Risk Factors
Intro
This doc will explore mortality and DALYs by risk factors
First to load the pre requisite packages and script for downloading data.
library(tidyverse)
source("download_completed_request.R")Next I will download the data from the request. (Only has to be done once so now set not to evaluate (run)).
The files can now be loaded and joined locally as follows
dta <- read_csv("raw_data/risk/1.csv") %>% bind_rows(read_csv("raw_data/risk/2.csv"))Parsed with column specification:
cols(
measure_id = col_integer(),
measure_name = col_character(),
location_id = col_integer(),
location_name = col_character(),
sex_id = col_integer(),
sex_name = col_character(),
age_id = col_integer(),
age_name = col_character(),
cause_id = col_integer(),
cause_name = col_character(),
rei_id = col_integer(),
rei_name = col_character(),
metric_id = col_integer(),
metric_name = col_character(),
year = col_integer(),
val = col_double(),
upper = col_double(),
lower = col_double()
)
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|===================================================================================================| 100% 78 MBParsed with column specification:
cols(
measure_id = col_integer(),
measure_name = col_character(),
location_id = col_integer(),
location_name = col_character(),
sex_id = col_integer(),
sex_name = col_character(),
age_id = col_integer(),
age_name = col_character(),
cause_id = col_integer(),
cause_name = col_character(),
rei_id = col_integer(),
rei_name = col_character(),
metric_id = col_integer(),
metric_name = col_character(),
year = col_integer(),
val = col_double(),
upper = col_double(),
lower = col_double()
)dtaglimpse(dta)Observations: 501,900
Variables: 18
$ measure_id <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...
$ measure_name <chr> "DALYs (Disability-Adjusted Life Years)", "DALYs (Disability-Adjusted Life Years)", "DA...
$ location_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
$ location_name <chr> "Global", "Global", "Global", "Global", "Global", "Global", "Global", "Global", "Global...
$ sex_id <int> 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, ...
$ sex_name <chr> "Male", "Female", "Male", "Female", "Male", "Female", "Male", "Female", "Male", "Female...
$ age_id <int> 1, 1, 1, 1, 1, 1, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 2...
$ age_name <chr> "Under 5", "Under 5", "Under 5", "Under 5", "Under 5", "Under 5", "All Ages", "All Ages...
$ cause_id <int> 294, 294, 294, 294, 294, 294, 294, 294, 294, 294, 294, 294, 294, 294, 294, 294, 294, 29...
$ cause_name <chr> "All causes", "All causes", "All causes", "All causes", "All causes", "All causes", "Al...
$ rei_id <int> 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 16...
$ rei_name <chr> "Occupational exposure to silica", "Occupational exposure to silica", "Occupational exp...
$ metric_id <int> 1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, ...
$ metric_name <chr> "Number", "Number", "Percent", "Percent", "Rate", "Rate", "Number", "Number", "Percent"...
$ year <int> 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 199...
$ val <dbl> 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.0...
$ upper <dbl> 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.4...
$ lower <dbl> 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 5.9...The _id suffix columns can be used to join to the lookup tables. In this case for risk (by rei_id)
lookup <- readxl::read_excel("raw_data/IHME_GBD_2016_CODEBOOK/IHME_GBD_2016_REI_HIERARCHY_Y2018M04D26.XLSX")
lookupglimpse(lookup)Observations: 144
Variables: 5
$ rei_id <dbl> 169, 202, 203, 104, 171, 191, 82, 83, 84, 238, 85, 86, 87, 88, 89, 90, 91, 126, 127, 150, ...
$ rei_name <chr> "All risk factors", "Environmental/occupational risks", "Behavioral risks", "Metabolic ris...
$ parent_id <dbl> 169, 169, 169, 169, 171, 191, 202, 82, 82, 82, 202, 85, 85, 85, 202, 89, 89, 202, 126, 127...
$ level <dbl> 0, 1, 1, 1, 0, 0, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...
$ sort_order <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,...In this example I’ll join then filter only level 1 categories in the rei hierarchy
dta %>%
left_join(lookup) %>%
