The American Journal of Managed Care July 2009
Automated Messaging to Improve Compliance With Diabetes Test Monitoring
A randomized controlled trial was conducted to compare the effectiveness of automated telephone and mail outreach to prompt compliance with periodic diabetes laboratory testing.
There were 13,057 subjects in the study (7108 in phase 1 and 5949 in phase 2). Characteristics of the subjects are listed in Table 1. The mean age of subjects was 51 years. Most subjects (54%) were male. Administrative records indicated that 10% of subjects preferred communicating with their healthcare provider in Spanish. The median (SD) household income based on address of residence and US census block was $57,700 ($25,900). Phase 1 occurred in the third quarter of 2005, and phase 2 occurred in the first quarter of 2006.
Response (ie, compliance) was best in the interventions using both mail and letter outreach. The results of each intervention at 8 weeks and 12 weeks are given in Table 2. Compliance in the phase 1 and phase 2 control groups was 12% to 13% at 8 weeks and 18% to 19% at 12 weeks. A letter alone and a call alone were not significantly different versus controls (P = .06 [letter] and P = .23 [call] at 8 weeks and P = .21 [letter] and P = .31 [call] at 12 weeks). Compliance with laboratory testing was significantly greater (P <.01) when outreach involved 2 or more attempts at contact (letter-call, call-letter, and letter-call-letter) than when either no contact was made (controls) or only 1 attempt at contact was made (letter or call). The letter-call group compliance was 21% at 8 weeks and 25% at 12 weeks; corresponding compliance for the call-letter group was 19% and 25%, respectively. The letter-call-letter group compliance (21% at 8 weeks and 26% at 12 weeks) was not significantly different from that of the letter-call group at 12 weeks (P = .65). Using logistic regression analysis to control for study phase, age, sex, and race/ethnicity, there was no significant difference between the letter and call groups at 8 weeks and at 12 weeks (P = .28 and P = .48, respectively) or between the letter-call and call-letter groups at 12 weeks (P = .66). The multivariable-adjusted ORs of compliance in each intervention group versus controls were 1.23 (95% confidence interval [CI], 0.91-1.66) for letter, 1.58 (95% CI, 1.16-2.15) for letter- call, 1.67 (95% CI, 1.23-2.27) for letter-call-letter, 1.09 (95% CI, 0.92-1.28) for call, and 1.46 (95% CI, 1.25-1.72) for call-letter.
Some demographic groups had greater compliance following the interventions (Table 3). Characteristics associated with an increased odds of compliance across all study groups included older age (by decades from 40 to >70 years, P <.001 across all ages), Asian race/ethnicity (OR, 1.29 vs whites; P = .02), and Spanish-language preference (OR, 1.18 vs English; P = .03). The compliance of men and women did not differ (P = .91), and household income was not clearly associated with compliance (P = .07).
Our main finding is the success of the paired combination of automated letter and telephone messaging compared with either intervention alone. Both a letter followed in 4 weeks by a call and a call followed in 4 weeks by a letter produced a 12-week compliance rate of 25%, which was 6% to 7% higher in absolute response rate versus the no-contact control group (about a 50% increase in odds). These findings help clarify which outreach options may be most effective to promote disease monitoring by laboratory tests among large, diverse populations with diabetes.
The modest increase in compliance observed in our study is not unexpected given that the target population had not had diabetes monitoring tests in more than 1 year, despite full insurance coverage. Response rates for immunization outreach range from a 1% to 20% absolute increase,5 and the increase in the odds of a response ranges from 1.5 to 2.5 for immunizations and breast cancer screening.5,7 Response rates may have been reduced somewhat by requiring compliance with all 3 tests, which involves not only a blood draw but also a urine sample.
The success of the intervention pair in relation to the alternatives studied seems understandable. The combination of the 2 outreach methods may help overcome some of the disadvantages of either method. For example, a mail message may reach those without a telephone or who are hard of hearing, while a call may reach those with difficulty reading or who discard mail. A second contact may communicate to patients that their health plan or provider is giving needed advice. Perhaps by the third contact in the letter-call-letter group, those individuals susceptible to the outreach had already responded, suggesting little need for continuing the same effort.8 Rather than abandoning those who remain noncompliant, they can be referred to diabetes or chronic disease case managers who attempt in-person calls; alternatively, medical records of noncompliant patients can be flagged to help prioritize testing on their next visit.
