Epidemiology and Social
Relationships of deterrence and law enforcement to drug-related harms among drug injectors in US metropolitan areas
Friedman, Samuel Ra,b; Cooper, Hannah LFc,d; Tempalski, Barbaraa; Keem, Mariaa; Friedman, Risaa; Flom, Peter La; Des Jarlais, Don Ca,e
From the aNational Development and Research Institutes, New York, New York
bDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
cMedical and Health Research Association of New York/National Development and Research Institutes, Inc., New York
dColumbia Mailman School of Public Health, New York
eBeth Israel Medical Center, New York, New York, USA.
Received 22 June, 2005
Revised 15 September, 2005
Accepted 20 September, 2005
Correspondence to Samuel R. Friedman, National Development and Research Institutes, 71 West 23rd Street, 8th Floor, New York, NY 10010, USA. E-mail: firstname.lastname@example.org
Objective: To understand associations of punitive policies to the population prevalence of injection drug users and to HIV seroprevalence among injectors.
Design and methods: A lagged-cross-sectional analysis of metropolitan statistical area data. Estimates of drug injectors per capita and of HIV seroprevalence among injectors in 89 large US metropolitan areas were regressed on three measures of legal repressiveness (hard drug arrests per capita; police employees per capita; and corrections expenditures per capita) controlling for other metropolitan area characteristics.
Results: No legal repressiveness measures were associated with injectors per capita; all three measures of legal repressiveness were positively associated with HIV prevalence among injectors.
Conclusions: These findings suggest that legal repressiveness may have little deterrent effect on drug injection and may have a high cost in terms of HIV and perhaps other diseases among injectors and their partners – and that alternative methods of maintaining social order should be investigated.
Becker , Wilson , and many other analysts argue that punishment and stigmatization deter criminal behavior by making it costly for the perpetrator. Politicians and the mass media have highlighted crimes and drugs as the cause of many social problems, and have thereby stigmatized criminals and, in particular, drug users [3,4]. Perhaps as a consequence of these arguments and stigma-inducing activities, there has been a great increase in the USA in state and local law enforcement personnel (from 770 000 in 1992 to 951 000 in 2002), expenditures for incarceration (from US$ 100 per capita in 1991 to US$ 184 per capita in 2001), and arrests and imprisonments in the USA in recent decades (jail and prison inmates increased from 1.2 million in 1990 to 2.1 million in 2001) . Much of this repressive effort has focused on the ‘War on Drugs’; arrests for drug possession in the United States increased from 540 800 in 1982 to 1 235 700 in 2002 .
Punishment and stigmatization may have unanticipated effects on public health in general and on drug-related harm in particular. As injection is a more efficient means of taking drugs than intranasal use, a number of researchers have suggested that punishment and stigmatization might increase the pressures on non-injecting heroin users and perhaps cocaine users to take up injection drug use (and for injectors to continue injecting) through decreasing the supply of drugs or driving up their costs [7,8]. Others have found that aggressive police tactics and/or stigmatization may lead injection drug users (IDUs) to engage in hurried injection behaviors, to share syringes more often, and/or to inject in high-risk environments [9–16] and, in addition, to impede the creation or functioning of syringe exchange [17–20], drug treatment or other programs to improve users’ health .
This study investigated whether three measures of legal repressiveness in large US metropolitan areas were associated with the population prevalence of injection drug use and with HIV prevalence among IDUs.
‘Sample’ and its statistical implications
The sample was the 96 largest metropolitan statistical areas (MSA) in the USA in 1993. MSAs, as defined by the US Census Bureau, are contiguous counties that contain a central city of 50 000 people or more and that form a socio-economic unity as defined by commuting patterns and social and economic integration within the constituent counties [22,23]. The paper thus studies a ‘population’ rather than a sample, so there is no sampling error (although there is measurement error). The relevance of statistical inference is debatable. Some researchers studying similar populations use ‘P-values’ or ‘confidence intervals’ as heuristic devices to avoid over-interpreting model parameters [24–29] (We refer here to ‘pseudo-confidence intervals’.) Other analysts might view the population as a random sample of ‘possible universes’; in this interpretation ‘pseudo-confidence intervals’ has a probabilistic interpretation.
