Literature DB >> 29036366

Accounting for Selectivity Bias and Correlation Across the Sequence From Elevated Blood Pressure to Hypertension Diagnosis and Treatment.

Penny Gordon-Larsen1,2, Samantha M Attard1,2, Annie Green Howard3, Barry M Popkin1,2, Bing Zhang4, Shufa Du1,2, David K Guilkey2,5.   

Abstract

BACKGROUND: It is unknown whether efforts to reduce hypertension burden in countries with very high prevalence, would be more effective if directed at hypertension diagnosis vs. treatment. Most analyses do not address bias and correlation across the sequence from elevated blood pressure (BP) to hypertension diagnosis and treatment, leading to potentially misleading findings.
METHODS: Using data spanning 18 years of the China Health and Nutrition Survey (n = 18,926; ages 18-75 years), we used an innovative 3-step, integrated system of equations to predict the sequence from: (i) elevated BP (systolic/diastolic BP ≥ 140/90 mm Hg) to (ii) diagnosed hypertension conditional on elevated BP, and to (iii) treatment (medication use) conditional on diagnosis, accounting for measured and unmeasured individual- and community-level confounders at each of the 3 steps. We compared results to separate traditional logistic regression models without control for unmeasured confounding.
RESULTS: Using our 3-step model, elevated BP increased from 12.6% and 8.5% (1991) to 36.8% and 29% (2009) in men and women, respectively, but diagnosis remained under 50%. We found widening disparities in hypertension diagnosis (higher hypertension at lower vs. higher education (difference of 2% in 1991 that widened to 5% in 2009)) and narrowing disparities in education (difference of 6% in 1991 to 4% in 2009) and insurance status (difference of 7% in 1991 to 2% in 2009) for treatment.
CONCLUSIONS: Our 3-step model improved model fit over traditionally used models. Our findings highlight serious barriers to hypertension diagnosis in Chinese adults, particularly among men and individuals of low attained education. © American Journal of Hypertension, Ltd 2017. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Entities:  

Keywords:  China; health infrastructure; health insurance; urbanization

Mesh:

Year:  2017        PMID: 29036366      PMCID: PMC5861577          DOI: 10.1093/ajh/hpx137

Source DB:  PubMed          Journal:  Am J Hypertens        ISSN: 0895-7061            Impact factor:   2.689


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