Causes of Mortality from 1999 to 2016

Recently in conversation, a relative mentioned anecdotally how heart complications seemed to be getting more and more treatable, and cited several examples of heart-attack victims she knew who had made successful recoveries from things that would have almost certainly been death sentences once. I thought it might be interesting to try and visualize this decline over the years, in addition to seeing if there are similar declines for other illnesses. I’ve also heard women and minorities talk about their struggles with the healthcare system, so I thought it would also be illuminating to see how the trends in mortality differ by race and gender.

To start with, I went to the CDC compressed mortality dataset and queried the 1999-2016 database. Each database organizes cause of death based on the International Classification of Disease (ICD), which has updated several times over the years. While older data is available, the classification system would be slightly different the further back I went, and I’d have to try to find some way of equating them all. Fortunately for my purposes, the 1999 to 2016 data, which uses the 10th edition of the ICD, should be plenty. I queried the system for information organized by ICD sub-chapter, year, age, race, and gender.

For this analysis, I’m not actually interested in examining the data by age, but I still need to query the age data in order to create age-adjusted death rates, which is what the CDC recommends using when comparing multiple populations. The reason for this is that some causes of death are far more common for certain age groups. Various illnesses are common for older adults but rare for children, or vice-versa. Alternatively, some things are much more dangerous depending on age group. For example, chicken-pox is considered fairly harmless for children, but can cause severe complications in adults. If we had a population with relatively few children, but lots of older adults, we might expect to see a much higher rate of death for chicken-pox, even if the rate of chicken-pox is exactly the same as a more child-heavy group. In order to account for this, age-adjusted mortality rates take a weighted average of different age groups in order to transform each population into a standardized population, where the proportions of each age group are the same. Currently the CDC uses the estimated population for the year 2000 as their standardized population. The estimates and the related age weights can be found here.

Unfortunately, the CDC Wonder query system won’t output any query that produces more than 75,000 results. In order to get all the data I wanted, I had to submit a separate query for each individual age range, meaning I actually have 14 datasets to work with.

Data Cleaning

Before I start analyzing the data, I need to do some basic data cleaning, and some formatting as well. Fortunately for this analysis, I’m just using data that was output by a single query system, and I was able to remove a lot of unwanted input at the time of the query, so there shouldn’t be too much cleaning needed.

To start with, I’ll have to merge my 14 datasets into a single dataframe. After that, I want to get a clear idea of what I’m working with, so I’m also going to ask for the dimensions of the dataframe as well as its basic structure.

library(tidyverse)
library(knitr)
library(kableExtra)

# Load data by age

df_1     <- read.delim("data/mortality_1999-2016_1.txt")
df_1_4   <- read.delim("data/mortality_1999-2016_1-4.txt")
df_5_9   <- read.delim("data/mortality_1999-2016_5-9.txt")
df_10_14 <- read.delim("data/mortality_1999-2016_10-14.txt")
df_15_19 <- read.delim("data/mortality_1999-2016_15-19.txt")
df_20_24 <- read.delim("data/mortality_1999-2016_20-24.txt")
df_25_34 <- read.delim("data/mortality_1999-2016_25-34.txt")
df_35_44 <- read.delim("data/mortality_1999-2016_35-44.txt")
df_35_44 <- read.delim("data/mortality_1999-2016_35-44.txt")
df_45_54 <- read.delim("data/mortality_1999-2016_45-54.txt")
df_55_64 <- read.delim("data/mortality_1999-2016_55-64.txt")
df_65_74 <- read.delim("data/mortality_1999-2016_65-74.txt")
df_75_84 <- read.delim("data/mortality_1999-2016_75-84.txt")
df_85    <- read.delim("data/mortality_1999-2016_85+.txt")

# Merge into single dataframe

df <- rbind(df_1, 
            df_1_4, 
            df_5_9, 
            df_10_14, 
            df_15_19, 
            df_20_24, 
            df_25_34, 
            df_35_44,
            df_45_54,
            df_55_64,
            df_65_74,
            df_75_84,
            df_85)

dim(df)

## [1] 138420     14

str(df)

## 'data.frame':    138420 obs. of  14 variables:
##  $ Notes               : chr  "" "" "" "" ...
##  $ ICD.Sub.Chapter     : chr  "Intestinal infectious diseases" "Intestinal infectious diseases" "Intestinal infectious diseases" "Intestinal infectious diseases" ...
##  $ ICD.Sub.Chapter.Code: chr  "A00-A09" "A00-A09" "A00-A09" "A00-A09" ...
##  $ Year                : int  1999 1999 1999 1999 2000 2000 2000 2000 2000 2001 ...
##  $ Year.Code           : int  1999 1999 1999 1999 2000 2000 2000 2000 2000 2001 ...
##  $ Age.Group           : chr  "< 1 year" "< 1 year" "< 1 year" "< 1 year" ...
##  $ Age.Group.Code      : chr  "1" "1" "1" "1" ...
##  $ Gender              : chr  "Female" "Female" "Male" "Male" ...
##  $ Gender.Code         : chr  "F" "F" "M" "M" ...
##  $ Race                : chr  "Black or African American" "White" "Black or African American" "White" ...
##  $ Race.Code           : chr  "2054-5" "2106-3" "2054-5" "2106-3" ...
##  $ Deaths              : int  2 4 3 12 1 3 1 4 8 2 ...
##  $ Population          : int  297942 1452001 307443 1528776 302401 1447232 84431 312650 1524372 316708 ...
##  $ Crude.Rate          : chr  "0.7 (Unreliable)" "0.3 (Unreliable)" "1.0 (Unreliable)" "0.8 (Unreliable)" ...

The only immediate issues I see are that several columns have redundant information. Most of them are also currently character strings, whereas I’d prefer them to be factors. From the look of the first few entries in the character string columns, there’s no immediately apparent reason why they can’t be simply converted into factors. I’ll take a closer look later, but for now no problems are jumping out for those columns.

One potentially larger issue is that I’d like the crude death rate to be numeric, rather than a character string. Because that column contains a lot of non-numeric values, such as several cells being marked “Unreliable”, it won’t convert simply into a numeric value. Normally I’d want to remove those character strings or split them off into a different column. However, I don’t need to do that for this dataframe, because I don’t actually want this column of crude death rates at all. The crude rate is calculated by the formula “(Deaths * 100,000) / Population”. This means that the crude rates I have right now only work for this specific grouping of the data. If, for example, I wanted to examine the change in death rate across race and cause of death, but not across gender, I’d have to calculate new crude rates using the sum of the deaths and population for both men and women. In fact, the only reason I included the crude rate in my query was because it’s mandatory output for the CDC Wonder query system. So, instead of trying to convert this column to being numeric, I can simply remove it from the dataframe later on, and then calculate the death rates for the groups I’m interested in when I need them.

The fact that some of the rates are marked as unreliable also seems like it might be a problem for the validity of my analysis, but it’s also not an issue. The reason they’ve been marked as unreliable is because the CDC doesn’t consider a crude rate to be reliable when there are fewer than 20 deaths in the population. The sample size is too small for the number to be meaningful. The reason the number of deaths I have in each field is so low, however, is because I split each population into 14 different age groups. When I calculate the age-adjusted death rates, all those age groups will be added to each other, resulting in an age-adjusted death rate with a decent sample size.

As far as the redundant columns go, for several fields, such as the ICD sub-chapter, gender, year, age group, and race, the query system automatically outputs both a plain English version of the data and a reference code. This means I have a lot of columns with perfectly correlated information. I’ll deal with those in a moment, but before I do I want to make sure I don’t have any data cells marked as “NA” in the dataframe.

apply(df, 2, function(x) sum(is.na(x)))

##                Notes      ICD.Sub.Chapter ICD.Sub.Chapter.Code 
##                    0                    0                    0 
##                 Year            Year.Code            Age.Group 
##                  756                  756                    0 
##       Age.Group.Code               Gender          Gender.Code 
##                   60                    0                    0 
##                 Race            Race.Code               Deaths 
##                    0                    0                  756 
##           Population           Crude.Rate 
##                  756                    0

It looks like I have several NA values. The number of NA values in Year.Code, Year, Population, and Deaths are all the same, so it’s a fair bet that it’s the same rows containing NA values for each of them. There’s also a much smaller number of NA values in Age.Group.Code. I’ll subset both groups of NA values and examine them to try and see what the cause is.

age_group_na <- subset(df, subset = is.na(df$Age.Group.Code))
year_na <- subset(df, subset = is.na(df$Year))

kable(age_group_na, style = "html") %>%
    kable_styling() %>%
  scroll_box(width = "100%", height = "400px")

