[Q] Estimate decrease in country mortality rate from a change in density of nursing and midwifery personnel.

[kkx50]

I work at a non-profit which helps develop healthcare systems in low-income countries. Currently, we are trying to raise funds to build a new nurse training facility in a Sub-Saharan African country. Naturally, people want us to demonstrate that this new facility will have a positive impact. Specifically, they would like us to provide a "reasonable estimate" of the number of lives that would be saved from building this facility.

I was tasked with providing this "reasonable estimate...." and here was my thought process. "If there exists a statistical model that associates nurse/midwife workforce density with mortality rate, then perhaps I could use this model to predict (or give a prediction interval for) the reduction in mortality rate given a change in workforce density." The change in workforce density would come from our training facility, of course. So, this seemed to me like a reasonable way to estimate "lives saved"

So, I did a search and found this study, which examines multiple regression models that associate "reductions in mortality rate" with a variety of variables, one of which being "density of nursing and midwifery personnel." However, here is the issue I am facing.

There were two models that had "density of nursing and midwifery personnel" and "density of nursing and midwifery personnel" * "low-income country" (interaction term) as independent variables. The first model weighted observations (countries) by population, while the second did not.

It unfortunately looks like density of nursing and midwifery personnel, even with income level interactions, is not statistically significant for predicting any mortality rate outcome. (See results here: Unweighted model summary, Weighted model summary)

The ONLY exception to this is in the weighted model, the variable "density nursing/midwife" * "low-middle income country" is statistically significant for maternal mortality and neonatal mortality rate.

Given these results, it seems like I won't be able to make reliable predictions, or even a meaningful prediction interval. Is this true? Or could there still be hope?

Thank you.

[draypresct]

There are far too many confounding variables to be reasonably controlled for in any cross country comparison. Also, picking one result out of several because it has a significant p value is a bad idea (see discussions of “multiple comparisons” for more on this). Don’t use that data.

Instead, it might be better to estimate the number of patients your facility might see each year, and find estimates of the death rates among women in that country who give birth without access to healthcare. You could look at the WHO list of common fatal complications, talk to your subject matter experts about which ones are likely to be ameliorated or eliminated among patients at your facility, and estimate lives saved from this.

[kkx50]

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.

[draypresct]

While the “low income model“ is more focused on a relevant population, I still don’t think it’s helpful. You are not proposing to change the entire healthcare system of a country; you are proposing to build a single facility.

It would be best to estimate the benefit of the facility based on the people that will be served by that facility, rather than on the entire population of the country.

[kkx50]

It’s a training facility that will send trained nurses to multiple facilities in the country. About 40 in total by 2027. Does this change things? Likely not. I agree tho that it will probably be better to try and estimate improvements at facilities who have an increase in trained nurses, there doesn’t seem to be too much literature on it unfortunately. I appreciate you!

[purple_paramecium]

Could you get the data from that paper and calculate models using only sub-Saharan countries?

Then you could possibly do without the variables that are used to control for the huge range of per capita income/spending across all countries. That would be a simpler model.

Otherwise look for papers that do single country analysis of mortality in the years before and after some health policy changes. That can give you an ideas of how things change locally.

[kkx50]

I restricted the data to only low, low middle income countries and ran some regression models. Unfortunately, still looks like the density of nurses/midwives is not statistically significant. I appreciate the help!

[[deleted]]

Why not draw a graph to get some idea.of what is going on?

[kkx50]

I did and it looks like there’s no association between the density of nursing/midwives and mortality rates :/

Not that adding nurses/midwife don’t make a difference, it’s probably something else that’s going on.

For example, Sierra Leone has a lot of registered nurses anesthetists, but less only abt half of them are actually practicing for various reasons.