Total combined dataset with both Docket cases and OSS search. Of docket 397 cases were reviewed; of these 176 were included.
In total we analyzed 767 cases. This includes the Westlaw and Lexis OSS.
library(readxl)
JD <- read_excel("C:/Users/telsabawi/Dropbox/1-Research/1- GW BOP White Paper/Datasets/combined_Jail_deaths_data_3.20.21.xlsx",
na = "NA")########################Formatting All Variables
JD$OFC_SUE<-factor(JD$OFC_SUE,
labels = c(`0` = "No", `1` = "Yes"))
JD$HEA_SUE<-factor(JD$HEA_SUE,
labels = c(`0` = "No", `1` = "Yes"))
JD$PHYS<-factor(JD$PHYS,
labels = c(`0` = "No", `1` = "Yes"))
JD$CRT<-factor(JD$CRT,labels = c(`1` = "Federal", `2` = "State"))
JD$City_Sue <-factor(JD$City_Sue,
labels = c(`0` = "No", `1` = "Yes"))
JD$Jail_Sue <-factor(JD$Jail_Sue,
labels = c(`0` = "No", `1` = "Yes"))
JD$Officer_Sue <-factor(JD$Officer_Sue, labels = c(`0` = "No", `1` = "Yes"))
JD$OUTCOME<-as.factor(JD$OUTCOME)
JD$STATE<-as.factor(JD$STATE)
JD$COUNTY_CIT<-as.factor(JD$COUNTY_CIT)
JD$PREX<-as.factor(JD$PREX)
JD$MENTAL <- factor(JD$MENTAL,labels = c(`0` = "No", `1` = "Yes"))
JD$SU <- factor(JD$SU,labels = c(`0` = "No", `1` = "Yes"))
JD$PHYS <- factor(JD$PHYS,labels = c(`0` = "No", `1` = "Yes"))
JD$COD_CAT<-as.factor(JD$COD_CAT)
JD$Withdraw <- factor(JD$Withdraw,labels = c(`0` = "No", `1` = "Yes"))
JD$AGE <- as.numeric(JD$AGE)
JD$TTL <- as.numeric(JD$TTL)
#loop via lapply multiple variables to factor
#names <- c(19:33)
#JD[,names] <- lapply(JD[,names] , factor)
JD$Suic_Idea<-factor(JD$Suic_Idea, labels = c(`0` = "No", `1` = "Yes"))library(knitr)
library(kableExtra)kable(summary(JD$CRT), align = "ll", col.names = "No. of Cases", caption = "Federal or State Court")| No. of Cases | |
|---|---|
| Federal | 315 |
| State | 57 |
| NA’s | 2 |
A<-table(JD$CRT)
A##
## Federal State
## 315 57Percentages:
round(100*prop.table(A),digits=0)##
## Federal State
## 85 15kable(summary(JD$OFC_SUE), align = "ll", col.names = "No. of Cases", caption = "No. of Cases That Listed Law enforcement Officials as Defendants")| No. of Cases | |
|---|---|
| No | 158 |
| Yes | 211 |
| NA’s | 5 |
B<-table(JD$OFC_SUE)Percentages:
round(100*prop.table(B),digits=0)##
## No Yes
## 43 57kable(summary(JD$Officer_Sue), align = "ll", col.names = "No. of Cases", caption = "No. of Cases That Listed Law Enforcement Officers Personally")| No. of Cases | |
|---|---|
| No | 43 |
| Yes | 149 |
| NA’s | 182 |
D<-table(JD$Officer_Sue)Percentages:
round(100*prop.table(D),digits=0)##
## No Yes
## 22 78kable(summary(JD$Jail_Sue), align = "ll", col.names = "No. of Cases", caption = "No. of Cases That Listed Law Enforcement Officers Personally")| No. of Cases | |
|---|---|
| No | 93 |
| Yes | 99 |
| NA’s | 182 |
E<-table(JD$Jail_Sue)Percentages:
round(100*prop.table(E),digits=0)##
## No Yes
## 48 52kable(summary(JD$City_Sue), align = "ll", col.names = "No. of Cases", caption = "No. of Cases That Listed Law Enforcement Officers Personally")| No. of Cases | |
|---|---|
| No | 49 |
| Yes | 143 |
| NA’s | 182 |
F<-table(JD$City_Sue)Percentages:
round(100*prop.table(F),digits=0)##
## No Yes
## 26 74kable(summary(JD$HEA_SUE), align = "ll", col.names = "No. of Cases", caption = "No. of Cases That Listed Healthcare Professionals (Or Facilities) as Defendants")| No. of Cases | |
|---|---|
| No | 170 |
| Yes | 198 |
| NA’s | 6 |
C<-table(JD$HEA_SUE)Percentages:
round(100*prop.table(C),digits=0)##
## No Yes
## 46 54kable(summary(JD$OUTCOME), col.names = "No. of Cases", align = "ll", caption = "Case Outcomes.")| No. of Cases | |
|---|---|
| Defense Judgment | 40 |
| Defense Verdict | 33 |
| Plaintiff Verdict | 22 |
| Settlement | 211 |
| NA’s | 68 |
V<-table(JD$OUTCOME)Percentages:
round(100*prop.table(V),digits=0)##
## Defense Judgment Defense Verdict Plaintiff Verdict Settlement
## 13 11 7 69tapply(JD$TTL, JD$OUTCOME, summary)## $`Defense Judgment`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 0 0 0 0 0 2
##
## $`Defense Verdict`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## $`Plaintiff Verdict`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 119000 859914 1600000 3570482 5195000 11857344 3
##
## $Settlement
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4000 175000 500000 1348717 1100000 12850000 82library(kableExtra)
kable(summary(JD$STATE), col.names = "State of Incarceration", align = "ll")| State of Incarceration | |
|---|---|
| Alabama | 6 |
| Alaska | 5 |
| Arizona | 4 |
| Arkansas | 1 |
| California | 34 |
| Colorado | 10 |
| Florida | 11 |
| Georgia | 8 |
| Hawaii | 1 |
| Idaho | 3 |
| Illinois | 41 |
| Indiana | 20 |
| Iowa | 2 |
| Kansas | 1 |
| Kentucky | 7 |
| Louisiana | 3 |
| Maine | 2 |
| Maryland | 1 |
| Massachusetts | 2 |
| Michigan | 14 |
| Mississippi | 9 |
| Missouri | 10 |
| Nebraska | 2 |
| Nevada | 1 |
| New Jersey | 9 |
| New Mexico | 6 |
| New York | 28 |
| North Carolina | 4 |
| North Dakota | 1 |
| NV | 1 |
| Ohio | 19 |
| Oklahoma | 14 |
| Oregon | 10 |
| Pennsylvania | 23 |
| South Carolina | 8 |
| Tennessee | 1 |
| Texas | 23 |
| Virginia | 11 |
| Washington | 7 |
| Wisconsin | 9 |
| NA’s | 2 |
kable(summary(JD$COUNTY_CIT), col.names = "City or County of Incarceration", align = "ll")| City or County of Incarceration | |
|---|---|
| Cook County | 15 |
| Los Angeles County | 9 |
| Lake County | 7 |
| Allegheny County | 6 |
| Monroe County | 6 |
| Queens County | 6 |
| Franklin County | 5 |
| Monterey County | 5 |
| Nassau County | 5 |
| Anchorage County | 4 |
| Butler County | 4 |
| Cumberland County | 4 |
| Orange County | 4 |
| Alameda County | 3 |
| Bronx County | 3 |
| Creek County | 3 |
| Delaware County | 3 |
| Jefferson County | 3 |
| Lancaster County | 3 |
| Maricopa County | 3 |
| Oklahoma County | 3 |
| Schenectady County | 3 |
| York County | 3 |
| Adams County | 2 |
| Albany County | 2 |
| Bowie County | 2 |
| Bucks County | 2 |
| City of Chicago | 2 |
| City of Cottage Grove | 2 |
| City of Los Angeles | 2 |
| City of Portsmouth County | 2 |
| Clark County | 2 |
| Cobb County | 2 |
| Columbia County | 2 |
