Equity in use of procedures and survival among AMI-patients: Evidence from a modern welfare state*

Equity in use of procedures and survival among AMI-patients: Evidence from a modern welfare state* Terje P. Hagen 1, Unto Häkkinen 1,2, Tor Iversen 1 and Tron Anders Moger 1 1 Department of Health Management and Health Economics, University of Oslo, Oslo 2 Centre for Health and Social Economics (CHESS), National Institute for Health and Welfare (THL), Helsinki Paper prepared for the 1 st ICPP conference, Grenoble, 26 th - 28 th June 2013. First rough draft: 4 th June 2013. Please do not site! * The paper in funded by the Norwegian Research Council (project 191390 More for less ) and EUs 7 th Framework Programme (project 241721 EuroHOPE ).

Abstract Objective: Variation in the use of procedures and survival differs by patients socioeconomic status (SES) in elective treatment like for example treatment of cancer. By this study we make a detailed analyses of determinants both of the use of technology, in particular percutaneous coronary interventions (PCI), and hospital differences in outcomes among acute myocardial infarction (AMI) patients in Norway. In addition to the analyses of the effects of SES the paper present an innovative approach to risk adjustment based on the patients co-morbidities (COM). Data and methods: Micro-data from the national patient register describing treatment episodes are linked with data from prescription registers, death cause registers, databases describing travel distances and registers covering education and income. Use of procedures are intermediate variable that lies on the causal pathway between SES- and COM-variables and survival. By structural equation modelling we quantify both direct and indirect effects along these pathways. Results: Preliminary analyses indicate that both use of procedures and mortality differs with SES. Although some significant effects, the effects are small in size. 1

Introduction Low socioeconomic status (SES) are in numerous studies shown to affect both access to cardiac procedures like angiography and percutan coronary interventions (PCI), and mortality adversely (1, 2). However, most of these studies either originate from health care systems where private insurance play a major role (3, 4) or are based on data from periods were cardiac treatment facilities were less developed (5). This analysis is based on 2009-data from Norway, a tax funded health care system where equal access to health care services is a major aim. In addition to analyzing the effects of SES the paper present an innovative approach to risk adjustment based on the patients co-morbidities (COM) where co-morbidities are registered either from previous hospitalizations or on data from the national prescription register. We also include variables describing travel distances from the patient home municipality to nearest local hospital and to nearest PCI facility. Travel distances are of specific interest since Norway implemented a relatively centralized structure of PCI centres in the late 1990s. Because of complex statistical relationships between SES, COM, travel distances, use of cardiac procedures and mortality, traditional regression analyses are not well suited for estimation. In particular, the effects of SES on mortality might be underestimated because the use of procedures are intermediate variables that lies in the causal pathway between SES, COM and travel distances on the one side and mortality on the other. SES may therefore have both direct effects on mortality and indirect effects on mortality by affecting the probability of receiving specific cardiac procedures. Structural equation modeling (SEM) is a statistical method that allows us to investigate such complex relationships by quantifying effects along direct and indirect pathways and thus finally determine the total effect on the variable of interest to the outcome. To our knowledge no former studies has used structural equation modeling to assess the relationships between SES, COM, travel distances, use of procedures and mortality (see however (6) for a SEM analyses of depression and AMI mortality). Institutional setting 2

Data and methods Basic concepts and data sources By making use of available databases through a disease based approach, this project - the EuroHOPE project (European Health Care Outcomes, Performance and Efficiency) - evaluates the performance of European health care systems in terms of outcomes, quality, use of resources and cost. EuroHOPE uses patient-level data available from linkable national or regional registers and other data sources that allow for measuring the outcome in terms of mortality and the use of resources (such as use of procedures) in the selected well-defined and risk adjusted patient groups. The concept of a hospital episode is crucial to the project. The hospital episode included definition of start (the index day) and finish dates for the treatment as well as inclusion and exclusion criteria. For the AMI patients the episode started with the first inpatient day (the index day) and terminated with the first discharge home, death, or after 90 days of continuous inpatient care. Patient transferrals between hospitals were included in one episode if the time between the hospitals stays were <= 1 day. Included patients were registered according to the International Classification of Diseases (ICD) 10. The definitions of AMI patients comprised I21 and I22. Patients aged 18 and above admitted to hospital with AMI in Norway during 2009 were included. Patients admitted with AMI during the past 365 days prior to the index day were excluded, as well as patients dying within the first 2 days (explained below). Modelling approach The SEM model is pictured in Figure 1. Variables acting only as dependent variables in the SEM are indicators of mortality within the first 14 days after primary admission to hospital and mortality within 365 days after primary admission to hospital, given that the patient has survived the first two weeks. Variables acting both as dependents and independents are indicators of angiography within 14 days of admission and PCI within 14 days of admission. Independent variables are divided into two groups. Background variables are age, gender, list of comorbidities and distance to nearest PCI/angiography facility. Variables describing socio-economic status (SES) include income, wealth, education level and disability pension. The structure of the SEM 3

