- Email: [email protected]
Association between poverty and appropriate statin prescription for the treatment of hyperlipidemia in the United States: An analysis from the ACC NCDR PINNACLE registry
Journal Pre-proof Association between poverty and appropriate statin prescription for the treatment of hyperlipidemia in the United States: An analysis from the ACC NCDR PINNACLE registry
Varsha K. Tanguturi, Kevin F. Kennedy, Salim S. Virani, Thomas M. Maddox, Katrina Armstrong, Jason H. Wasfy PII:
S1553-8389(19)30827-9
DOI:
https://doi.org/10.1016/j.carrev.2019.12.026
Reference:
CARREV 1788
To appear in:
Cardiovascular Revascularization Medicine
Received date:
20 September 2019
Revised date:
19 December 2019
Accepted date:
19 December 2019
Please cite this article as: V.K. Tanguturi, K.F. Kennedy, S.S. Virani, et al., Association between poverty and appropriate statin prescription for the treatment of hyperlipidemia in the United States: An analysis from the ACC NCDR PINNACLE registry, Cardiovascular Revascularization Medicine(2019), https://doi.org/10.1016/j.carrev.2019.12.026
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2019 Published by Elsevier.
Journal Pre-proof
1
Title Association between poverty and appropriate statin prescription for the treatment of hyperlipidemia in the United States: an analysis from the ACC NCDR PINNACLE registry Authors: Varsha K. Tanguturi MD1,2, Kevin F. Kennedy MS3, Salim S. Virani MD PhD4, Thomas M. Maddox MD MSc5, Katrina Armstrong MD MSCE2, Jason H. Wasfy MD MPhil1,6
ro
of
Affiliations: 1—Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 2—Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 3—St. Luke’s Mid-America Heart Institute, Kansas City, MO 4—Michael E. DeBakey Veterans Affairs Medical Center, Baylor College of Medicine, Houston, TX 5—Cardiology Division, Department of Medicine, Washington University School of Medicine, St. Louis, MO 6—Massachusetts General Physicians Organization, Boston, MA
re
-p
Sources of Funding: The primary funding for this work was from the National Cardiovascular Data Registry of the American College of Cardiology. This work was also supported by grants from the American Heart Association (18CDA34110215) as well as the National Institutes of Health through Harvard Catalyst (KL2 TR001100), both awarded to Dr. Wasfy.
lP
Corresponding Author: Varsha K. Tanguturi MD and Jason H. Wasfy MD MPhil, Cardiology Division Yawkey 5B, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114; Tel: (617) 724-0359; Fax: (617) 6431620; Email: [email protected] and [email protected]
Jo
ur
na
Subject Codes: Poverty, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Hypercholesterolemia, Delivery of Health Care, Healthcare Disparities
1
Journal Pre-proof
2
Abstract Background Poverty is associated with a higher risk of myocardial infarction and cardiac death, both of which are decreased by treatment of hyperlipidemia. There may be differences in the appropriate treatment of hyperlipidemia between richer and poorer Americans. In this study, we aimed to evaluate the association between income level and appropriate lipid-lowering therapy.
of
Methods We identified outpatient visits in the National Cardiovascular Data Registry’s Practice Innovation and Clinical Excellence (PINNACLE) Registry and determined appropriateness of lipid-lowering therapy among patients in different income quintiles (Quintile 5 being the highest income quintile). Logistic regression at the patient level was performed to evaluate the independent association of income and the primary outcome of appropriate statin therapy. The analysis was repeated before and after November 2013 given a change in guideline definitions.
lP
re
-p
ro
Results The study included 1,655,723 patients. Overall, 68-73% of patients were treated appropriately under the ATP III Guidelines and 57-62% of patients were treated appropriately under the ACC/AHA Guidelines. Patients in the wealthiest quintile had higher odds of appropriate statin therapy under both guidelines relative to patients in the poorest quintile (OR 1.06 [1.05-1.07] for ATP III and OR 1.03 [1.01-1.04] for ACC/AHA). In the whole sample, patients with higher estimated income had a small but significant increased likelihood of appropriate statin therapy (point-biserial correlation 0.035 [p < 0.001] for ATP III and 0.026 [p < 0.001] for ACC/AHA).
Jo
ur
na
Conclusions Here we describe a small association between appropriate statin use and income. Further investigation into barriers in the use of evidence-based therapies in poorer populations is needed.
2
Journal Pre-proof
3
Introduction Appropriate treatment of cholesterol with hydroxymethylglutaryl-CoA reductase inhibitors (statins) reduces the risk of myocardial infarction and cardiac death.(1) Because 1 million Americans suffer from acute myocardial infarction annually and over 600,000 Americans suffer cardiac death, ensuring that patients receive statin therapy when they need it is vital to public health.(2) Low-income patients often suffer from greater prevalence and poorer outcomes in cardiovascular disease. (3–10)
The mechanism of this disparity is unclear and little has been done to investigate the association of income and lipid therapy thus far. Targeting the critical step of lipid treatment in the management and
of
prevention of coronary artery disease could improve our care delivery in poorer populations that are
ro
already disproportionately affected.(1,4,11) As such, we investigated the relationship of appropriate statin therapy and patient income level between 2008 and 2016. We hypothesized that patient income
-p
level was independently associated with appropriate lipid therapy in eligible patients.
re
Methods Data Source
lP
The National Cardiovascular Data Registry’s Practice Innovation and Clinical Excellence (PINNACLE) Registry is a national, voluntary clinical registry of visits from outpatient cardiovascular practices.(16) A
na
wide range of data are collected from patient visits, including medications, clinical events, laboratory values, associated diagnoses, and vital signs. (17) These data are standardized using established
Jo
Study Population
ur
definitions and adjudicated with quality checks.
We considered all patients in the PINNACLE registry since the registry began in 2008 until March 2016. Those less than 18 years old, missing LDL-C levels, tobacco status, other risk factors to determine their risk of coronary artery disease (CAD), and those without zip code were excluded. (Figure 1) Those with New York Heart Association (NYHA) Class III-IV heart failure were excluded as there are no conclusive recommendations for this population.(13) Those with end-stage liver disease were excluded given risks associated with statin therapy.(12) Because there are additional guidelines for those with end stage renal disease, those on dialysis were also excluded.(18)
Outcomes and Covariates During this study period, 2 sets of guidelines were used in clinical practice. As such, we defined appropriateness specifically to match the guidelines in effect at the time. Before 2013, the Adult
3
Journal Pre-proof
4
Treatment Panel (ATP III) guidelines by the National Cholesterol Education Program recommended that patients be treated to achieve target levels of low-density lipoprotein cholesterol (LDL-C) depending on risk factors.(12) Then in November 2013, the American College of Cardiology (ACC) and American Heart Association (AHA) published guidelines that placed more emphasis on statin intensity as opposed to achieving a target LDL-C level.(13) The primary outcome of appropriate lipid therapy was measured by achievement of appropriate LDL-C goals (under the ATP III guidelines) or prescription of appropriate statin intensity (under the ACC/AHA guidelines) as described below.
The primary exposure of interest was estimated income, divided by quintile. Additional covariates were
of
chosen by clinical relevance and use in prior analyses from the PINNACLE registry.(7) Age, diabetes,
ro
CAD or prior myocardial infarction, history of coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI), dyslipidemia, hypertension, history of stroke and transient ischemic attack
-p
(TIA), and peripheral arterial disease along with date of treatment were used to determine the appropriateness of lipid therapy. Gender and zip code were obtained as demographic variables. To
re
estimate patient income, we used the median income of the patient’s zip code as determined from the U.S.
lP
Census, a validated method of estimating income in population-health research.(7,19)
Ascertaining Patient Eligibility and Appropriate Treatment for Statin Therapy
na
For visits before November 2013, the ATP III guidelines were used to determine whether or not a patient was appropriate for statin prescription, and after November 2013, the ACC/AHA guidelines were used.
Jo
period.
ur
Achievement of appropriate treatment could have occurred at any point after the first visit during each
Before November 2013 (Under ATP III Guidelines) Risk factors in accordance with the ATP III guidelines were identified from the patients’ first visit. For the highest-risk group, the patients were identified as having at least one or none of the following risk factors: CAD, diabetes mellitus (DM), other vascular disease (peripheral arterial disease, carotid disease, and abdominal aortic aneurysm), or multiple risk factors resulting in a 10-year estimated risk of coronary artery disease of >20% as estimated from the Framingham risk score (including tobacco use, blood pressure, high-density lipoprotein cholesterol (HDL-C) level, and total cholesterol levels).(12,20) Patients with any of the above risk factors and LDL-C 100mg/dl were deemed appropriate to be on a statin.(12) Remaining patients with the above risk factors but were already on a statin medication, were also deemed appropriate to be on a statin.