filter(level == 1) -> dta_lvl1Joining, by = c("rei_id", "rei_name")Now to start exploring: age-standardised, global, deaths, by lvl 1 risk factor and gender
dta_lvl1 %>%
filter(measure_name == "Deaths") %>%
filter(location_name == "Global") %>%
filter(age_name == "Age-standardized") %>%
filter(metric_name == "Rate") %>%
filter(cause_name == "All causes") %>%
select(year, sex = sex_name, risk_factor = rei_name, death_rate = val) %>%
ggplot(aes(x = year, y = death_rate, linetype = sex, colour = sex)) +
facet_wrap(~ risk_factor) +
geom_line()
Now to do the same by SDI
dta_lvl1 %>%
filter(measure_name == "Deaths") %>%
filter(location_name != "Global") %>%
filter(age_name == "Age-standardized") %>%
filter(metric_name == "Rate") %>%
filter(cause_name == "All causes") %>%
select(year, sex = sex_name, sdi = location_name, risk_factor = rei_name, death_rate = val) %>%
mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>%
mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T)) %>%
ggplot(aes(x = year, y = death_rate, linetype = sex, colour = sex)) +
facet_grid(risk_factor ~ sdi) +
geom_line()
And now let’s see what this implies for relative inequalities
dta_lvl1 %>%
filter(measure_name == "Deaths") %>%
filter(location_name != "Global") %>%
filter(age_name == "Age-standardized") %>%
filter(metric_name == "Rate") %>%
filter(cause_name == "All causes") %>%
select(year, sex = sex_name, sdi = location_name, risk_factor = rei_name, death_rate = val) %>%
mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>%
mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T)) %>% spread(sex, death_rate) %>% mutate(ratio = Male / Female) %>%
ggplot(aes(x = year, y = ratio, colour = risk_factor)) +
geom_line() +
facet_grid(. ~ sdi) +
geom_hline(yintercept = 1)
DALY decomposition
Let’s look at the contribution of different risk factors to DALY difference
dta_lvl1 %>%
filter(measure_name == "DALYs (Disability-Adjusted Life Years)") %>%
filter(location_name != "Global") %>%
filter(cause_name == "All causes") %>%
filter(age_name == "Age-standardized") %>%
filter(metric_name == "Rate") %>%
select(year, sex = sex_name, sdi = location_name, risk_factor = rei_name, daly_rate = val) %>%
mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>%
mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T)) %>%
ggplot(aes(x = year, y = daly_rate, fill = risk_factor), colour = "black") +
geom_area() +
facet_grid(sdi~sex)
Now proportion of all DALYs attributable to each risk factor by year and SDI
dta_lvl1 %>%
filter(measure_name == "DALYs (Disability-Adjusted Life Years)") %>%
filter(location_name != "Global") %>%
filter(cause_name == "All causes") %>%
filter(age_name == "Age-standardized") %>%
filter(metric_name == "Rate") %>%
select(year, sex = sex_name, sdi = location_name, risk_factor = rei_name, daly_rate = val) %>%
mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>%
mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T)) %>%
group_by(year, sex, sdi) %>%
mutate(total = sum(daly_rate)) %>%
mutate(proportion_total = daly_rate / total) %>%
ungroup() %>%
ggplot(aes(x = year, y = proportion_total, fill = risk_factor), colour = "black") +
geom_area() +
facet_grid(sdi~sex)
Prop total deaths attributable to diff risk factors
Now proportion of total deaths
by_factor <- dta_lvl1 %>%
filter(measure_name == "Deaths") %>%
select(-measure_name, -measure_id) %>%
filter(location_name != "Global") %>%
filter(age_name == "Age-standardized") %>%
filter(metric_name == "Rate") %>%
filter(cause_name == "All causes") %>%
select(year, sex = sex_name, sdi = location_name, rei = rei_name, rate = val) %>% mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>%
mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T))
overall <- dta %>%
filter(rei_name == "All risk factors") %>%
filter(measure_name == "Deaths") %>%
select(-measure_name, -measure_id) %>%
filter(location_name != "Global") %>%
filter(age_name == "Age-standardized") %>%
filter(metric_name == "Rate") %>%
filter(cause_name == "All causes") %>%
select(year, sex = sex_name, sdi = location_name, rei = rei_name, rate = val) %>% mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>%
mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T))
joined <- bind_rows(by_factor, overall)