	Notes	ICD.Sub.Chapter	ICD.Sub.Chapter.Code	Year	Year.Code	Age.Group	Age.Group.Code	Gender	Gender.Code	Race	Race.Code	Deaths	Population	Crude.Rate
9352	—			NA	NA		NA					NA	NA
9353	Dataset: Compressed Mortality, 1999-2016			NA	NA		NA					NA	NA
9354	Query Parameters:			NA	NA		NA					NA	NA
9355	Title: mortality_1999-2016_<1			NA	NA		NA					NA	NA
9356	Age Group: < 1 year			NA	NA		NA					NA	NA
9357	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA		NA					NA	NA
9358	Show Totals: False			NA	NA		NA					NA	NA
9359	Show Zero Values: False			NA	NA		NA					NA	NA
9360	Show Suppressed: False			NA	NA		NA					NA	NA
9361	Calculate Rates Per: 100,000			NA	NA		NA					NA	NA
9362	—			NA	NA		NA					NA	NA
9363	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA		NA					NA	NA
9364	—			NA	NA		NA					NA	NA
9365	Query Date: Sep 13, 2022 7:12:58 PM			NA	NA		NA					NA	NA
9366	—			NA	NA		NA					NA	NA
9367	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA		NA					NA	NA
9368	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA		NA					NA	NA
9369	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA		NA					NA	NA
9370	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA		NA					NA	NA
9371	7:12:58 PM			NA	NA		NA					NA	NA
9372	—			NA	NA		NA					NA	NA
9373	Messages:			NA	NA		NA					NA	NA
9374	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA		NA					NA	NA
9375	—			NA	NA		NA					NA	NA
9376	Caveats:			NA	NA		NA					NA	NA
9377	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA		NA					NA	NA
9378	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA		NA					NA	NA
9379	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA		NA					NA	NA
9380	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA		NA					NA	NA
9381	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA		NA					NA	NA
9382	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA		NA					NA	NA
9383	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA		NA					NA	NA
9384	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA		NA					NA	NA
9385	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA		NA					NA	NA
9386	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA		NA					NA	NA
9387	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA		NA					NA	NA
9388	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA		NA					NA	NA
9389	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA		NA					NA	NA
9390	The population figures used in the calculation of death rates for the age group ‘under 1 year’ are the estimates of the			NA	NA		NA					NA	NA
9391	resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/cmf.html#Age Group.			NA	NA		NA					NA	NA
9392	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA		NA					NA	NA
9393	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA		NA					NA	NA
9394	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA		NA					NA	NA
9395	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA		NA					NA	NA
9396	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA		NA					NA	NA
9397	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA		NA					NA	NA
9398	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA		NA					NA	NA
9399	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA		NA					NA	NA
9400	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA		NA					NA	NA
9401	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA		NA					NA	NA
9402	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA		NA					NA	NA
9403	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA		NA					NA	NA
9404	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA		NA					NA	NA
9405	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA		NA					NA	NA
9406	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA		NA					NA	NA
9407	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA		NA					NA	NA
9408	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA		NA					NA	NA
9409	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA		NA					NA	NA
9410	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA		NA					NA	NA
9411	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA		NA					NA	NA

kable(year_na, style = "html") %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "400px")

