AU_SAH_DELL_X

General

• How many clients were served between 2016 and 2022?

  • “The SAH Program served 4,753…in FY 2016-2022…”

    • DISAGREE; the program served 4,741
    • ##IMPORTANT## CSTAR125577 HAS NO WARD, AGE, OR SEX LISTED---DESPITE BEING ONE OF 4,741 SERVED

Eligible vs. Ineligible

• How many applications were completed?

  • “… there were 11,361 intake applications completed…”

    • AGREE

• What percent of people were deemed eligible?

EligibleX <- AU_SAH_INTAKE_COMPLETED_DELL%>%
  select(IneligibleReasons)%>%
  filter(is.na(IneligibleReasons))
print(EligibleX)
(7299/11361)*100

#TOTAL AMOUNT OF ELIGIBLE PEOPLE: 7,299
#PERCENT OF ELIGIBLE PEOPLE: 64.2%

• What percent of people were deemed ineligible?

  • “In FY 2016 -2022 there were 11,361 intake applications completed of which 58% were ineligible…(n = 6,620).”

    • DISAGREE; 35.8% were ineligible (n = 4,062)

IneligibleX <- AU_SAH_INTAKE_COMPLETED_DELL%>%
  select(IneligibleReasons)
print(na.omit(IneligibleX))
(4062/11361)*100

#TOTAL AMOUNT OF INELIGIBLE PEOPLE: 4,062
#PERCENT OF INELIGIBLE PEOPLE: 35.8%

• Of the ineligibles, what percentage were aged 60 and over?

  • “…of ineligible participants, 94.5% (n = 3719) were aged 60 or older.”

    • DISAGREE; 91.5% (n = 3,718)

Ineligible60AndAboveX <- AU_SAH_INTAKE_COMPLETED_DELL %>%
  select(AGE, IneligibleReasons)%>%
  filter(AGE >=60)
print(na.omit(Ineligible60AndAboveX))
(3718/4062)*100

#TOTAL AMOUNT OF INELIBLE PEOPLE AGED 60 AND OLDER: 3,718
#PERCENT OF PEOPLE AGED 60 AND OLDER DEEMED INELIGIBLE: 32.0%

• Of the ineligibles, what percentage were aged below 60?

IneligibleBelow60X <- AU_SAH_INTAKE_COMPLETED_DELL %>%
  select(AGE, IneligibleReasons)%>%
  filter(AGE <60)
print(na.omit(IneligibleBelow60X))
(339/4062)*100

#TOTAL AMOUNT OF INELIGBLE PEOPLE BELOW 60: 339
#PERCENT OF INELIGIBLE PEOPLE BELOW 60: 8.35%

• What were the most common reasons for ineligibility?

  • “The most common reasons for ineligibility were incomplete documents (58%) and unresponsive/no longer interested (31%).”

    • DISAGREE; Incomplete documents (56.5%), unresponsiveness / lack of interest (27.3%).

5.59+0.54

[1] 6.13

• Is there a difference between ineligibility and DC Healthy Benefits
  

HaveDCPBIneligible <- AU_SAH_INTAKE_COMPLETED_DELL %>%
  select(CSTAR, DCPublicBenefits, IneligibleReasons)%>%
  na.omit(filter(DCPublicBenefits == "Yes"))
print(HaveDCPBIneligible)
(1528/4062)*100

#TOTAL AMOUNT OF INELIGIBLES WHO HAVE DC PUBLIC BENEFITS: 1,528
#PERCENT OF INELIGIBLES WHO HAVE DC PUBLIC BENEFITS: 37.6%

NoDCPBIneligible <- AU_SAH_INTAKE_COMPLETED_DELL %>%
  na.omit(select(CSTAR, DCPublicBenefits, IneligibleReasons))%>%
  filter(is.na(DCPublicBenefits))
(2534/4062)*100

#TOTAL AMOUNT OF INELIGIBLES WHO DO NOT HAVE DC PUBLIC BENEFITS: 2,534
#PERCENT OF INELGIBLES WHO DO NOT HAVE DC PUBLIC BENEFITS: 62.4%

HaveDCPBEligible <- AU_SAH_INTAKE_COMPLETED_DELL %>%
  select(DCPublicBenefits, IneligibleReasons)%>%
  filter(DCPublicBenefits == "Yes",is.na(IneligibleReasons))
print(HaveDCPBEligible)
(2797/7299)*100

#TOTAL AMOUNT OF ELIGIBLES WHO HAVE DC PUBLIC BENEFITS: 2,797 
#PERCENT OF ELIGIBLES WHO HAVE DC PUBLIC BENEFITS: 38.3%

NoDCPBEligible <- AU_SAH_INTAKE_COMPLETED_DELL %>%
  select(DCPublicBenefits, IneligibleReasons)%>%
  filter(is.na(DCPublicBenefits), is.na(IneligibleReasons))
print(NoDCPBEligible)
(4502/7922)*100

#TOTAL AMOUNT OF ELIGIBLES WHO DO NOT HAVE PUBLIC BENEFITS: 4,502
#PERCENT OF ELIGIBLES WHO DO NOT HAVE PUBLIC BENEFITS: 56.8%

GENDER

• What percent of served clients were female?

  • “…the majority of [served clients] were women (79%)…”

    • AGREE

FemaleClients <- AU_SAH_DELL%>%
  select(Gender)%>%
  filter(Gender == "Female")
print(FemaleClients)
(3766/4741)*100

femaleClient <- AU_SAH_DELL %>%
  select(Gender)%>%
  filter(Gender == "female")
print(femaleClient)

FEMALEClient <- AU_SAH_DELL %>%
  select(Gender)%>%
  filter(Gender == "FEMALE")
print(FEMALEClient)

#TOTAL AMOUNT OF FEMALE CLIENTS: 3,766
#PERCENT OF FEMALE SERVED CLIENTS: 79.4%

• What percent of served clients were male?

