The document describes a Data Quality Review (DQR) framework for assessing the quality of health facility data. The DQR is a multi-pronged approach that builds on previous data quality assessment tools to evaluate data quality in a more systematic way. It includes a desk review of existing health facility data and indicators as well as facility surveys to verify data accuracy and assess health management information systems. The DQR framework provides a standardized method for stakeholders to evaluate routine health data quality and link these findings to health sector planning activities.

1. Data Quality
Review (DQR)
Framework
Review of quality of
health facility data

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Why is health facility data important?Why is health facility data important?
 For many indicators it is the only
continuous/frequent source of data
 It is most often the only data source that is
available at the subnational level -- important for
equity;
 For many key indicators, it is the sole source of
data. For example, PMTCT, ART, TB treatment
outcomes, TB notification, confirmed malaria
cases, causes of death, etc.

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3
Quality of health facility data – why do we care?Quality of health facility data – why do we care?
 High-quality data provide evidence to providers
and managers to optimize healthcare coverage,
quality, and services.
 High-quality data help:
― Form an accurate picture of health needs, programs, and
services in specific areas
― Inform appropriate planning and decision making
― Inform effective and efficient allocation of resources
― Support ongoing monitoring, by identifying best practices
and areas where support and corrective measures are
needed

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Most common problems affecting data qualityMost common problems affecting data quality
 Lack of guidelines to fill out the main data sources
and reporting forms
 Personnel not adequately trained
 Misunderstanding about how to compile data,
use tally sheets, and prepare reports
 Un-standardized source documents and reporting
forms
 Arithmetic errors during data compilating
 Lack of a reviewing process, before report
submission to next level

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Many tools have been used to address data qualityMany tools have been used to address data quality
 GAVI DQA
 WHO Immunization Data Quality Self-assessment
(DQS)
 Global Fund/MEASURE Evaluation DQA
 RDQA - Self assessment version of DQA (with in-
country adaptations)
 Global Fund OSDV
 PRISM
 WHO Data Quality Report Card (DQRC)

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DQR - harmonized approach to assessing and
improving data quality
DQR - harmonized approach to assessing and
improving data quality
 The DQR is a multi-pronged , multi-partner framework for
country-led data quality assurance that proposes a
harmonized approach to assessing data quality
 It is a framework that builds on the earlier program-
specific quality tools and methods while proposing the
examination of data quality in a more systemic way that
can meet the needs of multiple stakeholders
 It is a framework that also includes the examination of
existing facility data (that does not require additional
data collection) missing from earlier tools
 It provides valuable information for fitness-for-purpose to
support the Health Sector Strategic Planning Cycle (e.g.
health sector or program reviews)

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Why are we recommending a harmonized
approach to data quality?
Why are we recommending a harmonized
approach to data quality?
 Data quality is a systems issue - multiple assessments for
different diseases/programs are inefficient and burdensome
for the health system
 Can we satisfy the needs in data quality assurance of all
stakeholders with one holistic data quality assessment?
 The application of a standard framework to evaluate data
quality enables the understanding of the adequacy of
routine data used for health sector planning – can we link
data quality assessment to health planning efforts?
 Permits stakeholders to know that the routine data have
undergone a known minimum level of scrutiny which lends
credibility and confidence in the data

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DQR FrameworkDQR Framework

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Recommended Core Program IndicatorsRecommended Core Program Indicators
Program Area Indicator Name Full Indicator
Maternal
Health
Antenatal care 1st
visit (ANC1)
Number (%) of pregnant women who attended at least
once during their pregnancy
Immunization DTP3/Penta3 Number (%) of children < 1 year receiving three doses of
DTP/Penta vaccine
HIV/AIDS ART coverage Number and % of people living with HIV who are
currently receiving ART
TB Notified cases of
all forms of TB
Number (%) of all forms of TB cases (i.e. bacteriologically
confirmed plus clinically diagnosed) reported to the
national health authority in the past year (new and
relapse)
Malaria Confirmed malaria
cases
Number (%) of all suspected malaria cases that were
confirmed by microscopy or RDT

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DQR methodologyDQR methodology
C
om
ponent
s

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Domains of Data QualityDomains of Data Quality

12. Data Quality
Review (DQR)
Desk review

13. 13
 Completeness and timeliness
― Completeness of reports
― Completeness of data
― Timeliness of reports
 Internal consistency
― Accuracy
― Outliers
― Trends
― Consistency between indicators
 External consistency
― Data triangulation
― Comparison with data surveys
― Consistency of population trends
 External comparisons (population denominators)
Metrics for Data Quality Performance

