Health Insurance System Research Office
How to deal with data
problems?
By Ms. Alice Molinier (ILO) and
Ms. Orawan Prasitsiriphon
(HISRO)
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Health Insurance System Research Office
Agenda
• Handling data problems
• Coffee Break
• Concrete examples
สำนักงำนวิจัยเพื่อกำรพัฒนำหลักประกันสุขภำพไทย
Health Insurance System Research Office
Type of data problems
• Incorrect or inaccurate data
– incomplete or incorrect recording of
elementary data or incorrect aggregation
data etc.
• Incomplete data
– data on administrative costs are often not
available.
• Missing data
– the national health statistical system in
question simply does not report a specific
item which the modeler considers.
Source: Cichon,1999,Modeling in health care financing
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Handling data problems
Master data management.jpg
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Gathering data
Some tips
•
•
•
•
•
•
•
•
Plan it in advance!
List all the data you will need and the institutions that you will need to
contact
In each institution you need at least two counterparts
– 1 decision maker
– 1 technician
Be aware of the formal procedure
Data are never perfect when you receive them on the first day!
Explain why you need the data so that people do not have false
expectations and are ready to share information “for free”.
The minimum required data is to be mentioned in the letters; but a direct
access to the databases is to be preferred when possible.
Explain your assumptions and report your sources
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How to assess the quality of the data?
Some tips
•
•
•
•
•
Ask for non consolidated data (best: direct access to the database)
Ask following questions:
– a. How do they obtain the data?
– b. How frequently do they actualize the database?
– c. What are the updating processes of the databases?
Try to cross check data
Generate graphs (they help to visualize possible problems: frauds,
inconsistencies,etc.) and try to find a logic in the data (if you cannot
explain a phenomenon – there may be a problem)
Compare data in the reports (official accounts) and the databases, and if
there are differences try to understand why (e.g., non effective updating
processes…) with an aim to get the “true picture”
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How to assess the quality of
data?
Plausibility
Reliability
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Filling data gaps.
1. Develop system to gather the data.
2. Gather a sample of the data.
3. Make assumptions to develop
substitutes for the data.
4. Use proxy data from similar countries.
Source: Cichon,1999,Modeling in health care financing
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Agenda
• Handling data problems
• Coffee Break
• Concrete examples
– Incorrect or inaccurate data (cohort +
problem of two data source for
fertility rate)
– Incomplete data (fertility rate)
– Missing data (mortality rate)
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Population projection
• The cohort component technique uses
the components of demographic change
to project population growth.
• The technique projects the population by
age groups and sex.
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population projection
•
The cohort component is based on the components of demographic
change including births, deaths, and migration (assume net migration=0)
Year
Age
0
1
T
T+1
T+2
Populationt+1 =
Populationt
+
newbornst+1 – deathst+1
+
immigrantst+1 – emigrantst+1.
2
3
4
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1. Incorrect or inaccurate
data
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MALE POPULATION (single age, in thousands)
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
2005
2006
379
391
437
435
454
482
452
529
593
578
625
559
618
602
646
2007
382
394
440
405
434
454
445
491
574
568
611
540
602
577
655
2008
364
422
436
433
413
450
444
482
520
536
629
559
613
582
622
2009
356
386
434
455
421
444
424
475
483
515
582
585
594
577
632
Source: The last census in 2000 from NSO
2010
349
370
430
428
442
454
411
452
483
495
529
520
609
568
646
356
374
404
449
415
434
434
465
458
449
535
476
542
603
653
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MALE POPULATION (single age, in thousands)
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
2005
379
391
437
435
454
482
452
529
593
578
625
559
618
602
646
2006
382
394
440
405
434
454
445
491
574
568
611
540
602
577
655
2007
364
422
436
433
413
450
444
482
520
536
629
559
613
582
622
2008
356
386
434
455
421
444
424
475
483
515
582
585
594
577
632
Source: The last census in 2000 from NSO
2009
349
370
430
428
442
454
411
452
483
495
529
520
609
568
646
2010
356
374
404
449
415
434
434
465
458
449
535
476
542
603
653
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Pyramid Population
2005
2010
65 +
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
4,000 3,000 2,000 1,000
65 +
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
0
Female
1,000 2,000 3,000 4,000
Male
4,000 3,000 2,000 1,000
0
Female
1,000 2,000 3,000 4,000
Male
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How to deal?
Accept / Reject
Accept
plausibility
and
reliability
Have influences
to model
Reject
Have any data
which are more
appropriate
Have any
mathematics to
adjust data
Health model
RAP model
Limitation of data
e.g. HWS:
population by
scheme
Mean, Revised,
Linear equation
etc.
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2. Incomplete data
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Fertility rate
• Fertility rate is used to calculate number of
newborns.
