PArisk.RdUses the 'new' binge model methods to calculate a relative risk for each individual for experiencing each cause during one year.
PArisk(
interval_prob_vec = NULL,
SODFreq = NULL,
Weight = NULL,
Widmark_r = NULL,
cause = "Transport",
grams_ethanol_per_unit = 8,
grams_ethanol_per_std_drink = 12.8,
liver_clearance_rate_h = 0.017,
getcurve = FALSE,
grams_ethanol = NULL
)Column of vectors - the probabilities that each individual drinks each amount of grams of ethanol (1:600) on a single drinking occasion.
Numeric vector - the expected number of drinking occasions that each individual has each week.
Numeric vector - each individual's body weight in kg.
Numeric vector - the fraction of the body mass in which alcohol would be present if it were distributed at concentrations equal to that in blood. See examples of use of the Widmark equation in Watson (1981) and Posey and Mozayani (2007).
Character - the acute cause being considered.
Numeric value giving the conversion factor for the number of grams of pure ethanol in one UK standard unit of alcohol.
Numeric value giving the conversion factor for the number of grams of ethanol in one standard drink.
The rate at which blood alcohol concentration declines (percent / hour).
Logical - do you just want to look at the risk function curve?
Numeric vector - if getcurve is TRUE, use this to specify the values of grams of ethanol for which curve values should be returned.
Returns a numeric vector of each individual's relative risk of the acute consequences of drinking.
This calculation treats an occasion as a single point in time and therefore does not detail about the rate of alcohol absorption (i.e. there is no alcohol absorption rate constant) or the time interval between drinks within an occasion. This could introduce inaccuracies if e.g. a drinking occasion lasted several hours. The methods to calculate the total time spent intoxicated (with blood alcohol content greater than zero) are discussed in Taylor et al 2011 and the discussion paper by Hill-McManus 2014. The relative risks for alcohol-related injuries are taken from Cherpitel et al 2015.
if (FALSE) {
## Further explanation
# For a male with the following characteristics:
Weight <- 70 # weight in kg
Height <- 2 # height in m
Age <- 25 # age in years
# We can estimate their r value from the Widmark equation
# using parameter values from Posey and Mozayani (2007)
Widmark_r <- 0.39834 + ((12.725 * Height - 0.11275 * Age + 2.8993) / Weight)
# They might drink from 1 to 100 grams of ethanol on one occassion
grams_ethanol <- 1:100
# In minutes, We would expect them to remain intoxicated
# (with blood alcohol content > 0 percent) for
Duration_m <- 100 * grams_ethanol / (Widmark_r * Weight * 1000 * (liver_clearance_rate_h / 60))
# and hours
Duration_h <- Duration_m / 60
# Duration is the length of time taken to clear all alcohol from the blood
# so we don't apply any thresholds of intoxication,
# we just calculate the expected length of time with a bac greater than 0.
# Now suppose that on average our example male has 5 drinking occasions per week, and that
# on average they drink 3 units of alcohol on an occasion,
# and that the standard deviation of amount drunk on an occasion is 14 units.
# The cumulative probability distribution of each amount of alcohol being drunk on an occassion is
x <- pnorm(grams_ethanol, 2 * 8, 14 * 8)
# Convert from the cumulative distribution to the
# probability that each level of alcohol is consumed on a drinking occasion
interval_prob <- x - c(0, x[1:(length(x) - 1)])
# The probability-weighted distribution of time spent intoxicated during a year (52 weeks) is
Time_intox <- 5 * 52 * interval_prob * Duration_h
# And the expected total time spent intoxicated is
Time_intox_sum <- sum(Time_intox)
# The relative risk of a transport injury corresponding to each amount drunk on a single occasion
# corresponds to the number of standard drinks consumed
# We convert to standard drinking and apply the risk parameters from Cherpitel
v <- grams_ethanol / 12.8
v1 <- (v + 1) / 100
# Parameters from Cherpitel
b1 <- 3.973538882
b2 <- 6.65184e-6
b3 <- 0.837637
b4 <- 1.018824
# Apply formula for the risk curve from Cherpital
lvold_1 <- log(v1) + b1
lvold_2 <- (v1^3) - b2
logitp <- lvold_1 * b3 + lvold_2 * b4
p <- boot::inv.logit(logitp)
# The relative risk associated with each amount drunk on an occasion
rr <- p / p[1]
# The relative risk multiplied by the time exposed to that level of risk
Current_risk <- rr * Time_intox
# The sum of the relative risk associated with the time spent intoxicated during one year
Risk_sum <- sum(Current_risk)
# The average annual relative risk, considering that time in the year spent with a
# blood alcohol content of zero has a relative risk of 1.
Annual_risk <- min(
(Risk_sum + 1 * (365 * 24 - Time_intox_sum)) / (365 * 24),
365 * 24, na.rm = T)
# THE FOLLOWING ARE NOT CONSIDERED IN THIS CALCULATION
# Elapsed time in minutes since consuming alcohol
t <- 30
# Alcohol absorbtion rate constant
k_empty_stomach <- 6.5 / 60 # grams of ethanol per minute
# Alcohol absorbtion
alcohol_absorbed <- grams_ethanol * (1 - exp(-k_empty_stomach * t))
# Calculate blood alcohol content using the Widemark eqn
bac <- (100 * alcohol_absorbed / (Widmark_r * Weight * 1000)) - ((liver_clearance_rate_h / 60) * t)
}