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R/u5mr_cohort.R
In u5mr: Under-Five Child Mortality Estimation
Defines functions
u5mr_cohort
Documented in
u5mr_cohort
#' Estimating under-five mortality using Maternal age cohort-derived methods
#'
#' @description
#'
#' `r lifecycle::badge('stable')`
#'
#' `u5mr_cohort()` uses the maternal age cohort-derived methods (MAC) through summary
#' birth history information and maternal age (or time since first birth) to
#' calculate the under-five mortality estimates.
#'
#' @param data preprocessed data
#' @param women total number of women
#' @param child_born children ever born
#' @param child_dead children dead
#' @param agegrp age grouping or time since first birth
#' @param iso3 the `iso3` code of the country from which the survey data come
#' @param svy_year end of the survey
#'
#'
#' @details
#'
#' In this cohort-derived method, under-five mortality and reference time are estimated
#' through summary birth history information from the mothers and her age or time since
#' her first birth.
#'
#' In case sample weights are available for the mothers, final variables
#' should be multiplied by these weights before summarizing.
#'
#' \strong{Computational Procedure}
#'
#' Two formulas were used to quantify MAC method:
#'
#' \strong{\eqn{5q0} component}
#'
#' logit(\out{5q0<sub>ijk</sub>}) = \eqn{\beta}\out{<sub>0i</sub>} + \out{U<sub>ij</sub>} +
#' \eqn{\beta}\out{<sub>1i</sub>} x logit(\out{CD<sub>ijk</sub>} /
#' \out{CEB<sub>ijk</sub>}) + \eqn{\beta}\out{<sub>2i</sub>} x \out{CEB<sub>ijk</sub>} +
#' \eqn{\beta}\out{<sub>3i</sub>} x PR1 + \eqn{\beta}\out{<sub>4i</sub>} x PR2 +
#' \eqn{\epsilon}\out{<sub>ijk</sub>}
#'
#' where
#'
#' i = 5-year maternal age group \eqn{\epsilon} (15-19, 20-24, ... , 45-49)
#' j = country
#' k = survey
#' \out{CD<sub>i</sub>} = total dead children from maternal age group `i`
#' \out{CEB<sub>i</sub>} = total children ever born from maternal age group `i`
#' PR1 = ratio of parity among maternal age group 15-19 and parity among maternal age
#' group 20-24
#' PR2 = ratio of parity among maternal age group 20-24 and parity among maternal age
#' group 25-29
#' \out{U<sub>ij</sub>} = country-specific random effects
#'
#' All coefficients vary by maternal age group, as indicated by `i` and the random
#' effects also vary by country, as indicated by `j`.
#'
#'
#' \strong{Reference time component}
#'
#' \out{reftime<sub>ijk</sub>} = \eqn{\beta}\out{<sub>0i</sub>} +
#' \eqn{\beta}\out{<sub>1i</sub>} x (\out{CD<sub>ijk</sub>} /
#' \out{CEB<sub>ijk</sub>}) +
#' \eqn{\beta}\out{<sub>2i</sub>} x \out{CEB<sub>ijk</sub>} +
#' \eqn{\beta}\out{<sub>3i</sub>} x PR1 + \eqn{\beta}\out{<sub>4i</sub>} x PR2 +
#' \eqn{\epsilon}\out{<sub>ijk</sub>}
#'
#'
#'
#' @references
#'
#' Rajaratnam JK, Tran LN, Lopez AD, Murray CJL (2010) Measuring Under-Five Mortality: Validation of New Low-Cost Methods. PLOS Medicine 7(4): e1000253.