14. 14
Completeness and Timeliness of Data
This examines the extent to which:
Data reported through the system are available and
adequate for the intended purpose
All entities that are supposed to report are actually reporting
Data elements in submitted reports are complete
Reports are submitted/received on time through the levels
of the information system data flow

15. 15
• Completeness of reports (%) =
# total reports available or received
# total reports expected
• Completeness of indicator data (%) =
# indicator values entered (not missing) in the
report
# total expected indicator values
• Timeliness (%) =
# reports submitted or received on time
# total reports available or received
Completeness and Timeliness of Data

16. 16
Internal Consistency of Reported Data
This dimension examines:
The accuracy of reporting of selected indicators, by
reviewing source documents
Whether data are free of outliers (within bounds), by
assessing whether specific reported values within the
selected period (such as monthly) are extreme, relative to
the other values reported
Trends in reporting over time, to identify extreme or
implausible values year-to-year
The program indicator compared to other indicators with
which they have a predicable relationship, to determine
whether the expected relationship exists between the two
indicators

17. 17
Internal Consistency: Outliers
Metric Severity
Definition
National Level Subnational Level
Outliers
(Analyze
each
indicator
separately.)
Extreme
(At least 3
standard
deviations from the
mean)
% of monthly
subnational unit
values that are
extreme outliers
# (%) of subnational units in
which ≥1 of the monthly
subnational unit values over the
course of 1 year is an extreme
outlier value
Moderate
(Between 2–3
standard
deviations from the
mean, or >3.5 on
modified Z-score
method)
% of subnational
unit values that
are moderate
outliers
# (%) of subnational units in
which ≥2 of the monthly
subnational unit values over the
course of 1 year are moderate
outliers
17

18. 18
Example: Outliers in a Given Year
Dist
Month
Total
Outliers
%
Outliers
1 2 3 4 5 6 7 8 9 10 11 12
A 2543 2482 2492 2574 3012 2709 3019 2750 3127 2841 2725 2103 1 8.3%
B 1184 1118 1195 1228 1601 1324 1322 711 1160 1178 1084 1112 2 16.7%
C 776 541 515 527 857 782 735 694 687 628 596 543 0 0%
D 3114 2931 2956 4637 6288 4340 3788 3939 3708 4035 3738 3606 1 8.3%
E 1382 1379 1134 1378 1417 1302 1415 1169 1369 1184 1207 1079 0 0%
Nat’l 0 0 0 0 2 0 0 1 0 0 0 1 4 6.7%
Months with at least one moderate outlier on the district monthly reports
are shown in red.

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Metric
Definition
National Level Subnational Level
Trends/
Consistency
over Time
(Analyze
each
indicator
separately.)
Conduct one of the following, based on
indicator’s expected trend:
• Indicators or programs with expected growth:
Compare current year to the value predicted
from the trend in the 3 preceding years
• Indicators or programs expected to remain
constant: Compare current year to the average
of 3 preceding years
# (%) of districts
whose ratio of
current year to
predicted value (or
current year to
average of
preceding 3 years) is
at least ± 33% of
national ratio
Graphic depiction of trend to determine
plausibility based on programmatic knowledge
Internal Consistency: Trends Over Time

20. 20
Example: Trends over Time
District
Year
Mean of
Preceding
3 Years
(2010-
2012)
Ratio of 2013
to Mean of
2010-2012
% Difference
between National
and District Ratios2010 2011 2012 2013
A 30242 29543 26848 32377 28878 1.12 0.03
B 19343 17322 16232 18819 17632 1.07 0.08
C 7512 7701 7403 7881 7539 1.05 0.09
D 15355 15047 14788 25123 15063 1.67 0.44
E 25998 23965 24023 24259 24662 0.98 0.16
National 98450 93578 89294 108459 93774 1.16
Consistency trend: Comparison of district ratios to national ratios
Any difference between district and national ratio that is ≥33% is
highlighted in red.

