Neighborhood socioeconomic disparities and 1-year case fatality after incident myocardial infarction: the Atherosclerosis Risk in Communities (ARIC) Community Surveillance (1992-2002)

Liver Fibrosis Scores and Prostate Cancer Risk and Mortality in the Atherosclerosis Risk in Communities Study

Subclinical liver impairment due to fibrosis could influence the development and detectability of prostate cancer. To investigate the association between liver fibrosis and prostate cancer incidence and mortality, we included 5,284 men (mean age: 57.6 years, 20.1% Black) without cancer or liver disease at Visit 2 in the Atherosclerosis Risk in Communities study. Liver fibrosis was assessed using the aspartate aminotransferase to platelet ratio index, fibrosis 4 index (FIB-4), and nonalcoholic fatty liver disease fibrosis score (NFS). Over 25 years, 215 Black and 511 White men were diagnosed with prostate cancer, and 26 Black and 51 White men died from the disease. We estimated HRs for total and fatal prostate cancer using Cox regression. FIB-4 [quintile 5 vs. 1: HR = 0.47, 95% confidence interval (CI): 0.29-0.77, Ptrend = 0.004] and NFS (HR = 0.56, 95% CI: 0.33-0.97, Ptrend = 0.03) were inversely associated with prostate cancer risk in Black men. Compared with no abnormal score, men with ≥1 abnormal score had a lower prostate cancer risk if they were Black (HR = 0.46, 95% CI: 0.24-0.89), but not White (HR = 1.04, 95% CI: 0.69-1.58). Liver fibrosis scores did not appear to be associated with fatal prostate cancer in Black or White men. Among men without a clinical diagnosis of liver disease, higher liver fibrosis scores were associated with lower incidence of prostate cancer in Black men, but not in White men, and not with fatal prostate cancer in either race. Further research is needed to understand the influence of subclinical liver disease on prostate cancer development versus detectability and the racial differences observed.

PREVENTION RELEVANCE

Investigating the link between liver fibrosis and prostate cancer risk and mortality, our study reveals the potential influence of liver health on prostate cancer development and on detection using PSA test, urging further research to understand the differential findings by race and to optimize prevention and intervention strategies.

SES and correlated factors do not explain the association between periodontal disease, edentulism, and cancer risk

PURPOSE

Severe periodontal disease and edentulism have been previously reported to be significantly associated with cancer risk and mortality, including in the Atherosclerosis Risk in Communities study (2018); however, complex sources of confounding by socioeconomic status (SES), and characteristics correlated with SES, could have been present in earlier analyses.

METHODS

To capture life course SES and its correlates, we generated a propensity score and included it, along with other potential confounders such as smoking and obesity, into a Cox regression model to examine the association between periodontal disease and cancer risk. In addition, we stratified the model with the propensity score by low and high SES. All statistical tests were two-sided.

RESULTS

Compared with our previous study, the associations for severe periodontitis and cancer incidence remained comparable after weighting by the propensity score (e.g., for total cancer: before weighting, hazard ratio = 1.24, 95% confidence interval = 1.07-1.42 vs. after weighting, hazard ratio = 1.23, 95% confidence interval = 1.05-1.44 when comparing severe periodontitis to no or mild periodontitis). Associations were comparable in low and high SES strata and statistically significant among participants with high SES.

CONCLUSIONS

Complex sources of confounding by SES and its correlates are unlikely to fully account for the positive associations observed for periodontal disease and edentulism and cancer risk.

Neighborhood-level and individual-level socioeconomic status and self-reported management of ischaemic heart disease: cross-sectional results from the Korea Health Examinees Study

OBJECTIVE

Several studies identified neighbourhood context as a predictor of prognosis in ischaemic heart disease (IHD). The present study investigates the relationships of neighborhood-level and individual-level socioeconomic status with the odds of ongoing management of IHD, using baseline survey data from the Korea Health Examinees-Gem study.

DESIGN

In this cross-sectional study, we estimated the association of the odds of self-reported ongoing management with the neighborhood-level income status and percentage of college graduates after controlling for individual-level covariates using two-level multilevel logistic regression models based on the Markov Chain Monte Carlo function.

SETTING

A survey conducted at 17 large general hospitals in major Korean cities and metropolitan areas during 2005-2013.

PARTICIPANTS

2932 adult men and women.

