Related Covariates Research Articles

The Relationship between Drug Clearance and Body Size

A variety of body size covariates have been used in population pharmacokinetic analyses to describe variability in drug clearance (CL), such as total body weight (TBW), body surface area (BSA), lean body weight (LBW) and allometric TBW. There is controversy, however, as to which body size covariate is most suitable for describing CL across the whole population. Given the increasing worldwide prevalence of obesity, it is essential to identify the best size descriptor so that dosing regimens can be developed that are suitable for patients of any size. The aim of this study was to explore the use of body size covariates in population pharmacokinetic analyses for describing CL. In particular, we sought to determine if any body size covariate was preferential to describe CL and quantify its relationship with CL, and also identify study design features that result in the identification of a nonlinear relationship between TBW and CL. Population pharmacokinetic articles were identified from MEDLINE using defined keywords. A database was developed to collect information about study designs, model building and covariate analysis strategies, and final reported models for CL. The success of inclusion for a variety of covariates was determined. A meta-analysis of studies was then performed to determine the average relationship reported between CL and TBW. For each study, CL was calculated across the range of TBW for the study population and normalized to allow comparison between studies. BSA, LBW, and allometric TBW and LBW relationships with exponents of 3/4, 2/3, and estimated values were evaluated to determine the relationship that best described the data overall. Additionally, joint distributions of TBW were compared between studies reporting a 'nonlinear' relationship between CL and TBW (i.e. LBW, BSA and allometric TBW-shaped relationships) and those reporting 'other' relationships (e.g. linear increase in CL with TBW, ideal body weight or height). A total of 458 out of 2384 articles were included in the analysis, from which 484 pharmacokinetic studies were reviewed. Fifty-six percent of all models for CL included body size as a covariate, with 52% of models including a nonlinear relationship between CL and TBW. No single size descriptor was more successful than others for describing CL. LBW with a fixed exponent of 2/3, i.e. (LBW/50.45)(2/3), or estimated exponent of 0.646, i.e. ∼2/3, was found to best describe the average reported relationship between CL and TBW. The success of identifying a nonlinear increase in CL with TBW was found to be higher for those studies that included a wider range of subject TBW. To the best of our knowledge, this is the first study to have performed a meta-analysis of covariate relationships between CL and body size. Although many studies reported a linear relationship between CL and TBW, the average relationship was found to be nonlinear. LBW with an allometric exponent of ∼2/3 may be most suitable for describing an increase in CL with body size as it accounts for both body composition and allometric scaling principles concerning differences in metabolic rates across size.

Association of persistent hyperglycemia with outcome of severe traumatic brain injury in pediatric population

Hyperglycemia is a common secondary insult associated with an increased risk of mortality and poor outcome in traumatic brain injury (TBI), but the effect of hyperglycemia on outcomes of severe TBI in children and adolescents is less apparent. The aim of this study was to evaluate the association of hyperglycemia with mortality in pediatric patients with severe TBI. In this cross-sectional study, data of all children and adolescents with severe TBI admitted to Poursina Hospital in Rasht, including age, gender, Glasgow Coma Scale (GCS) upon admission, mortality rate, hospital length of stay, and serial blood glucose during the first three consecutive ICU days following admission, were reviewed from April 2007 to May 2011. After univariate analysis and adjustment for related covariates, logistic regression model was established to determine the association between persistent hyperglycemia and outcome. One-hundred and twenty-two children were included with a median admission GCS of 6 (interquartile range (IQR) 5-7) and a median age of 13 years (IQR 7.75-17). Among them, 91 were boys (74.6%) and 31 were girls (26.6%); the overall mortality was 40.2% (n=49). Patients who died had a significantly greater blood glucose levels than survivors for the first 3 days of admission (P=0.003, P<0.001, P=0.001, respectively). Moreover, persistent hyperglycemia during the first 3 days of admission had an adjusted odds ratio of 11.11 for mortality (P<0.001). Early hyperglycemia is associated with poor outcome, and persistent hyperglycemia is a powerful and independent predictor of mortality in children and adolescents with severe TBI.

