readme.morthz Data Group Name: morthz Description: Age-Specific Mortality Hazard of the Total US Population,from Ages 0-109 Data Source(s): Source: U.S. Decennial Life Tables for 1989-91. Volume 1 Number 1 United States Life Tables from the Centers for Disease Control and Prevention / National Center for Health Statistics. U.S. Department of Health and Human Services. http://www.cdc.gov/nchs/data/lifetables/life89_1_1.pdf Table 1. Life Table for the total population:1989-91, pp. 6-7. Table 2. Life table for males:1989-91, pp. 8-9. Table 3. Life table for females:1989-91, pp. 10-11. Table 4. Life table for the white population:1989-91, pp. 12-13. Table 5. Life table for white males:1989-91, pp. 14-15. Table 6. Life table for white females:1989-91, pp. 16-17. Table 7. Life table for the population other than white:1989-91, pp.18-1 Table 8. Life Table for males other than white:1989-91. pp. 20-21. Table 9. Life Table for females other than white:1989-91. pp. 22-23. Table 10. Life table for the black population:1989-91, PP. 24-25. Table 11. Life table for black males:1989-91, pp. 26-27. Table 12. Life table for black females:1989-91, pp. 28-29. Comments: The annual life tables differ in two main respects from the decennial life tables. The annual tables are based on deaths in a single year and, except for census years, on postcensal population estimates rather than on the data from a decennial census, and the annual tables are calculated by abbreviated methods. Also there are 3 more categories in the decennial life tabls : other than white,other than white male and other than white female. Usage: To look up values of these data for a set of key variable values: (in SAS) : a) In your SAS program, before your data step add: %include 'morthz.fmt' ; b) set SUPERKEY to the combination of key variable values you want to look up. See "SUPERKEY=" under key variable description below. c) use a formatted put function of SUPERKEY with the data series format (See examples under "Usage" in detailed description of each data series). Key vars: Varname Typ Description YEAROLD Num YEAR OLD Min=0 Max=109 SUPERKEY =YEAROLD; Data series [summary]: Varname Fmtname Typ Description ------- ------- --- ----------- MHUS MHUS. Num Mortality Hazard-Total US Pop MHUSM MHUSM. Num Mortality Hazar-US Males MHUSF MHUSF. Num Mortality Hazar-US Females MHUSW MHUSW. Num Mortality Hazar-US White MHUSB MHUSB. Num Mortality Hazar-US Black MHUSNW MHUSNW. Num Mortality Hazar-US Other Than White MHUSWM MHUSWM. Num Mortality Hazar-US White Males MHUSWF MHUSWF. Num Mortality Hazar-US White Females MHUSBM MHUSBM. Num Mortality Hazar-US Black Males MHUSBF MHUSBF. Num Mortality Hazar-US Black Females MHNWM MHNWM. Num Mortality Hazar-US Other Than White Males MHNWF MHNWF. Num Mortality Hazar-US Other Than White Females ---------------------------- Data series MHUS ---------------------------- Varname Fmtname Typ Description MHUS MHUS. Num Mortality Hazard-Total US Pop Units= Min=0.00016 Max=0.50525 Mean=0.07148127 N=110 N_Missing=0 How missings handled: Comments: Usage: MYVAR=put(SUPERKEY,MHUS.); ---------------------------- Data series MHUSM ---------------------------- Varname Fmtname Typ Description MHUSM MHUSM. Num Mortality Hazar-US Males Units= Min=0.00017 Max=0.51978 Mean=0.08134964 N=110 N_Missing=0 How missings handled: Comments: Usage: MYVAR=put(SUPERKEY,MHUSM.); ---------------------------- Data series MHUSF ---------------------------- Varname Fmtname Typ Description MHUSF MHUSF. Num Mortality Hazar-US Females Units= Min=0.00015 Max=0.50068 Mean=0.06712509 N=110 N_Missing=0 How missings handled: Comments: Usage: MYVAR=put(SUPERKEY,MHUSF.); ---------------------------- Data series MHUSW ---------------------------- Varname