4
Journal Pre-proof
5
Remaining patients, were grouped by the aggregate number of the following risk factors: tobacco use, hypertension (140/90mmHg or on anti-hypertensive medications), low HDL-C cholesterol (<40mg/dl), family history of CAD (<55 years old in males, <65 years old in females), and age (men 45 years old, women 65 years old). Per the ATP III guidelines, those with 2 or more of these risk factors and LDL-C 130mg/dl should be on a statin medication.(12) Those with 0-1 risk factors and LDL-C 160mg/dl were considered appropriate for statin therapy.(12) Those with the same risk-factor profiles, but already on a statin, were also deemed appropriate for statin therapy. Lower LDL-C target goals were also chosen to
of
account for pre-existing statin use that lowered measured LDL-C at the first visit.(12)
For those with the highest-risk profile, an LDL-C of 100mg/dL or less was considered appropriate
ro
treatment. An LDL-C less than or equal to 160mg/dL was used for the lowest-risk subgroup, and less
After November 2013 (Under ACC/AHA Guidelines)
-p
than or equal to 130mg/dL was used for the remaining groups.(12)
re
The first visit for a given patient under ACC/AHA guidelines was used to determine appropriateness for
lP
lipid-therapy. This was defined to include those with atherosclerotic cardiovascular disease (ASCVD) as defined by documented CAD, a history of acute coronary syndrome, stroke or transient ischemic attacks, peripheral arterial disease, myocardial infarction, coronary artery bypass grafting, or percutaneous
na
coronary intervention, as well as those aged 40-75 years old with documented diabetes but without ASCVD.(13) Those with an LDL-C 190mg/dl without ASCVD or those with diabetes were also
ur
thought to be appropriate to receive a statin under the ACC/AHA guidelines.(13) For all patients under the ACC/AHA guidelines, the algorithm was performed hierarchically. If the patient had an indication
Jo
for statin therapy based on ASCVD, LDL-C 190mg/dl, or diabetes, the patient was considered appropriate for statin. For all remaining patients, those with a calculated 10-year risk of ASCVD 7.5% as determined by the pooled cohort risk equation recommended by the 2013 ACC/AHA guidelines were deemed appropriate to receive a statin.
The highest-risk profile, including those younger than 75 years old with ASCVD and those with an LDLC greater than 190mg/dL, was determined to have appropriate treatment if the patient was on highintensity statin therapy (atorvastatin 40mg or 80mg daily or rosuvastatin 20mg or 40mg daily). The remaining groups were considered to have appropriate treatment if the patient was placed on at least a moderate-potency statin including any dose of atorvastatin and rosuvastatin or at least 40mg of lovastatin, 2mg of pitavastatin, 40mg of pravastatin, 20mg of simvastatin, or 80mg fluvastatin.(13)
5
Journal Pre-proof
6
Statistical Analysis For the main analyses, the unit of analysis was the patient. As such, analyses were performed at the patient level using the first visit to determine patient characteristics and demographics. Patient characteristics were summarized and compared among different guideline periods and income quintiles with chi-squared tests and t-tests as appropriate.
Logistic regression was performed to compare each quintile to the lowest income quintile to determine the likelihood of receiving appropriate lipid-lowering treatment. Adjustment for age, gender, and clinical comorbidities of CAD, peripheral arterial disease, DM, diagnosis of hyperlipidemia, hypertension, and
of
cerebrovascular disease was performed. Results were reported as odds ratios (ORs) with 95% confidence
ro
intervals (CIs) and p-values. In addition, the relationship between estimated income category and receiving appropriate treatment was investigated with a Point-Biserial correlation coefficient. All
-p
analyses were done in both the ATP III and ACC/AHA time periods separately.
re
This work was approved by the PINNACLE Research and Publications Committee of the ACC National
institutional review board (IRB).
na
Results
lP
Cardiovascular Data Registry. The work was approved under the overall approval of the Advarra
Of 28,265,967 patient encounters in the PINNACLE database during the study period, 1,655,723 unique
ur
patients remained after application of inclusion and exclusion criteria. (Figure 1) Of those, 751,217 patients (45.4%) were evaluated prior to the ACC/AHA guidelines, and 904,506 patients (54.6%) were
Jo
evaluated after the release of the 2013 ACC/AHA guidelines.
The study population is described in Table 1. The mean LDL-C in the pre-ACC/AHA guidelines group was 92.0 mg/dL (SD 41.9) and in the post-ACC/AHA guidelines group was 91.5 mg/dL (SD 37.9). The population after the publication of the ACC/AHA guidelines had a lower prevalence of CAD (73.1%), DM (28.6%), and hypertension (83.9%), compared to the prior population (75.7%, 31.6%, and 86%, respectively). Peripheral arterial disease had higher prevalence during the ACC/AHA period (14.3% vs 13.7% previously).
Quintile 1 (the lowest income quintile) was defined as an estimated income of less than $41,831. Quintile 2 included estimated incomes of $41,832-50,796, Quintile 3, $50,797-61,780, Quintile 4, $61,781-77,683, and Quintile 5 (the highest income quintile), an estimated income of greater than $77,684. Patient
6
Journal Pre-proof
7
demographics in each income quintile are shown in Table 2. More patients had CAD in the lowest income quintile than in the highest income quintile (75.2% vs. 70.8%, p <0.001). In the lowest income quintile, 33.3% of patients had diabetes mellitus compared to 25.9% of patients in the highest estimated income quintile (p <0.001), and 16.2% of patients had peripheral arterial disease compared to 11.6% of patients in the highest estimated income quintile (p <0.001). Most patients, 86.9%, in the lowest estimated income quintile and 81.9% in the highest estimated income quintile, had hypertension. Mean LDL-C was 93.9 mg/dL (SD 42.2) in the lowest estimated income quintile and 90.9 mg/dL (38.4) in the highest estimated income quintile.
of
Lipid lowering therapy under ATP III guidelines
ro
For patients treated under the ATP III guidelines, unadjusted percentages of eligible patients who were appropriately treated were 68%, 70%, 72%, 73%, and 73% for Quintiles 1-5. (Figure 2, Table 3a) When
-p
adjusted and compared to Quintile 1 (the lowest estimated income quintile), each income Quintile was seen to have progressively higher odds of receiving appropriate lipid therapy. (Table 3b) Quintile 2 had
re
an odds ratio of 1.02 [1.01-1.03], Quintile 3, an odds ratio of 1.04 [1.03-1.05], Quintile 4 an odds ratio of 1.05 [1.04-1.06], and Quintile 5 an odds ratio of 1.06 [1.05-1.07]. Results were all significant with a
lP
p<0.001. The Point-Biserial correlation coefficient for the trend was 0.035 (p < 0.001). Other variables found to be significantly associated with the likelihood of receiving appropriate lipid therapy were DM
na
(OR 1.08 [1.07-1.09]), CAD (OR 1.15 [1.14-1.16]), dyslipidemia (OR 0.82 [0.81-0.83]), HTN (1.13
ur
[1.12-1.15]), stroke or TIA (OR 1.02 [1.00-1.03]), and PAD (OR 1.02 [1.01-1.03]).
Lipid lowering therapy under 2013 ACC/AHA guidelines
Jo
When treated under the ACC/AHA guidelines, the proportion of patients appropriately receiving statin therapy was 57%, 60%, 58%, 61%, and 62% for Quintiles 1-5, respectively. (Figure 2, Table 3a) When adjusted for gender and clinical comorbidities, Quintile 2 did not have a statistically significant difference in likelihood of appropriate treatment (p=0.1253) when compared to Quintile 1 (the lowest income quintile). (Table 3b) Quintiles 3 and 4 had increased odds of receiving appropriate statin treatment with odds ratios of 1.01 [1.00-1.03] and 1.02 [1.00-1.03] respectively when compared to the Quintile 1 (the lowest income quintile), as did Quintile 5 (the highest income quintile) with an odds ratio of 1.03 [1.011.04]. The Point-Biserial correlation coefficient for the trend was 0.026 (p < 0.001). Other variables found to be significantly associated with the likelihood of receiving appropriate lipid therapy were DM (OR 1.09 [1.08- 1.1]), CAD (OR 1.43 [1.41-1.44], dyslipidemia (OR 1.63 [CI 1.60-1.66]), HTN (OR 1.04 [1.03- 1.05], stroke or TIA (OR 1.04 [1.02- 1.05]), and PAD (OR 1.04 [1.03-1.05]).
7
Journal Pre-proof
8
Sensitivity Analyses A sensitivity analysis using race as a covariate was performed in patients in whom race data was reported. Due to a high proportion of missing race data, and the possibility of biased estimates from non-random missing data, the results of this sensitivity analysis have uncertain significance. When adjusted for in the ATP III era, higher income quintiles were associated with greater odds of appropriate statin prescription. (Table 4) In the ACC/AHA era, only income Quintiles 2 and 5 had significantly greater odds of appropriate lipid therapy when compared to Quintile 1 (Quintile 2 OR 1.03 [1.00-1.05], Quintile 5 OR 1.03 [1.00-1.07]). Across all quintiles, the association was significant (p <0.001) during both the ATP III period and the ACC/AHA period. Non-Hispanic Black patients were found to have lower odds of
of
appropriate lipid therapy in both time periods (prior to November 2013, OR 0.85 [0.83-0.86] and after
ro
November 2013, OR 0.67 [0.65-0.70]).
-p
A second sensitivity analysis was performed in the study cohort as a whole rather than with each cohort as divided by different guideline eras. (Table 5) Higher income quintiles were significantly associated with
re
greater odds of appropriate statin prescription. The Point-Biserial correlation coefficient for the trend was
lP
0.025 (p < 0.001).
Discussion
na
In this work, we have found a slight but statistically significant association of patient income with likelihood of receiving statin therapy. These findings are critically important as clinical outcomes of
ur
cardiovascular disease are worse for poor patients. Our findings support the need for greater attention to
Jo
the treatment of lipid therapy in lower income patients.