The Use and Interpretation of Competing Risks Regression Models

Competing risks observations, in which patients are subject to a number of potential failure events, are a feature of most clinical cancer studies. With competing risks, several modeling approaches are available to evaluate the relationship of covariates to cause-specific failures. We discuss the use and interpretation of commonly used competing risks regression models. For competing risks analysis, the influence of covariate can be evaluated in relation to cause-specific hazard or on the cumulative incidence of the failure types. We present simulation studies to illustrate how covariate effects differ between these approaches. We then show the implications of model choice in an example from a Radiation Therapy Oncology Group (RTOG) clinical trial for prostate cancer. The simulation studies illustrate that, depending on the relationship of a covariate to both the failure type of principal interest and the competing failure type, different models can result in substantially different effects. For example, a covariate that has no direct influence on the hazard of a primary event can still be significantly associated with the cumulative probability of that event, if the covariate influences the hazard of a competing event. This is a logical consequence of a fundamental difference between the model formulations. The example from RTOG similarly shows differences in the influence of age and tumor grade depending on the endpoint and the model type used. Competing risks regression modeling requires that one considers the specific question of interest and subsequent choice of the best model to address it.

Updating and Extending the IRDF-2002 Dosimetry Library

Abstract The International Reactor Dosimetry File (IRDF)-2002 released in 2004 by the IAEA (see http://www-nds.iaea.org/irdf2002/) contains cross-section data and corresponding uncertainties for 66 dosimetry reactions. New cross-section evaluations have become available recently that re-define some of these dosimetry reactions including: (1) high-fidelity evaluation work undertaken by one of the authors (KIZ); (2) evaluations from the US ENDF/B-VII.0 and candidate evaluations from the US ENDF/B-VII.1 libraries that cover reactions within the International Evaluation of Neutron Cross-Section Standards; (3) European JEFF3.1 library; and (4) Japanese JENDL-4.0 library. Additional high-threshold reactions not included in IRDF-2002 (e.g.,59Co(n,3n) and 209Bi(n,3n)) have been also evaluated to characterize higher-energy neutron fields. Overall, 37 new evaluations of dosimetry reactions have been assessed and intercomparisons made with integral measurements in reference neutron fields to determine whether they should be adopted to update and improve IRDF-2002. Benchmark calculations performed for newly evaluated reactions using the ENDF/B-VII.0 235U thermal fission and 252Cf spontaneous fission neutron spectra show that calculated integral cross sections exhibit improved agreement with evaluated experimental data when compared with the equivalent data from the IRDF-2002 library. Data inconsistencies or deficiencies of new evaluations have been identified for 63Cu(n,2n), 60Ni(n,p)60m+g Co, 55Mn(n,γ), and 232Th(n,f) reactions. Compared with IRDF-2002, the upper neutron energy boundary was formally increased from the actual maximum energy of typically 20 MeV up to 60 MeV by using the TENDL-2010 cross sections and covariance matrices. This extension would allow the updated IRDF library to be also used in fusion dosimetry applications. Uncertainties in the cross sections for all new evaluations are given in the form of relative covariance matrices. Newly evaluated excitation functions should be considered as suitable candidates in the preparation of an improved version of the IRDF that was planned to be released for testing in December 2011.

Cord blood 8-isoprostane in the preterm infant

BackgroundCord blood 8-isoprostane (8-IP) is a marker of lipid peroxidation in the peripartum period. The independent association with degree of prematurity is not well-described. ObjectiveTo identify patterns of lipid peroxidation among early, moderate and late preterm infants, and to understand how cord blood 8-IP varies with gestational age (GA) and related covariates. Study designMother–infant pairs from 237 preterm births were studied as part of a longitudinal birth cohort study. GA subgroups were defined as extremely (≤28w), moderately (29–33w), and late (34–36w) preterm. Cord blood 8-IP was measured using EIA. Elevated 8-IP (4th quartile) was the primary outcome for multivariate logistic regression models, which were adjusted for maternal age/race, multiple gestation and infant gender, as well as other relevant covariates. ResultsElevated 8-IP was associated with extremely preterm birth (OR=4.31; 95% CI=1.90, 9.76), and was inversely associated with increasing GA (OR=0.88; 95% CI=0.80, 0.97). Elevated 8-IP was also associated with decreasing birth weight (BW), clinical chorioamnionitis, fetal inflammatory response of the placenta (FIR), and signs of perinatal depression. The GA on 8-IP association appeared to be modified by several maternal disease and fetal–infant factors. Lastly, the indirect associations between log-transformed 8-IP, GA and BW appeared to be most prominent for GA<30w and for BW<2000g. ConclusionLipid peroxidation in preterm birth, and the relative influence of accompanying peripartum factors, varies according to degree of prematurity. These findings have important implications for the developmental regulation of antioxidant defense and its impact on neonatal outcomes.

Volume of home- and community-based services and time to nursing-home placement.

