- Email: [email protected]
Unemployment: A Social Risk Factor Associated with Early Ischemic Stroke Mortality? Results from the Argentinean National Stroke Registry (ReNACer)
Unemployment: A Social Risk Factor Associated with Early Ischemic Stroke Mortality? Results from the Argentinean National Stroke Registry (ReNACer) Luciano A. Sposato, MD,* Pablo Ioli, MD,† Guillermo Povedano, MD,‡ Marıa Martha Esnaola y Rojas, MD,x and Gustavo Saposnik, MD, FAHA,{ on behalf of the Argentinean Neurological Society and ReNACer Investigators
Employment is an indicator of socioeconomic status. Unemployment is a worldwide social challenge, especially in emerging countries, accounting for a proportion of the overall higher mortality rates found in these nations. We assessed the relationship between employment status and in-hospital mortality among acute ischemic stroke patients participating in the Argentinean National Stroke Registry (ReNACer), a prospective, country-wide, hospital-based stroke registry aimed at improving quality of stroke care in Argentina. We compared demographic and socioeconomic characteristics, risk factors, acute treatment, and stroke severity between employed and unemployed patients with acute ischemic stroke participating in ReNACer. We developed a multiple logistic regression model to identify predictors of in-hospital mortality. Among the 726 patients with acute ischemic stroke included in the study, 39.5% were unemployed. In-hospital mortality was higher in the patients who were unemployed at the time of the stroke compared with those who were employed (12.0% v 5.0%; P 5 .003). On multivariate analysis, being unemployed (odds ratio [OR], 3.58; 95% confidence interval [CI], 1.36-7.37; P 5 .005), stroke severity (OR, 3.54; 95% CI 1.1110.40; P 5.018), and infarct size .15 mm (OR, 2.80; 95% CI, 1.18-6.60; P 5.019) were associated with in-hospital mortality after adjusting for relevant covariates. Social factors may influence poor outcomes after stroke. In the present study, unemployment was associated with a higher risk of adjusted in-hospital mortality. Strategies targeting individuals at high risk of cardiovascular diseases and poorer outcomes should be implemented to reduce stroke impact. Key Words: Cerebrovascular disease— prognosis—outcome—demographic. Ó 2012 by National Stroke Association
According to the World Bank, high unemployment rates will continue to characterize the global economy in the coming years, with the greatest impact on emerging
countries.1 Employment status is the single most common instrumental variable used by governments, national and international agencies, and other organizations to track
From the *Neurovascular Clinic, INECO, Vascular Research Unit, INECO Foundation, and Stroke Center, Institute of Neurosciences, University Hospital, Favaloro Foundation, Buenos Aires, Argentina; †Neurology Department/Clinical Research Unit, Hospital Privado de la Comunidad, Mar del Plata, Argentina; ‡Neurology Department, Complejo Medico Policial Churruca-Visca, Buenos Aires, Argentina; xNeurology Service, Unidad Asistencial Por Mas Salud, Hospital Dr Cesar Milstein, Buenos Aires, Argentina; and {Stroke Outcomes Research Unit, Departments of Medicine and Health Policy Management and Evaluation, and Institute for Clinical Evaluative Sciences,
St Michael’s Hospital, University of Toronto, Toronto, Ontario, Canada. Received December 2, 2010; accepted February 27, 2011. Supported in part by a VIGI1A grant from the Argentinean Ministry of Health and the World Bank. Address correspondence to Luciano A. Sposato, MD, Pacheco de Melo 1860, Ciudad de Buenos Aires, Argentina. E-mail: lsposato@ ineco.org.ar; [email protected]. 1052-3057/$ - see front matter Ó 2012 by National Stroke Association doi:10.1016/j.jstrokecerebrovasdis.2011.02.018
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the course of an economy. Moreover, at the population level, unemployment is an indicator of economic recession.2 At the individual level, unemployment is associated with lower health status (eg, higher prevalence of comorbid conditions, lower access to care) and poorer clinical outcomes.3 Both unemployment and low socioeconomic status predominate in emerging countries and might account for, at least in part, the higher overall mortality rates seen in these nations.4 Very few studies have analyzed the association between employment status and stroke outcome, and there are no available data on the association between employment status and early outcome of ischemic stroke in emerging countries. We sought to assess the relationship between employment status and in-hospital mortality among acute ischemic stroke patients participating in the Argentinean National Stroke Registry (ReNACer).
