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Low socioeconomic status increases short-term mortality of acute myocardial infarction despite universal health coverage
International Journal of Cardiology 172 (2014) 82–87
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International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Low socioeconomic status increases short-term mortality of acute myocardial infarction despite universal health coverage Jen-Yu Wang a,1, Cheng-Yi Wang a,1, Shiun-Yang Juang b, Kuang-Yung Huang c,d, Pesus Chou e, Chih-Wei Chen c,f,⁎, Ching-Chih Lee c,e,g,h,i,⁎⁎ a
Department of Internal Medicine, Cardinal Tien Hospital, School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan Department of Medical Research, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan School of Medicine, Tzu Chi University, Hualian, Taiwan d Division of Allergy, Immunology, and Rheumatology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan e Community Medicine Research Center and Institute of Public Health, National Yang-Ming University, Taipei, Taiwan f Division of Cardiology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan g Department of Otolaryngology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan h Department of Education, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan i Center for Clinical Epidemiology and Biostatistics, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan b c
a r t i c l e
i n f o
Article history: Received 30 July 2013 Received in revised form 16 December 2013 Accepted 20 December 2013 Available online 7 January 2014 Keywords: Socioeconomic status Acute myocardial infarction National Health Insurance
a b s t r a c t Background: This nationwide population-based study investigated the relationship between individual and neighborhood socioeconomic status (SES) and mortality rates for acute myocardial infarction (AMI) in Taiwan. Methods: A population-based follow-up study included 23,568 patients diagnosed with AMI from 2004 to 2008. Each patient was monitored for 2 years, or until their death, whichever came first. The individual income-related insurance payment amount was used as a proxy measure of patient's individual SES. Neighborhood SES was defined by household income, and neighborhoods were grouped as advantaged or disadvantaged. The Cox proportional hazards model was used to compare the mortality rates between the different SES groups after adjusting for possible confounding risk factors. Results: After adjusting for potential confounding factors, AMI patients with low individual SES had an increased risk of death than those with high individual SES who resided in advantaged neighborhoods. In contrast, the cumulative readmission rate from major adverse cardiovascular events did not differ significantly between the different individual and neighborhood SES groups. AMI patients with low individual SES had a lower rate of diagnostic angiography and subsequent percutaneous coronary intervention (P b 0.001). The presence of congestive heart failure, chronic kidney disease, chronic obstructive pulmonary disease, pneumonia, septicemia, and shock revealed an incremental increase with worse SES (P b 0.001). Conclusions: The findings indicate that AMI patients with low individual SES have the greatest risk of short-term mortality despite being under a universal health-care system. Public health strategies and welfare policies must continue to focus on this vulnerable group. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Numerous studies have linked socioeconomic status (SES) to health outcomes [1–7]. Cardiovascular disease (CVD), and acute myocardial infarction (AMI), is an important cause of death worldwide. Both individual SES and neighborhood SES are independently and significantly ⁎ Correspondence to: C.-W. Chen, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, No.2, Ming-Sheng Road, Dalin Town, Chiayi, 622, Taiwan. Tel.: +886 5 2648000x5919; fax: +886 5 2648006. ⁎⁎ Correspondence to: C.-C. Lee, Center for Clinical Epidemiology and Biostatistics, Cancer Center, Department of Otolaryngology, Taiwan. Tel.: + 886 5 2648000x5919; fax: +886 5 2648006. E-mail addresses: [email protected] (C.-W. Chen), [email protected] (C.-C. Lee). 1 JY Wang and CY Wang contributed equally to this article. 0167-5273/$ – see front matter © 2014 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijcard.2013.12.082
associated with the incidence of CVD and AMI [8–19]. The effect of neighborhood SES on AMI is well documented—those living in deprived areas experience the largest burden of the disease with higher incidence [8–18] and mortality rates [13,19]. Furthermore, patients with low individual SES have been shown to have a decreased treatment utilization rate and a higher mortality risk [20–24]. However, different types of medical coverage may impact treatment and outcomes for patients with AMI [25–27]. Universal health-care systems have been implemented in many industrialized countries, including Taiwan, and these systems have resulted in improved access to medical care and reduced mortality; however, disparities between different SES groups in medical care remain. Interestingly, the association between AMI and SES has been demonstrated most from Western society. The objective of this study was to examine the association between the cross-level effect of individual
J.-Y. Wang et al. / International Journal of Cardiology 172 (2014) 82–87
and neighborhood SESs on the mortality rates of patients with AMI in the National Health Insurance Research Database (NHIRD) in Taiwan. 2. Methods and materials In 1995 Taiwan implemented a National Health Insurance (NHI) program that requires mandatory enrollment in the government-run, universal, single-payer insurance system and provides comprehensive benefits coverage. Currently, up to 99% of the 23 million residents of Taiwan receive medical care through the NHI program. Over 97% of the hospitals and clinics in Taiwan are contracted to provide health care services [28], which are reimbursed by the Bureau of NHI, and all data related to these services are collected and input into the National Health Research Institute Database (NHIRD) by the National Health Research Institutes to provide a comprehensive record of medical care. The data consisted of ambulatory care records, inpatient care records, and the registration files of the insured. The data set included all claims data from Taiwan's NHI program, which was implemented as a means of financing health care for all Taiwanese citizens. This observational study was conducted in a retrospective cohort of the Chinese population from the 2004–2008 NHIRD in Taiwan. The National Health Insurance Bureau of Taiwan randomly reviews the charts of 1 out of every 100 ambulatory cases and one out of every 20 inpatient cases, as well as performing patient interviews to verify the accuracy of the diagnosis [29,30]. The in-hospital health care database makes an epidemiological study of AMI possible because nearly all patients with AMI are hospitalized in order to receive optimal medical care. Inpatients ≥18 years of age diagnosed with AMI between 2004 and 2008 and with a discharge diagnosis that met the International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) code of 410.X were recruited for the study. From 2004 to 2008, 80,726 patients with a new diagnosis of AMI were identified. Because there was a significant difference in proportion of gender and mean age between the 3 individual SES groups. The selection criteria were further refined by randomly selecting 23,568 age (with a caliber of 5) and gender-matched patients. The key dependent variable of interest was 6-month, 1-year and 2-year in-hospital mortality rate. A more comprehensive outcome included cumulative re-hospitalization rate due to AMI, stroke and congestive heart failure. The key independent variables were the contextual effects of individual SES and neighborhood SES. Patients were then linked to the mortality data covering the years from 2004 to 2008 to calculate mortality rate. Each patient was tracked for 2 years from the time of their first curative treatment using administrative data to identify all patients who died during the study period. Patient characteristics included age, gender, geographic location, treatment modality, co-morbidities, and monthly income. Risk factors were included in the analyses if they had previously been shown to be associated with CVD or AMI or if they were candidates on theoretical grounds [31,32]. Co-morbid conditions and therapeutic procedures were identified according to the ICD9-CM system and were further grouped into the following broad categories to construct a logistic regression model: hypertension, diabetes mellitus, dyslipidemia, atrial fibrillation, congestive heart failure, chronic kidney disease, pneumonia, chronic obstructive pulmonary disease, septicemia, and shock.
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of residences as urban (urbanization level 1), sub-urban (urbanization levels 2–3), or rural (urbanization levels 4–7) was also recorded. The hospitals were categorized by ownership (public, nonprofit, or for-profit), and hospital level (medical center, regional, or district hospital). The geographic regions where the AMI patients resided were recorded as Northern, Central, Southern, and Eastern Taiwan. 2.4. Statistical analysis The SAS statistical package (version 9.2; SAS Institute, Inc., Cary, NC, USA), and SPSS (version 15, SPSS Inc., Chicago, IL, USA) were used for data analysis. Pearson's chi-square test was used for categorical variables such as gender, level of urbanization, geographic regions of residence, comorbidities, treatment modality, and hospital characteristics (teaching level, ownership, and caseload) in AMI patients. Continuous variables were analyzed using a one-way ANOVA test. Mortality rate, stratified by individual SES and neighborhood SES, were measured from the time of diagnosis using overall mortality as the event variable. The cumulative 6-month mortality rates and the mortality curves were constructed and compared using the logrank test. The Cox proportional hazards regression model adjusting for patients' characteristics (age, gender, co-morbidities, urbanization, and area of residence), treatment modality (percutaneous coronary intervention [PCI], coronary artery bypass graft or medical therapy [CABG]), and hospital characteristics (ownership, teaching level, and caseload) was used to compare outcomes between different SES categories. SES variables were introduced into the Cox model, with the high individual SES and advantaged neighborhood group as the reference group. A two-sided P-value (P b 0.05) was used to determine statistical significance.
3. Results 3.1. Demographic data and clinical characteristics The study cohort consisted of 23,568 patients with a new diagnosis of AMI who were matched for age and gender. The distribution of demographic characteristics and selected comorbidities for the 3 groups is shown in Table 1. AMI patients with high individual SES were more likely to reside in urban areas, especially in northern Taiwan, and to undergo treatment in a medical center, compared with their moderate- and low-individual SES counterparts (P b 0.001). AMI patients with low individual SES also demonstrated a lower rate of diagnostic angiography and subsequent PCI (P b 0.001). Hypertension was the most common co-morbidity, followed by DM, and dyslipidemia across all SES groups. CHF, CKD, COPD, pneumonia, septicemia, and shock increased incrementally as SES decreased (P b 0.001).
2.1. Individual-level measures
3.2. Univariate analysis The four-factor Hollingshead scale uses marital status, gender, education and occupation [33]. This study used the income-related insurance payment amount as a proxy measure of individual SES at the time of diagnosis, which is an important prognostic factor for AMI [4,27,34–42]. The AMI patients were classified into three groups: (1) low SES: lower than US $528 per month (New Taiwan Dollar (NT$) 15840); (2) moderate SES: between US $528–833 per month (NT$15841–25000); and (3) high SES: US $833 per month (NT$25001) or more [43]. We selected NT$15,840 as the low income level cutoff point because this was the government-stipulated minimum wage for full-time employees in Taiwan in 2006. 2.2. Neighborhood-level Socioeconomic Status Winkleby et al. identified the following variables to characterize neighborhood SES: percentage high-school education, median annual family income, percentage blue collar workers, percentage unemployed, and median household income [44]. In this study, we adopted median household income as a proxy of neighborhood SES. For neighborhood SES, household income is a contextual characteristic representing averages and percentages measured at the enumeration level in the 2001 Taiwan Census. Neighborhood household income was measured using per capita personal income by township acquired from the 2001 income tax statistics released by Taiwan's Ministry of Finance (http://www.fdc. gov.tw/dp.asp?mp=5). Advantaged and disadvantaged neighborhoods were distinguished based on the median values of neighborhood household income (NT $540000 or US $17992 per year) for neighborhood characteristics, with advantaged neighborhoods having higher-than-median neighborhood household incomes, and disadvantaged neighborhoods having lower-than median household incomes. 2.3. Other variables Patient residences were classified into 7 urbanization levels based on 5 indices in Taiwan: population density, percentage of residents with college level or higher education, percentage of residents N65 years of age, percentage of residents who were agriculture workers, and the number of physicians per 100,000 people [45]. The urbanization level
Interaction effects between gender and several other variables were noted, and the patients were further stratified into two groups: male and female. The result of a Kaplan–Meier analysis showed that patients with low individual SES had poorer prognoses at 6 months after the AMI and was maintained thereafter in both male and female (Fig. 1a and b). The 6-month, 1-year and 2-year overall mortality rates stratified by gender are shown in Table 2. Although most deaths occurred within the first 6 months after the AMI, the effects of low individual SES on survival persisted at 2 years (P b 0.001). Analysis of the combined effect of individual SES and neighborhood SES revealed that mortality rates were highest among male and female AMI patients with low individual SES residing in either advantaged or disadvantaged neighborhoods. The 2-year cumulative mortality rates were highest among AMI patients who had low individual SES resided in advantaged neighborhood (14.8% of male and 16.2% of female, respectively). 3.3. Multivariate analysis After adjustment for covariates, the combined effect of individual SES and neighborhood SES remained statistically significant in the Cox proportional hazards regression model (Table 3). Adjusted hazard ratios revealed that, female AMI patients with low individual SES in disadvantaged neighborhoods had the highest risk of mortality (HR = 2.37; 95% CI, 1.66 to 3.4, P b 0.001) compared with females with high individual SES residing in advantaged neighborhoods. Male AMI patients with
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Table 1 Baseline characteristics (n = 23,568). Variables
Individual socioeconomic status High SES
Moderate SES
(n = 7856) Mean age, years (±SD) Gender Male (%) Female (%) Urbanization of residence Urban (%) Suburban (%) Rural (%) Geographic of residence Northern (%) Central (%) Southern (%) Eastern (%) Percutaneous transluminal coronary angioplasty Yes (%) No (%) Coronary artery bypass graft Yes (%) No (%) Angiography Yes (%) No (%) Comorbidities Hypertension Yes (%) No (%) Diabetes Yes (%) No (%) Dyslipidemia Yes (%) No (%) Atrial fibrillation Yes (%) No (%) Congestive heart failure Yes (%) No (%) Chronic kidney disease Yes (%) No (%) Pneumonia Yes (%) No (%) Chronic obstructive pulmonary disease Yes (%) No (%) Septicemia Yes (%) No (%) Shock Yes (%) No (%) Hospital characteristics Hospital ownership Public Nonprofit Profit Teaching level Medical center (%) Regional (%) District (%)
54
Low SES
(n = 7856) ±12
56
P value
(n = 7856) ±11
56
±12
NA NA
6213 1643
(79.1) (20.9)
6213 1643
(79.1) (20.9)
6213 1643
(79.1) (20.9)
2833 3831 1192
(36.0) (48.8) (15.2)
1440 3067 3349
(18.3) (39.0) (42.7)
2063 3763 2030
(26.3) (47.9) (25.8)
5153 1023 1476 204
(65.6) (13.0) (18.8) (2.6)
3215 1404 2945 292
(40.9) (17.9) (37.5) (3.7)
4394 1240 1962 260
(55.9) (15.8) (25.0) (3.3)
4200 3656
(53.5) (46.5)
3564 4292
(45.4) (54.6)
3389 4467
(43.1) (56.9)
622 7234
(7.9) (92.1)
665 7191
(8.5) (91.5)
752 7104
(9.6) (90.4)
4945 2911
(62.9) (37.1)
4378 3478
(55.7) (44.3)
4183 3673
(53.2) (46.8)
3676 4180
(46.8) (53.2)
3821 4035
(48.6) (51.4)
3797 4059
(48.3) (51.7)
2494 5362
(31.7) (68.3)
2868 4988
(36.5) (63.5)
3026 4830
(38.5) (61.5)
2755 5101
(35.1) (64.9)
2180 5676
(27.7) (72.3)
2076 5780
(26.4) (73.6)
363 7493
(4.6) (95.4)
443 7413
(5.6) (94.4)
441 7415
(5.6) (94.4)
1116 6740
(14.2) (85.8)
1484 6372
(18.9) (81.1)
1687 6169
(21.5) (78.5)
1184 6672
(15.1) (84.9)
1561 6295
(19.9) (80.1)
1765 6091
(22.5) (77.5)
872 6984
(11.1) (88.9)
1303 6553
(16.6) (83.4)
1544 6312
(19.7) (80.3)
312 7544
(4.0) (96.0)
608 7248
(7.7) (92.3)
699 7157
(8.9) (91.1)
684 7172
(8.7) (91.3)
1056 6800
(13.4) (86.6)
1180 6676
(15.0) (85.0)
730 7126
(9.3) (90.7)
1052 6804
(13.4) (86.6)
1164 6692
(14.8) (85.2)
2221 4024 1611
(28.3) (51.2) (20.5)
1743 4159 1954
(22.2) (52.9) (24.9)
2312 3628 1916
(29.4) (46.2) (24.4)
4362 2830 664
(55.5) (36.0) (8.5)
3576 3157 1123
(45.5) (40.2) (14.3)
3767 3054 1035
(48.0) (38.9) (13.1)
b0.001
b0.001
b0.001
0.001
b0.001
0.046
b0.001
b0.001
0.005
b0.001
b0.001
b0.001
b0.001
b0.001
b0.001
b0.001
b0.001
Abbreviation: SES, socioeconomic status.
low individual SES in advantaged and disadvantaged neighborhoods also demonstrated a large increase in mortality risk (HR = 1.76 and 1.83, respectively). No statistically significant differences in mortality rates were found between AMI patients with moderate and high
individual SES. In addition, we have calculated cumulative readmission risk (Appendix S1) and the adjusted hazard ratios for major adverse cardiovascular events (MACE) during the 6-month, 1-year and 2-year follow-up periods (Appendix S2). The rate of hospitalization from
J.-Y. Wang et al. / International Journal of Cardiology 172 (2014) 82–87
Fig. 1. a. The combined effect of individual and neighborhood SES on mortality rates in male acute myocardial infarction patients. b. The combined effect of individual and neighborhood SES on mortality rates in female acute myocardial infarction patients.
MACE did not differ significantly between the different individual and neighborhood SES groups. Baseline sociodemographic characteristics by individual and neighborhood SES are presented in Table 4. AMI patients in disadvantaged neighborhoods had lower resources of health care (e.g., physicians and pharmacists per 10,000 residents). AMI patients in advantaged neighborhoods also had a higher level of education (percentage of education ≥ high school), a higher median household income, and a lower mortality rate. 4. Discussion This study examined the combined effect of individual and neighborhood SES on the risk of all-cause mortality and readmission from MACE in AMI patients in a population-based study under a national
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health insurance system in Taiwan. AMI patients with low individual SES had significantly increased all-cause mortality after adjustment for a variety of comorbidities. Our findings are consistent with the results of other studies investigating the relationship between individual SES and post-AMI survival in other populations [10,12,18,20–24]. For example, Stjärne et al. noted that low-personal disposable income and lowincome level in the neighborhood seemed to have an additive effect on cardiovascular outcomes in Sweden [10]. Similar results have been found in developing countries in the African region [18]. Several plausible mechanisms may explain why low SES was associated with increased all-cause mortality. In this study, patients with lower individual SES tended to have a greater number of co-morbidities and cardiovascular risk factors, except dyslipidemia. Lower utilization rates of CAG and PCI in the low individual SES group may contribute to worse outcomes [24,46,47]. Patients with low SES may also have a greater chance of dying from other factors, such as sepsis, pneumonia, or other co-morbidities. Moreover, social isolation, a low sense of control, and occupational stress are more prevalent among patients with low SES. However, the risk of AMI mortality associated with individual SES was not consistent across different SES groups in the current study. Patients with moderate individual SES did not have a higher risk of mortality than those with high individual SES residing in advantaged neighborhoods. The current findings regarding combined individual and neighborhood SES effects on AMI mortality are inconsistent with previous studies. An analysis based on the records of 51,591 patients hospitalized for AMI in Canadian health system from 1994 to 1997 showed that a significant gradient in mortality across income quintiles [27]. The type of medical coverage may affect patients' treatment and outcome. In patients with AMI, analyses from the French National Health insurance showed that short-term mortality rate was higher and longterm adherence to therapy was lower in uninsured patients, compared with insured patients [25]. The NHI program in Taiwan aimed to eliminate primary health inequality, and may play an important role in closing the medical accessibility gap between patients based on SES. In Taiwan, co-payments are notably low and homogeneously priced which has made cost less of a barrier to receiving proper medical care. AMI patients with low individual SES living in disadvantaged neighborhoods had the highest risk of death after adjustment—risk of death in these cases was increased by up to 83% in males and 137% in females compared with high SES patients in advantaged neighborhoods. Several previous studies examining the cross-level interaction between individual and neighborhood SES on CVD and mortality have produced diverse results [48–50]. Subjects of low individual SES in high neighborhood SESs might experience a lower risk of mortality than subject of low individual SES in low neighborhood SES, because they benefit from the more healthcare-related sources in their neighborhoods [48]. Alternatively, subjects of low individual SES in high neighborhood SES might experience a higher risk of dying because of relative deprivation, low relative social standing, or both [50]. In our study, cross-level interaction between individual and neighborhood SES was noted after univariate analysis. However, AMI patients with low individual SES living in disadvantaged neighborhoods still had the highest risk of death after adjustment for covariates. This finding is consistent with previous analysis in the Atherosclerosis Risk in Communities Study (ARIC) [48]. This study has several limitations. First, the diagnosis of non-ST segment elevation myocardial infarction (NSTEMI), and ST segment elevation myocardial infarction (STEMI) could not be differentiated further due to the limitations of the ICD-9-CM system and the lack of actual clinical data in the NHIRD. Furthermore, we do not have data on patients with silent or unrecognized AMI, or patients with out-of-hospital sudden cardiac deaths. Second, the database does not contain information on tobacco use, dietary habits or other behavioral factors, which may be risk factors and prognostic factors for AMI patients. Third, the Killip classification, the risk stratification of AMI [51], was not included in the dataset. Fourth, the use of dichotomies to classify neighborhood SES is oversimplified. However, neighborhood SES is difficult
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J.-Y. Wang et al. / International Journal of Cardiology 172 (2014) 82–87
Table 2 Cumulative mortality risk in acute myocardial infarction patients(n = 23,568). Events and cumulative risk Socioeconomic status
6-month
1-year
2-year
Risk (%)
Risk (%)
Risk (%)
(95% CI)
(95% CI)
(95% CI)
Male High SES in advantaged neighborhood(n = 3775) High SES in disadvantaged neighborhood (n = 2438) Moderate SES in advantaged neighborhood (n = 2346) Moderate SES in disadvantaged neighborhood (n = 3867) Low SES in advantaged neighborhood (n = 3186) Low SES in disadvantaged neighborhood (n = 3027)
5.2 (4.42–5.98) 3.4 (2.62–4.18) 6.1 (5.12–7.08) 5.4 (4.62–6.18) 11.8 (10.62–12.98) 10.2 (9.02–11.38)
6.1 (5.32–6.88) 4.1 (3.32–4.88) 7.1 (6.12–8.08) 5.9 (5.12–6.68) 13.2 (12.02–14.38) 11.3 (10.12–12.48)
7.1 (6.32–7.88) 4.8 (4.02–5.58) 8.3 (7.12–9.48) 6.7 (5.92–7.48) 14.8 (13.62–15.98) 12.8 (11.62–13.98)
Female High SES in advantaged neighborhood (n = 1082) High SES in disadvantaged neighborhood (n = 561) Moderate SES in advantaged neighborhood (n = 647) Moderate SES in disadvantaged neighborhood (n = 996) Low SES in advantaged neighborhood (n = 814) Low SES in disadvantaged neighborhood (n = 829)
5.3 (3.93–6.67) 5 (3.24–6.76) 7.1 (5.14–9.06) 4.4 (3.03–5.77) 13.2 (10.85–15.55) 10.5 (8.34–12.66)
6.3 (4.93–7.67) 5.7 (3.74–7.66) 7.9 (5.74–10.06) 5.3 (3.93–6.67) 14.8 (12.45–17.15) 11.5 (9.34–13.66)
7.3 (5.73–8.87) 6.1 (4.14–8.06) 9 (6.84–11.16) 6.3 (4.73–7.87) 16.1 (13.55–18.65) 12.8 (10.45–15.15)
Abbreviations: 95% CI, 95% confidence interval; SES, socioeconomic status.