	Notes	ICD.Sub.Chapter	ICD.Sub.Chapter.Code	Year	Year.Code	Age.Group	Age.Group.Code	Gender	Gender.Code	Race	Race.Code	Deaths	Population	Crude.Rate
9352	—			NA	NA		NA					NA	NA
9353	Dataset: Compressed Mortality, 1999-2016			NA	NA		NA					NA	NA
9354	Query Parameters:			NA	NA		NA					NA	NA
9355	Title: mortality_1999-2016_<1			NA	NA		NA					NA	NA
9356	Age Group: < 1 year			NA	NA		NA					NA	NA
9357	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA		NA					NA	NA
9358	Show Totals: False			NA	NA		NA					NA	NA
9359	Show Zero Values: False			NA	NA		NA					NA	NA
9360	Show Suppressed: False			NA	NA		NA					NA	NA
9361	Calculate Rates Per: 100,000			NA	NA		NA					NA	NA
9362	—			NA	NA		NA					NA	NA
9363	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA		NA					NA	NA
9364	—			NA	NA		NA					NA	NA
9365	Query Date: Sep 13, 2022 7:12:58 PM			NA	NA		NA					NA	NA
9366	—			NA	NA		NA					NA	NA
9367	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA		NA					NA	NA
9368	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA		NA					NA	NA
9369	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA		NA					NA	NA
9370	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA		NA					NA	NA
9371	7:12:58 PM			NA	NA		NA					NA	NA
9372	—			NA	NA		NA					NA	NA
9373	Messages:			NA	NA		NA					NA	NA
9374	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA		NA					NA	NA
9375	—			NA	NA		NA					NA	NA
9376	Caveats:			NA	NA		NA					NA	NA
9377	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA		NA					NA	NA
9378	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA		NA					NA	NA
9379	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA		NA					NA	NA
9380	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA		NA					NA	NA
9381	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA		NA					NA	NA
9382	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA		NA					NA	NA
9383	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA		NA					NA	NA
9384	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA		NA					NA	NA
9385	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA		NA					NA	NA
9386	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA		NA					NA	NA
9387	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA		NA					NA	NA
9388	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA		NA					NA	NA
9389	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA		NA					NA	NA
9390	The population figures used in the calculation of death rates for the age group ‘under 1 year’ are the estimates of the			NA	NA		NA					NA	NA
9391	resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/cmf.html#Age Group.			NA	NA		NA					NA	NA
9392	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA		NA					NA	NA
9393	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA		NA					NA	NA
9394	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA		NA					NA	NA
9395	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA		NA					NA	NA
9396	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA		NA					NA	NA
9397	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA		NA					NA	NA
9398	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA		NA					NA	NA
9399	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA		NA					NA	NA
9400	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA		NA					NA	NA
9401	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA		NA					NA	NA
9402	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA		NA					NA	NA
9403	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA		NA					NA	NA
9404	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA		NA					NA	NA
9405	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA		NA					NA	NA
9406	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA		NA					NA	NA
9407	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA		NA					NA	NA
9408	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA		NA					NA	NA
9409	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA		NA					NA	NA
9410	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA		NA					NA	NA
9411	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA		NA					NA	NA
16420	—			NA	NA							NA	NA
16421	Dataset: Compressed Mortality, 1999-2016			NA	NA							NA	NA
16422	Query Parameters:			NA	NA							NA	NA
16423	Title: mortality_1999-2016_1-4			NA	NA							NA	NA
16424	Age Group: 1-4 years			NA	NA							NA	NA
16425	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA							NA	NA
16426	Show Totals: False			NA	NA							NA	NA
16427	Show Zero Values: False			NA	NA							NA	NA
16428	Show Suppressed: False			NA	NA							NA	NA
16429	Calculate Rates Per: 100,000			NA	NA							NA	NA
16430	—			NA	NA							NA	NA
16431	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA							NA	NA
16432	—			NA	NA							NA	NA
16433	Query Date: Sep 13, 2022 7:15:39 PM			NA	NA							NA	NA
16434	—			NA	NA							NA	NA
16435	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA							NA	NA
16436	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA							NA	NA
16437	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA							NA	NA
16438	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA							NA	NA
16439	7:15:39 PM			NA	NA							NA	NA
16440	—			NA	NA							NA	NA
16441	Messages:			NA	NA							NA	NA
16442	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA							NA	NA
16443	—			NA	NA							NA	NA
16444	Caveats:			NA	NA							NA	NA
16445	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA							NA	NA
16446	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA							NA	NA
16447	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA							NA	NA
16448	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA							NA	NA
16449	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA							NA	NA
16450	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA							NA	NA
16451	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA							NA	NA
16452	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA							NA	NA
16453	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA							NA	NA
16454	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA							NA	NA
16455	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA							NA	NA
16456	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA							NA	NA
16457	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA							NA	NA
16458	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA							NA	NA
16459	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA							NA	NA
16460	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA							NA	NA
16461	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA							NA	NA
16462	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA							NA	NA
16463	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA							NA	NA
16464	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA							NA	NA
16465	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA							NA	NA
16466	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA							NA	NA
16467	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA							NA	NA
16468	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA							NA	NA
16469	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA							NA	NA
16470	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA							NA	NA
16471	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA							NA	NA
16472	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA							NA	NA
16473	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA							NA	NA
16474	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA							NA	NA
16475	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA							NA	NA
16476	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA							NA	NA
16477	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA							NA	NA
21906	—			NA	NA							NA	NA
21907	Dataset: Compressed Mortality, 1999-2016			NA	NA							NA	NA
21908	Query Parameters:			NA	NA							NA	NA
21909	Title: mortality_1999-2016_5-9			NA	NA							NA	NA
21910	Age Group: 5-9 years			NA	NA							NA	NA
21911	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA							NA	NA
21912	Show Totals: False			NA	NA							NA	NA
21913	Show Zero Values: False			NA	NA							NA	NA
21914	Show Suppressed: False			NA	NA							NA	NA
21915	Calculate Rates Per: 100,000			NA	NA							NA	NA
21916	—			NA	NA							NA	NA
21917	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA							NA	NA
21918	—			NA	NA							NA	NA
21919	Query Date: Sep 13, 2022 7:18:14 PM			NA	NA							NA	NA
21920	—			NA	NA							NA	NA
21921	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA							NA	NA
21922	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA							NA	NA
21923	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA							NA	NA
21924	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA							NA	NA
21925	7:18:14 PM			NA	NA							NA	NA
21926	—			NA	NA							NA	NA
21927	Messages:			NA	NA							NA	NA
21928	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA							NA	NA
21929	—			NA	NA							NA	NA
21930	Caveats:			NA	NA							NA	NA
21931	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA							NA	NA
21932	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA							NA	NA
21933	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA							NA	NA
21934	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA							NA	NA
21935	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA							NA	NA
21936	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA							NA	NA
21937	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA							NA	NA
21938	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA							NA	NA
21939	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA							NA	NA
21940	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA							NA	NA
21941	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA							NA	NA
21942	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA							NA	NA
21943	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA							NA	NA
21944	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA							NA	NA
21945	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA							NA	NA
21946	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA							NA	NA
21947	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA							NA	NA
21948	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA							NA	NA
21949	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA							NA	NA
21950	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA							NA	NA
21951	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA							NA	NA
21952	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA							NA	NA
21953	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA							NA	NA
21954	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA							NA	NA
21955	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA							NA	NA
21956	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA							NA	NA
21957	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA							NA	NA
21958	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA							NA	NA
21959	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA							NA	NA
21960	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA							NA	NA
21961	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA							NA	NA
21962	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA							NA	NA
21963	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA							NA	NA
27869	—			NA	NA							NA	NA
27870	Dataset: Compressed Mortality, 1999-2016			NA	NA							NA	NA
27871	Query Parameters:			NA	NA							NA	NA
27872	Title: mortality_1999-2016_10-14			NA	NA							NA	NA
27873	Age Group: 10-14 years			NA	NA							NA	NA
27874	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA							NA	NA
27875	Show Totals: False			NA	NA							NA	NA
27876	Show Zero Values: False			NA	NA							NA	NA
27877	Show Suppressed: False			NA	NA							NA	NA
27878	Calculate Rates Per: 100,000			NA	NA							NA	NA
27879	—			NA	NA							NA	NA
27880	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA							NA	NA
27881	—			NA	NA							NA	NA
27882	Query Date: Sep 13, 2022 7:20:31 PM			NA	NA							NA	NA
27883	—			NA	NA							NA	NA
27884	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA							NA	NA
27885	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA							NA	NA
27886	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA							NA	NA
27887	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA							NA	NA
27888	7:20:31 PM			NA	NA							NA	NA
27889	—			NA	NA							NA	NA
27890	Messages:			NA	NA							NA	NA
27891	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA							NA	NA
27892	—			NA	NA							NA	NA
27893	Caveats:			NA	NA							NA	NA
27894	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA							NA	NA
27895	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA							NA	NA
27896	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA							NA	NA
27897	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA							NA	NA
27898	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA							NA	NA
27899	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA							NA	NA
27900	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA							NA	NA
27901	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA							NA	NA
27902	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA							NA	NA
27903	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA							NA	NA
27904	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA							NA	NA
27905	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA							NA	NA
27906	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA							NA	NA
27907	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA							NA	NA
27908	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA							NA	NA
27909	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA							NA	NA
27910	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA							NA	NA
27911	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA							NA	NA
27912	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA							NA	NA
27913	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA							NA	NA
27914	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA							NA	NA
27915	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA							NA	NA
27916	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA							NA	NA
27917	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA							NA	NA
27918	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA							NA	NA
27919	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA							NA	NA
27920	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA							NA	NA
27921	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA							NA	NA
27922	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA							NA	NA
27923	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA							NA	NA
27924	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA							NA	NA
27925	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA							NA	NA
27926	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA							NA	NA
35124	—			NA	NA							NA	NA
35125	Dataset: Compressed Mortality, 1999-2016			NA	NA							NA	NA
35126	Query Parameters:			NA	NA							NA	NA
35127	Title: mortality_1999-2016_15-19			NA	NA							NA	NA
35128	Age Group: 15-19 years			NA	NA							NA	NA
35129	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA							NA	NA
35130	Show Totals: False			NA	NA							NA	NA
35131	Show Zero Values: False			NA	NA							NA	NA
35132	Show Suppressed: False			NA	NA							NA	NA
35133	Calculate Rates Per: 100,000			NA	NA							NA	NA
35134	—			NA	NA							NA	NA
35135	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA							NA	NA
35136	—			NA	NA							NA	NA
35137	Query Date: Sep 13, 2022 7:23:35 PM			NA	NA							NA	NA
35138	—			NA	NA							NA	NA
35139	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA							NA	NA
35140	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA							NA	NA
35141	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA							NA	NA
35142	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA							NA	NA
35143	7:23:35 PM			NA	NA							NA	NA
35144	—			NA	NA							NA	NA
35145	Messages:			NA	NA							NA	NA
35146	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA							NA	NA
35147	—			NA	NA							NA	NA
35148	Caveats:			NA	NA							NA	NA
35149	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA							NA	NA
35150	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA							NA	NA
35151	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA							NA	NA
35152	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA							NA	NA
35153	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA							NA	NA
35154	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA							NA	NA
35155	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA							NA	NA
35156	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA							NA	NA
35157	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA							NA	NA
35158	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA							NA	NA
35159	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA							NA	NA
35160	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA							NA	NA
35161	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA							NA	NA
35162	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA							NA	NA
35163	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA							NA	NA
35164	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA							NA	NA
35165	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA							NA	NA
35166	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA							NA	NA
35167	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA							NA	NA
35168	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA							NA	NA
35169	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA							NA	NA
35170	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA							NA	NA
35171	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA							NA	NA
35172	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA							NA	NA
35173	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA							NA	NA
35174	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA							NA	NA
35175	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA							NA	NA
35176	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA							NA	NA
35177	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA							NA	NA
35178	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA							NA	NA
35179	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA							NA	NA
35180	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA							NA	NA
35181	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA							NA	NA
43605	—			NA	NA							NA	NA
43606	Dataset: Compressed Mortality, 1999-2016			NA	NA							NA	NA
43607	Query Parameters:			NA	NA							NA	NA
43608	Title: mortality_1999-2016_20-24			NA	NA							NA	NA
43609	Age Group: 20-24 years			NA	NA							NA	NA
43610	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA							NA	NA
43611	Show Totals: False			NA	NA							NA	NA
43612	Show Zero Values: False			NA	NA							NA	NA
43613	Show Suppressed: False			NA	NA							NA	NA
43614	Calculate Rates Per: 100,000			NA	NA							NA	NA
43615	—			NA	NA							NA	NA
43616	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA							NA	NA
43617	—			NA	NA							NA	NA
43618	Query Date: Sep 13, 2022 7:26:38 PM			NA	NA							NA	NA
43619	—			NA	NA							NA	NA
43620	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA							NA	NA
43621	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA							NA	NA
43622	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA							NA	NA
43623	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA							NA	NA
43624	7:26:38 PM			NA	NA							NA	NA
43625	—			NA	NA							NA	NA
43626	Messages:			NA	NA							NA	NA
43627	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA							NA	NA
43628	—			NA	NA							NA	NA
43629	Caveats:			NA	NA							NA	NA
43630	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA							NA	NA
43631	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA							NA	NA
43632	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA							NA	NA
43633	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA							NA	NA
43634	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA							NA	NA
43635	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA							NA	NA
43636	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA							NA	NA
43637	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA							NA	NA
43638	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA							NA	NA
43639	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA							NA	NA
43640	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA							NA	NA
43641	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA							NA	NA
43642	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA							NA	NA
43643	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA							NA	NA
43644	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA							NA	NA
43645	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA							NA	NA
43646	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA							NA	NA
43647	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA							NA	NA
43648	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA							NA	NA
43649	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA							NA	NA
43650	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA							NA	NA
43651	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA							NA	NA
43652	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA							NA	NA
43653	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA							NA	NA
43654	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA							NA	NA
43655	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA							NA	NA
43656	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA							NA	NA
43657	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA							NA	NA
43658	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA							NA	NA
43659	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA							NA	NA
43660	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA							NA	NA
43661	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA							NA	NA
43662	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA							NA	NA
54907	—			NA	NA							NA	NA
54908	Dataset: Compressed Mortality, 1999-2016			NA	NA							NA	NA
54909	Query Parameters:			NA	NA							NA	NA
54910	Title: mortality_1999-2016_25-34			NA	NA							NA	NA
54911	Age Group: 25-34 years			NA	NA							NA	NA
54912	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA							NA	NA
54913	Show Totals: False			NA	NA							NA	NA
54914	Show Zero Values: False			NA	NA							NA	NA
54915	Show Suppressed: False			NA	NA							NA	NA
54916	Calculate Rates Per: 100,000			NA	NA							NA	NA
54917	—			NA	NA							NA	NA
54918	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA							NA	NA
54919	—			NA	NA							NA	NA
54920	Query Date: Sep 13, 2022 4:22:07 PM			NA	NA							NA	NA
54921	—			NA	NA							NA	NA
54922	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA							NA	NA
54923	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA							NA	NA
54924	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA							NA	NA
54925	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA							NA	NA
54926	4:22:07 PM			NA	NA							NA	NA
54927	—			NA	NA							NA	NA
54928	Messages:			NA	NA							NA	NA
54929	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA							NA	NA
54930	—			NA	NA							NA	NA
54931	Caveats:			NA	NA							NA	NA
54932	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA							NA	NA
54933	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA							NA	NA
54934	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA							NA	NA
54935	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA							NA	NA
54936	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA							NA	NA
54937	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA							NA	NA
54938	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA							NA	NA
54939	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA							NA	NA
54940	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA							NA	NA
54941	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA							NA	NA
54942	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA							NA	NA
54943	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA							NA	NA
54944	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA							NA	NA
54945	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA							NA	NA
54946	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA							NA	NA
54947	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA							NA	NA
54948	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA							NA	NA
54949	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA							NA	NA
54950	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA							NA	NA