Methods ReNACer is a prospective, countrywide, hospitalbased stroke registry. We included in the registry every consecutive patient admitted for an acute stroke to the participating institutions between November 2004 and October 2006. For the present analysis, we considered patients with acute ischemic stroke aged 18 years and older with less than 72 hours between symptom onset and admission to any of the 74 participating institutions. After excluding patients aged .65 years (n 5 1265) and those with subarachnoid hemorrhage (n 5 103), hemorrhagic stroke (n 5 416), and transient ischemic attack (TIA; n 5 74), the study cohort comprised 726 patients with acute ischemic stroke. Data were recorded prospectively on a real-time basis during each patient’s hospital stay and completed after discharge. Further details have been reported elsewhere.5 Every patient underwent head computed tomography, magnetic resonance imaging, or both. We assessed demographic and socioeconomic characteristics, vascular risk factors, stroke severity, and care received. Stroke was defined according to World Health Organization criteria.6 Stroke severity was stratified according to the number of neurologic deficits5,7 and was dichotomized as #2 (mild stroke) or .2 (moderate to severe stroke). Neurologic deficits included in the score were motor deficit, sensory deficit, presence of cortical signs (ie, aphasia, hemianopia, or neglect), dysarthria, ataxia, and involvement of cranial nerves. We selected this score because not all neurologists were certified in or familiar with the administration of the National Institutes of Health Stroke Scale. Employment status was defined as having a continuously paying job in the previous 3 months before stroke onset. Individuals who had no full-time or part-time job during this specified period were classified as ‘‘unemployed.’’ Because of retirement arrangements in Argentina, individuals beyond
age 65 years do not continue in paid employment. Regarding educational level, lack of education was defined as incomplete elementary school or less. Patients were classified as ‘‘low-educated’’ if they had complete elementary school or less. Concerning medical insurance, Argentina’s health insurance system includes workers’ trade union organizations, or ‘‘obras sociales’’ (40%), government-funded social insurance for part of the retired population (PAMI) (8%), and private health insurers and providers (9%). Patients belonging to these groups were considered insured. The uninsured (43%) have access to public hospitals funded by the government. The study’s outcome measure was in-hospital mortality. The study was approved by the Argentinean Ministry of Public Health and local research ethics boards.
Statistical Analysis The c2 and Student t tests were used to compare categorical variables, and the Mann-Whitney U test was used to compare continuous variables. We developed a forward step-by-step multiple logistic regression model to identify variables associated with in-hospital mortality. Age, sex, academic versus nonacademic nature of participating centers, and variables with P values , .20 in univariate analysis were included in the model. Double-sided P values , .05 were considered significant for a variable to be retained in the final logistic regression model. Associations between significant covariates and in-hospital mortality are expressed as odds ratios (ORs) and 95% confidence intervals (95% CIs). Because employment status might be confounded by age and educational level, and considering that stroke severity may be determined by infarct size, we also assessed the following interaction terms in the logistic regression model: employment status 3 educational level, employment status 3 age, employment status 3 sex, and infarct size 3 stroke severity. All statistical analyses was conducted using SPSS version 13.0 (SPSS Inc, Chicago, IL).
Results The study cohort comprised 726 patients with acute ischemic stroke (62.1% male; mean age, 55 6 8 years), of whom 39.5% were unemployed. Baseline characteristics stratified by employment status are shown in Table 1. The unemployed patients were older than their employed counterparts. Male sex, diabetes mellitus, no education, and lack of health insurance were more prevalent in the unemployed patients. The unemployed patients were less likely to be admitted to academic centers and had a lower proportion of smokers. Average length of stay (LOS) and in-hospital mortality were higher in the unemployed patients. Six patients (0.83%) received systemic thrombolysis with recombinant tissue plasminogen activator; of these, 4 were employed and 2 were unemployed (P 5 .78). Antiplatelets were prescribed within the first 48
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Table 1. Baseline characteristics and outcome measures of employed and unemployed patients Employment status Demographic and socioeconomic characteristics Age, years, mean 6 SD Male sex, n (%) Health insurance, n (%) Low educational level, n (%) Incomplete elementary school or less, n (%) Nonacademic center, n (%) Risk factor profile Hypertension, n (%) Previous stroke or TIA, n (%) Diabetes mellitus, n (%) Hyperlipidemia, n (%) Smoking, n (%) Atrial fibrillation, n (%) Stroke severity Infarct size .15 mm, n (%) Moderate to severe stroke, n (%) Outcome measures ALOS, days In-hospital mortality, n (%)
Unemployed
Employed
P value
56.1 6 8.2 104/208 (50.0) 61/185 (33.0) 107/137 (78.1) 9/137 (6.6) 131/208 (63.0)
54.6 6 8.0 241/319 (75.5) 164/311 (52.7) 131/251 (52.2) 3/251 (1.2) 233/319 (73.0)
.04 ,.001 ,.001 ,.001 .003 .015
154/201 (76.6) 53/208 (25.5) 68/207 (32.9) 63/181 (34.8) 59/189 (31.2) 57/208 (27.4)
221/305 (72.5) 78/319 (24.5) 70/318 (22.0) 93/295 (31.5) 132/301 (43.9) 94/319 (29.5)
.29 .79 .006 .46 .005 .61
57/208 (27.4) 22/208 (10.6)
94/319 (29.5) 33/319 (10.3)
.61 .93
11.0 6 18.8 25/208 (12.0)
8.0 6 9.6 16/319 (5.0)
.019 .003
Abbreviation: ALOS, average length of stay. Low educational level refers to complete elementary school or less. Moderate to severe stroke: .2 neurologic deficits.