rm(by_factor, overall)Unfortunately it seems ‘all risk factors’ is not the same as ‘total’, i.e. the absolute differences in age-standardised death rates from each of the three level 1 risk factors do not add up to the ‘all risk factors’ absolute difference
joined %>%
spread(sex, rate) %>%
mutate(abs_diff = Male - Female) %>%
group_by(year, sdi) %>%
mutate(perc_of_abs_diff = 100 * abs_diff / abs_diff[rei=="All risk factors"]) %>%
summarise(discrepancy = sum(perc_of_abs_diff[rei != "All risk factors"]) - 100) %>%
ungroup() %>%
ggplot(aes(x = year, y = discrepancy, colour = sdi)) +
geom_line() 
So, the risk factors might not be mutually exclusive and exhaustive. Let’s take middle SDI in 2000 as an example
joined %>%
filter(sdi == "Middle") %>%
filter(year == 2000) %>%
spread(sex, rate) %>%
mutate(abs_diff = Male - Female) %>%
group_by(year, sdi) %>%
mutate(perc_of_abs_diff = 100 * abs_diff / abs_diff[rei=="All risk factors"]) So, the total of the three bottom rows adds up to around 160, when I might expect them to add to 100. A simple decomposition assumption doesn’t seem to apply when using the ‘all risk factors’ designation as total.
To try to account for this let’s look at a different dataset, with:
- Age standardised ages
- All cause
- Mortality rates
- By SDI
Let’s try to grab this data with a new query
The query link is here.
url_body <- "http://s3.healthdata.org/gbd-api-2016-production/c9bf1a5197bf46a966c1467c3103e8a9_files"
url_head <- "IHME-GBD_2016_DATA-c9bf1a51-"
outdir <- "raw_data/rate_all_cause"
download_completed_request(url_body, url_head, outdir, flush = T)And now to load this file
dta_total <- read_csv("raw_data/rate_all_cause/1.csv")Parsed with column specification:
cols(
measure = col_character(),
location = col_character(),
sex = col_character(),
age = col_character(),
cause = col_character(),
metric = col_character(),
year = col_integer(),
val = col_double(),
upper = col_double(),
lower = col_double()
)by_factor <- dta_lvl1 %>%
filter(measure_name == "Deaths") %>%
select(-measure_name, -measure_id) %>%
filter(location_name != "Global") %>%
filter(age_name == "Age-standardized") %>%
filter(metric_name == "Rate") %>%
filter(cause_name == "All causes") %>%
select(year, sex = sex_name, sdi = location_name, rei = rei_name, rate = val) %>% mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>%
mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T))
overall <- dta_total %>% filter(location != "Global") %>% mutate(rei = "total") %>% mutate(sdi = stringr::str_replace(location, " SDI", "")) %>%
mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T)) %>% select(year, sex, sdi, rei, rate = val)Let’s see if the totals add up this time. The three level 1 risk factors are shown as stacked areas, and the total is shown as a black line…
by_factor %>%
ggplot(aes(x = year, y = rate, fill = rei)) +
geom_area() +
facet_grid(sex ~ sdi) +
geom_line(aes(x = year, y = rate), inherit.aes = F, data = overall)
So, there is still discrepancy, which seems to be largest for low SDI, and smallest for high-middle SDI, but this might be decomposable subject to some assumptions.
Let’s now compare with previous attempt at an ‘overall’ estimate (based on ‘all risk factors’). In the following this is added as a black dashed line. We might expect the dashed and solid lines to be the same, but are they?
overall2 <- dta %>%
filter(rei_name == "All risk factors") %>%
filter(measure_name == "Deaths") %>%
select(-measure_name, -measure_id) %>%
filter(location_name != "Global") %>%
filter(age_name == "Age-standardized") %>%
filter(metric_name == "Rate") %>%
filter(cause_name == "All causes") %>%
select(year, sex = sex_name, sdi = location_name, rei = rei_name, rate = val) %>% mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>%
mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T))
by_factor %>%
ggplot(aes(x = year, y = rate, fill = rei)) +
geom_area() +
facet_grid(sex ~ sdi) +
geom_line(aes(x = year, y = rate), inherit.aes = F, data = overall) +
geom_line(aes(x = year, y = rate), inherit.aes = F, data = overall2, linetype = "dashed")
