A Study of Factors Affecting Record Linkage 

in Federal Statistical Databases 

Michael D. Larsen1 and Yuan Zhao2 

1The George Washington University, Department of Statistics 

2The George Washington University, Biostatistics Center 

1

6110 Executive Boulevard, Suite 750, Rockville, MD 20852; mlarsen@bsc.gwu.edu 

2

6110 Executive Boulevard, Suite 750, Rockville, MD 20852; yzhao@bsc.gwu.edu 

Abstract: Record linkage, or exact file matching, consists of bringing together records in two or 

more files on the same population. Files are linked for the purposes of creating a larger database, 

enabling analyses that would otherwise not be possible, and counting the population. When 

unique, error-free identification codes are not available on both files, then record linkage can be 

accomplished through probabilistic methods. When implementing matching algorithms, one must 

choose matching variables, define for each variable what it means to agree or disagree, choose 

blocking factors that restrict the space of comparison pairs, and decide the level of evidence 

required to declare that a pair of records is probable match. The National Center for Health 

Statistics (NCHS) uses record linkage to match surveys, such as the National Health Interview 

Survey (NHIS) or the National Health and Nutrition Examination Survey (NHANES) to the 

National Death Index (NDI) for studies of mortality and morbidity. Based on files simulated to 

be similar to the NDI and a NCHS health survey, some choices that affect the performance of 

probabilistic record linkage are studied. The impact of several choices as well as file sizes and 

recording errors are compared. The work has direct relevance for improving and evaluating record 

linkage operations in the federal statistical system. 

Key words and phrases: Exact matching; file linking; blocking; probabilistic linkage; NHIS; 

NHANES; National Death index. 

1. Introduction 

A simulated National Death Index-User data set provided by Bryan Sayer was analyzed using latent class analysis 

record linkage models. Factors studied included blocking factors, the impact of the inclusion of select variables in 

the latent class models, and the impact of errors in matching variables. Blocking factors in the NDI-User data files 

are very important in identifying correct matches and in reducing computational effort. The NDI-User data files 

can be successfully matched as long as there are sufficient matching variables available and errors in files are not too 

large. More matching errors are made when fewer variables are available for matching and there are more errors in 

the NDI-User data files. 

A simulated NDI-User data set was created by Bryan Sayer. He describes the creation of the data set as follows: 

The first name data is from the 250 most popular sex specific first names. Middle names are also drawn from 

the first name file, but use all unique first names to draw from. Middle initial is chosen based on the probability 

of occurrence, and then a middle name is chosen that matches the middle initial. Not all records have middle 

initial or name, as about 25% of NDI records have no middle initial. Year of birth is constrained to 1900 to 

1919. Year of Death is chosen to approximate age at death based on the 2000 data. Sex is split 50/50. Month 

and day values are chosen randomly, except that day must agree with month. All other values are chosen with 

probability equal to the values in the NDI file for 1979 to 2000. 

Latent class analysis for record linkage has been described by Larsen and Rubin (2001), Lahiri and Larsen (2005), 

Larsen (2004), and references therein. See also Larsen (2010). It is a model-based statistical method for estimating 

the probability that two record pairs, one of each from two files, pertain to the same record. One output of the 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

algorithm is an effective scoring system for measuring the contribution to the likelihood of agreement overall based 

on individual variables. When different combinations of variables are used in the algorithm, it produces different 

estimated probabilities of agreement. Estimation is accomplished by maximizing the latent class log likelihood 

function via the EM algorithm (Dempster, Laird, and Rubin 1977). 

Factors studied in this research on the simulated NDI-User data set include blocking factors, the impact of the 

inclusion of select variables in the latent class models, and the impact of errors in matching variables. 

2. Blocking Factors 

Blocking factors are used to decrease the number of comparisons that must be made. A comparison of all 10,000 

simulated user submissions to all 995,043 simulated NDI records would yield a huge number of comparisons. 

Ideally a blocking factor eliminates record pairs that are not matches, but does not exclude any record pairs that are 

matches. One possible blocking criterion is SSN. Exact agreement on SSN, however, is rather strict, even for a set 

of actual matches. It would tend to produce blocks with zero or one potential matching pair of records. As such, it 

is an excellent matching variable when it is available, but it does not have much potential as a blocking variable, at 

least in the sense of which blocking variables are construed for this research. 

