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161 lines
7.0 KiB
161 lines
7.0 KiB
#' Pneumonia and Influenza Mortality Surveillance
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#'
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#' The National Center for Health Statistics (NCHS) collects and disseminates the Nation's
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#' official vital statistics. NCHS collects death certificate data from state vital
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#' statistics offices for virtually all deaths occurring in the United States. Pneumonia
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#' and influenza (P&I) deaths are identified based on ICD-10
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#' multiple cause of death codes.\cr
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#' \cr
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#' NCHS Mortality Surveillance System data are presented by the week the death occurred
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#' at the national, state, and HHS Region levels. Data on the percentage of deaths due
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#' to P&I on a national level are released two weeks after the week of death to allow
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#' for collection of enough data to produce a stable percentage. States and HHS regions
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#' with less than 20% of the expected total deaths (average number of total deaths
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#' reported by week during 2008-2012) will be marked as insufficient data. Collection
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#' of complete data is not expected at the time of initial report, and a reliable
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#' percentage of deaths due to P&I is not anticipated at the U.S. Department of Health
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#' and Human Services region or state level within this two week period. The data for
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#' earlier weeks are continually revised and the proportion of deaths due to P&I may
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#' increase or decrease as new and updated death certificate data are received by NCHS.\cr
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#' \cr
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#' The seasonal baseline of P&I deaths is calculated using a periodic regression model
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#' that incorporates a robust regression procedure applied to data from the previous
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#' five years. An increase of 1.645 standard deviations above the seasonal baseline
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#' of P&I deaths is considered the "epidemic threshold," i.e., the point at which
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#' the observed proportion of deaths attributed to pneumonia or influenza was
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#' significantly higher than would be expected at that time of the year in the
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#' absence of substantial influenza-related mortality. Baselines and thresholds are
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#' calculated at the national and regional level and by age group.
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#'
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#' @md
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#' @param coverage_area coverage area for data (national, state or region)
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#' @param years a vector of years to retrieve data for (i.e. `2014` for CDC
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#' flu season 2014-2015). CDC has data for this API going back to 2009
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#' and up until the current, active flu season.
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#' Default value (`NULL`) means retrieve **all** years. NOTE: if you
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#' happen to specify a 2-digit season value (i.e. `57` == 2017-2018)
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#' the function is smart enough to retrieve by season ID vs convert that
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#' to a year.
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#' @note Queries for "state" and "region" are not necessarily as "instantaneous" as other API endpoints and can near or over 30s retrieval delays.
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#'
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#' For consistency with \code{\link{ilinet}} columns indicating the week now contain 'week' instead of the previously abbreviation 'wk'.
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#' @references
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#' - [Pneumonia and Influenza Mortality Surveillance Portal](https://gis.cdc.gov/grasp/fluview/mortality.html)
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#' @export
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#' @examples \dontrun{
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#' ndf <- pi_mortality()
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#' sdf <- pi_mortality("state")
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#' rdf <- pi_mortality("region")
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#' }
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pi_mortality <- function(coverage_area=c("national", "state", "region"), years=NULL) {
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coverage_area <- match.arg(tolower(coverage_area), choices = c("national", "state", "region"))
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us_states <- read.csv("https://gis.cdc.gov/grasp/fluview/Flu7References/Data/USStates.csv",
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stringsAsFactors=FALSE)
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us_states <- setNames(us_states, c("region_name", "subgeoid", "state_abbr"))
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us_states <- us_states[,c("region_name", "subgeoid")]
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us_states$subgeoid <- as.character(us_states$subgeoid)
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meta <- jsonlite::fromJSON("https://gis.cdc.gov/grasp/flu7/GetPhase07InitApp?appVersion=Public")
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mapcode_df <- setNames(meta$nchs_mapcode[,c("mapcode", "description")], c("map_code", "callout"))
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mapcode_df$map_code <- as.character(mapcode_df$map_code)
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geo_df <- meta$nchs_geo_dim