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Internal Consistency: Comparing Related Indicators
Metric
Definition
National Level Subnational Level
Consistency
among
related
indicators
Maternal Health: ANC1 - IPT1 or TT1
(should be roughly equal)
# (%) of subnational units where there is
an extreme difference (≥ ± 10%)
Immunization: DTP3 dropout rate =
(DTP1 - DTP3)/DTP1
(should not be negative)
# (%) of subnational units with # of DTP3
immunizations > DTP1 immunizations
(negative dropout)
HIV/AIDS: ART coverage - HIV
coverage (should be <1)
# (%) of subnational units where there is
an extreme difference (≥ ± 10%)
TB: TB cases notified - TB cases on
treatment (should be roughly
equal)
# (%) of subnational units where there is
an extreme difference (≥ ± 10%)
Malaria: # confirmed malaria cases
reported - cases testing positive
(should be roughly equal)
# (%) of subnational units where there is
an extreme difference (≥ ± 10%)

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Example: Internal Consistency
District ANC1 IPT1
Ratio of
ANC1 to IPT1
% Difference between
National & District Ratios
A 20995 18080 1.16 0.02
B 18923 16422 1.15 0.02
C 7682 6978 1.10 0.07
D 12663 9577 1.32 0.12
E 18214 15491 1.18 0
National 78477 66548 1.18
% difference between ANC1 and IPT1, by district
Districts with % difference ≥10% are flagged in red.

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External Consistency with Other Data Sources
This dimension examines the level of agreement
between two sources of data measuring the same
health indicator.
The two most common sources of data are:
The routinely collected and reported data from the
health management information system (HMIS) or program-
specific information system
A periodic population-based survey

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External Consistency: Compare with Survey Results
Examples of
Indicators
Definition
National Level Subnational Level
ANC 1st
visit
Ratio of facility
ANC1 coverage
rates to survey
ANC1 coverage
rates
# (%) of aggregation units used for the most recent
population-based survey, such as
province/state/region, whose ANC1 facility-based
coverage rates and survey coverage rates differ
by at least 33%
3rd
dose
DTP3
vaccine
Ratio of DTP3
coverage rates
from routine data
to survey DTP3
coverage rates
# (%) of aggregation units used for the most recent
population-based survey, such as
province/state/region, whose DTP3 facility-based
coverage rates and survey coverage rates differ
by at least 33%

25. 25
Example: External Consistency
District
Facility
Coverage
Rate
Survey
Coverage
Rate
Ratio of Facility
to Survey Rates
% Difference
between Official and
Alternate
Denominator
A 1.05 0.95 1.10 10%
B 0.93 0.98 0.96 4%
C 1.39 0.90 1.54 54%
D 1.38 0.92 1.50 50%
E 0.76 0.95 0.80 20%
National 1.10 0.94 1.17 17%
Comparison of HMIS and survey coverage rates for ANC1
Differences ≥ 33% are highlighted in red.

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External Comparison of Population Data
This dimension examines two points:
The adequacy of the population data used in the
calculation of health indicators
The comparison of two different sources of population
estimates (for which the values are calculated differently) to
see the level of congruence between the two sources

27. 27
External Comparison of Population Data
Metric
Definition
National Level Subnational Level
Consistency of
population
projections
Ratio of population projection
of live births from the country
census bureau/bureau of
statistics to a United Nations
live births projection for the
country
NA
Consistency of
denominator
between program
data & official
government
population
statistics
Ratio of population projection
for select indicator(s) from the
census to values used by
programs
# (%) of subnational units
where there is an extreme
difference (e.g., ±10%)
between the 2 denominators

28. 28
External Comparisons of Population Denominators
District
Official Government
Estimate for Live
Births
Health Program
Estimate for Live
Births
Ratio of Official
Government to Health
Program Estimates
A 29855 29351 1.02
B 25023 30141 0.83
C 6893 7420 0.93
D 14556 14960 0.97
E 25233 25283 1.00
National 101560 107155 0.95
Comparison of national and subnational administrative unit ratios of
official government live birth estimates
Administrative units with differences ≥ ±10% are highlighted in red.

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How do we do the desk review?
A data quality app has been created for DHIS and can be
downloaded by users and applied to their country DHIS
databases
For those that do not have DHIS, an Excel tool has been
developed to support data quality analysis
The principles of the desk review can be applied in any
software that a country has and are not limited to the
tools presented

30. Data Quality
Review (DQR)
Data verification and
system
assessment

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Facility Survey Component of DQR
There are 2 components:
― Data verification – Examines the accuracy of reporting of selected
indicators, by reviewing source documents
― System assessment -- Review adequacy of system to collect, compile,
transmit, analyze, and use HMIS & program data
Survey at 2 levels
― Health facility
― District