Prior analyses of patients with peripheral arterial disease have demonstrated income disparities in lipid treatment for patients with peripheral arterial disease. (7) Our findings here extend these results beyond peripheral arterial disease. Interestingly, the 2nd lowest income quintile (Quintile 2) did not have a significantly different likelihood of appropriate treatment under the ACC/AHA guidelines suggesting the possibility of a threshold effect that merits further exploration. These findings have direct implications for improving the prevention of coronary disease throughout the United States. While we know that poorer patients have worse outcomes in ischemic heart disease, and that various biological, social, and environmental causes have been implicated, changes in care delivery strategies could alleviate these disparities.(21,22)
Notably, however, we also found that fewer patients overall under the ACC/AHA guidelines received
8
Journal Pre-proof
9
appropriate treatment in every income quintile. (Table 3A) Those results may reflect early adoption of the ACC/AHA lipid-lowering therapy guidelines from November 2013-2016. There may be continued growth in the overall appropriate treatment rate, but the rate of increase appears to have slowed by 2016 (Figure 2). Lipid-therapy guideline updates in 2018 follow a similar treatment paradigm to the 2013 guidelines, and thus we expect the income disparities could persist. (23) Further study in the current era, now 6 years from the initial ACC/AHA treatment model, is necessary to better understand the adoption and dissemination of new lipid-therapy strategies. Continued evaluation and efforts to improve the diffusion of lipid lowering therapy should be considered.
of
Our study should be interpreted in the setting of important limitations. First, our analysis is limited by
ro
ecological inference bias. In particular, our definition of “low-income” uses the assumption that patients within a given zip code have the same zip code mean income.(19) We are reassured, however, that this
-p
method of using zip code as regional estimator of income is well established, and has been shown to track with household reported income.(24,25) Secondly, our results may be affected by missing poor or very
re
poor patients. In particular, practices that serve low-income groups may have fewer resources, and thus may not be able to participate in a voluntary registry such as PINNACLE. In addition, poor patients with
lP
lack of access to care may not be able to present to outpatient visits, and thus may be inadequately captured in our database. A large percentage of patients in the PINNACLE registry do not have LDL-C
na
levels recorded, contributing to the overwhelming majority of excluded patients. This may disproportionately exclude patients with lower incomes within zip codes, especially as data collection
ur
often occurs at the time of visits and missed visits correlate with income level.(27) Thirdly, variables with high missingness that could not be adjusted for—race and insurance status—prevent us from fully
Jo
evaluating additional confounders. We are reassured that a sensitivity analysis for patients with race data using race as an additional covariate shows similar results as the main analysis. Fourth, we are not able to fully assess the reasons why patients with indications for statins are not receiving them. Although we can assess appropriateness, we cannot understand the full complexity of the patient’s care that may make appropriate therapy unwise. While there are some genetic risk factors and known ethnic risk factors for statin intolerance, there is no known pattern that would disproportionately affect one income level over others and thus should not differentially alter our results.(28) Finally, we performed a patient-level analysis rather than a visit-level analysis to avoid bias from clustering by patient and over-sampling of patients with frequent visits in panel data. As such, we cannot detect clinical events such as a new myocardial infarction in a patient previously being treated for primary prevention that would change the definition of appropriate care. This approach of a patient-level analysis is consistent with prior studies.(7)
9
Journal Pre-proof
10
Our analysis shows a small disparity in lipid treatment amongst those of low and high income levels under both the ATP III guidelines and the ACC/AHA guidelines. This suggests the need for further
Jo
ur
na
lP
re
-p
ro
of
investigation into barriers to using evidence-based therapies in poorer populations.
10
Journal Pre-proof
11
References 1.
Taylor F., Huffman MD., Macedo AF., et al. Statins for the primary prevention of cardiovascular disease. Cochrane Database Syst Rev 2013;1:CD004816. Doi: 10.1002/14651858.CD004816.pub5.
2.
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(10):e146–603. Doi: 10.1161/CIR.0000000000000485.
3.
Kaplan GA., Keil JE. Socioeconomic factors and cardiovascular disease: a review of the literature. Circulation 1993;88(4 Pt 1):1973–98. Muntaner C., Sorlie P., O’Campo P., Johnson N., Backlund E. Occupational hierarchy, economic
of
4.
ro
sector, and mortality from cardiovascular disease among men and women. Findings from the National Longitudinal Mortality Study. Ann Epidemiol 2001;11(3):194–201. Doi: S1047-
5.
-p
2797(00)00210-6 [pii].
Alter DA., Chong A., Austin PC., et al. Socioeconomic status and mortality after acute myocardial
6.
re
infarction. Ann Intern Med 2006;144(2):82–93. Doi: 144/2/82 [pii]. Epstein AM., Ayanian JZ., Keogh JH., et al. Racial disparities in access to renal transplantation--
lP
clinically appropriate or due to underuse or overuse? N Engl J Med 2000;343(21):1537–44, 2 p preceding 1537. Doi: 10.1056/NEJM200011233432106. Subherwal S., Patel MR., Tang F., et al. Socioeconomic disparities in the use of cardioprotective
na
7.
medications among patients with peripheral artery disease: an analysis of the American College of
ur
Cardiology’s NCDR PINNACLE Registry. J Am Coll Cardiol 2013;62(1):51–7. Doi: S07351097(13)01743-9 [pii]10.1016/j.jacc.2013.04.018. Philbin EF., McCullough PA., DiSalvo TG., Dec GW., Jenkins PL., Weaver WD. Socioeconomic
Jo
8.
status is an important determinant of the use of invasive procedures after acute myocardial infarction in New York State. Circulation 2000;102(19 Suppl 3):III107-15. 9.
Rao S V., Schulman KA., Curtis LH., Gersh BJ., Jollis JG. Socioeconomic status and outcome following acute myocardial infarction in elderly patients. Arch Intern Med 2004;164(10):1128–33. Doi: 10.1001/archinte.164.10.1128164/10/1128 [pii].
10.
Rathore SS., Berger AK., Weinfurt KP., et al. Race, sex, poverty, and the medical treatment of acute myocardial infarction in the elderly. Circulation 2000;102(6):642–8.
11.
Deans KA., Bezlyak V., Ford I., et al. Differences in atherosclerosis according to area level socioeconomic deprivation: cross sectional, population based study. BMJ 2009;339:b4170.
12.
Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults
11
Journal Pre-proof
12
(Adult Treatment Panel III). JAMA 2001;285(19):2486–97. Doi: jsc10094 [pii]. 13.
Stone NJ., Robinson JG., Lichtenstein AH., et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2014;63(25 Pt B):2889–934. Doi: S0735-1097(13)06028-2 [pii]10.1016/j.jacc.2013.11.002.
14.
Pokharel Y., Tang F., Jones PG., et al. Adoption of the 2013 American College of Cardiology/American Heart Association Cholesterol Management Guideline in Cardiology Practices Nationwide. JAMA Cardiol 2017;2(4):361. Doi: 10.1001/jamacardio.2016.5922. Verma AA., Jimenez MP., Subramanian S V., Sniderman AD., Razak F. Race and Socioeconomic
of
15.
ro
Differences Associated With Changes in Statin Eligibility Under the 2013 American College of Cardiology/American Heart Association Cholesterol Guidelines. Circ Cardiovasc Qual Outcomes
16.
-p
2017;10(9). Doi: 10.1161/CIRCOUTCOMES.117.003764.
Chan PS., Oetgen WJ., Spertus JA. The Improving Continuous Cardiac Care (IC(3)) program and
[pii]10.1016/j.amjmed.2009.09.019.
Masoudi FA., Ponirakis A., Yeh RW., et al. Cardiovascular care facts: a report from the national
lP
17.
re
outpatient quality improvement. Am J Med 2010;123(3):217–9. Doi: S0002-9343(09)00949-8
cardiovascular data registry: 2011. J Am Coll Cardiol 2013;62(21):1931–47. Doi: S0735-
18.
na
1097(13)05088-2 [pii]10.1016/j.jacc.2013.05.099. Wanner C., Tonelli M. KDIGO Clinical Practice Guideline for Lipid Management in CKD:
ur
summary of recommendation statements and clinical approach to the patient. Kidney Int 2014;85(6):1303–9. Doi: ki201431 [pii]10.1038/ki.2014.31. Burstin HR., Lipsitz SR., Brennan TA. Socioeconomic status and risk for substandard medical
Jo
19.
care. JAMA 1992;268(17):2383–7. 20.
Wilson PW., D’Agostino RB., Levy D., Belanger AM., Silbershatz H., Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation 1998;97(18):1837–47.
21.
Havranek EP., Mujahid MS., Barr DA., et al. Social Determinants of Risk and Outcomes for Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation 2015;132(9):873–98. Doi: CIR.0000000000000228 [pii]10.1161/CIR.0000000000000228.
22.
Odutayo A., Gill P., Shepherd S., et al. Income Disparities in Absolute Cardiovascular Risk and Cardiovascular Risk Factors in the United States, 1999-2014. JAMA Cardiol 2017;165(11):786– 94. Doi: 10.1001/jamacardio.2017.1658.
23.
Grundy SM., Stone NJ., Bailey AL., et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on
12
Journal Pre-proof
13
the Management of Blood Cholesterol. J Am Coll Cardiol 2018:25709. Doi: 10.1016/j.jacc.2018.11.003. 24.
Auger KA., Kahn RS., Simmons JM., et al. Using Address Information to Identify Hardships Reported by Families of Children Hospitalized With Asthma. Academic Pediatrics, vol. 17. Elsevier Inc.; 2017. p. 79–87.
25.
Krieger N., Chen JT., Waterman PD., Rehkopf DH., Subramanian S V. Race/Ethnicity, Gender, and Monitoring Socioeconomic Gradients in Health: Comparison of Area-Based Socioeconomic Measures - The Public Health Disparities Geocoding Project. Am J Public Health 2003;93(10):1655–71. Doi: 10.2105/AJPH.93.10.1655. Lemstra M., Blackburn D., Crawley A., Fung R. Proportion and Risk Indicators of Nonadherence
of
26.
ro
to Statin Therapy: A Meta-analysis. Can J Cardiol 2012;28(5):574–80. Doi: 10.1016/j.cjca.2012.05.007.
