We excluded 112 participants with missing information on sex, marital status, education, occupation and metabolic data, and those with extreme values of sleep duration (Fig. 1 ). Finally, total participants 8 272 participants were included in our analyses. Demographic and metabolic features were showed based on gender using percentages for categorical variables and means ± SD for continuous variables. Prevalence of MetS and its components were calculated for the overall population and different subgroups, such as age groups, gender, BMI groups, sedentary behaviors and sleep duration. Sleep duration was classified as <6, 6–9, and >9 hours. Sedentary time behavior was divided into <3, 3–6, and > 6 hours. The relationships between sleep duration and MetS were evaluated by Restricted Cubic Spline Regression, adjusting for age, sex, education, occupation, smoking category, alcohol intake, physical activities, and BMI. The number of dots was chosen to 3 according to criteria to balance best fit and overfitting. MetS rate standardization was done using Henan province population in 2010. All the statistical analyses were performed with SAS version 9.2 (SAS Institute Inc). Figures used Stata 12.0 version (StataCorp, College Station, TX, USA).
Stata 12.0 version
Manufactured by StataCorp
Sourced in United States
Stata 12.0 is a comprehensive statistical software package developed by StataCorp. It provides a wide range of data management, analysis, and visualization tools for researchers and data analysts. Stata 12.0 offers a user-friendly interface and supports a variety of data formats, enabling efficient data manipulation and statistical modeling.
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Lab products found in correlation
3 protocols using stata 12.0 version
1
Metabolic Syndrome Prevalence and Sleep
2
Heterogeneity Assessment in Meta-analysis
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In the meta-analysis, we used SIR with its 95% CI to combine these pieces of data. Heterogeneity assumption in studies was checked with the Q statistic [9 (link)]. Meanwhile, we assessed the effect of heterogeneity in these studies by the following method: I2 = 100% × (Q − df)/Q [10 (link)]. A significant Q statistic (P < 0.10) suggested heterogeneity across studies, and then the result of the random-effect model was selected. If not, the result of the fixed effect model was selected. Additionally, publication bias was investigated with the funnel plot and Egger's linear regression test [11 (link)]. All the analyses were performed using the software Stata 12.0 version (StataCorp LP, College Station, TX, USA).
3
Examining Non-Linear Weather-Hospital Admission Relationships
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During the second phase, we plotted two-way fractional-polynomial prediction plots with confidence intervals (CI) in order to better describe the potential non-linear relationships between the weather and hospital admissions (the y axis in each figure denotes log-hazard ratios). Due to the German law prohibiting reporting of individual admissions and hospitals, correlational analysis was only made on a daily basis but not hourly. From the generated PETs in 2009–2011 (see Fig. 2 ), it was observed that there was no heat stress (PET >35 °C in Western Europe; Matzarakis and Mayer 1996 ). PETs were mainly between −10 and 20 °C throughout 3 years in 2009–2011. Statistical software STATA 12.0 version (STATA, College Station, TX, USA) was used to perform all the statistical analyses.![]()
Averaged PET by month and by day over 3 years in 2009–2011
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