You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

127 lines
5.7 KiB

#' Pneumonia and Influenza Mortality Surveillance
#' The National Center for Health Statistics (NCHS) collects and disseminates the Nation's
#' official vital statistics. NCHS collects death certificate data from state vital
#' statistics offices for virtually all deaths occurring in the United States. Pneumonia
#' and influenza (P&I) deaths are identified based on ICD-10
#' multiple cause of death codes.\cr
#' \cr
#' NCHS Mortality Surveillance System data are presented by the week the death occurred
#' at the national, state, and HHS Region levels. Data on the percentage of deaths due
#' to P&I on a national level are released two weeks after the week of death to allow
#' for collection of enough data to produce a stable percentage. States and HHS regions
#' with less than 20% of the expected total deaths (average number of total deaths
#' reported by week during 2008-2012) will be marked as insufficient data. Collection
#' of complete data is not expected at the time of initial report, and a reliable
#' percentage of deaths due to P&I is not anticipated at the U.S. Department of Health
#' and Human Services region or state level within this two week period. The data for
#' earlier weeks are continually revised and the proportion of deaths due to P&I may
#' increase or decrease as new and updated death certificate data are received by NCHS.\cr
#' \cr
#' The seasonal baseline of P&I deaths is calculated using a periodic regression model
#' that incorporates a robust regression procedure applied to data from the previous
#' five years. An increase of 1.645 standard deviations above the seasonal baseline
#' of P&I deaths is considered the "epidemic threshold," i.e., the point at which
#' the observed proportion of deaths attributed to pneumonia or influenza was
#' significantly higher than would be expected at that time of the year in the
#' absence of substantial influenza-related mortality. Baselines and thresholds are
#' calculated at the national and regional level and by age group.
#' @md
#' @param coverage_area coverage area for data (national, state or region)
#' @note Queries for "state" and "region" are not "instantaneous" and can near or over 30s retrieval delays.
#' @references
#' - [Pneumonia and Influenza Mortality Surveillance Portal](
#' @export
#' @examples \dontrun{
#' ndf <- pi_mortality()
#' sdf <- pi_mortality("state")
#' rdf <- pi_mortality("region")
#' }
pi_mortality <- function(coverage_area=c("national", "state", "region")) {
coverage_area <- match.arg(tolower(coverage_area), choices = c("national", "state", "region"))
us_states <- read.csv("",
us_states <- setNames(us_states, c("region_name", "subgeoid", "state_abbr"))
us_states <- us_states[,c("region_name", "subgeoid")]
us_states$subgeoid <- as.character(us_states$subgeoid)
meta <- jsonlite::fromJSON("")
mapcode_df <- setNames(meta$nchs_mapcode[,c("mapcode", "description")], c("map_code", "callout"))
mapcode_df$map_code <- as.character(mapcode_df$map_code)
geo_df <- meta$nchs_geo_dim
geo_df$geoid <- as.character(geo_df$geoid)
age_df <- setNames(meta$nchs_ages, c("ageid", "age_label"))
age_df$ageid <- as.character(age_df$ageid)
sum_df <- meta$nchs_summary
sum_df$seasonid <- as.character(sum_df$seasonid)
sum_df$ageid <- as.character(sum_df$ageid)
sum_df$geoid <- as.character(sum_df$geoid)
url = "",
Origin = "",
Accept = "application/json, text/plain, */*",
Referer = ""
encode = "json",
body = list(
AppVersion = "Public",
AreaParameters = list(list(ID=.geoid_map[coverage_area])),
SeasonsParameters = lapply(meta$seasons$seasonid, function(.x) { list(ID=as.integer(.x)) }),
AgegroupsParameters = list(list(ID="1"))
# httr::verbose(),
) -> res
res <- httr::content(res, as="parsed", flatten=TRUE)
dplyr::bind_rows(res$seasons) %>%
dplyr::left_join(mapcode_df, "map_code") %>%
dplyr::left_join(geo_df, "geoid") %>%
dplyr::left_join(age_df, "ageid") %>%
dplyr::left_join(dplyr::mutate(mmwrid_map, mmwrid=as.character(mmwrid)), "mmwrid") -> xdf
xdf <- dplyr::mutate(xdf, coverage_area = coverage_area)
if (coverage_area == "state") {
xdf <- dplyr::left_join(xdf, us_states, "subgeoid")
} else if (coverage_area == "region") {
xdf$region_name <- sprintf("Region %s", xdf$subgeoid)
} else {
xdf$region_name <- "national"
xdf[,c("seasonid", "baseline", "threshold", "percent_pni",
"percent_complete", "number_influenza", "number_pneumonia",
"all_deaths", "Total_PnI", "weeknumber", "geo_description",
"age_label", "wk_start", "wk_end", "year_wk_num", "mmwrid",
"coverage_area", "region_name", "callout")] -> xdf
suppressWarnings(xdf$baseline <- to_num(xdf$baseline) / 100)
suppressWarnings(xdf$threshold <- to_num(xdf$threshold) / 100)
suppressWarnings(xdf$percent_pni <- to_num(xdf$percent_pni) / 100)
suppressWarnings(xdf$percent_complete <- to_num(xdf$percent_complete) / 100)
suppressWarnings(xdf$number_influenza <- to_num(xdf$number_influenza))
suppressWarnings(xdf$number_pneumonia <- to_num(xdf$number_pneumonia))
suppressWarnings(xdf$all_deaths <- to_num(xdf$all_deaths))
suppressWarnings(xdf$Total_PnI <- to_num(xdf$Total_PnI))
xdf <- .mcga(xdf)