hours after admission to 82.8% of employed patients and 74.5% of unemployed patients (P 5 .022). As shown in Table 2, unemployment, health insurance, low educational level, incomplete elementary school or less, previous stroke or TIA, atrial fibrillation, stroke severity, and infarct size were associated with higher inhospital mortality on univariate analysis (Table 2). Table 2. Univariate analysis for in-hospital mortality OR (95% CI) Age Male sex Nonacademic center Unemployment Health insurance Low educational level Incomplete elementary school or less Hypertension Previous stroke or TIA Diabetes mellitus Hyperlipidemia Smoking Atrial fibrillation Stroke severity Infarct size .15 mm
0.98 (0.94-1.02) 1.39 (0.78-2.48) 1.40 (0.75-2.61) 2.58 (1.34-4.97) 0.50 (0.27-0.92) 1.88 (0.83-4.25) 7.05 (2.05-24.26) 0.97 (0.52-1.83) 1.49 (0.85-2.64) 1.21 (0.67-2.17) 1.33 (0.74-2.39) 1.03 (0.58-1.83) 2.55 (1.31-4.97) 2.10 (1.04-4.24) 4.22 (2.43-7.34)
P value .33 .26 .29 .004 .026 .12 .002 .93 .17 .52 .34 .92 .006 .039 ,.001
Low educational level refers to complete elementary school or less. Health insurance refers to patients whose medical costs are covered by ‘‘obras socials,’’ private health insurers, or PAMI (see Materials and Methods). Stroke severity (moderate to severe stroke): .2 neurologic deficits.
The results of the multivariate analysis are presented in Table 3. Because stroke severity is related to infarct size, we assessed the interaction term moderate to severe stroke 3 infarct size .15 mm. This term was associated with in-hospital mortality (OR, 1.89; 95% CI, 1.27-2.83; P 5.002). After including this interaction term in the logistic regression model, unemployment also was a significant determinant of in-hospital mortality (OR 3.34, 95% CI 1.417.92; P 5 .002). Considering that age, sex, and educational level are determinants of retirement, we assessed the interaction terms unemployment 3 age, unemployment 3 sex, and unemployment 3 low educational level. None of those terms was associated with in-hospital mortality.
Discussion Employment status is a major indicator of socioeconomic background, and unemployment is an ongoing concern for policy-makers in emerging countries because of its direct link to health inequalities. Limited data are available on the effect of unemployment on early mortality. This effect is more relevant for emergent countries such as Argentina. In the present study, which included patients with an acute ischemic stroke participating in ReNACer, unemployment was associated with a higher risk (OR 3.58, 95% CI, 1.36-7.37) of in-hospital death after adjusting for demographic variables, educational level, stroke severity, and relevant cardiovascular risk factors. In agreement with previous studies, stroke severity and infarct size also were associated with in-hospital mortality.7,8
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Table 3. Forward step-by-step multiple logistic regression model for in-hospital mortality
Unemployment Stroke severity Lesion size .15 mm
OR (95% CI)
P value
3.58 (1.36-7.37) 3.54 (1.11-10.14) 2.80 (1.18-6.60)
.005 .018 .019
Multivariate analysis adjusted for age, sex, nonacademic centers, health insurance, previous stroke or TIA, and atrial fibrillation. Stroke severity: .2 neurologic deficits on admission.