So, there are clear differences between age-standardised rates drawn from all causes (solid line), and age-standardised rates drawm from all risk factors (dashed lines), with the latter below the former. Again, this raises questions about how to perform a decomposition into risk factors, the effect of age-standardization, and so on.
Level 1 internal decomposition
Let’s not use level 0 for decomposing level 1 etc, and instead just produce estimates of total by summing up level 1 risks. Then, do the same for level 2.
contributions <- by_factor %>%
inner_join(
by_factor %>%
group_by(year, sex, sdi) %>%
summarise(total_rate = sum(rate)) %>%
ungroup()
) %>%
group_by(year, sdi) %>%
mutate(abs_sex_diff = total_rate[sex == "Male"][1] - total_rate[sex == "Female"][1]) %>%
group_by(year, sdi, rei) %>%
mutate(rei_abs_sex_diff = rate[sex == "Male"] - rate[sex == "Female"]) %>%
ungroup() %>%
mutate(contribution = 100 * rei_abs_sex_diff / abs_sex_diff)Joining, by = c("year", "sex", "sdi")Now to plot the contribution over time by SDI
contributions %>%
select(year, sex, sdi, rei, contribution) %>%
distinct() %>%
ggplot(aes(x = year, y= contribution, colour = rei)) +
geom_line() +
facet_wrap(~sdi)
Let’s think again about how to present this in the case of three risk factors, such that it could be scaled up to more factors
by_factor %>%
spread(sex, rate) %>%
mutate(diff_abs = Male - Female) %>%
group_by(year, sdi) %>%
mutate(diff_cumulative = cumsum(diff_abs)) -> cumulative_contributionsNow to plot for two years, 1990 and 2010
# use geom_segement
cumulative_contributions %>%
filter(year %in% c(1990, 2010)) %>%
mutate(start_pos = diff_cumulative - diff_abs) %>%
mutate(is_increasing = diff_cumulative > start_pos) %>%
group_by(year, sdi) %>%
mutate(max_cumulative = diff_cumulative[length(diff_cumulative)]) %>%
ungroup() %>%
ggplot(aes(x = start_pos, xend = diff_cumulative, y = rei, yend = rei, colour = is_increasing)) +
geom_segment( arrow = arrow(length = unit(0.2, "npc"))) +
facet_grid(sdi ~ year) +
geom_vline(xintercept = 0) +
geom_vline(aes(xintercept = max_cumulative), linetype = "dashed") +
guides(colour = FALSE) +
labs(x = "Gender difference in rates", y = "Risk factors (level 1)", title = "Contribution of risk factors to gender differences in all-cause mortality rates", subtitle = "Age-standardised rates per 100 000", caption = "Source: GBD")
ggsave("figures/contribution_of_level1_riskfactors_death_rate.png", height = 30, width = 30, units = "cm", dpi = 300)
This seems to work as a visualisation. Let’s try to scale up to level 2
by_factor_l2 <- dta %>%
left_join(lookup) %>%
filter(level == 2) %>%
filter(measure_name == "Deaths") %>%
select(-measure_name, -measure_id) %>%
filter(location_name != "Global") %>%
filter(age_name == "Age-standardized") %>%
filter(metric_name == "Rate") %>%
filter(cause_name == "All causes") %>%
select(year, sex = sex_name, sdi = location_name, rei = rei_name, rate = val) %>% mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>%
mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T)) Joining, by = c("rei_id", "rei_name")by_factor_l2 %>%
spread(sex, rate) %>%
mutate(diff_abs = Male - Female) %>%
group_by(year, sdi) %>%
mutate(diff_cumulative = cumsum(diff_abs)) %>%
filter(year %in% c(1990, 2010)) %>%
mutate(start_pos = diff_cumulative - diff_abs) %>%
mutate(is_increasing = diff_cumulative > start_pos) %>%
group_by(year, sdi) %>%
mutate(max_cumulative = diff_cumulative[length(diff_cumulative)]) %>%
ungroup() %>%
ggplot(aes(x = start_pos, xend = diff_cumulative, y = rei, yend = rei, colour = is_increasing)) +
geom_segment( arrow = arrow(length = unit(0.05, "npc"))) +
facet_grid(sdi ~ year) +
geom_vline(xintercept = 0) +
geom_vline(aes(xintercept = max_cumulative), linetype = "dashed") +
guides(colour = FALSE) +
labs(x = "Gender difference in rates", y = "Risk factors (level 2)", title = "Contribution of risk factors to gender differences in all-cause mortality rates", subtitle = "Age-standardised rates per 100 000", caption = "Source: GBD")
ggsave("figures/contribution_of_level2_riskfactors_death_rate.png", height = 30, width = 30, units = "cm", dpi = 300)
This seems to work pretty well though is perhaps at the limit of visual complexity. More time periods etc would likely make this image too busy.
---
title: "Risk Factors"
output:
  html_notebook: default
  word_document: default
---