The last four digits of SSN, however, can be used as a blocking variable. For each value of the last four digits of 

SSN there are several potential matches in the data set. In the simulated NDI-User data sets, there are on average 

nearly 100 cases with the same last four digits of SSN: 995,043/10,000 = 99.5. The table below summarizes the 

number of NDI cases by SSN4. If SSN4 is used as a blocking variable by itself, it would produce 99.5*10,000 = 

9,950,000 comparison pairs. At most 5,043 of these (the number of deaths in the simulated user file) are true 

matches. For the purposes of this research, the number of pairs according to this blocking definition was too large to 

be considered. 

Summary of distribution of number of cases with matching SSN4 

values in the NDI data set for each SSN4 in the User data set 

Minimum 1

st

 quartile Median 3rd quartile Maximum Mean Standard deviation n 

65 93 99 106 139 99.5 10.1 995,043 

Blocking based on names is another possibility. Matches to names can be made by matching last name on the User 

and NDI files, birth surname on the User and NDI files, and last name on the User file and birth surname on the NDI 

file. The third name match scenario primarily is relevant for females who might change their name at time of 

marriage, and is referred to as ‘maiden name’ below, even though agreement would happen for individuals who had 

not changed their name. As can be seen in the table below, the number of record pairs defined by name-based 

blocking criteria is even larger than the number of record pairs defined by the SSN4 blocking criterion. For the 

purposes of this research, the number of pairs according to these blocking definitions was too large to be considered. 

It is noteworthy, however, that a significant number of potential record pairs are allowed by one blocking criterion 

but not the others. In considering a name-based blocking criterion for the NDI, therefore, one should consider 

simultaneous inclusion of cases based on multiple name criteria. 

Total number of record pairs allowed by some name-based blocking criteria 

Name-Based Blocking Variables, Agree on Any in a Given Row Number of Resulting Record Pairs 

Last name 13,435,375 

Birth surname 13,159,741 

Maiden name 13,422,836 

Last name Birth surname 22,205,314 

Last name Maiden name 19,228,383 

Birth surname Maiden name 18,869,853 

Last name Birth surname Maiden name 24,670,117 

The next blocking criterion considered is one that requires agreement on SSN4 and on a name-based match. The 

table below gives the average number of record pairs per user record and the maximum number of record pairs per 

user record that are produced by these blocking criteria for the NDI-User data set. The results below illustrate the 

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extreme impact that blocking can make in an application such as matching to the NDI. Changing the blocking 

criteria can make more than a 100 (or a 1,000) fold decrease in the number of candidate pairs. We will refer to the 

blocking method in the last line of the table below as the ‘narrow’ blocking criterion in the rest of this article. 

Average and total number of record pairs allowed by SSN4 + name-based blocking criteria 

Blocking Variables, 

Agree on All in a Given Row 

Average number of record pairs per user 

record 

Total number of record pairs 

SSN4 99.5 995,043 

SSN4 Last name 0.6356 6,356 

SSN4 Birth surname 0.6320 6,320 

SSN4 Maiden name 0.4162 4,162 

SSN4 Any of 3 names 0.7429 7,429 

The next blocking criterion considered is one that requires agreement on first letter of first name plus agreement on 

some last name variable. First letter of last name in principle divides the NDI records into 26 classes. It is not as 

extreme as SSN4, which has 10,000 classes. For the purposes of this research, however, the number of pairs 

according to this blocking definition was too large to be considered. As before, the results below illustrate the 

extreme impact that blocking can make in an application such as matching a survey to the NDI. 

Maximum and total number of record pairs allowed by 

first letter of first name + name-based blocking criteria 

Blocking Variables, 

Agree on All in a Given Row 

Maximum number of 

record pairs per user record 

Total number of record 

pairs 

First of first name Last name 1,770 917,227 

First of first name Birth surname 1,711 916,897 

First of first name Maiden name 1,711 914,328 

First of first name Last name and/or birth surname 2,984 1,492,664 

First of first name Last name and/or maiden name 2,984 1,308,696 

First of first name Birth surname and/or maiden name 2,775 1,298,410 

First of first name Any of three names 3,530 1,690,309 

To further refine the blocking criteria, we then considered agreement on state of residence in addition to agreement 

on first letter of first name plus agreement on some last name variable. Inclusion of state of residence in principle 

should cut the size of blocking classes by a factor of 50. The blocking criterion defined in the last row of the table 

below will be referred to as the ‘broad’ blocking criterion in the rest of this article. 

