ABSTRACT
Background@#Numerous studies have shown the effect of particulate matter exposure on brain imaging markers. However, little evidence exists about whether the effect differs by the level of low-grade chronic systemic inflammation. We investigated whether the level of c-reactive protein (CRP, a marker of systemic inflammation) modifies the associations of particulate matter exposures with brain cortical gray matter thickness and white matter hyperintensities (WMH). @*Methods@#We conducted a cross-sectional study of baseline data from a prospective cohort study including adults with no dementia or stroke. Long-term concentrations of particulate matter ≤ 10 µm in diameter (PM10) and ≤ 2.5 µm (PM2.5) at each participant’s home address were estimated. Global cortical thickness (n = 874) and WMH volumes (n = 397) were estimated from brain magnetic resonance images. We built linear and logistic regression models for cortical thickness and WMH volumes (higher versus lower than median), respectively. Significance of difference in the association between the CRP group (higher versus lower than median) was expressed as P for interaction. @*Results@#Particulate matter exposures were significantly associated with a reduced global cortical thickness only in the higher CRP group among men (P for interaction = 0.015 for PM10 and 0.006 for PM2.5). A 10 μg/m3 increase in PM10 was associated with the higher volumes of total WMH (odds ratio, 1.78; 95% confidence interval, 1.07–2.97) and periventricular WMH (2.00; 1.20–3.33). A 1 μg/m3 increase in PM2.5 was associated with the higher volume of periventricular WMH (odds ratio, 1.66; 95% confidence interval, 1.08–2.56). These associations did not significantly differ by the level of high sensitivity CRP. @*Conclusion@#Particulate matter exposures were associated with a reduced global cortical thickness in men with a high level of chronic inflammation. Men with a high level of chronic inflammation may be susceptible to cortical atrophy attributable to particulate matter exposures.
ABSTRACT
Background@#Although influenza poses substantial mortality burden, most studies have estimated excess mortality using time-aggregated data. Here, we estimated mortality risk and population attributable fraction (PAF) attributed to seasonal influenza using individual-level data from a nationwide matched cohort. @*Methods@#Individuals with influenza during four consecutive influenza seasons (2013–2017) (n = 5,497,812) and 1:4 age- and sex-matched individuals without influenza (n = 20,990,683) were identified from a national health insurance database. The endpoint was mortality within 30 days after influenza diagnosis. All-cause and cause-specific mortality risk ratios (RRs) attributed to influenza were estimated. Excess mortality, mortality RR, and PAF of mortality were determined, including for underlying disease subgroups. @*Results@#Excess mortality rate, mortality RR, and PAF of all-cause mortality were 49.5 per 100,000, 4.03 (95% confidence interval [CI], 3.63–4.48), and 5.6% (95% CI, 4.5–6.7%). Cause-specific mortality RR (12.85; 95% CI, 9.40–17.55) and PAF (20.7%; 95% CI, 13.2– 27.0%) were highest for respiratory diseases. In subgroup analysis according to underlying disorders, PAF of all-cause mortality was 5.9% (95% CI, 0.6–10.7%) for liver disease, 5.8% (95% CI, 2.9–8.5%) for respiratory disease, and 3.8% (95% CI, 1.4–6.1%) for cancer. @*Conclusion@#Individuals with influenza had a 4-fold higher mortality risk than individuals without influenza. Preventing seasonal influenza may lead to 5.6% and 20.7% reductions in all-cause and respiratory mortality, respectively. Individuals with respiratory disease, liver disease, and cancer may benefit from prioritization when establishing influenza prevention strategies.
ABSTRACT
40 years or new hires with less than 1 year of service.