Health Care Quality Indicators - HEALTH_HCQI

Data - OECD

Info

LAST_DOWNLOAD

Code
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/oecd/HEALTH_HCQI.RData")$mtime)) %>%
  print_table_conditional()
LAST_DOWNLOAD
2023-09-09

LAST_COMPILE

LAST_COMPILE
2024-09-15

VAL

Code
HEALTH_HCQI %>%
  left_join(HEALTH_HCQI_var$VAL, by = "VAL") %>%
  group_by(VAL, Val) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
VAL Val Nobs
LOW_CI Lower confidence interval 31722
UP_CI Upper confidence interval 31722
AS_STD_RATE_MPOP Age-sex standardised rate per 100 000 population 17588
AS_STD_RATE_CPAT Age-sex standardised rate per 100 patients 10465
CRUDE_RATE_MORT NA 5397
CRUDE_RATE_CPAT Crude rate per 100 patients 3230
ASTD_SURVIVAL Age-standardised survival (%) 2150
DDD_POP DDDs per 1000 population per day 1380
PERDIAB Percentage of diabetic patients 1215
NUM_MILE Number per 1 000 patients aged 65 and over 1197
AS_STD_RATIO Age-sex standardised ratio 987
CRUDE_DIS_SURG Crude rate per 100 000 hospital discharges (using unlinked data) 928
PER_ANT Percentage of all antibiotics prescribed 654
CRUDE_RATE_CVAG Crude rate per 100 vaginal deliveries 633
RATIO Ratio 576
NUM_COAG Number per 100 patients receiving anticoagulating drugs 468
CRUDE_DIS_ALL Crude rate per 100 000 hospital discharges (using linked data) 391

AGE

Code
HEALTH_HCQI %>%
  left_join(HEALTH_HCQI_var$AGE, by = "AGE") %>%
  group_by(AGE, Age) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
AGE Age Nobs
TOTAL_15 15 years old and over 51557
TOTAL_45 45 years old and over 37026
TOTAL All age groups 9016
TOTAL_16 16 years and over 5758
TOTAL_65 65 years old and over 4038
0-14Y 0-14 years old 1014
TOTAL_1574 15-74 years old 987
TOTAL_18 18 years old and over 908
75Y_OVER 75 years old and over 399

GEN

Code
HEALTH_HCQI %>%
  left_join(HEALTH_HCQI_var$GEN, by = "GEN") %>%
  group_by(GEN, Gen) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
GEN Gen Nobs
T Total 37464
F Female 37320
M Male 35919

IND

Code
HEALTH_HCQI %>%
  left_join(HEALTH_HCQI_var$IND, by = "IND") %>%
  group_by(IND, Ind) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

PER

Code
HEALTH_HCQI %>%
  left_join(HEALTH_HCQI_var$PER, by = "PER") %>%
  group_by(PER, Per) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
PER Per Nobs
TIME19 2019 7743
TIME16 2016 7543
TIME20 2020 7435
TIME17 2017 7314
TIME18 2018 7259
TIME15 2015 7065
TIME14 2014 6772
TIME13 2013 6702
TIME21 NA 5760
TIME12 2012 5215
TIME10 2010 5116
TIME11 2011 4750
TIME09 2009 4272
TIME08 2008 3369
TIME07 2007 3120
TIME06 2006 2580
TIME05 2005 2358
PERC14 2010-2014 2211
PERC09 2005-2009 2175
PERC04 2000-2004 2064
TIME04 2004 2019
TIME22 NA 1799
TIME03 2003 1740
TIME02 2002 1559
TIME01 2001 1404
TIME00 2000 1359

COU

Code
HEALTH_HCQI %>%
  left_join(HEALTH_HCQI_var$COU, by = "COU") %>%
  group_by(COU, Cou) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Cou))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}