Neighborhood socioeconomic characteristics and statin medication in patients with myocardial infarction: a Swedish nationwide follow-up study
Forsberg et al. BMC Cardiovascular Disorders
Neighborhood socioeconomic characteristics and statin medication in patients with myocardial infarction: a Swedish nationwide follow-up study
Per-Ola Forsberg 0
Xinjun Li 0
Kristina Sundquist 0
0 Center for Primary Health Care Research, Clinical Research Centre (CRC), Lund University , building 28, floor 11, Jan Waldenströms gata 35, SE-205 02 Malmö , Sweden
Background: Coronary heart disease (CHD) and myocardial infarction (MI) are associated with neighborhood-level socioeconomic status (SES). Statins are important drugs for secondary prevention of MI. However, no study has determined whether neighborhood-level SES is associated with statin medication in MI patients. We aimed to determine whether there is a difference in statin medication rate in MI patients across different levels of neighborhood SES. Methods: All patients in Sweden, diagnosed with incident MI from January 1st, 2000 until December 31st 2010, were followed (n = 116,840). Of these, 89.7 % received statin medication. Data were analyzed by multilevel logistic regression, with individual-level characteristics (age, marital status, family income, educational attainment, country of origin, urban/rural status and comorbidities/chronic conditions related to MI) as covariates. Results: Low neighborhood-level SES was significantly associated with low statin medication rate (Odds Ratio 0.80). In the full model, which took into account individual-level socioeconomic characteristics and MI comorbidities, the odds no longer remained significant. Conclusions: Individual-level approaches may be most important in health care policies regarding statin medication in MI patients.
Myocardial infarction; Neighborhood deprivation; Socioeconomic status; Statins
Coronary heart disease (CHD) and in particular myocardial
infarction (MI) is a major cause of morbidity, mortality and
health expense worldwide [
]. Up to 70 % of CHD
incidence can be explained by individual-level
sociodemographic characteristics (age, sex, socioeconomic status
(SES)); health behaviors (smoking, physical inactivity, poor
diet); and metabolic risk factors (hypertension, diabetes,
]. Today, CHD prevention includes
both primary and secondary prevention with lifestyle
changes as well as medical treatment of risk factors,
primarily hypertension, diabetes, and hypercholesterolemia. HMG
CoA-reductase inhibitors, or statins, are a class of
cholesterol lowering drugs that are widely used in this context, in
particular for secondary prevention of MI [
reduce both mortality and morbidity for CHD patients [
Although individual-level sociodemographic
characteristics, such as low individual SES, are important risk factors
for CHD incidence, recent work has also shown that low
neighborhood SES is associated with an increased risk of
CHD, after adjustment for individual SES [
However, no study has examined whether
neighborhood SES is associated with statin medication rates, after
taking individual socioeconomic factors into account.
Such studies are important because if there are
differences in statin medication by certain neighborhood-level
characteristics, interventions will need to target such
Sweden is a suitable setting for such a study because it
is a country which has universal health coverage for all
residents and good availability of health-related
]. It is therefore possible to examine whether
statin medication patterns differ, by both individual and
neighborhood level SES, in a country that has almost
equal availability of health care.
The aim of this study is to determine whether the
statin medication rates for MI patients differ across
neighborhoods with different levels of neighborhood SES,
after taking individual factors into account.
All data were linked by the personal Swedish
identification number, which was replaced by a serial number in
the dataset in order to maintain anonymity for all
individuals. Health care data were retrieved from nationwide
Swedish in-patient as well as out-patient specialist care;
the latter in order to increase the number of cases as
well as generalizability of findings. The Swedish Hospital
Discharge Register contains nationwide hospital main
and secondary discharge diagnoses encoded in the
International Classification of Diseases (ICD) format. The
tenth revision (ICD-10) has been in use since 1997 [
The diagnoses in the Hospital Discharge Register have a
positive predictive value between 85 and 95 %, and the
validity of many cardiovascular diseases are even higher;
e.g., MI, angina pectoris and atrial fibrillation have a
positive predictive value of > 95 % [
]. Since 2000,
the proportion of missing primary diagnoses has been
only 0.5–0.9 % [
Inclusion criteria was MI registered as the main
diagnosis in the Hospital Discharge Register or Outpatient
Register within the study interval (2000 to 2010) using ICD-10
codes I21, I22, and I23. Only patients who previously had
no CHD were included. This was achieved by excluding
patients with a recorded main or secondary diagnosis of
CHD (ICD-10 I20-I25) during a 3-year period, i.e., from
1997 to 1999, before study start. Patients, who died
between 1 January 2000 and 30 June 2005, were excluded as
medication data was not available for this time period.
Also, all patients who died within one month of MI
diagnosis were excluded, as we assumed that some of them
might not have had a chance to pick up the medication.
Patients above 80 years of age were excluded as the
proportion of patients aged 80+ with statin medication was
The individual-level variables used in models were sex,
age at the start of the study, marital status, family income,
education level, country of origin, urban/rural status, and
chronic conditions related to MI (comorbidities) [
Sex: Male or female.
Age was assessed at start of follow-up. Age was used
as a continuous variable in the models.
Marital status: Individuals were classified as married/
cohabitating or widowed/divorced/never married.
Family income by quartile: Information on family
income in 2001 came from the Total Population Register,
provided by Statistics Sweden. Income was categorized
into quartiles: low income, middle-low income,
middlehigh income, and high income. The income was divided
by the number of people in the family. A weighted
system was also used; small children were given lower
weights than adolescents since the costs of living for a
small child are lower than those for an adolescent.
Education level was classified as completion of
compulsory school or less (≤9 years), vocational high school
or some theoretical high school (10–12 years), and
completed theoretical high school and/or college (>12 years).
Country of origin: Born in: 1) Sweden (reference), 2)
Finland, 3) Western countries, 4) Eastern European
countries, 5) Middle Eastern countries, or 6) other
countries. Urban/rural status: Residence in major cities
(Stockholm, Gothenburg or Malmö), southern or
Chronic conditions related to MI–comorbidities were
defined as the first diagnosis (main or additional
diagnosis) ten years before the study and during the follow-up
period of: 1) chronic lower respiratory diseases (ICD-9:
490–496; ICD-10: J40-J49), 2) alcoholism and
alcoholrelated liver disease (ICD-9: 291, 303, and 571; ICD-10:
F10 and K70), 3) hypertension (ICD-9: 401–405;
ICD10: I10-I19), and 4) diabetes mellitus (ICD-9: 250;
Outcome (dependent) variable
The outcome variable of medication of statins was defined
according to the ATC code C10AA for the Medication
The statins (HMG CoA reductase inhibitors, code
C10AA) included were:
Neighborhood-level socioeconomic status (SES)
The home addresses of all Swedish individuals have been
geocoded to small geographical units that have boundaries
defined by homogeneous types of buildings. These
neighborhood areas, developed by Statistics Sweden, are called
small areas for market statistics (SAMS) and have an
average of 1000 people each. SAMS were used as proxies
for neighborhoods, as in previous research [
summary index was calculated to characterize
neighborhoodlevel SES . The neighborhood index was based on
information on men and women aged 20–64, who lived in the
neighborhood, because people in this age group are the
most socioeconomically active. In other words, as a
population group they have a stronger impact on the
socioeconomic structure of the neighborhood than children,
younger men/women and retirees. The neighborhood index
was based on four items: low education level (<10 years of
formal education), low income (income from all sources,
including that from interest and dividends, defined as less
than 50 % of the median individual income),
unemployment (excluding full-time students, those completing
compulsory military service, and early retirees) and receipt of
social welfare. The index was categorized into the following
three groups: low neighborhood SES (more than 1 SD
below the mean), middle neighborhood SES (within 1 SD
of the mean), and high neighborhood SES (more than 1 SD
above the mean) [
]. The neighborhood SES index in the
year 2000 was used in the models.
Multilevel (hierarchical) logistic regression models were
used to estimate odds ratios (OR) for statin medication
rates for different levels of neighborhood SES. The
analyses were performed using MLwiN version 2.27. The
first model only included neighborhood-level SES to
determine the crude odds of statin medication by level of
neighborhood SES (Model 1). The second model also
included the individual-level characteristics age and sex
(Model 2). The third model added family income,
marital status, country of origin, educational attainment and
urban/rural status (Model 3). Last, a full model was
created which also included hospitalization due to chronic
lower respiratory disease, alcoholism and related liver
disease, type 2 diabetes mellitus or hypertension (Model
4, not shown in tables).
The between-neighborhood variance was estimated
with a random intercept. It was regarded to be
significant if it was more than 1.96 times the size of
the standard error, in accordance with the precedent
set in previous studies [
We computed the intraclass correlation (ICC)—that is,
the intra-neighborhood correlation—in order to estimate to
what extent the individual chance of statin medication for
individuals, within the same SAMS, was similar compared
with individuals in other SAMS areas. The ICC expresses
the proportion of the total variance that is at the
neighborhood level. The ICC in multilevel logistic regression can be
estimated by different procedures. We applied the latent
variable method [
] as exemplified by:
ICC ¼ V n þ π2=3
where Vn is the variance between neighborhoods and
π2/3 is the variance between individuals.
