Abstract
Background
Atrial fibrillation (AF) is treated by many types of physician specialists, including primary care physicians (PCPs). Health plans have different policies for how patients encounter these providers, and these may affect selection of AF treatment strategy.
Hypothesis
We hypothesized that healthcare plans with PCP‐gatekeeping to specialist access may be associated with different pharmacologic treatments for AF.
Methods
We performed a retrospective cohort study using a commercial pharmaceutical claims database. We utilized logistic regression models to compare odds of prescription of oral anticoagulant (OAC), non‐vitamin K‐dependent oral anticoagulant (NOAC), rate control, and rhythm control medications used to treat AF between patients with PCP‐gated healthcare plans (eg, HMO, EPO, POS) and patients with non‐PCP‐gated healthcare plans (eg, PPO, CHDP, HDHP, comprehensive) between 2007 and 2012. We also calculated median time to receipt of therapy within 90 days of index AF diagnosis.
Results
We found similar odds of OAC prescription at 90 days following new AF diagnosis in patients with PCP‐gated plans compared to those with non‐PCP‐gated plans (OR: OAC 1.01, P = 0.84; warfarin 1.05, P = 0.08). Relative odds were similar for rate control (1.17, P < 0.01) and rhythm control agents (0.93, P = 0.03). However, PCP‐gated plan patients had slightly lower likelihood of being prescribed NOACs (0.82, P = 0.001) than non‐gated plan patients. Elapsed time until receipt of medication was similar between PCP‐gated and non‐gated groups across drug classes.
Conclusions
Pharmaceutical claims data do not suggest that PCP‐gatekeeping by healthcare plans is a structural barrier to AF therapy, although it was associated with lower use of NOACs.
Keywords: arrhythmia/all, atrial fibrillation, socio‐economic aspects, thrombosis/hypercoagulable states
1. INTRODUCTION
Atrial fibrillation (AF) is the most commonly encountered arrhythmia in clinical practice.1, 2, 3 The prevalence of AF in the United States is believed to range from 2.7 to 6.1 million, and is projected to increase to 12.1 million by 2030.1, 4 AF has widely varying treatments for stroke prevention and for rhythm management; some of these may be more accessible to patients through specialty care, such as cardiology or cardiac electrophysiology.5, 6 At the same time, because of its high prevalence and wide‐ranging clinical manifestations, AF is treated by many different provider types, from primary care physicians (PCPs) to cardiovascular specialists. Insurance plans with restricted access to specialists could potentially introduce structural barriers for AF care. We therefore sought to determine if PCP‐gated health plans, which require PCP referral for specialist consultation, affect selection of treatment and treatment strategy in atrial fibrillation.
2. METHODS
We performed a retrospective cohort study using data from the Truven Health Marketscan Commercial Claims and Encounters and Medicare Supplemental Databases (Truven Health Analytics Inc., Cambridge, Massachusetts). We included patients with newly‐diagnosed AF (International Classification of Diseases, ninth Revision code 427.31 or 427.32 in any inpatient or outpatient encounter with no AF diagnoses in the preceding two years) between January 1, 2007 and December 31, 2011 who were eligible for oral anticoagulant (OAC) therapy. All drug data were extracted from the Outpatient Pharmaceutical Claims file. Our analysis cohort included all patients with nonvalvular AF (Figure 1—Flow Diagram) and a CHADS2 score ≥ 2. We utilized the CHADS2 score for anticoagulation eligibility because CHA2DS2‐VASc was not incorporated into US professional society guidelines during the study period.7 All patients had continuous insurance enrollment in the Marketscan database for at least 1 year following diagnosis and were followed until December 31, 2012. We excluded any patients who did not receive an outpatient prescription within 365 days of first AF diagnosis to maximize the chance that each prescription was due to the new AF diagnosis, rather than for another indication. We finally excluded patients with valvular AF on the basis of mitral stenosis, history of prior significant bleeding, or history of end‐stage renal disease (Table S1, Supporting information).
Figure 1.
Flow diagram: Summary of the inclusion/exclusion criteria for developing the study cohort
The primary predictor variable was enrollment in a health plan requiring PCP referral for specialist consultation. Non‐PCP‐gated health plans were defined as Basic/Major Medical, Comprehensive (Fee for Service), Preferred Provider Organization (PPO), Consumer‐Driven Health Plan (CDHP), and High Deductible Health Plan (HDHP). We determined PCP‐gated health plans to be Exclusive Provider Organization (EPO), Health Maintenance Organization (HMO), Non‐Capitated Point‐of‐Service (POS), Fully‐ or Partially‐Capitated POS (Table S2).
The principal outcomes of interest were receipt of oral anticoagulation, rhythm control drug therapy (sotalol, amiodarone, dronedarone, propafenone, flecainide, disopyramide, procainamide, quinidine, mexiletine), and rate control agents (metoprolol, carvedilol, atenolol, diltiazem, verapamil, digoxin). For OACs, we examined warfarin, dabigatran, and rivaroxaban only, as apixaban and edoxaban were not yet approved for use in the United States during the study period.
