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prevalence_state_demographic.R
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prevalence_state_demographic.R
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library(tidyverse)
library(RODBC)
library(scales)
library(usmap)
plot_list = list()
sex_test = data.frame(disease=character(), sex = character(), patients=integer(),
total=integer(), non_patient=integer(), chisq_pvalue=double(),
fisher_pvalue=double(), or= double(), lb=double(), ub=double())
age_test = data.frame(disease=character(), age = integer(), patients=integer(),
total=integer(), non_patient=integer(), chisq_pvalue=double())
age_sex_test = data.frame(disease=character(), age = integer(), sex = character(),
patients=integer(), total=integer(),
non_patient=integer(), chisq_pvalue=double(),
fisher_pvalue=double(), or= double(), lb=double(), ub=double())
patient_list = data.frame(disease = character(), z_patid = integer(), sex = character(),
age_band_cd = integer(), mbr_zip_5_cd = integer(), mbr_state = character())
ch = odbcConnect('vertica',
uid = 'XXXXX',
pwd = 'XXX')
icd10 = list('Cystic_Fibrosis' = c("'E840'", "'E8411'", "'E8419'", "'E848'", "'E849'"),
'Sickle_Cell'= c("'D5700'", "'D5701'", "'D5702'", "'D571'", "'D5720'",
"'D57211'", "'D57212'", "'D57212'", "'D57219'",
"'D5740'", "'D57411'", "'D57412'", "'D57419'",
"'D5780'", "'D57811'", "'D57812'", "'D57819'"),
'Muscular_Dystrophy' = c("'G710'", "'G7101'", "'G7100'", "'G7102'",
"'G7109'", "'G7111'"),
'Lennox_Gastaut_Syndrome' = c("'G40811'", "'G40813'", "'G40812'",
"'G40814'"),
'Urea_Cycle_Disorder' = c("'E7220'","'E7221'","'E7222'","'E7223'",
"'E7229'", "'E724'"),
"Takayasu's_Arteritis" = c("'M314'"),
'Pheochromocytoma' = c("'C7410'", "'D3500'"),
'Hereditary_Hemorrhagic_Telangiectasia' = c("'I780'"),
'Osteogenesis_Imperfecta' = c("'Q780'"),
'Eosinophilic_Esophagitis' = c("'K200'"),
'Charcot_Marie_Tooth' = c("'G600'"),
'Batten_Disease' = c("'E754'", "'G111'"),
'Focal_and_Segmental_Glomerulosclerosis' = c("'N040'","'N041'", "'N042'",
"'N047'","'N031'"),
'Mitochondrial_Neurogastrointestinal_Encephalopathy' = c("'E8849'")
)
state_members = sqlQuery(ch, paste("SELECT mbr_state,
count(distinct z_patid) as members
from HCCI_2.MBR_SDDV2
where yr >'2015'
group by 1;"))
write.csv(state_members %>% drop_na(), 'state_members.csv', row.names = FALSE)
state_members = read.csv('state_members.csv')
state_members = state_members %>% drop_na()
state_members_sex_age = read.csv('state_members_sex_age.csv')
state_members_sex_age = state_members_sex_age %>% drop_na()
state_members_yr = sqlQuery(ch, paste("SELECT mbr_state,yr,
count(distinct z_patid) as members
from HCCI_2.MBR_SDDV2
where yr >'2015'
group by 1,2;"))
write.csv(state_members_yr %>% drop_na(), 'state_members_yr.csv', row.names = FALSE)
state_members_yr = read.csv('state_members_yr.csv')
state_members_yr = state_members_yr %>% drop_na()
diseases = names(icd10)
t = Sys.time()
for(i in 1:length(diseases)){
print(i)
print(diseases[i])
report_directory = paste(getwd(), "/", 'reports/prevalence/overall/', diseases[i], sep = "")
ifelse(!dir.exists(report_directory), dir.create(report_directory),FALSE)
condition = paste(
paste('inp.icd10_cm',1:10, ' in (',
paste(icd10[[diseases[i]]], collapse = ","),
') or',sep = ''),collapse = ' ')
condition = substr(condition, 1, nchar(condition)-3)
inp = sqlQuery(ch, paste("SELECT inp.z_patid, inp.fst_dt,
SUM(inp.calc_allwd) as cost
from HCCI_2.INP_SDDV2 inp
where",
condition,
"
group by 1,2
having SUM(inp.calc_allwd) > 0;"))
