The reason the datasets contain these variables is the fact that lack of health insurance itself is a major influencing factor for the access to healthcare. It is directly correlated with the other socioeconomic factors (variables). At the same time, other variables play a direct role in limiting access to healthcare and so, they behave as confounders in assessing the impact of health insurance to the access to healthcare.
Because of this scenario, the uninsured status and other socioeconomic variables contained in this dataset was created to help decision makers to evaluate the weight of every variable that behaves like a barrier to healthcare, avoidance of confounding effects during the statistical assessment of weight, and prioritization of actions in order to mitigate the effects on the population’s health status. The estimated data for the socioeconomic variables could be used at tract level, at county level or at state level with mention of the homologous name of counties and states.
The source of the original dataset is CDC (Centers for Disease Control and Prevention), which releases an updated dataset every 5 years, the next update is expected to be published in 2020. The original dataset contains all the relevant data used for 2014 Social Vulnerability Index (SVI) assessment. The original dataset (published by CDC) is based on the data collected by American Community Survey for the time period 2010-2014. ACS offers estimates for 1, 3 and 5-year periods, the most accurate estimate is based on the data from 5 years. To avoid any misunderstanding, the estimates are not for every year of the period 2010-2014 but for the entire period.
From the 2014 SVI dataset, were extracted, organized in a logical order and renamed (in an user-friendly way), the variables and all their data, which could help the end user in for above-described goal. With regard to the census tract description, only the census tract number was kept, the data related to the county and state (which were already in distinct fields), were removed.
Each variable is doubled by its margin of error (MOE), which could help in determining the confidence interval, by subtracting and by adding the margin of error value to the estimated value for a variable.