More than 15 million people have diabetes in the United States, and it is now the sixth leading cause of death (1,2). The United Kingdom Prospective Diabetes Study and Kumamoto Study confirm that improved glucose control reduces the microvascular complications of type 2 diabetes such as retinopathy, nephropathy, and neuropathy (3,4). Because of these findings, new standards of care and new models of health care delivery have emerged to reduce the complications of diabetes and thus improve quality of life (5).

Nonadherence to diabetes treatment strategies prevents patients from meeting optimal standards of care. Jacobsen et al found that up to 34% of patients with type 1 diabetes defaulted from care, and their glycemic control was significantly worse than those who attended their visits (6). Evidence suggests that patients with diabetes who are unable or unwilling to adhere to treatment regimens suffer greater morbidity than those under regular medical supervision (7).

Studies have investigated patients' reasons for non-attendance in diabetes care clinics. Reasons include financial or transportation issues, inability to get time off work, forgetting about appointments, feeling too ill, crowded clinic settings, administrative errors, and feeling the appointment was unnecessary. Other studies have addressed nonadherence in diabetes clinic settings and found significant factors to be smoking, poor education, living long distances from the clinic, and dietary treatments (8).

The purpose of this study was to investigate the factors associated with patients dropping out of Project Dulce, a nurse-based diabetes disease-management system in San Diego, Calif (9). The factors studied included demographic, treatment, clinical, and behavioral variables. The program, started in 1998, is an initiative of Community Health Improvement Partners, the Council of Community Clinics, and The Whittier Institute for Diabetes. Project Dulce uses a multifaceted approach and focuses on providing care to racial/ethnic groups that often lack access to medical services.

Table 1 shows the descriptive and univariate statistics. There appear to have been fewer Asians and more whites in the dropout group compared with the control group, while the percentage of Hispanics remained consistent among both groups. More of the dropout population (58.6%) spoke English than the control population (47.2%), and more of the dropout population was insured by County Medical Service (60.9%) than the control population (43.5%). Among the dropouts, high baseline blood pressures (defined here as ⩾ 130 mm Hg for SBP and ⩾ 80 mm Hg for DBP) were more common compared to the control population: 46.1% of the dropout population had high SBP compared to 29.5% of the control, and 26.9% of the dropout population had high DBP compared to 11.7% of control. More of the dropouts compared to controls were current smokers (20.3%, dropouts compared to 12.5%, controls) or past smokers (47.3%, dropouts compared to 32.0%, controls).

Table 2 presents the results of the final logistic model. Insurance status, initial blood pressure, baseline A1c, and smoking habit were significant predictors of dropout status. The odds of dropping out of Project Dulce were 95% lower for a patient without insurance compared to a patient with insurance. The odds of being a dropout were 1.8 times higher for a patient with a SBP ⩾130 mm Hg compared to a patient with a SBP <130 mm Hg and 2.3 times higher for a patient with a DBP ⩾80 mm Hg compared to a patient with DBP <80 mm Hg. For every two-unit increase in baseline A1c, the odds of being a dropout increased 1.3 times. Compared with a patient who never smoked, the odds of dropping out of the program were 3.7 times higher for a current smoker and 2.9 times higher for a past smoker. The variables mentioned above were significant after controlling for all other variables in the model (sex, primary language, insurance status, blood pressure, baseline A1c, and smoking status).

Univariate associations between dropouts and controls showed that race/ethnicity, primary language, insurance status, baseline blood pressure, and smoking varied among the two groups. In multivariate analysis, insurance status, baseline blood pressure, baseline A1c, and smoking habit were different between dropouts and controls.

