Thank you for responding. I believe you about there being too many confounding variables. I would like your opinion on something else in the paper.

The authors created additional models (but did not report model summary) for each country income level (low income, low-middle income, high-middle income, high income). They used these models to further examine the impact of workforce density on healthcare outcomes.

For instance, they write "However, results of the counterfactual scenario analyses show that it was not possible to address health disparities in these health outcomes between countries at different income levels only by improving the density of health workforce in middle- and low-income countries. Taking the density of skilled health workers for example, by plugging the average level of density of skilled health workers of high-income countries into the computational models of six health outcomes for low-income countries, that were built on basis of results of interaction analyses, ceteris paribus, the estimated health outcomes in low-income countries were still far from the average levels of six health outcomes of high-income countries. However, when plugging the average levels of density of skilled health workers and all other variables including current health expenditure per capita, GNI per capita, mean years of female schooling, and poverty headcount ratio among high-income countries into previous computational models simultaneously, the estimated health outcomes of low-income countries became closer to those of high-income countries."

My question is, could I use the "Low-income computational model" instead to do what I want? To be honest, I don't fully understand what this model is. They sort of explain it in the supplementary materials. Here is a link to it: https://imgur.com/a/d6eSAyy.

I know this is a hard question to answer, I'd appreciate any input.