Background
Colorectal cancer (CRC) is one of the leading causes of cancer mortality [
1,
2]. The US Preventive Services Task Force (USPSTF) gives CRC screening an A-level recommendation for adults aged 50 to 75 [
3], and this service is among the highest rated clinical preventive services in the USPSTF’s portfolio for its potential to avoid morbidity and mortality and also save costs [
4]. A microsimulation model estimated that annual fecal immunochemical testing (FIT) among adults aged 50 to 75 would result in 244 life-years gained per 1000 persons, and other CRC screening methods (e.g., periodic sigmoidoscopy and colonoscopy) showed similar levels of benefit [
5]. Despite this, CRC screening is well below targets set by both Healthy People 2020 [
6] and the National Colorectal Cancer Roundtable [
7].
In addition, there are disparities in CRC screening rates. According to the National Health Interview Survey, CRC screening rates are lower for those with low income, lack of health insurance, low education levels, who lack a source of regular medical care, or who are recent immigrants [
8]. Rates are also lower in several race/ethnicity subgroups, including patients who are Hispanic, Native Hawaiian or other Pacific Islander, and American Indian/Alaska Native [
9]. CRC screening is also associated with a number of health-related factors, such as the presence of medical conditions [
10‐
13] and utilization of other preventive health services [
10,
12].
CRC screening is typically initiated at a medical visit, but there are important known barriers to this approach, such as cost, lack of health insurance, and difficulty attending medical appointments. A mail-based intervention may boost CRC screening rates and reduce disparities in underserved populations by reducing these barriers. A number of studies have shown that mailing FIT kits directly to patients can substantially increase screening rates in low-income, minority, and racially diverse settings [
14‐
20]. Screening rates were variable in these studies, ranging from 2 to 37% at baseline, and with the introduction of a FIT kit, mailing program rates increased by a factor of two to six, with absolute changes typically ranging from 21 to 29 percentage points. Two trials found that mailing FIT kits to patients who were unscreened was more effective in increasing CRC screening than phoning people to schedule colonoscopy appointments after 1 year [
16,
20], although this effect did not hold up with a 3-year follow-up [
21,
22]. Further, a recent study in a health maintenance organization (HMO) setting demonstrated that, among patients who had completed one FIT, 75–86% completed two additional rounds of screening within 4 years, suggesting good acceptability of this screening method among those who had used it [
23]. Similarly, in a study of veterans who had completed a FIT, 89% found it easy to use and convenient, and 97% reported that they were likely to complete a FIT by mail annually [
24]. In this group of veterans, 79% completed a second annual FIT test by mail [
24].
Understanding whether mailed FIT interventions are broadly effective could assure health systems administrators that this approach would benefit a wide swath of patients and be unlikely to exacerbate or introduce disparities. This study explores whether sociodemographic and health-related factors moderate the effect of an automated direct mail of FIT kit program delivered to patients receiving care at health clinics serving primarily low-income populations.
Results
We included 30,667 individuals from 26 clinics who were aged 50–74 and were not current on CRC screening. The intervention and usual care groups showed very similar distributions on baseline characteristics, generally within one to four percentage points of each other (Table
2).