Maximum and total number of record pairs allowed by state of residence + 

first letter of first name + name-based blocking criteria 

Blocking Variables, 

Agree on All in a Given Row 

Maximum number of 

record pairs per user 

record 

Total number of 

record pairs 

State reside First of first name Last name 164 41,740 

State reside First of first name Birth surname 167 41,739 

State reside First of first name Maiden name 167 39,374 

State reside First of first name Last name and/or birth surname 280 65,116 

State reside First of first name Last name and/or maiden name 280 57,542 

State reside First of first name Birth surname and/or maiden name 256 57,163 

State reside First of first name Any of three names 327 73,151 

3. Matching Variables 

Ten matching variables are considered in analyses in this paper. They are listed in the table below. The variable 

‘state.resid’ (state of residence) cannot be used for matching with the ‘broad’ blocking criterion, because all 

candidate pairs are required to agree on state of residence between the NDI and the User file. SSN could potentially 

be used as a matching variable, but doing so might be a little odd in the NDI application. Instead, SSN would likely 

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be used to find exact matches or to confirm matches found with other means. SSN4 could potentially be considered 

a matching variable in other contexts. First name can be used as a matching variable because only first letter of first 

name is used as a blocking variable. Birth surname can be used as a matching variable because some record pairs 

qualified under the blocking criteria based on last name or maiden name alone. Further research could assess 

whether birth surname could effectively be excluded from matching after it plays some role in blocking. 

Matching Variables 

Abbreviation Variable Match variable with narrow blocking Match variable with broad blocking 

Fname First name Yes Yes 

Sex Sex Yes Yes 

Race Race Yes Yes 

Birth.m Birth month Yes Yes 

Birth.d Birth day Yes Yes 

Birth.y Birth year Yes Yes 

Marital Marital status Yes Yes 

State.resid State of residence Yes 

State.birth State of birth Yes Yes 

Surname.birth Surname at birth Yes Yes 

In the studies reported below, matching variables are used in different combinations for matching the NDI-User 

records. First, all available matching variables are used together in a single latent class model. Second, one variable 

from the list above is removed and the analysis is redone. This yields nine analyses under the narrow blocking 

criterion and eight analyses under the broad blocking criterion. Third, four subsets of matching variables are 

examined. The subsets are described in the table below. In the first scenario, there is no birthday information. In 

the second scenario, there is no first name and only birth year. In the third scenario, there is no first name, only birth 

year, and no state of birth. In the fourth scenario, there is no first name, only birth year, and no surname at birth. 

Subsets used with the broad blocking criterion exclude the variable state of residence in addition to the matching 

variables indicated in the four scenarios. Future work could consider subsets of variables actually being considered 

for NDI-survey matches. 

Matching Variables used in Four Subset Matching Scenarios; 

subsets used with the broad blocking criterion exclude variable state of residence 

Variable Scenario 1 Scenario 2 Scenario 3 Scenario 4 

First name Yes 

Sex Yes Yes Yes Yes 

Race Yes Yes Yes Yes 

Birth month 

Birth day 

Birth year Yes Yes Yes 

Marital status Yes Yes Yes Yes 

State of residence Yes Yes Yes Yes 

State of birth Yes Yes Yes 

Surname at birth Yes Yes Yes 

4. Random Errors 

Random errors were introduced into the matching process by randomly changing a comparison vector element from 

1 to 0 or from 0 to 1. Changing a comparison vector element from a 1 to a 0 implies that there is an error in 

comparing two entries that should agree on a field of comparison. Changing a comparison vector element from a 0 

to a 1 implies that there is an error in comparing two entries that should disagree on a field of comparison. 

Random errors were introduced independently for each of the ten comparison fields. Error rates studied were taken 

as 5%, 10%, or 20%. When there are these levels of random errors many records have some error. Most records, 

however, do not have many errors. A random agreement in most cases is insufficient to make a nonmatching record 

pair appear to be a matching record pair. The table below shows the probability of having 0, 1, .., 10 random errors 

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I I I I I I I I I I 

in an individual comparison vector when there are 5%, 10%, or 20% errors per comparison field. Future work could 

consider scenarios in which comparison fields have different rates of errors and scenarios in which errors across 

fields of comparison are correlated. 

