Even if you have a proper budget for your data projects--we can all benefit from a deeper understanding of what assumptions are being made from the data we curate.
Non-proprietary data sources, at least the reputable resources will have a data dictionary of sorts. Typically it is the PDF file in the midst of your CSVs or other statistical file types.
I live in the world of real world data and rely on data generated from Census Data or more recent American Community Survey (ACS) data as well as a variety of other government sites.
Many of us work quite comfortably in the raw data world but if that isn't how you earn your keep there are also tools and instructional resources--and of course--ahem, those of us willing to work with you to reach your goal. First step is to understand the variables and measures. The 2016 ACS provides Subject Definitions for your use.
One of the critical concerns when using data for social correlates of health might be how the measures are calculated. Especially if you are combining multiple data sources.
Census data is a good place to start and many argue poverty is more than just an income threshold.
Poverty Is More Than a Matter of Income--RAY BOSHARA The New York Times
It's not that the government doesn't spend on the poor. It's that it spends very differently on the poor.
Here is a quick demonstration of a population analysis interactive by Public Insight that does all of the heavy lifting for you. But you will need to be well-versed with at least an introductory level of data knowledge.
I don't share any data interactive or resource that requires a bloated budget or an "arm and a leg" to access. Some of us like doing the data cleaning and modeling in-house and are perfectly capable.
My blog is for learning about new resources for in-house data teams, finding the right data professional for collaboration (ahem), or pointing toward a tool that might be a business solution.
Here are the different ACS variables available for analyses: