Faculty Bibliography

BACKGROUND:Patients with chronic kidney disease (CKD) and coronary artery disease frequently undergo preemptive revascularization before kidney transplant listing. OBJECTIVES/OBJECTIVE:In this post-hoc analysis from ISCHEMIA-CKD, we compared outcomes of patients not listed versus those listed according to management strategy. METHODS:In ISCHEMIA-CKD (n=777), 194 patients (25%) with chronic coronary syndromes and at least moderate ischemia were listed for transplant. The primary (all-cause mortality or nonfatal myocardial infarction [MI]) and secondary (death, nonfatal MI, hospitalization for unstable angina, heart failure, resuscitated cardiac arrest, or stroke) outcomes were analyzed using Cox multivariable modeling. Heterogeneity of randomized treatment effect between listed versus not listed groups was assessed. RESULTS:Compared with those not listed, listed patients were younger (60 versus 65 years), less likely of Asian race (15% versus 29%), more likely on dialysis (83% versus 44%), had fewer anginal symptoms, and more likely to have coronary angiography and coronary revascularization irrespective of treatment assignment. Among patients assigned to an invasive strategy versus conservative strategy, the adjusted hazard ratios (aHR) (95% confidence interval [CI]) for the primary outcome were 0.91 (0.54-1.54) and 1.03 (0.78-1.37) for those listed and not listed, respectively (pinteraction=0.68). Adjusted HR for secondary outcomes were 0.89 (0.55-1.46) in listed and 1.17 (0.89-1.53) in those not listed (pinteraction=0.35). CONCLUSIONS:In ISCHEMIA-CKD, an invasive strategy in kidney transplant candidates did not improve outcomes compared with conservative management. These data do not support routine coronary angiography or revascularization in patients with advanced CKD and chronic coronary syndromes listed for transplant.

Purpose : Ophthalmology clinics with minimal statistical analytics capabilities may struggle to implement a quality monitoring system to evaluate patient outcomes. We performed a quality improvement study by designing a quality monitoring system for a low-resource ophthalmology clinic that would use small samples to estimate outcomes for larger patient populations. Methods : We evaluated the proportion of primary open-angle glaucoma (POAG) patients who were treated successfully according to American Academy of Ophthalmology Preferred Practice Patterns (PPPs). We analyzed 100 patients seen in the clinic over 3 months in 2019 as the input for our Bayesian analysis. We also created a standardized note template for use by clinicians in the electronic medical record (EMR) for POAG patient visits to facilitate monitoring of treatment successes without requiring clinical expertise. We evaluated adherence to clinician template use in POAG patient notes on the weekly day of glaucoma patient visits over 9 weeks in 2020. Results : Using Bayesian analysis based on our initial data, we created tables that allow for quality monitoring of future 3 month intervals using parameters from smaller samples. Using a small sample of 30 patients, we were able to determine that there was a 100% probability that the clinic as a whole was not achieving the pre-defined target percentage of 80% of POAG patients successfully treated, as defined by the PPPs. Regarding adherence to use of the note template for POAG patients, the median proportion of patients for which the template was used on a single clinic day was 27%, with a maximum of 53% and a minimum of 11%. Conclusions : Based on the input parameters of the number of patients in the sample, the number of treatment successes in that sample, and the target proportion of successfully treated patients, a small sample Bayesian analysis can be used for quality monitoring of patient outcomes for larger patient populations in low-resource clinical settings. Future work includes creation of an open-access database of tables derived from Bayesian analysis for use as a reference by other low-resource clinics in quality monitoring efforts

Background: The efficacy of an aggressive multiple risk factor intervention approach - optimal medical therapy (OMT) - to reduce major adverse cardiovascular events in patients with CKD has not been tested.
Objective(s): to examine OMT goal attainment in patients with CKD on dialysis (CKD-D) and non-dialysis CKD (CKD-ND) in the ISCHEMIA-CKD trial.
Method(s): OMT was recommended to all participants in ISCHEMIA-CKD. Longitudinal trajectories of individual OMT components (smoking cessation, systolic blood pressure (SBP) <140 mmHg, low density lipoprotein (LDL) cholesterol <70 mg/dL, high-intensity statin use, and aspirin use) were modeled over study follow-up. Covariateadjusted percentage point difference in each OMT goal achieved at 24 months between CKD-D and CKD-ND groups (% difference [95% credible interval (CrI)]) was estimated.
Result(s): There were 415 CKD-D and 362 CKD-ND patients at baseline. CKD-D were younger (61 v 67 yrs, p<0.001) and less often diabetic (53% v 62%, p=0.023). CKD-D patients were 7.9 % (0.7%, 14.8%) more likely than CKD-ND to attain the SBP goal at 24 months (Figure). CKD-D patients were 22.7% (-33.3%, -11.4%) less likely to receive high-intensity statins. There was a steady and similar increase in proportional achievement of OMT during follow up.
Conclusion(s): OMT improved over time in advanced CKD-ND and CKD-D. CKD-D achieved the SBP goal more than CKD-ND, yet CKD-D were less likely to be treated with high-intensity statin. Future studies should explore systemic and patient-related barriers to attainment of OMT in this high-risk cohort.(Figure Presented)

