Public ARV Procurement Data: Using Information to Obtain Lower Prices Brenda Waning March 6, 2008 Open Society Institute Seminar Kiev, Ukraine Funded by United Kingdom Department of International Development, Medicine Transparency Alliance (MeTA) Project

Background to Procurement Practices Countries typically struggle with medicine procurement –Unreliable suppliers, unknown medicine quality –Insufficient funds and financial systems –Unsure of fair, market price (information asymmetry) –Result is enormous variation in price and across countries Global Fund (GF) Offers Level Playing Field –“Sufficient” financing –Public posting of prices paid by all recipients –Limited # pre-qualified ARVs and suppliers –Expect little price variation in this environment

Reality and Remedy Extreme price variation exists for identical ARV products across countries 2 Publicly available databases can provide price information historically impossible to obtain – –Global Fund Purchase Price Report – –WHO Global Price Reporting Mechanism

Public ARV Procurement Databases Global Fund Purchase Price Report – –ARV procurement transactions reported by GF principle recipients WHO Global Price Reporting Mechanism – –Includes GF data PLUS ARV procurements reported by international procurement agencies (IDA, UNICEF), WHO country offices, John Snow, MSH, etc. Both available on the web in Excel format

Using Existing Data to Assess ARV Prices Merged GF & Global Price Reporting Mechanism (GPRM) Data Sets – –Country – –Date of order – –Generic name – –Strength – –Dosage form – –Total # units – –Price per unit – –Type of package – –Manufacturer – –Country of manufacturer – –INCO term Expanded data set created at Boston University –Price per unit calculated –Year of procurement –WHO Region of country –World Bank Income of country –ARV drug class –Fixed-Dose Combination –Generic/Brand –PEPFAR country –Clinton Foundation eligible –Differential Price eligible –WHO 1 st /2 nd line regimens 2003 & 2006 guidelines –Daily dose Final Data Set: 9,731 procurements July 02 – Oct countries $636 million Extensive Data Cleaning

stavudine 15mg Guyana high outlier prices verified High price outliers: screens to assess efficiency, policy, governance nevirapine 200mg Russia 20x times more expensive than median price lamivudine 150mg 76 similar high price outliers removed price per tablet volume price per tablet volume

Extreme Price Variability Highest Price/Lowest Price

Percentile Definition & Comparisons 25 th percentile – –25% of prices paid for that particular ARV were equal to or less than the price you paid 75 th percentile – –75% of prices paid for that particular ARV were equal to or less than the price you paid If we compare each procurement made by a country with the global price distribution for that exact same product, the desire is to be in the <25 th percentile (few countries paid less than you)

Country Benchmarking % of Country Procurements Across Quartiles of Global Price Distribution* Country (#) <25 th percentile 25 th -50 th percentile 51 st -75 th percentile >75 th percentile Armenia (19) 5.3%10.5% 15.8%68.4% Belarus (14) 7.1%42.9%14.3%35.7% Estonia (11) 0% 100% Georgia (15) 20% 40% Kazakhstan (7)28.6% 0%42.9% Russian Fed. (32)0%6.3%0%93.8% Tajikistan (9)22.2% 33.3% Ukraine (47) 17%19.1%34%29.8% Uzbekistan (8)0% 50% Prices for July 2006-June 2007

Amount of Money Spent in Excess of Global Median Price (7/06-6/07)* Country (# procurements) Amount Spent Above Median Armenia (19) $18,252 Belarus (14) $148,216 Estonia (11) $1,075,273 Georgia (15) $33,483 Kazakhstan (7) $74,208 Russian Federation (32) $12,399,841 Tajikistan (9) $3,773 Ukraine (47) $3,615,215 Uzbekistan $79,804

% Generic Procurements CountryTotal # Procurements # Generic (%) Armenia 194 (21%) Belarus 147 (50%) Estonia 110 (0%) Georgia 159 (60%) Kazakhstan 75 (71%) Russian Federation 320 (0%) Tajikistan 99 (100%) Ukraine 4722 (47%) Uzbekistan 87 (88%)

stavudine 40mg Global Median Price = $0.05/tablet EstoniaRussian Federation UkraineUzbekistan 3.03* 3.17* 0.69* * July June 2007 *Brand Name ARV

efavirenz 600mg Global Median Price = $0.65/tablet EstoniaKazakhstanRussian Feder- ation TajikistanUzbekistan $2.07*$0.21$0.65* $0.98* $0.67$0.92 July June 2007 *Brand Name ARV

zidovudine 300mg/lamivudine 150mg Global Median Price = $0.19/tablet EstoniaKazakh- stan RussiaUkraineTajiki- stan Uzbeki- stan $4.32*$0.38$1.33* $1.71* $0.16 $0.18 $0.21 $0.23 July June 2007 *Brand Name ARV TJ: $151 pp/year KZ: $274 pp/yr Russia: $958 pp/yr

lopinavir 133mg/ritonavir 33mg Global Median Price = $0.33/tablet KazakhstanRussian Federation Ukraine $1.95*1.87* $1.44* $0.52* $1.12* July June 2007 *Brand Name ARV

