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Table 1 5 Models streamlined during model selection

From: Bayesian estimation of the effect of health inequality in disease detection

Model No.

Combination of λt,s and πs

P(m|z)

Model 4

\(\begin {array}{rl} \lambda _{t,s} \; = \log (\text {Pop}_{s}) \; & + \; \alpha _{0} \; + \alpha _{1.1} \; \text {seifa}_{s} + \alpha _{1.2} \; \text {seifa}_{s} + \alpha _{2} \; \text {hhsize}_{s}\\ & + \; \alpha _{4} \; \text {AccessDifficulty}_{s} + \; \phi _{s} \; + \; \theta _{s} \; + \; f(t) \end {array}\)

0.054

 

πs =β0+β1 GPs+β2 GPclinics

 

Model 13

\(\begin {array}{rl} \lambda _{t,s} \; = \log (\text {Pop}_{s}) \; & + \; \alpha _{0} \; + \alpha _{1.1} \; \text {seifa}_{s} + \alpha _{1.2} \; \text {seifa}_{s} + \alpha _{2} \; \text {hhsize}_{s} \\ &+ \; \alpha _{3} \; \text {homeless}_{s} + \; \phi _{s} \; + \; \theta _{s} \; + \; f(t) \end {array}\)

0.109

 

πs =β0+β1 GPs+β2 GPclinics+β3 AccessDifficultys

 

Model 16

\(\begin {array}{rl} \lambda _{t,s} \; = \log (\text {Pop}_{s}) \; & + \; \alpha _{0} \; + \alpha _{1.1} \; \text {seifa}_{s} + \alpha _{1.2} \; \text {seifa}_{s} + \alpha _{2} \; \text {hhsize}_{s} \\ & + \; \phi _{s} \; + \; \theta _{s} \; + \; f(t) \end {array}\)

0.164

 

πs =β0+β1 GPs+β2 GPclinics+β3 AccessDifficultys

 

Model 19

\(\begin {array}{rl} \lambda _{t,s} \; = \log (\text {Pop}_{s}) \; & + \; \alpha _{0} \; + \alpha _{1.1} \; \text {seifa}_{s} + \alpha _{1.2} \; \text {seifa}_{s} \\ & + \; \alpha _{3} \; \text {homeless}_{s} + \; \phi _{s} \; + \; \theta _{s} \; + \; f(t) \end {array}\)

0.062

 

πs =β0+β1 GPs+β2 GPclinics+β3 AccessDifficultys

 

Model 22

\(\begin {array}{rl} \lambda _{t,s} \; = \log (\text {Pop}_{t,s}) \; & + \; \alpha _{0} \; + \alpha _{1.1} \; \text {seifa}_{s} + \alpha _{1.2} \; \text {seifa}_{s} \\ & + \; \phi _{s} \; + \; \theta _{s} \; + \; f(t) \end {array}\)

0.402

 

πs =β0+β1 GPs+β2 GPclinics+β3 AccessDifficultys