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Working on mortality update, and full simulation

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Alex Gebben Work 2025-11-19 17:37:39 -07:00
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1_Run_Full_Simulation.r
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#####Packages #####Packages
library(tidyverse) #Cleaning data library(tidyverse) #Cleaning data
library(fixest) #Estimating a model of birth rates, to provide variance in the birth rate Monte Carlo using a fixed effect model. library(fixest) #Estimating a model of birth rates, to provide variance in the birth rate Monte Carlo using a fixed effect model.
library(forecast) #Fore ARIMA migration simulations
library(parallel) library(parallel)
library(uuid) #To add a index to each batch library(uuid) #To add a index to each batch
####If the prelimnary data needs to be reloaded run the supplied bash script to download, process, and generate all needed data sets for the Monte Carlo popopulation Simulation. Otherwise skip this step to save time ####If the prelimnary data needs to be reloaded run the supplied bash script to download, process, and generate all needed data sets for the Monte Carlo population Simulation. Otherwise skip this step to save time
RELOAD_DATA <- TRUE RELOAD_DATA <- FALSE
if(RELOAD_DATA){system("bash Prelim_Process.sh")} if(RELOAD_DATA){system("bash Prelim_Process.sh")}
#Load custom functions needed for the simulation #Load custom functions needed for the simulation
source("Scripts/Birth_Simulation_Functions.r") source("Scripts/Load_Custom_Functions/Migration_Simulation_Functions.r")
source("Scripts/Monte_Carlo_Functions.r") source("Scripts/Load_Custom_Functions/Birth_Simulation_Functions.r")
source("Scripts/Migration_Simulation_Functions.r") source("Scripts/Load_Custom_Functions/Increment_Data_Year.r")
#######Preliminary Model Inputs
YEARS_AHEAD <- 10
NUM_SIMULATIONS <- 10^4
ST_YEAR <- 2017
################################Load Data
DEMO <- readRDS("Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2016_Starting_Kemmerer_Diamondville_Demographics_Matrix.Rds")
BIRTH_MOD <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Birth_Regression_2016.Rds")
#Must add region as a factor with multiple levels for predict to work. Seems to check for multiple levels although that is not needed econometrics.
BIRTH_DATA <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Regression_Data/Birth_Simulation_Key_Starting_Points.Rds") %>% mutate(Region=factor(Region)) %>% filter(Region=='Kemmerer & Diamondville',Year==2016)
MIGRATION_ARIMA <- readRDS("Data/Intermediate_Inputs/Migration_ARIMA_Models/Kemmerer_Diamondville_Net_Migration_ARIMA_2016.Rds")
MIGRATION_ODDS <- readRDS("Data/Intermediate_Inputs/Migration_Trends/Migration_Age_Probability_Zero_to_85.Rds")
MIGRATION_MATRIX <- simulate(nsim=NUM_SIMULATIONS,MIGRATION_ARIMA,n=YEARS_AHEAD)
MIGRATION_MATRIX <- do.call(cbind, mclapply(1:NUM_SIMULATIONS,function(x)(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA) )),mc.cores = detectCores()-1))
rownames(MIGRATION_MATRIX) <- ST_YEAR:(ST_YEAR+YEARS_AHEAD-1)
colnames(MIGRATION_MATRIX) <- 1:NUM_SIMULATIONS
#####################START YEAR BY SIMULATIONS
CURRENT_YEARS_AHEAD <- 1
CURRENT_SIM_NUM <- 58
DEMO <- DEMOGRAPHICS_AFTER_MIGRATION(DEMO, MIGRATION_MATRIX[CURRENT_YEARS_AHEAD ,CURRENT_SIM_NUM],MIGRATION_ODDS )
BIRTH_DATA$Year <- BIRTH_DATA$Year+1
BIRTH_DATA$Lag_Two_Births <- BIRTH_DATA$Lag_Births
BIRTH_DATA$Lag_Births <- BIRTH_DATA$Births
BIRTH_DATA$Births <- NA
##We grab one year earlier than the window because they are one year older this year. Because the ages are from 0-85, row 18 is year 17, but one year is added making it 18 years in the current year. The birth windows are 18-28 for women and 18-30 for men.
BIRTH_DATA$Min_Birth_Group <- min(sum(DEMO[18:30,1]),sum(DEMO[18:28,2]))
NEW_BORNS <- BIRTH_SIM(BIRTH_MOD,BIRTH_DATA)
DEMO <- INCREMENT_AGES(DEMO,NEW_BORNS)
DEMO
#####User Configuration Values #####User Configuration Values
KEMMER_SIM <- TRUE #Wether the simulation should predict Kemmerer (and Diamondville) or Lincoln County as a whole. TRUE, is Kemmerer False is Lincoln KEMMER_SIM <- TRUE #Wether the simulation should predict Kemmerer (and Diamondville) or Lincoln County as a whole. TRUE, is Kemmerer False is Lincoln

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Data/Raw_Data/Mortality_Rates_New/Lincoln_Age_Adjusted_1999-2020.csv Normal file
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"Notes","Year","Year Code","Sex","Sex Code",Deaths,Population,Crude Rate,Crude Rate Standard Error,Age Adjusted Rate,Age Adjusted Rate Standard Error
,"1999","1999","Female","F",44,7084,621.1,93.6,671.3,102.2
,"1999","1999","Male","M",63,7254,868.5,109.4,1036.9,138.9
,"2000","2000","Female","F",49,7213,679.3,97.0,710.8,102.3
,"2000","2000","Male","M",53,7360,720.1,98.9,852.8,122.8
,"2001","2001","Female","F",51,7318,696.9,97.6,704.5,99.0
,"2001","2001","Male","M",64,7379,867.3,108.4,1006.8,131.6
,"2002","2002","Female","F",38,7366,515.9,83.7,495.7,80.7
,"2002","2002","Male","M",68,7492,907.6,110.1,1047.8,131.8
,"2003","2003","Female","F",60,7449,805.5,104.0,748.9,96.9
,"2003","2003","Male","M",62,7668,808.6,102.7,957.5,126.3
,"2004","2004","Female","F",54,7662,704.8,95.9,646.7,88.7
,"2004","2004","Male","M",64,7877,812.5,101.6,989.8,130.8
,"2005","2005","Female","F",58,7825,741.2,97.3,721.1,95.4
,"2005","2005","Male","M",69,8092,852.7,102.7,928.7,115.3
,"2006","2006","Female","F",47,8020,586.0,85.5,589.8,86.6
,"2006","2006","Male","M",47,8409,558.9,81.5,673.3,102.4
,"2007","2007","Female","F",62,8232,753.2,95.7,750.8,96.6
,"2007","2007","Male","M",75,8781,854.1,98.6,1002.2,122.6
,"2008","2008","Female","F",52,8529,609.7,84.5,613.4,86.3
,"2008","2008","Male","M",62,9100,681.3,86.5,795.2,105.2
,"2009","2009","Female","F",46,8777,524.1,77.3,528.7,78.9
,"2009","2009","Male","M",66,9305,709.3,87.3,854.8,109.8
,"2010","2010","Female","F",51,8804,579.3,81.1,578.0,81.9
,"2010","2010","Male","M",63,9302,677.3,85.3,774.4,102.1
,"2011","2011","Female","F",60,8839,678.8,87.6,650.3,85.3
,"2011","2011","Male","M",71,9232,769.1,91.3,778.1,97.8
,"2012","2012","Female","F",38,8774,433.1,70.3,372.8,62.1
,"2012","2012","Male","M",66,9187,718.4,88.4,765.1,98.1
,"2013","2013","Female","F",45,9053,497.1,74.1,433.7,66.6
,"2013","2013","Male","M",72,9311,773.3,91.1,861.4,105.2
,"2014","2014","Female","F",60,9158,655.2,84.6,602.0,80.1
,"2014","2014","Male","M",67,9409,712.1,87.0,709.3,92.0
,"2015","2015","Female","F",64,9191,696.3,87.0,642.1,82.1
,"2015","2015","Male","M",64,9531,671.5,83.9,668.8,88.4
,"2016","2016","Female","F",66,9360,705.1,86.8,590.6,74.5
,"2016","2016","Male","M",76,9750,779.5,89.4,800.6,97.1
,"2017","2017","Female","F",58,9454,613.5,80.6,508.2,68.3
,"2017","2017","Male","M",68,9811,693.1,84.1,639.1,81.8
,"2018","2018","Female","F",55,9504,578.7,78.0,502.6,69.5
,"2018","2018","Male","M",86,9930,866.1,93.4,806.2,91.8
,"2019","2019","Female","F",75,9726,771.1,89.0,660.2,77.9
,"2019","2019","Male","M",81,10104,801.7,89.1,777.8,89.9
,"2020","2020","Female","F",72,9915,726.2,85.6,603.9,73.0
,"2020","2020","Male","M",105,10338,1015.7,99.1,885.6,91.0
"---"
"Dataset: Multiple Cause of Death, 1999-2020"
"Query Parameters:"
"Title: Lincoln_Age_Adjusted_1999-2020"
"States: Lincoln County, WY (56023)"
"Group By: Year; Sex"
"Show Totals: False"
"Show Zero Values: True"
"Show Suppressed: True"
"Standard Population: 2000 U.S. Std. Population"
"Calculate Rates Per: 100,000"
"Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)"
"---"
"Help: See http://wonder.cdc.gov/wonder/help/mcd.html for more information."
"---"
"Query Date: Nov 19, 2025 11:53:33 PM"
"---"
"Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics"
"System, Mortality 1999-2020 on CDC WONDER Online Database, released in 2021. Data are from the Multiple Cause of Death Files,"
"1999-2020, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative"
"Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Nov 19, 2025 11:53:33 PM"
"---"
Caveats:
"1. As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#2014-Revision."
"2. The populations used to calculate standard age-adjusted rates are documented here: More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#2000 Standard Population."
"3. The method used to calculate age-adjusted rates is documented here: More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#Age-Adjusted Rates."
"4. Deaths for persons of unknown age are included in counts and crude rates, but are not included in age-adjusted rates."
"5. The method used to calculate standard errors is documented here: More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#Standard-Errors."
"6. The population figures for year 2020 are bridged-race estimates of the July 1 resident population, from the Vintage 2020"
"postcensal series released by NCHS on September 22, 2021. The population figures for year 2019 are bridged-race estimates of the"
"July 1 resident population, from the Vintage 2019 postcensal series released by NCHS on July 9, 2020. The population figures for"
"year 2018 are bridged-race estimates of the July 1 resident population, from the Vintage 2018 postcensal series released by NCHS"
"on June 25, 2019. The population figures for year 2017 are bridged-race estimates of the July 1 resident population, from the"
"Vintage 2017 postcensal series released by NCHS on June 27, 2018. The population figures for year 2016 are bridged-race"
"estimates of the July 1 resident population, from the Vintage 2016 postcensal series released by NCHS on June 26, 2017. The"
"population figures for year 2015 are bridged-race estimates of the July 1 resident population, from the Vintage 2015 postcensal"
"series released by NCHS on June 28, 2016. The population figures for year 2014 are bridged-race estimates of the July 1 resident"
"population, from the Vintage 2014 postcensal series released by NCHS on June 30, 2015. The population figures for year 2013 are"
"bridged-race estimates of the July 1 resident population, from the Vintage 2013 postcensal series released by NCHS on June 26,"
"2014. The population figures for year 2012 are bridged-race estimates of the July 1 resident population, from the Vintage 2012"
"postcensal series released by NCHS on June 13, 2013. Population figures for 2011 are bridged-race estimates of the July 1"
"resident population, from the county-level postcensal Vintage 2011 series released by NCHS on July 18, 2012. Population figures"
"for 2010 are April 1 Census counts. The population figures for years 2001 - 2009, are bridged-race estimates of the July 1"
"resident population, from the revised intercensal county-level 2000 - 2009 series released by NCHS on October 26, 2012."
"Population figures for 2000 are April 1 Census counts. Population figures for 1999 are from the 1990-1999 intercensal series of"
"July 1 estimates. Population figures for Infant Age Groups are the number of live births. <br/><b>Note:</b> Rates and population"
"figures for years 2001 - 2009 differ slightly from previously published reports, due to use of the population estimates which"
"were available at the time of release."
"7. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the"
"resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/mcd.html#Age Group."
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Data/Raw_Data/Mortality_Rates_New/Lincoln_Not_Age_Adjusted_2018-2023.csv Normal file
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"Notes","Year","Year Code","Sex","Sex Code",Deaths,Population,Crude Rate,Crude Rate Standard Error
,"2018","2018","Female","F",55,9504,578.7,78.0
,"2018","2018","Male","M",86,9930,866.1,93.4
,"2019","2019","Female","F",75,9726,771.1,89.0
,"2019","2019","Male","M",81,10104,801.7,89.1
,"2020","2020","Female","F",72,9915,726.2,85.6
,"2020","2020","Male","M",105,10338,1015.7,99.1
,"2021","2021","Female","F",82,9785,838.0,92.5
,"2021","2021","Male","M",91,10368,877.7,92.0
,"2022","2022","Female","F",57,10007,569.6,75.4
,"2022","2022","Male","M",100,10653,938.7,93.9
,"2023 ","2023","Female","F",69,10117,682.0,82.1
,"2023 ","2023","Male","M",100,10763,929.1,92.9
"---"
"Dataset: Multiple Cause of Death, 2018-2023, Single Race"
"Query Parameters:"
"Title: Lincoln_Not_Age_Adjusted_2018-2023"
"States: Lincoln County, WY (56023)"
"Group By: Year; Sex"
"Show Totals: False"
"Show Zero Values: True"
"Show Suppressed: True"
"Calculate Rates Per: 100,000"
"Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)"
"---"
"Help: See http://wonder.cdc.gov/wonder/help/mcd-expanded.html for more information."
"---"
"Query Date: Nov 19, 2025 11:51:02 PM"
"---"
"Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics"
"System, Mortality 2018-2023 on CDC WONDER Online Database, released in 2024. Data are from the Multiple Cause of Death Files,"
"2018-2023, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative"
"Program. Accessed at http://wonder.cdc.gov/mcd-icd10-expanded.html on Nov 19, 2025 11:51:02 PM"
"---"
Caveats:
"1. The method used to calculate standard errors is documented here: More information:"
"http://wonder.cdc.gov/wonder/help/mcd-expanded.html#Standard-Errors."
"2. The population figures for years 2023 are single-race estimates of the July 1 resident population, from the Vintage 2023"
"postcensal series released by the Census Bureau on June 27, 2024. The 2023 series is based on the Modified Blended Base produced"
"by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Modified Blended Base consists of the blend"
"of Vintage 2020 postcensal population estimates for April 1, 2020, 2020 Demographic Analysis Estimates, and 2020 Census data"
"from the internal Census Edited File (CEF). The population figures for years 2022 are single-race estimates of the July 1"
"resident population, from the Vintage 2022 postcensal series released by the Census Bureau on June 22, 2023. The 2022 series is"
"based on the Modified Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The"
"Modified Blended Base consists of the blend of Vintage 2020 postcensal population estimates for April 1, 2020, 2020 Demographic"
"Analysis Estimates, and 2020 Census data from the internal Census Edited File (CEF). The population figures for year 2021 are"
"single-race estimates of the July 1 resident population, based on the Blended Base produced by the US Census Bureau in lieu of"
"the April 1, 2020 decennial population count, from the Vintage 2021 postcensal series released by the Census Bureau on June 30,"
"2022. The population figures for year 2020 are single-race estimates of the July 1 resident population, from the Vintage 2020"
"postcensal series based on April 2010 Census, released by the Census Bureau on July 27, 2021. The population figures for year"
"2019 are single-race estimates of the July 1 resident population, from the Vintage 2019 postcensal series based on April 2010"
"Census, released by the Census Bureau on June 25, 2020. The population figures for year 2018 are single-race estimates of the"
"July 1 resident population, from the Vintage 2018 postcensal series based on April 2010 Census, released by the Census Bureau on"
"June 20, 2019. More information: http://wonder.cdc.gov/wonder/help/mcd-expanded.html#Population Data."
"3. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the"
"resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/mcd-expanded.html#Age"
"Group."
"4. After the creation of the final 2023 dataset, North Carolina updated the cause of death information for over 900 death"
"certificates to include a cause of death code indicating drug overdose (ICD-10 underlying cause-of-death codes: X40-X44,"
"X60-X64, X85, and Y10-Y14). Jurisdictions can continue to update death certificates after the closing of the mortality file. As"
"a result, users should consider that the actual death count for drug overdose deaths for North Carolina in 2023 is over 4,400"
"deaths, with a crude rate of approximately 41.0 per 100,000 population, and an age-adjusted rate of approximately 42.1 per"
"100,000 population. These deaths will not be updated on the final mortality datasets."