model comes from the fact that all patients in the data undergoing a PCI undergo an angiography first. All variables are categorized, and an overview is given in table 1. For age the categories are 18-49 years, then five year intervals up to 89 years and finally age 90+. For the comorbidities, we include indicators on whether the comorbidity is present or not based on hospital admissions with the comorbidity as main or secondary diagnosis and medication purchases for the comorbidity during the year prior to AMI admission. For distance from the patients resident municipality to the nearest angiography/pci facility, we use the travel distance in minutes by car as given by Google Maps. In Norway, patients living within 90 minutes of the facility by car or by helicopter are in the catchment area. We use 60 minute categories up to 180 minutes+ for this variable. For income and wealth, we use the average gross value for the years 2000-2008. The categories are NKR 0-150000, 150-300000, 300-500000 and 500000+. Education is categorized into primary, secondary, and university/college education. Finally, disability pension is included as an indicator on whether the patient has received it or not prior to 2009 (i.e. in any year since start of records in 1960. The relationship between the background/ses variables and variables within these two groups is left unanalyzed in the SEM. The background variables are adjusted for in all analyses, as we would like to remove the effect of those when studying the effects of the SES. Hence, the terms total, direct and indirect effects are used in relation to the SES variables influence on angiography, PCI and mortality, but always adjusted for effects of background variables. Statistical analysis The SEM approach used in the paper, is a pragmatic one. We use linear regression to estimate the effects of variables. See Hellevik (2009) for a review of linear regression for binary outcomes. In order to get some information on the time effects, the analysis is divided into two parts: First mortality within 14 days of primary admission is used as the final outcome, giving effects of the SES on early mortality. Second, mortality within 365 days given survival within the first two weeks is the final outcome, studying more long-term effects. The choice of using 14 days and 365 days as cut-off values was based on having a significant number of deaths in each model, a sufficiently short first time period, and at the same time avoiding some of the long-term effects occurring assumed to occur after 365 days due to e.g. better lifestyle and resources of patients with higher SES. More than 95% of patients receiving angiography/pci in the data 4

receive it during the first two weeks. Finally, as the time aspect is not taken care of in detail in this model, we excluded deaths occurring within two days of admission. This is done to remove the problem of patients dying before being evaluated for PCI/angiography in the data and removes 10% (199 cases) of the deaths occurring within the first 365 days. As mentioned in the previous section, the evaluation should be done quickly, and patients living further away from the nearest angiography/pci facility than 90 minutes by helicopter are put on thrombolytic treatment instead. They may however receive angiography/pci later. Also, one may argue that if a patient dies within the first two days, the case is so acute that the patient would likely die regardless of SES and is as such not relevant for the research question. The data were analyzed using Stata version 12. The category with the lowest SES for each SES is used as baseline (i.e. lowest income and wealth category, lowest education level), except for disability pension where no is baseline. Results 10524 patients were included. For descriptive statistics see Table 1. 525 patients (5.0%) died within 14 days, leaving 9999 patients for the second part of the SEM of which 1267 (12.7%) died within 365 days. The regression coefficients for each model fitted in the SEM is given in the Appendix while the main results are presented in Table 2. Tables 3 and 4 (appendix) show the results of an analysis of the total effects of SES on mortality, i.e. adjusted for background variables but not for angiography/pci within 14 days. From the coefficients in table 3 and 4, it is apparent that only income give any substantial effects among the SES for 14 day mortality, with e.g. patients in the income category NKR300-500000 having 2.8% lower risk of dying than individuals in the baseline category NKR0-150000. This effect seems to be reduced for 365 days mortality given survival of the first two weeks. However, some of the wealth indicators are significant instead, with patients in the highest wealth category having 2.2% lower risk. In tables 5 and 6 the direct effects of SES on mortality can be found, i.e. adjusted for angiography/pci within 14 days. The direct effect of income on mortality is still fairly substantial for 7 day mortality, but neither of the SESs are significant for 365 days mortality given survival of the first two weeks. Tables 7 and 8 show the effect of SES adjusted for background variables on angiography within 14 days, for all patients and patients having 5

survived the first week, respectively. Most SES categories give 2-3% higher likelihood of receiving an angiography compared to baseline, not substantial compared to the overall average of 59% of patients having received angiography within the first two weeks (Table 1). Tables 9 and 10 show the direct effects of SES on PCI within 14 days, i.e. adjusted for angiography within 14 days. Only disability pension show an effect of more than 2% in these tables. Finally, tables 11 and 12 show the total effects of SES on PCI within 14 days. We see that the total effect for disability pension and income on PCI is quite much larger than the direct effects. Using both the coefficients for these variables on angiography from tables 7 and 8 and the angiography estimate on PCI in tables 9 and 10, one finds that most of the difference between the total and direct effect comes from the indirect path through angiography. Table 2 gives an overview of the contributions of direct, indirect and unanalyzed effects to the total effects of SES for both two to 14 days mortality and 14 to 365 days mortality. We see that the effects are mainly direct. However, when bearing in mind that there is an overall average of 5% mortality in the two to 14 days period and 12.7% mortality in the 14 to 365 days period, the overall effects of SES on mortality are small. The only exception worth mentioning, is income for short term mortality. Discussion 6

References: 1. Payne N, Saul C. Variations in use of cardiology services in a health authority: Comparison of coronary artery revascularisation rates with prevalence of angina and coronary mortality. Brit Med J. 1997 Jan 25;314(7076):257-61. PubMed PMID: WOS:A1997WE63300023. 2. Alter DA, Naylor CD, Austin P, Tu JV. Effects of socioeconomic status on access to invasive cardiac procedures and on mortality after acute myocardial infarction. New Engl J Med. 1999 Oct;341(18):1359-67. PubMed PMID: WOS:000083357800006. English. 3. Bernheim SM, Spertus JA, Reid KJ, Bradley EH, Desai RA, Peterson ED, et al. Socioeconomic disparities in outcomes after acute myocardial infarction. American Heart Journal. 2007 Feb;153(2):313-9. PubMed PMID: WOS:000244047600026. 4. Popescu I, Vaughan-Sarrazin MS, Rosenthal GE. Differences in mortality and use of revascularization in black and white patients with acute MI admitted to hospitals with and without revascularization services. JAMA : the journal of the American Medical Association. 2007 Jun 13;297(22):2489-95. PubMed PMID: 17565083. 5. Rosvall M, Chaix B, Lynch J, Lindstrom M, Merlo J. The association between socioeconomic position, use of revascularization procedures and five-year survival after recovery from acute myocardial infarction. Bmc Public Health. 2008 Feb;8. PubMed PMID: WOS:000254550400001. 6. Thombs BD, Ziegelstein RC, Parakh K, Stewartc DE, Abbey SE, Grace SL. Probit structural equation regression model: general depressive symptoms predicted post-myocardial infarction mortality after controlling for somatic symptoms of depression. J Clin Epidemiol. 2008 Aug;61(8):832-9. PubMed PMID: WOS:000257720800013. English. 7

