Abstract
BACKGROUND: Cardiovascular disease is the leading cause of death among women in the United States. Past research has highlighted the importance of the relationship between female-specific demographics and traditional risk factors. The present analysis aimed to identify the prevalence of modifiable risk factors in women attending a community cardiovascular health screening. METHODS: Data collected between 2011 and 2019 were obtained from the Houston HeartReach Registry. Participants were classified as having or not having each of 4 traditional cardiometabolic risk factors: hypertension, diabetes, body mass index indicating overweight or obesity, and dyslipidemia. Differences in prevalence were compared using the Pearson χ2 test. RESULTS: Most participants had hypertension, overweight or obesity, and dyslipidemia. Older women (≥65 years) had the highest prevalence of all cardiometabolic risk factors. Black participants had a higher prevalence of hypertension (P = .006) and a lower prevalence of dyslipidemia (P = .009) than non-Black participants. Hispanic participants had a lower prevalence of hypertension (P < .001) and a higher prevalence of overweight or obesity (P = .03) than non-Hispanic participants. Participants in the lowest household income bracket (<$25,000) were more likely to have diabetes (P = .001) and overweight or obesity (P = .004) than participants in the highest income bracket (≥$50,000). Unemployed participants had a higher prevalence of diabetes (P < .001), overweight or obesity (P = .004), and dyslipidemia (P < .001) than employed participants. Comorbidity analysis revealed clustering of multiple cardiometabolic risk factors. Moreover, risk factor hot spots were identified by zip code, which could help select future sites for targeted screening. CONCLUSION: The analysis found that cardiometabolic risk factor prevalence varies with demographic and socioeconomic status. Geographic areas where cardiometabolic risk factor prevalence was highest were also identified. Further participant recruitment and analysis are required to create predictive models of cardiovascular disease risk in women.