Missing values on the three measures of legal repressiveness reduced the number of MSA (N) to 89.
As the derivations of both dependent variables (injectors per capita and HIV prevalence among injectors) have been described previously, they are described only briefly here.
Drug injectors per capita in the MSA population in 1998. This was estimated in a three-step process . The number of persons who had injected drugs in the USA in 1998 was first estimated by adjusting and averaging others’ prior estimates [31,32]. This number was then allocated to each MSA using four multipliers (using data on drug injectors among drug abuse treatment populations, HIV counseling and testing clients, and AIDS cases, and estimates of numbers of injectors and HIV prevalence among them in 1993) . These four estimated numbers of injectors in each MSA were then averaged; and the mean divided by the MSA population.
HIV prevalence. This was defined as the proportion of IDUs who were HIV positive among IDUs in 95 MSAs in 1998, and was estimated by taking the mean of two estimates . The first estimate was calculated by modifying Centers for Disease Control (CDC) voluntary HIV counseling and testing data to correct for their inherent underestimation of prevalence. Research-based data on HIV prevalence for 25 MSAs were used to calculate regression equations to perform these adjustments. The second estimate was based on methods developed by Lieb et al. . Briefly, the estimated total number of HIV-positive IDUs (including those who are also men who have had sex with men) living in an MSA was designated as k (and estimated by adjusting data on AIDS cases). The estimated numbers of IDUs (a)  and the estimated HIV prevalence among IDUs (b) were variables related by the function, k = ab; thus, b = a/k.
Almost all of the independent variables precede the dependent variables in time so that the temporal sequence is correct. (This does not, of course, take account of the high degree of autocorrelation over time in many of these variables.)
The main independent variables were the three measures of legal repressiveness. They are: (1) arrests for possession or sale of heroin or cocaine (1994–1997), taken from US Federal Bureau of Investigation (FBI) data; (2) police employees per capita (1994–1997), taken from FBI data [35,36]; and (3) ‘corrections’ expenditures per capita (1997), taken from United States Census Bureau data on government finances .
Arrests for possession or sale of heroin or cocaine (1994–1997). Arrest of drug users may be an indicator of pressures on police; and the fear of arrest may encourage drug users to become or remain drug injectors and also may lead injectors to inject less safely [9,10,14–16].
Police employees per capita. This may reflect a public willingness to spend money and person-power on policing. It may also indicate more direct effects on HIV risk; for example, Davis et al.  found that police presence, as distinct from arrests, was associated with less use of syringe exchanges in Philadelphia.
Correction expenditures per capita. These are an indicator of public willingness to spend resources on local incarceration and probation systems as well as an indicator of the number of people arrested and the average time they spend in jail before and after trial, which would tend to increase fear of arrest and thus to risk of using drugs by injection in unsafe ways (as discussed above).
Consideration was given to using factor analysis as a data reduction strategy, but the three variables had inter-correlations that were too low for the resulting single factor to be meaningful (it captured slightly less than 50% of the variance; correlations were 0.08 between police employees per capita and corrections expenditures per capita; 0.19 between police employees per capita and heroin and cocaine arrests per capita; and 0.36 between heroin and cocaine arrests and corrections expenditures per capita.).
Based on a review of relevant literature, a number of variables were included in the analyses as control variables because they might affect the dependent variables at the metropolitan area of analysis. These included the following items.
Region. US regions differ politically and culturally, and on mean values of both dependent variables and the three legal repressiveness measures. In order to make our categories for regions more homogeneous politically, culturally and economically, US Census categories for Region were adjusted by moving Maryland, Delaware and Washington, DC, to the Northeast Region; Texas to the West; and Oklahoma to the Midwest. Midwest was treated as the reference category because it had the lowest mean value on drug injectors per capita. (It had the second lowest mean value, 4.85%, on HIV prevalence, which was statistically indistinguishable [P(t) = 0.59] from the mean 4.56 HIV prevalence in the West.)
