2 question on second draft fix

Increased Death Rates of Domestic Violence Victims
from Arresting vs. Warning Suspects in the Milwaukee
Domestic Violence Experiment (MilDVE)
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Authors & Funding & Ethics
Authors: rence W. Sherman and Heather M. Harris

Staff assistance provided by Chief Edward Flynn of the Milwaukee Police Department (MPD).

The original trial was funded by US National Institute of Justice (NIJ) Grants 86-IJ-CX-KO43 and 86-IJ-CX-0037.

The current follow-up study was funded by the University of Maryland

The current study was approved by the IRB of the University of Maryland in 2012, title 334834-2

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Research Strategy and Purpose
Sherman and Harris (2014) conducted a secondary analysis of vital statistics on participants of the MilDVE experiment

The Raison D’etre of the current study is the fact that “[n]o prospective-longitudinal or experimental design…has previously examined the association between partner arrest and mortality of domestic violence victims” (Sherman and Harris 2014)

According to Sherman and Harris (2014), “…this analysis was to explore any possible connection between arrest and long-term mortality rates. Milwaukee offered the experiment with the highest success rate (98 %) of the six 1980s trials in delivering treatments as randomly assigned (Sherman 1992). This trial arguably provides an opportunity to discover causal links of arrest to death even without directly measuring any causal pathways triggered by the randomly assigned treatments.”

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Recap of the MilDVE, Sherman et al. (1992)
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Sample Design
The sample was a non-probability purposive sample of calls regarding misdemeanor domestic assault cases

Unit of observation is the individual

Calls received by the Milwaukee Police regarding misdemeanor domestic assault were screened by police officers to establish eligibility for the experiment. Eligible calls were referred to the Crime Control Institute staff, who randomly assigned one of three treatments. Selection of cases continued until 1,200 eligible cases were obtained out of 2,054 cases (ICPSR)

“Cases of serious injury were ineligible, as were attempts to inflict serious injury. While 13 % of the eligible cases had an injury for which victims sought medical attention, only 5 % of victims went to hospital after the police came (Sherman 1992). Both suspects and victims were required to be older than 18 and in a domestic relationship. A team of 36 officers screened, enrolled, and accepted random assignment of 1,200 cases from April 6, 1987, through August 8, 1988 (Sherman 1992)” (quoted from Sherman and Harris, 2014).

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Random Assignment: Each Case had ~ Chance for Any One of 3 Treatments
Research protocol involved 35 patrol officers in four Milwaukee police districts screening domestic violence cases for eligibility, then calling police headquarters to request a randomly-assigned disposition (ICPSR, 1994).

The three possible randomly assigned dispositions were (ICPSR, 1994):
(1) Code 1, which consisted of arrest and at least one night in jail (mean 11.1 hours), unless the
suspect posted bond,
(2) Code 2, which consisted of arrest and immediate release on recognizance from the booking
area at police headquarters, or as soon as possible (mean 4.5 hours), and
(3) Code 3, which consisted of a standard Miranda-style script warning read by police to both
suspect and victim.

Each domestic violence case had an equal likelihood of being assigned to any one of the three codes

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Procedure & Masking
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Assignments were enclosed in sequentially numbered, opaque, sealed envelopes opened one at a time by the independent research team (Sherman & Harris 2014).

Details of the sequence remained unknown to all police and members of the research team until police called researchers to report identifying details for both suspect and victim, and each envelope was opened while police officers remained on the phone and researchers communicated the assigned treatment (Sherman & Harris 2014).

Because randomization assigned suspects to different legal statuses, it was impossible to mask the treatment from the research staff or participants (Sherman & Harris 2014).

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The Posttest Only Multiple Treatment Design

Group Posttest

Full Arrest

Short Arrest

XFA O1

XSA O2

Warning

XW O3

Where:
R – random assignment of the cases to the experimental control groups (1/3 chance of being assigned any one of the 3 treatment groups)
Oi – ith observation, i = 1, 2, and 3, speed is measured by a research assistant
X – independent variable (group = Full Arrest; Short Arrest; or, Warning)

Potential Confounders Treatment Recidivism

Randomization

Randomization (if it works) disconnects Treatment from potential confounders

R

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MilDVE Exhibits High Degree of Experimental Protocol Compliance

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Perfect Compliance Cases: 400 + 384 + 396 = 1,180 (98.3%)
Sherman et al. (1992), p. 148
7.3% (or 88 of 1,200) the randomization cases were repeat couples

The prevalence of repeat
violence over time is displayed for the warning treatment and the combined arrest (short/full ) treatment for n = 1,133 person with employment data available

