A PREDICTIVE TOOL FOR FORECASTING ANTI-HYPERTENSION MEDICATION REQUIREMENTS IN LOW- AND MIDDLE-INCOME COUNTRIES. (April 2021)

• Record Type: Journal Article
• Authors: Sahoo, Swagata Kumar; Pathni, Anupam K.; Kaur, Prabhdeep; Kunwar, Abhishek; Sharma, Bhawna; Farrell, Margaret L.; Moran, Andrew E.; Komrska, Jan; Frieden, Thomas R.
• Is Part Of: Journal of hypertension. Volume 39(2021)e-Supplement 1
• Publication Date: 2021-04
• Subjects: Hypertension -- Periodicals; Hypertension -- Periodicals; 616.132005
• DOI: 10.1097/01.hjh.0000747584.46815.5c
• Languages: English
• ISSNs: 1473-5598

Abstract

Abstract : Objective: To develop a mathematical tool for estimating medication requirement that would improve the uninterrupted supply of medications for large-scale hypertension control programs in low- and middle-income countries. Design and method: In a steady state, annual drug procurement forecasts can simply replicate the previous year's consumption. For a new or rapidly growing hypertension control program, however, forecasting drug requirement requires consideration of not only patients currently on medication, but also rates of new patient registration and drop-out. To forecast antihypertensive drug requirements, a simple mathematical tool was developed based on the morbidity method, requiring minimal inputs and assumptions. The tool hinges on use of a standard step-wise treatment protocol which uses specific medications and doses and considers hypertension prevalence, number of existing patients, expected new monthly enrollments, proportion of patients with controlled blood pressure at each protocol step, and expected loss-to-follow up over time. Outputs include forecasted quantities of specific medications for any specific time period and associated budgets. The tool provides flexibility for correction of projected requirements based on programmatic data, thus making the estimation more accurate. Results: The tool was implemented in an Indian hypertension control program starting with a priori assumptions and refined iteratively as program data became available. It was customised for use in specific Indian states based on their treatment protocol, population size and primary health care coverage. The monthly drug requirement projected by the tool helped to quantify drug volumes for periodic procurement, and to overcome shortages observed in some areas in first year of the program. More than 10 states in India have adopted this tool for procurement and budget planning. Conclusions: This simple, pragmatic antihypertension drug forecasting tool can help ensure prudent resource allocation in hypertension control programs and facilitate reliable delivery of medications for patients in low- and middle-income countries. This tool can also quantify the financial implications of alternative treatment protocols, inform protocol design, and be adapted to other chronic disease control programs.