Table 3 Hazard ratios of individual socioeconomic status for mortality in advantaged and disadvantaged neighborhoodsa. Socioeconomic status
6-month mortality
1-year mortality
2-year mortality
Adjusted
95%
Adjusted
95%
Adjusted
95%
HR
CI
HR
CI
HR
CI
Male High SES in advantaged neighborhood High SES in disadvantaged neighborhood Moderate SES in advantaged neighborhood Moderate SES in disadvantaged neighborhood Low SES in advantaged neighborhood Low SES in disadvantaged neighborhood
1 0.84 1.00 1.20 1.76 1.83
0.64–1.08 0.81–1.24 0.97–1.49 1.48–2.09 1.51–2.22
1 0.84 0.99 1.11 1.65 1.69
0.66–1.06 0.81–1.21 0.91–1.36 1.40–1.94 1.41–2.02
1 0.85 0.99 1.07 1.59 1.64
0.68–1.06 0.82–1.20 0.88–1.29 1.37–1.85 1.39–1.94
Female High SES in advantaged neighborhood High SES in disadvantaged neighborhood Moderate SES in advantaged neighborhood Moderate SES in disadvantaged neighborhood Low SES in advantaged neighborhood Low SES in disadvantaged neighborhood
1 1.22 1.20 1.06 2.05 2.37
0.77–1.93 0.81–1.78 0.68–1.65 1.48–2.84 1.66–3.40
1 1.20 1.12 1.14 1.94 2.26
0.78–1.84 0.77–1.61 0.76–1.70 1.43–2.63 1.62–3.15
1 1.07 1.07 1.06 1.83 2.02
0.71–1.61 0.76–1.50 0.73–1.54 1.37–2.43 1.47–2.76
Abbreviations: Adjusted HR, adjusted hazard ratio; 95% CI, 95% confidence interval; SES, socioeconomic status. a Adjusted for the patient gender, age, urbanization, geographic region, co-morbidities, and hospital characteristics.
to measure and no single measure to stratify risk in patients is better than the others [52]. Finally, the rates of coronary angiography and intervention in AMI patients are lower than what were predicted because of the incomplete ICD coding in real-world practice.
5. Conclusion Although Taiwan's National Health Insurance has reduced financial barriers to medical care, disparities in medical care of AMI remain. Our
Table 4 Sociodemographic characteristics by individual and neighborhood socioeconomic status (n = 23,568). High individual SES
Number of patients Number of deaths (%) Mean age, mean ± SD Male gender, % Education≧high school, % Median household income, NT$1000 Health care-related resources Physicians per 10,000 persons, mean ± SD Pharmacists per 10,000 persons, mean ± SD a
Pearson Chi-Square.
Moderate individual SES
Low individual SES
P value
Advantaged neighborhood
Disadvantaged neighborhood
Advantaged neighborhood
Disadvantaged neighborhood
Advantaged neighborhood
Disadvantaged neighborhood
4857 347 (7.1) 54 ± 12 77.7 99.4 617 ± 72
2999 152 (5.1) 55 ± 12 81.3 83.9 497 ± 33
2993 253 (8.5) 54 ± 11 78.4 98.8 596 ± 58
4863 322 (6.6) 59 ± 12 79.5 57.3 479 ± 37
4000 603 (15.1) 57 ± 12 79.7 99 606 ± 61
3856 494 (12.8) 56 ± 12 78.5 71.9 488 ± 37
b0.001a b0.001 b0.001a b0.001a b0.001
26 ± 21 9 ± 12
12 ± 12 4±4
24 ± 19 7±7
10 ± 10 3±3
24 ± 21 8 ± 10
11 ± 10 4±3
b0.001 b0.001
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findings indicate that AMI patients with low individual SES have the highest risk of mortality despite a universal health-care system. Public health strategies and welfare policies must continue to focus their attention on this vulnerable group. Acknowledgments This study was initiated after approval by the Institutional Review Board of Buddhist Dalin Tzu Chi General Hospital, Taiwan. Since all identifying personal information was removed from the secondary files prior to analysis, the review board waived the requirement for written informed consent from the patients involved. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ijcard.2013.12.082. References [1] Marmot MG, Smith GD, Stansfeld S, et al. Health inequalities among British civil servants: the Whitehall II study. Lancet 1991;337:1387–93. [2] Pappas G, Queen S, Hadden W, Fisher G. The increasing disparity in mortality between socioeconomic groups in the United States, 1960 and 1986. N Engl J Med 1993;329:103–9. [3] Huisman M, Read S, Towriss CA, Deeg DJ, Grundy E. Socioeconomic inequalities in mortality rates in old age in the World Health Organization Europe region. Epidemiol Rev 2013;35:84–97. [4] Alter DA, Franklin B, Ko DT, et al. Socioeconomic status, functional recovery, and long-term mortality among patients surviving acute myocardial infarction. PLoS One 2013;8:e65130. [5] Elstad JI, Dahl E, Hofoss D. Associations between relative income and mortality in Norway: a register-based study. Eur J Public Health 2006;16:640–4. [6] Kawachi I, Kennedy BP, Lochner K, Prothrow-Stith D. Social capital, income inequality, and mortality. Am J Public Health 1997;87:1491–8. [7] Pearce N, Davey Smith G. Is social capital the key to inequalities in health? Am J Public Health 2003;93:122–9. [8] Sundquist K, Winkleby M, Ahlen H, Johansson SE. Neighborhood socioeconomic environment and incidence of coronary heart disease: a follow-up study of 25,319 women and men in Sweden. Am J Epidemiol 2004;159:655–62. [9] Sundquist K, Malmstrom M, Johansson SE. Neighbourhood deprivation and incidence of coronary heart disease: a multilevel study of 2.6 million women and men in Sweden. J Epidemiol Community Health 2004;58:71–7. [10] Stjarne MK, Fritzell J, De Leon AP, Hallqvist J. Neighborhood socioeconomic context, individual income and myocardial infarction. Epidemiology 2006;17:14–23. [11] Morrison C, Woodward M, Leslie W, Tunstall-Pedoe H. Effect of socioeconomic group on incidence of, management of, and survival after myocardial infarction and coronary death: analysis of community coronary event register. BMJ 1997;314:541–6. [12] Henriksson G, Weitoft GR, Allebeck P. Associations between income inequality at municipality level and health depend on context—a multilevel analysis on myocardial infarction in Sweden. Soc Sci Med 2010;71:1141–9. [13] Chaix B, Rosvall M, Merlo J. Neighborhood socioeconomic deprivation and residential instability: effects on incidence of ischemic heart disease and survival after myocardial infarction. Epidemiology 2007;18:104–11. [14] Davies CA, Dundas R, Leyland AH. Increasing socioeconomic inequalities in first acute myocardial infarction in Scotland, 1990–92 and 2000–02. BMC Public Health 2009;9:134. [15] Deguen S, Lalloue B, Bard D, Havard S, Arveiler D, Zmirou-Navier D. A small-area ecologic study of myocardial infarction, neighborhood deprivation, and sex: a Bayesian modeling approach. Epidemiology 2010;21:459–66. [16] Diez Roux AV, Merkin SS, Arnett D, et al. Neighborhood of residence and incidence of coronary heart disease. N Engl J Med 2001;345:99–106. [17] Picciotto S, Forastiere F, Stafoggia M, D'Ippoliti D, Ancona C, Perucci CA. Associations of area based deprivation status and individual educational attainment with incidence, treatment, and prognosis of first coronary event in Rome, Italy. J Epidemiol Community Health 2006;60:37–43. [18] Stringhini S, Viswanathan B, Gedeon J, Paccaud F, Bovet P. The social transition of risk factors for cardiovascular disease in the African region: evidence from three cross-sectional surveys in the Seychelles. Int J Cardiol 2013;168:1201–6. [19] Winkleby M, Sundquist K, Cubbin C. Inequities in CHD incidence and case fatality by neighborhood deprivation. Am J Prev Med 2007;32:97–106. [20] Rosengren A, Subramanian SV, Islam S, et al. Education and risk for acute myocardial infarction in 52 high, middle and low-income countries: INTERHEART case–control study. Heart 2009;95:2014–22. [21] Rasmussen JN, Rasmussen S, Gislason GH, et al. Mortality after acute myocardial infarction according to income and education. J Epidemiol Community Health 2006;60:351–6. [22] Lindenauer PK, Lagu T, Rothberg MB, et al. Income inequality and 30 day outcomes after acute myocardial infarction, heart failure, and pneumonia: retrospective cohort study. BMJ 2013;346:f521.
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Effects of socioeconomic status on access to invasive cardiac procedures and on mortality after acute myocardial infarction. N Engl J Med 1999;341:1359–67. [28] Chiang TL. Taiwan's 1995 health care reform. Health Policy 1997;39:225–39. [29] Tseng CH. Mortality and causes of death in a national sample of diabetic patients in Taiwan. Diabetes Care 2004;27:1605–9. [30] Website BoNHI. http://www.nhi.gov.tw/information/bulletin_file/421_089003646519.doc; 2006. [31] Lim S, Choi HJ, Shin H, et al. Subclinical atherosclerosis in a community-based elderly cohort: the Korean Longitudinal Study on Health and Aging. Int J Cardiol 2012;155:126–33. [32] Chien KL, Hsu HC, Su TC, et al. Constructing a point-based prediction model for the risk of coronary artery disease in a Chinese community: a report from a cohort study in Taiwan. Int J Cardiol 2012;157:263–8. [33] Hollingshead AB. Four factor index of social status; 1975. [34] Latour-Perez J, Gutierrez-Vicen T, Lopez-Camps V, et al. 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Contents lists available at ScienceDirect
International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Low socioeconomic status increases short-term mortality of acute myocardial infarction despite universal health coverage Jen-Yu Wang a,1, Cheng-Yi Wang a,1, Shiun-Yang Juang b, Kuang-Yung Huang c,d, Pesus Chou e, Chih-Wei Chen c,f,⁎, Ching-Chih Lee c,e,g,h,i,⁎⁎ a
Department of Internal Medicine, Cardinal Tien Hospital, School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan Department of Medical Research, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan School of Medicine, Tzu Chi University, Hualian, Taiwan d Division of Allergy, Immunology, and Rheumatology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan e Community Medicine Research Center and Institute of Public Health, National Yang-Ming University, Taipei, Taiwan f Division of Cardiology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan g Department of Otolaryngology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan h Department of Education, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan i Center for Clinical Epidemiology and Biostatistics, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan b c
a r t i c l e
i n f o
Article history: Received 30 July 2013 Received in revised form 16 December 2013 Accepted 20 December 2013 Available online 7 January 2014 Keywords: Socioeconomic status Acute myocardial infarction National Health Insurance
a b s t r a c t Background: This nationwide population-based study investigated the relationship between individual and neighborhood socioeconomic status (SES) and mortality rates for acute myocardial infarction (AMI) in Taiwan. Methods: A population-based follow-up study included 23,568 patients diagnosed with AMI from 2004 to 2008. Each patient was monitored for 2 years, or until their death, whichever came first. The individual income-related insurance payment amount was used as a proxy measure of patient's individual SES. Neighborhood SES was defined by household income, and neighborhoods were grouped as advantaged or disadvantaged. The Cox proportional hazards model was used to compare the mortality rates between the different SES groups after adjusting for possible confounding risk factors. Results: After adjusting for potential confounding factors, AMI patients with low individual SES had an increased risk of death than those with high individual SES who resided in advantaged neighborhoods. In contrast, the cumulative readmission rate from major adverse cardiovascular events did not differ significantly between the different individual and neighborhood SES groups. AMI patients with low individual SES had a lower rate of diagnostic angiography and subsequent percutaneous coronary intervention (P b 0.001). The presence of congestive heart failure, chronic kidney disease, chronic obstructive pulmonary disease, pneumonia, septicemia, and shock revealed an incremental increase with worse SES (P b 0.001). Conclusions: The findings indicate that AMI patients with low individual SES have the greatest risk of short-term mortality despite being under a universal health-care system. Public health strategies and welfare policies must continue to focus on this vulnerable group. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Numerous studies have linked socioeconomic status (SES) to health outcomes [1–7]. Cardiovascular disease (CVD), and acute myocardial infarction (AMI), is an important cause of death worldwide. Both individual SES and neighborhood SES are independently and significantly ⁎ Correspondence to: C.-W. Chen, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, No.2, Ming-Sheng Road, Dalin Town, Chiayi, 622, Taiwan. Tel.: +886 5 2648000x5919; fax: +886 5 2648006. ⁎⁎ Correspondence to: C.-C. Lee, Center for Clinical Epidemiology and Biostatistics, Cancer Center, Department of Otolaryngology, Taiwan. Tel.: + 886 5 2648000x5919; fax: +886 5 2648006. E-mail addresses: [email protected] (C.-W. Chen), [email protected] (C.-C. Lee). 1 JY Wang and CY Wang contributed equally to this article. 0167-5273/$ – see front matter © 2014 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijcard.2013.12.082
associated with the incidence of CVD and AMI [8–19]. The effect of neighborhood SES on AMI is well documented—those living in deprived areas experience the largest burden of the disease with higher incidence [8–18] and mortality rates [13,19]. Furthermore, patients with low individual SES have been shown to have a decreased treatment utilization rate and a higher mortality risk [20–24]. However, different types of medical coverage may impact treatment and outcomes for patients with AMI [25–27]. Universal health-care systems have been implemented in many industrialized countries, including Taiwan, and these systems have resulted in improved access to medical care and reduced mortality; however, disparities between different SES groups in medical care remain. Interestingly, the association between AMI and SES has been demonstrated most from Western society. The objective of this study was to examine the association between the cross-level effect of individual
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and neighborhood SESs on the mortality rates of patients with AMI in the National Health Insurance Research Database (NHIRD) in Taiwan. 