Health expenditure and financing - SHA

Data - OECD

Author

François Geerolf

HF

Code 
SHA |>
  left_join(SHA_var$HF, by = "HF") |>
  group_by(HF, Hf) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
HF Hf Nobs
HFTOT All financing schemes 2102835
HF1 Government/compulsory schemes 544719
HF2HF3 Voluntary schemes/household out-of-pocket payments 450722
HF11 Government schemes 410559
HF3 Household out-of-pocket payments 351674
HF12HF13 Compulsory contributory health insurance schemes 327551
HF2 Voluntary health care payment schemes 320875
HF121 Social health insurance schemes 246188
HF21 Voluntary health insurance schemes 241386
HF22 NPISH financing schemes 122707
HF31 Out-of-pocket excluding cost-sharing 119141
HF23 Enterprise financing schemes 100645
HF32 Cost-sharing with third-party payers 95846
HF122 Compulsory private insurance schemes 71370
HF4 Rest of the world financing schemes (non-resident) 14388
HF0 Financing schemes unknown 380

HC

Code 
SHA |>
  left_join(SHA_var$HC, by = "HC") |>
  group_by(HC, Hc) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()

HP

Code 
SHA |>
  left_join(SHA_var$HP, by = "HP") |>
  group_by(HP, Hp) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()

MEASURE

Code 
SHA |>
  left_join(SHA_var$MEASURE, by = "MEASURE") |>
  group_by(MEASURE, Measure) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
MEASURE Measure Nobs
MLLNCU Current prices 450244
MTMOPP Current prices, current PPPs 448707
UNPPER Per capita, current prices 448273
VALREL Constant prices, OECD base year 446598
PPPPER Per capita, current prices, current PPPs 446550
VRPPPT Constant prices, constant PPPs, OECD base year 446314
REPPER Per capita, constant prices, OECD base year 445080
VRPPPR Per capita, constant prices, constant PPPs, OECD base year 444367
PARCUR Share of current expenditure on health 421779
PARPIB Share of gross domestic product 413258
PARHP Share of provider 372806
PARHC Share of function 371479
PARHF Share of financing scheme 365531

LOCATION

Code 
SHA |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  group_by(LOCATION, Location) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(Location)),
         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 .}

obsTime

Code 
SHA |>
  group_by(obsTime) |>
  summarise(Nobs = n()) |>
  arrange(desc(obsTime)) |>
  print_table_conditional()

Financing Sources (% of GDP)

By HC

Code 
SHA |>
  filter(MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT",
         LOCATION %in% c("FRA", "DEU", "KOR", "USA")) |>
  left_join(SHA_var$HC, by = "HC") |>
  group_by(LOCATION, HC, Hc) |>
  summarise(value = last(obsValue)) |>
  spread(LOCATION, value) |>
  print_table_conditional()

France, Germany, Italy

Table

Code 
SHA |>
  filter(MEASURE == "PARPIB",
         HP == "HPTOT",
         HC == "HCTOT",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$HF, by = "HF") |>
  group_by(LOCATION, HF, Hf) |>
  summarise(value = last(obsValue)) |>
  spread(LOCATION, value) |>
  print_table_conditional()
HF Hf DEU FRA ITA
HF1 Government/compulsory schemes 10.665 9.302 7.412
HF11 Government schemes 0.764 0.612 7.395
HF121 Social health insurance schemes 8.292 7.938 0.016
HF122 Compulsory private insurance schemes 0.841 0.751 NA
HF12HF13 Compulsory contributory health insurance schemes 9.132 8.689 0.016
HF2 Voluntary health care payment schemes 0.323 0.781 0.252
HF21 Voluntary health insurance schemes 0.163 0.712 0.191
HF22 NPISH financing schemes 0.105 0.001 0.020
HF23 Enterprise financing schemes 0.048 0.069 0.040
HF2HF3 Voluntary schemes/household out-of-pocket payments 1.865 1.810 2.304
HF3 Household out-of-pocket payments 1.542 1.029 2.053
HF31 Out-of-pocket excluding cost-sharing NA 0.530 NA
HF32 Cost-sharing with third-party payers NA 0.499 NA
HFTOT All financing schemes 12.530 12.377 9.716

HFTOT

Code 
SHA |>
  filter(HC == "HCTOT",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("% of GDP") + xlab("")

HF1

Code 
SHA |>
  filter(HC == "HCTOT",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HF1",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("% of GDP") + xlab("")

HF11

Code 
SHA |>
  filter(HC == "HCTOT",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HF11",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("% of GDP") + xlab("")

HF121

Code 
SHA |>
  filter(HC == "HCTOT",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HF121",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("% of GDP") + xlab("")