# Intro

This doc will explore mortality and DALYs by risk factors 

First to load the pre requisite packages and script for downloading data. 

```{r}
library(tidyverse)

source("download_completed_request.R")
```

Next I will download the data from the request. (Only has to be done once so now set not to evaluate (run)).


```{r, eval = F, comment= F, echo = F}


url_body <- "http://s3.healthdata.org/gbd-api-2016-production/6790a3f2d5948a86929939c83525f81e_files"
url_head <- "IHME-GBD_2016_DATA-6790a3f2-"
outdir <- "raw_data/risk"

download_completed_request(url_body, url_head, outdir, flush = T)


```

The files can now be loaded and joined locally as follows 

```{r}
dta <- read_csv("raw_data/risk/1.csv") %>% bind_rows(read_csv("raw_data/risk/2.csv"))

dta
glimpse(dta)

```

The `_id` suffix columns can be used to join to the lookup tables. In this case for risk (by rei_id)

```{r}
lookup <- readxl::read_excel("raw_data/IHME_GBD_2016_CODEBOOK/IHME_GBD_2016_REI_HIERARCHY_Y2018M04D26.XLSX")
lookup
glimpse(lookup)

```

In this example I'll join then filter only level 1 categories in the rei hierarchy 

```{r}
dta %>% 
  left_join(lookup) %>% 
  filter(level == 1) -> dta_lvl1
```

Now to start exploring: age-standardised, global, deaths, by lvl 1 risk factor and gender 

```{r}

dta_lvl1 %>%
  filter(measure_name == "Deaths") %>% 
  filter(location_name == "Global") %>% 
  filter(age_name == "Age-standardized") %>% 
  filter(metric_name == "Rate") %>% 
  filter(cause_name == "All causes") %>% 
  select(year, sex = sex_name, risk_factor = rei_name, death_rate = val) %>% 
  ggplot(aes(x = year, y = death_rate, linetype = sex, colour = sex)) + 
  facet_wrap(~ risk_factor) +
  geom_line()

```

Now to do the same by SDI

```{r}

dta_lvl1 %>%
  filter(measure_name == "Deaths") %>% 
  filter(location_name != "Global") %>% 
  filter(age_name == "Age-standardized") %>% 
  filter(metric_name == "Rate") %>% 
  filter(cause_name == "All causes") %>% 
  select(year, sex = sex_name, sdi = location_name, risk_factor = rei_name, death_rate = val) %>% 
  mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>% 
  mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T)) %>% 
  ggplot(aes(x = year, y = death_rate, linetype = sex, colour = sex)) + 
  facet_grid(risk_factor ~ sdi) +
  geom_line()

```

And now let's see what this implies for relative inequalities


```{r}

dta_lvl1 %>%
    filter(measure_name == "Deaths") %>% 
    filter(location_name != "Global") %>% 
    filter(age_name == "Age-standardized") %>% 
    filter(metric_name == "Rate") %>% 
    filter(cause_name == "All causes") %>% 
    select(year, sex = sex_name, sdi = location_name, risk_factor = rei_name, death_rate = val) %>% 
    mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>% 
    mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T)) %>% spread(sex, death_rate) %>% mutate(ratio = Male / Female) %>% 
  ggplot(aes(x = year, y = ratio, colour = risk_factor)) + 
  geom_line() + 
  facet_grid(.  ~ sdi) + 
  geom_hline(yintercept = 1)

```

# DALY decomposition 

Let's look at the contribution of different risk factors to DALY difference


```{r}
dta_lvl1 %>% 
  filter(measure_name == "DALYs (Disability-Adjusted Life Years)") %>% 
  filter(location_name != "Global") %>% 
  filter(cause_name == "All causes") %>% 
  filter(age_name == "Age-standardized") %>% 
  filter(metric_name == "Rate") %>% 
  select(year, sex = sex_name, sdi = location_name, risk_factor = rei_name, daly_rate = val) %>% 
  mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>% 
  mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T)) %>% 
  ggplot(aes(x = year, y = daly_rate, fill = risk_factor), colour = "black") + 
  geom_area() + 
  facet_grid(sdi~sex)

```


Now proportion of all DALYs attributable to each risk factor by year and SDI
```{r}
dta_lvl1 %>% 
  filter(measure_name == "DALYs (Disability-Adjusted Life Years)") %>% 
  filter(location_name != "Global") %>% 
  filter(cause_name == "All causes") %>% 
  filter(age_name == "Age-standardized") %>% 
  filter(metric_name == "Rate") %>% 
  select(year, sex = sex_name, sdi = location_name, risk_factor = rei_name, daly_rate = val) %>% 
  mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>% 
  mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T)) %>% 
  group_by(year, sex, sdi) %>% 
  mutate(total = sum(daly_rate)) %>% 
  mutate(proportion_total = daly_rate / total) %>% 
  ungroup() %>%
  ggplot(aes(x = year, y = proportion_total, fill = risk_factor), colour = "black") + 
  geom_area() + 
  facet_grid(sdi~sex)

```

# Prop total deaths attributable to diff risk factors


Now proportion of total deaths 
```{r}
by_factor <- dta_lvl1 %>% 
  filter(measure_name == "Deaths") %>% 
  select(-measure_name, -measure_id) %>% 
  filter(location_name != "Global") %>% 
  filter(age_name == "Age-standardized") %>% 
  filter(metric_name == "Rate") %>% 
  filter(cause_name == "All causes") %>% 
  select(year, sex = sex_name, sdi = location_name, rei = rei_name, rate = val) %>%   mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>% 
  mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T))

overall <- dta %>% 
  filter(rei_name == "All risk factors") %>% 
  filter(measure_name == "Deaths") %>% 
  select(-measure_name, -measure_id) %>% 
  filter(location_name != "Global") %>% 
  filter(age_name == "Age-standardized") %>% 
  filter(metric_name == "Rate") %>% 
  filter(cause_name == "All causes") %>% 
  select(year, sex = sex_name, sdi = location_name, rei = rei_name, rate = val) %>%   mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>% 
  mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T))

joined <- bind_rows(by_factor, overall)
rm(by_factor, overall)

```

Unfortunately it seems 'all risk factors' is not the same as 'total', i.e. the absolute differences in age-standardised death rates from each of the three level 1 risk factors do not add up to the 'all risk factors' absolute difference


```{r}
joined %>% 
  spread(sex, rate) %>% 
  mutate(abs_diff = Male - Female) %>% 
  group_by(year, sdi) %>% 
  mutate(perc_of_abs_diff = 100 * abs_diff / abs_diff[rei=="All risk factors"]) %>% 
  summarise(discrepancy = sum(perc_of_abs_diff[rei != "All risk factors"]) - 100) %>% 
  ungroup() %>% 
  ggplot(aes(x = year, y = discrepancy, colour = sdi)) + 
  geom_line() 

```

So, the risk factors might not be mutually exclusive and exhaustive. Let's take middle SDI in 2000 as an example

```{r}
joined %>% 
  filter(sdi == "Middle") %>% 
  filter(year == 2000) %>% 
  spread(sex, rate) %>% 
  mutate(abs_diff = Male - Female) %>% 
  group_by(year, sdi) %>% 
  mutate(perc_of_abs_diff = 100 * abs_diff / abs_diff[rei=="All risk factors"]) 



```
So, the total of the three bottom rows adds up to around 160, when I might expect them to add to 100. A simple decomposition assumption doesn't seem to apply when using the 'all risk factors' designation as total. 