Probabilities of number of errors Number of random errors in ten comparisons 

at 3 levels of random error 0 1 2 3 4 5 6 7 8-10 

5% random error by field 0.60 0.32 0.07 0.01 0.00 0.00 0.00 0.00 0.00 

10% random error by field 0.35 0.39 0.19 0.06 0.01 0.00 0.00 0.00 0.00 

20% random error by field 0.11 0.27 0.30 0.20 0.09 0.03 0.01 0.00 0.00 

5. Results 

Results when using the narrow blocking criterion 

Results are reported in this section when the narrow blocking criterion, which is defined as agreement on SSN4 and 

one of three last name variables, is used to define blocks. Results using the broad blocking criterion are reported in 

the next section. In interpreting these results, it is important to remember that the blocking criterion reduces the 

number of potential matches to a small number. Some individuals in the User file have only one or sometimes just 

two potential matches in the NDI file. Thus, the goal is for the matching variables to further clarify the matching 

situation for each user record. A latent class model with two latent classes was fit to the multidimensional 

contingency table defined by the several (binary) matching variables. 

When all ten matching variables are used, the algorithm produces estimated probabilities that correctly match the 

5,043 deceased individuals in the User file to their records in the NDI simulated file. That is, for the deceased 

individuals it makes no errors in finding their matched cases. 

When one variable is removed at a time and the algorithm re-run, the algorithm again produces estimated 

probabilities that correctly match the 5,043 deceased individuals in the User file to their records in the NDI 

simulated file. That is, using any nine of the available predictor variables, for the deceased individuals it makes no 

errors in finding their matched cases. 

When groups of variables are removed together as defined in the four scenarios of Section 3 and the algorithm rerun, the algorithm yet again produces estimated probabilities that correctly match the 5,043 deceased individuals in 

the User file to their records in the NDI simulated file. That is, using the predictor variables in these four sets of 

predictors, for the deceased individuals it makes no errors in finding their matched cases. 

Why is there such a high success rate? In interpreting these results, it is important to remember that the blocking 

criterion reduces the number of potential matches to a small number. Some individuals in the User file have only 

one or sometimes just two potential matches in the NDI file. The matching variables, then, are able to clarify the 

matching situation. The cases with the highest estimated probabilities of matching are the true matching cases. 

A 5% chance of an error in comparing matching variables was randomly introduced. When all ten matching 

variables are used, the algorithm produces estimated probabilities that correctly match the 5,043 deceased 

individuals in the User file to their records in the NDI simulated file. That is, for the deceased individuals it makes 

no errors in finding their matched cases. Thus, the 5% random errors did not decrease effectiveness. As was 

discussed, most cases have zero fields or one field with random errors, so information is not substantially degraded. 

When nine matching variables are used, the algorithm again produced zero errors, except in the case when day of 

birth is removed. In part this could be random variation due to simulation. It could be, however, that day of birth, 

which has 31 levels, is an important variable for further clarifying match status. In the case that day of birth was 

not used in matching 1 error was made in finding the 5,043 deceased individuals in the NDI file. When the four 

subsets of variables are used for matching and 5% errors are present, matching scenarios 1, 2, 3, and 4 made 1, 4, 4, 

and 5 errors, respectively. As expected, the reduced set of matching variables lead to more difficulty in matching. 

Still, the latent class model results are quite good. 

A 10% chance of an error in comparing matching variables was randomly introduced. When all ten matching 

variables are used, the algorithm produces estimated probabilities that correctly match the 5,043 deceased 

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individuals in the User file to their records in the NDI simulated file. That is, for the deceased individuals it makes 

no errors in finding their matched cases. Thus, the 10% random errors did not decrease effectiveness. When nine 

matching variables are used, the algorithm again produced zero errors, except in the case when day of birth is 

removed. In the case that day of birth was not used in matching 1 error was made in finding the 5,043 deceased 

individuals in the NDI file. When the four subsets of variables are used for matching and 10% errors are present, 

matching scenarios 1, 2, 3, and 4 made 4, 5, 15, and 7 errors, respectively. As expected, the reduced set of 

matching variables lead to more difficulty in matching. Still, the latent class model results are quite good. 

A 20% chance of an error in comparing matching variables was randomly introduced. When all ten matching 

variables are used, the algorithm produces estimated probabilities that correctly match all but 9 of the 5,043 

deceased individuals in the User file to their records in the NDI simulated file. Thus, even the 20% random errors 

did not significantly decrease effectiveness. When nine matching variables are used, the algorithm produced some 

more errors. The number of errors when a variable is excluded from matching is given in the table below. 

