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Hypertension (HT) is a common chronic disease that often causes target-organ damage and severe complications, contributing to cardiovascular morbidity and mortality worldwide. Accumulating evidence suggests that inflammation plays a prominent role in the initiation and progression of HT. Multiple inflammatory biomarkers have been proposed to predict HT. Several new hematological parameters can reflect the inflammatory response and platelet activation. The major advantage of hematological parameters over conventional inflammatory markers is that they are relatively inexpensive and easily obtained from routine blood tests. Numerous studies have investigated several hematological parameters for their utility as predictive biomarkers for the diagnosis and prognosis of HT. Among them, the neutrophil to lymphocyte ratio (NLR), monocyte to high density lipoprotein cholesterol ratio (MHR), red cell distribution width (RDW), platelet to lymphocyte ratio (PLR), mean platelet volume (MPV), platelet distribution width (PDW), and systemic immune-inflammation index (SII) have recently received attention. We searched PubMed and Embase databases (up to September 18, 2022) to assess the relationships between hematological parameters and HT. This review discusses the diagnostic and prognostic value of these hematological parameters in HT, providing an important basis for early screening, risk stratification, and optimal management of hypertensive patients. [ABSTRACT FROM AUTHOR]

Respondent-driven sampling (RDS) is a method for recruiting “hidden” populations through a network-based, chain and peer referral process. RDS recruits hidden populations more effectively than other sampling methods and promises to generate unbiased estimates of their characteristics. RDS’s faithful representation of hidden populations relies on the validity of core assumptions regarding the unobserved referral process. With empirical recruitment data from an RDS study of female sex workers (FSWs) in Shanghai, we assess the RDS assumption that participants recruit nonpreferentially from among their network alters. We also present a bootstrap method for constructing the confidence intervals around RDS estimates. This approach uniquely incorporates real-world features of the population under study (e.g., the sample’s observed branching structure). We then extend this approach to approximate the distribution of RDS estimates under various peer recruitment scenarios consistent with the data as a means to quantify the impact of recruitment bias and of rejection bias on the RDS estimates. We find that the hierarchical social organization of FSWs leads to recruitment biases by constraining RDS recruitment across social classes and introducing bias in the RDS estimates. [ABSTRACT FROM PUBLISHER]