To try to account for this let's look at a different dataset, with:

* Age standardised ages
* All cause
* Mortality rates 
* By SDI

Let's try to grab this data with a new query 

The query link is [here](http://ghdx.healthdata.org/gbd-results-tool/result/c9bf1a5197bf46a966c1467c3103e8a9).


```{r, eval = F}

url_body <- "http://s3.healthdata.org/gbd-api-2016-production/c9bf1a5197bf46a966c1467c3103e8a9_files"
url_head <- "IHME-GBD_2016_DATA-c9bf1a51-"
outdir <- "raw_data/rate_all_cause"

download_completed_request(url_body, url_head, outdir, flush = T)
```


And now to load this file 

```{r}

dta_total <- read_csv("raw_data/rate_all_cause/1.csv")
```


```{r}

by_factor <- dta_lvl1 %>% 
  filter(measure_name == "Deaths") %>% 
  select(-measure_name, -measure_id) %>% 
  filter(location_name != "Global") %>% 
  filter(age_name == "Age-standardized") %>% 
  filter(metric_name == "Rate") %>% 
  filter(cause_name == "All causes") %>% 
  select(year, sex = sex_name, sdi = location_name, rei = rei_name, rate = val) %>%   mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>% 
  mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T))

overall <- dta_total %>% filter(location != "Global") %>% mutate(rei = "total") %>% mutate(sdi = stringr::str_replace(location, " SDI", "")) %>% 
    mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T)) %>% select(year, sex, sdi, rei, rate = val)


```


Let's see if the totals add up this time. The three level 1 risk factors are shown as stacked areas, and the total is shown as a black line... 


```{r}
by_factor %>% 
  ggplot(aes(x = year, y = rate, fill = rei)) + 
  geom_area() + 
  facet_grid(sex ~ sdi) +
  geom_line(aes(x = year, y = rate), inherit.aes = F, data = overall)

```


So, there is still discrepancy, which seems to be largest for low SDI, and smallest for high-middle SDI, but this might be decomposable subject to some assumptions.

Let's now compare with previous attempt at an 'overall' estimate (based on 'all risk factors'). In the following this is added as a black dashed line. We might expect the dashed and solid lines to be the same, but are they? 


```{r}
overall2 <- dta %>% 
  filter(rei_name == "All risk factors") %>% 
  filter(measure_name == "Deaths") %>% 
  select(-measure_name, -measure_id) %>% 
  filter(location_name != "Global") %>% 
  filter(age_name == "Age-standardized") %>% 
  filter(metric_name == "Rate") %>% 
  filter(cause_name == "All causes") %>% 
  select(year, sex = sex_name, sdi = location_name, rei = rei_name, rate = val) %>%   mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>% 
  mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T))

by_factor %>% 
  ggplot(aes(x = year, y = rate, fill = rei)) + 
  geom_area() + 
  facet_grid(sex ~ sdi) +
  geom_line(aes(x = year, y = rate), inherit.aes = F, data = overall) +
  geom_line(aes(x = year, y = rate), inherit.aes = F, data = overall2, linetype = "dashed")


```

So, there are clear differences between age-standardised rates drawn from all causes (solid line), and age-standardised rates drawm from all risk factors (dashed lines), with the latter below the former. Again, this raises questions about how to perform a decomposition into risk factors, the effect of age-standardization, and so on. 

# Level 1 internal decomposition 

Let's not use level 0 for decomposing level 1 etc, and instead just produce estimates of total by summing up level 1 risks. Then, do the same for level 2. 

```{r}

contributions <- by_factor %>% 
  inner_join(
    by_factor %>% 
      group_by(year, sex, sdi) %>% 
      summarise(total_rate = sum(rate)) %>% 
      ungroup()
    ) %>% 
  group_by(year, sdi) %>% 
  mutate(abs_sex_diff = total_rate[sex == "Male"][1] - total_rate[sex == "Female"][1]) %>% 
  group_by(year, sdi, rei) %>% 
  mutate(rei_abs_sex_diff = rate[sex == "Male"] - rate[sex == "Female"]) %>% 
  ungroup() %>% 
  mutate(contribution = 100 * rei_abs_sex_diff / abs_sex_diff)

