U.S. Maternally Linked Birth Records May Be Biased for Hispanics and Other Population Groups

Leiss, Jack K.; Giles, Denise; Sullivan, Karen; Mathews, Rahel; Sentelle, Glenda; Tomashek, Kay M.

doi:10.1016/j.annepidem.2009.09.003

Cited by 7 publications

(7 citation statements)

References 40 publications

(63 reference statements)

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“…10,11,13 However, because not having a SSN in this analysis is due to the combination of participants not knowing, not having, not being asked (per study protocol) or unwilling to provide it, the percentages in this analysis are substantially higher. Consistent with earlier studies, 9,11,13,26 the higher percentage of estimated missed links provides further evidence -Miller et al that the linkage process may be less effective for the Hispanic population. However, this analysis contributes to the literature by quantifying the expected missed links due to missing SSN, finding that the estimated missed links were almost 90% higher among Hispanic and Asian groups compared with White non-Hispanic, groups.…”

Section: Discussionsupporting

confidence: 79%

Racial and Ethnic Differences in a Linkage With the National Death Index

2017

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Methods:We used preventive fraction methods based on race/ethnicity-specific Cox proportional hazards models of the relationship between availability of SSN and mortality based on observed links, adjusted for survey year, sex, age, respondent-rated health, education, and US nativity.Results: Availability of a SSN and observed percent linked were significantly lower for Hispanic and Asian/Pacific Islander (PI) participants compared with White non-Hispanic participants. We estimated that more than 18% of expected links were missed due to lack of SSNs among Hispanic and Asian/PI participants compared with about 10% among White non-Hispanic participants. However, correcting the observed links for expected missed links appeared to only have a modest impact on mortality disparities by race/ethnicity. Conclusions:Researchers conducting analyses of mortality disparities using the NDI or other linked death records, need to be cognizant of the potential for differential linkage to contribute to their results. Ethn

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Section: Discussionsupporting

confidence: 79%

Racial and Ethnic Differences in a Linkage With the National Death Index

2017

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“…Information is classified into three groups: 1) sensitive identifiers (including first and last name, date of birth, date of diagnosis and date of death), 2) epidemiological data (sex, month and year of birth, residential code, diagnosis), 3) linkage identifiers (a set of 22 fields of standardized 3 identifiers and phonetically encoded identifiers).…”

Section: A the Current German Standard For Linking Registriesmentioning

confidence: 99%

“…In practice, either phonetic codes or Bloom filter encodings might be used for the encryption. For efficient blocking and linkage, keyed HMACs for all numerical identifiers (hospital ID, state, sex, 3 The standardization includes reformatting values, removal of special characters, within-word punctuation and professional titles. multifetal sequence number, date of birth and hour of birth) might be considered.…”

Section: B the New Proposalmentioning

confidence: 99%

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Building a National Perinatal Data Base without the Use of Unique Personal Identifiers

Schnell

Borgs

2015

2015 IEEE International Conference on Data Mining Workshop (ICDMW)

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Abstract-To assess the quality of hospital care, national databases of standard medical procedures are common. A widely known example are national databases of births. If unique personal identification numbers are available (as in Scandinavian countries), the construction of such databases is trivial from a computational point of view. However, due to privacy legislation, such identifiers are not available in all countries. Given such constraints, the construction of a national perinatal database has to rely on other patient identifiers, such as names and dates of birth. These kind of identifiers are prone to errors. Furthermore, some jurisdictions require the encryption of personal identifiers. The resulting problem is therefore an example of Privacy Preserving Record Linkage (PPRL). This contribution describes the design considerations for a national perinatal database using data of about 600,000 births in about 1,000 hospitals. Based on simulations, recommendations for parameter settings of Bloom filter based PPRL are given for this real world application. I. BACKGROUNDFor the medical assessment of German hospitals, a federal institution (GBA) 1 is obliged by law to link administrative records of more than 600,000 births yearly. The records are scattered across about 1,000 independent perinatal and neonatal units. The linked data is used for monitoring hospital performance and epidemiological analyses like spatial prevalence of very low birth weights. Due to privacy regulations, patient databases of hospitals are not linked by an electronic network. The hospitals use different electronic medical record systems, but have to use the same data exchange format. All details of the data exchange are part of a mandatory regulation. Because the German health insurance system has no common unique personal identifier number, other patient identifiers have to be used. Since the current regulations do not allow names in any form, encrypted or not, current linkage is based on different combinations of health insurance numbers, birth weight and hospital identifiers. Given the described constraints, only about 80% of the records can be linked [1].From a statistical point of view, non-linked records might cause a missing data problem [2]. If the fact, that a true link is missed, depends on variables of interest, this is referred to as differential linkage error [3], [4]. This might result in biased estimates of causal effects and population parameters [5], [6]. Concerning our field of application, evidence of bias caused by differences between linked and non-linked maternal data sets has been published [7], [8]. The easiest way to 1 For details, see www.english.g-ba.de.reduce differential linkage bias is improving the linkage rate. Therefore, using additional identifiers has been proposed to the regulatory authority [9]. As previous research has shown [10] [30]. In this paper, we will describe the design of a national perinatal database using Bloom filters for PPRL. 2 We are not aware of any published attack against encryp...

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“…From a statistical point of view, unlinked records result in a missing data problem [7]. If a true link is missed, but the link is crucial for variables of interest, this is referred to as differential linkage error [8], [9]. Hence, a differential linkage error may result in biased estimates of causal effects and population parameters [10], [11].…”

Section: Introductionmentioning

confidence: 99%