Acknowledgements:

We recognize that there are some limitations to gathering data in this method, predominantly, that some people with sensory, intellectual or physical disabilities may not be physically able to access the survey.

Introduction:

In Australia alone, 4.3 million people are estimated to be living with a disability. This equates to approximately 18.3% of the population. Living with a disability comes hand in hand with emotional, medical and accessibility issues. I conducted a survey in order to discover the extent of these issues. The purpose of this survey was to highlight the issues that disabled people face throughout life due to their disability, and to shed light on the fact that a disability causes more than just medical problems.

Domain Knowledge:

Data for this project was collected from social media platforms, specifically, Facebook groups utilized by those who experience some degree of disability. Research was conducted in the attempt to broadly understand the experiences of those living with a disability of some form. Some things to note: A mental disability differs from an intellectual disability. Someone with an intellectual disability has difficulties taking in, processing or expressing information, whereas a mental illness affects emotions, moods, perceptions and behaviour and can be suffered by people of all levels of intellectual ability.

The criteria to participate in the survey is as follows:

  1. participants must be aged 18 or older

  2. participants must be diagnosed with a disability

It was important for all participants to be diagnosed with a disability in order to highlight the issues within the community.

The survey was conducted through Survey Monkey and spread through various social media channels. All participants aside from 2 are completely unknown to me in order to remove bias. All responses were anonymous. In total, there were 93 participants.

Results

The survey included 10 questions, with certain questions allowing participants to choose not to respond due to the nature of the question. The questions are as follows:

  1. Please tick all boxes that apply in regards to your disability: physical, intellectual, mental, and / or sensory.
disability <- c(86, 6, 53, 19)
lbls <- c("Physical", "Intellectual", "Mental", "Sensory")
pct <- round(disability/sum(disability)*100)
lbls <- paste(lbls, pct) 
lbls <- paste(lbls, "%",sep="") 
pie(disability,labels = lbls, col=rainbow(length(lbls)),
    main="Types of Disability")

  1. Do you suffer from more than one condition?
condition <-c(81,10)
barplot(condition, main="Do You Suffer From Multiple Conditions?", names.arg=c("Yes", "No"), xlab="Number of Responses", ylab="Number", border="blue", density=c(10,20,30,40,50,60,70,80,90))

  1. Do you have issues attaining or keeping employment due to your condition?
employment <- c(71,22)
barplot(employment, main="Issues Attaining Employment", names.arg=c("Yes", "No"), border="blue", xlab="Response", ylab="Number of Responses", density=c(10,20,30,40,50,60,70,80))

  1. Do you face accessibility issues in your school, place of work or living situation?
accessibility <- c(48,45)
barplot(accessibility, main="Accessibility Issues", names.arg=c("Yes", "No"), border="blue", xlab="Response", ylab="Number of Responses", density=c(10,20,30,40,50))

  1. Do you use a mobility aid: never, occasionally, most of the time, or always?
mobility <- c(42,35,7,7)
lbls <- c("Never", "Occasionally", "Usually", "Always")
pct <- round(mobility/sum(mobility)*100)
lbls <- paste(lbls, pct) 
lbls <- paste(lbls, "%",sep="") 
pie(mobility,labels = lbls, col=rainbow(length(lbls)),
    main="Use of Mobility Aids")

  1. Have you faced discrimination, a hate crime, or a hateful attitude towards your disability in a public place?
discrimination <- c(40,45,8)
barplot(discrimination, main="Discrimination In Public Places", names.arg=c("Yes", "No", "Prefer Not To Respond"), xlab="Responses", ylab="Number of Responses", density=c(10,20,30,40,50))

  1. Have you suffered from mental illness due to your condition?
mental <- c(73,15,5)
lbls <- c("Yes", "No", "Prefer Not To Respond")
pct <- round(mental/sum(mental)*100)
lbls <- paste(lbls, pct)
lbls <- paste(lbls, "%",sep="") 
pie(mental,labels = lbls, col=rainbow(length(lbls)),
    main="Mental Illness")

  1. Have you had your condition from birth, or has it been acquired over time?
acquired <-c(29,64)
lbls <- c("Birth", "Acquired"
)
pct <- round(acquired/sum(acquired)*100)
lbls <- paste(lbls, pct) 
lbls <- paste(lbls, "%",sep="") 
pie(acquired,labels = lbls, col=rainbow(length(lbls)),
    main=("Acquired Disability"))

