Estimating the Potential Health Care Cost-Savings from a Flax-Based Treatment for Hypertension.
Luc ClairJared KashtonGrant N PiercePublished in: Nutrients (2024)
Hypertension contributes to the increase in health care spending in Canada through two primary mechanisms. First, it directly increases costs, as individuals with hypertension require medical care to manage the condition. Second, it indirectly raises expenses by serving as a risk factor for numerous chronic diseases, leading to increased health care utilization among those affected. Therefore, reducing hypertension prevalence could alleviate its resulting strain on the Canadian health care system. Clinical trials have demonstrated that daily flaxseed consumption effectively lowers both systolic and diastolic blood pressure. This study employs a four-step cost-of-illness analysis to estimate the potential health care cost-savings from a flaxseed-based treatment for hypertension. The analysis begins by assessing the proportion of individuals with hypertension likely to adopt the flaxseed regimen. It then evaluates the impact of flaxseed consumption on systolic and diastolic blood pressure. Next, data from the Canadian Health Measures Survey, Cycles 5 and 6, are used to estimate the prevalence of hypertension and the expected reduction in prevalence due to the flaxseed treatment. Finally, the potential reduction in health care spending is calculated. To incorporate uncertainty, partial sensitivity analysis and Monte Carlo simulations were utilized, varying the intake success rate and other model parameters, respectively. The most conservative estimate suggests a potential health care cost-savings of CAD 96,284,344 in Canada for the year 2020.
Keyphrases
- blood pressure
- healthcare
- hypertensive patients
- heart rate
- clinical trial
- left ventricular
- risk factors
- human health
- monte carlo
- blood glucose
- coronary artery disease
- randomized controlled trial
- health information
- adipose tissue
- type diabetes
- body mass index
- risk assessment
- machine learning
- big data
- combination therapy
- climate change
- artificial intelligence
- data analysis
- phase iii
- smoking cessation
- replacement therapy