The proportion of the second level variance explained
by the different variables was calculated as:
V EXPLAINED ¼
V 0−V 1
where Vo is the age adjusted variance in the initial
model and V1 is the second level variance in the
First order interactions between neighborhood
deprivation and individual-level characteristics for statin
medication in MI patients were also analyzed.
For comparison, we also calculated logistic regression
models using the SAS statistical package (version 9.3;
SAS Institute, Cary, NC, USA).
Table 1 shows the distribution of the study population and
number of patients receiving statins by neighborhood-level
SES. For statin medication, the number (No) of patients
receiving statins as well as the share of patients on statin
medication within each patient group (%) is presented. Of
the 116,840 patients with MI included in this study,
104,766 (89.7 %) received statins during the study period.
Of the total population, 19 %, 62 %, and 19 % lived in high,
middle, and low SES neighborhoods, respectively.
Agestandardized statin medication rates was 90.6 % in
neighborhoods with high SES; 89.7 % in neighborhoods with
middle SES; and 88.6 % in neighborhoods with low SES.
This slight difference in statin medication rate by
neighborhood-level SES was observed across all
individuallevel variables, as shown in Table 1.
Table 2 shows the age-adjusted ORs for each covariate.
Patients with low family income had significantly lower
odds of statin medication (OR 0.49) compared to
patients with high family income. Low educational
attainment was also associated with lower odds of statin
medication (OR 0.71) compared to patients with high
Table 3 shows the results of the different multilevel
logistic regression models. In the crude model (Model 1), the
odds of statin medication were lower for patients living in
neighborhoods with low SES. The OR for statin medication
in patients with MI living in neighborhoods with low
compared to high neighborhood-level SES was 0.80 (95 %
confidence interval (CI) 0.75–0.86). In Model 2,
neighborhoodlevel SES remained significantly associated with statin
medication after adjustment for age and sex (OR 0.80, 95 %
However, in the third model, after adding
individuallevel sociodemographic variables, the ORs no longer
remained significant. The fourth model also included the
variables for comorbidities, which did not change the ORs
further, and the results are therefore not shown.
The between-neighborhood variance was significant in all
models. The explained variance was 6 % in Model 1,
indicating that the neighborhood variable explained only a
small proportion of the total variance, and increased to
23 % in Model 2 and 39 % in Model 3.
The ICC was low, varying between 1.6 and 2.4 % in
the different models, indicating that the clustering
within neighborhoods was low.
Analyzing first order interactions between
neighborhood SES and individual-level characteristics for statin
medication in MI patients showed significant interactions
between neighborhood level SES and education level only.
The results of this analysis are shown in Additional file 1:
The present study shows that MI patients living in low
SES neighborhoods had 20 % lower odds of statin
medication compared to those living in middle and high SES
neighborhoods. However, the odds no longer remained
significant after adjusting for the individual-level
sociodemographic characteristics and comorbidities. To the
best of our knowledge, the potential influence of
neighborhood SES on statin use in MI patients has not been
Previous studies have shown that statin medication
rates in CHD patients may vary due to individual-level
factors such as the patients’ age and comorbidities. In
addition, low individual-level SES may affect medication
patterns negatively [
] and it has been shown that
when a larger proportion of the medication costs are
required from the patients, statin utilization decreases,
in particular for less regularly compliant patients .
Individuals in neighborhoods with low SES are likely to
have small financial margins [
], which might affect
their willingness to pay for prescribed drugs. The
findings of the present study indicate that the lower odds of
statin medication in MI patients living in deprived
neighborhoods may be explained by individual-level
socioeconomic factors. This could imply that the
neighborhood in itself does not affect statin medication in this
Although the present study was unable to detect a
neighborhood effect on statin medication in CHD patients,
previous studies have shown that neighborhood-level
deprivation is a strong predictor for CHD incidence and
fatality from CHD after adjusting for individual-level
sociodemographic characteristics [
]. In addition, a recent
study of ours has shown that neighborhood-level
deprivation affects prescription patterns of statins in
patients with atrial fibrillation, after adjusting for
individuallevel factors [
]. Adults with atrial fibrillation living in high
SES neighborhoods were more often prescribed statins
(men and women, OR 1.23) compared to their counterparts
residing in middle SES neighborhoods [
]. It is unclear
why neighborhood-level deprivation affects statin
medication for patients with atrial fibrillation, but not for the MI
patients in the present study.
One possible explanation could be that statin
medication after MI is initiated almost exclusively at the
hospital, i.e., in immediate relation to the event, whereas
statin medication, for other reasons, might be initiated
at a local health care center where the potential
neighborhood effects may be stronger than in hospitals that
cover a wider geographic area. Previous research has
shown that prescription patterns among doctors working
in local health care centers may be affected by
neighborhood SES [
]. Additionally, individuals living in more
affluent neighborhoods may be better informed as
patients, irrespective of individual-level SES.