OUTCOME MEASURE

The self-reported status of management after incident angina or myocardial infarction.

RESULTS

At the neighbourhood level, residence in a higher-income neighbourhood was associated with the self-reported ongoing management of IHD, after controlling for individual-level covariates [OR: 1.22, 95% credible interval (CI): 1.01 to 1.61). At the individual level, higher education was associated with the ongoing IHD management (high school graduation, OR: 1.33, 95% CI: 1.08 to 1.65); college or higher, OR: 1.63, 95% CI: 1.22 to 2.12; reference, middle school graduation or below).

CONCLUSIONS

Our study suggests that policies or interventions aimed at improving the quality and availability of medical resources in low-income areas may associate with ongoing IHD management. Moreover, patient-centred education is essential for ongoing IHD management, especially when targeted to patients with IHD with a low education level.

Value of Neighborhood Socioeconomic Status in Predicting Risk of Outcomes in Studies That Use Electronic Health Record Data

IMPORTANCE

Data from electronic health records (EHRs) are increasingly used for risk prediction. However, EHRs do not reliably collect sociodemographic and neighborhood information, which has been shown to be associated with health. The added contribution of neighborhood socioeconomic status (nSES) in predicting health events is unknown and may help inform population-level risk reduction strategies.

OBJECTIVE

To quantify the association of nSES with adverse outcomes and the value of nSES in predicting the risk of adverse outcomes in EHR-based risk models.

DESIGN, SETTING, AND PARTICIPANTS

Cohort study in which data from 90 097 patients 18 years or older in the Duke University Health System and Lincoln Community Health Center EHR from January 1, 2009, to December 31, 2015, with at least 1 health care encounter and residence in Durham County, North Carolina, in the year prior to the index date were linked with census tract data to quantify the association between nSES and the risk of adverse outcomes. Machine learning methods were used to develop risk models and determine how adding nSES to EHR data affects risk prediction. Neighborhood socioeconomic status was defined using the Agency for Healthcare Research and Quality SES index, a weighted measure of multiple indicators of neighborhood deprivation.

MAIN OUTCOMES AND MEASURES

Outcomes included use of health care services (emergency department and inpatient and outpatient encounters) and hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke.

RESULTS

Among the 90 097 patients in the training set of the study (57 507 women and 32 590 men; mean [SD] age, 47.2 [17.7] years) and the 122 812 patients in the testing set of the study (75 517 women and 47 295 men; mean [SD] age, 46.2 [17.9] years), those living in neighborhoods with lower nSES had a shorter time to use of emergency department services and inpatient encounters, as well as a shorter time to hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke. The predictive value of nSES varied by outcome of interest (C statistic ranged from 0.50 to 0.63). When added to EHR variables, nSES did not improve predictive performance for any health outcome.

CONCLUSIONS AND RELEVANCE

Social determinants of health, including nSES, are associated with the health of a patient. However, the results of this study suggest that information on nSES may not contribute much more to risk prediction above and beyond what is already provided by EHR data. Although this result does not mean that integrating social determinants of health into the EHR has no benefit, researchers may be able to use EHR data alone for population risk assessment.

Neighborhood Socioeconomic Disadvantage and Care After Myocardial Infarction in the National Cardiovascular Data Registry

Background:

Patients living in disadvantaged neighborhoods are at high risk for adverse outcomes after acute myocardial infarction (MI). Whether residential socioeconomic status (SES) is associated with quality of in-hospital care among patients presenting with MI is unclear.

Methods and Results:

Multivariable logistic regression was used to examine the relationship between SES, quality of care, and in-hospital cardiovascular outcomes among patients with MI from diverse SES neighborhoods from July 2008 to December 2013, at 586 participating hospitals in the Acute Coronary Treatment and Intervention Outcomes Network Registry–Get With The Guidelines quality improvement program. Patients were categorized according to which SES summary measure group they resided in through linkage with US census block data. Outcomes were in-hospital mortality and major adverse cardiovascular events. Quality of MI care was assessed with the defect-free care measure that delineates the proportion of eligible patients who received all acute and discharge guideline-recommended therapies. Among 390 692 patients, there was a substantially longer median arrival-to-angiography time in lower SES neighborhoods (lowest 8.0 hours, low 5.5 hours, medium 4.8 hours, high 4.5 hours, highest 3.4 hours; P <0.0001), and a higher proportion of ST-segment–elevation myocardial infarction patients treated with fibrinolysis (lowest 23.1%, low 20.2%, medium 18.0%, high 14.2%, highest 5.9%; P <0.0001). However, after adjustment for clinical risk factors, insurance status, and hospital characteristics, socioeconomic disadvantage was not associated with lower rates of guideline-recommended defect-free acute care. Patients presenting from more disadvantaged neighborhoods had a progressively higher independent risk of in-hospital mortality ( P global =0.03) and major bleeding ( P global <0.001), along with lower quality of discharge care.

