Eric A. Whitsel

Pharmacologic 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.

Atrial fibrillation (AF) is the most common cardiac arrhythmia seen in clinical practice, and makes an important contribution to cardiovascular disease (CVD) and all-cause mortality. The focus of AF research has recently shifted, from concentrating on treatments and complications, to the evaluation of putative risk factors including ambient air pollution. Although the present study pertains specifically to AF, much of its content is drawn from, and therefore is applicable to, the study of other arrhythmias, the conduct of which is confronted by many of the same challenges. Meeting these challenges involves recognising the collective importance of 1. large, ethnically and geographically diverse, clinically well-characterised populations; 2. methods for reducing uncertainty in outcome ascertainment, distinguishing effects of pervasive environmental exposures and improving their estimation; 3. approaches to evaluation of susceptibility; and 4. strategies for informing regulatory policies designed to help control population-level risks for CVD.

Metabolic neuropathophysiology underlying the prediabetic state may confer susceptibility to the adverse health effects of ambient particulate matter 10). The authors therefore examined whether impaired glucose homeostasis modifies the effect of PM₁₀ on heart rate variability in a stratified, random sample of 4,295 Women's Health Initiative clinical trial participants, among whom electrocardiograms and fasting blood draws were repeated at 3-year intervals from 1993 to 2004. In multilevel, mixed models weighted for sampling design and adjusted for clinical and environmental covariables, PM₁₀ exposure was inversely associated with heart rate variability. Inverse PM₁₀–heart rate variability associations were strongest for the root mean square of successive differences in normal-to-normal RR intervals (RMSSD). Among participants with impaired fasting glucose, there were −8.3% (95% confidence interval: −13.9, −2.4) versus −0.6% (95% confidence interval: −2.4, 1.3), −8.4% (95% confidence interval: −13.8, −2.7) versus −0.3% (95% confidence interval: −2.1, 1.6), and −4.3% (95% confidence interval: −9.4, 1.0) versus −0.8% (95% confidence interval: −2.7, 1.0) decreases in the RMSSD per 10-μg/m³ increase in PM₁₀ at high versus low levels of insulin (P < 0.01), insulin resistance (P < 0.01), and glucose (P = 0.16), respectively. These associations were stronger among participants with diabetes and weaker among those without diabetes or impaired fasting glucose. The findings suggest that insulin and insulin resistance exacerbate the adverse effect of PM₁₀ on cardiac autonomic control and thus risk of coronary heart disease among nondiabetic, postmenopausal women with impaired fasting glucose.

Geocoding methods vary among spatial epidemiology studies. Errors in the geocoding process and differential match rates may reduce study validity. We compared two geocoding methods using 8,157 Washington State addresses. The multi-stage geocoding method implemented by the state health department used a sequence of local and national reference files. The single-stage method used a single national reference file. For each address geocoded by both methods, we measured the distance between the locations assigned by each method. Area-level characteristics were collected from census data, and modeled as predictors of the discordance between geocoded address coordinates.

Background: Published studies of geocoding accuracy often focus on a single geographic area, address source or vendor, do not adjust accuracy measures for address characteristics, and do not examine effects of inaccuracy on exposure measures. We addressed these issues in a Women's Health Initiative ancillary study, the Environmental Epidemiology of Arrhythmogenesis in WHI. Results: Addresses in 49 U.S. states (n = 3,615) with established coordinates were geocoded by four vendors (A-D). There were important differences among vendors in address match rate (98%; 82%; 81%; 30%), concordance between established and vendor-assigned census tracts (85%; 88%; 87%; 98%) and distance between established and vendor-assigned coordinates (mean ρ [meters]: 1809; 748; 704; 228). Mean ρ was lowest among street-matched, complete, zip-coded, unedited and urban addresses, and addresses with North American Datum of 1983 or World Geodetic System of 1984 coordinates. In mixed models restricted to vendors with minimally acceptable match rates (A-C) and adjusted for address characteristics, within-address correlation, and among-vendor heteroscedasticity of ρ, differences in mean ρ were small for street-type matches (280; 268; 275), i.e. likely to bias results relying on them about equally for most applications. In contrast, differences between centroid-type matches were substantial in some vendor contrasts, but not others (5497; 4303; 4210) p_interaction < 10^-4, i.e. more likely to bias results differently in many applications. The adjusted odds of an address match was higher for vendor A versus C (odds ratio = 66, 95% confidence interval: 47, 93), but not B versus C (OR = 1.1, 95% CI: 0.9, 1.3). That of census tract concordance was no higher for vendor A versus C (OR = 1.0, 95% CI: 0.9, 1.2) or B versus C (OR = 1.1, 95% CI: 0.9, 1.3). Misclassification of a related exposure measure – distance to the nearest highway – increased with mean ρ and in the absence of confounding, non-differential misclassification of this distance biased its hypothetical association with coronary heart disease mortality toward the null. Conclusion: Geocoding error depends on measures used to evaluate it, address characteristics and vendor. Vendor selection presents a trade-off between potential for missing data and error in estimating spatially defined attributes. Informed selection is needed to control the trade-off and adjust analyses for its effects.

