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# Quantifying the Impoverishing Effects of Purchasing Medicines: A Cross-Country Comparison of the Affordability of Medicines in the Developing World

• niens@bmg.eur.nl

Affiliation: Institute for Medical Technology Assessment and Institute for Health Policy & Management, Erasmus University Rotterdam, The Netherlands

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• Affiliation: Essential Medicines and Pharmaceutical Policies, World Health Organization, Geneva, Switzerland

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• Affiliation: Institute for Medical Technology Assessment and Institute for Health Policy & Management, Erasmus University Rotterdam, The Netherlands

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• Affiliation: Health Action International Global, Amsterdam, The Netherlands

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• Affiliation: Institute for Medical Technology Assessment and Institute for Health Policy & Management, Erasmus University Rotterdam, The Netherlands

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• Affiliation: Essential Medicines and Pharmaceutical Policies, World Health Organization, Geneva, Switzerland

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• Published: August 31, 2010
• DOI: 10.1371/journal.pmed.1000333

## Abstract

### Background

Increasing attention is being paid to the affordability of medicines in low- and middle-income countries (LICs and MICs) where medicines are often highly priced in relation to income levels. The impoverishing effect of medicine purchases can be estimated by determining pre- and postpayment incomes, which are then compared to a poverty line. Here we estimate the impoverishing effects of four medicines in 16 LICs and MICs using the impoverishment method as a metric of affordability.

### What Do These Findings Mean?

Although the purchasing of medicines represents only part of the costs associated with the management of an illness, it is clear that the high cost of medicines have catastrophic effects on poor people. In addition, as the treatment of chronic conditions often requires a combination of medicines, the cost of treating and managing a chronic condition such as asthma, diabetes, and cardiovascular disease is likely to be even more unaffordable than what is reported in this study. Therefore concerted action is urgently required to improve medicine affordability and prevent poor populations from being pushed further into poverty. Such action could include: governments, civil society organizations, and others making access to essential medicines more of a priority and to consider this strategy as an integral part of reducing poverty; the development, implementation, and enforcement of sound national and international price policies; actively promoting the use of quality assured, low-cost generic drugs; ensuring the availability of essential medicines in the public sector at little or no charge to poor people; establishing health insurance systems with outpatient medicine benefits; encouraging pharmaceutical companies to differentially price medicines that are still subject to patent restrictions.

Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1​000333.

### Introduction

In developing countries the cost of medicines accounts for a relatively large portion of total healthcare costs [1][4]. As the majority of people in developing countries do not have health insurance [5] and medicines provided free through the public sector are often unavailable [4], medicines are often paid for out of pocket at the time of illness. Consequently, where medicine prices are high, people may be unable to procure them and therefore forego treatment or they may go into debt. For this reason, the World Health Organization (WHO) has designated affordable prices as a determinant of access to medicines (together with rational selection and use, sustainable financing, and reliable health and supply systems) [6]. In several international treaties, access to healthcare has been established as a right [7],[8]. States have a legal obligation to make essential medicines available to those who need them at an affordable cost. Determining the degree of affordability of medicines, especially in low- and middle-income countries (LICs and MICs), is an important, yet complex undertaking as affordability is a vague concept.

Medicine affordability has been investigated in terms of the days' wages that a country's lowest paid unskilled government worker (LPGW) needs to spend on a standard course of treatment [4],[9]. However, this metric is limited because it does not provide insight into the affordability of medicines for the often large sections of the population that earn less than the LPGW [4],[10]. Recently, Niëns et al. have proposed two alternative methods to gain insight into the affordability of medicines in the developing world [11]. A first method focuses on the catastrophic impact of expenditures on medicines, while the second approach consists of studying the impoverishing effect of these expenditures. Here we discuss the application of the latter approach and present the results of a cross-country analysis of the affordability of four medicines in 16 developing countries.

### Methods

Our measurement of the affordability of medicines is based on the approach taken by Van Doorslaer et al. [3], who reassessed poverty estimates in 11 Asian countries after taking into account household expenditures on health care. The impoverishment approach has also been used in other fields of study such as housing affordability [12],[13] and health insurance [14].

The impoverishing effect of a medicine is defined in terms of the percentage of the population that would be pushed below an income level of US$1.25 or US$2 per day when having to purchase the medicine. Although different income levels have been used/proposed [3],[15], the US$1.25 and US$2 poverty lines were chosen because they are the most recent widely recognized poverty indicators as used by the World Bank [16]. Thus, the approach essentially compares households' daily per capita income before and after (the hypothetical) procurement of a medicine. If the prepayment income is above the US$1.25 (or US$2) poverty line and the postpayment income falls below these lines, purchasing the medicine impoverishes people. We used this method to generate “impoverishment rates,” which denote the percentage of the population that would become impoverished. The unaffordability of a medicine then refers to the percentage of the population that either already is or would fall below the poverty line when having to procure the medicine. First we consider the affordability of medicines in the total population at risk of becoming ill. We also indicate, using prevalence rates for the three chronic diseases, the expected number of patients actually affected.

#### Data

To conduct the first analysis, three types of data were required per country: medicine prices, aggregated income data, and information on the income distribution. In calculating expected numbers of patients affected, prevalence data are also required. Medicine prices were taken from standardized surveys using the WHO/Health Action International (HAI) price measurement methodology, which report median patient prices for a selection of commonly used medicines in the private sector, for both originator brand (OB) and lowest priced generic (LPG) products [17]. We focused on the private sector because the availability of essential medicines in the public sector is much lower [4]. In the countries studied here, therefore, many people will depend on the private sector for their medicines.

