The association between deprivation and hospital mortality for admissions to critical care units in England

Equity in the provision of helicopter emergency medical services in the United Kingdom: a geospatial analysis using indices of multiple deprivation

BACKGROUND

Helicopter Emergency Medical Services (HEMS) in the United Kingdom (UK) are provided in a mixed funding model, with the majority of services funded by charities alongside a small number of government-funded operations. More socially-deprived communities are known to have greater need for critical care, such as that provided by HEMS in the UK. Equity of access is an important pillar of medical care, describing how resource should be allocated on the basis of need; a concept that is particularly relevant to resource-intensive services such as HEMS. However, the Inverse Care Law describes the tendency of healthcare provision to vary inversely with population need, where healthcare resource does not meet the expected needs in areas of higher deprivation. It is not known to what extent the Inverse Care Law applies to HEMS in the UK.

METHODS

Modelled service areas were created with each small unit geography locus in the UK assigned to its closest HEMS operational base. The total population, median decile on index of multiple deprivation, and geographic area for each modelled service area was determined from the most recently available national statistics. Linear regression was used to determine the association between social deprivation, geographic area, and total population served for each modelled service area.

RESULTS

The provision of HEMS in the UK varied inversely to expected population need; with HEMS operations in more affluent areas serving smaller populations. The model estimated that population decreases by 18% (95% confidence interval 1-32%) for each more affluent point in median decile of index of multiple deprivation. There was no significant association between geographic area and total population served.

CONCLUSION

The provision of HEMS in the UK is consistent with the Inverse Care Law. HEMS operations in more deprived areas serve larger populations, thus providing a healthcare resource inversely proportional with the expected needs of these communities. Funding structures may explain this variation as charities are more highly concentrated in more affluent areas.

Association of socioeconomic deprivation with outcomes in critically ill adult patients: an observational prospective multicenter cohort study

BACKGROUND

The influence of socioeconomic deprivation on health inequalities is established, but its effect on critically ill patients remains unclear, due to inconsistent definitions in previous studies.

METHODS

Prospective multicenter cohort study conducted from March to June 2018 in eight ICUs in the Greater Paris area. All admitted patients aged ≥ 18 years were enrolled. Socioeconomic phenotypes were identified using hierarchical clustering, based on education, health insurance, income, and housing. Association of phenotypes with 180-day mortality was assessed using Cox proportional hazards models.

RESULTS

A total of 1,748 patients were included. Median age was 62.9 [47.4-74.5] years, 654 (37.4%) patients were female, and median SOFA score was 3 [1-6]. Study population was clustered in five phenotypes with increasing socioeconomic deprivation. Patients from phenotype A (n = 958/1,748, 54.8%) were without socioeconomic deprivation, patients from phenotype B (n = 273/1,748, 15.6%) had only lower education levels, phenotype C patients (n = 117/1,748, 6.7%) had a cumulative burden of 1[1-2] deprivations and all had housing deprivation, phenotype D patients had 2 [1-2] deprivations, all of them with income deprivation, and phenotype E patients (n = 93/1,748, 5.3%) included patients with 3 [2-4] deprivations and included all patients with health insurance deprivation. Patients from phenotypes D and E were younger, had fewer comorbidities, more alcohol and opiate use, and were more frequently admitted due to self-harm diagnoses. Patients from phenotype C (predominant housing deprivation), were more frequently admitted with diagnoses related to chronic respiratory diseases and received more non-invasive positive pressure ventilation. Following adjustment for age, sex, alcohol and opiate use, socioeconomic phenotypes were not associated with increased 180-day mortality: phenotype A (reference); phenotype B (hazard ratio [HR], 0.85; 95% confidence interval CI 0.65-1.12); phenotype C (HR, 0.56; 95% CI 0.34-0.93); phenotype D (HR, 1.09; 95% CI 0.78-1.51); phenotype E (HR, 1.20; 95% CI 0.73-1.96).

CONCLUSIONS

In a universal health care system, the most deprived socioeconomic phenotypes were not associated with increased 180-day mortality. The most disadvantaged populations exhibit distinct characteristics and medical conditions that may be addressed through targeted public health interventions.

Linking Sepsis with chronic arterial hypertension, diabetes mellitus, and socioeconomic factors in the United States: A scoping review

RATIONALE

Sepsis is a syndrome of life-threatening organ dysfunction caused by a dysregulated host immune response to infection. Social risk factors including location and poverty are associated with sepsis-related disparities. Understanding the social and biological phenotypes linked with the incidence of sepsis is warranted to identify the most at-risk populations. We aim to examine how factors in disadvantage influence health disparities related to sepsis.

METHODS

A scoping review was performed for English-language articles published in the United States from 1990 to 2022 on PubMed, Web of Science, and Scopus. Of the 2064 articles found, 139 met eligibility criteria and were included for review.

RESULTS

There is consistency across the literature of disproportionately higher rates of sepsis incidence, mortality, readmissions, and associated complications, in neighborhoods with socioeconomic disadvantage and significant poverty. Chronic arterial hypertension and diabetes mellitus also occur more frequently in the same geographic distribution as sepsis, suggesting a potential shared pathophysiology.

CONCLUSIONS

The distribution of chronic arterial hypertension, diabetes mellitus, social risk factors associated with socioeconomic disadvantage, and sepsis incidence, are clustered in specific geographical areas and linked by endothelial dysfunction. Such population factors can be utilized to create equitable interventions aimed at mitigating sepsis incidence and sepsis-related disparities.

