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PURPOSE Individuals of lower socioeconomic status have higher rates of hospitalization due to ambulatory care-sensitive conditions, particularly chronic obstructive pulmonary disease and asthma. We examined whether differences in patient demographics, ambulatory care use, or physician characteristics could explain this disparity in avoidable hospitalizations.METHODS Using administrative data from the city of Winnipeg, Manitoba, Canada, we identified all adults aged 18 to 70 years with chronic obstructive pulmonary disease or asthma, grouped together as obstructive airway disease. We divided patients into census-derived income quintiles using average household income. We performed a series of multivariate logistic regression analyses to determine how the association of socioeconomic status with the risk of obstructive airway disease-related hospitalizations changed after controlling for blocks of covariates related to patient demographics (socioeconomic status, age, sex, and comorbidity), ambulatory care use (continuity influenza vaccination and specialist referral), and characteristics of the patient's usual physician (eg, payment mechanism, sex, years in practice). RESULTSWe included 34,741 patients with obstructive airway disease, 729 (2.1%) of whom were hospitalized with a related diagnosis during a 2-year period. Patients having a lower income were more likely to be hospitalized than peers having the highest income, and this effect of socioeconomic status remained virtually unchanged after controlling for every other variable studied. In a fully adjusted model, patients in the lowest income quintile had approximately 3 times the odds of hospitalization relative to counterparts in the highest income quintile (odds ratio = 2.93; 95% confidence limits: 2.19, 3.93). CONCLUSIONSIn the setting of universal health care, the income-based disparity in hospitalizations for respiratory ambulatory care-sensitive conditions cannot be explained by factors directly related to the use of ambulatory services that can be measured using administrative data. Our findings suggest that we look beyond the health care system at the broader social determinants of health to reduce the number of avoidable hospitalizations among the poor.

Resting-state fMRI (R-fMRI) has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition and analysis, as fMRI data is particularly sensitive to structured noise resulting from hardware, software, and environmental differences. Here, we investigated whether a novel clean up tool for structured noise was capable of reducing center-related R-fMRI differences between healthy subjects. We analyzed three Tesla R-fMRI data from 72 subjects, half of whom were scanned with eyes closed in a Philips Achieva system in The Netherlands, and half of whom were scanned with eyes open in a Siemens Trio system in the UK. After pre-statistical processing and individual Independent Component Analysis (ICA), FMRIB's ICA-based X-noiseifier (FIX) was used to remove noise components from the data. GICA and dual regression were run and non-parametric statistics were used to compare spatial maps between groups before and after applying FIX. Large significant differences were found in all resting-state networks between study sites before using FIX, most of which were reduced to non-significant after applying FIX. The between-center difference in the medial/primary visual network, presumably reflecting a between-center difference in protocol, remained statistically significant. FIX helps facilitate multi-center R-fMRI research by diminishing structured noise from R-fMRI data. In doing so, it improves combination of existing data from different centers in new settings and comparison of rare diseases and risk genes for which adequate sample size remains a challenge.