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Table 3 Backwards stepwise regression on predictors of willingness to pay for COVID-19 vaccine (n = 559)

From: Financing COVID-19 vaccination in sub-Saharan Africa: lessons from a nation-wide willingness to pay (WTP) survey in Ghana

Willingness to pay OR Std.Err P > z [95%Conf Interval]
Elderly male 0.199 0.225 0.153 0.022 1.820
Educated male 0.550 0.114 0.004 0.366 0.826
Adherent to COVID-19 protocolsa 0.887 0.107 0.320 0.700 1.123
Married Christian 0.549 0.173 0.057 0.296 1.018
Perception of vaccineb 2.404 0.911 0.021 1.144 5.054
Educated elderly 3.178 3.738 0.326 0.317 31.871
Elderly married 5.205 4.892 0.079 0.825 32.844
Elderly Christian 0.373 0.290 0.204 0.082 1.709
Response to COVID-19c 1.171 0.123 0.134 0.952 1.439
Educated married 2.188 0.791 0.030 1.077 4.445
Elderly Christian 1.422 0.325 0.124 0.908 2.225
Married health worker 0.428 0.149 0.015 0.217 0.845
_cons 0.573 0.298 0.285 0.207 1.589
  1. Legend: aindexed score of overall adherence level to COVID-19 protocols on a five-point Likert scale where higher values depict better adherence and vice-versa; bindexed score of perception of COVID-19 where higher values depict positive perception and vice-versa; cindexed score on perceived government response strategy against COVID-19 on a five-point Likert scale where higher values depict better perceived response and vice-versa
  2. Note: Sample size (n = 559) in the regression model is the valid responses of persons who will accept to take the COVID-19 vaccine and those who did not respond in the affirmative were dropped from the regression model
  3. Step-wise backwards regression beginning with full model;
  4. p = 0.9929 >  = 0.3300 removing Male Christian
  5. p = 0.9461 >  = 0.3300 removing Educated health worker
  6. p = 0.9266 >  = 0.3300 removing Male health worker
  7. p = 0.8447 >  = 0.3300 removing Perceived impact of COVID-19
  8. p = 0.6085 >  = 0.3300 removing Perception of COVID-19
  9. p = 0.5769 >  = 0.3300 removing Christian health worker
  10. p = 0.4735 >  = 0.3300 removing Married male
  11. Logistic regression (model specification)
  12. Number of obs. = 559
  13. LR chi2(12)= 31.99
  14. Prob > chi2 = 0.0014
  15. Log likelihood = -369.81995
  16. Pseudo R2 = 0.0415