Association Between Socioeconomic Status and Outcomes in Critical Care: A Systematic Review and Meta-Analysis

OBJECTIVES

Socioeconomic status is well established as a key determinant of inequalities in health outcomes. Existing literature examining the impact of socioeconomic status on outcomes in critical care has produced inconsistent findings. Our objective was to synthesize the available evidence on the association between socioeconomic status and outcomes in critical care.

DATA SOURCES

A systematic search of CINAHL, Ovid MEDLINE, and EMBASE was undertaken on September 13, 2022.

STUDY SELECTION

Observational cohort studies of adults assessing the association between socioeconomic status and critical care outcomes including mortality, length of stay, and functional outcomes were included. Two independent reviewers assessed titles, abstracts, and full texts against eligibility and quality criteria.

DATA EXTRACTION

Details of study methodology, population, exposure measures, and outcomes were extracted.

DATA SYNTHESIS

Thirty-eight studies met eligibility criteria for systematic review. Twenty-three studies reporting mortality to less than or equal to 30 days following critical care admission, and eight reporting length of stay, were included in meta-analysis. Random-effects pooled analysis showed that lower socioeconomic status was associated with higher mortality at less than or equal to 30 days following critical care admission, with pooled odds ratio of 1.13 (95% CIs, 1.05-1.22). Meta-analysis of ICU length of stay demonstrated no significant difference between socioeconomic groups. Socioeconomic status may also be associated with functional status and discharge destination following ICU admission.

CONCLUSIONS

Lower socioeconomic status was associated with higher mortality following admission to critical care.

Patterns of Healthcare Resource Utilisation of Critical Care Survivors between 2006 and 2017 in Wales: A Population-Based Study

In this retrospective cohort study, we used the Secure Anonymised Information Linkage (SAIL) Databank to characterise and identify predictors of the one-year post-discharge healthcare resource utilisation (HRU) of adults who were admitted to critical care units in Wales between 1 April 2006 and 31 December 2017. We modelled one-year post-critical-care HRU using negative binomial models and used linear models for the difference from one-year pre-critical-care HRU. We estimated the association between critical illness and post-hospitalisation HRU using multilevel negative binomial models among people hospitalised in 2015. We studied 55,151 patients. Post-critical-care HRU was 11-87% greater than pre-critical-care levels, whereas emergency department (ED) attendances decreased by 30%. Age ≥50 years was generally associated with greater post-critical-care HRU; those over 80 had three times longer hospital readmissions than those younger than 50 (incidence rate ratio (IRR): 2.96, 95% CI: 2.84, 3.09). However, ED attendances were higher in those younger than 50. High comorbidity was associated with 22-62% greater post-critical-care HRU than no or low comorbidity. The most socioeconomically deprived quintile was associated with 24% more ED attendances (IRR: 1.24 [1.16, 1.32]) and 13% longer hospital stays (IRR: 1.13 [1.09, 1.17]) than the least deprived quintile. Critical care survivors had greater 1-year post-discharge HRU than non-critical inpatients, including 68% longer hospital stays (IRR: 1.68 [1.63, 1.74]). Critical care survivors, particularly those with older ages, high comorbidity, and socioeconomic deprivation, used significantly more primary and secondary care resources after discharge compared with their baseline and non-critical inpatients. Interventions are needed to ensure that key subgroups are identified and adequately supported.

Screening for Social Risk Factors in the ICU During the Pandemic

Due to limitations in data collected through electronic health records, the social risk factors (SRFs) that predate severe illness and restrict access to critical care services are poorly understood.

OBJECTIVES

This study explored the feasibility and utility of directly eliciting SRFs in the ICU by implementing a screening program.

DESIGN SETTING AND PARTICIPANTS

Five hundred sixty-six critically ill patients at the medical ICU of Robert Wood Johnson University Hospital from July 1, 2019, to September 31, 2021, were interviewed for SRFs using an adapted version of the American Academy of Family Physicians' Social Needs Screening Tool.

MAIN OUTCOMES AND MEASURES

For each SRFs, we compared basic demographic factors, proxies of socioeconomic status, and severity score between those with and without the SRFs through chi-square tests and Wilcoxon rank-sum tests. Furthermore, we determined the prevalence of SRFs overall, before, and during the COVID-19 pandemic.

RESULTS

Of critically ill patients, 39.58% reported at least one SRF. Age, zip-code matched median household income, and insurance type differed depending on the SRFs. Notably, patients with SRFs were admitted with a lower average severity score, indicating reduced risk in mortality. Since March 2020, the prevalence of SRFs in the ICU overall fell from 54.47% to 35.44%. Conversely, the proportion of patients unable to afford healthcare increased statistically significantly from 7.32% to 18.06%.

CONCLUSIONS AND RELEVANCE

Screening for SRFs in the ICU detected the presence of disproportionally low-risk patients whose access to critical care services became restricted throughout the pandemic.

The relative impact of socioeconomic position and frailty varies by population setting

Introduction

Frailty and socioeconomic position (SEP) are well-established determinants of health. However, we know less about the contributions of frailty and SEP in older adults, especially in acute settings. We set out to answer how frailty and SEP might influence health outcomes in older people, comparing a population sample and patients managed by a speciality acute frailty service.

Methods

We used the Delirium and Population Health Informatics Cohort, a population sample of 1510 individuals aged ≥70 years from the London Borough of Camden and 1750 acute frailty patients. SEP was determined using the Index of Multiple Deprivation. Linear and Cox proportional hazard regression models were conducted to assess SEP on frailty, readmission, and mortality outcomes.

