Randi E. Foraker

National High School Athlete Concussion Rates From 2005-2006 to 2011-2012

Background: High school athletes are at risk for concussions. Although a previously published study showed an increase in concussion rates for a single school district, it remains unknown if the rate of concussions among high school athletes is increasing nationally. Purpose: To investigate national high school athlete concussion rates over time. Study Design: Descriptive epidemiologic study. Methods: The rate of concussions per 1000 athlete-exposures was calculated for academic years 2005-2006 through 2011-2012 using the High School Reporting Information Online sports injury surveillance system. Results: During the 7-year period of this study, High School Reporting Information Online captured 4024 concussions with overall concussion diagnosis rates increasing significantly from 0.23 to 0.51 (P = .004). Concussion diagnosis rates increased for each of the 9 sports studied, with 5 sports having statistically significant increases over this 7-year period. Conclusion: The study analysis indicates that national concussion diagnosis rates for high school sports have increased significantly over time.

Socioeconomic Status, Medicaid Coverage, Clinical Comorbidity, and Rehospitalization or Death After an Incident Heart Failure Hospitalization Atherosclerosis Risk in Communities Cohort (1987 to 2004)

Background— Among patients with heart failure (HF), early readmission or death and repeat hospitalizations may be indicators of poor disease management or more severe disease. Methods and Results— We assessed the association of neighborhood median household income (nINC) and Medicaid status with rehospitalization or death in the Atherosclerosis Risk in Communities cohort study (1987 to 2004) after an incident HF hospitalization in the context of individual socioeconomic status and evaluated the relationship for modification by demographic and comorbidity factors. We used generalized linear Poisson mixed models to estimate rehospitalization rate ratios and 95% CIs and Cox regression to estimate hazard ratios (HRs) and 95% CIs of rehospitalization or death. In models controlling for race and study community, sex, age at HF diagnosis, body mass index, hypertension, educational attainment, alcohol use, and smoking, patients with a high burden of comorbidity who were living in low-nINC areas at baseline had an elevated hazard of all-cause rehospitalization (HR, 1.40; 95% CI, 1.10 to 1.77), death (HR, 1.36; 95% CI, 1.02 to 1.80), and rehospitalization or death (HR, 1.36; 95% CI, 1.08 to 1.70) as well as increased rates of hospitalization compared to those with a high burden of comorbidity living in high-nINC areas. Medicaid recipients with a low level of comorbidity had an increased hazard of all-cause rehospitalization (HR, 1.19; 95% CI, 1.05 to 1.36) and rehospitalization or death (HR, 1.21; 95% CI, 1.07 to 1.37) and a higher rate of repeat hospitalizations compared to non-Medicaid recipients. Conclusions— Comorbidity burden appears to influence the association among nINC, Medicaid status, and rehospitalization and death in patients with HF.

Socioeconomic Status, Medicaid Coverage, Clinical Comorbidity, and Rehospitalization or Death After an Incident Heart Failure HospitalizationClinical Perspective

Background— Among patients with heart failure (HF), early readmission or death and repeat hospitalizations may be indicators of poor disease management or more severe disease. Methods and Results— We assessed the association of neighborhood median household income (nINC) and Medicaid status with rehospitalization or death in the Atherosclerosis Risk in Communities cohort study (1987 to 2004) after an incident HF hospitalization in the context of individual socioeconomic status and evaluated the relationship for modification by demographic and comorbidity factors. We used generalized linear Poisson mixed models to estimate rehospitalization rate ratios and 95% CIs and Cox regression to estimate hazard ratios (HRs) and 95% CIs of rehospitalization or death. In models controlling for race and study community, sex, age at HF diagnosis, body mass index, hypertension, educational attainment, alcohol use, and smoking, patients with a high burden of comorbidity who were living in low-nINC areas at baseline had an elevated hazard of all-cause rehospitalization (HR, 1.40; 95% CI, 1.10 to 1.77), death (HR, 1.36; 95% CI, 1.02 to 1.80), and rehospitalization or death (HR, 1.36; 95% CI, 1.08 to 1.70) as well as increased rates of hospitalization compared to those with a high burden of comorbidity living in high-nINC areas. Medicaid recipients with a low level of comorbidity had an increased hazard of all-cause rehospitalization (HR, 1.19; 95% CI, 1.05 to 1.36) and rehospitalization or death (HR, 1.21; 95% CI, 1.07 to 1.37) and a higher rate of repeat hospitalizations compared to non-Medicaid recipients. Conclusions— Comorbidity burden appears to influence the association among nINC, Medicaid status, and rehospitalization and death in patients with HF.

