COH Project
Alex Blohm
1/30/2019
Cleaning
Reading in the Data as one big data frame
coh = read.csv("COH Testing FY18 - FY17 - FY16 (De-Identified).csv")Cleaning Times
#Time in
split <- strsplit(as.character(coh$Time.of.Session),":")
time <- as.data.frame(do.call(rbind,split))
hour.in <- as.numeric(as.character(time[,1])) + as.numeric(as.character(time[,2]))/60 + 12*(as.character(time[,3])=="00 PM")
coh$hour.in = hour.in
#Time out
split <- strsplit(as.character(coh$Time.of.Session.1),":")
time <- as.data.frame(do.call(rbind,split))
hour.out <- as.numeric(as.character(time[,1])) + as.numeric(as.character(time[,2]))/60 + 12*(as.character(time[,3])=="00 PM")## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion#Hour Difference
time.dif = hour.out - hour.in## Warning in hour.out - hour.in: longer object length is not a multiple of
## shorter object lengthtime.dif2 = ifelse (time.dif > 9, time.dif - 12,
ifelse (time.dif < -9, time.dif + 12,
time.dif))
#Length of session in Minutes
coh$Session.Length.min = time.dif2*60Test Result
coh$pos = ifelse(coh$Test.Result == "True Positive", 1, 0)Testing “Location” at the center or offsite
coh$Location = ifelse((coh$Testing.Location == "Center on Halsted") | (coh$Testing.Location == "Center on Halsted Conference Room") | (coh$Testing.Location == "Center on Halsted MH Space") | (coh$Testing.Location == "Center on Halsted NAMES Office") | (coh$Testing.Location == "Center on Halsted Testing Office") | (coh$Testing.Location == "Center on Halsted Youth Space"), "Center on Halsted", ifelse((coh$Testing.Location == "Outside Center on Halsted"), "Offsite", NA))Client new categories general screeners and Grant-Funded,
unknown NA
coh$Client = ifelse(coh$Client.Type == "General - Screen", "General Screen", ifelse(coh$Client.Type =="Unknown", NA, "Grant Funded"))HLM Hispanic/Latino/Mexican Variable;
split <- strsplit(as.character(coh$Ethnicity.and.Race),"/")
Eth <- as.data.frame(do.call(rbind,split))## Warning in (function (..., deparse.level = 1) : number of columns of result
## is not a multiple of vector length (arg 1)coh$HLM = ifelse(Eth[,1] == "Hispanic", "Hispanic", "Not Hispanic")Black
(Not HLM and not Multi-Racial)
coh$Black = ifelse((Eth[,3] == "a - Black" & Eth[,1] == "Non-Hispanic"), "Black", "Not Black")H.Risk.Eth: High Risk Ethnicities
(Black and Latino)
coh$H.Risk.Eth = ifelse((coh$HLM == "Hispanic" | coh$Black == "Black"), "High Risk", "Low Risk")SISO Self-Identified Secual Orientation
groups LGBTQ or Heterosexual
coh$SISO = ifelse ( (coh$Self.Identified.Sexual.Orientation == "Other" | coh$Self.Identified.Sexual.Orientation == "Questioning" | coh$Self.Identified.Sexual.Orientation == "Refused" | coh$Self.Identified.Sexual.Orientation == "Unknown / Unreported"), "Unknown, Unreported, Questioning, Refused, or Other", ifelse( coh$Self.Identified.Sexual.Orientation == "Heterosexual", "Heterosexual", "LGBT"))Age Group 1 (Age1): Youth 24 and younger
split <- strsplit(as.character(coh$Age)," ")
Age <- as.data.frame(do.call(rbind,split))
coh$a = as.integer(as.character(Age[,1]))
coh$age = ifelse(coh$a < 0 | coh$a>100, NA, coh$a)
coh$Youth24 = ifelse((coh$a >100 | coh$a < 0), NA, ifelse( coh$a <= 24, "Youth", "Adult"))Age Group 2 (Age2): Youth 29 and younger
coh$Youth29 = ifelse((coh$a >100 | coh$a < 0), NA, ifelse( coh$a <= 29, "Youth", "Adult"))High Risk Sexual Behavior (MSM):
Men who have Sex with men or Male-to-Female in group “MSM or MTF”“, or”neither"
coh$MSM = ifelse(( coh$Sexual.Partners == "MSM" | coh$Sexual.Partners == "MSMFTM" | coh$Sexual.Partners == "MSMMTF" | coh$Sexual.Partners == "MSMW" | coh$Sexual.Partners == "MSMWMTF" | coh$Sexual.Partners == "MSWMTF" | coh$Sexual.Partners == "MTF"), "MSM or MTF", "Neither")Season
coh$Season = ifelse( (coh$Month.of.Session == "January" | coh$Month.of.Session == "February" | coh$Month.of.Session == "December"), "Winter", ifelse ((coh$Month.of.Session == "March" | coh$Month.of.Session == "April" | coh$Month.of.Session == "May"), "Spring", ifelse( ( coh$Month.of.Session == "June" | coh$Month.of.Session == "July" | coh$Month.of.Session =="August"), "Summer", ifelse ( (coh$Month.of.Session == "September" | coh$Month.of.Session == "October" | coh$Month.of.Session == "November"), "Fall", "Miscoded"))))Separate Data into Fiscal Years (For Grants)
Failed code idea: coh\(FY = for (i in 2015:2017){ ifelse(((coh\)Year.of.Session == i+1) & (coh\(Month.of.Session == "January" | coh\)Month.of.Session == “February” | coh\(Month.of.Session == "March" | coh\)Month.of.Session == “April” | coh\(Month.of.Session == "May" | coh\)Month.of.Session == “June”)), i+1, ifelse((coh\(Year.of.Session == i) & (coh\)Month.of.Session == “July” | coh\(Month.of.Session == "August" | coh\)Month.of.Session == “September” | coh\(Month.of.Session == "October" | coh\)Month.of.Session == “November” | coh$Month.of.Session == “December”), i+1,NA))
}
coh$Fiscal.Year = ifelse(((coh$Year.of.Session == 2018) | (coh$Year.of.Session == 2017 & (coh$Month.of.Session == "July" | coh$Month.of.Session == "August" | coh$Month.of.Session == "September" | coh$Month.of.Session == "October" | coh$Month.of.Session == "November" | coh$Month.of.Session == "December") ) ), 2018, ifelse (((coh$Year.of.Session == 2017 & (coh$Month.of.Session == "January" | coh$Month.of.Session == "February" | coh$Month.of.Session == "March" | coh$Month.of.Session == "April" | coh$Month.of.Session == "May" | coh$Month.of.Session == "June")) | (coh$Year.of.Session == 2016 & (coh$Month.of.Session == "July" | coh$Month.of.Session == "August" | coh$Month.of.Session == "September" | coh$Month.of.Session == "October" | coh$Month.of.Session == "November" | coh$Month.of.Session == "December") ) ), 2017, 2016))