According to Sherman et al. (1992), “[f]igure 1 shows the “survival” trend in the prevalence of repeat violence over time, with an obviously clear advantage for the arrested suspects in the early days. At about seven to nine months after the presenting incidents, however, the arrest and non-arrest curves cross over, and from there on out the arrest group does worse” (p. 153-154)

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Survival Analysis of Arrestees Versus the Warned

Mandatory Arrest for Unemployed Suspects After the Fourth Month has the Highest Rate of Recidivism Over Time

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MilDVE (1992) Findings
“The Milwaukee domestic violence experiment finds no evidence of an overall long-term deterrent effect of arrest. The initial deterrent effects observed for up to thirty days quickly disappear. By one year later, short arrest alone, and short and full arrest combined, produce an escalation effect. The first reported act of repeat violence following combined arrest treatments occurs an average of twenty percent sooner than it does following the warning treatment.” (Sherman et al. 1992, p. 167)

“The Milwaukee experiment does find strong evidence that arrest has different effects on different kinds of people. Employed, married, high school graduate and white suspects are all less likely to have any incident of repeat violence reported to the domestic violence hotline if they are arrested than if they are not. Unemployed, unmarried, high school dropouts and black suspects, on average, are reported much more frequently to the domestic violence hotline if they are arrested than if they are not. The magnitudes of the increased domestic violence associated with arrest of the latter groups are substantial, ranging up to sixty percent. The Milwaukee findings are replicated clearly in Omaha, as well as by a more limited data set in Colorado Springs.” (Sherman et al. 1992, p. 167-168)

Sherman et al. (1992) suggest a need for other approaches to the control of domestic violence among marginalized groups (e.g., the unemployed), such as greater investment in battered women’s shelters (p. 169).

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Back to Sherman and Harris (2014)

Main Dependent Variable
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Victim Mortality

Main Covariates (Independent Variables)
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Suspect Arrested

Victim has prior arrest

Suspect has prior

Victim’s race

Victim’s sex

Victim employed

Data Corpus (Sherman & Harris, 2014)
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Mortality data is type of vital data that concerns a population, such as the number deaths and cause-of-death.

The data corpus of the mortality study consists of two sources for vital data:
(1) purchased mortality data Wisconsin Office of Vital
(2) Death Master File (DMF) is commercially known as the Social Security
Death Index (SSDI)
(3) MILDVE (1992) experiment data

According to National Information Technical Service Website “[t]he Death Master File (DMF) from the Social Security Administration (SSA) contains over 83 million records of deaths that have been reported to SSA. This file includes the following information on each decedent, if the data are available to the SSA: social security number, name, date of birth, and date of death.”

Mortality Follow-up Study (Sherman & Harris, 2014)
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In 2012–2013, the present authors obtained the list of victim names and dates of birth in MilDVE, then purchased mortality data from the Wisconsin Office of Vital that we cross-checked and expanded through searches of the Social Security Death Index (SSDI), which features national coverage of all persons who have ever received Federal welfare benefits of any kind.

Since only 10% of the Death Master File (DMF) cases found in Sherman and Harris (2014) study lacked records in Wisconsin

To avoid errors, multiple dates of birth, cities in which decedents had resided, and other checks were used to determine matches.

Cause of death is not known for the nine deaths (out of 91) identified only by the DMF.

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(CONsolidated Standards of Reporting Trials)

Intention-to-treat Analysis (ITT)
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ITT data in which all participants are included in the group to which they were assigned, whether or not they actually received or completed the intervention given to the group.

According to Sherman and ‘Harris (2014), “[f]or purposes of assessing long-term differences in mortality, we analyzed the random assignment of intention-to-treat in the first case in which each individual appeared as a victim, even if they were subsequently treated as an offender or as a victim whose partner received a different treatment.

Only 1% of victims (13 of 1,125) had previously been treated in the experiment as suspects.”

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Statistical Analysis: Tables 1 to 3
and
Figures 1 to 5

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Unbalanced Covariates: the groups are not clones of each other with respect to these three covariates

Main Effects of Suspect Arrest on 23-year Victim Mortality

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Dead Alive Total

Arrest 70 (9.28%) 684 (90.72%) 754 (100.00%)

Warn 21 (5.66%) 350 (94.34%) 371 (100.00%)

91 (8.09%) 1,034 (91.91%) 1,125 (100.00%)

Table p-value = 0.02

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Heart Disease and Other Internal Causes of Partner Arrest on Victim Mortality by Cause of Death
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Heart Disease Dead Alive Total

Arrest 19 (2.52%) 735 (97.48%) 754 (100.00%)

Warn 4 (1.08%) 367 (98.92%) 371 (100.00%)

23 (2.04%) 1,102 (97.96%) 1,125 (100.00%)

Other Internal Cause Dead Alive Total

Arrest 23 (3.03%) 731 (96.95%) 754 (100.00%)