Matching Errors under Narrow Blocking when there is a 20% chance of error by variable 

and 9 of 10 variables are used for matching 

Abbreviation Variable excluded from matching Number of errors when variable is not used for matching 

Fname First name 13 

Sex Sex 12 

Race Race 10 

Birth.m Birth month 17 

Birth.d Birth day 15 

Birth.y Birth year 14 

Marital Marital status 12 

State.resid State of residence 17 

State.birth State of birth 19 

Surname.birth Surname at birth 10 

When the four subsets of variables are used for matching and 5% errors are present, matching scenarios 1, 2, 3, and 

4 made 36, 36, 59, and 38 errors, respectively. As expected, the reduced set of matching variables lead to more 

difficulty in matching. Still, the latent class model results are quite good. 

Results when using the broad blocking criterion 

Results are reported in this section when the broad blocking criterion, which is defined as agreement on state of 

residence, first letter of first name, and one of three last name variables, is used to define blocks. 

When all ten matching variables are used, the algorithm produces estimated probabilities that correctly match the 

5,043 deceased individuals in the User file to their records in the NDI simulated file. That is, for the deceased 

individuals it makes no errors in finding their matched cases. When one variable is removed at a time and the 

algorithm re-run, the algorithm produces estimated probabilities that correctly match the 5,043 deceased individuals 

in the User file to their records in the NDI simulated file. That is, using any nine of the available predictor variables, 

for the deceased individuals it makes no errors in finding their matched cases. 

When groups of variables are removed together as defined in the four scenarios of Section 3 and the algorithm rerun, the algorithm produces estimated probabilities that correctly match most of the 5,043 deceased individuals in 

the User file to their records in the NDI simulated file, but some errors are made. The matching scenarios 1, 2, 3, 

and 4 made 8, 4, 25, and 4 errors, respectively. Scenario 3, which did not use first name, day or month of birth, or 

state of birth as matching variables had the most problem. 

A 5% chance of an error in comparing matching variables was randomly introduced. When all nine matching 

variables are used with the broad blocking criterion, the algorithm produces estimated probabilities that correctly 

match the 5,043 deceased individuals in the User file to their records in the NDI simulated file. That is, for the 

deceased individuals it makes no errors in finding their matched cases. Thus, the 5% random errors did not decrease 

effectiveness. When eight of the nine matching variables are used, the algorithm again produced zero errors. When 

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the four subsets of variables are used for matching and 5% errors are present, matching scenarios 1, 2, 3, and 4 made 

1, 0, 8, and 0 errors, respectively. The result is paradoxical, because results are better with a little error introduced. 

Why could this happen? This could happen because one or more variables received an exaggerated weight under 

one of the scenarios with the original data. The random errors could have lead to a reduction in the importance of a 

particular variable in terms of determining matching probabilities, which ultimately increased the match rate. As 

mentioned before, these are simulated results so there could be a degree of simulation variance involved. Further 

study of this phenomenon could be undertaken in future work. 

A 10% chance of an error in comparing matching variables was randomly introduced. When all nine matching 

variables are used under the broad blocking criterion, the algorithm produces estimated probabilities that correctly 

match the 5,043 deceased individuals in the User file to their records in the NDI simulated file. When eight of the 

nine matching variables are used, the algorithm again produced zero errors. When the four subsets of variables are 

used for matching and 10% errors are present, matching scenarios 1, 2, 3, and 4 made 7, 4, 17, and 3 errors, 

respectively. As expected, the reduced set of matching variables lead to more difficulty in matching. Still, the 

latent class model results are quite good. 

 A 20% chance of an error in comparing matching variables was randomly introduced. When all nine matching 

variables are used, the algorithm produces estimated probabilities that correctly match all the deceased individuals in 

the User file to their records in the NDI simulated file. Thus, even the 20% random errors did not significantly 

decrease effectiveness. When eight of the nine matching variables are used, the algorithm produced some errors: 

there was one error each in matching deceased individuals when variable “birth month” or variable “birth year” were 

removed. Why could results be better for the broad blocking scenario versus the narrow blocking scenario? In the 

particular cases considered for the NDI-User data files, the narrow blocking scenario contains very few NDI cases 

per user record. In many aspects, the user and NDI records match quite closely. Therefore they might be difficult to 

distinguish. In the broad blocking scenario, there are multiple NDI cases per user record. Many of those NDI cases 

are quite dissimilar from the user records. Therefore, many of the additional NDI cases have little risk of being 

finally associated with the user cases, resulting in a false match. This rationale is speculative, but it will be 

interesting to study in future applications. When the four subsets of variables are used for matching and 20% errors 

are present, matching scenarios 1, 2, 3, and 4 made 23, 8, 48, and 10 errors, respectively. As expected, the reduced 

set of matching variables lead to more difficulty in matching. 