coh$Fiscal.Year = as.factor(coh$Fiscal.Year)
#FY 18 July 2017-June 2018
coh18 = subset(coh, coh$Year.of.Session == 2018)
coh17 = subset(coh, coh$Year.of.Session == 2017)
coh17.p2 = subset(coh17, coh17$Month.of.Session == "July" | coh17$Month.of.Session == "August" | coh17$Month.of.Session == "September" | coh17$Month.of.Session == "October" | coh17$Month.of.Session == "November" | coh17$Month.of.Session == "December")
cohFY18=rbind(coh18,coh17.p2)
#FY 17 July 2016-June 2017
coh17.p1 = subset(coh17, coh17$Month.of.Session == "January" | coh17$Month.of.Session == "February" | coh17$Month.of.Session == "March" | coh17$Month.of.Session == "April" | coh17$Month.of.Session == "May" | coh17$Month.of.Session == "June")
coh16 = subset(coh, coh$Year.of.Session == 2016)
coh16.p2 = subset(coh16, coh16$Month.of.Session == "July" | coh16$Month.of.Session == "August" | coh16$Month.of.Session == "September" | coh16$Month.of.Session == "October" | coh16$Month.of.Session == "November" | coh16$Month.of.Session == "December")
cohFY17=rbind(coh17.p1,coh16.p2)
#FY 17 July 2016-June 2017
coh16.p1 = subset(coh16, coh16$Month.of.Session == "January" | coh16$Month.of.Session == "February" | coh16$Month.of.Session == "March" | coh16$Month.of.Session == "April" | coh16$Month.of.Session == "May" | coh16$Month.of.Session == "June")
coh15 = subset(coh, coh$Year.of.Session == 2015)
cohFY16=rbind(coh16.p1,coh15)Descriptive Statistics
Positive
t = table(coh$pos, coh$Fiscal.Year); addmargins(t)##
## 2016 2017 2018 Sum
## 0 3862 4590 4104 12556
## 1 34 41 24 99
## Sum 3896 4631 4128 12655prop.table(t)##
## 2016 2017 2018
## 0 0.305175820 0.362702489 0.324298696
## 1 0.002686685 0.003239826 0.001896484mosaicplot(t)
#Who is getting tested at the center vs. events?
Overall
Table = table(coh$Location); Table##
## Center on Halsted Offsite
## 11208 1437prop.table(Table)##
## Center on Halsted Offsite
## 0.8863582 0.1136418library(ggplot2)
ggplot(coh, aes(x = Location)) + geom_bar() + theme(axis.text.x = element_text(angle = 45, hjust = 1))
2018
Table = table(cohFY18$Location); Table##
## Center on Halsted Offsite
## 3703 424prop.table(Table)##
## Center on Halsted Offsite
## 0.8972619 0.1027381ggplot(cohFY18, aes(x = Location)) + geom_bar() + theme(axis.text.x = element_text(angle = 45, hjust = 1))
2017
Table = table(cohFY17$Location); Table##
## Center on Halsted Offsite
## 4083 540prop.table(Table)##
## Center on Halsted Offsite
## 0.8831927 0.1168073ggplot(cohFY17, aes(x = Location)) + geom_bar() + theme(axis.text.x = element_text(angle = 45, hjust = 1))
2016
Table = table(cohFY16$Location); Table##
## Center on Halsted Offsite
## 3422 473prop.table(Table)##
## Center on Halsted Offsite
## 0.8785623 0.1214377ggplot(cohFY16, aes(x = Location)) + geom_bar() + theme(axis.text.x = element_text(angle = 45, hjust = 1))
Association between Testing Status
chi = chisq.test(coh$Location, coh$Testing.Status); chi## Warning in chisq.test(coh$Location, coh$Testing.Status): Chi-squared
## approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: coh$Location and coh$Testing.Status
## X-squared = 6.1881, df = 2, p-value = 0.04532chi$stdres## coh$Testing.Status
## coh$Location Anonymous Confidential
## Center on Halsted -0.2436168 2.4764989 -2.4104776
## Offsite 0.2436168 -2.4764989 2.4104776\(\alpha = 0.01\) level shows NO association between Anonomous vs. confidential status and location
Association between Client Type
t = table(coh$Location, coh$Client);addmargins(t)##
## General Screen Grant Funded Sum
## Center on Halsted 1858 9349 11207
## Offsite 359 1078 1437
## Sum 2217 10427 12644chi = chisq.test(t); chi##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 61.628, df = 1, p-value = 4.149e-15chi$stdres##
## General Screen Grant Funded
## Center on Halsted -7.887189 7.887189
## Offsite 7.887189 -7.887189library(fmsb)
oddsratio(t)## Disease Nondisease Total
## Exposed 1858 9349 11207
## Nonexposed 359 1078 1437
## Total 2217 10427 12644## Warning in N1 * N0 * M1: NAs produced by integer overflow##
## Odds ratio estimate and its significance probability
##
## data: t
## p-value = NA
## 95 percent confidence interval:
## 0.5243370 0.6792024
## sample estimates:
## [1] 0.5967671\(\alpha = 0.01\) level shows an association between location and Client Type. Much more general screeners at off site testing locations (almost 8 standard deviations above expected).
Association between Hispanic
t = table(coh$Location, coh$HLM);addmargins(t)##
## Hispanic Not Hispanic Sum
## Center on Halsted 2828 8380 11208
## Offsite 412 1025 1437
## Sum 3240 9405 12645chi = chisq.test(t); chi##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 7.7243, df = 1, p-value = 0.005448chi$stdres##
## Hispanic Not Hispanic
## Center on Halsted -2.811351 2.811351
## Offsite 2.811351 -2.811351oddsratio(t)## Disease Nondisease Total
## Exposed 2828 8380 11208
## Nonexposed 412 1025 1437
## Total 3240 9405 12645## Warning in N1 * N0 * M1: NAs produced by integer overflow##
## Odds ratio estimate and its significance probability
##
## data: t
## p-value = NA
## 95 percent confidence interval:
## 0.7431392 0.9485362
## sample estimates:
## [1] 0.8395799\(\alpha = 0.01\) level shows an association between location and Hispanic/Latino. More at offsite testing locations.
