Clustering of Social Determinants of Health Among Patients

Introduction/Objectives: Many health systems screen patients for social determinants of health and refer patients with social needs to community organizations for assistance. Understanding how social determinants cluster together may help guide assistance programs. Methods: This study examined patients screened by The MetroHealth System in Cleveland, Ohio for 9 social determinants, including food insecurity, financial strain, transportation limitations, inability to pay for housing or utilities, intimate partner violence, social isolation, infrequent physical activity, daily stress, and lack of internet access. Clustering analyses were performed to determine which combination of social determinants occurred together more often than would be expected if each determinant were independent of each other. Results: Among 23 161 screened patients, there were 19 dyads, 13 triads, and one tetrad of social determinants that clustered together. The most prevalent triad of food insecurity, social isolation, and inability to pay for housing or utilities occurred among 1095 patients but would be expected to occur among 284 patients, for an observed/expected ratio of 3.85 (95% confidence interval 3.64-4.07). In multivariate analyses, younger, Black, and lower income patients were 2 to 3 times more likely to have this triad compared to older, White, and wealthier patients. Conclusions: Social determinants of health frequently cluster together, and such clustering is associated with patient demographic characteristics. Further work is needed to determine how social determinant clusters impact health and cost outcomes and to develop programs that can address multiple co-existing social needs

Changes in Characteristics and Treatment Patterns of Patients with Newly Diagnosed Type 2 Diabetes in a Large United States Integrated Health System between 2008 and 2013

To assess changes in the clinical characteristics and treatment patterns of patients with newly diagnosed type 2 diabetes (T2D), the electronic health record system at Cleveland Clinic was used to create cross-sectional summaries of all patients with new-onset T2D in 2008 and 2013. Differences between the 2008 and 2013 data sets were assessed after adjusting for age, gender, race, and income. Approximately one-third of patients with newly diagnosed T2D in 2008 and 2013 had an A1C ≤8%, suggesting the continued presence of a delayed recognition of the disease. Patients with newly diagnosed T2D in 2008 were older than those in 2013. Hypertension, cardiovascular disease, and neuropathy were highly prevalent among patients diagnosed with T2D. The prevalence of neuropathy, cerebrovascular disease, and peripheral vascular disease increased from 2008 to 2013. Metformin was the most commonly prescribed antidiabetic medication. Sulfonylurea usage remained unchanged, while use of thiazolidinediones decreased considerably

Models for Predicting Recurrence, Complications, and Health Status in Women After Pelvic Organ Prolapse Surgery.

ObjectiveTo develop statistical models predicting recurrent pelvic organ prolapse, surgical complications, and change in health status 12 months after apical prolapse surgery.MethodsLogistic regression models were developed using a combined cohort from three randomized trials and two prospective cohort studies from 1,301 participants enrolled in surgical studies conducted by the Pelvic Floor Disorders Network. Composite recurrent prolapse was defined as prolapse beyond the hymen; the presence of bothersome bulge symptoms; or prolapse reoperation or retreatment within 12 months after surgery. Complications were defined as any serious adverse event or Dindo grade III complication within 12 months of surgery. Significant change in health status was defined as a minimum important change of SF-6D utility score (±0.035 points) from baseline. Thirty-two candidate risk factors were considered for each model and model accuracy was measured using concordance indices. All indices were internally validated using 1,000 bootstrap resamples to correct for bias.ResultsThe models accurately predicted composite recurrent prolapse (concordance index=0.72, 95% CI 0.69-0.76), bothersome vaginal bulge (concordance index=0.73, 95% CI 0.68-0.77), prolapse beyond the hymen (concordance index=0.74, 95% CI 0.70-0.77), serious adverse event (concordance index=0.60, 95% CI 0.56-0.64), Dindo grade III or greater complication (concordance index=0.62, 95% CI 0.58-0.66), and health status improvement (concordance index=0.64, 95% CI 0.62-0.67) or worsening (concordance index=0.63, 95% CI 0.60-0.67). Calibration curves demonstrated all models were accurate through clinically useful predicted probabilities.ConclusionThese prediction models are able to provide accurate and discriminating estimates of prolapse recurrence, complications, and health status 12 months after prolapse surgery

Models for Predicting Recurrence, Complications, and Health Status in Women After Pelvic Organ Prolapse Surgery.

ObjectiveTo develop statistical models predicting recurrent pelvic organ prolapse, surgical complications, and change in health status 12 months after apical prolapse surgery.MethodsLogistic regression models were developed using a combined cohort from three randomized trials and two prospective cohort studies from 1,301 participants enrolled in surgical studies conducted by the Pelvic Floor Disorders Network. Composite recurrent prolapse was defined as prolapse beyond the hymen; the presence of bothersome bulge symptoms; or prolapse reoperation or retreatment within 12 months after surgery. Complications were defined as any serious adverse event or Dindo grade III complication within 12 months of surgery. Significant change in health status was defined as a minimum important change of SF-6D utility score (±0.035 points) from baseline. Thirty-two candidate risk factors were considered for each model and model accuracy was measured using concordance indices. All indices were internally validated using 1,000 bootstrap resamples to correct for bias.ResultsThe models accurately predicted composite recurrent prolapse (concordance index=0.72, 95% CI 0.69-0.76), bothersome vaginal bulge (concordance index=0.73, 95% CI 0.68-0.77), prolapse beyond the hymen (concordance index=0.74, 95% CI 0.70-0.77), serious adverse event (concordance index=0.60, 95% CI 0.56-0.64), Dindo grade III or greater complication (concordance index=0.62, 95% CI 0.58-0.66), and health status improvement (concordance index=0.64, 95% CI 0.62-0.67) or worsening (concordance index=0.63, 95% CI 0.60-0.67). Calibration curves demonstrated all models were accurate through clinically useful predicted probabilities.ConclusionThese prediction models are able to provide accurate and discriminating estimates of prolapse recurrence, complications, and health status 12 months after prolapse surgery