32. 32
Accuracy: Data Verification
Quantitative:
Compares recounted to reported
data
Implement in 2 stagesImplement in 2 stages
Assess on a limited
scale if sites are
collecting and
reporting data
accurately and on
time
In-depth verifications at
the service delivery sites
Follow-up verifications at
the intermediate and
central levels
32

33. 33
Data Verification Following Data Flow
33

34. 34
Accuracy: Verification Factor
Verification Factor
Numerator: Recounted data
Denominator: Reported data
 Over-reporting: <100%
 Under-reporting: >100%
Suggested range of
acceptability:
100% +/- 10%
(90% –110%)
34

35. 35
Verification factor
 Weighted mean of verification ratios
 Summarizes information on the reliability of reporting of
the data reporting system
 Indicates the degree of over-/under-reporting in the
system
― e.g. VF = 0.80 indicates that of the total reported number of
events, approximately 80% could be verified in source
documents -> over-reporting

36. 36
Verification Factor Example
v
Indicator 1 Indicator 2
Recounted Reported VF Recounted Reported VF
A 1212 1065 1.14 4009 4157 0.96
B 1486 1276 1.16 3518 3686 0.95
C 357 387 0.92 672 779 0.86
D 2987 3849 0.78 1361 1088 1.25
E 4356 4509 0.97 4254 3970 1.07
Data accuracy by district
Indicators flagged in red are verification factors ≥ ±10% of 1.
36

37. 37
Verification Factors Plotted Graphically
over-reported
underreported
37

38. 38
Data verification
Recommended maximum 5 indicators for review
—ANC1, DTP3/Penta 3, ART coverage, TB cases, malaria
cases (confirmed)
Select a time period for the verification (3 months)
— e.g. : July, August, September 2016
For each indicator:
—Review the source documents and reports
—Recount the number of events
—Compare the recount to the reported events
—Determine reasons for any discrepancies

39. 39
System Assessment Indicators
Indicator
Level
Facility District
Presence of trained staff X X
Presence of guidelines X X
No recent stock out of data collection tools X X
Recently received supervision and written feedback X X
Evidence of analysis and use data X X

40. 40
If the sampling permits it,
system assessment findings can be disaggregated by strata

41. 41
Are there tools to support data verification
and system assessment?
 A paper-based questionnaire is available that can be adapted to a
country situation
 A data collection program has been developed in CSPro for tablets
 An analysis tool has been developed in Excel for support the analysis
of the data collected during this exercise

42. Data Quality
Review (DQR)
Desk review
Excel Tool

43. 43
43
Opening Screen

44. 44
Input Basic Information Tab – Parameters of the
analysis

45. 45
Input Administrative Units for the Analysis

46. 46
Input Program Areas and Indicators

47. 47
Input Quality Thresholds
Recommended User-defined
Domain 1: Completeness and Consistency of Reporting/Indicator Data Col 1 Col 2
1a
1a1a 75%
1a1b 75%
1a2a 75%
1a2b 75%
1a3a 75%
1a3b 75%
1a4a 75%
1a4b 75%
1b
Program Area 1: Maternal_Health
1b1 Indicator 1: ANC 1st Visit 90%
Program Area 2: Immunization
1b2 Indicator 1: 3rd dose DPT-containing vaccine 67%
Program Area 3: HIV_AIDS
1b3 Indicator 1: Number of HIV+ persons currently on ART 90%
Program Area 4: Malaria
1b4 Indicator 1: Number of confirmed malaria cases reported 90%
Program Area 5: TB
1b5 Indicator 1: Number of Notified TB cases (all forms of TB) 75%
Completeness of Region Level Reporting
Completeness of Indicator Reporting: % of data elements that are non-zero values; % of data elements
that are non-missing values
Timeliness of Region Level Reporting
Completeness of District Level Reporting
Threshold
Completeness and Timliness of Reporting from Health Facilities and Aggregation Levels: District, Region,
Province
Completeness of Province Level Reporting
Timeliness of Province Level Reporting
Timeliness of District Level Reporting
Completeness of Health Facility Level Reporting
Timeliness of Health Facility Level Reporting
Quality Thresholds:
'Quality thresholds'are the values that set the limits of acceptable error in data reporting. The analyses in the DQR
compare results to these thresholds to judge the quality of the data. Recommended values are included for each
metric in column 1. User-defined thresholds can be input into col 2 which will take precedence over the values in
col 1.

