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NCHS

Quarterly Provisional Estimates - Technical Notes - Mortality, 2015-Quarter 1, 2017

Nature and sources of data

Provisional estimates are based on all complete death records received and processed by the Centers for Disease Control and Prevention’s National Center for Health Statistics (NCHS) as of a specified cutoff date. National provisional estimates include events occurring only within the 50 states and the District of Columbia. NCHS receives the death records and monthly provisional occurrence counts from state vital registration systems through the Vital Statistics Cooperative Program.  A complete death record includes both demographic and medical information.

Individual death records are weighted, when necessary, to independent provisional counts of deaths occurring in each state by month. These monthly state-specific provisional counts serve as control totals and are the basis for the record weights used for computing provisional estimates. If the number of complete records is greater than the provisional count received from the state, the state-specific number of complete records is used instead and the weight is set at 1.

Table I shows the percent completeness of the provisional data by month for the United States and each jurisdiction. The percent completeness is obtained by dividing the number of complete records from each state for each month by the corresponding provisional count and multiplying by 100. Although data by place of occurrence are used to compute the weights, all rate estimates are for residents of the 50 states and District of Columbia.

 

Table I. Mortality data completeness as a percentage of monthly provisional count: United States, each state, District of Columbia, and New York City, as of June 18, 2017
Area 2016 2017
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar
Total U.S. 100 100 100 100 100 100 100 100 100 100 100 100 95 89 82
Alabama 100 100 100 100 100 100 100 100 100 100 100 100 97 72 71
Alaska 100 100 100 100 100 100 100 100 100 100 100 100 98 91 85
Arizona 100 100 100 100 100 100 100 100 100 100 100 100 99 90 81
Arkansas 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
California 100 100 100 100 100 100 100 100 100 100 100 100 97 89 77
Colorado 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Connecticut 100 100 100 100 100 100 100 100 100 100 100 100 83 69 0
Delaware 100 100 100 100 100 100 100 100 100 100 100 100 97 86 76
District of Columbia 100 100 100 100 100 100 100 100 100 100 100 100 98 89 77
Florida 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Georgia 100 100 100 100 100 100 100 100 100 100 100 100 97 87 81
Hawaii 100 100 100 100 100 100 100 100 100 100 100 100 99 88 81
Idaho 100 100 100 100 100 100 100 100 100 100 100 100 98 88 75
Illinois 100 100 100 100 100 100 100 100 100 100 100 100 100 93 78
Indiana 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Iowa 100 100 100 100 100 100 100 100 100 100 100 100 98 78 74
Kansas 100 100 100 100 100 100 100 100 100 100 100 100 100 91 81
Kentucky 100 100 100 100 100 100 100 100 100 100 100 100 96 80 71
Louisiana 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Maine 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Maryland 100 100 100 100 100 100 100 100 100 100 100 100 91 80 80
Massachusetts 100 100 100 100 100 100 100 100 100 100 100 100 95 87 79
Michigan 100 100 100 100 100 100 100 100 100 100 100 100 100 100 92
Minnesota 100 100 100 100 100 100 100 100 100 100 100 100 98 88 69
Mississippi 100 100 100 100 100 100 100 100 100 100 100 100 97 90 83
Missouri 100 100 100 100 100 100 100 100 100 100 100 99 86 84 75
Montana 100 100 100 100 100 100 100 100 100 100 100 100 99 77 72
Nebraska 100 100 100 100 100 100 100 100 100 100 100 100 80 81 81
Nevada 100 100 100 100 100 100 100 100 100 100 100 100 99 85 70
New Hampshire 100 100 100 100 100 100 100 100 100 100 100 100 99 89 74
New Jersey 100 100 100 100 100 100 100 100 100 100 100 100 100 95 77
New Mexico 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
New York1 100 100 100 100 100 100 100 100 100 100 100 100 98 89 77
North Carolina 100 100 100 100 100 100 100 100 100 100 100 100 98 83 76
North Dakota 100 100 100 100 100 100 100 100 100 100 100 100 83 84 55
Ohio 100 100 100 100 100 100 100 100 100 100 100 100 99 99 99
Oklahoma 100 100 100 100 100 100 100 100 100 100 100 100 99 88 79
Oregon 100 100 100 100 100 100 100 100 100 100 100 100 96 78 68
Pennsylvania 100 100 100 100 100 100 100 100 100 100 100 100 72 39 21
Rhode Island 100 100 100 100 100 100 100 100 100 100 100 100 99 87 78
South Carolina 100 100 100 100 100 100 100 100 100 100 100 100 99 77 69
South Dakota 100 100 100 100 100 100 100 100 100 100 100 100 99 88 71
Tennessee 100 100 100 100 100 100 100 100 100 100 100 100 79 79 78
Texas 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Utah 100 100 100 100 100 100 100 100 100 100 100 100 100 90 81
Vermont 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Virginia 100 100 100 100 100 100 100 100 100 100 100 100 100 89 54
Washington 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
West Virginia 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Wisconsin 100 100 100 100 100 100 100 100 100 100 100 100 43 70 79
Wyoming 100 100 100 100 100 100 100 100 100 100 100 100 98 83 71
New York City2 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

1 Excludes New York City.

2 New York City, excluding the rest of the state of New York.

NOTE: Percent completeness equals 100 times the number of death records received at NCHS divided by an individual provisional count of deaths reported by each jurisdiction. NCHS receives the death records and monthly provisional occurrence counts from state vital registration systems through the Vital Statistics Cooperative Program.