Comparison of WHO 2003, 2006, and 2008 ART Guidelines WHO 2003 ART Guidelines – –12 ARVs – –10 regimens: 4 first line, 6 second line WHO 2006 Revised ART Guidelines – –23 ARVs – –108 regimens: 24 first line, 84 second line WHO nd Line Prioritization – –20 ARVs (1 st and 2 nd line) – –42 2 nd line regimens (10 Urgent, 22 High, 10 Important)

2003 WHO regimens Generic: $170-$431 Brand: $500-$619 WHO First line regimens: generic price < brand (Median Prices July 2005-June 2006) 2006 WHO regimens Generic: $170-$1,234 Brand: $431-$1,393 Low range of cost of brand regimens is higher than upper end of cost generic regimens Upper end of cost of generic regimens is 3 times higher than STG big potential $ impact, even for 1 st line

WHO 2nd line regimens: generic price > brand (Median Prices July 2005-June 2006) 2003 WHO regimens Generic: $2,913 - $4,117 Brand: $1,714 - $2, WHO regimens Generic: $948 - $4,245 Brand: $865 - $2,577 Mostly due to price of protease inhibitors

WHO 1 st and 2 nd line Regimens Median Prices Paid July 2006-June 2007 Prices changing dramatically Decisions about country adoption MUST involve price, among other things – –Median 1st line prices   Generics range $102-$730 pp/year   Brand range $595-$1,015 pp/year – –Median 2 nd line prices range   Generics range $540-$6231 pp/year   Brands range $880-$2,044 pp/year Generic PIs more competitive with brands, except Lop/r

Multiple Regression to Examine Predictors of Price Regression model included 7 variables: – –volume (divided in tertiles) – –year of procurement – –INCO terms – –generic/brand – –CHAI-eligible – –Differential price-eligible – –Country World Bank income stage

Higher Volume does NOT mean Lower Price High Volume Middle Volume Low Volume Efavirenz 600mg Ref3.75%6.85% Lop+rit mg Ref12.18%23.87% Sta+lam+nvp mg Ref12.87%19.03% Zidovudine 300mg Ref4.57%2.00% Only 4 of 24 dosage forms showed volume-price association

Remaining 20 Dosage Forms No Price-Volume Association Abacavir 300mg Didanosine 100mg Didanosine 200mg Didanosine 400mg Efavirenz 50mg Efavirenz 200mg Indinavir 400mg Lamivudine 150mg Nelfinavir 250mg Nevirapine 200mg Ritonavir 100mg Stavudine 20mg Stavudine 30mg Stavudine 40mg Sta+lam mg Sta+lam mg Sta+lam+nvp mg Tenofovir 300mg Zidovudine 100mg Zid+lam mg

CHAI Generic Prices Compared to Non-CHAI Generic Prices 11 comparisons – –1 (EFV 600mg) CHAI 22% less expensive Non-CHAI – –4 ARVs CHAI 8-13% less expensive Non-CHAI – –6 ARVs CHAI price was <5% less than non-CHAI

CHAI Inferences: CHAI Setting Market Prices for All? Oct 2003Jan 2006

Differential Prices (DP) 19 ARVs available under DP scheme – –Most less expensive than brand prices when DP not available 7 of the 19 DP-ARVs compared to generic prices – –5 of these 7 DP ARVs more expensive than generics DP Offers little savings over generics for most ARVs

Summary & Conclusions Transparency of medicine prices using electronic databases can potentially lead to increased access to medicines Knowledge can be power – –BUT only if information is accurate, comprehensive (this data represents ~40-50% of all GF procurements), High level political commitment needed from donors to mandate data be reported – –No Policy from World Bank or PEPFAR to report (although SCMS is voluntarily reporting)

Summary & Conclusions (2) Databases have potential to inform and create accountability – –donors, countries, and programs – –Rapidly changing market place; requires constant monitoring Need to understand how data can be most useful to implementers – –User-friendly interfaces – –Accessible in low resource settings – –Incorporate into procurement system

Acknowledgements: – –Funding: United Kingdom Department for International Development – –Michael Borowitz (DFID) – –Research team: Warren Kaplan, Matthew Fox, Alexis King, Danielle Lawrence, Richard Laing, Bert Leufkens, Rose Radin, Sapna Mahajan Thank you! Brenda Waning: