1. Comparative Profile of Violent Clients of Prostitute Women Devon D. Brewer Jonathan A. Dudek Stephen Q. Muth John M. Roberts, Jr. John J. Potterat Research supported by NIJ grants 2003-IJ-CX-1036 and 2001-IJ-CX-0001. Points of view are those of the authors alone. London IP 2005

2. “Most traffic accidents occur within 25 miles of home.” “More pedestrians get hit by cars when crossing the street in crosswalks than elsewhere.” Are these useful facts for assessing risk (profiling) or planning intervention?

3. “Most traffic accidents occur within 25 miles of home.” “More pedestrians get hit by cars when crossing the street in crosswalks than elsewhere.” Are these useful facts for assessing risk (profiling) or planning intervention? NO! At least not by themselves … Need comparative, actuarial approach (contrast with frequency of trips within 25 miles of home, frequency of street crossings in crosswalks)

4. Investigative and scientific value of an offender profile depends on: specificity discriminative power (between offenders & non-offenders) feasibility of assessing profiled traits Case-control research designs needed (Cook et al., 2005; Fox & Levin, 1998) cases = offenders controls = non-offenders in underlying reference population Apply this approach to violent clients of prostitute women and clients in general

5. Prostitute women have the highest homicide victimization rate of any set of women studied, account for ~ 3% of female homicide victims in US (Potterat et al, 2004) clients ~ 80-90%+ of perpetrators in U.S., ~ 35% of prostitute homicides committed by serial killers (Brewer et al.) violent crimes against prostitutes among the most difficult to solve (Lowman & Fraser, 1996; Decker et al., 2002) need effective profile of offenders

6. Cases = violent clients charged with/ convicted of assaulting, raping, and/or killing street prostitute women -- cleared cases identified from extensive search of newspaper databases, World Wide Web, academic/true crime literature, incidental discoveries; sought crime reports Controls = clients identified from local/state CJ agency prostitution arrest records patronizing-specific charges, patronizing evident in arrest narrative, or inferred by 3- 5+ male arrests in a day or multiple male arrests close in space/time

7. Matching For each case, randomly matched 5 controls arrested: in same city/county jurisdiction as and within 10 years (48% w/in 1 year) of case’s (median) attack 85 cases, 425 controls

8. Case-control jurisdictions cases and controls included in present analyses cases and controls in process no cases and/or no controls

9. Distribution of cases by (median) year of attack

10. Variables available by state/city

11. Characteristics of perpetrators 61% convicted of violence against prostitutes 5% attacked only offstreet prostitute women 7% were team perpetrators (3 sets) number of prostitute victims (fatal or nonfatal): mean = 3, median = 1, 45% = 2+, range = 1-48 number killed by 55 prostitute killers: mean = 3, median = 1, 42% = 2+, range = 1-47

12. Overlap between perpetrators of fatal and nonfatal attacks Perps of fatal attacks (n = 55) Perps of nonfatal attacks (n = 47) Overlap (n = 17) Conservative estimate of proportion of perps of fatal attacks who also had nonfatal attacks

13. Multivariate conditional logistic regressions Model 1: CT/FL/MN/WA n = 306 Model 2: CT/FL/WA n = 198 a p <.05 b p <.01 c p <.001

14. Violent and/or sex offense criminal history incompletely identifies potential suspects: all perpetrators = 28% (21/74) prostitute killers = 37% (18/49) [this study] 40% (4/10) in 33 US counties, 1988 (USDOJ, 1988) 81% (56/69) in FBI data, 1985-2000 (Dudek, 2001) scope of crim hx: this study = arrests/conv. in state, 33 counties = unk, FBI = arrests in US serial killers = 48% (7/15) [this study]; only 1/7 had “miscellaneous” criminal history 77% (17/22) in FBI data (Dudek, 2001)

15. Violent and/or sex offense history incompletely identifies potential suspects such suspects still a large pool; prioritize local residents with non-extreme BMI, rank them by estimated “relative odds” from multivariate logistic model, integrate with facts of case other source for identifying suspects: bad date (ugly mugs/dodgy punters) lists at least 10% of perpetrators known by prostitutes to be violent (none had violent/sex criminal history) another 4% with no violent/sex history caught due to info provided by prostitutes

16. Age OR (per year) = 1.01 (95% CI 0.99-1.03) (based on conditional logistic regression, which incorporates case-control matching)

17. Race % white: cases = 64%, controls = 70% OR = 0.71 (95% CI 0.39-1.27) serial prostitute killers: cases (n = 22) = 64%, controls (n = 110) = 72% OR = 0.63 (95% CI 0.21-1.86)

18. Height (inches) OR (per inch) = 1.11 (95% CI 1.01-1.21)

19. Weight (pounds) OR (per pound) = 1.01 (95% CI 1.00-1.02)

20. Body Mass Index (BMI) OR (per unit) = 1.02 (95% CI 0.95-1.09) Controls more likely to have extreme BMI (< 20 or  35): 8% vs. 0% (p <.05 in unmatched analysis)

21. Journey-to-crime road distance some jurisdictions provided only city of arrest/residence for controls % < 50 km: cases (n = 49) = 90% controls (n = 245) = 95% OR = 0.44 (95% CI 0.14-1.34) OR (per km) = 1.01 (95% CI 0.99-1.02) precise distances

22. Birth state same as attack/arrest state crude indicator of lifetime geographic mobility % same: cases (n = 29) = 21% controls (n = 145) = 23% OR = 0.89 (95% CI 0.34-2.32)

23. Violent criminal history (including rape) any arrest or conviction prior to case’s first attack/control’s prostitution arrest in state criminal history repository overall: cases (n = 60) = 27% controls (n = 300) = 15% OR = 2.14 (95% CI 1.09-2.43) prostitute killers: cases (n = 38) = 34% controls (n = 190) = 15% OR = 3.29 (95% CI 1.44-7.54)

24. Property offense criminal history overall: cases (n = 60) = 25% controls (n = 300) = 14% OR = 2.22 (95% CI 1.11-4.44)

25. Sex offense criminal history (including rape) overall: cases (n = 60) = 8% controls (n = 300) = 2% OR = 5.00 (95% CI 1.45-17.27) prostitute killers: cases (n = 38) = 13% controls (n = 190) = 2% OR = 8.33 (95% CI 1.99-34.87)

26. Patronizing (buying sex) criminal history overall: cases (n = 71) = 1% controls (n = 355) = 4% OR = 0.36 (95% CI 0.05-2.88) underascertainment for both cases & controls, as such misdemeanors often not reported to state repositories

27. “Miscellaneous” criminal history history of non-violent/non-property/non-sex/ non-patronizing offenses (e.g., driving while intoxicated, drug offenses, driving without a license, etc.) overall: cases (n = 60) = 13% controls (n = 300) = 22% OR = 0.53 (95% CI 0.24-1.21)

28. Conclusions Whites not more likely to be perpetrators or serial killers (cf. Fox & Levin, 1998) Prostitutes’ perceptions that young and/or black clients are more violent (Miller, 1993; Williamson & Folaron, 2001) unsupported (also Lowman & Fraser, 1996) Perpetrators unlikely to be extremely under- or overweight (need strength/agility to attack?) Both violent clients and clients overall are overwhelmingly local journey-to-crime ~ journey-to-routine-activity (patronizing in this instance)

29. Matched case-control analyses provide rigorous confirmation of association between violent/sex criminal history and client violence suggested by prior indirect/informal comparisons (Kinnel, 1993; Salfati et al.) Patronizing history not predictive of client violence however, several perpetrators caught in stings subsequent to first attack, leading to their arrest for prior violence

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