Table 2Baseline individual-level patient characteristics
Age |
50–64 | 12,249 | 82.2 | 12,749 | 80.9 | 24,998 | 81.5 |
65–75 | 2655 | 17.8 | 3014 | 19.1 | 5669 | 18.5 |
Gender |
Female | 8381 | 56.2 | 8605 | 54.6 | 16,986 | 55.4 |
Male | 6523 | 43.8 | 7158 | 45.4 | 13,681 | 44.6 |
Race |
Asian | 545 | 3.8 | 950 | 6.4 | 1495 | 5.1 |
Black | 629 | 4.4 | 743 | 5.0 | 1372 | 4.7 |
Hawaiian/Pacific Islander | 72 | 0.5 | 59 | 0.4 | 131 | 0.4 |
Native American | 142 | 1.0 | 146 | 1.0 | 288 | 1.0 |
Other | 9 | 0.1 | 25 | 0.2 | 34 | 0.1 |
White | 12,886 | 90.2 | 13,010 | 87.1 | 25,896 | 88.6 |
Ethnicity |
Non-Hispanic | 12,227 | 84.6 | 13,370 | 88.2 | 25,597 | 86.4 |
Hispanic | 2225 | 15.4 | 1789 | 11.8 | 4014 | 13.6 |
Language |
English | 12,032 | 81.6 | 12,600 | 81.6 | 24,632 | 81.6 |
Spanish | 1797 | 12.2 | 1361 | 8.8 | 3158 | 10.5 |
Other | 920 | 6.2 | 1475 | 9.6 | 2395 | 7.9 |
Insurance status |
Uninsured | 3494 | 23.8 | 3248 | 20.8 | 6742 | 22.3 |
Medicaid | 5642 | 38.5 | 6004 | 38.5 | 11,646 | 38.5 |
Medicare | 3562 | 24.3 | 3868 | 24.8 | 7430 | 24.5 |
Commercial | 1885 | 12.8 | 2392 | 15.3 | 4277 | 14.1 |
Other | 88 | 0.6 | 101 | 0.6 | 189 | 0.6 |
Federal poverty level |
< 100% | 5870 | 48.8 | 6282 | 53.8 | 12,152 | 51.2 |
100–150% | 2594 | 21.6 | 2529 | 21.7 | 5123 | 21.6 |
151–200% | 1258 | 10.4 | 1126 | 9.6 | 2384 | 10.0 |
200%+ | 2316 | 19.2 | 1738 | 14.9 | 4054 | 17.1 |
Flu shot in 12 months prior to index date |
No | 11,144 | 74.8 | 11,970 | 75.9 | 23,114 | 75.4 |
Yes | 3760 | 25.2 | 3793 | 24.1 | 7553 | 24.6 |
Mammogram in 2 years prior to index date (women, n = 16,986) |
No | 5765 | 68.8 | 5976 | 69.4 | 11,741 | 69.1 |
Yes | 2616 | 31.2 | 2629 | 30.6 | 5245 | 30.9 |
Pap in 3 years prior to index date (women under age 65, n = 13,634) |
No | 4085 | 60.3 | 4268 | 62.3 | 8353 | 61.3 |
Yes | 2694 | 39.7 | 2587 | 37.7 | 5281 | 38.7 |
Tobacco use |
Current | 3892 | 30.0 | 4237 | 30.4 | 8129 | 30.2 |
Former | 3335 | 25.7 | 3690 | 26.5 | 7025 | 26.1 |
Never | 5768 | 44.4 | 6018 | 43.2 | 11,786 | 43.8 |
BMI |
< 18.5 (underweight) | 190 | 1.4 | 203 | 1.4 | 393 | 1.4 |
18.5–25 (normal weight) | 3232 | 23.0 | 3541 | 24.5 | 6773 | 23.8 |
25–30 (overweight) | 4456 | 31.7 | 4431 | 30.7 | 8887 | 31.2 |
≥ 30 (obese) | 6164 | 43.9 | 6265 | 43.4 | 12,429 | 43.6 |
Charlson score, based on past 12 months |
0 | 7968 | 53.5 | 8648 | 54.9 | 16,616 | 54.2 |
1 | 4095 | 27.5 | 4208 | 26.7 | 8303 | 27.1 |
2 | 1633 | 11.0 | 1637 | 10.4 | 3270 | 10.7 |
3+ | 1208 | 8.1 | 1270 | 8.1 | 2478 | 8.1 |
Visit for chronic pulmonary condition, past 12 months |
No | 11,972 | 80.3 | 12,716 | 80.7 | 24,688 | 80.5 |
Yes | 2932 | 19.7 | 3047 | 19.3 | 5979 | 19.5 |
Visit for diabetes, past 12 months |
No | 11,590 | 77.8 | 12,430 | 78.9 | 24,020 | 78.3 |
Yes | 3314 | 22.2 | 3333 | 21.1 | 6647 | 21.7 |
Visit for depression, past 12 months |
No | 11,080 | 74.7 | 12,083 | 77.0 | 23,163 | 75.9 |
Yes | 3749 | 25.3 | 3606 | 23.0 | 7355 | 24.1 |
Neighborhood ED visits per 1000 Medicaid/Medicare population |