Presence of laws against over-the-counter sales of syringes. These laws were associated with higher levels of injectors per capita, and with greater HIV prevalence and incidence among IDUs, in 1993 .
Unemployment rate in 1990 . A number of studies have found that economic conditions are associated with rates of substance use and/or HIV prevalence [38,40,41].
Proportion of the MSA population who are black. Many studies have found that black injectors are more likely than other injectors to be HIV infected and/or to have AIDS [42–48]; and earlier research from this project shows that higher percentages of black populations than of whites in these metropolitan areas are injection drug users .
For analyzing HIV prevalence, injection drug users per 10 000 population in 1993  was also used as a control variable. It was a predictor of HIV prevalence among injectors in 1993 .
Since the unit of analysis in this study is the metropolitan area, dependent variables are rates for a given metropolitan area. Correlation and linear regression are used to estimate associations among variables. Standardized coefficients (betas) are reported to facilitate comparisons of magnitudes of association. Regression diagnostics including collinearity diagnostics, tests of the assumptions of the model and tests for outliers and influential points were assessed. Sensitivity analyses assessed the effects of metropolitan areas with particularly high or low values of the dependent variables as well as possible measurement error in the dependent variables. Statistical analyses were done in SAS version 9 .
Injectors per capita range from one-fifth of 1% to almost 2% of the population in these metropolitan areas (Table 1). HIV prevalence among injectors also has considerable range, from 2.4 to 27.4%.
In bivariate relationships, both per capita heroin and cocaine arrests and corrections expenditures per capita are associated with the number of injectors per capita (Table 2). After controlling for region and other variables, however, none of the three measures of legal repressiveness has a pseudo-P below 0.2.
All three measures of legal repressiveness are associated with higher HIV prevalence in bivariate analysis and with other potential predictors statistically controlled. Police employees per capita has the strongest association (beta = 0.358) with HIV prevalence, such that a one standard deviation increase in police employees per capita would yield a 0.358 standard deviation increase in the predicted value of HIV prevalence (Table 3).
The data used for the dependent variables were subject to considerable potential error whose magnitude and variance are not well-characterized. To assess the sensitivity of results to such error, we re-ran the regressions based on two different logics. First, we re-ran them for each dependent variable after removing the two metropolitan areas with the highest and the two with the lowest values on that dependent variable. (Hence, N = 85 for each of these analyses.)
Second, each dependent variable had been created as the mean of separate partial estimates – four separate estimates of drug injectors per capita  and two separate estimates for HIV prevalence . We thus re-ran the analyses separately using each of the separate partial estimates in turn as the dependent variable.
Between analyses removing the extreme values and those using the separate partial estimates, the sensitivity analyses produced three separate regression equations for HIV prevalence, each of which estimated the regression coefficients for each of the three indicators of repressiveness, and five equations for injectors per capita. The nine estimated parameters for HIV prevalence were all positive in the three sensitivity regressions for that dependent variable, with seven of them significantly so. None of the 15 parameters in the five sensitivity regressions for injectors per capita was significantly different from zero.
We then compared the parameter estimates for the new equations with those in the original run, using t-tests and treating the original value as fixed. Of the 24 comparisons, one was significant at P < 0.05, and another at P < 0.10, which is approximately what would be expected from a set of random numbers. Thus, the models do not appear to be very sensitive to likely errors in the dependent variables.