BACKGROUND:Few studies examine relationships between built environment (BE) and type 2 diabetes mellitus (T2DM) using spatial models, investigate BE domains apart from food environment or physical activity resources or conduct sensitivity analysis of methodological choices made in measuring BE. We examine geographic heterogeneity of T2DM, describe how heterogeneity in T2DM relates to BE and estimate associations of T2DM with BE. METHODS:Individual-level electronic health records (n=41 203) from the Duke Medicine Enterprise Data Warehouse (2007-2011) were linked to BE based on census block. Data on housing damage, property disorder, territoriality, vacancy and public nuisances were used to estimate BE based on four different construction methods (CMs). We used race-stratified aspatial and spatial Bayesian models to assess geographic heterogeneity in T2DM and associations of T2DM with BE. RESULTS:Among whites, a 1 SD increase in poor quality BE was associated with a 1.03 (95% credible interval 1.01 to 1.06) and 1.06 (95 % credible interval 1.02 to 1.11) increased risk of T2DM for poor quality BE CM1 and CM2, respectively. Among blacks/African Americans, associations between T2DM and BE overlapped with the null for all CMs. The addition of BE to white models reduced residual geographic heterogeneity in T2DM by 4%-15%, depending on CM. In black/African-American models, BE did not affect residual heterogeneity. CONCLUSION/CONCLUSIONS:Associations of T2DM with BE were sensitive to CM and geographic heterogeneity in T2DM differed by race/ethnicity. Findings underscore the need to consider multiple methods of estimating BE and consider differences in relationships by race/ethnicity.

Neighborhood characteristics such as racial segregation may be associated with type 2 diabetes mellitus, but studies have not examined these relationships using spatial models appropriate for geographically patterned health outcomes. We constructed a local, spatial index of racial isolation (RI) for black residents in a defined area, measuring the extent to which they are exposed only to one another, to estimate associations of diabetes with RI and examine how RI relates to spatial patterning in diabetes. We obtained electronic health records from 2007-2011 from the Duke Medicine Enterprise Data Warehouse. Patient data were linked to RI based on census block of residence. We used aspatial and spatial Bayesian models to assess spatial variation in diabetes and relationships with RI. Compared with spatial models with patient age and sex, residual geographic heterogeneity in diabetes in spatial models that also included RI was 29% and 24% lower for non-Hispanic white and black residents, respectively. A 0.20-unit increase in RI was associated with an increased risk of diabetes for white (risk ratio = 1.24, 95% credible interval: 1.17, 1.31) and black (risk ratio = 1.07, 95% credible interval: 1.05, 1.10) residents. Improved understanding of neighborhood characteristics associated with diabetes can inform development of policy interventions.

Globally, the majority of childhood deaths in the post-neonatal period are caused by infections that can be effectively treated or prevented with inexpensive interventions delivered through even very basic health facilities. To understand the role of inadequate health systems on childhood mortality in Kenya, we assemble a large, retrospective cohort of children (born 1996-2013) and describe the health systems context of each child using health facility survey data representative of the province at the time of a child's birth. We examine the relationship between survival beyond 59 months of age and geographic distribution of health facilities, quality of services, and cost of services. We find significant geographic heterogeneity in survival that can be partially explained by differences in distribution of health facilities and user fees. Higher per capita density of health facilities resulted in a 25% reduction in the risk of death (HRR = 0.73, 95% CI:0.58 to 0.91) and accounted for 30% of the between-province heterogeneity in survival. User fees for sick-child visits increased risk by 30% (HRR = 1.30, 95% CI:1.11 to 1.53). These results implicate health systems constraints in child mortality, quantify the contribution of specific domains of health services, and suggest priority areas for improvement to accelerate reductions in child mortality.

