Hypertension | Current Treatment | Detailed, Expanded Analysis: Treatment Algorithms: Claims Data Analysis (US)

Publish date: May 2019

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Primary, or essential, hypertension is defined as persistently elevated blood pressure (BP) without any identifiable cause. It is a major risk factor for various cardiovascular diseases (e.g., coronary artery disease, stroke, heart failure), renal failure, and death. Hypertension is treated with a vast array of drugs belonging to a large number of classes, including numerous fixed-dose combination (FDC) products. Standard first-line treatment may involve monotherapy or combination therapy, and guidelines emphasize the additive effects of using two or more drugs from different antihypertensive drug classes. The Hypertension Treatment Algorithm provides insights into drug class prescribing patterns among a wealth of treatment options. Owing to the wide availability of generics, pricing pressures are of minimal concern.

QUESTIONS ANSWERED

  • What patient shares do key therapies and brands garner by line of therapy in newly diagnosed hypertensive patients? What are the trends in prescribing among recently treated and newly diagnosed hypertensive patients?
  • How have FDCs been integrated into the treatment algorithm?
  • What percentage of hypertensive patients receive drug therapy within one year of diagnosis, and how quickly? What percentage of patients progress to later lines of therapy within one year of diagnosis?
  • What percentage of hypertensive patients receive monotherapy versus combination therapy? What are the most commonly used combinations?
  • What are the product-level compliance and persistency rates among drug-treated patients?

PRODUCT DESCRIPTION

Treatment Algorithms: Claims Data Analysis provides detailed, quantitative analysis of the treatment journey and brand usage across lines of therapy and overall using real-world, patient-level claims data so that marketers can accurately assess their source of business, benchmark usage against competitors, and quantify areas of opportunity for their marketed or emerging brand.

Table of contents

  • Detailed, Expanded Analysis: Treatment Algorithms: Claims Data Analysis (US)
    • Treatment Algorithm Claims Data Analysis | Hypertension | US | June 2019

Author(s): Kahkashan Resham, Ph.D

Kahkashan Resham is a senior research associate in the Cardiovascular, Metabolic and Renal disorders team at Decision Resources Group. Her current work is focused on writing treatment algorithm reports, key opinion leader identification, clinical trial pipeline pulls, as well as secondary research across multiple cardiovascular and metabolic indications. Prior to joining DRG, Resham obtained her doctorate in Pharmacology from National Institute of Pharmaceutical Education and Research (NIPER), Nagar, India. She completed her M. Pharm. from the NIPER Hyderabad and was awarded a gold medal for securing 1st rank in the Institute. She holds a bachelor’s degree in Pharmacy from Birla Institute of Technology, Ranchi, India. Resham has also published several peer-reviewed research articles in the area of drug metabolism, diabetes and chemotherapy-induced peripheral neuropathic pain.