Ide BA., Curry MA., Drobnies B. Factors related to the keeping of appointments by indigent
-p
27.
clients. J Heal Care Poor Underserved 1993;4(1):21–39.
ur
na
lP
[pii]10.1016/j.amjcard.2014.02.033.
re
Ahmad Z. Statin intolerance. Am J Cardiol 2014;113(10):1765–71. Doi: S0002-9149(14)00725-5
Jo
28.
13
Journal Pre-proof
14
Table 1. Study Population Characteristics Under Different Guidelines Total (n=1655723)
2008-November 2013 (n=751,217)
November 2013-March 2016 (n=904,506)
Jo
ur
na
lP
re
-p
ro
of
Age (years) (mean, (SD)) 68.84 (11.7) 68.8 (11.9) 68.9 (11.4) Sex (n, %) Male 982668 (60) 448913 (60.4) 533755 (59.6) Female 656132 (40) 294388 (39.6) 361744 (40.4) Race (n, %) Non-Hispanic Black 85226 (5.1) 33875 (4.5) 51351 (5.7) Hispanic 49119 (3.0) 18558 (2.4) 30561 (3.4) Asian 15185 (0.92) 5994 (0.8) 9191 (1.0) White 1070921 (64.7) 463429 (61.7) 607492 (67.2) Other 6,941 (0.4) 3005 (0.4) 205911 (0.4) Missing 428331 (25.9) 226356 (30.1) 201975 (22.3) Income ($) (mean, SD) 60428.3 (23952) 60483.6 (23983.6) 60382.5 (23925.6) CAD1 (n, %) 1098246 (74.3) 539903 (75.7) 558343 (73.1) Diabetes Mellitus (n, %) 406553 (30) 194112 (31.6) 212441 (28.6) Hypertension (n, %) 1219974 (84.92) 592427 (86) 627547 (83.9) PAD2 (n, %) 205549 (14) 86730 (13.7) 118819 (14.3) 3 LDL-C (mg/dL) 91.73 (39.8) 92 (41.9) 91.5 (37.9) (mean, SD) HDL-C4 (mg/dL) 47.2 (17.5) 46.6 (16.5) 47.65 (18.3) (mean, SD) 1. Coronary Artery Disease (CAD), 2. Peripheral Arterial Disease (PAD), 3. Low-Density Lipoprotein Cholesterol (LDL-C), 4. High-Density Lipoprotein Cholesterol (HDL-C). All p-values are <0.001.
14
Journal Pre-proof
15
Table 2. Study Population Characteristics By Income Quintile Total (n=1655723)
Quintile 1 (n=336282)
Quintile 2 (n=336655)
Quintile 3 (n=330473)
Quintile 4 (n=326647)
Quintile 5 (n=303826)
Age (mean, 68.84 (11.7) SD) Sex (n, %) Male 982668 (60) 656132 (40) Female
68.4 (11.7)
69.3 (11.6)
69.0 (11.5)
69.0 (11.6)
68.6 (11.9)
187843 (56.3) 1459980 (43.7)
196689 (58.9) 196796 (60.2) 198294 (61.7) 137414 (41.1) 130235(39.8) 122918 (38.3)
189415 (62.9) 111516 (37.1)
Race (n, %)
Jo
ur
na
lP
re
-p
ro
of
NonHispanic 85211 (5.1) 38770 (11.5) 14645 (4.4) 12181 (3.7) 10555 (3.2) 7467 (2.5) Black Hispanic 49119 (3.0) 15546 (4.6) 10764 (3.2) 8916 (2.7) 8186 (2.5) 5401 (1.8) Asian 15185 (0.92) 1706 (0.5) 1943 (0.6) 2322 (0.7) 3624 (1.1) 5449 (1.8) White 1070921 (64.7) 201341 (59.9) 227111 (67.5) 228256 (69.1) 216453 (66.3) 184491 (60.7) Other 6956 (0.4) 78919 (0.4) 994 (0.3) 1287 (0.4) 1486 (0.5) 1356 (0.4) Missing 428331 (25.9) 77627 (23.1) 81198 (24.1) 77511 (23.4) 86343 (26.4) 99662 (32.8) CAD1 (n,%) 1098246 (74.3) 232391(75.2) 237615 (77) 226290 (74) 216284 (74) 171334 (70.8) Diabetes 406553 (30) 95321 (33.3) 82998 (31.3) 80363 (30.2) 77635(28.8) 64809 (25.9) Mellitus (n,%) Hypertension 1219974 (84.9) 255506 (86.9) 248548 (86) 247263 (85) 244020 (84.5) 208541 (81.9) (n, %) PAD2 (n, %) 205549 (14) 48745 (14) 42789 (14.5) 40035 (14) 40474 (14.7) 31150 (11.6) 3 LDL-C 91.73 (39.8) 93.9 (42.2) 91.8 (40) 91.5 (39.3) 90.3 (38.6) 90.9 (38.4) (mg/dL) (mean, SD) 1. Coronary Artery Disease (CAD), 2. Peripheral Artery Disease (PAD), 3. Low-Density Lipoprotein Cholesterol(LDL-C), 4. High-Density Lipoprotein Cholesterol (HDL-C). All p-values are <0.001.
15
Journal Pre-proof
16
ro
of
Table 3. Likelihood of Receiving Appropriate Statin Therapy Among Different Income Groups. 3a) Proportion of those Receiving Appropriate Statin Therapy Under Each Set of Guidelines. Unadjusted analysis. % Of Those Needing % Of Those Needing Lipid Therapy Lipid Therapy Appropriately Treated Appropriately Treated Income Under ATP III Under ACC/AHA Quintile Guidelines Guidelines P-value 57% p < 0.01 Quintile 1 68% 60% p < 0.01 Quintile 2 70% 58% p < 0.01 Quintile 3 72% 61% p < 0.01 Quintile 4 73% 62% p < 0.01 Quintile 5 73%
Quintile 5
re
lP
na
Quintile 4
ur
Quintile 3
Odds of Appropriate Treatment Under ACC/AHA Guidelines (95% CI) Ref 1.01 (1.00-1.02) p=0.13 1.01 (1.00-1.03) 1.02 p=0.04 1.03 (1.00-1.03) 1.04 p=0.01 1.03 (1.01-1.04) p=0.01
Jo
Income Quintile Quintile 1 Quintile 2
Odds of Appropriate Treatment Under ATP III Guidelines (95% CI) Ref 1.02 (1.01-1.03) p<0.01 1.04 (1.03-1.05) p<0.01 1.05 (1.04-1.06) p<0.01 1.06 (1.05-1.07) p<0.01
-p
3b) Odds of Receiving Appropriate Statin Therapy Under Each Set of Guidelines. Analysis adjusted for age, gender, coronary artery disease, peripheral arterial disease, diabetes mellitus, hyperlipidemia, hypertension, and cerebrovascular disease.
16
Journal Pre-proof
17
Table 4. Sensitivity Analysis of Odds of Receiving Appropriate Lipid Therapy Under Each Set of Guidelines in Patients with Race Data. Race has a high level of missing-ness in the study population. This analysis adjusted for race, age, gender, coronary artery disease, peripheral arterial disease, diabetes mellitus, hyperlipidemia, hypertension, and cerebrovascular disease. Odds of Appropriate Treatment Under ACC/AHA Guidelines (95% CI) Ref
Quintile 2 vs. Quintile 1 Quintile 3 vs. Quintile 1 Quintile 4 vs. Quintile 1 Quintile 5 vs. Quintile 1
1.01 (1.00-1.02) p < 0.04 1.02 (1.01-1.04) p < 0.01 1.03 (1.02-1.05) p < 0.01 1.04 (1.02-1.06) p < 0.01
1.03 (1.00-1.05) p=0.02 1.02 (0.99-1.05) p=0.12 1.01 (0.99-1.04) p=0.29 1.03 (1.00-1.07) p=0.04
Non-Hispanic Black vs. White Other vs. White
0.85 (0.83-0.86) p < 0.01
0.67 (0.65-0.7) p < 0.01
ro
-p
re
lP
0.94 (0.88, 1.01) p=0.08
Jo
ur
na
1 (0.96-1.03) p = 0.80
of
Quintile 1
Odds of Appropriate Treatment Under ATP III Guidelines (95% CI) Ref
17
Journal Pre-proof
18
Table 5. Sensitivity Analysis of Odds of Receiving Appropriate Lipid Therapy in Full Study Cohort. Patients were evaluated as an entire study cohort rather than within each separate guideline era. This analysis adjusted for race, age, gender, coronary artery disease, peripheral arterial disease, diabetes mellitus, hyperlipidemia, hypertension, and cerebrovascular disease.
of ro -p re
lP
Quintile 5
na
Quintile 4
ur
Quintile 3
Odds of Appropriate Treatment (95% CI) Ref 1.01 (1.00-1.02) p=0.03 1.02 (1.01-1.03) p<0.01 1.02 (1.01-1.03) p<0.01 1.03 (1.02-1.04) p<0.01
Jo
Income Quintile Quintile 1 Quintile 2
18
Journal Pre-proof
19
Figure 1. Study Design. n = number of encounters
Pinnacle Encounters: n=28,265,967
ro
of
Exclusions: 1) Age <18: n=310,679 2) Missing LDL: n=19,390,649 3) Missing Zip Code: n=353,984 4) NYHA Class III or IV: n=146,143 5) Patients on Dialysis: n=2,337
Jo
ur
na
lP
re
-p
Cohort: n=8,062,175
19
Journal Pre-proof
20
100 80 60
of
40
ro
20
re
-p
0
Income Q2
Income Q4
Income Q5
Income Q3
ur
na
lP
Income Q1
Jo
Patients Receiving Appropriate Lipid Therapy (%)
Figure 2. Proportion of Patients in Different Income Groups Receiving Appropriate Statin Treatment Over Time. The transition to the ACC/AHA Guidelines ocurred in 2013Q3.
