Scoliosis school screening of 139,922 multi-ethnic children in Dali, southwestern China: A large epidemiological study

Figures

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  Figure 1. The geographical location of 8 counties in this study

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  Figure 2. Percentage of students screened by age and sex for idiopathic scoliosis

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  Figure 3. Relative frequency of the population that was investigated in different ethnicity groups

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  Figure 4. The relative frequency of the population that was investigated in different counties

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  Figure 5. The prevalence of scoliosis screening positive rate by ethnicity

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  Figure 6. The prevalence of scoliosis screening positive rate of different ethnicities by living counties

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  Figure 7. Correlation Matrix of Selected Variables This matrix displays the Pearson correlation coefficients between key variables, including age, height, weight, BMI, altitude, longitude, and latitude. Each cell in the matrix represents the correlation coefficient between two variables, with values closer to 1 or -1 indicating stronger positive or negative correlations, respectively.

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  Figure 8. ScatterPlots of height, weight, and BMI relationships A series of scatterplots visualizing the relationships between height, weight, and BMI. Each plot juxtaposes two of the variables on the x and y axes, with individual data points representing observations. The trend lines provide a visual representation of the linear relationships between the variables.

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  Figure 9. LASSO Coefficient Paths This graph illustrates the coefficient paths for the variables in the LASSO regression as the penalty parameter (log lambda) changes. The x axis represents the log-transformed lambda values, while the y axis depicts the standardized coefficients of the variables. The variables are represented by numbers as follows: 1 - Age, 2 - Height, 3 - Weight, 4 - BMI, 5 - Altitude, 6 - Longitude, and 7 - Latitude. As lambda increases, the coefficients of some variables shrink to zero, indicating their exclusion from the predictive model.