Baseline characteristics were compared according to alcohol drinking status using a one-way analysis of variance or the chi-square test. Given known sex differences in drinking behavior, all analyses were stratified by sex. Multivariate logistic regression using sampling weights was performed to assess the relationship between alcohol drinking status and depression after adjusting for age, sex, BMI, marital status, education, monthly household income, residence, smoking status, comorbidity (hypertension, diabetes, and arthritis), self-rated health, and physical activity. We assessed the effect modification of age on the association between alcohol drinking status and depression and determined the prevalence of depression according to age after adjusting for confounders. The Wald test was used to test the interaction. All analyses were performed using STATA statistical software version 16.0 (Stata Corp., College Station, TX, USA).
Stata statistical software version 16
Manufactured by StataCorp
Sourced in United States
Stata is a statistical software package designed for data analysis, visualization, and modeling. Version 16 of Stata provides a comprehensive set of tools and features for a wide range of statistical techniques, including regression analysis, time series analysis, survey data analysis, and much more. Stata 16 is available for Windows, macOS, and Linux operating systems.
Automatically generated - may contain errors
131 protocols using stata statistical software version 16
1
Alcohol Drinking and Depression Prevalence
2
Stata-powered Meta & Network Analysis
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The ‘meta’ and ‘network’ packages of Stata statistical software version 16.0 (StataCorp, College Station, Texas, USA) will be used for PMA and NMA, respectively. The statistical significance estimation of all ESs will happen at p<0.05 and 95% confidence interval.
3
Racial Disparities in Health Insurance and Care
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In this cross-sectional study, we grouped 2011-2019 NHIS data by the period before the national ACA implementation (2011-2013), the start of the ACA implementation (2014-2015), the implementation of the health insurance mandate (2016-2018), and the year the individual mandate was eliminated (2019). We limited the sample to participants aged 18 to 64 years. All results were nationally representative. Since NHIS is publicly available with deidentified observations, the Drexel University human research protection program deemed it exempt from institutional review board approval. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE ) reporting guideline for cross-sectional studies.
We estimated weighted predictive probabilities for the following 4 measures according to self-reported race and ethnicity during the 4 periods: (1) being currently uninsured, (2) having a usual source of care, (3) any emergency department (ED) visit in the past year, and (4) any delay of care due to cost in the past year. Usual source of care is a global measure that does not differentiate types of care. Confidence intervals were used to measure uncertainty. Data analyses were performed using Stata statistical software, version 16.0 (StataCorp LLC).
We estimated weighted predictive probabilities for the following 4 measures according to self-reported race and ethnicity during the 4 periods: (1) being currently uninsured, (2) having a usual source of care, (3) any emergency department (ED) visit in the past year, and (4) any delay of care due to cost in the past year. Usual source of care is a global measure that does not differentiate types of care. Confidence intervals were used to measure uncertainty. Data analyses were performed using Stata statistical software, version 16.0 (StataCorp LLC).
4
Statistical Analysis of Outcome Data
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STATA statistical software version 16.0 was used to analyze the data. Descriptive statistics using mean, standard deviation, frequencies, and percentages were used to describe categorical and continuous variables. Cross-tabulation computation was done for the outcome variable across the key explanatory variables. At a cut-off p-value of ≤0.05, a chi-square test of independence or Fisher exact test was calculated between the outcome variable and key explanatory variables and those that were not significant were not entered into the multivariate analysis. At a 95% confidence interval (95% CI), two logistic regression models were fitted between the outcome variable and key explanatory variables. The first model (Model I) explored a bivariate association whilst Model II followed a multivariate approach. The results were reported in crude odds ratio (COR) and adjusted odds ratio (AOR) for Model I and Model II respectively. A multi-collinearity test was performed for the key explanatory variables using the Variance Inflation Factor (VIF). The VIF results indicated no evidence of multi-collinearity between the explanatory variables (Mean VIF = 1.06, Maximum VIF = 1.12 Minimum VIF = 1.02) (see S1 Table ). Finally, the Hosmer-Lemeshow test was applied to measure the model fit which indicated no evidence of poor fit (P = 0.72).
5
Meta-Analysis of NAFLD and CRN Risk
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OR and 95% CI were used to measure the relationship between NAFLD and the risk of CRN. The chi-square test and I2 value were used to evaluate the data heterogeneity from different studies. Stata statistical software version 16.0 was used throughout the meta-analysis.
6
Analyzing Blood Pressure Parameters and AKI
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Stata statistical software version 16.0 (Stata Corp LP, College Station, TX, United States) was used for the data analysis. Continuous variables were reported as means ± SD or median (IQR), and categorical variables were expressed as percentages (%). Student's t‐test would be used to compare continuous variables, whereas the Chi‐square test would be used to compare categorical variables. The logistic regression analysis was used to determine the correlation between each blood pressure parameter (maximum, minimum, SD, Range, and CV) and AKI. Multivariate model 1 was unadjusted for variables. Multivariate model 2 was adjusted for variables that differed between groups with and without AKI (admission systolic blood pressure and history of alcohol drinking). p values of <.05 were considered statistically significant.
7
Descriptive Statistics and Logistic Regression
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The data were imported by STATA statistical software, version 16.0 (College Station, Texas, USA), and descriptive statistics were used to obtain the absolute and relative frequencies of categorical variables. For bivariable analysis, Pearson's chi-square test and Fisher's exact test were applied for expected values <5. Influencing factors were examined using a logistic regression model (logit model) with an odds ratio (OR). All analyses were carried out considering a significance level of 5% (p < 0.05).
8
Racial Disparities in Health Insurance and Care
9
Comprehensive Statistical Analysis of Research
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PMA and NMA will transpire using the ‘meta’ and ‘network’ packages of Stata statistical software version 16.0 (StataCorp, College Station, Texas, USA). Statistical significance estimation will happen at p<0.05 and 95% CI.
10
Factors Influencing Sanitation Surcharge Willingness
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Data was cleaned, checked for consistency and validated using self-written commands before analysis. Data management, as well as statistical analysis, were performed using STATA statistical software version 16.0.39
First, descriptive computations were conducted to describe the general sampled characteristics. At the 5% alpha threshold, a chi-square test of independence was conducted to ascertain the association between dependent and independent variables. As such, any independent variable that could not meet the cut-off point of 5% was not entered into the regression model.
Subsequently, at 95% confidence level and 5% alpha threshold, two-level binary logistic regression models were built. Model I (unadjusted model) examined the relationship between the independent variables and willingness to pay sanitation surcharge, whilst Model II (adjusted model) accounted for the effect of other covariates. Our findings were reported in Odds Ratio (OR), and odds above 1 were explained as having a likelihood to pay sanitation surcharge, whilst odds below 1 meant otherwise. The Hosmer-Lemeshow post-estimation test was used to assess the model fitness, and the results indicated no evidence of poor fit.
First, descriptive computations were conducted to describe the general sampled characteristics. At the 5% alpha threshold, a chi-square test of independence was conducted to ascertain the association between dependent and independent variables. As such, any independent variable that could not meet the cut-off point of 5% was not entered into the regression model.
Subsequently, at 95% confidence level and 5% alpha threshold, two-level binary logistic regression models were built. Model I (unadjusted model) examined the relationship between the independent variables and willingness to pay sanitation surcharge, whilst Model II (adjusted model) accounted for the effect of other covariates. Our findings were reported in Odds Ratio (OR), and odds above 1 were explained as having a likelihood to pay sanitation surcharge, whilst odds below 1 meant otherwise. The Hosmer-Lemeshow post-estimation test was used to assess the model fitness, and the results indicated no evidence of poor fit.
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