Predictors of quitting support from nonsmoking mothers for smoking fathers: a cross-sectional study from Chinese pupils' families.

BACKGROUND: Quitting support from smokers' partners can predict quit attempts and smoking abstinence but research on factors that predict such support has been limited. To add more evidence for partner support and the improved interventions for smoking cessation, we analyzed some new potential predictors of quitting support from smokers' spouses. METHOD: This cross-sectional study was conducted in in 2022 and 2023, selecting the students' families in which fathers smoked and mothers didn't smoke from grade 1-5 of 13 primary schools in Qingdao, China. Parents who met the criteria completed the online questionnaires and 1018 families were included in the analysis. We measured personal information related to smokers and their spouses such as age, education and nicotine dependence, and variables related to family and marital relationship such as family functioning, perceived responsiveness and power in decision-making of quitting smoking. Quitting support from smokers' spouses was measured by Partner Interaction Questionnaire and generalized linear model was used to explore the potential predictors of partner support. RESULTS: In this study, the mean age of smokers was 39.97(SD = 5.57) and the mean age of smokers' spouses was 38.24(SD = 4.59). The regression analysis showed that for smokers and their spouses, the older age groups showed the lower ratio of positive/negative support(P < 0.05) and smokers with high education showed the less positive and negative partner support(P < 0.05). Nicotine dependence was positively associated with negative support (ß = 0.120, P < 0.01), and perceived responsiveness (ß = 0.124, P < 0.05) as well as family functioning (ß = 0.059, P < 0.05) was positively associated with positive support. These three factors were associated with ratio of positive/negative support(P < 0.05). In addition, power of smoker's spouse in decision-making of quitting smoking was positively associated with the positive (ß = 0.087, P < 0.001) and negative support (ß = 0.084, P < 0.001). CONCLUSIONS: Nicotine dependence, family functioning, power in decision-making of quitting smoking and perceived responsiveness were found to be the predictors of quitting support from smokers' spouses. By incorporating predictors of partner support and integrating some established theories that can improve family functioning and marital relationships, smoking cessation interventions can be further improved.

Influence factors analysis of COVID-19 Prevention behavior of chinese Citizens: a path analysis based on the hypothetical model.

BACKGROUND: Under the outbreak of Coronavirus disease 2019 (COVID-19), a structural equation model was established to determine the causality of important factors that affect Chinese citizens' COVID-19 prevention behavior. METHODS: The survey in Qingdao covered several communities in 10 districts and used the method of cluster random sampling. The research instrument used in this study is a self-compiled Chinese version of the questionnaire. Of the 1215 questionnaires, 1188 were included in our analysis. We use the rank sum test, which is a non-parametric test, to test the influence of citizens'basic sociodemographic variables on prevention behavior, and the rank correlation test to analyze the influencing factors of prevention behavior. IBM AMOS 24.0 was used for path analysis, including estimating regression coefficients and evaluating the statistical fits of the structural model, to further explore the causal relationships between variables. RESULTS: The result showed that the score in the prevention behavior of all citizens is a median of 5 and a quartile spacing of 0.31. The final structural equation model showed that the external support for fighting the epidemic, the demand level of health information, the cognition of (COVID-19) and the negative emotions after the outbreak had direct effects on the COVID-19 prevention behavior, and that negative emotions and information needs served as mediating variables. CONCLUSIONS: The study provided a basis for relevant departments to further adopt epidemic prevention and control strategies.

Increased prevalence of a rare mutant of pandemic H1N1 influenza virus in a Eurasian region.

In 2009, a novel swine-origin H1N1 influenza virus sparked an influenza pandemic. The emergence of mutations in the viral genome is therefore of ongoing concern. In this study, the hemagglutinin (HA) gene sequences of 3444 pandemic H1N1 influenza viruses reported to the GenBank database and the sequences of 48 pandemic H1N1 influenza viruses detected in the Chinese city of Qingdao were analyzed. Among the 3492 viruses, 101 carried a serine to proline substitution at position 128 (S128P) in the viral HA gene. All the 101 S128P mutants belonged to Clade 7 which has become dominant worldwide since the summer of 2009. Among the 3492 viruses, 1646 were collected before July 25, 2009, and none of these viruses carried the S128P mutation. Furthermore, after July 25, 2009, the prevalence of the S128P mutant was 33.56% (99/295) in a region of Eurasia including Russia, Mongolia, mainland China and South Korea, but only 0.11% (2/1846) in the rest of the world. The data suggested that the originally rare S128P mutant has become prevalent in the Eurasia region, indicating that the S128P mutant likely transmitted more efficiently than other strains of the virus. Therefore, it is of significance to observe whether the S128P mutant will be more dominant worldwide in the coming future and investigate the exact effects of the S128P mutation.