Karen J. Wernli, PhD

 Fecal immunochemical tests (FITs) can efficiently screen for colorectal cancer (CRC), but little is known on the timing to their completion. We investigate the time to return of a FIT following an order and describe patient characteristics associated with FIT return.

 We identified 63,478 members of Kaiser Permanente Washington, aged 50 to 74 years, who received a FIT order from 2011 through 2012. Patient characteristics were ascertained through administrative and electronic health record data sources. We compared time from FIT order to return by patient characteristics using Kaplan-Meier and Cox regression methods.

 About half (53.7%) of members completed a FIT. Median time from order to return was 13 days (mean, 44.5 days; interquartile range, 6-42 days). There was higher completion of FITs among Asian patients (hazard ratio [HR], 1.43; 95% CI, 1.38-1.48), black patients (HR, 1.13; 95% CI, 1.08-1.19), and Hispanic patients (HR, 1.10; 95% CI, 1.04-1.16) compared with white patients; among patients with recent CRC testing (vs no testing in past 2 years; HR, 1.90; 95% CI, 1.86-1.95); and among patients with Medicare insurance (vs commercial; HR, 1.30; 95% CI, 1.24-1.37). Factors associated with decreased FIT completion included younger age (50-54 years vs 70-74 years; HR, 0.87; 95% CI, 0.82-0.92), obesity (vs normal body mass index; HR, 0.88; 95% CI, 0.86-0.91), and higher Charlson Comorbidity Index score (≥3 vs 0; HR, 0.82; 95% CI, 0.79-0.87).

 Time to return of FIT varies by patient characteristics. We observed greater FIT completion among people of color, suggesting that racial disparities in CRC may not be due to patient completion of the test after an order is received.

Fecal immunochemical tests (FITs) are efficient and cost-effective means of colorectal cancer screening if completed in a timely manner.

About half (53.7%) of patients in our study sample returned their annual FIT within 1 year of clinician order.

Among completed FITs, more than half were returned within 2 weeks from the time of clinician order.

We did not observe disparities in FIT return by race, but younger age and higher body mass index were significantly associated with lower FIT completion.

The US Preventive Services Task Force (USPSTF) recommends several colorectal cancer (CRC) screening strategies for average-risk adults aged 50 to 75 years.

 These screening options received the highest grade by the USPSTF, reflecting the strength of evidence that shows high certainty of substantial benefit from CRC screening.

One option is an annual high-sensitivity guaiac-based fecal occult blood test (gFOBT); another option is an annual fecal immunochemical test (FIT).

 FITs are consistently a preferred screening tool among patients at average risk for CRC.

 Compared with colonoscopy, FIT use is less invasive, is less expensive, and carries less risk of adverse events, giving it the potential to translate into increased CRC screening participation by underscreened groups and a lower overall cost to screening programs.

 As stool-based screening tests, FITs are an improvement over previous stool tests due to a higher sensitivity in detection of CRC and reduced burden on patients (ie, FIT requires only 1 stool test, whereas gFOBT requires 3).

 However, no previous research has investigated the overall completion of FIT orders in a community-based setting and assessed the time to completion for this modality.

The process of cancer screening completion consists of several steps, from identification of the eligible population to completion of the test and follow-up of testing results.

 Understanding where failures in the screening process occur (ie, uptake, longitudinal adherence, follow-up of positive tests) is critical for improving the process. Previous research has highlighted the deficits in overall compliance with CRC screening recommendations without identifying failures at specific steps in the screening process.

 In a previous study among members of Kaiser Permanente Washington (KPWA), we noted the prominent use of stool tests (either gFOBT or FIT) among first-time screenees, with 72% of study participants using a stool-based test for their first CRC screening after age 50 years.

 Further, the retrospective cohort showed that important patient characteristics, including higher body mass index (BMI) and female gender, were associated with lower uptake of any CRC screening tests when considering all screening methods combined. Other studies have shown disparities in general CRC screening for all modalities in socioeconomically disadvantaged populations.