The purpose of this study was to determine whether the volume of Home- and Community-Based Services (HCBS) that target Activities of Daily Living disabilities, such as attendant care, homemaking services, and home-delivered meals, increases recipients' risk of transitioning from long-term care provided through HCBS to long-term care provided in a nursing home. Data are from the Indiana Medicaid enrollment, claims, and Insite databases. Insite is the software system that was developed for collecting and reporting data for In-Home Service Programs. Enrollees in Indiana Medicaid's Aged and Disabled Waiver program were followed forward from time of enrollment to assess the association between the volume of attendant care, homemaking services, home-delivered meals, and related covariates, and the risk for nursing-home placement. An extension of the Cox proportional hazard model was computed to determine the cumulative hazard of nursing-home placement in the presence of death as a competing risk. Of the 1354 Medicaid HCBS recipients followed in this study, 17% did not receive any attendant care, homemaking services, or home-delivered meals. Among recipients who survived through 24 months after enrollment, one in five transitioned from HCBS to a nursing-home. Risk for nursing-home placement was significantly lower for each five-hour increment in personal care (HR=0.95, 95% CI=0.92-0.98) and homemaking services (HR=0.87, 95% CI=0.77-0.99). Future policies and practices that are focused on optimizing long-term care outcomes should consider that a greater volume of HCBS for an individual is associated with reduced risk of nursing-home placement.

Unveiling the Values Inculcation Model among Mathematics Teachers in Developing Country: A Conceptual Approach

The demand for valued-education particularly science education and mathematics education is increasing in light of the global technological advancement. However, research on the area of values associated with mathematics teaching and learning among mathematics teachers remains in its infancy in Nigeria. This study was conducted to unveil the effectiveness of the mathematical values inculcation model among secondary schools mathematics teachers in Nigeria. A sample of 509 mathematics teachers was drawn from the six states of the North Eastern Region of Nigeria. A self-constructed survey instrument was used to gather the data of the study. The independent variables of the study consist of ideological, attitudinal, sociological, computational and motivational mathematical values. Structural equation modeling technique and confirmatory factor analysis (CFA) were conducted. The findings of the study show that there is a significant covariant relationship among the latent constructs for values inculcation in mathematics teaching and learning. There is also adequate support of the values inculcation model among Nigerian mathematics teachers as the model fits the data.

Prior for Structural Materials

The construction of a library containing prior cross sections and related covariance matrices for nine frequently measured reaction channels of 276 structural materials from 1 to 150 MeV is presented. This library provides a starting point for future evaluations and reduces them to the analysis of experimental data. The prior cross sections were calculated using the nuclear reaction programs of TALYS1.0 and TALYS1.2 with the implemented global and local neutron and proton optical potential parameter sets. The prior covariance matrices contain contributions stemming from parameter uncertainties and model deficiencies. The associated procedures are discussed and an example evaluation with these prior cross sections and covariance matrices is shown for one specific isotope. These developments are of great importance with regard to the reliability of evaluations strongly based on model calculations. This is particularly true for the extension of the energy range as requested by the development of novel nuclear technologies.

Environmental Determinants of Child Mortality in Nigeria

Globally, childhood mortality rates have decline over the years due majorly to various action plans and interventions targeted at various communicable diseases and other immunizable childhood infections which have been major causes of child mortality, but the situation seems to remain unchanged in sub-Saharan African countries, as approximately half of these deaths occur in sub-Saharan Africa despite the region having only one fifth of the world’s children population. Many covariates associated with variations in infant and child mortality are interrelated, and it is important to attempt to isolate the effects of individual variables for proper and effective interventions. This study examined the environmental determinants of child mortality using principal component analysis as a data reduction technique with varimax rotation to assess the underlying structure for sixty-five measured variables, explaining the covariance relationships amongst the large correlated variables in a more parsimonious way and simultaneous multiple regression for child mortality modelling in Nigeria. For purpose of robustness, a model selection technique procedure was implemented. Estimation from the stepwise regression model shows that household environmental characteristics do have significant impact on mortality.