Results

In the population sample, SEP was significantly associated with frailty and mortality with successive increases in rate of death for each IMD quintile (HR = 1.28, 95% CI 1.11 to 1.49, P < 0.005). Increasing SEP, age, and admission status among hospitalized individuals were associated with greater frailty. For individuals seen by the speciality frailty service, SEP was not associated with frailty, mortality, or readmission.

Discussion

When older people experience acute illness severe enough to require secondary care, particularly specialist services, this overcomes any prior advantages conferred by a higher SEP.

Nationwide case-control study of risk factors and outcomes for community-acquired sepsis

Sepsis is the main cause of death in the intensive care units (ICU) and increasing incidences of ICU admissions for sepsis are reported. Identification of patients at risk for sepsis and poor outcome is therefore of outmost importance. We performed a nation-wide case-control study aiming at identifying and quantifying the association between co-morbidity and socio-economic factors with intensive care admission for community-acquired sepsis. We also explored 30-day mortality. All adult patients (n = 10,072) with sepsis admitted from an emergency department to an intensive care unit in Sweden between 2008 and 2017 and a control population (n = 50,322), matched on age, sex and county were included. In the sepsis group, 69% had a co-morbid condition at ICU admission, compared to 31% in the control group. Multivariable conditional logistic regression analysis was performed and there was a large variation in the influence of different risk factors associated with ICU-admission, renal disease, liver disease, metastatic malignancy, substance abuse, and congestive heart failure showed the strongest associations. Low income and low education level were more common in sepsis patients compared to controls. The adjusted OR for 30-day mortality for sepsis patients was 132 (95% CI 110-159) compared to controls.

An Innovative Individual-Level Socioeconomic Measure Predicts Critical Care Outcomes in Older Adults: A Population-Based Study

BACKGROUND

Little is known about the impact of socioeconomic status (SES) as a key element of social determinants of health on intensive care unit (ICU) outcomes for adults.

OBJECTIVE

We assessed whether a validated individual SES index termed HOUSES (HOUsing-based SocioEconomic status index) derived from housing features was associated with short-term outcomes of critical illness including ICU mortality, ICU-free days, hospital-free days, and ICU readmission.

METHODS

We performed a population-based cohort study of adult patients living in Olmsted County, Minnesota, admitted to 7 intensive care units at Mayo Clinic from 2011 to 2014. We compared outcomes between the lowest SES group (HOUSES quartile 1 [Q1]) and the higher SES group (HOUSES Q2-4). We stratified the cohort based on age (<50 years old and ≥50 years old).

RESULTS

Among 4134 eligible patients, 3378 (82%) patients had SES successfully measured by the HOUSES index. Baseline characteristics, severity of illness, and reason for ICU admission were similar among the different SES groups as measured by HOUSES except for larger number of intoxications and overdoses in younger patients from the lowest SES. In all adult patients, there were no overall differences in mortality, ICU-free days, hospital-free days, or ICU readmissions in patients with higher SES compared to lower SES. Among older patients (>50 years), those with higher SES (HOUSES Q2-4) compared to those with lower SES (HOUSES Q1) had lower mortality rates (hazard ratio = 0.72; 95% CI: 0.56-0.93; adjusted P = .01), increased ICU-free days (mean 1.08 days; 95% CI: 0.34-1.84; adjusted P = .004), and increased hospital-free days (mean 1.20 days; 95% CI: 0.45-1.96; adjusted P = .002). There were no differences in ICU readmission rates (OR = 0.74; 95% CI: 0.55-1.00; P = .051).

CONCLUSION

Individual-level SES may be an important determinant or predictor of critical care outcomes in older adults. Housing-based socioeconomic status may be a useful tool for enhancing critical care research and practice.

Associations Between Socioeconomic Status, Patient Risk, and Short-Term Intensive Care Outcomes

OBJECTIVES

To investigate the association of socioeconomic status as measured by the average socioeconomic status of the area where a person resides on short-term mortality in adults admitted to an ICU in Queensland, Australia.

DESIGN

Secondary data analysis using de-identified data from the Australian and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation linked to the publicly available area-level Index of Relative Socioeconomic Advantage and Disadvantage from the Australian Bureau of Statistics.

SETTING

Adult ICUs from 35 hospitals in Queensland, Australia, from 2006 to 2015.

PATIENTS

A total of 218,462 patient admissions.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The outcome measure was inhospital mortality. The main study variable was decile of Index of Relative Socioeconomic Advantage and Disadvantage. The overall crude inhospital mortality was 7.8%; 9% in the most disadvantaged decile and 6.9% in the most advantaged decile (p < 0.001). Increasing socioeconomic disadvantage was associated with increasing severity of illness as measured by Acute Physiology and Chronic Health Evaluation III score, admission with a diagnosis of sepsis or trauma, cardiac, respiratory, renal, and hepatic comorbidities, and remote location. Increasing socioeconomic advantage was associated with elective surgical admission, hematological and oncology comorbidities, and admission to a private hospital (all p < 0.001). After excluding patients admitted after elective surgery, in the remaining 106,843 patients, the inhospital mortality was 13.6%, 13.3% in the most disadvantaged, and 14.1% in the most advantaged. There was no trend in mortality across deciles of socioeconomic status after excluding elective surgery patients. In the logistic regression model adjusting for severity of illness and diagnosis, there was no statistically significant difference in the odds ratio of inhospital mortality for the most disadvantaged decile compared with other deciles. This suggests variables used for risk adjustment may lie on the causal pathway between socioeconomic status and outcome in ICU patients.