Figure 1 The structural equation model Age Gender Comorbidities Distance Unanalyzed PCI during first 7 days Mortality: 7 day 7-365 days Income Wealth Education Disability pension Angio during first 7 days 8

Variable % (n) Variable % (n) 14 day mortality 5.0% (525) Dementia 3.9% (408) 365 day mortality 17.0% (1792) Depression 12.5% (1320) 14 day angio 59.9% (6305) Parkinson's disease 1.1% (121) 14 day PCI 41.7% (4388) Mental disorders 3.2% (341) Gender male 62.0% (6526) Renal insufficiency 3.1% (323) Age: 18-49 7.3% (772) Alcoholism 0.8% (80) 50-54 6.2% (651) Stroke 3.5% (367) 55-59 8.2% (860) Distance: 0-60 min 46.3% (4874) 60-64 10.8% (1139) 60-120 min 23.1% (2431) 65-69 9.8% (1038) 120-180 min 10.7% (1131) 70-74 10.2% (1069) 180+ min 19.9% (2088) 75-79 12.1% (1277) Income: NKR0-150k 18.0% (1865) 80-84 13.5% (1422) 150-300k 54.5% (5643) 85-89 13.6% (1436) 300-500k 22.3% (2308) 90+ 8.2% (858) 500+k 5.2% (708) Hypertension 62.9% (6623) Wealth: NKR0-150k 18.2% (1889) Coronary artery disease 13.9% (1463) 150-300k 23.9% (2480) Atrial fibrillation 6.5% (685) 300-500k 25.1% (2608) Cardiac insufficiency 6.0% (629) 500+k 32.8% (3547) Diabetes mellitus 15.5% (1633) Education: primary 42.9% (4426) Atherosclerosis 1.9% (198) Secondary 44.9% (4640) Cancer 2.2% (229) Univ/college 12.2% (1263) COPD and asthma 16.7% (1752) Disability pension 16.2% (1703) Table 1. Descriptive statistics of the variables used in the structural equation modelling. N=10524. 9

Mortality between two and 14 days Mortality between 14 and 365 days SEV Direct Indirect Unanal. Total Direct Indirect Unanal. Total Income: NKR0-150k Baseline Baseline -1.8% 0.0% -0.2% -2.0%* -1.2% 0.0% -0.3% -1.5% 150-300k -2.6% 0.0% -0.1% -2.7%* -1.7% 0.0% -0.3% -2.0% 300-500k -1.4% 0.0% -0.1% -1.5% -0.4% 0.0% -0.2% -0.6% 500+k Wealth: NKR0-150k Baseline Baseline -0.1% 0.0% -0.2% -0.3% -0.5% 0.0% -0.3% -0.8% 150-300k 0.2% 0.0% -0.2% 0.0% -1.8% 0.0% -0.2% -2.0%* 300-500k 0.1% 0.0% -0.1% 0.0% -1.9% 0.0% -0.3% -2.2%* 500+k Education: primary Baseline Baseline -0.4% 0.0% -0.1% -0.5% 0.2% 0.0% 0.0% 0.2% Secondary -0.5% 0.0% -0.2% -0.7% -1.2% 0.0% -0.3% -1.5% Univ/college Disability pension yes 0.2% 0.0% 0.1% 0.3% 0.2% 0.0% 0.2% 0.4% Table 2. Path coefficients for the socio-economic variables, divided into direct effects, indirect effects as sum of effects going through both angiography/pci and PCI only, unanalyzed effects and total effects. Effects can be compared to overall rates of 5% dead within two and 14 days and 12.7% dead within two weeks and 365 days. *Denotes significant total effects based on the not neccessarily unbiased p-value from the linear regression of binary outcomes. 10

Appendix: Regression coefficients used to calculate the path coefficients in Table 2. d14 Coef. Std. Err. t P> t [95% Conf. Interval] male.004889.0046949 1.04 0.298 -.004314.014092 a00_49 -.1093951.0121965-8.97 0.000 -.1333026 -.0854876 a50_54 -.1130604.0126789-8.92 0.000 -.1379135 -.0882073 a55_59 -.1145832.0118619-9.66 0.000 -.1378349 -.0913315 a60_64 -.1128373.0112775-10.01 0.000 -.1349435 -.0907311 a65_69 -.100106.0113326-8.83 0.000 -.1223201 -.0778919 a70_74 -.089151.010582-8.42 0.000 -.1098938 -.0684082 a75_79 -.079044.0098068-8.06 0.000 -.0982672 -.0598208 a80_84 -.057617.0094175-6.12 0.000 -.0760772 -.0391567 a85_89 -.0319714.0092944-3.44 0.001 -.0501902 -.0137526 htn -.0068016.0048252-1.41 0.159 -.01626.0026568 cad -.0230197.0068612-3.36 0.001 -.0364689 -.0095705 af.015167.0094147 1.61 0.107 -.0032876.0336216 ci.0332012.010171 3.26 0.001.0132641.0531383 dm.0107478.0060266 1.78 0.075 -.0010655.0225611 ath.0438831.0156904 2.80 0.005.0131269.0746394 can -.0091204.014547-0.63 0.531 -.0376354.0193945 cpd.0002662.0058154 0.05 0.963 -.0111331.0116656 dem.0536309.01116 4.81 0.000.031755.0755067 dep.0071326.0065528 1.09 0.276 -.0057121.0199772 pd.0179662.0197551 0.91 0.363 -.0207576.05669 md.0345861.0122381 2.83 0.005.0105971.0585751 ri.0761373.012851 5.92 0.000.0509467.1013278 alc -.0268856.0254855-1.05 0.291 -.0768423.0230711 str.0215444.0117824 1.83 0.067 -.0015513.0446402 dist60_120.0072917.0054101 1.35 0.178 -.0033132.0178965 dist120_180.0028061.0071579 0.39 0.695 -.0112248.016837 dist180.0022541.0057092 0.39 0.693 -.008937.0134452 inc150_300 -.0200894.0062923-3.19 0.001 -.0324236 -.0077552 inc300_500 -.0277291.0086328-3.21 0.001 -.0446511 -.0108071 inc500 -.0154601.0126767-1.22 0.223 -.0403089.0093888 wealth150_300 -.0027658.0067179-0.41 0.681 -.0159342.0104025 wealth300_500.0004586.006843 0.07 0.947 -.012955.0138722 wealth500 -.0006703.0070767-0.09 0.925 -.0145421.0132015 edu_secondary -.0052146.0047431-1.10 0.272 -.014512.0040828 edu_univ -.0069356.0075624-0.92 0.359 -.0217595.0078882 dis_pens.0029798.007 0.43 0.670 -.0107416.0167011 _cons.1377253.0104394 13.19 0.000.1172621.1581886 Table 3. Total effects for the socio-economic variables on mortality within two to 14 days, i.e. not adjusted for angiography and PCI within 14 days. 11

