Association between Non-Cigarette/Smokeless Tobacco and Hypertension in the National Health Interview Survey: A Pseudo-Panel Analysis

Abstract

This study sought to examine assumption between having ever used noncigarette tobacco or smokeless tobacco, and a diagnosis of hypertension among a sample of 13, 086 United States adults participating in the National Health Interview Series from 2012-2014. A pseudo-panel analysis of data extracted from the Integrated Health Interview Series Survey was conducted. The generalized linear mixed model was used to quantify the effect of a history of non-cigarette tobacco, smokeless tobacco, and socio-demographic predictor variables on the response variable, a diagnosis of hypertension. The transformed data, based on the pseudo-panel technique, resulted in fiftyseven (57) birth cohorts and followed in 2012, 2013, and 2014. The mean age was 51.6 years (±12.4). The findings of this study revealed that the odds of hypertension diagnosis for non-cigarette tobacco users was 0.8846 times lower (95% CI: 0.7907, 0.9896) than non-users after adjusting for possible confounders such as age, language, education, income and years of smoking. Our study suggested that the association between the use of non-cigarette tobacco and the diagnosis of hypertension among the sample population is consistent enough to assume a less plausible association between the two variables.

The health impact of smokeless tobacco products: a systematic review

“…Of 15 studies reporting on SLT-product data from USA [ 10 – 14 , 42 – 50 , 53 ], eight were exclusively in USA [ 42 – 46 , 48 – 50 , 53 ].…”

“…A ‘poor’-rated meta-analysis reported no association between SLT-products and stroke in US data [ 11 ]. A US cross-sectional study reported lower self-reported hypertension (OR = 0.88; 95% CI 0.79–0.98) in SLT-product users (adjusted for smoking status and duration) [ 48 ].…”

Plateletpheresis Donation Trend and a Ban on Family/Replacement Donation in China: A Pseudo-Panel Data Approach Analysis

“…The pseudo-panel data approach is actually a solution to transform the individual-level cross-sectional data into the group-level data (i.e., pseudo-panel data)such that the typical longitudinal models can be applied to e ciently and consistently estimate the change of the interested outcome variable over time. [13] This approach has been increasingly applied to public health [14,15] . According to the methods described in the literature [16,17], we constructed two pseudo-panel datasets from our discovery and replicate data by grouping three time-invariant variables -gender, birth year, and blood donation history.…”

“…Our study was actually an observational study using pseudo-panel approach to analysis the data [13][14][15]. Thus, we used the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement to ensure standardisation and enhance the quality of the reporting [18].…”

“…For overall GZ pseudo-panel set, we plotted outcome values (i.e., average apheresis platelet units per donor per cell) from each cohort group that had common gender, birth year, and blood donation history versus time (i.e., 14 cross-sections) as well as overall average outcome values from all 24 cohort groups versus time to visualize whether the trend of the outcome over time is linear or non-linear. Given that generalized linear mixed model and mixed-generalized ordered logit model have been successfully applied to analyze the trend of the outcome variable (proportion or probability) over time within a pseudo-panel dataset [14,19], for our pseudo-panel dataset with a continuous outcome variable, we applied a linear mixed model for modelling the linear trend, or modelling the nonlinear trend using a piecewise linear mixed model with the de ned time (i.e., cross-section) breakpoint(s) based on the above-mentioned visualization [20]. We conducted a model selection starting from an unadjusted model (i.e., model 1 -pure time trend model without any covariates) to an adjusted model (i.e., model 2 = model 1 + signi cant covariates), and then to a nal model (i.e., model 3 = model 2 + signi cant interaction terms).…”