We characterized the distribution of all measures and performed Pearson χ2 tests to determine statistical significance by e-cigarette ever use, including both current use and prior use. We used multivariate logistic regressions to measure the association of e-cigarette ever and current use with language proficiency and length of stay. Considering sex and smoking status as moderators, we further analyzed regression models with interaction terms between length of stay in the United States, English language proficiency, and acculturation proxies. We used Stata 14.0 SE (StataCorp, College Station, TX, USA) to adjust for complex survey design and weights in all the analyses.
Stata 14.0 se
Manufactured by StataCorp
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Stata 14.0 SE is a statistical software package developed by StataCorp. It provides a comprehensive set of tools for data analysis, management, and visualization. Stata 14.0 SE supports a wide range of statistical models and procedures, including linear regression, logistic regression, time series analysis, and more. The software is designed to be user-friendly and offers a variety of features to help researchers and analysts efficiently analyze their data.
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Lab products found in correlation
8 protocols using stata 14.0 se
1
E-cigarette Use, Language, and Acculturation
2
Adolescent Substance Use Prevalence and Factors
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Data of this study were analyzed using STATA 14.0 SE (StataCorp. LP, College Station, TX, USA). The prevalence of tobacco and other substance use were assessed for total sample and by subgroups. Chi-squared tests were used to assess bivariate comparisons. Assessment of the extent to which various adverse psychosocial and social-environmental factors are associated with adolescents' tobacco and other substance use were carried out using a logistic regression approach. We estimated both unadjusted and adjusted odds ratios and their 95% confidence intervals were calculated. In all statistical analyses, we set α = 0.05. Imputation using a logistic regression model was used to estimate missing values from known values to account for missing data, most frequently for sexual history (i.e., for 11.6% of respondents) [40 (link)]. Age, gender, and school grade were included as covariates in the imputation. Multicollinearity of the variables was checked using variance inflation factor (VIF), and in all cases, the values of VIF were found less than 2, indicating multicollinearity was not an issue. In all analyses, the complex survey design and sampling weights were considered.
3
Nutrient Levels and Determinants
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The analysis was conducted using STATA 14.0 SE. We computed mean and median levels of nutrients and the proportion of people with deficiency or low levels of nutrients under study. We estimated median and proportions for age group, parity and pregnancy/lactation status to study patterns. We developed histogram for each of the nutrients and found that serum folate, vitamin B12 and ferritin levels had skewed distribution. So we also estimated geometric means for these nutrients. We used log-transformed values to test for association with factors such as age, parity and current pregnancy or breastfeeding status using multivariable linear regression (model-1). We also conducted regression analysis (model-2) adjusting additionally for serum protein and BMI levels. We did not find any significant association of protein and BMI levels with the four main nutrients under study. The pattern and magnitudes of effect in model-1 did not change in model-2, so we did not report model-2.
4
Chronic Disease Prevalence in Migrant Groups
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Five waves of the HABITAT survey data were analyzed to determine the prevalence of self-reported chronic diseases and behavioral risk factors over 9 years by group country of birth: Australian-born, immigrants from high-income countries, and immigrants from low–middle-income countries. Poisson regression was used to estimate the prevalence ratios (PRs) because of its ability to estimate PR consistently and effectively in prospective studies. Poisson regression provides better analysis than logistic regression because of providing unbiased, more interpretable, and easier to communicate ratios [20 (link)]. To avoid overestimating the error of the estimated risk, a robust error variance procedure (sandwich estimation) was used (Zou, 2004). Preliminary bivariate analyses indicated that employment status, income, and education qualifications were not significantly associated with any of the diseases or risk factors under study. All regression models were adjusted for age, sex, education, and gross household income. All socio-demographic covariates analysed are listed in Table 1 . We reported estimated PRs with 95% confidence intervals (CIs) at a significance level of p < 0.05. All analyses were conducted using Stata 14.0 SE.
5
Logistic Regression for Binary Outcomes
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We tested for statistical significance by fitting logistic regression analysis to compare differences in binary outcomes where time was the predictor, using the Pearson’s chi-squared test for direct comparison of the two proportions. For pre-post intervention comparisons with fewer than n = 10 observed outcomes, Fisher’s exact statistic was used to calculate p-values, in lieu of chi-square statistics. A two-sided p-value < 0.05 was considered significant. Statistical analyses were performed using Stata 14.0 SE.
6
Factors Associated with COVID-19 Vulnerability
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Differences across the COVID-19 vulnerability categories were estimated using the Pearson's chisquare test. Logistic regression models were used to estimate the odds ratios (OR) and their 95% confidence intervals (95%CI) to assess factors associated with the highest COVID-19 vulnerability. Multivariate analyses were sequentially performed by adding blocks of characteristics in the following order: (1) gender-related characteristics; (2) sociodemographic characteristics; and (3) healthrelated characteristics. The fully adjusted model included only variables with p < 0.20 in the block analyses due to evidence of multicollinearity (variance inflation factor > 5). Hosmer-Lemeshow goodness-of-fit test was implemented to assess model fit after fitting the logistic regression final models. Post-stratification was used to estimate weights according to Brazilian regions, considering the population estimates of the general Brazilian population aged ≥ 18 years used in the 2019 Brazilian National Health Survey (PNS 2019). This procedure was used to enhance representativeness, since the participants' selection probability was unknown 26 and the participants were concentrated in the Southeast Region. All analyses were performed using Stata 14.0 SE (https://www.stata.com).
7
Evaluating Pediatric TB Infection Rates
8
Prescription Opioid Misuse Characteristics
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