 Such findings encompass 2 critical aspects in the cancer care continuum: (1) the administration of screening options to patients by clinicians as part of regular primary care, resulting in CRC screening order; and (2) the follow-through of screening on the part of the patient once given a screening order.

Here we focus on understanding factors associated with time to completion of FIT orders, as this step is required for FIT use to be effective. Building on previous findings regarding the acceptability and performance of FIT use,

 we characterized patterns of return of a FIT following clinician order and identified patient characteristics associated with lower rates of FIT screening completion within an integrated healthcare delivery system.

The Population-based Research to Optimize the Screening Process (PROSPR) consortium was created by the National Cancer Institute to allow multiple study sites to coordinate research for the improvement of screening practices for breast, cervical, and colorectal cancers in community settings.

 One aim of the consortium is to understand screening processes and potential failures and successes of the screening process to improve overall patient health. The collection of data throughout the screening process can further inform comparative effectiveness research for CRC screening.

 KPWA, a mixed-model health insurance and care delivery system in Washington state, is a member of the PROSPR consortium. KPWA data were extracted in 2016 from the Virtual Data Warehouse (VDW), which houses patient information in separate content areas: enrollment/demographics, utilization, laboratory, pharmacy, census, tumor registry, vital signs, and social history (tobacco, alcohol, sexual activity, etc).

The study protocol received institutional review board approval through the KPWA Human Subjects Division for waiver of consent to enroll participants, link study data, and perform statistical analyses.

Identification of FIT Orders and Test Return

CRC screening guidelines at KPWA follow USPSTF recommendations as previously described.

 In 2011, KPWA clinicians replaced offering of the 3-sample SENSA gFOBT with the 1-sample FIT because of the improved diagnostic characteristics of the test, and they began to offer and distribute the FIT at routine clinical visits. Routine reminders are not part of the usual clinical workflow. Our cohort was defined as patients who received an order for a FIT between January 1, 2011, and December 31, 2012. From clinical laboratory data, we identified the date of FIT receipt with Current Procedural Terminology codes (ie, 82270, 82271, 82272, 82273, 82274) and Healthcare Common Procedure Coding System codes (ie, G01017, G0328, G0394).

We excluded FIT orders due to (1) standing future orders (ie, automatic orders for future receipt of FIT) (n = 104); (2) orders that had a valid reason for being cancelled (see 

]), such as a new order for colonoscopy or flexible sigmoidoscopy, as those individuals are unlikely to complete a FIT in addition to other CRC screening (n = 2358); (3) inpatient orders (n = 44); and (4) orders included in the treatment arm of an ongoing randomized controlled trial (n = 81).

 Among the remaining orders, we selected the first order per patient as the index order for the start of follow-up. Our study was focused on average-risk adults; hence, orders from patients were excluded if they had a prior diagnosis of CRC (n = 66); full or partial colectomy, ileostomy, or proctectomy (n = 79); Crohn disease (n = 457); or ulcerative colitis (n = 496).

Patient characteristics were selected based on identified risk factors for CRC.

 We obtained patient characteristics through administrative and clinical patient records. Age at the time of the FIT order was calculated based on patient date of birth. The calculated variable of BMI, based on the most recent measurements within the past year of FIT order, was extracted by the VDW from vital signs recorded at the corresponding clinical visit. We used the Charlson Comorbidity Index (CCI) score as of the date of FIT order, as well as status for specific comorbidities of interest.

Self-reported race/ethnicity was grouped as non-Hispanic white (reference group), non-Hispanic black, Hispanic (all races), non-Hispanic Asian, non-Hispanic multiracial, and non-Hispanic unknown/missing race. Gender was binary as male (reference group) and female. Age at FIT order was divided into 5-year categories (50-54, 55-59, 60-64, 65-69, and 70-74 years), with the oldest as the reference category. Insurance status was grouped according to Medicare, commercial (reference group), and other payment options (including private pay, self-pay, and Medicaid). The most recent BMI measurement (within the past year) at the time of the first FIT order was categorized as underweight (below 18.5 kg/m

) (reference category), overweight (25.0-29.9 kg/m

 was calculated with the lowest score (0) as the reference group and compared with groups of higher scores (1, 2, and 3 or more). Additional analyses were performed for 3 comorbidities with the highest prevalence in the sample population: diabetes, chronic pulmonary disease, and history of myocardial infarction. Receipt of any CRC testing (eg, FIT, colonoscopy) within the 2 years prior to the index date was recorded as a binary variable.

