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Use of Data for Quality and Program Improvement

Hugh SturrockAimee Leidich

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OUTLINE

• Introduction to data quality• Basics of data visualization• Introduction to pivot tables• Example data exercise• Real-world data exploration

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INTRODUCTION TO DATA QUALITY

Why good data is important

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Facility Level • Serves as basis for planning and developing Interventions• Allows providers to identify patients/clients in need of services and/or referrals• Improves efficiency through administrative organization• Inventories resources and determines which supplies and medicines are available and which need to

be ordered when• Monitors and evaluates quality of care

Region/district level• Informs acquisition and distribution of resources• Provides evidence for construction and/or expansion of

facilities• Explains human resource capabilities and challenges• Assists with more precise budgeting• Assists council authorities in planning interventions and

monitoring those activities• Demonstrates trends in calculated indicators used to

estimate future changes• Demonstrates trends in calculated indicators used to

estimate future changes

National level• Informs policy • Assists in planning and assessing

various interventions to make strategic decisions about the improvement of those interventions

• Works towards meeting the overall national goal of reducing the burden of poor health

• Provides evidence towards meeting targets

• Provides the basis for M&E

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What is data quality?

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Key Terms

• Data• Indicator• Quality Data• Quality Control• Data Quality Checks• Data Quality Assessment

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Quality Data

• Data that is reliable and accurately represents the measure it was intended to present and is valid for the use to which it is applied. Decision makers have confidence in and rely upon quality data.

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Quality Control• Process of controlling the usage of data with

known quality measurement for an application or a process.

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Data Quality AssessmentProcedure for determining whether or not a data set is suitable for its intended purpose.

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Data Quality Checks• Procedures for verifying that forms, registers

and databases are completely and correctly filled at each step of the reporting process.– Examples:

• Spot-checks• Cross-verifications

Spot-checks of actual service delivery toolsPerform spot checks to verify the complete and accurate documentation of delivery of services or commodities.

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Test date Unique ID No. Patient clinic ID

Name Sex Age Result

SurnameGiven name

 

07/01/2007 KS0031 1852 Michelle f 44 Pos 07/02/2007 KS0014 1824 Mary m 31

KS0088 1864 Andrew m 26 14/07/2007 KS0013 1754 Charles m 71 Neg

Missing date Incorrect gender entryMissing data

Cross-check with other data-sources

Cross-check the verified report totals with other data-sources (e.g. inventory records, laboratory reports, aggregated reports etc).

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Quarterly Report

Facility 1 25

Facility 2 20

TOTAL 45

Facility 1: Cases: 25

Facility 2: Cases: 20

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Data Quality Guiding Principles

• Accuracy• Reliability• Completeness• Precision• Timeliness• Integrity• Confidentiality

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Accuracy

• Also known as validity. Accurate data are considered correct when the data measure what they are intended to measure. Accurate data minimize error (e.g., recording or interviewer bias, transcription error, sampling error) to a point of being negligible.

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Precision

• Data have sufficient detail meaning they have all the parameters and details needed to produce the required information.

Completeness• All variables in either reporting or recording

tools must be filled. It represents the complete list of eligible persons or units and not just a fraction of the list.

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Timeliness• Data are up-to-date (current) and information

is available on time. This implies all the reports produced are submitted to the next level within the recommended timeframe.

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Due May 7th

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Reliability

• The data generated by a program’s information system are based on protocols and procedures that do not change according to who is using them and when or how often they are used. The data are reliable because they are measured and collected consistently.

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Integrity

• Data have integrity when the system used to generate them are protected from deliberate bias or manipulation for political or personal reasons.

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Confidentiality

• Clients are assured that their data will be maintained according to national and/or international standards for data. This means that personal data are not disclosed inappropriately and that data in hard copy and electronic form are treated with appropriate levels of security (e.g. kept in locked cabinets and in password protected files).

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Factors that contribute to poor data quality

• Data entry errors• Inconsistent reporting forms• Missing data• Delayed reporting• Failure to report

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Common Sources of Errors• Transposition• Copying• Coding• Routing• Consistency• Range• Gaps• Calculation

Indicator ResultNumber of Pregnant Women 21

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Transposition Error

When two numbers are switched. Usually caused by typing mistakes. (e.g. 12 is entered as 21)

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Transposition error

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Copying Error

When a number or letter is copied as the wrong number or letter. (e.g. 0 entered as the letter O)

Number

0 Entered as

Letter

O

Study ID SNo101 SNo102

1 54 32 30 13 22 24 43 35 33 26 30 2

11 37 3

Sno. Maswali Mpangilio wa kundi (Kodi) 101 Una miaka mingapi?

