Assessing Cancer History Accuracy in Primary Care Electronic Health Records Through Cancer Registry Linkage

Megan Hoopes, Robert Voss, Heather Angier, Miguel Marino, Teresa Schmidt, Jennifer E DeVoe, Jeffrey Soule, Nathalie Huguet, Megan Hoopes, Robert Voss, Heather Angier, Miguel Marino, Teresa Schmidt, Jennifer E DeVoe, Jeffrey Soule, Nathalie Huguet

Abstract

Background: Many cancer survivors receive primary care in community health centers (CHCs). Cancer history is an important factor to consider in the provision of primary care, yet little is known about the completeness or accuracy of cancer history data contained in CHC electronic health records (EHRs).

Methods: We probabilistically linked EHR data from more than1.5 million adult CHC patients to state cancer registries in California, Oregon, and Washington and estimated measures of agreement (eg, kappa, sensitivity, specificity). We compared demographic and clinical characteristics of cancer patients as estimated by each data source, evaluating distributional differences with absolute standardized mean differences.

Results: A total 74 707 cancer patients were identified between the 2 sources (EHR only, n = 22 730; registry only, n = 23 616; both, n = 28 361). Nearly one-half of cancer patients identified in registries were missing cancer documentation in the EHR. Overall agreement of cancer ascertainment in the EHR vs cancer registries (gold standard) was moderate (kappa = 0.535). Cancer site-specific agreement ranged from substantial (eg, prostate and female breast; kappa > 0.60) to fair (melanoma and cervix; kappa < 0.40). Comparing population characteristics of cancer patients as ascertained from each data source, groups were similar for sex, age, and federal poverty level, but EHR-recorded cases showed greater medical complexity than those ascertained from cancer registries.

Conclusions: Agreement between EHR and cancer registry data was moderate and varied by cancer site. These findings suggest the need for strategies to improve capture of cancer history information in CHC EHRs to ensure adequate delivery of care and optimal health outcomes for cancer survivors.

© The Author(s) 2020. Published by Oxford University Press.

Figures

Figure 1.
Figure 1.
Diagram of electronic health record (EHR) linkage to California, Oregon, and Washington state cancer registries and resulting subgroups for validation analyses.
Figure 2.
Figure 2.
Percentage of leading cancers identified by source of ascertainment. Total number of cases ascertained from either source presented in parentheses. Width of bars is proportional to this combined case count. EHR = electronic health record.
Figure 3.
Figure 3.
Distribution of year of diagnosis and age at diagnosis among matched cases, electronic health record (EHR) vs cancer registry. Comparisons include matched patient*sites (same cancer site and patient in both EHR and registry data), n = 27 026. We imputed mid-year for dates where only the year was known and mid-month if day was unknown.