We evaluated the association of demographic characteristics, CCI score, and medical conditions with the return of a FIT following clinician order. We performed time-to-event analysis from the date recorded of first FIT order until the date recorded for a received FIT. Individuals were censored for death, for disenrollment from KPWA, and at 365 days from the date of the first FIT order. We used 365 days as the end point because FITs are recommended as an annual screening tool; thus, patients could expect another order at the time of their annual visit. Time to completion was described using nonparametric Kaplan-Meier estimates, and differences between groups within variables were initially estimated by log-rank tests, with graphical display through 50 days from first FIT order. A univariate analysis was used to describe the mean, median, and interquartile range of time to completion of the FIT among those with a returned order. Among all patients in our population, Cox proportional hazard models were used to estimate unadjusted and adjusted hazard ratios (HRs) and 95% CIs comparing the time from order to receipt of FIT by patient-level factors with the variable-specific referent category. The adjusted model included all variables, without the specific comorbidities included in the CCI. Indicator variables were used for all variables in the models, which included an indicator for missing data where applicable. To estimate the adjusted HR for specific comorbidities, CCI score was removed from the model and replaced by each of the comorbidities in separate models. We used Breslow’s method for ties.

Completion of FIT order, rather than incompletion, was the outcome of interest in this analysis to maintain consistency of interpretation with the results of the time-to-event analysis. Therefore, an HR greater than 1 indicates better FIT completion, a clinically positive outcome, and conversely, an HR less than 1 indicates a higher risk of the adverse outcome (ie, incomplete FIT).

All analyses were performed using SAS version 9.4 (SAS Institute; Cary, North Carolina). Additional analyses of each model were conducted in which categorical variables for age, BMI, and CCI scores were replaced with the corresponding continuous variable and tested for trends.

We identified 63,478 men and women aged 50 to 74 years who were members of KPWA in 2011-2012 and received a FIT order for inclusion in our final study population. Among those with a FIT order, about half (54%) of the sample population returned a FIT to the laboratory within 1 year of the order date. In our sample population, nearly 60% of FIT order recipients were female and approximately 75% were white (

). There was an even distribution of subjects in each age group. Among those with a recorded BMI (71.0%), nearly 40% were obese. Approximately 25% of our patients had a CCI score of 1 or greater.

The proportion of patients who returned a completed FIT varied by patient race/ethnicity, age, and BMI (Table 1). Among patients who completed a FIT, the median return time of the FIT was 13 days. There was little variation in the median time to completion by patient characteristics (

), with a few exceptions. On average, members of Asian race/ethnicity completed a FIT 4 days earlier than white members. Older adults (70-74 years) returned a FIT 4 days earlier than those in the youngest categories (50-54 and 55-59 years). The substantial difference between the mean and median across groups, along with the difference between the median and third quartile, suggests a heavily right-skewed distribution of time to return (Table 2). The Kaplan-Meier curves display the time-to-completion experience comparing member groups, namely age groups, race/ethnicity, BMI, and CCI score (

), patient factors statistically significantly associated with decrease in FIT completion included being a woman, younger age compared with oldest age (70-74 years), being overweight, CCI score (≥1), and, more specifically, diagnosis with diabetes, chronic pulmonary disease, and myocardial infarction. Compared with white race/ethnicity, Asian, black, and Hispanic race/ethnicity were statistically significantly associated with improvements in FIT completion (Asian: adjusted HR, 1.43; 95% CI, 1.38-1.48; black: adjusted HR, 1.13; 95% CI, 1.07-1.19; Hispanic: adjusted HR, 1.10; 95% CI, 1.04-1.16).

A sensitivity analysis in which time was censored at 6 months, rather than 1 year, showed near-identical results, with very small attenuations of HRs (data not shown).

