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      Modeling of smoking intensity by age at smoking onset among Iranian adult male using generalized additive model

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          Abstract

          Because the age at which a person first starts smoking has such a strong correlation with future smoking behaviours, it's crucial to examine its relationship with smoking intensity. However, it is still challenging to accurately prove this relationship due to limitations in the methodology of the performed studies. Therefore the main purpose of this study is to evaluate the potential risk factors affecting the intensity of smoking, especially the age of smoking onset among Iranian adult male smokers over 18 years of age using a generalized additive model (GAM). In GAM a latent variable with logistic distribution and identity link function was considered. Data from 913 Iranian male current smokers over the age of 18 was evaluated from a national cross-sectional survey of non-communicable disease (NCD) risk factors in 2016. Individuals were classified into: light, moderate, and heavy smokers. A GAM was used to assess the relationship. The results showed that 246 (26.9%) subjects were light smokers, 190 (20.8%) subjects were moderate smokers and 477 (52.2%) subjects were heavy smokers. According to the GAM results, the relationship was nonlinear and smokers who started smoking at a younger age were more likely to become heavy smokers. The factors of unemployment (OR = 1.364, 95% CI 0.725–2.563), retirement (OR = 1.217, 95% CI 0.667–2.223), and exposure to secondhand smoke at home (OR = 1.364, 95% CI 1.055–1.763) increased the risk of heavy smoking. but, smokers with high-income (OR = 0.742, 95% CI 0.552–0.998) had a low tendency to heavy smoking. GAM identified the nonlinear relationship between the age of onset of smoking and smoking intensity. Tobacco control programs should be focused on young and adolescent groups and poorer socio-economic communities.

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          Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

          Summary Background Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk–outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk–outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk–outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51–12·1) deaths (19·2% [16·9–21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12–9·31) deaths (15·4% [14·6–16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253–350) DALYs (11·6% [10·3–13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0–9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10–24 years, alcohol use for those aged 25–49 years, and high systolic blood pressure for those aged 50–74 years and 75 years and older. Interpretation Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Funding Bill & Melinda Gates Foundation.
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            Early smoking initiation and nicotine dependence in a cohort of young adults.

            We examined the extent to which nicotine dependence and daily smoking might vary by age at first cigarette. The potential confounding effects of sex, race and history of childhood behaviour problems were examined as well. A sample of 1200 was randomly selected from the subset of 21-30-year-old members of a large HMO in the Detroit SMSA; 1007 (84%) agreed to participate. Personal interviews were conducted in respondents' homes, using the NIMH-DIS to elicit information on DSM-III-R diagnoses, including nicotine dependence. Controlling for sex and race, persons who smoked their first cigarette at 14 to 16 years of age were 1.6 times more likely to become dependent than those who initiated smoking at an older age (P = 0.03). The association was unchanged when history of childhood behaviour problems was also controlled. Smoking initiation before age 14 was not associated with increased probability of dependence. Persons who initiated smoking before age 14 had a longer lag time to daily smoking and a lower likelihood of progressing to daily smoking, compared to persons who initiated smoking later on. The findings suggest that, among persons who have ever smoked, there might be two distinct groups in whom the chances of developing dependence are considerably reduced. The first comprises persons who delayed first use until age 17. The second comprises persons who smoked their first cigarette before age 14, a group in whom the progression to daily smoking might be markedly slower than in persons who initiated smoking when they were older.
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              Income, occupation and education: Are they related to smoking behaviors in China?

              Background The association between socioeconomic status (SES) and smoking behaviors may differ across countries. This study aimed to estimate the association between socioeconomic status (income, occupation and education) and multiple measures of smoking behaviors among the Chinese elderly population. Methods Using data from the China Health and Retirement Longitudinal Study in 2013, we examined the relationship between socioeconomic status and smoking behaviors through multivariate regression analysis. Sample selection models were applied to correct for sample selection bias. Smoking behaviors were measured by four indicators: smoking status, cigarette consumption, health risks related to smoking, and smoking dependence. Analyses were stratified by gender and urban-rural residence. Results Among Chinese people aged 45 years or older, smokers accounted for 40% of the population in 2013, smoking 19 cigarettes per day. It was also found that 79% of smokers were at an increased health risk. Overall, although the influence of income on smoking behaviors was small and even insignificant, occupation and education levels were significantly associated with smoking behaviors. Managers or professionals were more likely to smoke, however there was no significant relationship with smoking dependence. Individuals with higher educational attainment were less likely to be associated with smoking behaviors. In addition, gender and urban-rural differences existed in the relationship between SES and smoking behaviors. Conclusions Smoking disparities among diverse levels of socioeconomic status existed but varied greatly by SES indicators and population characteristics. Tobacco control policies in China should be increasingly focused on populations with low socioeconomic status in order to break the link between socioeconomic disadvantage and smoking behaviors. Further actions should mitigate inequalities in education, improve the social culture of cigarette use, and tailor interventions based on characteristics of the population.
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                Author and article information

                Contributors
                moghimb@yahoo.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                6 October 2022
                6 October 2022
                2022
                : 12
                : 16700
                Affiliations
                [1 ]GRID grid.411950.8, ISNI 0000 0004 0611 9280, Department of Biostatistics, Student Research Committee, , Hamadan University of Medical Sciences, ; Hamadan, Iran
                [2 ]GRID grid.411950.8, ISNI 0000 0004 0611 9280, Modeling of Noncommunicable Diseases Research Center, Department of Biostatistics, School of Public Health, , Hamadan University of Medical Sciences, ; Hamadan, Iran
                [3 ]GRID grid.411705.6, ISNI 0000 0001 0166 0922, Department of Biostatistics and Epidemiology, Faculty of Health & Health, Safety and Environment Research Center, , Alborz University of Medical Sciences, ; Karaj, Iran
                Article
                21194
                10.1038/s41598-022-21194-4
                9537518
                36202896
                485f8afb-9335-4b69-9133-d856ba40dfa9
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 December 2021
                : 23 September 2022
                Funding
                Funded by: Vice-chancellor for Research and Technology, Hamadan University of Medical Sciences
                Award ID: 9904031998
                Award ID: 9904031998
                Award ID: 9904031998
                Award Recipient :
                Categories
                Article
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                © The Author(s) 2022

                Uncategorized
                health care,medical research,risk factors
                Uncategorized
                health care, medical research, risk factors

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