We therefore conducted a case-control study of PCA in a large prepaid health plan, Group Health Cooperative of Puget Sound. We included patients age 18–79 years who were enrolled for ≥1 year or had four or more clinic visits in the prior year, had physician-diagnosed diabetes noted in their ambulatory care medical record or were treated with oral hypoglycemics or insulin, and had an ECG recorded before their index date (see below). We excluded enrollees with prior physician-diagnosed heart disease (Table 1).

Background: Although current evidence suggests that the spatial T wave axis captures important information about ventricular repolarization abnormalities, there are only a few and discordant epidemiologic studies addressing the ability of the spatial T wave axis to predict coronary heart disease (CHD) occurrence. Methods: This prospective study analyzed data from 12,256 middle-aged African American and white men and women, from the Atherosclerosis Risk in Communities Study (ARIC). Following a standardized protocol, resting standard 12-lead, 10-second electrocardiograms were digitized and analyzed with the Marquette GE program. The median follow-up time was 12.1 years; incident coronary heart disease comprised fatal and non-fatal CHD events. Results: The incidence rate of CHD was 4.26, 4.18, 4.28 and 5.62 per 1000 person-years respectively, across the spatial T wave axis quartiles. Among women for every 10 degrees increase in the spatial T wave axis deviation, there was an estimated increase in the risk of CHD of 1.16 (95% CI 1.04–1.28). After adjustment for age, height, weight, smoking, hypertension, diabetes, QRS axis and minor T wave abnormalities, this hazard rate ratio for women fell to 1.03 (0.92–1.14). The corresponding crude and adjusted hazard ratios for men were 1.05 (95% CI 0.96–1.15) and 0.95 (0.86–1.04) respectively. Conclusions: In conclusion, this prospective, population-based, bi-ethnic study of men and women free of coronary heart disease at baseline shows that spatial T wave axis deviation is not associated with incident coronary events during long-term follow up. It is doubtful that spatial T wave axis deviation would add benefit in the prediction of CHD events above and beyond the current traditional risk factors.

The authors estimated accuracy and repeatability of commercial geocoding to guide vendor selection in the Life Course Socioeconomic Status, Social Context and Cardiovascular Disease study (2001–2002). They submitted 1,032 participant addresses (97% in Maryland, Minnesota, Mississippi, or North Carolina) to vendor A twice over 9 months and measured repeatability as agreement between levels of address matching, discordance (%) between statistical tabulation areas, and median distance (d, in meters) and bearing (θ, in degrees) between coordinates assigned on each occasion (H_o:Σ_{i = 1 →n} [θ_i /n] = 180°). They also submitted 75 addresses of nearby air pollution monitors (77% urban/suburban; 69% residential/commercial) to vendors A and B and then measured accuracy by comparing vendor- and US Environmental Protection Agency (EPA)–assigned geocodes using the above measures. Repeatability of geocodes assigned by vendor A was high (kappa = 0.90; census block group discordance = 5%; d < 1 m; θ = 177°). The match rate for EPA monitor addresses was higher for vendor B versus A (88% vs. 76%), but discordance at census block group, tract, and county levels also was, respectively, 1.4-, 1.9-, and 5.0-fold higher for vendor B. Moreover, coordinates assigned by vendor B were further from those assigned by the EPA (d = 212 m vs. 149 m; θ = 131° vs. 171°). These findings suggest that match rates, repeatability, and accuracy should be used to guide vendor selection.