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measure_AN_table_com2019.R
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################################################################################
### entitywise measure calculation
### version controlled by git
################################################################################
## prerequisites
source('E:/CT_APCD/Sai/2019spring/data-organization/R/utils.R')
library(knitr)
library(scales)
library(data.table)
dir_code <- input_dir('E:/CT_APCD/Sai/lds2021/lds2021code/Attribution_2018_2019/')
dir.in <- input_dir('E:/CT_APCD/Sai/lds2021/lds2021file/Attribution_result/output/measure_AN_2019/detail/')
dir.in2 <- input_dir("E:/CT_APCD/Sai/lds2021/lds2021file/Attribution_result/output/Double-Attribution_2019/")
dir.out.tb <- output_dir('E:/CT_APCD/Sai/lds2021/lds2021file/Attribution_result/output/measure_AN_2019/table/')
## dir.out <- 'E:/CT_APCD/Hongfei/output/toShiny/'
# dir.out <- output_dir('E:/CT_APCD/Sai/Attribution_result/output/measure_AN_0_64_com/shiny-apps-data/')
source(paste0(dir_code, 'myDoc.R'))
source(paste0(dir_code, 'myFun.R'))
myear <- 2019
## read in output format sample
name_dat_samp <- readRDS(paste0(dir_code, 'name_dat_samp.rds'))
## measure_info from myDoc.R
measure_info[, measure_label := paste0(myear, measure_label)]
### entitywise rate output
Attri2_final <- fread(paste0(dir.in2, 'Attri2_final.csv'),
header = TRUE, colClasses = 'character', encoding = 'UTF-8',
nrows = 1)
entityNameList <- names(Attri2_final)[9:27]
LETTERS702 <- c(LETTERS, sapply(LETTERS, function(x) paste0(x, LETTERS)))
measurelist <- paste0(myear,
c(NULL,
# 'BCS',
# 'CCS',
# "CHL",
# "NCS",
# "AWC",
# "ImAdo1",
# "ImAdo2",
# "ImAdo3",
# "ImAdo4",
# 'FUH_30d',
# 'FUH_7d',
# "IET_opioid_Enga", "IET_opioid_IESD", "IET_opioid_Init", "IET_opioid_Neg",
# "IET_drug_Enga", "IET_drug_IESD", "IET_drug_Init", "IET_drug_Neg",
# "IET_alcohol_Enga", "IET_alcohol_IESD", "IET_alcohol_Init", "IET_alcohol_Neg",
# "IET_Enga", "IET_IESD", "IET_Init", "IET_Neg",
# "LBP",
# "Nephropathy",
# "HbA1c",
"Diabetes",
NULL))
for (measureName in measurelist){
measure <- fread(paste0(dir.in, measureName, '.csv'))
measure[, Rate := as.numeric(Rate)*100]
measure[, `:=` (score = Rate)]
## measure[, `:=` (score = round(as.numeric(Rate) * 100, 2))]
## measure[as.numeric(denom) >= 30, `:=` (CI_95lb = as.character(round(as.numeric(CI_95lb)*100, 2)),
## CI_95rb = as.character(round(as.numeric(CI_95rb)*100, 2)),
## CI_90lb = as.character(round(as.numeric(CI_90lb)*100, 2)),
## CI_90rb = as.character(round(as.numeric(CI_90rb)*100, 2)))]
measure[1:length(entityNameList),
EntityID := LETTERS702[1:length(entityNameList)]]
measure[Entity == "Att_OutAN", EntityID := 'ZZ']
if (measureName %in% measurelist_lowerbetter){
measure[, score := 1 - score]
}
measure.mean <- measure[Entity == 'Attributed']$score
measure.top_performer <- max(measure[!is.na(EntityID) & !is.na(score)
& denom >= 30]$score) ## higher rate, better
measure.top_performer_AN <- max(measure[entityType == "AN" & !is.na(score)
& denom >= 30]$score)