Warn 4 (1.08%) 367 (98.92%) 371 (100.00%)

27 (2.39%) 1,098 (97.60%) 1,125 (100.00%)

2.34
2.83

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Significant Moderators

Main Effect     Dead Alive     RR

    Arrest 70 684 754   1.640141

    Warn 21 350 371    

      91 1034 1,125    

               

Black     Dead Alive      

    Arrest 52 477 529   1.981096

    Warn 13 249 262    

      65 726 791    

               

Suspect No Prior     Dead Alive      

    Arrest 30 222 252   2.291667

    Warn 8 146 154    

      38 368 406    

               

Victim Employed     Dead Alive      

    Arrest 18 206 224   4.258929

    Warn 2 104 106    

      20 310 330    

               

Victim Employed and Black     Dead Alive      

    Arrest 14 112 126   #DIV/0!

    Warn 0 69 69    

      14 181 195    

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Effect size refers to the extent to which a relationship or phenomena exists in a population: effect Size 0.2 are small and effect sizes 0.8 are large

Aside: Cox Regression
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Cox Hazard Model
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Cox regression or Cox hazard model is used to adjust treatment comparisons for subject characteristics or to identify prognostic factors for time to event data (the outcome)

Cox regression models the hazard rate

The hazard rate is the risk of failure (i.e., the risk or probability of suffering the event of interest), given that the participant has survived up to a specific time

Cox regression enables us to model the relative hazard (risk) or the hazard ratio which enables the comparison of groups with respect to their hazards

The hazard (risk) ratio in Sherman and Harris (2014) is the likelihood of experiencing death (within the 23 follow-up time period) for DV survivors whose partners were arrested versus warned adjusting for background variables which may affect the outcome (time to death)

The hazard ratio (HR) is roughly analogous to the relative risk (RR)

The Relative Hazard Interpreted as Relative Risk
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The coefficients of the Cox regression model are interpretable as “relative risk” (RR) type ratios

Roughly speaking, the relative risk (RR) is the measure of the risk of a certain event happening in one group compared to the risk of the same event happening in another group

In a simple comparison between an the arrest group and the warned group:

RR = 1 means there is no difference in risk of death between the two groups.
RR < 1 means death is less likely to occur in the arrest group than in the warning group. RR > 1 means death is more likely to occur in the arrest group than in the warned group.

For example, consider table 5, model 1, in Sherman and Harris (2014) and the two level (dichotomous) covariate suspect arrested with value of 0 = warned & 1 = arrested is observed, hazard ratio is

The measure indicates that DV survivors whose partners were arrested (suspect arrested = 1) are 85% more likely to die than the DV survivors whose partners were warned, suspect arrested = 0, adjusting for victim has prior, suspect has prior and suspect employed

Statistical Analysis:
Tables 4 to 6
and
Figure 6

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Unbalanced Covariates: Variables Unevenly Distributed Over the Groups (Random Assignment Could Not Balance Them)
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To increase precision of the estimated main effect (suspect arrest), Sherman and Harris (2014) ran a Cox hazard model adjusting (or controlling) for the predictive influence of three imbalanced covariates at baseline (cf, table 1)

Victim has prior arrest
Suspect employed
Suspect has prior arrest

According to Sherman and Harris (2014) “[b]ased on the moderator analyses (Table 3), the arrested group had slightly more prior victim arrests, which lowered their predicted mortality; more employed suspects, which raised predicted victim mortality; and more suspects with priors, which lowered predicted victim mortality

Cf, figure 6

The results slightly increase the main effect of partner arrest on victim mortality over the unadjusted raw increase of 64%, with the adjusted hazard rate showing victims in the partner-arrest group 85% more likely to die than those in the partner-warned group (Sherman and Harris, 2014)

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Suspect Arrested is a key covariate that is “robust” to whether a particular variable is added or dropped as long as the three unbalanced covariates are always included in the model …

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“The results show higher mortality for the standard length of time in custody than for the artificially reduced time” (Sherman and Harris, 2014)

Table 6 Data In More Detail
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Dead Alive Total

Full Arrest 40 (10.61%) 337 (89.39%) 377 (100.00%)

Warn 21 (5.66%) 350 (94.34%) 371 (100.00%)

91 (8.16%) 687 (91.84%) 748 (100.00%)

Dead Alive Total

Short Arrest 30 (7.96%) 337 (92.04%) 377 (100.00%)

Warn 21 (5.66%) 350 (94.34%) 371 (100.00%)

91 (6.82%) 687 (93.18%) 748 (100.00%)

Dead Alive Total

Full 40 (10.61%) 337 (89.39%) 377 (100.00%)

Short 30 (7.96%) 337 (92.04%) 377 (100.00%)

70 (9.28%) 687 (90.72%) 754 (100.00%)

Counterfactual Analysis
According to the World Health Organization, the contribution of a risk factor to an outcome or a death is quantified using the population attributable fraction (PAF)

In the context of the paper, the PAF is the proportional reduction in population mortality that would occur if exposure to a risk factor were reduced to an alternative ideal exposure scenario (e.g., no arrest)

Hence, the counterfactual that Sherman and Harris (2014) considered was what would have been the morality of DV survivors whose partners were arrested, if the arrest had not occurred.

