The relationship between payer type and lipid outcomes in response to clinical lifestyle interventions in youth with dyslipidemia

As part of an ongoing QI project, we prospectively collected clinical and demographic data from patients under the age of 25 years who were assessed in the Preventive Cardiology Program at a large regional medical center from September 1, 2010 to March 31, 2016. Patients were eligible for this study if they had an initial visit to the Preventive Cardiology Program during the observation period with a lipid profile that included a total cholesterol, triglyceride level, HDL-C, and LDL-C, regardless of the reason for referral. For example, patients who were referred to the Preventive Cardiology Program for hypertension, but obtained a lipid profile as part of the visit. Patients were excluded if they were prescribed lipid-lowering medications during follow up at any time during the observation period, if they were over the age of 25 years, or if they did not have a baseline visit in the observation period. This study was approved by the research ethics board with a waiver of individual participant consent.

The QI initiative was implemented for all children and adolescents seen for a lipid disorder in a Preventive Cardiology Program as part of a large, regional medical system starting September 1, 2010. This initiative provided management suggestions and prospectively collected clinical data via the use of a Standardized Clinical Assessment and Management Plan (SCAMP). Providers (physicians, nurse practitioners, and nurses) completed the standardized forms for each patient encounter and when interval laboratory measurements were obtained. In addition to collecting pertinent health information, the SCAMP suggested management by way of treatment algorithms generally consistent with the 2011 US National Heart, Lung, and Blood Institute Pediatric Integrated CVD Guidelines [10]. Patients were counseled on heart healthy lifestyle behaviors based on the 2011 Guidelines; patients visited with a registered dietician and a nurse practitioner or physician. The intervention included nutrition and physical activity counseling tailored for the type of lipid disorder. For example, for those patients with high triglycerides, the focus was on reducing intake of simple sugars and replacing them with vegetables and whole grains. In contrast, for patients with elevated LDL-C, the focus was more on reducing saturated fat and increasing dietary fiber intake in the form of fruits, vegetables and whole grains. Physical activity counseling focused on stepwise increases in activity to target a minimum of 5 h of moderate-to-vigorous physical activity per week. Patients had the opportunity to meet with a registered dietician at each visit. In addition to in-person counselling, patients were provided with printed educational materials. After three visits, additional visits did not necessarily include a visit with a dietician. Clinical information was extracted from this QI dataset and supplemented with information from the electronic health record (EHR).

The participant’s payer-type was determined for the purposes of this investigation from the EHR at the initial clinic visit and was held constant during all longitudinal analyses. Government-sponsored coverage was defined as having any coverage through Medicaid, the State Children’s Health Insurance Program, or Medicare. Private coverage was defined as having health insurance that was not provided through government-sponsored coverage and also included international patients. No patients were identified as uninsured in our study. Out of state patients had insurance coverage by their respective state and were classified as private or government-sponsored in a similar fashion. Payer-type was present on administrative paperwork at each visit.

Weight was recorded by a trained clinical provider via a standing scale (Scale-Tronix Stand-on Scale, Scale-Tronix, White Plains, NY) to the nearest 0.1 kg with patients in their own clothing without a jacket or shoes. However, it was not possible to know if a patient’s weight was measured on the same scale at each visit. Height was measured with a vertical stadiometer in patients without shoes to the nearest 0.1 cm. Body mass index (BMI) was calculated as weight in kilograms divided by height in centimeters squared. BMI percentiles and Z-scores were generated using the Centers for Disease Control (CDC) 2000 reference tables, as implemented in CDC EpiInfo version 7.

Lipid measurements were obtained from peripheral blood samples, generally after an 8-h fast. Measurements were made using standard enzymatic assays. Total cholesterol, HDL-C, and triglycerides were measured and LDL-C was calculated according to the Friedewald equation [19]. Non-HDL-C, a surrogate for all atherogenic Apolipoprotein B containing particles, was determines as total cholesterol minus HDL-C. If triglycerides were ≥ 400 mg/dL (4.52 mmol/L), a direct LDL-C was obtained in most cases.