The World Bank's World Development Indicators (WDIs) provided Household Final Consumption Expenditure (HHFCE) data and information on income distribution [18]. Although WDIs have shortcomings (highlighted in the Discussion section), they have the advantage of being available for a wide range of countries. Moreover, in this context commonly used household surveys are often not available on a yearly basis and are not conducted in a standardized way, limiting the comparability of results across countries and over time [3],[19]. Here we use an affordability measure that can be quite easily applied in LICs and MICs where the use of more detailed household survey data may be limited.

HHFCE was selected as an aggregate income measure rather than gross domestic product (GDP) per capita as it better reflects households' resources [19], while GDP also includes consumption, gross investment, and net trade. Because the WDI did not provide any information on HHFCE for Nigeria and Yemen, the Economist Intelligence Unit (EIU) nominal private consumption figure was used for these countries [20]. For simplicity, we refer to “income” as measured by HHFCE or nominal private consumption. Apart from average income, the WDIs also provide some information on a country's income distribution by listing the proportion of total income earned in seven income groups; five income quintiles, with the poorest and richest quintiles split into deciles.

At the time of analysis, medicine price surveys were available for 53 countries. In large countries such as India and China, price surveys were carried out on a state or provincial level [4]. Because the WDIs do not provide state-level income distributions, HHFCE, and population figures, these countries were excluded from the current study. To ensure cross-country comparability, the analysis was limited to countries where income distributions (WDI data) were available from the year 2000 onwards. We used WDI income data from the same year as the WHO/HAI price data. Data on income distributions for the same year were used when possible, if not, the most recent income distribution data prior to the year of the price and income data were used.

Table 1 provides an overview of all countries and data used in this study. When discussing results, countries were grouped into LICs and MICs according to the 2008 World Bank's classification [21]. Sixteen countries were selected on the basis of the availability of WHO/HAI data. They are not representative of the developing world as a whole. However, as these countries vary substantially in terms of economic development, health care infrastructure, and medicine prices, they provide an interesting sample to study affordability of medicines.

We selected four medicines for which price data were available for the majority of countries and for which treatment regimens are relatively standard across countries. While these may not lead to results that are in a strict sense generalisable, they provide valuable insight into the affordability of common medicines in the selected countries. Table 2 lists the medicine, the ill health conditions for which these medicines are used, the total number of units per treatment course, and the treatment duration in days [17]. Three of the four study medicines are used to treat chronic conditions (asthma, diabetes, and hypertension). For each of these, we also calculated the expected numbers of patients becoming impoverished, using the prevalence data shown in Table S1. We could not do this for adult respiratory infection because of unavailability of comparable prevalence data.

The emphasis on medicines for chronic disease is justified by the fact that these conditions require ongoing, usually lifelong expenditures, making it more difficult for households to use financing strategies like borrowing and selling assets [22]. Table 2 shows that the treatment duration for these medicines was set at 30 d to represent the monthly treatment costs. The affordability of one acute condition (adult respiratory infection) treated with a 7-d treatment course of amoxicillin was also studied. Recently, the WHO increased the guidelines for treatment of adult respiratory infection with amoxicillin to a daily regimen of three times 500 mg amoxicillin. This change implies that the affordability of this medicine is likely to be lower than reported here [23].

#### Calculation Methods

Our method of estimating the impoverishing effect of procuring medicines was based on the method used by Van Doorslaer et al. [3]. However, using aggregate data requires some simplifying assumptions about the income distribution across population groups. For a detailed discussion of the method used to calculate the impoverishing effect of medicines, we refer to Niëns et al. [11]. The basic idea is to compare poverty estimates before and after a (potential) purchase of the medicines listed in Table 1. Average per capita income within each income group is estimated by combining information on the proportion of total income earned across income groups with data on the HHFCE (as provided by the WDIs). As only data on average income in the different quintiles and deciles were available, we assumed linearity of the income distribution within these relevant groups in which the US$1.25 and US$2 poverty lines were located in calculating poverty and impoverishment. The proportion of the population that would earn less than US$1.25 or US$2 per day after buying a medicine but not before would therefore be impoverished because of purchasing medicines. The medicine is deemed affordable for the proportion of the population that would remain above the poverty line after having purchased it. We also estimated the actual number of patients with one of the three chronic illnesses for which the medicine is unaffordable. To make this estimation, we used prevalence rates from various data sources and again assume that the respective disease is evenly spread over the income distribution.

### Supporting Information

Alternative Language Abstract S1.

Abstract translated into French by Ellen Van de Poel and Gabriela Flores.

doi:10.1371/journal.pmed.1000333.s001

(0.02 MB DOC)

Alternative Language Abstract S2.

Abstract translated into Spanish by Laurens M. Niëns and Isaac Corro Ramos.

doi:10.1371/journal.pmed.1000333.s002

(0.02 MB DOC)

Table S1.

The prevalence of three chronic diseases.

doi:10.1371/journal.pmed.1000333.s003

(0.05 MB DOC)

### Acknowledgments

We would like to thank Dele Abegunde for his critical review of the manuscript and Eddy Van Doorslaer and Frans Rutten for their useful ideas on this topic.

### Author Contributions

ICMJE criteria for authorship read and met: LMN AC EvdP ME WBFB RL. Agree with the manuscript's results and conclusions: LMN AC EvdP ME WBFB RL. Designed the experiments/the study: LMN AC WBFB RL. Analyzed the data: LMN AC ME. Collected data/did experiments for the study: LMN. Wrote the first draft of the paper: LMN. Contributed to the writing of the paper: LMN AC EvdP ME WBFB RL.

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