d365 Coef. Std. Err. t P> t [95% Conf. Interval] male.0140391.0069071 2.03 0.042.0004998.0275785 a00_49 -.3177149.0180965-17.56 0.000 -.3531878 -.2822419 a50_54 -.3209296.018731-17.13 0.000 -.3576464 -.2842129 a55_59 -.3116088.0175777-17.73 0.000 -.3460647 -.2771529 a60_64 -.3046286.0167727-18.16 0.000 -.3375066 -.2717506 a65_69 -.2949761.0168555-17.50 0.000 -.3280163 -.2619358 a70_74 -.2668289.0158415-16.84 0.000 -.2978815 -.2357762 a75_79 -.2435241.0147846-16.47 0.000 -.272505 -.2145432 a80_84 -.1679182.0142979-11.74 0.000 -.1959451 -.1398913 a85_89 -.0679602.0141993-4.79 0.000 -.0957938 -.0401267 htn -.0001276.0070531-0.02 0.986 -.0139532.0136979 cad.0113796.0101133 1.13 0.261 -.0084446.0312039 af.0467206.0140753 3.32 0.001.01913.0743112 ci.1164788.0153337 7.60 0.000.0864216.1465361 dm.0339578.0088846 3.82 0.000.0165421.0513734 ath.0291596.0236899 1.23 0.218 -.0172774.0755967 can.2614221.0213247 12.26 0.000.2196211.303223 cpd.0250242.0085406 2.93 0.003.0082829.0417655 dem.157158.017166 9.16 0.000.123509.190807 dep.0227488.0096942 2.35 0.019.0037461.0417514 pd.029905.0294912 1.01 0.311 -.027904.0877141 md.0478833.0182224 2.63 0.009.0121636.0836029 ri.1729228.0198559 8.71 0.000.1340011.2118446 alc.0729629.0366934 1.99 0.047.0010361.1448897 str.0102417.0177243 0.58 0.563 -.0245015.044985 dist60_120 -.0118503.0079322-1.49 0.135 -.0273991.0036984 dist120_180.009646.0104904 0.92 0.358 -.0109174.0302095 dist180.0025365.0083688 0.30 0.762 -.0138681.018941 inc150_300 -.0153813.0093684-1.64 0.101 -.0337453.0029827 inc300_500 -.0197959.0126392-1.57 0.117 -.0445714.0049797 inc500 -.0062262.0184657-0.34 0.736 -.0424228.0299704 wealth150_300 -.0081877.0098588-0.83 0.406 -.0275131.0111377 wealth300_500 -.0204238.0100622-2.03 0.042 -.0401478 -.0006998 wealth500 -.0221616.0104112-2.13 0.033 -.0425697 -.0017534 edu_secondary.0018623.0069631 0.27 0.789 -.0117869.0155116 edu_univ -.0145737.0110048-1.32 0.185 -.0361455.0069981 dis_pens.0041047.0100997 0.41 0.684 -.0156928.0239021 _cons.3298184.0157773 20.90 0.000.2988916.3607451 Table 4. Total effects for the socio-economic variables on mortality within 365 days given survival of the two weeks, i.e. not adjusted for angiography and PCI within 14 days. 12

d14 Coef. Std. Err. t P> t [95% Conf. Interval] male.0080635.0046801 1.72 0.085 -.0011103.0172374 a00_49 -.062167.0128181-4.85 0.000 -.0872929 -.037041 a50_54 -.063742.0133294-4.78 0.000 -.0898702 -.0376138 a55_59 -.0668389.0125194-5.34 0.000 -.0913793 -.0422985 a60_64 -.0670222.0119156-5.62 0.000 -.090379 -.0436653 a65_69 -.0544734.0119595-4.55 0.000 -.0779165 -.0310303 a70_74 -.0464059.0111728-4.15 0.000 -.0683069 -.024505 a75_79 -.0433423.0102432-4.23 0.000 -.0634209 -.0232637 a80_84 -.0362248.0095476-3.79 0.000 -.0549401 -.0175095 a85_89 -.0238347.0092647-2.57 0.010 -.0419953 -.0056741 htn -.0068177.0048032-1.42 0.156 -.016233.0025977 cad -.0265037.0068265-3.88 0.000 -.0398851 -.0131224 af.0113812.0093634 1.22 0.224 -.0069729.0297354 ci.0281938.0101182 2.79 0.005.0083602.0480275 dm.0083483.0059931 1.39 0.164 -.0033995.020096 ath.0404411.0155964 2.59 0.010.009869.0710132 can -.0220429.0145021-1.52 0.129 -.0504699.0063841 cpd -.0016026.005785-0.28 0.782 -.0129423.0097371 dem.0404967.0111513 3.63 0.000.0186379.0623556 dep.0039262.0065191 0.60 0.547 -.0088525.0167049 pd.0093651.0196483 0.48 0.634 -.0291494.0478796 md.0319154.012165 2.62 0.009.0080697.0557612 ri.0693759.0127858 5.43 0.000.0443132.0944385 alc -.0382606.0253488-1.51 0.131 -.0879494.0114281 str.013383.0117316 1.14 0.254 -.0096132.0363793 dist60_120.0055109.0053823 1.02 0.306 -.0050395.0160614 dist120_180 -.0004389.0071197-0.06 0.951 -.014395.0135172 dist180 -.0015299.0056857-0.27 0.788 -.0126751.0096153 inc150_300 -.0186404.0062549-2.98 0.003 -.0309012 -.0063796 inc300_500 -.0259382.0085813-3.02 0.003 -.0427592 -.0091173 inc500 -.0142846.0125989-1.13 0.257 -.038981.0104117 wealth150_300 -.0012075.0066777-0.18 0.857 -.0142972.0118822 wealth300_500.001715.0068016 0.25 0.801 -.0116174.0150474 wealth500.00113.0070349 0.16 0.872 -.0126598.0149199 edu_secondary -.004421.0047151-0.94 0.348 -.0136635.0048215 edu_univ -.0053312.0075179-0.71 0.478 -.0200678.0094054 dis_pens.0017894.0069591 0.26 0.797 -.0118519.0154306 ang_14 -.063649.0068328-9.32 0.000 -.0770427 -.0502553 pci_14.0005697.0059424 0.10 0.924 -.0110785.0122179 _cons.143537.0103953 13.81 0.000.1231602.1639138 Table 5. Direct effects of socio-economic variables on mortality within two and 14 days, i.e. adjusted for both angiography and PCI within 14 days. 13