• These data is grouped by 5-year age group.
(to protect fluctuate and abnormal data)
• Single age,
• It can be derived from the method of
interpolations e.g. Sprague multipliers,
Polynomial Interpolation, Karup-King thirddifference formula etc.
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Example 2
• Maternity Allowance for all informal
working women in year 2008-2010.
• What do you do when you have two
different source of information?
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Data and Information
What do we have?
– Fertility rate assumption of 2008-2010
– Number of population, employees in formal
sector and economically active population by
age and sex since 2008-2010
What do we want?
– Number of newborns that would be born with
women in informal workers in 2008-2010.
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Health Insurance System Research Office
Informal workers in 2006-2010
In thousand
กลุ่มอำยุ
Age Group
Total
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 49
50 - 59
60 +
2549
2006
9,875
360
601
796
1,146
1,397
2,862
1,869
845
2550
2007
10,849
309
706
848
1,166
1,518
3,093
2,156
1,053
2551
2008
11,100
283
743
897
1,188
1,496
3,147
2,285
1,060
2552
2009
11,121
300
756
845
1,136
1,426
3,172
2,307
1,179
2553
2010
11,133
273
711
895
1,080
1,409
3,228
2,366
1,171
Source: NSO
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How to calculate?
• Determine alternative between
– Group fertility rate assumption data from age group
40-44 and 45-49 to 40-49
– Separate female informal worker data from age
group 40-49 to 40-44 and 45-49
• Calculate number of newborns who born with
women in informal workers
#Newborn=# of women in fertile age * fertility rate
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Group fertility rate assumption
Age Group 2005-2010
15-19
0.0098
20-24
0.0691
25-29
0.1243
30-34
0.0796
35-39
0.0308
40-44
0.0074
45-49
0.0008
TFR
1.6090
Age Group 2005-2010
15-19
0.0098
20-24
0.0691
25-29
0.1243
30-34
0.0796
35-39
0.0308
40-49
0.0082
TFR
1.6090
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Separate female informal worker
• Assume proportion of total economically
active population of age group 40-44 and
45-49 equal proportion of female
informal workers in the same age groups.
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Separate female informal worker (2)
Total economically active population in thousand unit
Age
2006
2007
2008
2009
2010
Age
2006
2007
2008
2009
2010
40-49
8,845.7
9,072.1
9,301.0
9,525.0
9,627.5
40-49
8,845.7
9,072.1
9,301.0
9,525.0
9,627.5
40-44
4,672.7
4,785.1
4,927.0
4,989.7
4,983.6
40-44
4,672.7
4,785.1
4,927.0
4,989.7
4,983.6
45-49
4,173.0
4,287.0
4,374.1
4,535.3
4,643.9
45-49
4,173.0
4,287.0
4,374.1
4,535.3
4,643.9
Proportion
Proportion
0.53:0.47
0.53:0.47
0.53:0.47
0.53:0.47
0.53:0.47
0.53:0.47
0.52:0.48
0.52:0.48
0.52:0.48
0.52:0.48
Female informal workers in thousand unit
Age
2006
2007
2008
2009
2010
40-49
2,861.82
3,093.31
3,147.47
3,172.22
3,227.90
40-44
1,511.76
1,631.56
1,667.29
1,661.77
1,670.89
45-49
1,350.06
1,461.75
1,480.19
1,510.45
1,557.01
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Number of newborns in thousand unit
Group fertility rate assumption
Age
Group 2006 2007 2008 2009 2010
Total
302
322
332
321
319
Separate female informal worker
Age
Group 2006 2007 2008 2009 2010
Total 291 310 320 308 306
15 - 19
3.5
3.0
2.8
2.9
2.7
15 - 19
3.5
3.0
2.8
2.9
2.7
20 - 24 41.5
48.8
51.3
52.2
49.1
20 - 24 41.5
48.8
51.3
52.2
49.1
25 - 29 99.0
105.4
111.6
105.0
111.3
25 - 29 99.0 105.4 111.6 105.0 111.3
30 - 34 91.2
92.8
94.6
90.5
86.0
30 - 34 91.2
92.8
94.6
90.5
86.0
35 - 39 43.0
46.8
46.1
43.9
43.4
35 - 39 43.0
46.8
46.1
43.9
43.4
40 - 49 23.5
25.4
25.8
26.0
26.5
40 - 44 11.2
12.1
12.3
12.3
12.4
45 - 49
1.2
1.2
1.2
1.2
1.1
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3. Missing data
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Mortality pattern
• In Thailand, no standard mortality
pattern.
• It has only mortality statistic and life
expectancy.
• Use proxy data from similar countries by
‘West model’ mortality pattern then
adjust by life expectancy in Thailand.
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