A study of the association between atmospheric particulate matter and blood pressure in the population.

This systematic review and meta-analysis was conducted to assess the association between the level of atmospheric particulate matter (PM) and the increase in blood pressure (BP) for different exposure terms (≤ 7 vs > 7 days) and age groups (< 65 vs ≥ 65 years). Some databases were searched to investigate the association between increased atmospheric PM (diameter < 2.5 mm [PM2.5] or < 10 mm [PM10]) and BP (systolic blood pressure [SBP] and diastolic blood pressure [DBP]). Among a total of 719 identified articles, 68 were reviewed in depth, of which only 20 satisfied the inclusion criteria. A significant association was found between PM10 levels and higher BP. The ß values were 0.270mmHg (95% confidence interval [CI] 0.068-0.482) for SBP and 0.215mmHg (95% CI 0.058-0.372) for DBP. These ß values mean that, for every 10 mg/m(3) increase in PM10, SBP increased by 0.270mmHg and DBP by 0.215 mmHg. Subgroup analyses were conducted for different exposure terms and age groups. A positive association was seen between PM2.5 and SBP. The ß value of SBP was 0.495mmHg (95% CI 0.03-0.96) with every 10 mg/m(3) increase in PM2.5. There were no significant associations in both age groups and non-older groups. There was no significant association between PM2.5 and DBP, either in the overall effect or in the subgroup effects. In conclusion, significant associations were found between higher BP and higher PM10 levels, but the association between BP and levels of PM2.5 levels was unclear.

Spatial-Temporal Epidemiology of Tuberculosis in Mainland China: An Analysis Based on Bayesian Theory.

OBJECTIVE: To explore the spatial-temporal interaction effect within a Bayesian framework and to probe the ecological influential factors for tuberculosis. METHODS: Six different statistical models containing parameters of time, space, spatial-temporal interaction and their combination were constructed based on a Bayesian framework. The optimum model was selected according to the deviance information criterion (DIC) value. Coefficients of climate variables were then estimated using the best fitting model. RESULTS: The model containing spatial-temporal interaction parameter was the best fitting one, with the smallest DIC value (-4,508,660). Ecological analysis results showed the relative risks (RRs) of average temperature, rainfall, wind speed, humidity, and air pressure were 1.00324 (95% CI, 1.00150-1.00550), 1.01010 (95% CI, 1.01007-1.01013), 0.83518 (95% CI, 0.93732-0.96138), 0.97496 (95% CI, 0.97181-1.01386), and 1.01007 (95% CI, 1.01003-1.01011), respectively. CONCLUSIONS: The spatial-temporal interaction was statistically meaningful and the prevalence of tuberculosis was influenced by the time and space interaction effect. Average temperature, rainfall, wind speed, and air pressure influenced tuberculosis. Average humidity had no influence on tuberculosis.