The migraine signature study: Methods and baseline results.

OBJECTIVE: To characterize patients who utilize services for migraine in a large integrated health care network, and describe patterns of care and utilization. BACKGROUND: Within health care systems, migraine is a common reason for seeking primary and neurology care, but relatively little is documented about who seeks care and the factors that explain variation in utilization. METHODS: We conducted a retrospective cohort study using electronic health record (EHR) data from Sutter Health primary care (PC) patients who had at least one office visit to a PC clinic between 2013 and 2017. Migraine status was ascertained from diagnosis codes and medication orders. Control status was assigned to those with no evidence of care for any type of headache. We divided the primary care migraine cohort into two groups: those who received all their care for migraine from PC (denoted PC-M) and those who had ≥1 encounter with a neurologist for migraine (denoted N-M). Migraine cases were also designated as having preexisting migraine if they had an encounter with a migraine diagnosis within (±) 6 months of their first study period PC visit and, otherwise, designated as first migraine consult. Two levels of contrasts included: patients with migraine and controls; and within the group of patients with migraine, PC-M and N-M groups. Comorbid conditions were determined from EHR encounter diagnosis codes. RESULTS: We identified 94,149 patients with migraine (including 21,525 N-M and 72,624 PC-M) and 1,248,763 controls. Comorbidities: Proportions of psychiatric [29.8% (n = 28,054) vs. 11.8% (n = 147,043)], autoimmune [(4.4% (n = 4162) vs. 2.6% (n = 31,981)], pain [13.2% (n = 12,439) vs. 5.8% (n = 72,049)], respiratory [24.6% (n = 23,186) vs. 12.3% (n = 153,692)], neurologic [2.9% (n = 2688) vs. 0.9% (n = 11,321)], and cerebrovascular [1.0% (n = 945) vs. 0.6% (n = 7500)] conditions were higher in the migraine group compared to controls, all p < 0.001. Among patients with migraine, the N-M group was similar to the PC-M group in sex, age, ethnicity, and marital status, but were more likely to have preexisting migraine (49.9% (n = 10,734) vs. 36.2% (n = 26,317), p < 0.001). Proportions of comorbid conditions were higher among the N-M group than the PC-M group {psychiatric [38.5% (n = 8291) vs. 27.2% (n = 19,763)], autoimmune [6.3% (n = 1365) vs. 3.9% (n = 2797)], pain [19.6% (n = 4218) vs. 11.3% (n = 8211)], respiratory [30.3% (n = 6516) vs. 23.0% (n = 16,670)], neurologic [6.0% (n = 1288) vs. 1.9% (n = 1400)], cardiovascular [9.7% (n = 2091) vs. 7.0% (n = 5076)], and cerebrovascular [2.3% (n = 500) vs. 0.6% (n = 445)], all p < 0.001}. Medications: During the study period, 82.6% (n = 77,762) of patients with migraine received ≥1 prescription order for an acute migraine medication [89.4% (n = 19,250) of N-M vs. 80.6% (n = 58,512) of PC]. Opioids were prescribed to 52.9% (n = 49,837) of patients with migraine [63.5% (n = 13,669) for N-M and 49.8% (n = 36,168) for PC-M patients). During the study period, 61.4% (n = 57,810) of patients received ≥1 prescription for a migraine preventive medication [81.4% (n = 17,521) of N-M and 55.5% (n = 40,289) of PC-M patients]. The most commonly prescribed classes of preventive medications were antidepressants. CONCLUSIONS: Among patients with migraine in a large health system, those who were also cared for in neurology were more likely to receive both acute and preventive medication migraine orders than those patients who did not see a neurologist, with triptans and antidepressants the most commonly prescribed classes of acute and preventive pharmacotherapies, respectively. Opioids were prescribed to approximately half of the total sample and more common in the N-M group. Adjusting for demographics, patients with migraine had higher rates of nearly every comorbidity we assessed and were more likely to utilize services compared to those without migraine. Overall, patients with migraine also cared for in neurology practices used more of all health care resource types under consideration and had more medical issues, which may be due in some part to a more severe, frequent and disabling disease state compared to those who sought care exclusively from PC practices.

Risk factors for drug dependence among out-patients on opioid therapy in a large US health-care system.

AIMS: Our study sought to assess the prevalence of and risk factors for opioid drug dependence among out-patients on long-term opioid therapy in a large health-care system. METHODS: Using electronic health records, we identified out-patients receiving 4+ physician orders for opioid therapy in the past 12 months for non-cancer pain within a large US health-care system. We completed diagnostic interviews with 705 of these patients to identify opioid use disorders and assess risk factors. RESULTS: Preliminary analyses suggested that current opioid dependence might be as high as 26% [95% confidence interval (CI) = 22.0-29.9] among the patients studied. Logistic regressions indicated that current dependence was associated with variables often in the medical record, including age <65 [odds ratio (OR) = 2.33, P = 0.001], opioid abuse history (OR = 3.81, P < 0.001), high dependence severity (OR = 1.85, P = 0.001), major depression (OR = 1.29, P = 0.022) and psychotropic medication use (OR = 1.73, P = 0.006). Four variables combined (age, depression, psychotropic medications and pain impairment) predicted increased risk for current dependence, compared to those without these factors (OR = 8.01, P < 0.001). Knowing that the patient also had a history of severe dependence and opioid abuse increased this risk substantially (OR = 56.36, P < 0.001). CONCLUSION: Opioid misuse and dependence among prescription opioid patients in the United States may be higher than expected. A small number of factors, many documented in the medical record, predicted opioid dependence among the out-patients studied. These preliminary findings should be useful in future research efforts.