In our study, adults with the intention to return a FIT kit did so within 2 to 3 weeks from order date; otherwise, the FIT kit was more likely to not be returned. These results provide important evidence on the timing of return, which has not been previously reported and can support outreach interventions to improve FIT return. Further, we identify some differences by patient characteristics that indicate which patients might need additional support for stool-based CRC screening.

Our observation that women who receive a FIT order are less likely to return a completed FIT compared with men is consistent with previous study findings, suggesting gender-specific factors that influence completion of CRC screening, such as prior breast cancer screening.

 Women have been shown to be less likely to participate in CRC screening, possibly due to the additional need to participate in screening programs for breast and cervical cancers.

We saw a consistent linear trend with improved rates of FIT return with increasing age. Interventions aimed at improving initiation of CRC screening through referrals, tracking of patient outcomes, and mitigating patient barriers have been shown to be effective at increasing CRC screening among adults younger than 65 years and may help to attenuate the observed differences in FIT completion by age.

 Being insured by Medicare remained significantly associated with return in the adjusted model accounting for age, which is appropriate with increasing risk of CRC.

Across all variables, missing data were associated with slow return and overall incompletion of FITs. The attenuated HR in the adjusted models compared with that in the unadjusted models was likely a result of correlated missingness across variables. Although there are a variety of reasons for which members could have missing capture of data variables, the most probable case is that it reflects overall healthcare engagement. Thus, the observation that those patients who are missing data for a given variable less commonly returned their FIT or did so over more time is not surprising. For cases in which missing data are a reflection of lower healthcare engagement, additional support to understand and overcome barriers for such patients may prove to be challenging but could result in the greatest return on investment.

Higher BMI has been associated with less engagement with healthy behaviors, particularly in women,

 and it is associated with reduced participation in CRC screening compared with patients with normal BMI.

 CRC is particularly relevant to overweight and obese adults, as increased weight is a strong risk factor for CRC.

 In a subanalysis using a Cox proportional hazards model, we did see a marginally significant interaction (

 = .04) between gender and obesity on time to FIT completion (results not shown). The presence of comorbidities overall was associated with a slight reduction in completion of a FIT. We did not specifically evaluate interactions between patient characteristics, but important subgroups, such as overweight women with diabetes, could be potential target populations for screening follow-up.

 Our findings regarding differences by race/ethnicity contrast with those of previous studies, which have suggested lower rates of CRC screening among people of color and lower completion of FITs,

specifically for black and Hispanic individuals.

 Burnett-Hartman et al evaluated in stratified analyses the entire PROSPR network, indicating disparities in completion by race

; our results, however, support results in stratified analysis suggesting that Asian/Pacific Islanders are the earliest both to complete a FIT and to complete CRC screening overall. Results by other racial groups are similar to the stratified analysis but not for the pooled analysis reflective of larger samples in other systems. In our population, non-Hispanic black patients were more likely to complete FIT screening than non-Hispanic white patients. This is contrary to findings of previous studies, which have stated that a considerable proportion of the disparities in overall CRC survival between black and white patients might be attributable to differences in screening.

 However, our results are consistent with those of a recent randomized clinical trial that found that nonwhite participants were more likely to adhere to gFOBT than white participants, whereas white participants adhered more often to colonoscopy.

 As use of colonoscopy is generally more common than gFOBT/FIT for CRC screening in fee-for-service systems, this could explain part of the overall disparity in CRC screening adherence among minority populations.

It is important to note that completing a FIT is only 1 step in the CRC screening continuum, and a positive FIT requires a follow-up colonoscopy, which might incur out-of-pocket costs, even for insured patients.

 The potential for disparities in such follow-up merits further investigation. A recent study conducted at KPWA using mailed FITs and support over the phone was shown to double the number of adults who were currently compliant with CRC screening recommendations.

 Mailed outreach was effective for improving rates of CRC screening among underserved populations and had a markedly higher effect on screening with FITs compared with invitation for colonoscopy.

 Results from another study suggested that improved CRC screening will most likely be achieved through optimizing the time during current primary care visits rather than through outreach to encourage patients to attend primary care visits.

