calculate total rate based on point estimates

calculate total rate based on point estimates

Usage

aggregate_maternal_estimates(
  estimates,
  ego.dat,
  sib.dat,
  only_females = TRUE,
  age_prop = NULL,
  vis_res = NULL
)

Arguments

estimates

the output of sibling_estimator

ego.dat

the prepped ego data

sib.dat

the prepped sibling data

only_females

only keep female estimates? Defaults to TRUE; see Details, below

age_prop

optional, the respondent age distribution from get_ego_age_distn. Computed internally when NULL (the default); pass it in to avoid recomputing it; see Details

vis_res

optional, the visibility results from get_visibility. Computed internally when NULL (the default); pass it in to avoid recomputing it; see Details

Value

either a tibble with the estimates aggregated across age groups OR, if there are bootstrap results, then a list with three entries:

• point - the point estimates (should be the same as running w/out bootstraps)

• boot_summ - estimates w/ confidence intervals calculated from bootstrap reps

• boot - results for each bootstrap rep

Details

This function aggregates age-specific maternal mortality quantities like the maternal death rate or the pregnancy-related death rate. It uses the age distribution of the survey respondents as the reference population.

Note that, by default, this will only consider estimates for females aged 15-49, as this is what makes sense for aggregating maternal estimates. It might occasionally be useful to run this function for all-cause mortality; in that case, setting the parameter only_female=FALSE will include male estimates, too (but will still restrict to ages 15-49)

age_prop and vis_res are computed from ego.dat and sib.dat when they are not supplied, which is the usual case. They are exposed because callers frequently need them for their own age-specific output, and frequently call this function more than once per survey (for example, once for all-cause and once for pregnancy-related mortality). Computing them once and passing them in avoids repeating identical work; it does not change any result.