CONCLUSIONS

Socioeconomic status as defined as Index of Relative Socioeconomic Advantage and Disadvantage of the area in which a patient lives was associated with ICU admission diagnosis, comorbidities, severity of illness, and crude inhospital mortality in this study. Socioeconomic status was not associated with inhospital mortality after excluding elective surgical patients or when adjusted for severity of illness and admission diagnosis. Commonly used measures for risk adjustment in intensive care improve understanding of the pathway between socioeconomic status and outcomes.

Epidemiology of the First Wave of COVID-19 ICU Admissions in South Wales-The Interplay Between Ethnicity and Deprivation

On the 9th March 2020, the first patient with COVID-19 was admitted to ICU in the Royal Gwent Hospital (RGH), Newport, Wales. We prospectively recorded the rate of ICU admissions of 52 patients with COVID-19 over 60 days, focusing on the epidemiology of ethnicity and deprivation because these factors have emerged as significant risk factors. Patients were 65% (34 of 52) male and had a median (IQR) age of 55 (48–62) years. Prevalent comorbidities included obesity (52%); diabetes (33%), and asthma (23%). COVID-19 hospital and ICU inpatient numbers peaked on days 23 and 39, respectively—a lag of 16 days. The ICU mortality rate was 33% (17 of 52). People of black, Asian, and minority ethnic descent (BAME group) represented 35% of ICU COVID-19 admissions (18 of 52) and 35% of deaths (6 of 17). Amongst the BAME group, 72% (13 of 18) of patients were found to reside in geographical areas representing the 20% most deprived in Wales, vs. 27% of patients in the Caucasian group (9 of 33). Less than 5% of the population within the area covered by RGH are of BAME descent, yet this group had a disproportionately high ICU admission and mortality rate from COVID-19. The interplay between ethnicity and deprivation, which is complex, may be a factor in our findings. This in turn could be related to an increased prevalence of co-morbidities; higher community exposure; larger proportion of lower band key worker roles; or genetic polymorphisms.

Impact of Critical Illness on Resource Utilization: A Comparison of Use in the Year Before and After ICU Admission

Supplemental Digital Content is available in the text.

Increasingly, patients admitted to an ICU survive to hospital discharge; many with ongoing medical needs. The full impact of an ICU admission on an individual’s resource utilization and survivorship trajectory in the United States is not clear. We sought to compare healthcare utilization among ICU survivors in each year surrounding an ICU admission.

Influence of deprivation on initial severity and prognosis of patients admitted to the ICU: the prospective, multicentre, observational IVOIRE cohort study

Background

The influence of socioeconomic status on patient outcomes is unclear. We assessed the impact of socioeconomic deprivation on severity of illness at intensive care unit (ICU) admission, and on the risk of death at 3 months after ICU admission.

Methods

The IVOIRE study was a prospective, observational, multicentre cohort study in the ICU of 8 participating hospitals in France, including patients aged ≥ 18 years admitted to the ICU and receiving at least one life support therapy for organ failure. The primary outcomes were severity at admission (assessed by SAPSII score), and mortality at 3 months. Socioeconomic data were obtained from interviews with patients or family. Deprivation was assessed using the EPICES score.

Results

Among 1294 patents included between 2013 and 2016, 629 (48.6%) were classed as deprived and differed significantly from non-deprived subjects in terms of sociodemographic characteristics and pre-existing conditions. The mean SAPS II score at admission was 50.1 ± 19.4 in deprived patients and 52.3 ± 17.3 in non-deprived patients, with no significant difference by multivariable analysis (β = − 1.85 [95% CI − 3.86; + 0.16, p = 0.072]). The proportion of death was 31.1% at 3 months, without significant differences between deprived and non-deprived patients, even after adjustment for confounders.

Conclusions

Deprivation is frequent in patients admitted to the ICU and is not associated with disease severity at admission, or with mortality at 3 months between deprived and non-deprived patients.

Trial registration The IVOIRE cohort is registered with ClinicalTrials.gov under the identifier NCT01907581, registration date 17/7/2013

Risk Factors for 1-Year Mortality and Hospital Utilization Patterns in Critical Care Survivors: A Retrospective, Observational, Population-Based Data Linkage Study

OBJECTIVES

Clear understanding of the long-term consequences of critical care survivorship is essential. We investigated the care process and individual factors associated with long-term mortality among ICU survivors and explored hospital use in this group.

DESIGN

Population-based data linkage study using the Secure Anonymised Information Linkage databank.

SETTING

All ICUs between 2006 and 2013 in Wales, United Kingdom.

PATIENTS

We identified 40,631 patients discharged alive from Welsh adult ICUs.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Primary outcome was 365-day survival. The secondary outcomes were 30- and 90-day survival and hospital utilization in the 365 days following ICU discharge. Kaplan-Meier curves were plotted to compare survival rates. Cox proportional hazards regression models were used to determine risk factors of mortality. Seven-thousand eight-hundred eighty-three patients (19.4%) died during the 1-year follow-up period. In the multivariable Cox regression analysis, advanced age and comorbidities were significant determinants of long-term mortality. Expedited discharge due to ICU bed shortage was associated with higher risk. The rate of hospitalization in the year prior to the critical care admission was 28 hospitalized days/1,000 d; post critical care was 88 hospitalized days/1,000 d for those who were still alive; and 57 hospitalized days/1,000 d and 412 hospitalized days/1,000 d for those who died by the end of the study, respectively.