20
Figure 1
Figure 2
Varsha K. Tanguturi, Kevin F. Kennedy, Salim S. Virani, Thomas M. Maddox, Katrina Armstrong, Jason H. Wasfy PII:
S1553-8389(19)30827-9
DOI:
https://doi.org/10.1016/j.carrev.2019.12.026
Reference:
CARREV 1788
To appear in:
Cardiovascular Revascularization Medicine
Received date:
20 September 2019
Revised date:
19 December 2019
Accepted date:
19 December 2019
Please cite this article as: V.K. Tanguturi, K.F. Kennedy, S.S. Virani, et al., Association between poverty and appropriate statin prescription for the treatment of hyperlipidemia in the United States: An analysis from the ACC NCDR PINNACLE registry, Cardiovascular Revascularization Medicine(2019), https://doi.org/10.1016/j.carrev.2019.12.026
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2019 Published by Elsevier.
Journal Pre-proof
1
Title Association between poverty and appropriate statin prescription for the treatment of hyperlipidemia in the United States: an analysis from the ACC NCDR PINNACLE registry Authors: Varsha K. Tanguturi MD1,2, Kevin F. Kennedy MS3, Salim S. Virani MD PhD4, Thomas M. Maddox MD MSc5, Katrina Armstrong MD MSCE2, Jason H. Wasfy MD MPhil1,6
ro
of
Affiliations: 1—Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 2—Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 3—St. Luke’s Mid-America Heart Institute, Kansas City, MO 4—Michael E. DeBakey Veterans Affairs Medical Center, Baylor College of Medicine, Houston, TX 5—Cardiology Division, Department of Medicine, Washington University School of Medicine, St. Louis, MO 6—Massachusetts General Physicians Organization, Boston, MA
re
-p
Sources of Funding: The primary funding for this work was from the National Cardiovascular Data Registry of the American College of Cardiology. This work was also supported by grants from the American Heart Association (18CDA34110215) as well as the National Institutes of Health through Harvard Catalyst (KL2 TR001100), both awarded to Dr. Wasfy.
lP
Corresponding Author: Varsha K. Tanguturi MD and Jason H. Wasfy MD MPhil, Cardiology Division Yawkey 5B, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114; Tel: (617) 724-0359; Fax: (617) 6431620; Email: [email protected] and [email protected]
Jo
ur
na
Subject Codes: Poverty, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Hypercholesterolemia, Delivery of Health Care, Healthcare Disparities
1
Journal Pre-proof
2
Abstract Background Poverty is associated with a higher risk of myocardial infarction and cardiac death, both of which are decreased by treatment of hyperlipidemia. There may be differences in the appropriate treatment of hyperlipidemia between richer and poorer Americans. In this study, we aimed to evaluate the association between income level and appropriate lipid-lowering therapy.
of
Methods We identified outpatient visits in the National Cardiovascular Data Registry’s Practice Innovation and Clinical Excellence (PINNACLE) Registry and determined appropriateness of lipid-lowering therapy among patients in different income quintiles (Quintile 5 being the highest income quintile). Logistic regression at the patient level was performed to evaluate the independent association of income and the primary outcome of appropriate statin therapy. The analysis was repeated before and after November 2013 given a change in guideline definitions.
lP
re
-p
ro
Results The study included 1,655,723 patients. Overall, 68-73% of patients were treated appropriately under the ATP III Guidelines and 57-62% of patients were treated appropriately under the ACC/AHA Guidelines. Patients in the wealthiest quintile had higher odds of appropriate statin therapy under both guidelines relative to patients in the poorest quintile (OR 1.06 [1.05-1.07] for ATP III and OR 1.03 [1.01-1.04] for ACC/AHA). In the whole sample, patients with higher estimated income had a small but significant increased likelihood of appropriate statin therapy (point-biserial correlation 0.035 [p < 0.001] for ATP III and 0.026 [p < 0.001] for ACC/AHA).
Jo
ur
na
Conclusions Here we describe a small association between appropriate statin use and income. Further investigation into barriers in the use of evidence-based therapies in poorer populations is needed.
2
Journal Pre-proof
3
Introduction Appropriate treatment of cholesterol with hydroxymethylglutaryl-CoA reductase inhibitors (statins) reduces the risk of myocardial infarction and cardiac death.(1) Because 1 million Americans suffer from acute myocardial infarction annually and over 600,000 Americans suffer cardiac death, ensuring that patients receive statin therapy when they need it is vital to public health.(2) Low-income patients often suffer from greater prevalence and poorer outcomes in cardiovascular disease. (3–10)
The mechanism of this disparity is unclear and little has been done to investigate the association of income and lipid therapy thus far. Targeting the critical step of lipid treatment in the management and
of
prevention of coronary artery disease could improve our care delivery in poorer populations that are
ro
already disproportionately affected.(1,4,11) As such, we investigated the relationship of appropriate statin therapy and patient income level between 2008 and 2016. We hypothesized that patient income
-p
level was independently associated with appropriate lipid therapy in eligible patients.
re
Methods Data Source
lP
The National Cardiovascular Data Registry’s Practice Innovation and Clinical Excellence (PINNACLE) Registry is a national, voluntary clinical registry of visits from outpatient cardiovascular practices.(16) A
na
wide range of data are collected from patient visits, including medications, clinical events, laboratory values, associated diagnoses, and vital signs. (17) These data are standardized using established
Jo
Study Population
ur
definitions and adjudicated with quality checks.
We considered all patients in the PINNACLE registry since the registry began in 2008 until March 2016. Those less than 18 years old, missing LDL-C levels, tobacco status, other risk factors to determine their risk of coronary artery disease (CAD), and those without zip code were excluded. (Figure 1) Those with New York Heart Association (NYHA) Class III-IV heart failure were excluded as there are no conclusive recommendations for this population.(13) Those with end-stage liver disease were excluded given risks associated with statin therapy.(12) Because there are additional guidelines for those with end stage renal disease, those on dialysis were also excluded.(18)
Outcomes and Covariates During this study period, 2 sets of guidelines were used in clinical practice. As such, we defined appropriateness specifically to match the guidelines in effect at the time. Before 2013, the Adult
3
Journal Pre-proof
4
Treatment Panel (ATP III) guidelines by the National Cholesterol Education Program recommended that patients be treated to achieve target levels of low-density lipoprotein cholesterol (LDL-C) depending on risk factors.(12) Then in November 2013, the American College of Cardiology (ACC) and American Heart Association (AHA) published guidelines that placed more emphasis on statin intensity as opposed to achieving a target LDL-C level.(13) The primary outcome of appropriate lipid therapy was measured by achievement of appropriate LDL-C goals (under the ATP III guidelines) or prescription of appropriate statin intensity (under the ACC/AHA guidelines) as described below.
The primary exposure of interest was estimated income, divided by quintile. Additional covariates were
of
chosen by clinical relevance and use in prior analyses from the PINNACLE registry.(7) Age, diabetes,
ro
CAD or prior myocardial infarction, history of coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI), dyslipidemia, hypertension, history of stroke and transient ischemic attack
-p
(TIA), and peripheral arterial disease along with date of treatment were used to determine the appropriateness of lipid therapy. Gender and zip code were obtained as demographic variables. To
re
estimate patient income, we used the median income of the patient’s zip code as determined from the U.S.
lP
Census, a validated method of estimating income in population-health research.(7,19)
Ascertaining Patient Eligibility and Appropriate Treatment for Statin Therapy
na
For visits before November 2013, the ATP III guidelines were used to determine whether or not a patient was appropriate for statin prescription, and after November 2013, the ACC/AHA guidelines were used.
Jo
period.
ur
Achievement of appropriate treatment could have occurred at any point after the first visit during each
Before November 2013 (Under ATP III Guidelines) Risk factors in accordance with the ATP III guidelines were identified from the patients’ first visit. For the highest-risk group, the patients were identified as having at least one or none of the following risk factors: CAD, diabetes mellitus (DM), other vascular disease (peripheral arterial disease, carotid disease, and abdominal aortic aneurysm), or multiple risk factors resulting in a 10-year estimated risk of coronary artery disease of >20% as estimated from the Framingham risk score (including tobacco use, blood pressure, high-density lipoprotein cholesterol (HDL-C) level, and total cholesterol levels).(12,20) Patients with any of the above risk factors and LDL-C 100mg/dl were deemed appropriate to be on a statin.(12) Remaining patients with the above risk factors but were already on a statin medication, were also deemed appropriate to be on a statin.
4
Journal Pre-proof
5
Remaining patients, were grouped by the aggregate number of the following risk factors: tobacco use, hypertension (140/90mmHg or on anti-hypertensive medications), low HDL-C cholesterol (<40mg/dl), family history of CAD (<55 years old in males, <65 years old in females), and age (men 45 years old, women 65 years old). Per the ATP III guidelines, those with 2 or more of these risk factors and LDL-C 130mg/dl should be on a statin medication.(12) Those with 0-1 risk factors and LDL-C 160mg/dl were considered appropriate for statin therapy.(12) Those with the same risk-factor profiles, but already on a statin, were also deemed appropriate for statin therapy. Lower LDL-C target goals were also chosen to
of
account for pre-existing statin use that lowered measured LDL-C at the first visit.(12)
For those with the highest-risk profile, an LDL-C of 100mg/dL or less was considered appropriate
ro
treatment. An LDL-C less than or equal to 160mg/dL was used for the lowest-risk subgroup, and less
After November 2013 (Under ACC/AHA Guidelines)
-p
than or equal to 130mg/dL was used for the remaining groups.(12)
re
The first visit for a given patient under ACC/AHA guidelines was used to determine appropriateness for
lP
lipid-therapy. This was defined to include those with atherosclerotic cardiovascular disease (ASCVD) as defined by documented CAD, a history of acute coronary syndrome, stroke or transient ischemic attacks, peripheral arterial disease, myocardial infarction, coronary artery bypass grafting, or percutaneous
na
coronary intervention, as well as those aged 40-75 years old with documented diabetes but without ASCVD.(13) Those with an LDL-C 190mg/dl without ASCVD or those with diabetes were also
ur
thought to be appropriate to receive a statin under the ACC/AHA guidelines.(13) For all patients under the ACC/AHA guidelines, the algorithm was performed hierarchically. If the patient had an indication
Jo
for statin therapy based on ASCVD, LDL-C 190mg/dl, or diabetes, the patient was considered appropriate for statin. For all remaining patients, those with a calculated 10-year risk of ASCVD 7.5% as determined by the pooled cohort risk equation recommended by the 2013 ACC/AHA guidelines were deemed appropriate to receive a statin.