HF122

Code 
SHA |>
  filter(HC == "HCTOT",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HF122",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, .1),
                     labels = scales::percent_format(accuracy = .1)) +
  ylab("% of GDP") + xlab("")

HF2HF3

Code 
SHA |>
  filter(HC == "HCTOT",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HF2HF3",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.2),
                     labels = scales::percent_format(accuracy = .1)) +
  ylab("% of GDP") + xlab("")

HF2

Code 
SHA |>
  filter(HC == "HCTOT",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HF2",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.2),
                     labels = scales::percent_format(accuracy = .1)) +
  ylab("% of GDP") + xlab("")

HF21

Code 
SHA |>
  filter(HC == "HCTOT",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HF21",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.2),
                     labels = scales::percent_format(accuracy = .1)) +
  ylab("% of GDP") + xlab("")

HF22

Code 
SHA |>
  filter(HC == "HCTOT",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HF22",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.01),
                     labels = scales::percent_format(accuracy = .01)) +
  ylab("% of GDP") + xlab("")

HF23

Code 
SHA |>
  filter(HC == "HCTOT",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HF23",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.01),
                     labels = scales::percent_format(accuracy = .01)) +
  ylab("% of GDP") + xlab("")

HF3

Code 
SHA |>
  filter(HC == "HCTOT",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HF3",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.2),
                     labels = scales::percent_format(accuracy = .1)) +
  ylab("% of GDP") + xlab("")

HF31

Code 
SHA |>
  filter(HC == "HCTOT",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HF31",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.01),
                     labels = scales::percent_format(accuracy = .01)) +
  ylab("% of GDP") + xlab("")

HF32

Code 
SHA |>
  filter(HC == "HCTOT",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HF32",
         LOCATION %in% c("FRA", "DEU", "ITA")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.01),
                     labels = scales::percent_format(accuracy = .01)) +
  ylab("% of GDP") + xlab("")

HC6 - Preventive Care

All Countries - First/ Last Obs

Code 
SHA |>
  filter(HC == "HC6",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT") |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  group_by(LOCATION, Location) |>
  summarise(Nobs = n(),
            `Year 1` = first(obsTime),
            `Preventive Care 1` = first(obsValue),
            `Year 2` = last(obsTime),
            `Preventive Care 2` = last(obsValue)) |>
  arrange(-`Preventive Care 2`) |>
  print_table_conditional()

France, Germany, South Korea

Code 
SHA |>
  filter(HC == "HC6",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT",
         LOCATION %in% c("FRA", "DEU", "KOR")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.05),
                     labels = scales::percent_format(accuracy = 0.01)) +
  ylab("Preventive Care (% of GDP)") + xlab("")

HC62 - Immunisation programmes

All Countries - First/ Last Obs

Code 
SHA |>
  filter(HC == "HC62",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT") |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  group_by(LOCATION, Location) |>
  summarise(Nobs = n(),
            `Year 1` = first(obsTime),
            `Immunisation 1 (% of GDP)` = first(obsValue),
            `Year 2` = last(obsTime),
            `Immunisation 2 (% of GDP)` = last(obsValue)) |>
  arrange(-`Immunisation 2 (% of GDP)`) |>
  print_table_conditional()
LOCATION Location Nobs Year 1 Immunisation 1 (% of GDP) Year 2 Immunisation 2 (% of GDP)
COL Colombia 5 2013 0.125 2017 0.133
BRA Brazil 5 2015 0.076 2019 0.108
DEU Germany 28 1992 0.025 2019 0.064
ISL Iceland 18 2003 0.070 2020 0.053
SWE Sweden 19 2001 0.035 2019 0.049
SVN Slovenia 7 2012 0.035 2019 0.047
GBR United Kingdom 7 2013 0.039 2019 0.041
KOR Korea 37 1970 0.001 2020 0.040
LVA Latvia 7 2013 0.031 2019 0.034
LTU Lithuania 14 2006 0.006 2019 0.031
CZE Czech Republic 7 2013 0.081 2019 0.029
RUS Russia 5 2015 0.013 2019 0.021
MEX Mexico 17 2003 0.016 2019 0.020
DNK Denmark 10 2010 0.006 2019 0.018
EST Estonia 8 2012 0.028 2019 0.014
FIN Finland 21 2000 0.004 2020 0.013
LUX Luxembourg 9 2011 0.018 2019 0.013
GRC Greece 17 2003 0.005 2019 0.007
POL Poland 7 2013 0.007 2019 0.007
CRI Costa Rica 9 2011 0.005 2019 0.006
FRA France 14 2006 0.004 2019 0.006
BEL Belgium 17 2003 0.003 2019 0.002

