Rapid Assessment

1
Rapid Assessment for Avoidable
Blindness Dr. Hans Limburg
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Why do we need surveys?

• We need baseline data on avoidable blindness and
  poor vision in a defined area to make adequate
  planning and management of intervention
  strategies possible
• We need follow-up data to monitor progress of
  blindness intervention

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What do you want to know?Why do you want to
know this?
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Indicators used

• prevalence of all blindness, severe visual
  impairment (SVI) and visual impairment (VI)
• main causes of blindness, SVI and VI
• prevalence of cataract blindness
• (incidence of (cataract) blindness)
• prevalence of (pseudo)aphakia
• Cataract Surgical Coverage
• prevalence of low vision
• visual outcome after cataract surgery
• cause of poor visual outcome
• barriers to cataract surgery
• age at time of surgery, place of surgery, type of
  surgery, costs, satisfaction

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Definitions (WHO)

• Blindness
• VA lt3/60 with best correction in the better eye
  or visual field of 10 degrees or less around
  visual axis
• Severe visual impairment
• VA lt6/60 3/60 with best correction in the
  better eye or visual field of 20 degrees or less,
  but more than 10 degrees
• Visual impairment
• VA lt6/18 6/60 with best correction in the
  better eye or visual field of 30 degrees or less,
  but more than 20 degrees

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Definitions (WHO)

• .... with best correction ....
• this means that refractive errors are corrected
  at time of survey
• problem of refractive error is not acknowledged
• .... with available correction ....
• this measures vision with the correction
  available with the patient
• allows estimation of magnitude of refractive
  error problem
• RAAB measures first with available correction,
  followed by pinhole vision

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The survey should

• provide baseline indicators that are needed for
  adequate planning and for monitoring over time
• be a simple and cheap procedure
• be carried out by local staff
• be used again after 5-8 years to measure how much
  has changed

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Survey Design
persons of all ages in survey area
21,000
persons 50
3,000
persons 50 with operable cataract or
(pseudo)aphakia
150
Blind SVI VI male female
100
50
(pseudo)aphakia
cataract
Cataract Surgical Coverage
Outcome
Barriers
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Examination procedures in RAAB

• Measure VA with tumbling E-chart and with
  available correction and pinhole correction
• Assess lens status in each eye with distant
  direct ophthalmoscopy and/or portable slitlamp
• If VAlt6/18 and not due to cataract, corneal scar
  or refractive error, then dilate pupil and
  examine with direct ophthalmoscope and/or
  slitlamp
• Assess main cause of VAlt6/18 in each eye and for
  the person
• Poor vision due to cataract ask for barriers
• If operated for cataract ask details of surgery

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Survey form
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Avoidable blindness

• Main causes
• Cataract
• Trachoma
• Onchocerciasis
• Childhood blindness
• Refractive errors and low vision
• Glaucoma
• Diabetic retinopathy
• ARMD

• Population at risk
• People 50
• Special surveys
• Special surveys
• Special surveys
• Children and people 40-50
• Mainly people 50
• Mainly people 50
• People 50

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Focus on people 50

• 85 of all blindness in people 50
• Nearly all cataracts in people 50
• Prevalence high in people 50, hence sample size
  can be small
• Elderly people often not far away from the house
• Generally good compliance

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Why survey people aged 50 and not people of all
ages?(Madan Mohan, Survey of Blindness India.
Summary results, New Delhi 1989)
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Why survey people aged 50 and not people aged
40?(Madan Mohan, Survey of Blindness India.
Summary results, New Delhi 1989)
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Select survey area

• Total population in survey area between 0.5 and 5
  million people
• Area has management structure for eye care
• Population data by sex and by 5-year age groups
  available
• Population by sub-unit (village, town,
  neighbourhood, enumeration area, polling station,
  etc.) are available
• Entire area is accessible for survey teams
• No problems with security

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Calculate sample size (S)

• For Simple random sampling
• Estimated prevalence of condition (P)
• (cataract) blindness (VAlt3/60) e.g. 4
• Acceptable variation around estimate (D)
• Typically 20 of P e.g. 3.2 - 4.8
• Confidence in estimate (Z)
• Typically 95
• Non-compliance (absence, refusals)
• e.g. lt10
• Sinfinite population ZZ(P(1-P))/DD
• Sfinite population Sinf./(1(Sinf./population))

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Sampling methods

• Simple Random Sampling
• Same chance of selection for every individual
• List of all persons aged 50 in survey area
• Too much traveling
• Cluster Random sampling
• Same chance of selection for every group of
  people (cluster)
• List of all population units in survey area
• Less traveling
• Within cluster people share similar conditions,
  leading to less variation in results. To correct
  that loss of variation, a correction factor has
  to be applied Design Effect (DEFF)

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Enumeration areas
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Sample size cluster sampling

