1. New Haven Needle Exchange Program Was it effective in reducing HIV transmission? Was it cost-effective?

2. US History of HIV 1981 CDC MMWR reports unusual pneumonia in 5 gay men in LA 1982 CDC coins the name AIDS 1983 HIV virus discovered 1985 HIV test approved 1986 AZT approved 1987 US bans HIV+ immigrants and visitors 1991 More drugs approved 1997 Combination therapy becomes standard

3. Drug use and the spread of HIV IDU = injection drug user 1/3 of US AIDS cases can be traced to drug injection 1/2 of new HIV infections can be traced to drug injection Spread of HIV among IDUs in NYC –1985: prevalence close to 0 –1988: 40% of IDUs infected Becomes clear by 1987 that IDUs are dominant mode of transmission in New Haven Reducing spread among IDUs a priority!

4. Reducing spread of HIV among IDUs Drug abuse treatment (e.g., detox, rapid detaox, residential programs) Maintenance treatment (e.g., methadone, buprenorphine) Bleach/education programs Needle exchange programs

5. Politics around needle exchange Proponents: –Reduce HIV spread –Doesn’t increase drug use –Helps vulnerable minority populations Opponents: –No evidence they reduce HIV spread –Encourages drug use –Admits defeat in war on drugs

6. History of Needle Exchange 1984 Implemented in Amsterdam 1988 First US program in Tacoma, Washington 1988 Use of federal funds is banned 1990 May Connecticut legislature allows New Haven needle sharing program Nov. Program starts 1991 March initial data reported 1992 Syringe possession decriminalized in Connecticut 1993 Paper wins Edelman Award 1998 Dept of HHS report “NEPs: Part of a Comprehensive HIV Prevention Strategy” Currently ~200 needle exchange programs in US

7. Early needle exchange studies Relied on self-reported behavior about reduction in risky behavior Did not incorporate quantity of needles exchanged

8. New Haven program Used needles exchanged 1-1 (up to 5) for new ones Program clients and needles had IDs Date, location, client ID, and needle IDs recorded at distribution and return of needles Samples of needles tested for HIV

9. Initial data from random testing % HIV infected 91.5% (44/48) needles from “shooting gallery” 67.5% (108/160) street needles at program start 50.3% (291/579) program needles (first 15mo) 40.5% (147/367) program needles (next 12mo) But how does reduced needle prevalence translate into reduced HIV transmission?

10. Circulation Approach Needle exchange… –Keeps number of needles in circulation constant –Increases needle turnover, thus reducing the time a needle is in circulation Shorter circulation time reduces the number of uses (and users) per needle Thus, decrease in number of infected needles

11. Notation and Parameter Estimates = 0.674shared drug injections / client / year  = 0.84probability a needle is bleached before injection  = 0.1removal rate / HIV-infected client / year  = 0.0066 Pr [ HIV transmission probability | infected needle]  = 20.5 needle exchanges / circulating needle / year  = 0.1675 # clients / #circulating needles  (t) fraction of circulating needles infected with HIV  (0)=0.675  (t)HIV prevalence among program clients  (0)=0.636 C( T )new infections over time period T / IDU

12. Model  ´(t) = [1-  (t)] (1-  )  (t)  -  (t)   ´(t) = [1-  (t)]   (t) -  (t)[  +  (1-  (t))] C( T ) =  t=0..T  [1-  (t)] (1-  )  (t)  dt HIV spread: IDU -> needle -> IDU Malaria spread: humans -> mosquitoes -> humans Needle exchange and bleach ~ replacing infected mosquitoes with uninfected ones

13. Effectiveness One year horizon No needle exchange (  =0) –C(1)=0.064 = 6.4 HIV infections / 100 IDUs / year With needle exchange (  =20.5) –C(1)=0.043 = 4.3 HIV infections / 100 IDUs / year Incidence reduced by 33%.

14. Other Outcomes No evidence of increase in drug injection 1/6 of IDUs in program enter treatment Program attracts minority clients –Local drug treatment: 60% white –Program clients: 60% nonwhite.

15. Cost Effectiveness Program cost: ~ $150,000 / year Lifetime hospital costs / infection ~ $50k-100k 20 infections averted Cost saving!

16. Sensitivity Assume  (t) constant Approximately,  I decreases as – increases –  decreases –  decreases Results robust  I =  (0)  +  (0)+1-  (0)]

17. Estimating rate of shared injections Self-reported 2.14 injections / client / day Sharing rate –Self-reported 8.4% –But 31.5% of program needles returned by different client than originally issued to –Assume 31.5% (conservative) Thus, 

18. Estimating probability of bleaching  Bleach outreach program begun in 1987 Self-reported 84% use of bleach Thus,  =0.84

19. Estimating IDU departure rate  Departures due to –Development of AIDS –Drug treatment (1/6 of clients) –Hospitalization, jail, relocation,… Assume departures due only to AIDS (conservative) Mean time to AIDS ~ 10 years Thus, 

20. Estimating initial conditions  (0),  (0) From needle data (108 infected / 160 tested) –Thus,  Other studies on HIV prevalence among IDUs,  –13% seeking treatment –36% at STD clinics –67% of African American men entering treatment Assume at equilibrium before program starts: –  ´(0)=0,  ´(0)=0,  =0 –Thus,  (0)=  (1-  )  ] = 0.636

21. Estimating infectivity per injection  Studies of accidental needle sticks –Chance of transmission ~ 0.003-0.005 Drug injection has higher probability Assume at equilibrium before program start: –  ´(0)=0,  ´(0)=0,  =0 –  Thus,  =  (1-  )(1-  = 0.066

22. Estimating the needle exchange rate  Random variable T r = time until needle returned –Exponential dist with rate  –  = needle exchanges / needle / year Random variable T l = time until needle is lost –Exponential dist. with rate  –  = rate at which needles lost / year Random variable L = 1 if needle is legible, else 0 –Bernoulli with probability l –l=0.86 fraction of needles whose code is legible

23. Estimating the needle exchange rate  x i = 1 if the i th needle has been returned, else 0 t i = observed (censored) circulation time of i th needle If x i =1, then t i =T r <T l and L=1 –Likelihood =  exp[-(  )t i ] · l If x i =0, then T l <T r Likelihood:  /(  ) or (T r <T l and L=0)Likelihood:  /(  ) · (1-l) or (t i <min{T r,T l } and T r <T l and L=1) Likelihood:  /(  ) · exp[-(  )t i ] · l

24. Estimating the needle exchange rate  max  log L = ∑ i I(x i =1) log [  exp[-(  )t i ]l] + I(x i =0)log[  /(  (1-l)  /(  ) + l[  /(  )]exp[-(  )t i ]] Max likelihood estimates –  = 20.5 needle exchanges / needle / year –  = 23.1 lost needles / circulating needle / year

25. Estimating #clients / #needles   = D/N N = number of needles in circulation D = number of IDUs in the program Assume number of needles constant, N  = D  = 20.5 needle exchanges / needle / year = 122.4 needles distributed / IDU / year Thus,  20.5/122.4=0.1675