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Disaggregating
routine
monitoring data
by disability
-
an example from
eye health
Emma Jolley, Pauline
Thivillier, Dominic...
Sightsavers
• Vision: a world where no one is
blind from avoidable causes
and where visually impaired
people participate e...
Eye Health Programmes
• Globally 285 million people with visual
impairments
• 80% of this is avoidable: cataract,
uncorrec...
Monitoring equity of access
How inclusive are your eye health programmes in terms of gender?
In 2014:
– 49% cataract opera...
Disability Disaggregation pilot
project
The objectives of this project are to:
• Understand whether people with disabiliti...
Methods
• Data on disability integrated in to routine data collection tools
at hospital and primary care level – paper and...
Washington Group Short Set
“The next questions ask about difficulties you may have doing certain
activities because of a H...
Results from Bhopal, India
Data
• 21,681 patients’ data collected in 15 months (until December
2015)
• 52% female, 48% mal...
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 proj...
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 ques...
Which factors are associated with
disability?
Variable Values WG severe
difficulties (6Q)
WG severe
difficulties (6Q)
excl...
Which factors are associated with
disability?
* p-value < 0.05
** p-value < 0.01
***p-value < 0.001
Variable Values WG sev...
Lessons and next steps
• A significant proportion of clients have functional difficulties
• The women attending our servic...
Further information:
http://www.sightsavers.org/
everybodycounts/
ejolley@sightsavers.org
pthivillier@sightsavers.org
fsmi...
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Disaggregating routine monitoring data by disability – an example from eye health

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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.

Published in: Government & Nonprofit
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Disaggregating routine monitoring data by disability – an example from eye health

  1. 1. Disaggregating routine monitoring data by disability - an example from eye health Emma Jolley, Pauline Thivillier, Dominic Haslam & Archana Bhambal
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 14. Further information: http://www.sightsavers.org/ everybodycounts/ ejolley@sightsavers.org pthivillier@sightsavers.org fsmith@sightsavers.org

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