Similar to the findings of Graber et al (10) and Jacobson et al (6), sex and age were not significant predictors of dropout. Other studies have found that men and younger patients are less likely to adhere to treatment (11-13). Race/ethnicity was a predictor of dropout in univariate analysis but lost its significance after controlling for other factors. Dove and Schneider (14) and Goldman et al (11) also found this to be the case. Primary language also lost its significance in multivariate analysis. Disease duration and type of medicine used to treat diabetes did not predict patient adherence to the program. This finding is consistent with Jacobson et al (6) and Graber et al (10) but contradicts Irvine and Mitchell (15).

Insurance status was a significant predictor of patient adherence. The uninsured were 95% less likely to drop out of the program compared to those with insurance. The uninsured pay an enrollment fee to cover nurse visits, laboratory measurements, and diabetes-related medications. They are not eligible for county or federal health services because of documentation status or income limits. The initial monetary investment and lack of affordable health care options are clearly reasons that the uninsured were less likely to drop out. Those with County Medical Services and Medicaid/Medicare do not pay to enroll in Project Dulce and have other provider options for their diabetes care.

Those with high initial blood pressure or A1c were more likely to drop out of the program. These clinical markers may indicate that these patients were more ill and conceivably unable to keep their outpatient appointments. Perhaps their time was occupied with other primary care or specialty visits to address other medical problems. Regardless of the reasons for patient nonadherence, these clinical markers are important. Higher A1c levels put the cases at increased risk for developing microvascular disease, including retinopathy, nephropathy, and neuropathy (3,4). Elevated initial blood pressure put the cases at increased risk of heart attacks, heart failure, strokes, and kidney disease (16).

Smokers that have type 2 diabetes are at even greater risk of micro- and macrovascular disease (17). This study showed that those who smoked or smoked in the past were more likely to drop out of the program. Why these patients are leaving is not clear. Perhaps smokers are less interested in their health, which would explain their lack of interest in Project Dulce. Another possible explanation is that smokers feel rejected by providers that admonish their behavior (10). Smoking could also be a surrogate for life stressors that prevent appointment attendance due to unstable living or financial environments (18). Regardless of the reason, the clinical and behavioral markers that predict nonadherence leave these patients at high risk of morbidity and mortality associated with microvascular and macrovascular disease (19).

This study has limitations. According to Griffin (8), adherence may be less related to demographic factors and more to patients' perceptions, beliefs, and attitudes. Perhaps some patients may not have understood the importance of consistent glucose control, had a different perception of the disease, or were fearful of the multiple medications needed to treat diabetes. Others may have experienced adverse side effects to medication, stopped treatment, and were ashamed to return to their provider. Patient perceptions could explain nonadherence, but none of this information was available for this study.

Similarly, other possible confounders, such as distance living from the clinic, transportation issues, work schedules, whether they knew they had an appointment, stability of living environment, alcohol and drug problems, other comorbid medical/psychological problems, and patient satisfaction and ability to talk with providers, were not available. These factors could also significantly predict dropouts in Project Dulce. Some of the demographic, clinical, and behavioral factors significant in this model may lose statistical significance depending on the importance of the unmeasured predictors. Lastly, 18 dropouts and 69 controls were excluded because of missing data, which could potentially introduce a selection bias. Because patient identifiers were scrambled for privacy prior to copying the dataset, medical records could not be reviewed to recover this data.

In summary, with the available data, this predictor analysis found that Project Dulce patients with higher initial blood pressure readings and A1c values and those who smoked or smoked in the past were more likely to drop out of the program. These results have clinical applicability in that those patients in most need of care were more likely to drop out of the program. Investigating reasons for dropping out would best be done with patient interviews. In this way, patient perceptions, attitudes, and beliefs could be explored in addition to other logistical reasons.

Project Dulce can use the results of this study to minimize future patient loss to follow-up. Although all patients are case-managed by nurses, an intensified approach would be beneficial in those patients with higher initial A1c and blood pressure measurements and in those who smoke or smoked in the past. Acquiring additional information from patients who drop out could help explain the obstacles facing this low-income, multiethnic population. Addressing these obstacles with interventions and/or organizational changes may lead to better clinical outcomes.