Our study has several strengths in methodology, including a sufficient sample size to evaluate patient characteristics, data systems to capture return of FIT kits with a contemporary sample, and ability to ascertain patient covariates as confounders in the analysis. However, there are some limitations because of our study population that may limit the generalizability of our findings. It is important to recognize that ours is an insured population. Our results might not reflect the experience of uninsured or underinsured adults who may not have access to consistent healthcare. As a screening test, a FIT is a covered service under the Affordable Care Act; thus, included members should not have experienced any direct expense associated with completing a FIT during the study period.

 Despite federally mandated coverage, nonparticipation in CRC screening has been shown to be associated with concern for out-of-pocket costs among insured people with low socioeconomic status or in racial minority populations.

 However, for those who are uninsured, FIT use may be the most economically feasible method of receiving CRC screening. We primarily investigated patient characteristics for which previous research has asserted disparities in screening compliance. The VDW is limited in the ability to explore the reasons behind the observed patterns, but this research provides an important groundwork for future design for interventions to improve overall completion.

To our knowledge, this is the first study to assess screening completion based on return of FITs following clinician order. This research is important to reach the Healthy People 2020 goal of 70.5% completion of CRC screening and is a valuable contribution to the existing knowledge on CRC screening to reduce underuse.

 We have demonstrated that among adults eligible for CRC screening, the majority of those who complete the test do so within 2 weeks of the order. Of note, we did observe higher rates of FIT completion by nonwhite race/ethnicity. Targeted interventions, beyond mailed kits, and clinic workflows to improve return of FITs should be investigated as potential means to increase overall return rates and address disparities by patient characteristics such as obesity and age.

This study’s contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute, the National Institutes of Health, or the Agency for Healthcare Research and Quality.

Kaiser Permanente Washington Health Research Institute (CBH, JC, KJW), Seattle, WA; Department of Epidemiology, University of Washington (CBH, AIP, AH, JC), Seattle, WA; Fred Hutchinson Cancer Research Center (CBH, AIP), Seattle, WA.

 Research in this publication was supported by the National Cancer Institute of the National Institutes of Health under award number U54 CA163261 and award number T32 CA094880 (“Cancer Prevention Training: Epidemiology, Nutrition, Genetics & Survivorship”) to Mr Haas.

 Drs Chubak and Wernli are employees of Kaiser Permanente Washington, have received several grants on colorectal cancer, and present on colorectal cancer research at meetings and conferences. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (CBH, AH, KJW); acquisition of data (JC, KJW); analysis and interpretation of data (CBH, AIP, JC, KJW); drafting of the manuscript (CBH, AIP, KJW); critical revision of the manuscript for important intellectual content (CBH, AIP, AH, JC, KJW); statistical analysis (CBH); provision of patients or study materials (KJW); obtaining funding (JC, KJW); administrative, technical, or logistic support (KJW); and supervision (AH, KJW).

 Cameron B. Haas, MPH, Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Ste 1600, Seattle, WA 98101. Email: haas.c@ghc.org.

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Articles

Targeted interventions by patient characteristics to improve fecal immunochemical test completion could reduce disparities in colorectal cancer screening and improve overall compliance with screening recommendations.

Time to Fecal Immunochemical Test Completion for Colorectal Cancer

To evaluate woman-level characteristics associated with timing of follow-up after abnormal mammograms in an integrated healthcare system with an active breast health program.

 The study included women aged 40-84 years who had an abnormal mammogram (20,060 screening and 3184 diagnostic) recommended for follow-up. We compared characteristics of women who received any follow-up evaluation within <7, 8 to 14, 15 to 21, and 22 to 180 days. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) using multivariate ordinal logistic regression.