Association between Black
t = table(coh$Location, coh$Black);addmargins(t)##
## Black Not Black Sum
## Center on Halsted 2675 8533 11208
## Offsite 319 1118 1437
## Sum 2994 9651 12645chi = chisq.test(t); chi##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 1.8694, df = 1, p-value = 0.1715chi$stdres##
## Black Not Black
## Center on Halsted 1.400226 -1.400226
## Offsite -1.400226 1.400226oddsratio(t)## Disease Nondisease Total
## Exposed 2675 8533 11208
## Nonexposed 319 1118 1437
## Total 2994 9651 12645## Warning in N1 * N0 * M1: NAs produced by integer overflow##
## Odds ratio estimate and its significance probability
##
## data: t
## p-value = NA
## 95 percent confidence interval:
## 0.963040 1.253435
## sample estimates:
## [1] 1.098685No significant association between Black and offsite testing.
Association between High Risk Ethnicity
t = table(coh$Location, coh$H.Risk.Eth);addmargins(t)##
## High Risk Low Risk Sum
## Center on Halsted 5503 5705 11208
## Offsite 731 706 1437
## Sum 6234 6411 12645chi = chisq.test(t); chi##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 1.5282, df = 1, p-value = 0.2164chi$stdres##
## High Risk Low Risk
## Center on Halsted -1.264231 1.264231
## Offsite 1.264231 -1.264231No significant association between high risk ethnicity groups and offsite testing.
Association between Sexual Orientation
t = table(coh$Location, coh$SISO);addmargins(t)##
## Heterosexual LGBT
## Center on Halsted 2277 8720
## Offsite 457 954
## Sum 2734 9674
##
## Unknown, Unreported, Questioning, Refused, or Other
## Center on Halsted 211
## Offsite 26
## Sum 237
##
## Sum
## Center on Halsted 11208
## Offsite 1437
## Sum 12645chi = chisq.test(t); chi##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 99.449, df = 2, p-value < 2.2e-16chi$stdres##
## Heterosexual LGBT
## Center on Halsted -9.9582345 9.6074306
## Offsite 9.9582345 -9.6074306
##
## Unknown, Unreported, Questioning, Refused, or Other
## Center on Halsted 0.1927912
## Offsite -0.1927912p-value 2.540678310^{-22} extremely small. Heterosexuals testing offsite 9 standard deviations above expected.
Association between First Test
t = table(coh$Location, coh$First.Test.);addmargins(t)##
## FALSE TRUE Sum
## Center on Halsted 10143 1065 11208
## Offsite 1255 182 1437
## Sum 11398 1247 12645chi = chisq.test(t); chi##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 13.983, df = 1, p-value = 0.0001845chi$stdres##
## FALSE TRUE
## Center on Halsted 3.786359 -3.786359
## Offsite -3.786359 3.786359Significant result, more first tests offsite (which makes sense with the high number of general screeners/heterosexuals tested offsite). However, note the 1065 first HIV tests at the center.
Association between Youth under 24
t = table(coh$Location, coh$Youth24);addmargins(t)##
## Adult Youth Sum
## Center on Halsted 6594 4613 11207
## Offsite 1083 353 1436
## Sum 7677 4966 12643chi = chisq.test(t); chi##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 146.01, df = 1, p-value < 2.2e-16chi$stdres##
## Adult Youth
## Center on Halsted -12.11217 12.11217
## Offsite 12.11217 -12.11217Very significant association. Just over 12 standard deviations more Youth tested at the Center.
Association between Youth under 29
t = table(coh$Location, coh$Youth29);addmargins(t)##
## Adult Youth Sum
## Center on Halsted 3611 7596 11207
## Offsite 656 780 1436
## Sum 4267 8376 12643chi = chisq.test(t); chi##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 102.56, df = 1, p-value < 2.2e-16chi$stdres##
## Adult Youth
## Center on Halsted -10.1569 10.1569
## Offsite 10.1569 -10.1569Result holds for “youth under 29,” very significant association. Just over 10 standard deviations more Youth tested at the Center.
Association between MSM
t = table(coh$Location, coh$MSM);addmargins(t)##
## MSM or MTF Neither Sum
## Center on Halsted 8689 2519 11208
## Offsite 943 494 1437
## Sum 9632 3013 12645chi = chisq.test(t); chi##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 98.757, df = 1, p-value < 2.2e-16chi$stdres##
## MSM or MTF Neither
## Center on Halsted 9.970564 -9.970564
## Offsite -9.970564 9.970564oddsratio(t)## Disease Nondisease Total
## Exposed 8689 2519 11208
## Nonexposed 943 494 1437
## Total 9632 3013 12645## Warning in N1 * N0 * M1: NAs produced by integer overflow##
## Odds ratio estimate and its significance probability
##
## data: t
## p-value = NA
## 95 percent confidence interval:
## 1.606599 2.032387
## sample estimates:
## [1] 1.806995Very significant association. Just under 10 standard deviations more Men who have sex with men (MSM) tested at the Center than offsite (follows our general screener association).
Failed/Extra code
Comparing Years
plot(coh\(Location~coh\)Fiscal.Year) lm=lm(coh\(Location~coh\)Fiscal.Year) t = table(coh\(Location, coh\)Fiscal.Year) anova(t)
prop.test(t) prop.trend.test(t)
Our questions to investigate:
Where do people live
By State
Table = table(coh$State); Table##
## Unknown Unreported Alabama Arizona
## 3 4 1 2 3
## California Colorado Connecticut DC Delaware
## 19 5 2 3 1
## Florida Georgia Hawaii Idaho Illinois
## 12 7 1 2 12291
## Indiana Iowa Kansas Kentucky Louisiana
## 99 7 3 9 3
## Maryland Massachusetts Michigan Minnesota Missouri
## 1 3 34 13 10
## Nebraska New Hampshire New Jersey New York North Carolina
## 1 1 5 16 2
## North Dakota Ohio Oregon Pennsylvania Rhode Island
## 2 18 5 8 1
## South Carolina Tennessee Texas Virginia Washington
## 1 1 14 5 1
## Wisconsin
## 36#ggplot(coh, aes(x = State)) + geom_bar() + theme(axis.text.x = element_text(angle = 45, hjust = 1))By Zip
Who’s coming alone vs. with partner
table(coh$Testing.Modality)##
## Tested & Family Member Tested
## 1 67
## Tested & Friend Tested Tested & Partner Tested
## 483 754
## Tested Alone Tested with COH Staff Support
## 10599 11
## Tested with Family Member Support Tested with Friend Support
## 72 306
## Tested with Partner Support Unknown / Unreported
## 156 206#subset(coh, Testing.Modality == "Tested with Friend Support")First Test
t = table(coh$First.Test.);t##
## FALSE TRUE
## 11408 1247prop.table(t)##
## FALSE TRUE
## 0.90146187 0.09853813pie(t)
barplot(t)
Repeat Testers at Center
t = table(coh$Tested.at.COH.before.);t##
## FALSE TRUE
## 6832 5823prop.table(t)##
## FALSE TRUE
## 0.5398657 0.4601343Association between Client type and repeat testers at the COH
t = table(coh$Tested.at.COH.before., coh$Client);addmargins(t)##
## General Screen Grant Funded Sum
## FALSE 1633 5198 6831
## TRUE 585 5238 5823
## Sum 2218 10436 12654chi = chisq.test(t); chi##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 416.73, df = 1, p-value < 2.2e-16chi$stdres##
## General Screen Grant Funded
## FALSE 20.43736 -20.43736
## TRUE -20.43736 20.43736barplot(t)
mosaicplot(t)
Very significant association. Many more Grant Funded people repeat testers (20 standard deviations higher) GOOD!