Data on employment status and ischemic stroke mortality are scarce and sometimes conflicting. Carson et al9 reported an increased stroke risk among employed women, whereas other authors found an increased stroke mortality in unemployed men and women.2,10 There are several plausible explanations for the association between unemployment and in-hospital mortality in patients with ischemic stroke. First, differences in demographic characteristics and a higher prevalence of chronic comorbid conditions (eg, hypertension, smoking, atrial fibrillation) in unemployed patients could explain these findings. Second, unemployment has been associated with an underuse of health care services.11 This low use of medical services could lead to less access to preventive treatments by unemployed individuals. Moreover, employment status is an indicator of socioeconomic background,2 and individuals of low socioeconomic status may have limited access to pharmacologic therapy and may encounter barriers to medical services, such of lack of transportation and no usual place of care.12 Third, low educational level has been associated with an increased mortality from all causes,10 as well as causes related to stroke.13 In our study, the employed patients were more likely to have a higher educational level. Fourth, harmful lifestyle habits acquired during childhood could have conditioned biological differences as well.14 A deleterious preconditioning during childhood, the product of a low socioeconomic background, has been proposed as a strong determinant of cardiovascular risk.14 This detrimental association may result in higher and/or longer exposure to vascular risk factors with limited access to preventive interventions. Finally, as has been documented in patients with acute myocardial infarction,15 social deprivation may predispose to prehospital delays in seeking or getting medical attention. Our study has several limitations. First, the healthy worker effect should be considered as a potential bias.16 Employers might hire healthier people, and alternatively, unhealthy individuals could be more likely to lose their jobs.17 Differences in baseline characteristics between the employed and unemployed patients (eg, prevalence of diabetes mellitus and smoking) may have affected our results. Second, we were not able to assess prestroke
disability. If some patients were unemployed before stroke because they were disabled, it would be reasonable to expect a higher mortality due to this factor. Third, we cannot rule out the possibility of residual confounding, which might have overestimated the magnitude of the effect between employment status and mortality. Fourth, given that a considerable number of patients are not admitted to a hospital after stroke, by considering only patients admitted to the participating hospitals, our conclusions might not be representative of the whole spectrum of cerebrovascular disease in emerging countries.18 Fifth, we cannot rule out a selection bias, given our relatively high proportion of patients with unknown employment status. Finally, the observational design of our study limits its inference of causation. In conclusion, in the present study including patients with an acute ischemic stroke from an emergent country, unemployment was associated with higher in-hospital mortality. Our results remain consistent after adjusting for relevant covariates and after testing several interaction terms. Differences in demographic characteristics and prevalence of chronic comorbid conditions might help explain our findings. This study represents an initial step in the understanding of the early influence of socioeconomic factors and associated variables on stroke outcomes. It also reveals health care implications from unemployment, a current major challenge for governments worldwide, particularly in emerging countries. Strategies targeting individuals at high risk for cardiovascular diseases and poorer outcomes should be implemented with the goal of reducing the impact of cardiovascular diseases.
References 1. World Bank. Global economic prospects: Crisis, finance and growth, 2010. Available from: http://siteresources. worldbank.org/INTGEP2010/Resources/GEP2010-FullReport.pdf/. Accessed December 1, 2010. 2. Fujino Y, Iso H, Tamakoshi A, et al, for the JACC Study Group. A prospective cohort study of employment status and mortality from circulatory disorders among Japanese workers. J Occup Health 2005;47:510-517. 3. Davey Smith G, Blane D, Bartley M. Explanations for socioeconomic differentials in mortality: Evidence from Britain and elsewhere. Eur J Pub Health 1994;4:131-144. 4. World Health Organization. World health statistics 2009. Available from: http://www.who.int/whosis/whostat/ EN_WHS09_Full.pdf. Accessed December 1, 2010. 5. Sposato LA, Esnaola MM, Zamora R, et al, on behalf of ReNACer Investigators and the Argentinean Neurological Society. Quality of ischemic stroke care in emerging countries: The Argentinean National Stroke Registry (ReNACer). Stroke 2008;39:3036-3041. 6. Hatano S. Experience from a multicentre stroke register: A preliminary report. Bull World Health Organ 1976; 54:541-553. 7. Heuschmann PU, Kolominsky-Rabas PL, Misselwitz B, et al, for the German Stroke Registers Study Group. Predictors of in-hospital mortality and attributable risks of
UNEMPLOYMENT: A SOCIAL RISK FACTOR FOR ISCHEMIC STROKE MORTALITY?