We ascertained baseline patient characteristics using comorbidity‐specific ICD‐9 codes within the study period (up to 1 year prior to date of incident AF diagnosis), and calculated the CHADS2 stroke risk score and Charlson and Selim Comorbidity Indices for each patient. These diagnoses and scores were derived using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System and algorithms established in our previous work.5
Time until receipt of therapy was defined as the number of days between the date of a new AF diagnosis and the date a drug prescription was filled for each patient. This outcome was calculated for the period of time within 90 days following the index diagnosis, as well as within 365 days following the index diagnosis.
We compared baseline clinical and demographic characteristics between cohorts of patients in different health plan groups using Analysis of Variance (anova) tests for continuous variables and χ 2 tests for categorical variables. For outcomes, we reported the number of patients in each health plan group receiving each type of medication, as well as the percentage of each group receiving a prescription for that medication. To control for potential confounders, we calculated adjusted odds ratios using multivariable logistic regression models which incorporated age, sex, region, Charlson comorbidity index, CHADS2Vasc score, hypertension, diabetes, stroke/TIA, prior myocardial infarction, peripheral artery disease, and antiplatelet medication use.
We compared time to prescription outcomes using medians with interquartile ranges and the Wilcoxon rank‐sum test. All analyses were performed using Stata, version 12.0 (College Station, Texas) and SAS, version 9.2 (Cary, North Carolina). This study was approved by the local institutional IRB (Stanford, California) and the VA Research and Development Committee (Palo Alto, California) and were in accordance with the Declaration of Helsinki.
3. RESULTS
We identified 40 744 patients who were newly diagnosed with AF between 2007 and 2011 (age 76.6 ± 11.1 years, 49.8% female, Table 1). Of these patients, 5708 were enrolled in PCP‐gated health plans (14%), while 35 036 were enrolled in non‐PCP‐gated health plans (86%). Mean CHADS2 score, Selim and Charlson Comorbidity Indices did not significantly differ between PCP‐gated and non‐PCP‐gated groups.
Table 1.
Baseline clinical characteristics
| Total (N = 40 744) | PCP‐gated population (N = 5708) | Non‐PCP‐gated population (N = 35 036) | P value | |
|---|---|---|---|---|
| Age (years) | 76.6 ± 11.1 | 72.6 ± 12.4 | 77.3 ± 10.7 | <0.01 |
| Sex (female) | 20 298 (49.8%) | 2688 (47.1%) | 17 610 (50.3%) | <0.01 |
| Selim comorbidity index | 4.1 ± 2.0 | 4.1 ± 2.0 | 4.1 ± 2.0 | 0.09 |
| Charlson comorbidity index | 1.8 ± 1.3 | 1.9 ± 1.3 | 1.8 ± 1.3 | <0.01 |
| CHADS2 score (overall) | 2.5 ± 0.8 | 2.5 ± 0.8 | 2.5 ± 0.8 | <0.01 |
| CHADS2 score (by group) | <0.01 | |||
| CHADS2 2 | 26 288 (64.5%) | 3860 (67.0%) | 22 428 (64.0%) | |
| CHADS2 3 | 8996 (22.1%) | 1178 (20.6%) | 7818 (22.3%) | |
| CHADS2 4 | 4104 (10.1%) | 498 (8.7%) | 3606 (10.3%) | |
| CHADS2 5 | 1194 (2.9%) | 137 (2.4%) | 1057 (3.0%) | |
| CHADS2 6 | 162 (0.4%) | 35 (0.6%) | 127 (0.4%) | |
| CHA2DS2‐VASc score | 4.2 ± 1.2 | 4.0 ± 1.3 | 4.2 ± 1.2 | <0.01 |
| Heart failure | 14 401 (35.4%) | 1986 (34.8%) | 12 415 (35.4%) | 0.35 |
| Hypertension | 34 590 (84.9%) | 5029 (88.1%) | 29 561 (84.4%) | <0.01 |
| Diabetes | 18 130 (44.5%) | 2911 (51.0%) | 15 219 (43.4%) | <0.01 |
| Prior stroke/transient ischemic attack | 5461 (13.4%) | 698 (12.2%) | 4763 (13.6%) | <0.01 |
| Prior myocardial infarction | 3065 (7.5%) | 499 (8.7%) | 2566 (7.3%) | <0.01 |
| Anemia | 5718 (14.0%) | 808 (14.2%) | 4910 (14.1%) | 0.78 |
| Prior bleeding | 2388 (5.9%) | 315 (5.5%) | 2073 (5.9%) | 0.24 |
| Peripheral artery disease | 4174 (10.2%) | 483 (8.5%) | 3691 (10.5%) | <0.01 |
| Chronic kidney disease | 1732 (4.3%) | 294 (5.2%) | 1438 (4.1%) | <0.01 |
| Region (United States) | <0.01 | |||
| Northeast | 5879 (14.3%) | 1035 (18.1%) | 4844 (13.8%) | |
| North central | 14 436 (35.4%) | 999 (17.5%) | 13 437 (38.4%) | |
| South | 13 133 (32.2%) | 1980 (34.7%) | 11 153 (31.8%) | |
| West | 7169 (17.6%) | 1671 (29.3%) | 5496 (15.7%) | |
| Unknown | 127 (0.3%) | 23 (0.4%) | 104 (0.3%) |
Abbreviations: PCP, primary care physician.