CONCLUSIONS

One in five ICU survivors die within 1 year, with advanced age and comorbidity being significant predictors of outcome, leading to high resource use. Care process factors indicating high system stress were associated with increased risk. More detailed understanding is needed on the effects of the potentially modifiable factors to optimize service delivery and improve long-term outcomes of the critically ill.

Health inequities in the diagnosis and outcome of sepsis in Argentina: a prospective cohort study

Background

Socioeconomic variables impact health outcomes but have rarely been evaluated in critical illness. Low- and middle-income countries bear the highest burden of sepsis and also have significant health inequities. In Argentina, public hospitals serve the poorest segment of the population, while private institutions serve patients with health coverage. Our objective was to analyze differences in mortality between public and private hospitals, using Sepsis-3 definitions.

Methods

This is a multicenter, prospective cohort study including patients with sepsis admitted to 49 Argentine ICUs lasting 3 months, beginning on July 1, 2016. Epidemiological, clinical, and socioeconomic status variables and hospital characteristics were compared between patients admitted to both types of institutions.

Results

Of the 809 patients included, 367 (45%) and 442 (55%) were admitted to public and private hospitals, respectively. Those in public institutions were younger (56 ± 18 vs. 64 ± 18; p < 0.01), with more comorbidities (Charlson score 2 [0–4] vs. 1 [0–3]; p < 0.01), fewer education years (7 [7–12] vs. 12 [10–16]; p < 0.01), more frequently unemployed/informally employed (30% vs. 7%; p < 0.01), had similar previous self-rated health status (70 [50–90] vs. 70 [50–90] points; p = 0.30), longer pre-admission symptoms (48 [24–96] vs. 24 [12–48] h; p < 0.01), had been previously evaluated more frequently in any healthcare venue (28 vs. 20%; p < 0.01), and had higher APACHE II, SOFA, lactate levels, and mechanical ventilation utilization. ICU admission as septic shock was more frequent in patients admitted to public hospitals (47 vs. 35%; p < 0.01), as were infections caused by multiresistant microorganisms. Sepsis management in the ICU showed no differences. Twenty-eight-day mortality was higher in public hospitals (42% vs. 24%; p < 0.01) as was hospital mortality (47% vs. 30%; p < 0.01). Admission to a public hospital was an independent predictor of mortality together with comorbidities, lactate, SOFA, and mechanical ventilation; in an alternative prediction model, it acted as a correlate of pre-hospital symptom duration and infections caused by multiresistant microorganisms.

Conclusions

Patients in public hospitals belonged to a socially disadvantaged group and were sicker at admission, had septic shock more frequently, and had higher mortality. Unawareness of disease severity and delays in the health system might be associated with late admission. This marked difference in outcome between patients served by public and private institutions constitutes a state of health inequity.

Electronic supplementary material

The online version of this article (10.1186/s13054-019-2522-6) contains supplementary material, which is available to authorized users.

Association of Household Income Level and In-Hospital Mortality in Patients With Sepsis: A Nationwide Retrospective Cohort Analysis

OBJECTIVE

Associations between low socioeconomic status (SES) and poor health outcomes have been demonstrated in a variety of conditions. However, the relationship in patients with sepsis is not well described. We investigated the association of lower household income with in-hospital mortality in patients with sepsis across the United States.

METHODS

Retrospective nationwide cohort analysis utilizing the Nationwide Inpatient Sample (NIS) from 2011. Patients aged 18 years or older with sepsis were included. Socioeconomic status was approximated by the median household income of the zip code in which the patient resided. Multivariate logistic modeling incorporating a validated illness severity score for sepsis in administrative data was performed.

RESULTS

A total of 8 023 590 admissions from the 2011 NIS were examined. A total of 671 858 patients with sepsis were included in the analysis. The lowest income residents compared to the highest were younger (66.9 years, standard deviation [SD] = 16.5 vs 71.4 years, SD = 16.1, P < .01), more likely to be female (53.5% vs 51.9%, P < .01), less likely to be white (54.6% vs 76.6%, P < .01), as well as less likely to have health insurance coverage (92.8% vs 95.9%, P < .01). After controlling for severity of sepsis, residing in the lowest income quartile compared to the highest quartile was associated with a higher risk of mortality (odds ratio [OR]: 1.06, 95% confidence interval [CI]: 1.03-1.08, P < .01). There was no association seen between the second (OR: 1.02, 95% CI: 0.99-1.05, P = .14) and third (OR: 0.99, 95% CI: 0.97-1.01, P = .40) quartiles compared to the highest.

CONCLUSION

After adjustment for severity of illness, patients with sepsis who live in the lowest median income quartile had a higher risk of mortality compared to residents of the highest income quartile. The association between SES and mortality in sepsis warrants further investigation with more comprehensive measures of SES.

Intensive Care Syndrome: Promoting Independence and Return to Employment (InS:PIRE). Early evaluation of a complex intervention

BACKGROUND

Many patients suffer significant physical, social and psychological problems in the months and years following critical care discharge. At present, there is minimal evidence of any effective interventions to support this patient group following hospital discharge. The aim of this project was to understand the impact of a complex intervention for ICU survivors.

