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Use of Data for Quality and Program 
Improvement 
Hugh Sturrock 
Aimee Leidich 
1
OUTLINE 
2 
• Introduction to data quality 
• Basics of data visualization 
• Introduction to pivot tables 
• Example data...
INTRODUCTION TO DATA QUALITY 
3
Why good data is important 
4 
Facility Level 
• Serves as basis for planning and developing Interventions 
• Allows provi...
What is data quality? 
5
Key Terms 
• Data 
• Indicator 
• Quality Data 
• Quality Control 
• Data Quality Checks 
• Data Quality Assessment 
6
Quality Data 
• Data that is reliable and accurately represents 
the measure it was intended to present and is 
valid for ...
Quality Control 
• Process of controlling the usage of data with 
known quality measurement for an 
application or a proce...
Data Quality Assessment 
Procedure for determining whether or not a 
data set is suitable for its intended purpose. 
9
Data Quality Checks 
• Procedures for verifying that forms, registers 
and databases are completely and correctly 
filled ...
Spot-checks of actual service delivery tools 
Perform spot checks to verify the complete and accurate 
documentation of de...
Cross-check with other data-sources 
Cross-check the verified report totals with 
other data-sources (e.g. inventory recor...
Data Quality Guiding Principles 
• Accuracy 
• Reliability 
• Completeness 
• Precision 
• Timeliness 
• Integrity 
• Conf...
Accuracy 
• Also known as validity. Accurate data are 
considered correct when the data measure 
what they are intended to...
Precision 
• Data have sufficient detail meaning they have 
all the parameters and details needed to 
produce the required...
Completeness 
• All variables in either reporting or recording 
tools must be filled. It represents the 
complete list of ...
Timeliness 
• Data are up-to-date (current) and information 
is available on time. This implies all the 
reports produced ...
Reliability 
• The data generated by a program’s 
information system are based on protocols 
and procedures that do not ch...
Integrity 
• Data have integrity when the system used to 
generate them are protected from deliberate 
bias or manipulatio...
Confidentiality 
• Clients are assured that their data will be 
maintained according to national and/or 
international sta...
Factors that contribute to poor data 
quality 
• Data entry errors 
• Inconsistent reporting forms 
• Missing data 
• Dela...
22 
Common Sources of Errors 
• Transposition 
• Copying 
• Coding 
• Routing 
• Consistency 
• Range 
• Gaps 
• Calculati...
Indicator Result 
Number of Pregnant 
Women 21 
23 
Transposition Error 
When two numbers are switched. Usually caused by ...
24 
Copying Error 
When a number or letter is copied as the wrong number 
or letter. (e.g. 0 entered as the letter O) 
Num...
Study ID SNo101 SNo102 
1 54 3 
2 30 1 
3 22 2 
4 43 3 
5 33 2 
6 30 2 
11 37 3 
Sno. Maswali Mpangilio wa kundi (Kodi) 
1...
Registration and Personal Information 
Unique CTC ID 
Number 
Why eligible 
(Transfer in) 
Sex 
Age/ 
DOB 
(under-5) 
Name...
Unique CTC ID 
Number 
Why eligible 
(Transfer in) 
Sex 
Age/ 
DOB 
(under-5) 
Name 
211852 2 
Michelle 
Bamba F 44 
33182...
Unique CTC ID 
Number 
Why eligible 
(Transfer in) 
Name Sex 
Weight 
Age/ 
DOB 
(under-5) 
211852 2 
Michelle 
Bamba F 44...
Registration and Personal Information 
Unique CTC ID 
Number 
Why eligible 
(Transfer in) 
Sex 
Age/ 
DOB 
(under-5) 
Name...
Calculation 
When data is not calculated correctly. (e.g. 3+1 = 5) 
Indicator TOTAL 
(Males + 
Females) 
Males Females 
To...
INTRODUCTION TO DATA USAGE 
AND VISUALIZATION 
31
Why Do We Spend So Much Time and 
Energy Collecting All This Data ?! 
Strengthen M&E 
programs 
Use evidence for 
decision...
Data Is At The Center of M&E 
Improve 
coverage, reach, 
intensity of 
services 
DATA 
Improve 
quality of 
data 
Priority...
Use Data To Guide Resource 
Allocation 
• A program needs adequate resources and staff in 
order to achieve its goals. 