d365 Coef. Std. Err. t P> t [95% Conf. Interval] male.0198519.00687 2.89 0.004.0063852.0333186 a00_49 -.2381696.0189432-12.57 0.000 -.2753023 -.2010369 a50_54 -.2381185.0196199-12.14 0.000 -.2765776 -.1996594 a55_59 -.2313758.0184743-12.52 0.000 -.2675892 -.1951624 a60_64 -.2276488.0176392-12.91 0.000 -.2622254 -.1930722 a65_69 -.2175585.0177292-12.27 0.000 -.2523114 -.1828055 a70_74 -.1939058.0166702-11.63 0.000 -.2265829 -.1612287 a75_79 -.1821294.0153952-11.83 0.000 -.2123072 -.1519515 a80_84 -.1301237.0144668-8.99 0.000 -.1584817 -.1017658 a85_89 -.0531456.0141219-3.76 0.000 -.0808275 -.0254638 htn -.0000915.0070041-0.01 0.990 -.0138211.0136381 cad.005067.0100384 0.50 0.614 -.0146104.0247444 af.0396341.0139654 2.84 0.005.0122591.0670091 ci.1068635.0152196 7.02 0.000.07703.1366971 dm.0305876.0088117 3.47 0.001.0133149.0478603 ath.0236949.0234888 1.01 0.313 -.0223482.0697379 can.2400147.0212042 11.32 0.000.1984501.2815793 cpd.0219363.0084744 2.59 0.010.0053246.038548 dem.1340819.0171087 7.84 0.000.1005453.1676186 dep.0170585.0096215 1.77 0.076 -.0018016.0359186 pd.014536.0292611 0.50 0.619 -.042822.0718939 md.0447214.018067 2.48 0.013.0093063.0801365 ri.1624033.0197007 8.24 0.000.1237857.2010209 alc.0535354.0364074 1.47 0.141 -.0178306.1249015 str -.0038102.0176036-0.22 0.829 -.0383171.0306966 dist60_120 -.0150108.0078722-1.91 0.057 -.030442.0004204 dist120_180.0039772.0104093 0.38 0.702 -.0164273.0243817 dist180 -.0044448.0083166-0.53 0.593 -.0207469.0118574 inc150_300 -.0125906.00929-1.36 0.175 -.030801.0056198 inc300_500 -.0166463.0125325-1.33 0.184 -.0412127.0079201 inc500 -.0035142.0183072-0.19 0.848 -.0394.0323717 wealth150_300 -.0053604.0097759-0.55 0.583 -.0245233.0138024 wealth300_500 -.0182362.0099765-1.83 0.068 -.0377923.0013199 wealth500 -.0192134.0103239-1.86 0.063 -.0394503.0010236 edu_secondary.0025869.0069043 0.37 0.708 -.010947.0161209 edu_univ -.0122015.0109125-1.12 0.264 -.0335923.0091893 dis_pens.0024874.0100154 0.25 0.804 -.017145.0221197 ang_14 -.1058682.0099425-10.65 0.000 -.1253576 -.0863787 pci_14 -.0015525.0085191-0.18 0.855 -.0182517.0151467 _cons.3400955.0156726 21.70 0.000.3093739.3708171 Table 6. Direct effects of socio-economic variables on mortality within 365 days given survival of the first two weeks, i.e. adjusted for both angiography and PCI within 14 days. 14