Baseline clinical characteristics of the study population, including age, sex, patient localization in United States, and clinical comorbidities. Age, comorbidity indices, and CHADS2 score are reported as means and standard deviations, while all other values are reported as frequencies with proportions.
The differences between PCP‐gated and non‐PCP‐gated plans in proportion of anticoagulants prescribed were small and the values were similar: 44.2% of PCP‐gated plan members were prescribed any oral anticoagulant medication, compared to 42.3% of non‐gated plan patients (P < 0.01) (Table 2). Of PCP‐gated plan members, 39.1% were prescribed warfarin, compared to 37.1% of non‐PCP‐gated plan patients (P < 0.01). There was no significant difference in NOAC prescription between groups (5.9% gated vs 6.0% non‐gated, P = 0.64). Overall, 55.8% of PCP‐gated plan members were on no anticoagulation vs 57.7% of non‐PCP‐gated plan patients (P = 0.01). Findings were similar when prescription proportions were compared at 365 days following AF diagnosis (Table S3).
Table 2.
Prescriptions within 90 days following new AF diagnosis, stratified by PCP‐gated vs non‐PCP‐gated health plansa
| Therapy | Total (n = 40 744) | PCP‐gated (n = 5708) | Non‐PCP‐gated (n = 35 036) | P value | |
|---|---|---|---|---|---|
| Anticoagulant medications | Any OAC | 17 356 (42.6%) | 2522 (44.2%) | 14 834 (42.3%) | <0.01 |
| Warfarin (any prescription) | 15 239 (37.4%) | 2232 (39.1%) | 13 007 (37.1%) | <0.01 | |
|
Warfarin
(as only OAC) |
14 910 (36.6%) | 2187 (38.3%) | 12 723 (36.3%) | <0.01 | |
| Dabigatran (any prescription) | 2399 (5.9%) | 334 (5.9%) | 2065 (5.9%) | 0.90 | |
| Dabigatran (as only OAC) | 2069 (5.1%) | 288 (5.1%) | 1781 (5.1%) | 0.90 | |
| Rivaroxaban (any prescription) | 57 (0.1%) | 2 (0.04%) | 55 (0.2%) | 0.02 | |
| Rivaroxaban (as only OAC) | 40 (0.1%) | 1 (0.02%) | 39 (0.1%) | 0.04 | |
| Any NOAC | 2446 (6.0%) | 335 (5.9%) | 2111 (6.0%) | 0.64 | |
| Warfarin and NOAC | 329 (0.8%) | 45 (0.8%) | 284 (0.8%) | 0.86 | |
| Antiplatelet agents only (no OAC) | 3780 (9.3%) | 423 (7.4%) | 3357 (9.6%) | <0.01 | |
| No anticoagulation | 23 388 (57.4%) | 3186 (55.8%) | 20 202 (57.7%) | 0.01 | |
| Rate & rhythm control medications | Rhythm control | 9997 (24.5%) | 1394 (24.4%) | 8603 (24.6%) | 0.83 |
| Rate control | 30 060 (73.8%) | 4361 (76.4%) | 25 699 (73.4%) | <0.01 | |
Abbreviations: AF, atrial fibrillation; NOAC, non‐vitamin‐K‐dependent oral anticoagulant; OAC, oral anticoagulant; PCP, primary care physician.
Prescriptions by number and proportion (in parentheses) of patients enrolled in PCP‐gated vs non‐PCP‐gated health plans at 90 days from index AF diagnosis.
Table 3 presents multivariable‐adjusted odds ratios for prescriptions within 90 days following a new AF diagnosis for PCP‐gated plan patients relative to non‐PCP‐gated plan patients. The odds ratios (ORs) were largely similar between the two groups, with the OR of any OAC 1.01 (95% CI: 0.95‐1.07, P = 0.84), and warfarin 1.05 (0.99‐1.12, P = 0.08) with neither result statistically significant. The adjusted analyses found statistically‐significant ORs of small effect size showing lower odds of being prescribed any NOAC (OR = 0.82 [95% CI: 0.72‐0.92], P < 0.01), dabigatran (OR = 0.83 [0.74‐0.94], P < 0.01), and rivaroxaban (OR = 0.18 [0.04‐0.75], P = 0.02) in the PCP‐gated group compared to the non‐PCP‐gated group.