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Data/Raw_Data/Mortality_Rates_New/Other/Wyoming_10_Year_Age_Groups_1999-2020.csv Normal file
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"Notes","Year","Year Code","Sex","Sex Code","Ten-Year Age Groups","Ten-Year Age Groups Code",Deaths,Population,Crude Rate,Crude Rate Standard Error
,"1999","1999","Female","F","< 1 year","1",13,2892,Unreliable,124.7
,"1999","1999","Female","F","1-4 years","1-4",Suppressed,12231,Suppressed,Suppressed
,"1999","1999","Female","F","5-14 years","5-14",10,35881,Unreliable,8.8
,"1999","1999","Female","F","15-24 years","15-24",21,36262,57.9,12.6
,"1999","1999","Female","F","25-34 years","25-34",18,28737,Unreliable,14.8
,"1999","1999","Female","F","35-44 years","35-44",68,40269,168.9,20.5
,"1999","1999","Female","F","45-54 years","45-54",100,34926,286.3,28.6
,"1999","1999","Female","F","55-64 years","55-64",154,21368,720.7,58.1
,"1999","1999","Female","F","65-74 years","65-74",332,16237,2044.7,112.2
,"1999","1999","Female","F","75-84 years","75-84",555,11115,4993.3,212.0
,"1999","1999","Female","F","85+ years","85+",701,4600,15239.1,575.6
,"1999","1999","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"1999","1999","Male","M","< 1 year","1",29,3178,912.5,169.5
,"1999","1999","Male","M","1-4 years","1-4",Suppressed,12790,Suppressed,Suppressed
,"1999","1999","Male","M","5-14 years","5-14",21,37968,55.3,12.1
,"1999","1999","Male","M","15-24 years","15-24",63,39446,159.7,20.1
,"1999","1999","Male","M","25-34 years","25-34",39,30290,128.8,20.6
,"1999","1999","Male","M","35-44 years","35-44",92,40234,228.7,23.8
,"1999","1999","Male","M","45-54 years","45-54",177,36606,483.5,36.3
,"1999","1999","Male","M","55-64 years","55-64",255,21868,1166.1,73.0
,"1999","1999","Male","M","65-74 years","65-74",415,14867,2791.4,137.0
,"1999","1999","Male","M","75-84 years","75-84",590,8067,7313.7,301.1
,"1999","1999","Male","M","85+ years","85+",385,1948,19763.9,1007.3
,"1999","1999","Male","M","Not Stated","NS",Suppressed,Not Applicable,Not Applicable,Not Applicable
,"2000","2000","Female","F","< 1 year","1",22,2919,753.7,160.7
,"2000","2000","Female","F","1-4 years","1-4",Suppressed,12128,Suppressed,Suppressed
,"2000","2000","Female","F","5-14 years","5-14",Suppressed,35247,Suppressed,Suppressed
,"2000","2000","Female","F","15-24 years","15-24",27,36083,74.8,14.4
,"2000","2000","Female","F","25-34 years","25-34",22,29055,75.7,16.1
,"2000","2000","Female","F","35-44 years","35-44",65,39523,164.5,20.4
,"2000","2000","Female","F","45-54 years","45-54",95,36129,262.9,27.0
,"2000","2000","Female","F","55-64 years","55-64",168,22008,763.4,58.9
,"2000","2000","Female","F","65-74 years","65-74",318,16324,1948.1,109.2
,"2000","2000","Female","F","75-84 years","75-84",546,11274,4843.0,207.3
,"2000","2000","Female","F","85+ years","85+",671,4718,14222.1,549.0
,"2000","2000","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2000","2000","Male","M","< 1 year","1",20,3211,622.9,139.3
,"2000","2000","Male","M","1-4 years","1-4",Suppressed,12682,Suppressed,Suppressed
,"2000","2000","Male","M","5-14 years","5-14",12,37256,Unreliable,9.3
,"2000","2000","Male","M","15-24 years","15-24",40,39275,101.8,16.1
,"2000","2000","Male","M","25-34 years","25-34",46,30799,149.4,22.0
,"2000","2000","Male","M","35-44 years","35-44",97,39242,247.2,25.1
,"2000","2000","Male","M","45-54 years","45-54",183,37950,482.2,35.6
,"2000","2000","Male","M","55-64 years","55-64",225,22582,996.4,66.4
,"2000","2000","Male","M","65-74 years","65-74",395,15019,2630.0,132.3
,"2000","2000","Male","M","75-84 years","75-84",595,8341,7133.4,292.4
,"2000","2000","Male","M","85+ years","85+",359,2017,17798.7,939.4
,"2000","2000","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2001","2001","Female","F","< 1 year","1",23,2989,769.5,160.4
,"2001","2001","Female","F","1-4 years","1-4",Suppressed,12082,Suppressed,Suppressed
,"2001","2001","Female","F","5-14 years","5-14",Suppressed,34158,Suppressed,Suppressed
,"2001","2001","Female","F","15-24 years","15-24",18,36664,Unreliable,11.6
,"2001","2001","Female","F","25-34 years","25-34",27,28275,95.5,18.4
,"2001","2001","Female","F","35-44 years","35-44",56,38079,147.1,19.7
,"2001","2001","Female","F","45-54 years","45-54",121,37891,319.3,29.0
,"2001","2001","Female","F","55-64 years","55-64",157,22527,696.9,55.6
,"2001","2001","Female","F","65-74 years","65-74",305,16489,1849.7,105.9
,"2001","2001","Female","F","75-84 years","75-84",535,11373,4704.1,203.4
,"2001","2001","Female","F","85+ years","85+",716,4800,14916.7,557.5
,"2001","2001","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2001","2001","Male","M","< 1 year","1",13,3187,Unreliable,113.1
,"2001","2001","Male","M","1-4 years","1-4",Suppressed,12633,Suppressed,Suppressed
,"2001","2001","Male","M","5-14 years","5-14",Suppressed,36201,Suppressed,Suppressed
,"2001","2001","Male","M","15-24 years","15-24",64,40108,159.6,19.9
,"2001","2001","Male","M","25-34 years","25-34",56,30344,184.6,24.7
,"2001","2001","Male","M","35-44 years","35-44",101,37785,267.3,26.6
,"2001","2001","Male","M","45-54 years","45-54",192,39735,483.2,34.9
,"2001","2001","Male","M","55-64 years","55-64",250,23494,1064.1,67.3
,"2001","2001","Male","M","65-74 years","65-74",402,15150,2653.5,132.3
,"2001","2001","Male","M","75-84 years","75-84",588,8601,6836.4,281.9
,"2001","2001","Male","M","85+ years","85+",378,2092,18068.8,929.4
,"2001","2001","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2002","2002","Female","F","< 1 year","1",26,3056,850.8,166.9
,"2002","2002","Female","F","1-4 years","1-4",Suppressed,12233,Suppressed,Suppressed
,"2002","2002","Female","F","5-14 years","5-14",Suppressed,33685,Suppressed,Suppressed
,"2002","2002","Female","F","15-24 years","15-24",22,37529,58.6,12.5
,"2002","2002","Female","F","25-34 years","25-34",25,28811,86.8,17.4
,"2002","2002","Female","F","35-44 years","35-44",60,36659,163.7,21.1
,"2002","2002","Female","F","45-54 years","45-54",126,38790,324.8,28.9
,"2002","2002","Female","F","55-64 years","55-64",167,24106,692.8,53.6
,"2002","2002","Female","F","65-74 years","65-74",324,16709,1939.1,107.7
,"2002","2002","Female","F","75-84 years","75-84",557,11422,4876.6,206.6
,"2002","2002","Female","F","85+ years","85+",722,4785,15088.8,561.5
,"2002","2002","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2002","2002","Male","M","< 1 year","1",18,3351,Unreliable,126.6
,"2002","2002","Male","M","1-4 years","1-4",Suppressed,12932,Suppressed,Suppressed
,"2002","2002","Male","M","5-14 years","5-14",14,35766,Unreliable,10.5
,"2002","2002","Male","M","15-24 years","15-24",69,40889,168.7,20.3
,"2002","2002","Male","M","25-34 years","25-34",67,30697,218.3,26.7
,"2002","2002","Male","M","35-44 years","35-44",95,36510,260.2,26.7
,"2002","2002","Male","M","45-54 years","45-54",190,40562,468.4,34.0
,"2002","2002","Male","M","55-64 years","55-64",242,25302,956.4,61.5
,"2002","2002","Male","M","65-74 years","65-74",420,15321,2741.3,133.8
,"2002","2002","Male","M","75-84 years","75-84",602,8768,6865.9,279.8
,"2002","2002","Male","M","85+ years","85+",411,2134,19259.6,950.0
,"2002","2002","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2003","2003","Female","F","< 1 year","1",15,3269,Unreliable,118.5
,"2003","2003","Female","F","1-4 years","1-4",Suppressed,12165,Suppressed,Suppressed
,"2003","2003","Female","F","5-14 years","5-14",Suppressed,33189,Suppressed,Suppressed
,"2003","2003","Female","F","15-24 years","15-24",20,37875,52.8,11.8
,"2003","2003","Female","F","25-34 years","25-34",19,28866,Unreliable,15.1
,"2003","2003","Female","F","35-44 years","35-44",64,35334,181.1,22.6
,"2003","2003","Female","F","45-54 years","45-54",137,39716,344.9,29.5
,"2003","2003","Female","F","55-64 years","55-64",163,25404,641.6,50.3
,"2003","2003","Female","F","65-74 years","65-74",319,16872,1890.7,105.9
,"2003","2003","Female","F","75-84 years","75-84",540,11609,4651.6,200.2
,"2003","2003","Female","F","85+ years","85+",734,4829,15199.8,561.0
,"2003","2003","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2003","2003","Male","M","< 1 year","1",24,3426,700.5,143.0
,"2003","2003","Male","M","1-4 years","1-4",10,13173,Unreliable,24.0
,"2003","2003","Male","M","5-14 years","5-14",Suppressed,35385,Suppressed,Suppressed
,"2003","2003","Male","M","15-24 years","15-24",54,41439,130.3,17.7
,"2003","2003","Male","M","25-34 years","25-34",51,30757,165.8,23.2
,"2003","2003","Male","M","35-44 years","35-44",116,35171,329.8,30.6
,"2003","2003","Male","M","45-54 years","45-54",186,41176,451.7,33.1
,"2003","2003","Male","M","55-64 years","55-64",315,26913,1170.4,65.9
,"2003","2003","Male","M","65-74 years","65-74",427,15621,2733.5,132.3
,"2003","2003","Male","M","75-84 years","75-84",547,9035,6054.2,258.9
,"2003","2003","Male","M","85+ years","85+",407,2229,18259.3,905.1
,"2003","2003","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2004","2004","Female","F","< 1 year","1",25,3291,759.6,151.9
,"2004","2004","Female","F","1-4 years","1-4",Suppressed,12401,Suppressed,Suppressed
,"2004","2004","Female","F","5-14 years","5-14",Suppressed,33096,Suppressed,Suppressed
,"2004","2004","Female","F","15-24 years","15-24",24,38019,63.1,12.9
,"2004","2004","Female","F","25-34 years","25-34",18,29632,Unreliable,14.3
,"2004","2004","Female","F","35-44 years","35-44",53,34199,155.0,21.3
,"2004","2004","Female","F","45-54 years","45-54",118,40526,291.2,26.8
,"2004","2004","Female","F","55-64 years","55-64",196,26688,734.4,52.5
,"2004","2004","Female","F","65-74 years","65-74",294,16931,1736.5,101.3
,"2004","2004","Female","F","75-84 years","75-84",568,11845,4795.3,201.2
,"2004","2004","Female","F","85+ years","85+",672,4878,13776.1,531.4
,"2004","2004","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2004","2004","Male","M","< 1 year","1",35,3474,1007.5,170.3
,"2004","2004","Male","M","1-4 years","1-4",Suppressed,13499,Suppressed,Suppressed
,"2004","2004","Male","M","5-14 years","5-14",Suppressed,35190,Suppressed,Suppressed
,"2004","2004","Male","M","15-24 years","15-24",59,41972,140.6,18.3
,"2004","2004","Male","M","25-34 years","25-34",53,31403,168.8,23.2
,"2004","2004","Male","M","35-44 years","35-44",73,34394,212.2,24.8
,"2004","2004","Male","M","45-54 years","45-54",211,41788,504.9,34.8
,"2004","2004","Male","M","55-64 years","55-64",263,28358,927.4,57.2
,"2004","2004","Male","M","65-74 years","65-74",393,15942,2465.2,124.4
,"2004","2004","Male","M","75-84 years","75-84",543,9270,5857.6,251.4
,"2004","2004","Male","M","85+ years","85+",339,2310,14675.3,797.1
,"2004","2004","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2005","2005","Female","F","< 1 year","1",18,3473,Unreliable,122.2
,"2005","2005","Female","F","1-4 years","1-4",Suppressed,12731,Suppressed,Suppressed
,"2005","2005","Female","F","5-14 years","5-14",Suppressed,32902,Suppressed,Suppressed
,"2005","2005","Female","F","15-24 years","15-24",19,37633,Unreliable,11.6
,"2005","2005","Female","F","25-34 years","25-34",24,30463,78.8,16.1
,"2005","2005","Female","F","35-44 years","35-44",56,33329,168.0,22.5
,"2005","2005","Female","F","45-54 years","45-54",134,41037,326.5,28.2
,"2005","2005","Female","F","55-64 years","55-64",202,28130,718.1,50.5
,"2005","2005","Female","F","65-74 years","65-74",287,17159,1672.6,98.7
,"2005","2005","Female","F","75-84 years","75-84",554,11864,4669.6,198.4
,"2005","2005","Female","F","85+ years","85+",661,5005,13206.8,513.7
,"2005","2005","Female","F","Not Stated","NS",Suppressed,Not Applicable,Not Applicable,Not Applicable
,"2005","2005","Male","M","< 1 year","1",31,3506,884.2,158.8
,"2005","2005","Male","M","1-4 years","1-4",Suppressed,13728,Suppressed,Suppressed
,"2005","2005","Male","M","5-14 years","5-14",Suppressed,34849,Suppressed,Suppressed
,"2005","2005","Male","M","15-24 years","15-24",69,41893,164.7,19.8
,"2005","2005","Male","M","25-34 years","25-34",60,32181,186.4,24.1
,"2005","2005","Male","M","35-44 years","35-44",94,33799,278.1,28.7
,"2005","2005","Male","M","45-54 years","45-54",183,42321,432.4,32.0
,"2005","2005","Male","M","55-64 years","55-64",306,29995,1020.2,58.3
,"2005","2005","Male","M","65-74 years","65-74",386,16273,2372.0,120.7
,"2005","2005","Male","M","75-84 years","75-84",604,9426,6407.8,260.7
,"2005","2005","Male","M","85+ years","85+",393,2460,15975.6,805.9
,"2005","2005","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2006","2006","Female","F","< 1 year","1",27,3500,771.4,148.5
,"2006","2006","Female","F","1-4 years","1-4",Suppressed,13319,Suppressed,Suppressed
,"2006","2006","Female","F","5-14 years","5-14",Suppressed,32778,Suppressed,Suppressed
,"2006","2006","Female","F","15-24 years","15-24",20,37609,53.2,11.9
,"2006","2006","Female","F","25-34 years","25-34",27,31534,85.6,16.5
,"2006","2006","Female","F","35-44 years","35-44",53,32706,162.0,22.3
,"2006","2006","Female","F","45-54 years","45-54",128,41653,307.3,27.2
,"2006","2006","Female","F","55-64 years","55-64",209,29782,701.8,48.5
,"2006","2006","Female","F","65-74 years","65-74",323,17487,1847.1,102.8
,"2006","2006","Female","F","75-84 years","75-84",528,11799,4475.0,194.7
,"2006","2006","Female","F","85+ years","85+",748,5194,14401.2,526.6
,"2006","2006","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2006","2006","Male","M","< 1 year","1",27,3916,689.5,132.7
,"2006","2006","Male","M","1-4 years","1-4",Suppressed,14012,Suppressed,Suppressed
,"2006","2006","Male","M","5-14 years","5-14",12,34925,Unreliable,9.9
,"2006","2006","Male","M","15-24 years","15-24",57,42058,135.5,18.0
,"2006","2006","Male","M","25-34 years","25-34",75,33320,225.1,26.0
,"2006","2006","Male","M","35-44 years","35-44",86,33839,254.1,27.4
,"2006","2006","Male","M","45-54 years","45-54",227,42734,531.2,35.3
,"2006","2006","Male","M","55-64 years","55-64",292,31672,921.9,54.0
,"2006","2006","Male","M","65-74 years","65-74",414,16804,2463.7,121.1
,"2006","2006","Male","M","75-84 years","75-84",621,9499,6537.5,262.3
,"2006","2006","Male","M","85+ years","85+",420,2527,16620.5,811.0
,"2006","2006","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2007","2007","Female","F","< 1 year","1",20,3816,524.1,117.2
,"2007","2007","Female","F","1-4 years","1-4",Suppressed,14066,Suppressed,Suppressed
,"2007","2007","Female","F","5-14 years","5-14",Suppressed,33194,Suppressed,Suppressed
,"2007","2007","Female","F","15-24 years","15-24",15,37845,Unreliable,10.2
,"2007","2007","Female","F","25-34 years","25-34",36,32880,109.5,18.2
,"2007","2007","Female","F","35-44 years","35-44",68,32471,209.4,25.4
,"2007","2007","Female","F","45-54 years","45-54",133,42153,315.5,27.4
,"2007","2007","Female","F","55-64 years","55-64",195,31267,623.7,44.7
,"2007","2007","Female","F","65-74 years","65-74",300,17959,1670.5,96.4
,"2007","2007","Female","F","75-84 years","75-84",542,11900,4554.6,195.6
,"2007","2007","Female","F","85+ years","85+",738,5225,14124.4,519.9
,"2007","2007","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2007","2007","Male","M","< 1 year","1",38,4140,917.9,148.9
,"2007","2007","Male","M","1-4 years","1-4",Suppressed,14840,Suppressed,Suppressed
,"2007","2007","Male","M","5-14 years","5-14",Suppressed,35661,Suppressed,Suppressed
,"2007","2007","Male","M","15-24 years","15-24",75,41793,179.5,20.7
,"2007","2007","Male","M","25-34 years","25-34",71,35570,199.6,23.7
,"2007","2007","Male","M","35-44 years","35-44",109,34080,319.8,30.6
,"2007","2007","Male","M","45-54 years","45-54",208,42943,484.4,33.6
,"2007","2007","Male","M","55-64 years","55-64",306,33393,916.4,52.4
,"2007","2007","Male","M","65-74 years","65-74",409,17408,2349.5,116.2
,"2007","2007","Male","M","75-84 years","75-84",572,9612,5950.9,248.8
,"2007","2007","Male","M","85+ years","85+",411,2660,15451.1,762.1
,"2007","2007","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2008","2008","Female","F","< 1 year","1",25,3954,632.3,126.5
,"2008","2008","Female","F","1-4 years","1-4",Suppressed,14664,Suppressed,Suppressed
,"2008","2008","Female","F","5-14 years","5-14",Suppressed,34160,Suppressed,Suppressed
,"2008","2008","Female","F","15-24 years","15-24",24,37676,63.7,13.0
,"2008","2008","Female","F","25-34 years","25-34",23,34400,66.9,13.9
,"2008","2008","Female","F","35-44 years","35-44",56,32355,173.1,23.1
,"2008","2008","Female","F","45-54 years","45-54",160,42085,380.2,30.1
,"2008","2008","Female","F","55-64 years","55-64",187,32760,570.8,41.7
,"2008","2008","Female","F","65-74 years","65-74",303,18689,1621.3,93.1
,"2008","2008","Female","F","75-84 years","75-84",547,11902,4595.9,196.5
,"2008","2008","Female","F","85+ years","85+",719,5337,13472.0,502.4
,"2008","2008","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2008","2008","Male","M","< 1 year","1",31,4016,771.9,138.6
,"2008","2008","Male","M","1-4 years","1-4",Suppressed,15543,Suppressed,Suppressed
,"2008","2008","Male","M","5-14 years","5-14",Suppressed,36422,Suppressed,Suppressed
,"2008","2008","Male","M","15-24 years","15-24",50,42055,118.9,16.8
,"2008","2008","Male","M","25-34 years","25-34",88,37040,237.6,25.3
,"2008","2008","Male","M","35-44 years","35-44",91,34435,264.3,27.7
,"2008","2008","Male","M","45-54 years","45-54",229,42894,533.9,35.3
,"2008","2008","Male","M","55-64 years","55-64",299,34906,856.6,49.5
,"2008","2008","Male","M","65-74 years","65-74",395,18277,2161.2,108.7
,"2008","2008","Male","M","75-84 years","75-84",532,9659,5507.8,238.8
,"2008","2008","Male","M","85+ years","85+",438,2814,15565.0,743.7
,"2008","2008","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2009","2009","Female","F","< 1 year","1",17,4008,Unreliable,102.9
,"2009","2009","Female","F","1-4 years","1-4",Suppressed,15510,Suppressed,Suppressed
,"2009","2009","Female","F","5-14 years","5-14",Suppressed,35045,Suppressed,Suppressed
,"2009","2009","Female","F","15-24 years","15-24",22,38027,57.9,12.3
,"2009","2009","Female","F","25-34 years","25-34",36,36198,99.5,16.6
,"2009","2009","Female","F","35-44 years","35-44",58,32181,180.2,23.7
,"2009","2009","Female","F","45-54 years","45-54",137,41892,327.0,27.9
,"2009","2009","Female","F","55-64 years","55-64",194,34430,563.5,40.5
,"2009","2009","Female","F","65-74 years","65-74",325,19336,1680.8,93.2
,"2009","2009","Female","F","75-84 years","75-84",480,11980,4006.7,182.9
,"2009","2009","Female","F","85+ years","85+",743,5439,13660.6,501.2
,"2009","2009","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2009","2009","Male","M","< 1 year","1",30,4164,720.5,131.5
,"2009","2009","Male","M","1-4 years","1-4",Suppressed,16296,Suppressed,Suppressed
,"2009","2009","Male","M","5-14 years","5-14",Suppressed,37370,Suppressed,Suppressed
,"2009","2009","Male","M","15-24 years","15-24",69,41976,164.4,19.8
,"2009","2009","Male","M","25-34 years","25-34",73,40016,182.4,21.4
,"2009","2009","Male","M","35-44 years","35-44",87,34914,249.2,26.7
,"2009","2009","Male","M","45-54 years","45-54",188,42649,440.8,32.1
,"2009","2009","Male","M","55-64 years","55-64",333,36568,910.6,49.9
,"2009","2009","Male","M","65-74 years","65-74",415,19221,2159.1,106.0
,"2009","2009","Male","M","75-84 years","75-84",568,9663,5878.1,246.6
,"2009","2009","Male","M","85+ years","85+",484,2968,16307.3,741.2
,"2009","2009","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2010","2010","Female","F","< 1 year","1",22,3800,578.9,123.4
,"2010","2010","Female","F","1-4 years","1-4",Suppressed,15807,Suppressed,Suppressed
,"2010","2010","Female","F","5-14 years","5-14",Suppressed,35373,Suppressed,Suppressed
,"2010","2010","Female","F","15-24 years","15-24",20,37519,53.3,11.9
,"2010","2010","Female","F","25-34 years","25-34",38,36978,102.8,16.7
,"2010","2010","Female","F","35-44 years","35-44",54,32115,168.1,22.9
,"2010","2010","Female","F","45-54 years","45-54",145,41414,350.1,29.1
,"2010","2010","Female","F","55-64 years","55-64",239,35707,669.3,43.3
,"2010","2010","Female","F","65-74 years","65-74",313,19887,1573.9,89.0