PURPOSE:To determine how strabismus diagnosis varies within a given community and across communities among children with Medicaid health insurance. DESIGN:Retrospective cohort analysis. PARTICIPANTS:Children aged ≤10 years enrolled in Medicaid in Michigan or North Carolina during 2009. METHODS:Children who met the study inclusion criteria were identified from the Medicaid Analytic Extract database, which includes claims data for all children enrolled in Medicaid throughout the United States. Residential location was determined by the last known 5-digit ZIP code for each child, which was linked to the centroid of a ZIP Code Tabulation Area (ZCTA) for geo-referencing and spatial analyses. International Classification of Diseases, 9th Revision, Clinical Modification billing codes were used to identify children diagnosed with strabismus (code 378.xx). Bayesian hierarchical intrinsic conditional autoregressive spatial probit models were used to determine the risk of a child receiving a strabismus diagnosis in communities throughout Michigan and North Carolina. Maps display communities (ZCTAs) where the 95% credible intervals for the spatial random effects estimates do not cross zero, allowing for identification of locations with increased and decreased strabismus diagnosis risk relative to other communities in the states. MAIN OUTCOME MEASURES:Likelihood of receiving a diagnosis of strabismus. RESULTS:In 2009, among 519 212 eligible children in Michigan, 7535 (1.5%) received ≥1 strabismus diagnosis, and in North Carolina, 5827 of 523 886 eligible children (1.1%) were diagnosed with strabismus. In both states, the proportion receiving a strabismus diagnosis among black (0.9% in Michigan; 0.7% in North Carolina) and Hispanic (1.1% in Michigan; 0.8% in North Carolina) children was lower than the proportion for white children (1.8% in Michigan; 1.6% in North Carolina). Children living in poorer communities in both states were less likely to be diagnosed with strabismus independent of their race/ethnicity. CONCLUSIONS:A child's likelihood of being diagnosed with strabismus is associated with characteristics of the residential community where he or she resides. The findings of this study highlight the importance of ensuring that children who live in less affluent communities have access to the necessary services and eye care professionals to properly diagnose and treat them for this condition.

BACKGROUND:Researchers and policymakers are increasingly focused on combined exposures to social and environmental stressors, especially given how often these stressors tend to co-locate. Such exposures are equally relevant in urban and rural areas and may accrue disproportionately to particular communities or specific subpopulations. OBJECTIVES:To estimate relationships between racial isolation (RI), a measure of the extent to which minority racial/ethnic group members are exposed to only one another, and long-term particulate matter with an aerodynamic diameter of <2.5μ (PM2.5) and ozone (O3) levels in urban and nonurban areas of the eastern two-thirds of the US. METHODS:Long-term (5year average) census tract-level PM2.5 and O3 concentrations were calculated using output from a downscaler model (2002-2006). The downscaler uses a linear regression with additive and multiplicative bias coefficients to relate ambient monitoring data with gridded output from the Community Multi-scale Air Quality (CMAQ) model. A local, spatial measure of RI was calculated at the tract level, and tracts were classified by urbanicity, RI, and geographic region. We examined differences in estimated pollutant exposures by RI, urbanicity, and demographic subgroup (e.g., race/ethnicity, education, socioeconomic status, age), and used linear models to estimate associations between RI and air pollution levels in urban, suburban, and rural tracts. RESULTS:High RI tracts (≥80th percentile) had higher average PM2.5 levels in each category of urbanicity compared to low RI tracts (<20th percentile), with the exception of the rural West. Patterns in O3 levels by urbanicity and RI differed by region. Linear models indicated that PM2.5 concentrations were significantly and positively associated with RI. The largest association between PM2.5 and RI was observed in the rural Midwest, where a one quintile increase in RI was associated with a 0.90μg/m(3) (95% confidence interval: 0.83, 0.99μg/m(3)) increase in PM2.5 concentration. Associations between O3 and RI in the Northeast, Midwest and West were positive and highest in suburban and rural tracts, even after controlling for potential confounders such as percentage in poverty. CONCLUSION:RI is associated with higher 5year estimated PM2.5 concentrations in urban, suburban, and rural census tracts, adding to evidence that segregation is broadly associated with disparate air pollution exposures. Disproportionate burdens to adverse exposures such as air pollution may be a pathway to racial/ethnic disparities in health.