 The proportion of women seeking care within 7 days was 23% for screening and 69% for diagnostic mammograms. Characteristics associated with later follow-up (>8 days vs <7 days) after an abnormal screening mammogram included being older (OR = 1.15; 95% CI, 1.04-1.26 [age 70-79 years]; OR = 1.31; 95% CI, 1.14-1.51 [age 80 years]), Asian (OR = 1.18; 95% CI, 1.04-1.33), or having a college degree (OR = 1.10; 95% CI, 1.01-1.19). Characteristics associated with earlier follow-up included family history of breast cancer (OR = 0.93; 95% CI, 0.88-0.98), symptoms at time of mammogram (OR = 0.79; 95% CI, 0.70-0.88), or extremely dense breasts (OR = 0.82; 95% CI, 0.69-0.96). For diagnostic mammograms, symptoms at time of mammogram (OR = 0.47; 95% CI, 0.39-0.56) and being obese (OR = 0.79; 95% CI, 0.65-0.98) were associated with earlier follow-up.

 Several woman-level characteristics were associated with timely follow-up after an abnormal screening exam, but only presence of symptoms and being obese was associated with timely follow-up after an abnormal diagnostic exam.

	Within an integrated healthcare system with an active breast health program to facilitate care after an abnormal mammogram, there remains woman-level characteristics associated with timely follow-up.

		Presence of symptoms was associated with timely follow-up after both abnormal screening and diagnostic mammogram.

		Women who were older, Asian, or more educated were less likely to return earlier after an abnormal screening mammogram. Efforts to understand and reduce barriers might be important in these groups of women.

	Approximately 75% of US women report having at least 1 screening mammogram to detect breast cancer.

 Abnormal mammograms, which require additional imaging or surgical follow-up, occur at a rate of almost 10% and 8% of screening and diagnostic exams, respectively.

 There are currently no published national data available for mammography facilities and practices benchmarks for the appropriate timing for receipt of follow-up evaluation after an abnormal mammogram.

	A delay in follow-up can be attributed to characteristics of the woman (ie, not making an appointment), the provider (ie, not communicating the importance of follow-up), or the healthcare system (ie, insufficient scheduling space for prompt return). Failure of 1 or all of these systems results in delayed follow-up by the patient, which may contribute to a delayed diagnosis of breast cancer, later stage disease,

 Woman-level characteristics associated with a delay or lack of follow-up after an abnormal mammogram include being older,

	Evaluating the follow-up of breast health within an HMO setting provides opportunities to monitor women after an abnormal mammographic exam when access to appropriate follow-up care should be equitable. Two prior studies have evaluated follow-up after an abnormal mammogram within an HMO setting,

 but neither study had an active program for breast health, which would identify and schedule care for women with abnormal mammograms. In our study, we investigated woman-level characteristics associated with  follow-up after an abnormal mammography examination in women enrolled in a health plan with an active breast health program.

	Women aged 40 to 84 years who received a mammogram between 1996 and 2003 in Group Health, an integrated delivery system in the Puget Sound region of Washington State, and were enrolled for at least 12 months were eligible for this study. All women had access to free mammography through the health plan, including screening and diagnostic exams. This analysis is restricted to women with an abnormal result from mammography defined as a Breast Imaging Reporting and Data System (BI-RADS)

 assessment of 0 (incomplete), 4 (suspicious abnormality), or 5 (highly suspicious of malignancy). We specifically excluded women recommended for 6-month short-interval follow-up (BI-RADS 3) because we were interested in abnormal exams that required immediate attention. Further, we restricted the sample to women who were recommended to receive additional follow-up with 1 of 4 procedures: (1) biopsy, (2) fine needle aspiration, (3) surgical evaluation, or (4) ultrasound. Women had to be continuously enrolled for 12 months after the abnormal mammogram to ensure adequate information on follow-up. We identified 25,272 and 5074 abnormal screening or diagnostic mammograms, respectively.

 Breast Cancer Screening Program at Group Health

	Since 1985, the Breast Cancer Screening Program at Group Health has sent reminder letters to women to encourage participation in mammography screening.