,"2010","2010","Female","F","75-84 years","75-84",547,12040,4543.2,194.3
,"2010","2010","Female","F","85+ years","85+",798,5549,14381.0,509.1
,"2010","2010","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2010","2010","Male","M","< 1 year","1",29,3986,727.5,135.1
,"2010","2010","Male","M","1-4 years","1-4",Suppressed,16610,Suppressed,Suppressed
,"2010","2010","Male","M","5-14 years","5-14",Suppressed,37795,Suppressed,Suppressed
,"2010","2010","Male","M","15-24 years","15-24",53,40941,129.5,17.8
,"2010","2010","Male","M","25-34 years","25-34",67,40671,164.7,20.1
,"2010","2010","Male","M","35-44 years","35-44",77,34851,220.9,25.2
,"2010","2010","Male","M","45-54 years","45-54",236,42163,559.7,36.4
,"2010","2010","Male","M","55-64 years","55-64",370,37806,978.7,50.9
,"2010","2010","Male","M","65-74 years","65-74",426,19681,2164.5,104.9
,"2010","2010","Male","M","75-84 years","75-84",522,9880,5283.4,231.2
,"2010","2010","Male","M","85+ years","85+",460,3053,15067.1,702.5
,"2010","2010","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2011","2011","Female","F","< 1 year","1",21,3726,563.6,123.0
,"2011","2011","Female","F","1-4 years","1-4",Suppressed,15697,Suppressed,Suppressed
,"2011","2011","Female","F","5-14 years","5-14",Suppressed,35787,Suppressed,Suppressed
,"2011","2011","Female","F","15-24 years","15-24",24,37558,63.9,13.0
,"2011","2011","Female","F","25-34 years","25-34",22,37613,58.5,12.5
,"2011","2011","Female","F","35-44 years","35-44",61,31920,191.1,24.5
,"2011","2011","Female","F","45-54 years","45-54",130,40085,324.3,28.4
,"2011","2011","Female","F","55-64 years","55-64",233,37552,620.5,40.6
,"2011","2011","Female","F","65-74 years","65-74",301,20509,1467.6,84.6
,"2011","2011","Female","F","75-84 years","75-84",511,12207,4186.1,185.2
,"2011","2011","Female","F","85+ years","85+",750,5747,13050.3,476.5
,"2011","2011","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2011","2011","Male","M","< 1 year","1",28,3822,732.6,138.4
,"2011","2011","Male","M","1-4 years","1-4",Suppressed,16304,Suppressed,Suppressed
,"2011","2011","Male","M","5-14 years","5-14",Suppressed,38126,Suppressed,Suppressed
,"2011","2011","Male","M","15-24 years","15-24",54,41146,131.2,17.9
,"2011","2011","Male","M","25-34 years","25-34",79,41383,190.9,21.5
,"2011","2011","Male","M","35-44 years","35-44",84,34753,241.7,26.4
,"2011","2011","Male","M","45-54 years","45-54",214,40851,523.9,35.8
,"2011","2011","Male","M","55-64 years","55-64",359,39568,907.3,47.9
,"2011","2011","Male","M","65-74 years","65-74",436,20569,2119.7,101.5
,"2011","2011","Male","M","75-84 years","75-84",557,10049,5542.8,234.9
,"2011","2011","Male","M","85+ years","85+",501,3186,15725.0,702.5
,"2011","2011","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2012","2012","Female","F","< 1 year","1",16,3671,Unreliable,109.0
,"2012","2012","Female","F","1-4 years","1-4",Suppressed,15466,Suppressed,Suppressed
,"2012","2012","Female","F","5-14 years","5-14",Suppressed,36362,Suppressed,Suppressed
,"2012","2012","Female","F","15-24 years","15-24",20,37995,52.6,11.8
,"2012","2012","Female","F","25-34 years","25-34",32,38444,83.2,14.7
,"2012","2012","Female","F","35-44 years","35-44",50,32553,153.6,21.7
,"2012","2012","Female","F","45-54 years","45-54",146,38962,374.7,31.0
,"2012","2012","Female","F","55-64 years","55-64",223,38594,577.8,38.7
,"2012","2012","Female","F","65-74 years","65-74",339,21797,1555.3,84.5
,"2012","2012","Female","F","75-84 years","75-84",514,12455,4126.9,182.0
,"2012","2012","Female","F","85+ years","85+",769,5832,13185.9,475.5
,"2012","2012","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2012","2012","Male","M","< 1 year","1",23,3779,608.6,126.9
,"2012","2012","Male","M","1-4 years","1-4",Suppressed,15676,Suppressed,Suppressed
,"2012","2012","Male","M","5-14 years","5-14",Suppressed,38819,Suppressed,Suppressed
,"2012","2012","Male","M","15-24 years","15-24",52,41866,124.2,17.2
,"2012","2012","Male","M","25-34 years","25-34",88,42655,206.3,22.0
,"2012","2012","Male","M","35-44 years","35-44",91,35715,254.8,26.7
,"2012","2012","Male","M","45-54 years","45-54",201,40002,502.5,35.4
,"2012","2012","Male","M","55-64 years","55-64",382,40345,946.8,48.4
,"2012","2012","Male","M","65-74 years","65-74",464,21717,2136.6,99.2
,"2012","2012","Male","M","75-84 years","75-84",550,10318,5330.5,227.3
,"2012","2012","Male","M","85+ years","85+",499,3389,14724.1,659.1
,"2012","2012","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2013","2013","Female","F","< 1 year","1",16,3734,Unreliable,107.1
,"2013","2013","Female","F","1-4 years","1-4",Suppressed,15205,Suppressed,Suppressed
,"2013","2013","Female","F","5-14 years","5-14",Suppressed,37328,Suppressed,Suppressed
,"2013","2013","Female","F","15-24 years","15-24",15,38413,Unreliable,10.1
,"2013","2013","Female","F","25-34 years","25-34",44,38834,113.3,17.1
,"2013","2013","Female","F","35-44 years","35-44",45,32994,136.4,20.3
,"2013","2013","Female","F","45-54 years","45-54",132,37642,350.7,30.5
,"2013","2013","Female","F","55-64 years","55-64",237,39514,599.8,39.0
,"2013","2013","Female","F","65-74 years","65-74",338,22932,1473.9,80.2
,"2013","2013","Female","F","75-84 years","75-84",502,12642,3970.9,177.2
,"2013","2013","Female","F","85+ years","85+",762,6142,12406.4,449.4
,"2013","2013","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2013","2013","Male","M","< 1 year","1",19,3852,Unreliable,113.2
,"2013","2013","Male","M","1-4 years","1-4",Suppressed,15556,Suppressed,Suppressed
,"2013","2013","Male","M","5-14 years","5-14",Suppressed,39662,Suppressed,Suppressed
,"2013","2013","Male","M","15-24 years","15-24",52,42495,122.4,17.0
,"2013","2013","Male","M","25-34 years","25-34",74,42993,172.1,20.0
,"2013","2013","Male","M","35-44 years","35-44",84,36234,231.8,25.3
,"2013","2013","Male","M","45-54 years","45-54",210,38616,543.8,37.5
,"2013","2013","Male","M","55-64 years","55-64",380,40897,929.2,47.7
,"2013","2013","Male","M","65-74 years","65-74",460,22882,2010.3,93.7
,"2013","2013","Male","M","75-84 years","75-84",577,10490,5500.5,229.0
,"2013","2013","Male","M","85+ years","85+",549,3601,15245.8,650.7
,"2013","2013","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2014","2014","Female","F","< 1 year","1",21,3747,560.4,122.3
,"2014","2014","Female","F","1-4 years","1-4",Suppressed,15050,Suppressed,Suppressed
,"2014","2014","Female","F","5-14 years","5-14",Suppressed,37655,Suppressed,Suppressed
,"2014","2014","Female","F","15-24 years","15-24",22,38056,57.8,12.3
,"2014","2014","Female","F","25-34 years","25-34",37,39321,94.1,15.5
,"2014","2014","Female","F","35-44 years","35-44",52,33444,155.5,21.6
,"2014","2014","Female","F","45-54 years","45-54",137,36136,379.1,32.4
,"2014","2014","Female","F","55-64 years","55-64",251,39821,630.3,39.8
,"2014","2014","Female","F","65-74 years","65-74",365,24087,1515.3,79.3
,"2014","2014","Female","F","75-84 years","75-84",495,12530,3950.5,177.6
,"2014","2014","Female","F","85+ years","85+",804,6264,12835.2,452.7
,"2014","2014","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2014","2014","Male","M","< 1 year","1",28,3867,724.1,136.8
,"2014","2014","Male","M","1-4 years","1-4",Suppressed,15507,Suppressed,Suppressed
,"2014","2014","Male","M","5-14 years","5-14",Suppressed,39691,Suppressed,Suppressed
,"2014","2014","Male","M","15-24 years","15-24",54,42193,128.0,17.4
,"2014","2014","Male","M","25-34 years","25-34",91,43304,210.1,22.0
,"2014","2014","Male","M","35-44 years","35-44",96,36486,263.1,26.9
,"2014","2014","Male","M","45-54 years","45-54",191,37236,512.9,37.1
,"2014","2014","Male","M","55-64 years","55-64",397,40998,968.3,48.6
,"2014","2014","Male","M","65-74 years","65-74",497,24295,2045.7,91.8
,"2014","2014","Male","M","75-84 years","75-84",584,10682,5467.1,226.2
,"2014","2014","Male","M","85+ years","85+",523,3783,13825.0,604.5
,"2014","2014","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2015","2015","Female","F","< 1 year","1",15,3770,Unreliable,102.7
,"2015","2015","Female","F","1-4 years","1-4",Suppressed,15054,Suppressed,Suppressed
,"2015","2015","Female","F","5-14 years","5-14",Suppressed,38270,Suppressed,Suppressed
,"2015","2015","Female","F","15-24 years","15-24",10,37183,Unreliable,8.5
,"2015","2015","Female","F","25-34 years","25-34",41,39624,103.5,16.2
,"2015","2015","Female","F","35-44 years","35-44",66,33863,194.9,24.0
,"2015","2015","Female","F","45-54 years","45-54",122,34916,349.4,31.6
,"2015","2015","Female","F","55-64 years","55-64",265,40192,659.3,40.5
,"2015","2015","Female","F","65-74 years","65-74",385,25176,1529.2,77.9
,"2015","2015","Female","F","75-84 years","75-84",521,12758,4083.7,178.9
,"2015","2015","Female","F","85+ years","85+",793,6400,12390.6,440.0
,"2015","2015","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2015","2015","Male","M","< 1 year","1",24,3907,614.3,125.4
,"2015","2015","Male","M","1-4 years","1-4",Suppressed,15664,Suppressed,Suppressed
,"2015","2015","Male","M","5-14 years","5-14",Suppressed,40215,Suppressed,Suppressed
,"2015","2015","Male","M","15-24 years","15-24",70,41346,169.3,20.2
,"2015","2015","Male","M","25-34 years","25-34",96,43218,222.1,22.7
,"2015","2015","Male","M","35-44 years","35-44",105,36936,284.3,27.7
,"2015","2015","Male","M","45-54 years","45-54",194,36154,536.6,38.5
,"2015","2015","Male","M","55-64 years","55-64",401,41096,975.8,48.7
,"2015","2015","Male","M","65-74 years","65-74",519,25626,2025.3,88.9
,"2015","2015","Male","M","75-84 years","75-84",599,10875,5508.0,225.1
,"2015","2015","Male","M","85+ years","85+",528,3864,13664.6,594.7
,"2015","2015","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2016","2016","Female","F","< 1 year","1",12,3610,Unreliable,96.0
,"2016","2016","Female","F","1-4 years","1-4",Suppressed,14757,Suppressed,Suppressed
,"2016","2016","Female","F","5-14 years","5-14",Suppressed,38407,Suppressed,Suppressed
,"2016","2016","Female","F","15-24 years","15-24",23,36528,63.0,13.1
,"2016","2016","Female","F","25-34 years","25-34",27,39396,68.5,13.2
,"2016","2016","Female","F","35-44 years","35-44",72,34064,211.4,24.9
,"2016","2016","Female","F","45-54 years","45-54",99,33642,294.3,29.6
,"2016","2016","Female","F","55-64 years","55-64",284,40234,705.9,41.9
,"2016","2016","Female","F","65-74 years","65-74",405,26442,1531.7,76.1
,"2016","2016","Female","F","75-84 years","75-84",503,12917,3894.1,173.6
,"2016","2016","Female","F","85+ years","85+",773,6562,11779.9,423.7
,"2016","2016","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2016","2016","Male","M","< 1 year","1",25,3796,658.6,131.7
,"2016","2016","Male","M","1-4 years","1-4",Suppressed,15982,Suppressed,Suppressed
,"2016","2016","Male","M","5-14 years","5-14",Suppressed,40244,Suppressed,Suppressed
,"2016","2016","Male","M","15-24 years","15-24",47,40765,115.3,16.8
,"2016","2016","Male","M","25-34 years","25-34",82,42552,192.7,21.3
,"2016","2016","Male","M","35-44 years","35-44",95,37270,254.9,26.2
,"2016","2016","Male","M","45-54 years","45-54",184,35410,519.6,38.3
,"2016","2016","Male","M","55-64 years","55-64",393,41032,957.8,48.3
,"2016","2016","Male","M","65-74 years","65-74",547,26798,2041.2,87.3
,"2016","2016","Male","M","75-84 years","75-84",550,11120,4946.0,210.9
,"2016","2016","Male","M","85+ years","85+",582,3973,14648.9,607.2
,"2016","2016","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2017","2017","Female","F","< 1 year","1",15,3653,Unreliable,106.0
,"2017","2017","Female","F","1-4 years","1-4",Suppressed,14246,Suppressed,Suppressed
,"2017","2017","Female","F","5-14 years","5-14",Suppressed,37820,Suppressed,Suppressed
,"2017","2017","Female","F","15-24 years","15-24",23,35196,65.3,13.6
,"2017","2017","Female","F","25-34 years","25-34",27,38159,70.8,13.6
,"2017","2017","Female","F","35-44 years","35-44",55,34385,160.0,21.6
,"2017","2017","Female","F","45-54 years","45-54",96,32548,294.9,30.1
,"2017","2017","Female","F","55-64 years","55-64",275,40123,685.4,41.3
,"2017","2017","Female","F","65-74 years","65-74",409,27711,1475.9,73.0
,"2017","2017","Female","F","75-84 years","75-84",553,13494,4098.1,174.3
,"2017","2017","Female","F","85+ years","85+",769,6542,11754.8,423.9
,"2017","2017","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2017","2017","Male","M","< 1 year","1",17,3826,Unreliable,107.8
,"2017","2017","Male","M","1-4 years","1-4",Suppressed,15306,Suppressed,Suppressed
,"2017","2017","Male","M","5-14 years","5-14",0,39812,Unreliable,0.0
,"2017","2017","Male","M","15-24 years","15-24",40,39163,102.1,16.1
,"2017","2017","Male","M","25-34 years","25-34",70,41355,169.3,20.2
,"2017","2017","Male","M","35-44 years","35-44",98,37234,263.2,26.6
,"2017","2017","Male","M","45-54 years","45-54",181,34151,530.0,39.4
,"2017","2017","Male","M","55-64 years","55-64",422,40731,1036.1,50.4
,"2017","2017","Male","M","65-74 years","65-74",567,28233,2008.3,84.3
,"2017","2017","Male","M","75-84 years","75-84",545,11660,4674.1,200.2
,"2017","2017","Male","M","85+ years","85+",592,3967,14923.1,613.3
,"2017","2017","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2018","2018","Female","F","< 1 year","1",18,3335,Unreliable,127.2
,"2018","2018","Female","F","1-4 years","1-4",Suppressed,13944,Suppressed,Suppressed
,"2018","2018","Female","F","5-14 years","5-14",Suppressed,37466,Suppressed,Suppressed
,"2018","2018","Female","F","15-24 years","15-24",13,35241,Unreliable,10.2
,"2018","2018","Female","F","25-34 years","25-34",27,37316,72.4,13.9
,"2018","2018","Female","F","35-44 years","35-44",56,34948,160.2,21.4
,"2018","2018","Female","F","45-54 years","45-54",126,31591,398.8,35.5
,"2018","2018","Female","F","55-64 years","55-64",265,39806,665.7,40.9
,"2018","2018","Female","F","65-74 years","65-74",401,28793,1392.7,69.5
,"2018","2018","Female","F","75-84 years","75-84",613,14083,4352.8,175.8
,"2018","2018","Female","F","85+ years","85+",869,6680,13009.0,441.3
,"2018","2018","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2018","2018","Male","M","< 1 year","1",17,3488,Unreliable,118.2
,"2018","2018","Male","M","1-4 years","1-4",Suppressed,15145,Suppressed,Suppressed
,"2018","2018","Male","M","5-14 years","5-14",Suppressed,39485,Suppressed,Suppressed
,"2018","2018","Male","M","15-24 years","15-24",53,39038,135.8,18.6
,"2018","2018","Male","M","25-34 years","25-34",72,40462,177.9,21.0
,"2018","2018","Male","M","35-44 years","35-44",100,37765,264.8,26.5
,"2018","2018","Male","M","45-54 years","45-54",170,33289,510.7,39.2
,"2018","2018","Male","M","55-64 years","55-64",399,40043,996.4,49.9
,"2018","2018","Male","M","65-74 years","65-74",601,29375,2046.0,83.5
,"2018","2018","Male","M","75-84 years","75-84",645,12443,5183.6,204.1
,"2018","2018","Male","M","85+ years","85+",610,4001,15246.2,617.3
,"2018","2018","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2019","2019","Female","F","< 1 year","1",20,3205,624.0,139.5
,"2019","2019","Female","F","1-4 years","1-4",Suppressed,13622,Suppressed,Suppressed
,"2019","2019","Female","F","5-14 years","5-14",Suppressed,37347,Suppressed,Suppressed
,"2019","2019","Female","F","15-24 years","15-24",15,35383,Unreliable,10.9
,"2019","2019","Female","F","25-34 years","25-34",36,36758,97.9,16.3
,"2019","2019","Female","F","35-44 years","35-44",47,35803,131.3,19.1
,"2019","2019","Female","F","45-54 years","45-54",100,31049,322.1,32.2
,"2019","2019","Female","F","55-64 years","55-64",288,39443,730.2,43.0
,"2019","2019","Female","F","65-74 years","65-74",429,29971,1431.4,69.1
,"2019","2019","Female","F","75-84 years","75-84",519,14585,3558.5,156.2
,"2019","2019","Female","F","85+ years","85+",861,6863,12545.5,427.6
,"2019","2019","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2019","2019","Male","M","< 1 year","1",26,3367,772.2,151.4
,"2019","2019","Male","M","1-4 years","1-4",Suppressed,14737,Suppressed,Suppressed
,"2019","2019","Male","M","5-14 years","5-14",10,39387,Unreliable,8.0
,"2019","2019","Male","M","15-24 years","15-24",54,39207,137.7,18.7
,"2019","2019","Male","M","25-34 years","25-34",73,39675,184.0,21.5
,"2019","2019","Male","M","35-44 years","35-44",125,38482,324.8,29.1
,"2019","2019","Male","M","45-54 years","45-54",179,32804,545.7,40.8
,"2019","2019","Male","M","55-64 years","55-64",385,39311,979.4,49.9
,"2019","2019","Male","M","65-74 years","65-74",613,30619,2002.0,80.9
,"2019","2019","Male","M","75-84 years","75-84",714,13013,5486.8,205.3
,"2019","2019","Male","M","85+ years","85+",617,4128,14946.7,601.7
,"2019","2019","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2020","2020","Female","F","< 1 year","1",15,3091,Unreliable,125.3
,"2020","2020","Female","F","1-4 years","1-4",Suppressed,13361,Suppressed,Suppressed
,"2020","2020","Female","F","5-14 years","5-14",Suppressed,37125,Suppressed,Suppressed
,"2020","2020","Female","F","15-24 years","15-24",17,35739,Unreliable,11.5
,"2020","2020","Female","F","25-34 years","25-34",45,36394,123.6,18.4
,"2020","2020","Female","F","35-44 years","35-44",56,36454,153.6,20.5
,"2020","2020","Female","F","45-54 years","45-54",137,30942,442.8,37.8
,"2020","2020","Female","F","55-64 years","55-64",314,38693,811.5,45.8
,"2020","2020","Female","F","65-74 years","65-74",506,31617,1600.4,71.1
,"2020","2020","Female","F","75-84 years","75-84",700,15211,4601.9,173.9
,"2020","2020","Female","F","85+ years","85+",926,6810,13597.7,446.8
,"2020","2020","Female","F","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
,"2020","2020","Male","M","< 1 year","1",16,3232,Unreliable,123.8
,"2020","2020","Male","M","1-4 years","1-4",Suppressed,14101,Suppressed,Suppressed
,"2020","2020","Male","M","5-14 years","5-14",Suppressed,39462,Suppressed,Suppressed
,"2020","2020","Male","M","15-24 years","15-24",50,39558,126.4,17.9
,"2020","2020","Male","M","25-34 years","25-34",112,39712,282.0,26.6
,"2020","2020","Male","M","35-44 years","35-44",137,39072,350.6,30.0
,"2020","2020","Male","M","45-54 years","45-54",185,32995,560.7,41.2
,"2020","2020","Male","M","55-64 years","55-64",487,38621,1261.0,57.1
,"2020","2020","Male","M","65-74 years","65-74",788,32197,2447.4,87.2
,"2020","2020","Male","M","75-84 years","75-84",792,13737,5765.5,204.9
,"2020","2020","Male","M","85+ years","85+",679,4204,16151.3,619.8
,"2020","2020","Male","M","Not Stated","NS",0,Not Applicable,Not Applicable,Not Applicable
"---"
"Dataset: Multiple Cause of Death, 1999-2020"
"Query Parameters:"
"Title: Wyoming_10_Year_Age_Groups_1999-2020"
"States: Wyoming (56)"
"Group By: Year; Sex; Ten-Year Age Groups"
"Show Totals: Disabled"
"Show Zero Values: True"
"Show Suppressed: True"
"Calculate Rates Per: 100,000"
"Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)"
"---"
"Help: See http://wonder.cdc.gov/wonder/help/mcd.html for more information."
"---"
"Query Date: Nov 19, 2025 11:59:31 PM"
"---"
"Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics"
"System, Mortality 1999-2020 on CDC WONDER Online Database, released in 2021. Data are from the Multiple Cause of Death Files,"
"1999-2020, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative"
"Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Nov 19, 2025 11:59:31 PM"
"---"
Messages:
"1. Totals are not available for these results due to suppression constraints. More Information:"
"http://wonder.cdc.gov/wonder/help/faq.html#Privacy."
"---"
Caveats:
"1. As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#2014-Revision."
"2. Data are Suppressed when the data meet the criteria for confidentiality constraints. More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#Assurance of Confidentiality."
"3. Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#Unreliable."
"4. Deaths of persons with Age ""Not Stated"" are included in ""All"" counts and rates, but are not distributed among age groups,"
"so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#Not Stated."
"5. The method used to calculate standard errors is documented here: More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#Standard-Errors."
