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Mistry R, Bondarenko I, Jeon J, Brouwer AF, Mattingly DT, Hirschtick JL, Jimenez-Mendoza E, Levy DT, Land SR, Elliott MR, Taylor JMG, Meza R, Fleischer NL. Latent class analysis of use frequencies for multiple tobacco products in US adults. Prev Med 2021; 153:106762. [PMID: 34358593 PMCID: PMC8595688 DOI: 10.1016/j.ypmed.2021.106762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 07/23/2021] [Accepted: 07/30/2021] [Indexed: 10/20/2022]
Abstract
A persistent challenge is characterizing patterns of tobacco use in terms of product combinations and frequency. Using Wave 4 (2016-17) Population Assessment of Tobacco and Health Study adult data, we conducted latent class analyses (LCA) of past 30-day frequency of use for 9 tobacco products. One-step LCA with joint multinomial logistic regression models compared sociodemographic factors between users (n = 13,716) and non-users (n = 17,457), and between latent classes of users. We accounted for survey design and weights. Our analyses identified 6 classes: in addition to non-users (C0: 75.7%), we found 5 distinct latent classes of users: daily exclusive cigarette users (C1: 15.5%); occasional cigarette and polytobacco users (C2: 3.8%); frequent e-product and occasional cigarette users (C3: 2.2%); daily smokeless tobacco (SLT) and infrequent cigarette users (C4: 2.0%); and occasional cigar users (C5: 0.8%). Compared to C1: C2 and C3 had higher odds of being male (versus female), younger (especially 18-24 versus 55 years), and having higher education; C2 had higher, while C3 and C4 had lower, odds of being a racial/ethnic minority (versus Non-Hispanic White); C4 and C5 had much higher odds of being male (versus female) and heterosexual (versus sexual minority) and having higher income; and C5 had higher odds of college or more education. We identified three classes of daily or frequent users of a primary product (cigarettes, SLT or e-products) and two classes of occasional users (cigarettes, cigars and polytobacco). Sociodemographic differences in class membership may influence tobacco-related health disparities associated with specific patterns of use.
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Affiliation(s)
- Ritesh Mistry
- University of Michigan, Department of Health Behavior and Health Education, Ann Arbor, MI, United States of America.
| | - Irina Bondarenko
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, United States of America
| | - Jihyoun Jeon
- University of Michigan, Department of Epidemiology, Ann Arbor, MI, United States of America
| | - Andrew F Brouwer
- University of Michigan, Department of Epidemiology, Ann Arbor, MI, United States of America
| | - Delvon T Mattingly
- University of Michigan, Department of Epidemiology, Ann Arbor, MI, United States of America
| | - Jana L Hirschtick
- University of Michigan, Department of Epidemiology, Ann Arbor, MI, United States of America
| | - Evelyn Jimenez-Mendoza
- University of Michigan, Department of Epidemiology, Ann Arbor, MI, United States of America
| | - David T Levy
- Georgetown University, School of Medicine, Washington, DC, United States of America
| | - Stephanie R Land
- National Institutes of Health, National Cancer Institute, Bethesda, MD, United States of America
| | - Michael R Elliott
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, United States of America
| | - Jeremy M G Taylor
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, United States of America
| | - Rafael Meza
- University of Michigan, Department of Epidemiology, Ann Arbor, MI, United States of America
| | - Nancy L Fleischer
- University of Michigan, Department of Epidemiology, Ann Arbor, MI, United States of America
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Dolley S. Big Data's Role in Precision Public Health. Front Public Health 2018; 6:68. [PMID: 29594091 PMCID: PMC5859342 DOI: 10.3389/fpubh.2018.00068] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 02/20/2018] [Indexed: 01/01/2023] Open
Abstract
Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.
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