Prevalence rates of sexual dissatisfaction according to smoking characteristics were calculated. Socio-demographic and clinical characteristics of smokers and non-smokers compared by χ2 test. Because smokers and non-smokers were not comparable in terms of some socio-demographic and clinical variables, the smoking-sexual dissatisfaction association was tested with multiple binary logistic regression model, which entered sexual satisfaction as the outcome variable, smoking status as the predictor, and socio-demographic and clinical variables at once to adjust for the potential confounding effects of these socio-demographic and clinical factors. By using the sample of smokers and the same analytic procedures, two multiple binary logistic regression models (one included the level of smoking as the predictor, and the other included the level of nicotine dependence as the predictor) were established to further examine the relationships between the two variables and sexual satisfaction. We used odds ratios (ORs) and 95% confidence intervals (CIs) to quantify the associations between variables and sexual satisfaction. The statistical significance level was set at P < 0.05 (two-sided). SPSS software version 15.0 package (SPSS Inc, Chicago, IL) was used for all analyses.
Spss software version 15.0 package
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SPSS software version 15.0 is a statistical analysis software package developed by IBM. The core function of SPSS is to provide users with statistical analysis tools and capabilities for data management, analysis, and presentation.
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Lab products found in correlation
5 protocols using spss software version 15.0 package
1
Smoking Impacts on Sexual Satisfaction
2
Examining Sexual Dissatisfaction in CSP
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The prevalence of dissatisfaction with one’s sexual life was described. Rates of dissatisfaction between patients with and without CSP were compared by chi-squared test. The association between CSP and sexual life dissatisfaction was examined with multiple ordinary logistic regression that entered SLS as the outcome variable, CSP as the predictor, and sociodemographic, psychological, and clinical covariates at once to adjust for the potential confounding effects of these sociodemographic, psychological, and clinical variables. The statistical significance level was set at P<0.05 (two-sided). SPSS software version 15.0 package (SPSS Inc., Chicago, IL, USA) was used for all analyses.
3
Factors Influencing Nightmare Prevalence
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Prevalence rates of FNs in the whole patient sample and different patient cohorts were calculated and the chi-squared test was used to compare rates between groups. Multivariable logistic regression model with backward stepwise selection that entered significant variables in the chi-squared test as independent variables and nightmares as the dependent variable was used to identify factors significantly and independently associated with nightmares. OR and 95% CIs were used to quantify the associations between factors and nightmares. Independent samples Mann–Whitney U test was used to compare ShDS scores of patients with and without nightmares. Multiple linear regression models were used to examine the strength of association between nightmares and ShDS score, controlling for socio-demographic variables, drug use characteristics, physical health, mental health, and insomnia. The statistical significance level was set P<0.05 (two-sided). SPSS software version 15.0 package was used for all analyses (SPSS Inc., Chicago, IL, USA).
4
Insomnia Prevalence and Correlates
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Prevalence of clinical insomnia was calculated. Chi-square test was used to compare rates of insomnia between/across subgroups according to characteristics such as sex and education levels. Binary logistic regression analysis with the forward selection (Wald) method was used to examine the independent correlates of insomnia. Statistically significant variables from the Chi-square test were included into the multiple logistic regression model. Odds Ratios (ORs) and their 95% confidence intervals (CIs) were used to quantify the associations between insomnia and correlates. The statistical significance level was set at P < 0.05 (two-sided). SPSS software version 15.0 package (SPSS Inc., Chicago, IL, USA) was used for all analyses.
5
Loneliness and Mental Health in Pandemic
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Prevalence rates of loneliness in the whole sample and different subgroups were calculated. Chi-square test was used to compare rates between subgroups according to socio-demographic and epidemic characteristics. Multiple logistic regression with a backward stepwise entry of all significant variables in univariate analysis was used to identify factors associated with loneliness. We used Chi-square test to compare rates of mental health needs between lonely and not lonely residents. The independent association of loneliness with mental health needs was further examined with multiple logistic regression analysis which included perceived needs for mental health care the outcome variable, loneliness as the predictor, and socio-demographic and epidemic characteristics and common mental health problems all together as covariates. The association between loneliness and mental health services utilization was examined in the same manner. Odds ratios (ORs) and their 95 % confidence intervals (95 %CIs) were used to quantify associations between factors and the outcome variables. The statistical significance level was set at P < 0.05 (two-sided). SPSS software version 15.0 package (SPSS Inc., Chicago, IL, USA) was used for all analyses.
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