Table 3.
Multivariable‐adjusted odds ratios for prescriptions within 90 days following new AF diagnosis, for PCP‐gated vs non‐PCP‐gated health plansa
| Adjusted odds ratio | ||
|---|---|---|
| PCP‐gated vs non‐PCP‐gatedWith non‐PCP‐gated as reference group | ||
| (95% CI) | P value | |
| Any OAC | 1.01 (0.95‐1.07) | 0.84 |
| Warfarin | 1.05 (0.99‐1.12) | 0.08 |
| Dabigatran | 0.83 (0.74‐0.94) | <0.01 |
| Rivaroxaban | 0.18 (0.04‐0.75) | 0.02 |
| Any NOAC | 0.82 (0.72‐0.92) | <0.01 |
| Rhythm control strategy | 0.93 (0.87‐0.99) | 0.03 |
| Rate control strategy | 1.17 (1.09‐1.25) | <0.01 |
Abbreviations: AF, atrial fibrillation; NOAC, non‐vitamin‐K‐dependent oral anticoagulant; OAC, oral anticoagulant; PCP, primary care physician.
Odds ratios for the prescription of AF medications for PCP‐gated plan patients relative to non‐PC‐gated plan patients. Logistic regression models adjusted for the following covariates: Age, sex, region, Charlson comorbidity index, CHADS2Vasc score, hypertension, diabetes, stroke/TIA, prior myocardial infarction, peripheral artery disease, and antiplatelet medication use.
There was no difference in the prescription of rhythm control medications in PCP‐gated and non‐gated groups (24.4% vs 24.6%, P = 0.83). PCP‐gated patients were prescribed rate control agents at a slightly higher proportion than non‐PCP‐gated patients (76.4% vs 73.4%, P < 0.01). Trends were similar when prescription proportions were compared at 365 days following AF diagnosis (Table S3). Adjusted ORs for both rate and rhythm control (1.17 [95% CI: 1.09‐1.25], P < 0.01 and 0.93 [0.87‐0.99], P = 0.03, respectively) were statistically significant but of very small effect size.
Compared to non‐PCP‐gated health plans, PCP‐gated healthcare plans were not associated with an increase in time to prescription of any of our examined medication classes (Table 4). Median times from the date of index diagnosis to prescription receipt for dabigatran, rivaroxaban, and rhythm control medications were very small and clinically similar, although statistically significant. Time to prescription receipt for warfarin in PCP‐gated vs non‐PCP‐gated groups was 4 ± 14 vs 5 ± 16 days (P < 0.01). Time to receipt of antiplatelet agents as only AF treatment was 23 ± 36 vs 26 ± 43 (P = 0.01), while that for any OAC was 4 ± 14 vs 5 ± 16 (P < 0.01), and for rate control medications was 10 ± 25 vs 11 ± 30 (P < 0.01).
Table 4.
Time to patient receipt of therapy among prescriptions filled within 90 days following new AF diagnosisa
| Median in days ± IQR | |||
|---|---|---|---|
| PCP‐gated | Non‐PCP‐gated | P value | |
| Any OAC | 4 ± 14 | 5 ± 16 | <0.01 |
| Warfarin (any prescription) | 4 ± 14 | 5 ± 16 | <0.01 |
| Warfarin (as only OAC) | 4 ± 13 | 5 ± 14 | <0.01 |
| Dabigatran (any prescription) | 7 ± 26 | 6 ± 22 | 0.36 |
| Dabigatran (as only OAC) | 6 ± 20 | 5 ± 18 | 0.18 |
| Rivaroxaban (any prescription) | 24 ± 8 | 29 ± 38 | 0.85 |
| Rivaroxaban (as only OAC) | 20 ± 0 | 23 ± 36 | 1.0 |
| Antiplatelet agents only (no OAC prescription) | 23 ± 36 | 26 ± 43 | 0.01 |
| Any NOAC | 7 ± 26 | 6 ± 23 | 0.29 |
| Rhythm control | 13 ± 35 | 13 ± 34 | 0.87 |
| Rate control | 10 ± 25 | 11 ± 30 | <0.01 |
Abbreviations: AF, atrial fibrillation; IQR, interquartile range; NOAC, Non‐vitamin‐K‐dependent oral anticoagulant; OAC, oral anticoagulant; PCP, primary care physician.
Median time to receipt of prescriptions (in days) of the various AF medications listed at 90 days following index AF diagnosis.