48. 48
Input Information on Completeness and Timeliness

49. 49
Input Population Data

50. 50
Input Data on Indicator Trends

51. 51
Input Indicator Data

52. 52
Summary Dashboard
No. Indicator Definition
National Score
(%)
# of districts not
attaining quality
threhold
% of districts not
attaining quality
threshold
1a
Completeness of District
Reporting
National district reporting completeness rate and
districts with poor completeness of reporting
99.1% 1 2.1%
1b
Timeliness of District
Reporting
National district reporting timeliness rate and districts
with poor timeliness of reporting
90.3% 3 6.4%
1c
Completeness of Facility
Reporting
National facility reporting completeness rate and districts
with poor facility reporting completeness
96.1% 8 17.0%
1d
Timeliness of Facility
Reporting
National facility reporting timeliness rate and districts
with poor facility reporting timeliness
89.6% 11 23.4%
Maternal_Health - ANC 1st Visit 98.9% 1 2.1%
Immunization - 3rd dose DPT-containing vaccine 99.3%
HIV_AIDS - Number of HIV+ persons in palliative care 99.8%
Malaria - Number of confirmed malaria cases reported 99.8%
Immunization - OPV3 98.9% 1 2.1%
Multi-program - Penta 1st doses 99.1%
Maternal_Health - ANC 1st Visit 98.9% 2 4.3%
Immunization - 3rd dose DPT-containing vaccine 99.5%
HIV_AIDS - Number of HIV+ persons in palliative care 100.0%
Malaria - Number of confirmed malaria cases reported 99.5% 1 2.1%
Immunization - OPV3 100.0%
Multi-program - Penta 1st doses 100.0%
1f.1
Consistency of Reporting
Completeness - District
Reporting
Consistency of district reporting completeness and
districts deviating from the expected trend
105.8%
1f.2
Consistency of Reporting
Completeness - Facility
Reporting
Consistency of facility reporting completeness and
Districts deviating from the expected trend
103.5%
DOMAIN 1: COMPLETENESS OF REPORTING
BURUNDI - ANNUAL DATA QUALITY REVIEW: RESULTS, 2016
DOMAIN 2: INTERNAL CONSISTENCY OF REPORTED DATA
Completeness of indicator
data (missing values)
1e.1
1e.2
Indicator 1: Completeness and timeliness of reporting
Indicator 1e: Completeness of indicator data - presence of missing and zero values
Indicator 1f: Consistency of reporting completeness over time
Completeness of indicator
data (zero values)

53. 53
Domain 1: Completeness of Reporting

54. 54
Domain 1: Completeness of Indicator Data
National
score
Program Area and Indicator
Quality
Threshold
Type % No. % Name
Missing 98.9% 1 2.1%
Zero 98.9% 2 4.3%
Missing 99.3% 1 2.1%
Zero 99.5% 1 2.1%
Missing 99.8%
Zero 100.0%
Missing 99.8%
Zero 99.5% 1 2.1%
Missing 98.9% 1 2.1%
Zero 100.0%
Missing 99.1% 1 2.1%
Zero 100.0%
Missing 99.3% 4 8.5%
Zero 99.6% 4 8.5%
Malaria - Number of confirmed malaria
cases reported
<= 90%
-
Multi-program - Penta 1st doses <= 90%
District 21
-
Indicator 1f: Consistency of Reporting Completeness
Immunization - OPV3 <= 90%
District 7
-
Total (all indicators combined)
HIV_AIDS - Number of HIV+ persons in
palliative care
<= 90%
-
-
Indicator 1e: Completeness of Indicator Reporting - Presence of Missing and Zero Values
2016
Maternal_Health - ANC 1st Visit
District 44
District 17, District 29
<= 90%
Districts with > user-defined % of zero or missing values
Immunization - 3rd dose DPT-
containing vaccine
<= 90%
District 19
District 17
Interpretation of results: Indicator 1e
•
•
•
•

55. 55
Domain 2: Internal Consistency - Outliers
National
score
% No. % Name
0.2% 1 2.1%
0.0%
0.5% 3 6.4%
0.0%
0.4% 2 4.3%
0.0%
0.2%
Indicator 2a.1: Extreme Outliers (>3 SD from the mean) 2016
Malaria - Number of confirmed malaria cases reported
Immunization - OPV3
Multi-program - Penta 1st doses
Total (all indicators combined)
DOMAIN 2: INTERNAL CONSISTENCY OF REPORTED DATA
-
Districts with extreme outliers relative to the mean
District 39
Indicator 2a: Identification of Outliers
-
District 31, District 39
-
Program Area and Indicator
District 9, District 38, District 41
Maternal_Health - ANC 1st Visit
Immunization - 3rd dose DPT-containing vaccine
HIV_AIDS - Number of HIV+ persons in palliative care
Interpretation of results - Indicator 2a1:
•
•
•
•
•
•