#' (\doi{10.1371/journal.pmed.1000253}{10.1371/journal.pmed.1000253})
#'
#' @return `data.frame`
#'
#' * `iso3` - total number of women
#' * `svy_year` - total number of children ever born
#' * `agegrp` - five-year age groups
#' * `ref_time` - time between survey year and interpolated year
#' * `year` - reference year
#' * `q5` - under-five mortality
#'
#' @examples
#'
#' ## Example using fake survey data from Cambodia
#' data(cambodia)
#' camb <- u5mr_cohort(cambodia, women = "women", child_born = "child_born",
#' child_dead = "child_dead", agegrp = "agegrp", iso3 = "KHM", svy_year = 1234)
#'
#' with(camb,
#' plot(year, q5 * 1000, type = "b", pch = 19,
#' col = "black", xlab = "Year", ylab = "U5MR per 1000 live births",
#' main = paste0("Under-five mortality, q(5) in Bangladesh, estimated\n",
#' "using the maternal age cohort-derived method")))
#'
#' @export
u5mr_cohort <- function(data, women = "women",
child_born = "child_born", child_dead = "child_dead",
agegrp = "agegrp", iso3 = "KHM", svy_year = 2010) {
agegrp <- data[[agegrp]]
women <- data[[women]]
child_born <- data[[child_born]]
child_dead <- data[[child_dead]]
pi <- child_born / women
di <- child_dead / child_born
logit_di <- log(di / (1 - di))
pr1 <- pi[1] / pi[2]
pr2 <- pi[2] / pi[3]
re <- mac_re$re[mac_re$iso3 == iso3]
## if there is no country that matches, then re = 0
if (length(re) == 0) re <- 0
## calculate logit 5q0 values
logit_5q0 <- mac_5q0$coef_intercept + re + mac_5q0$coef_log_cdceb *
logit_di + mac_5q0$coef_ceb * pi + mac_5q0$coef_pr1 * pr1 +
mac_5q0$coef_pr2 * pr2
## convert back to normal values by exponentiating
q5 <- exp(logit_5q0) / (1 + exp(logit_5q0))
## calculate reference time
ref_time <- mac_reftime$coef_intercept + mac_reftime$coef_cdceb * di +
mac_reftime$coef_ceb * pi + mac_reftime$coef_pr1 * pr1 +
mac_reftime$coef_pr2 * pr2
year <- svy_year - ref_time
data.frame(iso3, svy_year, agegrp, ref_time, year, q5)
}
# HELPERS -----------------------------------------------------------------
mac_5q0 <- data.frame(
agegrp = c('15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49'),
coef_intercept = c(-0.6975273, 0.1258313, 0.0167552, -0.1034484, -0.0852121, -0.237591, 0.044768),
coef_log_cdceb = c(0.5555944, 0.8853436, 0.9853121, 0.9679322, 0.9728206, 0.9951801, 1.01955),
coef_ceb = c(2.573548, 0.2111931, 0.0523679, 0.0180773, 0.0147395, 0.0206229, 0.0016674),
coef_pr1 = c(-3.923797, -0.0087424, 0.0705175, -0.0029778, 0.2246798, 0.162713, 0.8637883),
coef_pr2 = c(-0.2960762, -1.110689, -0.2891437, -0.0033048, -0.2317203, -0.0566164, -0.8015343)
)
mac_reftime <- data.frame(
agegrp = c('15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49'),
coef_intercept = c(0.9432311, 2.533395, 4.303299, 7.268542, 11.42926, 15.48549, 19.90218),
coef_cdceb = c(0.0015192, 0.0006606, -0.0017479, -0.0000676, -0.0012584, -0.002121, -0.001287),
coef_ceb = c(0.1923932, 0.2812458, 0.1005495, -0.3103958, -0.4738352, -0.5497375, -0.5534297),
coef_pr1 = c(0.7542738, 0.8857344, 0.2908919, 0.5750943, 1.502711, 3.027917, 6.079983),
coef_pr2 = c(0.543375, 0.6180932, 2.843069, 4.691129, 4.441438, 3.965783, 0.9077534)
)
mac_re <- data.frame(
agegrp = c("15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29",
"30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39", "35-39",
"40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49"),
iso3 = c('ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD', 'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN', 'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE', 'ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD', 'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN', 'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE', 'ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD', 'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN', 'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE', 'ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD',