54951	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA							NA	NA
54952	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA							NA	NA
54953	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA							NA	NA
54954	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA							NA	NA
54955	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA							NA	NA
54956	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA							NA	NA
54957	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA							NA	NA
54958	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA							NA	NA
54959	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA							NA	NA
54960	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA							NA	NA
54961	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA							NA	NA
54962	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA							NA	NA
54963	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA							NA	NA
54964	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA							NA	NA
67559	—			NA	NA							NA	NA
67560	Dataset: Compressed Mortality, 1999-2016			NA	NA							NA	NA
67561	Query Parameters:			NA	NA							NA	NA
67562	Title: mortality_1999-2016_35-44			NA	NA							NA	NA
67563	Age Group: 35-44 years			NA	NA							NA	NA
67564	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA							NA	NA
67565	Show Totals: False			NA	NA							NA	NA
67566	Show Zero Values: False			NA	NA							NA	NA
67567	Show Suppressed: False			NA	NA							NA	NA
67568	Calculate Rates Per: 100,000			NA	NA							NA	NA
67569	—			NA	NA							NA	NA
67570	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA							NA	NA
67571	—			NA	NA							NA	NA
67572	Query Date: Sep 13, 2022 4:06:23 PM			NA	NA							NA	NA
67573	—			NA	NA							NA	NA
67574	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA							NA	NA
67575	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA							NA	NA
67576	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA							NA	NA
67577	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA							NA	NA
67578	4:06:23 PM			NA	NA							NA	NA
67579	—			NA	NA							NA	NA
67580	Messages:			NA	NA							NA	NA
67581	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA							NA	NA
67582	—			NA	NA							NA	NA
67583	Caveats:			NA	NA							NA	NA
67584	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA							NA	NA
67585	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA							NA	NA
67586	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA							NA	NA
67587	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA							NA	NA
67588	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA							NA	NA
67589	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA							NA	NA
67590	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA							NA	NA
67591	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA							NA	NA
67592	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA							NA	NA
67593	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA							NA	NA
67594	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA							NA	NA
67595	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA							NA	NA
67596	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA							NA	NA
67597	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA							NA	NA
67598	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA							NA	NA
67599	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA							NA	NA
67600	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA							NA	NA
67601	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA							NA	NA
67602	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA							NA	NA
67603	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA							NA	NA
67604	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA							NA	NA
67605	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA							NA	NA
67606	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA							NA	NA
67607	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA							NA	NA
67608	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA							NA	NA
67609	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA							NA	NA
67610	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA							NA	NA
67611	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA							NA	NA
67612	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA							NA	NA
67613	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA							NA	NA
67614	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA							NA	NA
67615	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA							NA	NA
67616	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA							NA	NA
81333	—			NA	NA							NA	NA
81334	Dataset: Compressed Mortality, 1999-2016			NA	NA							NA	NA
81335	Query Parameters:			NA	NA							NA	NA
81336	Title: mortality_1999-2016_45-54			NA	NA							NA	NA
81337	Age Group: 45-54 years			NA	NA							NA	NA
81338	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA							NA	NA
81339	Show Totals: False			NA	NA							NA	NA
81340	Show Zero Values: False			NA	NA							NA	NA
81341	Show Suppressed: False			NA	NA							NA	NA
81342	Calculate Rates Per: 100,000			NA	NA							NA	NA
81343	—			NA	NA							NA	NA
81344	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA							NA	NA
81345	—			NA	NA							NA	NA
81346	Query Date: Sep 13, 2022 4:03:48 PM			NA	NA							NA	NA
81347	—			NA	NA							NA	NA
81348	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA							NA	NA
81349	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA							NA	NA
81350	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA							NA	NA
81351	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA							NA	NA
81352	4:03:48 PM			NA	NA							NA	NA
81353	—			NA	NA							NA	NA
81354	Messages:			NA	NA							NA	NA
81355	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA							NA	NA
81356	—			NA	NA							NA	NA
81357	Caveats:			NA	NA							NA	NA
81358	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA							NA	NA
81359	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA							NA	NA
81360	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA							NA	NA
81361	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA							NA	NA
81362	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA							NA	NA
81363	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA							NA	NA
81364	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA							NA	NA
81365	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA							NA	NA
81366	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA							NA	NA
81367	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA							NA	NA
81368	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA							NA	NA
81369	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA							NA	NA
81370	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA							NA	NA
81371	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA							NA	NA
81372	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA							NA	NA
81373	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA							NA	NA
81374	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA							NA	NA
81375	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA							NA	NA
81376	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA							NA	NA
81377	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA							NA	NA
81378	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA							NA	NA
81379	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA							NA	NA
81380	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA							NA	NA
81381	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA							NA	NA
81382	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA							NA	NA
81383	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA							NA	NA
81384	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA							NA	NA
81385	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA							NA	NA
81386	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA							NA	NA
81387	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA							NA	NA
81388	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA							NA	NA
81389	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA							NA	NA
81390	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA							NA	NA
95555	—			NA	NA							NA	NA
95556	Dataset: Compressed Mortality, 1999-2016			NA	NA							NA	NA
95557	Query Parameters:			NA	NA							NA	NA
95558	Title: mortality_1999-2016_55-64			NA	NA							NA	NA
95559	Age Group: 55-64 years			NA	NA							NA	NA
95560	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA							NA	NA
95561	Show Totals: False			NA	NA							NA	NA
95562	Show Zero Values: False			NA	NA							NA	NA
95563	Show Suppressed: False			NA	NA							NA	NA
95564	Calculate Rates Per: 100,000			NA	NA							NA	NA
95565	—			NA	NA							NA	NA
95566	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA							NA	NA
95567	—			NA	NA							NA	NA
95568	Query Date: Sep 13, 2022 4:01:53 PM			NA	NA							NA	NA
95569	—			NA	NA							NA	NA
95570	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA							NA	NA
95571	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA							NA	NA
95572	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA							NA	NA
95573	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA							NA	NA
95574	4:01:53 PM			NA	NA							NA	NA
95575	—			NA	NA							NA	NA
95576	Messages:			NA	NA							NA	NA
95577	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA							NA	NA
95578	—			NA	NA							NA	NA
95579	Caveats:			NA	NA							NA	NA
95580	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA							NA	NA
95581	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA							NA	NA
95582	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA							NA	NA
95583	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA							NA	NA
95584	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA							NA	NA
95585	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA							NA	NA
95586	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA							NA	NA
95587	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA							NA	NA
95588	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA							NA	NA
95589	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA							NA	NA
95590	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA							NA	NA
95591	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA							NA	NA
95592	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA							NA	NA
95593	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA							NA	NA
95594	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA							NA	NA
95595	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA							NA	NA
95596	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA							NA	NA
95597	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA							NA	NA
95598	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA							NA	NA
95599	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA							NA	NA
95600	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA							NA	NA
95601	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA							NA	NA
95602	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA							NA	NA
95603	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA							NA	NA
95604	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA							NA	NA
95605	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA							NA	NA
95606	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA							NA	NA
95607	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA							NA	NA
95608	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA							NA	NA
95609	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA							NA	NA
95610	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA							NA	NA
95611	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA							NA	NA
95612	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA							NA	NA
109990	—			NA	NA							NA	NA
109991	Dataset: Compressed Mortality, 1999-2016			NA	NA							NA	NA
109992	Query Parameters:			NA	NA							NA	NA
109993	Title: mortality_1999-2016_65-74			NA	NA							NA	NA
109994	Age Group: 65-74 years			NA	NA							NA	NA
109995	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA							NA	NA
109996	Show Totals: False			NA	NA							NA	NA
109997	Show Zero Values: False			NA	NA							NA	NA
109998	Show Suppressed: False			NA	NA							NA	NA
109999	Calculate Rates Per: 100,000			NA	NA							NA	NA
110000	—			NA	NA							NA	NA
110001	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA							NA	NA
110002	—			NA	NA							NA	NA
110003	Query Date: Sep 13, 2022 3:59:35 PM			NA	NA							NA	NA
110004	—			NA	NA							NA	NA
110005	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA							NA	NA
110006	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA							NA	NA
110007	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA							NA	NA
110008	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA							NA	NA
110009	3:59:35 PM			NA	NA							NA	NA
110010	—			NA	NA							NA	NA
110011	Messages:			NA	NA							NA	NA
110012	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA							NA	NA
110013	—			NA	NA							NA	NA
110014	Caveats:			NA	NA							NA	NA
110015	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA							NA	NA
110016	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA							NA	NA
110017	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA							NA	NA
110018	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA							NA	NA
110019	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA							NA	NA
110020	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA							NA	NA
110021	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA							NA	NA
110022	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA							NA	NA
110023	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA							NA	NA
110024	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA							NA	NA
110025	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA							NA	NA
110026	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA							NA	NA
110027	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA							NA	NA
110028	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA							NA	NA
110029	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA							NA	NA
110030	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA							NA	NA
110031	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA							NA	NA
110032	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA							NA	NA
110033	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA							NA	NA
110034	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA							NA	NA
110035	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA							NA	NA
110036	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA							NA	NA
110037	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA							NA	NA
110038	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA							NA	NA
110039	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA							NA	NA
110040	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA							NA	NA
110041	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA							NA	NA
110042	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA							NA	NA
110043	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA							NA	NA
110044	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA							NA	NA
110045	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA							NA	NA
110046	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA							NA	NA
110047	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA							NA	NA
124533	—			NA	NA							NA	NA
124534	Dataset: Compressed Mortality, 1999-2016			NA	NA							NA	NA
124535	Query Parameters:			NA	NA							NA	NA
124536	Title: mortality_1999-2016_75-84			NA	NA							NA	NA
124537	Age Group: 75-84 years			NA	NA							NA	NA
124538	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA							NA	NA
124539	Show Totals: False			NA	NA							NA	NA
124540	Show Zero Values: False			NA	NA							NA	NA
124541	Show Suppressed: False			NA	NA							NA	NA
124542	Calculate Rates Per: 100,000			NA	NA							NA	NA
124543	—			NA	NA							NA	NA
124544	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA							NA	NA
124545	—			NA	NA							NA	NA
124546	Query Date: Sep 13, 2022 3:57:48 PM			NA	NA							NA	NA
124547	—			NA	NA							NA	NA
124548	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA							NA	NA
124549	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA							NA	NA
124550	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA							NA	NA
124551	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA							NA	NA
124552	3:57:48 PM			NA	NA							NA	NA
124553	—			NA	NA							NA	NA
124554	Messages:			NA	NA							NA	NA
124555	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA							NA	NA
124556	—			NA	NA							NA	NA
124557	Caveats:			NA	NA							NA	NA
124558	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA							NA	NA
124559	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA							NA	NA
124560	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA							NA	NA
124561	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA							NA	NA
124562	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA							NA	NA
124563	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA							NA	NA
124564	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA							NA	NA
124565	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA							NA	NA
124566	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA							NA	NA
124567	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA							NA	NA
124568	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA							NA	NA
124569	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA							NA	NA
124570	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA							NA	NA
124571	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA							NA	NA
124572	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA							NA	NA
124573	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA							NA	NA
124574	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA							NA	NA
124575	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA							NA	NA
124576	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA							NA	NA
124577	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA							NA	NA
124578	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA							NA	NA
124579	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA							NA	NA
124580	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA							NA	NA
124581	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA							NA	NA
124582	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA							NA	NA
124583	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA							NA	NA
124584	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA							NA	NA
124585	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA							NA	NA
124586	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA							NA	NA
124587	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA							NA	NA
124588	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA							NA	NA
124589	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA							NA	NA
124590	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA							NA	NA
138363	—			NA	NA							NA	NA
138364	Dataset: Compressed Mortality, 1999-2016			NA	NA							NA	NA
138365	Query Parameters:			NA	NA							NA	NA
138366	Title: mortality_1999-2016_85+			NA	NA							NA	NA
138367	Age Group: 85+ years			NA	NA							NA	NA
138368	Group By: ICD Sub-Chapter; Year; Age Group; Gender; Race			NA	NA							NA	NA
138369	Show Totals: False			NA	NA							NA	NA
138370	Show Zero Values: False			NA	NA							NA	NA
138371	Show Suppressed: False			NA	NA							NA	NA
138372	Calculate Rates Per: 100,000			NA	NA							NA	NA
138373	—			NA	NA							NA	NA
138374	Help: See http://wonder.cdc.gov/wonder/help/cmf.html for more information.			NA	NA							NA	NA
138375	—			NA	NA							NA	NA
138376	Query Date: Sep 13, 2022 3:54:39 PM			NA	NA							NA	NA
138377	—			NA	NA							NA	NA
138378	Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics			NA	NA							NA	NA
138379	System, Mortality: Compressed Mortality File 1999-2016 on CDC WONDER Online Database, released June 2017. Data are from the			NA	NA							NA	NA
138380	Compressed Mortality File 1999-2016 Series 20 No. 2U, 2016, as compiled from data provided by the 57 vital statistics			NA	NA							NA	NA
138381	jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/cmf-icd10.html on Sep 13, 2022			NA	NA							NA	NA
138382	3:54:39 PM			NA	NA							NA	NA
138383	—			NA	NA							NA	NA
138384	Messages:			NA	NA							NA	NA
138385	Rows with zero Deaths are hidden. Use Quick Options above to show zero rows.			NA	NA							NA	NA
138386	—			NA	NA							NA	NA
138387	Caveats:			NA	NA							NA	NA
138388	Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:			NA	NA							NA	NA
138389	http://wonder.cdc.gov/wonder/help/cmf.html#Unreliable.			NA	NA							NA	NA
138390	About national population figures: population figures for 1999 are from the 1990-1999 series of bridged-race intercensal			NA	NA							NA	NA
138391	estimates of the July 1 resident population; population figures for 2000 and 2010 are bridged-race April 1 census counts;			NA	NA							NA	NA
138392	population figures for 2001-2009 are from the revised 2000-2009 series of bridged-race intercensal estimates of the July 1			NA	NA							NA	NA
138393	resident population; population figures for 2011 are bridged-race postcensal estimates of the July 1 resident population, from			NA	NA							NA	NA
138394	the Vintage 2011 series released by NCHS on July 18, 2012; population figures for 2012 are bridged-race postcensal estimates of			NA	NA							NA	NA
138395	the July 1 resident population, from the Vintage 2012 series released by NCHS on June 13, 2013; population figures for 2013 are			NA	NA							NA	NA
138396	bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2013 series released by NCHS on June 26,			NA	NA							NA	NA
138397	2014; population figures for 2014 are bridged-race postcensal estimates of the July 1 resident population, from the Vintage 2014			NA	NA							NA	NA
138398	series released by NCHS on June 30, 2015. and population figures for 2015 are bridged-race postcensal estimates of the July 1			NA	NA							NA	NA
138399	resident population, from the Vintage 2015 series released by NCHS on June 28, 2016. More information:			NA	NA							NA	NA
138400	http://wonder.cdc.gov/wonder/help/cmf.html#Population Information.			NA	NA							NA	NA
138401	Deaths of persons with Age “Not Stated” are included in “All” counts and rates, but are not distributed among age groups,			NA	NA							NA	NA
138402	so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:			NA	NA							NA	NA
138403	http://wonder.cdc.gov/wonder/help/cmf.html#Not Stated.			NA	NA							NA	NA
138404	Information included on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the			NA	NA							NA	NA
138405	funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis			NA	NA							NA	NA
138406	of observation. Race and ethnicity information from the census is by self-report. To the extent that race and Hispanic origin			NA	NA							NA	NA
138407	are inconsistent between these two data sources, death rates will be biased. More information:			NA	NA							NA	NA
138408	http://wonder.cdc.gov/wonder/help/cmf.html#Racial Differences.			NA	NA							NA	NA
138409	As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:			NA	NA							NA	NA
138410	http://wonder.cdc.gov/wonder/help/cmf.html#2014-Revision.			NA	NA							NA	NA
138411	Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, “Other			NA	NA							NA	NA
138412	ill-defined and unspecified causes of mortality” for deaths occurring in years 2000 and 2001. Caution should be used in			NA	NA							NA	NA
138413	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#California-Reporting-Anomalies.			NA	NA							NA	NA
138414	Circumstances in Georgia for the years 2008-2009 have resulted in unusually high death counts for the ICD-10 cause of death			NA	NA							NA	NA
138415	code R99, “Other ill-defined and unspecified causes of mortality.” Caution should be used in interpreting these data. More			NA	NA							NA	NA
138416	information: http://wonder.cdc.gov/wonder/help/cmf.html#Georgia-Reporting-Anomalies.			NA	NA							NA	NA
138417	Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code			NA	NA							NA	NA
138418	R99, “Other ill-defined and unspecified causes of mortality” and therefore unusually low death counts in other ICD-10 codes,			NA	NA							NA	NA
138419	most notably R95, “Sudden Infant Death Syndrome” and X40-X49, “Unintentional poisoning.” Caution should be used in			NA	NA							NA	NA
138420	interpreting these data. More information: http://wonder.cdc.gov/wonder/help/cmf.html#New-Jersey-Reporting-Anomalies.			NA	NA							NA	NA