(Miaka kamili)Miaka_____________

102 Umesoma mpaka darasa la ngapi?

Hajasoma 0Hakumaliza elimu ya msingi 1 Amemaliza elimu ya msingi 2

Hakumaliza elimu ya sekondari 3 Amemaliza elimu ya sekondari 4

Elimu ya juu (Chuo,chuo kikuu, n.k.) 5

Hakujibu 98

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Coding ErrorWhen the wrong code is entered. (e.g. interview subject circled 1 = Yes, but the coder copied 2 (= No) during coding) Entered as 4

during interview

Coded as 3 in the dataset

 Registration and Personal Information

Unique CTC ID Number

Why eligible (Transfer in)

SexAge/ DOB

(under-5)Name

211852 2Michelle Bamba F 44

331824 2 Mary Musa   F

121864 2Andrew Matua M 26

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Routing When a number is placed in the wrong field or in the wrong order (e.g. gender entered into the age category)

Gender erroneously entered into the age category

Unique CTC ID Number

Why eligible (Transfer in)

SexAge/ DOB

(under-5)

Name

211852 2Michelle Bamba F 44

331824 2 Mary Musa M  34

121864 2Andrew Matua M 26

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ConsistencyWhen two or more responses on the same questionnaire are contradictory (e.g. birth date and age; name and gender) Mary erroneously

entered as a male

Unique CTC ID Number

Why eligible (Transfer in)

SexAge/ DOB

(under-5)Weight Name

211852 2Michelle Bamba F 44 600

331824 2 Mary Musa M  34 42

121864 2Andrew Matua M 26 41 28

RangeWhen a number lies outside the range of probable or possible values (e.g. Age = 151 yrs)

Weight erroneously entered as 600kg

 Registration and Personal Information

Unique CTC ID Number

Why eligible (Transfer in)

SexAge/ DOB

(under-5)Name

2Michelle Bamba F 44

2 Mary Musa M  34

2Andrew Matua M 26

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GapsWhen data are not filled in

Unique ID is missing

CalculationWhen data is not calculated correctly. (e.g. 3+1 = 5)

230110 +340 =Total males and females added erroneously

IndicatorTOTAL

(Males + Females)

Males Females

Total

<1 year

1-4 years

5-14 years

≥15 years

Total

<1 year

1-4 years

5-14 years

≥15 years

1.1 Cumulative number of persons ever enrolled in care at this facility at beginning of the reporting quarter 350 110 3 2 8 97 230 5 7 17 201

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INTRODUCTION TO DATA USAGE AND VISUALIZATION

Why Do We Spend So Much Time and Energy Collecting All This Data ?!

Strengthen M&E programs

Use evidence for decision making

Strengthen capacity of staff

Improve program planning and

resource allocation

Gain efficiency and effectiveness

Improve data quality

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Data Is At The Center of M&E

DATA

Improve coverage, reach,

intensity of services

Improve quality of

data

Priority setting and resource

allocation

Accountability

But…..only if we review, discuss, interpret, and use it regularly! 33

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Use Data To Guide Resource Allocation

• A program needs adequate resources and staff in order to achieve its goals.

• Presenting high-quality program data can help program managers to advocate for additional resources.

Our malaria surveillance data suggest we need

more vehicles!

Our malaria surveillance data

suggest we need more trained nurses!

Our RDT data suggest we need faster allocation of RDTs to avoid stockouts

Data Use for Decision Making

• No one “gold standard” approach• Hybrid of approaches depending on the

context– Dissemination in all appropriate forums– Motivate/incentivise efforts in data use– Reduce institutional and behavioural barriers to

data use (e.g. accountability and performance measurement; attitudes)

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BASICS OF VISUALLY PRESENTING DATA

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Key Definitions• Results: Simple description/observations of your results (who,

what, where, when, magnitude, trend). • Interpretation: Explanation of why your results may have

occurred. • Conclusion: the key message of your results, implications and

the “action-plan” that you recommend based on your results.– The “Take Away”

Nine elephants damaged storefronts on Market St in San Francisco in 2010, one elephant damaged a

store in 2013.