56. 56
Domain 2: Consistency over time
Quality threshold
National score (%)
Number of districts with divergent scores
Percent of districts with divergent scores
Names of districts with divergent scores:
20%
Expected trend Increasing
Compare districts to: expected result
2b3: Consistency of 'General_Service_Statistics -
OPD Total Visits' over time
Year 2014
100%
5
38.5%
District 6, District 7, District 8, District 9, District 11
0
100,000
200,000
300,000
400,000
500,000
600,000
0 100,000 200,000 300,000 400,000 500,000
General_Service_Statistics-OPDTotalVisits
eventsforyearofanalysis
Forcasted General_Service_Statistics - OPD Total Visits value for
current year based on preceding years (3 years max)
0
1,000,000
2,000,000
3,000,000
2011 2012 2013 2014
Trend over time: General_Service_Statistics -OPD Total Visits
Interpretation of results - Indicator 2c3:
•This indicator is increasing over time (Outpatient visits are
increasing - something we were expecting given social mobiliation for
public health services.
•Comparison of expected result (that the forecasted value is equal to the
actual value for 2014) yeilds 5 districts with ratios that exceed the
quality threhold of 20%. 3 are inferior of the quality threshold while 2
are greater.
• Errors are not systematic (e.g. all in one direction) Review district
outpatient registers in affected districts to confirm reported values.

57. 57
Domain 2: Consistency between related indicators
Percent of districts with divergent scores 15.4%
Names of districts with divergent scores:
District 5, District 6
Indicator 2c: Internal Consistency - Consistency Between Related Indicators
Consistency between related indicators - Ratio of two related indicators and Districts with ratios significantly different from the
national ratio *
2c1: Maternal Health Comparison: ANC 1st Visit :
IPT 1st Dose
Year 2014
Expected relationship
National Score (%) 114%
Number of districts with divergent scores 2
equal
Quality Threshold 10%
Compare districts with: national rate
Interpretationof results - Indicator 2c1:
• Data seem pretty good - only district 5 has a largely discrepant value
• IPT seens consistently lower than ANC1 - more pregnant women should be receiving IPT
• Stock out of fansidar in Region 2 could explain low number of IPTin Districts 5 . Call DHIO in these districts to
investigate
•National rate is 114% - most districts are close to this value. District 6 is performing well relative to the other districts
but is 'discrepant' relative to the national rate. - no follow up needed.
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
ANC1eventsforyearofanalysis
IPT 1st Dose eventsfor year of analysis
Scatter Plot: ANC 1st Visit : IPT1st Dose(Districts compared tonational
rate)

58. 58
Domain 3: External Consistency – Consistency
with survey values

59. 59
Domain 4: Consistency of population data –
Comparison of denominators in use in-country
Names of districts with divergent scores:
District 1, District 5, District 7, District 12
National Score (%) 106%
Number of districts with divergent scores 4
Percent of districts with divergent scores 30.8%
Indicator4b: Consistency of denominatorbetween program data and official government population statistics
Indicator4b1- Comparing the official Live Births
denominator to aprogram denominator, if
applicable
Year 2014
Quality Threshold 10%
0.00
10000.00
20000.00
30000.00
40000.00
50000.00
60000.00
70000.00
ProgramdenominatorforLiveBirths
Official government denominator for Live Births
Interpretationof results- Indicator 4b1:
• the Program denominatorsin Districts1, 7, and 12 seemtoo large - and too small inDistrict 5. Review growth rates
used by program to estimateintercensal yearly values forlivebirths.
•

60. Data Quality
Review (DQR)
Data verification and
system
Assessment
Excel
Chartbook

61. 61
Opening Screen

62. 62
CSPro Data Entry Application

63. 63
Create DQR Indicators in CSPro

64. 64
Export Data From CSPro and Paste into the
Chartbook

65. 65
Input Analysis Disaggregations

66. 66
General Facility Information

67. 67
Availability of Source Documents and
Reports

68. 68
Timeliness and Completeness

69. 69
Verification Factors

70. 70
Program Specific Verification Factors

71. 71
Program Specific – Reasons for
Discrepancies

72. 72
System Assessment Results

73. 73
System Assessment – Subnational Results