br This analysis has several limitations particularly its re
This analysis has several limitations, particularly its reliance on retrospective questionnaire responses from implementing staﬀ. Although this Spectinomycin is appropriate for micro-level data collection (Frick, 2009), such an approach has inherent recall issues. We strove to minimize these issues by focusing on key informants at each organi-zation and attempting to facilitate consistent understanding of terms and concepts. However, the heterogeneity of experiences and ability to recall events, magnified by high staﬀ turnover at some sites, compli-cated data collection. Whenever possible, we re-contacted our in-formants to clarify responses, but considerable ambiguity remains. Future economic research should explore new methods of extracting micro-level implementation data vital to understanding the economics
of screening programs. Data collection methods that are not perceived by clinic staﬀ as intrusions into ongoing patient care are especially valuable. A checklist for the conduct and reporting of micro-costing studies may be helpful in this regard (Ruger and Reiﬀ, 2016). In ad-dition, “time-driven” activity-based costing (TDABC), a modified form of the standard ABC methodology applied in this study, has been de-scribed as a micro-costing approach well suited to accommodate com-plex health care cost accounting (Kaplan and Anderson, 2004; Kaplan and Porter, 2011). Standard ABC is considered a resource-intensive approach to data collection, which can inhibit its use. TDABC is in-tended to maintain the validity of cost data while reducing the re-sources needed to acquire them. TDABC requires only two key para-meters: the capacity cost rate (the cost of capacity-supplying resources divided by their practical, not theoretical, capacity), and the time re-quired to perform service delivery activities. To date, TDABC has been used primarily to analyze hospital and clinic services (Keel et al., 2017), but its utility in facilitating evaluation of FQHC screening programs such as STOP CRC should also be explored.
In addition, program-level data did not distinguish between costs of screening and diagnostic colonoscopy for each organization, which complicates understanding the true intervention eﬀects in terms of improving CRC screening rates. Improved organization-level data sys-tems will mitigate Linking number issue. Also, our implementation cost estimates are based on eight organizations. Finally, cross-organizational diﬀer-ences in patient population, management commitment to STOP CRC, resource availability, and other latent factors may well contribute to our reported cost diﬀerences.
Our results indicate the implications for cost-eﬀectiveness of im-plementing a standard CRC screening intervention within a pragmatic trial setting involving multiple FQHCs with varied patient populations,
clinical structures, and resource availability. The variation in perfor-mance across organizations serves to emphasize the need for future similar evaluations that can contribute to our knowledge of how to introduce such screening programs to underserved populations most eﬀectively and eﬃciently.
This work was supported by the National Cancer Institute of the National Institutes of Health (UH3CA188640).
We gratefully acknowledge the contributions of Bill Vollmer, Jennifer Schneider, Jennifer Rivelli, and Sacha Reich (Center for Health Research, Kaiser Permanente Northwest). The content is solely the re-sponsibility of the authors and does not necessarily represent the oﬃ-cial views of the National Institutes of Health. Dr. Meenan was pri-marily responsible for data analysis and paper writing. Ms. Petrik contributed to data generation and analysis as well as paper editing. Drs. Coronado and Green contributed to study design, data analysis and paper editing. The STOP CRC trial is registered at ClinicalTrials.gov (NCT01742065). No financial disclosures were reported by the authors of this paper.