METHODS AND RESULTS: We used 2010 to 2018 ambulatory visit data from children aged 2 to 12 years within CAPRICORN (Chicago Area Patient-Centered Outcomes Research Network), an electronic health record network in Chicago. This study included 87 549 children who attended 197 559 well-child encounters. Across all encounters, children were 51.5% male and mean (SD) age 6.4 (3.3) years. For each child who attended a well-child visit and met age and/or risk-based criteria, receipt of body mass index, blood pressure, lipids, and/or hemoglobin A1c or fasting blood glucose measurements were assessed. We used generalized estimating equations to calculate proportion adherence for each metric overall and stratified by age, sex, race and ethnicity, and insurance status. Universal age-based screening prevalence (95% CI) per 100 eligible visits was 77.1 (76.8-77.3) for body mass index, 33.4 (33.1-33.7) for blood pressure, and 9.6 (9.3-9.9) for lipids. Risk-based screening prevalence (95% CI) per 100 eligible visits was 13.9 (12.2-15.9) for blood pressure, 6.9 (6.4-7.5) for lipids, and 13.3 (12.6-14.1) for blood glucose.
CONCLUSIONS: Early screening of cardiovascular health risk factors could lead to earlier interventions, which could alter cardiovascular health trajectories across the lifetime. Low-to-moderate levels of adherence to universal age-based and risk-based cardiovascular health screening highlight the gap between recommendations and clinical practice, emphasizing the need to understand and address barriers to screening in pediatric populations.
METHODS: We used data spanning 2010-2018 from children aged 2-12 years within the Chicago Area Patient-Centered Outcomes Research Network-an electronic health record network. Four clinical systems comprised the derivation sample and a fifth the validation sample. Body mass index, blood pressure, cholesterol, and blood glucose were categorized as ideal, intermediate, and poor using clinical measurements, laboratory readings, and International Classification of Diseases diagnosis codes and summed for an overall CVH score. Group-based trajectory modeling was used to create CVH score trajectories which were assessed for classification accuracy in the validation sample.
RESULTS: Using data from 122,363 children (47% female, 47% non-Hispanic White) three trajectories were identified: 59.5% maintained high levels of clinical CVH, 23.4% had high levels of CVH that declined, and 17.1% had intermediate levels of CVH that further declined with age. A similar classification emerged when the trajectories were fitted in the validation sample.
CONCLUSIONS: Stratification of CVH was present by age 2, implicating the need for early life and preconception prevention strategies.
METHODS: Data were used from children and adolescents aged 8-19 years in six pooled childhood cohorts (19,261 participants, collected between 1972 and 2010) to create reference standards for fasting glucose and total cholesterol. Using the models for glucose and cholesterol as well as previously published reference standards for body mass index and blood pressure, clinical cardiovascular health charts were developed. All models were estimated using sex-specific random-effects linear regression, and modeling was performed during 2020-2022.
RESULTS: Models were created to generate charts with smoothed means, percentiles, and standard deviations of clinical cardiovascular health for each year of childhood. For example, a 10-year-old girl with a body mass index of 16 kg/m2 (30th percentile), blood pressure of 100/60 mm Hg (46th/50th), glucose of 80 mg/dL (31st), and total cholesterol of 160 mg/dL (46th) (lower implies better) would have a clinical cardiovascular health percentile of 62 (higher implies better).
CONCLUSIONS: Clinical cardiovascular health charts based on pediatric data offer a standardized approach to express clinical cardiovascular health as an age- and sex-standardized percentile for clinicians to assess cardiovascular health in childhood to consider preventive approaches at early ages and proactively optimize lifetime trajectories of cardiovascular health.
METHODS: We included nulliparous individuals with singleton pregnancies who self-identified as Hispanic, non-Hispanic Black (NHB), or non-Hispanic White (NHW) and participated in the nuMoM2b cohort study (Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be). First-trimester CVH was quantified using 6 routinely assessed factors in pregnancy included in the American Heart Association Life's Essential 8 score (0-100 points), in which higher scores indicate better CVH. Oaxaca-Blinder decomposition evaluated the extent to which racial and ethnic differences in CVH were explained by differences in individual- and neighborhood-level factors (age, socioeconomic characteristics, psychosocial factors, nativity, perceived racial discrimination, and area deprivation index).
RESULTS: Among 9104 participants, the mean age was 26.8 years, 18.7% identified as Hispanic, 15.6% identified as NHB, and 65.8% identified as NHW. Mean (SD) CVH scores were 76.7 (14.1), 69.8 (15.1), and 79.9 (14.3) in the Hispanic, NHB, and NHW groups, respectively (P<0.01). The individual- and neighborhood-level factors evaluated explained all differences in CVH between Hispanic and NHW groups and 82% of differences between NHW and NHB groups. Racial and ethnic differences in educational attainment explained the greatest proportion of differences in CVH. If mean years of education among the Hispanic (14.0 [2.5]) and NHB (13.4 [2.4]) groups were the same as the NHW (15.8 [2.4]) group, mean CVH scores would be higher by 2.98 points (95% CI, 2.59-3.37) in the Hispanic and 4.28 points (95% CI, 3.77-4.80) in NHB groups.
CONCLUSIONS: Racial and ethnic differences in early pregnancy CVH were largely explained by differences in individual- and neighborhood-level factors.