Using Medicare data to estimate the number of cases missed by a cancer registry: a 3-source capture-recapture model.
BACKGROUND
Cancer surveillance is essential for assessing patterns of cancer occurrence. State cancer registries do not capture all available cases potentially biasing results. Secondary data may be useful in identifying new cases and estimating the number of cases missed.


OBJECTIVE
We sought to create 2 distinct data sources from Medicare claims to use in combination with registry data as 3 sources for a capture-recapture analysis to estimate the capture rate and bias in capture of a statewide cancer registry.


METHODS
Data from the Virginia cancer registry (Registry) were merged with Medicare inpatient (Part A) as well as Medicare outpatient and physician claims (Part B) to provide 3 sources to estimate missing cases. A 3-source loglinear model was used to estimate the number of missing cancer cases for breast, lung, colorectal, and prostate cancer. Models included main effects and interactions. Additional analysis looked at the effect of demographic and comorbidity variables.


RESULTS
Loglinear models demonstrated mostly positive dependence between the 3 sources, implying that 2-source models would underestimate missing cases and overestimate capture rates. Using capture-recapture estimates of total number of cancer cases as the denominator, capture rates for Registry ranged from 59% (colorectal) to 74% (lung). When the aggregate of cases found by either Medicare or Registry were used the capture rates ranged from 74% (prostate) to 89% (breast). Further analysis indicated that capture rates differed by demographic characteristics.


CONCLUSIONS
We conclude that Medicare claims are useful to supplement a Registry, estimate the number of missing cases, and assess bias in capture.
McClish
Donna
D
Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298-0032, USA. mcclish@hsc.vcu.edu
Penberthy
Lynne
L
D000368:Aged; D001943:Breast Neoplasms / Q000191:economics / Q000453:epidemiology; D015179:Colorectal Neoplasms / Q000191:economics / Q000453:epidemiology; D005260:Female; D006801:Humans; D016014:Linear Models; D008175:Lung Neoplasms / Q000191:economics / Q000453:epidemiology; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data*; D015233:Models, Statistical; D009369:Neoplasms / Q000145:classification / Q000191:economics / Q000453:epidemiology*; D011159:Population Surveillance / Q000379:methods*; D011471:Prostatic Neoplasms / Q000191:economics / Q000453:epidemiology; D040442:Public Health Informatics; D012042:Registries / Q000706:statistics & numerical data*; D018511:Systems Integration; D014768:Virginia / Q000453:epidemiology