain procedure SCV
over femur fracture repair´s SCV (RSCVff). Obviously the value of RSCVff for
femur fracture hospitalisation will be 1; an RSCVff of 2 for a procedure
should be interpreted as the variability in the utilisation of the procedure
doubling that registered for the femur fracture (i.e., based on need).
7. Autonomous Community effect
Although healthcare area is considered the main unit of analysis, the
specific way in what the SNS is organized, full devolution to the AACC with
complete responsibility on regulation, funding and provision, requires
analyze the relative effect of the AC level, in variation. Thus, Autonomous
Community effect, considered as a proxy of regional policy effect, is studied
by using Intraclass Correlation Coefficients or Rho statistics (as estimated
by using multilevel analysis). Both of them draw out what portion of the
variation is attributable to differences across regions, beyond the
differences across areas. The higher the value, the more likely to signal
AACC as a relevant underlying factor of variation (i.e. differences in
healthcare policies might be considered as a associated factor)
Exploring underlying factors in Atlas VPM
Atlas VPM-SNS’ analyses are not only aimed to describe rates and their
variation, but also to explore factors associated to the variability. In this sense,
Atlas VPM-SNS bivariate and multivariate analyses are meant to seek
association among supply or demand factors and the variation in rates.
Supply factors
The micro data from the National Statistic on Inpatient Regime Centres (EESRI)
are the information source for variables regarding health services supply; data
are retrieved from the wave closest in time to the corresponding period of
analysis. In the case of the Atlas on Oncological Surgery micro data from the
2004 EESRI were pulled from the Ministry of Health, Social Policy and Equality
(http://www.msc.es/estadEstudios/estadisticas/estadisticas/microdatos/frmBusq
uedaMicrodatos.jsp ).4,5 The availability of resources is then counted in the
healthcare area where each hospital is located. Typically, the levels of
resources are grouped into tertiles. The list of variables includes:
4
Instituto de Información Sanitaria. Estadística de Establecimientos Sanitarios con Régimen de
Internado. Handbook. (Accessed May 2009). Available at: http://www.
msc.es/estadEstudios/estadisticas/estadisticas/microdatos/frmListadoMicrodatos.jsp
5
Instituto de Información Sanitaria. Estadística de Establecimientos Sanitarios con Régimen de
Internado. Questionnaire. (Accessed May 2009). Available at:
http://www.msc.es/estadEstudios/estadisticas/estadisticas/microdatos/frmListadoMicrodatos.jsp
1

11. Methodology of the Atlas VPM 6
1. Hospital beds per 1,000 inhabitants. As functioning beds existing in the
public network hospitals over the corresponding area population. Three
categories matching the tertiles of the distribution are defined: 0.82-1.71,
1.72-2.42 and 2.43-6.15 beds per 1,000 inhabitants;
2. Hospital size (beds). Using three categories: below 300 beds, between 300
and 599 beds and 600 beds or above;
3. Internal Medical Residents (MIR). It allows characterising each hospital as
postgraduate training and teaching centre. The variable splits hospitals into
MIR training for any medical specialty or non-MIR training hospitals;
4. Hospital doctors per 1,000 inhabitants. Specialists (any specialty) in the
public network counted in full time equivalent (FTE); tertiles of the
distribution yield the following three categories: from 0.06 to 1.05, from 1.06
to 1.30 and from 1.31 to 3.16 specialist doctors per 1,000 inhabitants;
5. Surgeons per 10,000 inhabitants. It refers to surgeons in the public hospital
network. The tertiles divided the distribution into below 0.15, from 0.15 to
0.40 and from 0.41 to 0.61 surgeons per 10,000 inhabitants;
6. Surgical beds per 10,000 inhabitants. Functioning beds attached to general
surgery and digestive tract services. The tertiles configure the following
groups: below 0.38, from 0.39 to 0.56 and from 0.57 to 1.27 surgical beds
per 10,000 habitants.
Socio-economic factors
Socioeconomic variables are extracted from Spanish Economic Yearbook
annually published by La Caixa6. This publication offers information
disaggregated by municipality for those nodes over 1,000 inhabitants. In order
to build “average” values for each socioeconomic variable, and map them into
the healthcare areas, the figures for municipalities were aggregated and
weighted by their population volume; this way, an average value was obtained
for each healthcare area. As for the municipalities below 1,000 inhabitants, they
were assigned the average value of the health area where they were included
(generally none of these municipalities represents more than 5-10% of any
healthcare area population).
On the other hand, some of the relevant economic variables may only be
available with a number of years lag; for instance the 2006 yearbook offers a
suite of 2006 data but when it comes to income it only shows 2003 data and
2001 for educational level. All editions are searched to get data as closest to
the period of analysis as possible. The list of variables included:
1. Economic level 2003: available family income per inhabitant estimated
by geographical area (municipal level) for year 2003. The Spanish
Economic Yearbook defines 10 levels which for 2003 correspond to the
following year income intervals: 1) <7,200 €; 2) 7,200-8,300; 3) 8,300-
9,300; 4) 9,300-10,200; 5) 10,200-11,300; 6) 11,300-12,100; 7) 12,100-
6
Servicio de Estudios. Anuario Económico de España 2005. Barcelona: La Caixa;
2005. (Acceso mayo 2009). Available at:
http://www.lacaixa.comunicacions.com/se/index.php?idioma=esp

12. 12,700; 8) 12,700-13,500; 9) 13,500-14-500 and 10) >14,500. Personal
income available can be defined as the income on hand for household
economies to expend and save, or rather the adding up of all effective
revenue obtained by a household for a period of time. It could be
considered as all revenue coming from salary, capital derived revenue,
social benefits and cash transfers, deducting direct taxes and social
security contributions by the family members discounted. For the Atlas,
economic level was organised into tertiles: from 1.76 to 3.95,
corresponding to areas of average available income below 10,000 € per
inhabitant and year; from 3.98 to 6.46 corresponding to areas with an
average available income between 10,000 and 11,700 € per inhab./year;
and, from 6.47 to 9.40, areas with average available income between
11,700 and 14,500 or above per person and year
2. Evolution of family income over the period 1999-2003. The Spanish
Economic Yearbook estimates the evolution of available household
income per municipality (period 1998-2003) in two steps. First the
available household income for 1998 is estimated and then the
coefficient of variation compared to 2003 estimations is calculated; once
the variation in average household available income is obtained,
intervals are defined corresponding to 10 different levels of household
income variation: 1) below 10%; 2) 10 to 16%; 3) 16% - 21%; 4) 21% -
26%; 5) 26% - 34%; 6) 34% - 42%; 7) 42% - 50%; 8) 50% - 60%; 9)
60% - 72%; 10) More than 72%. Alternatively, the Atlas purposed tertiles
are defined: from 2.75% to 5.00%; from 5.01% to 6.00%; and, from
6.01% to 10.
3. Unemployment rate over total population in 2004. The Spanish
Economic Yearbook defines it as the number of people registered in the
employment bureau at each municipality by July the first 2004 with
respect to the population of the municipality according to the census
data by January first 2003 (registered unemployed*100/population). This
unemployment rate displays by sex and age (16-24 years old; 25 to 49
years old and age 50 and over). The first choice for the denominator of
this rate would be the active population in each municipality however the
sample used for the national active population survey is not
representative at municipal level rendering it useless for the level of
disaggregation here required (it works fine for province and regional
levels, though). Nevertheless, the unemployment rate obtained against
the resident population is still a good indicator for comparison across
municipalities. The distribution tertiles are: 0.90 to 2.90; 2.91 to 4.22;
and 4.23 to 7.96.
4. Unemployment rate over active population (24 to 49 years old). Similar
to the previous variable but referred to the young adult population.
Distribution tertiles group areas into the following intervals: 1.20 to 5.36;
5.42 to 8.00; and 8.02 to 14.25
5. Landline phones per 100 inhabitants by January 1st 2004. Distribution
tertiles group areas into the following intervals: 23.9 to 32.8; 32.9 to
39.0; and, 39.1 to 61.1.
6. Cars per 100 inhabitants. Automobile fleet (excluding vans and trucks)
registered in each area by January 1st 2004. Distribution tertiles group
1

13. Methodology of the Atlas VPM 6
areas into the following intervals: 21.1 to 36.9; 37.0 to 42.1; and 42.2 to
62.4.
7. Percentage of illiterates and without formal education in the year 2000.
The variable takes as reference the household “head” level of education.
Distribution tertiles group areas into the following intervals: 4.7% to
12.9%; 13.0% to 17.9%; and 18.0%-37.8%.
8. Percentage of university level educated persons in the year 2000.
Likewise the previous, the variable takes as reference the household
“head” level of education. Distribution tertiles group areas into the
following intervals: 4.7% to 9.0%; 9.1% to 12.4; and 12.4% to 25.7%.