Using Community-Based Participatory Research to Develop Geospatial Models Toward Improving Community Health for Disadvantaged Hispanic Populations in Charlotte, NC.

Hispanic immigrant communities across the U.S. experience persistent health disparities and barriers to primary care. We examined whether community-based participatory research (CBPR) and geospatial modeling could systematically and reproducibly pinpoint neighborhoods in Charlotte, North Carolina with large proportions of Hispanic immigrants who were at-risk for poor health outcomes and health disparities. Using a CBPR framework, we identified 21 social determinants of health measures and developed a geospatial model from a subset of those measures to identify neighborhoods with large proportions of Hispanic immigrant populations at risk for poor health outcomes. The geospatial model included four measures-poverty, English ability, acculturation and violent crime-which comprised our Hispanic Health Risk Index (HHRI). We developed a Primary Care Barrier Index (PCBI) to determine (1) how well the HHRI correlated with a statistically derived composite measure incorporating all 21 measures identified through the CBPR process as being associated with access to primary care; (2) whether the HHRI predicted primary care access as well as the statistically-derived composite measure in a statistical model; and (3) whether the HHRI identified similar neighborhoods as the statistically derived composite measure. We collapsed 17 of the 21 social determinants using principal components analysis to develop the PCBI. We determined the correlation of each index with inappropriate emergency department (ED) visits, a proxy for primary care access, using logistic generalized estimating equations. Results from logistic regression models showed positive associations of both the HHRI and the PCBI with the use of the ED for primary care treatable conditions. Enhanced by the knowledge of the local community, the CBPR process with geospatial modeling can guide the multi-tiered validation of social determinants of health and identify neighborhoods that are at-risk for poor health outcomes and health disparities.

Population pharmacokinetics of recombinant factor VIII Fc fusion protein.

Population pharmacokinetics (PK) of FVIII activity-time profiles following recombinant factor VIII Fc fusion protein (rFVIIIFc) and recombinant factor VIII (rFVIII) dosing were evaluated in previously treated patients with severe hemophilia A (from two clinical trials). Potential covariates that may be determinants of variability in FVIII activity were identified. A 2-compartment model adequately described the PK of both compounds. von Willebrand Factor (VWF) concentration was the major covariate for rFVIIIFc clearance, reflecting its protective role in FVIII activity clearance. The effect of body weight and hematocrit on the central volume of distribution of rFVIIIFc was minor. The results of these analyses confirmed that rFVIIIFc clearance (1.65 dL/h) is much lower than that of rFVIII (2.53 dL/h), while the steady state volumes of distribution were similar. The strong positive correlations between the PK parameters of rFVIIIFc and rFVIII suggest that individuals who have high time-related PK characteristics with rFVIII are likely to have comparable characteristics with rFVIIIFc. Steady-state activity-time profiles for selected rFVIIIFc dosing regimens were simulated accounting for uncertainty in model parameters. These population PK analyses and simulations provide a comprehensive characterization of the PK of rFVIIIFc and rFVIII and may be useful for designing dosing regimens.

Using Geographic Information Systems (GIS) to understand a community's primary care needs.

INTRODUCTION: A key element for reducing health care costs and improving community health is increased access to primary care and preventative health services. Geographic information systems (GIS) have the potential to assess patterns of health care utilization and community-level attributes to identify geographic regions most in need of primary care access. METHODS: GIS, analytical hierarchy process, and multiattribute assessment and evaluation techniques were used to examine attributes describing primary care need and identify areas that would benefit from increased access to primary care services. Attributes were identified by a collaborative partnership working within a practice-based research network using tenets of community-based participatory research. Maps were created based on socioeconomic status, population density, insurance status, and emergency department and primary care safety-net utilization. RESULTS: Individual and composite maps identified areas in our community with the greatest need for increased access to primary care services. CONCLUSIONS: Applying GIS to commonly available community- and patient-level data can rapidly identify areas most in need of increased access to primary care services. We have termed this a Multiple Attribute Primary Care Targeting Strategy. This model can be used to plan health services delivery as well as to target and evaluate interventions designed to improve health care access.

Geographic Information Systems (GIS) demonstrating primary care needs for a transitioning hispanic community.

BACKGROUND: Hispanics are the largest and fastest growing minority group in the United States. Charlotte, NC, had the 4th fastest growing Hispanic community in the nation between 1990 to 2000. Gaining understanding of the patterns of health care use for this changing population is a key step toward designing improved primary care access and community health. METHODS: The Multiple Attribute Primary Care Targeting Strategy process was applied to key patient- and community-level attributes describing the Charlotte Hispanic community. Maps were created based on socioeconomic status, population density, insurance status, and use of the emergency department as a primary care safety net. Each of these variables was weighed and added to create a single composite map. RESULTS: Individual attribute maps and the composite map identified geographic locations where Hispanic community members would most benefit from increased access to primary care services. CONCLUSIONS: Using the Multiple Attribute Primary Care Targeting Strategy process we were able to identify geographic areas within our community where many Hispanic immigrants face barriers to accessing appropriate primary care services. These areas can subsequently be targeted for interventions that improve access to primary care and reduce emergency department use. The geospatial model created through this process can be monitored over time to determine the effectiveness of these interventions.