• ...
Data Use for Decision Making 
• No one “gold standard” approach 
• Hybrid of approaches depending on the 
context 
– Disse...
BASICS OF VISUALLY PRESENTING 
DATA 
36
Key Definitions 
• Results: Simple description/observations of your results 
(who, what, where, when, magnitude, trend). 
...
RESULTS 
38
Presenting Data In Tables 
• Tables may be the only presentation format needed when the 
data are few, relationships are s...
Bar Charts Are Useful to Show Simple 
Comparisons, Esp. Differences in Quantity. 
Fig. 7. Partner HIV testing among pregna...
Line Charts Are Good for Showing 
Change Over Time (Trend) 
Fig. 8. Percentage of patients alive on ART at 12 months after...
Bar and Line Charts Can Be Used Together to 
Show Trends Of Several Related Indicators 
25,000 
20,000 
15,000 
10,000 
5,...
43 
Maps show geographic relationships 
Est. no. HIV + per sq km
Figure title 
• Be sure to include: 
What (the indicator) 
• HIV prevalence 
• % circumcised 
• % alive on ART 
Who 
• pre...
Who ? 
70% 
60% 
50% 
40% 
30% 
20% 
10% 
0% 
What ? 
When ? 
2009-10 2010-11 2011-12 
% distribution of ARV type 
Fig. 11...
Presenting Data Tips (2) 
• All relevant information needed to interpret the table, 
figure, or map should be included so ...
100% 
90% 
80% 
70% 
60% 
50% 
40% 
30% 
20% 
10% 
0% 
Clear chart title 
Fig. 12. Distribution of ARV prophylaxes used fo...
Stratification of Data 
• What is stratification? 
– Dividing into subgroups 
• What are common levels of data stratificat...
What Do You Think About This 
Figure? 
100% 
90% 
80% 
70% 
60% 
50% 
40% 
30% 
20% 
10% 
0% 
Fig. 13. Percentage of patie...
We Can Stratify By Time, e.g. 
Initiation Cohort… 
Fig. 14. Percentage of patients alive on ART at 12 months 
after initia...
We Can Stratify by Age Group 
100% 
95% 
90% 
85% 
80% 
75% 
70% 
65% 
60% 
55% 
50% 
Fig. 15. Percentage of patients aliv...
We Can Stratify By Geographic Area 
100% 
95% 
90% 
85% 
80% 
75% 
70% 
65% 
60% 
55% 
50% 
Fig.19. Percentage of adult pa...
We Can Stratify By Facilities Within 
Geographic Areas 
Fig. 20.Percentage of adult patients alive on ART at 12 months aft...
We Can Stratify By Sex and Geography … 
Three indicators for HIV testing by sex and province. Zambia. 2007 
Females 
54 
M...
INTERPRETATION 
55
Magnitude and Trend (1) 
• Magnitude : 
– the amount of coverage 
– The size of the difference between sub-groups or 
time...
Magnitude and Trend Statements (2) 
“ From 1992 to 2002, HIV prevalence among pregnant women 
increased (trend) from 4.2% ...
Interpretation of Results 
• Descriptive results are what you see, 
interpretation is how you see it. 
• Why do you think ...
Interpretation Statement (3) 
“ Retention in District A is declining much more rapidly compared to the national 
average. ...
CONCLUSIONS 
60
Drawing Conclusions (1) 
• Conclusions are the “take-away” message, i.e. what you want 
your audience to remember and do a...
Conclusion Statement (2) 
“Patient and facility level factors predictive of patient loss that are unique to District A 
sh...