France, Germany, South Korea

Code 
SHA |>
  filter(HC == "HC62",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT",
         LOCATION %in% c("FRA", "DEU", "KOR")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.01),
                     labels = scales::percent_format(accuracy = 0.01)) +
  ylab("Immunisation (% of GDP)") + xlab("")

HC63 - Early disease detection programmes

All Countries - First/ Last Obs

Code 
SHA |>
  filter(HC == "HC62",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT") |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  group_by(LOCATION, Location) |>
  summarise(Nobs = n(),
            `Year 1` = first(obsTime),
            `Early Detection 1 (% of GDP)` = first(obsValue),
            `Year 2` = last(obsTime),
            `Early Detection 2 (% of GDP)` = last(obsValue)) |>
  arrange(-`Early Detection 2 (% of GDP)`) |>
  print_table_conditional()
LOCATION Location Nobs Year 1 Early Detection 1 (% of GDP) Year 2 Early Detection 2 (% of GDP)
COL Colombia 5 2013 0.125 2017 0.133
BRA Brazil 5 2015 0.076 2019 0.108
DEU Germany 28 1992 0.025 2019 0.064
ISL Iceland 18 2003 0.070 2020 0.053
SWE Sweden 19 2001 0.035 2019 0.049
SVN Slovenia 7 2012 0.035 2019 0.047
GBR United Kingdom 7 2013 0.039 2019 0.041
KOR Korea 37 1970 0.001 2020 0.040
LVA Latvia 7 2013 0.031 2019 0.034
LTU Lithuania 14 2006 0.006 2019 0.031
CZE Czech Republic 7 2013 0.081 2019 0.029
RUS Russia 5 2015 0.013 2019 0.021
MEX Mexico 17 2003 0.016 2019 0.020
DNK Denmark 10 2010 0.006 2019 0.018
EST Estonia 8 2012 0.028 2019 0.014
FIN Finland 21 2000 0.004 2020 0.013
LUX Luxembourg 9 2011 0.018 2019 0.013
GRC Greece 17 2003 0.005 2019 0.007
POL Poland 7 2013 0.007 2019 0.007
CRI Costa Rica 9 2011 0.005 2019 0.006
FRA France 14 2006 0.004 2019 0.006
BEL Belgium 17 2003 0.003 2019 0.002

France, Germany, South Korea

Code 
SHA |>
  filter(HC == "HC63",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT",
         LOCATION %in% c("FRA", "DEU", "KOR")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.01),
                     labels = scales::percent_format(accuracy = 0.01)) +
  ylab("Early disease detection programmes (% of GDP)") + xlab("")

HC65 - Epidemiological surveillance and risk and disease control programmes

All Countries - First/ Last Obs

Code 
SHA |>
  filter(HC == "HC62",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT") |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  group_by(LOCATION, Location) |>
  summarise(Nobs = n(),
            `Year 1` = first(obsTime),
            `Surveillance 1 (% of GDP)` = first(obsValue),
            `Year 2` = last(obsTime),
            `Surveillance 2 (% of GDP)` = last(obsValue)) |>
  arrange(-`Surveillance 2 (% of GDP)`) |>
  print_table_conditional()
LOCATION Location Nobs Year 1 Surveillance 1 (% of GDP) Year 2 Surveillance 2 (% of GDP)
COL Colombia 5 2013 0.125 2017 0.133
BRA Brazil 5 2015 0.076 2019 0.108
DEU Germany 28 1992 0.025 2019 0.064
ISL Iceland 18 2003 0.070 2020 0.053
SWE Sweden 19 2001 0.035 2019 0.049
SVN Slovenia 7 2012 0.035 2019 0.047
GBR United Kingdom 7 2013 0.039 2019 0.041
KOR Korea 37 1970 0.001 2020 0.040
LVA Latvia 7 2013 0.031 2019 0.034
LTU Lithuania 14 2006 0.006 2019 0.031
CZE Czech Republic 7 2013 0.081 2019 0.029
RUS Russia 5 2015 0.013 2019 0.021
MEX Mexico 17 2003 0.016 2019 0.020
DNK Denmark 10 2010 0.006 2019 0.018
EST Estonia 8 2012 0.028 2019 0.014
FIN Finland 21 2000 0.004 2020 0.013
LUX Luxembourg 9 2011 0.018 2019 0.013
GRC Greece 17 2003 0.005 2019 0.007
POL Poland 7 2013 0.007 2019 0.007
CRI Costa Rica 9 2011 0.005 2019 0.006
FRA France 14 2006 0.004 2019 0.006
BEL Belgium 17 2003 0.003 2019 0.002