Conclusions:

In this national registry of MI, patients living in the most disadvantaged neighborhoods received equitable in-hospital care compared with advantaged neighborhoods. However, they experienced substantial delays in receiving angiography. Furthermore, patients living in disadvantaged neighborhoods remain at higher risk of adverse in-hospital outcomes after MI, including mortality. These observations suggest there are further opportunities for improvement in acute and discharge MI care.

Trends in Early Prenatal Care Among Women with Pre-Existing Diabetes: Have Income Disparities Changed?

BACKGROUND

Women with pre-existing diabetes are at high maternal risk for comorbidities and death, particularly when early prenatal care is not received. Low income is a known barrier to early prenatal care. It is unknown whether recent policies to expand access to prenatal care have reduced income disparities. We hypothesized that income disparities would be minimized and that the odds of receipt of first trimester prenatal care among women with pre-existing diabetes would become similar across income strata over time.

MATERIAL AND METHODS

Using the Colorado birth certificate registry from 2007 to 2014, receipt of prenatal care was assessed retrospectively in 2,497 women with pre-existing diabetes. Logistic regression was used to examine the association between high (>$50,000), medium ($25,000-50,000), and low (<$25,000) income strata and receipt of first trimester prenatal care by birth year, adjusted for demographics.

RESULTS

High, medium, and low income represented 29.5%, 19.0%, and 51.5% of the cohort, respectively. Women with high income were more likely to receive first trimester care than women with low income from 2007 [adjusted odds ratio, 95% confidence interval: 2.16 (1.18, 3.96)] through 2013 [1.66 (1.01, 2.73)], but significant differences were no longer observed in 2014 [1.59 (0.89, 2.84)]. The likelihood of receiving first trimester prenatal care was not significantly different between medium- and low-income strata from 2007 [1.07 (0.66, 1.74)] through 2014 [0.77 (0.48, 1.23)].

CONCLUSIONS

From 2007 to 2013, women in Colorado with diabetes were more likely to receive early prenatal care if they were in the highest income stratum than in the lowest stratum. In 2014, receipt of first trimester care became equitable across all income strata. Future work should examine national patterns of income with receipt of prenatal care and outcomes among women with pre-existing diabetes.

County socioeconomic characteristics and heart transplant outcomes in the United States

BACKGROUND

Geographic disparities in survival after heart transplantation have received mixed support in prior studies, and specific geographic characteristics that might be responsible for these differences are unclear. We tested for differences in heart transplant outcomes across United States (US) counties after adjustment for individual-level covariates. Our secondary aim was to evaluate whether specific county-level socioeconomic characteristics explained geographic disparities in survival.

METHODS

Data on patients aged ≥18 years undergoing a first-time heart transplant between July 2006 and December 2014 were obtained from the United Network for Organ Sharing. Residents of counties represented by <5 patients were excluded. Patient survival (censored in March 2016) was analyzed using multivariable Cox regression. Shared frailty models were used to test for residual differences in overall all-cause mortality across counties after adjusting for recipient and donor characteristics. Measures of county economic disadvantage, inequality, and racial segregation were obtained from US Census data and coded into quintiles. A likelihood ratio test determined whether adjusting for each county measure improved the fit of the Cox model.

RESULTS

Multivariable analysis of 10,879 heart transplant recipients found that, adjusting for individual-level characteristics, there remained statistically significant variation in mortality hazard across US counties (P=.004). Adjusting for quintiles of community disadvantage, economic inequality, or racial segregation did not significantly improve model fit (likelihood ratio test P=.092, P=.273, and P=.107, respectively) and did not explain residual differences in patient survival across counties.

CONCLUSIONS

Heart transplantation outcomes vary by county, but this difference is not attributable to county-level socioeconomic disadvantage.