Fmtname Typ Description MHUSW MHUSW. Num Mortality Hazar-US White Units= Min=0.00015 Max=0.512 Mean=0.07190418 N=110 N_Missing=0 How missings handled: Comments: Usage: MYVAR=put(SUPERKEY,MHUSW.); ---------------------------- Data series MHUSB ---------------------------- Varname Fmtname Typ Description MHUSB MHUSB. Num Mortality Hazar-US Black Units= Min=0.00022 Max=0.42925 Mean=0.06697764 N=110 N_Missing=0 How missings handled: Comments: Usage: MYVAR=put(SUPERKEY,MHUSB.); ---------------------------- Data series MHUSNW ---------------------------- Varname Fmtname Typ Description MHUSNW MHUSNW. Num Mortality Hazar-US Other Than White Units= Min=0.00019 Max=0.43452 Mean=0.06596536 N=110 N_Missing=0 How missings handled: Comments: Usage: MYVAR=put(SUPERKEY,MHUSNW.); ---------------------------- Data series MHUSWM ---------------------------- Varname Fmtname Typ Description MHUSWM MHUSWM. Num Mortality Hazar-US White Males Units= Min=0.00016 Max=0.52797 Mean=0.08193173 N=110 N_Missing=0 How missings handled: Comments: Usage: MYVAR=put(SUPERKEY,MHUSWM.); ---------------------------- Data series MHUSWF ---------------------------- Varname Fmtname Typ Description MHUSWF MHUSWF. Num Mortality Hazar-US White Females Units= Min=0.00014 Max=0.50712 Mean=0.06756855 N=110 N_Missing=0 How missings handled: Comments: Usage: MYVAR=put(SUPERKEY,MHUSWF.); ---------------------------- Data series MHUSBM ---------------------------- Varname Fmtname Typ Description MHUSBM MHUSBM. Num Mortality Hazar-US Black Males Units= Min=0.00022 Max=0.44864 Mean=0.07799491 N=110 N_Missing=0 How missings handled: Comments: Usage: MYVAR=put(SUPERKEY,MHUSBM.); ---------------------------- Data series MHUSBF ---------------------------- Varname Fmtname Typ Description MHUSBF MHUSBF. Num Mortality Hazar-US Black Females Units= Min=0.00022 Max=0.42548 Mean=0.06195491 N=110 N_Missing=0 How missings handled: Comments: Usage: MYVAR=put(SUPERKEY,MHUSBF.); ---------------------------- Data series MHNWM ---------------------------- Varname Fmtname Typ Description MHNWM MHNWM. Num Mortality Hazar-US Other Than White Males Units= Min=0.0002 Max=0.45345 Mean=0.07612409 N=110 N_Missing=0 How missings handled: Comments: Usage: MYVAR=put(SUPERKEY,MHNWM.); ---------------------------- Data series MHNWF ---------------------------- Varname Fmtname Typ Description MHNWF MHNWF. Num Mortality Hazar-US Other Than White Females Units= Min=0.00019 Max=0.42821 Mean=0.06105127 N=110 N_Missing=0 How missings handled: Comments: Usage: myvar=put(superkey,MHNWF.); ---------------------------------------------------------------------------- The CONTENTS Procedure Data Set Name: SCRLIB.MORTHZ90 Observations: 110 Member Type: DATA Variables: 13 Engine: V8 Indexes: 0 Created: 11:29 Wednesday, March 19, 2003 Observation Length: 104 Last Modified: 11:29 Wednesday, March 19, 2003 Deleted Observations: 0 Protection: Compressed: NO Data Set Type: Sorted: NO Label: -----Alphabetic List of Variables and Attributes----- # Variable Type Len Pos Label ----------------------------------------------------------------------------------- 12 MHNWF Num 8 80 Mortality Hazar-US Other Than White Females 13 MHNWM Num 8 88 Mortality Hazar-US Other Than White Males 1 MHUS Num 8 0 Mortality Hazard-Total US Pop 6 MHUSB Num 8 32 Mortality Hazar-US Black 10 MHUSBF Num 8 64 Mortality Hazar-US Black Females 11 MHUSBM Num 8 72 Mortality Hazar-US Black Males 3 MHUSF Num 8 8 Mortality Hazar-US Females 4 MHUSM Num 8 16 Mortality Hazar-US Males 7 MHUSNW Num 8 40 Mortality Hazar-US Other Than White 5 MHUSW Num 8 24 Mortality Hazar-US White 8 MHUSWF Num 8 48 Mortality Hazar-US White Females 9 MHUSWM Num 8 56 Mortality Hazar-US White Males 2 YEAROLD Num 4 96 YEAR OLD