 To date, the program has mailed more than 500,000 letters. Breast clinic nurses were responsible for much of the follow-up after a mammogram. For 

, the radiologist interpreted the image the next working day after the mammogram. The breast clinic nurses called the patient within 24 to 48 hours after the report was issued with the patient’s result. If a patient had an abnormal result, the breast clinic nurse attempted to schedule the next available appointment for the patient. For 

, the radiologist interpreted the image the same day and reported the result of the mammogram to the woman. A patient with an abnormal diagnostic mammogram would have scheduled the next available follow-up appointment recommended by the radiologist with the breast clinic nurse. Among nonresponders for either screening or diagnostic mammograms, the breast clinic nurse would call the patient to schedule the follow-up appointment.

	At the time of the mammography visit, women completed a self-administered questionnaire,

 which collected information on age, reproductive risk factors, breast screening, medical history, and concerns regarding women’s breasts at the time of the exam. Women completed the questionnaire before knowing the results of their mammogram. The  uestionnaire served as the data source for woman-level characteristics under evaluation.

	Information about follow-up procedures was available through electronic administrative data including pathology, radiology, utilization, and claims databases. Time to follow-up care was calculated from the date of the mammogram until the first date of follow-up care with any of the 4 procedures. Women might have received a procedure that was different from the one recommended, but was still 1 of the 4 procedures of interest. We categorized women according to receipt of follow-up care within <7, 8 to 14, 15 to 21, and 22 to 180 calendar days, based on ability to seek follow-up care within sequential weeks up to 4 weeks.

	This study was approved by the institutional review board at Group Health Research Institute.

	Woman-level characteristics of interest for this analysis included the following risk factors: age, race, body mass index (kg/m2), education, family history of breast cancer in first- or second-degree relatives, self-reported symptoms at the time of mammography, biopsy history prior to the mammography examination, radiologists’ interpretation of mammographic breast density,

 and use of postmenopausal hormone therapy. Women with missing data for any of the risk factors or without follow-up within 180 days were excluded from the  analysis. The final sample size included 20,060 screening and 3184 diagnostic mammograms. Descriptive summary statistics were calculated for each variable by interval of follow-up time.

	We used a multivariate ordinal logistic regression model (ologit) for ordered outcome variables to compare differences in the follow-up timing intervals.

An ordinal logistic model is a model for multinomial data that generalizes the logistic regression model. The model allows the consideration of differences in “early” versus “later” follow-up at each of the 3 thresholds of the outcome. The interpretation of the odds ratio is the comparison of early versus late defined in our analysis as <7 days versus >8 days. All analyses included all potential risk factors and used robust methods to account for clustering for multiple records per woman. Tests for trends were evaluated by entering the ordinal term representing  the categorical variable into the ordinal model. We evaluated the contribution of each variable using a likelihood ratio test, comparing the full model with the reduced model. All hypothesis tests were 2-sided and assumed a type I error rate of alpha = .05.

	All analyses were conducted in Stata/MP 11.0 (Stata-Corp, College Station, TX).

	For women who had an abnormal screening mammogram, the median time from receipt of the exam to followup was 13 days. The proportion of women receiving care within <7, 8 to 14, 15 to 21, and 22 to 180 days was 23.3%, 36.3%, 22.0%, and 18.3%, respectively, among women with abnormal screening mammogram (

). For women who had an abnormal diagnostic mammogram, the median time to further follow-up was 5 days. Further, the proportion of women receiving care within 7, 8 to 14, 15 to 21, and >22 days was 69.1%, 10.6%, 7.0%, and 13.2%, respectively (

	Among women with an abnormal screening mammogram, those more likely to receive follow-up care after 7 days were older (70-79 and >80 years) (P-trend <.001), Asian or Pacific Islander, and college graduates (P-trend = .006). The woman-level factors associated with being more likely to receive follow-up care within 7 days included reporting symptoms at the time of the exam, having a positive family history of breast cancer, and having extremely dense breasts (P-trend = .001) (Table 1).

	Among women with an abnormal diagnostic mammogram, few woman-level characteristics were associated with earlier or later follow-up. Older women had later follow-up than younger women, though the results were not statistically significant at all levels. Women with higher body mass index P-trend = .026) and symptoms at the time of the mammogram were more likely to have follow-up care earlier rather than later.

