IMPORTANCEThe elimination of racial and ethnic differences in health status and health care access is a US goal, but it is unclear whether the country has made progress over the last 2 decades. OBJECTIVE To determine 20-year trends in the racial and ethnic differences in self-reported measures of health status and health care access and affordability among adults in the US.

After recent UK policy developments, considerable attention has been focused upon how clinical specialties measure and report on the quality of care delivered to patients. Defining the right indicators alone is insufficient to close the feedback loop. This narrative review aims to describe and synthesize a diverse body of research relevant to the question of how information from quality indicators can be fed back and used effectively to improve care. Anaesthesia poses certain challenges in the identification of valid outcome indicators sensitive to variations in anaesthetic care. Metrics collected during the immediate post-anaesthetic recovery period, such as patient temperature, patient-reported quality of recovery, and pain and nausea, provide potentially useful information for the anaesthetist, yet this information is not routinely fed back. Reviews of the effects of feeding back performance data to healthcare providers suggest that this may result in small to moderate positive effects upon outcomes and professional practice, with stronger effects where feedback is integrated within a broader quality improvement strategy. The dominant model for use of data within quality improvement is based upon the industrial process control approach, in which care processes are monitored continuously for process changes which are rapidly detectable for corrective action. From this review and experience of implementing these principles in practice, effective feedback from quality indicators is timely, credible, confidential, tailored to the recipient, and continuous. Considerable further work is needed to understand how information from quality indicators can be fed back in an effective way to clinicians and clinical units, in order to support revalidation and continuous improvement.

Objectives To determine whether the Child Opportunity Index (COI), a nationally-available measure of relative educational, health/environmental, and social/economic opportunity across census tracts within metropolitan areas, was associated with population- and patient-level asthma morbidity. Study design This population-based retrospective cohort study was conducted between 2011–2013 in 1 Southwest Ohio county. Participants included all children, 1–16 years, with hospitalizations or emergency department visits for asthma or wheezing at a major pediatric hospital. Patients were identified using discharge diagnosis codes and geocoded to their home census tract. The primary population-level outcome was census tract asthma hospitalization rate. The primary patient-level outcome was re-hospitalization within 12 months of index hospitalization. Census tract opportunity was characterized using the COI and its educational, health/environmental, and social/economic domains. Results Across 222 in-county census tracts, there were 2,539 geocoded hospitalizations. The median asthma-related hospitalization rate was 5.0 per 1,000 children per year (interquartile range: 1.9–8.9). Median hospitalization rates in very low, low, moderate, high, and very high opportunity tracts were 9.1, 7.6, 4.6, 2.1, and 1.8 per 1,000, respectively (P < .0001). The social/economic domain had the most variables significantly associated with the outcome at the population level. The adjusted patient-level analyses showed that the COI was not significantly associated with a patient’s risk of re-hospitalization within 12 months. Conclusions The Child Opportunity Index was associated with population-level asthma morbidity. The detail provided by the COI may inform interventions aimed at increasing opportunity and reducing morbidity across regions.

BACKGROUND: Disparities in health service use have been described across a range of sociodemographic factors. Patterns of PICU use have not been thoroughly assessed. METHODS: This was a population-level, retrospective analysis of admissions to the Cincinnati Children's Hospital Medical Center PICU between 2011 and 2016. Residential addresses of patients were geocoded and spatially joined to census tracts. Pediatric patients were eligible for inclusion if they resided within Hamilton County, Ohio. PICU admission and bed-day rates were calculated by using numerators of admissions and bed days, respectively, over a denominator of tract child population. Relationships between tract-level PICU use and child poverty were assessed by using Spearman's r and analysis of variance. Analyses were event based; children admitted multiple times were counted as discrete admissions. RESULTS: There were 4071 included admissions involving 3129 unique children contributing a total of 12 297 PICU bed days. Child poverty was positively associated with PICU admission rates (r = 0.59; P , .001) and bed-day rates (r = 0.47; P , .001). When tracts were grouped into quintiles based on child poverty rates, the PICU bed-day rate ranged from 23.4 days per 1000 children in the lowest poverty quintile to 81.9 days in the highest poverty quintile (P , .001). CONCLUSIONS: The association between poverty and poor health outcomes includes pediatric intensive care use. This association exists for children who grow up in poverty and around poverty. Future efforts should characterize the interplay between patient-and neighborhoodlevel risk factors and explore neighborhood-level interventions to improve child health. WHAT'S KNOWN ON THIS SUBJECT: Poverty adversely affects health. The health impacts of socioeconomic status and poverty occur at the individual and community levels. Socioeconomic disparities in PICU use have not been as robustly assessed compared with other medical disciplines. WHAT THIS STUDY ADDS: Socioeconomic disparities extend to pediatric critical illness. Neighborhood poverty affects children' s need for intensive care. We argue that this association represents more than an aggregate of individual risk factors, and the interplay of individual and community demographics merits further investigation.