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9.
10.
11.
12.
13.
death after ischemic stroke. Arch Intern Med 2004; 164:1761-1768. Baird AE, Dambrosia J, Janket S, et al. A three-item scale for the early prediction of stroke recovery. Lancet 2001; 357:2095-2099. Carson AP, Rose KM, Catellier DJ, et al. Employment status, coronary heart disease, and stroke among women. Ann Epidemiol 2009;19:630-636. Hirokawa K, Tsutusmi A, Kayaba K. Impacts of educational level and employment status on mortality for Japanese women and men: The Jichi Medical School Cohort Study. Eur J Epidemiol 2006;21:641-651. Virtanen P, Kivim€ aki M, Vahtera J, et al. Employment status and differences in the one-year coverage of physician visits: Different needs or unequal Access to services? BMC Health Serv Res 2006;6:126-136. Levine DA, Kiefe CI, Howard G, et al. Reduced medication access: A marker for vulnerability in US stroke survivors. Stroke 2007;38:1557-1564. Avenda~ no M, Kunst AE, Huisman M, et al. Educational level and stroke mortality: A comparison of 10
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European populations during the 1990s. Stroke 2004; 35:432-437. Hinkle LE Jr, Whitney LH, Lehman EW, et al. Occupation, education, and coronary heart disease: Risk is influenced more by education and background than by occupational experiences in the Bell System. Science 1968;161:238-246. Morrison C, Woodward M, Leslie W, et al. Effect of socioeconomic group on incidence of, management of, and survival after myocardial infarction and coronary death: Analysis of a community coronary event register. BMJ 1997;314:541-546. Li CY, Sung FC. A review of the healthy worker effect in occupational epidemiology. Occup Med 1999;49:225-229. Bartley M, Owen C. Relation between socioeconomic status, employment, and health during economic change, 1973-93. BMJ 1996;313:445-449. Smeeton NC, Corbin DOC, Hennis AJ, et al. A comparison of acute and long-term management of stroke patients in Barbados and South London. Cerebrovasc Dis 2009;27:328-335.
Employment is an indicator of socioeconomic status. Unemployment is a worldwide social challenge, especially in emerging countries, accounting for a proportion of the overall higher mortality rates found in these nations. We assessed the relationship between employment status and in-hospital mortality among acute ischemic stroke patients participating in the Argentinean National Stroke Registry (ReNACer), a prospective, country-wide, hospital-based stroke registry aimed at improving quality of stroke care in Argentina. We compared demographic and socioeconomic characteristics, risk factors, acute treatment, and stroke severity between employed and unemployed patients with acute ischemic stroke participating in ReNACer. We developed a multiple logistic regression model to identify predictors of in-hospital mortality. Among the 726 patients with acute ischemic stroke included in the study, 39.5% were unemployed. In-hospital mortality was higher in the patients who were unemployed at the time of the stroke compared with those who were employed (12.0% v 5.0%; P 5 .003). On multivariate analysis, being unemployed (odds ratio [OR], 3.58; 95% confidence interval [CI], 1.36-7.37; P 5 .005), stroke severity (OR, 3.54; 95% CI 1.1110.40; P 5.018), and infarct size .15 mm (OR, 2.80; 95% CI, 1.18-6.60; P 5.019) were associated with in-hospital mortality after adjusting for relevant covariates. Social factors may influence poor outcomes after stroke. In the present study, unemployment was associated with a higher risk of adjusted in-hospital mortality. Strategies targeting individuals at high risk of cardiovascular diseases and poorer outcomes should be implemented to reduce stroke impact. Key Words: Cerebrovascular disease— prognosis—outcome—demographic. Ó 2012 by National Stroke Association
According to the World Bank, high unemployment rates will continue to characterize the global economy in the coming years, with the greatest impact on emerging
countries.1 Employment status is the single most common instrumental variable used by governments, national and international agencies, and other organizations to track
From the *Neurovascular Clinic, INECO, Vascular Research Unit, INECO Foundation, and Stroke Center, Institute of Neurosciences, University Hospital, Favaloro Foundation, Buenos Aires, Argentina; †Neurology Department/Clinical Research Unit, Hospital Privado de la Comunidad, Mar del Plata, Argentina; ‡Neurology Department, Complejo Medico Policial Churruca-Visca, Buenos Aires, Argentina; xNeurology Service, Unidad Asistencial Por Mas Salud, Hospital Dr Cesar Milstein, Buenos Aires, Argentina; and {Stroke Outcomes Research Unit, Departments of Medicine and Health Policy Management and Evaluation, and Institute for Clinical Evaluative Sciences,
St Michael’s Hospital, University of Toronto, Toronto, Ontario, Canada. Received December 2, 2010; accepted February 27, 2011. Supported in part by a VIGI1A grant from the Argentinean Ministry of Health and the World Bank. Address correspondence to Luciano A. Sposato, MD, Pacheco de Melo 1860, Ciudad de Buenos Aires, Argentina. E-mail: lsposato@ ineco.org.ar; [email protected]. 1052-3057/$ - see front matter Ó 2012 by National Stroke Association doi:10.1016/j.jstrokecerebrovasdis.2011.02.018
