This web report is accessible at https://tin6150.github.io/phw251_group_z/milestone6_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 information for each of the 58 counties in California. It includes information 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
of chronic diseases as filter, 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. As we went back to double check our selection code, we confirmed that funding was higher in large populous counties such as those around the greater Los Angeles and San Francisco. However, we also noted that while many rural counties had no funding for “In Closure”, they did report funding for projects “In Construction”. While our main variable focuses on “In Closure” to help our improvement plan drive new spending for rural counties with high and variable mortality rates, we also compared “In Construction” funding to help narrow selection.
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 demographics and disease profile for the California’s
rural counties, which according to the National
Rural Development Partnership, are counties with population density
below 20 person per square miles.
There are 11 counties in California that qualify per this definition,
and they are highlighted in green.
Highlighted in blue are places where age, renter to owner ratio, or Chronic Mortality is higher than the state-wide average. 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, and disease selection is per CDC guideline.
As background reference, across all 58 CA counties, we found these statistics:
We observe that while only one of these counties have rent:owner
ratio higher than the state-wide average, many are still fairly
high.
They all have $0 in the latest HCAI funding that are in the Closure
state.
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 across the 11 rural counties in CA (as defined by National Rural Development Partnership, 20 person/sq.mile)
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.
The Mortality Rate numbers are running yearly averages over the 5 year period of 2016-2020.
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. Table 1 list these the 11 rural counties, as well as highlight those that have a median age higher than the state-wide average (there were 8, and the ONE director wants to narrow down to a list of 5).
Using Figures 1 and 2 we compared demographics, funding and chronic mortality rates between counties. Notably, Figure 1, subplot 3, displays funding received for projects “In Construction” as of August 2022. Lassen, Inyo and Siskiyou reported funding of “In Construction” projects over 4 million dollars. Counties that reported zero dollar amounts in both categories include Alpine, Colusa, Mariposa, Mono, Modoc, and Sierra. While Plumas and Trinity received funding, it was under 1 million dollars. Together with Figure 2, we can see that Trinity has a lower chronic mortality rate than Plumas. We also see that Lassen, Colusa and Mono have lower chronic mortality rates, as well as median ages below the state-wide average. Since Lassen also has high funding of projects in construction, we do not recommend funding the county at this time. Additionally, 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.
The counties we selected have the highest chronic mortality rates of rural counties, have median ages above the state-wide average, and fit at least one other selection criteria better than those not selected. We propose funding development projects for healthcare facility improvement in Inyo, Mariposa, Modoc, Plumas and Siskiyou counties. As shown in Figure 2, we recommend these future improvement projects focus on heart disease and cancer, as they the most common disease reported.