• Sample size is determined also by size of
  clusters and variance between and within clusters
  Design Effect (DEFF)
• Cluster size 40 gt DEFF 1.3
• Cluster size 50 gt DEFF 1.4
• Cluster size 60 gt DEFF 1.5
• Multiply sample size for SRS with DEFF
• DEFF is close to 1.0 when condition is evenly
  spread in community and 2.0 or higher when it
  clusters in families or geographical areas

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Calculate sample size
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Multistage sampling

• Select population area by systematic sampling
  from sampling frame
• census enumeration areas with population, or
• list of settlements with population, or
• other list of geographic distribution of total
  population
• Sub-divide selected population unit in segments
  with equal population, enough to provide required
  number of people aged 50
• Randomly select one segment
• Visit all houses in selected segment
• Examine all eligible people in these houses,
  until required number has been examined

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Sampling frame
- census enumeration areas with population, or -
list of settlements with population, or - other
list of geographic distribution of total
population
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Systematic sampling
1

• Suppose we want to test a sample of size 100 from
  a population of 2000, we test every 2000/100
  every 20th member (sampling interval)
• The starting point is determined by multiplying
  the sampling interval by a random number between
  0 and 1 (e.g. 0.45103 x 20 9.0206)
• The first member to be tested is 9. The next
  member will be 9 20 29, then 49, 69, 89, etc.
• In this way 100 members are selected, evenly
  spread over the population

9
29
49
69
1929
1949
1969
1989
2000
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Selection of clusters from sampling frame
1

• Sampling frame is list of all population units
  with their population in survey area
• Create cumulative column of population
• This cumulative list represents all persons in
  survey area and in each geographical sub-area
• Divide the total population by required number of
  clusters to calculate sampling interval
• determine starting point by multiplying sampling
  interval by a random number between 0 and 1
• Add sampling interval to starting point to
  identify second person, etc.
• Make a list of the population units where the
  selected persons are living. These are the
  locations of the clusters.
• This procedure ensures random selection of
  clusters with a probability proportional to the
  population size

1,000,000
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How to select the required number of people aged
50 in the cluster location

• 1. Random walk
• 2. Compact segment sampling

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Start at centre of cluster area
Select direction by spinning a bottle
At every crossing, select direction by spinning
bottle
Continue until you have examined the required
number in the cluster
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Compact segment sampling

• If the area from which the cluster has to be
  selected has a large population, bias may be
  introduced through the selection process which
  houses are to be visited. This can be reduced by
  dividing the area in equal segments with each
  enough population to provide required number of
  eligible people (aged 50)
• Population in segment
• Cluster size / population 50 (e.g. 50 / 21.1
  237)
• Number of segments
• Population area / population in segment (1482 /
  237 6.3)

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Total population 1482
21.1 of population is aged 50
50 / 0.211 237 on average, 50 people aged 50
in every 240 people
2
divide area in 6 segments of around 240 people
3
1
6
4
5
Select one segment at random and examine all
houses until you have completed 50 people aged
50
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Compact segment sampling

• Advantages
• Less bias, because
• No subjective decisions to be taken by staff on
  which direction to continue
• All households in selected segment are visited
• Higher compliance
• All people aged 50 or higher are sure to be
  visited
• Less people have to stay home for examination

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Compact segment sampling

• Advance team
• will visit selected cluster area 3-5 days before
  survey team
• will divide the population unit in equal segments
  on the census map
• if no map is available, they will make a sketch
  map of the population unit and divide this in
  segments of similar size
• will locate next nearest population unit in case
  original unit has not enough eligible people
• will contact local leaders to announce purpose
  and date of survey
• will contact local health worker/social worker to
  ensure they accompany the survey team

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Materials needed per team

• 1 direct ophthalmoscope (spare batteries)
• 1 portable slit lamp and mydriatic (optional)
• 1 pinhole, preferable with multiple holes
• 1 torch with spare batteries
• 2 E charts (see attachment for sizes)
• 2 pencils with eraser and sharpener
• 1 rope-measure for 6 and 3 meter
• 1 clipboard to hold the forms
• as many survey forms as cluster size
• map of population unit divided in segments
• referral slips and basic drugs for treatment
• identity card
• shoulder bag to carry all materials

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Inter-observer variation

• Before the teams start the actual field work it
  has to be checked whether they can make an
  adequate diagnosis
• Each team examines 40-50 people aged 50
• The teams should not know the findings of the
  other teams
• Findings of most experienced team are considered
  correct
• Findings of other teams are compared with those
  of most experienced team (Gold Standard)
• Agreement is measured by Kappa statistic.