Legal repressiveness is not independently associated with higher rates of injectors in metropolitan populations but is associated with higher HIV prevalence among injectors. These findings are subject to a number of limitations. First, causal mechanisms are hard to study at a single level of analysis as both higher-level and lower-level variables may affect observed relationships. Similarly, although almost all independent variables precede the dependent variables in time, all variables are subject to considerable temporal autocorrelation. Thus, causal inference would have been stronger if longitudinal data had been used. Such analyses are planned for the relatively near future, including further study of the possibly two-directional relationships between legal repressiveness and injectors per capita. ‘Police employees per capita’ is not the same as police on duty in drug-using areas or in drug squads, which may or may not have attenuated the effects of this variable. As police squads that exclusively target drug activities have been found to have particularly deleterious effects on HIV risk behaviors , future research should study the effects of particular categories of police personnel on drug injection and on HIV.
These data offer no support for deterrence theories that hold that legal repressiveness reduces levels of injection drug use since the multiple regression coefficients had pseudo-P values above 0.2. In this, they parallel the findings of Reinarman et al.  that legal repressiveness is not associated with patterns of cannabis or other drug use (among cannabis users) in a comparison of two cities with different drug policies.
The main finding of this paper is that higher rates of three measures of legal repressiveness are associated with higher HIV prevalence among injectors. This may be because fear of arrest and/or punishment leads drug injectors to avoid using syringe exchanges [13,19,20,52], or to inject hurriedly [9,10,16] or to inject in shooting galleries or other multiperson injection settings  to escape detection. Numerous studies have found that hurried injection and injection in shooting galleries and similar locations, as well as injecting while incarcerated [54,55], are associated with riskier injection practices [14,15,52,53]. The comparatively large magnitude of the association between police employees per capita and HIV prevalence suggests that the total size of police departments may be an important factor in heightening these risks. In addition, the stigmatization side of legal repressiveness may create, among drug users, lowered self-concepts and other psychological or social conditions conducive of greater risk ; and may lead to public opinion that makes it more difficult to set up, fund, or find locations for syringe exchanges and drug treatment facilities [21,57,58].
It is critical to note that although large numbers of police employees and high expenditures on corrections may generally be associated with high rates of HIV among injectors, they do not always preclude implementation of effective HIV prevention programs. Although New York City is high on the measures of legal repressiveness, New York State legalized and funded large-scale syringe exchange programs carried out by non-profit organizations beginning in 1992. This was followed by an 80% reduction in HIV incidence among IDUs in the city . From our experience, we would suggest that the development of co-operative working relationships between public health authorities and law enforcement authorities to promote HIV prevention programs for IDUs was crucial. Although policy and legal changes facilitated this co-operation, the existence of co-operation at the street level was key to this success.
The implications of this paper are clear. In terms of research, more study is needed to determine the sequelae of legal repressiveness (including determining whether the associations found in this paper are causal) and the mechanisms through which these sequelae arise. In terms of policy and practice, these findings suggest that legal repressiveness may have little deterrent effect on drug injection and may have a high cost in terms of HIV and perhaps other diseases among injectors, their partners, and the broader community, and that alternative methods of maintaining social order should be investigated.
The authors wish to thank the many persons who have helped us assemble data sets. At the US Centers for Disease Control and Prevention, this includes Abu Abdul-Quader, Bob Byers, Patricia Fleming, Stacie Greby, Robert Janssen, Steven Jones, Meade Morgan, and Santas Xen. At the Office of Applied Studies of the Substance Abuse and Mental Health Services Administration, this includes Cathie Alderks and Coleen Sanderson. At the US Census Bureau, this includes Michael Ratcliffe. We would also like to acknowledge and thank hundreds of local experts who provided us with information about conditions or events in their states or metropolitan areas. We would like to acknowledge our project director, Elizabeth Lambert, for her superb support at all times, but especially in the wake of the destruction of our offices at the World Trade Center in 2001 when this support was most needed.
Points of view, opinions, and conclusions in this paper do not necessarily represent the official position of the US Government, Medical and Health Association of New York City, Inc., Beth Israel, Columbia University, the Johns Hopkins University, or National Development and Research Institutes, Inc.