To our knowledge, previous studies have not examined combinations of automated outreach for chronic disease monitoring; however, there is an extensive and reviewed literature on interventions to improve preventive services such as immunizations and breast cancer screening.5,7 A few studies compared combinations of telephone calls and mailed letters or postcards to encourage compliance with tests or treatments. In a study9 similar to ours that compared letters and automated telephone messages for increasing immunization rates among children (n = 648), the combination of a letter followed by a call 1 week later was more effective than either a call or a letter alone. In that immunization study, the letter-call combination was not significantly better than the call-letter combination but was preferred on the basis of costeffectiveness. A letter followed by a telephone call has also been noted to improve breast cancer screening versus a nointervention control group or a letter alone.10-12 The setting and target populations may have an effect on study results. In a study13 aimed to increase childhood immunization rates among an inner-city population, an automated call was no less effective than a combination of an automated call that was followed by a live call versus a mailed reminder; however, the call was only slightly better than no intervention.
Some demographic characteristics were associated with compliance in the present study. Although the reasons are not apparent from our study, there was an increasing response across all intervention groups with increasing age. Among racial/ethnic groups, Asians appeared to have a slightly greater response rate than whites. Although Hispanics (as a racial/ethnic group) did not respond more than non-Hispanic whites, those whose preferred language was Spanish were slightly more likely to respond across all intervention groups than English speakers. A similar finding of an increased response rate among Spanish speakers was found using reminder or order cards for immunization rates among children.14 Although sufficient evidence has not yet accumulated, there may be a greater potential to reach patients with chronic disease who face a language barrier and are not yet aware of recommended care than there is among English speakers, who may have other reasons for noncompliance.
In this study and in the most similar published study,9 the letter-call and call-letter interventions were similarly effective. These paired interventions had an advantage over the letter-call-letter approach in that they required only 2 attempts at contact, had the same response rate, and were more cost-effective. In any given setting, the choice between letter-call and call-letter is perhaps best made on a cost basis. If these 2 approaches are deemed equivalent, then whichever intervention is more expensive could be used second, targeting nonresponders; however, this calculation is complicated somewhat by differential response to the first contact. This study is potentially limited in a general sense by the fact that there are many possible variations in outreach details that may affect patient compliance. For example, such details include an electronic signature from the patient’s primary care provider or a telephone message in the voice of the provider. Other combinations of interventions were possible (such as call-letter-call, letter-letter-call, etc) but for practical reasons were not tested. It is possible that some providers initiated contact with noncompliant patients themselves, but any such efforts would be randomly distributed and would not bias comparisons.
The study results may not be generalizable to other populations and to other outreach needs such as medication adherence. For example, diabetes-related testing may be easier or harder to do than testing for other chronic conditions based on ease of testing, patient knowledge, or the perceived value of the test. The population of the health plan studied herein is diverse and of middle household income, and results may not generalize well to populations of very low socioeconomic status or to health systems that are not integrated and capable of electronically identifying and monitoring chronic disease status.
When paired, automated mail and telephone outreach can effectively increase compliance with diabetes laboratory test monitoring. At least in the case of a third attempt at contact using a letter, there was nothing gained by the additional effort. Patients who remain noncompliant will likely require a change in outreach method to achieve recommended disease monitoring (such as live contact by a case manager). These findings may help streamline similar outreach efforts in chronic diseases and perhaps in other clinical scenarios (such as cancer prevention) that require periodic compliance with test monitoring.
This research was supported by Merck Health Management Services, West Point, PA. We thank Richard Contreras for preparing data on median household income, Jiaxiao Shi for assistance with statistical tests, the entire Southern California Kaiser Permanente outreach team, and Julie Frame and Kelly Green for their support in the implementation of this study.
Author Affiliations: Department of Research and Evaluation (SFD), Southern California Kaiser Permanente, Pasadena, CA; Division of Pharmacy Services (RKN), Southern California Kaiser Permanente, Downey, CA; and Division of Endocrinology (FHZ), Southern California Kaiser Permanente, Woodland Hills.
Author Disclosure: The authors (SFD, RKN, FHZ) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Funding Source: This research was supported by Merck & Co Health Management Services.
Authorship Information: Concept and design (SFD, RKN, FHZ); acquisition of data (SFD, RKN); analysis and interpretation of data (SFD, FHZ); drafting of the manuscript (SFD, FHZ); critical revision of the manuscript for important intellectual content (SFD, RKN, FHZ); statistical analysis (SFD); provision of study materials or patients (FHZ); obtaining funding (SFD, FHZ); and supervision (SFD, FHZ).
Address correspondence to: Stephen F. Derose, MD, MS, Department of Research and Evaluation, Southern California Kaiser Permanente, 100 S Los Robles Ave, 2nd Fl, Pasadena, CA 91101. E-mail: firstname.lastname@example.org.
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