Equity in use of procedures and survival among AMI-patients: Evidence from a modern welfare state*
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1 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 First rough draft: 4 th June Please do not site! * The paper in funded by the Norwegian Research Council (project More for less ) and EUs 7 th Framework Programme (project EuroHOPE ).
2 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
3 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
4 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
5 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 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 The categories are NKR , , and 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 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
6 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 patients were included. For descriptive statistics see Table 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 NKR having 2.8% lower risk of dying than individuals in the baseline category NKR 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
7 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
8 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 Jan 25;314(7076): PubMed PMID: WOS:A1997WE 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 Oct;341(18): PubMed PMID: WOS: 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 Feb;153(2): PubMed PMID: WOS: 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 Jun 13;297(22): PubMed PMID: 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 Feb;8. PubMed PMID: WOS: 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 Aug;61(8): PubMed PMID: WOS: English. 7
9 Figure 1 The structural equation model Age Gender Comorbidities Distance Unanalyzed PCI during first 7 days Mortality: 7 day days Income Wealth Education Disability pension Angio during first 7 days 8
10 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: % (772) Alcoholism 0.8% (80) % (651) Stroke 3.5% (367) % (860) Distance: 0-60 min 46.3% (4874) % (1139) min 23.1% (2431) % (1038) min 10.7% (1131) % (1069) 180+ min 19.9% (2088) % (1277) Income: NKR0-150k 18.0% (1865) % (1422) k 54.5% (5643) % (1436) k 22.3% (2308) % (858) 500+k 5.2% (708) Hypertension 62.9% (6623) Wealth: NKR0-150k 18.2% (1889) Coronary artery disease 13.9% (1463) k 23.9% (2480) Atrial fibrillation 6.5% (685) k 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=
11 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% k -2.6% 0.0% -0.1% -2.7%* -1.7% 0.0% -0.3% -2.0% k -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% k 0.2% 0.0% -0.2% 0.0% -1.8% 0.0% -0.2% -2.0%* k 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
12 Appendix: Regression coefficients used to calculate the path coefficients in Table 2. d14 Coef. Std. Err. t P> t [95% Conf. Interval] male a00_ a50_ a55_ a60_ a65_ a70_ a75_ a80_ a85_ htn cad af ci dm ath can cpd dem dep pd md ri alc str dist60_ dist120_ dist inc150_ inc300_ inc wealth150_ wealth300_ wealth edu_secondary edu_univ dis_pens _cons 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
13 d365 Coef. Std. Err. t P> t [95% Conf. Interval] male a00_ a50_ a55_ a60_ a65_ a70_ a75_ a80_ a85_ htn cad af ci dm ath can cpd dem dep pd md ri alc str dist60_ dist120_ dist inc150_ inc300_ inc wealth150_ wealth300_ wealth edu_secondary edu_univ dis_pens _cons 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
14 d14 Coef. Std. Err. t P> t [95% Conf. Interval] male a00_ a50_ a55_ a60_ a65_ a70_ a75_ a80_ a85_ htn cad af ci dm ath can cpd dem dep pd md ri alc str dist60_ dist120_ dist inc150_ inc300_ inc wealth150_ wealth300_ wealth edu_secondary edu_univ dis_pens ang_ pci_ _cons 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
15 d365 Coef. Std. Err. t P> t [95% Conf. Interval] male a00_ a50_ a55_ a60_ a65_ a70_ a75_ a80_ a85_ htn cad af ci dm ath can cpd dem dep pd md ri alc str dist60_ dist120_ dist inc150_ inc300_ inc wealth150_ wealth300_ wealth edu_secondary edu_univ dis_pens ang_ pci_ _cons 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
16 ang_14 Coef. Std. Err. t P> t [95% Conf. Interval] male a00_ a50_ a55_ a60_ a65_ a70_ a75_ a80_ a85_ htn cad af ci dm ath can cpd dem dep pd md ri alc str dist60_ dist120_ dist inc150_ inc300_ inc wealth150_ wealth300_ wealth edu_secondary edu_univ dis_pens _cons Table 7. Direct effects of socio-economic variables on angiography within 14 days for all patients. 15
17 ang_14 Coef. Std. Err. t P> t [95% Conf. Interval] male a00_ a50_ a55_ a60_ a65_ a70_ a75_ a80_ a85_ htn cad af ci dm ath can cpd dem dep pd md ri alc str dist60_ dist120_ dist inc150_ inc300_ inc wealth150_ wealth300_ wealth edu_secondary edu_univ dis_pens _cons Table 8. Direct effects of socio-economic variables on angiography within 14 days for patients having survived the first two weeks. 16
18 pci_14 Coef. Std. Err. t P> t [95% Conf. Interval] male a00_ a50_ a55_ a60_ a65_ a70_ a75_ a80_ a85_ htn cad af ci dm ath can cpd dem dep pd md ri alc str dist60_ dist120_ dist inc150_ inc300_ inc wealth150_ wealth300_ wealth edu_secondary edu_univ dis_pens ang_ _cons 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
19 pci_14 Coef. Std. Err. t P> t [95% Conf. Interval] male a00_ a50_ a55_ a60_ a65_ a70_ a75_ a80_ a85_ htn cad af ci dm ath can cpd dem dep pd md ri alc str dist60_ dist120_ dist inc150_ inc300_ inc wealth150_ wealth300_ wealth edu_secondary edu_univ dis_pens ang_ _cons 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
20 pci_14 Coef. Std. Err. t P> t [95% Conf. Interval] male a00_ a50_ a55_ a60_ a65_ a70_ a75_ a80_ a85_ htn cad af ci dm ath can cpd dem dep pd md ri alc str dist60_ dist120_ dist inc150_ inc300_ inc wealth150_ wealth300_ wealth edu_secondary edu_univ dis_pens _cons 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
21 pci_14 Coef. Std. Err. t P> t [95% Conf. Interval] male a00_ a50_ a55_ a60_ a65_ a70_ a75_ a80_ a85_ htn cad af ci dm ath can cpd dem dep pd md ri alc str dist60_ dist120_ dist inc150_ inc300_ inc wealth150_ wealth300_ wealth edu_secondary edu_univ dis_pens _cons 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
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