Sponsorship: This project was supported by National Institute of Drug Abuse grant R01 DA13336 (Community Vulnerability and Response to IDU-Related HIV). A Behavioral Science Training in Drug Abuse Research post-doctoral fellowship, sponsored by the Medical and Health Research Association of New York and the National Development and Research Institutes with funding from the National Institute on Drug Abuse (5T32 DA07233), supported H.C.
1. Becker GS. The economics of discrimination
. Chicago, Illinois: University of Chicago Press; 1957.
2. Wilson JQ. Drugs and crime
. Chicago, Illinois: University of Chicago Press; 1990.
3. Bertram E, Blachman M, Sharpe K, Andreas P. Drug war politics
. Los Angeles, California: University of California Press; 1996.
4. Friedman SR. The political economy of drug-user scapegoating – and the philosophy and politics of resistance. Drugs: Educ Prev Policy 1998; 5:15–32.
5. US Census Bureau. Statistical abstract of the United States 1994
. Washington, DC: US Bureau of the Census; 2004.
6. Federal Bureau of Investigations. Crime in the United States. Vol 2003
. Washington, DC: Federal Bureau of Investigation. 2003.
7. Wodak A. Health, HIV infection, human rights, and injecting drug use. Health Hum Rights 1998; 2:25–41.
8. Neaigus A, Atillasoy A, Friedman SR, Andrade X, Miller M, Ildefonso G, et al
. Trends in the non-injected use of heroin and factors associated with the transition to injecting. In: Inciardi JA, Harrison LD, editors. Heroin in the age of crack cocaine. Newbury Park, CA: Sage Publications; 1998. pp. 131–159.
9. Maher L, Dixon D. Policing and public health: law enforcement and harm minimization in a street-level drug market. Br J Criminol 1999; 39:488–512.
10. Cooper H, Moore L, Gruskin S, Krieger N. The impact of a police drug crackdown on drug injectors’ ability to practice harm reduction: a qualitative study. Soc Sci Med 2005; 61:673–684.
11. Strathdee SA, Fraga WD, Case P, Firestone M, Brouwer KC, Perez SG, et al
. Vivo para consumirla y la consumo para vivir” [“I live to inject and inject to live”]: high-risk injection behaviors in Tijuana, Mexico. J Urban Health 2005; 82:iv58–iv73.
12. Strathdee SA, Zafar T, Brahmbhatt H, Baksh A, ul Hassan S. Rise in needle sharing among injection drug users in Pakistan during the Afghanistan war. Drug Alcohol Depend 2003; 71:17–24.
13. Wood E, Spittal PM, Small W, Kerr T, Li K, Hogg RS, et al
. Displacement of Canada's largest public illicit drug market in response to a police crackdown. CMAJ 2004; 170:1551–1556.
14. Bluthenthal RN, Kral AH, Erringer EA, Edlin BR. Drug paraphernalia laws and injection-related infectious disease risk among drug injectors. Drug Issues 1999; 22:1–16.
15. Bluthenthal RN, Lorvick J, Kral AH, Erringer EA, Kahn JG. Collateral damage in the war on drugs: HIV risk behaviors among injection drug users. Int J Drug Policy 1999; 10:25–38.
16. Aitken C, Moore D, Higgs D, Kelsall J, Kerger M. The impact of a police crackdown on a street drug scene: evidence from the street. Int J Drug Policy 2002; 13:189–198.
17. Wood E, Kerr T, Small W, Jones J, Schecter MT, Tyndall MW. The impact of police presence on access to needle exchange programs. J Acquir Immune Defic Syndr Hum Retrovirol 2003; 34:116–118.
18. Rich JD, Strong L, Towe CW, McKenzie M. Obstacles to needle exchange participation in Rhode Island. J Acquir Immune Defic Syndr 1999; 21:396–400.
19. Bluthenthal RN, Kral AH, Lorvick J, Watters JK. Impact of law enforcement on syringe exchange programs: a look at Oakland and San Francisco. Med Anthropol 1997; 18:61–83.
