We have decided to take a leap and try plot_ly and knit to html output for this milestone. The html page is hosted as Github Pages and is accessible in this address: https://tin6150.github.io/phw251_group_z/milestone4_groupZ.html
(This code block omitted for brevity, please refer to source at our github repo )
Table 1 shows that the 5 counties we selected as deserving more HCAI
funding.
They are the counties of Siskiyou, Inyo, Mariposa, Plumas and Modoc. All
these 5 counties have low population density (they qualify as Rural per
National Rural Development Partnership’s definition), have a high median
age, and fairly high ratio of renters. More importantly, they have one
of the highest percentage of chronic diseases mortality, and no HCAI
fundings that are “In Closure” as of 2022-08-11.
As background reference, across all 58 CA counties, we found these statistics:
Note that mortality rate is calculated based on the latest available population data: 2012.
viz_focus = viz_fund_dem_chron %>%
filter( rural_class == "rural",
high_med_age == TRUE
##high_rental == TRUE
)
focus_table = viz_focus %>%
mutate( USD_amount = currency( Numeric_Cost, digits=0L)) %>%
select( County,
pop12_sqmi,
med_age,
rent_own_ratio,
pct, # prevalence,
USD_amount,
) %>%
arrange( desc( pct )) %>%
rename(
`Pop Density` = pop12_sqmi,
`Median Age` = med_age,
`Rent:Own Ratio` = rent_own_ratio,
`% Chronic` = pct,
`HCAI Fund in 2022` = USD_amount
) %>%
head( 5 )
kable( focus_table,
format = "html",
booktabs = T,
digits = c(0,1,1,2,2,0L),
align = c('lccccr'),
format.args = list(big.mark=','),
caption = "Rural Counties with high median age, rental ratio, and chronic disease rate",
)| County | Pop Density | Median Age | Rent:Own Ratio | % Chronic | HCAI Fund in 2022 |
|---|---|---|---|---|---|
| Siskiyou | 7.1 | 46.8 | 0.54 | 2.86 | $0 |
| Inyo | 1.8 | 45.5 | 0.57 | 1.88 | $0 |
| Mariposa | 12.6 | 49.2 | 0.47 | 1.72 | $0 |
| Plumas | 7.7 | 49.5 | 0.44 | 1.69 | $0 |
| Modoc | 2.3 | 46.0 | 0.46 | 1.43 | $0 |
Figure 1 is a bar graph of Mortality Rate for Chronic diseases (as defined by CDC) across 11 rural counties (as defined by National Rural Development Partnership) The 5 counties of focus have the highest mortality rates in this group.
Note that we don’t have disease data for Alpine or Sierra county.
chronic_focus_counties = inner_join(
demographics_chronic,
rural_counties,
by = "County" ) %>%
mutate( ctyColor = case_when(
County == "Siskiyou" ~ "red",
County == "Inyo" ~ "darkorange" ,
County == "Mariposa" ~ "blue",
County == "Plumas" ~ "darkblue",
County == "Modoc" ~ "darkcyan",
TRUE ~ "rgb(187, 216, 228)"
) )
fig3 = plot_ly( data = chronic_focus_counties ) %>%
add_trace(
x = ~County,
y = ~pct,
name = 'Chronic Mortality Rates by Counties',
marker = list(color = ~ctyColor),
hoverinfo = "text",
text = ~paste(round(pct, 1), "%" ),
type = 'bar') %>%
layout(
title = "Chronic Mortality Rates For California's Rural Counties",
yaxis = list( title="% Mortality Rate"),
xaxis = list( title="County", categoryorder='total descending' )
)
fig3Figure 2: The following boxplot summarizes chronic disease mortality rates from all CA counties, grouped according to HCAI funding amounts for in closure projects as of August 2022. The funding amounts were categorized as “high” if they were above the mean amount, low if they were below the mean, and “no funding” if no funding for in closure projects was reported. Variability of mortality rate is highest for counties in the “No funding” category, quality/predictability likely will increase with HCAI funding.
funding_chronic <- funding_data %>%
filter( `OSHPD Project Status` == "In Closure") %>%
filter( `Data Generation Date` == as_date( "2022-08-11")) %>%
mutate(funding_amount = case_when(
Numeric_Cost > 12239849 ~ "High Funding",
Numeric_Cost == 0 ~ "No Funding",
Numeric_Cost < 12239849 ~ "Low Funding"
)) %>%
inner_join(demographics_chronic, funding_data_all_counties, by = "County") %>%
select(pct, County, funding_amount, rural_class, Numeric_Cost)
plot_ly(
funding_chronic,
y=~pct,
color= ~funding_amount,
type="box"
) %>%
layout(
title="Chronic Disease Mortality Rates & HCAI Funding",
yaxis=list(title="Chronic Disease Rate"))Table 2 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
table_data_e<- inner_join(rural_not_rural, chronic_mortality_data, by= "County") %>%
select(c("County", "rural_class", "Cause","Count", "Year")) %>% filter(Year%in%2020) %>%
filter(rural_class=="rural") %>%
group_by(County,Cause) %>% summarize(count_cause=sum(Count)) %>% arrange(County,desc(c(count_cause))) %>% slice(c(1,11,21,31,41,51,61,71,81,91,101))
table_data_e[1,2]<-"Not Available"
table_data_e[9,2]<-"Not Available"
common_chronic<- kable(table_data_e, col.names=c("County", "Chronic Disease", "Number Reported"),
digits=0, booktabs=T, escape=F, align="ccc", caption="Most Common Chronic Disease by Rural County in 2020")
common_chronic| County | Chronic Disease | Number Reported |
|---|---|---|
| Alpine | Not Available | 0 |
| Colusa | HTD | 172 |
| Inyo | HTD | 226 |
| Lassen | HTD | 352 |
| Mariposa | HTD | 200 |
| Modoc | HTD | 68 |
| Mono | CAN | 22 |
| Plumas | HTD | 226 |
| Sierra | Not Available | 0 |
| Siskiyou | CAN | 756 |
| Trinity | CAN | 62 |