ang_14 Coef. Std. Err. t P> t [95% Conf. Interval] male.0505233.0083044 6.08 0.000.0342451.0668014 a00_49.7467381.021573 34.61 0.000.7044507.7890255 a50_54.7793917.0224263 34.75 0.000.7354318.8233516 a55_59.7548951.0209813 35.98 0.000.7137676.7960225 a60_64.7244212.0199476 36.32 0.000.68532.7635225 a65_69.7210551.0200449 35.97 0.000.6817631.7603471 a70_74.6752543.0187173 36.08 0.000.6385646.711944 a75_79.5639634.0173461 32.51 0.000.5299615.5979652 a80_84.3379733.0166576 20.29 0.000.305321.3706255 a85_89.1286082.0164398 7.82 0.000.0963829.1608334 htn -.0006703.0085348-0.08 0.937 -.0174002.0160596 cad -.0552284.012136-4.55 0.000 -.0790173 -.0314395 af -.0600373.0166526-3.61 0.000 -.0926797 -.0273949 ci -.0792858.0179903-4.41 0.000 -.1145504 -.0440211 dm -.0378763.0106598-3.55 0.000 -.0587715 -.016981 ath -.0544921.027753-1.96 0.050 -.1088935 -.0000907 can -.2040862.0257305-7.93 0.000 -.2545232 -.1536493 cpd -.0298251.0102862-2.90 0.004 -.0499882 -.0096621 dem -.2076329.0197398-10.52 0.000 -.2463267 -.1689391 dep -.0508223.0115904-4.38 0.000 -.0735419 -.0281027 pd -.136194.0349426-3.90 0.000 -.2046883 -.0676996 md -.0420792.0216466-1.94 0.052 -.0845107.0003524 ri -.106719.0227308-4.69 0.000 -.1512758 -.0621621 alc -.1801265.0450786-4.00 0.000 -.2684894 -.0917636 str -.1289673.0208405-6.19 0.000 -.1698189 -.0881158 dist60_120 -.0284331.0095693-2.97 0.003 -.0471908 -.0096754 dist120_180 -.0511766.0126608-4.04 0.000 -.0759943 -.0263588 dist180 -.0600348.0100983-5.95 0.000 -.0798295 -.04024 inc150_300.0228343.0111298 2.05 0.040.0010176.0446509 inc300_500.0284276.0152696 1.86 0.063 -.0015039.0583591 inc500.0186756.0224225 0.83 0.405 -.0252768.062628 wealth150_300.0246163.0118825 2.07 0.038.0013242.0479084 wealth300_500.0198386.0121038 1.64 0.101 -.0038872.0435644 wealth500.0283733.0125173 2.27 0.023.0038371.0529096 edu_secondary.0124087.0083895 1.48 0.139 -.0040364.0288538 edu_univ.0251768.0133763 1.88 0.060 -.0010435.0513971 dis_pens -.0190423.0123815-1.54 0.124 -.0433124.0052278 _cons.0924583.018465 5.01 0.000.0562632.1286535 Table 7. Direct effects of socio-economic variables on angiography within 14 days for all patients. 15

ang_14 Coef. Std. Err. t P> t [95% Conf. Interval] male.053789.0085322 6.30 0.000.0370641.0705139 a00_49.7437133.0223543 33.27 0.000.6998942.7875323 a50_54.7748938.0231381 33.49 0.000.7295382.8202493 a55_59.7501433.0217134 34.55 0.000.7075806.792706 a60_64.7196771.020719 34.74 0.000.6790635.7602906 a65_69.7245496.0208213 34.80 0.000.6837356.7653637 a70_74.6827652.0195687 34.89 0.000.6444065.7211239 a75_79.5748972.0182631 31.48 0.000.5390976.6106968 a80_84.3537892.0176619 20.03 0.000.3191681.3884103 a85_89.1386014.0175401 7.90 0.000.1042191.1729837 htn.0010401.0087125 0.12 0.905 -.0160383.0181186 cad -.0587983.0124928-4.71 0.000 -.0832868 -.0343097 af -.0659467.017387-3.79 0.000 -.1000289 -.0318646 ci -.089652.0189414-4.73 0.000 -.1267811 -.0525229 dm -.0316245.010975-2.88 0.004 -.0531377 -.0101113 ath -.0509011.0292636-1.74 0.082 -.108264.0064618 can -.2005233.026342-7.61 0.000 -.2521592 -.1488873 cpd -.0284087.01055-2.69 0.007 -.0490889 -.0077285 dem -.2157547.0212049-10.17 0.000 -.2573207 -.1741887 dep -.052943.011975-4.42 0.000 -.0764166 -.0294694 pd -.1432304.03643-3.93 0.000 -.2146408 -.07182 md -.0298538.0225097-1.33 0.185 -.0739776.01427 ri -.0985629.0245276-4.02 0.000 -.1466422 -.0504835 alc -.1811834.0453267-4.00 0.000 -.2700332 -.0923336 str -.1314317.0218944-6.00 0.000 -.1743494 -.088514 dist60_120 -.0290823.0097985-2.97 0.003 -.0482894 -.0098752 dist120_180 -.0532212.0129587-4.11 0.000 -.0786228 -.0278195 dist180 -.0649072.0103378-6.28 0.000 -.0851714 -.0446431 inc150_300.0262208.0115726 2.27 0.023.0035361.0489055 inc300_500.0292904.015613 1.88 0.061 -.0013144.0598952 inc500.0252359.0228103 1.11 0.269 -.0194771.0699489 wealth150_300.0264757.0121784 2.17 0.030.0026034.050348 wealth300_500.0205171.0124296 1.65 0.099 -.0038476.0448818 wealth500.0277105.0128608 2.15 0.031.0025007.0529204 edu_secondary.0070003.0086014 0.81 0.416 -.0098604.0238609 edu_univ.0224867.0135941 1.65 0.098 -.0041605.0491339 dis_pens -.0147505.0124759-1.18 0.237 -.039206.0097049 _cons.095065.0194893 4.88 0.000.0568617.1332682 Table 8. Direct effects of socio-economic variables on angiography within 14 days for patients having survived the first two weeks. 16

