Table 7

Multiple linear regression with stepwise inclusion of variables (HI superregion serves as reference for other superregions)

Model 1: Original data set	Headache, N = 181 (5 variables)		Migraine, N = 242 (8 variables)		TTH, N = 109 (4 variables)		H15+, N = 58 (2 variables)
Whole model	F = 10.3, p < 0.001, R2 adj: 0.208		F = 13.9, p < 0.001, R2 adj: 0.299		F = 6.9, p < 0.001, R2 adj: 0.181		F = 6.6, p = 0.003, R2 adj: 0.165
Variables with significance	β standardised	SPCsqr	β standardised	SPCsqr	β standardised	SPCsqr	β standardised	SPCsqr
Year of publication			0.258	0.060
Number (ln transformed) of participants	−0.207	0.040	− 0.146	0.018
dMig and pMig			0.269	0.064
dTTH and pTTH							0.321	0.102
Health-care personnel			0.173	0.022
Sampling method			−0.169	0.022
Type of screening question	−0.307	0.090			−0.285	0.080
Number of conditions	−0.162	0.026
Mode of engagement							0.281	0.078
SSA superregion	−0.148	0.021	−0.173	0.028	−0.232	0.052
SEEAO superregion	−0.171	0.029	−0.136	0.018	−0.294	0.084
NAME superregion			−0.113	0.011	−0.231	0.051
Model 2: ln transformed dependent variable and outliers (±1.5 interquartile range) removed	Headache, N = 176 (4 variables)		Migraine, N = 238 (7 variables)		TTH, N = 103 (4 variables)		H15+, N = 57 (2 variables)
Whole model	F = 11.3 p < 0.001, R2 adj: 0.191		F = 13.6, p < 0.001, R2 adj: 0.271		F = 5.5, p < 0.001, R2adj:0.150		F = 6.5, p = 0.003, R2 adj: 0.164
Variables with significance	β standardised	SPCsqr	β standardised	SPCsqr	β standardised	SPCsqr	β standardised	SPCsqr
Year of publication			0.259	0.064
Number (ln transformed) of participants	−0.186	0.032	−0.137	0.016			−0.354	0.125
dMig and pMig			0.291	0.078
dTTH and pTTH	0.166	0.027
Health-care personnel			0.133	0.013
Sampling method			−0.169	0.022
Type of screening question	−0.277	0.072			−0.233	0.054
SEEAO superregion			−0.110	0.012	−0.206	0.058	−0.250	0.062
SSA superregion	−0.207	0.042	−0.180	0.031	−0.245	0.042
NAME superregion					−0.264	0.068

R² adj (Adjusted R²): The proportion of variability explained by the whole model (“Goodness of fit”), adjusted for overestimation caused by the number of effects

β-standardised: The direction and strength of its correlation with the dependent prevalence variable

SPC^sqr: the proportion of the variability explained by that variable alone