Code
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/oecd/HEALTH_HCQI.RData")$mtime)) %>%
print_table_conditional()
LAST_DOWNLOAD |
---|
2023-09-09 |
Data - OECD
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/oecd/HEALTH_HCQI.RData")$mtime)) %>%
print_table_conditional()
LAST_DOWNLOAD |
---|
2023-09-09 |
LAST_COMPILE |
---|
2024-09-15 |
%>%
HEALTH_HCQI left_join(HEALTH_HCQI_var$VAL, by = "VAL") %>%
group_by(VAL, Val) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
VAL | Val | Nobs |
---|---|---|
LOW_CI | Lower confidence interval | 31722 |
UP_CI | Upper confidence interval | 31722 |
AS_STD_RATE_MPOP | Age-sex standardised rate per 100 000 population | 17588 |
AS_STD_RATE_CPAT | Age-sex standardised rate per 100 patients | 10465 |
CRUDE_RATE_MORT | NA | 5397 |
CRUDE_RATE_CPAT | Crude rate per 100 patients | 3230 |
ASTD_SURVIVAL | Age-standardised survival (%) | 2150 |
DDD_POP | DDDs per 1000 population per day | 1380 |
PERDIAB | Percentage of diabetic patients | 1215 |
NUM_MILE | Number per 1 000 patients aged 65 and over | 1197 |
AS_STD_RATIO | Age-sex standardised ratio | 987 |
CRUDE_DIS_SURG | Crude rate per 100 000 hospital discharges (using unlinked data) | 928 |
PER_ANT | Percentage of all antibiotics prescribed | 654 |
CRUDE_RATE_CVAG | Crude rate per 100 vaginal deliveries | 633 |
RATIO | Ratio | 576 |
NUM_COAG | Number per 100 patients receiving anticoagulating drugs | 468 |
CRUDE_DIS_ALL | Crude rate per 100 000 hospital discharges (using linked data) | 391 |
%>%
HEALTH_HCQI left_join(HEALTH_HCQI_var$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 |
%>%
HEALTH_HCQI left_join(HEALTH_HCQI_var$GEN, by = "GEN") %>%
group_by(GEN, Gen) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
GEN | Gen | Nobs |
---|---|---|
T | Total | 37464 |
F | Female | 37320 |
M | Male | 35919 |
%>%
HEALTH_HCQI left_join(HEALTH_HCQI_var$IND, by = "IND") %>%
group_by(IND, Ind) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
%>%
HEALTH_HCQI left_join(HEALTH_HCQI_var$PER, by = "PER") %>%
group_by(PER, Per) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
PER | Per | Nobs |
---|---|---|
TIME19 | 2019 | 7743 |
TIME16 | 2016 | 7543 |
TIME20 | 2020 | 7435 |
TIME17 | 2017 | 7314 |
TIME18 | 2018 | 7259 |
TIME15 | 2015 | 7065 |
TIME14 | 2014 | 6772 |
TIME13 | 2013 | 6702 |
TIME21 | NA | 5760 |
TIME12 | 2012 | 5215 |
TIME10 | 2010 | 5116 |
TIME11 | 2011 | 4750 |
TIME09 | 2009 | 4272 |
TIME08 | 2008 | 3369 |
TIME07 | 2007 | 3120 |
TIME06 | 2006 | 2580 |
TIME05 | 2005 | 2358 |
PERC14 | 2010-2014 | 2211 |
PERC09 | 2005-2009 | 2175 |
PERC04 | 2000-2004 | 2064 |
TIME04 | 2004 | 2019 |
TIME22 | NA | 1799 |
TIME03 | 2003 | 1740 |
TIME02 | 2002 | 1559 |
TIME01 | 2001 | 1404 |
TIME00 | 2000 | 1359 |
%>%
HEALTH_HCQI left_join(HEALTH_HCQI_var$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 .} {