	This is the largest study to date to investigate the timing of follow-up after an abnormal mammogram. Unique features of this analysis include the available data from an integrated healthcare system and active surveillance by nurses to encourage and assist patients in returning to evaluate the abnormality noted on the mammogram. Further, we had sufficient data to investigate follow-up for both screening and diagnostic mammograms.

	We confirmed that women of older age were more likely to seek care later rather than earlier for both screening and diagnostic mammograms.

 We also detected that having symptoms at the time of the mammogram was associated with earlier follow-up for both screening and diagnostic exams. Our finding is supported by Caplan and colleagues, who determined that women with breast symptoms were diagnosed with breast cancer 6 days earlier than women without breast symptoms.

	In this study, we found that women of Asian/Pacific Islander descent were more likely than white women to delay follow-up care after an abnormal screening mammogram. Karliner and colleagues reported that Asian women did not fully understand their physicians’ reports of their mammograms, particularly Chinese-speaking women.

 It is unclear whether language barriers were responsible for the longer delay in seeking care among Asian/Pacific Islander women in our study, but language interpreters or written materials might help reduce delay in other programs.

	Women with extremely dense breasts were more likely to seek follow-up care quickly compared with women with less dense breasts. Prior studies have not reported on breast density and delay in follow-up care. The sensitivity of mammography in women with extremely dense breasts is only 30% compared with 80% in women with fatty breasts.

 These women are also likely to have an increased risk of interval cancers,

 which likely results in their prompt follow-up.

	In our study, fewer than 1% and 4% of women with abnormal screening and diagnostic mammograms, respectively, did not receive any follow-up within 180 days (data not shown). Other studies have reported lack of follow-up in women after abnormal exams at rates between 8% and 28%.

 The difference in rates of follow-up is likely due to the ability of women to receive additional medical services within our health system at no financial burden and with the assistance of specially trained nurses working in the breast program. The use of computer monitoring to follow up with women postmammography reduces the delays in seeking care.

 Group Health’s Breast Cancer Screening Program implemented active follow-up of women with breast clinic nurses and now uses the electronic medical record to help track women.

	This study was conducted in a well-characterized defined population of women who receive healthcare through an integrated delivery system. There was thorough assessment and follow-up of mammography screening because of the Breast Cancer Screening Program at Group Health. We might have missed some clinical care that occurred at outside facilities that was not submitted for a claim. However, this is unlikely for women with their primary insurance at the health plan, because filing a claim would enable payment for outside services and women would be motivated to do so.

	Clinical guidelines for promptness in obtaining an evaluation after an abnormality is noted on mammography examination are not well established. An assessment of the National Initiative on Cancer Care Quality in the United States suggests that 90% of patients should be seen within 3 weeks of an abnormal screening mammogram for a diagnostic mammogram, within 14 days from needing an open biopsy to cancer diagnosis, and within 5 working days from breast cancer diagnosis to an appointment with the treating physician.

 While we cannot make a direct comparison to the National Initiative for Cancer Care Quality, we can report that 82% of women received the recommended follow-up care within 3 weeks of their abnormal screening mammogram. Further, 69% of women with abnormal diagnostic mammograms received recommended follow-up care within 7 days. In 2004, the National Quality Measures for Breast Centers identified 2 measures of timely follow-up: imaging timeliness of care (ie, time between screening mammogram, diagnostic mammogram, needle biopsy, surgery) and surgical timeliness (ie, time between diagnostic mammogram, open surgical biopsy/excision, initial breast cancer surgery).

 There are characteristics that influence women’s timeliness to follow-up that future interventions may address to reduce delays in breast cancer diagnosis.

 From Group Health Research Institute (KJW, EJAB, SH, DSMB), Seattle, WA; and Department of General Internal Medicine (JGE), University of Washington, Seattle, WA.

 This research was supported by the National Cancer Institute (Dr Buist, Principal Investigator, U01CA63731; and Dr Elmore, Principal Investigator, K05CA104699).

 The authors (KJW, EJAB, SH, JGE, DSMB) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Woman-level characteristics such as breast symptoms at mammography are associated with earlier follow-up within an integrated health system with an active breast health plan.

Karen J. Wernli, PhD

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