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the course of an economy. Moreover, at the population level, unemployment is an indicator of economic recession.2 At the individual level, unemployment is associated with lower health status (eg, higher prevalence of comorbid conditions, lower access to care) and poorer clinical outcomes.3 Both unemployment and low socioeconomic status predominate in emerging countries and might account for, at least in part, the higher overall mortality rates seen in these nations.4 Very few studies have analyzed the association between employment status and stroke outcome, and there are no available data on the association between employment status and early outcome of ischemic stroke in emerging countries. We sought to assess the relationship between employment status and in-hospital mortality among acute ischemic stroke patients participating in the Argentinean National Stroke Registry (ReNACer).
Methods ReNACer is a prospective, countrywide, hospitalbased stroke registry. We included in the registry every consecutive patient admitted for an acute stroke to the participating institutions between November 2004 and October 2006. For the present analysis, we considered patients with acute ischemic stroke aged 18 years and older with less than 72 hours between symptom onset and admission to any of the 74 participating institutions. After excluding patients aged .65 years (n 5 1265) and those with subarachnoid hemorrhage (n 5 103), hemorrhagic stroke (n 5 416), and transient ischemic attack (TIA; n 5 74), the study cohort comprised 726 patients with acute ischemic stroke. Data were recorded prospectively on a real-time basis during each patient’s hospital stay and completed after discharge. Further details have been reported elsewhere.5 Every patient underwent head computed tomography, magnetic resonance imaging, or both. We assessed demographic and socioeconomic characteristics, vascular risk factors, stroke severity, and care received. Stroke was defined according to World Health Organization criteria.6 Stroke severity was stratified according to the number of neurologic deficits5,7 and was dichotomized as #2 (mild stroke) or .2 (moderate to severe stroke). Neurologic deficits included in the score were motor deficit, sensory deficit, presence of cortical signs (ie, aphasia, hemianopia, or neglect), dysarthria, ataxia, and involvement of cranial nerves. We selected this score because not all neurologists were certified in or familiar with the administration of the National Institutes of Health Stroke Scale. Employment status was defined as having a continuously paying job in the previous 3 months before stroke onset. Individuals who had no full-time or part-time job during this specified period were classified as ‘‘unemployed.’’ Because of retirement arrangements in Argentina, individuals beyond
age 65 years do not continue in paid employment. Regarding educational level, lack of education was defined as incomplete elementary school or less. Patients were classified as ‘‘low-educated’’ if they had complete elementary school or less. Concerning medical insurance, Argentina’s health insurance system includes workers’ trade union organizations, or ‘‘obras sociales’’ (40%), government-funded social insurance for part of the retired population (PAMI) (8%), and private health insurers and providers (9%). Patients belonging to these groups were considered insured. The uninsured (43%) have access to public hospitals funded by the government. The study’s outcome measure was in-hospital mortality. The study was approved by the Argentinean Ministry of Public Health and local research ethics boards.
Statistical Analysis The c2 and Student t tests were used to compare categorical variables, and the Mann-Whitney U test was used to compare continuous variables. We developed a forward step-by-step multiple logistic regression model to identify variables associated with in-hospital mortality. Age, sex, academic versus nonacademic nature of participating centers, and variables with P values , .20 in univariate analysis were included in the model. Double-sided P values , .05 were considered significant for a variable to be retained in the final logistic regression model. Associations between significant covariates and in-hospital mortality are expressed as odds ratios (ORs) and 95% confidence intervals (95% CIs). Because employment status might be confounded by age and educational level, and considering that stroke severity may be determined by infarct size, we also assessed the following interaction terms in the logistic regression model: employment status 3 educational level, employment status 3 age, employment status 3 sex, and infarct size 3 stroke severity. All statistical analyses was conducted using SPSS version 13.0 (SPSS Inc, Chicago, IL).