4. DISCUSSION
We found that, overall, plans with PCP gatekeeping to specialist referral were not associated with clinically meaningful differences in prescription rates or delays in time to prescription of oral anticoagulation, rate control, and rhythm control drug therapy. However, PCP gatekeeping plans had very small, but statistically significant, decreased odds of being prescribed non‐vitamin K‐dependent oral anticoagulants. These findings suggest that PCP gatekeeping may not be a major structural barrier in receipt of medications for atrial fibrillation, although non‐PCP‐gated plans may very slightly favor facilitating the prescription of NOACs.
There are several potential explanations for these results. Although several studies, including one published from our research group, have shown that patients with new AF and early treatment by cardiology specialty care are more likely to receive anticoagulation and rhythm control, non‐cardiologist physicians are increasingly prescribing AF medications.5, 6, 8, 9, 10 The overall percentage of patients in our cohort with CHADS2 score ≥ 2 receiving OAC prescriptions was similar to that reported in registries and observational studies.5, 11, 12, 13, 14
There is evidence to suggest that over time, PCPs have increased rates of novel medication prescriptions. Initially, uptake of new or premium drugs is higher among specialists than PCPs soon after their introduction to the market, which has been well described for anti‐retroviral drug therapy for human immunodeficiency virus and for oral hypoglycemic agents in diabetes.15, 16, 17, 18 The diffusion of innovation theory postulates that an S‐shaped curve characterizes the adoption of a new technology or scientific advance, with initial uptake by a small number of early adopters followed by rapid dissemination of knowledge and utilization by the remainder of a population.19, 20, 21, 22 The process can occur quite quickly, as in the case of protease inhibitors for use in HIV.17 An analysis of sildenafil prescriptions in the 24 weeks following its availability in an HMO plan showed that 85% of the prescriptions were placed within the first 12 weeks of availability, primarily by PCPs.23 Our findings that overall OAC prescriptions did not differ by PCP gating status may suggest completion of the rapid dissemination and uptake phase for most AF treatments. The small but statistically significant odds ratios favoring the non‐PCP‐gated populations in NOACs further suggests that in this newer drug group, the process is ongoing, with more specialists representing early adopters. Interestingly, the low primary care odds ratio of rivaroxaban use, relative to dabigatran, may be indicative of a gradient of uptake of later‐generation NOACs, although interpretability is limited by the small number of patients in the rivaroxaban group.
Another potential explanation for the similar rates of AF prescriptions is that PCP gatekeeping may paradoxically lead to comparable or increased referrals to specialists (and thus, comparable rates of specialty drug prescriptions) compared to non‐PCP‐gated groups as seen for other conditions. A study of health plan type on prescription drug access and use found no difference between Medicare HMO and FFS (fee for service) populations in ability to obtain necessary prescribed medications, average number of prescriptions per beneficiary, and percentage of generic drug use per user.24 HMO patients, when compared to FFS plan patients were as likely, or sometimes more likely, to have been prescribed a variety of medications including beta blockers, calcium channel blockers, nitrates, digitalis with diuretics, ACE‐inhibitors, angiotensin receptor blockers, and troglitazone.25, 26, 27, 28 They also did not differ in rates of prior coronary angiography, stress ECG, or thallium scans.26, 27, 29 PCP gatekeeping itself as a variable within a single healthcare plan did not result in lower rates of guideline‐based ophthalmologic diabetic retinal examinations.30
A number of studies have looked directly at the effect of PCP gatekeeping measures on the referral process to specialists.31, 32, 33, 34, 35, 36, 37, 38, 39 Several retrospective and prospective analyses within health plans reported no differences in the number of referrals to specialists and total specialist visits between PCP‐gated and non‐gated arrangements.35, 36, 37, 38 Other analyses found that patients in PCP‐gated plans were in fact more likely to be referred to a specialist.33, 34, 39 These findings were not explained by patient/family preference or payment capitation. Furthermore, the phenomenon was not limited to the United States, but was observed in some Western European countries as well.32 These studies suggest that the involvement of specialists in the care of patients is a complicated process not explained by PCP gatekeeping alone. Therefore, even though we found that gatekeeping itself was not associated with major pharmacologic treatment differences, there may be other barriers that limit specialty referral even in non‐gated plans. We have previously reported that in newly‐diagnosed AF across the entire Veterans Affairs system, cardiology care (compared to primary care only) was associated with lower risk of stroke that was mediated by higher rates of OAC prescription.6 Patients who lived closer to VA specialty centers were more likely to receive cardiology care. These and other factors, such as patient self‐perception of illness severity, total health care expenditures, and prescription burden,24, 25, 29 deserve further inquiry.
5. LIMITATIONS
There are several limitations of our present study. As is the case for all observational studies, there exist potential unidentified confounders. For example, our data source could not ascertain AF subtype (eg, permanent or paroxysmal) or symptom severity. We could not adjust for individual‐level socioeconomic status because the claims data source does not include details such patient household income, education status, or zip code. Health plan prescription copayment and formulary restrictions could also be important for comparison of individual drugs, although our principal aim was to look broadly at treatment strategies when multiple drugs may be used (OAC, rate and rhythm control). Recent results from our group suggest that insurance plans with greater benefits (and generally lower copayment) may be associated with increased odds of NOAC prescription.40 Further characterization of these factors will help researchers better understand the drivers of AF treatment choices by healthcare providers.