# measure.top_performer_FQHC <- max(measure[entityType == "FQHC" & !is.na(score)
# & denom >= 30]$score)
measure.sd <- sd(measure[!is.na(EntityID) & !is.na(score) & denom >= 30]$score)
measure.sd.AN <- sd(measure[Entity %in% c(measure[entityType == "AN"]$Entity, "Att_OutAN") &
denom >= 30 &
!is.na(score)]$score)
LLSDM1_AN <- measure.mean - measure.sd.AN
LLHSDM1_AN <- measure.mean - 0.5*measure.sd.AN
ULHSDM1_AN <- measure.mean + 0.5*measure.sd.AN
ULSDM1_AN <- measure.mean + measure.sd.AN
# measure.sd.FQHC <- sd(measure[Entity %in% c(measure[entityType == "FQHC"]$Entity, "Att_OutFQHC") &
# denom >= 30 &
# !is.na(score)]$score)
# LLSDM1_FQHC <- measure.mean - measure.sd.FQHC
# LLHSDM1_FQHC <- measure.mean - 0.5*measure.sd.FQHC
# ULHSDM1_FQHC <- measure.mean + 0.5*measure.sd.FQHC
# ULSDM1_FQHC <- measure.mean + measure.sd.FQHC
measure[!is.na(EntityID) & EntityID != "ZZ",
`:=` (state_average = measure.mean)]
measure[, rate_in_SD := 0]
measure[!is.na(EntityID) & denom >= 30 & EntityID != "ZZ",
`:=` (sd = measure.sd,
rate_in_SD = 1)]
measure[denom >= 30 & entityType == "AN",
`:=` (top_performer_AN = measure.top_performer_AN,
sd.AN = measure.sd.AN,
LLSDM1_AN = LLSDM1_AN,
LLHSDM1_AN = LLHSDM1_AN,
ULHSDM1_AN = ULHSDM1_AN,
ULSDM1_AN = ULSDM1_AN)]
measure[denom >= 30 & entityType == "AN" & score < LLSDM1_AN, quality_care_rating_AN := 1
][denom >= 30 & entityType == "AN" & score >= LLSDM1_AN & score <= LLHSDM1_AN, quality_care_rating_AN := 2
][denom >= 30 & entityType == "AN" & score >= LLHSDM1_AN & score <= ULHSDM1_AN, quality_care_rating_AN := 3
][denom >= 30 & entityType == "AN" & score >= ULHSDM1_AN & score <= ULSDM1_AN, quality_care_rating_AN := 4
][denom >= 30 & entityType == "AN" & score >= ULSDM1_AN, quality_care_rating_AN := 5
]
# measure[denom >= 30 & entityType == "FQHC",
# `:=` (top_performer_FQHC = measure.top_performer_FQHC,
# sd.FQHC = measure.sd.FQHC,
# LLSDM1_FQHC = LLSDM1_FQHC,
# LLHSDM1_FQHC = LLHSDM1_FQHC,
# ULHSDM1_FQHC = ULHSDM1_FQHC,
# ULSDM1_FQHC = ULSDM1_FQHC)]
# measure[denom >= 30 & entityType == "FQHC" & score < LLSDM1_FQHC, quality_care_rating_FQHC := 1
# ][denom >= 30 & entityType == "FQHC" & score >= LLSDM1_FQHC & score <= LLHSDM1_FQHC, quality_care_rating_FQHC := 2
# ][denom >= 30 & entityType == "FQHC" & score >= LLHSDM1_FQHC & score <= ULHSDM1_FQHC, quality_care_rating_FQHC := 3
# ][denom >= 30 & entityType == "FQHC" & score >= ULHSDM1_FQHC & score <= ULSDM1_FQHC, quality_care_rating_FQHC := 4
# ][denom >= 30 & entityType == "FQHC" & score >= ULSDM1_FQHC, quality_care_rating_FQHC := 5
# ]
# measure[
# !is.na(Entity) & abs(score - measure.mean) < 1/2 * measure.sd &
# denom >= 30,
# quality_care_rating := 3][
# !is.na(Entity) & (score - measure.mean) > -1 * measure.sd & (score - measure.mean) < -1/2 * measure.sd &
# denom >= 30,
# quality_care_rating := 2][
# !is.na(Entity) & (score - measure.mean) < -1 * measure.sd &
# denom >= 30,
# quality_care_rating := 1][
# !is.na(Entity) & (score - measure.mean) > 1/2 * measure.sd & (score - measure.mean) < 1 * measure.sd &
# denom >= 30,
# quality_care_rating := 4][
# !is.na(Entity) & (score - measure.mean) > 1 * measure.sd &
# denom >= 30,
# quality_care_rating := 5]
measure[!Entity %in% entityNameList, quality_care_rating := NA]