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Inter-observer variation
Each team examines 40-50 people aged 50, e.g.
patients and their company from the outpatient
department
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Inter-observer variation
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Report on inter-observer variation
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Cleaning of data

• During data entry
• In case of missing or inconsistent entry
• field turns red
• when curser is placed on a red field, a message
  appears indicating the error
• when saving this record the same message appears.
• Checking all survey and IOV records
• Enter data immediately after a cluster was
  completed and use the menu Reports Consistency
  check to check consistency of all records
  entered so far
• Print out errors and ask team leader to correct
• Validation of double data entry
• Enter all records twice by different operators.
• Compare data files. File is considered clean if
  there are no differences

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Consistency checks
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Consistency checks
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Consistency checks

• Enter survey records immediately after the teams
  return from the field and run the consistency
  check.
• In case of any errors, the field staff may still
  remember the patient and the error can be
  corrected.
• Fragment from consistency report.

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Validate double entry

• Survey records are entered by two different data
  entry operators
• Use Records Validation through double data
  entry menu to compare survey data files
• When completed, both data files are compared by
  linking the patient ID
• When all records are exactly the same, both
  records are considered to be entered correctly
• When records differ, both records have to be
  compared with the survey form and be corrected.
• Run validate again until both data files show no
  differences.

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Data analysis
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Prevalence reports

• Sample prevalence
• Prevalence as calculated from findings in the
  survey
• Age and sex adjusted prevalence
• Compare age and sex composition of sample differs
  with actual population in the survey area.
• Population file gives population by sex and
  5-year age group in survey area.
• If different, prevalence is adjusted by software

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Cataract Surgical Coverage

• Objective
• - coverage indicator
• - measure accessibility utilisation of
  services
• - measures actual field situation
• - independent of reporting system
• CSC eyes
• Where a no. (pseudo)aphakic eyes
• b no. eyes with operable cataract
• CSC persons
• Where x persons with bilateral
  (pseudo)aphakia
• y persons with 1 (ps)aphakic and 1 operable
  cataract eye
• z persons with bilateral operable cataract

a a b
x y x y z
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Age and sex adjusted prevalence
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Barriers to cataract surgery

• Ask open question
• Why have you not been operated yet?
• List of pre-defined barriers
• Interviewer selects 1-2 barriers from list that
  reflect best the patients reply
• Analysis of barriers by
• Males, females
• Bilateral and unilateral blindness and SVI due to
  cataract (VAlt3/60 VAlt6/60)

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Barriers
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Barriers to cataract surgery
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Visual outcome after cataract surgery
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Long-term visual outcome after cataract surgery

• with IOL non-IOL couching
• with available correction best corrected or
  pinhole vision
• during last 5 years more than 5 years ago
• outcome by place of surgery
• major causes of poor outcome
• age at time of surgery
• satisfaction
• causes of poor outcome after surgery
• type of surgery by sex

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WHO guidelines on Visual Outcome of Cataract
Surgery
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Best corrected vision after 1 year in clinical
trials
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Average visual outcome in population based
studies
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Visual outcome after cataract surgery in
population based surveysPlease note variation in

• Post-operative period varies from weeks to
  decades
• Quality of surgical facilities (basic to
  excellent)
• Experience and skills of surgeons (couchers)
• Supply and replacement of spectacles
• Initial good outcome may go down due to other eye
  disorders, reducing vision with age
• Outcome data from surveys may not do justice to
  recent advancements in IOL surgery, but may very
  well reflect what the public sees and what
  determines their expectations and trust to regain
  sight after surgery

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Also . . . .

• Good outcome will motivate other patients to come
  forward for surgery
• Poor outcome will deter other cases
• In most surveys fear of losing sight was major
  reason not to come for surgery
• When causes of poor outcome are known, it will be
  possible to address these causes and thereby
  improve results of cataract surgery

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Service indicators

• Age at time of surgery
• Place of surgery, by sex
• Costs of services provided, by sex
• Use of spectacles, by sex
• Type of surgery (IOL non-IOL)
• Cause of poor outcome
• Satisfied with results of operation

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Tables and graphs
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Sampling error and Design effect
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RACSS / RAAB conducted in

• India, 1995 onwards
• Paraguay, 1999
• Turkmenistan, 2000
• Vietnam, 2000
• Pakistan, 2000
• Myanmar, 2001
• Cambodia, 2001
• Mauretania, 2001
• Mali, 2001
• Guadalajara, Mexico, 2001
• Peru, 2002
• BA, Argentina, 2003
• Campinas, Brazil, 2003
• Nigeria,
• Venezuela, 2004
• Guatemala, 2004
• Lombok, Indonesia, 2004

• Cuba, 2004
• Nakuru, Kenya, 2005
• Philippines, 2005, 2006
• Colombia, 2005
• Bangladesh, 2005
• Botswana, 2006
• Nuevo Leon, Mexico, 2005
• Rwanda, 2006
• Bio Bio, Chile, 2006
• Kunming, China 2006
• Laos, 2006
• Kericho, Kenya, 2007
• Vietnam, 2007
• Zanzibar, 2007
• Jiangxi, China, 2007
• Cambodia, 2007

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Thank you!
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