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      Effect of Socioeconomic Status on Mortality after Bacteremia in Working-Age Patients. A Danish Population-Based Cohort Study

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      1 , 2 , * , 2 , 1 , 2 , 1 , 2 , the Danish Collaborative Bacteremia Network (DACOBAN)
      PLoS ONE
      Public Library of Science

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          Abstract

          Objectives

          To examine the effect of socioeconomic status (SES) on mortality in patients with bacteremia and the underlying factors that may mediate differences in mortality.

          Methods

          We conducted a population-based cohort study in two Danish regions. All patients 30 to 65 years of age with first time bacteremia from 2000 through 2008 were identified in a population-based microbiological bacteremia database (n = 8,653). Individual-level data on patients’ SES (educational level and personal income) and comorbid conditions were obtained from public and medical registries. We used Cox regression to examine mortality within 30 days after bacteremia with and without cumulative adjustment for potential mediators.

          Results

          Bacteremia patients of low SES were more likely to live alone and be unmarried than patients of high SES. They also had more pre-existing comorbidity, more substance abuse, more Staphylococcus aureus and nosocomial infections, and more admissions to small nonteaching hospitals. Overall, 1,374 patients (15.9%) died within 30 days of follow-up. Patients of low SES had consistently higher mortality after bacteremia than those of high SES crude hazard ratio for low vs. high education, 1.38 [95% confidence interval (CI), 1.18–1.61]; crude hazard ratio for low-income vs. high-income tertile, 1.58 [CI, 1.39–1.80]. Adjustment for differences in social support, pre-existing comorbidity, substance abuse, place of acquisition of the infection, and microbial agent substantially attenuated the effect of SES on mortality (adjusted hazard ratio for low vs. high education, 1.15 [95% CI, 0.98–1.36]; adjusted hazard ratio for low-income vs. high-income tertile, 1.29 [CI, 1.12–1.49]). Further adjustment for characteristics of the admitting hospital had minimal effect on observed mortality differences.

          Conclusions

          Low SES was strongly associated with increased 30-day mortality after bacteremia. Less social support, more pre-existing comorbidity, more substance abuse, and differences in place of acquisition and agent of infection appeared to mediate much of the observed disparities in mortality.

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          Most cited references24

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          The natural history of the systemic inflammatory response syndrome (SIRS). A prospective study.

          Define the epidemiology of the four recently classified syndromes describing the biologic response to infection: systemic inflammatory response syndrome (SIRS), sepsis, severe sepsis, and septic shock. Prospective cohort study with a follow-up of 28 days or until discharge if earlier. Three intensive care units and three general wards in a tertiary health care institution. Patients were included if they met at least two of the criteria for SIRS: fever or hypothermia, tachycardia, tachypnea, or abnormal white blood cell count. Development of any stage of the biologic response to infection: sepsis, severe sepsis, septic shock, end-organ dysfunction, and death. During the study period 3708 patients were admitted to the survey units, and 2527 (68%) met the criteria for SIRS. The incidence density rates for SIRS in the surgical, medical, and cardiovascular intensive care units were 857, 804, and 542 episodes per 1000 patient-days, respectively, and 671, 495, and 320 per 1000 patient-days for the medical, cardiothoracic, and general surgery wards, respectively. Among patients with SIRS, 649 (26%) developed sepsis, 467 (18%) developed severe sepsis, and 110 (4%) developed septic shock. The median interval from SIRS to sepsis was inversely correlated with the number of SIRS criteria (two, three, or all four) that the patients met. As the population of patients progressed from SIRS to septic shock, increasing proportions had adult respiratory distress syndrome, disseminated intravascular coagulation, acute renal failure, and shock. Positive blood cultures were found in 17% of patients with sepsis, in 25% with severe sepsis, and in 69% with septic shock. There were also stepwise increases in mortality rates in the hierarchy from SIRS, sepsis, severe sepsis, and septic shock: 7%, 16%, 20%, and 46%, respectively. Of interest, we also observed equal numbers of patients who appeared to have sepsis, severe sepsis, and septic shock but who had negative cultures. They had been prescribed empirical antibiotics for a median of 3 days. The cause of the systemic inflammatory response in these culture-negative populations is unknown, but they had similar morbidity and mortality rates as the respective culture-positive populations. This prospective epidemiologic study of SIRS and related conditions provides, to our knowledge, the first evidence of a clinical progression from SIRS to sepsis to severe sepsis and septic shock.
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            Education, income, and occupational class cannot be used interchangeably in social epidemiology. Empirical evidence against a common practice.

            S. Geyer (2006)
            Education, income, and occupational class are often used interchangeably in studies showing social inequalities in health. This procedure implies that all three characteristics measure the same underlying phenomena. This paper questions this practice. The study looked for any independent effects of education, income, and occupational class on four health outcomes: diabetes prevalence, myocardial infarction incidence and mortality, and finally all cause mortality in populations from Sweden and Germany. Sweden: follow up of myocardial infarction mortality and all cause mortality in the entire population, based on census linkage to the Cause of Death Registry. Germany: follow up of myocardial infarction morbidity and all cause mortality in statutory health insurance data, plus analysis of prevalence data on diabetes. Multiple regression analyses were performed to calculate the effects of education, income, and occupational class before and after mutual adjustments. Sweden (all residents aged 25-64) and Germany (Mettman district, Nordrhein-Westfalen, all insured persons aged 25-64). Correlations between education, income, and occupational class were low to moderate. Which of these yielded the strongest effects on health depended on type of health outcome in question. For diabetes, education was the strongest predictor and for all cause mortality it was income. Myocardial infarction morbidity and mortality showed a more mixed picture. In mutually adjusted analyses each social dimension had an independent effect on each health outcome in both countries. Education, income, and occupational class cannot be used interchangeably as indicators of a hypothetical latent social dimension. Although correlated, they measure different phenomena and tap into different causal mechanisms.
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              Statistics review 12: Survival analysis

              This review introduces methods of analyzing data arising from studies where the response variable is the length of time taken to reach a certain end-point, often death. The Kaplan–Meier methods, log rank test and Cox's proportional hazards model are described.
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                Author and article information

                Contributors
                On behalf of : for the Danish Collaborative Bacteremia Network (DACOBAN)
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                25 July 2013
                : 8
                : 7
                : e70082
                Affiliations
                [1 ]Department of Clinical Microbiology, Aalborg University Hospital, Aalborg, Denmark
                [2 ]Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
                University of Cambridge, United Kingdom
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: KK MN HCS MS. Performed the experiments: KK MN HCS RWT MS. Analyzed the data: KK. Contributed reagents/materials/analysis tools: KK MN HCS RWT MS. Wrote the paper: KK. Data management: KK. Critical revision of the manuscript: KK MN HCS RWT MS. Final approval of the submitted manuscript: KK MN HCS RWT MS.

                Article
                PONE-D-13-04511
                10.1371/journal.pone.0070082
                3723741
                23936145
                a4ec3205-4da7-4e7b-a214-cfa66d1abd2f
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 January 2013
                : 16 June 2013
                Page count
                Pages: 10
                Funding
                This study received financial support from the Institute of Clinical Medicine, Aarhus University Hospital, Denmark. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine
                Clinical Research Design
                Cohort Studies
                Epidemiology
                Observational Studies
                Critical Care and Emergency Medicine
                Sepsis
                Epidemiology
                Clinical Epidemiology
                Infectious Disease Epidemiology
                Social Epidemiology
                Infectious Diseases
                Bacterial Diseases
                Bacteremia
                Public Health
                Socioeconomic Aspects of Health

                Uncategorized
                Uncategorized

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