METHODS

Quality improvement project conducted between September 2014 and June 2016, enrolling 49 selected patients from one ICU in Scotland. To evaluate the impact of this programme outcomes were compared to an existing cohort of patients from the same ICU from 2008-2009. Patients attended a five week peer supported rehabilitation programme. This multidisciplinary programme included pharmacy, physiotherapy, nursing, medical, and psychology input. The primary outcome in this evaluation was the EQ-5D, a validated measure of health-related quality of life. The minimally clinically important difference (MCID) in the EQ-5D is 0.08. We also measured change in self-efficacy over the programme duration. Based on previous research, this study utilised a 2.4 (6%) point change in self-efficacy scores as a MCID.

RESULTS

40 patients (82%) completed follow-up surveys at 12 months. After regression adjustment for those factors known to impact recovery from critical care, there was a 0.07-0.16 point improvement in quality of life for those patients who took part in the intervention compared to historical controls from the same institution, depending on specific regression strategy used. Self-efficacy scores increased by 2.5 points (6.25%) over the duration of the five week programme (p = 0.003), and was sustained at one year post intervention. In the year following ICU, 15 InS:PIRE patients returned to employment or volunteering roles (88%) compared with 11 (46%) in the historical control group (p = 0.15).

CONCLUSIONS AND RELEVANCE

This historical control study suggests that a complex intervention may improve quality of life and self-efficacy in survivors of ICU. A larger, multi-centre study is needed to investigate this intervention further.

Development and validation of the new ICNARC model for prediction of acute hospital mortality in adult critical care

PURPOSE

To develop and validate an improved risk model to predict acute hospital mortality for admissions to adult critical care units in the UK.

MATERIALS AND METHODS

155,239 admissions to 232 adult critical care units in England, Wales and Northern Ireland between January and December 2012 were used to develop a risk model from a set of 38 candidate predictors. The model was validated using 90,017 admissions between January and September 2013.

RESULTS

The final model incorporated 15 physiological predictors (modelled with continuous nonlinear models), age, dependency prior to hospital admission, chronic liver disease, metastatic disease, haematological malignancy, CPR prior to admission, location prior to admission/urgency of admission, primary reason for admission and interaction terms. The model was well calibrated and outperformed the current ICNARC model on measures of discrimination (area under the receiver operating characteristic curve 0.885 versus 0.869) and model fit (Brier's score 0.108 versus 0.115). On average, the new model reclassified patients into more appropriate risk categories (net reclassification improvement 19.9; P<0.0001). The model performed well across patient subgroups and in specialist critical care units.

CONCLUSIONS

The risk model developed in this study showed excellent discrimination and calibration and when validated on a different period of time and across different types of critical care unit. This in turn allows improved accuracy of comparisons between UK critical care providers.

Validation and analysis of prognostic scoring systems for critically ill patients with cirrhosis admitted to ICU

Introduction

The number of patients admitted to ICU who have liver cirrhosis is rising. Current prognostic scoring tools to predict ICU mortality have performed poorly in this group. In previous research from a single centre, a novel scoring tool which modifies the Child-Turcotte Pugh score by adding Lactate concentration, the CTP + L score, is strongly associated with mortality. This study aims to validate the use of the CTP + L scoring tool for predicting ICU mortality in patients admitted to a general ICU with cirrhosis, and to determine significant predictive factors for mortality with this group of patients. This study will also explore the use of the Royal Free Hospital (RFH) score in this cohort.

Methods

A total of 84 patients admitted to the Glasgow Royal Infirmary ICU between June 2012 and Dec 2013 with cirrhosis were included. An additional cohort of 115 patients was obtained from two ICUs in London (St George’s and St Thomas’) collected between October 2007 and July 2009. Liver specific and general ICU scoring tools were calculated for both cohorts, and compared using area under the receiver operating characteristic (ROC) curves. Independent predictors of ICU mortality were identified by univariate analysis. Multivariate analysis was utilised to determine the most predictive factors affecting mortality within these patient groups.

Results

Within the Glasgow cohort, independent predictors of ICU mortality were identified as Lactate (p < 0.001), Bilirubin (p = 0.0048), PaO₂/FiO₂ Ratio (p = 0.032) and PT ratio (p = 0.012). Within the London cohort, independent predictors of ICU mortality were Lactate (p < 0.001), PT ratio (p < 0.001), Bilirubin (p = 0.027), PaO₂/FiO₂ Ratio (p = 0.0011) and Ascites (p = 0.023). The CTP + L and RFH scoring tools had the highest ROC value in both cohorts examined.

Conclusion

The CTP + L and RFH scoring tool are validated prognostic scoring tools for predicting ICU mortality in patients admitted to a general ICU with cirrhosis.

Ensuring comparisons of health-care providers are fair: development and validation of risk prediction models for critically ill patients

Background

National clinical audit has a key role in ensuring quality in health care. When comparing outcomes between providers, it is essential to take the differing case mix of patients into account to make fair comparisons. Accurate risk prediction models are therefore required.

Objectives

To improve risk prediction models to underpin quality improvement programmes for the critically ill (i.e. patients receiving general or specialist adult critical care or experiencing an in-hospital cardiac arrest).

Design

Risk modelling study nested within prospective data collection.

Setting

Adult (general/specialist) critical care units and acute hospitals in the UK.

Participants

Patients admitted to an adult critical care unit and patients experiencing an in-hospital cardiac arrest attended by the hospital-based resuscitation team.

Interventions

None.