| Cuyahoga County | 2 |
| DeKalb County | 2 |
| Hamilton County | 2 |
| Harris County | 2 |
| Lane County | 2 |
| Lehigh County | 2 |
| Lincoln County | 2 |
| Macomb County | 2 |
| Madison County | 2 |
| Marion County | 2 |
| Milwaukee County | 2 |
| Montgomery County | 2 |
| Morgan County | 2 |
| Not Applicable | 2 |
| Ocean County | 2 |
| Richmond County | 2 |
| San Diego County | 2 |
| Santa Cruz County | 2 |
| Santa Fe County | 2 |
| Spartanburg County | 2 |
| St. Clair County | 2 |
| Tulsa County | 2 |
| Union County | 2 |
| Warren County | 2 |
| Washington County | 2 |
| Wayne County | 2 |
| Whiteside County | 2 |
| Albemarle County | 1 |
| Arapahoe County | 1 |
| Benton County | 1 |
| Bernalillo County | 1 |
| Bexar County | 1 |
| Boundary County | 1 |
| Bradley County | 1 |
| Brazoria County | 1 |
| Broome County | 1 |
| Brown County | 1 |
| Caddo County | 1 |
| Calhoun County | 1 |
| Canyon County | 1 |
| Carbon County | 1 |
| Carter County | 1 |
| Cass County | 1 |
| Chatham County | 1 |
| City and County of Honolulu | 1 |
| City of Arlington | 1 |
| City of Baton Rouge | 1 |
| City of Bell | 1 |
| City of Cedar Rapids | 1 |
| City of El Centro | 1 |
| City of Eupora | 1 |
| City of Eureka; Humboldt County | 1 |
| City of Harvey | 1 |
| City of Indianola | 1 |
| City of La Habra | 1 |
| City of Newark | 1 |
| City of Tallahassee | 1 |
| City of Toledo | 1 |
| City of Tulsa | 1 |
| Cochise County | 1 |
| Conecuh County | 1 |
| Cook | 1 |
| Crawford County | 1 |
| Dawes County | 1 |
| (Other) | 121 |
| NA’s | 30 |
tapply(JD$TTL ~ JD$STATE, JD$OUTCOME, sum)TOTAL AMOUNT OF SETTLEMENTS AND PLAINTIFFS VERDICTS
z <-sum(JD$TTL, na.rm = TRUE)
prettyNum(z, big.mark=",")## [1] "256,823,674"Age
JD$AGE <- as.numeric(JD$AGE)
mean(JD$AGE, na.rm=TRUE)## [1] 37.21839summary(JD$PREX)COUNT
summary(JD$MENTAL)## No Yes NA's
## 195 135 44Y<-table(JD$MENTAL)PERCENTAGES
round(100*prop.table(Y),digits=0)##
## No Yes
## 59 41COUNT
summary(JD$SU)## No Yes NA's
## 223 103 48G <-table(JD$SU)PERCENTAGE
round(100*prop.table(G),digits=0)##
## No Yes
## 68 32Table
table(JD$COD_CAT, JD$SU)##
## No Yes
## INM Force 9 0
## Lack Treat Phys 91 8
## OFC Force 21 10
## Overdose 0 17
## Suicide 90 21
## Withdraw 8 46count
summary(JD$PHYS)## No Yes NA's
## 213 115 46H <- table(JD$PHYS)PERCENTAGE
round(100*prop.table(H),digits=0)##
## No Yes
## 65 35COUNT
summary(JD$COD_CAT)## INM Force Lack Treat Phys OFC Force Overdose Suicide
## 9 102 33 17 129
## Withdraw NA's
## 54 30K <- table (JD$COD_CAT)PERCENTAGE
round(100*prop.table(K),digits=0)##
## INM Force Lack Treat Phys OFC Force Overdose Suicide
## 3 30 10 5 38
## Withdraw
## 16AVERAGE SETTLEMENT/VERDICT AMOUNT BY CAUSE OF DEATH
kable(tapply(JD$TTL, JD$COD_CAT, mean, na.rm=TRUE), digits = 2, format.args = list(big.mark = ",", scientific = FALSE))| x | |
|---|---|
| INM Force | 403,839.3 |
| Lack Treat Phys | 1,168,581.5 |
| OFC Force | 1,817,971.7 |
| Overdose | 843,928.6 |
| Suicide | 995,756.1 |
| Withdraw | 1,671,339.1 |