Longitudinal evaluation of CA-125 velocity and prediction of ovarian cancer

ObjectiveTo determine whether CA-125 velocity is a statistically significant predictor of ovarian cancer and develop a classification rule to screen for ovarian cancer. MethodsIn the ovarian component of the PLCO cancer screening trial, 28,038 women aged 55–74 had at least two CA-125 screening tests. Ovarian cancer was diagnosed in 72 (0.26%) women. A multiple logistic regression model was developed to evaluate CA-125 velocity and other related covariates as predictors of ovarian cancer. Predictive accuracy was assessed by the concordance index and measures of discrimination and calibration while the fit of the model was assessed by the Hosmer and Lemeshow's goodness-of-fit χ2test. ResultsCA-125 velocity decreased as the number of CA-125 measurements increased but was unaffected by age at baseline screen and family history of ovarian cancer. The average velocity (19.749U/ml per month) of the cancer group was more than 500 times the average velocity (0.035U/ml per month) of the non-cancer group. ConclusionAmong six covariates used in the model, CA-125 velocity and time intervals between baseline and second to last screening test and between last two screening tests were statistically significant predictors of ovarian cancer. The chance of having ovarian cancer increased as velocity increased, and the chance decreased when the time intervals between baseline and the second to last screening test and between last two screening tests of an individual increased.

Two advanced methods for adjusting the main coefficient in logistic regression

A binary disease outcome is commonly modeled with continuous covariates (e.g., biochemical concentration) in medical research, and the corresponding exploration may employ a normal discrimination approach. The covariate relationship affects the estimated association between binary outcome and the interesting covariate. The method of value deviated from a fitted value (fractional polynomial), which is abbreviated as VDFV, may reduce the estimation bias especially when the relationship between the covariates is nonlinear. However, when the extraneous variable relates to the outcome, the pooled data (cases and controls) are replaced by the control data only for the purpose of fitting values. Based on two association patterns, the extraneous variable unrelated to the outcome (I) and that related to the outcome (II), the simulation study reveals that VDFV-p (using pooled data) is reliable, with less bias and a smaller mean square error (MSE) in pattern (I) and that VDFV-c (using control data) shows less bias in pattern (II). The conventional covariate adjustment performs worse in (I) but fairly well in (II). Note that a huge MSE is never observed in VDFV-p or VDFV-c, while this is a common issue related to small sample size or sparse data in logistic regression. Two fetal studies are illustrated--one for pattern (I) and one for pattern (II).

Optimal arbitrage under model uncertainty

In an equity market model with “Knightian” uncertainty regarding the relative risk and covariance structure of its assets, we characterize in several ways the highest return relative to the market that can be achieved using nonanticipative investment rules over a given time horizon, and under any admissible configuration of model parameters that might materialize. One characterization is in terms of the smallest positive supersolution to a fully nonlinear parabolic partial differential equation of the Hamilton–Jacobi–Bellman type. Under appropriate conditions, this smallest supersolution is the value function of an associated stochastic control problem, namely, the maximal probability with which an auxiliary multidimensional diffusion process, controlled in a manner which affects both its drift and covariance structures, stays in the interior of the positive orthant through the end of the time-horizon. This value function is also characterized in terms of a stochastic game, and can be used to generate an investment rule that realizes such best possible outperformance of the market.

What Can Genetic Studies of Left Ventricular Mass Tell Us?

Left ventricular hypertrophy (LVH), whether measured by electrocardiography, echocardiography, or at autopsy, is associated with increased risk of cardiovascular morbidity and mortality.1,2 Although LVH has been associated with several clinical characteristics such as age, sex, body mass index, and hypertension, the effects and their directionality have varied according to the criteria for LVH.3 Aggregation within families suggests a heritable component due to genetics,4 although a recent study of echocardiographic left ventricular mass in a large sample was unable to identify common genetic factors.5 Treatment of hypertensives with LVH can lead to its regression and with it reduction in risk of cardiovascular disease.6 Thus, it appears to be a modifiable risk factor, although LVH induced by exercise training is thought to be benign. Thus, LVH is a complex trait with diverse definitions, has multiple clinical and genetic contributors and is a modifiable risk factor in some but not all settings. Article see p 626 It is not surprising therefore that the (patho-) physiological mechanisms that underlie LVH remain incompletely characterized. Human genetics offers one opportunity to expose mechanisms not previously recognized to play a role in the development of LVH. Shah et al report in this issue of Circulation Cardiovascular Genetics a genetic screen to identify common genetic variants related to electrocardiographic correlates of left ventricular mass (ECG LVM): 2 reflecting simple voltage measures—Sokolow-Lyon voltage and 12-lead QRS voltage sum—and 2 incorporating QRS duration—Cornell product and QRS voltage product.7 The investigators genotyped gene-focused single-nucleotide polymorphisms (SNPs) found on the ITMAT-Broad-CARE (IBC) array, marketed by Illumina as the HumanCVD BeadChip 50K array, with ≈35 000 SNPs passing quality control. The array includes SNPs at 2100 candidate genes selected for being implicated in processes thought to be involved in cardiovascular disease as well as some …