Variation in Rates of Fatal Coronary Heart Disease by Neighborhood Socioeconomic Status: The Atherosclerosis Risk in Communities Surveillance (1992–2002)

PURPOSE Racial and gender disparities in out-of-hospital deaths from coronary heart disease (CHD) have been well-documented, yet disparities by neighborhood socioeconomic status (nSES) have been less systematically studied in US population-based surveillance efforts. METHODS We examined the association of nSES, classified into tertiles, with 3,743 out-of-hospital fatal CHD events, and a subset of 2,191 events classified as sudden, among persons aged 35 to 74 years in four US communities under surveillance by the Atherosclerosis Risk in Communities (ARIC). Poisson generalized linear mixed models generated age-, race- (white, black) and gender-specific standardized mortality rate ratios and 95% confidence intervals (RR, 95% CI). RESULTS Regardless of nSES measure used, inverse associations of nSES with all out-of-hospital fatal CHD and sudden fatal CHD were seen in all race-gender groups. The magnitude of these associations was larger among women than men. Further, among blacks, associations of low nSES (vs. high nSES) were stronger for sudden cardiac deaths (SCD) than for all out-of-hospital fatal CHD. CONCLUSIONS Low nSES was associated with an increased risk of out-of-hospital CHD death and SCD. Measures of the neighborhood context are useful tools in population-based surveillance efforts for documenting and monitoring socioeconomic disparities in mortality over time.

Socioeconomic status and the trajectory of self-rated health

BACKGROUND self-rated health (SRH) likely reflects both mental and physical health domains, and is assessed by asking individuals to describe their health status. Poor SRH is associated with disease incidence and subsequent mortality. Changes in SRH across time in persons with different incident diseases are uncharacterised. METHODS SRH was assessed in the Atherosclerosis Risk in Communities study via annual telephone interviews over a median of 17.6 years. Individual quadratic growth models were used for repeated measures of SRH in persons who remained disease-free during follow-up (n = 11,188), as well as among those who were diagnosed with myocardial infarction (MI; n = 1,071), stroke (n = 809), heart failure (HF; n = 1,592) or lung cancer (n = 433) and those who underwent a cardiac revascularisation procedure (n = 1,340) during follow-up. RESULTS among disease-free participants and across time, there was a trend for lowest mean SRH among persons living in low socioeconomic areas and highest mean SRH among persons living in high socioeconomic areas. Factors contributing to the decline in SRH over time included advanced age, lower educational attainment, smoking and obesity. CONCLUSION addressing factors related to poor SRH trajectories among patients pre- and post-incident disease may favourably affect health outcomes among patients regardless of type of disease.

Neighborhood socioeconomic status, Medicaid coverage and medical management of myocardial infarction: Atherosclerosis risk in communities (ARIC) community surveillance

BackgroundPharmacologic treatments are efficacious in reducing post-myocardial infarction (MI) morbidity and mortality. The potential influence of socioeconomic factors on the receipt of pharmacologic therapy has not been systematically examined, even though healthcare utilization likely influences morbidity and mortality post-MI. This study aims to investigate the association between socioeconomic factors and receipt of evidence-based treatments post-MI in a community surveillance setting.MethodsWe evaluated the association of census tract-level neighborhood household income (nINC) and Medicaid coverage with pharmacologic treatments (aspirin, beta [β]-blockers and angiotensin converting enzyme [ACE] inhibitors; optimal therapy, defined as receipt of two or more treatments) received during hospitalization or at discharge among 9,608 MI events in the ARIC community surveillance study (1993-2002). Prevalence ratios (PR, 95% CI), adjusted for the clustering of hospitalized MI events within census tracts and within patients, were estimated using Poisson regression.ResultsSeventy-eight percent of patients received optimal therapy. Low nINC was associated with a lower likelihood of receiving β-blockers (0.93, 0.87-0.98) and a higher likelihood of receiving ACE inhibitors (1.13, 1.04-1.22), compared to high nINC. Patients with Medicaid coverage were less likely to receive aspirin (0.92, 0.87-0.98), compared to patients without Medicaid coverage. These findings were independent of other key covariates.ConclusionsnINC and Medicaid coverage may be two of several socioeconomic factors influencing the complexities of medical care practice patterns.

Risk Factors for Incident Hospitalized Heart Failure With Preserved Versus Reduced Ejection Fraction in a Multiracial Cohort of Postmenopausal Women