6. Summary 

A simulated NDI-User data set provided by Bryan Sayer was analyzed using latent class analysis record linkage 

models. Factors studied included blocking factors, the impact of the inclusion of select variables in the latent class 

models, and the impact of errors in matching variables. Blocking factors in the NDI-User data files are very 

important in identifying correct matches and in reducing computational effort. The NDI-User data files can be 

successfully matched as long as there are sufficient matching variables available and errors in files are not too large. 

More matching errors are made when fewer variables are available for matching and there are more errors in the 

NDI-User data files. 

Future work could consider additional blocking criterion and alternate uses of matching variables. The alternate 

uses of matching variables could include degree of agreement, agreement simultaneously on two or more variable 

(to incorporate interactions between variables), and partial agreement definitions. Future work could also study the 

impact of matching errors on analyses performed after matching. The latter study interest would require a different 

simulated or actual data set than the one used in this article. Finally, a few research questions were identified as

part of this research, as mentioned in the primary text of this paper. These topics could be investigated in future 

research and could be reasons that it is quite difficult to improve record linkage effectiveness in some NDI 

situations. 

References 

Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977). Maximum likelihood estimation from incomplete data via the 

EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B, 78, 221-227. 

Lahiri, P., and Larsen, M.D. (2005). Regression analysis with linked data. Journal of the American 

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Statistical Association, 100, 222-230. 

Larsen, M.D. (2004). Record linkage using finite mixture models. Applied Bayesian Modeling and Causal 

Inference from Incomplete-Data Perspectives. Wiley. 309-318. 

Larsen, M.D. (2006). Entry for the Sage Encyclopedia of Measurement and Statistics on ``Latent Class Analysis.'' 

Larsen, M.D. (2006). Entry for the Sage Encyclopedia of Measurement and Statistics on ``Record Linkage.'' 

Larsen, M.D. (2006). Ideas for secure record linkage. Privacy in Statistical Databases 2006. [CDROM]. CENEXSDC Conference. Rome, Italy. 

Larsen, M.D. (2010). Record Linkage Modeling in Federal Statistical Databases. FCSM research conference 2009. 

http://www.fcsm.gov/09papers/Larsen_II-C.pdf. 

Larsen, M.D., and Rubin, D.B. (2001). Iterative automated record linkage using mixture models. 

Journal of the American Statistical Association, 96, 32-41. 

Sayer, B. (2006). Comparing BigMatch Results to Current National Death Index (NDI) Selection Methods. 

Proceedings of the Survey Research Methods Section (Alexandria, VA: American Statistical Association): 3648-55. 

Appendix: File Layouts 

NDI File (N=995,043) 

Item Start End 

State of Death 1 2 

NYC indicator (not used) 2 5 

Certificate Number 6 11 

Last Name 12 31 

First Name 32 46 

Middle Initial 47 47 

Sex 49 49 

Month of Death 50 51 

Day of Death 52 53 

Year of Death (two digit) 54 55 

Race 56 56 

Age Units (for age at death) 57 57 

Age (2 digits) 58 59 

Month of Birth 60 61 

Day of Birth 62 63 

Year of Birth (two digit) 64 65 

State of Birth 66 67 

Marital Status 68 68 

Social Security Number 69 77 

State of Residence 78 79 

Birth Surname 80 88 

NYSIIS Last Name 100 109 

NYSIIS First Name 110 119 

NYSIIS Birth Surname 120 129 

Year of Death (4 digits) 135 138 

Year of Birth (4 digits) 139 142 

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User File (10,000 people, 5,043 deceased, 4,957 non-deceased 128,270 submission records) 

Item Start End 

Last Name 1 20 

First Name 21 35 

Middle Initial 36 36 

Social Security Number 37 45 

Month of Birth 46 47 

Day of Birth 48 49 

Year of Birth (4 digit) 50 53 

Birth Surname 54 71 

Age Unit 72 72 

Age (2 digit) 73 74 

Sex 75 75 

Race 76 76 

Marital Status 77 77 

State of Residence 78 79 

State of Birth 80 81 

Year of Death (or Interview) 82 85 

State of Death 86 87 

Certificate Number 88 93 

Type of Submission Record 98 99 

Vital Status (Deceased or Alive) 100 100 

Middle Name 101 115 

NYSIIS Last Name 116 125 

NYSIIS First Name 126 135 

NYSIIS Middle Name 136 145 

NYSIIS Birth Surname 146 155 

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