Association between High Risk Ethnicities and repeat testers at the COH
t = table(coh$Tested.at.COH.before., coh$H.Risk.Eth );addmargins(t)##
## High Risk Low Risk Sum
## FALSE 3409 3423 6832
## TRUE 2833 2990 5823
## Sum 6242 6413 12655chi = chisq.test(t); chi##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 1.9019, df = 1, p-value = 0.1679chi$stdres##
## High Risk Low Risk
## FALSE 1.396946 -1.396946
## TRUE -1.396946 1.396946Not much evidence of association between High Risk Ethnicities and repeat testers.
Association between Sexual Orientation and repeat testers at the COH
t = table(coh$Tested.at.COH.before., coh$SISO);addmargins(t)##
## Heterosexual LGBT
## FALSE 2075 4611
## TRUE 660 5072
## Sum 2735 9683
##
## Unknown, Unreported, Questioning, Refused, or Other Sum
## FALSE 146 6832
## TRUE 91 5823
## Sum 237 12655chi = chisq.test(t); chi##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 690.73, df = 2, p-value < 2.2e-16chi$stdres##
## Heterosexual LGBT
## FALSE 25.93295 -25.93959
## TRUE -25.93295 25.93959
##
## Unknown, Unreported, Questioning, Refused, or Other
## FALSE 2.37502
## TRUE -2.37502Lots of evidence of association! LGBT much more likely to be repeat testers at the Center (almost 26 standard deviations above expected).
Monthly Trends
t = table(coh$Month.of.Session);t##
## April August December February January July June
## 960 1070 1079 923 1188 1115 1077
## March May November October September
## 1107 1034 1058 1062 982pt = prop.table(t);pt##
## April August December February January July
## 0.07585934 0.08455156 0.08526274 0.07293560 0.09387594 0.08810747
## June March May November October September
## 0.08510470 0.08747531 0.08170684 0.08360332 0.08391940 0.07759779chi = chisq.test(pt, p = c(0.082, 0.085, 0.085, 0.077, 0.085, 0.085, 0.082, 0.085, 0.085, 0.082, 0.085, 0.082)); chi## Warning in chisq.test(pt, p = c(0.082, 0.085, 0.085, 0.077, 0.085, 0.085, :
## Chi-squared approximation may be incorrect##
## Chi-squared test for given probabilities
##
## data: pt
## X-squared = 0.0023167, df = 11, p-value = 1chi$observed##
## April August December February January July
## 0.07585934 0.08455156 0.08526274 0.07293560 0.09387594 0.08810747
## June March May November October September
## 0.08510470 0.08747531 0.08170684 0.08360332 0.08391940 0.07759779chi$expected## April August December February January July June
## 0.082 0.085 0.085 0.077 0.085 0.085 0.082
## March May November October September
## 0.085 0.085 0.082 0.085 0.082chi$stdres##
## April August December February January
## -0.0223813610 -0.0016079913 0.0009421271 -0.0152458057 0.0318268947
## July June March May November
## 0.0111426008 0.0113159654 0.0088758289 -0.0118084651 0.0058437500
## October September
## -0.0038747633 -0.0160451116chi$residuals##
## April August December February January
## -0.0214441005 -0.0015381343 0.0009011976 -0.0146470860 0.0304442174
## July June March May November
## 0.0106585253 0.0108420886 0.0084902302 -0.0112954619 0.0055990323
## October September
## -0.0037064293 -0.0153731931chi$method## [1] "Chi-squared test for given probabilities"Association between Client (and month)
t = table(coh$Month.of.Session, coh$Client); addmargins(t)##
## General Screen Grant Funded Sum
## April 209 751 960
## August 180 890 1070
## December 164 915 1079
## February 143 779 922
## January 176 1012 1188
## July 188 927 1115
## June 247 830 1077
## March 192 915 1107
## May 228 806 1034
## November 161 897 1058
## October 179 883 1062
## September 151 831 982
## Sum 2218 10436 12654chi = chisq.test(t); chi##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 69.174, df = 11, p-value = 1.753e-10chi$stdres##
## General Screen Grant Funded
## April 3.5966520 -3.5966520
## August -0.6344979 0.6344979
## December -2.1036601 2.1036601
## February -1.6740082 1.6740082
## January -2.5839513 2.5839513
## July -0.6135038 0.6135038
## June 4.8784467 -4.8784467
## March -0.1684495 0.1684495
## May 3.9912078 -3.9912078
## November -2.0649964 2.0649964
## October -0.6027445 0.6027445
## September -1.8461731 1.8461731Significant association, more general screeners April, May, June.
Association between High Risk Ethnicities (and month)
t = table(coh$Month.of.Session, coh$H.Risk.Eth); addmargins(t)##
## High Risk Low Risk Sum
## April 484 476 960
## August 495 575 1070
## December 533 546 1079
## February 465 458 923
## January 566 622 1188
## July 561 554 1115
## June 541 536 1077
## March 551 556 1107
## May 533 501 1034
## November 510 548 1058
## October 515 547 1062
## September 488 494 982
## Sum 6242 6413 12655chi = chisq.test(t); chi##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 10.034, df = 11, p-value = 0.5273chi$stdres##
## High Risk Low Risk
## April 0.70416392 -0.70416392
## August -2.09434589 2.09434589
## December 0.05029424 -0.05029424
## February 0.66572387 -0.66572387
## January -1.21765457 1.21765457
## July 0.69208816 -0.69208816
## June 0.62295600 -0.62295600
## March 0.31334859 -0.31334859
## May 1.49204137 -1.49204137
## November -0.76133121 0.76133121
## October -0.56591332 0.56591332
## September 0.24155256 -0.24155256No major associations.
Association between Sexual Orientation (and month)
t = table(coh$Month.of.Session, coh$SISO); addmargins(t)##
## Heterosexual LGBT
## April 224 716
## August 198 854
## December 253 813
## February 207 697
## January 244 931
## July 238 862
## June 250 806
## March 246 829
## May 210 806
## November 223 808
## October 247 790
## September 195 771
## Sum 2735 9683
##
## Unknown, Unreported, Questioning, Refused, or Other Sum
## April 20 960
## August 18 1070
## December 13 1079
## February 19 923
## January 13 1188
## July 15 1115
## June 21 1077
## March 32 1107
## May 18 1034
## November 27 1058
## October 25 1062
## September 16 982
## Sum 237 12655chi = chisq.test(t); chi##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 38.171, df = 22, p-value = 0.01753chi$stdres##
