Baozhen Qiao

Three cohorts including the Fire Department of the City of New York (FDNY), the World Trade Center Health Registry (WTCHR), and the General Responder Cohort (GRC), each funded by the World Trade Center Health Program have reported associations between WTC-exposures and cancer. Results have generally been consistent with effect estimates for excess incidence for all cancers ranging from 6 to 14% above background rates. Pooling would increase sample size and de-duplicate cases between the cohorts. However, pooling required time consuming steps: obtaining Institutional Review Board (IRB) approvals and legal agreements from entities involved; establishing an honest broker for managing the data; de-duplicating the pooled cohort files; applying to State Cancer Registries (SCRs) for matched cancer cases; and finalizing analysis data files. Obtaining SCR data use agreements ranged from 6.5 to 114.5 weeks with six states requiring >20 weeks. Records from FDNY (n = 16,221), WTCHR (n = 29,372), and GRC (n = 33,427) were combined de-duplicated resulting in 69,102 unique individuals. Overall, 7894 cancer tumors were matched to the pooled cohort, increasing the number cancers by as much as 58% compared to previous analyses. Pooling resulted in a coherent resource for future research for studies on rare cancers and mortality, with more representative of occupations and WTC- exposure.

Social Security numbers (SSNs) collected by cancer surveillance registries in the United States are used for patient matching, deduplication, follow-up, and linkage studies. However, due to various reasons, a small proportion of patient records have missing or inaccurate SSNs. Recently, New York State Cancer Registry (NYSCR) data have been linked to LexisNexis data to obtain patient demographic information, including SSNs. The current study evaluated the feasibility of using LexisNexis to improve SSN information in the NYSCR. Patients diagnosed during the years 2005–2016, aged 21 or older, in the NYSCR were linked to LexisNexis data. For the matched patients, LexisNexis returned demographic information, including SSNs as available. Percentages of patients without LexisNexis matches or without LexisNexis SSNs were examined by demographic characteristics. We used multivariate logistic regression analyses to further evaluate how patient demographic characteristics affected the likelihood of no LexisNexis matches or of no SSNs returned. For patients with SSNs returned, LexisNexis SSNs were compared with registry SSNs. If patients had prior missing registry SSNs or if LexisNexis SSNs were inconsistent with registry SSNs, we used Match*Pro to review and verify match status. Registry SSNs were updated for those confirmed to be true matches. Improvement of SSNs was assessed based on percentage reduction of missingness. Of 1,396,078 patient records submitted for LexisNexis linkage, 1.6% were not matched. Among those matched, 1.5% did not have SSNs returned. Multivariate logistic regression analyses indicated that patients who were female, Black, Asian Pacific Islander (API), Hispanic, born outside the United States, deceased, or living in poorer census tracts were more likely to not have LexisNexis matches, or to not have SSNs returned. Among 47,271 patients with missing registry SSNs (3.4%), 26,895 had SSNs returned from LexisNexis, and 24,919 were confirmed to be true matches. After registry SSNs updates, the percentage of SSN missingness was reduced to 1.7%, with a larger absolute reduction observed among those who were younger than 60 years, API, or alive. For 33,057 patients with inconsistent SSNs, 11,474 were due to incorrect consolidations of SSNs in the registry, and those SSNs were subsequently fixed. LexisNexis is a valuable resource for improving the quality of SSN information in registries. Our results showed that the overall percentage of patients with missing SSNs was reduced from 3.4% to 1.7% after LexisNexis link-age, and SSNs that were initially incorrectly consolidated for some patients were also identified and subsequently fixed. However, the magnitude of SSN improvement varied by patient demographic characteristics. Data quality improvements often require resources, and this evaluation can assist registries with decisions related to similar efforts.

State cancer registries in the United States are data sources for estimating population-based cancer survival. However, the completeness of patient follow-up can affect the accuracy of survival estimates. Like many registries, the New York State Cancer Registry (NYSCR) conducts patient follow-up largely through linkages with other data sources. Even after expending great effort on linkages, a small proportion of patients remain lost to follow-up (LTFU). In this study, we identified factors that are associated with the likelihood of LTFU in the NYSCR. First primary cancers (sequence number, 00 or 01 and excluding death-certificate- and autopsy-only cases) diagnosed during 2000-2018 among New York State residents were selected for study. All patients were followed through December 31, 2018. Based on each patient's vital status and last contact date, follow-up status was categorized into 2 groups: patients LTFU and patients not LTFU. Patients LTFU were examined by demographic and tumor characteristics. Multivariate logistic regression analyses were performed to evaluate the association between demographic/tumor characteristics and likelihood of LTFU. For patients LTFU, the timing of LTFU (within 1 year, 1 to <5 years, 5 to 10 years) was further described. LTFU rates within 5 years after cancer diagnosis were also examined. Among 1,797,228 patients, 74,722 were LTFU prior to December 31, 2018, representing 4.2% of all patients and 7.6% of alive patients. About 60% of LTFU occurred within 1 year after cancer diagnosis. Compared to the reference group, logistic regression analyses indicated that patients LTFU were more likely to be female, Black, Asian/Pacific Islander (API), Hispanic, foreign born, insured by Medicaid, uninsured, aged <20 years, and living in New York City or metropolitan counties. Cases reported by laboratories only and physician offices also had a higher likelihood of LTFU. Similar patterns and effects were identified when evaluating 5-year LTFU. Identifying factors associated with patient LTFU is important for cancer registries to improve follow-up data. We found that LTFU is not random; rather, certain patient groups have higher LTFU rates than others. For registries that conduct follow-up through linkages, it is critical to collect high-quality and complete demographic data, especially for females, children, the foreign born, and minority race/ethnicity groups.