It looks like the Notes column of each query I made contains information about the dataset and the query used to generate it. Every other column is either empty or NA for those rows. For some reason the Age.Group.Code column came back as NA for one query but blank for all the others, which is why it had only 60 NA values. This means I can simply remove all the NA values from the main dataframe, as they all contain no data. It’s a little concerning that this happened, and makes me wonder if I accidentally changed something on one of my 14 queries. Fortunately, the notes fields for these NA values includes a description of each query, and I can verify that the query structure of the oddball query is the same as all the other ones. However, the oddball query is for the <1 year old age group. The query system allows for examination of further infant ranges inside the <1 group, so my best guess is that the existence of this subset of age groups inside the <1 age group is the cause of the minor difference in how the query system output the data here.

Regardless, it looks like I should simply be able to delete these rows, as they contain no actual data. The fact that they contain information on the nature of each exact query, and some of the documentation for the dataset is useful. Conveniently, having just subset those rows into a new dataframe, I’ll still have access to them if I need them, even after removing them from the main dataframe.

df <- subset(df, subset = !is.na(df$Year))

With the NA values taken care of, I also want to check to see if there are any other empty cells in the dataframe.

apply(df, 2, function(x) sum(x == ""))

##                Notes      ICD.Sub.Chapter ICD.Sub.Chapter.Code 
##               137664                    0                    0 
##                 Year            Year.Code            Age.Group 
##                    0                    0                    0 
##       Age.Group.Code               Gender          Gender.Code 
##                    0                    0                    0 
##                 Race            Race.Code               Deaths 
##                    0                    0                    0 
##           Population           Crude.Rate 
##                    0                    0

There are 137,664 empty cells in the Notes column. I know from earlier that the dataframe used to have 138,420 rows, and I just deleted 756, so our dataframe is currently 137,664 rows long. In other words, every cell in the Notes column is empty, so I can feel free to delete the entire column later on when I prune the dataframe down.

Now that the data appears to be fairly clean, I actually want to add a new column. For my analysis, I know that I’m going to want to first look at ICD chapter, rather than ICD sub-chapter. That way I can get a general idea of what the data looks like, then more narrowly examine areas of interest when they come up. Unfortunately, the CDC query system only allows for the data to be grouped by five different fields, and I used all five on sub-chapter, year, race, gender, and age. Fortunately, the system the ICD uses for chapter and sub-chapter codes is simple. The individual cause of death codes are a letter and two digits ranging from A00 to Z99. The code for a chapter is simply a range of values (e.g. “A00-B99”). The code for a sub-chapter is the same, just with a narrower range (e.g. “A00-A09”, “A16-A19”, etc). I already have the sub-chapter ranges, so in order to get a column of chapter ranges, I just need to make a new column and fill each cell with the ICD chapter codes that encompass the sub-chapter range.

To start, I want to define a new dataframe that includes both the chapter codes and short versions of the titles for each chapter in the ICD 10. Fortunately the WHO maintains a webpage with details on the ICD 10 I can use to get this information. Once I have the dataframe with the ICD chapter codes, I’m going to join the codes to the main dataframe. Most R functions for joining dataframes that I know of don’t have the kind of inequality join I need to do this, so I’m going to load in the “sqldf” library to write an SQL join query instead.

# Make ICD Chapter and Chapter Code dataframe
icd_key <- data.frame(
  ICD.Chapter.Code = c(
    "A00-B99",
    "C00-D48",
    "D50-D89",
    "E00-E90",
    "F00-F99",
    "G00-G99",
    "H00-H59",
    "H60-H95",
    "I00-I99",
    "J00-J99",
    "K00-K93",
    "L00-L99",
    "M00-M99",
    "N00-N99",
    "O00-O99",
    "P00-P96",
    "Q00-Q99",
    "R00-R99",
    "S00-T98",
    "V01-Y98",
    "Z00-Z99",
    "U00-U99"
  ),
  ICD.Chapter = c(
    "Parasitic/Infectious Diseases",
    "Neoplasms",
    "Blood Diseases",
    "Endocrine Diseases",
    "Mental Disorders",
    "Nervous System Diseases",
    "Eye Diseases",
    "Ear Diseases",
    "Circulatory Diseases",
    "Respiratory Diseases",
    "Digestive Diseases",
    "Skin Diseases",
    "Musculoskeletal Diseases",
    "Genitourinary Diseases",
    "Pregnancy/Childbirth",
    "Perinatal Conditions",
    "Congenital Malformations",
    "Not Classified Elsewhere",
    "Consequences of External Causes",
    "External Causes",
    "Healthcare Factors",
    "Special Purposes"
  )
)

# Join ICD Chapter Codes to dataframe

library(sqldf)

df <- sqldf("SELECT df.*, icd_key.'ICD.Chapter.Code' 
      FROM df 
      JOIN icd_key ON 
        LEFTSTR(df.'ICD.Sub.Chapter.Code', 3) >= 
          LEFTSTR(icd_key.'ICD.Chapter.Code', 3)
        AND LEFTSTR(df.'ICD.Sub.Chapter.Code', 3) <= 
            RIGHTSTR(icd_key.'ICD.Chapter.Code', 3)")

Next I want to make some changes to the data structure. I want the age group, gender, race, ICD chapter and ICD sub-chapter data to be factors, rather than character strings. Before I make any conversions, I want to make sure there aren’t any unexpected values in any of those columns that will wind up being coerced to NA if I try to convert to factor, or give me weird factor levels I didn’t expect.

unique(df$ICD.Chapter.Code)

##  [1] "A00-B99" "C00-D48" "D50-D89" "E00-E90" "F00-F99" "G00-G99" "H00-H59"
##  [8] "H60-H95" "I00-I99" "J00-J99" "K00-K93" "L00-L99" "M00-M99" "N00-N99"
## [15] "P00-P96" "Q00-Q99" "R00-R99" "V01-Y98" "U00-U99" "O00-O99"

unique(df$ICD.Sub.Chapter.Code)

##   [1] "A00-A09" "A16-A19" "A20-A28" "A30-A49" "A50-A64" "A70-A74" "A80-A89"
##   [8] "A90-A99" "B00-B09" "B15-B19" "B20-B24" "B25-B34" "B35-B49" "B50-B64"
##  [15] "B99-B99" "C00-C97" "D10-D36" "D37-D48" "D50-D53" "D55-D59" "D60-D64"
##  [22] "D65-D69" "D70-D76" "D80-D89" "E00-E07" "E10-E14" "E15-E16" "E20-E34"
##  [29] "E40-E46" "E50-E64" "E65-E68" "E70-E88" "F01-F09" "F10-F19" "F30-F39"
##  [36] "F40-F48" "F70-F79" "F80-F89" "F90-F98" "G00-G09" "G10-G14" "G20-G25"
##  [43] "G30-G31" "G35-G37" "G40-G47" "G50-G58" "G60-G64" "G70-G72" "G80-G83"
##  [50] "G90-G98" "H10-H11" "H15-H21" "H25-H27" "H30-H35" "H40-H40" "H43-H44"
##  [57] "H53-H54" "H65-H74" "H80-H83" "H90-H93" "I05-I09" "I10-I15" "I20-I25"
##  [64] "I26-I28" "I30-I51" "I60-I69" "I70-I78" "I80-I89" "I95-I99" "J00-J06"
##  [71] "J09-J18" "J20-J22" "J30-J39" "J40-J47" "J60-J70" "J80-J84" "J85-J86"
##  [78] "J90-J94" "J96-J98" "K00-K14" "K20-K31" "K35-K38" "K40-K46" "K50-K52"
##  [85] "K55-K63" "K65-K66" "K70-K76" "K80-K86" "K90-K92" "L00-L08" "L20-L30"
##  [92] "L50-L53" "L80-L98" "M00-M25" "M30-M35" "M40-M54" "M60-M79" "M80-M94"
##  [99] "M95-M99" "N00-N07" "N10-N15" "N17-N19" "N20-N23" "N25-N28" "N30-N39"
## [106] "N40-N50" "N70-N76" "N80-N98" "P00-P04" "P05-P08" "P10-P15" "P20-P29"
## [113] "P35-P39" "P50-P61" "P70-P74" "P76-P78" "P80-P83" "P90-P96" "Q00-Q07"
## [120] "Q10-Q18" "Q20-Q28" "Q30-Q34" "Q35-Q37" "Q38-Q45" "Q50-Q56" "Q60-Q64"
## [127] "Q65-Q79" "Q80-Q89" "Q90-Q99" "R00-R09" "R10-R19" "R20-R23" "R25-R29"
## [134] "R40-R46" "R47-R49" "R50-R68" "R70-R79" "R90-R94" "R95-R99" "V01-V99"
## [141] "W00-X59" "X85-Y09" "Y10-Y34" "Y35-Y36" "Y40-Y84" "A65-A69" "A75-A79"
## [148] "B65-B83" "B90-B94" "F20-F29" "F60-F69" "H49-H52" "H55-H57" "I00-I02"
## [155] "L40-L44" "L55-L59" "L60-L75" "U00-U49" "Y85-Y89" "D00-D09" "F50-F59"
## [162] "F99-F99" "H00-H05" "L10-L13" "X60-X84" "B85-B89" "H46-H47" "O00-O07"
## [169] "O10-O16" "O20-O29" "O30-O48" "O60-O75" "O95-O99" "O85-O92" "N60-N64"
## [176] "H60-H61" "R80-R82" "R30-R39" "R83-R89"

unique(df$Age.Group.Code)

##  [1] "1"     "1-4"   "5-9"   "10-14" "15-19" "20-24" "25-34" "35-44" "45-54"
## [10] "55-64" "65-74" "75-84" "85+"

unique(df$Gender.Code)

## [1] "F" "M"

unique(df$Race.Code)

## [1] "2054-5" "2106-3" "A-PI"   "1002-5"

It doesn’t look like there are any nasty surprises hidden in the data, so I can go ahead and convert these columns to factors.

factors <- c("ICD.Chapter.Code",
             "ICD.Sub.Chapter.Code",
             "Age.Group.Code",
             "Gender.Code",
             "Race.Code")

df[,factors] <- lapply(df[,factors], factor)

The next thing I want to fix is that I have too many different sub-groups for age. I’ve mentioned a few times that I have 14 different age groups. However, there are only 11 age group weights to use when doing the age adjustment. This is because the age weights use the groups “5-14 years” and “15-24 years”, while our dataset has the groups “5-9 years,” “10-14 years,” “15-19 years,” and “20-24 years”. This means I have four age groups that I want to collapse into two age groups, summing together the deaths and population. To start, I’ll rename the age groups in my dataframe to match the weights used to calculate age adjustments.

df$Age.Group <- gsub("(5-9 years)|(10-14 years)", "5-14 years", df$Age.Group)
df$Age.Group <- gsub("(15-19 years)|(20-24 years)", "15-24 years", df$Age.Group)
df$Age.Group.Code <- fct_collapse(
  df$Age.Group.Code,
  `5-14` = c("5-9", "10-14"), 
  `15-24` = c("15-19", "20-24")
)

Now my factor levels match the ones I need, but I have two different “5-14” and “15-24” rows for each group. I’ll sum those rows together shortly, but it’ll be a simpler operation if I remove the redundant columns from my dataset first. In general, I want to keep the “.Code” columns, because their version of the data is much shorter, which makes it easier to type, and makes the code easier to read (with the exception of the Year.Code column, which is identical to the regular Year column in every way). However, when visualizing the data, I’ll want more descriptive labels, closer to what’s in the plain English columns. Instead of deleting the plain English columns outright, I’m going to set up a relational structure by making several new dataframes that match the “.Code” version of each factor level to the plain English version. That way I can reference the plain English description of each variable level whenever I need it, but it doesn’t clog up the dataframe, or require overly long strings to filter the data.

gender_key <- unique(df[, c("Gender.Code", "Gender")])
age_key <- unique(df[, c("Age.Group.Code", "Age.Group")])
icd_sub_key <- unique(
  x = df[, c("ICD.Chapter.Code", "ICD.Sub.Chapter.Code", "ICD.Sub.Chapter")]
)
race_key <- unique(df[, c("Race.Code", "Race")])

# Sort keys alphabetically where not already alphabetical

race_key <- arrange(race_key, Race.Code)
icd_key <- arrange(icd_key, ICD.Chapter.Code)
icd_sub_key <- arrange(icd_sub_key, ICD.Sub.Chapter.Code)

Now that I’ve made sure I have all the information about what each column means somewhere, I can go ahead and delete the redundant columns. Additionally, I noted earlier I wanted to remove the Notes and Crude.Rate columns. In addition, the Year.Code column is the same as the Year column, so I certainly don’t need both. “Year” is a lot easier to type than “Year.Code”, so I’ll be removing the latter. I’d also like the ICD.Chapter.Code column to appear as the first column, rather than the last column as it current does.

# Remove unwanted columns
df <- subset(df, select = -c(1, 2, 5, 6, 8, 10, 14))

# Put ICD.Chapter.Code first

df <- select(df, ICD.Chapter.Code, everything())

Finally, I need to sum across the 5-14 and 15-24 age groups that I created earlier.

df <- df %>% 
  group_by(
    ICD.Chapter.Code,
    ICD.Sub.Chapter.Code,
    Year,
    Age.Group.Code,
    Gender.Code,
    Race.Code
  ) %>%
  summarize_all(sum) %>%
  ungroup()

Now that my dataframe is clean and easy to work with, I can start having some fun with data!

Exploratory Analysis

As I mentioned earlier, I want to look at age-adjusted death rates, rather than just the crude death rate. To calculate the age-adjusted mortality rate itself is fairly simple. You multiply the crude death rate for each individual age group by the age weight, and then add each of those results together. When I queried the data, I could have actually had the age-adjusted mortality rates included but, as with the crude rate, I want to define the groups of interest myself and calculate their specific age-adjusted rates.

The first thing I want to do is add the age weights to the age group dataframe I made earlier. In fact, because the age group and age group code are so similar, and I’m not planning to report on any age values, the main reason I made this dataframe to begin with was to have a place to put these age weights. The only other reason to have it is as a reminder that the age group code “1” actually means “<1”.

age_key$Age.Weight <- c(0.013818, # <1
                        0.055317, # 1-4
                        0.145565, # 5-14
                        0.138646, # 15-24
                        0.135573, # 25-34
                        0.162613, # 35-44
                        0.134834, # 45-54
                        0.087247, # 55-64
                        0.066037, # 65-74
                        0.044842, # 75-84
                        0.015508  # >=85
)

If I’m going to make a lot of different figures based on different groupings of people, I’m also going to want a way to easily and quickly calculate the age-adjusted mortality rates by group. That means I need to write a function that outputs just that. It should allow me to work out the age-adjusted mortality rates for the populations I want in just a few commands and pipe the results into a figure.

# Function takes the name of the dataframe, then the name(s) of any grouping 
# variables of interest
age_adjust <- function(X, ...){
  
  # Age.Group.Call needs to be included alongside the specific groups listed in 
  # the function call.
  X <- group_by(X, ..., Age.Group.Code)
  
  X <- summarise(X, Population = sum(unique(Population)), Deaths = sum(Deaths))

  # Calculate crude rate for each age group
  X$Crude.Rate <- (X$Deaths * 100000) / X$Population
   
  # Add in the age weights from the age_key dataframe
  X <- merge(
    x = X, 
    y = age_key[, c("Age.Group.Code", "Age.Weight")], 
    by = "Age.Group.Code"
  )

  # Multiply crude rates by age weight and sum across age groups for finished 
  # product
  X$Age.Adjustment <- X$Age.Weight * X$Crude.Rate

  X <- group_by(X, ...)

  X <- summarise(X, Crude.Rate.Adjusted = sum(Age.Adjustment))
  
  return(X)
}

What are the leading causes of death from 1999 to 2016?

The first thing I want to know is what the top overall causes of death are. For this, I’m not going to use the age-adjusted rates. I just want to get an idea of what the most common causes of death are across this entire time-span. To do that, I’ll just sum the deaths by cause across the dataframe. Because there are over a hundred sub-chapters in the ICD, I just want to look at the main chapters at first to get a general picture.

total_deaths <- df %>%
  left_join(icd_key, by = c("ICD.Chapter.Code" = "ICD.Chapter.Code")) %>%
  group_by(ICD.Chapter.Code, ICD.Chapter) %>%
  summarise(Deaths = sum(Deaths)) %>%
  arrange(desc(Deaths))

kable(total_deaths) %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px")

ICD.Chapter.Code	ICD.Chapter	Deaths
I00-I99	Circulatory Diseases	15251874
C00-D48	Neoplasms	10504677
J00-J99	Respiratory Diseases	4355028
V01-Y98	External Causes	3281894
G00-G99	Nervous System Diseases	2379786
E00-E90	Endocrine Diseases	1853280
F00-F99	Mental Disorders	1761991
K00-K93	Digestive Diseases	1648674
A00-B99	Parasitic/Infectious Diseases	1198825
N00-N99	Genitourinary Diseases	1120420
R00-R99	Not Classified Elsewhere	612842
M00-M99	Musculoskeletal Diseases	248895
P00-P96	Perinatal Conditions	238935
Q00-Q99	Congenital Malformations	182947
D50-D89	Blood Diseases	177261
L00-L99	Skin Diseases	74236
O00-O99	Pregnancy/Childbirth	14267
U00-U99	Special Purposes	2931
H60-H95	Ear Diseases	1240
H00-H59	Eye Diseases	829

Diseases of the circulatory system and neoplasms lead the other top causes of death by an order of magnitude. This isn’t too surprising, as those are the chapters that include cancer and heart disease. Our least common causes of death include diseases of the eye, ear, deaths from codes reserved for special purposes, pregnancy and childbirth, and skin diseases. I’m actually quite surprised to see pregnancy and childbirth as low on the list as they are, given how dangerous childbirth has historically been.

This table also gives me some information on how I want to break down my figures. Looking at every ICD chapter by year would be too much information to show on a line graph and have it still be readable, so I already knew I’d want to split them across multiple figures. Now I know that I probably want to graph neoplasms and circulatory diseases separately from other causes. As a general rule of thumb, I like to keep the scale of a figure inside the same order of magnitude.

Has the death rate for leading causes of death changed over time?

This whole project was started by a discussion of how heart disease has become more treatable, so naturally I want to delve deeper into the death rate for circulatory disease immediately. Neoplasms works well as a cause of death to graph alongside it, even if it weren’t the only other one in the same order of magnitude. Treating cancer is extremely difficult, and the subject of a massive amount of research, so it’ll be good to see how they compare next to each other.

death_cause <- c("C00-D48", "I00-I99")

filter(df, ICD.Chapter.Code %in% death_cause) %>%
  age_adjust(ICD.Chapter.Code, Year) %>%
  ggplot(aes(x = Year, y = Crude.Rate.Adjusted, color = ICD.Chapter.Code)) +
  geom_line() +
  geom_point() +
  ylab("Age-Adjusted Crude Death Rate") +
  labs(title = "Top Causes of Death Over Time") +
  scale_color_discrete(
    labels = icd_key$ICD.Chapter[icd_key$ICD.Chapter.Code %in% death_cause],
    name = "Cause of Death"
  )

There’s an overall decline for both causes, but it’s much steeper for circulatory diseases than neoplasms. The decline for neoplasms is impressively linear. I wouldn’t normally expect to see such a steady and consistent decline in real-world data. By contrast the slope on the circulatory disease appears less consistent. Just eyeballing it, I’d say it’s probably linear, but there are some potential hints of a logarithmic curve to it, meaning the decline in the death rate for circulatory disease might be starting to flatten out. I don’t think I have data across enough years to get meaningful results out of trying to fit both functions to the data and seeing which performs better. In another analysis it might be worth querying some of the databases that use older versions of the ICD and trying to equate their measures to get a bigger sample size.

Is the decline in leading causes of death the same across gender and race?

Earlier I’d mentioned race and gender as potential factors, so it may be interesting to see if these factors interact with the data.

labels_top <- c("Neoplasms", "Circulatory Diseases")
names(labels_top) <- death_cause

filter(df, ICD.Chapter.Code %in% death_cause) %>%
  age_adjust(ICD.Chapter.Code, Year, Race.Code, Gender.Code) %>%
  ggplot(
    aes(
      x = Year, 
      y = Crude.Rate.Adjusted, 
      color = Race.Code, 
      shape = Gender.Code, 
      linetype = Gender.Code
    )
  ) +
  geom_line() +
  geom_point() +
  facet_wrap(
    facets = ~ICD.Chapter.Code, 
    labeller = labeller(ICD.Chapter.Code = labels_top)
  ) +
  ylab("Age-Adjusted Crude Death Rate") +
  labs(title = "Top Causes of Death Over Time by Race and Gender") +
  scale_color_discrete(labels = race_key$Race, name = "Race") +
  scale_shape_discrete(labels = gender_key$Gender, name = "Gender") +
  scale_linetype_manual(
    labels = gender_key$Gender, 
    name = "Gender", 
    values = c(1, 3)
  ) +
  guides(colour=guide_legend(override.aes=list(shape=NA)))

There are differences in death rate based on race, with Black and African American people having the highest death rates for both neoplasms and circulatory disease, but Asian people and Pacific Islanders having the lowest. In general the death rates are also higher for men than women. The overall reduction over time is fairly similar for each group though, indicating that while there is definitely an inequality in death rates, the decline in death rates doesn’t affect minority groups disproportionately less than white people. In fact, the slopes for neoplasms appear to be steeper for groups that had higher initial death rates. This may indicate that for neoplasms the amount of inequality is starting to lessen over time, though it would help to see more recent data in order to see if this is a lasting trend.