Additionally, the timeframe of our analysis was chosen because our goal was to examine a period of varying drug uptake, namely the first 2 years and 1 year following dabigatran and rivaroxaban's respective US approval. We aimed to characterize the effects of health plan gatekeeping on prescriptions of new classes of medications shortly after their introduction to the market, a phenomenon which has implications for other new cardiovascular drugs, such as in heart failure. Consequently, the interval does not include the more recently‐approved NOACs such as apixaban and edoxaban. Future investigation could include the timeframes of introduction of these medications, as well as that of procedural interventions in AF, such as catheter ablation.
6. CONCLUSIONS
In newly diagnosed AF, pharmaceutical claims data do not suggest that PCP gatekeeping of US healthcare plan is a major structural barrier to AF drug therapy, although it was associated with lower use of NOACs.
Supporting information
TABLE S1. ICD‐9 codes for exclusion
TABLE S2. Reproduced from marketscan clinical claims and encounters user guide
TABLE S3. Prescriptions within 365 days following new AF diagnosis
ACKNOWLEDGEMENTS
All authors contributed materially to study concept and design, acquisition and interpretation of data, or drafting and critical revision of the manuscript for important intellectual content. The work was supported by a VA Health Services and Development MERIT Award (IIR 09‐092). The funding source did not play a role in the study design; collection, analysis, and interpretation of data; in writing the report; and in the decision to submit the article for publication.
Conflicts of interest
Andrew Young Chang: None; Mariam Askari: None; Jun Fan: None; Paul A. Heidenreich: None; P. Michael Ho: Janssen Pharmaceuticals, American Heart Association; Kenneth W. Mahaffey: Ablynx, Afferent, Amgen, AstraZeneca, BAROnova, Bio2 Medical, BioPrint Fitness, Boehringer Ingelheim, Bristol Myers Squibb, Cardiometabolic Health Congress, Cubist, Daiichi, Eli Lilly, Elsevier, Epson, Ferring, Glaxo Smith Kline, Google (Verily), Johnson & Johnson, Medtronic Inc., Merck, Mt. Sinai, Myokardia, Novartis, Oculeve, Portola, Radiomeer, Sanofi, Springer Publishing, St Jude Medical, The Medicine Company, Theravance, UCSF, Vindico, WebMD; Aditya Jathin Ullal: None; Alexander Carroll Perino: None; Mintu P. Turakhia: Janssen Pharmaceuticals, Medtronic Inc., AztraZeneca, Veterans Health Administration, AliveCor, St. Jude Medical, Boehringer Ingelheim, Precision Health Economics, Zipline Medical, iBeat Inc., Akebia, Cardiva Medical, Medscape/theheart.org, Amazon, iRhythm, JAMA Cardiology.
Chang AY, Askari M, Fan J, et al. Association of Healthcare Plan with atrial fibrillation prescription patterns. Clin Cardiol. 2018;41:1136–1143. 10.1002/clc.23042
REFERENCES
- 1. Benjamin EJ, Blaha MJ, Chiuve SE, et al. Heart Disease and Stroke Statistics—2017 update: a report from the American Heart Association. Circulation. 2017;135:e146‐e603. CIR–0000000000000485. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Miyasaka Y. Secular trends in incidence of atrial fibrillation in Olmsted County, Minnesota, 1980 to 2000, and implications on the projections for future prevalence. Circulation. 2006;114(2):119‐125. 10.1161/CIRCULATIONAHA.105.595140. [DOI] [PubMed] [Google Scholar]
- 3. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics‐‐2014 update: a report from the American Heart Association. Circulation. 2014;129(3):e28‐e292. 10.1161/01.cir.0000441139.02102.80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Colilla S, Crow A, Petkun W, Singer DE, Simon T, Liu X. Estimates of current and future incidence and prevalence of atrial fibrillation in the U.S. adult population. Am J Cardiol. 2013;112(8):1142‐1147. 10.1016/j.amjcard.2013.05.063. [DOI] [PubMed] [Google Scholar]
- 5. Turakhia MP, Hoang DD, Xu X, et al. Differences and trends in stroke prevention anticoagulation in primary care vs cardiology specialty management of new atrial fibrillation: The Retrospective Evaluation and Assessment of Therapies in AF (TREAT‐AF) study. Am Heart J. 2013;165((1)):93‐101.e1. 10.1016/j.ahj.2012.10.010. [DOI] [PubMed] [Google Scholar]
- 6. Perino AC, Fan J, Schmitt SK, et al. Treating specialty and outcomes in newly diagnosed atrial fibrillation: from the TREAT‐AF study. J Am Coll Cardiol. 2017;70(1):78‐86. 10.1016/j.jacc.2017.04.054. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Fuster V, Rydén LE, Cannom DS, et al. 2011 ACCF/AHA/HRS focused updates incorporated into the ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation: a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines. Circulation. 2011;123(10):e269‐e367. 10.1161/CIR.0b013e318214876d. [DOI] [PubMed] [Google Scholar]
- 8. Friberg L, Hammar N, Ringh M, Pettersson H, Rosenqvist M. Stroke prophylaxis in atrial fibrillation: who gets it and who does not? Report from the Stockholm cohort‐study on atrial fibrillation (SCAF‐study). Eur Heart J. 2006;27(16):1954‐1964. 10.1093/eurheartj/ehl146. [DOI] [PubMed] [Google Scholar]
- 9. Choudhry NK, Soumerai SB, Normand S‐LT, Ross‐Degnan D, Laupacis A, Anderson GM. Warfarin prescribing in atrial fibrillation: the impact of physician, patient, and hospital characteristics. Am J Med. 2006;119(7):607‐615. 10.1016/j.amjmed.2005.09.052. [DOI] [PubMed] [Google Scholar]
- 10. Fosbol EL, Holmes DN, Piccini JP, et al. Provider specialty and atrial fibrillation treatment strategies in United States community practice: findings from the ORBIT‐AF registry. J Am Heart Assoc. 2013;2(4):e000110 10.1161/JAHA.113.000110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Hsu JC, Maddox TM, Kennedy KF, et al. Oral anticoagulant therapy prescription in patients with atrial fibrillation across the Spectrum of stroke risk: insights from the NCDR PINNACLE registry. JAMA Cardiol. 2016;1(1):55‐62. 10.1001/jamacardio.2015.0374. [DOI] [PubMed] [Google Scholar]
- 12. Hess PL, Kim S, Fonarow GC, et al. Absence of oral anticoagulation and subsequent outcomes among outpatients with atrial fibrillation. Am J Med. 2016;130:449‐456. 10.1016/j.amjmed.2016.11.001. [DOI] [PubMed] [Google Scholar]
- 13. Ogilvie IM, Newton N, Welner SA, et al. Underuse of oral anticoagulants in atrial fibrillation: a systematic review. Am J Med. 2010;123(7):638‐645.e4. 10.1016/j.amjmed.2009.11.025. [DOI] [PubMed] [Google Scholar]
- 14. McCormick D, Gurwitz JH, Goldberg RJ, et al. Prevalence and quality of warfarin use for patients with atrial fibrillation in the long‐term care setting. Arch Intern Med. 2001;161(20):2458‐2463. [DOI] [PubMed] [Google Scholar]
- 15. De Smet BD, Fendrick AM, Stevenson JG, et al. Over and under‐utilization of cyclooxygenase‐2 selective inhibitors by primary care physicians and specialists: the tortoise and the hare revisited. J Gen Intern Med. 2006;21(7):694‐697. 10.1111/j.1525-1497.2006.00463.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Pugh MJ, Anderson J, Pogach LM, et al. Differential adoption of pharmacotherapy recommendations for type 2 diabetes by generalists and specialists. Med Care Res Rev MCRR. 2003;60(2):178‐200. [DOI] [PubMed] [Google Scholar]
- 17. Landon BE, Wilson IB, Cohn SE, et al. Physician specialization and antiretroviral therapy for HIV. J Gen Intern Med. 2003;18(4):233‐241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Chin MH, Friedmann PD, Cassel CK, Lang RM. Differences in generalist and specialist physicians' knowledge and use of angiotensin‐converting enzyme inhibitors for congestive heart failure. J Gen Intern Med. 1997;12(9):523‐530. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Rogers EM. Diffusion of Innovations. 4th ed. New York, NY: Free Press; 1995. [Google Scholar]
- 20. Dunn AG, Braithwaite J, Gallego B, Day RO, Runciman W, Coiera E. Nation‐scale adoption of new medicines by doctors: an application of the bass diffusion model. BMC Health Serv Res. 2012;12:248 10.1186/1472-6963-12-248. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Garjón FJ, Azparren A, Vergara I, Azaola B, Loayssa JR. Adoption of new drugs by physicians: a survival analysis. BMC Health Serv Res. 2012;12:56 10.1186/1472-6963-12-56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Huskamp HA, O'Malley AJ, Horvitz‐Lennon M, Taub AL, Berndt ER, Donohue JM. How quickly do physicians adopt new drugs? The case of second‐generation antipsychotics. Psychiatr Serv Wash DC. 2013;64(4):324‐330. 10.1176/appi.ps.201200186. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Harrold LR, Gurwitz JH, Field TS, et al. The diffusion of a novel therapy into clinical practice: the case of sildenafil. Arch Intern Med. 2000;160(22):3401‐3405. [DOI] [PubMed] [Google Scholar]
