The Market Dynamics of Generic Medicines in the Private Sector of 19 Low and Middle Income Countries between 2001 and 2011: A Descriptive Time Series Analysis

<div><p>This observational study investigates the private sector, retail pharmaceutical market of 19 low and middle income countries (LMICs) in Latin America, Asia and the Middle East/South Africa analyzing the relationships between volume market share of generic and originator medicines over a time series from 2001 to 2011. Over 5000 individual pharmaceutical substances were divided into generic (unbranded generic, branded generic medicines) and originator categories for each country, including the United States as a comparator. In 9 selected LMICs, the market share of those originator substances with the largest decrease over time was compared to the market share of their counterpart generic versions. Generic medicines (branded generic plus unbranded generic) represent between 70 and 80% of market share in the private sector of these LMICs which exceeds that of most European countries. Branded generic medicine market share is higher than that of unbranded generics in all three regions and this is in contrast to the U.S. Although switching from an originator to its generic counterpart can save money, this narrative in reality is complex at the level of individual medicines. In some countries, the market behavior of some originator medicines that showed the most temporal decrease, showed switching to their generic counterpart. In other countries such as in the Middle East/South Africa and Asia, the loss of these originators was not accompanied by any change at all in market share of the equivalent generic version. For those countries with a significant increase in generic medicines market share and/or with evidence of comprehensive “switching” to generic versions, notably in Latin America, it would be worthwhile to establish cause-effect relationships between pharmaceutical policies and uptake of generic medicines. The absence of change in the generic medicines market share in other countries suggests that, at a minimum, generic medicines have not been strongly promoted.</p></div

Time series of “unbranded generic” market share in 19 LMICs and the U.S.

<p>LEGEND: The trend (change in branded generic market share/yr) was calculated using a simple linear regression model.Trend: United States 2.90%/yr; LAC 1.46%/yr; Middle East plus South Africa (MeSA) −0.08%/yr; Asia −0.27%/yr. A t test for regressions were all significant [p<0.05]. The number of countries in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074399#pone-0074399-g003" target="_blank">Figure 3</a> are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074399#pone-0074399-g001" target="_blank">Figure 1</a>.</p

Time series “branded generic” market share in 19 LMICs and the U.S.

<p>LEGEND: The trend (change in branded generic market share/yr) was calculated using a simple linear regression model.Trend: United States −1.15%/yr; LAC −0.34%/yr; Middle East plus South Africa (MeSA) 0.47%/yr; Asia 0.61%/yr. A t test for regressions were all significant [p<0.05]. The number of countries in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074399#pone-0074399-g002" target="_blank">Figure 2</a> are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074399#pone-0074399-g001" target="_blank">Figure 1</a>.</p

“Diagnostic ratios”: definitions, examples and explanation.

<p>“Diagnostic ratios”: definitions, examples and explanation.</p

Time series of “total generic market share” in 19 LMICs and the U.S.

<p>LEGEND: The trend (change in total generic market share/yr) was calculated using a simple linear regression model. Trend: United States 1.54%/yr; LAC 1.12%/yr; Middle East plus South Africa (MeSA) 0.38%/yr; Asia 0.31%/yr. A t test for regressions were all significant [p<0.05].</p

The distribution of diagnostic ratios (in the 4 categories) for each countries' 30 originator pharmaceutical substances.

<p>The number in each bar is the number of medicines falling into the respective category.</p

HIV program funding needs, contributions and determinants, for 84 low- and middle-income countries in US$ per person living with HIV (PLWH), unless indicated: median (and inter-quartile range) across countries.

<p>ART is antiretroviral treatment. GNI is gross national income. MRY: most recent year is 2009, 2008 or 2007. PEPFAR is the US President’s Emergency Plan for AIDS Relief. PLWH is person living with HIV/AIDS; pc is per capita. Resource needs per prevalent case for ART and non-ART do not exactly add to total HIV Resource Need, because these are medians, not means.</p><p>Of PEPFAR’s 15 initial focus countries, Guyana, Ethiopia, Haiti and Tanzania were not included in our analysis because of insufficient data on predictor variables. For Egypt, the resource need estimate used excludes an implausibly high HIV counseling and testing (HCT) cost line item in the UNAIDS Investment Framework, which would imply a total national resource need of over $1.4 billion, to instead assume a more plausible$39.4 million total need. For the estimated gap, we used the maximum between expected domestic contribution and actual domestic contribution within each country.</p

Domestic HIV Contribution Deviation: Actual minus expected domestic contributions in 2009 (in US$ per person living with HIV).

<p>Bars to the right (blue in color) represent positive deviation: Actual domestic contribution>EDC. Bars to the left (orange in color) are negative deviations: Actual domestic contributionTable 1.</p

National HIV control resource availability and gaps, and the balance between domestic and donor funding contributions, in 2009.

<p>The x-axis is the national domestic contribution per person living with HIV (PLWH) in US$minus the expected domestic contribution (EDC) per PLWH in US$; the y-axis is the HIV/AIDS resource gap: resource need minus international donor funding and minus actual domestic contribution, each per PLWH in US$. The color of the circle refers to region: red for Africa, purple for East Asia/Pacific, gray for South Asia, green for Middle East, yellow for Europe and Central Asia, and light blue for the Americas. Outliers not shown on graph (but included in analyses and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067565#pone-0067565-t001" target="_blank">Table 1</a>) are: in Quadrant I (Argentina, Azerbaijan, Bulgaria, Egypt, El Salvador, Paraguay, Philippines, Georgia, Belarus, Kazakhstan, Romania, Uzbekistan); Quadrant II (Botswana, Costa Rica, Chile, Nicaragua, Mongolia); Quadrant III (Fiji, Gabon, Malaysia); and Quadrant IV (Algeria, Armenia, Lebanon, Sri Lanka).</p

Relative Domestic Contribution Deviation and Funding Gap.

<p>The x-axis presents the domestic contribution deviation in relative terms, as percentage of the expected domestic contribution (EDC); while the y-axis shows the relative resource gap as a percentage of resource need. The following outliers fall off the scale: Benin (549%, 13%), Fiji (−11%, −220%), Guatemala (74%, −130%), Madagascar (2876%, 44%), Mongolia (1739%, −314%), Nicaragua (695%, −69%) and Romania (325%, 14%). All countries are included in the analyses and in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067565#pone-0067565-t001" target="_blank">Table 1</a>.</p