summary(JD$DAYS)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 1.25 5.00 45.60 20.75 1569.00 112DAYS TO DEATH BY COD
tapply(JD$DAYS, JD$COD_CAT, mean, na.rm=TRUE)## INM Force Lack Treat Phys OFC Force Overdose Suicide
## 138.000000 80.759494 82.230769 5.615385 23.781609
## Withdraw
## 9.039216tapply(JD$DAYS, JD$COD_CAT, median, na.rm=TRUE)## INM Force Lack Treat Phys OFC Force Overdose Suicide
## 158 15 3 1 4
## Withdraw
## 3tapply(JD$DAYS, JD$COD_CAT, range, na.rm=TRUE)## $`INM Force`
## [1] 16 220
##
## $`Lack Treat Phys`
## [1] 1 583
##
## $`OFC Force`
## [1] 1 1569
##
## $Overdose
## [1] 1 60
##
## $Suicide
## [1] 1 332
##
## $Withdraw
## [1] 1 209Deaths within 24 hours
Days1 <-JD[ which(JD$DAYS==1), ] #subset data where day to death is 1 or less
#COD
kk <- table(Days1$COD_CAT)PERCENTAGES
round(100*prop.table(kk),digits=0)##
## INM Force Lack Treat Phys OFC Force Overdose Suicide
## 0 14 14 17 33
## Withdraw
## 23table(JD$COD_CAT, JD$MENTAL)##
## No Yes
## INM Force 7 2
## Lack Treat Phys 86 14
## OFC Force 20 11
## Overdose 11 5
## Suicide 24 92
## Withdraw 46 6SU and MENTAL HEALTH
table(JD$MENTAL, JD$SU)##
## No Yes
## No 126 69
## Yes 97 30Doesn’t appear to be a relationship worth exploring here.
EVIDENCE OF SUBSTANCE USE AND COD
table(JD$COD_CAT, JD$SU)##
## No Yes
## INM Force 9 0
## Lack Treat Phys 91 8
## OFC Force 21 10
## Overdose 0 17
## Suicide 90 21
## Withdraw 8 46EVIDENCE OF MENT HEALTH ISSUES AND COD
table(JD$COD_CAT, JD$MENTAL)##
## No Yes
## INM Force 7 2
## Lack Treat Phys 86 14
## OFC Force 20 11
## Overdose 11 5
## Suicide 24 92
## Withdraw 46 6WITHDRAWAL SYMPTOMS AND COD
table(JD$COD_CAT, JD$Withdraw)##
## No Yes
## INM Force 9 0
## Lack Treat Phys 95 4
## OFC Force 24 7
## Overdose 16 1
## Suicide 88 24
## Withdraw 1 53withdrawal and suicide may have a there there.
kable(tapply(JD$TTL, JD$STATE, mean, na.rm=TRUE), digits = 2, format.args = list(big.mark = ",", scientific = FALSE))| x | |
|---|---|
| Alabama | 0.00 |
| Alaska | 400,000.00 |
| Arizona | 602,009.00 |
| Arkansas | 350,000.00 |
| California | 1,579,216.17 |
| Colorado | 1,978,571.43 |
| Florida | 68,333.33 |
| Georgia | 248,750.00 |
| Hawaii | 0.00 |
| Idaho | 31,666.67 |
| Illinois | 249,785.71 |
| Indiana | 867,243.00 |
| Iowa | 0.00 |
| Kansas | 75,000.00 |
| Kentucky | 3,768,750.00 |
| Louisiana | 81,500.00 |
| Maine | 1,000,000.00 |
| Maryland | NaN |
| Massachusetts | NaN |
| Michigan | 232,500.00 |
| Mississippi | 2,500,000.00 |
| Missouri | 481,474.12 |
| Nebraska | 0.00 |
| Nevada | NaN |
| New Jersey | 1,066,666.67 |
| New Mexico | 241,887.50 |
| New York | 1,959,071.43 |
| North Carolina | 1,325,000.00 |
| North Dakota | NaN |
| NV | 15,000.00 |
| Ohio | 625,714.29 |
| Oklahoma | 3,242,857.14 |
| Oregon | 2,288,888.89 |
| Pennsylvania | 4,202,669.71 |
| South Carolina | 272,812.50 |
| Tennessee | NaN |
| Texas | 488,887.50 |
| Virginia | 2,337,500.00 |
| Washington | 2,027,000.00 |
| Wisconsin | 1,153,928.57 |
kable(tapply(JD$TTL, JD$STATE, max, na.rm=TRUE), digits = 2, format.args = list(big.mark = ",", scientific = FALSE))| x | |
|---|---|
| Alabama | 0 |
| Alaska | 400,000 |
| Arizona | 2,000,000 |
| Arkansas | 350,000 |
| California | 9,300,000 |
| Colorado | 4,650,000 |
| Florida | 206,000 |
| Georgia | 995,000 |
| Hawaii | 0 |
| Idaho | 95,000 |
| Illinois | 1,700,000 |
| Indiana | 2,750,000 |
| Iowa | 0 |
| Kansas | 75,000 |
| Kentucky | 15,000,000 |
| Louisiana | 163,000 |
| Maine | 2,000,000 |
| Maryland | -Inf |
| Massachusetts | -Inf |
| Michigan | 950,000 |
| Mississippi | 10,000,000 |
| Missouri | 1,311,793 |
| Nebraska | 0 |
| Nevada | -Inf |
| New Jersey | 1,550,000 |
| New Mexico | 800,000 |
| New York | 7,890,000 |
| North Carolina | 2,000,000 |
| North Dakota | -Inf |
| NV | 15,000 |
| Ohio | 4,000,000 |
| Oklahoma | 12,500,000 |
| Oregon | 10,000,000 |
| Pennsylvania | 11,857,344 |
| South Carolina | 750,000 |
| Tennessee | -Inf |
| Texas | 2,421,650 |
| Virginia | 12,850,000 |
| Washington | 8,000,000 |
| Wisconsin | 6,750,000 |
table(JD$STATE, JD$COD_CAT)##
## INM Force Lack Treat Phys OFC Force Overdose Suicide Withdraw
## Alabama 0 1 1 0 3 0
## Alaska 1 0 0 0 2 2
## Arizona 3 0 0 0 1 0
## Arkansas 0 0 0 0 0 1
## California 0 7 5 5 11 5
## Colorado 0 2 0 1 1 4
## Florida 1 3 0 0 5 1
## Georgia 0 6 1 0 0 0
## Hawaii 0 0 1 0 0 0
## Idaho 0 3 0 0 0 0
## Illinois 0 15 1 1 17 5
## Indiana 1 5 2 0 8 4
## Iowa 0 0 1 0 0 0
## Kansas 0 0 0 0 0 1
## Kentucky 0 1 0 0 1 4
## Louisiana 0 0 1 0 1 1
## Maine 0 0 1 0 1 0
## Maryland 0 1 0 0 0 0
## Massachusetts 0 0 0 0 2 0
## Michigan 0 2 2 0 7 1
## Mississippi 0 2 1 1 3 0
## Missouri 0 3 1 0 5 0
## Nebraska 0 2 0 0 0 0
## Nevada 0 1 0 0 0 0
## New Jersey 0 0 0 0 8 0
## New Mexico 0 1 0 1 0 4
## New York 0 11 0 0 12 2
## North Carolina 0 0 0 1 2 0
## North Dakota 0 1 0 0 0 0
## NV 1 0 0 0 0 0
## Ohio 1 4 4 2 4 4
## Oklahoma 1 1 5 1 5 1
## Oregon 0 3 1 0 3 3
## Pennsylvania 0 8 0 1 8 5
## South Carolina 0 1 2 0 4 0
## Tennessee 0 0 0 0 1 0
## Texas 0 6 1 1 8 3
## Virginia 0 7 1 0 1 1
## Washington 0 2 1 0 2 2
## Wisconsin 0 3 0 2 3 0library('MASS')
n_model<- glm.nb(DAYS ~ COD_CAT, data=JD)
summary(n_model)##
## Call:
## glm.nb(formula = DAYS ~ COD_CAT, data = JD, init.theta = 0.4366961016,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6809 -1.1351 -0.8253 -0.2458 4.0181
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.9273 0.7578 6.502 7.93e-11 ***
## COD_CATLack Treat Phys -0.5358 0.7768 -0.690 0.490373
## COD_CATOFC Force -0.5177 0.8141 -0.636 0.524835
## COD_CATOverdose -3.2017 0.8742 -3.663 0.000250 ***
## COD_CATSuicide -1.7583 0.7753 -2.268 0.023333 *
## COD_CATWithdraw -2.7257 0.7883 -3.458 0.000545 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.4367) family taken to be 1)
##
## Null deviance: 399.29 on 259 degrees of freedom
## Residual deviance: 315.52 on 254 degrees of freedom
## (114 observations deleted due to missingness)
## AIC: 2181
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.4367
## Std. Err.: 0.0326
##
## 2 x log-likelihood: -2167.0330