Relationship between neighborhood poverty rate and bloodstream infections in the critically ill*

Mendu, Mallika L.; Zager, Sam; Gibbons, Fiona K.; Christopher, Kenneth B.

doi:10.1097/ccm.0b013e318241e51e

Cited by 31 publications

(30 citation statements)

References 99 publications

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“…Analogously, Mendu et al (2012) elucidated that neighborhoods with greater than 20% poverty rates had 32% increased odds of community-acquired infections compared to those with neighborhood poverty rate <5%. 18 Additionally, we found that race was a significant determinant in regional disparities of sepsis mortality. Many epidemiolgic studies based on hospital discharge records have reported that black persons have higher rates of sepsis, hospitalization mortality, and are twice as likely to develop sepsis as white persons.…”

Section: Discussionmentioning

confidence: 76%

“…18-21 Numerous studies have shown that uninsured critically ill patients are less likely to receive life-saving critical care procedures, receive less post acute care during critical illness recovery, and have increased mortality compared to those with insurance. 22-27 As seen in our study, strongly clustered counties had greater socioeconomic and access to care disparities.…”

Section: Discussionmentioning

confidence: 99%

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Defining Sepsis Mortality Clusters in the United States*

Moore

Donnelly

Griffin

et al. 2016

Critical Care Medicine

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Objective In the United States (US) sepsis is a major public health problem accounting for over 200,000 annual deaths. The aims of this study were to identify US counties with high sepsis mortality and to assess the community characteristics associated with sepsis mortality. Design We performed a descriptive analysis of 2003 through 2012 Compressed Mortality File (CMF) data summarized at the county level. We defined sepsis deaths as deaths associated with an infection as the CMF is derived from death certificates and classified according to the International Classification of Diseases, Version 10 (ICD-10). Measurements We identified county-level sepsis clustering groups: strongly clustered, moderately clustered, and non-clustered. We approximated the mean crude, age-adjusted, and community-adjusted sepsis mortality rates nationally and for clustering groups. We contrasted demographic and community characteristics between clustering groups. We performed logistic regression for the association between strongly clustered counties and community characteristics. Main Results Among 3,108 US counties, the age-adjusted sepsis mortality rate was 59.6 deaths per 100,000 persons (95% CI: 58.9 – 60.4). Among 161 (5.2%) counties categorized as strongly clustered, the age-adjusted sepsis mortality rate was 93.1 deaths per 100,000 persons (95% CI: 90.5 – 95.7). Strongly clustered sepsis counties were more likely to be located in the South (92.6%, p <0.001), had lower education, larger population in poverty, without medical insurance, and higher unemployment rates (p < 0.001). Conclusion Sepsis mortality clustering is prevalent in Southern US, with three definitive sepsis clusters: “Mississippi Valley”, “Middle Georgia”, and “Central Appalachia,” characterized by lower education, income, employment and insurance coverage.

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Section: Discussionmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Defining Sepsis Mortality Clusters in the United States*

Moore

Donnelly

Griffin

et al. 2016

Critical Care Medicine

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“…In a review of 38,917 ICU admissions at 2 academic hospitals in Boston, neighbourhood poverty rate was not associated with 30-day, 90-day, 365-day, or hospital mortality [15]. However, within this same cohort, patients living in neighborhoods with high poverty rates were significantly more likely to have positive blood cultures within 48 hours of admission to ICU [16]. Neighbourhood poverty rate was ascertained using residential addresses and thus individuals without a fixed address were systematically excluded from these studies.…”

Section: Discussionmentioning

confidence: 99%

Characteristics, clinical course, and outcomes of homeless and non-homeless patients admitted to ICU: A retrospective cohort study

