Table 3.
Statistically significant results of univariate and multivariate ordinal and binary logistic regression of PCPs knowledge related to PM, FXTAS, FXPOI and FMR1 gene testing.
| Univariate and multivariate ordinal logistic regression analysis: dependent variable is premutation factual knowledge score | ||||||
|---|---|---|---|---|---|---|
| Univariate ordinal logistic regression analysis | Multivariate ordinal logistic regression analysis | |||||
| Independent variable | OR (95% CI) | P | OR (95% CI) | P | ||
| Age (year) | 0.97 (0.94–1.00) | 0.027* | ||||
| PCPs Settings (BG vs. in. Serbia) | 0.28 (0.17–0.49) | <0.001* | 0.37 (0.21–0.65) | <0.001* | ||
| Experience | 0.62 (0.40–0.95) | 0.029* | ||||
| Univariate and multivariate ordinal logistic regression analysis: dependent variable is knowledge of empirical evidence refers to FMR1 gene testing | ||||||
| Univariate ordinal logistic regression analysis | Multivariate ordinal logistic regression analysis | |||||
| Independent variable | OR (95% CI) | P | OR (95% CI) | P | ||
| PCPs Settings (BG vs. in. Serbia) | 0.23 (0.14–0.38) | 0.000* | 0.23 (0.14–0.38) | 0.000* | ||
| Experience | 0.61 (0.40–0.92) | 0.019* | 0.62 (0.40–0.95) | 0.029* | ||
| Univariate and multivariate binary logistic regression analysis: dependent variable is question “Do you know that FMR1 gene premutation can cause symptoms like those of Parkinson's Disease?”(question: II/2/4) | ||||||
| Univariate binary logistic regression analysis | Multivariate binary logistic regression analysis | |||||
| Independent variable | B (SE) | OR (95% CI) | P | B (SE) | OR (95% CI) | P |
| PCPs Settings (BG vs. in. Serbia) | 1.28 (0.29) | 3.61 (2.03–6.42) | 0.000* | 1.03 (0.31) | 2.81 (1.52–5.20) | 0.001* |
| Experience | −0.66(0.23) | 0.52 (0.33–0.82) | 0.005* | −0.71 (0.29) | 0.49 (0.28–0.87) | 0.015* |
| Univariate binary logistic regression analysis: dependent variable is question “Do you know thatFMR1 gene premutation can cause primary ovarian insufficiency?” (question: II/2/5) | ||||||
| Univariate binary logistic regression analysis | ||||||
| Independent variable | B (SE) | OR (95% CI) | P | |||
| PCPs Settings (BG vs. in. Serbia) | 1.53 (0.32) | 4.64 (2.47–8.71) | 0.000* | |||
| Univariate and multivariate binary logistic regression analysis: dependent variable is question “Are you aware of the availability of early, precise genetic/medical diagnosis of FMR1 gene mutations?”(qusetion: II/3/1) | ||||||
| Univariate binary logistic regression analysis | ||||||
| Independent variable | B (SE) | OR (95% CI) | P | |||
| PCPs Settings (BG vs. in. Serbia) | 1.58 (0.29) | 4.87 (2.77–8.57) | 0.000* | |||
| Experience | −0.59 (0.23) | 0.56 (0.36–0.87) | 0.010* | |||
| Univariate binary logistic regression analysis: dependent variable is question “Do you know that there is the professional organizations' recommendation on FMR1 gene testing in individuals diagnosed with autism spectrum disorders?” (question: II/3/2) | ||||||
| Univariate binary logistic regression analysis | ||||||
| Independent variable | B (SE) | OR (95% CI) | P | |||
| PCPs Settings (BG vs. in. Serbia) | 1.28 (0.29) | 3.61 (2.03–6.42) | 0.000* | |||
Only statistically significant predictors are shown in the table.
PCPs, primary care physicians; FMR1, gene-Fragile X Mental Retardation 1 gene; BG, Belgrade; in Serbia, inner Serbia; GP, general practice; Experience refers to total years (length of clinical experience).
Statistically significant data; P values, probability of the data arising by chance; B, the coefficient for the constant (also called the “intercept”) in the model; SE, the standard error around the coefficient for the constant. OR-an odds ratio as a measure of association between an exposure and an outcome; CI, 95% confidence interval is used to estimate the precision of the OR.