Abstract
BACKGROUND: National welfare policies have the potential to influence population health. Yet, no research has investigated the influence that welfare spending levels have on primary prevention interventions.
 
 METHODS: This study uses generalized linear mixed model Bayesian analysis to explore how welfare spending influences the relationship between measles counts and measles vaccination rates at a national level. Furthermore, models include random effects to account for the nested structure of countries within regions. A conditional autoregressive model was also developed to test for the influence of spatial relationships among the variables of interest.
 
 RESULTS: Analysis of the Bayesian Information Criterion (BIC) indicated that the non-spatial model (BIC=19743.090) was preferred over the spatial model (BIC = 24225.730). The final model found that both the first dose of measles vaccine (B = -0.835, 95% Cr. I. = -0.975, -0.699), public social protection (B = -0.936, 95% Cr. I. = -1.132, -0.744), and their interaction (B = -0.239, 95% Cr. I. -0.319, -0.156) had a negative influence on national measles counts.
 
 CONCLUSIONS: This finding indicates that welfare spending may enhance primary prevention interventions, like measles vaccination.
Highlights
National welfare policies have the potential to influence population health
I. -0.319, -0.156) had a negative influence on national measles counts. This finding indicates that welfare spending may enhance primary prevention interventions, like measles vaccination
To examine national welfare spending, this study looks at public social protection (PSP) expenditure as a proportion of gross domestic product (GDP)
Summary
The purpose of this research is to explore relationships among variables, and not to make predictions
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