 

Imputation of incomplete data

When the available data for a specific state and month are less than 50% complete (Table I), they are not used for computing provisional estimates. For these states and months, the number of deaths by underlying cause is imputed based on data from the same state and month in the previous year. These counts are then weighted by the ratio of the state’s population in the same month of both years. For this release, imputation was needed for West Virginia, Pennsylvania, and Connecticut in the first quarter of 2017. As a result, 0.2% of death records were imputed.

 

Cause-of-death classification

Mortality statistics are compiled in accordance with World Health Organization (WHO) regulations specifying that WHO member nations classify and code causes of death in accordance with the current revision of the International Statistical Classification of Diseases and Related Health Problems (ICD). ICD provides the basic guidance used in virtually all countries to code and classify causes of death. It provides not only disease, injury, and poisoning categories but also the rules used to select the single underlying cause of death for tabulation from the several diagnoses that may be reported on a single death certificate, as well as definitions, tabulation lists, the format of the death certificate, and regulations on use of the classification. Causes of death for data presented in this report were coded according to ICD guidelines described in annual issues of Part 2a of the NCHS Instruction Manual (1).

 

Population denominators

Population estimates used for computing rates are postcensal estimates, which originate from the U.S. Census Bureau and are based on the 2010 census. All population estimates used for a single data year are based on population estimates of the same vintage (that is, the same reference year). Changes in rates between years in part reflect differences between vintages of the population estimates. Quarterly rates are based on the average of the population estimates for the two dates nearest the midpoint of the quarter. Rates for 12-month periods are based on the population estimate at the midpoint of each period.

 

Computing rates

Death rates are on an annual basis per 100,000 estimated population. Age-adjusted death rates are used to compare relative mortality risks over time; however, they should be viewed as relative indexes rather than as actual measures of mortality risk. They were computed by the direct method; that is, by applying age-specific death rates to the U.S. standard population (relative age distribution of year 2000 projected population of the United States) (2).

 

Accuracy of estimates

Provisional estimates by causes of death are subject to some nonrandom sampling error. This is because the delay in receiving the report of a death depends on the cause of death. The quarterly provisional estimates are based on data that is more incomplete for the most recent months. Causes of death with more delayed reporting tend to be underrepresented in the sample, so the weighting scheme tends to underestimate their rates in the most recent months.

Furthermore, for some deaths, the final cause may not be available at the time the provisional estimates are computed. In those cases, the causes of death may be reported as unknown or pending investigation and coded to the category Other ill-defined and unspecified causes of mortality (ICD–10 code R99), a subcategory of symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified (ICD–10 codes R00–R99). In the final data, some of the deaths of unknown cause will be reassigned to specific causes if further, more specific cause-of-death information is provided.

Even if no differential delay occurred in the reporting of final cause of death, some sampling error would still exist for rate estimates, because they are based on incomplete data. A guideline for the size of this sampling error is given by deriving the variation that would occur if the data were missing at random (3).

Partly because of the factors discussed above, these provisional estimates are rarely higher than the true rate.  The estimates’ accuracy is stated as how much lower they might be than the true rate, based on experience with recent reporting practices.  Because reporting practices have been improving, accuracy of the estimates may be better than recent experience suggests.

Separate assessments of accuracy will be described for two groups of estimates:  1) those released with a 3-month lag, and (2) those that are released with a longer 6- or 9- month lag.  For those released with a 3-month lag the 3-month estimates are expected to be less than 2% below the true rate and estimates for the 12-month period are expected to be less than 1% below the true rate.

Rate estimates for most external causes of death tend to be less reliable and are released with a longer lag (4).  Estimates for homicide, falls for persons aged 65 and over, firearm-related injuries, suicide and unintended injuries are released with a 6 month lag and estimates for drug overdose are released with a 9 month lag.  In both cases estimates for 3-month periods are expected to be less than 7% below the true rate and estimates for 12-month periods less than 3% below the true rate.  Estimates for previously released quarters are revised based on all new data and updates received since the previous release.  As a result, the reliability of estimates for a specific quarter will improve with each quarterly release.

 

Interpretation of changes over time

Most causes of death have a seasonal pattern. This is well-known for influenza but has also been documented for other causes. The bulleted text accompanying estimates for each cause of death compares periods 1 year apart to minimize seasonal influence. Unless otherwise specified, a difference is reported only if statistically significant at the 0.05 level by the z test given in reference 3.

 

Acknowledgements

The interactive dashboard was designed by Anthony Lipphardt.

 

References

  1. NCHS. National Vital Statistics System. Instructions for classifying the underlying cause of death. In: NCHS instruction manual; Part 2a. Published annually.
  2. Kochanek KD, Murphy SL, Xu JQ, Tejada-Vera B. Deaths: Final data for 2014. National vital statistics reports; vol 65 no 4. Hyattsville, MD: National Center for Health Statistics. 2016.
  3. Hoyert DL, Xu JQ. Deaths: Preliminary data for 2011. National vital statistics reports; vol 61 no 6. Hyattsville, MD: National Center for Health Statistics. 2012.
  4. Spencer MR, Ahmad F. Timeliness of death certificate data for mortality surveillance and provisional estimates [PDF – 210 KB]. National Center for Health Statistics. January 2017.

 

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