Background Utilizing Adverse Childhood Experiences (ACEs) measurement scales to assess youths’ adversities has expanded exponentially in health and justice studies. However, most of the ACEs assessment scales have yet to meet critical psychometric standards, especially for key demographic and minority groups. It is critical that any assessment or screening tool is not reinforcing bias, warranting the need for validating ACEs tools that are equitable, reliable and accurate. The current study aimed to examine the structural validity of an ACEs scale. Using data from the 2019 Behavioral Risk Factor Surveillance System (BRFSS), which collected of 97,314 responses collected from adults across sixteen states. This study assessed the psychometric properties and measurement invariance of the ACEs tool under the structural equation modeling framework. Results We found the 11-item ACEs screening tool as a second-order factor with three subscales, all of which passed the measurement invariance tests at metric and scalar levels across age, race, sex, socioeconomic status, gender identity, and sexual orientation. We also found that minority groups experienced more childhood adversity with small effect size, with the exception of the gender identity. Conclusion The ACEs measurement scale from the BRFSS is equitable and free from measurement bias regardless of one’s age, race, sex, socioeconomic status, gender identity, and sexual orientation, and thus is valid to be used to compare group mean differences within these groups. The scale is a potentially valid, viable, and predictive risk assessment in health and justice and research settings to identify high-risk groups or individuals for treatments.
Effect of vitamin D on apoptosis of peripheral blood T-lymphocyte subsets in treatment of neonatal sepsis was investigated. A total of 150 neonatal patients with sepsis were randomly divided into vitamin D treatment group (observation group) and treatment control group, while 100 healthy newborns were selected as healthy control group. T-lymphocyte subsets were detected by flow cytometer, the levels of tumor necrosis factor-α, interleukin-1 and calcitonin were determined by double-antibody immunoluminometric assay, and the effect of vitamin D on the above indicators in the treatment of sepsis was observed. Serum 25(OH)D (22.52±5.56 mg/l) in the treatment group was obviously increased compared with that in the treatment group (14.85±6.14 mg/l) (P<0.05), but the levels in the two groups were remarkably lower than that in the normal control group (26.38±6.56 mg/l), and the differences were statistically significant (P<0.05). Cluster of differentiation 4 (CD4+) T-lymphocyte subset in sepsis patients was obviously reduced compared with that in the healthy control group (P<0.01); the difference in comparison of CD8+ T-lymphocyte subset between sepsis patients and healthy people was not statistically significant (P>0.05). After treatment for 72 h, CD4+ T-lymphocytes were increased, and the ratio of CD4+ to CD8+ was close to 1, suggesting that the effect was superior to that in the treatment control group. The inflammatory factor levels in children with sepsis were evidently higher than those in the healthy control group (P<0.01), and high-level states of inflammatory factors were significantly improved after treatment with vitamin D for 72 h, indicating that the effect was superior to that in the treatment group. The results indicated that the prognosis of sepsis patients treated with vitamin D is improved, and the mechanism may be achieved by regulating T-lymphocyte subsets and inflammatory factors.
Firstly, from the external structure of the multivariate time series ( ) matrix, the article defines the similarity function based on the Range and the number of the different samples of the two matrixes difference; Afterward, considering the correlations between the column vectors of the matrix, the internal factors of the matrix, defines the similarity function based on the weighted square norm of the corresponding covariance matrix; and constructs the similarity function by weighted the two defined functions. Lastly, applying the similarity function of two s to the clustering the database, the algorithm is very effective.
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