> 419 visits | 13,088 | 88.4 | 15,167 | 96.9 | 28,255 | 92.8 |
≤ 419 visits | 1722 | 11.6 | 485 | 3.1 | 2207 | 7.2 |
Neighborhood Gini inequality score |
> 0.4106 | 8197 | 56.9 | 9206 | 60.0 | 17,403 | 58.5 |
≤ 0.4106 | 6216 | 43.1 | 6146 | 40.0 | 12,362 | 41.5 |
Neighborhood median household income |
≤ $68,426 | 13,114 | 91.0 | 14,654 | 95.4 | 27,768 | 93.3 |
> $68,426 | 1299 | 9.0 | 698 | 4.6 | 1997 | 6.7 |
Neighborhood percentage college graduates |
≤ 41% | 11,537 | 80.0 | 12,854 | 83.7 | 24,391 | 81.9 |
> 41% | 2876 | 20.0 | 2499 | 16.3 | 5375 | 18.1 |
Neighborhood population density per square mile |
≤ 1000 (rural) | 4423 | 30.7 | 5907 | 38.5 | 10,330 | 34.7 |
> 1000 (non-rural) | 9991 | 69.3 | 9446 | 61.5 | 19,437 | 65.3 |
Neighborhood poverty (percentage below 100% FPL) |
> 17.6% | 7384 | 51.2 | 8281 | 53.9 | 15,665 | 52.6 |
≤ 17.6% | 7029 | 48.8 | 7071 | 46.1 | 14,100 | 47.4 |
Neighborhood unemployment rate |
> 6.6% | 12,691 | 88.0 | 13,978 | 91.0 | 26,669 | 89.6 |
≤ 6.6% | 1722 | 12.0 | 1375 | 9.0 | 3097 | 10.4 |
Most of the persons in the sample were aged 50–64 (81.5%), White (88.6%), and non-Hispanic (86.4%), and more than half were female (55.4%). Most of the participants had household incomes that were below 200% of the federal poverty level (82.3%), and the most common form of health coverage was Medicaid (38.5%), followed by Medicare (24.5%), and no insurance coverage (22.3%). Records suggested relatively low completion preventive services; 24.6% had a flu shot in the past year, 30.9% of women had a mammogram in the past 2 years, and 38.7% of age-eligible women had a recent Pap smear.
Tables
3 and
4 show the percentage of patients who had completed a FIT in the subgroups of interest, by intervention group. Although not always statistically significant, we saw a consistent pattern of increased FIT return rates among intervention participants compared to control participants across all subgroups studied, with incidence rate ratios (IRRs) generally ranging from 1.25 to 1.50. FIT completion in the intervention group ranged from 15 and 25% for most subgroups, typically three to six percentage points higher than the control group participants. Also shown in Tables
3 and
4 are the relative risks for having completed a FIT (vs. not) in each subgroup and the
P value for the treatment*moderator interaction. The only moderator with a statistically significant interaction was race; persons of Asian descent showed a twofold response to the intervention (adjusted incident rate ratio [aIRR] = 2.06, 95% confidence interval [CI] 1.41 to 3.00). Intervention response was in the more typical range for participants who were White (aIRR = 1.32, 95% CI 0.99 to 1.76) and Black (aIRR = 1.28, 95% CI 0.85 to 1.92). Among persons of Asian descent, 18.9% in the usual care group completed a FIT, compared with 37.7% in the intervention group. In contrast, usual care completion rates among White and Black persons were 12.9 and 14.9%, respectively, compared to 15.8 and 20.2% for the intervention group participants.