Main outcome measures

Acute hospital mortality (adult critical care); return of spontaneous circulation (ROSC) greater than 20 minutes and survival to hospital discharge (in-hospital cardiac arrest).

Data sources

The Case Mix Programme (adult critical care) and National Cardiac Arrest Audit (in-hospital cardiac arrest).

Results

The current Intensive Care National Audit & Research Centre (ICNARC) model was externally validated using data for 29,626 admissions to critical care units in Scotland (2007–9) and outperformed the Acute Physiology And Chronic Health Evaluation (APACHE) II model in terms of discrimination (c-index 0.848 vs. 0.806) and accuracy (Brier score 0.140 vs. 0.157). A risk prediction model for cardiothoracic critical care was developed using data from 17,002 admissions to five units (2010–12) and validated using data from 10,238 admissions to six units (2013–14). The model included prior location/urgency, blood lactate concentration, Glasgow Coma Scale (GCS) score, age, pH, platelet count, dependency, mean arterial pressure, white blood cell (WBC) count, creatinine level, admission following cardiac surgery and interaction terms, and it had excellent discrimination (c-index 0.904) and accuracy (Brier score 0.055). A risk prediction model for admissions to all (general/specialist) adult critical care units was developed using data from 155,239 admissions to 232 units (2012) and validated using data from 90,017 admissions to 216 units (2013). The model included systolic blood pressure, temperature, heart rate, respiratory rate, partial pressure of oxygen in arterial blood/fraction of inspired oxygen, pH, partial pressure of carbon dioxide in arterial blood, blood lactate concentration, urine output, creatinine level, urea level, sodium level, WBC count, platelet count, GCS score, age, dependency, past medical history, cardiopulmonary resuscitation, prior location/urgency, reason for admission and interaction terms, and it outperformed the current ICNARC model for discrimination and accuracy overall (c-index 0.885 vs. 0.869; Brier score 0.108 vs. 0.115) and across unit types. Risk prediction models for in-hospital cardiac arrest were developed using data from 14,688 arrests in 122 hospitals (2011–12) and validated using data from 7791 arrests in 143 hospitals (2012–13). The models included age, sex (for ROSC > 20 minutes), prior length of stay in hospital, reason for attendance, location of arrest, presenting rhythm, and interactions between rhythm and location. Discrimination for hospital survival exceeded that for ROSC > 20 minutes (c-index 0.811 vs. 0.720).

Limitations

The risk prediction models developed were limited by the data available within the current national clinical audit data sets.

Conclusions

We have developed and validated risk prediction models for cardiothoracic and adult (general and specialist) critical care units and for in-hospital cardiac arrest.

Future work

Future development should include linkage with other routinely collected data to enhance available predictors and outcomes.

Funding details

The National Institute for Health Research Health Services and Delivery Research programme.

Socioeconomic Status and in-hospital Mortality of Acute Coronary Syndrome: Can Education and Occupation Serve as Preventive Measures?

Background:

Socioeconomic status (SES) can greatly affect the clinical outcome of medical problems. We sought to assess the in-hospital mortality of patients with the acute coronary syndrome (ACS) according to their SES.

Methods:

All patients admitted to Tehran Heart Center due to 1^st-time ACS between March 2004 and August 2011 were assessed. The patients who were illiterate/lowly educated (≤5 years attained education) and were unemployed were considered low-SES patients and those who were employed and had high educational levels (>5 years attained education) were regarded as high-SES patients. Demographic, clinical, paraclinical, and in-hospital medical progress data were recorded. Death during the course of hospitalization was considered the end point, and the impact of SES on in-hospital mortality was evaluated.

Results:

A total of 6246 hospitalized patients (3290 low SES and 2956 high SES) were included (mean age = 60.3 ± 12.1 years, male = 2772 [44.4%]). Among them, 79 (1.26%) patients died. Univariable analysis showed a significantly higher mortality rate in the low-SES group (1.9% vs. 0.6%; P < 0.001). After adjustment for possible cofounders, SES still showed a significant effect on the in-hospital mortality of the ACS patients in that the high-SES patients had a lower in-hospital mortality rate (odds ratio: 0.304, 95% confidence interval: 0.094–0.980; P = 0.046).

Conclusions:

This study found that patients with low SES were at a higher risk of in-hospital mortality due to the ACS. Furthermore, the results suggest the need for increased availability of jobs as well as improved levels of education as preventive measures to curb the unfolding deaths owing to coronary artery syndrome.

Socioeconomic indicators of health inequalities and female mortality: a nested cohort study within the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS)

Background

Evidence is mounting that area-level socioeconomic indicators are important tools for predicting health outcomes. However, few studies have examined these alongside individual-level education. This nested cohort study within the control arm of the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) assesses the association of mutually adjusted individual (education) and area-level (Index of Multiple Deprivation-IMD 2007) socioeconomic status indicators and all-cause female mortality.

Methods

Participants resident in England who had completed both baseline (Wave 1) and follow up (Wave 2) questionnaires were included. Follow-up was through the Health and Social Care Information Centre with deaths censored on 31st December 2012. IMD, education and a range of covariates were explored. Cox regression models adjusted for all covariates were used. Sensitivity analysis using imputation was performed (1) including those with missing data and (2) on the entire cohort who had completed the baseline questionnaire.