condition = paste(
paste('op.diag_icd10_cm',1:10, ' in (',
paste(icd10[[diseases[i]]], collapse = ","),
') or',sep = ''),collapse = ' ')
condition = substr(condition, 1, nchar(condition)-3)
op = sqlQuery(ch, paste("SELECT op.z_patid, op.fst_dt,
SUM(op.calc_allwd) as cost
from HCCI_2.OP_SDDV2 op
where",
condition,
"group by 1,2
having SUM(op.calc_allwd) > 0;"))
condition = paste(
paste('ph.diag_icd10_cm',1:10, ' in (',
paste(icd10[[diseases[i]]], collapse = ","),
') or',sep = ''),collapse = ' ')
condition = substr(condition, 1, nchar(condition)-3)
ph = sqlQuery(ch, paste("SELECT ph.z_patid, ph.fst_dt,
SUM(ph.calc_allwd) as cost
from HCCI_2.PHYS_SDDV2 ph
where",
condition,
"group by 1,2
having SUM(ph.calc_allwd) > 0;"))
pt = inp %>%
bind_rows(op) %>%
bind_rows(ph) %>%
group_by(z_patid,fst_dt) %>%
summarise(cost = sum(cost)) %>%
group_by(z_patid) %>%
mutate(rank = row_number(fst_dt),
behind = lag(fst_dt),
time_diff= as.numeric(fst_dt-behind)) %>%
drop_na() %>%
filter(time_diff < 91) %>%
group_by(z_patid) %>%
summarise(claims = n()+1)
patients_query = paste("'", pt$z_patid, "'", sep = "")
members = sqlQuery(ch, paste("select ss.z_patid, ss.sex, ss.age_band_cd, ss.mbr_zip_5_cd, ss.mbr_state
from(
SELECT *,
ROW_NUMBER() over(PARTITION by z_patid ORDER by yr DESC , mnth DESC) as rn
FROM HCCI_2.MBR_SDDV2 ms
where ms.z_patid in (", paste(patients_query, collapse = ','),
")) as ss
where ss.rn = 1;"))
patient_temp = members %>% mutate(disease = diseases[i])
patient_list = patient_list %>% bind_rows(patient_temp)
df_main = inp %>%
bind_rows(op) %>%
bind_rows(ph) %>%
mutate(yr = lubridate::year(fst_dt)) %>%
group_by(z_patid, yr) %>%
summarise(cost = sum(cost),
claim_count = n()) %>%
filter(z_patid %in% pt$z_patid) %>%
full_join(members)
## tests
tmp_t = df_main %>%
group_by(sex) %>%
summarise(patients = n_distinct(z_patid)) %>%
left_join(state_members_sex_age %>%
group_by(sex) %>%
summarise(total = sum(members))) %>%
mutate(non_patient = total-patients) %>%
drop_na()
M = as.matrix(tmp_t[,c(F,T,F,T)])
ct = chisq.test(t(M))$p.value
ft = fisher.test(M)
sex_test_tmp = data.frame(disease=character(), sex = character(), patients=integer(), total=integer(),
non_patient=integer(), chisq_pvalue=double(),
fisher_pvalue=double(), or= double(), lb=double(), ub=double())
sex_test_tmp[1:nrow(tmp_t),1] =diseases[i]
sex_test_tmp[,c('sex',"patients", "total", "non_patient")] = tmp_t
sex_test_tmp$chisq_pvalue = chisq.test(t(M))$p.value
sex_test_tmp$fisher_pvalue = ft$p.value
sex_test_tmp$or = ft$estimate
sex_test_tmp[,'lb'] = ft$conf.int[1]
sex_test_tmp[,'ub'] = ft$conf.int[2]
sex_test = sex_test %>% bind_rows(sex_test_tmp)
tmp_t = df_main %>%
group_by(age_band_cd) %>%
summarise(patients = n_distinct(z_patid)) %>%
left_join(state_members_sex_age %>%
group_by(age_band_cd) %>%
summarise(total = sum(members))) %>%
mutate(non_patient = total-patients) %>%
drop_na()
M = as.matrix(tmp_t[,c(F,T,F,T)])
age_test_tmp = data.frame(disease=character(), age = integer(), patients=integer(), total=integer(),
non_patient=integer(), chisq_pvalue=double())
age_test_tmp[1:nrow(tmp_t),1] =diseases[i]
age_test_tmp[,c('age',"patients", "total", "non_patient")] = tmp_t
age_test_tmp$chisq_pvalue = chisq.test(t(M))$p.value
age_test = age_test %>% bind_rows(age_test_tmp)
for(j in 1:7){
tmp_t = df_main %>%
filter(age_band_cd==j) %>%
group_by(sex) %>%
summarise(patients = n_distinct(z_patid)) %>%
left_join(state_members_sex_age %>%
filter(age_band_cd==j) %>%
group_by(sex) %>%
summarise(total = sum(members))) %>%