Table 3FIT completion by individual-level patient characteristics
Age |
50–64 | 24,998 | 15.2 | 13.0 | 17.3 | 1.36 (1.02, 1.81) | 0.58 |
65–74 | 5669 | 16.9 | 14.4 | 19.0 | 1.41 (1.03, 1.94) |
Gender |
Female | 16,986 | 16.3 | 14.2 | 18.3 | 1.33 (1.00, 1.78) | 0.33 |
Male | 13,681 | 14.5 | 12.0 | 16.8 | 1.42 (1.05, 1.90) |
Race |
White | 25,896 | 14.4 | 12.9 | 15.8 | 1.32 (0.99, 1.76) | 0.003 |
Black | 1372 | 17.8 | 14.9 | 20.2 | 1.28 (0.85, 1.92) |
Asian | 1495 | 30.8 | 18.9 | 37.7 | 2.06 (1.41, 3.01) |
Hispanic ancestry |
Non-Hispanic | 25,597 | 14.8 | 12.5 | 16.8 | 1.39 (1.05, 1.85) | 0.19 |
Hispanic | 4014 | 21.2 | 18.7 | 24.3 | 1.25 (0.91, 1.71) |
Primary language |
English | 24,632 | 13.5 | 11.8 | 15.1 | 1.38 (1.04, 1.84) | 0.89 |
Non-English | 5553 | 25.5 | 20.2 | 30.6 | 1.40 (1.03, 1.90) |
Insurance status |
Uninsured | 6742 | 16.0 | 13.3 | 19.1 | 1.29 (0.94, 1.78) | 0.67 |
Medicaid | 11,646 | 16.2 | 13.3 | 18.9 | 1.38 (1.02, 1.88) |
Medicare | 7430 | 15.5 | 13.8 | 17.1 | 1.34 (0.98, 1.84) |
Commercial | 4227 | 13.0 | 12.8 | 13.3 | 1.48 (1.05, 2.08) |
Federal poverty level |
≤ 100% | 12,152 | 16.8 | 14.4 | 19.0 | 1.29 (0.95, 1.75) | 0.42 |
> 100–150% | 5123 | 16.1 | 13.3 | 18.9 | 1.37 (0.99, 1.89) |
> 151–200% | 2384 | 14.9 | 13.0 | 17.1 | 1.27 (0.88, 1.83) |
> =200% | 4054 | 14.4 | 11.9 | 17.7 | 1.51 (1.07, 2.11) |
Flu shot past year |
No | 23,114 | 14.2 | 12.1 | 16.2 | 1.39 (1.04, 1.86) | 0.46 |
Yes | 7553 | 19.4 | 16.7 | 22.2 | 1.32 (0.98, 1.79) |
Mammogram in past 2 years |
No | 11,741 | 14.7 | 12.7 | 16.5 | 1.27 (0.96, 1.67) | 0.18 |
Yes | 5245 | 20.0 | 17.5 | 22.5 | 1.42 (1.06, 1.90) |
Pap test in last 3 years |
No | 8353 | 14.2 | 12.4 | 16.0 | 1.28 (0.97, 1.70) | 0.40 |
Yes | 5281 | 19.0 | 16.6 | 21.5 | 1.37 (1.03, 1.84) |
Smoking status |
Former or never | 18,811 | 17.3 | 14.6 | 19.9 | 1.40 (1.04, 1.87) | 0.16 |
Current | 8129 | 12.3 | 10.8 | 13.7 | 1.25 (0.91, 1.72) |
Body mass index |
< 30.0 kg/m2 | 16,053 | 15.8 | 13.0 | 18.5 | 1.44 (1.07, 1.93) | 0.08 |
≥ 30.0 kg/m2 | 12,429 | 15.7 | 13.9 | 17.4 | 1.28 (0.95, 1.72) |
Charlson comorbidities |
0–2 | 28,189 | 15.7 | 13.5 | 17.8 | 1.35 (1.01, 1.80) | 0.16 |
≥ 3 | 2478 | 13.2 | 10.4 | 15.9 | 1.61 (1.11, 2.32) |
Visits for chronic pulmonary disease |
No | 24,688 | 15.7 | 13.4 | 17.8 | 1.35 (1.01, 1.80) | 0.50 |
Yes | 5979 | 14.7 | 12.3 | 17.0 | 1.42 (1.04, 1.95) |
Visits for diabetes |
No | 24,020 | 14.8 | 12.4 | 17.0 | 1.42 (1.06, 1.89) | 0.06 |
Yes | 6647 | 18.1 | 16.3 | 20.0 | 1.23 (0.91, 1.67) |
Visits for depression |
No | 23,163 | 16.1 | 13.7 | 18.3 | 1.37 (1.03, 1.84) | 0.66 |
Yes | 7355 | 13.9 | 12.1 | 15.8 | 1.33 (0.98, 1.81) |
Table 4FIT completion by patients’ neighborhood characteristics
ED visits per 1000 Medicaid/Medicare population |
> 419 visits | 28,255 | 16.1 | 14.2 | 17.8 | 1.34 (1.00, 1.78) | 0.36 |
≤ 419 visits | 2207 | 8.5 | 6.8 | 14.6 | 1.73 (0.95, 3.14) |