'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN', 'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE', 'ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD', 'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN', 'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE', 'ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD', 'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN',
'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE', 'ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD', 'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN', 'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE'),
re = c(-0.0740473806025451, -0.795791117654689, 0.0155759526158037, 0.167607916035274,
0.312169911719107, -0.296688023813686, -0.0347382273558816, -0.0807181991540473,
-0.00587074151497604, 0.0341199997542009, 0.0987730517784896, 0.214199601121983,
0.101434384497789, -0.252926194970638, 0.201941057537439, -0.335415771831015,
-0.13894049257474, -0.17633911342114, 0.308017811901251, 0.0622887918523105,
0.0495851202778916, 0.348063608603817, 0.269097292481718, -0.0822907533315262,
-0.45401938813222, 0.242555390947918, -0.0414108708084773, -0.235634428567539,
-0.410657587622013, 0.461005429816519, -0.114078617228897, 0.124042825789835,
0.112012316556841, 0.177191559784913, -0.533245228365606, 0.244690752241358,
-0.253770138470807, 0.0427130903724443, -0.151949500816286, 0.221563125229524,
0.121359808817806, 0.251304137747192, 0.141382927962772, 0.0979680619614078,
0.362533204668132, 0.468893124782236, -0.238506315601834, -0.215977145004483,
-0.268123473779954, -0.131684862494324, -0.256291791064001, -0.503895050382619,
0.435827395207834, -0.00818867178546823, 0.202913086086713, 0.388892640993358,
0.116454974260369, 0.239297053094394, 0.206897543532596, -0.444633894827663,
0.102355012116847, -0.265088057216033, 0.171283416422014, 0.068321016313176,
-0.399809598084503, -0.0282595054955628, -0.113126794403926, 0.0296563717984058,
0.278059822173844, -0.149931652478513, 0.0226273023933315, 0.191677673151666,
0.0793470615451354, 0.0432160268164751, 0.0454145682561387, -0.00101334949487753,
-0.125846269439958, -0.105600603498696, -0.0327436405540945, 0.0261922848616024,
-0.0519554128785404, -0.0107890447217732, 0.0735177332022234, -0.0613379469231933,
-0.049151442842105, -0.183711910937571, 0.0114849866708276, -0.0301466123730515,
0.133021245486241, -0.0241454496412133, -0.0193380761575792, 0.0156187626309521,
0.115575865490356, -0.0993359505210646, -0.0674513480183834, -0.034396588453635,
0.065713848824011, -0.0172355741140534, -0.234405361204415, 0.116528251911681,
-0.0268324932077755, -0.0793378313527664, 0.126938872898581, 0.0190697557242236,
0.128202523894199, 0.0743618838667504, -0.0959220218156416, 0.016882362050265,
0.0343723054707976, 0.0841920122399618, 0.02395193795347, -0.0461280053433558,
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Defines functions u5mr_cohort
Documented in u5mr_cohort
#' Estimating under-five mortality using Maternal age cohort-derived methods
#'
#' @description
#'
#' `r lifecycle::badge('stable')`
#'
#' `u5mr_cohort()` uses the maternal age cohort-derived methods (MAC) through summary
#' birth history information and maternal age (or time since first birth) to
#' calculate the under-five mortality estimates.
#'
#' @param data preprocessed data
#' @param women total number of women
#' @param child_born children ever born
#' @param child_dead children dead
#' @param agegrp age grouping or time since first birth
#' @param iso3 the `iso3` code of the country from which the survey data come
#' @param svy_year end of the survey
#'
#'
#' @details
#'
#' In this cohort-derived method, under-five mortality and reference time are estimated
#' through summary birth history information from the mothers and her age or time since
#' her first birth.
#'
#' In case sample weights are available for the mothers, final variables
#' should be multiplied by these weights before summarizing.