Results The study cohort comprised 726 patients with acute ischemic stroke (62.1% male; mean age, 55 6 8 years), of whom 39.5% were unemployed. Baseline characteristics stratified by employment status are shown in Table 1. The unemployed patients were older than their employed counterparts. Male sex, diabetes mellitus, no education, and lack of health insurance were more prevalent in the unemployed patients. The unemployed patients were less likely to be admitted to academic centers and had a lower proportion of smokers. Average length of stay (LOS) and in-hospital mortality were higher in the unemployed patients. Six patients (0.83%) received systemic thrombolysis with recombinant tissue plasminogen activator; of these, 4 were employed and 2 were unemployed (P 5 .78). Antiplatelets were prescribed within the first 48
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Table 1. Baseline characteristics and outcome measures of employed and unemployed patients Employment status Demographic and socioeconomic characteristics Age, years, mean 6 SD Male sex, n (%) Health insurance, n (%) Low educational level, n (%) Incomplete elementary school or less, n (%) Nonacademic center, n (%) Risk factor profile Hypertension, n (%) Previous stroke or TIA, n (%) Diabetes mellitus, n (%) Hyperlipidemia, n (%) Smoking, n (%) Atrial fibrillation, n (%) Stroke severity Infarct size .15 mm, n (%) Moderate to severe stroke, n (%) Outcome measures ALOS, days In-hospital mortality, n (%)
Unemployed
Employed
P value
56.1 6 8.2 104/208 (50.0) 61/185 (33.0) 107/137 (78.1) 9/137 (6.6) 131/208 (63.0)
54.6 6 8.0 241/319 (75.5) 164/311 (52.7) 131/251 (52.2) 3/251 (1.2) 233/319 (73.0)
.04 ,.001 ,.001 ,.001 .003 .015
154/201 (76.6) 53/208 (25.5) 68/207 (32.9) 63/181 (34.8) 59/189 (31.2) 57/208 (27.4)
221/305 (72.5) 78/319 (24.5) 70/318 (22.0) 93/295 (31.5) 132/301 (43.9) 94/319 (29.5)
.29 .79 .006 .46 .005 .61
57/208 (27.4) 22/208 (10.6)
94/319 (29.5) 33/319 (10.3)
.61 .93
11.0 6 18.8 25/208 (12.0)
8.0 6 9.6 16/319 (5.0)
.019 .003
Abbreviation: ALOS, average length of stay. Low educational level refers to complete elementary school or less. Moderate to severe stroke: .2 neurologic deficits.
hours after admission to 82.8% of employed patients and 74.5% of unemployed patients (P 5 .022). As shown in Table 2, unemployment, health insurance, low educational level, incomplete elementary school or less, previous stroke or TIA, atrial fibrillation, stroke severity, and infarct size were associated with higher inhospital mortality on univariate analysis (Table 2). Table 2. Univariate analysis for in-hospital mortality OR (95% CI) Age Male sex Nonacademic center Unemployment Health insurance Low educational level Incomplete elementary school or less Hypertension Previous stroke or TIA Diabetes mellitus Hyperlipidemia Smoking Atrial fibrillation Stroke severity Infarct size .15 mm
0.98 (0.94-1.02) 1.39 (0.78-2.48) 1.40 (0.75-2.61) 2.58 (1.34-4.97) 0.50 (0.27-0.92) 1.88 (0.83-4.25) 7.05 (2.05-24.26) 0.97 (0.52-1.83) 1.49 (0.85-2.64) 1.21 (0.67-2.17) 1.33 (0.74-2.39) 1.03 (0.58-1.83) 2.55 (1.31-4.97) 2.10 (1.04-4.24) 4.22 (2.43-7.34)
P value .33 .26 .29 .004 .026 .12 .002 .93 .17 .52 .34 .92 .006 .039 ,.001
Low educational level refers to complete elementary school or less. Health insurance refers to patients whose medical costs are covered by ‘‘obras socials,’’ private health insurers, or PAMI (see Materials and Methods). Stroke severity (moderate to severe stroke): .2 neurologic deficits.