## Heterosexual LGBT
## April 1.3478961 -1.4688555
## August -2.5810185 2.6597549
## December 1.5317021 -0.9460508
## February 0.6246768 -0.7447881
## January -0.9441899 1.5817556
## July -0.2265939 0.6551358
## June 1.3342497 -1.3579044
## March 0.5163680 -1.3377444
## May -1.0619060 1.1355401
## November -0.4412468 -0.1159859
## October 1.3616045 -1.7086362
## September -1.3908994 1.5379056
##
## Unknown, Unreported, Questioning, Refused, or Other
## April 0.5006058
## August -0.4805193
## December -1.6922870
## February 0.4322986
## January -2.0793901
## July -1.3606260
## June 0.1950940
## March 2.6153297
## May -0.3266563
## November 1.7024156
## October 1.2087810
## September -0.5859585Some evidence of association. More Heterosexual December, April, June, October. Very strange.
Seasonal Trends
t = table(coh$Season);t##
## Fall Spring Summer Winter
## 3102 3101 3262 3190pt = prop.table(t);pt##
## Fall Spring Summer Winter
## 0.2451205 0.2450415 0.2577637 0.2520743chisq.test(pt, p = c(.249, .252, .252, .247))## Warning in chisq.test(pt, p = c(0.249, 0.252, 0.252, 0.247)): Chi-squared
## approximation may be incorrect##
## Chi-squared test for given probabilities
##
## data: pt
## X-squared = 0.00048866, df = 3, p-value = 1Association Between Season and Client
t = table(coh$Season, coh$Client); addmargins(t)##
## General Screen Grant Funded Sum
## Fall 491 2611 3102
## Spring 629 2472 3101
## Summer 615 2647 3262
## Winter 483 2706 3189
## Sum 2218 10436 12654chi = chisq.test(t); chi##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 38.972, df = 3, p-value = 1.759e-08chi$stdres##
## General Screen Grant Funded
## Fall -2.865519 2.865519
## Spring 4.645268 -4.645268
## Summer 2.311038 -2.311038
## Winter -4.091161 4.091161As suspected, more general screeners Spring and Summer.
Model First Test
Repeat.Tester = glm(Tested.at.COH.before. ~ pos + Stage.of.Change.at.Time.of.Testing + Location + HLM + Black + SISO + age + Fiscal.Year, family=binomial(link=logit),data=coh)
summary(Repeat.Tester)##
## Call:
## glm(formula = Tested.at.COH.before. ~ pos + Stage.of.Change.at.Time.of.Testing +
## Location + HLM + Black + SISO + age + Fiscal.Year, family = binomial(link = logit),
## data = coh)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7635 -1.0850 -0.5259 1.0709 2.4443
##
## Coefficients:
## Estimate
## (Intercept) -1.928594
## pos -0.777023
## Stage.of.Change.at.Time.of.TestingContemplation 0.378295
## Stage.of.Change.at.Time.of.TestingMaintenance 0.616870
## Stage.of.Change.at.Time.of.TestingPre-Contemplation -0.076977
## Stage.of.Change.at.Time.of.TestingPreparation 0.415528
## Stage.of.Change.at.Time.of.TestingRelapse / Adjustment 0.788553
## Stage.of.Change.at.Time.of.TestingUnknown / Unreported -0.537302
## LocationOffsite -1.085206
## HLMNot Hispanic 0.047031
## BlackNot Black -0.227298
## SISOLGBT 1.298631
## SISOUnknown, Unreported, Questioning, Refused, or Other 0.665279
## age 0.007602
## Fiscal.Year2017 0.297099
## Fiscal.Year2018 0.899269
## Std. Error z value
## (Intercept) 0.101194 -19.058
## pos 0.227191 -3.420
## Stage.of.Change.at.Time.of.TestingContemplation 0.073987 5.113
## Stage.of.Change.at.Time.of.TestingMaintenance 0.047153 13.082
## Stage.of.Change.at.Time.of.TestingPre-Contemplation 0.159348 -0.483
## Stage.of.Change.at.Time.of.TestingPreparation 0.060358 6.884
## Stage.of.Change.at.Time.of.TestingRelapse / Adjustment 0.105838 7.451
## Stage.of.Change.at.Time.of.TestingUnknown / Unreported 0.197059 -2.727
## LocationOffsite 0.070894 -15.307
## HLMNot Hispanic 0.046605 1.009
## BlackNot Black 0.049522 -4.590
## SISOLGBT 0.052715 24.635
## SISOUnknown, Unreported, Questioning, Refused, or Other 0.145881 4.560
## age 0.002045 3.718
## Fiscal.Year2017 0.047678 6.231
## Fiscal.Year2018 0.049814 18.052
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## pos 0.000626 ***
## Stage.of.Change.at.Time.of.TestingContemplation 3.17e-07 ***
## Stage.of.Change.at.Time.of.TestingMaintenance < 2e-16 ***
## Stage.of.Change.at.Time.of.TestingPre-Contemplation 0.629042
## Stage.of.Change.at.Time.of.TestingPreparation 5.81e-12 ***
## Stage.of.Change.at.Time.of.TestingRelapse / Adjustment 9.29e-14 ***
## Stage.of.Change.at.Time.of.TestingUnknown / Unreported 0.006399 **
## LocationOffsite < 2e-16 ***
## HLMNot Hispanic 0.312902
## BlackNot Black 4.44e-06 ***
## SISOLGBT < 2e-16 ***
## SISOUnknown, Unreported, Questioning, Refused, or Other 5.11e-06 ***
## age 0.000201 ***
## Fiscal.Year2017 4.62e-10 ***
## Fiscal.Year2018 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 17447 on 12642 degrees of freedom
## Residual deviance: 15771 on 12627 degrees of freedom
## (12 observations deleted due to missingness)
## AIC: 15803
##
## Number of Fisher Scoring iterations: 4df = data.frame (pos = c(rep(0,2)), Stage.of.Change.at.Time.of.Testing = c(rep("Maintenance",2)), Location = c(rep("Center on Halsted",2)), HLM = c("Hispanic","Not Hispanic"), Black = c("Black", "Not Black"), SISO = c("Heterosexual", "LGBT"), age = c(30,30), Fiscal.Year = c(rep("2018",2)))
exp(predict(Repeat.Tester,new=df))/(1+exp(predict(Repeat.Tester,new=df)))## 1 2
## 0.4540301 0.7178788Positive test Model
t = table(coh$pos, coh$Fiscal.Year); addmargins(t)##
## 2016 2017 2018 Sum
## 0 3862 4590 4104 12556
## 1 34 41 24 99
## Sum 3896 4631 4128 12655prop.table(t)##
## 2016 2017 2018
## 0 0.305175820 0.362702489 0.324298696
## 1 0.002686685 0.003239826 0.001896484glm = glm(pos ~ Fiscal.Year + Stage.of.Change.at.Time.of.Testing + HLM + Black + SISO + age, family=binomial(link=logit),data=coh)
summary(glm)##