20. Davis CS, Burris S, Kraut-Becher KG, Lynch KG, Metzger D. Effects of an intensive street-level police intervention on syringe exchange program use in Philadelphia, PA. Am J Public Health 2005; 95:233–236.
21. Des Jarlais DC, Paone D, Friedman SR, Peyser N, Newman RG. Regulating controversial programs for unpopular people: Methadone maintenance and syringe exchange programs. Am J Public Health 1995; 85:1577–1584.
22. Office of Management and Budget. Standards for defining metropolitan and micropolitan statistical areas. Federal Register
23. US Bureau of the Census. State and metropolitan area data book, 1997–1998
. Washington, DC: US Bureau of the Census; 1998.
24. Friedman J. Central business district and residential urban renewal: Response to undesired change? Soc Sci Quart 1977; 58:45–49.
25. Friedman JJ. Community action on water pollution. Hum Ecol 1977; 5:329–353.
26. Kaplan GA, Pamuk ER, Lynch JW, Cohen RD, Balfour JL. Inequality in income and mortality in the United States: Analysis of mortality and potential pathways. BMJ 1996; 312:999–1003.
27. Lynch JW, Kaplan GA, Pamuk ER, Cohen RD, Heck KE, Balfour JL, et al
. Income inequality and mortality in metropolitan areas of the United States. Am J Public Health 1998; 88:1074–1080.
28. McCarthy JD, Wolfson M, Baker DP, Mosakowski E. The founding of social movement organizations: Local citizens’ groups opposing drunken driving. In: Carroll GR, editor. Ecological models of organization. Cambridge, Massachusetts: Ballinger; 1988. pp. 71–84.
29. Turk H: Organizations in modern life
. Washington, DC: Jossey-Bass; 1977.
30. Friedman SR, Tempalski B, Cooper H, Perlis T, Keem M, Friedman R, et al
. Estimating numbers of injecting drug users in metropolitan areas for structural analyses of community vulnerability and for assessing relative degrees of service provision for injecting drug users. J Urban Health 2004; 81:377–400.
31. Substance Abuse and Mental Health Services Administration. National household survey on drug abuse: main findings 1992
. Rockville, Maryland: SAMSHA, Office of Applied Studies.
32. Holmberg SD. The estimated prevalence and incidence of HIV in 96 large US metropolitan areas. Am J Public Health 1996; 86:642–654.
33. Friedman SR, Lieb S, Tempalski B, Cooper H, Keem M, Friedman R, et al
. HIV among injection drug users in large US metropolitan areas. J Urban Health 2005; 82:434–445.
34. Lieb S, Friedman SR, Zeni MB, Chitwood DD, Liberti TM, Gates GJ, et al
. An HIV prevalence-based model for estimating risk-populations of injection drug users and men who have sex with men. J Urban Health 2004; 81:401–415.
35. Police Employee (LEOKA) Data. Federal Bureau of Investigations. US uniform crime reporting program. 93
. Washington DC: Federal Bureau of Investigations.
36. County-Level Detailed Arrest and Offense Data. Federal Bureau of Investigation. US uniform crime reporting program. 93
. Washington DC: Federal Bureau of Investigations.
37. US Census Bureau. United States census annual surveys of government finances. 1997
. Washington, DC: US Bureau of the Census; 1998.
38. Friedman SR, Perlis T, Lynch J, Des Jarlais DC. Economic inequality, poverty, and laws against syringe access as predictors of metropolitan area rates of drug injection and HIV infection. Proceedings of the Global Research Network Meeting
, Durban, South Africa, July 2000. 2001. pp. 147–149.
39. Geronimus AT, Bound J. Use of census-bases aggregate variables to proxy for socioeconomic group: Evidence from national samples. Am J Epidemiol 1998; 148:475–486.
40. Dee TS. Alcohol abuse and economic conditions: evidence from repeated cross-sections of individual-level data. Health Econ 2001; 10:257–270.