Likelihood based approaches to handling data below the quantification limit using NONMEM VI.

PURPOSE: To evaluate the likelihood-based methods for handling data below the quantification limit (BQL) using new features in NONMEM VI. METHODS: A two-compartment pharmacokinetic model with first-order absorption was chosen for investigation. Methods evaluated were: discarding BQL observations (M1), discarding BQL observations but adjusting the likelihood for the remaining data (M2), maximizing the likelihood for the data above the limit of quantification (LOQ) and treating BQL data as censored (M3), and like M3 but conditioning on the observation being greater than zero (M4). These four methods were compared using data simulated with a proportional error model. M2, M3, and M4 were also compared using data simulated from a positively truncated normal distribution. Successful terminations and bias and precision of parameter estimates were assessed. RESULTS: For the data simulated with a proportional error model, the overall performance was best for M3 followed by M2 and M1. M3 and M4 resulted in similar estimates in analyses without log transformation. For data simulated with the truncated normal distribution, M4 performed better than M3. CONCLUSIONS: Analyses that maximized the likelihood of the data above the LOQ and treated BQL data as censored provided the most accurate and precise parameter estimates.

Population pharmacokinetics of the active metabolite of leflunomide in pediatric subjects with polyarticular course juvenile rheumatoid arthritis.

Leflunomide is a pyrimidine synthesis inhibitor used in the treatment of rheumatoid arthritis. Data from two clinical studies were used to establish a population pharmacokinetic (PPK) model for the active metabolite (M1) of leflunomide in patients with juvenile rheumatoid arthritis (JRA) and determine appropriate pediatric doses. Seventy-three subjects 3-17 years of age provided 674 M1 concentrations. The PPK model was derived from nonlinear mixed-effects modeling and qualified by cross-study evaluation and predictive check. A one-compartment model with first-order input described M1 PPK well. Body weight (WT) correlated weakly with oral clearance (CL/F = 0.020.[WT/40](0.430)) and strongly with volume of distribution (V/F = 5.8.[WT/40](0.769)). Steady-state concentrations (C(ss)) of M1 in JRA were compared for a variety of leflunomide dose regimens using Monte-Carlo simulation. To achieve comparable C(ss) values in pediatric patients with JRA to that in adult patients, doses of leflunomide should be adjusted modestly: 10 mg/d for 10-20 kg, 15 mg/d for 20-40 kg, and 20 mg/d for > 40 kg.

Estimation of population pharmacokinetic parameters in the presence of non-compliance.

In population pharmacokinetic (PK) studies, patients' drug plasma profiles are routinely analyzed assuming that all patients took their drug at the times and in the amounts specified. However, patient non-compliance with the prescribed drug regimen is a leading source of failure to drug therapy. It has been reported that over 30% of patients routinely skip doses regardless of their disease, prognosis, or symptoms. This brings into question the assumption regarding full compliance for population PK analyses. This paper describes the estimation of population PK parameters in the presence and absence of non-compliance while either assuming full compliance or estimating compliance using a hierarchical Bayesian approach. Assessment of compliance for a given dose was limited to one of three possibilities: no dose was taken at the prescribed time, the prescribed dose was taken at the prescribed time, or twice the prescribed dose was taken at the prescribed time. Simulated data sets based on a one-compartment pharmacokinetic model with first order elimination were analyzed using WinBUGS (Bayesian inference Using Gibbs Sampling) software. An initial feasibility simulation experiment, using a simple, but informative PK sampling design with bolus input of drug, was performed. A second simulation study was then carried out using a more realistic sampling design and first-order input of drug. The simulated sampling design included observations after known doses as well as after uncertain doses. Results from the feasibility study revealed that when compliance was estimated instead of being assumed to be 100%, the relative prediction error for clearance (CL) decreased from 0.25 to 0.10 for 60% compliance and from 0.6 to 0.2 for 35% compliance. Estimates of the interoccasion variability of clearance were improved by compliance estimation but still had substantial positive bias. Estimated of interindividual variability were relatively insensitive to compliance estimation. Estimates for volume of distribution (V) and its associated variances were not affected by incorporation of compliance estimates, perhaps due to the specific sampling design that was used. The design was relatively uninformative regarding V. In the more realistic study, estimates for CL, V and the difference between the absorption rate constant and the elimination rate constant (KA-K) were improved by the incorporation of compliance estimation. The median relative errors were reduced from 0.51 to -0.01 for CL, from 0.49 to 0.04 for V, and from 0.49 to -0.02 for Ka-K. The bias in interoccasion variances for V and CL appeared to be reduced by compliance estimation while estimates of interindividual variability were not affected in a systematic fashion. The bias in the residual error variance was decreased from a relative error of about 2 to close to 0. The use of hierarchical Bayesian modeling with the incorporation of compliance estimation decreased the bias in the typical value parameter but the effects on variance parameters were less consistent. The encouraging results of these simulation experiments will hopefully stimulate further evaluation of this methodology for the estimation of population pharmacokinetic parameters in the presence of potential patient noncompliance.