This case study is part of the OpenCaseStudies project. This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 (CC BY-NC 3.0) United States License.

To cite this case study:

Kuo, Pei-Lun and Jager, Leah and Taub, Margaret and Hicks, Stephanie. (2019, February 14). opencasestudies/ocs-healthexpenditure: Exploring Health Expenditure using State-level data in the United States (Version v1.0.0). Zenodo. http://doi.org/10.5281/zenodo.2565307

Exploring health expenditure using state-level data in the United States

Health policy in the United States is complicated, and several forms of healthcare coverage exist, including both coverage by federal goverment-led healthcare policy, and by private insurance companies. Before making any inference about the relationship between health condition and health policy, it is important for us to have a general idea about healthcare economics in the United States. Thus, We are interested in getting sense of healthcare coverage and healthcare spending across the United States.

1. Is there a relationship between healthcare coverage and healthcare spending in the United States?
2. How does the spending distribution change across geographic regions in the Unied States?
3. Does the relationship between healthcare coverage and healthcare spending in the United States change from 2013 to 2014?

The data for this demonstration come from Henry J Kaiser Family Foundation (KFF).

• Health Insurance Coverage of the Total Population (Includes years 2013-2016)

• Health Care Expenditures by State of Residence (in millions) (Includes years 1991-2014)

For educational purposes, the data have been downloaded and relative paths are used for this demonstration. Note: If students are not familiar with relative paths, it will be helpful to briefly introduce the idea for absolute paths and relative paths.

We also introduce library(datasets) for States information.

The skills, methods, and concepts that students will be familiar with by the end of this case study are:

Data Science Learning Objectives:

1. Load data from a package (datasets)
2. Import data from a csv (readr)
3. View, filter, join, and summarize data (dplyr)
4. Reshape data into different formats (tidyr)
5. Create data visualizations (ggplot2) with labels (ggrepel) and facets for different groups

We use the R package library(readr) for data import in this tutorial.

Two R package library(tidyr), library(dplyr) are used for data wrangling in this tutorial.

We explain what tidy data is, and further introduce the concepts of “wide format” and “long format.” We also demonstrate how to convert from one format to the other using gather() and spread().

We also demonstrate some other useful functions for data wrangling, including selecting columns using select(), Selecting rows using filter(), arranging or re-orderomg rows using arrange(), joining two datasets using join(), adding columns using mutate(), creating summaries of columns using summarize(), and grouping operations using group_by().

For exploratory analysis, we use data visulization for exploratory analysis. ggplot2 is the R package we demonstrate in this tutorial.

We explain how to create plots using ggplot() with basic syntax for ggplot2. We also demonstrate how to create scatter plots using geom_point(), how to add layers of text using geom_text(), how to facet across a variable using facet_wrap(), how to create boxplots using geom_boxplot(), and how to facet by two variables using facet_grid.

The total healthcare expenditure is associated with the population. To make a fair comparison, we create “healthcare expenditure per capita.” Further, the exploratory analysis via data visualization showed higher speding in healthcare per capita is positively associated with higher employer coverage proportion and is negatively associated with the porportion of uninsured population across the States.

Packages used in this case study:

In order to run this code please ensure you have these packages installed.

The objective of this tutorial is for student to get familiar with important skills in data science, including data import (readr), data wrangling (dplyr), and data visualization (ggplot2). This material is designed for 4.5 teaching hours. (One potential way to teach this tutorial is to divide the material into three 1.5 hour sessions. The first session focuses on data import, the second session focuses on data wrangling, and the third portion focuses on visualization.) The session starting with (*) can be made as exercise for students’ practice.