The highest-risk profile, including those younger than 75 years old with ASCVD and those with an LDLC greater than 190mg/dL, was determined to have appropriate treatment if the patient was on highintensity statin therapy (atorvastatin 40mg or 80mg daily or rosuvastatin 20mg or 40mg daily). The remaining groups were considered to have appropriate treatment if the patient was placed on at least a moderate-potency statin including any dose of atorvastatin and rosuvastatin or at least 40mg of lovastatin, 2mg of pitavastatin, 40mg of pravastatin, 20mg of simvastatin, or 80mg fluvastatin.(13)
5
Journal Pre-proof
6
Statistical Analysis For the main analyses, the unit of analysis was the patient. As such, analyses were performed at the patient level using the first visit to determine patient characteristics and demographics. Patient characteristics were summarized and compared among different guideline periods and income quintiles with chi-squared tests and t-tests as appropriate.
Logistic regression was performed to compare each quintile to the lowest income quintile to determine the likelihood of receiving appropriate lipid-lowering treatment. Adjustment for age, gender, and clinical comorbidities of CAD, peripheral arterial disease, DM, diagnosis of hyperlipidemia, hypertension, and
of
cerebrovascular disease was performed. Results were reported as odds ratios (ORs) with 95% confidence
ro
intervals (CIs) and p-values. In addition, the relationship between estimated income category and receiving appropriate treatment was investigated with a Point-Biserial correlation coefficient. All
-p
analyses were done in both the ATP III and ACC/AHA time periods separately.
re
This work was approved by the PINNACLE Research and Publications Committee of the ACC National
institutional review board (IRB).
na
Results
lP
Cardiovascular Data Registry. The work was approved under the overall approval of the Advarra
Of 28,265,967 patient encounters in the PINNACLE database during the study period, 1,655,723 unique
ur
patients remained after application of inclusion and exclusion criteria. (Figure 1) Of those, 751,217 patients (45.4%) were evaluated prior to the ACC/AHA guidelines, and 904,506 patients (54.6%) were
Jo
evaluated after the release of the 2013 ACC/AHA guidelines.
The study population is described in Table 1. The mean LDL-C in the pre-ACC/AHA guidelines group was 92.0 mg/dL (SD 41.9) and in the post-ACC/AHA guidelines group was 91.5 mg/dL (SD 37.9). The population after the publication of the ACC/AHA guidelines had a lower prevalence of CAD (73.1%), DM (28.6%), and hypertension (83.9%), compared to the prior population (75.7%, 31.6%, and 86%, respectively). Peripheral arterial disease had higher prevalence during the ACC/AHA period (14.3% vs 13.7% previously).
Quintile 1 (the lowest income quintile) was defined as an estimated income of less than $41,831. Quintile 2 included estimated incomes of $41,832-50,796, Quintile 3, $50,797-61,780, Quintile 4, $61,781-77,683, and Quintile 5 (the highest income quintile), an estimated income of greater than $77,684. Patient
6
Journal Pre-proof
7
demographics in each income quintile are shown in Table 2. More patients had CAD in the lowest income quintile than in the highest income quintile (75.2% vs. 70.8%, p <0.001). In the lowest income quintile, 33.3% of patients had diabetes mellitus compared to 25.9% of patients in the highest estimated income quintile (p <0.001), and 16.2% of patients had peripheral arterial disease compared to 11.6% of patients in the highest estimated income quintile (p <0.001). Most patients, 86.9%, in the lowest estimated income quintile and 81.9% in the highest estimated income quintile, had hypertension. Mean LDL-C was 93.9 mg/dL (SD 42.2) in the lowest estimated income quintile and 90.9 mg/dL (38.4) in the highest estimated income quintile.
of
Lipid lowering therapy under ATP III guidelines
ro
For patients treated under the ATP III guidelines, unadjusted percentages of eligible patients who were appropriately treated were 68%, 70%, 72%, 73%, and 73% for Quintiles 1-5. (Figure 2, Table 3a) When
-p
adjusted and compared to Quintile 1 (the lowest estimated income quintile), each income Quintile was seen to have progressively higher odds of receiving appropriate lipid therapy. (Table 3b) Quintile 2 had
re
an odds ratio of 1.02 [1.01-1.03], Quintile 3, an odds ratio of 1.04 [1.03-1.05], Quintile 4 an odds ratio of 1.05 [1.04-1.06], and Quintile 5 an odds ratio of 1.06 [1.05-1.07]. Results were all significant with a
lP
p<0.001. The Point-Biserial correlation coefficient for the trend was 0.035 (p < 0.001). Other variables found to be significantly associated with the likelihood of receiving appropriate lipid therapy were DM
na
(OR 1.08 [1.07-1.09]), CAD (OR 1.15 [1.14-1.16]), dyslipidemia (OR 0.82 [0.81-0.83]), HTN (1.13
ur
[1.12-1.15]), stroke or TIA (OR 1.02 [1.00-1.03]), and PAD (OR 1.02 [1.01-1.03]).
Lipid lowering therapy under 2013 ACC/AHA guidelines
Jo
When treated under the ACC/AHA guidelines, the proportion of patients appropriately receiving statin therapy was 57%, 60%, 58%, 61%, and 62% for Quintiles 1-5, respectively. (Figure 2, Table 3a) When adjusted for gender and clinical comorbidities, Quintile 2 did not have a statistically significant difference in likelihood of appropriate treatment (p=0.1253) when compared to Quintile 1 (the lowest income quintile). (Table 3b) Quintiles 3 and 4 had increased odds of receiving appropriate statin treatment with odds ratios of 1.01 [1.00-1.03] and 1.02 [1.00-1.03] respectively when compared to the Quintile 1 (the lowest income quintile), as did Quintile 5 (the highest income quintile) with an odds ratio of 1.03 [1.011.04]. The Point-Biserial correlation coefficient for the trend was 0.026 (p < 0.001). Other variables found to be significantly associated with the likelihood of receiving appropriate lipid therapy were DM (OR 1.09 [1.08- 1.1]), CAD (OR 1.43 [1.41-1.44], dyslipidemia (OR 1.63 [CI 1.60-1.66]), HTN (OR 1.04 [1.03- 1.05], stroke or TIA (OR 1.04 [1.02- 1.05]), and PAD (OR 1.04 [1.03-1.05]).
7
Journal Pre-proof
8
Sensitivity Analyses A sensitivity analysis using race as a covariate was performed in patients in whom race data was reported. Due to a high proportion of missing race data, and the possibility of biased estimates from non-random missing data, the results of this sensitivity analysis have uncertain significance. When adjusted for in the ATP III era, higher income quintiles were associated with greater odds of appropriate statin prescription. (Table 4) In the ACC/AHA era, only income Quintiles 2 and 5 had significantly greater odds of appropriate lipid therapy when compared to Quintile 1 (Quintile 2 OR 1.03 [1.00-1.05], Quintile 5 OR 1.03 [1.00-1.07]). Across all quintiles, the association was significant (p <0.001) during both the ATP III period and the ACC/AHA period. Non-Hispanic Black patients were found to have lower odds of
of
appropriate lipid therapy in both time periods (prior to November 2013, OR 0.85 [0.83-0.86] and after
ro
November 2013, OR 0.67 [0.65-0.70]).
-p
A second sensitivity analysis was performed in the study cohort as a whole rather than with each cohort as divided by different guideline eras. (Table 5) Higher income quintiles were significantly associated with
re
greater odds of appropriate statin prescription. The Point-Biserial correlation coefficient for the trend was
lP
0.025 (p < 0.001).
Discussion
na
In this work, we have found a slight but statistically significant association of patient income with likelihood of receiving statin therapy. These findings are critically important as clinical outcomes of
ur
cardiovascular disease are worse for poor patients. Our findings support the need for greater attention to
Jo
the treatment of lipid therapy in lower income patients.