The number of elephants on Market St in San

Francisco has decreased since 2010 because a zookeeper has started

laying a trail of peanuts to Ocean Beach

Citizens should be sensitized to encourage elephants to play at the

beach instead of on Market St

Result Interpretation Conclusion

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RESULTS

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Presenting Data In Tables• Tables may be the only presentation format needed when the

data are few, relationships are straightforward and when display of exact values is important.

Table X. PEPFAR annual progress reporting, PMTCT indicators, FY12-13, NamibiaIndicator Estimate

Number of pregnant women that are tested or know their HIV status at ANC and L&D 62,142

Number of pregnant women with known positive status at entry to ANC or L&D 7,546

Number of pregnant women newly tested positive 4,251

Source: PEPFAR Annual Progress Report, Namibia 2013

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Bar Charts Are Useful to Show Simple Comparisons, Esp. Differences in Quantity.

2009 -10 2010 -11 2011-120

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

55,097 57,219

70,025

2,659 (4.8%) 2,490 (4.4%) 2,546 (3.6%)

Fig. 7. Partner HIV testing among pregnant women attend-ing ANC, Country X, 2009-10 to 2011-12.

Pregnant women attending ANC Partner tested for HIVYear

# of

wom

en o

r par

tner

s

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Line Charts Are Good for Showing Change Over Time (Trend)

2003 2004 2005 2006 2007 2008 2009 2010 2011 201250%55%60%65%70%75%80%85%90%95%

100%

77%

87%91% 92% 91%

88% 88% 88% 87%82%

Fig. 8. Percentage of patients alive on ART at 12 months after initiation in Country X, by initiation cohort year.

Initiation cohort year

% a

live

on A

RT

Bar and Line Charts Can Be Used Together to Show Trends Of Several Related Indicators

2005 2006 2007 2008 2009 2010 2011 20120

5

10

15

20

25

30

35

0

5,000

10,000

15,000

20,000

25,000

Fig. 9. Estimated MTCT rate at 6 weeks and MTCT rate at 6 weeks including breastfeeding, Country X, 2005-2012

Number infants exposed MTCT rate (excluding breastfeeding infants)

MTCT rate including breastfeeding infantsYear

% in

fant

s in

fect

ed

# in

fant

s ex

pose

d

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Est. no. HIV + per sq km

Maps show geographic relationships

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Figure title

• Be sure to include:

What (the indicator)• HIV prevalence • % circumcised • % alive on ART

Who• pregnant women age 15-49

• adults males age 15-49• pediatric ART patients

Where• in Namibia

• in Ohangwena region • at Engela Hospital Clinic

When• in 2012

• from 2008 to 2012

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2009-10 2010-11 2011-120%

10%

20%

30%

40%

50%

60%

70%

Fig. 11. Distribution of ARV prophylaxes used for PMTCT among HIV positive pregnant women attending antenatal care in Namibia,

2009-10 to 2011-12.

Single-dose NVP Combination ARV HAART

% d

istr

ibuti

on o

f ARV

type

Source: Namibia MOHSS (2012) Annual Implementation Progress Report for the National Strategic Framework (NSF) 2011/12.

What ?

When ?

Where ?

Who ?

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Presenting Data Tips (2)• All relevant information needed to interpret the table,

figure, or map should be included so that the reader can understand without reference to text (i.e. in a report)

• Clearly label your X and Y axes, format consistently (font, font size, style, position)

• Use data series legends /labels• Make the scale appropriate for the findings you want to

convey.• Reference the source of your data

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2009-10 2010-11 2011-120%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Fig. 12. Distribution of ARV prophylaxes used for PMTCT among HIV positive pregnant women attending antenatal care in Namibia, 2009-10 to 2011-12.

Single-dose NVP Combination ARV HAART

Reporting period

% d

istr

ibuti

on o

f ARV

type

Source: Namibia MOHSS (2012) Annual Implementation Progress Report for the National Strategic Framework (NSF) 2011/12.

Clear chart title

X-axis label

Y-axis label

Series legend Data source reference

X-axis label

Scale spans to 100% to display

complete picture

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Stratification of Data

• What is stratification?– Dividing into subgroups

• What are common levels of data stratification?– Year, age, sex, geographic region, facility

• Why do we stratify?– Let’s look at stratification within the indicator:

• % of patients alive on ART 12 months after initiation

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What Do You Think About This Figure?