We generated summary statistics including counts, percentages, means and medians, as appropriate for distribution, to describe categorical and continuous study variables. Baseline characteristics were compared between payer-type with t-tests or chi-squared tests for continuous and categorical data, respectively. The triglyceride level was natural log transformed due to non-normal distribution. We defined abnormal lipid levels for males and females as an LDL-C greater than or equal to 130 mg/dL (3.3 mmol/L), an HDL-C less than 40 mg/dL (1.03 mmol/L), non-HDL-C greater than 145 mg/dL (3.75 mmol/L), and a triglyceride level greater than or equal to 150 mg/dL (1.69 mmol/L). Race/ethnicity was self-reported and categorized as White, Black, Hispanic, Asian, other, and unknown. BMI was categorized as underweight (BMI <5th percentile), normal (BMI 5th – 84th percentile), overweight (BMI ≥ 85th to 94th percentile), and obese (BMI ≥95th percentile) for age and sex in patients younger than 18 years old. For young adults ages 18–25 years old we used absolute BMI cutoffs of normal (BMI < 25 kg/m²), overweight (25 – < 30 kg/m²), and obese (≥30 kg/m²). Corresponding BMI categories were then combined for those younger and older than 18 years of age.

In age, sex, and race/ethnicity-adjusted models, patients with government-sponsored health coverage were found to have a baseline HDL-C that was 3.5 (1.0) mg/dL lower than patients with private insurance (p < 0.001). Patients with government-sponsored health coverage were also found to have higher baseline lnTG (+ 0.14 [0.04], p < 0.001), which equates to a triglyceride concentration of 1.15 times higher with government-sponsored care as compared to private insurance. However, the association of government-sponsored health insurance with lower HDL-C and higher lnTG was no longer present after adjusting for BMI category (− 1.1 [0.9], p = 0.13, and + 0.05 [0.04], p = 0.2 for HDL-C and lnTG, respectively). There was no difference in baseline LDL-C between patients with government-sponsored health coverage and private insurance (+ 3.4 mg/dL [3.0], p = 0.3). We did not detect effect modification by age, sex, race/ethnicity, or obesity/overweight status on the relationship between payer-type and baseline lipid parameter.

The lipid values from the baseline visit through the third follow-up visit are presented in Table 3. Overall, for patients with baseline abnormal lipid-specific values, from the baseline to third follow-up visit, there was a 15 (39) mg/dL decrease in LDL-C, a 3 [7] mg/dL increase in HDL-C, a 20 (37) mg/dL decrease in non-HDL-C, and a 69 (222) mg/dL decrease in triglyceride levels. We observed no association between payer-type and change in lipid values from baseline to first through third follow-up visit, adjusting for age, sex, race/ethnicity, and time between visits (Figs. 1 and 2). Despite no main effect of an association between payer-type and change in LDL-C between visits, we did detect an interaction between patients with self-reported White race and the relationship between payer-type and change in LDL-C (p = 0.03). In race-stratified models (White only, abnormal LDL-C at baseline with at least 1 follow-up, n = 248), White patients with private insurance had a 12.8 (5.5) mg/dL (p = 0.02) additional decrease in LDL-C between baseline and first follow-up as compared to White patients with government-sponsored health coverage, after adjusting for age, sex, time between visits, and baseline LDL-C (Fig. 3). The association was unchanged after additionally adjusting for BMI category. There was no observed association between payer-type and change in LDL-C from baseline to first follow-up in the other race/ethnic groups, although the sample sizes were smaller limiting our power to detect a difference (n = 25, 29, 23, and 69 for Black, Hispanic, other, and unknown, respectively). There was no observed effect modification by sex, age, and BMI category on the relationship between payer-type and change in lipid values.

The strengths of our study include prospectively collected data and a relatively large pediatric cohort. However, this study also has limitations. First, the type of health coverage captures only one aspect of SES [14, 32]; parental education level and self-reported incomes are known to be important SES measures that were not available in the dataset. Second, we may not have had sufficient power to detect an overall difference in response to treatment as we had a small number of patients with government-sponsored coverage. However, if this were the case in our data, the undetected effects would likely not be of a clinical significance. Patients self-classification of race/ethnicity was frequently incomplete. Similarly, a progressively decreasing sample size, prevented us from making a valid conclusion in regards to longer-term outcomes beyond three visits. Our study also is limited by the inability to control for a number of factors that could potentially effect behavioral change, including family history and smoking history. In our study, we used payer-type as a marker of socioeconomic status; other markers were not available to us. Another limitation was that providers had access to the patient’s health insurance type at the visit and it cannot be guaranteed that this information did not influence patient counseling. As the population evaluated in this study was derived from a referral population, the patients may have been more motivated than the general population to make lifestyle changes and may have had more resources to make lifestyle changes. For instance, patients in our population have already demonstrated their engagement by visiting a preventive cardiology clinic.