Socioeconomic Status as a Predictor of Mortality in Patients Admitted With Atrial Fibrillation

Lower socioeconomic status (SES) is associated with a higher risk of cardiovascular disease. However, the association between SES and mortality in patients with atrial fibrillation (AF) is not clear. We examined whether SES predicts all-cause mortality in patients hospitalized with AF. This is a retrospective study of patients aged >18 years, admitted with a primary diagnosis of AF to Montefiore Medical Center between 2000 and 2010. Multivariable logistic regression models were used to determine predictors of survival adjusted for age, gender, heart failure, diabetes mellitus, chronic kidney disease, previous myocardial infraction, chronic obstructive pulmonary disease, hypertension, peripheral vascular disease, and SES. SES was determined using the New York City Department of Health Standardized Score (a log composite score of household income, value of housing units, net rental income, household occupations, and educational level). The cohort was divided into quartiles based on SES score, with Q4 the highest and Q1 the lowest SES score. There were 4,503 patients identified with a mean follow up of 4.5 years in the following SES quartiles: Q1 (n = 1,132), Q2 (n = 1,119), Q3 (n = 1,126), and Q4 (n = 1,126). The unadjusted mortality varied across quartiles (Q1 to Q4), 54%, 58%, 56%, and 59%, respectively (p = 0.004). After controlling for other variables in the multivariable analysis, patients with the lowest SES (Q1) had a significantly higher mortality than patients in the quartile with the highest (Q4) SES (odds ratio 1.3, CI 1.1 to 1.5). In conclusion, patients admitted to the hospital with AF have varying mortality based on their SES. After controlling for co-morbidities, patients with AF and lower SES scores had higher mortality. Further research studies are warranted to study this risk of increased mortality in AF population.

Impact of Insurance Type on Initial Rejection Post Heart Transplant

BACKGROUND

Heart transplantation allocation is often restricted from patients with low socioeconomic status (SES) due to concern for worse outcomes. We hypothesised that comorbidities would have a greater impact on risk of severe rejection post-orthotopic heart transplant than would Medicaid insurance and Median Household Income (MHI).

METHODS

A retrospective study of 171 patients who underwent orthotopic heart transplant between 7/1999-11/2013 at our facility were followed until 9/2014 for rejection hospitalisations or death. Survival and multivariable analyses with adjustment for age, race, and gender were performed to estimate the risk of severe cellular rejection, ≥2r (hazard ratio [HR], 95% confidence interval [CI]).

RESULTS

Eighteen per cent of patients had Medicaid, and 72% of patients had low or medium MHI. Severe rejection occurred in 23% of patients. In the univariable analysis, Medicaid and diabetes were associated with increased risk of rejection while age >60 years, Caucasian race, and male sex were associated with reduced risk [Medicaid 2.32(1.20,4.51), diabetes 2.49(1.09,5.69), age 0.41(0.20,0.84), Caucasian 0.44(0.21,0.93), male 0.49(0.26,0.92)]. Median Household Income had no correlation [MHI 0.79(0.51,1.23)]. In the multivariable adjusted model, Medicaid was not associated with rejection [1.65(0.79,3.41)]; diabetes was strongly associated with risk of severe rejection [3.9(1.59,9.39)], and age >60 years was associated with risk reduction [0.42(0.20,0.82)].

CONCLUSIONS

Medicaid insurance and MHI were not associated with increased risk of severe cellular rejection requiring hospitalisation post-orthotopic heart transplant in the adjusted model. Rather the presence of diabetes and age ≤60 years were associated with increased risk.

Statistical Assessment of Neighborhood Socioeconomic Deprivation Environment in Spatial Epidemiologic Studies

Neighborhood socioeconomic deprivation has been associated with health behaviors and outcomes. However, neighborhood socioeconomic status has been measured inconsistently across studies. It remains unclear whether appropriate socioeconomic indicators vary over geographic areas and geographic levels. The aim of this study is to compare the composite socioeconomic index to six socioeconomic indicators reflecting different aspects of socioeconomic environment by both geographic areas and levels. Using 2000 U.S. Census data, we performed a multivariate common factor analysis to identify significant socioeconomic resources and constructed 12 composite indexes at the county, the census tract, and the block group levels across the nation and for three states, respectively. We assessed the agreement between composite indexes and single socioeconomic variables. The component of the composite index varied across geographic areas. At a specific geographic region, the component of the composite index was similar at the levels of census tracts and block groups but different from that at the county level. The percentage of population below federal poverty line was a significant contributor to the composite index, regardless of geographic areas and levels. Compared with non-component socioeconomic indicators, component variables were more agreeable to the composite index. Based on these findings, we conclude that a composite index is better as a measure of neighborhood socioeconomic deprivation than a single indicator, and it should be constructed on an area- and unit-specific basis to accurately identify and quantify small-area socioeconomic inequalities over a specific study region.