2. Methods and materials In 1995 Taiwan implemented a National Health Insurance (NHI) program that requires mandatory enrollment in the government-run, universal, single-payer insurance system and provides comprehensive benefits coverage. Currently, up to 99% of the 23 million residents of Taiwan receive medical care through the NHI program. Over 97% of the hospitals and clinics in Taiwan are contracted to provide health care services [28], which are reimbursed by the Bureau of NHI, and all data related to these services are collected and input into the National Health Research Institute Database (NHIRD) by the National Health Research Institutes to provide a comprehensive record of medical care. The data consisted of ambulatory care records, inpatient care records, and the registration files of the insured. The data set included all claims data from Taiwan's NHI program, which was implemented as a means of financing health care for all Taiwanese citizens. This observational study was conducted in a retrospective cohort of the Chinese population from the 2004–2008 NHIRD in Taiwan. The National Health Insurance Bureau of Taiwan randomly reviews the charts of 1 out of every 100 ambulatory cases and one out of every 20 inpatient cases, as well as performing patient interviews to verify the accuracy of the diagnosis [29,30]. The in-hospital health care database makes an epidemiological study of AMI possible because nearly all patients with AMI are hospitalized in order to receive optimal medical care. Inpatients ≥18 years of age diagnosed with AMI between 2004 and 2008 and with a discharge diagnosis that met the International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) code of 410.X were recruited for the study. From 2004 to 2008, 80,726 patients with a new diagnosis of AMI were identified. Because there was a significant difference in proportion of gender and mean age between the 3 individual SES groups. The selection criteria were further refined by randomly selecting 23,568 age (with a caliber of 5) and gender-matched patients. The key dependent variable of interest was 6-month, 1-year and 2-year in-hospital mortality rate. A more comprehensive outcome included cumulative re-hospitalization rate due to AMI, stroke and congestive heart failure. The key independent variables were the contextual effects of individual SES and neighborhood SES. Patients were then linked to the mortality data covering the years from 2004 to 2008 to calculate mortality rate. Each patient was tracked for 2 years from the time of their first curative treatment using administrative data to identify all patients who died during the study period. Patient characteristics included age, gender, geographic location, treatment modality, co-morbidities, and monthly income. Risk factors were included in the analyses if they had previously been shown to be associated with CVD or AMI or if they were candidates on theoretical grounds [31,32]. Co-morbid conditions and therapeutic procedures were identified according to the ICD9-CM system and were further grouped into the following broad categories to construct a logistic regression model: hypertension, diabetes mellitus, dyslipidemia, atrial fibrillation, congestive heart failure, chronic kidney disease, pneumonia, chronic obstructive pulmonary disease, septicemia, and shock.
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of residences as urban (urbanization level 1), sub-urban (urbanization levels 2–3), or rural (urbanization levels 4–7) was also recorded. The hospitals were categorized by ownership (public, nonprofit, or for-profit), and hospital level (medical center, regional, or district hospital). The geographic regions where the AMI patients resided were recorded as Northern, Central, Southern, and Eastern Taiwan. 2.4. Statistical analysis The SAS statistical package (version 9.2; SAS Institute, Inc., Cary, NC, USA), and SPSS (version 15, SPSS Inc., Chicago, IL, USA) were used for data analysis. Pearson's chi-square test was used for categorical variables such as gender, level of urbanization, geographic regions of residence, comorbidities, treatment modality, and hospital characteristics (teaching level, ownership, and caseload) in AMI patients. Continuous variables were analyzed using a one-way ANOVA test. Mortality rate, stratified by individual SES and neighborhood SES, were measured from the time of diagnosis using overall mortality as the event variable. The cumulative 6-month mortality rates and the mortality curves were constructed and compared using the logrank test. The Cox proportional hazards regression model adjusting for patients' characteristics (age, gender, co-morbidities, urbanization, and area of residence), treatment modality (percutaneous coronary intervention [PCI], coronary artery bypass graft or medical therapy [CABG]), and hospital characteristics (ownership, teaching level, and caseload) was used to compare outcomes between different SES categories. SES variables were introduced into the Cox model, with the high individual SES and advantaged neighborhood group as the reference group. A two-sided P-value (P b 0.05) was used to determine statistical significance.
3. Results 3.1. Demographic data and clinical characteristics The study cohort consisted of 23,568 patients with a new diagnosis of AMI who were matched for age and gender. The distribution of demographic characteristics and selected comorbidities for the 3 groups is shown in Table 1. AMI patients with high individual SES were more likely to reside in urban areas, especially in northern Taiwan, and to undergo treatment in a medical center, compared with their moderate- and low-individual SES counterparts (P b 0.001). AMI patients with low individual SES also demonstrated a lower rate of diagnostic angiography and subsequent PCI (P b 0.001). Hypertension was the most common co-morbidity, followed by DM, and dyslipidemia across all SES groups. CHF, CKD, COPD, pneumonia, septicemia, and shock increased incrementally as SES decreased (P b 0.001).
2.1. Individual-level measures
3.2. Univariate analysis The four-factor Hollingshead scale uses marital status, gender, education and occupation [33]. This study used the income-related insurance payment amount as a proxy measure of individual SES at the time of diagnosis, which is an important prognostic factor for AMI [4,27,34–42]. The AMI patients were classified into three groups: (1) low SES: lower than US $528 per month (New Taiwan Dollar (NT$) 15840); (2) moderate SES: between US $528–833 per month (NT$15841–25000); and (3) high SES: US $833 per month (NT$25001) or more [43]. We selected NT$15,840 as the low income level cutoff point because this was the government-stipulated minimum wage for full-time employees in Taiwan in 2006. 2.2. Neighborhood-level Socioeconomic Status Winkleby et al. identified the following variables to characterize neighborhood SES: percentage high-school education, median annual family income, percentage blue collar workers, percentage unemployed, and median household income [44]. In this study, we adopted median household income as a proxy of neighborhood SES. For neighborhood SES, household income is a contextual characteristic representing averages and percentages measured at the enumeration level in the 2001 Taiwan Census. Neighborhood household income was measured using per capita personal income by township acquired from the 2001 income tax statistics released by Taiwan's Ministry of Finance (http://www.fdc. gov.tw/dp.asp?mp=5). Advantaged and disadvantaged neighborhoods were distinguished based on the median values of neighborhood household income (NT $540000 or US $17992 per year) for neighborhood characteristics, with advantaged neighborhoods having higher-than-median neighborhood household incomes, and disadvantaged neighborhoods having lower-than median household incomes. 2.3. Other variables Patient residences were classified into 7 urbanization levels based on 5 indices in Taiwan: population density, percentage of residents with college level or higher education, percentage of residents N65 years of age, percentage of residents who were agriculture workers, and the number of physicians per 100,000 people [45]. The urbanization level