Gini Inequality score |
> 0.4106 | 17,403 | 15.2 | 13.1 | 17.1 | 1.37 (1.02, 1.83) | 0.81 |
≤ 0.4106 | 12,362 | 16.1 | 13.5 | 18.8 | 1.39 (1.04, 1.86) |
Median household income |
≤ $68,426 | 27,768 | 15.6 | 13.2 | 17.7 | 1.37 (1.03, 1.82) | 0.67 |
> $68,426 | 1997 | 15.4 | 14.1 | 17.8 | 1.44 (0.99, 2.10) |
Percentage college graduates |
≤ 41% | 24,391 | 15.7 | 13.2 | 17.9 | 1.38 (1.04, 1.84) | 0.61 |
> 41% | 5375 | 15.1 | 13.6 | 16.8 | 1.33 (0.96, 1.83) |
Population density per square mile |
≤ 1000 (rural) | 10,330 | 13.4 | 10.6 | 15.5 | 1.45 (1.07, 1.96) | 0.26 |
> 1000 (non-rural) | 19,437 | 16.7 | 14.5 | 19.2 | 1.32 (0.99, 1.78) |
Poverty (percentage below 100% FPL) |
> 17.6% | 15,665 | 16.2 | 13.8 | 18.5 | 1.30 (0.97, 1.74) | 0.06 |
≤ 17.6% | 14,100 | 14.8 | 12.8 | 16.9 | 1.47 (1.10, 1.98) |
Unemployment rate |
> 6.6% | 26,669 | 15.4 | 13.1 | 17.4 | 1.37 (1.03, 1.82) | 0.50 |
≤ 6.6% | 3097 | 17.4 | 14.6 | 20.9 | 1.46 (1.04, 2.04) |
Although no other interaction tests were statistically significant, a few other characteristics were statistically significant at P = 0.10. Specifically, we found larger effects for those with non-obese range BMIs than for participants with BMI ≥ 30.0 (aIRR = 1.44 vs. 1.28, P = 0.08), for those without vs. with a visit for diabetes in the past year (aIRR = 1.42 vs. 1.23, P = 0.06), and those living in lower poverty vs. higher poverty neighborhoods (aIRR = 1.47 vs. 1.30, P = 0.06). However, the preponderance of evidence suggests that intervention effects were fairly consistent across patient subpopulations. We reran the analyses of BMI and the neighborhood-level characteristics that we dichotomized, keeping the moderators as continuous variables (data not shown). None of the interaction terms were statistically significant in these analyses (P > 0.14 in all cases), supporting the robustness of these findings.
Discussion
In this population, drawn from safety net clinics in Oregon, Washington, and California serving low-income patients, a wide range of patient subpopulations generally showed fairly comparable responses to the mailed FIT intervention. However, the intervention effect was largest among persons of Asian descent, with a statistically significant incident rate ratio of 2.06 (95% CI 1.41 to 3.00). It is unclear why this subgroup showed large effects, and this result needs replication. One possible explanation we explored was that 77% of persons of Asian descent in the study population reported that English was not their preferred language, so it was possible that the wordless FIT instructions developed for this trial were particularly helpful for the Asian subpopulation. However, we did not find a greater benefit of the intervention among non-English speakers in general, nor was there a parallel effect in persons of Hispanic descent, who had a similar proportion of non-English speakers (76%) as the Asian subpopulation.