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Data use overview

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Introduction to Data Use for Health

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Data use overview

  1. 1. Use of Data for Quality and Program Improvement Hugh Sturrock Aimee Leidich 1
  2. 2. OUTLINE 2 • Introduction to data quality • Basics of data visualization • Introduction to pivot tables • Example data exercise • Real-world data exploration
  3. 3. INTRODUCTION TO DATA QUALITY 3
  4. 4. Why good data is important 4 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
  5. 5. What is data quality? 5
  6. 6. Key Terms • Data • Indicator • Quality Data • Quality Control • Data Quality Checks • Data Quality Assessment 6
  7. 7. 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. 7
  8. 8. Quality Control • Process of controlling the usage of data with known quality measurement for an application or a process. 8
  9. 9. Data Quality Assessment Procedure for determining whether or not a data set is suitable for its intended purpose. 9
  10. 10. 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 10
  11. 11. Spot-checks of actual service delivery tools Perform spot checks to verify the complete and accurate documentation of delivery of services or commodities. 11 Test date Unique ID No. Patient clinic ID Name Sex Age Result Surname Given 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 entry Missing data
  12. 12. 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). 12 Quarterly Report Facility 1 25 Facility 2 20 TOTAL 45 Facility 1: Cases: 25 Facility 2: Cases: 20
  13. 13. Data Quality Guiding Principles • Accuracy • Reliability • Completeness • Precision • Timeliness • Integrity • Confidentiality 13
  14. 14. 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. 14
  15. 15. Precision • Data have sufficient detail meaning they have all the parameters and details needed to produce the required information. 15
  16. 16. 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. 16
  17. 17. 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. 17 Due May 7th
  18. 18. 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. 18
  19. 19. Integrity • Data have integrity when the system used to generate them are protected from deliberate bias or manipulation for political or personal reasons. 19
  20. 20. 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). 20
  21. 21. Factors that contribute to poor data quality • Data entry errors • Inconsistent reporting forms • Missing data • Delayed reporting • Failure to report 21
  22. 22. 22 Common Sources of Errors • Transposition • Copying • Coding • Routing • Consistency • Range • Gaps • Calculation
  23. 23. Indicator Result Number of Pregnant Women 21 23 Transposition Error When two numbers are switched. Usually caused by typing mistakes. (e.g. 12 is entered as 21) 12 Transposition error
  24. 24. 24 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
  25. 25. Study ID SNo101 SNo102 1 54 3 2 30 1 3 22 2 4 43 3 5 33 2 6 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 0 Hakumaliza 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 25 Coding Error When 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
  26. 26. Registration and Personal Information Unique CTC ID Number Why eligible (Transfer in) Sex Age/ DOB (under-5) Name 211852 2 Michelle Bamba F 44 331824 2 Mary Musa F 121864 2 Andrew Matua M 26 26 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
  27. 27. Unique CTC ID Number Why eligible (Transfer in) Sex Age/ DOB (under-5) Name 211852 2 Michelle Bamba F 44 331824 2 Mary Musa M 34 121864 2 Andrew Matua M 26 27 Consistency When 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
  28. 28. Unique CTC ID Number Why eligible (Transfer in) Name Sex Weight Age/ DOB (under-5) 211852 2 Michelle Bamba F 44 600 331824 2 Mary Musa M 34 42 121864 2 Andrew Matua M 26 41 28 Range When a number lies outside the range of probable or possible values (e.g. Age = 151 yrs) Weight erroneously entered as 600kg
  29. 29. Registration and Personal Information Unique CTC ID Number Why eligible (Transfer in) Sex Age/ DOB (under-5) Name 2 Michelle Bamba F 44 2 Mary Musa M 34 2 Andrew Matua M 26 29 Gaps When data are not filled in Unique ID is missing
  30. 30. Calculation When data is not calculated correctly. (e.g. 3+1 = 5) Indicator TOTAL (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 17 201 340 = 110 + 230 Total males and females added erroneously
  31. 31. INTRODUCTION TO DATA USAGE AND VISUALIZATION 31
  32. 32. 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 32
  33. 33. Data Is At The Center of M&E Improve coverage, reach, intensity of services DATA Improve quality of data Priority setting and resource allocation Accountability But…..only if we review, discuss, interpret, and use it regularly! 33
  34. 34. 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 34
  35. 35. 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)
  36. 36. BASICS OF VISUALLY PRESENTING DATA 36
  37. 37. 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” 37 Result Interpretation Conclusion 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
  38. 38. RESULTS 38
  39. 39. 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, Namibia Indicator 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 39