et al. 2017

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BackgroundLittle is known about homeless patients in intensive care units (ICUs).ObjectivesTo compare clinical characteristics, treatments, and outcomes of homeless to non-homeless patients admitted to four ICUs in a large inner-city academic hospital.Methods63 randomly-selected homeless compared to 63 age-, sex-, and admitting-ICU-matched non-homeless patients.ResultsCompared to matched non-homeless, homeless patients (average age 48±12 years, 90% male, 87% admitted by ambulance, 56% mechanically ventilated, average APACHE II 17) had similar comorbidities and illness severity except for increased alcohol (70% vs 17%,p<0.001) and illicit drug(46% vs 8%,p<0.001) use and less documented hypertension (16% vs 40%,p = 0.005) or prescription medications (48% vs 67%,p<0.05). Intensity of ICU interventions was similar except for higher thiamine (71% vs 21%,p<0.0001) and nicotine (38% vs 14%,p = 0.004) prescriptions. Homeless patients exhibited significantly lower Glasgow Coma Scores and significantly more bacterial respiratory cultures. Longer durations of antibiotics, vasopressors/inotropes, ventilation, ICU and hospital lengths of stay were not statistically different, but homeless patients had higher hospital mortality (29% vs 8%,p = 0.005). Review of all deaths disclosed that withdrawal of life-sustaining therapy occurred in similar clinical circumstances and proportions in both groups, regardless of family involvement. Using multivariable logistic regression, homelessness did not appear to be an independent predictor of hospital mortality.ConclusionsHomeless patients, admitted to ICU matched to non-homeless patients by age and sex (characteristics most commonly used by clinicians), have higher hospital mortality despite similar comorbidities and illness severity. Trends to longer durations of life supports may have contributed to the higher mortality. Additional research is required to validate this higher mortality and develop strategies to improve outcomes in this vulnerable population.

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“…Prior studies have found correlations between a wide range of neighborhood social characteristics and health care outcomes [19–23]. Using the percentage of African American residents by ZIP code as the primary predictor variable, Rodriguez et al [21] analyzed the time from day 90 of end-stage renal disease to death from any cause and time to first renal transplantation of adult dialysis patients.…”

Section: Introductionmentioning

confidence: 99%

“…Colvin et al [22] further emphasized the effect of SES on mortality by showing an inverse linear association between SES and in-hospital pediatric mortality. In adult patients with critical illness, Mendu et al [23] found that neighborhood poverty rate was associated with blood stream infection near critical care initiation. The adjusted risk of community-acquired bloodstream infection was 1.32 (95 % CI 1.11–1.56; p = 0.002) and 1.51 (95 % CI 1.04–2.20; p = 0.03) for patients from neighborhoods with 20–40 and >40 % poverty rates, respectively, compared to patients with a neighborhood poverty rate <5 %.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Neighborhood and Individual Characteristics on Pediatric Critical Illness

et al. 2014

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The relationship between neighborhood/individual characteristics and pediatric intensive care unit (PICU) outcomes is largely unexplored. We hypothesized that individual- level racial/ethnic minority status and neighborhood-level low socioeconomic status and minority concentration would adversely affect children’s severity of illness on admission to the PICU. Retrospective analyses (1/1/2007–5/ 23/2011) of clinical, geographic, and demographic data were conducted at an academic, tertiary children’s hospital PICU. Clinical data included age, diagnosis, insurance, race/ethnicity, Pediatric Index of Mortality 2 score on presentation to the PICU (PIM2), and mortality. Residential addresses were geocoded and linked with 2010 US Census tract data using geographic information systems geocoding techniques. Repeated measures models to predict PIM2 and mortality were constructed using three successive models with theorized covariates including the patient’s race/ethnicity, the predominant neighborhood racial/ethnic group, interactions between patient race/ethnicity and neighborhood race/ethnicity, neighborhood socioeconomic status, and insurance type. Of the 5,390 children, 57.8 %were Latino and 70.1 %possessed government insurance. Latino children (β = 0.31; p < 0.01), especially Latino children living in a Latino ethnic enclave (β = 1.13; p < 0.05), had higher PIM2 scores compared with non-Latinos. Children with government insurance (β = 0.29; p < 0.01) had higher PIM2 scores compared to children with other payment types and median neighborhood income was inversely associated with PIM2 scores (β = −0.04 per $10,000/year of income; p < 0.05). Lower median neighborhood income, Latino ethnicity, Latino children living in a predominantly Latino neighborhood, and children possessing government insurance were associated with a higher severity of illness on PICU admission. The reasons why these factors affect critical illness severity require further exploration.

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Relationship between neighborhood poverty rate and bloodstream infections in the critically ill*

Abstract: Within the limitations of our study design, increased neighborhood poverty rate, a proxy for decreased socioeconomic status, appears to be associated with risk of bloodstream infection among patients who receive critical care.

Cited by 31 publications

References 99 publications

Defining Sepsis Mortality Clusters in the United States*

Defining Sepsis Mortality Clusters in the United States*

Characteristics, clinical course, and outcomes of homeless and non-homeless patients admitted to ICU: A retrospective cohort study

The Effect of Neighborhood and Individual Characteristics on Pediatric Critical Illness

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