- 24. Saleh SS, Weller W, Hannan E. The effect of insurance type on prescription drug use and expenditures among elderly Medicare beneficiaries. J Health Hum Serv Adm. 2007;30(1):50‐74. [PubMed] [Google Scholar]
- 25. Stafford RS, Davidson SM, Davidson H, Miracle‐McMahill H, Crawford SL, Blumenthal D. Chronic disease medication use in managed care and indemnity insurance plans. Health Serv Res. 2003;38(2):595‐612. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Paone G, Higgins RSD, Spencer T, Silverman NA. Enrollment in the health alliance plan HMO is not an independent risk factor for coronary artery bypass graft surgery. Circulation. 1995;92(9):69‐72. 10.1161/01.CIR.92.9.69. [DOI] [PubMed] [Google Scholar]
- 27. Starr A, Furnary AP, Grunkemeier GL, He GW, Ahmad A. Is referral source a risk factor for coronary surgery? Health maintenance organization versus fee‐for‐service system. J Thorac Cardiovasc Surg. 1996;111(4):708‐717. [DOI] [PubMed] [Google Scholar]
- 28. Skelding PC, Majumdar SR, Kleinman K, et al. Clinical and nonclinical correlates of adherence to prescribing guidelines for hypertension in a large managed care organization. J Clin Hypertens Greenwich Conn. 2006;8(6):414‐419. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Rask KJ, Deaton C, Culler SD, et al. The effect of primary care gatekeepers on the management of patients with chest pain. Am J Manag Care. 1999;5(10):1274‐1282. [PubMed] [Google Scholar]
- 30. Schillinger D, Bibbins‐Domingo K, Vranizan K, Bacchetti P, Luce JM, Bindman AB. Effects of primary care coordination on public hospital patients. J Gen Intern Med. 2000;15(5):329‐336. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Garrido MV, Zentner A, Busse R. The effects of gatekeeping: a systematic review of the literature. Scand J Prim Health Care. 2011;29(1):28‐38. 10.3109/02813432.2010.537015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Gérvas J, Pérez Fernández M, Starfield BH. Primary care, financing and gatekeeping in Western Europe. Fam Pract. 1994;11(3):307‐317. [DOI] [PubMed] [Google Scholar]
- 33. Forrest CB, Reid RJ. Passing the baton: HMOs' influence on referrals to specialty care. Health Aff (Millwood). 1997;16(6):157‐162. 10.1377/hlthaff.16.6.157. [DOI] [PubMed] [Google Scholar]
- 34. Forrest CB, Glade GB, Starfield B, Baker AE, Kang M, Reid RJ. Gatekeeping and referral of children and adolescents to specialty care. Pediatrics. 1999;104(1 Pt 1):28‐34. [DOI] [PubMed] [Google Scholar]
- 35. Joyce GF, Kapur K, Van Vorst KA, et al. Visits to primary care physicians and to specialists under gatekeeper and point‐of‐service arrangements. Am J Manag Care. 2000;6(11):1189‐1196. [PubMed] [Google Scholar]
- 36. Ferris TG, Chang Y, Blumenthal D, Pearson SD. Leaving gatekeeping behind—effects of opening access to specialists for adults in a health maintenance organization. N Engl J Med. 2001;345(18):1312‐1317. [DOI] [PubMed] [Google Scholar]
- 37. Krasna IH. Abdominal pain and appendicitis: is there a difference in referrals between HMO pediatricians and private pediatricians? J Pediatr Surg. 2000;35(7):1084‐1086. 10.1053/jpsu.2000.7831. [DOI] [PubMed] [Google Scholar]
- 38. Ferris TG, Chang Y, Perrin JM, Blumenthal D, Pearson SD. Effects of removing gatekeeping on specialist utilization by children in a health maintenance organization. Arch Pediatr Adolesc Med. 2002;156(6):574‐579. [DOI] [PubMed] [Google Scholar]
- 39. Forrest CB, Nutting P, Werner JJ, Starfield B, von Schrader S, Rohde C. Managed health plan effects on the specialty referral process: results from the ambulatory sentinel practice network referral study. Med Care. 2003;41(2):242‐253. 10.1097/01.MLR.0000044903.91168.B6. [DOI] [PubMed] [Google Scholar]
- 40. Yong CM, Liu Y, Apruzzese P, et al. Association of insurance type with receipt of oral anticoagulation in insured patients with atrial fibrillation: a report from the American College of Cardiology NCDR PINNACLE registry. Am Heart J. 2018;195:50‐59. 10.1016/j.ahj.2017.08.010. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
TABLE S1. ICD‐9 codes for exclusion
TABLE S2. Reproduced from marketscan clinical claims and encounters user guide
TABLE S3. Prescriptions within 365 days following new AF diagnosis