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Table 3 Logistic regression analysis for each single variable and adjusted independent risk factors

From: Risk factors for high-altitude headache upon acute high-altitude exposure at 3700 m in young Chinese men: a cohort study

Variable

β-coefficient

Odds ratio

(95% CI) a

p value

Demographic factors

    

Age

0.05

1.05

1.01–1.10

0.020

BMI

0.03

1.03

0.97–1.09

0.352

Smoking

0.03

0.97

0.77–1.22

0.790

Alcohol consumption

–0.04

0.96

0.82–1.14

0.680

History

    

Primary headache(yes)

1.51

4.52

2.44–8.37

<0.001

High altitude exposure (yes)

–1.64

0.85

0.60–1.20

0.353

Athletic training(yes)

0.15

1.16

0.71–1.91

0.554

Psychological scale

    

SAS

0.20

1.22

1.16–1.29

<0.001

Physiological factors

    

SBP

0.01

1.01

0.99–1.02

0.457

DBP

0

1.00

0.99–1.02

0.581

ΔBPBP

0

1.00

0.98–1.02

0.671

MAP

0.01

1.00

0.99–1.02

0.507

SaO2

–0.09

0.91

0.86–0.96

0.001

HR

0.02

1.02

1.01–1.04

0.002

Sleep

    

Insomnia(yes)

0.97

2.64

1.92–3.65

<0.001

ESS

0.09

1.09

1.05–1.14

<0.001

PLI

–0.23

0.80

0.64–1.00

0.045

Adjusted independent risk factors

    

Primary headache history (yes)

1.20

3.30

1.73–6.30

<0.001

Insomnia(yes)

0.65

1.91

1.35–2.70

<0.001

SaO2

–0.06

0.94

0.89–1.00

0.035

HR

0.020

1.020

1.00–1.03

0.009

SAS

0.16

1.18

1.11–1.25

<0.001

  1. a: 95% CI: 95% confidence intervals. A binary logistic regression model was used to identify the risk factors for HAH.