High Level of Selenium Exposure in the Strong Heart Study: A Cause for Incident Cardiovascular Disease?

Increasing evidence suggests that high selenium (Se) exposure is associated with adverse health effects. However, limited evidence exists on the association of Se exposure with cardiovascular disease (CVD), especially in communities affected by high naturally occurring Se in environmental media. We evaluated the prospective association between urinary Se levels and CVD incidence and mortality for 2727 American Indian adults who participated in the Strong Heart Study, with urinary Se levels measured at baseline (1989-1991) and CVD outcomes ascertained through 2017. The median (interquartile range) of urinary Se was 49.0 (36.7-67.4) µg/g creatinine. The multivariable adjusted hazard ratios (95% confidence interval) of incident CVD, coronary heart disease, and stroke comparing the 75th versus 25th percentile of urinary Se distributions were 1.11 (1.01-1.22), 1.05 (0.94-1.17), and 1.08 (0.88-1.33), respectively. In flexible dose-response models, increased risk for CVD incidence was only observed when the urinary Se level exceeded 60 µg/g creatinine. For CVD mortality, a nonstatistically significant U-shaped relationship was found across urinary Se levels. There was no evidence of effect modification by other urinary metal/metalloid levels. Our observation leads to the hypothesis that elevated Se exposure is a risk factor for CVD, especially in Se-replete populations. Antioxid. Redox Signal. 37, 990-997.

Risk factors for Lyme disease stage and manifestation using electronic health records.

BACKGROUND: Little is known about risk factors for early (e.g., erythema migrans) and disseminated Lyme disease manifestations, such as arthritis, neurological complications, and carditis. No study has used both diagnoses and free text to classify Lyme disease by disease stage and manifestation. METHODS: We identified Lyme disease cases in 2012-2016 in the electronic health record (EHR) of a large, integrated health system in Pennsylvania. We developed a rule-based text-matching algorithm using regular expressions to extract clinical data from free text. Lyme disease cases were then classified by stage and manifestation using data from both diagnoses and free text. Among cases classified by stage, we evaluated individual, community, and health care variables as predictors of disseminated stage (vs. early) disease using Poisson regression models with robust errors. Final models adjusted for sociodemographic factors, receipt of Medical Assistance (i.e., Medicaid, a proxy for low socioeconomic status), primary care contact, setting of diagnosis, season of diagnosis, and urban/rural status. RESULTS: Among 7310 cases of Lyme disease, we classified 62% by stage. Overall, 23% were classified using both diagnoses and text, 26% were classified using diagnoses only, and 13% were classified using text only. Among the staged diagnoses (n = 4530), 30% were disseminated stage (762 arthritis, 426 neurological manifestations, 76 carditis, 95 secondary erythema migrans, and 76 other manifestations). In adjusted models, we found that persons on Medical Assistance at least 50% of time under observation, compared to never users, had a higher risk (risk ratio [95% confidence interval]) of disseminated Lyme disease (1.20 [1.05, 1.37]). Primary care contact (0.59 [0.54, 0.64]) and diagnosis in the urgent care (0.22 [0.17, 0.29]), compared to the outpatient setting, were associated with lower risk of disseminated Lyme disease. CONCLUSIONS: The associations between insurance payor, primary care status, and diagnostic setting with disseminated Lyme disease suggest that lower socioeconomic status and less health care access could be linked with disseminated stage Lyme disease. Intervening on these factors could reduce the individual and health care burden of disseminated Lyme disease. Our findings demonstrate the value of both diagnostic and narrative text data to identify Lyme disease manifestations in the EHR.

Obstacles to diagnosis and treatment of Lyme disease in the USA: a qualitative study.

OBJECTIVE: For many individuals with Lyme disease, prompt treatment leads to rapid resolution of infection. However, severe complications can occur if treatment is delayed. Our objective was to identify themes around belated diagnosis or treatment of Lyme disease using the General Model of Total Patient Delay (GMTPD). DESIGN: We conducted a qualitative interview study using indepth telephone interviews. SETTING: Participants were patients from a large, integrated health system in the state of Pennsylvania, USA. PARTICIPANTS: There were 26 participants. Participants had to have a diagnosis of Lyme disease between 2014 and 2017 and a positive IgG western blot. We used a stratified purposeful sampling design to identify patients with and without late Lyme disease manifestations. To ensure variation in care experiences, we oversampled patients diagnosed outside of primary care. OUTCOME MEASURES: We asked participants about their experience from first Lyme disease symptoms to treatment. We applied an iterative coding process to identify key themes and then synthesised codes into higher order codes representing the GMTPD stages: appraisal delay (symptom to recognition of illness); illness delay (inferring illness to deciding to seek help); behavioural delay (deciding to seek help to the act of seeking help); scheduling delay (seeking help to attending an appointment); and treatment delay (attending appointment to treatment). RESULTS: Appraisal delay themes included symptom misattribution, intermittent symptoms and misperceptions about the necessity of a bull's-eye rash. Health insurance status was a driver of illness and behavioural delays. Scheduling delay was not noted by participants, in part, because 10 of the 26 patients went to urgent care or emergency department settings. Misdiagnoses were more common in these settings, contributing to treatment delay. CONCLUSION: Our study identified potentially modifiable risk factors for belated treatment. Targeting these risk factors may minimise time to treatment and reduce the occurrence of preventable complications.