Info

LAST_DOWNLOAD

Code

tibble(LAST_DOWNLOAD = as.Date(file.info("~/iCloud/website/data/oecd/HEALTH_HCQI.RData")$mtime)) %>%
  print_table_conditional()

LAST_DOWNLOAD
2023-09-09

LAST_COMPILE

LAST_COMPILE
2025-11-17

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 .}