pci_14 Coef. Std. Err. t P> t [95% Conf. Interval] male.0387412.0078091 4.96 0.000.0234339.0540485 a00_49.0322306.0214115 1.51 0.132 -.0097402.0742014 a50_54 -.010399.0222678-0.47 0.641 -.0540483.0332504 a55_59.0322314.0209124 1.54 0.123 -.0087611.0732239 a60_64.0340108.0199033 1.71 0.088 -.0050035.0730252 a65_69 -.0193916.0199787-0.97 0.332 -.0585538.0197706 a70_74 -.0374985.0186616-2.01 0.045 -.0740789 -.000918 a75_79 -.0340845.0171089-1.99 0.046 -.0676213 -.0005477 a80_84 -.0147383.0159496-0.92 0.355 -.0460027.0165261 a85_89.0007057.0154776 0.05 0.964 -.0296334.0310449 htn -.0462581.0080112-5.77 0.000 -.0619615 -.0305546 cad -.0180984.011403-1.59 0.113 -.0404505.0042537 af -.022492.0156409-1.44 0.150 -.0531513.0081673 ci -.0159426.0169027-0.94 0.346 -.0490753.0171901 dm.005372.010012 0.54 0.592 -.0142535.0249975 ath -.0100784.0260552-0.39 0.699 -.0611518.040995 can.0172078.0242266 0.71 0.478 -.0302811.0646968 cpd -.0320547.0096591-3.32 0.001 -.0509886 -.0131209 dem -.0050674.0186294-0.27 0.786 -.0415847.0314499 dep -.0160786.0108896-1.48 0.140 -.0374244.0052673 pd -.0280449.0328233-0.85 0.393 -.092385.0362952 md.014549.0203223 0.72 0.474 -.0252867.0543847 ri.0162177.0213593 0.76 0.448 -.0256508.0580862 alc -.0380219.0423461-0.90 0.369 -.1210287.0449848 str.002704.0195988 0.14 0.890 -.0357135.0411214 dist60_120 -.0320586.0089861-3.57 0.000 -.0496732 -.0144441 dist120_180.0123723.0118936 1.04 0.298 -.0109415.0356862 dist180 -.0253147.0094953-2.67 0.008 -.0439273 -.0067021 inc150_300 -.0074879.0104491-0.72 0.474 -.0279703.0129945 inc300_500.0135876.0143352 0.95 0.343 -.0145122.0416875 inc500.0108583.0210475 0.52 0.606 -.030399.0521155 wealth150_300 -.0014261.0111558-0.13 0.898 -.0232938.0204415 wealth300_500 -.0021787.0113627-0.19 0.848 -.0244518.0200944 wealth500 -.0090221.0117523-0.77 0.443 -.0320588.0140147 edu_secondary -.014863.0078756-1.89 0.059 -.0303008.0005748 edu_univ -.020204.0125578-1.61 0.108 -.0448199.0044118 dis_pens -.0253821.0116232-2.18 0.029 -.0481658 -.0025983 ang_14.664224.0093178 71.29 0.000.6459594.6824887 _cons.0671849.0173536 3.87 0.000.0331686.1012013 Table 9. Direct effects of socio-economic variables on PCI within 14 days for all patients, i.e. adjusted for angiography within 14 days. 17

pci_14 Coef. Std. Err. t P> t [95% Conf. Interval] male.0404108.0082036 4.93 0.000.0243301.0564915 a00_49.0273734.022647 1.21 0.227 -.0170196.0717663 a50_54 -.0159958.0234572-0.68 0.495 -.0619769.0299852 a55_59.027591.0220863 1.25 0.212 -.0157027.0708847 a60_64.0299802.0210875 1.42 0.155 -.0113557.071316 a65_69 -.0235571.0211959-1.11 0.266 -.0651055.0179912 a70_74 -.0414416.0199266-2.08 0.038 -.080502 -.0023812 a75_79 -.0396941.0184022-2.16 0.031 -.0757663 -.0036218 a80_84 -.0164068.0172959-0.95 0.343 -.0503103.0174968 a85_89 -.001195.0168843-0.07 0.944 -.0342918.0319018 htn -.0483204.0083598-5.78 0.000 -.0647073 -.0319335 cad -.0174517.0120007-1.45 0.146 -.0409757.0060722 af -.0237364.0166954-1.42 0.155 -.0564629.0089901 ci -.0202892.0181955-1.12 0.265 -.0559563.0153779 dm.0067479.0105351 0.64 0.522 -.0139031.0273989 ath -.0151164.0280831-0.54 0.590 -.0701652.0399324 can.0183563.0253513 0.72 0.469 -.0313375.0680501 cpd -.0328605.0101266-3.24 0.001 -.0527107 -.0130102 dem -.0076735.0204552-0.38 0.708 -.04777.0324231 dep -.0197318.0115018-1.72 0.086 -.0422777.0028141 pd -.0372043.0349829-1.06 0.288 -.1057781.0313696 md.0190084.0216003 0.88 0.379 -.0233327.0613494 ri.0108032.0235542 0.46 0.646 -.0353679.0569744 alc -.0380194.0435274-0.87 0.382 -.1233422.0473034 str -.0012437.0210471-0.06 0.953 -.0425005.0400131 dist60_120 -.0332231.009406-3.53 0.000 -.0516608 -.0147853 dist120_180.0132148.0124448 1.06 0.288 -.0111797.0376092 dist180 -.0274678.0099394-2.76 0.006 -.0469511 -.0079844 inc150_300 -.0079085.011107-0.71 0.476 -.0296805.0138634 inc300_500.0119014.0149836 0.79 0.427 -.0174695.0412723 inc500.0092235.0218881 0.42 0.673 -.0336817.0521287 wealth150_300 -.0019175.0116882-0.16 0.870 -.0248288.0209938 wealth300_500 -.0036589.011928-0.31 0.759 -.0270403.0197226 wealth500 -.0090622.012343-0.73 0.463 -.033257.0151327 edu_secondary -.0152783.0082534-1.85 0.064 -.0314567.0009002 edu_univ -.0203782.0130455-1.56 0.118 -.0459501.0051937 dis_pens -.026058.0119716-2.18 0.030 -.0495249 -.0025911 ang_14.6644751.0097726 67.99 0.000.6453187.6836315 _cons.0739019.0187233 3.95 0.000.0372003.1106034 Table 10. Direct effects of socio-economic variables on PCI within 14 days for patients having survived the first two weeks, i.e. adjusted for angiography within 14 days. 18