  40. 40. Bar Charts Are Useful to Show Simple Comparisons, Esp. Differences in Quantity. Fig. 7. Partner HIV testing among pregnant women attending ANC, Country X, 2009-10 to 2011-12. 55,097 57,219 70,025 2,659 (4.8%) 2,490 (4.4%) 2,546 (3.6%) 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 2009 -10 2010 -11 2011-12 # of women or partners Year Pregnant women attending ANC Partner tested for HIV 40
  41. 41. Line Charts Are Good for Showing Change Over Time (Trend) Fig. 8. Percentage of patients alive on ART at 12 months after initiation in Country X, by initiation cohort year. 77% 87% 91% 92% 91% 88% 88% 88% 87% 82% 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 % alive on ART Initiation cohort year 41
  42. 42. Bar and Line Charts Can Be Used Together to Show Trends Of Several Related Indicators 25,000 20,000 15,000 10,000 5,000 0 35 30 25 20 15 10 5 0 Fig. 9. Estimated MTCT rate at 6 weeks and MTCT rate at 6 weeks including breastfeeding, Country X, 2005-2012 2005 2006 2007 2008 2009 2010 2011 2012 # infants exposed % infants infected Year Number infants exposed MTCT rate (excluding breastfeeding infants) MTCT rate including breastfeeding infants
  43. 43. 43 Maps show geographic relationships Est. no. HIV + per sq km
  44. 44. 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 44
  45. 45. Who ? 70% 60% 50% 40% 30% 20% 10% 0% What ? When ? 2009-10 2010-11 2011-12 % distribution of ARV type 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 Where ? Source: Namibia MOHSS (2012) Annual Implementation Progress Report for the National Strategic Framework (NSF) 2011/12. 45
  46. 46. 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 46
  47. 47. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Clear chart title Fig. 12. Distribution of ARV prophylaxes used for PMTCT among HIV positive pregnant women attending antenatal care in Namibia, 2009-10 to 2011-12. 2009-10 2010-11 2011-12 % distribution of ARV type Reporting period X-axis label Single-dose NVP Combination ARV HAART X-axis label Source: Namibia MOHSS (2012) Annual Implementation Progress Report for the National Strategic Framework (NSF) 2011/12. Y-axis label Series legend Data source reference 47 Scale spans to 100% to display complete picture
  48. 48. 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 48
  49. 49. What Do You Think About This Figure? 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Fig. 13. Percentage of patients alive on ART at 12 months after ART initiation. 49
  50. 50. We Can Stratify By Time, e.g. Initiation Cohort… Fig. 14. Percentage of patients alive on ART at 12 months after initiation in Country X, by initiation cohort year. 77% 87% 91% 92% 91% 88% 88% 88% 87% 82% 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 % alive on ART Initiation cohort year 50
  51. 51. We Can Stratify by Age Group 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% Fig. 15. Percentage of patients alive on ART at 12 months after initiation in Country X, by cohort year and adult vs. pediatric patients. 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 % alive on ART Initiation cohort year Adults Children 51
  52. 52. We Can Stratify By Geographic Area 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% Fig.19. Percentage of adult patients alive on ART at 12 months after initiation by cohort year and selected districts in Country X. 2004 2005 2006 2007 2008 2009 2010 2011 2012 % alive on ART Initiation cohort year District A District B District C 52
  53. 53. We Can Stratify By Facilities Within Geographic Areas Fig. 20.Percentage of adult patients alive on ART at 12 months after initiation by selected facilities within District Q in Country X. 0.87 0.91 0.89 0.81 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 2009 2010 2011 2012 % alive on ART Initiation cohort year Q: Health Centre 1 Q: Health Centre 2 Q: District Hospital District Q overall 53
  54. 54. We Can Stratify By Sex and Geography … Three indicators for HIV testing by sex and province. Zambia. 2007 Females 54 Males Source: DHS 2007
  55. 55. INTERPRETATION 55
  56. 56. 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) 56
  57. 57. 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).” 57 Fig. 23. HIV prevalence among pregnant women receiving antenatal care at public facilities in Country X, 1992-2012
  58. 58. 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? 58
  59. 59. 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. 59 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% Fig. 28. Percentage of adult patients alive on ART at 12 months after initiation by cohort year and selected districts in Country X. 2004 2005 2006 2007 2008 2009 2010 2011 2012 % alive on ART Initiation cohort year District A District B District C
  60. 60. CONCLUSIONS 60
  61. 61. 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 61
  62. 62. 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. 62 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% Fig.31. Percentage of adult patients alive on ART at 12 months after initiation by cohort year and selected districts in country X. 2004 2005 2006 2007 2008 2009 2010 2011 2012 % alive on ART Initiation cohort year District A District B District C

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