Interaction effects between gender and several other variables were noted, and the patients were further stratified into two groups: male and female. The result of a Kaplan–Meier analysis showed that patients with low individual SES had poorer prognoses at 6 months after the AMI and was maintained thereafter in both male and female (Fig. 1a and b). The 6-month, 1-year and 2-year overall mortality rates stratified by gender are shown in Table 2. Although most deaths occurred within the first 6 months after the AMI, the effects of low individual SES on survival persisted at 2 years (P b 0.001). Analysis of the combined effect of individual SES and neighborhood SES revealed that mortality rates were highest among male and female AMI patients with low individual SES residing in either advantaged or disadvantaged neighborhoods. The 2-year cumulative mortality rates were highest among AMI patients who had low individual SES resided in advantaged neighborhood (14.8% of male and 16.2% of female, respectively). 3.3. Multivariate analysis After adjustment for covariates, the combined effect of individual SES and neighborhood SES remained statistically significant in the Cox proportional hazards regression model (Table 3). Adjusted hazard ratios revealed that, female AMI patients with low individual SES in disadvantaged neighborhoods had the highest risk of mortality (HR = 2.37; 95% CI, 1.66 to 3.4, P b 0.001) compared with females with high individual SES residing in advantaged neighborhoods. Male AMI patients with
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J.-Y. Wang et al. / International Journal of Cardiology 172 (2014) 82–87
Table 1 Baseline characteristics (n = 23,568). Variables
Individual socioeconomic status High SES
Moderate SES
(n = 7856) Mean age, years (±SD) Gender Male (%) Female (%) Urbanization of residence Urban (%) Suburban (%) Rural (%) Geographic of residence Northern (%) Central (%) Southern (%) Eastern (%) Percutaneous transluminal coronary angioplasty Yes (%) No (%) Coronary artery bypass graft Yes (%) No (%) Angiography Yes (%) No (%) Comorbidities Hypertension Yes (%) No (%) Diabetes Yes (%) No (%) Dyslipidemia Yes (%) No (%) Atrial fibrillation Yes (%) No (%) Congestive heart failure Yes (%) No (%) Chronic kidney disease Yes (%) No (%) Pneumonia Yes (%) No (%) Chronic obstructive pulmonary disease Yes (%) No (%) Septicemia Yes (%) No (%) Shock Yes (%) No (%) Hospital characteristics Hospital ownership Public Nonprofit Profit Teaching level Medical center (%) Regional (%) District (%)
54
Low SES
(n = 7856) ±12
56
P value
(n = 7856) ±11
56
±12
NA NA
6213 1643
(79.1) (20.9)
6213 1643
(79.1) (20.9)
6213 1643
(79.1) (20.9)
2833 3831 1192
(36.0) (48.8) (15.2)
1440 3067 3349
(18.3) (39.0) (42.7)
2063 3763 2030
(26.3) (47.9) (25.8)
5153 1023 1476 204
(65.6) (13.0) (18.8) (2.6)
3215 1404 2945 292
(40.9) (17.9) (37.5) (3.7)
4394 1240 1962 260
(55.9) (15.8) (25.0) (3.3)
4200 3656
(53.5) (46.5)
3564 4292
(45.4) (54.6)
3389 4467
(43.1) (56.9)
622 7234
(7.9) (92.1)
665 7191
(8.5) (91.5)
752 7104
(9.6) (90.4)
4945 2911
(62.9) (37.1)
4378 3478
(55.7) (44.3)
4183 3673
(53.2) (46.8)
3676 4180
(46.8) (53.2)
3821 4035
(48.6) (51.4)
3797 4059
(48.3) (51.7)
2494 5362
(31.7) (68.3)
2868 4988
(36.5) (63.5)
3026 4830
(38.5) (61.5)
2755 5101
(35.1) (64.9)
2180 5676
(27.7) (72.3)
2076 5780
(26.4) (73.6)
363 7493
(4.6) (95.4)
443 7413
(5.6) (94.4)
441 7415
(5.6) (94.4)
1116 6740
(14.2) (85.8)
1484 6372
(18.9) (81.1)
1687 6169
(21.5) (78.5)
1184 6672
(15.1) (84.9)
1561 6295
(19.9) (80.1)
1765 6091
(22.5) (77.5)
872 6984
(11.1) (88.9)
1303 6553
(16.6) (83.4)
1544 6312
(19.7) (80.3)
312 7544
(4.0) (96.0)
608 7248
(7.7) (92.3)
699 7157
(8.9) (91.1)
684 7172
(8.7) (91.3)
1056 6800
(13.4) (86.6)
1180 6676
(15.0) (85.0)
730 7126
(9.3) (90.7)
1052 6804
(13.4) (86.6)
1164 6692
(14.8) (85.2)
2221 4024 1611
(28.3) (51.2) (20.5)
1743 4159 1954
(22.2) (52.9) (24.9)
2312 3628 1916
(29.4) (46.2) (24.4)
4362 2830 664
(55.5) (36.0) (8.5)
3576 3157 1123
(45.5) (40.2) (14.3)
3767 3054 1035
(48.0) (38.9) (13.1)
b0.001
b0.001
b0.001
0.001
b0.001
0.046
b0.001
b0.001
0.005
b0.001
b0.001
b0.001
b0.001
b0.001
b0.001
b0.001
b0.001
Abbreviation: SES, socioeconomic status.
low individual SES in advantaged and disadvantaged neighborhoods also demonstrated a large increase in mortality risk (HR = 1.76 and 1.83, respectively). No statistically significant differences in mortality rates were found between AMI patients with moderate and high
individual SES. In addition, we have calculated cumulative readmission risk (Appendix S1) and the adjusted hazard ratios for major adverse cardiovascular events (MACE) during the 6-month, 1-year and 2-year follow-up periods (Appendix S2). The rate of hospitalization from
J.-Y. Wang et al. / International Journal of Cardiology 172 (2014) 82–87
Fig. 1. a. The combined effect of individual and neighborhood SES on mortality rates in male acute myocardial infarction patients. b. The combined effect of individual and neighborhood SES on mortality rates in female acute myocardial infarction patients.
MACE did not differ significantly between the different individual and neighborhood SES groups. Baseline sociodemographic characteristics by individual and neighborhood SES are presented in Table 4. AMI patients in disadvantaged neighborhoods had lower resources of health care (e.g., physicians and pharmacists per 10,000 residents). AMI patients in advantaged neighborhoods also had a higher level of education (percentage of education ≥ high school), a higher median household income, and a lower mortality rate. 4. Discussion This study examined the combined effect of individual and neighborhood SES on the risk of all-cause mortality and readmission from MACE in AMI patients in a population-based study under a national
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health insurance system in Taiwan. AMI patients with low individual SES had significantly increased all-cause mortality after adjustment for a variety of comorbidities. Our findings are consistent with the results of other studies investigating the relationship between individual SES and post-AMI survival in other populations [10,12,18,20–24]. For example, Stjärne et al. noted that low-personal disposable income and lowincome level in the neighborhood seemed to have an additive effect on cardiovascular outcomes in Sweden [10]. Similar results have been found in developing countries in the African region [18]. Several plausible mechanisms may explain why low SES was associated with increased all-cause mortality. In this study, patients with lower individual SES tended to have a greater number of co-morbidities and cardiovascular risk factors, except dyslipidemia. Lower utilization rates of CAG and PCI in the low individual SES group may contribute to worse outcomes [24,46,47]. Patients with low SES may also have a greater chance of dying from other factors, such as sepsis, pneumonia, or other co-morbidities. Moreover, social isolation, a low sense of control, and occupational stress are more prevalent among patients with low SES. However, the risk of AMI mortality associated with individual SES was not consistent across different SES groups in the current study. Patients with moderate individual SES did not have a higher risk of mortality than those with high individual SES residing in advantaged neighborhoods. The current findings regarding combined individual and neighborhood SES effects on AMI mortality