## Call:
## glm(formula = pos ~ Fiscal.Year + Stage.of.Change.at.Time.of.Testing +
## HLM + Black + SISO + age, family = binomial(link = logit),
## data = coh)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.3645 -0.1467 -0.0819 -0.0552 4.0253
##
## Coefficients:
## Estimate
## (Intercept) -5.2531248
## Fiscal.Year2017 -0.0299984
## Fiscal.Year2018 -0.4127912
## Stage.of.Change.at.Time.of.TestingContemplation 0.0521155
## Stage.of.Change.at.Time.of.TestingMaintenance -1.9226548
## Stage.of.Change.at.Time.of.TestingPre-Contemplation -0.0280171
## Stage.of.Change.at.Time.of.TestingPreparation -0.2793148
## Stage.of.Change.at.Time.of.TestingRelapse / Adjustment -0.3438780
## Stage.of.Change.at.Time.of.TestingUnknown / Unreported 0.5346977
## HLMNot Hispanic -0.5537393
## BlackNot Black -1.1714088
## SISOLGBT 2.4231313
## SISOUnknown, Unreported, Questioning, Refused, or Other 2.5732280
## age 0.0008028
## Std. Error z value
## (Intercept) 0.7405281 -7.094
## Fiscal.Year2017 0.2466671 -0.122
## Fiscal.Year2018 0.2803048 -1.473
## Stage.of.Change.at.Time.of.TestingContemplation 0.3034532 0.172
## Stage.of.Change.at.Time.of.TestingMaintenance 0.3692261 -5.207
## Stage.of.Change.at.Time.of.TestingPre-Contemplation 0.7329177 -0.038
## Stage.of.Change.at.Time.of.TestingPreparation 0.2773237 -1.007
## Stage.of.Change.at.Time.of.TestingRelapse / Adjustment 0.5297549 -0.649
## Stage.of.Change.at.Time.of.TestingUnknown / Unreported 0.4542713 1.177
## HLMNot Hispanic 0.2653758 -2.087
## BlackNot Black 0.2490914 -4.703
## SISOLGBT 0.5913125 4.098
## SISOUnknown, Unreported, Questioning, Refused, or Other 0.8248933 3.119
## age 0.0119877 0.067
## Pr(>|z|)
## (Intercept) 1.31e-12 ***
## Fiscal.Year2017 0.90320
## Fiscal.Year2018 0.14085
## Stage.of.Change.at.Time.of.TestingContemplation 0.86364
## Stage.of.Change.at.Time.of.TestingMaintenance 1.92e-07 ***
## Stage.of.Change.at.Time.of.TestingPre-Contemplation 0.96951
## Stage.of.Change.at.Time.of.TestingPreparation 0.31385
## Stage.of.Change.at.Time.of.TestingRelapse / Adjustment 0.51626
## Stage.of.Change.at.Time.of.TestingUnknown / Unreported 0.23918
## HLMNot Hispanic 0.03692 *
## BlackNot Black 2.57e-06 ***
## SISOLGBT 4.17e-05 ***
## SISOUnknown, Unreported, Questioning, Refused, or Other 0.00181 **
## age 0.94661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1157.6 on 12652 degrees of freedom
## Residual deviance: 1049.1 on 12639 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 1077.1
##
## Number of Fisher Scoring iterations: 9glm = glm(pos ~ Fiscal.Year + HLM + Black + SISO + age, family=binomial(link=logit),data=coh)
summary(glm)##
## Call:
## glm(formula = pos ~ Fiscal.Year + HLM + Black + SISO + age, family = binomial(link = logit),
## data = coh)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.2394 -0.1492 -0.1118 -0.0900 3.8747
##
## Coefficients:
## Estimate
## (Intercept) -5.341195
## Fiscal.Year2017 -0.155769
## Fiscal.Year2018 -0.505082
## HLMNot Hispanic -0.611771
## BlackNot Black -1.248565
## SISOLGBT 2.385884
## SISOUnknown, Unreported, Questioning, Refused, or Other 2.510558
## age -0.005321
## Std. Error z value
## (Intercept) 0.731215 -7.305
## Fiscal.Year2017 0.236223 -0.659
## Fiscal.Year2018 0.269635 -1.873
## HLMNot Hispanic 0.264117 -2.316
## BlackNot Black 0.247744 -5.040
## SISOLGBT 0.590766 4.039
## SISOUnknown, Unreported, Questioning, Refused, or Other 0.822234 3.053
## age 0.011938 -0.446
## Pr(>|z|)
## (Intercept) 2.78e-13 ***
## Fiscal.Year2017 0.50963
## Fiscal.Year2018 0.06104 .
## HLMNot Hispanic 0.02054 *
## BlackNot Black 4.66e-07 ***
## SISOLGBT 5.38e-05 ***
## SISOUnknown, Unreported, Questioning, Refused, or Other 0.00226 **
## age 0.65582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1157.6 on 12652 degrees of freedom
## Residual deviance: 1097.9 on 12645 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 1113.9
##
## Number of Fisher Scoring iterations: 9Age group breakdown histogram
attach(coh)## The following objects are masked _by_ .GlobalEnv:
##
## Age, hour.insummary(age)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 14.00 22.00 26.00 28.94 32.00 83.00 2hist(age)
detach(coh)Age vs. high risk group
t = table(coh$Youth24, coh$H.Risk.Eth); addmargins(t)##
## High Risk Low Risk Sum
## Adult 3423 4255 7678
## Youth 2818 2157 4975
## Sum 6241 6412 12653prop.table(t)##
## High Risk Low Risk
## Adult 0.2705287 0.3362839
## Youth 0.2227140 0.1704734mosaicplot(t)
t = table(coh$Youth29, coh$H.Risk.Eth); addmargins(t)##
## High Risk Low Risk Sum
## Adult 1733 2534 4267
## Youth 4508 3878 8386
## Sum 6241 6412 12653prop.table(t)##
## High Risk Low Risk
## Adult 0.1369636 0.2002687
## Youth 0.3562791 0.3064886mosaicplot(t)
Age vs. Stage
t = table(coh$Youth24, coh$Stage.of.Change.at.Time.of.Testing); addmargins(t)##
## Action Contemplation Maintenance Pre-Contemplation Preparation
## Adult 2344 422 3299 130 974
## Youth 1313 663 1743 75 964
## Sum 3657 1085 5042 205 1938
##
## Relapse / Adjustment Unknown / Unreported Sum
## Adult 322 187 7678
## Youth 133 84 4975
## Sum 455 271 12653prop.table(t)##
## Action Contemplation Maintenance Pre-Contemplation
## Adult 0.185252509 0.033351774 0.260728681 0.010274243
## Youth 0.103769857 0.052398641 0.137753892 0.005927448
##
## Preparation Relapse / Adjustment Unknown / Unreported
## Adult 0.076977792 0.025448510 0.014779104
## Youth 0.076187465 0.010511341 0.006638742mosaicplot(t)
barplot(t)
Who is taking their first test?