Series10%

10%20%30%40%50%60%70%80%90%

100%

Fig. 13. Percentage of patients alive on ART at 12 months after ART initiation.

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We Can Stratify By Time, e.g. Initiation Cohort…

2003 2004 2005 2006 2007 2008 2009 2010 2011 201250%55%60%65%70%75%80%85%90%95%

100%

77%

87%91% 92% 91%

88% 88% 88% 87%82%

Fig. 14. Percentage of patients alive on ART at 12 months after initiation in Country X, by initiation cohort year.

Initiation cohort year

% a

live

on A

RT

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We Can Stratify by Age Group

2003 2004 2005 2006 2007 2008 2009 2010 2011 201250%55%60%65%70%75%80%85%90%95%

100%

Fig. 15. Percentage of patients alive on ART at 12 months after initia-tion in Country X, by cohort year and adult vs. pediatric patients.

Adults Children

Initiation cohort year

% a

live

on A

RT

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We Can Stratify By Geographic Area

2004 2005 2006 2007 2008 2009 2010 2011 201250%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

Fig.19. Percentage of adult patients alive on ART at 12 months after initiation by cohort year and selected districts in Country X.

District A District B District C

Initiation cohort year

% a

live

on A

RT

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We Can Stratify By Facilities Within Geographic Areas

2009 2010 2011 201250%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

0.87 0.910.89

0.81

Fig. 20.Percentage of adult patients alive on ART at 12 months after initiation by selected facilities within District Q in Country X.

Q: Health Centre 1 Q: Health Centre 2 Q: District HospitalDistrict Q overall

Initiation cohort year

% a

live

on A

RT

Females

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Three indicators for HIV testing by sex and province. Zambia. 2007

Males

We Can Stratify By Sex and Geography …

Source: DHS 2007

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INTERPRETATION

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Magnitude and Trend (1)

• Magnitude : – the amount of coverage– The size of the difference between sub-groups or

time points• Trend:

– the direction of change over time (i.e. increasing, decreasing, or remaining stable)

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Magnitude and Trend Statements (2)

“ From 1992 to 2002, HIV prevalence among pregnant women increased (trend) from 4.2% to 22% (magnitude).

After peaking at 22% in 2002 (magnitude), HIV prevalence has remained fairly stable from 2004-2012 (trend) at around 18-20% (magnitude).”

Fig. 23. HIV prevalence among pregnant women receiving antenatal care at public facilities in Country X, 1992-2012

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Interpretation of Results

• Descriptive results are what you see, interpretation is how you see it.

• Why do you think your results are what they are? What are 1-2 possible programmatic explanations: – Programmatic/guidelines changes? (e.g. CD4 ART eligibility,

Option B+)– Increased/decreased access to services at facilities within

district/region?– Staff reductions? Staff trained in new areas (e.g. IMAI)– Are data missing from some time points, facilities, sub-groups?– Are there facilities or districts that are not reporting,

underreporting for this time period, or reporting data differently?

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Interpretation Statement (3)“ Retention in District A is declining much more rapidly compared to the national average. These declines may be related to the higher than average loss of ART doctors within this district, which may have effected access and quality of care. Alternatively, the observed trend in District A may be a result of incomplete data reported in the ePMS.

2004 2005 2006 2007 2008 2009 2010 2011 201250%55%60%65%70%75%80%85%90%95%

100%

Fig. 28. Percentage of adult patients alive on ART at 12 months after initiation by cohort year and selected districts in Country X.

District A District B District C

Initiation cohort year

% a

live

on A

RT

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CONCLUSIONS

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Drawing Conclusions (1)• Conclusions are the “take-away” message, i.e. what you want

your audience to remember and do after the presentation.• Especially relating to programmatic implications of results.• Conclusion can include the presenter’s recommendations for:

• Program improvement • Additional data verification/quality checks

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Conclusion Statement (2)“Patient and facility level factors predictive of patient loss that are unique to District A should be identified and corrected. Best practices from higher performing districts should be shared. Failure to do so may result in increased AIDS mortality and drug resistance in this district. The completeness of data from this district should also be confirmed to validate our results.

2004 2005 2006 2007 2008 2009 2010 2011 201250%55%60%65%70%75%80%85%90%95%

100%

Fig.31. Percentage of adult patients alive on ART at 12 months after initiation by cohort year and selected districts in country X.

District A District B District C

Initiation cohort year

% a

live

on A

RT