MaleClients <- AU_SAH_DELL%>%
  select(Gender)%>%
  filter(Gender == "Male")
print(MaleClients)
(974/4741)*100

#TOTAL AMOUNT OF MALE CLIENTS: 974
#PERCENT OF MALE SERVED CLIENTS: 20.5%

AGE

• What percent of clients were 60 and older?

  • “…served [clients]…over age 60 (92%).”

    • AGREE; SHOULD MENTION IN DOCUMENT THAT YOU MEAN 60 AND OVER--NOT JUST THOSE OVER THE AGE OF 60.

(3766/4741)*100

[1] 79.43472

• What percent of clients were below 60?

BelowSixty <- AU_SAH_DELL%>%
  select(Age)%>%
  filter(Age <60)
print(BelowSixty)
(360/4741)*100

#TOTAL AMOUNT OF CLIENTS YOUNGER THAN 60: 360
#PERCENT OF CLIENTS UNDER 60 YEARS OF AGE: 7.59%

##IMPORTANT## INDIVIDUAL WITH CSTAR 251515 HAS AGE LISTED AS 0!
NewborneElder <- AU_SAH_DELL %>%
  select(CSTAR, Age)%>%
  filter(Age == 0)
print(NewborneElder)

WARD

• What percentage of clients were served by given ward?

  • “Almost 50% of clients live in Ward 7 and Ward 5.”

    • AGREE; 45.9%

Ward1 <- AU_SAH_DELL%>%
  select(Ward)%>%
  filter(Ward == 1)
print(Ward1)
(212/4741)*100

#TOTAL AMOUNT OF CLIENTS RESIDING IN WARD 1: 212
#PERCENT OF CLIENTS RESIDING IN WARD 1: 4.47%

Ward2 <- AU_SAH_DELL %>%
  select(Ward)%>%
  filter(Ward == 2)
print(Ward2)
(80/4741)*100

#TOTAL AMOUNT OF CLIENTS RESIDING IN WARD 2: 80
#PERCENT OF CLIENTS RESIDING IN WARD 2: 1.69%

Ward3 <- AU_SAH_DELL %>%
  select(Ward)%>%
  filter(Ward == 3)
print(Ward3)
(83/4741)*100

#TOTAL AMOUNT OF CLIENTS RESIDING IN WARD 3: 83 
#PERCENT OF CLIENTS RESIDING IN WARD 3: 1.75%

Ward4 <- AU_SAH_DELL %>%
  select(Ward)%>%
  filter(Ward == 4)
print(Ward4)
(844/4741)*100

#TOTAL AMOUNT OF CLIENTS RESIDING IN WARD 4: 844
#PERCENT OF CLIENTS RESIDING IN WARD 4: 17.8%

Ward5 <- AU_SAH_DELL %>%
  select(Ward)%>%
  filter(Ward == 5)
print(Ward5)
(981/4741)*100

#TOTAL AMOUNT OF CLIENTS RESIDING IN WARD 5: 981
#PERCENT OF CLIENTS RESIDING IN WARD 5: 20.7%

Ward6 <- AU_SAH_DELL %>%
  select(Ward)%>%
  filter(Ward == 6)
print(Ward6)
(452/4741)*100

#TOTAL AMOUNT OF CLIENTS RESIDING IN WARD 6: 452
#PERCENT OF CLIENTS RESIDING IN WARD 6: 9.53%

Ward7 <- AU_SAH_DELL %>%
  select(Ward)%>%
  filter(Ward == 7)
print(Ward7)
(1197/4741)*100

#TOTAL AMOUNT OF CLIENTS RESIDING IN WARD 7: 1,197
#PERCENT OF CLIENTS RESIDING IN WARD 7: 25.2%

Ward8 <- AU_SAH_DELL %>%
  select(Ward)%>%
  filter(Ward == 8)
print(Ward8)
(890/4741)*100

#TOTAL AMOUNT OF CLIENTS RESIDING IN WARD 8: 890 
#PERCENT OF CLIENTS RESIDING IN WARD 8: 18.8%

##IMPORTANT## INDIVIDUAL WITH CSTAR 247745 HAVE WARD LISTED AS 0!
Ward0 <- AU_SAH_DELL %>%
  select(CSTAR, Ward)%>%
  filter(Ward == 0)
print(Ward0)

Fiscal Years

• Of the fiscal years, which year had the highest contractor visits

  • 2016: 1 contractor visit; 0.021%

    • (1/4741)*100

  • 2017: 302 contractor visits; 6.37%

    • (302/4741)*100

  • 2018: 1,004 contractor visits; 21.2%

    • (1004/4741)*100

  • 2019: 1,174 contractor visits; 24.7%

    • (1173/4741)*100

  • 2020: 624 contractor visits; 13.2%

    • (624/4741)*100

  • 2021: 961 contractor visits; 20.3%

    • (961/4741)*100

  • 2022: 594 contractor visits; 12.5%

    • (594/4741)*100

  • NA: 81; 1.71%

    • (81/4741)*100

NADateOfVisit <- DateOfVisit %>%
  filter(is.na(DateOfVisit))
print(NADateOfVisit)

Second Episode

• What percent of clients needed a second episode?

SecondEpisode <- AU_SAH_Date_Visit %>%
  select(SecondEpisode) %>%
  filter(SecondEpisode == "Yes")
print(SecondEpisode)
(572/4741)*100

#TOTAL AMOUNT OF CLIENTS WHO NEEDED A SECOND EPISODE: 572
#PERCENT OF CLIENTS WHO NEEDED A SECOND EPISODE: 12.1%