Results

Of the 54,539 women resident in England who completed both Wave 1 and Wave 2 questionnaires, 4,510 had missing data. The remaining 50,029 women were included in the primary analysis. Area-level IMD was positively associated with all-cause mortality for the most deprived group compared to the least deprived (HR=1.42, CI=1.14-1.78) after adjusting for all potential confounders. Sensitivity analyses showed similar results with stronger associations in the entire cohort (HR=1.90, CI=1.68-2.16). The less educated an individual, the higher the mortality risk (test for trend p=<0.001). However, the crude effect on mortality of having no formal education compared to college/university education disappeared when adjusted for IMD rank (HR=1.08, CI=0.93-1.26).

Conclusion

Women living in more deprived areas continue to have higher mortality even in this less deprived cohort and after adjustment for a range of potential confounders.

Trial Registration

This study is registered as an International Standard Randomised Controlled Trial, number ISRCTN22488978.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-015-1609-5) contains supplementary material, which is available to authorized users.

Comorbidity and Intensive Care Outcome — A Multivariable Analysis

Decisions regarding admission to intensive care are made considering both the physiological state of the patient and the burden of comorbidity. Despite many retrospective cohort studies looking at isolated comorbidities, there has been little work to study multiple comorbidities and their effect upon intensive care outcome. In this retrospective cohort analysis, detailed comorbidity and demographic data were gathered on 1,029 patients from the West of Scotland and matched to both unit and hospital mortality at 30 days. Logistic regression was performed to investigate the factors associated with death within 30 days at both hospital and unit level. Variables with a p-value <0.25 at the univariable level were considered in a multivariable model. Variable selection for the multivariable modelling was carried out using backward selection and then replicated using forward selection to check for model stability. A modelling tool was constructed for both unit and hospital mortality at 30 days. This modelling has shown significant odds ratios for hospital death for alcoholic liver disease (OR 4.83), age (1.03), rheumatological diseases (1.93) and functional exercise tolerance prior to admission (3.08). Results from this work may inform a national prospective study to validate the modelling tool on a wider population.

Investigating risk factors for psychological morbidity three months after intensive care: a prospective cohort study

INTRODUCTION

There is growing evidence of poor mental health and quality of life among survivors of intensive care. However, it is not yet clear to what extent the trauma of life-threatening illness, associated drugs and treatments, or patients' psychological reactions during intensive care contribute to poor psychosocial outcomes. Our aim was to investigate the relative contributions of a broader set of risk factors and outcomes than had previously been considered in a single study.

METHODS

A prospective cohort study of 157 mixed-diagnosis highest acuity patients was conducted in a large general intensive care unit (ICU). Data on four groups of risk factors (clinical, acute psychological, socio-demographic and chronic health) were collected during ICU admissions. Post-traumatic stress disorder (PTSD), depression, anxiety and quality of life were assessed using validated questionnaires at three months (n = 100). Multivariable analysis was used.

RESULTS

At follow-up, 55% of patients had psychological morbidity: 27.1% (95% CI: 18.3%, 35.9%) had probable PTSD; 46.3% (95% CI: 36.5%, 56.1%) probable depression, and 44.4% (95% CI: 34.6%, 54.2%) anxiety. The strongest clinical risk factor for PTSD was longer duration of sedation (regression coefficient = 0.69 points (95% CI: 0.12, 1.27) per day, scale = 0 to 51). There was a strong association between depression at three months and receiving benzodiazepines in the ICU (mean difference between groups = 6.73 points (95% CI: 1.42, 12.06), scale = 0 to 60). Use of inotropes or vasopressors was correlated with anxiety, and corticosteroids with better physical quality of life.

CONCLUSIONS

Strikingly high rates of psychological morbidity were found in this cohort of intensive care survivors. The study's key finding was that acute psychological reactions in the ICU were the strongest modifiable risk factors for developing mental illness in the future. The observation that use of different ICU drugs correlated with different psychological outcomes merits further investigation. These findings suggest that psychological interventions, along with pharmacological modifications, could help reduce poor outcomes, including PTSD, after intensive care.

Socioeconomic status, severity of disease and level of family members' care in adult surgical intensive care patients: the prospective ECSSTASI study

Background

Low socioeconomic status (SES) is associated with increased mortality from cardiovascular disease, cancer and trauma. However, individual-level prospective data on SES in relation to health outcomes among critically ill patients admitted to intensive care units (ICU) are unavailable.

Methods

In a cohort of 1,006 patients at a 24-bed surgical ICU of an academic tertiary care facility in Germany, we examined levels of SES in relation to disease severity at admission, time period of mechanical ventilation, length of stay and frequency of phone calls and visits by next-of-kin.

Findings

Patients with low SES had higher risk for Sequential Organ Failure Assessment (SOFA) score greater or equal to 5 [multivariate-adjusted odds ratio (OR) 1.49; 95% confidence interval (CI) 0.95–2.33; p = 0.029] and a trend for higher risk for Simplified Acute Physiology Score (SAPS II) greater or equal to 31 (OR 1.28; 95% CI 0.80–2.05; p = 0.086) at admission as compared with patients with high SES. When compared with men with high SES, those with low SES had greater risk for ICU treatment ≥5 days (multivariate-adjusted OR 1.99; 95% CI 1.06–3.74; p = 0.036) and showed a trend for a low number of visits from next-of-kin (<0.5 visits per day) (OR 1.85; 95% CI 0.79–4.30; p = 0.054). In women such associations could not be demonstrated.

Interpretation

Socioeconomic status is inversely related to severity of disease at admission and to length of stay in ICU, and positively associated with the level of care by next-of-kin. Whether relations differ by gender requires further examination.