"6. The population figures for year 2020 are bridged-race estimates of the July 1 resident population, from the Vintage 2020"
"postcensal series released by NCHS on September 22, 2021. The population figures for year 2019 are bridged-race estimates of the"
"July 1 resident population, from the Vintage 2019 postcensal series released by NCHS on July 9, 2020. The population figures for"
"year 2018 are bridged-race estimates of the July 1 resident population, from the Vintage 2018 postcensal series released by NCHS"
"on June 25, 2019. The population figures for year 2017 are bridged-race estimates of the July 1 resident population, from the"
"Vintage 2017 postcensal series released by NCHS on June 27, 2018. The population figures for year 2016 are bridged-race"
"estimates of the July 1 resident population, from the Vintage 2016 postcensal series released by NCHS on June 26, 2017. The"
"population figures for year 2015 are bridged-race estimates of the July 1 resident population, from the Vintage 2015 postcensal"
"series released by NCHS on June 28, 2016. The population figures for year 2014 are bridged-race estimates of the July 1 resident"
"population, from the Vintage 2014 postcensal series released by NCHS on June 30, 2015. The population figures for year 2013 are"
"bridged-race estimates of the July 1 resident population, from the Vintage 2013 postcensal series released by NCHS on June 26,"
"2014. The population figures for year 2012 are bridged-race estimates of the July 1 resident population, from the Vintage 2012"
"postcensal series released by NCHS on June 13, 2013. Population figures for 2011 are bridged-race estimates of the July 1"
"resident population, from the county-level postcensal Vintage 2011 series released by NCHS on July 18, 2012. Population figures"
"for 2010 are April 1 Census counts. The population figures for years 2001 - 2009, are bridged-race estimates of the July 1"
"resident population, from the revised intercensal county-level 2000 - 2009 series released by NCHS on October 26, 2012."
"Population figures for 2000 are April 1 Census counts. Population figures for 1999 are from the 1990-1999 intercensal series of"
"July 1 estimates. Population figures for Infant Age Groups are the number of live births. <br/><b>Note:</b> Rates and population"
"figures for years 2001 - 2009 differ slightly from previously published reports, due to use of the population estimates which"
"were available at the time of release."
"7. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the"
"resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/mcd.html#Age Group."
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"Notes","Year","Year Code","Sex","Sex Code","Ten-Year Age Groups","Ten-Year Age Groups Code",Deaths,Population,Crude Rate,Crude Rate Standard Error
,"1999","1999","Female","F","< 1 year","1",12291,1852123,663.6,6.0
,"1999","1999","Female","F","1-4 years","1-4",2274,7493614,30.3,0.6
,"1999","1999","Female","F","5-14 years","5-14",3103,19908282,15.6,0.3
,"1999","1999","Female","F","15-24 years","15-24",8242,18860184,43.7,0.5
,"1999","1999","Female","F","25-34 years","25-34",12790,19945090,64.1,0.6
,"1999","1999","Female","F","35-44 years","35-44",32138,22669604,141.8,0.8
,"1999","1999","Female","F","45-54 years","45-54",57315,18632305,307.6,1.3
,"1999","1999","Female","F","55-64 years","55-64",96255,12378763,777.6,2.5
,"1999","1999","Female","F","65-74 years","65-74",197680,10125424,1952.3,4.4
,"1999","1999","Female","F","75-84 years","75-84",357620,7411198,4825.4,8.1
,"1999","1999","Female","F","85+ years","85+",436152,2960708,14731.3,22.3
,"1999","1999","Female","F","Not Stated","NS",79,Not Applicable,Not Applicable,Not Applicable
,"1999","1999","Male","M","< 1 year","1",15646,1943639,805.0,6.4
,"1999","1999","Male","M","1-4 years","1-4",2975,7846168,37.9,0.7
,"1999","1999","Male","M","5-14 years","5-14",4492,20911542,21.5,0.3
,"1999","1999","Male","M","15-24 years","15-24",22414,19815847,113.1,0.8
,"1999","1999","Male","M","25-34 years","25-34",28276,20233316,139.7,0.8
,"1999","1999","Male","M","35-44 years","35-44",57118,22407073,254.9,1.1
,"1999","1999","Male","M","45-54 years","45-54",95659,17945514,533.1,1.7
,"1999","1999","Male","M","55-64 years","55-64",142724,11399263,1252.0,3.3
,"1999","1999","Male","M","65-74 years","65-74",254920,8293485,3073.7,6.1
,"1999","1999","Male","M","75-84 years","75-84",340970,4813716,7083.3,12.1
,"1999","1999","Male","M","85+ years","85+",209989,1193310,17597.2,38.4
,"1999","1999","Male","M","Not Stated","NS",277,Not Applicable,Not Applicable,Not Applicable
,"2000","2000","Female","F","< 1 year","1",12317,1856631,663.4,6.0
,"2000","2000","Female","F","1-4 years","1-4",2155,7508434,28.7,0.6
,"2000","2000","Female","F","5-14 years","5-14",3012,20034103,15.0,0.3
,"2000","2000","Female","F","15-24 years","15-24",8236,19105073,43.1,0.5
,"2000","2000","Female","F","25-34 years","25-34",12561,19771195,63.5,0.6
,"2000","2000","Female","F","35-44 years","35-44",32501,22700729,143.2,0.8
,"2000","2000","Female","F","45-54 years","45-54",59943,19180722,312.5,1.3
,"2000","2000","Female","F","55-64 years","55-64",97525,12629328,772.2,2.5
,"2000","2000","Female","F","65-74 years","65-74",193801,10087712,1921.2,4.4
,"2000","2000","Female","F","75-84 years","75-84",360226,7481827,4814.7,8.0
,"2000","2000","Female","F","85+ years","85+",443429,3012589,14719.2,22.1
,"2000","2000","Female","F","Not Stated","NS",67,Not Applicable,Not Applicable,Not Applicable
,"2000","2000","Male","M","< 1 year","1",15718,1949017,806.5,6.4
,"2000","2000","Male","M","1-4 years","1-4",2824,7861716,35.9,0.7
,"2000","2000","Male","M","5-14 years","5-14",4401,21043474,20.9,0.3
,"2000","2000","Male","M","15-24 years","15-24",23071,20078818,114.9,0.8
,"2000","2000","Male","M","25-34 years","25-34",27890,20120529,138.6,0.8
,"2000","2000","Male","M","35-44 years","35-44",57297,22447798,255.2,1.1
,"2000","2000","Male","M","45-54 years","45-54",100398,18497230,542.8,1.7
,"2000","2000","Male","M","55-64 years","55-64",143321,11645356,1230.7,3.3
,"2000","2000","Male","M","65-74 years","65-74",247408,8303274,2979.6,6.0
,"2000","2000","Male","M","75-84 years","75-84",340219,4879353,6972.6,12.0
,"2000","2000","Male","M","85+ years","85+",214742,1226998,17501.4,37.8
,"2000","2000","Male","M","Not Stated","NS",289,Not Applicable,Not Applicable,Not Applicable
,"2001","2001","Female","F","< 1 year","1",12091,1962905,616.0,5.6
,"2001","2001","Female","F","1-4 years","1-4",2208,7468536,29.6,0.6
,"2001","2001","Female","F","5-14 years","5-14",2927,20075767,14.6,0.3
,"2001","2001","Female","F","15-24 years","15-24",8289,19585847,42.3,0.5
,"2001","2001","Female","F","25-34 years","25-34",12926,19567043,66.1,0.6
,"2001","2001","Female","F","35-44 years","35-44",33510,22642429,148.0,0.8
,"2001","2001","Female","F","45-54 years","45-54",63217,20047081,315.3,1.3
,"2001","2001","Female","F","55-64 years","55-64",99181,13043566,760.4,2.4
,"2001","2001","Female","F","65-74 years","65-74",189379,10043126,1885.7,4.3
,"2001","2001","Female","F","75-84 years","75-84",361187,7589733,4758.9,7.9
,"2001","2001","Female","F","85+ years","85+",447998,3051430,14681.6,21.9
,"2001","2001","Female","F","Not Stated","NS",91,Not Applicable,Not Applicable,Not Applicable
,"2001","2001","Male","M","< 1 year","1",15477,2049753,755.1,6.1
,"2001","2001","Male","M","1-4 years","1-4",2899,7817023,37.1,0.7
,"2001","2001","Male","M","5-14 years","5-14",4168,21076273,19.8,0.3
,"2001","2001","Male","M","15-24 years","15-24",23963,20627723,116.2,0.8
,"2001","2001","Male","M","25-34 years","25-34",28757,19904479,144.5,0.9
,"2001","2001","Male","M","35-44 years","35-44",58164,22409323,259.6,1.1
,"2001","2001","Male","M","45-54 years","45-54",104848,19339187,542.2,1.7
,"2001","2001","Male","M","55-64 years","55-64",144958,12061729,1201.8,3.2
,"2001","2001","Male","M","65-74 years","65-74",241581,8341053,2896.3,5.9
,"2001","2001","Male","M","75-84 years","75-84",340742,5003885,6809.5,11.7
,"2001","2001","Male","M","85+ years","85+",217533,1261064,17250.0,37.0
,"2001","2001","Male","M","Not Stated","NS",331,Not Applicable,Not Applicable,Not Applicable
,"2002","2002","Female","F","< 1 year","1",12317,1932939,637.2,5.7
,"2002","2002","Female","F","1-4 years","1-4",2052,7564711,27.1,0.6
,"2002","2002","Female","F","5-14 years","5-14",2952,20071157,14.7,0.3
,"2002","2002","Female","F","15-24 years","15-24",8630,19898124,43.4,0.5
,"2002","2002","Female","F","25-34 years","25-34",12619,19518779,64.7,0.6
,"2002","2002","Female","F","35-44 years","35-44",33547,22439455,149.5,0.8
,"2002","2002","Female","F","45-54 years","45-54",64663,20357603,317.6,1.2
,"2002","2002","Female","F","55-64 years","55-64",101979,13853658,736.1,2.3
,"2002","2002","Female","F","65-74 years","65-74",185969,10016658,1856.6,4.3
,"2002","2002","Female","F","75-84 years","75-84",364150,7664104,4751.4,7.9
,"2002","2002","Female","F","85+ years","85+",455170,3077446,14790.5,21.9
,"2002","2002","Female","F","Not Stated","NS",75,Not Applicable,Not Applicable,Not Applicable
,"2002","2002","Male","M","< 1 year","1",15717,2018522,778.6,6.2
,"2002","2002","Male","M","1-4 years","1-4",2806,7913020,35.5,0.7
,"2002","2002","Male","M","5-14 years","5-14",4198,21062681,19.9,0.3
,"2002","2002","Male","M","15-24 years","15-24",24416,20956011,116.5,0.7
,"2002","2002","Male","M","25-34 years","25-34",28736,19830867,144.9,0.9
,"2002","2002","Male","M","35-44 years","35-44",57593,22201194,259.4,1.1
,"2002","2002","Male","M","45-54 years","45-54",107722,19634591,548.6,1.7
,"2002","2002","Male","M","55-64 years","55-64",151363,12849674,1178.0,3.0
,"2002","2002","Male","M","65-74 years","65-74",237021,8371877,2831.2,5.8
,"2002","2002","Male","M","75-84 years","75-84",343504,5100760,6734.4,11.5
,"2002","2002","Male","M","85+ years","85+",225906,1291362,17493.6,36.8
,"2002","2002","Male","M","Not Stated","NS",282,Not Applicable,Not Applicable,Not Applicable
,"2003","2003","Female","F","< 1 year","1",12123,1942634,624.0,5.7
,"2003","2003","Female","F","1-4 years","1-4",2139,7636693,28.0,0.6
,"2003","2003","Female","F","5-14 years","5-14",2804,20026122,14.0,0.3
,"2003","2003","Female","F","15-24 years","15-24",8898,20180290,44.1,0.5
,"2003","2003","Female","F","25-34 years","25-34",12698,19500922,65.1,0.6
,"2003","2003","Female","F","35-44 years","35-44",33026,22206703,148.7,0.8
,"2003","2003","Female","F","45-54 years","45-54",66099,20776919,318.1,1.2
,"2003","2003","Female","F","55-64 years","55-64",106058,14519419,730.5,2.2
,"2003","2003","Female","F","65-74 years","65-74",182076,10048202,1812.0,4.2
,"2003","2003","Female","F","75-84 years","75-84",360692,7713157,4676.3,7.8
,"2003","2003","Female","F","85+ years","85+",459640,3127975,14694.5,21.7
,"2003","2003","Female","F","Not Stated","NS",71,Not Applicable,Not Applicable,Not Applicable
,"2003","2003","Male","M","< 1 year","1",15902,2033237,782.1,6.2
,"2003","2003","Male","M","1-4 years","1-4",2826,7979882,35.4,0.7
,"2003","2003","Male","M","5-14 years","5-14",4150,21010082,19.8,0.3
,"2003","2003","Male","M","15-24 years","15-24",24670,21208564,116.3,0.7
,"2003","2003","Male","M","25-34 years","25-34",28602,19742873,144.9,0.9
,"2003","2003","Male","M","35-44 years","35-44",56435,21947503,257.1,1.1
,"2003","2003","Male","M","45-54 years","45-54",110682,20043035,552.2,1.7
,"2003","2003","Male","M","55-64 years","55-64",156461,13489526,1159.9,2.9
,"2003","2003","Male","M","65-74 years","65-74",231421,8452713,2737.8,5.7
,"2003","2003","Male","M","75-84 years","75-84",342332,5183281,6604.5,11.3
,"2003","2003","Male","M","85+ years","85+",228212,1338201,17053.6,35.7
,"2003","2003","Male","M","Not Stated","NS",271,Not Applicable,Not Applicable,Not Applicable
,"2004","2004","Female","F","< 1 year","1",12218,1962216,622.7,5.6
,"2004","2004","Female","F","1-4 years","1-4",2136,7713175,27.7,0.6
,"2004","2004","Female","F","5-14 years","5-14",2835,19946937,14.2,0.3
,"2004","2004","Female","F","15-24 years","15-24",8834,20433434,43.2,0.5
,"2004","2004","Female","F","25-34 years","25-34",12509,19520225,64.1,0.6
,"2004","2004","Female","F","35-44 years","35-44",31685,22015734,143.9,0.8
,"2004","2004","Female","F","45-54 years","45-54",66534,21184129,314.1,1.2
,"2004","2004","Female","F","55-64 years","55-64",106665,15185747,702.4,2.2
,"2004","2004","Female","F","65-74 years","65-74",176775,10110363,1748.5,4.2
,"2004","2004","Female","F","75-84 years","75-84",350561,7738742,4529.9,7.7
,"2004","2004","Female","F","85+ years","85+",445123,3166584,14056.9,21.1
,"2004","2004","Female","F","Not Stated","NS",72,Not Applicable,Not Applicable,Not Applicable
,"2004","2004","Male","M","< 1 year","1",15718,2052042,766.0,6.1
,"2004","2004","Male","M","1-4 years","1-4",2649,8058452,32.9,0.6
,"2004","2004","Male","M","5-14 years","5-14",3999,20918980,19.1,0.3
,"2004","2004","Male","M","15-24 years","15-24",24587,21514678,114.3,0.7
,"2004","2004","Male","M","25-34 years","25-34",28359,19746331,143.6,0.9
,"2004","2004","Male","M","35-44 years","35-44",53677,21784541,246.4,1.1
,"2004","2004","Male","M","45-54 years","45-54",111163,20445801,543.7,1.6
,"2004","2004","Male","M","55-64 years","55-64",158032,14119557,1119.2,2.8
,"2004","2004","Male","M","65-74 years","65-74",222891,8557170,2604.7,5.5
,"2004","2004","Male","M","75-84 years","75-84",333669,5251161,6354.2,11.0
,"2004","2004","Male","M","85+ years","85+",226650,1379299,16432.3,34.5
,"2004","2004","Male","M","Not Stated","NS",274,Not Applicable,Not Applicable,Not Applicable
,"2005","2005","Female","F","< 1 year","1",12422,1956682,634.9,5.7
,"2005","2005","Female","F","1-4 years","1-4",1991,7785123,25.6,0.6
,"2005","2005","Female","F","5-14 years","5-14",2749,19822282,13.9,0.3
,"2005","2005","Female","F","15-24 years","15-24",8725,20657595,42.2,0.5
,"2005","2005","Female","F","25-34 years","25-34",12642,19547698,64.7,0.6
,"2005","2005","Female","F","35-44 years","35-44",31476,21865343,144.0,0.8
,"2005","2005","Female","F","45-54 years","45-54",69058,21614529,319.5,1.2
,"2005","2005","Female","F","55-64 years","55-64",109872,15868410,692.4,2.1
,"2005","2005","Female","F","65-74 years","65-74",175548,10198569,1721.3,4.1
,"2005","2005","Female","F","75-84 years","75-84",351430,7753835,4532.3,7.6
,"2005","2005","Female","F","85+ years","85+",464373,3249455,14290.8,21.0
,"2005","2005","Female","F","Not Stated","NS",56,Not Applicable,Not Applicable,Not Applicable
,"2005","2005","Male","M","< 1 year","1",16018,2047711,782.2,6.2
,"2005","2005","Male","M","1-4 years","1-4",2765,8127884,34.0,0.6
,"2005","2005","Male","M","5-14 years","5-14",3853,20779364,18.5,0.3
,"2005","2005","Male","M","15-24 years","15-24",25509,21788574,117.1,0.7
,"2005","2005","Male","M","25-34 years","25-34",29283,19710949,148.6,0.9
,"2005","2005","Male","M","35-44 years","35-44",53309,21640195,246.3,1.1
,"2005","2005","Male","M","45-54 years","45-54",114472,20881375,548.2,1.6
,"2005","2005","Male","M","55-64 years","55-64",165429,14773087,1119.8,2.8
,"2005","2005","Male","M","65-74 years","65-74",222807,8683128,2566.0,5.4
,"2005","2005","Male","M","75-84 years","75-84",335235,5320967,6300.3,10.9
,"2005","2005","Male","M","85+ years","85+",238796,1443844,16538.9,33.8
,"2005","2005","Male","M","Not Stated","NS",199,Not Applicable,Not Applicable,Not Applicable
,"2006","2006","Female","F","< 1 year","1",12547,1975919,635.0,5.7
,"2006","2006","Female","F","1-4 years","1-4",2090,7775092,26.9,0.6
,"2006","2006","Female","F","5-14 years","5-14",2524,19817117,12.7,0.3
,"2006","2006","Female","F","15-24 years","15-24",8817,20836077,42.3,0.5
,"2006","2006","Female","F","25-34 years","25-34",12764,19626340,65.0,0.6
,"2006","2006","Female","F","35-44 years","35-44",30893,21727611,142.2,0.8
,"2006","2006","Female","F","45-54 years","45-54",69859,22011575,317.4,1.2
,"2006","2006","Female","F","55-64 years","55-64",112414,16532054,680.0,2.0
,"2006","2006","Female","F","65-74 years","65-74",171928,10350638,1661.0,4.0
,"2006","2006","Female","F","75-84 years","75-84",340023,7732685,4397.2,7.5
,"2006","2006","Female","F","85+ years","85+",460414,3347539,13753.8,20.3
,"2006","2006","Female","F","Not Stated","NS",49,Not Applicable,Not Applicable,Not Applicable
,"2006","2006","Male","M","< 1 year","1",15980,2065819,773.5,6.1
,"2006","2006","Male","M","1-4 years","1-4",2541,8122053,31.3,0.6
,"2006","2006","Male","M","5-14 years","5-14",3625,20760709,17.5,0.3
,"2006","2006","Male","M","15-24 years","15-24",26070,22007767,118.5,0.7
,"2006","2006","Male","M","25-34 years","25-34",30188,19768839,152.7,0.9
,"2006","2006","Male","M","35-44 years","35-44",52150,21516190,242.4,1.1
,"2006","2006","Male","M","45-54 years","45-54",115172,21274584,541.4,1.6
,"2006","2006","Male","M","55-64 years","55-64",168987,15398059,1097.5,2.7
,"2006","2006","Male","M","65-74 years","65-74",218165,8852389,2464.5,5.3
,"2006","2006","Male","M","75-84 years","75-84",327315,5362466,6103.8,10.7
,"2006","2006","Male","M","85+ years","85+",241578,1518390,15910.1,32.4
,"2006","2006","Male","M","Not Stated","NS",171,Not Applicable,Not Applicable,Not Applicable
,"2007","2007","Female","F","< 1 year","1",12845,2027181,633.6,5.6
,"2007","2007","Female","F","1-4 years","1-4",2069,7813088,26.5,0.6
,"2007","2007","Female","F","5-14 years","5-14",2562,19810765,12.9,0.3
,"2007","2007","Female","F","15-24 years","15-24",8666,21001489,41.3,0.4
,"2007","2007","Female","F","25-34 years","25-34",12780,19788593,64.6,0.6
,"2007","2007","Female","F","35-44 years","35-44",29501,21502002,137.2,0.8
,"2007","2007","Female","F","45-54 years","45-54",70230,22337962,314.4,1.2
,"2007","2007","Female","F","55-64 years","55-64",113492,17148671,661.8,2.0
,"2007","2007","Female","F","65-74 years","65-74",170894,10595802,1612.8,3.9
,"2007","2007","Female","F","75-84 years","75-84",331879,7694491,4313.2,7.5
,"2007","2007","Female","F","85+ years","85+",464781,3446309,13486.3,19.8
,"2007","2007","Female","F","Not Stated","NS",45,Not Applicable,Not Applicable,Not Applicable
,"2007","2007","Male","M","< 1 year","1",16293,2120816,768.2,6.0
,"2007","2007","Male","M","1-4 years","1-4",2634,8164877,32.3,0.6
,"2007","2007","Male","M","5-14 years","5-14",3585,20744888,17.3,0.3
,"2007","2007","Male","M","15-24 years","15-24",25316,22144326,114.3,0.7