mutate(non_patient = total-patients) %>%
drop_na()
if(nrow(tmp_t)==2){
M = as.matrix(tmp_t[,c(F,T,F,T)])
ct = chisq.test(t(M))$p.value
ft = fisher.test(M)
ft$p.value
c(ft$conf.int)
as.numeric(ft$estimate)
age_sex_test_tmp = data.frame(disease=character(),age=integer(), sex = character(),
patients=integer(), total=integer(),
non_patient=integer(), chisq_pvalue=double(),
fisher_pvalue=double(), or= double(), lb=double(), ub=double())
age_sex_test_tmp[1:nrow(tmp_t),1] =diseases[i]
age_sex_test_tmp[1:nrow(tmp_t),'age'] =j
age_sex_test_tmp[,c('sex',"patients", "total", "non_patient")] = tmp_t
age_sex_test_tmp$chisq_pvalue = chisq.test(t(M))$p.value
age_sex_test_tmp$fisher_pvalue = ft$p.value
age_sex_test_tmp$or = ft$estimate
age_sex_test_tmp[,'lb'] = ft$conf.int[1]
age_sex_test_tmp[,'ub'] = ft$conf.int[2]
age_sex_test = age_sex_test %>% bind_rows(age_sex_test_tmp)
}
}
## overall prevalence
tmp = df_main %>%
filter(yr>2015) %>%
group_by(mbr_state) %>%
summarise(count = n_distinct(z_patid))
data("statepop")
statepop = statepop %>%
left_join(tmp, by = c('abbr' = 'mbr_state')) %>%
replace(is.na(.), 0) %>%
left_join(state_members, by = c('abbr' = 'mbr_state')) %>%
mutate(count = replace(count, count>0 & count<10 , NA),
patient_per_hundered_thousands= count*100000/pop_2015,
patient_per_hundered_thousands_mem= count*100000/members)
p1 = plot_usmap(data = statepop,size = 0.1 ,
values = 'patient_per_hundered_thousands') +
labs(title = paste("Prevalence of ", gsub("_", " ", diseases[i]) ),
subtitle = '2016-2020')+
scale_fill_continuous(low = 'white', high = 'red',
name = gsub('_', ' ', 'Patients per 100,000 people\n'),
label = scales::comma)+
theme(legend.position = c(0.9, 0.2),
text = element_text(family = 'sans'),
plot.title = element_text(face = "bold",size = 10),
plot.subtitle = element_text(size = 8),
legend.title = element_text(face="bold.italic", size = 6),
legend.text = element_text(size = 4),
legend.key.size = unit(.15, 'in'),
legend.background = element_blank())
ggsave(plot = p1, paste(report_directory,'/',diseases[i], '.png', sep = ""),
dpi = 600, width = 7.2, height = 3, units = 'in')
plot_list[[paste(diseases[i],'_total_prev_pop', sep = '')]] = p1
p2 = plot_usmap(data = statepop,size = 0.1 ,
values = 'patient_per_hundered_thousands_mem') +
labs(title = paste("Prevalence of ", gsub("_", " ", diseases[i]) ),
subtitle = '2016-2020')+
scale_fill_continuous(low = 'white', high = 'red',
name = gsub('_', ' ', 'Patients per 100,000 members\n'),
label = scales::comma)+
theme(legend.position = c(0.9, 0.2),
text = element_text(family = 'sans'),
plot.title = element_text(face = "bold",size = 10),
plot.subtitle = element_text(size = 8),
legend.title = element_text(face="bold.italic", size = 6),
legend.text = element_text(size = 4),
legend.key.size = unit(.15, 'in'),
legend.background = element_blank())
ggsave(plot = p2,paste(report_directory,'/',diseases[i], '_members.png', sep = ""),
dpi = 600, width = 7.2, height = 3, units = 'in')
plot_list[[paste(diseases[i],'_total_prev_mem', sep = '')]] = p2
write.csv(statepop, paste(report_directory,'/',diseases[i], '.csv', sep = ""))
tmp = df_main %>%
group_by(yr, mbr_state) %>%
summarise(count = n_distinct(z_patid)) %>%
filter(yr>2015) %>%
spread(yr, count) %>%
replace(is.na(.),0) %>%
gather('yr', 'count', -mbr_state)
data("statepop")
statepop = statepop %>%
left_join(tmp,by = c('abbr' = 'mbr_state'))
if(sum(is.na(statepop$count))>0){
df_na = statepop[is.na(statepop$count),]
for(r in 1:nrow(df_na)){
for(year in (2016:2020)){
