Alzheimers: National and state

packages <- c("tidyverse", "lubridate", "writexl", "knitr",
              "MMWRweek", "DT")

if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
  install.packages(setdiff(packages, rownames(installed.packages())), repos = "http://cran.us.r-project.org")  
}

options(knitr.kable.NA = '')

library(tidyverse)
library(lubridate)
library(writexl)
library(knitr)
library(MMWRweek)
library(DT)

nat <- read_csv("alz.cause.summary.state.df_2020-08-20.csv") %>% 
  filter(state=="US") %>% 
  #mutate(week_end=mdy(week_end))
  mutate(week_end=ymd(week_end))

weeks_in <- 1:30
weeks_march <- 10:30

natlsum_ad <- nat %>%
  filter(mmwr_year==2020) %>% 
  filter(mmwr_week %in% weeks_in)

#national_weekly <- natlsum_ad %>% 
#  select(week, week_end_date, year,
#                     all_cause_deaths, expected_all_cause=baseline_all_cause,
#                     expected_all_cause_upper=baseline_all_cause_upper, 
#                     expected_all_cause_lower=baseline_all_cause_lower,
#                     excess_all_cause_deaths, covid19.nchs)

natlsum_ad %>% 
  ggplot(aes(week_end,obs)) +
  geom_ribbon(aes(ymin=lpi, ymax=upi), fill="gray70", alpha=.5) +
  geom_ribbon(aes(ymin=pred, ymax=obs), fill="sienna1", alpha=.5) +
  geom_line(data=natlsum_ad, aes(x=week_end, y=obs),color="sienna1", alpha=.4) +
  geom_line(color="black", size=1) +
  theme_minimal() +
  labs(title="U.S. Alzheimer and dementia deaths", y="Weekly deaths", x="") 

states <- read_csv("alz.cause.summary.state.df_2020-08-07.csv") %>% 
  filter(state!="US" ) %>% 
  mutate(week_end=mdy(week_end))

states_29 <- states %>% 
  filter(mmwr_year==2020) %>% 
  filter(mmwr_week==29) %>% 
  mutate(flag = case_when(
    obs < lpi ~ "Lower than baseline range",
    obs > upi ~ "Higher than baseline range",
    TRUE ~ "Within range"
  )) %>% 
  select(state, flag29=flag)

states_30 <- states %>% 
  filter(mmwr_year==2020) %>% 
  filter(mmwr_week==30) %>% 
  mutate(flag = case_when(
    obs < lpi ~ "Lower than baseline range",
    obs > upi ~ "Higher than baseline range",
    TRUE ~ "Within range"
  )) %>% 
  select(state, flag30=flag)

march <- states %>% 
  filter(mmwr_year==2020) %>% 
  filter(mmwr_week %in% weeks_march) %>% 
  group_by(state) %>%
  summarize(deaths=sum(obs, na.rm=T),
            expected=sum(pred, na.rm=T),
            excess_deaths=sum(unexplained.cases, na.rm=T)) %>% 
  mutate(x_excess=round((deaths-expected)/expected*100,2)) %>% 
  ungroup() %>% 
  left_join(states_29) %>% 
  left_join(states_30) 


higher <- march %>% 
  filter(flag29=="Higher than baseline range") %>% 
  pull(state)

within <- march %>% 
  filter(flag29=="Within range") %>% 
  pull(state)

lower <- march %>% 
  filter(flag29=="Lower than baseline range") %>% 
  pull(state)

States

states %>% 
  filter(mmwr_year==2020) %>% 
  filter(mmwr_week %in% weeks_in) %>% 
  ggplot(aes(week_end, obs)) +
  geom_ribbon(aes(ymin=lpi, ymax=upi), fill="gray70", alpha=.5) +
  geom_ribbon(aes(ymin=pred, ymax=obs), fill="sienna1", alpha=.5) +
  geom_line(aes(x=week_end, y=obs),color="sienna1", alpha=.4) +
  geom_line(color="black", size=.5) +
  facet_wrap(~state, scales="free", ncol=5) +
  theme_minimal()#, 

       #subtitle="Exceeds the expected range and with the least delays in reporting")

march %>% datatable()