Sherman and Harris (2014) observe that “[i]n the absence of arrest, 30 % of the observed mortality in this population over 23 years would have been avoided, holding all other factors constant”

“For black victims, 40 % of deaths would have been avoided; for whites it would have been 6 %” (Sherman and Harris, 2014)

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Conclusion
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Statistical Considerations
The results, according to Sherman and Harris (2014), were found to be robust as per multiple sensitivity tests, cf, table 5 and figure 7.

However, according to Sherman and Harris (2014), “imbalances in measured covariates suggest that, despite random assignment,…[their]…results could contain spurious imbalances in unmeasured factors predisposing mortality“

Also, there are external validity concerns about the MilDVE sample, which could be addressed by replication using data Charlotte, Miami, and Omaha

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Three Considerations
The results pose three related challenges of explanation:
1. By what causal pathways can arresting a suspect cause a victim to die?
2. What could explain why those pathways produced such different results for
black and white victims?
3. Why does victim employment double the lethal effect of suspect arrest among
black victims, but not among white victims?
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By what causal pathways can arresting a suspect
cause a victim to die?

Sherman and Harris (2014) suggest the interactions between arrest, race, and employment provide clues as to the direction those causal pathways took and they were inclined toward differential post-traumatic stress manifestations in victims whose partners were arrested

Sherman and Harris (2014) speculate: “If some domestic violence victims experience partner arrest as traumatic, that stress-related response chain could trigger an increase in coronary heart disease and other morbidity leading to premature death.”

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What could explain why those pathways produced such different results for black and white victims?

However, racial difference vitiates the Post Traumatic Stress Symptoms (PTSS) theory since a brief examination of the literature shows, at worst, that black domestic violence victims suffer less PTSS than whites, evincing greater resilience in relation to numerous symptoms and measures of PTSS.

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Why does victim employment double the lethal effect of suspect arrest among black victims, but not among white victims?

Sherman and Harris (2014) using data from table 3 note that for “employed black victims whose suspects were arrested, the 23-year death rate per 1,000 was 112; for unemployed black victims, the comparable figure was 94—less, but admittedly not lower than for employed white victims. For white unemployed victims, the death rate when partners were arrested was 111 per 1,000, almost the same as for employed black victims; for white employed victims, it was only 40, and was actually lower for those whose partners were arrested than for those whose partners were warned (which was 50).”

To investigate the race and employment differential effects, Sherman and Harris (2014) suggest two approaches:

One is gathering new evidence in the field from the Milwaukee sample (and the samples of the companion experiments).

Sherman and Harris (2014) believe that there are contextual variables not accounted for and consider variables at the census tract level in future analyses
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Findings and Claims
Sherman and Harris (2014) claims that arrests for domestic violence compromised women’s social status and respectability, especially for African American Women:

“African-American victims of domestic violence are disproportionately likely to die after partner arrests relative to white victims. The magnitude of the disparity strongly indicates that mandatory arrest laws, however well-intentioned, can create a racially discriminatory impact on victims. While 6 % of the deaths amongst the white partners of arrested suspects could have been avoided, 40 % of the deaths amongst the black partners of arrested suspects could have been avoided, if only their partners had been warned instead.”

This may have proven especially true for employed black women, 84 percent of whom were sole breadwinners living in poor neighborhoods

Chronic worry and other stress resulting from a sudden social demotion could have contributed to deaths from diseases, Sherman and Harris (2014) proposes.

“Whatever effect these findings may have on future research or policy decisions, they provide strong evidence for proposals to test criminal sanctions in the same way that medical treatments are tested. Evidence from controlled trials indicating a substantial cause of premature death for African-American victims should be taken seriously unless and until further strong evidence shows otherwise” (Sherman and Harris, 2014)
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References
Sherman, L. W. et al. (1992). Variable Effects of Arrest on Criminal Careers: The
Milwaukee Domestic Violence Experiment. Journal of Criminal and
Criminology, Vol. 83, Issue 1., pages 137-169

Sherman, L. W. and Harris, H. M. (2014). Increased death rates of
domestic violence victims from arresting vs. warning suspects in the
Milwaukee Domestic Violence Experiment (MilDVE). Journal of
Experimental Criminology.

http://www.who.int/healthinfo/global_burden_disease/metrics_paf/en/
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