For circulatory diseases, the shape of the data is starting to look a lot more logarithmic now, with a fairly clear trend towards the decline leveling off. While the death rate for Black or African American men has declined sharply, the curve flattens out dramatically around 2011, keeping it well above the other groups.

Have common causes of death changed over time?

Most of the ICD chapters showed a total number of deaths in the range of millions, so next I want to take a general look at all of those.

death_cause <- total_deaths$ICD.Chapter.Code[
  which(
    x = total_deaths$Deaths > 999999 & total_deaths$Deaths < 10000000
  )
]

filter(df, ICD.Chapter.Code %in% death_cause) %>%
  age_adjust(ICD.Chapter.Code, Year) %>%
  ggplot(aes(x = Year, y = Crude.Rate.Adjusted, color = ICD.Chapter.Code)) +
  geom_line() +
  geom_point(aes(shape = ICD.Chapter.Code)) + 
  ylab("Age-Adjusted Crude Death Rate") +
  labs(title = "Common Causes of Death Over Time") +
  scale_color_discrete(
    labels = icd_key$ICD.Chapter[icd_key$ICD.Chapter.Code %in% death_cause],
    name = "Cause of Death"
  ) +
  scale_linetype_discrete(
    labels = icd_key$ICD.Chapter[icd_key$ICD.Chapter.Code %in% death_cause],
    name = "Cause of Death"
  ) +
  scale_shape_manual(
    labels = icd_key$ICD.Chapter[icd_key$ICD.Chapter.Code %in% death_cause],
    name = "Cause of Death",
    values = c(15, 16, 17, 15, 16, 17, 15, 16)
  )

Most of these are fairly steady over time. Interestingly, death due to mental diseases shows a steady incline up to about 2013, after which it starts to decline again. Deaths from nervous system diseases, likewise, increase after that point. The other point of interest is deaths from external causes show a sudden massive uptick between 2014 and 2015, and continue to rise on this new, very steep slope into 2016.

Why is there a tradeoff between deaths from mental disorders and nervous system diseases?

There’s no guarantee these are related, but the difference between a mental disorder, as classified by the ICD 10 and a nervous system disease is a very fine line. In fact, several forms of dementia, such as dementia from Alzheimer’s or Parkinson’s disease, are listed under mental disorders but with a reference to codes in the nervous system diseases chapter for the underlying illnesses themselves. The divergence also appears immediately after the publication of the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). My guess would be that changes in the DSM-V caused a shift in reporting, where deaths were more often classified as being due to nervous system disorders rather than due to dementia stemming from those disorders. In general dementia from organic causes, such as nervous system diseases, is in the F01-F09 sub-chapter of the ICD 10. It looks like the corresponding sub-chapters for nervous system diseases include Pick’s disease (G31), Huntington’s Disease(G10), and Parkinson’s Disease (G20), so the sub-chapters we want there are G10-G14, G20-G25, and G30-G31.

death_cause <- c("F01-F09","G10-G14", "G20-G25", "G30-G31")

filter(df, ICD.Sub.Chapter.Code %in% death_cause) %>%
  age_adjust(ICD.Chapter.Code, ICD.Sub.Chapter.Code, Year) %>%
  ggplot(aes(x = Year, y = Crude.Rate.Adjusted, color = ICD.Sub.Chapter.Code)) +
  geom_line() +
  geom_point() +
  ylab("Age-Adjusted Crude Death Rate") +
  labs(
    title = "Deaths from Organic Mental Disorders and Nervous System Disorders"
  ) +
  scale_color_discrete(name = "ICD Sub-Chapters"
  )

The pattern of interest definitely seems to be related specifically to sub-chapter G30-G31, which includes Alzheimer’s disease and Pick’s disease. As in the overall graph by chapter, around 2013 the number of deaths in the ICD sub-chapter that includes dementia from nervous system diseases starts to decline, while at the same time deaths from Alzheimer’s and Pick’s disease start to rise. That definitely lines up with the idea that there wasn’t a major change in actual causes of death after 2013, just a change in how deaths were classified. The reason the pattern is seen in the Alzheimer’s and Pick’s disease group, but not the others, is probably simply due to Alzheimer’s disease being overwhelmingly more common, and thus making up the majority of the data.

How have deaths from external causes changed over time?

External causes is a broad category that includes essentially anything that isn’t a disease. Because of that, I suspect this is an area where we’re particularly likely to see differences based on things like socio-economic status, so I want to start by breaking the data down by race and gender.

labels_gender <- c(`F` = "Female", `M` = "Male")


filter(df, ICD.Chapter.Code == "V01-Y98") %>%
  age_adjust(ICD.Chapter.Code, Year, Race.Code, Gender.Code) %>%
  ggplot(aes(x = Year, y = Crude.Rate.Adjusted, color = Race.Code)) +
  facet_wrap(~Gender.Code, labeller = labeller(Gender.Code = labels_gender))+
  geom_line() +
  geom_point() +
  ylab("Age-Adjusted Crude Death Rate") +
  labs(title = "Deaths from External Causes by Race and Gender") +
  scale_color_discrete(labels = race_key$Race, name = "Race")

There are some differences due to race, most notably a dramatically lower rate of death via external cause for the Asian or Pacific Islander group. In terms of gender, the massive uptick in death rate for external causes seems to be accounted for almost entirely by the male groups, except for the Asian or Pacific Islander male group.

What causes the increase in deaths from external causes for men after 2014?

To try and work out why there’s a sudden increase in death from external causes for men, I want to break down the data by gender and sub-chapter to try and see what specific cause is behind this.

filter(df, ICD.Chapter.Code == "V01-Y98") %>%
  age_adjust(ICD.Sub.Chapter.Code, Year, Gender.Code) %>%
  ggplot(aes(x = Year, y = Crude.Rate.Adjusted, color = ICD.Sub.Chapter.Code)) +
  facet_wrap(~Gender.Code, labeller = labeller(Gender.Code = labels_gender))+
  geom_line() +
  geom_point() +
  ylab("Age-Adjusted Crude Death Rate") +
  labs(title = "Deaths from External Causes by Category and Gender") +
  scale_color_discrete(
    labels = str_wrap(
      icd_sub_key$ICD.Sub.Chapter[icd_sub_key$ICD.Chapter.Code == "V01-Y98"], 
      20
    ), 
    name = "Death Cause"
  )

Unfortunately the dramatic uptick in male deaths falls into the “Other” category, which contains well over a hundred unique causes of death, including falls, accidents involving machinery, animal attacks, and radiation exposure. With such a broad range, there’s no telling if the sudden spike in death rate for men in this category is due to a single factor or multiple external factors. I’ll likely do another analysis later on that dives into just the external cause of death information to get more answers.

Of additional interest is the decrease in death rate for men due to transport accidents after 2007. I wasn’t able to find any immediately obvious answers for what might have caused this, but it might be worth further investigation to try and determine if there were any likely laws, safety updates, or policy changes that caused this decline so that similar measures can be used to further reduce the death rate. There is a slight dip for women as well in the same time period, but much less noticeable. This is likely due to women having a much lower death rate from transport accidents overall. Any interventions or changes in transportation safety would have had a larger impact on those more likely to suffer accidents in the first place.

Summary

From this dataset, it’s clear to see that there is still a large deal of racial and gender inequality in causes of death. This is especially evident in racial inequality for circulatory diseases and neoplasms. However, there is some slight evidence to show that the inequality is starting to become less severe when it comes to death due to neoplasms. There has been a clear reduction in death rate due to neoplasms and circulatory disease for all groups, though the decline in circulatory disease may be starting to level out. Overall death rates are highest for Black or African American people, and lowest for people who are Asian or Pacific Islanders. This is in line with a 2002 report by the Population Reference Bureau and the trends do not appear to have changed since that time.

There has also been a rise in the Alzheimer’s and Pick’s disease death rates, likely due primarily to Alzheimer’s disease. While the rise in deaths due to Alzheimer’s disease appears to have jumped dramatically after 2013, it’s likely due to deaths that were formerly classified as being due to dementia now being classified as death due to Alzheimer’s disease. With this in mind, the increase in death due to Alzheimer’s disease appears to be a fairly steady trend.

Finally, there is a dramatic uptick in deaths due to external causes after 2014. This uptick is almost entirely accounted for by men who are not Asian or Pacific Islanders dying from causes classified as “other”. Inspection of more detailed data will be needed to try and tease out the trends behind this. Additionally, there was a dramatic drop in death rate from transport accidents after 2007 for men. The exact cause is unknown, but potentially of further interest. If the cause was due to a new policy of some kind, replication of that policy could further lower accident rates.

Overall, while this analysis does answer the original question it set out to examine, I feel as if it’s raised more questions than answers, which I consider to be about the perfect combination. There’s little more satisfying than finding that the answers to your questions come with new questions, and new depths to explore, so I look forward to coming back to this area and trying to see not only what answers I can come up with, but what new questions as well.