statepop[nrow(statepop)+1,] = c(df_na[r,1:4], as.character(year), 0)
}
}
}
statepop$yr = as.numeric(statepop$yr)
statepop = statepop %>%
drop_na() %>%
left_join(state_members_yr, by = c('abbr' = 'mbr_state', 'yr'='yr')) %>%
mutate(count = replace(count, count>0 & count<10 , NA),
patient_per_hundered_thousands = count*100000/pop_2015,
patient_per_hundered_thousands_mem = count*100000/members)
p3 = plot_usmap(data = statepop, size = 0.01,
values = 'patient_per_hundered_thousands') +
labs(title = paste("Prevalence of ", gsub("_", " ", diseases[i])),
subtitle = '2016-2020')+
scale_fill_continuous(low = 'white', high = 'red',
name = gsub('_', ' ', 'Patients per 100,000 people\n'),
label = scales::comma)+
facet_wrap(~yr,nrow = 1)+
theme(legend.position ='right',
text = element_text(family = 'sans'),
plot.title = element_text(face = "bold",size = 10),
plot.subtitle = element_text(size = 8),
legend.title = element_text(face="bold.italic", size = 4),
legend.text = element_text(size = 3),
legend.key.size = unit(.1, 'in'),
legend.background = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size = 6))
ggsave(plot = p3, paste(report_directory,'/',diseases[i], '_years.png', sep = ""),
dpi = 600, width = 7.2, height = 3, units = 'in')
plot_list[[paste(diseases[i],'_year_prev', sep = '')]] = p3
p4 = plot_usmap(data = statepop, size = 0.01,
values = 'patient_per_hundered_thousands_mem') +
labs(title = paste("Prevalence of ", gsub("_", " ", diseases[i])),
subtitle = '2016-2020')+
scale_fill_continuous(low = 'white', high = 'red',
name = gsub('_', ' ', 'Patients per 100,000 members\n'),
label = scales::comma)+
facet_wrap(~yr,nrow = 1)+
theme(legend.position ='right',
text = element_text(family = 'sans'),
plot.title = element_text(face = "bold",size = 10),
plot.subtitle = element_text(size = 8),
legend.title = element_text(face="bold.italic", size = 4),
legend.text = element_text(size = 3),
legend.key.size = unit(.1, 'in'),
legend.background = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size = 6))
ggsave(plot = p4, paste(report_directory,'/',diseases[i], '_years_members.png', sep = ""),
dpi = 600, width = 7.2, height = 3, units = 'in')
plot_list[[paste(diseases[i],'_year_prev_mem', sep = '')]] = p4
write.csv(statepop, paste(report_directory,'/',diseases[i], '_years.csv', sep = ""))
}
Sys.time()-t
report_directory = paste(getwd(), "/", 'reports/prevalence/overall/', sep = "")
write.csv(age_test, paste(report_directory,'age_test.csv', sep = ''), row.names = FALSE)
write.csv(sex_test, paste(report_directory,'sex_test.csv', sep = ''), row.names = FALSE)
write.csv(age_sex_test, paste(report_directory,'age_sex_test.csv', sep = ''), row.names = FALSE)
write.csv(patient_list, paste(report_directory,'patient_list.csv', sep = ''), row.names = FALSE)
report_directory = paste(getwd(), "/", 'reports/prevalence/overall/', sep = "")
saveRDS(object = plot_list, file = paste(report_directory, 'plot_list.RData',sep = ''))
plot_list = readRDS(file = 'reports/prevalence/overall/plot_list.RData')
p1 = plot_list$Cystic_Fibrosis_year_prev_mem+
ggtitle(label = 'a: Cystic Fibrosis',subtitle = '')+
theme(plot.margin = margin(0,0,0,0))
p2 = plot_list$Sickle_Cell_year_prev_mem+
ggtitle(label = 'b: Sickle Cell',subtitle = element_blank())+
theme(strip.text = element_blank(),
plot.title = element_text(margin = margin(0,0,-1,0,unit = 'cm')))
cowplot::plot_grid(p1,
p2,nrow = 2,
align = 'hv'
)
ggsave(paste(report_directory,'main_figure.png', sep = ""),
dpi = 600, width = 7.2, height = 3, units = 'in')