,"2007","2007","Male","M","25-34 years","25-34",29792,19924870,149.5,0.9
,"2007","2007","Male","M","35-44 years","35-44",50105,21294228,235.3,1.1
,"2007","2007","Male","M","45-54 years","45-54",114456,21601977,529.8,1.6
,"2007","2007","Male","M","55-64 years","55-64",173618,15979763,1086.5,2.6
,"2007","2007","Male","M","65-74 years","65-74",218344,9102925,2398.6,5.1
,"2007","2007","Male","M","75-84 years","75-84",320803,5392948,5948.6,10.5
,"2007","2007","Male","M","85+ years","85+",248866,1593236,15620.2,31.3
,"2007","2007","Male","M","Not Stated","NS",156,Not Applicable,Not Applicable,Not Applicable
,"2008","2008","Female","F","< 1 year","1",12390,2022889,612.5,5.5
,"2008","2008","Female","F","1-4 years","1-4",2037,7891294,25.8,0.6
,"2008","2008","Female","F","5-14 years","5-14",2371,19856962,11.9,0.2
,"2008","2008","Female","F","15-24 years","15-24",8171,21144714,38.6,0.4
,"2008","2008","Female","F","25-34 years","25-34",12640,20019411,63.1,0.6
,"2008","2008","Female","F","35-44 years","35-44",28684,21195733,135.3,0.8
,"2008","2008","Female","F","45-54 years","45-54",71504,22597536,316.4,1.2
,"2008","2008","Female","F","55-64 years","55-64",116632,17681763,659.6,1.9
,"2008","2008","Female","F","65-74 years","65-74",176179,11004244,1601.0,3.8
,"2008","2008","Female","F","75-84 years","75-84",331664,7656443,4331.8,7.5
,"2008","2008","Female","F","85+ years","85+",483470,3533026,13684.3,19.7
,"2008","2008","Female","F","Not Stated","NS",45,Not Applicable,Not Applicable,Not Applicable
,"2008","2008","Male","M","< 1 year","1",15669,2109846,742.7,5.9
,"2008","2008","Male","M","1-4 years","1-4",2693,8247098,32.7,0.6
,"2008","2008","Male","M","5-14 years","5-14",3280,20779295,15.8,0.3
,"2008","2008","Male","M","15-24 years","15-24",24027,22246778,108.0,0.7
,"2008","2008","Male","M","25-34 years","25-34",29635,20188062,146.8,0.9
,"2008","2008","Male","M","35-44 years","35-44",47686,20996753,227.1,1.0
,"2008","2008","Male","M","45-54 years","45-54",115038,21862911,526.2,1.6
,"2008","2008","Male","M","55-64 years","55-64",179550,16475300,1089.8,2.6
,"2008","2008","Male","M","65-74 years","65-74",225400,9501435,2372.3,5.0
,"2008","2008","Male","M","75-84 years","75-84",321896,5419659,5939.4,10.5
,"2008","2008","Male","M","85+ years","85+",261221,1662814,15709.6,30.7
,"2008","2008","Male","M","Not Stated","NS",102,Not Applicable,Not Applicable,Not Applicable
,"2009","2009","Female","F","< 1 year","1",11589,1959169,591.5,5.5
,"2009","2009","Female","F","1-4 years","1-4",1955,7943260,24.6,0.6
,"2009","2009","Female","F","5-14 years","5-14",2403,19963476,12.0,0.2
,"2009","2009","Female","F","15-24 years","15-24",8104,21263984,38.1,0.4
,"2009","2009","Female","F","25-34 years","25-34",13303,20267430,65.6,0.6
,"2009","2009","Female","F","35-44 years","35-44",28110,20841610,134.9,0.8
,"2009","2009","Female","F","45-54 years","45-54",72748,22797854,319.1,1.2
,"2009","2009","Female","F","55-64 years","55-64",119165,18329541,650.1,1.9
,"2009","2009","Female","F","65-74 years","65-74",175238,11375157,1540.5,3.7
,"2009","2009","Female","F","75-84 years","75-84",316695,7590640,4172.2,7.4
,"2009","2009","Female","F","85+ years","85+",470396,3631954,12951.6,18.9
,"2009","2009","Female","F","Not Stated","NS",78,Not Applicable,Not Applicable,Not Applicable
,"2009","2009","Male","M","< 1 year","1",14823,2044418,725.0,6.0
,"2009","2009","Male","M","1-4 years","1-4",2495,8297671,30.1,0.6
,"2009","2009","Male","M","5-14 years","5-14",3248,20879587,15.6,0.3
,"2009","2009","Male","M","15-24 years","15-24",22312,22312948,100.0,0.7
,"2009","2009","Male","M","25-34 years","25-34",29199,20455912,142.7,0.8
,"2009","2009","Male","M","35-44 years","35-44",46555,20646201,225.5,1.0
,"2009","2009","Male","M","45-54 years","45-54",114820,22069234,520.3,1.5
,"2009","2009","Male","M","55-64 years","55-64",184142,17076059,1078.4,2.5
,"2009","2009","Male","M","65-74 years","65-74",225794,9857942,2290.5,4.8
,"2009","2009","Male","M","75-84 years","75-84",311032,5432135,5725.8,10.3
,"2009","2009","Male","M","85+ years","85+",262782,1735347,15142.9,29.5
,"2009","2009","Male","M","Not Stated","NS",177,Not Applicable,Not Applicable,Not Applicable
,"2010","2010","Female","F","< 1 year","1",10884,1929877,564.0,5.4
,"2010","2010","Female","F","1-4 years","1-4",1856,7952058,23.3,0.5
,"2010","2010","Female","F","5-14 years","5-14",2225,20056351,11.1,0.2
,"2010","2010","Female","F","15-24 years","15-24",7761,21308500,36.4,0.4
,"2010","2010","Female","F","25-34 years","25-34",13067,20431857,64.0,0.6
,"2010","2010","Female","F","35-44 years","35-44",26599,20634607,128.9,0.8
,"2010","2010","Female","F","45-54 years","45-54",71189,22864357,311.4,1.2
,"2010","2010","Female","F","55-64 years","55-64",121507,18881581,643.5,1.8
,"2010","2010","Female","F","65-74 years","65-74",177447,11616910,1527.5,3.6
,"2010","2010","Female","F","75-84 years","75-84",313821,7584360,4137.7,7.4
,"2010","2010","Female","F","85+ years","85+",489608,3703754,13219.2,18.9
,"2010","2010","Female","F","Not Stated","NS",39,Not Applicable,Not Applicable,Not Applicable
,"2010","2010","Male","M","< 1 year","1",13702,2014276,680.2,5.8
,"2010","2010","Male","M","1-4 years","1-4",2460,8305151,29.6,0.6
,"2010","2010","Male","M","5-14 years","5-14",3054,20969500,14.6,0.3
,"2010","2010","Male","M","15-24 years","15-24",21790,22317842,97.6,0.7
,"2010","2010","Male","M","25-34 years","25-34",29192,20632091,141.5,0.8
,"2010","2010","Male","M","35-44 years","35-44",43434,20435999,212.5,1.0
,"2010","2010","Male","M","45-54 years","45-54",112018,22142359,505.9,1.5
,"2010","2010","Male","M","55-64 years","55-64",189295,17601148,1075.5,2.5
,"2010","2010","Male","M","65-74 years","65-74",229704,10096519,2275.1,4.7
,"2010","2010","Male","M","75-84 years","75-84",311830,5476762,5693.7,10.2
,"2010","2010","Male","M","85+ years","85+",275866,1789679,15414.3,29.3
,"2010","2010","Male","M","Not Stated","NS",87,Not Applicable,Not Applicable,Not Applicable
,"2011","2011","Female","F","< 1 year","1",10658,1952827,545.8,5.3
,"2011","2011","Female","F","1-4 years","1-4",1841,7909731,23.3,0.5
,"2011","2011","Female","F","5-14 years","5-14",2222,20067831,11.1,0.2
,"2011","2011","Female","F","15-24 years","15-24",7743,21366229,36.2,0.4
,"2011","2011","Female","F","25-34 years","25-34",13663,20746335,65.9,0.6
,"2011","2011","Female","F","35-44 years","35-44",26678,20404484,130.7,0.8
,"2011","2011","Female","F","45-54 years","45-54",71538,22698956,315.2,1.2
,"2011","2011","Female","F","55-64 years","55-64",126674,19703935,642.9,1.8
,"2011","2011","Female","F","65-74 years","65-74",180776,12005425,1505.8,3.5
,"2011","2011","Female","F","75-84 years","75-84",313532,7602197,4124.2,7.4
,"2011","2011","Female","F","85+ years","85+",505120,3843148,13143.4,18.5
,"2011","2011","Female","F","Not Stated","NS",35,Not Applicable,Not Applicable,Not Applicable
,"2011","2011","Male","M","< 1 year","1",13327,2043710,652.1,5.6
,"2011","2011","Male","M","1-4 years","1-4",2405,8255790,29.1,0.6
,"2011","2011","Male","M","5-14 years","5-14",3179,20971217,15.2,0.3
,"2011","2011","Male","M","15-24 years","15-24",21924,22431646,97.7,0.7
,"2011","2011","Male","M","25-34 years","25-34",30085,21044163,143.0,0.8
,"2011","2011","Male","M","35-44 years","35-44",43215,20223470,213.7,1.0
,"2011","2011","Male","M","45-54 years","45-54",111709,22019247,507.3,1.5
,"2011","2011","Male","M","55-64 years","55-64",196641,18358205,1071.1,2.4
,"2011","2011","Male","M","65-74 years","65-74",234276,10476313,2236.2,4.6
,"2011","2011","Male","M","75-84 years","75-84",312693,5573033,5610.8,10.0
,"2011","2011","Male","M","85+ years","85+",285425,1894025,15069.8,28.2
,"2011","2011","Male","M","Not Stated","NS",99,Not Applicable,Not Applicable,Not Applicable
,"2012","2012","Female","F","< 1 year","1",10490,1926339,544.6,5.3
,"2012","2012","Female","F","1-4 years","1-4",1823,7856870,23.2,0.5
,"2012","2012","Female","F","5-14 years","5-14",2164,20118347,10.8,0.2
,"2012","2012","Female","F","15-24 years","15-24",7726,21431588,36.0,0.4
,"2012","2012","Female","F","25-34 years","25-34",13808,20970529,65.8,0.6
,"2012","2012","Female","F","35-44 years","35-44",26233,20342813,129.0,0.8
,"2012","2012","Female","F","45-54 years","45-54",70249,22461868,312.7,1.2
,"2012","2012","Female","F","55-64 years","55-64",128655,19983308,643.8,1.8
,"2012","2012","Female","F","65-74 years","65-74",187411,12782530,1466.1,3.4
,"2012","2012","Female","F","75-84 years","75-84",309747,7624484,4062.5,7.3
,"2012","2012","Female","F","85+ years","85+",511204,3923297,13030.0,18.2
,"2012","2012","Female","F","Not Stated","NS",47,Not Applicable,Not Applicable,Not Applicable
,"2012","2012","Male","M","< 1 year","1",13139,2016738,651.5,5.7
,"2012","2012","Male","M","1-4 years","1-4",2395,8199397,29.2,0.6
,"2012","2012","Male","M","5-14 years","5-14",3036,21026407,14.4,0.3
,"2012","2012","Male","M","15-24 years","15-24",21456,22512317,95.3,0.7
,"2012","2012","Male","M","25-34 years","25-34",30783,21338792,144.3,0.8
,"2012","2012","Male","M","35-44 years","35-44",42929,20173607,212.8,1.0
,"2012","2012","Male","M","45-54 years","45-54",109214,21806870,500.8,1.5
,"2012","2012","Male","M","55-64 years","55-64",200951,18602894,1080.2,2.4
,"2012","2012","Male","M","65-74 years","65-74",244935,11202862,2186.4,4.4
,"2012","2012","Male","M","75-84 years","75-84",310681,5648150,5500.6,9.9
,"2012","2012","Male","M","85+ years","85+",294103,1964033,14974.4,27.6
,"2012","2012","Male","M","Not Stated","NS",100,Not Applicable,Not Applicable,Not Applicable
,"2013","2013","Female","F","< 1 year","1",10321,1925056,536.1,5.3
,"2013","2013","Female","F","1-4 years","1-4",1745,7790614,22.4,0.5
,"2013","2013","Female","F","5-14 years","5-14",2263,20159538,11.2,0.2
,"2013","2013","Female","F","15-24 years","15-24",7622,21429247,35.6,0.4
,"2013","2013","Female","F","25-34 years","25-34",14001,21203096,66.0,0.6
,"2013","2013","Female","F","35-44 years","35-44",26501,20307429,130.5,0.8
,"2013","2013","Female","F","45-54 years","45-54",69727,22198448,314.1,1.2
,"2013","2013","Female","F","55-64 years","55-64",131802,20359676,647.4,1.8
,"2013","2013","Female","F","65-74 years","65-74",196531,13419124,1464.6,3.3
,"2013","2013","Female","F","75-84 years","75-84",309673,7686002,4029.1,7.2
,"2013","2013","Female","F","85+ years","85+",520736,3999007,13021.6,18.0
,"2013","2013","Female","F","Not Stated","NS",37,Not Applicable,Not Applicable,Not Applicable
,"2013","2013","Male","M","< 1 year","1",13119,2016727,650.5,5.7
,"2013","2013","Male","M","1-4 years","1-4",2323,8135691,28.6,0.6
,"2013","2013","Male","M","5-14 years","5-14",3077,21061497,14.6,0.3
,"2013","2013","Male","M","15-24 years","15-24",20864,22525155,92.6,0.6
,"2013","2013","Male","M","25-34 years","25-34",31462,21641491,145.4,0.8
,"2013","2013","Male","M","35-44 years","35-44",43072,20145261,213.8,1.0
,"2013","2013","Male","M","45-54 years","45-54",107997,21569084,500.7,1.5
,"2013","2013","Male","M","55-64 years","55-64",206325,18956755,1088.4,2.4
,"2013","2013","Male","M","65-74 years","65-74",257898,11797642,2186.0,4.3
,"2013","2013","Male","M","75-84 years","75-84",315340,5760517,5474.2,9.7
,"2013","2013","Male","M","85+ years","85+",304462,2041782,14911.6,27.0
,"2013","2013","Male","M","Not Stated","NS",95,Not Applicable,Not Applicable,Not Applicable
,"2014","2014","Female","F","< 1 year","1",10329,1930493,535.0,5.3
,"2014","2014","Female","F","1-4 years","1-4",1658,7790662,21.3,0.5
,"2014","2014","Female","F","5-14 years","5-14",2122,20161424,10.5,0.2
,"2014","2014","Female","F","15-24 years","15-24",7674,21456371,35.8,0.4
,"2014","2014","Female","F","25-34 years","25-34",14480,21546290,67.2,0.6
,"2014","2014","Female","F","35-44 years","35-44",27303,20353904,134.1,0.8
,"2014","2014","Female","F","45-54 years","45-54",69540,22033807,315.6,1.2
,"2014","2014","Female","F","55-64 years","55-64",136610,20755699,658.2,1.8
,"2014","2014","Female","F","65-74 years","65-74",202893,14049245,1444.2,3.2
,"2014","2014","Female","F","75-84 years","75-84",308074,7789312,3955.1,7.1
,"2014","2014","Female","F","85+ years","85+",517441,4053362,12765.7,17.7
,"2014","2014","Female","F","Not Stated","NS",53,Not Applicable,Not Applicable,Not Applicable
,"2014","2014","Male","M","< 1 year","1",12886,2017857,638.6,5.6
,"2014","2014","Male","M","1-4 years","1-4",2172,8137871,26.7,0.6
,"2014","2014","Male","M","5-14 years","5-14",3128,21029648,14.9,0.3
,"2014","2014","Male","M","15-24 years","15-24",21117,22523450,93.8,0.6
,"2014","2014","Male","M","25-34 years","25-34",32697,21970214,148.8,0.8
,"2014","2014","Male","M","35-44 years","35-44",43693,20159229,216.7,1.0
,"2014","2014","Male","M","45-54 years","45-54",106377,21425044,496.5,1.5
,"2014","2014","Male","M","55-64 years","55-64",212198,19321882,1098.2,2.4
,"2014","2014","Male","M","65-74 years","65-74",268648,12349045,2175.5,4.2
,"2014","2014","Male","M","75-84 years","75-84",316430,5893378,5369.2,9.5
,"2014","2014","Male","M","85+ years","85+",308785,2108869,14642.2,26.3
,"2014","2014","Male","M","Not Stated","NS",110,Not Applicable,Not Applicable,Not Applicable
,"2015","2015","Female","F","< 1 year","1",10447,1942904,537.7,5.3
,"2015","2015","Female","F","1-4 years","1-4",1684,7786776,21.6,0.5
,"2015","2015","Female","F","5-14 years","5-14",2258,20129986,11.2,0.2
,"2015","2015","Female","F","15-24 years","15-24",8148,21382495,38.1,0.4
,"2015","2015","Female","F","25-34 years","25-34",15736,21838064,72.1,0.6
,"2015","2015","Female","F","35-44 years","35-44",27418,20386206,134.5,0.8
,"2015","2015","Female","F","45-54 years","45-54",68947,21889385,315.0,1.2
,"2015","2015","Female","F","55-64 years","55-64",140159,21163072,662.3,1.8
,"2015","2015","Female","F","65-74 years","65-74",212669,14658169,1450.9,3.1
,"2015","2015","Female","F","75-84 years","75-84",313720,7899603,3971.3,7.1
,"2015","2015","Female","F","85+ years","85+",537997,4112863,13080.8,17.8
,"2015","2015","Female","F","Not Stated","NS",43,Not Applicable,Not Applicable,Not Applicable
,"2015","2015","Male","M","< 1 year","1",13008,2035134,639.2,5.6
,"2015","2015","Male","M","1-4 years","1-4",2281,8142467,28.0,0.6
,"2015","2015","Male","M","5-14 years","5-14",3153,20979520,15.0,0.3
,"2015","2015","Male","M","15-24 years","15-24",22346,22465721,99.5,0.7
,"2015","2015","Male","M","25-34 years","25-34",35781,22299138,160.5,0.8
,"2015","2015","Male","M","35-44 years","35-44",45670,20203577,226.0,1.1
,"2015","2015","Male","M","45-54 years","45-54",105547,21298776,495.6,1.5
,"2015","2015","Male","M","55-64 years","55-64",217626,19714747,1103.9,2.4
,"2015","2015","Male","M","65-74 years","65-74",282347,12892348,2190.0,4.1
,"2015","2015","Male","M","75-84 years","75-84",323846,6023571,5376.3,9.4
,"2015","2015","Male","M","85+ years","85+",321704,2174298,14795.8,26.1
,"2015","2015","Male","M","Not Stated","NS",95,Not Applicable,Not Applicable,Not Applicable
,"2016","2016","Female","F","< 1 year","1",10294,1939667,530.7,5.2
,"2016","2016","Female","F","1-4 years","1-4",1789,7800517,22.9,0.5
,"2016","2016","Female","F","5-14 years","5-14",2362,20099377,11.8,0.2
,"2016","2016","Female","F","15-24 years","15-24",8562,21218116,40.4,0.4
,"2016","2016","Female","F","25-34 years","25-34",17359,22077505,78.6,0.6
,"2016","2016","Female","F","35-44 years","35-44",28577,20317393,140.7,0.8
,"2016","2016","Female","F","45-54 years","45-54",68430,21680540,315.6,1.2
,"2016","2016","Female","F","55-64 years","55-64",144061,21464106,671.2,1.8
,"2016","2016","Female","F","65-74 years","65-74",219338,15239221,1439.3,3.1
,"2016","2016","Female","F","75-84 years","75-84",312097,8056660,3873.8,6.9
,"2016","2016","Female","F","85+ years","85+",531107,4155488,12780.9,17.5
,"2016","2016","Female","F","Not Stated","NS",40,Not Applicable,Not Applicable,Not Applicable
,"2016","2016","Male","M","< 1 year","1",12867,2030478,633.7,5.6
,"2016","2016","Male","M","1-4 years","1-4",2256,8156375,27.7,0.6
,"2016","2016","Male","M","5-14 years","5-14",3141,20948655,15.0,0.3
,"2016","2016","Male","M","15-24 years","15-24",24013,22292911,107.7,0.7
,"2016","2016","Male","M","25-34 years","25-34",40257,22599738,178.1,0.9
,"2016","2016","Male","M","35-44 years","35-44",49215,20152763,244.2,1.1
,"2016","2016","Male","M","45-54 years","45-54",105086,21106139,497.9,1.5
,"2016","2016","Male","M","55-64 years","55-64",222384,19999038,1112.0,2.4
,"2016","2016","Male","M","65-74 years","65-74",292742,13391109,2186.1,4.0
,"2016","2016","Male","M","75-84 years","75-84",324819,6176874,5258.6,9.2
,"2016","2016","Male","M","85+ years","85+",323355,2224843,14533.8,25.6
,"2016","2016","Male","M","Not Stated","NS",97,Not Applicable,Not Applicable,Not Applicable
,"2017","2017","Female","F","< 1 year","1",9867,1924145,512.8,5.2
,"2017","2017","Female","F","1-4 years","1-4",1648,7818747,21.1,0.5
,"2017","2017","Female","F","5-14 years","5-14",2302,20109479,11.4,0.2
,"2017","2017","Female","F","15-24 years","15-24",8522,21100662,40.4,0.4
,"2017","2017","Female","F","25-34 years","25-34",18066,22351311,80.8,0.6
,"2017","2017","Female","F","35-44 years","35-44",29004,20506270,141.4,0.8
,"2017","2017","Female","F","45-54 years","45-54",66338,21468595,309.0,1.2
,"2017","2017","Female","F","55-64 years","55-64",146671,21737855,674.7,1.8
,"2017","2017","Female","F","65-74 years","65-74",227679,15806306,1440.4,3.0
,"2017","2017","Female","F","75-84 years","75-84",321088,8298676,3869.1,6.8
,"2017","2017","Female","F","85+ years","85+",543169,4189013,12966.5,17.6
,"2017","2017","Female","F","Not Stated","NS",38,Not Applicable,Not Applicable,Not Applicable
,"2017","2017","Male","M","< 1 year","1",12468,2015150,618.7,5.5
,"2017","2017","Male","M","1-4 years","1-4",2232,8180818,27.3,0.6
,"2017","2017","Male","M","5-14 years","5-14",3269,20973213,15.6,0.3
,"2017","2017","Male","M","15-24 years","15-24",23503,22149633,106.1,0.7
,"2017","2017","Male","M","25-34 years","25-34",42149,22991361,183.3,0.9
,"2017","2017","Male","M","35-44 years","35-44",50792,20369100,249.4,1.1
,"2017","2017","Male","M","45-54 years","45-54",103804,20906357,496.5,1.5
,"2017","2017","Male","M","55-64 years","55-64",225335,20257803,1112.3,2.3
,"2017","2017","Male","M","65-74 years","65-74",303931,13877140,2190.2,4.0