References

    1. Leach CR, Weaver KE, Aziz NM, et al.The complex health profile of long-term cancer survivors: prevalence and predictors of comorbid conditions. J Cancer Surviv. 2015;9(2):239–251.
    1. Institute of Medicine and National Research Council. From Cancer Patient to Cancer Survivor: Lost in Transition. Washington, DC: National Academies Press; 2006.
    1. Pollack LA, Adamache W, Ryerson AB, Eheman CR, Richardson LC.. Care of long‐term cancer survivors: physicians seen by Medicare enrollees surviving longer than 5 years. Cancer. 2009;115(22):5284–5295.
    1. Roy S, Vallepu S, Barrios C, Hunter K.. Comparison of comorbid conditions between cancer survivors and age-matched patients without cancer. J Clin Med Res. 2018;10(12):911–919.
    1. Krist AH, Beasley JW, Crosson JC, et al.Electronic health record functionality needed to better support primary care. J Am Med Inform Assoc. 2014;21(5):764–771.
    1. Sollie A, Roskam J, Sijmons RH, Numans ME, Helsper CW.. Do GPs know their patients with cancer? Assessing the quality of cancer registration in Dutch primary care: a cross-sectional validation study. BMJ Open. 2016;6(9):e012669.
    1. Zahnd WE, James AS, Jenkins WD, et al.Rural-Urban Differences in Cancer Incidence and Trends in the United States. Cancer Epidmiol Biomarkers Prev. ; 2018;27(11):1265–1274.
    1. Olaku OO, Taylor EA.. Cancer in the medically underserved population. Prim Care. 2017;44(1):87–97.
    1. Fernandez LM, Becker JA.. Women’s select health issues in underserved populations. Prim Care. 2017;44(1):47–55.
    1. Centers for Disease Control and Prevention. BRFSS Prevalence and Trends Data. . 2015. Accessed April 18, 2020.
    1. Hoopes M, Schmidt T, Huguet N, et al.Identifying and characterizing cancer survivors in the US primary care safety net. Cancer. 2019;125(19):3448–3456.
    1. Dregan A, Moller H, Murray-Thomas T, Gulliford M.. Validity of cancer diagnosis in a primary care database compared with linked cancer registrations in England. Population-based cohort study. Cancer Epidemiol. 2012;36(5):425–429.
    1. Boggon R, van Staa TP, Chapman M, Gallagher AM, Hammad TA, Richards MA.. Cancer recording and mortality in the General Practice Research Database and linked cancer registries. Pharmacoepidemiol Drug Saf. 2013;22(2):168–175.
    1. Thompson CA, Jin A, Luft HS, et al.Population-Based Registry Linkages to Improve Validity of Electronic Health Record-Based Cancer Research. Cancer Epidmiol Biomarkers Prev. 2020;29(4):796–806.
    1. Tucker TC, Durbin EB, McDowell JK, Huang B.. Unlocking the potential of population‐based cancer registries. Cancer. 2019;125(21):3729–3737.
    1. North American Association of Central Cancer Registries (NAACCR). . Accessed April 20, 2020.
    1. Petrik AF, Green BB, Vollmer WM, et al.The validation of electronic health records in accurately identifying patients eligible for colorectal cancer screening in safety net clinics. Fam Pract. 2016;33(6):639–643.
    1. Bailey SR, Heintzman JD, Marino M, et al.Measuring preventive care delivery: comparing rates across three data sources. Am J Prev Med. 2016;51(5):752–761.
    1. Centers for Disease Control and Prevention. Link Plus I Registry Plus. . Accessed March 31, 2020.
    1. National Cancer Institute. Download MatchPro Software. . Accessed March 31, 2020.
    1. Surveillance, Epidemiology, and End Results. SEER ICD-O-3 Coding Materials. . Accessed March 31, 2020.
    1. ICD Conversion Programs. . Accessed March 31, 2020.
    1. Cunningham M. More than just the kappa coefficient: a program to fully characterize inter-rater reliability between two raters. Paper presented at SAS Global Forum; March 22-25, 2009; Washington, DC.
    1. Landis JR, Koch GG.. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–174.
    1. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity‐score matched samples. Stat Med. 2009;28(25):3083–3107.
    1. Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Commun Stat-Simul Comput. 2009;38(6):1228–1234.
    1. Charlson ME, Charlson RE, Peterson JC, Marinopoulos SS, Briggs WM, Hollenberg JP.. The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients. J Clin Epidemiol. 2008;61(12):1234–1240.
    1. Hudson SV, Miller SM, Hemler J, et al.Cancer survivors and the patient-centered medical home. Behav Med Pract Policy Res. 2012;2(3):322–331.
    1. Rubinstein EB, Miller WL, Hudson SV, et al.Cancer survivorship care in advanced primary care practices: a qualitative study of challenges and opportunities. JAMA Intern Med. 2017;177(12):1726–1732.
    1. Tsui J, Howard J, O’Malley D, et al.Understanding primary care-oncology relationships within a changing healthcare environment. BMC Fam Pract. 2019;20(1):164.
    1. Hiatt RA, Tai CG, Blayney DW, et al.Leveraging state cancer registries to measure and improve the quality of cancer care: a potential strategy for California and beyond. JNCI. 2015;107(5):djv047.

Source: PubMed

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

3
Suscribir