Prior analyses of patients with peripheral arterial disease have demonstrated income disparities in lipid treatment for patients with peripheral arterial disease. (7) Our findings here extend these results beyond peripheral arterial disease. Interestingly, the 2nd lowest income quintile (Quintile 2) did not have a significantly different likelihood of appropriate treatment under the ACC/AHA guidelines suggesting the possibility of a threshold effect that merits further exploration. These findings have direct implications for improving the prevention of coronary disease throughout the United States. While we know that poorer patients have worse outcomes in ischemic heart disease, and that various biological, social, and environmental causes have been implicated, changes in care delivery strategies could alleviate these disparities.(21,22)
Notably, however, we also found that fewer patients overall under the ACC/AHA guidelines received
8
Journal Pre-proof
9
appropriate treatment in every income quintile. (Table 3A) Those results may reflect early adoption of the ACC/AHA lipid-lowering therapy guidelines from November 2013-2016. There may be continued growth in the overall appropriate treatment rate, but the rate of increase appears to have slowed by 2016 (Figure 2). Lipid-therapy guideline updates in 2018 follow a similar treatment paradigm to the 2013 guidelines, and thus we expect the income disparities could persist. (23) Further study in the current era, now 6 years from the initial ACC/AHA treatment model, is necessary to better understand the adoption and dissemination of new lipid-therapy strategies. Continued evaluation and efforts to improve the diffusion of lipid lowering therapy should be considered.
of
Our study should be interpreted in the setting of important limitations. First, our analysis is limited by
ro
ecological inference bias. In particular, our definition of “low-income” uses the assumption that patients within a given zip code have the same zip code mean income.(19) We are reassured, however, that this
-p
method of using zip code as regional estimator of income is well established, and has been shown to track with household reported income.(24,25) Secondly, our results may be affected by missing poor or very
re
poor patients. In particular, practices that serve low-income groups may have fewer resources, and thus may not be able to participate in a voluntary registry such as PINNACLE. In addition, poor patients with
lP
lack of access to care may not be able to present to outpatient visits, and thus may be inadequately captured in our database. A large percentage of patients in the PINNACLE registry do not have LDL-C
na
levels recorded, contributing to the overwhelming majority of excluded patients. This may disproportionately exclude patients with lower incomes within zip codes, especially as data collection
ur
often occurs at the time of visits and missed visits correlate with income level.(27) Thirdly, variables with high missingness that could not be adjusted for—race and insurance status—prevent us from fully
Jo
evaluating additional confounders. We are reassured that a sensitivity analysis for patients with race data using race as an additional covariate shows similar results as the main analysis. Fourth, we are not able to fully assess the reasons why patients with indications for statins are not receiving them. Although we can assess appropriateness, we cannot understand the full complexity of the patient’s care that may make appropriate therapy unwise. While there are some genetic risk factors and known ethnic risk factors for statin intolerance, there is no known pattern that would disproportionately affect one income level over others and thus should not differentially alter our results.(28) Finally, we performed a patient-level analysis rather than a visit-level analysis to avoid bias from clustering by patient and over-sampling of patients with frequent visits in panel data. As such, we cannot detect clinical events such as a new myocardial infarction in a patient previously being treated for primary prevention that would change the definition of appropriate care. This approach of a patient-level analysis is consistent with prior studies.(7)
9
Journal Pre-proof
10
Our analysis shows a small disparity in lipid treatment amongst those of low and high income levels under both the ATP III guidelines and the ACC/AHA guidelines. This suggests the need for further
Jo
ur
na
lP
re
-p
ro
of
investigation into barriers to using evidence-based therapies in poorer populations.
10
Journal Pre-proof
11
References 1.
Taylor F., Huffman MD., Macedo AF., et al. Statins for the primary prevention of cardiovascular disease. Cochrane Database Syst Rev 2013;1:CD004816. Doi: 10.1002/14651858.CD004816.pub5.
2.
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(10):e146–603. Doi: 10.1161/CIR.0000000000000485.
3.
Kaplan GA., Keil JE. Socioeconomic factors and cardiovascular disease: a review of the literature. Circulation 1993;88(4 Pt 1):1973–98. Muntaner C., Sorlie P., O’Campo P., Johnson N., Backlund E. Occupational hierarchy, economic
of
4.
ro
sector, and mortality from cardiovascular disease among men and women. Findings from the National Longitudinal Mortality Study. Ann Epidemiol 2001;11(3):194–201. Doi: S1047-
5.
-p
2797(00)00210-6 [pii].
Alter DA., Chong A., Austin PC., et al. Socioeconomic status and mortality after acute myocardial
6.
re
infarction. Ann Intern Med 2006;144(2):82–93. Doi: 144/2/82 [pii]. Epstein AM., Ayanian JZ., Keogh JH., et al. Racial disparities in access to renal transplantation--
lP
clinically appropriate or due to underuse or overuse? N Engl J Med 2000;343(21):1537–44, 2 p preceding 1537. Doi: 10.1056/NEJM200011233432106. Subherwal S., Patel MR., Tang F., et al. Socioeconomic disparities in the use of cardioprotective
na
7.
medications among patients with peripheral artery disease: an analysis of the American College of
ur
Cardiology’s NCDR PINNACLE Registry. J Am Coll Cardiol 2013;62(1):51–7. Doi: S07351097(13)01743-9 [pii]10.1016/j.jacc.2013.04.018. Philbin EF., McCullough PA., DiSalvo TG., Dec GW., Jenkins PL., Weaver WD. Socioeconomic
Jo
8.
status is an important determinant of the use of invasive procedures after acute myocardial infarction in New York State. Circulation 2000;102(19 Suppl 3):III107-15. 9.
Rao S V., Schulman KA., Curtis LH., Gersh BJ., Jollis JG. Socioeconomic status and outcome following acute myocardial infarction in elderly patients. Arch Intern Med 2004;164(10):1128–33. Doi: 10.1001/archinte.164.10.1128164/10/1128 [pii].
10.
Rathore SS., Berger AK., Weinfurt KP., et al. Race, sex, poverty, and the medical treatment of acute myocardial infarction in the elderly. Circulation 2000;102(6):642–8.
11.
Deans KA., Bezlyak V., Ford I., et al. Differences in atherosclerosis according to area level socioeconomic deprivation: cross sectional, population based study. BMJ 2009;339:b4170.
12.
Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults
11
Journal Pre-proof
12
(Adult Treatment Panel III). JAMA 2001;285(19):2486–97. Doi: jsc10094 [pii]. 13.
Stone NJ., Robinson JG., Lichtenstein AH., et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2014;63(25 Pt B):2889–934. Doi: S0735-1097(13)06028-2 [pii]10.1016/j.jacc.2013.11.002.
14.
Pokharel Y., Tang F., Jones PG., et al. Adoption of the 2013 American College of Cardiology/American Heart Association Cholesterol Management Guideline in Cardiology Practices Nationwide. JAMA Cardiol 2017;2(4):361. Doi: 10.1001/jamacardio.2016.5922. Verma AA., Jimenez MP., Subramanian S V., Sniderman AD., Razak F. Race and Socioeconomic
of
15.
ro
Differences Associated With Changes in Statin Eligibility Under the 2013 American College of Cardiology/American Heart Association Cholesterol Guidelines. Circ Cardiovasc Qual Outcomes
16.
-p
2017;10(9). Doi: 10.1161/CIRCOUTCOMES.117.003764.
Chan PS., Oetgen WJ., Spertus JA. The Improving Continuous Cardiac Care (IC(3)) program and
[pii]10.1016/j.amjmed.2009.09.019.
Masoudi FA., Ponirakis A., Yeh RW., et al. Cardiovascular care facts: a report from the national
lP
17.
re
outpatient quality improvement. Am J Med 2010;123(3):217–9. Doi: S0002-9343(09)00949-8
cardiovascular data registry: 2011. J Am Coll Cardiol 2013;62(21):1931–47. Doi: S0735-
18.
na
1097(13)05088-2 [pii]10.1016/j.jacc.2013.05.099. Wanner C., Tonelli M. KDIGO Clinical Practice Guideline for Lipid Management in CKD:
ur
summary of recommendation statements and clinical approach to the patient. Kidney Int 2014;85(6):1303–9. Doi: ki201431 [pii]10.1038/ki.2014.31. Burstin HR., Lipsitz SR., Brennan TA. Socioeconomic status and risk for substandard medical
Jo
19.
care. JAMA 1992;268(17):2383–7. 20.
Wilson PW., D’Agostino RB., Levy D., Belanger AM., Silbershatz H., Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation 1998;97(18):1837–47.
21.
Havranek EP., Mujahid MS., Barr DA., et al. Social Determinants of Risk and Outcomes for Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation 2015;132(9):873–98. Doi: CIR.0000000000000228 [pii]10.1161/CIR.0000000000000228.
22.
Odutayo A., Gill P., Shepherd S., et al. Income Disparities in Absolute Cardiovascular Risk and Cardiovascular Risk Factors in the United States, 1999-2014. JAMA Cardiol 2017;165(11):786– 94. Doi: 10.1001/jamacardio.2017.1658.
23.
Grundy SM., Stone NJ., Bailey AL., et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on
12
Journal Pre-proof
13
the Management of Blood Cholesterol. J Am Coll Cardiol 2018:25709. Doi: 10.1016/j.jacc.2018.11.003. 24.
Auger KA., Kahn RS., Simmons JM., et al. Using Address Information to Identify Hardships Reported by Families of Children Hospitalized With Asthma. Academic Pediatrics, vol. 17. Elsevier Inc.; 2017. p. 79–87.
25.
Krieger N., Chen JT., Waterman PD., Rehkopf DH., Subramanian S V. Race/Ethnicity, Gender, and Monitoring Socioeconomic Gradients in Health: Comparison of Area-Based Socioeconomic Measures - The Public Health Disparities Geocoding Project. Am J Public Health 2003;93(10):1655–71. Doi: 10.2105/AJPH.93.10.1655. Lemstra M., Blackburn D., Crawley A., Fung R. Proportion and Risk Indicators of Nonadherence
of
26.
ro
to Statin Therapy: A Meta-analysis. Can J Cardiol 2012;28(5):574–80. Doi: 10.1016/j.cjca.2012.05.007.
Ide BA., Curry MA., Drobnies B. Factors related to the keeping of appointments by indigent
-p
27.
clients. J Heal Care Poor Underserved 1993;4(1):21–39.
ur
na
lP
[pii]10.1016/j.amjcard.2014.02.033.
re
Ahmad Z. Statin intolerance. Am J Cardiol 2014;113(10):1765–71. Doi: S0002-9149(14)00725-5
Jo
28.