,"2017","2017","Male","M","75-84 years","75-84",336671,6407875,5254.0,9.1
,"2017","2017","Male","M","85+ years","85+",334866,2279669,14689.2,25.4
,"2017","2017","Male","M","Not Stated","NS",91,Not Applicable,Not Applicable,Not Applicable
,"2018","2018","Female","F","< 1 year","1",9399,1879703,500.0,5.2
,"2018","2018","Female","F","1-4 years","1-4",1587,7798370,20.4,0.5
,"2018","2018","Female","F","5-14 years","5-14",2369,20100339,11.8,0.2
,"2018","2018","Female","F","15-24 years","15-24",8146,20994345,38.8,0.4
,"2018","2018","Female","F","25-34 years","25-34",17980,22487065,80.0,0.6
,"2018","2018","Female","F","35-44 years","35-44",29004,20690288,140.2,0.8
,"2018","2018","Female","F","45-54 years","45-54",63807,21090497,302.5,1.2
,"2018","2018","Female","F","55-64 years","55-64",146563,21873773,670.0,1.8
,"2018","2018","Female","F","65-74 years","65-74",230867,16246231,1421.0,3.0
,"2018","2018","Female","F","75-84 years","75-84",328017,8659334,3788.0,6.6
,"2018","2018","Female","F","85+ years","85+",542962,4218810,12870.0,17.5
,"2018","2018","Female","F","Not Stated","NS",35,Not Applicable,Not Applicable,Not Applicable
,"2018","2018","Male","M","< 1 year","1",12068,1968505,613.1,5.6
,"2018","2018","Male","M","1-4 years","1-4",2243,8163697,27.5,0.6
,"2018","2018","Male","M","5-14 years","5-14",3081,20974830,14.7,0.3
,"2018","2018","Male","M","15-24 years","15-24",22008,21976455,100.1,0.7
,"2018","2018","Male","M","25-34 years","25-34",40864,23210709,176.1,0.9
,"2018","2018","Male","M","35-44 years","35-44",51376,20587600,249.5,1.1
,"2018","2018","Male","M","45-54 years","45-54",101030,20541202,491.8,1.5
,"2018","2018","Male","M","55-64 years","55-64",228273,20398863,1119.0,2.3
,"2018","2018","Male","M","65-74 years","65-74",312911,14246085,2196.5,3.9
,"2018","2018","Male","M","75-84 years","75-84",347188,6735040,5155.0,8.7
,"2018","2018","Male","M","85+ years","85+",337318,2325693,14504.0,25.0
,"2018","2018","Male","M","Not Stated","NS",109,Not Applicable,Not Applicable,Not Applicable
,"2019","2019","Female","F","< 1 year","1",9247,1847935,500.4,5.2
,"2019","2019","Female","F","1-4 years","1-4",1635,7719541,21.2,0.5
,"2019","2019","Female","F","5-14 years","5-14",2311,20053140,11.5,0.2
,"2019","2019","Female","F","15-24 years","15-24",8023,20877151,38.4,0.4
,"2019","2019","Female","F","25-34 years","25-34",17827,22581141,78.9,0.6
,"2019","2019","Female","F","35-44 years","35-44",29550,20867064,141.6,0.8
,"2019","2019","Female","F","45-54 years","45-54",61546,20702936,297.3,1.2
,"2019","2019","Female","F","55-64 years","55-64",147012,21949318,669.8,1.7
,"2019","2019","Female","F","65-74 years","65-74",235312,16783854,1402.0,2.9
,"2019","2019","Female","F","75-84 years","75-84",332927,8971649,3710.9,6.4
,"2019","2019","Female","F","85+ years","85+",535581,4228470,12666.1,17.3
,"2019","2019","Female","F","Not Stated","NS",44,Not Applicable,Not Applicable,Not Applicable
,"2019","2019","Male","M","< 1 year","1",11674,1935117,603.3,5.6
,"2019","2019","Male","M","1-4 years","1-4",2041,8074090,25.3,0.6
,"2019","2019","Male","M","5-14 years","5-14",3186,20941023,15.2,0.3
,"2019","2019","Male","M","15-24 years","15-24",21748,21810359,99.7,0.7
,"2019","2019","Male","M","25-34 years","25-34",41351,23359180,177.0,0.9
,"2019","2019","Male","M","35-44 years","35-44",53436,20792080,257.0,1.1
,"2019","2019","Male","M","45-54 years","45-54",98847,20171966,490.0,1.6
,"2019","2019","Male","M","55-64 years","55-64",227925,20499219,1111.9,2.3
,"2019","2019","Male","M","65-74 years","65-74",320247,14699579,2178.6,3.8
,"2019","2019","Male","M","75-84 years","75-84",355100,6998223,5074.1,8.5
,"2019","2019","Male","M","85+ years","85+",338165,2376488,14229.6,24.5
,"2019","2019","Male","M","Not Stated","NS",103,Not Applicable,Not Applicable,Not Applicable
,"2020","2020","Female","F","< 1 year","1",8723,1826869,477.5,5.1
,"2020","2020","Female","F","1-4 years","1-4",1503,7613266,19.7,0.5
,"2020","2020","Female","F","5-14 years","5-14",2261,20050413,11.3,0.2
,"2020","2020","Female","F","15-24 years","15-24",9332,20828241,44.8,0.5
,"2020","2020","Female","F","25-34 years","25-34",21654,22625267,95.7,0.7
,"2020","2020","Female","F","35-44 years","35-44",35996,21090324,170.7,0.9
,"2020","2020","Female","F","45-54 years","45-54",71298,20441441,348.8,1.3
,"2020","2020","Female","F","55-64 years","55-64",169422,21914243,773.1,1.9
,"2020","2020","Female","F","65-74 years","65-74",282508,17365858,1626.8,3.1
,"2020","2020","Female","F","75-84 years","75-84",393226,9228272,4261.1,6.8
,"2020","2020","Female","F","85+ years","85+",617885,4243727,14560.0,18.5
,"2020","2020","Female","F","Not Stated","NS",37,Not Applicable,Not Applicable,Not Applicable
,"2020","2020","Male","M","< 1 year","1",10859,1908141,569.1,5.5
,"2020","2020","Male","M","1-4 years","1-4",2026,7953016,25.5,0.6
,"2020","2020","Male","M","5-14 years","5-14",3362,20941721,16.1,0.3
,"2020","2020","Male","M","15-24 years","15-24",26484,21727443,121.9,0.7
,"2020","2020","Male","M","25-34 years","25-34",51832,23444379,221.1,1.0
,"2020","2020","Male","M","35-44 years","35-44",68494,21045868,325.5,1.2
,"2020","2020","Male","M","45-54 years","45-54",119844,19924692,601.5,1.7
,"2020","2020","Male","M","55-64 years","55-64",271127,20489434,1323.3,2.5
,"2020","2020","Male","M","65-74 years","65-74",391999,15183540,2581.7,4.1
,"2020","2020","Male","M","75-84 years","75-84",428858,7223275,5937.2,9.1
,"2020","2020","Male","M","85+ years","85+",394920,2414693,16354.9,26.0
,"2020","2020","Male","M","Not Stated","NS",79,Not Applicable,Not Applicable,Not Applicable
"---"
"Dataset: Multiple Cause of Death, 1999-2020"
"Query Parameters:"
"Title: US_10_Year_Age_Groups_1999-2020"
"Group By: Year; Sex; Ten-Year Age Groups"
"Show Totals: False"
"Show Zero Values: True"
"Show Suppressed: True"
"Calculate Rates Per: 100,000"
"Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)"
"---"
"Help: See http://wonder.cdc.gov/wonder/help/mcd.html for more information."
"---"
"Query Date: Nov 19, 2025 11:58:36 PM"
"---"
"Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics"
"System, Mortality 1999-2020 on CDC WONDER Online Database, released in 2021. Data are from the Multiple Cause of Death Files,"
"1999-2020, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative"
"Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Nov 19, 2025 11:58:36 PM"
"---"
Caveats:
"1. As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#2014-Revision."
"2. Deaths of persons with Age ""Not Stated"" are included in ""All"" counts and rates, but are not distributed among age groups,"
"so are not included in age-specific counts, age-specific rates or in any age-adjusted rates. More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#Not Stated."
"3. The method used to calculate standard errors is documented here: More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#Standard-Errors."
"4. The population figures for year 2020 are bridged-race estimates of the July 1 resident population, from the Vintage 2020"
"postcensal series released by NCHS on September 22, 2021. The population figures for year 2019 are bridged-race estimates of the"
"July 1 resident population, from the Vintage 2019 postcensal series released by NCHS on July 9, 2020. The population figures for"
"year 2018 are bridged-race estimates of the July 1 resident population, from the Vintage 2018 postcensal series released by NCHS"
"on June 25, 2019. The population figures for year 2017 are bridged-race estimates of the July 1 resident population, from the"
"Vintage 2017 postcensal series released by NCHS on June 27, 2018. The population figures for year 2016 are bridged-race"
"estimates of the July 1 resident population, from the Vintage 2016 postcensal series released by NCHS on June 26, 2017. The"
"population figures for year 2015 are bridged-race estimates of the July 1 resident population, from the Vintage 2015 postcensal"
"series released by NCHS on June 28, 2016. The population figures for year 2014 are bridged-race estimates of the July 1 resident"
"population, from the Vintage 2014 postcensal series released by NCHS on June 30, 2015. The population figures for year 2013 are"
"bridged-race estimates of the July 1 resident population, from the Vintage 2013 postcensal series released by NCHS on June 26,"
"2014. The population figures for year 2012 are bridged-race estimates of the July 1 resident population, from the Vintage 2012"
"postcensal series released by NCHS on June 13, 2013. Population figures for 2011 are bridged-race estimates of the July 1"
"resident population, from the county-level postcensal Vintage 2011 series released by NCHS on July 18, 2012. Population figures"
"for 2010 are April 1 Census counts. The population figures for years 2001 - 2009, are bridged-race estimates of the July 1"
"resident population, from the revised intercensal county-level 2000 - 2009 series released by NCHS on October 26, 2012."
"Population figures for 2000 are April 1 Census counts. Population figures for 1999 are from the 1990-1999 intercensal series of"
"July 1 estimates. Population figures for Infant Age Groups are the number of live births. <br/><b>Note:</b> Rates and population"
"figures for years 2001 - 2009 differ slightly from previously published reports, due to use of the population estimates which"
"were available at the time of release."
"5. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the"
"resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/mcd.html#Age Group."
Can't render this file because it has a wrong number of fields in line 530.

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Data/Raw_Data/Mortality_Rates_New/US_Age_Adjusted_1999-2020.csv Normal file
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"Notes","Year","Year Code","Sex","Sex Code",Deaths,Population,Crude Rate,Crude Rate Standard Error,Age Adjusted Rate,Age Adjusted Rate Standard Error
,"1999","1999","Female","F",1215939,142237295,854.9,0.8,734.0,0.7
,"1999","1999","Male","M",1175460,136802873,859.2,0.8,1067.0,1.0
,"2000","2000","Female","F",1225773,143368343,855.0,0.8,731.4,0.7
,"2000","2000","Male","M",1177578,138053563,853.0,0.8,1053.8,1.0
,"2001","2001","Female","F",1233004,145077463,849.9,0.8,725.6,0.7
,"2001","2001","Male","M",1183421,139891492,846.0,0.8,1035.4,1.0
,"2002","2002","Female","F",1244123,146394634,849.8,0.8,723.6,0.7
,"2002","2002","Male","M",1199264,141230559,849.2,0.8,1030.6,1.0
,"2003","2003","Female","F",1246324,147679036,843.9,0.8,715.2,0.6
,"2003","2003","Male","M",1201964,142428897,843.9,0.8,1010.3,0.9
,"2004","2004","Female","F",1215947,148977286,816.2,0.7,690.5,0.6
,"2004","2004","Male","M",1181668,143828012,821.6,0.8,973.3,0.9
,"2005","2005","Female","F",1240342,150319521,825.1,0.7,692.3,0.6
,"2005","2005","Male","M",1207675,145197078,831.7,0.8,971.9,0.9
,"2006","2006","Female","F",1224322,151732647,806.9,0.7,672.2,0.6
,"2006","2006","Male","M",1201942,146647265,819.6,0.7,943.5,0.9
,"2007","2007","Female","F",1219744,153166353,796.4,0.7,658.1,0.6
,"2007","2007","Male","M",1203968,148064854,813.1,0.7,922.9,0.9
,"2008","2008","Female","F",1245787,154604015,805.8,0.7,659.9,0.6
,"2008","2008","Male","M",1226197,149489951,820.3,0.7,918.8,0.8
,"2009","2009","Female","F",1219784,155964075,782.1,0.7,636.8,0.6
,"2009","2009","Male","M",1217379,150807454,807.2,0.7,890.9,0.8
,"2010","2010","Female","F",1236003,156964212,787.4,0.7,634.9,0.6
,"2010","2010","Male","M",1232432,151781326,812.0,0.7,887.1,0.8
,"2011","2011","Female","F",1260480,158301098,796.3,0.7,632.4,0.6
,"2011","2011","Male","M",1254978,153290819,818.7,0.7,875.3,0.8
,"2012","2012","Female","F",1269557,159421973,796.4,0.7,624.7,0.6
,"2012","2012","Male","M",1273722,154492067,824.5,0.7,865.1,0.8
,"2013","2013","Female","F",1290959,160477237,804.4,0.7,623.5,0.6
,"2013","2013","Male","M",1306034,155651602,839.1,0.7,863.6,0.8
,"2014","2014","Female","F",1298177,161920569,801.7,0.7,616.7,0.6
,"2014","2014","Male","M",1328241,156936487,846.4,0.7,855.1,0.8
,"2015","2015","Female","F",1339226,163189523,820.7,0.7,624.2,0.6
,"2015","2015","Male","M",1373404,158229297,868.0,0.7,863.2,0.7
,"2016","2016","Female","F",1344016,164048590,819.3,0.7,617.5,0.5
,"2016","2016","Male","M",1400232,159078923,880.2,0.7,861.0,0.7
,"2017","2017","Female","F",1374392,165311059,831.4,0.7,619.7,0.5
,"2017","2017","Male","M",1439111,160408119,897.2,0.7,864.5,0.7
,"2018","2018","Female","F",1380736,166038755,831.6,0.7,611.3,0.5
,"2018","2018","Male","M",1458469,161128679,905.2,0.7,855.5,0.7
,"2019","2019","Female","F",1381015,166582199,829.0,0.7,602.7,0.5
,"2019","2019","Male","M",1473823,161657324,911.7,0.8,846.7,0.7
,"2020","2020","Female","F",1613845,167227921,965.1,0.8,695.1,0.6
,"2020","2020","Male","M",1769884,162256202,1090.8,0.8,998.3,0.8
"---"
"Dataset: Multiple Cause of Death, 1999-2020"
"Query Parameters:"
"Title: US_Age_Adjusted_1999-2020"
"Group By: Year; Sex"
"Show Totals: False"
"Show Zero Values: True"
"Show Suppressed: True"
"Standard Population: 2000 U.S. Std. Population"
"Calculate Rates Per: 100,000"
"Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)"
"---"
"Help: See http://wonder.cdc.gov/wonder/help/mcd.html for more information."
"---"
"Query Date: Nov 19, 2025 11:54:57 PM"
"---"
"Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics"
"System, Mortality 1999-2020 on CDC WONDER Online Database, released in 2021. Data are from the Multiple Cause of Death Files,"
"1999-2020, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative"
"Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Nov 19, 2025 11:54:57 PM"
"---"
Caveats:
"1. As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#2014-Revision."
"2. The populations used to calculate standard age-adjusted rates are documented here: More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#2000 Standard Population."
"3. The method used to calculate age-adjusted rates is documented here: More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#Age-Adjusted Rates."
"4. Deaths for persons of unknown age are included in counts and crude rates, but are not included in age-adjusted rates."
"5. The method used to calculate standard errors is documented here: More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#Standard-Errors."
"6. The population figures for year 2020 are bridged-race estimates of the July 1 resident population, from the Vintage 2020"
"postcensal series released by NCHS on September 22, 2021. The population figures for year 2019 are bridged-race estimates of the"
"July 1 resident population, from the Vintage 2019 postcensal series released by NCHS on July 9, 2020. The population figures for"
"year 2018 are bridged-race estimates of the July 1 resident population, from the Vintage 2018 postcensal series released by NCHS"
"on June 25, 2019. The population figures for year 2017 are bridged-race estimates of the July 1 resident population, from the"
"Vintage 2017 postcensal series released by NCHS on June 27, 2018. The population figures for year 2016 are bridged-race"
"estimates of the July 1 resident population, from the Vintage 2016 postcensal series released by NCHS on June 26, 2017. The"
"population figures for year 2015 are bridged-race estimates of the July 1 resident population, from the Vintage 2015 postcensal"
"series released by NCHS on June 28, 2016. The population figures for year 2014 are bridged-race estimates of the July 1 resident"
"population, from the Vintage 2014 postcensal series released by NCHS on June 30, 2015. The population figures for year 2013 are"
"bridged-race estimates of the July 1 resident population, from the Vintage 2013 postcensal series released by NCHS on June 26,"
"2014. The population figures for year 2012 are bridged-race estimates of the July 1 resident population, from the Vintage 2012"
"postcensal series released by NCHS on June 13, 2013. Population figures for 2011 are bridged-race estimates of the July 1"
"resident population, from the county-level postcensal Vintage 2011 series released by NCHS on July 18, 2012. Population figures"
"for 2010 are April 1 Census counts. The population figures for years 2001 - 2009, are bridged-race estimates of the July 1"
"resident population, from the revised intercensal county-level 2000 - 2009 series released by NCHS on October 26, 2012."
"Population figures for 2000 are April 1 Census counts. Population figures for 1999 are from the 1990-1999 intercensal series of"
"July 1 estimates. Population figures for Infant Age Groups are the number of live births. <br/><b>Note:</b> Rates and population"
"figures for years 2001 - 2009 differ slightly from previously published reports, due to use of the population estimates which"
"were available at the time of release."
"7. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the"
"resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/mcd.html#Age Group."
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"Notes","Year","Year Code","Sex","Sex Code",Deaths,Population,Crude Rate,Age Adjusted Rate,Age Adjusted Rate Standard Error
,"2018","2018","Female","F",1380736,166038755,831.6,611.3,0.5
,"2018","2018","Male","M",1458469,161128679,905.2,855.5,0.7
,"2019","2019","Female","F",1381015,166582199,829.0,602.7,0.5
,"2019","2019","Male","M",1473823,161657324,911.7,846.7,0.7
,"2020","2020","Female","F",1613845,167227921,965.1,695.1,0.6
,"2020","2020","Male","M",1769884,162256202,1090.8,998.3,0.8
,"2021","2021","Female","F",1626123,167509003,970.8,733.3,0.6
,"2021","2021","Male","M",1838108,164384742,1118.2,1048.0,0.8
,"2022","2022","Female","F",1560607,168004004,928.9,666.1,0.5
,"2022","2022","Male","M",1719250,165283553,1040.2,954.5,0.7
,"2023 ","2023","Female","F",1473879,169165495,871.3,632.9,0.5
,"2023 ","2023","Male","M",1617085,165749400,975.6,884.3,0.7
"---"
"Dataset: Underlying Cause of Death, 2018-2023, Single Race"
"Query Parameters:"
"Title: US_Age_Adjusted_2018-2013"
"Group By: Year; Sex"
"Show Totals: False"
"Show Zero Values: True"
"Show Suppressed: True"
"Standard Population: 2000 U.S. Std. Population"
"Calculate Rates Per: 100,000"
"Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)"
"---"
"Help: See http://wonder.cdc.gov/wonder/help/ucd-expanded.html for more information."