41. Ruhm CJ. Are recessions good for your health? Quart J Econ 2000; 115:617–650.
42. Selik RM, Castro KG, Pappaioanou M. Racial/ethnic differences in the risk of AIDS in the United States. Am J Public Health 1988; 78:1539–1545.
43. Selik RM, Castro KG, Pappaioanou M, Buehler JW. Birthplace and the risk of AIDS among Hispanics in the United States. Am J Public Health 1989; 79:836–839.
44. Friedman SR, Sotheran JL, Abdul-Quader A, Primm BJ, Des Jarlais DC, Kleinman P, et al
. The AIDS epidemic among blacks and Hispanics. Millbank Quarterly 1987; 65:455–499.
45. Novick DM, Joseph H, Richman BL, Salsitz E, Pascarelli E, Des Jarlais DC, et al
. Medical maintenance: a new model for continuing treatment of socially rehabilitated methadone maintenance patients. In: Harris LS, editor. Problems of drug dependence 1988: Proceedings of the 50th
Annual Scientific Meeting [NIDA Monograph 93]. Washington, DC: US Government Printing Office; 1989. pp. 168–176.
46. Centers for Disease Control and Prevention: HIV/AIDS surveillance report 2001
. Atlanta, Georgia: Centers for Disease Control and Prevention; 2002.
47. US Census Bureau. Statistical Abstract of the United States. 2000
. Washington, DC: US Bureau of the Census; 2001.
48. Centers for Disease Control and Prevention. HIV prevalence trends in selected populations in the united states: results from national serosurveillance, 1993–1997
. Atlanta, Georgia: Centers for Disease Control and Prevention; 1998.
49. Cooper H, Friedman SR, Tempalski B, Friedman R, Keem M. Racial/ethnic disparities in injection drug use in 94 U.S. metropolitan statistical areas. Ann Epidemiol 2005; 15:326–334.
50. SAS Institute. SAS/STAT 9.1 user's guide
. Cary, North Carolina: SAS Institute Inc.; 2004.
51. Reinarman C, Cohen PDA, Kaal HL. The limited relevance of drug policy: Cannabis in Amsterdam and in San Francisco. Am J Public Health 2004; 94:836–842.
52. Rhodes T, Judd A, Mikhailova L, Sarang A, Khutorskoy M, Platt L, et al
. Injecting equipment sharing among injecting drug users in Togliatti City, Russian Federation: Maximizing the protective effects of syringe distribution. J Acquir Immune Defic Syndr Hum Retrovirol 2004; 35:293–300.
53. Rhodes T, Mikhailova L, Sarang A, Lowndes CM, Rylkov A, Khutorskoy M, et al
. Situation factors associated with drug injecting, risk reduction, and syringe exchange practices in Togliatti City, Russian Federation. Soc Sci Med 2003; 57:39–54.
54. Braithwaite RL, Hammett TM, Mayberry RM. Prisons and AIDS. A public health challenge
. San Francisco, California: Jossey-Bass Publishers; 1996.
55. Brewer TF, Vlahov D, Taylor E, Hall D, Munoz A, Polk F. Transmission of HIV-1 within a statewide prison system. AIDS 1998; 12:363–367.
56. Cooper H, Moore L, Gruskin S, Krieger N. Characterizing perceived police violence: Implications for public health. Am J Public Health 2004; 94:1109–1118.
57. Strike CJ, Myers T, Millson M. Finding a place for needle exchange programs. Crit Public Health 2004; 14:261–275.
58. Tempalski, B. The uneven geography of syringe exchange programs in the United States: Need, politics, and place
. Seattle, Washington: Department of Geography, University of Washington.
59. Des Jarlais DC, Perlis T, Arasteh K, Torian LV, Beatrice S, Milliken J, et al
. HIV incidence among injecting drug users in New York City, to 2002: Use of serological test algorithm to assess expansion of HIV prevention services. Am J Public Health 2005; 95:1439–1444.
HIV; injection drug use; law enforcement; repression; urban health; metropolitan areas
© 2006 Lippincott Williams & Wilkins, Inc.
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