#'
#' \strong{Computational Procedure}
#'
#' Two formulas were used to quantify MAC method:
#'
#' \strong{\eqn{5q0} component}
#'
#' logit(\out{5q0<sub>ijk</sub>}) = \eqn{\beta}\out{<sub>0i</sub>} + \out{U<sub>ij</sub>} +
#' \eqn{\beta}\out{<sub>1i</sub>} x logit(\out{CD<sub>ijk</sub>} /
#' \out{CEB<sub>ijk</sub>}) + \eqn{\beta}\out{<sub>2i</sub>} x \out{CEB<sub>ijk</sub>} +
#' \eqn{\beta}\out{<sub>3i</sub>} x PR1 + \eqn{\beta}\out{<sub>4i</sub>} x PR2 +
#' \eqn{\epsilon}\out{<sub>ijk</sub>}
#'
#' where
#'
#' i = 5-year maternal age group \eqn{\epsilon} (15-19, 20-24, ... , 45-49)
#' j = country
#' k = survey
#' \out{CD<sub>i</sub>} = total dead children from maternal age group `i`
#' \out{CEB<sub>i</sub>} = total children ever born from maternal age group `i`
#' PR1 = ratio of parity among maternal age group 15-19 and parity among maternal age
#' group 20-24
#' PR2 = ratio of parity among maternal age group 20-24 and parity among maternal age
#' group 25-29
#' \out{U<sub>ij</sub>} = country-specific random effects
#'
#' All coefficients vary by maternal age group, as indicated by `i` and the random
#' effects also vary by country, as indicated by `j`.
#'
#'
#' \strong{Reference time component}
#'
#' \out{reftime<sub>ijk</sub>} = \eqn{\beta}\out{<sub>0i</sub>} +
#' \eqn{\beta}\out{<sub>1i</sub>} x (\out{CD<sub>ijk</sub>} /
#' \out{CEB<sub>ijk</sub>}) +
#' \eqn{\beta}\out{<sub>2i</sub>} x \out{CEB<sub>ijk</sub>} +
#' \eqn{\beta}\out{<sub>3i</sub>} x PR1 + \eqn{\beta}\out{<sub>4i</sub>} x PR2 +
#' \eqn{\epsilon}\out{<sub>ijk</sub>}
#'
#'
#'
#' @references
#'
#' Rajaratnam JK, Tran LN, Lopez AD, Murray CJL (2010) Measuring Under-Five Mortality: Validation of New Low-Cost Methods. PLOS Medicine 7(4): e1000253.
#' (\doi{10.1371/journal.pmed.1000253}{10.1371/journal.pmed.1000253})
#'
#' @return `data.frame`
#'
#' * `iso3` - total number of women
#' * `svy_year` - total number of children ever born
#' * `agegrp` - five-year age groups
#' * `ref_time` - time between survey year and interpolated year
#' * `year` - reference year
#' * `q5` - under-five mortality
#'
#' @examples
#'
#' ## Example using fake survey data from Cambodia
#' data(cambodia)
#' camb <- u5mr_cohort(cambodia, women = "women", child_born = "child_born",
#' child_dead = "child_dead", agegrp = "agegrp", iso3 = "KHM", svy_year = 1234)
#'
#' with(camb,
#' plot(year, q5 * 1000, type = "b", pch = 19,
#' col = "black", xlab = "Year", ylab = "U5MR per 1000 live births",
#' main = paste0("Under-five mortality, q(5) in Bangladesh, estimated\n",
#' "using the maternal age cohort-derived method")))
#'
#' @export
u5mr_cohort <- function(data, women = "women",
child_born = "child_born", child_dead = "child_dead",
agegrp = "agegrp", iso3 = "KHM", svy_year = 2010) {
agegrp <- data[[agegrp]]
women <- data[[women]]
child_born <- data[[child_born]]
child_dead <- data[[child_dead]]
pi <- child_born / women
di <- child_dead / child_born
logit_di <- log(di / (1 - di))
pr1 <- pi[1] / pi[2]
pr2 <- pi[2] / pi[3]
re <- mac_re$re[mac_re$iso3 == iso3]
## if there is no country that matches, then re = 0
if (length(re) == 0) re <- 0
## calculate logit 5q0 values
logit_5q0 <- mac_5q0$coef_intercept + re + mac_5q0$coef_log_cdceb *
logit_di + mac_5q0$coef_ceb * pi + mac_5q0$coef_pr1 * pr1 +
mac_5q0$coef_pr2 * pr2
## convert back to normal values by exponentiating
q5 <- exp(logit_5q0) / (1 + exp(logit_5q0))
