• The total number of prevalent cases of primary prevention dyslipidemia in the countries under study will increase by 13%, from 67 million in 2016 to 76 million in 2026.
  • The total number of prevalent cases of secondary prevention dyslipidemia in the countries under study will increase by 25%, from 17.5 million in 2016 to 22 million in 2026.
  • Elevated LDL-cholesterol is the most common form of dyslipidemia among non-drug-treated persons in the countries under study.

Table of contents

  • Dyslipidemia - Epidemiology - Americas Data
    • Introduction
      • Key Findings
        • Overview
          • Prevalence of Primary Prevention Dyslipidemia per 1,000 People Aged 20+ in 2016 and 2026
          • Relative Sizes of the Contributing Factors to the Trend in Prevalent Cases of Primary Prevention Dyslipidemia over the Next Ten Years
          • Prevalence of Secondary Prevention Dyslipidemia per 1,000 People Aged 20+ in 2016 and 2026
          • Relative Sizes of the Contributing Factors to the Trend in Prevalent Cases of Secondary Prevention Dyslipidemia over the Next Ten Years
      • Epidemiology Data
      • Methods
        • Total Prevalent Cases of Dyslipidemia
        • Total Prevalent Cases of Primary and Secondary Dyslipidemia
      • Reference Materials
        • Primary and Secondary Dyslipidemia Disease Definition
          • Literature Review
            • Studies Included in the Analysis of Dyslipidemia
            • Studies Excluded from the Analysis of Dyslipidemia
          • Risk/Protective Factors
            • Risk/Protective Factors for Dyslipidemia
          • Bibliography

        Author(s): Ema Rodrigues, DSc, MPH

        Ema is an epidemiologist with expertise in forecasting incident and prevalent populations within oncology, as well as some cardiovascular indications such as venous thromboembolism. She has significant experience with statistical methods such as multivariate linear regression, conditional logistic regression, principal components analysis, mixed models, hierarchical modeling, and path analysis to account for the complex relationships among various predictors of health outcomes, particularly correlated variables.

        She completed her master’s and doctoral degree (MPH, D.Sc.) in Environmental Health at Boston University School of Public Health, where she worked on projects investigating significant predictors of various health outcomes including central nervous system cancer, cognitive function, and birth outcomes.

                     


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