Discretized and Aggregated: Modeling Dive Depth of Harbor Seals from Ordered Categorical Data with Temporal Autocorrelation

Ordered categorical data are pervasive in environmental and ecological data, and often arise from constraints that require discretizing a continuous variable into ordered categories. A great deal of data have been collected toward the study of marine mammal dive behavior using satellite depth recorders (SDRs), which often discretize a continuous variable such as depth. Additionally, data storage or transmission constraints may also necessitate the aggregation of data over time intervals of a specified length. The categorization and aggregation create a time series of ordered multicategory counts for each animal, which present challenges in terms of statistical modeling and practical interpretation. We describe an intuitive strategy for modeling such aggregated, ordered categorical data allowing for inference regarding the category probabilities and a measure of central tendency on the original scale of the data (e.g., meters), along with incorporation of temporal correlation and overdispersion. The strategy extends covariate-specific cutpoint models for ordinal data. We demonstrate the method in an analysis of SDR dive-depth data collected on harbor seals in Alaska. The primary goal of the analysis is to assess the relationship of covariates, such as time of day, with number of dives and maximum depth of dives. We also predict missing values and introduce novel graphical summaries of the data and results.

Risk Loci on Chromosome 8q24 are Associated With Prostate Cancer in Northern Chinese Men

Genome-wide association studies have identified several genetic variants at 8q24 that are strongly associated with prostate cancer risk in populations of European, American and Japanese ancestry. We investigated the contribution of these prostate cancer risk variants in the Chinese population. We evaluated the association of 14 single nucleotide polymorphisms at 8q24 with prostate cancer risk using high resolution melting curve combined gene sequencing methods in case-control groups, including 265 cases and 288 controls. We explored the association between clinical covariates (age at diagnosis, prostate specific antigen, Gleason score and tumor stage) and risk loci in our study to infer their impact on aggressive prostate cancer. Four of the 14 single nucleotide polymorphisms were associated with prostate cancer risk, including rs16901966 (OR 1.343, 95% CI 1.029-1.754, p = 0.030), rs1447295 (OR 1.499, 95% CI 1.109-2.027, p = 0.008), rs11986220 (OR 1.589, 95% CI 1.160-2.178, p = 0.004) and rs10090154 (OR 1.571, 95% CI 1.146-2.154, p = 0.005). Haplotype based association analysis of the risk alleles revealed significant differences between cases and controls. The risk alleles of rs16901966, rs1447295, rs11986220 and rs10090154 were associated with age at diagnosis and tumor stage compared with controls while rs16901966 was associated with aggressive prostate cancer (OR 1.538, 95% CI 1.076-2.099, p = 0.018). For northern Chinese men rs16901966, rs1447295, rs11986220 and rs10090154 at 8q24 (region 1, region 2) are associated with prostate cancer and prostate cancer related clinical covariates.

Using funnel plots in public health surveillance.

BackgroundPublic health surveillance is often concerned with the analysis of health outcomes over small areas. Funnel plots have been proposed as a useful tool for assessing and visualizing surveillance data, but their full utility has not been appreciated (for example, in the incorporation and interpretation of risk factors).MethodsWe investigate a way to simultaneously focus funnel plot analyses on direct policy implications while visually incorporating model fit and the effects of risk factors. Health survey data representing modifiable and nonmodifiable risk factors are used in an analysis of 2007 small area motor vehicle mortality rates in Alberta, Canada.ResultsSmall area variations in motor vehicle mortality in Alberta were well explained by the suite of modifiable and nonmodifiable risk factors. Funnel plots of raw rates and of risk adjusted rates lead to different conclusions; the analysis process highlights opportunities for intervention as risk factors are incorporated into the model. Maps based on funnel plot methods identify areas worthy of further investigation.ConclusionsFunnel plots provide a useful tool to explore small area data and to routinely incorporate covariate relationships in surveillance analyses. The exploratory process has at each step a direct and useful policy-related result. Dealing thoughtfully with statistical overdispersion is a cornerstone to fully understanding funnel plots.