  1. Is there a cure for your condition?
cure <- c(6,89)
barplot(cure, main="Curable Conditions", ylab="Number of Respondants", xlab="Response", border="blue", names.arg=c("Yes", "No"), density=c(10,20,30,40,50,60,70,80,90))

  1. Do you consider yourself to have a mild, moderate or severe disability?
mildmoderatesevere <- c(23,  54, 15)
lbls <- c("Mild", "Medium", "Severe")
pct <- round(mildmoderatesevere/sum(mildmoderatesevere)*100)
lbls <- paste(lbls, pct) # add percents to labels
lbls <- paste(lbls, "%",sep="") # ad % to labels
pie(mildmoderatesevere,labels = lbls, col=rainbow(length(lbls)),
    main="Severity of Disability")



Box Model

The box model explores and analyses chance variability.

library("DiagrammeR")

grViz(" 
  digraph CFA {

  # All
  node [fontname = Helvetica, fontcolor = White]

    # Box
    node [shape = box, style=filled, color=SteelBlue4, width=2 label='Yes, No'][fillcolor = SteelBlue4]
    a ; 

    # Sample
    node [shape = circle, style=filled, color=IndianRed, width=0.5, label='-'][fillcolor = IndianRed]
    b ; 

    # Draws
    a -> b [fontname = Helvetica,label = '93 draws',fontsize=8]
    b -> a  [fontname = Helvetica,color=grey,arrowsize = 0.5]
  }

"
)

To investigate the box model, we will look at question 7 of the survey: “Have you suffered from mental illness due to your condition?”

box=c(rep(1,73),rep(0,15))
N = 93
n = 88
# mean & sd of box (population)
meanbox=mean(box)
sdbox=sd(box)* sqrt(N-1)/sqrt(N)
# EV & SE of sum (sample)
n * meanbox
## [1] 73
sqrt(n) * sdbox
## [1] 3.528579
# EV & SE of percentage (sample)
meanbox * 100
## [1] 82.95455
1/sqrt(n) * sdbox * 100
## [1] 4.009749

This model shows that 82% of the sample answered that they had suffered from mental illness due to the condition, where the sample is the sum of respondants of yes, and no. The Prefer Not To Answer responses have been excluded from the sample size. There is a standard error of approximately 4 percentage points. ##Research Questions:

  1. Is your disability mild, moderate or severe, and how does this affect your daily life?
  2. Have you experiences difficulties because of your condition such as mental illness, hate crimes or discrimination?
  3. The nature of participants disabilities (e.g., physical/mental/intellectual etc, do they suffer from multiple conditions, do they require mobility assistance, is it curable and whether it was acquired at or after birth?)
  4. Is there a correlation between severity of disability and level of day to day difficulty experienced?

