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Cardiometabolic diseases have substantially increased in China in the past 20years and blood pressure is a primary modifiable risk factor. Using data fromthe China Health and Nutrition Survey, we examine blood pressure trends inChina from 1991 to 2009, with a concentration on age cohorts and urbanicity.Very large values of blood pressure are of interest, so we model theconditional quantile functions of systolic and diastolic blood pressure. Thisallows the covariate effects in the middle of the distribution to vary fromthose in the upper tail, the focal point of our analysis. We join thedistributions of systolic and diastolic blood pressure using a copula, whichpermits the relationships between the covariates and the two responses to shareinformation and enables probabilistic statements about systolic and diastolicblood pressure jointly. Our copula maintains the marginal distributions of thegroup quantile effects while accounting for within-subject dependence, enablinginference at the population and subject levels. Our population-level regressioneffects change across quantile level, year and blood pressure type, providing arich environment for inference. To our knowledge, this is the first quantilefunction model to explicitly model within-subject autocorrelation and is thefirst quantile function approach that simultaneously models multivariateconditional response. We find that the association between high blood pressureand living in an urban area has evolved from positive to negative, with thestrongest changes occurring in the upper tail. The increase in urbanizationover the last twenty years coupled with the transition from the positiveassociation between urbanization and blood pressure in earlier years to a moreuniform association with urbanization suggests increasing blood pressure overtime throughout China, even in less urbanized areas. Our methods are availablein the R package BSquare.

Quantile regression for mixed models with an application to examine blood pressure trends in China