Background—Heart failure is an important and growing public health problem in women. Risk factors for incident hospitalized heart failure with preserved ejection fraction (HFpEF) compared with heart failure with reduced ejection fraction (HFrEF) in women and differences by race/ethnicity are not well characterized. Methods and Results—We prospectively evaluated the risk factors for incident hospitalized HFpEF and HFrEF in a multiracial cohort of 42 170 postmenopausal women followed up for a mean of 13.2 years. Cox regression models with time-dependent covariate adjustment were used to define risk factors for HFpEF and HFrEF. Differences by race/ethnicity about incidence rates, baseline risk factors, and their population-attributable risk percentage were analyzed. Risk factors for both HFpEF and HFrEF were as follows: older age, white race, diabetes mellitus, cigarette smoking, and hypertension. Obesity, history of coronary heart disease (other than myocardial infarction), anemia, atrial fibrillation, and more than one comorbidity were associated with HFpEF but not with HFrEF. History of myocardial infarction was associated with HFrEF but not with HFpEF. Obesity was found to be a more potent risk factor for African American women compared with white women for HFpEF (P for interaction=0.007). For HFpEF, the population-attributable risk percentage was greatest for hypertension (40.9%) followed by obesity (25.8%), with the highest population-attributable risk percentage found in African Americans for these risk factors. Conclusions—In this multiracial cohort of postmenopausal women, obesity stands out as a significant risk factor for HFpEF, with the strongest association in African American women. Clinical Trial Registration—URL: http://www.clinicaltrials.gov. Unique identifier: NCT00000611.

Posttraumatic stress disorder and incident heart failure among a community-based sample of US veterans

OBJECTIVES We investigated the association between posttraumatic stress disorder (PTSD) and incident heart failure in a community-based sample of veterans. METHODS We examined Veterans Affairs Pacific Islands Health Care System outpatient medical records for 8248 veterans between 2005 and 2012. We used multivariable Cox regression to estimate hazard ratios and 95% confidence intervals for the development of heart failure by PTSD status. RESULTS Over a mean follow-up of 7.2 years, veterans with PTSD were at increased risk for developing heart failure (hazard ratio [HR] = 1.47; 95% confidence interval [CI] = 1.13, 1.92) compared with veterans without PTSD after adjustment for age, gender, diabetes, hyperlipidemia, hypertension, body mass index, combat service, and military service period. Additional predictors for heart failure included age (HR = 1.05; 95% CI = 1.03, 1.07), diabetes (HR = 2.54; 95% CI = 2.02, 3.20), hypertension (HR = 1.87; 95% CI = 1.42, 2.46), overweight (HR = 1.72; 95% CI = 1.25, 2.36), obesity (HR = 3.43; 95% CI = 2.50, 4.70), and combat service (HR = 4.99; 95% CI = 1.29, 19.38). CONCLUSIONS Ours is the first large-scale longitudinal study to report an association between PTSD and incident heart failure in an outpatient sample of US veterans. Prevention and treatment efforts for heart failure and its associated risk factors should be expanded among US veterans with PTSD.

Diagnosis-specific readmission risk prediction using electronic health data: a retrospective cohort study

BackgroundReadmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission.MethodsThis is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were included in the analysis.The main outcome was 30-day readmission. Demographic, comorbidity, laboratory, and medication data were collected on all patients from a comprehensive information warehouse. Using multivariable analysis with stepwise removal we created three risk disease-specific risk prediction models and a combined model. These models were then validated on separate cohorts.Results3572 patients were included in the derivation cohort. Overall there was a 16.2% readmission rate. The acute myocardial infarction and pneumonia readmission-risk models performed well on a random sample validation cohort (AUC range 0.73 to 0.76) but less well on a historical validation cohort (AUC 0.66 for both). The congestive heart failure model performed poorly on both validation cohorts (AUC 0.63 and 0.64).ConclusionsThe readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on a historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends. The poor performance of the congestive heart failure model may suggest that for chronic disease conditions social and behavioral variables are of greater importance and improved documentation of these variables within the electronic health record should be encouraged.

Community-level determinants of obesity: harnessing the power of electronic health records for retrospective data analysis

BackgroundObesity and overweight are multifactorial diseases that affect two thirds of Americans, lead to numerous health conditions and deeply strain our healthcare system. With the increasing prevalence and dangers associated with higher body weight, there is great impetus to focus on public health strategies to prevent or curb the disease. Electronic health records (EHRs) are a powerful source for retrospective health data, but they lack important community-level information known to be associated with obesity. We explored linking EHR and community data to study factors associated with overweight and obesity in a systematic and rigorous way.MethodsWe augmented EHR-derived data on 62,701 patients with zip code-level socioeconomic and obesogenic data. Using a multinomial logistic regression model, we estimated odds ratios and 95% confidence intervals (OR, 95% CI) for community-level factors associated with overweight and obese body mass index (BMI), accounting for the clustering of patients within zip codes.Results33, 31 and 35 percent of individuals had BMIs corresponding to normal, overweight and obese, respectively. Models adjusted for age, race and gender showed more farmers’ markets/1,000 people (0.19, 0.10-0.36), more grocery stores/1,000 people (0.58, 0.36-0.93) and a 10% increase in percentage of college graduates (0.80, 0.77-0.84) were associated with lower odds of obesity. The same factors yielded odds ratios of smaller magnitudes for overweight. Our results also indicate that larger grocery stores may be inversely associated with obesity.ConclusionsIntegrating community data into the EHR maximizes the potential of secondary use of EHR data to study and impact obesity prevention and other significant public health issues.