if (measureName %in% measurelist_lowerbetter){
measure[!is.na(EntityID) & denom >= 30 & EntityID != "ZZ", `:=`(score = 1 - score,
state_average = 1 - state_average,
measure.top_performer = 1 - measure.top_performer,
measure.top_performe_AN = 1 - measure.top_performer_AN)]
}
# score_dat <- rbind(score_dat, measure)
measure_tb <- measure[, .(Measure, entityType, EntityID, Entity,
num, denom, Rate, state_average,
top_performer_AN,
rate_in_SD,
sd, sd.AN,
LLSDM1_AN,
LLHSDM1_AN,
ULHSDM1_AN,
ULSDM1_AN,
quality_care_rating_AN,
# top_performer_FQHC,
# sd.FQHC,
# LLSDM1_FQHC,
# LLHSDM1_FQHC,
# ULHSDM1_FQHC,
# ULSDM1_FQHC,
# top_performer,
# quality_care_rating_FQHC,
NULL)] ## modify output csv format
# measure_tb[, Rate := as.numeric(Rate)*100]
# measure_tb[!is.na(EntityID), `:=`(
# state_average = round(state_average*100, 2),
# sd = round(sd*100, 2),
# top_performer = round(top_performer*100, 2)
# )]
measure_tb[EntityID == "ZZ", EntityID := ""]
fwrite(measure_tb, paste0(dir.out.tb, measureName, '_table.csv'))
}
## modify output rds format
# score_dat1 <- score_dat[
# , .(Measure, Entity, score, state_average, top_performer, quality_care_rating)]
# score_dat1 <- score_dat1[Entity %in% c(entityNameList, 'outAN')]
# score_dat2 <- org_crosswalk[score_dat1, on = c('org_abbr' = 'Entity')]
# ## merge 'measure_label'
# score_dat3 <- measure_info[score_dat2, on = c('measure_label' = 'Measure')]
#
# ## add 'outAN' to template
# score_dat_samp[org_name == "Farmington Valley Medical Associates",
# `:=`(org_name = 'Yale New Haven Health',
# org_url = 'https://www.ynhh.org/')]
# score_dat_samp1 <- unique(score_dat_samp[, .(
# ## insurance, ## 1
# measure_name, ## 2
# measure_category, ## 3
# measure_def, ## 4
# measure_hover_col, ## 5
# measure_hover_row, ## 6
# measure_abbr, ## 7
# measure_link1, ## 8
# measure_link2, ## 9
# measure_link3, ## 10
# measure_link4 ## 11
# )])[, org_name := 'Other Healthcare Providers']
# score_dat_samp2 <- rbind(score_dat_samp[, .(
# ## insurance, ## 1
# measure_name, ## 2
# measure_category, ## 3
# measure_def, ## 4
# measure_hover_col, ## 5
# measure_hover_row, ## 6
# measure_abbr, ## 7
# measure_link1, ## 8
# measure_link2, ## 9
# measure_link3, ## 10
# measure_link4, ## 11
# org_name ## 12
# )], score_dat_samp1)
#
# ## merge 'score'
# score_dat4 <- score_dat3[score_dat_samp2, on = c('measure_abbr', 'org_name')]
# dim(score_dat4) ## [1] 1273 19
# ## merge `quality_care_rating3`
# rating_info <- unique(score_dat_samp[, .(quality_care_rating, quality_care_rating3)])
# score_dat4 <- rating_info[score_dat4, on = 'quality_care_rating']
# corder <- colnames(score_dat_samp)
# setcolorder(score_dat4, corder)
#
# ## simplify template
# score_simp <- unique(score_dat4[measure_label %in% measurelist, .(
# org_name,
# measure_name,
# insurance,
# score,
# state_average,
# top_performer,
# quality_care_rating
# )])
#
# ## save
# fwrite(score_simp, file.path(dir.out, "score_dat.csv"))
## saveRDS(as.data.frame(score_dat4[, `:=`(measure_label = NULL,
## org_abbr = NULL)]),
## file = paste0(dir.out, "score_dat.rds"))