The geographic distribution of cardiovascular health in the stroke prevention in healthcare delivery environments (SPHERE) study

BACKGROUND

Community-level factors have been clearly linked to health outcomes, but are challenging to incorporate into medical practice. Increasing use of electronic health records (EHRs) makes patient-level data available for researchers in a systematic and accessible way, but these data remain siloed from community-level data relevant to health.

PURPOSE

This study sought to link community and EHR data from an older female patient cohort participating in an ongoing intervention at the Ohio State University Wexner Medical Center to associate community-level data with patient-level cardiovascular health (CVH) as well as to assess the utility of this EHR integration methodology.

MATERIALS AND METHODS

CVH was characterized among patients using available EHR data collected May through July of 2013. EHR data for 153 patients were linked to United States census-tract level data to explore feasibility and insights gained from combining these disparate data sources. Analyses were conducted in 2014.

RESULTS

Using the linked data, weekly per capita expenditure on fruits and vegetables was found to be significantly associated with CVH at the p<0.05 level and three other community-level attributes (median income, average household size, and unemployment rate) were associated with CVH at the p<0.10 level.

CONCLUSIONS

This work paves the way for future integration of community and EHR-based data into patient care as a novel methodology to gain insight into multi-level factors that affect CVH and other health outcomes. Further, our findings demonstrate the specific architectural and functional challenges associated with integrating decision support technologies and geographic information to support tailored and patient-centered decision making therein.

The Impact of Comorbid Depression on Educational Inequality in Survival after Acute Coronary Syndrome in a Cohort of 83 062 Patients and a Matched Reference Population

Background

Patients with low socioeconomic position have higher rates of mortality after diagnosis of acute coronary syndrome (ACS), but little is known about the mechanisms behind this social inequality. The aim of the present study was to examine whether any educational inequality in survival after ACS was influenced by comorbid conditions including depression.

Methods

From 2001 to 2009 all first-time ACS patients were identified in the Danish National Patient Registry. This cohort of 83 062 ACS patients and a matched reference population were followed for incident depression and mortality until December 2012 by linkage to person, patients and prescription registries. Educational status was defined at study entry and the impact of potential confounders and mediators (age, gender, cohabitation status, somatic comorbidity and depression) on the relation between education and mortality were identified by drawing a directed acyclic graph and analysed using multiple Cox regression analyses.

Findings

During follow-up, 29 583(35.6%) of ACS patients and 19 105(22.9%) of the reference population died. Cox regression analyses showed an increased mortality in the lowest educated compared to those with high education in both ACS patients and the reference population. Adjustment for previous and incident depression or other covariables only attenuated the relations slightly. This pattern of associations was seen for mortality after 30 days, 1 year and during total follow-up.

Conclusion

In this study the relative excess mortality rate in lower educated ACS patients was comparable with the excess risk associated with low education in the background population. This educational inequality in survival remained after adjustment for somatic comorbidity and depression.

Twenty-eight day and one-year case fatality after hospitalisation with an acute coronary syndrome: a nationwide data linkage study

OBJECTIVES

To determine 28-day and one-year case fatality in patients hospitalised with acute coronary syndromes (ACS) and identify factors associated with mortality.

METHODS

All New Zealand residents admitted with ACS between 2007 and 2009 were followed for one year using individual patient linkage of national hospitalisation and mortality datasets. Deaths from any cause were used to calculate 28-day and one-year case fatality. Cox-proportional hazards models were constructed to identify factors associated with mortality after an ACS hospitalisation.

RESULTS

The cohort included 42,920 ACS patients. Case fatality increased steeply with age. Māori and Pacific peoples had 1.5 times the risk of 28-day, and twice the risk of one-year, mortality as Europeans/Others. Low (compared to high) socioeconomic status was associated with significantly higher mortality at 28 days but not one year. Patients with unstable angina had half the risk of short-term mortality as NSTEMI patients, whereas STEMI patients had double the NSTEMI risk.