first.test.set = subset(coh, First.Test.==TRUE)
summary(subset(coh, First.Test.==TRUE))## Client.Type Testing.Status Month.of.Session
## IDPH :499 : 1 November:133
## General - Screen :466 Anonymous : 23 July :121
## CDC : 92 Confidential:1223 January :117
## IDPH QOL Grant : 66 August :115
## CDPH - Latino MSM: 61 March :115
## CDPH - White MSM : 58 December:106
## (Other) : 5 (Other) :540
## Year.of.Session Day.of.Session Time.of.Session Time.of.Session.1
## Min. :2015 Friday :167 4:00:00 PM : 77 3:45:00 PM: 27
## 1st Qu.:2016 Monday :168 2:00:00 PM : 70 2:45:00 PM: 25
## Median :2016 Saturday :221 3:00:00 PM : 66 4:45:00 PM: 24
## Mean :2016 Sunday : 93 5:00:00 PM : 61 7:45:00 PM: 24
## 3rd Qu.:2017 Thursday :193 7:00:00 PM : 50 4:30:00 PM: 23
## Max. :2018 Tuesday :164 12:00:00 PM: 44 5:45:00 PM: 23
## Wednesday:241 (Other) :879 (Other) :1101
## Reason.for.Session
## : 0
## HIV Related Counseling - No Testing : 0
## HIV Testing and Counseling :1247
## Referral / Linkage to Care - No Testing: 0
##
##
##
## Self.Identified.Gender Language
## Male :927 English :1183
## Female :305 Portuguese: 1
## Unknown : 6 Spanish : 63
## M-F Transgender: 4
## Queer : 2
## F-M Transgender: 1
## (Other) : 2
## Source zip Age
## Google Search :517 60613- : 76 20 yrs :110
## Friend :227 60657- : 76 23 yrs :104
## COH Off-site Testing Event:173 60614- : 53 19 yrs :100
## IMPACT/Northwestern Study : 97 60618- : 41 22 yrs : 95
## Other : 41 60640- : 41 21 yrs : 89
## COH Walk-In Sign in Lobby : 38 60626- : 39 18 yrs : 82
## (Other) :154 (Other) :921 (Other):667
## Ethnicity.and.Race Sexual.Partners
## Non-Hispanic/Latino/a - White :464 MSM :473
## Non-Hispanic/Latino/a - Black :220 MSW :293
## Non-Hispanic/Latino/a - Asian :116 WSM :229
## Hispanic/Latino/a - Mexican - White :107 MSMW :149
## Hispanic/Latino/a - Mexican - Declined : 97 WSWM : 39
## Hispanic/Latino/a - Other Hispanic - White: 36 WSW : 23
## (Other) :207 (Other): 41
## Self.Identified.Sexual.Orientation IL.County State
## Heterosexual:561 Cook :1132 Illinois :1212
## Gay :462 Outside IL: 35 Indiana : 13
## Bisexual :157 DuPage : 22 Michigan : 6
## Lesbian : 27 Lake : 20 Wisconsin : 3
## Queer : 18 Will : 12 California: 2
## Refused : 8 Kane : 7 Iowa : 2
## (Other) : 14 (Other) : 19 (Other) : 9
## Country First.Test.
## United States :1247 Mode:logical
## Argentina : 0 TRUE:1247
## Australia : 0
## Holland (see Netherlands): 0
## Italy : 0
## Japan : 0
## (Other) : 0
## Testing.Location
## Center on Halsted Testing Office :931
## Outside Center on Halsted :182
## Center on Halsted : 96
## Center on Halsted NAMES Office : 29
## Center on Halsted Youth Space : 7
## Center on Halsted Conference Room: 1
## (Other) : 1
## Testing.Modality Test.Specimen
## Tested Alone :925 : 0
## Tested & Friend Tested : 92 Fingerstick Blood :1225
## Tested & Partner Tested : 78 Oral Mucosal Transudate: 15
## Tested with Friend Support : 62 OraSure : 2
## Tested with Partner Support : 24 Venous Blood : 5
## Tested with Family Member Support: 21
## (Other) : 45
## Test.Result Stage.of.Change.at.Time.of.Testing
## True Negative :1238 Action :524
## True Positive : 7 Contemplation : 99
## Preliminary Positive: 2 Maintenance :315
## : 0 Pre-Contemplation : 41
## False Positive : 0 Preparation :219
## Invalid : 0 Relapse / Adjustment: 23
## (Other) : 0 Unknown / Unreported: 26
## By.Appointment..Leave.Blank.for.Walk.In.
## Mode :logical
## FALSE:644
## TRUE :603
##
##
##
##
## Fast.Track...Tracks.the.session.when.not.walk.in.Wednesday.
## Mode :logical
## FALSE:952
## TRUE :295
##
##
##
##
## Tested.at.COH.before. First.Time.at.COH. Mental.Health.Intake.Requested
## Mode :logical Mode :logical Mode :logical
## FALSE:1242 FALSE:239 FALSE:1247
## TRUE :5 TRUE :1008
##
##
##
##
## Mental.Health.Intake.Scheduled Crisis.Assistance.from.MH.Staff.Requested
## Mode :logical Mode :logical
## FALSE:1247 FALSE:1247
##
##
##
##
##
## Crisis.Assistance.from.Mental.Health.Staff.Given
## Mode :logical
## FALSE:1247
##
##
##
##
##
## Outreach.initiative
## General Outreach :742
## Walk In Wednesday :150
## Walk-In Saturdays :112
## Walgreens State Street: 40
## Walk In Sunday : 34
## Walgreens Lakeview : 24
## (Other) :145
## Al.Referrals
## :243
## State of IL AIDS/HIV & STD Hotline :156
## State of IL AIDS/HIV & STD Hotline, Other - STD Testing : 95
## Howard Brown Health Center, State of IL AIDS/HIV & STD Hotline, Other - STD Testing: 44
## Other - STD Testing : 37
## Howard Brown Health Center, Other - STD Testing : 35
## (Other) :637
## hour.in Session.Length.min pos Location
## Min. : 8.20 Min. :-525.00 Min. :0.000000 Length:1247
## 1st Qu.:13.33 1st Qu.: 30.00 1st Qu.:0.000000 Class :character
## Median :15.80 Median : 35.00 Median :0.000000 Mode :character
## Mean :15.86 Mean : 34.74 Mean :0.005614
## 3rd Qu.:18.00 3rd Qu.: 45.00 3rd Qu.:0.000000
## Max. :24.92 Max. : 540.00 Max. :1.000000
##
## Client HLM Black
## Length:1247 Length:1247 Length:1247
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## H.Risk.Eth SISO a age
## Length:1247 Length:1247 Min. :-1.00 Min. :14.00
## Class :character Class :character 1st Qu.:20.00 1st Qu.:20.00
## Mode :character Mode :character Median :23.00 Median :23.00
## Mean :26.02 Mean :26.04
## 3rd Qu.:28.00 3rd Qu.:28.00
## Max. :76.00 Max. :76.00
## NA's :1
## Youth24 Youth29 MSM
## Length:1247 Length:1247 Length:1247
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Season Fiscal.Year
## Length:1247 2016:421
## Class :character 2017:450
## Mode :character 2018:376
##
##
##
## Tested at COH before, what percent go to a grant and not a screen
repeat.test.set = subset(coh, First.Test.==FALSE)
t = table(repeat.test.set$Client)
prop.table(t)##
## General Screen Grant Funded
## 0.1535899 0.8464101barplot(t)
Time of day seeing youngest clients
