Descriptive statistics of sample characteristics were stratified by SPD status. Analyses by subgroup were conducted to test overall trends in health care utilization by using logistic regression and adjusting for sociodemographic characteristics, health insurance coverage, and chronic conditions. Statistical analyses were conducted by using Stata, version 12.1 SE, and accounting for the complex survey design of the NHIS. Because coefficients from different models of logistic regression may not be directly comparable, postestimation procedures using the margins command were conducted to obtain predicted average annual change in utilization by SPD status. In addition to performing an overall trend test assuming changes in utilization were linear, we conducted supplementary analyses when visual inspection of the raw data suggested nonlinear changes.
Stata version 12.1 se
Manufactured by StataCorp
Sourced in United States
Stata version 12.1 SE is a data analysis and statistical software package developed by StataCorp. It provides a wide range of tools for data management, analysis, and presentation. Stata 12.1 SE supports advanced statistical methods and offers a user-friendly interface for data exploration and visualization.
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Lab products found in correlation
4 protocols using stata version 12.1 se
1
Trends in Healthcare Utilization by SPD Status
2
Fasting Impact on Birth Outcomes
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Data were analysed using Stata Version 12.1 SE [9 ]. Descriptive relationships between demographic variables and fasting behaviour were explored using means, medians and ranges were used for continuous variables, whilst categorical variables were described using proportions. Logistic regression modelling was used to investigate the relationship between the decision to fast or not and the demographic variables. The relationship between fasting behaviours and birth outcomes, including preterm birth, birth weight and low birth weight were examined using logistic regression and linear regression respectively. For the analyses conducted using fasting categories we examined relationships between covariables and birth outcomes using multinomial logistic regression using non-fasters as the reference category. For all final models covariables were screened using univariate analysis and entered into final models if they were significant (p < 0.2). This study had 60% power at a 95% level of confidence to detect a 10% difference in low birth weight and preterm birth. The equivalent power value for differences in birth weight, between fasting and non-fasting mothers, was 90% [10 (link)].
3
Longitudinal Modelling of Behavioural Outcomes
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To describe baseline data, frequencies, means with standard deviations, and medians with inter-quartile ranges were presented. Linear mixed-effects modelling for repeated measures was applied to determine differences between intervention arms in development of standard scores for MOverall and the underlying subscales. Similar mixed-effects models were applied to determine the group differences in development of FOverall and the underlying sub-components. Maximum likelihood estimation was used for all models [28 (link)]. Akaike information criterion and Bayesian information criterion determined whether random intercepts or random slope models were preferred. Unstructured covariance matrix was applied when a random slope model was used. The normality of first level residuals, random intercepts and slopes, as well as homoscedasticity, were investigated in all models. For all statistical analysis, Stata version 12.1 SE (StataCorp LP, College Station, TX, USA) was used.
4
Trends in HIV-1 Subtype Epidemiology
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Demography and clinical parameters were assessed by Kruskal-Wallis equality-of-populations rank test for continuous variables and χ2 test for categorical variables. To assess trends of the HIV-1 subtypes, a multinomial logistic regression model [17] (link) was used for the 3967 patients with HIV diagnosis between 1983 and 2012. The outcome (the HIV-1 subtypes) was grouped into HIV-1B (reference group), HIV-1C, recombinants, and other pure subtypes. The year of HIV-1 diagnosis was the only covariate considered within the model. Square root of year of diagnosis was also added in the model to test for non-linearity, but was not significant. As sensitivity analysis and to get a more detailed description of which strains were primarily imported to Sweden and which strains actually have spread in Sweden, we performed analyses restricted to: 1) All patients infected in Sweden (n = 1551); 2) Patients heterosexually infected in Sweden (n = 307); 3) MSM infected in Sweden (n = 921); 4) All patients born and infected in Sweden (n = 1165); 5) All patients infected outside of Sweden (n = 2161); 6) All patients born in Sweden and infected outside of Sweden (n = 455); 7) Patients born and infected outside of Sweden (n = 1699). Statistical analyses were performed using Stata version 12.1 SE (StataCorp LP, USA)
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