are inconsistent with previous studies. An analysis based on the records of 51,591 patients hospitalized for AMI in Canadian health system from 1994 to 1997 showed that a significant gradient in mortality across income quintiles [27]. The type of medical coverage may affect patients' treatment and outcome. In patients with AMI, analyses from the French National Health insurance showed that short-term mortality rate was higher and longterm adherence to therapy was lower in uninsured patients, compared with insured patients [25]. The NHI program in Taiwan aimed to eliminate primary health inequality, and may play an important role in closing the medical accessibility gap between patients based on SES. In Taiwan, co-payments are notably low and homogeneously priced which has made cost less of a barrier to receiving proper medical care. AMI patients with low individual SES living in disadvantaged neighborhoods had the highest risk of death after adjustment—risk of death in these cases was increased by up to 83% in males and 137% in females compared with high SES patients in advantaged neighborhoods. Several previous studies examining the cross-level interaction between individual and neighborhood SES on CVD and mortality have produced diverse results [48–50]. Subjects of low individual SES in high neighborhood SESs might experience a lower risk of mortality than subject of low individual SES in low neighborhood SES, because they benefit from the more healthcare-related sources in their neighborhoods [48]. Alternatively, subjects of low individual SES in high neighborhood SES might experience a higher risk of dying because of relative deprivation, low relative social standing, or both [50]. In our study, cross-level interaction between individual and neighborhood SES was noted after univariate analysis. However, AMI patients with low individual SES living in disadvantaged neighborhoods still had the highest risk of death after adjustment for covariates. This finding is consistent with previous analysis in the Atherosclerosis Risk in Communities Study (ARIC) [48]. This study has several limitations. First, the diagnosis of non-ST segment elevation myocardial infarction (NSTEMI), and ST segment elevation myocardial infarction (STEMI) could not be differentiated further due to the limitations of the ICD-9-CM system and the lack of actual clinical data in the NHIRD. Furthermore, we do not have data on patients with silent or unrecognized AMI, or patients with out-of-hospital sudden cardiac deaths. Second, the database does not contain information on tobacco use, dietary habits or other behavioral factors, which may be risk factors and prognostic factors for AMI patients. Third, the Killip classification, the risk stratification of AMI [51], was not included in the dataset. Fourth, the use of dichotomies to classify neighborhood SES is oversimplified. However, neighborhood SES is difficult
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Table 2 Cumulative mortality risk in acute myocardial infarction patients(n = 23,568). Events and cumulative risk Socioeconomic status
6-month
1-year
2-year
Risk (%)
Risk (%)
Risk (%)
(95% CI)
(95% CI)
(95% CI)
Male High SES in advantaged neighborhood(n = 3775) High SES in disadvantaged neighborhood (n = 2438) Moderate SES in advantaged neighborhood (n = 2346) Moderate SES in disadvantaged neighborhood (n = 3867) Low SES in advantaged neighborhood (n = 3186) Low SES in disadvantaged neighborhood (n = 3027)
5.2 (4.42–5.98) 3.4 (2.62–4.18) 6.1 (5.12–7.08) 5.4 (4.62–6.18) 11.8 (10.62–12.98) 10.2 (9.02–11.38)
6.1 (5.32–6.88) 4.1 (3.32–4.88) 7.1 (6.12–8.08) 5.9 (5.12–6.68) 13.2 (12.02–14.38) 11.3 (10.12–12.48)
7.1 (6.32–7.88) 4.8 (4.02–5.58) 8.3 (7.12–9.48) 6.7 (5.92–7.48) 14.8 (13.62–15.98) 12.8 (11.62–13.98)
Female High SES in advantaged neighborhood (n = 1082) High SES in disadvantaged neighborhood (n = 561) Moderate SES in advantaged neighborhood (n = 647) Moderate SES in disadvantaged neighborhood (n = 996) Low SES in advantaged neighborhood (n = 814) Low SES in disadvantaged neighborhood (n = 829)
5.3 (3.93–6.67) 5 (3.24–6.76) 7.1 (5.14–9.06) 4.4 (3.03–5.77) 13.2 (10.85–15.55) 10.5 (8.34–12.66)
6.3 (4.93–7.67) 5.7 (3.74–7.66) 7.9 (5.74–10.06) 5.3 (3.93–6.67) 14.8 (12.45–17.15) 11.5 (9.34–13.66)
7.3 (5.73–8.87) 6.1 (4.14–8.06) 9 (6.84–11.16) 6.3 (4.73–7.87) 16.1 (13.55–18.65) 12.8 (10.45–15.15)
Abbreviations: 95% CI, 95% confidence interval; SES, socioeconomic status.
Table 3 Hazard ratios of individual socioeconomic status for mortality in advantaged and disadvantaged neighborhoodsa. Socioeconomic status
6-month mortality
1-year mortality
2-year mortality
Adjusted
95%
Adjusted
95%
Adjusted
95%
HR
CI
HR
CI
HR
CI
Male High SES in advantaged neighborhood High SES in disadvantaged neighborhood Moderate SES in advantaged neighborhood Moderate SES in disadvantaged neighborhood Low SES in advantaged neighborhood Low SES in disadvantaged neighborhood
1 0.84 1.00 1.20 1.76 1.83
0.64–1.08 0.81–1.24 0.97–1.49 1.48–2.09 1.51–2.22
1 0.84 0.99 1.11 1.65 1.69
0.66–1.06 0.81–1.21 0.91–1.36 1.40–1.94 1.41–2.02
1 0.85 0.99 1.07 1.59 1.64
0.68–1.06 0.82–1.20 0.88–1.29 1.37–1.85 1.39–1.94
Female High SES in advantaged neighborhood High SES in disadvantaged neighborhood Moderate SES in advantaged neighborhood Moderate SES in disadvantaged neighborhood Low SES in advantaged neighborhood Low SES in disadvantaged neighborhood
1 1.22 1.20 1.06 2.05 2.37
0.77–1.93 0.81–1.78 0.68–1.65 1.48–2.84 1.66–3.40
1 1.20 1.12 1.14 1.94 2.26
0.78–1.84 0.77–1.61 0.76–1.70 1.43–2.63 1.62–3.15
1 1.07 1.07 1.06 1.83 2.02
0.71–1.61 0.76–1.50 0.73–1.54 1.37–2.43 1.47–2.76
Abbreviations: Adjusted HR, adjusted hazard ratio; 95% CI, 95% confidence interval; SES, socioeconomic status. a Adjusted for the patient gender, age, urbanization, geographic region, co-morbidities, and hospital characteristics.
to measure and no single measure to stratify risk in patients is better than the others [52]. Finally, the rates of coronary angiography and intervention in AMI patients are lower than what were predicted because of the incomplete ICD coding in real-world practice.
5. Conclusion Although Taiwan's National Health Insurance has reduced financial barriers to medical care, disparities in medical care of AMI remain. Our
Table 4 Sociodemographic characteristics by individual and neighborhood socioeconomic status (n = 23,568). High individual SES
Number of patients Number of deaths (%) Mean age, mean ± SD Male gender, % Education≧high school, % Median household income, NT$1000 Health care-related resources Physicians per 10,000 persons, mean ± SD Pharmacists per 10,000 persons, mean ± SD a
Pearson Chi-Square.
Moderate individual SES
Low individual SES
P value
Advantaged neighborhood
Disadvantaged neighborhood
Advantaged neighborhood
Disadvantaged neighborhood
Advantaged neighborhood
Disadvantaged neighborhood
4857 347 (7.1) 54 ± 12 77.7 99.4 617 ± 72
2999 152 (5.1) 55 ± 12 81.3 83.9 497 ± 33
2993 253 (8.5) 54 ± 11 78.4 98.8 596 ± 58
4863 322 (6.6) 59 ± 12 79.5 57.3 479 ± 37
4000 603 (15.1) 57 ± 12 79.7 99 606 ± 61
3856 494 (12.8) 56 ± 12 78.5 71.9 488 ± 37
b0.001a b0.001 b0.001a b0.001a b0.001
26 ± 21 9 ± 12
12 ± 12 4±4
24 ± 19 7±7
10 ± 10 3±3
24 ± 21 8 ± 10
11 ± 10 4±3
b0.001 b0.001
J.-Y. Wang et al. / International Journal of Cardiology 172 (2014) 82–87
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