-Youth 24 and under, -Also see 29 and under
youth.set = subset(coh, Youth24 == "Youth")
hist(youth.set$a)
hist(youth.set$hour.in)
plot(youth.set$a, youth.set$hour.in)
June specifically- Who are we seeing because pride
june = subset(coh, Month.of.Session == "June")
summary(june)## Client.Type Testing.Status Month.of.Session
## IDPH :365 : 0 June :1077
## General - Screen :247 Anonymous : 15 April : 0
## CDPH - White MSM :219 Confidential:1062 August : 0
## CDPH - Latino MSM:136 December: 0
## CDC : 89 February: 0
## IDPH QOL Grant : 20 January : 0
## (Other) : 1 (Other) : 0
## Year.of.Session Day.of.Session Time.of.Session Time.of.Session.1
## Min. :2016 Friday :152 3:00:00 PM : 69 4:00:00 PM: 27
## 1st Qu.:2016 Monday :141 2:00:00 PM : 54 4:30:00 PM: 26
## Median :2017 Saturday :180 4:00:00 PM : 50 3:30:00 PM: 25
## Mean :2017 Sunday : 50 5:00:00 PM : 40 1:30:00 PM: 23
## 3rd Qu.:2018 Thursday :192 1:00:00 PM : 36 2:45:00 PM: 22
## Max. :2018 Tuesday :127 11:00:00 AM: 35 3:40:00 PM: 21
## Wednesday:235 (Other) :793 (Other) :933
## Reason.for.Session
## : 0
## HIV Related Counseling - No Testing : 0
## HIV Testing and Counseling :1077
## Referral / Linkage to Care - No Testing: 0
##
##
##
## Self.Identified.Gender Language
## Male :902 English :1038
## Female :142 Portuguese: 0
## M-F Transgender : 24 Spanish : 39
## Gender Non-Conforming: 2
## Non-Binary : 2
## Queer : 2
## (Other) : 3
## Source zip Age
## IMPACT/Northwestern Study :276 60613- : 80 21 yrs : 75
## Repeat Testing Client :251 60657- : 76 23 yrs : 72
## Google Search :219 60640- : 65 20 yrs : 70
## COH Off-site Testing Event:108 60614- : 38 25 yrs : 67
## Friend : 97 60647- : 38 19 yrs : 62
## COH Walk-In Sign in Lobby : 29 60618- : 30 22 yrs : 61
## (Other) : 97 (Other) :750 (Other):670
## Ethnicity.and.Race Sexual.Partners
## Non-Hispanic/Latino/a - White :419 MSM :667
## Non-Hispanic/Latino/a - Black :279 MSW :110
## Non-Hispanic/Latino/a - Asian : 73 WSM :107
## Hispanic/Latino/a - Mexican - White : 64 MSMW : 92
## Hispanic/Latino/a - Mexican - Declined : 56 WSWM : 25
## Hispanic/Latino/a - Other Hispanic - White: 29 MSMWMTF: 16
## (Other) :157 (Other): 60
## Self.Identified.Sexual.Orientation IL.County State
## Gay :649 Cook :980 Illinois :1042
## Heterosexual :250 Outside IL: 35 Indiana : 11
## Bisexual :119 DuPage : 16 Wisconsin: 5
## Queer : 25 Lake : 10 Michigan : 3
## Unknown / Unreported: 9 Will : 9 Texas : 3
## Lesbian : 7 Kane : 8 Missouri : 2
## (Other) : 18 (Other) : 19 (Other) : 11
## Country First.Test.
## United States :1077 Mode :logical
## Argentina : 0 FALSE:980
## Australia : 0 TRUE :97
## Holland (see Netherlands): 0
## Italy : 0
## Japan : 0
## (Other) : 0
## Testing.Location
## Center on Halsted Testing Office:681
## Center on Halsted :272
## Outside Center on Halsted :109
## Center on Halsted NAMES Office : 11
## Center on Halsted MH Space : 2
## Center on Halsted Youth Space : 2
## (Other) : 0
## Testing.Modality Test.Specimen
## Tested Alone :937 : 0
## Tested & Partner Tested : 55 Fingerstick Blood :1067
## Tested & Friend Tested : 38 Oral Mucosal Transudate: 0
## Tested with Friend Support : 22 OraSure : 2
## Tested with Partner Support : 12 Venous Blood : 8
## Tested with Family Member Support: 7
## (Other) : 6
## Test.Result Stage.of.Change.at.Time.of.Testing
## True Negative :1066 Action :277
## True Positive : 7 Contemplation :156
## Previously Known Positive: 4 Maintenance :403
## : 0 Pre-Contemplation : 29
## False Positive : 0 Preparation :183
## Invalid : 0 Relapse / Adjustment: 25
## (Other) : 0 Unknown / Unreported: 4
## By.Appointment..Leave.Blank.for.Walk.In.
## Mode :logical
## FALSE:460
## TRUE :617
##
##
##
##
## Fast.Track...Tracks.the.session.when.not.walk.in.Wednesday.
## Mode :logical
## FALSE:751
## TRUE :326
##
##
##
##
## Tested.at.COH.before. First.Time.at.COH. Mental.Health.Intake.Requested
## Mode :logical Mode :logical Mode :logical
## FALSE:575 FALSE:640 FALSE:1077
## TRUE :502 TRUE :437
##
##
##
##
## Mental.Health.Intake.Scheduled Crisis.Assistance.from.MH.Staff.Requested
## Mode :logical Mode :logical
## FALSE:1077 FALSE:1077
##
##
##
##
##
## Crisis.Assistance.from.Mental.Health.Staff.Given
## Mode :logical
## FALSE:1077
##
##
##
##
##
## Outreach.initiative
## General Outreach :705
## Walk In Wednesday :127
## Walk-In Saturdays : 89
## Walgreens State Street: 32
## Walgreens Lakeview : 25
## Walk In Sunday : 22
## (Other) : 77
## Al.Referrals
## :210
## State of IL AIDS/HIV & STD Hotline :108
## State of IL AIDS/HIV & STD Hotline, Other - STD Testing : 69
## Other - STD Testing : 47
## Howard Brown Health Center, State of IL AIDS/HIV & STD Hotline, Other - STD Testing: 44
## Howard Brown Health Center, Other - STD Testing : 40
## (Other) :559
## hour.in Session.Length.min pos Location
## Min. : 2.083 Min. :-540.00 Min. :0.0000 Length:1077
## 1st Qu.:13.417 1st Qu.: 26.00 1st Qu.:0.0000 Class :character
## Median :15.500 Median : 33.00 Median :0.0000 Mode :character
## Mean :15.690 Mean : 35.73 Mean :0.0065
## 3rd Qu.:17.833 3rd Qu.: 50.00 3rd Qu.:0.0000
## Max. :24.967 Max. : 540.00 Max. :1.0000
##
## Client HLM Black
## Length:1077 Length:1077 Length:1077
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## H.Risk.Eth SISO a age
## Length:1077 Length:1077 Min. :14.00 Min. :14.00
## Class :character Class :character 1st Qu.:22.00 1st Qu.:22.00
## Mode :character Mode :character Median :26.00 Median :26.00
## Mean :28.78 Mean :28.78
## 3rd Qu.:32.00 3rd Qu.:32.00
## Max. :80.00 Max. :80.00
##
## Youth24 Youth29 MSM
## Length:1077 Length:1077 Length:1077
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Season Fiscal.Year
## Length:1077 2016:395
## Class :character 2017:348
## Mode :character 2018:334
##
##
##
##