pci_14 Coef. Std. Err. t P> t [95% Conf. Interval] male.0722999.0095488 7.57 0.000.0535824.0910175 a00_49.528232.0248058 21.29 0.000.4796077.5768563 a50_54.5072917.0257869 19.67 0.000.4567442.5578392 a55_59.5336509.0241254 22.12 0.000.4863603.5809414 a60_64.5151889.0229368 22.46 0.000.4702282.5601495 a65_69.4595506.0230487 19.94 0.000.4143705.5047306 a70_74.4110217.0215222 19.10 0.000.3688339.4532095 a75_79.3405136.0199455 17.07 0.000.3014164.3796107 a80_84.2097517.0191539 10.95 0.000.1722063.247297 a85_89.0861304.0189034 4.56 0.000.049076.1231847 htn -.0467033.0098138-4.76 0.000 -.0659402 -.0274663 cad -.0547825.0139546-3.93 0.000 -.0821362 -.0274287 af -.0623702.0191481-3.26 0.001 -.0999042 -.0248362 ci -.0686061.0206863-3.32 0.001 -.1091553 -.0280569 dm -.0197863.0122572-1.61 0.107 -.0438128.0042402 ath -.0462733.0319119-1.45 0.147 -.108827.0162803 can -.1183512.0295863-4.00 0.000 -.1763462 -.0603561 cpd -.0518653.0118277-4.39 0.000 -.0750499 -.0286807 dem -.1429821.0226978-6.30 0.000 -.1874744 -.0984899 dep -.049836.0133273-3.74 0.000 -.0759602 -.0237118 pd -.1185082.0401788-2.95 0.003 -.1972667 -.0397497 md -.013401.0248904-0.54 0.590 -.0621911.0353891 ri -.0546676.0261371-2.09 0.037 -.1059014 -.0034338 alc -.1576663.0518338-3.04 0.002 -.2592707 -.0560618 str -.0829592.0239636-3.46 0.001 -.1299326 -.0359859 dist60_120 -.0509446.0110033-4.63 0.000 -.0725132 -.029376 dist120_180 -.0216204.0145581-1.49 0.138 -.0501571.0069164 dist180 -.0651912.0116116-5.61 0.000 -.0879523 -.0424301 inc150_300.0076792.0127977 0.60 0.548 -.0174068.0327652 inc300_500.0324699.0175578 1.85 0.064 -.0019469.0668868 inc500.023263.0257825 0.90 0.367 -.0272758.0738019 wealth150_300.0149246.0136632 1.09 0.275 -.0118578.0417071 wealth300_500.0109986.0139176 0.79 0.429 -.0162826.0382797 wealth500.0098242.014393 0.68 0.495 -.018389.0380373 edu_secondary -.0066208.0096467-0.69 0.493 -.0255303.0122886 edu_univ -.003481.0153808-0.23 0.821 -.0336305.0266685 dis_pens -.0380304.0142369-2.67 0.008 -.0659375 -.0101233 _cons.128598.0212321 6.06 0.000.0869789.1702171 Table 11. Total effects of socio-economic variables on PCI within 14 days for all patients, i.e. not adjusted for angiography within 14 days. 19

pci_14 Coef. Std. Err. t P> t [95% Conf. Interval] male.0761523.0099578 7.65 0.000.0566329.0956717 a00_49.5215523.0260893 19.99 0.000.4704117.5726929 a50_54.4989018.0270041 18.48 0.000.445968.5518356 a55_59.5260425.0253413 20.76 0.000.4763682.5757169 a60_64.5081877.0241808 21.02 0.000.4607882.5555872 a65_69.4578881.0243002 18.84 0.000.4102546.5055215 a70_74.4122389.0228383 18.05 0.000.367471.4570068 a75_79.3423108.0213146 16.06 0.000.3005296.384092 a80_84.2186773.020613 10.61 0.000.1782715.2590831 a85_89.0909022.0204708 4.44 0.000.0507751.1310293 htn -.0476293.0101683-4.68 0.000 -.0675613 -.0276973 cad -.0565217.0145802-3.88 0.000 -.0851019 -.0279415 af -.0675564.0202921-3.33 0.001 -.1073331 -.0277796 ci -.0798608.0221062-3.61 0.000 -.1231936 -.0365279 dm -.0142658.0128087-1.11 0.265 -.0393736.010842 ath -.0489389.0341532-1.43 0.152 -.1158862.0180085 can -.1148864.0307434-3.74 0.000 -.17515 -.0546229 cpd -.0517374.0123127-4.20 0.000 -.0758729 -.0276018 dem -.1510371.0247479-6.10 0.000 -.1995482 -.102526 dep -.0549111.0139759-3.93 0.000 -.0823068 -.0275154 pd -.1323773.0425169-3.11 0.002 -.2157194 -.0490352 md -.0008287.0262708-0.03 0.975 -.052325.0506675 ri -.0546894.0286259-1.91 0.056 -.110802.0014233 alc -.1584113.0529001-2.99 0.003 -.2621066 -.0547159 str -.0885768.0255527-3.47 0.001 -.1386654 -.0384881 dist60_120 -.0525475.0114357-4.60 0.000 -.0749638 -.0301312 dist120_180 -.0221494.0151239-1.46 0.143 -.0517953.0074966 dist180 -.070597.0120651-5.85 0.000 -.0942471 -.046947 inc150_300.0095146.0135062 0.70 0.481 -.0169604.0359895 inc300_500.0313641.0182217 1.72 0.085 -.0043543.0670825 inc500.0259921.0266216 0.98 0.329 -.0261917.078176 wealth150_300.015675.0142133 1.10 0.270 -.012186.043536 wealth300_500.0099742.0145064 0.69 0.492 -.0184615.0384099 wealth500.0093508.0150096 0.62 0.533 -.0200712.0387728 edu_secondary -.0106268.0100386-1.06 0.290 -.0303046.009051 edu_univ -.0054364.0158654-0.34 0.732 -.0365359.0256632 dis_pens -.0358594.0145605-2.46 0.014 -.0644009 -.0073178 _cons.1370702.0227457 6.03 0.000.0924837.1816566 Table 12. Total effects of socio-economic variables on PCI within 14 days for patients having survived the first two weeks, i.e. not adjusted for angiography within 14 days. 20