This web report is accessible at https://tin6150.github.io/phw251_group_z/milestone5_groupZ.html
Code used for analsys and visual generation is available at our github repo.
The California Department of Public Health Office of Health Equity (OHE) recently issued a new policy to create a public-private partnership to improve healthcare facilities in five rural counties across the state. Our team will evaluate and recommend which counties should receive development funding proposals based on equitable selection criteria created by OHE. Specifically, we will explore data to identify which rural counties have more non-homeowners, aging individuals, higher chronic mortality rates, and have received minimal funding from the Department of Health Care Access and Information.
We used 3 datasets for this project:
The first dataset is from the 2012 Census and contains demographics info for each of the 58 counties in California. It includes info such as population per square mile, median age, number of households who are renters vs owners, ethnicity, genders, etc. We calculated the renter to owner ratio for each county. We then calculated the average age and population density for the whole state and visually inspected the data to see how each county stack up. We ended up using the National Rural Development Partnership’s definition to determine if a given county’s population density is to be classified as rural, for which there were 11.
The second dataset is the mortality surveillance obtained from the CA
Open Data Portal. It contains a breakdown of total mortality for each
county by 15 disease areas. We used the CDC definition to filter for the
chronic diseases, for which 10 fit the criteria. The data range from
2014 to 2020, but we were tasked to focus on the last 5 years, thus we
applied a filter with Year >= 2016. As tasked, we also
performed filters with Geography_Type == "Occurrence" and
used Strata == "Total Population" to avoid over counting.
Any missing data were replaced with 0. Once the data was cleaned, we
summed all the disease occurrences within each county. We joined this
with the demographics data to obtain a mortality rate of chronic
conditions over 5 years for each county.
The third dataset is the HCAI funding, also obtained from the CA Open Data Portal. It contains healthcare spending for each county in 4 stages of project progression, updated about every 2 weeks. We focused on the latest available data, which was Aug 11, 2022, and those with state of “In Closure”. Many rural counties showed up with $0 amount, and we went back to double check our selection code. It checked out, much fundings are in large populous counties such as those around the greater Los Angeles and San Francisco. It was not that rural counties had no funding, there were fundings for example in the “In Construction” phase, but we decided to focus on “In Closure” to help our improvement plan to drive new spending for rural counties with high and variable mortality rates.
After cleaning and filtering the 3 datasets above, we joined them by county name, whereby we can see which counties had high renters, high chronic mortality rates, and the funding they received.
Table 1 shows CA counties that are rural (per National Rural Development Partnership’s definition), and have median age greater than the state-wide average.
We have pre-sorted them by decreasing renter to owner ratio. We observe that while none of these counties have rent:owner ratio higher than the state-wide average, they are still fairly high; and they have $0 in the latest HCAI funding that are in the Closure state.
As background reference, across all 58 CA counties, we found these statistics:
Where age, renter to owner ratio, or Chronic Mortality is higher than
the state-wide average, they are highlighted in blue.
Note that mortality rate is calculated based on the latest available
population data: 2012. Number of Chronic cases for each county is
actually the average number of yearly cases between 2016-2020.
Figure 1 visualizes demographic and funding characteristics to further rank and narrow the selection of rural counties. For each county, the first two subplots depict median age and rent:own ratio, respectively. The third subplot depicts the HCAI funding amount each county received on projects with a status of “in construction” as of August 2022. Since all rural counties had no funding for projects “in closure”, our team felt it was important to explore funding for projects “in construction”. Together these scatter plots provide a visual comparison of where each county falls on the measurement scales of each criteria.
Figure 2 is a bar graph of Mortality Rate for Chronic diseases (as defined by CDC) across the 11 rural counties in CA (as defined by National Rural Development Partnership)
The list for Chronic disease is selected according to CDC
definition.
We note that we don’t have disease data for Alpine or Sierra county.
This table shows the most common chronic disease in each of the rural counties, while also showing the number of people who have the illness in the year 2020. The counties of Alpine and Sierra did not have chronic disease data available. As we expand funding in the 5 select counties of focus, special emphasis should be placed on Heart Disease, as that’s the most common chronic illness causing high mortality.
*HTD= Heart Disease, CAN= Cancer
For the new public-private partnership to improve healthcare, the OHE director wanted to focus on rural areas that have high rental rates and high median age. However, no county perfectly fit all three attributes. Therefore, we offer visualizations and analysis with a holistic view of which counties best fit the selection criteria. Our first step narrowed down which counties are “rural” as defined by the National Rural Development Partnership, and had a median age higher than the state-wide average. These 8 counties are depicted in Table 1, which includes data on the other selection criteria. Here we see that none of these rural counties have HCAI funding for projects with an “In Closure” status for August 2022.
Figure 1 provides a visual comparison of demographics and funding in rural counties. Most notable is that the third subplot displays funding received for projects “In Construction” as of August 2022. Both Inyo and Siskiyou reported funding of “In Construction” projects over 4 million dollars. Counties that reported zero dollar amounts in both categories include Alpine, Mariposa, Modoc, and Sierra. While Plumas and Trinity received funding, it was under 1 million dollars. Plumas and Mariposa rank in the top 5 counties across all three subplots. Sierra, Siskiyou, and Modoc rank high in two criteria categories each. Evaluating chronic mortality rates from Figure 2 helps us narrow down the selection even more. The 5 counties with the highest mortality rate include Siskiyou, Inyo, Mariposa, Plumas and Modoc. These counties align with those found to have high median age and large renter to owner ratio in Table 1 and Figure 1. Clearly, with Inyo and Siskiyou having such high chronic mortality rates, funding on health care improvements is necessary. Without adequate data on chronic mortality rates in Sierra and Alpine counties, it is hard to justify allocating funding at this time, as we don’t know what would be most beneficial. We recommend further study in those areas to capture mortality and assess need for improvement projects. In the meantime, based on the information from counties that reported chronic disease information, heart disease seems to be the most common, so we now have a staring point for the future project we may want to focus on.
Ultimately, we propose funding development projects for healthcare facility improvement in Inyo, Siskiyou, Mariposa, Modoc, and Plumas counties.