OBJECTIVE: To develop two practical methods for measuring the affordability of medicines in developing countries. METHODS: The proposed methods--catastrophic and impoverishment methods--rely on easily accessible aggregated expenditure data and take into account a country's income distribution and absolute level of income. The catastrophic method quantifies the proportion of the population whose resources would be catastrophically reduced by spending on a given medicine; the impoverishment method estimates the proportion of the population that would be pushed below the poverty line by procuring a given medicine. These methods are illustrated by calculating the affordability of glibenclamide, an antidiabetic drug, in India and Indonesia. The results were validated by comparing them with the results obtained by using household micro data for India and Indonesia. FINDINGS: When accurate aggregate data are available, the proposed methods offer a practical way to obtain informative and accurate estimates of affordability. Their results are very similar to those obtained with household micro data analysis and are easily compared across countries. CONCLUSION: The catastrophic and impoverishment methods, based on macro data, can provide a suitable estimate of medicine affordability when the household level micro data needed to carry out more sophisticated studies are not available. Their usefulness depends on the availability of accurate aggregated data.
OBJECTIVE: To develop two practical methods for measuring the affordability of medicines in developing countries. METHODS: The proposed methods--catastrophic and impoverishment methods--rely on easily accessible aggregated expenditure data and take into account a country's income distribution and absolute level of income. The catastrophic method quantifies the proportion of the population whose resources would be catastrophically reduced by spending on a given medicine; the impoverishment method estimates the proportion of the population that would be pushed below the poverty line by procuring a given medicine. These methods are illustrated by calculating the affordability of glibenclamide, an antidiabetic drug, in India and Indonesia. The results were validated by comparing them with the results obtained by using household micro data for India and Indonesia. FINDINGS: When accurate aggregate data are available, the proposed methods offer a practical way to obtain informative and accurate estimates of affordability. Their results are very similar to those obtained with household micro data analysis and are easily compared across countries. CONCLUSION: The catastrophic and impoverishment methods, based on macro data, can provide a suitable estimate of medicine affordability when the household level micro data needed to carry out more sophisticated studies are not available. Their usefulness depends on the availability of accurate aggregated data.
Authors: Laurens M Niëns; Alexandra Cameron; Ellen Van de Poel; Margaret Ewen; Werner B F Brouwer; Richard Laing Journal: PLoS Med Date: 2010-08-31 Impact factor: 11.069
Authors: Fernando Antoñanzas; Robert Terkola; Paul M Overton; Natalie Shalet; Maarten Postma Journal: Pharmacoeconomics Date: 2017-08 Impact factor: 4.981
Authors: Laurens M Niëns; Alexandra Cameron; Ellen Van de Poel; Margaret Ewen; Werner B F Brouwer; Richard Laing Journal: PLoS Med Date: 2010-08-31 Impact factor: 11.069
Authors: Veronika J Wirtz; Hans V Hogerzeil; Andrew L Gray; Maryam Bigdeli; Cornelis P de Joncheere; Margaret A Ewen; Martha Gyansa-Lutterodt; Sun Jing; Vera L Luiza; Regina M Mbindyo; Helene Möller; Corrina Moucheraud; Bernard Pécoul; Lembit Rägo; Arash Rashidian; Dennis Ross-Degnan; Peter N Stephens; Yot Teerawattananon; Ellen F M 't Hoen; Anita K Wagner; Prashant Yadav; Michael R Reich Journal: Lancet Date: 2016-11-08 Impact factor: 79.321
Authors: Simon C Moore; Bella Orpen; Jesse Smith; Chinmoy Sarkar; Chenlu Li; Jonathan Shepherd; Sarah Bauermeister Journal: J Public Health (Oxf) Date: 2022-06-27 Impact factor: 5.058