Factors Associated with Insulin Resistance in a Middle-Aged Non-Obese Rural Population: The Chungju Metabolic Disease Cohort (CMC) Study

OBJECTIVESWe aimed to determine the characteristics affecting insulin resistance in non-obese middle-aged adults in a rural community.METHODSA total of 1,270 non-diabetic adults aged between 40 and 64 years old with body mass index (BMI) less than 25 kg/m2 were analyzed. Subjects with insulin resistance were defined as those who had the highest quartile value of the homeostasis model assessment of insulin resistance (HOMA-IR) in a non-diabetic population.RESULTSA total of 217 subjects (20.6%) had insulin resistance. Prevalence of metabolic syndrome was significantly higher in insulin-resistant subjects in both men (29.3% vs. 10.3%) and women (34.1% vs. 15.6%). Among metabolic syndrome components, elevated waist circumference and elevated triglyceride were higher in insulin-resistant subjects in both genders. After being controlled for socioeconomic status and lifestyle related covariates, the association between insulin resistance and BMI was statistically significant in the category of 23.0-24.9 kg/m2 in men (adjusted OR, 4.63; 95% confidence interval [95% CI], 1.77-12.15) using the category of 18.5-20.9 kg/m2 as a reference. In addition, the association between insulin resistance and abdominal obesity was statistically significant only for men (adjusted OR, 2.57; 95% CI, 1.29-5.11).CONCLUSIONInsulin resistance appears to be highly associated with high BMI and abdominal obesity, even in non-obese, non-diabetic middle-aged men.

Chronic Stress and Fatigue-Related Quality of Life After Mild to Moderate Traumatic Brain Injury

To determine relationships among chronic stress, fatigue-related quality of life (QOL-F), and related covariates after mild to moderate traumatic brain injury (TBI). Observational and cross-sectional. A total of 84 community-dwelling individuals with mild to moderate TBI recruited from multiple out patient rehabilitation clinics assessed on average 15 months after injury. Self-report surveys and chart abstraction. Neurofunctional Behavioral Inventory, Perceived Stress Scale-14, Impact of Events Scale, McGill Pain Short-form Scale, and modified version of the Fatigue Impact Scale. QOL-F was associated with somatic symptoms, perceived situational stress, but not with event-related stress (posttraumatic stress disorder symptoms) related to index TBI, preinjury demographic, or postinjury characteristics. Somatic symptoms and chronic situational stress accounted for 42% of the variance in QOL-F. QOL-F in community-dwelling individuals with mild to moderate TBI is associated with chronic situational stress and somatic symptoms. Symptom management strategies may need to include general stress management to reduce fatigue burden and improve quality of life.

Testing for stationarity using covariates: an application to purchasing power parity

We examine the evidence for Purchasing Power Parity (PPP) using post-Bretton Woods exchange rate data for 20 industrialized countries. The two tests used are the covariate tests for stationarity where the null hypothesis of stationarity is tested against the unit root alternative. These tests are generalizations of existing univariate stationarity tests and improve the power of univariate tests by utilizing the information contained in related stationary covariates. We conclude that PPP holds for 17 out of the 20 countries tested.

Air pollution and general practitioner access and utilization: a population based study in Sarnia, 'Chemical Valley,' Ontario

BackgroundHealth impacts of poor environmental quality have been identified in studies around the world and in Canada. While many of the studies have identified associations between air pollution and mortality or morbidity, few have focused on the role of health care as a potential moderator of impacts. This study assessed the determinants of health care access and utilization in the context of ambient air pollution in Sarnia, Ontario, Canada.MethodsResidents of Sarnia participated in a Community Health Study administered by phone, while several ambient air pollutants including nitrogen dioxide (NO2), sulphur dioxide (SO2) and the volatile organic compounds benzene, toluene, ethylbenzene, mp- and o-xylene (BTEX) were monitored across the city. Land Use Regression models were used to estimate individual exposures to the measured pollutants and logistic regression models were utilized to assess the relative influence of environmental, socioeconomic and health related covariates on general practitioner access and utilization outcomes.ResultsThe results show that general practitioner use increased with levels of exposure to nitrogen dioxide (NO2- Odds Ratio [OR]: 1.16, p < 0.05) and sulphur dioxide (SO2- OR: 1.61, p < 0.05). Low household income was a stronger predictor of having no family doctor in areas exposed to high concentrations of NO2 and SO2. Respondents without regular care living in high pollution areas were also more likely to report travelling or waiting for care in excess of 20 minutes (OR: 3.28, p < 0.05) than their low exposure counterparts (OR: 1.11, p > 0.05).ConclusionsThis study provides evidence for inequitable health care access and utilization in Sarnia, with particular relevance to its situation as a sentinel high exposure environment. Levels of exposure to pollution appears to influence utilization of health care services, but poor access to primary health care services additionally burden certain groups in Sarnia, Ontario, Canada.