1. Is your disability mild, moderate or severe, and how does this affect daily life?

cure <- c(6,89)
barplot(cure, main="Curable Conditions", ylab="Number of Respondants", xlab="Response", border="blue", names.arg=c("Yes", "No"), density=c(10,20,30,40,50,60,70,80,90))

``` The data gathered demonstrates that 96% of participants live with a non-curable disability, which means that the vast majority of participants will deal with their disability for life. By assessing the types of disability, we are able to gain an understanding of how this will affect day to day life.

disability <- c(86, 6, 53, 19)
lbls <- c("Physical", "Intellectual", "Mental", "Sensory")
pct <- round(disability/sum(disability)*100)
lbls <- paste(lbls, pct) 
lbls <- paste(lbls, "%",sep="") 
pie(disability,labels = lbls, col=rainbow(length(lbls)),
    main="Types of Disability")

This data shows that 52% of participants suffer from physical symptoms, which can result in mobility issues, pain and medical complications. Furthermore, 32% of respondants indicated that they suffer from mental illness related symptoms which can drastically reduce overall quality of life. 12% of respondants stated that they suffer from sensory related symptoms, and a further 7% stated that they suffer from an intellectual disability. These results indicate that every aspect of day to day life can be, and is affected by the disabilities of the respondants, with a wide range of symptoms and conditions represented. It should be noted that participants were able to select more than one response as many conditions include symptoms of different natures.

2. Have you experienced difficulties because of your condition such as mental illness, hate crimes or discrimination?

According to the Australian Bureau of Statistics, the unemployment rate of disabled citizens aged 15 to 64 was 47.3% in 2012. Furthermore, 33.6% of these people were permanently unable to work. However, 19.3% had no restrictions regarding ability to work whatsoever (Disability and Labour Force Participation, 2012). This indicates that there is some discrimination in the Australian work force refusing employment to a significant number of Australians due to their disability. The following table shows the result from question 3 of the survey, describing issues attaining or keeping employment due to the respondants’ disabilities.

load("Q3.Rda")
knitr:: kable(Q3, main="Unemployment")
Yes No
71 22

This result shows that 23.7% of respondants have had no issue attaining or keeping employment due to their disability. This is marginally higher than the national average, which was, as aforementoned, 19.3% in 2012. A shocking 76.3% indicated that they have faced issues regarding employment. However, the survey was not thorough enough to measure whether or not respondants were permanently or temporarily unable to work, which resulted in a higher percentage than the national average.

3. What is the nature of the participants’ disabilities?

(e.g., physical/mental/intellectual etc, do they suffer from multiple conditions, do they require mobility assistance, is it curable and whether it was acquired at or after birth?)

barplot(disability, main="Type of Disability",
        names.arg=c("Physical", "Intellectual", "Mental", "Sensory"),
        border="blue", xlab="Type", ylab="Number of Responses", density=c(10,20,30,40,50,60,70,80))

As aforementioned, the most prevalent type of symptoms experienced by the participants is physical in nature, however the survey collected a broad range of responses from diverse experiences of disabled people.

condition <-c(81,10)
barplot(condition, main="Multiple Conditions", names.arg=c("Yes", "No"), xlab="Number of Responses", ylab="Number", border="blue", density=c(10,20,30,40,50,60,70,80,90))

knitr:: kable(condition)
x
81
10

These graphical and numerical summaries show that comorbidity is exponentially common for those suffering from a disability. This increases the likelihood of various types of symptoms.

mobility <- c(42,35,7,7)
lbls <- c("Never", "Occasionally", "Usually", "Always")
pct <- round(mobility/sum(mobility)*100)
lbls <- paste(lbls, pct) 
lbls <- paste(lbls, "%",sep="") 
pie(mobility,labels = lbls, col=rainbow(length(lbls)),
    main="Use of Mobility Aids")

The data collected in this survey indicates that the majority of participants use a mobility aid at least occasionally. However, a significant proportion of participants never require mobility aids. This could be due to being unable to access a mobility aid due to cost, or that their symptoms do not cause significant mobility issues.

acquired <-c(29,64)

barplot(acquired, main="Present At Birth", names.arg=c("Birth", "Acquired"), xlab="Number of Responses", ylab="Number", border="blue", density=c(10,20,30,40,50,60,70,80,90))

This barplot shows that the vast majority of conditions assessed in this survey have been acquired over time. This shows that congenital conditions are much rarer than acquired conditions.

Overall, the nature of the conditions assessed is varied and diverse. This demonstrates that each disabled person has a different lived experience that cannot be completely accurately described in a short survey. However, it was shown that a significant proportion of respondants suffer from predominately physical symptoms and have acquired their condition over time.

4. Is there a correlation between severity of disability and level of day to day difficulty experienced?

barplot(mildmoderatesevere, main="Severity of Conditions", names.arg=c("Mild", "Moderate", "Severe"), xlab="Number of Responses", ylab="Number", border="blue", density=c(10,20,30,40,50,60,70,80,90))

par(mfrow = c(1, 2))
hist(mildmoderatesevere, main = "Severity",  
    freq = F)
hist(mental, main = "Mental Illness",  
    freq = F)   

    hist(accessibility, main="Accessibility Issues",
    freq = F)
    hist(mobility, main="Use of Mobility Aid",
    freq = F)

    hist(discrimination, main="Discrimination",
    freq = F)
    hist(employment, main="Issues Keeping Employment",
    freq = F)

These histograms compare the densities of the different issues assessed in daily life for disabled people. This shows that there appears to be a correlation between these issues - however, it is not completely the same. For example, there appears to be a correlation between level of discrimination experienced and issues keeping employment, perhaps due to discrimination in the workplace.

Conclusion:

In conclusion, the survey was conducted to explore and analyse the lived experiences of those suffering from a disability. Although, as mentioned in the acknowledgements section of this report, the survey isn’t an entirely accurate way of discovering this due to the limitations of the survey. It was discovered that there is a correlation between severity of illness and quality of life. This was largely due to discrimination, severity of symptoms, and accessibility issues in daily life.

References:

Australian Bureau Of Statistics (2015). 4433.0.55.006 - Disability and Labour Force Participation, 2012. [online] Available at: http://www.abs.gov.au/ausstats/abs@.nsf/mf/4433.0.55.006 [Accessed 1 May 2018].