Predicting severe pain after major surgery: a secondary analysis of the Peri-operative Quality Improvement Programme (PQIP) dataset

Acute postoperative pain is common, distressing and associated with increased morbidity. Targeted interventions can prevent its development. We aimed to develop and internally validate a predictive tool to pre-emptively identify patients at risk of severe pain following major surgery. We analysed data from the UK Peri-operative Quality Improvement Programme to develop and validate a logistic regression model to predict severe pain on the first postoperative day using pre-operative variables. Secondary analyses included the use of peri-operative variables. Data from 17,079 patients undergoing major surgery were included. Severe pain was reported by 3140 (18.4%) patients; this was more prevalent in females, patients with cancer or insulin-dependent diabetes, current smokers and in those taking baseline opioids. Our final model included 25 pre-operative predictors with an optimism-corrected c-statistic of 0.66 and good calibration (mean absolute error 0.005, p = 0.35). Decision-curve analysis suggested an optimal cut-off value of 20–30% predicted risk to identify high-risk individuals. Potentially modifiable risk factors included smoking status and patient-reported measures of psychological well-being. Non-modifiable factors included demographic and surgical factors. Discrimination was improved by the addition of intra-operative variables (likelihood ratio χ2 496.5, p < 0.001) but not by the addition of baseline opioid data. On internal validation, our pre-operative prediction model was well calibrated but discrimination was moderate. Performance was improved with the inclusion of peri-operative covariates suggesting pre-operative variables alone are not sufficient to adequately predict postoperative pain

Back to Black … and Green? Location and policy interventions in contemporary neighborhood housing markets

The postwar flight from U.S. central cities led to widespread decay and devaluation in downtown housing markets. In a reversal of fortunes, distant housing prices soared while the dense urban core lagged. However, over the course of the 2000–06 housing bubble, we find that the markets in often ignored mid-sized cities shifted back to the downtowns. This research examines the factors influencing neighborhood housing values, including location and public policy interventions. Our analysis period begins with 2000 and has two end points: one at the close of the national housing bubble in 2006 and another in 2008 during the housing market collapse. Based on OLS and spatial regression analyses of percent increases in neighborhood housing values for Louisville, Kentucky, we find that higher downtown property increases are due in large part to historic preservation districts, a university–community partnership, and a HOPE VI site. We confirm that our findings hold even through the 2007–2008 housing crisis. We ultimately theorize that higher downtown appreciation is due to three factors: green urbanism, planning/policy successes, and the surprising non-significance of the traditionally negative predictor race (nonwhite percentage)