Neighborhood Income, Health Insurance, and Prehospital Delay for Myocardial Infarction: The Atherosclerosis Risk in Communities Study

Department of Epidemiology, University of North Carolina at Chapel Hill, 137 E Franklin St, Ste 306, Chapel Hill, NC 27514, USA.
Archives of internal medicine (Impact Factor: 17.33). 10/2008; 168(17):1874-9. DOI: 10.1001/archinte.168.17.1874
Source: PubMed

ABSTRACT Outcomes following an acute myocardial infarction (AMI) are generally more favorable if prehospital delay time is minimized.
We examined the association of neighborhood household income (nINC) and health insurance status with prehospital delay among a weighted sample of 9700 men and women with a validated, definite, or probable AMI in the Atherosclerosis Risk in Communities (ARIC) community surveillance study (1993-2002). Weighted multinomial regression with generalized estimation equations was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) and to account for the clustering of patients within census tracts.
Low nINC was associated with a higher odds of long vs short delay (OR, 1.46; 95% CI, 1.09-1.96) and medium vs short delay (OR, 1.43; 95% CI, 1.12-1.81) compared with high nINC in a model including age, sex, race, diabetes, hypertension, presence of chest pain, arrival at the hospital via emergency medical service, distance from residence to hospital, study community, and year of AMI event. Meanwhile, compared with patients with prepaid insurance or prepaid plus Medicare, patients with Medicaid were more likely to have a long vs short delay (OR, 1.87; 95% CI, 1.10-3.19) and a medium vs short delay (OR, 1.76; 95% CI, 1.13-2.74).
Both low nINC and being a Medicaid recipient are associated with longer prehospital delay. Reducing socioeconomic and insurance disparities in prehospital delay is critical because excess delay time may hinder effective care for AMI.

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    • "Another area that needs more comprehensive education in patients is what to do when they think they are having a heart attack. In this study, only 39.8% of patients used an ambulance, a proportion that was lower than that in other studies [6] [8] [31] [50]. Consistent with previous multivariate studies, use of an ambulance was associated with significantly shorter delay times in MI patients but not in UA patients [14] [26]. "
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    ABSTRACT: Background: Few studies have had the opportunity to examine a broad range of predictors of pre-hospital delay from a multivariate perspective that includes not only sociodemographic and clinical features but also atypical symptoms, patient appraisal and behavior, across the acute coronary syndrome (ACS) spectrum. Methods: A total of 1894 hospitalized ACS patients were recruited predischarge after an ACS event. Patients completed a detailed questionnaire and clinical details were verified with their case notes. Results: The median pre-hospital delay times were, 4.06, 2.70, 4.51 and 5.50h, for all ACS, ST elevated myocardial infarction (STEMI), non-STEMI and unstable angina (UA) subgroups respectively. Multiple regression models examining 33 predictors of pre-hospital delay were significant (p<0.001), accounting for 32%, 42%, 34% and 29% of the variance for all ACS, STEMI, non-STEMI and UA subgroups respectively. The predictors that were singularly significantly associated with longer pre-hospital delay within all ACS were: taking medications, visiting family physician, and symptoms that were intermittent in nature. In the MI subgroups, not using an ambulance and gradual symptom onset, were also associated with longer delay. In STEMI patients non-attribution of symptoms to heart was also associated with longer pre-hospital delay. Conclusions: Multivariable analyses found that although sociodemographic, clinical history or situational predictors contributed to the variance in pre-hospital delay, the main predictors of pre-hospital delay were behavioral and symptom presentation factors. These factors should therefore be incorporated into patient education and interventions, to further improve patient pre-hospital delay time.
    International journal of cardiology 04/2013; 168(3). DOI:10.1016/j.ijcard.2013.03.022 · 4.04 Impact Factor
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    • "In previous studies, receipt of Medicaid has been associated with adverse health outcomes independent of neighborhood-level SES (Foraker et al., 2008; Ross & Mirowsky, 2000). Although we used educational level as a marker of individual-level SES in this cohort, it should be noted that Medicaid enrollment is often used as a surrogate for low individual-level SES in studies of hospital claims data (Croft et al., 1999), as its receipt is determined by having certain diseases or disabilities or an income below the poverty line (Ku, 2005; Rosenbaum S., 2002). "
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    ABSTRACT: Heart failure (HF) accounts for 6.5 million hospital days per year. It remains unknown if socioeconomic factors are associated with hospital length of stay (LOS). We analyzed predictors of longer hospital LOS [mean (days), 95% confidence interval (CI)] among participants with incident hospitalized HF (n = 1,300) in the Atherosclerosis Risk in Communities (ARIC) cohort from 1987 to 2005. In a statistical model adjusted for median household income, age, gender, race/study community, education level, hypertension, alcohol use, smoking, Medicaid status, and Charlson comorbidity index score, Medicaid recipients experienced a longer LOS (7.5, 6.3-8.9) compared to non-Medicaid recipients (6.2, 5.7-6.7), and patients with a higher burden of comorbidity had a longer LOS (7.5, 6.4-8.6) compared to patients with a lower burden (6.2, 5.7-6.9). Median household income and education were not associated with longer LOS in multivariable models. Medicaid recipients and patients with more comorbid disease may not have the resources for adequate, comprehensive, out-of-hospital management of HF symptoms, and may require a longer LOS due to the need for more care during the hospitalization because of more severe HF. Data on out-of-hospital management of chronic diseases as well as HF severity are needed to further elucidate the mechanisms leading to longer LOS among subgroups of HF patients.
    Journal for Healthcare Quality 12/2012; 36(1). DOI:10.1111/j.1945-1474.2012.00211.x · 1.40 Impact Factor
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    • "Shorter time of education has been also shown to increase the delay in an Australian study (24). It is conceivable that low level of patient education elicited slower reaction of patients, or it just might mirror the low socioeconomic status, which has been shown to be related to time delay (25). Night time onset and arrival via other hospitals were another factors, which was understandable and concurred with other study (17, 26). "
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    ABSTRACT: Despite recent successful efforts to shorten the door-to-balloon time in patients with acute ST-segment elevation myocardial infarction (STEMI), prehospital delay remains unaffected. Nonetheless, the factors associated with prehospital delay have not been clearly identified in Korea. We retrospectively evaluated 423 patients with STEMI. The mean symptom onset-to-door time was 255 ± 285 (median: 150) min. The patients were analyzed in two groups according to symptom onset-to-door time (short delay group: ≤ 180 min vs long delay group: > 180 min). Inhospital mortality was significantly higher in long delay group (6.9% vs 2.8%; P = 0.048). Among sociodemographic and clinical variables, diabetes, low educational level, triage via other hospital, use of private transport and night time onset were more prevalent in long delay group (21% vs 30%; P = 0.038, 47% vs 59%; P = 0.013, 72% vs 82%; P = 0.027, 25% vs 41%; P < 0.001 and 33% vs 48%; P = 0.002, respectively). In multivariate analysis, low educational level (1.66 [1.08-2.56]; P = 0.021), symptom onset during night time (1.97 [1.27-3.04]; P = 0.002), triage via other hospital (1.83 [1.58-5.10]; P = 0.001) and private transport were significantly associated with prehospital delay (3.02 [1.81-5.06]; P < 0.001). In conclusion, prehospital delay is more frequent in patients with low educational level, symptom onset during night time, triage via other hospitals, and private transport, and is associated with higher inhospital mortality.
    Journal of Korean medical science 08/2012; 27(8):864-9. DOI:10.3346/jkms.2012.27.8.864 · 1.27 Impact Factor
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