healthiar (version 0.2.1)

get_risk: Get the relative risk of an exposure level

Description

This function re-scales the relative risk from the increment value in the epidemiological study (e.g. for PM2.5 10 or 5 ug/m3) to the actual population exposure

Usage

get_risk(
  erf_shape = NULL,
  rr = NULL,
  rr_increment = NULL,
  erf_eq = NULL,
  cutoff = 0,
  exp
)

Value

This function returns the numeric relative risk(s) at the specified exposure level(s), referred to as rr_at_exp in the equations above.

Arguments

erf_shape

String value specifying the exposure-response function shape to be assumed. Options (no default): "linear", log_linear", "linear_log", "log_log". Only applicable in RR pathways; not required if erf_eq_... argument(s) already specified.

rr

Numeric vector containing the relative risk. The data frame must contain the central estimate as well as the lower and upper bound of the exposure-response function.

rr_increment

Numeric value specifying the exposure increment for which the provided relative risk is valid. See Details for more info. Only applicable in RR pathways; not required if erf_eq_... argument(s) already specified.

erf_eq

Equation of the user-defined exposure-response function that puts the relative risk (y) in relation with exposure (x). If the function is provided as string, it can only contains one variable: x (exposure). E.g. "3+x+x^2". If the function is provided as a function, the object should have a function class. If only the values of the x-axis (exposure) and y axis (relative risk) of the dots in the exposure-response function are available, a cubic spline natural interpolation can be assumed to get the function using, e.g., stats::splinefun(x, y, method="natural")

cutoff

Numeric value showing the cut-off exposure level in ug/m3 (i.e. the exposure level below which no health effects occur).

exp

Population exposure to the stressor (e.g. annual population-weighted mean).

Author

Alberto Castro & Axel Luyten

Details

Equations for scaling of relative risk

linear ERF $$rr\_at\_exp = 1 + \frac{(rr - 1)}{rr\_increment} \cdot (exp - cutoff)$$

log-linear ERF

$$rr\_at\_exp = e^{\frac{\log(\mathrm{rr})}{\mathrm{rr\_increment}} \cdot (\mathrm{exp} - \mathrm{cutoff})}$$

log-log ERF

$$rr\_at\_exp = (\frac{exp + 1}{cutoff + 1})^{\frac{\log(\mathrm{rr})}{\log(\mathrm{rr\_increment + cutoff + 1}) - \log(cutoff + 1)}}$$

linear-log ERF

$$rr\_at\_exp = 1 + \frac{\log(\mathrm{rr - 1})}{\log(\mathrm{rr\_increment + cutoff + 1}) - \log(cutoff + 1)} \cdot \frac{\log(exp + 1)}{\log(cutoff + 1)}$$

Sources

For the log-linear, log-log and linear-log exposure-response function equations see Pozzer et al. 2022 (https://doi.org/10.1029/2022GH000711).

Examples

# Goal: scale relative risk to observed exposure level
get_risk(
  rr = 1.05,
  rr_increment = 10,
  erf_shape = "linear",
  exp = 10,
  cutoff = 5
)