We found two other trials of mailed FIT interventions that reported on moderators of treatment effect [
14,
16], although these trials did not report specifically on differential effects in persons of Asian descent compared to other race/ethnic groups. One of these mailed FIT trials found that the intervention effect was comparable across age, gender, race/ethnicity (Hispanic vs. other), preferred language (English vs. Spanish), and insurance status, but did find a larger treatment effect among persons with no visits during the follow-up period than those with three or more visits, a variable we did not explore [
14]. In their study, among persons with no visits during follow-up, 3 % of the control group participants and 59% of the intervention group participants had completed a FIT within 6 months, a 56 percentage point difference between groups. Among those with three or more visits during follow-up, 58% of the control group and 86% of the intervention group completed a FIT within 6 months, a 28 percentage point difference. The other trial of mailed FITs that reported effect moderators found no differences in intervention response by gender or race/ethnicity (comparing non-Hispanic white, black, and Hispanic subgroups) [
16].
We adhered to most recommendations outlined by the checklist for the appraisal of moderators and predictors (CHAMP) [
37]. First, we examined characteristics related to those that have been shown to be related to CRC screening rates, including demographics, socioeconomic factors, health status, and use of preventive services. The broad factors were selected a priori; however, the specific fields were restricted to those available in the EHR and in the databases of neighborhood-level data use by this study. We used measures taken prior to the start of the interventions, employed statistical interaction testing, and presented results for all moderators examined. In addition, the setting and study population were comparable to the settings and populations in which the mail FIT would be used clinically. Because of the large number of moderators we examined, the relatively small number of participants of Asian descent, and the lack of an effect related to non-English language preference (a construct related to Asian ethnicity), we view the finding of a positive moderating effect in Asian patients as exploratory and in need of replication. We also believe the overall pattern of consistent benefit across a range of patient characteristics in this setting is plausible.
One of the main limitations of this study is related to our reliance on the EHR for capture of moderator variables. Patients in this low-income population may be more mobile than typical, both in terms of where they live and where they receive their healthcare. As such, the neighborhood-level characteristics may not be current for people who struggle with homelessness or insecure housing, and healthcare-related services may be received at non-study clinics. However, low-income patients’ mobility is likely primarily between neighborhoods with similar economic profiles, so we believe the information on neighborhood-level characteristics will often remain reasonably similar when patients have moved. However, EHRs are simply not always complete and accurate, so some patients will have been dropped from some analyses due to missing moderator data and some will have been misclassified in the EHR. In addition, some participants may have completed a FIT within a health system that was not covered by the OCHIN collaborative, so they would be misclassified as not completing a FIT.
Another limitation of our study is that we tested a larger number of potential moderators without adjusting our analyses to maintain a type I error rate of 5%. Thus, even though we did find one statistically significant interaction indicating a larger benefit for patients of Asian descent, this finding may be due to chance and not be robust to replication. An additional limitation is that we did not conduct power calculations specifically for the moderator analyses, given the wide range of subgroup sizes, and analyses for some subgroups may be underpowered.
Despite these limitations, our study has a number of important strengths. Our sample included more than 30,000 patients, and so had substantial numbers of patients across a variety of patient subgroups. In addition, these clinics are part of a collaborative that uses a common EHR system, meaning differences in data storage and capture were minimized across the clinics and that data on study participants seen at other clinics under the umbrella EHR provider would be captured. Another very important strength is that this was a pragmatic effectiveness trial, conducted in real-world safety net clinics, using the existing staff and infrastructure. While the overall effect of this intervention was not as large as that seen in some other trials of mailed FITs, the effect was robust across patient subpopulations and was implemented within the constraints of real-world, low-resourced clinics.
The relatively modest effects of an automated FIT mailing intervention were generally consistent across a wide range of patient subpopulations, suggesting broad impact that is unlikely to exaggerate existing disparities in CRC screening rates. Patients of Asian descent may be more likely to benefit from the intervention; however, this finding needs to be replicated.
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