Association of ambient air pollution with cardiovascular disease risks in people with type 2 diabetes: a Bayesian spatial survival analysis.

BACKGROUND: Evidence is limited on excess risks of cardiovascular diseases (CVDs) associated with ambient air pollution in diabetic populations. Survival analyses without considering the spatial structure and possible spatial correlations in health and environmental data may affect the precision of estimation of adverse environmental pollution effects. We assessed the association between air pollution and CVDs in type 2 diabetes through a Bayesian spatial survival approach. METHODS: Taiwan's national-level health claims and air pollution databases were utilized. Fine individual-level latitude and longitude were used to determine pollution exposure. The exponential spatial correlation between air pollution and CVDs was analyzed in our Bayesian model compared to traditional Weibull and Cox models. RESULTS: There were 2072 diabetic patients included in analyses. PM2.5 and SO2 were significant CVD risk factors in our Bayesian model, but such associations were attenuated or underestimated in traditional models; adjusted hazard ratio (HR) and 95% credible interval (CrI) or confidence interval (CI) of CVDs for a 1 µg/m3 increase in the monthly PM2.5 concentration for our model, the Weibull and Cox models was 1.040 (1.004-1.073), 0.994 (0.984-1.004), and 0.994 (0.984-1.004), respectively. With a 1 ppb increase in the monthly SO2 concentration, adjusted HR (95% CrI or CI) was 1.886 (1.642-2.113), 1.092 (1.022-1.168), and 1.091 (1.021-1.166) for these models, respectively. CONCLUSIONS: Against traditional non-spatial analyses, our Bayesian spatial survival model enhances the assessment precision for environmental research with spatial survival data to reveal significant adverse cardiovascular effects of air pollution among vulnerable diabetic patients.

Atosiban and Pregnancy Outcomes Following In Vitro Fertilization Treatment for Infertile Women Requiring One, Two, or More Embryo Transfer Cycles: A Longitudinal Cohort Study.

We assessed the effect of atosiban on pregnancy outcomes following in vitro fertilization (IVF) treatment among infertile women requiring different numbers of embryo transfer (ET) cycles (i.e., one, two, and more than two ET cycles). A longitudinal cohort study was conducted by utilizing the data from the Assisted Reproductive Technology Center in a university tertiary hospital during 2007-2017. Patients receiving IVF treatment with at least one ET cycle were included. Pregnancy outcomes following IVF treatment, including biochemical, clinical, and ongoing pregnancies, were investigated. The association between atosiban and IVF pregnancy was assessed using logistic generalized estimating equation models, with adjustment for time-varying clinical characteristics (e.g., maternal age) across multiple ET cycles for an individual. 403 women with 838 ET cycles were included, where 165 patients required one ET cycle, 133 patients required two ET cycles (a total of 266 ET cycles), and 105 patients required more than two ET cycles (a total of 407 ET cycles). Atosiban use was not significantly associated with pregnancy outcomes among all study infertile women undergoing IVF treatment. However, the results for women requiring more than two ET cycles showed significantly increased pregnancy rates associated with atosiban use (i.e., odds ratios [95% confidence interval] of 4.40 [1.52, 12.73] and 2.85 [1.45, 5.60] for clinical and ongoing pregnancies, respectively). This association was not observed for the women requiring only one or two ET cycles. Atosiban is a potential treatment for enhancing IVF pregnancy, especially among infertile women requiring more than two ET cycles.