The results of the multivariate analysis are presented in Table 3. Because stroke severity is related to infarct size, we assessed the interaction term moderate to severe stroke 3 infarct size .15 mm. This term was associated with in-hospital mortality (OR, 1.89; 95% CI, 1.27-2.83; P 5.002). After including this interaction term in the logistic regression model, unemployment also was a significant determinant of in-hospital mortality (OR 3.34, 95% CI 1.417.92; P 5 .002). Considering that age, sex, and educational level are determinants of retirement, we assessed the interaction terms unemployment 3 age, unemployment 3 sex, and unemployment 3 low educational level. None of those terms was associated with in-hospital mortality.
Discussion Employment status is a major indicator of socioeconomic background, and unemployment is an ongoing concern for policy-makers in emerging countries because of its direct link to health inequalities. Limited data are available on the effect of unemployment on early mortality. This effect is more relevant for emergent countries such as Argentina. In the present study, which included patients with an acute ischemic stroke participating in ReNACer, unemployment was associated with a higher risk (OR 3.58, 95% CI, 1.36-7.37) of in-hospital death after adjusting for demographic variables, educational level, stroke severity, and relevant cardiovascular risk factors. In agreement with previous studies, stroke severity and infarct size also were associated with in-hospital mortality.7,8
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Table 3. Forward step-by-step multiple logistic regression model for in-hospital mortality
Unemployment Stroke severity Lesion size .15 mm
OR (95% CI)
P value
3.58 (1.36-7.37) 3.54 (1.11-10.14) 2.80 (1.18-6.60)
.005 .018 .019
Multivariate analysis adjusted for age, sex, nonacademic centers, health insurance, previous stroke or TIA, and atrial fibrillation. Stroke severity: .2 neurologic deficits on admission.
Data on employment status and ischemic stroke mortality are scarce and sometimes conflicting. Carson et al9 reported an increased stroke risk among employed women, whereas other authors found an increased stroke mortality in unemployed men and women.2,10 There are several plausible explanations for the association between unemployment and in-hospital mortality in patients with ischemic stroke. First, differences in demographic characteristics and a higher prevalence of chronic comorbid conditions (eg, hypertension, smoking, atrial fibrillation) in unemployed patients could explain these findings. Second, unemployment has been associated with an underuse of health care services.11 This low use of medical services could lead to less access to preventive treatments by unemployed individuals. Moreover, employment status is an indicator of socioeconomic background,2 and individuals of low socioeconomic status may have limited access to pharmacologic therapy and may encounter barriers to medical services, such of lack of transportation and no usual place of care.12 Third, low educational level has been associated with an increased mortality from all causes,10 as well as causes related to stroke.13 In our study, the employed patients were more likely to have a higher educational level. Fourth, harmful lifestyle habits acquired during childhood could have conditioned biological differences as well.14 A deleterious preconditioning during childhood, the product of a low socioeconomic background, has been proposed as a strong determinant of cardiovascular risk.14 This detrimental association may result in higher and/or longer exposure to vascular risk factors with limited access to preventive interventions. Finally, as has been documented in patients with acute myocardial infarction,15 social deprivation may predispose to prehospital delays in seeking or getting medical attention. Our study has several limitations. First, the healthy worker effect should be considered as a potential bias.16 Employers might hire healthier people, and alternatively, unhealthy individuals could be more likely to lose their jobs.17 Differences in baseline characteristics between the employed and unemployed patients (eg, prevalence of diabetes mellitus and smoking) may have affected our results. Second, we were not able to assess prestroke
disability. If some patients were unemployed before stroke because they were disabled, it would be reasonable to expect a higher mortality due to this factor. Third, we cannot rule out the possibility of residual confounding, which might have overestimated the magnitude of the effect between employment status and mortality. Fourth, given that a considerable number of patients are not admitted to a hospital after stroke, by considering only patients admitted to the participating hospitals, our conclusions might not be representative of the whole spectrum of cerebrovascular disease in emerging countries.18 Fifth, we cannot rule out a selection bias, given our relatively high proportion of patients with unknown employment status. Finally, the observational design of our study limits its inference of causation. In conclusion, in the present study including patients with an acute ischemic stroke from an emergent country, unemployment was associated with higher in-hospital mortality. Our results remain consistent after adjusting for relevant covariates and after testing several interaction terms. Differences in demographic characteristics and prevalence of chronic comorbid conditions might help explain our findings. This study represents an initial step in the understanding of the early influence of socioeconomic factors and associated variables on stroke outcomes. It also reveals health care implications from unemployment, a current major challenge for governments worldwide, particularly in emerging countries. Strategies targeting individuals at high risk for cardiovascular diseases and poorer outcomes should be implemented with the goal of reducing the impact of cardiovascular diseases.
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