Presentation as part of the Evidence of Best Practice in Disability-Inclusive Development plenary sessions. It includes highlights of our pilot project in Bhopal including the methodology used to disaggregation data by disability, results, lessons learnt and next steps. Presented by Emma Jolley at the 2016 international symposium: Disability in the SDG: Forming Alliances and Building Evidence for the 2030 Agenda, in London, 18-19 February 2016.

1. Disaggregating
routine
monitoring data
by disability
-
an example from
eye health
Emma Jolley, Pauline
Thivillier, Dominic
Haslam & Archana
Bhambal

2. Sightsavers
• Vision: a world where no one is
blind from avoidable causes
and where visually impaired
people participate equally in
society
• Two streams of work:
• Eye health (includes NTDs)
• Inclusion, focusing on
gender and disability

3. Eye Health Programmes
• Globally 285 million people with visual
impairments
• 80% of this is avoidable: cataract,
uncorrected refractive error, trachoma…
• 90% live in developing countries
• Ultimate strategic aim for governments to
ensure good quality eye care is available to
all people as an integral part of wider
health systems

4. Monitoring equity of access
How inclusive are your eye health programmes in terms of gender?
In 2014:
– 49% cataract operations were on females
– 54% trachoma operations on females
– 52% glasses dispensed to females
But >60% visual impairment found in females
What about people with disabilities?
• What is the prevalence of visual impairment in this group?
• How are they accessing services?

5. Disability Disaggregation pilot
project
The objectives of this project are to:
• Understand whether people with disabilities are accessing
our services
• Build the evidence base on how to disaggregate routine
data by disability
• Ultimately make Sightsavers projects more inclusive of
people with disabilities.
The pilots are based in:
• Eye Health Project in Bhopal, India
• Neglected Tropical Disease (NTD) Projects in Tanzania and
Ghana

6. Methods
• Data on disability integrated in to routine data collection tools
at hospital and primary care level – paper and electronic
systems
• Monthly reports developed and shared for analysis in excel
and Stata
As this is a pilot we also collected data on:
• Experiences of people involved in the project
• Quality of the data collected
• Regular in-depth interviews and focus groups among staff
involved in pilot, data quality audits and patient exit
interviews [not addressed today]

7. Washington Group Short Set
“The next questions ask about difficulties you may have doing certain
activities because of a HEALTH PROBLEM:
1. Do you have difficulty seeing, even if wearing glasses?
2. Do you have difficulty hearing, even if using a hearing aid?
3. Do you have difficulty walking or climbing steps?
4. Do you have difficulty remembering or concentrating?
5. Do you have difficulty (with self-care such as) washing all over or
dressing?
6. Using your usual (customary) language, do you have difficulty
communicating, (for example understanding or being understood by
others)?”
Response categories:
a) No, no difficulty,
b) Yes, some difficulty,
c) Yes, a lot of difficulty and
d) Cannot do it at all.
7. Are you disabled? Yes/ No

8. Results from Bhopal, India
Data
• 21,681 patients’ data collected in 15 months (until December
2015)
• 52% female, 48% male
• Mean (and median) age 45 years
• 58% at hospital, 42% at primary care

9. 0.6%
9.0%
17.5%
0% 10% 20%
Initial data from Bhopal, India
What proportion of our
clients have a disability?
17.5% of project clients
report severe or
completely limiting
difficulties in at least one
domain.
9% when we exclude the
sight domain.
0.6% when we ask them
directly if they are disabled

10. How does this compare?
4.1%
3.8%
0.6%
7.5%
9.0%
17.5%
0% 5% 10% 15% 20%
2012 Census: Bhopal adults
Telengana*: Direct questioning
Pilot: Are you disabled?
Telengana*: WG severe or completely
limiting difficulties
Pilot: severe or completely limiting
difficulties (excluding seeing)
Pilot: severe or completely limiting
difficulties
* International Centre for Evidence in Disability (ICED), The Telengana Disability Study, India Country Report, London
School of Hygiene and Tropical Medicine (LSHTM) 2014 [available from http://disabilitycentre.lshtm.ac.uk]

11. Which factors are associated with
disability?
Variable Values WG severe
difficulties (6Q)
WG severe
difficulties (6Q)
excluding seeing
Are you
disabled?
Odds ratio
Sex Male - - -
Female 1.4*** 2.1*** 0.7*
Age - binary <50 - - -
50+ 3.4*** 3.3*** 1.7**
Location Hospital - - -
Primary centre 8.2*** 42.5*** 5.5***
* p-value < 0.05
** p-value < 0.01
***p-value < 0.001
Univariate associations with disability measures

12. Which factors are associated with
disability?
* p-value < 0.05
** p-value < 0.01
***p-value < 0.001
Variable Values WG severe
difficulties
(6Q)
WG severe
difficulties (6Q)
excluding seeing
Are you
disabled?
Odds ratio
Sex Male - -
Female 1.2*** 1.7*** 0.5***
Age - binary <50 - - -
50+ 3.6*** 3.5*** 1.5*
Location Hospital - - -
VC 8.4*** 40.8*** 5.8***
Multivariate associations with disability measures

13. Lessons and next steps
• A significant proportion of clients have functional difficulties
• The women attending our services are more likely to have
difficulties functioning than men and are less likely than
men to attend hospital services [not presented]
• People with disabilities, especially non-visual disabilities,
are much less likely to go to hospital services.
• Services are being reviewed to improve the approach to be
more gender and disability inclusive.
• Testing approaches to collecting disability data in other
ways, e.g. occasional monitoring

14. Further information:
http://www.sightsavers.org/
everybodycounts/
ejolley@sightsavers.org
pthivillier@sightsavers.org
fsmith@sightsavers.org