"---"
"Query Date: Nov 19, 2025 11:40:01 PM"
"---"
"Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics"
"System, Mortality 2018-2023 on CDC WONDER Online Database, released in 2024. Data are from the Multiple Cause of Death Files,"
"2018-2023, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative"
"Program. Accessed at http://wonder.cdc.gov/ucd-icd10-expanded.html on Nov 19, 2025 11:40:01 PM"
"---"
Caveats:
"1. The populations used to calculate standard age-adjusted rates are documented here: More information:"
"http://wonder.cdc.gov/wonder/help/ucd-expanded.html#2000 Standard Population."
"2. The method used to calculate age-adjusted rates is documented here: More information:"
"http://wonder.cdc.gov/wonder/help/ucd-expanded.html#Age-Adjusted Rates."
"3. Deaths for persons of unknown age are included in counts and crude rates, but are not included in age-adjusted rates."
"4. The method used to calculate standard errors is documented here: More information:"
"http://wonder.cdc.gov/wonder/help/ucd-expanded.html#Standard-Errors."
"5. The population figures for years 2023 are single-race estimates of the July 1 resident population, from the Vintage 2023"
"postcensal series released by the Census Bureau on June 27, 2024. The 2023 series is based on the Modified Blended Base produced"
"by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Modified Blended Base consists of the blend"
"of Vintage 2020 postcensal population estimates for April 1, 2020, 2020 Demographic Analysis Estimates, and 2020 Census data"
"from the internal Census Edited File (CEF). The population figures for years 2022 are single-race estimates of the July 1"
"resident population, from the Vintage 2022 postcensal series released by the Census Bureau on June 22, 2023. The 2022 series is"
"based on the Modified Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The"
"Modified Blended Base consists of the blend of Vintage 2020 postcensal population estimates for April 1, 2020, 2020 Demographic"
"Analysis Estimates, and 2020 Census data from the internal Census Edited File (CEF). The population figures for years 2021 are"
"single-race estimates of the July 1 resident population, based on the Blended Base produced by the US Census Bureau in lieu of"
"the April 1, 2020 decennial population count, from the Vintage 2021 postcensal series released by the Census Bureau on June 30,"
"2022. The population figures for year 2020 are single-race estimates of the July 1 resident population, from the Vintage 2020"
"postcensal series based on April 2010 Census, released by the Census Bureau on July 27, 2021. The population figures for year"
"2019 are single-race estimates of the July 1 resident population, from the Vintage 2019 postcensal series based on April 2010"
"Census, released by the Census Bureau on June 25, 2020. The population figures for year 2018 are single-race estimates of the"
"July 1 resident population, from the Vintage 2018 postcensal series based on April 2010 Census, released by the Census Bureau on"
"June 20, 2019. More information: http://wonder.cdc.gov/wonder/help/ucd-expanded.html#Population Data."
"6. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the"
"resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/ucd-expanded.html#Age"
"Group."
"7. Connecticut population estimates for 2022 and later years are reported for 9 planning regions as county equivalent areas,"
"instead of the former 8 legacy counties in the Vintage 2022 postcensal series released by the Census Bureau on June 22, 2023,"
"and in the Vintage 2023 postcensal series released by the Census Bureau on June 27, 2024. Populations estimates for the former"
"counties are not available for 2022 and later years. More information:"
"http://wonder.cdc.gov/wonder/help/ucd-expanded.html#Connecticut-2022."
"8. After the creation of the final 2023 dataset, North Carolina updated the cause of death information for over 900 death"
"certificates to include a cause of death code indicating drug overdose (ICD-10 underlying cause-of-death codes: X40-X44,"
"X60-X64, X85, and Y10-Y14). Jurisdictions can continue to update death certificates after the closing of the mortality file. As"
"a result, users should consider that the actual death count for drug overdose deaths for North Carolina in 2023 is over 4,400"
"deaths, with a crude rate of approximately 41.0 per 100,000 population, and an age-adjusted rate of approximately 42.1 per"
"100,000 population. These deaths will not be updated on the final mortality datasets."
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Data/Raw_Data/Mortality_Rates_New/US_Single_Age_1999-2020.csv Normal file
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Data/Raw_Data/Mortality_Rates_New/Wyoming_Age_Adjusted_1999-2020.csv Normal file
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"Notes","Year","Year Code","Sex","Sex Code",Deaths,Population,Crude Rate,Crude Rate Standard Error,Age Adjusted Rate,Age Adjusted Rate Standard Error
,"1999","1999","Female","F",1974,244518,807.3,18.2,751.9,17.0
,"1999","1999","Male","M",2068,247262,836.4,18.4,1083.6,24.7
,"2000","2000","Female","F",1945,245408,792.6,18.0,730.9,16.7
,"2000","2000","Male","M",1975,248374,795.2,17.9,1010.7,23.5
,"2001","2001","Female","F",1971,245327,803.4,18.1,728.3,16.5
,"2001","2001","Male","M",2058,249330,825.4,18.2,1022.1,23.3
,"2002","2002","Female","F",2040,247785,823.3,18.2,748.1,16.6
,"2002","2002","Male","M",2134,252232,846.0,18.3,1045.2,23.4
,"2003","2003","Female","F",2027,249128,813.6,18.1,730.8,16.3
,"2003","2003","Male","M",2145,254325,843.4,18.2,1009.5,22.6
,"2004","2004","Female","F",1978,251506,786.5,17.7,703.8,15.9
,"2004","2004","Male","M",1977,257600,767.5,17.3,896.1,20.8
,"2005","2005","Female","F",1963,253726,773.7,17.5,686.6,15.6
,"2005","2005","Male","M",2136,260431,820.2,17.7,948.7,21.2
,"2006","2006","Female","F",2072,257361,805.1,17.7,708.5,15.7
,"2006","2006","Male","M",2239,265306,843.9,17.8,974.0,21.2
,"2007","2007","Female","F",2054,262776,781.7,17.2,695.2,15.5
,"2007","2007","Male","M",2212,272100,812.9,17.3,928.6,20.3
,"2008","2008","Female","F",2059,267982,768.3,16.9,684.0,15.2
,"2008","2008","Male","M",2168,278061,779.7,16.7,885.9,19.6
,"2009","2009","Female","F",2021,274046,737.5,16.4,656.0,14.8
,"2009","2009","Male","M",2262,285805,791.4,16.6,901.5,19.5
,"2010","2010","Female","F",2185,276189,791.1,16.9,696.4,15.1
,"2010","2010","Male","M",2253,287437,783.8,16.5,865.4,18.8
,"2011","2011","Female","F",2058,278401,739.2,16.3,642.3,14.4
,"2011","2011","Male","M",2329,289757,803.8,16.7,881.8,18.8
,"2012","2012","Female","F",2119,282131,751.1,16.3,646.6,14.3
,"2012","2012","Male","M",2362,294281,802.6,16.5,858.3,18.2
,"2013","2013","Female","F",2102,285380,736.6,16.1,620.4,13.8
,"2013","2013","Male","M",2414,297278,812.0,16.5,858.3,17.9
,"2014","2014","Female","F",2192,286111,766.1,16.4,639.2,14.0
,"2014","2014","Male","M",2474,298042,830.1,16.7,852.0,17.6
,"2015","2015","Female","F",2228,287206,775.7,16.4,639.6,13.9
,"2015","2015","Male","M",2550,298901,853.1,16.9,863.5,17.6
,"2016","2016","Female","F",2208,286559,770.5,16.4,620.5,13.6
,"2016","2016","Male","M",2514,298942,841.0,16.8,833.2,17.1
,"2017","2017","Female","F",2229,283877,785.2,16.6,616.1,13.4
,"2017","2017","Male","M",2539,295438,859.4,17.1,824.1,16.9
,"2018","2018","Female","F",2394,283203,845.3,17.3,651.5,13.7
,"2018","2018","Male","M",2676,294534,908.6,17.6,855.8,17.0
,"2019","2019","Female","F",2323,284029,817.9,17.0,608.1,13.0
,"2019","2019","Male","M",2798,294730,949.3,17.9,881.0,17.1
,"2020","2020","Female","F",2725,285437,954.7,18.3,712.1,14.0
,"2020","2020","Male","M",3258,296891,1097.4,19.2,980.4,17.7
"---"
"Dataset: Multiple Cause of Death, 1999-2020"
"Query Parameters:"
"Title: Wyoming_Age_Adjusted_1999-2020"
"States: Wyoming (56)"
"Group By: Year; Sex"
"Show Totals: False"
"Show Zero Values: True"
"Show Suppressed: True"
"Standard Population: 2000 U.S. Std. Population"
"Calculate Rates Per: 100,000"
"Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)"
"---"
"Help: See http://wonder.cdc.gov/wonder/help/mcd.html for more information."
"---"
"Query Date: Nov 19, 2025 11:54:08 PM"
"---"
"Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics"
"System, Mortality 1999-2020 on CDC WONDER Online Database, released in 2021. Data are from the Multiple Cause of Death Files,"
"1999-2020, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative"
"Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Nov 19, 2025 11:54:08 PM"
"---"
Caveats:
"1. As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#2014-Revision."
"2. The populations used to calculate standard age-adjusted rates are documented here: More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#2000 Standard Population."
"3. The method used to calculate age-adjusted rates is documented here: More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#Age-Adjusted Rates."
"4. Deaths for persons of unknown age are included in counts and crude rates, but are not included in age-adjusted rates."
"5. The method used to calculate standard errors is documented here: More information:"
"http://wonder.cdc.gov/wonder/help/mcd.html#Standard-Errors."
"6. The population figures for year 2020 are bridged-race estimates of the July 1 resident population, from the Vintage 2020"
"postcensal series released by NCHS on September 22, 2021. The population figures for year 2019 are bridged-race estimates of the"
"July 1 resident population, from the Vintage 2019 postcensal series released by NCHS on July 9, 2020. The population figures for"
"year 2018 are bridged-race estimates of the July 1 resident population, from the Vintage 2018 postcensal series released by NCHS"
"on June 25, 2019. The population figures for year 2017 are bridged-race estimates of the July 1 resident population, from the"
"Vintage 2017 postcensal series released by NCHS on June 27, 2018. The population figures for year 2016 are bridged-race"
"estimates of the July 1 resident population, from the Vintage 2016 postcensal series released by NCHS on June 26, 2017. The"
"population figures for year 2015 are bridged-race estimates of the July 1 resident population, from the Vintage 2015 postcensal"
"series released by NCHS on June 28, 2016. The population figures for year 2014 are bridged-race estimates of the July 1 resident"
"population, from the Vintage 2014 postcensal series released by NCHS on June 30, 2015. The population figures for year 2013 are"
"bridged-race estimates of the July 1 resident population, from the Vintage 2013 postcensal series released by NCHS on June 26,"
"2014. The population figures for year 2012 are bridged-race estimates of the July 1 resident population, from the Vintage 2012"
"postcensal series released by NCHS on June 13, 2013. Population figures for 2011 are bridged-race estimates of the July 1"
"resident population, from the county-level postcensal Vintage 2011 series released by NCHS on July 18, 2012. Population figures"
"for 2010 are April 1 Census counts. The population figures for years 2001 - 2009, are bridged-race estimates of the July 1"
"resident population, from the revised intercensal county-level 2000 - 2009 series released by NCHS on October 26, 2012."
"Population figures for 2000 are April 1 Census counts. Population figures for 1999 are from the 1990-1999 intercensal series of"
"July 1 estimates. Population figures for Infant Age Groups are the number of live births. <br/><b>Note:</b> Rates and population"
"figures for years 2001 - 2009 differ slightly from previously published reports, due to use of the population estimates which"
"were available at the time of release."
"7. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the"
"resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/mcd.html#Age Group."
Can't render this file because it has a wrong number of fields in line 46.

69
Data/Raw_Data/Mortality_Rates_New/Wyoming_Age_Adjusted_2018-2023.csv Normal file
View File

@ -0,0 +1,69 @@
"Notes","Year","Year Code","Sex","Sex Code",Deaths,Population,Crude Rate,Age Adjusted Rate,Age Adjusted Rate Standard Error
,"2018","2018","Female","F",2394,283203,845.3,651.5,13.7
,"2018","2018","Male","M",2676,294534,908.6,855.8,17.0
,"2019","2019","Female","F",2323,284029,817.9,608.1,13.0
,"2019","2019","Male","M",2798,294730,949.3,881.0,17.1
,"2020","2020","Female","F",2725,285437,954.7,712.1,14.0
,"2020","2020","Male","M",3258,296891,1097.4,980.4,17.7
,"2021","2021","Female","F",2981,282589,1054.9,811.3,15.3
,"2021","2021","Male","M",3601,296214,1215.7,1098.0,19.0
,"2022","2022","Female","F",2707,283427,955.1,703.7,13.9
,"2022","2022","Male","M",3198,297954,1073.3,957.7,17.6
,"2023 ","2023","Female","F",2620,285032,919.2,680.1,13.6
,"2023 ","2023","Male","M",2966,299025,991.9,859.1,16.3
"---"
"Dataset: Underlying Cause of Death, 2018-2023, Single Race"
"Query Parameters:"
"Title: Wyoming_Age_Adjusted_2018-2013"
"States: Wyoming (56)"
"Group By: Year; Sex"
"Show Totals: False"
"Show Zero Values: True"
"Show Suppressed: True"
"Standard Population: 2000 U.S. Std. Population"
"Calculate Rates Per: 100,000"
"Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)"
"---"
"Help: See http://wonder.cdc.gov/wonder/help/ucd-expanded.html for more information."
"---"
"Query Date: Nov 19, 2025 11:42:49 PM"
"---"
"Suggested Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics"
"System, Mortality 2018-2023 on CDC WONDER Online Database, released in 2024. Data are from the Multiple Cause of Death Files,"
"2018-2023, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative"
"Program. Accessed at http://wonder.cdc.gov/ucd-icd10-expanded.html on Nov 19, 2025 11:42:49 PM"
"---"
Caveats:
"1. The populations used to calculate standard age-adjusted rates are documented here: More information:"
"http://wonder.cdc.gov/wonder/help/ucd-expanded.html#2000 Standard Population."
"2. The method used to calculate age-adjusted rates is documented here: More information:"
"http://wonder.cdc.gov/wonder/help/ucd-expanded.html#Age-Adjusted Rates."
"3. Deaths for persons of unknown age are included in counts and crude rates, but are not included in age-adjusted rates."
"4. The method used to calculate standard errors is documented here: More information:"
"http://wonder.cdc.gov/wonder/help/ucd-expanded.html#Standard-Errors."
"5. The population figures for years 2023 are single-race estimates of the July 1 resident population, from the Vintage 2023"
"postcensal series released by the Census Bureau on June 27, 2024. The 2023 series is based on the Modified Blended Base produced"
"by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Modified Blended Base consists of the blend"
"of Vintage 2020 postcensal population estimates for April 1, 2020, 2020 Demographic Analysis Estimates, and 2020 Census data"
"from the internal Census Edited File (CEF). The population figures for years 2022 are single-race estimates of the July 1"
"resident population, from the Vintage 2022 postcensal series released by the Census Bureau on June 22, 2023. The 2022 series is"
"based on the Modified Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The"
"Modified Blended Base consists of the blend of Vintage 2020 postcensal population estimates for April 1, 2020, 2020 Demographic"
"Analysis Estimates, and 2020 Census data from the internal Census Edited File (CEF). The population figures for years 2021 are"
"single-race estimates of the July 1 resident population, based on the Blended Base produced by the US Census Bureau in lieu of"
"the April 1, 2020 decennial population count, from the Vintage 2021 postcensal series released by the Census Bureau on June 30,"
"2022. The population figures for year 2020 are single-race estimates of the July 1 resident population, from the Vintage 2020"
"postcensal series based on April 2010 Census, released by the Census Bureau on July 27, 2021. The population figures for year"
"2019 are single-race estimates of the July 1 resident population, from the Vintage 2019 postcensal series based on April 2010"
"Census, released by the Census Bureau on June 25, 2020. The population figures for year 2018 are single-race estimates of the"
"July 1 resident population, from the Vintage 2018 postcensal series based on April 2010 Census, released by the Census Bureau on"
"June 20, 2019. More information: http://wonder.cdc.gov/wonder/help/ucd-expanded.html#Population Data."
"6. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the"
"resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/ucd-expanded.html#Age"
"Group."
"7. After the creation of the final 2023 dataset, North Carolina updated the cause of death information for over 900 death"
"certificates to include a cause of death code indicating drug overdose (ICD-10 underlying cause-of-death codes: X40-X44,"
"X60-X64, X85, and Y10-Y14). Jurisdictions can continue to update death certificates after the closing of the mortality file. As"
"a result, users should consider that the actual death count for drug overdose deaths for North Carolina in 2023 is over 4,400"
"deaths, with a crude rate of approximately 41.0 per 100,000 population, and an age-adjusted rate of approximately 42.1 per"
"100,000 population. These deaths will not be updated on the final mortality datasets."
Can't render this file because it has a wrong number of fields in line 14.