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geo_df$geoid <- as.character(geo_df$geoid)
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age_df <- setNames(meta$nchs_ages, c("ageid", "age_label"))
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age_df$ageid <- as.character(age_df$ageid)
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sum_df <- meta$nchs_summary
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sum_df$seasonid <- as.character(sum_df$seasonid)
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sum_df$ageid <- as.character(sum_df$ageid)
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sum_df$geoid <- as.character(sum_df$geoid)
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available_seasons <- sort(meta$seasons$seasonid)
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if (is.null(years)) { # ALL YEARS
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years <- available_seasons
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} else { # specified years or seasons or a mix
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years <- as.numeric(years)
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years <- ifelse(years > 1996, years - 1960, years)
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years <- sort(unique(years))
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years <- years[years %in% available_seasons]
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if (length(years) == 0) {
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years <- rev(sort(meta$seasons$seasonid))[1]
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curr_season_descr <- meta$seasons[meta$seasons$seasonid == years, "description"]
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message(sprintf("No valid years specified, defaulting to this flu season => ID: %s [%s]",
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years, curr_season_descr))
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}
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}
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httr::POST(
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url = "https://gis.cdc.gov/grasp/flu7/PostPhase07DownloadData",
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httr::user_agent(.cdcfluview_ua),
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httr::add_headers(
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Origin = "https://gis.cdc.gov",
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Accept = "application/json, text/plain, */*",
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Referer = "https://gis.cdc.gov/grasp/fluview/mortality.html"
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),
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encode = "json",
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body = list(
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AppVersion = "Public",
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AreaParameters = list(list(ID=.geoid_map[coverage_area])),
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SeasonsParameters = lapply(years, function(.x) { list(ID=as.integer(.x)) }),
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AgegroupsParameters = list(list(ID="1"))
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),
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# httr::verbose(),
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httr::timeout(.httr_timeout)
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) -> res
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httr::stop_for_status(res)
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res <- httr::content(res, as="parsed", flatten=TRUE)
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suppressWarnings(suppressMessages(dplyr::bind_rows(res$seasons) %>%
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dplyr::left_join(mapcode_df, "map_code") %>%
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dplyr::left_join(geo_df, "geoid") %>%
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dplyr::left_join(age_df, "ageid") %>%
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dplyr::left_join(dplyr::mutate(mmwrid_map, mmwrid=as.character(mmwrid)), "mmwrid") -> xdf))
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xdf <- dplyr::mutate(xdf, coverage_area = coverage_area)
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if (coverage_area == "state") {
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xdf <- dplyr::left_join(xdf, us_states, "subgeoid")
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} else if (coverage_area == "region") {
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xdf$region_name <- sprintf("Region %s", xdf$subgeoid)
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} else {
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xdf$region_name <- "national"
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}
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xdf[,c("seasonid", "baseline", "threshold", "percent_pni",
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"percent_complete", "number_influenza", "number_pneumonia",
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"all_deaths", "Total_PnI", "weeknumber", "geo_description",
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"age_label", "wk_start", "wk_end", "year_wk_num", "mmwrid",
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"coverage_area", "region_name", "callout")] -> xdf
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xdf$baseline <- to_num(xdf$baseline) / 100
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xdf$threshold <- to_num(xdf$threshold) / 100
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xdf$percent_pni <- to_num(xdf$percent_pni) / 100
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xdf$percent_complete <- to_num(xdf$percent_complete) / 100
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xdf$number_influenza <- to_num(xdf$number_influenza)
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xdf$number_pneumonia <- to_num(xdf$number_pneumonia)
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xdf$all_deaths <- to_num(xdf$all_deaths)
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xdf$Total_PnI <- to_num(xdf$Total_PnI)
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xdf <- xdf %>%
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dplyr::rename(
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week_start = wk_start,
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week_end = wk_end,
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year_week_num = year_wk_num
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)
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xdf <- .mcga(xdf)
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xdf
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}
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