13
Journal Pre-proof
14
Table 1. Study Population Characteristics Under Different Guidelines Total (n=1655723)
2008-November 2013 (n=751,217)
November 2013-March 2016 (n=904,506)
Jo
ur
na
lP
re
-p
ro
of
Age (years) (mean, (SD)) 68.84 (11.7) 68.8 (11.9) 68.9 (11.4) Sex (n, %) Male 982668 (60) 448913 (60.4) 533755 (59.6) Female 656132 (40) 294388 (39.6) 361744 (40.4) Race (n, %) Non-Hispanic Black 85226 (5.1) 33875 (4.5) 51351 (5.7) Hispanic 49119 (3.0) 18558 (2.4) 30561 (3.4) Asian 15185 (0.92) 5994 (0.8) 9191 (1.0) White 1070921 (64.7) 463429 (61.7) 607492 (67.2) Other 6,941 (0.4) 3005 (0.4) 205911 (0.4) Missing 428331 (25.9) 226356 (30.1) 201975 (22.3) Income ($) (mean, SD) 60428.3 (23952) 60483.6 (23983.6) 60382.5 (23925.6) CAD1 (n, %) 1098246 (74.3) 539903 (75.7) 558343 (73.1) Diabetes Mellitus (n, %) 406553 (30) 194112 (31.6) 212441 (28.6) Hypertension (n, %) 1219974 (84.92) 592427 (86) 627547 (83.9) PAD2 (n, %) 205549 (14) 86730 (13.7) 118819 (14.3) 3 LDL-C (mg/dL) 91.73 (39.8) 92 (41.9) 91.5 (37.9) (mean, SD) HDL-C4 (mg/dL) 47.2 (17.5) 46.6 (16.5) 47.65 (18.3) (mean, SD) 1. Coronary Artery Disease (CAD), 2. Peripheral Arterial Disease (PAD), 3. Low-Density Lipoprotein Cholesterol (LDL-C), 4. High-Density Lipoprotein Cholesterol (HDL-C). All p-values are <0.001.
14
Journal Pre-proof
15
Table 2. Study Population Characteristics By Income Quintile Total (n=1655723)
Quintile 1 (n=336282)
Quintile 2 (n=336655)
Quintile 3 (n=330473)
Quintile 4 (n=326647)
Quintile 5 (n=303826)
Age (mean, 68.84 (11.7) SD) Sex (n, %) Male 982668 (60) 656132 (40) Female
68.4 (11.7)
69.3 (11.6)
69.0 (11.5)
69.0 (11.6)
68.6 (11.9)
187843 (56.3) 1459980 (43.7)
196689 (58.9) 196796 (60.2) 198294 (61.7) 137414 (41.1) 130235(39.8) 122918 (38.3)
189415 (62.9) 111516 (37.1)
Race (n, %)
Jo
ur
na
lP
re
-p
ro
of
NonHispanic 85211 (5.1) 38770 (11.5) 14645 (4.4) 12181 (3.7) 10555 (3.2) 7467 (2.5) Black Hispanic 49119 (3.0) 15546 (4.6) 10764 (3.2) 8916 (2.7) 8186 (2.5) 5401 (1.8) Asian 15185 (0.92) 1706 (0.5) 1943 (0.6) 2322 (0.7) 3624 (1.1) 5449 (1.8) White 1070921 (64.7) 201341 (59.9) 227111 (67.5) 228256 (69.1) 216453 (66.3) 184491 (60.7) Other 6956 (0.4) 78919 (0.4) 994 (0.3) 1287 (0.4) 1486 (0.5) 1356 (0.4) Missing 428331 (25.9) 77627 (23.1) 81198 (24.1) 77511 (23.4) 86343 (26.4) 99662 (32.8) CAD1 (n,%) 1098246 (74.3) 232391(75.2) 237615 (77) 226290 (74) 216284 (74) 171334 (70.8) Diabetes 406553 (30) 95321 (33.3) 82998 (31.3) 80363 (30.2) 77635(28.8) 64809 (25.9) Mellitus (n,%) Hypertension 1219974 (84.9) 255506 (86.9) 248548 (86) 247263 (85) 244020 (84.5) 208541 (81.9) (n, %) PAD2 (n, %) 205549 (14) 48745 (14) 42789 (14.5) 40035 (14) 40474 (14.7) 31150 (11.6) 3 LDL-C 91.73 (39.8) 93.9 (42.2) 91.8 (40) 91.5 (39.3) 90.3 (38.6) 90.9 (38.4) (mg/dL) (mean, SD) 1. Coronary Artery Disease (CAD), 2. Peripheral Artery Disease (PAD), 3. Low-Density Lipoprotein Cholesterol(LDL-C), 4. High-Density Lipoprotein Cholesterol (HDL-C). All p-values are <0.001.
15
Journal Pre-proof
16
ro
of
Table 3. Likelihood of Receiving Appropriate Statin Therapy Among Different Income Groups. 3a) Proportion of those Receiving Appropriate Statin Therapy Under Each Set of Guidelines. Unadjusted analysis. % Of Those Needing % Of Those Needing Lipid Therapy Lipid Therapy Appropriately Treated Appropriately Treated Income Under ATP III Under ACC/AHA Quintile Guidelines Guidelines P-value 57% p < 0.01 Quintile 1 68% 60% p < 0.01 Quintile 2 70% 58% p < 0.01 Quintile 3 72% 61% p < 0.01 Quintile 4 73% 62% p < 0.01 Quintile 5 73%
Quintile 5
re
lP
na
Quintile 4
ur
Quintile 3
Odds of Appropriate Treatment Under ACC/AHA Guidelines (95% CI) Ref 1.01 (1.00-1.02) p=0.13 1.01 (1.00-1.03) 1.02 p=0.04 1.03 (1.00-1.03) 1.04 p=0.01 1.03 (1.01-1.04) p=0.01
Jo
Income Quintile Quintile 1 Quintile 2
Odds of Appropriate Treatment Under ATP III Guidelines (95% CI) Ref 1.02 (1.01-1.03) p<0.01 1.04 (1.03-1.05) p<0.01 1.05 (1.04-1.06) p<0.01 1.06 (1.05-1.07) p<0.01
-p
3b) Odds of Receiving Appropriate Statin Therapy Under Each Set of Guidelines. Analysis adjusted for age, gender, coronary artery disease, peripheral arterial disease, diabetes mellitus, hyperlipidemia, hypertension, and cerebrovascular disease.
16
Journal Pre-proof
17
Table 4. Sensitivity Analysis of Odds of Receiving Appropriate Lipid Therapy Under Each Set of Guidelines in Patients with Race Data. Race has a high level of missing-ness in the study population. This analysis adjusted for race, age, gender, coronary artery disease, peripheral arterial disease, diabetes mellitus, hyperlipidemia, hypertension, and cerebrovascular disease. Odds of Appropriate Treatment Under ACC/AHA Guidelines (95% CI) Ref
Quintile 2 vs. Quintile 1 Quintile 3 vs. Quintile 1 Quintile 4 vs. Quintile 1 Quintile 5 vs. Quintile 1
1.01 (1.00-1.02) p < 0.04 1.02 (1.01-1.04) p < 0.01 1.03 (1.02-1.05) p < 0.01 1.04 (1.02-1.06) p < 0.01
1.03 (1.00-1.05) p=0.02 1.02 (0.99-1.05) p=0.12 1.01 (0.99-1.04) p=0.29 1.03 (1.00-1.07) p=0.04
Non-Hispanic Black vs. White Other vs. White
0.85 (0.83-0.86) p < 0.01
0.67 (0.65-0.7) p < 0.01
ro
-p
re
lP
0.94 (0.88, 1.01) p=0.08
Jo
ur
na
1 (0.96-1.03) p = 0.80
of
Quintile 1
Odds of Appropriate Treatment Under ATP III Guidelines (95% CI) Ref
17
Journal Pre-proof
18
Table 5. Sensitivity Analysis of Odds of Receiving Appropriate Lipid Therapy in Full Study Cohort. Patients were evaluated as an entire study cohort rather than within each separate guideline era. This analysis adjusted for race, age, gender, coronary artery disease, peripheral arterial disease, diabetes mellitus, hyperlipidemia, hypertension, and cerebrovascular disease.
of ro -p re
lP
Quintile 5
na
Quintile 4
ur
Quintile 3
Odds of Appropriate Treatment (95% CI) Ref 1.01 (1.00-1.02) p=0.03 1.02 (1.01-1.03) p<0.01 1.02 (1.01-1.03) p<0.01 1.03 (1.02-1.04) p<0.01
Jo
Income Quintile Quintile 1 Quintile 2
18
Journal Pre-proof
19
Figure 1. Study Design. n = number of encounters
Pinnacle Encounters: n=28,265,967
ro
of
Exclusions: 1) Age <18: n=310,679 2) Missing LDL: n=19,390,649 3) Missing Zip Code: n=353,984 4) NYHA Class III or IV: n=146,143 5) Patients on Dialysis: n=2,337
Jo
ur
na
lP
re
-p
Cohort: n=8,062,175
19
Journal Pre-proof
20
100 80 60
of
40
ro
20
re
-p
0
Income Q2
Income Q4
Income Q5
Income Q3
ur
na
lP
Income Q1
Jo
Patients Receiving Appropriate Lipid Therapy (%)
Figure 2. Proportion of Patients in Different Income Groups Receiving Appropriate Statin Treatment Over Time. The transition to the ACC/AHA Guidelines ocurred in 2013Q3.
20
Figure 1
Figure 2