Record linkage of claims and cancer registries data-Evaluation of a deterministic linkage approach based on indirect personal identifiers

PURPOSE

In Germany, record linkage of claims and cancer registry data is cost- and time-consuming, since up until recently no unique personal identifier was available in both data sources. The aim of this study was to evaluate the feasibility and performance of a deterministic linkage procedure based on indirect personal identifiers included in the data sources.

METHODS

We identified users of glucose-lowering drugs with residence in four federal states in Northern and Southern Germany (Bavaria, Bremen, Hamburg, Lower Saxony) in the German Pharmacoepidemiological Research Database (GePaRD) and assessed colorectal and thyroid cancer cases. Cancer registries of the federal states selected all colorectal and thyroid cancer cases between 2004 and 2015. A deterministic linkage approach was performed based on indirect personal identifiers such as year of birth, sex, area of residence, type of cancer and an absolute difference between the dates of cancer diagnosis in both data sources of at most 90 days. Results were compared to a probabilistic linkage using "direct" personal identifiers (gold standard).

RESULTS

The deterministic linkage procedure yielded a sensitivity of 71.8% for colorectal cancer and 66.6% for thyroid cancer. For thyroid cancer, the sensitivity improved when using only inpatient diagnosis to define cancer in GePaRD (71.4%). Specificity was always above 99%. Using the probabilistic linkage to define cancer cases, the risk for colorectal cancer was estimated 10 percentage points lower than when using the deterministic approach.

CONCLUSIONS

Sensitivity of the deterministic linkage approach appears to be too low to be considered as reasonable alternative to the probabilistic linkage procedure.

Demystifying probabilistic linkage: Common myths and misconceptions

Many of the distinctions made between probabilistic and deterministic linkage are misleading. While these two approaches to record linkage operate in different ways and can produce different outputs, the distinctions between them are more a result of how they are implemented than because of any intrinsic differences. In the way they are generally applied, probabilistic and deterministic procedures can be little more than alternative means to similar ends-or they can arrive at very different ends depending on choices that are made during implementation. Misconceptions about probabilistic linkage contribute to reluctance for implementing it and mistrust of its outputs. We aim to explain how the outputs of either approach can be tailored to suit the intended application, but also to highlight the ways in which probabilistic linkage is generally more flexible, more powerful and more informed by the data. This is accomplished by examining common misconceptions about probabilistic linkage and its difference from deterministic linkage, highlighting the potential impact of design choices on the outputs of either approach. We hope that better understanding of linkage designs will help to allay concerns about probabilistic linkage, and help data linkers to select and tailor procedures to produce outputs that are appropriate for their intended use.

Probabilistic Matching Approach to Link Deidentified Data from a Trauma Registry and a Traumatic Brain Injury Model System Center

OBJECTIVE

There is no civilian traumatic brain injury database that captures patients in all settings of the care continuum. The linkage of such databases would yield valuable insight into possible care interventions. Thus, the objective of this article is to describe the creation of an algorithm used to link the Traumatic Brain Injury Model System (TBIMS) to trauma data in state and national trauma databases.

DESIGN

The TBIMS data from a single center was randomly divided into two sets. One subset was used to generate a probabilistic linking algorithm to link the TBIMS data to the center's trauma registry. The other subset was used to validate the algorithm. Medical record numbers were obtained and used as unique identifiers to measure the quality of the linkage. Novel methods were used to maximize the positive predictive value.

RESULTS

The algorithm generation subset had 121 patients. It had a sensitivity of 88% and a positive predictive value of 99%. The validation subset consisted of 120 patients and had a sensitivity of 83% and a positive predictive value of 99%.

CONCLUSIONS

The probabilistic linkage algorithm can accurately link TBIMS data across systems of trauma care. Future studies can use this database to answer meaningful research questions regarding the long-term impact of the acute trauma complex on health care utilization and recovery across the care continuum in traumatic brain injury populations.

Validation of an index to estimate the prevalence of frailty among community-dwelling seniors

BACKGROUND

This study validates cut-points for a frailty index (FI) to identify seniors at risk of a hospital-related event and estimates the number of frail seniors living in the community. The FI developed by Rockwood and Mitnitski defines levels of frailty based on scores of 0 to 1.0.

DATA AND METHODS

The cut-point validation was conducted using Stratum-Specific Likelihood Ratios applied to combined 2003 and 2005 Canadian Community Health Survey (CCHS) data, linked to hospital records from the Discharge Abstract Database (2002 to 2007). Based on the validated cut-points, frailty prevalence was estimated using 2009/2010 CCHS data.

RESULTS

Seniors scoring more than 0.21 on the FI were considered to be at elevated risk of hospital-related events. Four additional frailty levels were identified: non-frail (0 to ≤0.1), pre-frail (>0.1 to ≤0.21), more frail (>0.30 to ≤0.35) (women only), and most frail (frail-group subset) (0.45 or more). The number of community-dwelling seniors considered to be frail was estimated at about 1 million (24%) in 2009/2010; another 1.4 million (32%) could be considered pre-frail. Frailty prevalence rose with age; was higher among women than among men; and varied by geographic location.

INTERPRETATION

A cut-point of more than 0.21 can be used to identify frail seniors living in the community.