```

Now to plot the contribution over time by SDI

```{r}
contributions %>% 
  select(year, sex, sdi, rei, contribution) %>% 
  distinct() %>% 
  ggplot(aes(x = year, y= contribution, colour = rei)) + 
  geom_line() + 
  facet_wrap(~sdi)

```


Let's think again about how to present this in the case of three risk factors, such that it could be scaled up to more factors 

```{r}
by_factor %>% 
  spread(sex, rate) %>% 
  mutate(diff_abs = Male - Female) %>% 
  group_by(year, sdi) %>%
  mutate(diff_cumulative = cumsum(diff_abs)) -> cumulative_contributions


```


Now to plot for two years, 1990 and 2010

```{r}

# use geom_segement

cumulative_contributions %>% 
    filter(year %in% c(1990, 2010)) %>% 
    mutate(start_pos = diff_cumulative - diff_abs) %>% 
    mutate(is_increasing = diff_cumulative > start_pos) %>% 
  group_by(year, sdi) %>% 
  mutate(max_cumulative = diff_cumulative[length(diff_cumulative)]) %>% 
  ungroup() %>% 
  ggplot(aes(x = start_pos, xend = diff_cumulative, y = rei, yend = rei, colour = is_increasing)) + 
  geom_segment( arrow = arrow(length = unit(0.2, "npc"))) + 
  facet_grid(sdi ~ year) +
  geom_vline(xintercept = 0) + 
  geom_vline(aes(xintercept = max_cumulative), linetype = "dashed") + 
  guides(colour = FALSE) + 
  labs(x = "Gender difference in rates", y = "Risk factors (level 1)", title = "Contribution of risk factors to gender differences in all-cause mortality rates", subtitle = "Age-standardised rates per 100 000", caption = "Source: GBD")


ggsave("figures/contribution_of_level1_riskfactors_death_rate.png", height = 30, width = 30, units = "cm", dpi = 300)


```

This seems to work as a visualisation. Let's try to scale up to level 2

```{r}
by_factor_l2 <- dta %>% 
  left_join(lookup) %>% 
  filter(level == 2)  %>% 
  filter(measure_name == "Deaths") %>% 
  select(-measure_name, -measure_id) %>% 
  filter(location_name != "Global") %>% 
  filter(age_name == "Age-standardized") %>% 
  filter(metric_name == "Rate") %>% 
  filter(cause_name == "All causes") %>% 
  select(year, sex = sex_name, sdi = location_name, rei = rei_name, rate = val) %>%   mutate(sdi = stringr::str_replace(sdi, " SDI", "")) %>% 
  mutate(sdi = factor(sdi, levels = c("Low", "Low-middle", "Middle", "High-middle", "High"), ordered = T)) 
```


```{r}
by_factor_l2 %>% 
  spread(sex, rate) %>% 
  mutate(diff_abs = Male - Female) %>% 
  group_by(year, sdi) %>%
  mutate(diff_cumulative = cumsum(diff_abs)) %>% 
    filter(year %in% c(1990, 2010)) %>% 
    mutate(start_pos = diff_cumulative - diff_abs) %>% 
    mutate(is_increasing = diff_cumulative > start_pos) %>% 
  group_by(year, sdi) %>% 
  mutate(max_cumulative = diff_cumulative[length(diff_cumulative)]) %>% 
  ungroup() %>% 
  ggplot(aes(x = start_pos, xend = diff_cumulative, y = rei, yend = rei, colour = is_increasing)) + 
  geom_segment( arrow = arrow(length = unit(0.05, "npc"))) + 
  facet_grid(sdi ~ year) +
  geom_vline(xintercept = 0) + 
  geom_vline(aes(xintercept = max_cumulative), linetype = "dashed") + 
  guides(colour = FALSE) + 
  labs(x = "Gender difference in rates", y = "Risk factors (level 2)", title = "Contribution of risk factors to gender differences in all-cause mortality rates", subtitle = "Age-standardised rates per 100 000", caption = "Source: GBD")

ggsave("figures/contribution_of_level2_riskfactors_death_rate.png", height = 30, width = 30, units = "cm", dpi = 300)

```

This seems to work pretty well though is perhaps at the limit of visual complexity. More time periods etc would likely make this image too busy.