## calculate reference time
ref_time <- mac_reftime$coef_intercept + mac_reftime$coef_cdceb * di +
mac_reftime$coef_ceb * pi + mac_reftime$coef_pr1 * pr1 +
mac_reftime$coef_pr2 * pr2
year <- svy_year - ref_time
data.frame(iso3, svy_year, agegrp, ref_time, year, q5)
}
# HELPERS -----------------------------------------------------------------
mac_5q0 <- data.frame(
agegrp = c('15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49'),
coef_intercept = c(-0.6975273, 0.1258313, 0.0167552, -0.1034484, -0.0852121, -0.237591, 0.044768),
coef_log_cdceb = c(0.5555944, 0.8853436, 0.9853121, 0.9679322, 0.9728206, 0.9951801, 1.01955),
coef_ceb = c(2.573548, 0.2111931, 0.0523679, 0.0180773, 0.0147395, 0.0206229, 0.0016674),
coef_pr1 = c(-3.923797, -0.0087424, 0.0705175, -0.0029778, 0.2246798, 0.162713, 0.8637883),
coef_pr2 = c(-0.2960762, -1.110689, -0.2891437, -0.0033048, -0.2317203, -0.0566164, -0.8015343)
)
mac_reftime <- data.frame(
agegrp = c('15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49'),
coef_intercept = c(0.9432311, 2.533395, 4.303299, 7.268542, 11.42926, 15.48549, 19.90218),
coef_cdceb = c(0.0015192, 0.0006606, -0.0017479, -0.0000676, -0.0012584, -0.002121, -0.001287),
coef_ceb = c(0.1923932, 0.2812458, 0.1005495, -0.3103958, -0.4738352, -0.5497375, -0.5534297),
coef_pr1 = c(0.7542738, 0.8857344, 0.2908919, 0.5750943, 1.502711, 3.027917, 6.079983),
coef_pr2 = c(0.543375, 0.6180932, 2.843069, 4.691129, 4.441438, 3.965783, 0.9077534)
)
mac_re <- data.frame(
agegrp = c("15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "15-19", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "20-24", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29",
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"40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "40-44", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49", "45-49"),
iso3 = c('ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD', 'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN', 'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE', 'ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD', 'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN', 'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE', 'ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD', 'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN', 'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE', 'ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD',
'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN', 'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE', 'ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD', 'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN', 'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE', 'ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD', 'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN',
'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE', 'ARM', 'AZE', 'BDI', 'BEN', 'BFA', 'BGD', 'BOL', 'BRA', 'BWA', 'CAF', 'CIV', 'CMR', 'COG', 'COL', 'COM', 'DOM', 'ECU', 'EGY', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GTM', 'HND', 'HTI', 'IDN', 'IND', 'JOR', 'KAZ', 'KEN', 'KGZ', 'KHM', 'LBR', 'LKA', 'LSO', 'MAR', 'MDG', 'MEX', 'MLI', 'MOZ', 'MRT', 'MWI', 'NAM', 'NER', 'NGA', 'NIC', 'NPL', 'PAK', 'PER', 'PHL', 'PRY', 'RWA', 'SDN', 'SEN', 'SWZ', 'TCD', 'TGO', 'THA', 'TTO', 'TUN', 'TUR', 'TZA', 'UGA', 'UZB', 'VNM', 'YEM', 'ZAF', 'ZMB', 'ZWE'),
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