32
Mortality_Rate_Analysis.r Normal file
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@ -0,0 +1,32 @@
library(tidyverse)
LIN_1999 <- read_csv("Data/Raw_Data/Mortality_Rates_New/Lincoln_Age_Adjusted_1999-2020.csv") %>% select(Year,Sex,Mort_Rate=`Age Adjusted Rate`)%>% mutate(Region='Lincoln')
WY_1999 <- read_csv("Data/Raw_Data/Mortality_Rates_New/Wyoming_Age_Adjusted_1999-2020.csv") %>% select(Year,Sex,Mort_Rate=`Age Adjusted Rate`)%>% mutate(Region='Wyoming') %>% filter(Year<2018)
US_1999 <- read_csv("Data/Raw_Data/Mortality_Rates_New/US_Age_Adjusted_1999-2020.csv")%>% select(Year,Sex,Mort_Rate=`Age Adjusted Rate`) %>% filter(!is.na(Sex),!is.na(Year)) %>% mutate(Region="US")%>% filter(Year<2018)
WY_2018 <- read_csv("Data/Raw_Data/Mortality_Rates_New/Wyoming_Age_Adjusted_2018-2023.csv") %>% select(Year,Sex,Mort_Rate=`Age Adjusted Rate`) %>% mutate(Region='Wyoming')
US_2018 <- read_csv("Data/Raw_Data/Mortality_Rates_New/US_Age_Adjusted_2018-2023.csv")%>% select(Year,Sex,Mort_Rate=`Age Adjusted Rate`) %>% mutate(Region="US")
##No adjustment for later data allowed
LIN_2018<- read_csv("Data/Raw_Data/Mortality_Rates_New/Lincoln_Not_Age_Adjusted_2018-2023.csv") %>% select(Year,Sex,Mort_Rate=`Crude Rate`)%>% mutate(Region='Lincoln')
ADJUST_TERM <- LIN_2018 %>% rename(UNADJUSTED=Mort_Rate) %>% inner_join(LIN_1999) %>% filter(!is.na(Year)) %>% mutate(Ratio=Mort_Rate/UNADJUSTED) %>% group_by(Sex) %>% summarize(Ratio=mean(Ratio))
LIN_2018 <- LIN_2018 %>% filter(Year>2020) %>% left_join(ADJUST_TERM) %>% mutate(Mort_Rate=Mort_Rate*Ratio) %>% select(-Ratio)
DF <- rbind(LIN_1999,LIN_2018,WY_1999,US_1999,US_2018,WY_2018) %>% filter(!is.na(Year),!is.na(Sex))
ggplot(DF,aes(x=Year,y=Mort_Rate,group=Region,color=Region,fill=Region))+geom_point()+geom_smooth(method="lm")+ facet_grid(. ~ Sex)
ggplot(DF,aes(x=Year,y=Mort_Rate,group=Region,color=Region,fill=Region))+geom_point()+geom_smooth()+ facet_grid(. ~ Sex)
ggplot(DF,aes(x=Year,y=Mort_Rate,group=Region,color=Region,fill=Region))+geom_point()+ facet_grid(. ~ Sex)
ggplot(DF,aes(x=Year,y=Mort_Rate,group=Region,color=Region,fill=Region))+geom_point()+geom_smooth()
REG_DATA <- DF %>% pivot_wider(values_from=Mort_Rate,names_from=Region)
library(fixest)
REG_DATA
feols(log(Lincoln)~log(US)+Sex+Year,REG_DATA)
feols((Lincoln)~Sex+Year,REG_DATA)
%>% select(Year,Sex,County,Mort_Rate=`Age Adjusted Rate`) %>% filter(!is.na(County))

25
Scripts/2D_Overall_Migration_ARIMA.r
View File

@ -12,7 +12,7 @@ POP_OTHER_DATA <- readRDS("Data/Cleaned_Data/Population_Data/RDS/Other_Lincoln_P
TS_DATA <- POP_DATA %>% mutate(In_Migration=ifelse(Migration>0,1,0)) %>% group_by(County) %>% arrange(County,Year) %>% mutate(Prev_Pop=lag(Population)) %>% ungroup TS_DATA <- POP_DATA %>% mutate(In_Migration=ifelse(Migration>0,1,0)) %>% group_by(County) %>% arrange(County,Year) %>% mutate(Prev_Pop=lag(Population)) %>% ungroup
TS_DATA_TEST <- POP_DATA_TEST %>% mutate(In_Migration=ifelse(Migration>0,1,0)) %>% group_by(County) %>% arrange(County,Year) %>% mutate(Prev_Pop=lag(Population)) %>% ungroup TS_DATA <- POP_DATA %>% mutate(In_Migration=ifelse(Migration>0,1,0)) %>% group_by(County) %>% arrange(County,Year) %>% mutate(Prev_Pop=lag(Population)) %>% ungroup
TS_KEM_DATA <- POP_KEM_DATA %>% mutate(In_Migration=ifelse(Migration>0,1,0)) %>% group_by(County) %>% arrange(County,Year) %>% mutate(Prev_Pop=lag(Population)) %>% ungroup TS_KEM_DATA <- POP_KEM_DATA %>% mutate(In_Migration=ifelse(Migration>0,1,0)) %>% group_by(County) %>% arrange(County,Year) %>% mutate(Prev_Pop=lag(Population)) %>% ungroup
TS_OTHER_DATA <- POP_OTHER_DATA %>% mutate(In_Migration=ifelse(Migration>0,1,0)) %>% group_by(County) %>% arrange(County,Year) %>% mutate(Prev_Pop=lag(Population)) %>% ungroup TS_OTHER_DATA <- POP_OTHER_DATA %>% mutate(In_Migration=ifelse(Migration>0,1,0)) %>% group_by(County) %>% arrange(County,Year) %>% mutate(Prev_Pop=lag(Population)) %>% ungroup
@ -28,13 +28,10 @@ ST_YEAR_OTHER <- min(pull(TS_OTHER_DATA %>% filter(!is.na(Migration)),Year))
END_YEAR_OTHER <- max(pull(TS_OTHER_DATA %>% filter(!is.na(Migration)),Year)) END_YEAR_OTHER <- max(pull(TS_OTHER_DATA %>% filter(!is.na(Migration)),Year))
TS_WIDE <- TS_DATA %>% dplyr::select(Year,County,Migration) %>% pivot_wider(values_from=Migration,names_from=County) %>% arrange(Year) %>% filter(Year>ST_YEAR+1,Year<=END_YEAR) %>%ts(start=c(ST_YEAR+1),frequency=1)
TS_KEM_WIDE <- TS_KEM_DATA %>% dplyr::select(Year,County,Migration) %>% pivot_wider(values_from=Migration,names_from=County) %>% arrange(Year) %>% filter(Year>ST_YEAR+1,Year<=END_YEAR) %>%ts(start=c(ST_YEAR+1),frequency=1)
TS_OTHER_WIDE <- TS_OTHER_DATA %>% dplyr::select(Year,County,Migration) %>% pivot_wider(values_from=Migration,names_from=County) %>% arrange(Year) %>% filter(Year>ST_YEAR+1,Year<=END_YEAR) %>%ts(start=c(ST_YEAR+1),frequency=1)
LN <- TS_DATA %>% dplyr::select(Year,County,Migration) %>% pivot_wider(values_from=Migration,names_from=County) %>% arrange(Year) %>% dplyr::select(Lincoln,Year) %>% filter(Year>=ST_YEAR,Year<=END_YEAR) %>% dplyr::select(-Year) %>%ts(start=c(ST_YEAR),frequency=1) LN <- TS_DATA %>% dplyr::select(Year,County,Migration) %>% pivot_wider(values_from=Migration,names_from=County) %>% arrange(Year) %>% dplyr::select(Lincoln,Year) %>% filter(Year>=ST_YEAR,Year<=END_YEAR) %>% dplyr::select(-Year) %>%ts(start=c(ST_YEAR),frequency=1)
LN_2016 <- TS_DATA %>% dplyr::select(Year,County,Migration) %>% pivot_wider(values_from=Migration,names_from=County) %>% arrange(Year) %>% dplyr::select(Lincoln,Year) %>% filter(Year>=ST_YEAR,Year<=2016) %>% dplyr::select(-Year) %>%ts(start=c(ST_YEAR),frequency=1)
LN_1985 <- TS_DATA %>% dplyr::select(Year,County,Migration) %>% pivot_wider(values_from=Migration,names_from=County) %>% arrange(Year) %>% dplyr::select(Lincoln,Year) %>% filter(Year>=ST_YEAR,Year<=1985) %>% dplyr::select(-Year) %>%ts(start=c(ST_YEAR),frequency=1)
KEM <- TS_KEM_DATA %>% dplyr::select(Year,Region,Migration) %>% pivot_wider(values_from=Migration,names_from=Region) %>% arrange(Year) %>% dplyr::select('Kemmerer & Diamondville',Year) %>% filter(Year>=ST_YEAR_KEM,Year<=END_YEAR_KEM) %>% dplyr::select(-Year) %>%ts(start=c(ST_YEAR_KEM),frequency=1) KEM <- TS_KEM_DATA %>% dplyr::select(Year,Region,Migration) %>% pivot_wider(values_from=Migration,names_from=Region) %>% arrange(Year) %>% dplyr::select('Kemmerer & Diamondville',Year) %>% filter(Year>=ST_YEAR_KEM,Year<=END_YEAR_KEM) %>% dplyr::select(-Year) %>%ts(start=c(ST_YEAR_KEM),frequency=1)
OTHER <- TS_OTHER_DATA %>% dplyr::select(Year,Region,Migration) %>% pivot_wider(values_from=Migration,names_from=Region) %>% arrange(Year) %>% dplyr::select('Lincoln Other'=Lincoln_Other,Year) %>% filter(Year>=ST_YEAR_OTHER,Year<=END_YEAR_OTHER) %>% dplyr::select(-Year) %>%ts(start=c(ST_YEAR_OTHER),frequency=1) OTHER <- TS_OTHER_DATA %>% dplyr::select(Year,Region,Migration) %>% pivot_wider(values_from=Migration,names_from=Region) %>% arrange(Year) %>% dplyr::select('Lincoln Other'=Lincoln_Other,Year) %>% filter(Year>=ST_YEAR_OTHER,Year<=END_YEAR_OTHER) %>% dplyr::select(-Year) %>%ts(start=c(ST_YEAR_OTHER),frequency=1)
@ -45,6 +42,8 @@ OTHER <- TS_OTHER_DATA %>% dplyr::select(Year,Region,Migration) %>% pivot_wider
#adf.test(LN,k=1) #Stationary with one lag, otherwise not stationary #adf.test(LN,k=1) #Stationary with one lag, otherwise not stationary
#kpss.test(LN) #Stationary,default of program and has some model fit improvements #kpss.test(LN) #Stationary,default of program and has some model fit improvements
MOD <- auto.arima(LN) MOD <- auto.arima(LN)
MOD_2016 <- auto.arima(LN_2016)
MOD_1985 <- auto.arima(LN_1985)
MOD_KEM <- auto.arima(KEM) MOD_KEM <- auto.arima(KEM)
MOD_OTHER <- auto.arima(OTHER) MOD_OTHER <- auto.arima(OTHER)
@ -54,10 +53,18 @@ LN_ADJ_OTHER <- 1-1/LN_ADJ_KEM
#Downshift the sub-region data #Downshift the sub-region data
KEM_NEW <- LN/LN_ADJ_KEM KEM_NEW <- LN/LN_ADJ_KEM
KEM_2016_NEW <- LN_2016/LN_ADJ_KEM
KEM_1985_NEW <- LN_1985/LN_ADJ_KEM
OTHER_NEW <- LN/LN_ADJ_OTHER OTHER_NEW <- LN/LN_ADJ_OTHER
OTHER_2016_NEW <- LN_2016/LN_ADJ_OTHER
OTHER_1985_NEW <- LN_1985/LN_ADJ_OTHER
#Create new models for migration forecasts #Create new models for migration forecasts
MOD_KEM_ADJ <- auto.arima(KEM_NEW ,stationary=TRUE) MOD_KEM_ADJ <- auto.arima(KEM_NEW ,stationary=TRUE)
MOD_KEM_ADJ_2016 <- auto.arima(KEM_2016_NEW ,stationary=TRUE)
MOD_KEM_ADJ_1985 <- auto.arima(KEM_1985_NEW ,stationary=TRUE)
MOD_OTHER_ADJ <- auto.arima(OTHER_NEW,stationary=TRUE) MOD_OTHER_ADJ <- auto.arima(OTHER_NEW,stationary=TRUE)
MOD_OTHER_ADJ_2016 <- auto.arima(OTHER_2016_NEW,stationary=TRUE)
MOD_OTHER_ADJ_1985 <- auto.arima(OTHER_1985_NEW,stationary=TRUE)
#Save the models for use in the Monte Carlo simulation #Save the models for use in the Monte Carlo simulation
if(!exists("SAVE_LOC_ARIMA_MODELS")){SAVE_LOC_ARIMA_MODELS <-"./Data/Intermediate_Inputs/Migration_ARIMA_Models/"} if(!exists("SAVE_LOC_ARIMA_MODELS")){SAVE_LOC_ARIMA_MODELS <-"./Data/Intermediate_Inputs/Migration_ARIMA_Models/"}
dir.create(SAVE_LOC_ARIMA_MODELS, recursive = TRUE, showWarnings = FALSE) dir.create(SAVE_LOC_ARIMA_MODELS, recursive = TRUE, showWarnings = FALSE)
@ -65,7 +72,13 @@ saveRDS(MOD,paste0(SAVE_LOC_ARIMA_MODELS,"Full_Lincoln_County_Net_Migration_ARIM
saveRDS(MOD_KEM_ADJ,paste0(SAVE_LOC_ARIMA_MODELS,"Kemmerer_Diamondville_Net_Migration_ARIMA.Rds")) saveRDS(MOD_KEM_ADJ,paste0(SAVE_LOC_ARIMA_MODELS,"Kemmerer_Diamondville_Net_Migration_ARIMA.Rds"))
saveRDS(MOD_OTHER_ADJ,paste0(SAVE_LOC_ARIMA_MODELS,"Other_Lincoln_Net_Migration_ARIMA.Rds")) saveRDS(MOD_OTHER_ADJ,paste0(SAVE_LOC_ARIMA_MODELS,"Other_Lincoln_Net_Migration_ARIMA.Rds"))
saveRDS(MOD_2016,paste0(SAVE_LOC_ARIMA_MODELS,"Full_Lincoln_County_Net_Migration_ARIMA_2016.Rds"))
saveRDS(MOD_KEM_ADJ_2016,paste0(SAVE_LOC_ARIMA_MODELS,"Kemmerer_Diamondville_Net_Migration_ARIMA_2016.Rds"))
saveRDS(MOD_OTHER_ADJ_2016,paste0(SAVE_LOC_ARIMA_MODELS,"Other_Lincoln_Net_Migration_ARIMA_2016.Rds"))
saveRDS(MOD_1985,paste0(SAVE_LOC_ARIMA_MODELS,"Full_Lincoln_County_Net_Migration_ARIMA_1985.Rds"))
saveRDS(MOD_KEM_ADJ_1985,paste0(SAVE_LOC_ARIMA_MODELS,"Kemmerer_Diamondville_Net_Migration_ARIMA_1985.Rds"))
saveRDS(MOD_OTHER_ADJ_1985,paste0(SAVE_LOC_ARIMA_MODELS,"Other_Lincoln_Net_Migration_ARIMA_1985.Rds"))
##Save model summary results and create useful figures ##Save model summary results and create useful figures
if(!exists("SAVE_LOC_ARIMA_FIGURES")){SAVE_LOC_ARIMA_FIGURES <-"./Results/Migration_ARIMA/"} if(!exists("SAVE_LOC_ARIMA_FIGURES")){SAVE_LOC_ARIMA_FIGURES <-"./Results/Migration_ARIMA/"}

52
Scripts/2E_Move_Current_Demographic_Data_to_Current_Year.r
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@ -10,9 +10,36 @@ MORT <- readRDS("Data/Cleaned_Data/Mortality_Rate_Data/RDS/Lincoln_County_Mortal
source("Scripts/Load_Custom_Functions/Migration_Simulation_Functions.r") source("Scripts/Load_Custom_Functions/Migration_Simulation_Functions.r")
KEM_DEMOGRAPHIC["84",] <- colSums(KEM_DEMOGRAPHIC[c("84","85"),]) KEM_DEMOGRAPHIC["84",] <- colSums(KEM_DEMOGRAPHIC[c("84","85"),])
KEM_DEMOGRAPHIC <- KEM_DEMOGRAPHIC[1:85,] KEM_DEMOGRAPHIC <- KEM_DEMOGRAPHIC[1:85,]
#Manually add estimated new births 31 total, small assumption made that 15 are boys and 16 are girls. #######################Births
KEM_DEMOGRAPHIC <- rbind(c(15,15),KEM_DEMOGRAPHIC ) BIRTHS <- POPULATION %>% filter(Year==2023) %>% pull(Births)
if(floor(BIRTHS/2)==round(BIRTHS/2)){KEM_DEMOGRAPHIC <- rbind(c(BIRTHS/2,BIRTHS/2),KEM_DEMOGRAPHIC )}else { KEM_DEMOGRAPHIC <- rbind(c(floor(BIRTHS/2),round(BIRTHS/2)),KEM_DEMOGRAPHIC )}
rownames(KEM_DEMOGRAPHIC) <- 0:85 rownames(KEM_DEMOGRAPHIC) <- 0:85
############Apply a migration out of Kemmerer
source("Scripts/Load_Custom_Functions/Migration_Simulation_Functions.r")
#Run many simulations to pick a plausible distribution of people leaving
Migration <- POPULATION %>% filter(Year==2023) %>% pull(Migration)
NUM_RUNS <- 10^5
#In Parallel run the migration simulation many times, to find a distribution of migration given 2023 data.
#Find the average number of migrants leaving by age-sex using the Reduce function to collapse the list, and then dividing by number of runs selected.
RES <- Reduce('+',mclapply(1:NUM_RUNS,function(x){return(KEM_DEMOGRAPHIC-DEMOGRAPHICS_AFTER_MIGRATION(KEM_DEMOGRAPHIC,Migration,ODDS_LEAVE)) },mc.cores = detectCores()-1))/NUM_RUNS
#Add a factor such that the sum of all migration adds up to the desired 33. The average found should sum to 33, but when rounding is zero since each sub category sex-age is less than 50% chance of a migration
BEST_FACTOR <- NA
DIFF <- Inf
for(FACTOR in seq(0,1,by=0.0001)){
C_DIFF <- abs(sum(round(RES+FACTOR))+Migration)
if(C_DIFF==0){
BEST_FACTOR <- FACTOR
break}
if(C_DIFF<DIFF){
BEST_FACTOR <- FACTOR
DIFF<-C_DIFF}
}
sum(round(RES+BEST_FACTOR))
KEM_DEMOGRAPHIC <- KEM_DEMOGRAPHIC-round(RES+BEST_FACTOR)
#######################Apply Deaths
INDEX <- as.numeric(rownames(KEM_DEMOGRAPHIC)) INDEX <- as.numeric(rownames(KEM_DEMOGRAPHIC))
for(i in INDEX){ for(i in INDEX){
@ -26,24 +53,13 @@ DEATH_MAT <- round(KEM_DEMOGRAPHIC*RES+0.205) #Manually Manipulated factor to ma
DEATH_MAT[67:69,"Num_Female"] <- 0 DEATH_MAT[67:69,"Num_Female"] <- 0
sum(DEATH_MAT) sum(DEATH_MAT)
KEM_DEMOGRAPHIC <- KEM_DEMOGRAPHIC - DEATH_MAT KEM_DEMOGRAPHIC <- KEM_DEMOGRAPHIC - DEATH_MAT
############Apply a migration out of 33 people in Kemmerer
source("Scripts/Load_Custom_Functions/Migration_Simulation_Functions.r") #KEM_POP <- POPULATION %>% filter(Year==2024) %>% pull(Population)
#Run many simulations to pick a plausible distribution of people leaving #KEM_POP-sum(KEM_DEMOGRAPHIC)
NUM_RUNS <- 10^7 saveRDS(KEM_DEMOGRAPHIC,"Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Kemmerer_Diamondville_Demographics_Matrix.Rds")
#In Parallel run the migration simulation many times, to find a distribution of migration given 2023 data.
#Find the average number of migrants leaving by age-sex using the Reduce function to collapse the list, and then dividing by number of runs selected.
RES <- Reduce('+',mclapply(1:NUM_RUNS,function(x){return(KEM_DEMOGRAPHIC-DEMOGRAPHICS_AFTER_MIGRATION(KEM_DEMOGRAPHIC,-33,ODDS_LEAVE)) },mc.cores = detectCores()-1))/NUM_RUNS
#Add a factor such that the sum of all migration adds up to the desired 33. The average found should sum to 33, but when rounding is zero since each sub category sex-age is less than 50% chance of a migration
FACTOR <- 0.225
33-sum(round(RES+FACTOR))
KEM_DEMOGRAPHIC_NEW <- KEM_DEMOGRAPHIC-round(RES+FACTOR)
saveRDS(KEM_DEMOGRAPHIC_NEW,"Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Kemmerer_Diamondville_Demographics_Matrix.Rds")
TEST <- KEM_DEMOGRAPHIC_NEW -KEM_DEMOGRAPHIC
##Perhaps I can be more clever. I could average the direct simulation estimate as done above, with the Kemmerer values found when estimating the other region, and then subtracting from the Lincoln Total. ##Perhaps I can be more clever. I could average the direct simulation estimate as done above, with the Kemmerer values found when estimating the other region, and then subtracting from the Lincoln Total.
OTHER_DEMOGRAPHIC_NEW <- readRDS("Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Lincoln_County_Demographics_Matrix.Rds")-KEM_DEMOGRAPHIC_NEW OTHER_DEMOGRAPHIC_NEW <- readRDS("Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Lincoln_County_Demographics_Matrix.Rds")-KEM_DEMOGRAPHIC
saveRDS(OTHER_DEMOGRAPHIC_NEW ,"Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Kemmerer_Diamondville_Demographics_Matrix.Rds") saveRDS(OTHER_DEMOGRAPHIC_NEW ,"Data/Intermediate_Inputs/Starting_Demographic_Data_Sets_of_Monte_Carlo/2024_Starting_Kemmerer_Diamondville_Demographics_Matrix.Rds")
sum(KEM_DEMOGRAPHIC_NEW)
POPULATION

9
Scripts/Load_Custom_Functions/Birth_Simulation_Functions.r
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@ -8,8 +8,13 @@ BIRTH_SIM <- function(REG_MODEL,REG_DATA,NUM_SIMS=1){
YEAR <- REG_DATA %>% pull(Year) %>% unique YEAR <- REG_DATA %>% pull(Year) %>% unique
BIRTHS <- round(exp(rnorm(NUM_SIMS,mean=PRED_MEAN,sd=SE_PRED))) BIRTHS <- round(exp(rnorm(NUM_SIMS,mean=PRED_MEAN,sd=SE_PRED)))
MALE <- sapply(1:NUM_SIMS,function(x){ rbinom(1,BIRTHS[x],prob=0.5)}) MALE <- sapply(1:NUM_SIMS,function(x){ rbinom(1,BIRTHS[x],prob=0.5)})
RES <- cbind(rep(YEAR,NUM_SIMS),rep(0,NUM_SIMS),MALE,BIRTHS-MALE) %>% as_tibble RES <- cbind(MALE,BIRTHS-MALE)
colnames(RES) <- c("Year","Age","Num_Male","Num_Female") rownames(RES) <- "0"
colnames(RES) <- c("Num_Male","Num_Female")
#%>% as_tibble
#colnames(RES) <- c("Num_Male","Num_Female")
#"0"
return(RES) return(RES)
} }

9
Scripts/Load_Custom_Functions/Increment_Data_Year.r Normal file
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@ -0,0 +1,9 @@
#A function which takes the current demographics of a region, adds births as age zero, and then shifts all ages up one year.
#Used to account for aging in each round of data
INCREMENT_AGES <- function(DEMOGRAPHIC_DATA,MALE_AND_FEMALE_BIRTHS){
DEMOGRAPHIC_DATA[85,] <- colSums(DEMOGRAPHIC_DATA[85:86,] )
DEMOGRAPHIC_DATA <- DEMOGRAPHIC_DATA[1:85,]
rownames(DEMOGRAPHIC_DATA ) <- 1:85
return(rbind(MALE_AND_FEMALE_BIRTHS,DEMOGRAPHIC_DATA) )
}

2
Scripts/Load_Custom_Functions/Migration_Simulation_Functions.r
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@ -1,5 +1,5 @@
#Script to increment the migration, and death data to start simulation in 2024, projecting 2025 #Script to increment the migration, and death data to start simulation in 2024, projecting 2025
library(tidyverse) #library(tidyverse)
#setwd("../../") #setwd("../../")
#When migrants are leaving the area of study the current distention of ages and sex needs to be accounted for because people cannot leave who do not already live in the area. As a result two functions are made one looks at cases when the net migration is out, and the other looks at net migration in. In the later the identified distention of migrants by age is used with no direct tie to the current demographics #When migrants are leaving the area of study the current distention of ages and sex needs to be accounted for because people cannot leave who do not already live in the area. As a result two functions are made one looks at cases when the net migration is out, and the other looks at net migration in. In the later the identified distention of migrants by age is used with no direct tie to the current demographics