France, Germany, South Korea

Code 
SHA |>
  filter(HC == "HC65",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT",
         LOCATION %in% c("FRA", "DEU", "KOR")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.01),
                     labels = scales::percent_format(accuracy = 0.01),
                     limits = c(0, 0.0012)) +
  ylab("Epidemio. surveillance,risk & disease control (% of GDP)") + xlab("")

HC66 - Preparing for disaster and emergency response programmes

All Countries - First/ Last Obs

Code 
SHA |>
  filter(HC == "HC62",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT") |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  group_by(LOCATION, Location) |>
  summarise(Nobs = n(),
            `Year 1` = first(obsTime),
            `Disaster Preparedness 1 (% of GDP)` = first(obsValue),
            `Year 2` = last(obsTime),
            `Disaster Preparedness 2 (% of GDP)` = last(obsValue)) |>
  arrange(-`Disaster Preparedness 2 (% of GDP)`) |>
  print_table_conditional()
LOCATION Location Nobs Year 1 Disaster Preparedness 1 (% of GDP) Year 2 Disaster Preparedness 2 (% of GDP)
COL Colombia 5 2013 0.125 2017 0.133
BRA Brazil 5 2015 0.076 2019 0.108
DEU Germany 28 1992 0.025 2019 0.064
ISL Iceland 18 2003 0.070 2020 0.053
SWE Sweden 19 2001 0.035 2019 0.049
SVN Slovenia 7 2012 0.035 2019 0.047
GBR United Kingdom 7 2013 0.039 2019 0.041
KOR Korea 37 1970 0.001 2020 0.040
LVA Latvia 7 2013 0.031 2019 0.034
LTU Lithuania 14 2006 0.006 2019 0.031
CZE Czech Republic 7 2013 0.081 2019 0.029
RUS Russia 5 2015 0.013 2019 0.021
MEX Mexico 17 2003 0.016 2019 0.020
DNK Denmark 10 2010 0.006 2019 0.018
EST Estonia 8 2012 0.028 2019 0.014
FIN Finland 21 2000 0.004 2020 0.013
LUX Luxembourg 9 2011 0.018 2019 0.013
GRC Greece 17 2003 0.005 2019 0.007
POL Poland 7 2013 0.007 2019 0.007
CRI Costa Rica 9 2011 0.005 2019 0.006
FRA France 14 2006 0.004 2019 0.006
BEL Belgium 17 2003 0.003 2019 0.002

France, Germany, South Korea

Code 
SHA |>
  filter(HC == "HC65",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT",
         LOCATION %in% c("FRA", "DEU", "KOR")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.01),
                     labels = scales::percent_format(accuracy = 0.01),
                     limits = c(0, 0.0012)) +
  ylab("Disaster Preparedness (% of GDP)") + xlab("")

HC7 - Providers of health care system administration and financing

All Countries - First/ Last Obs

Code 
SHA |>
  filter(HC == "HC7",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT") |>
  left_join(SHA_var$LOCATION |>
              setNames(c("LOCATION", "Location")), by = "LOCATION") |>
  group_by(LOCATION, Location) |>
  summarise(Nobs = n(),
            `Year 1` = first(obsTime),
            `Administration 1 (% of GDP)` = first(obsValue),
            `Year 2` = last(obsTime),
            `Administration 2 (% of GDP)` = last(obsValue)) |>
  arrange(-`Administration 2 (% of GDP)`) |>
  print_table_conditional()

France, Germany, South Korea

Code 
SHA |>
  filter(HC == "HC7",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT",
         LOCATION %in% c("FRA", "DEU", "KOR")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.05),
                     labels = scales::percent_format(accuracy = 0.01)) +
  ylab("Administration (% of GDP)") + xlab("")

HC72 - Administration of health financing

All Countries - First/ Last Obs

Code 
SHA |>
  filter(HC == "HC72",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT") |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  group_by(LOCATION, Location) |>
  summarise(Nobs = n(),
            `Year 1` = first(obsTime),
            `Administration 1 (% of GDP)` = first(obsValue),
            `Year 2` = last(obsTime),
            `Administration 2 (% of GDP)` = last(obsValue)) |>
  arrange(-`Administration 2 (% of GDP)`) |>
  print_table_conditional()

France, Germany, South Korea

Code 
SHA |>
  filter(HC == "HC72",
         MEASURE == "PARPIB",
         HP == "HPTOT",
         HF == "HFTOT",
         LOCATION %in% c("FRA", "DEU", "KOR")) |>
  left_join(SHA_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  left_join(colors, by = c("Location" = "country")) |>
  mutate(obsValue = obsValue/100) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 60, 0.05),
                     labels = scales::percent_format(accuracy = 0.01)) +
  ylab("Administration (% of GDP)") + xlab("")