Finally, differences in medication patterns may in
some cases be explained by “medication deserts”, where
pharmacies in low SES neighborhoods may have lower
availability of drugs [
]. In Sweden, with universal
health care and good availability of drugs, this is unlikely
The results of the current study, including all MI patients
in Sweden during the study period, show that statin
medication rates for these patients are generally high. Of the
116,840 patients with MI, 89.7 % received statins. For
comparison, 89 and 88 % of patients were treated with statins
according to Italian data from the REAL registry [
French data from the FAST-MI registry [
In the US, several studies of statin medication for CHD
patients have shown medication rates of about 60-70 %
]. However, those studies only included a sub-set of
MI patients, and the authors of one of the studies that
reported high medication rates explained their findings as a
result of only including highly motivated medical practices
]. It has previously been shown that in countries without
universal health insurance, lack of individual health
insurance is associated with lower likelihood of being treated
with a statin [
]. It is also possible that availability of
generic statins will result in lower costs for the patients and
improved compliance. The high statin medication rates in
this study indicates that universal health care may improve
statin medication for MI patients, which could be an
argument in support of providing universal health care to the
Limitations and strengths
Our study has some limitations. Residual confounding
may exist as socioeconomic measures only represent
proxies for individual-level status. However, as the lower
odds of statin medication in those MI patients living in
deprived neighborhoods no longer remained significant
after the statistical adjustments, it is likely that our
socioeconomic measures are relatively good proxies of
individual-level socioeconomic status. Another
limitation is that we did not account for mobility, i.e., move to
a different neighborhood during the follow-up period.
However, we have previously checked the mobility in
this age group and found it to be low. In addition, the
follow-up period was relatively short, which implies that
most people likely remained in their neighborhood
during the study period.
This study has also, however, several strengths. The
large cohort, which included practically all patients
diagnosed with MI in Sweden during the study period,
increases the generalizability of our results as very little
data is missing. Another strength is the use of personal
identification numbers, which made it possible to link
health care data to socioeconomic data as well as
following individuals in different national registers. A further
strength is the use of the Hospital Discharge Register,
which has high validity, especially for cardiovascular
]. Finally, the use of multilevel modeling
helped us to separate neighborhood-level and
individuallevel effects and allowed for consideration of both
random and fixed effects.
Neighborhood-level SES was modestly associated with
statin medication rates in MI patients and this
association was no longer significant after adjusting for
individual-level sociodemographic factors. These
findings raise important clinical and public health concerns,
and indicate that individual-level approaches may be
most important in health care policies regarding statin
medication in MI patients.
This is the first study to determine whether neighborhood socioeconomic status is associated with statin medication rates in myocardial infarction (MI) patients.
Low neighborhood-level socioeconomic status was
significantly associated with lower statin medication
rates (odds ratio 0.80).
After adjustment for individual-level socioeconomic
characteristics and comorbidities, this association
was no longer significant.
Individual-level approaches may be most important
in health care policies regarding statin medication in
Additional file 1: Table S1. Evaluating the interaction between
neighborhood deprivation and individual-level characteristics for statin
medication in myocardial infarction patients. (DOCX 31 kb)
CHD, Coronary heart disease; ICC, intraclass correlation; ICD, International
Classification of Diseases; MI, myocardial infarction; OR, Odds ratio; SES,
This work was supported by the Swedish Research Council to Kristina
Sundquist and the National Heart, Lung, And Blood Institute of the National
Institutes of Health under Award Number R01HL116381 to Kristina Sundquist.
The content is solely the responsibility of the authors and does not
necessarily represent the official views of the National Institutes of Health.
The funding sources had no role in the design, conduct, or analysis of the
study or in the decision to submit the manuscript for publication.
Availability of data and materials
The dataset used in the present study was retrieved from nationwide
registers provided by the National Board of Health and Welfare (health care
data) and Statistics Sweden, the Swedish Government-owned statistics
bureau (population data).
All authors contributed to the conception and design of the study; KS
contributed to the acquisition of data; XL contributed to the analysis and
interpretation of data; POF drafted the manuscript; and all authors revised it
critically and approved the final version. All authors had full access to all of
the data (including statistical reports and tables) and take responsibility for
the integrity of the data and the accuracy of their analysis.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
This study was approved by the Ethics Committee of Lund University,
Sweden, and was conducted in accordance with the 1975 Declaration of
Helsinki. No individual consent was needed as this study was based on
secondary data from nationwide registers.
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