CONCLUSIONS AND IMPLICATIONS

The major determinant of increasing case fatality was increasing age. There were also substantial differences in case fatality by ethnicity, deprivation and diagnostic category. Further research is needed to explore the possible mechanisms by which ethnic and deprivation disparities occur and effective strategies to address them.

Differential Outcomes by Race and Ethnicity in Patients with Coronary Heart Disease: A Contemporary Review

Coronary heart disease (CHD) is a leading cause of death for people of most ethnicities in the USA. However, several racial and ethnic minority groups are disproportionately burdened by CHD and experience higher mortality rates and rehospitalization rates compared with whites. Contemporary CHD research has been dedicated in part to broadening our understanding of the root causes of racial and ethnic disparities in CHD outcomes. Several factors contribute, including socioeconomic and comorbid conditions. These factors may be amenable to change, and targets for initiatives to reduce disparities and improve CHD outcomes. In this article, we review the recently published research related to the distribution and determinants of racial and ethnic differences in CHD outcomes in the USA.

Educational inequalities in 28 day and 1-year mortality after hospitalisation for incident acute myocardial infarction--a nationwide cohort study

BACKGROUND

There is little recent evidence on the impact of comorbidities and access to revascularisation procedures on educational inequalities in mortality after acute myocardial infarction (AMI). The aim of the study was to investigate educational inequalities in mortality among all patients hospitalised for an incident AMI during 2001-2009 in Norway.

METHODS

Data were obtained through the Cardiovascular Disease in Norway (CVDNOR) project. Incident AMI was defined as an AMI-hospitalisation without any AMI-events in the previous 7 years. Education was categorised as basic, upper secondary or tertiary (college/university). Cox regression was used to assess educational differences in 28-day and 29-365-day mortality after an incident AMI in terms of hazard ratios and relative index of inequality (RII). RII can be interpreted as the ratio in mortality between the 0 th and the 100th percentile of the education distribution.

RESULTS

111 993 incident AMIs were included (39.4% women). Among patients aged 35-69, RIIs (95% CI) adjusted for age, sex and year were 1.86 (1.59-2.18) and 2.10 (1.69-2.59) for 28-day and 29-365-day mortality respectively. Among patients aged 70-94 the corresponding RIIs were 1.12 (1.06-1.30) and 1.28 (1.19-1.38). Educational inequalities in mortality were attenuated after adjustment for comorbidities and revascularisation, but were still significant. Educational inequalities did not decrease during 2001-2009.

CONCLUSION

Educational inequalities in both 28-day and 29-365 day mortality were strong and persistent during 2001-2009. Further research is needed to investigate if these disparities are driven by inequalities in the severity of the AMI or by inequitable access to treatment and rehabilitation.

Long-term survival among older patients with myocardial infarction differs by educational level: results from the MONICA/KORA myocardial infarction registry

Background

Socioeconomic disparities in survival after acute myocardial infarction (AMI) have been found in many countries. However, population-based results from Germany are lacking so far. Thus, the objective of this study was to examine the association between educational status and long-term mortality in a population-based sample of people with AMI.

Methods

The sample consisted of 2,575 men and 844 women, aged 28–74 years, hospitalized with a first-time AMI between 1 January 2000 and 31 December 2008, recruited from a population-based AMI registry. Patients were followed up until December 2011. Data on education, risk factors and co-morbidities were collected by individual interviews; data on clinical characteristics and AMI treatment by chart review. Cox proportional hazards models were used to assess the relationship between educational status and long-term mortality.

Results

During follow-up, 19.1% of the patients with poor education died compared with 13.1% with higher education. After adjustment for covariates, no effect of education on mortality was found for the total sample and for patients aged below 65 years. In older people, however, low education level was significantly associated with increased mortality (hazard ratio (HR) 1.44, 95% confidence interval (CI) 1.05–1.98, p = 0.023). Stratified analyses showed that women older than 64 years with poor education were significantly more likely to die than women in the same age group with higher education (HR 1.57, 95% CI 1.02–2.41, p = 0.039).

Conclusions

Elderly, poorly educated patients with AMI, and particularly women, have poorer long-term survival than their better educated peers. Further research is required to illuminate the reasons for this finding.